Moving to Opportunity for Fair Housing

Demonstration Program

Final Impacts Evaluation

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Moving to Opportunity for Fair

Housing Demonstration Program

Final Impacts Evaluation

Prepared for:

U.S. Department of Housing and Urban Development

Office of Policy Development & Research

Prepared by:

Lisa Sanbonmatsu

Jens Ludwig, Project Director

Lawrence F. Katz, Principal Investigator

Lisa A. Gennetian

Greg J. Duncan

Ronald C. Kessler

Emma Adam

Thomas W. McDade

Stacy Tessler Lindau

with:

Matthew Sciandra

Fanghua Yang

Ijun Lai

William Congdon

Joe Amick

Ryan Gillette

Michael A. Zabek

Jordan Marvakov

Sabrina Yusuf

Nicholas A. Potter

National Bureau of Economic Research

October 14, 2011

Acknowledgments

This report was prepared under HUD Contract C–CHI–00808 to the National Bureau of Economic Research (NBER). For their support, advice, and open-mindedness about the study design, we are grateful to the Government Technical Representative for the study, Todd M. Richardson (Associate Deputy Assistant Secretary for Policy Development), as well as Mark D. Shroder (Associate Deputy Assistant Secretary for Research, Evaluation, and Monitoring), Kevin J. Neary (Deputy Assistant Secretary for Research, Evaluation, and Monitoring), Erika Poethig (Deputy Assistant Secretary for Policy Development), and Raphael Bostic (Assistant Secretary for Policy Development and Research). Michelle Sternthal, Jennifer Stoloff, and Ronald Wilson at HUD also provided valuable comments on an earlier draft of this report.

Additional support for the long-term (10–15 year) MTO follow-up study reported on here came from the National Science Foundation (SES–0527615), the National Institute for Child Health and Human Development (R01–HD040404, R01–HD040444), the Centers for Disease Control (R49–CE000906), the National Institute of Mental Health (R01–MH077026), the National Institute for Aging (P20–AG012180, R56–AG031259, P01–AG005842–22S1), the National Opinion Research Center’s Population Research Center (through R24–HD051152–04 from the National Institute of Child Health and Human Development), University of Chicago’s Center for Health Administration Studies, the U.S. Department of Education/Institute of Education Sciences (R305U070006), the John D. and Catherine T. MacArthur Foundation, the Smith Richardson Foundation, the Spencer Foundation, the Bill & Melinda Gates Foundation, and the Russell Sage Foundation. This study was also supported by a grant from the Annie E. Casey Foundation to the Urban Institute.

Outstanding advice about the design, execution, and interpretation of the MTO study was provided by our Technical Review Panel, which included: J. Lawrence Aber (New York University); Jeanne Brooks-Gunn (Columbia University); Charles Brown (University of Michigan); Thomas Cook (Northwestern University); Kenneth Dodge (Duke University); Felton Earls (Harvard Medical School); Ronald Ferguson (Harvard University); John Goering (City University of New York); Christopher Jencks (Harvard University); Kathleen Mullan Harris (University of North Carolina at Chapel Hill); Edgar Olsen (University of Virginia); Robert Sampson (Harvard University); Eldar Shafir (Princeton University); Laurence Steinberg (Temple University); and Jane Waldfogel (Columbia University). We also thank Larry Orr for feedback, and are particularly grateful to Jeffrey R. Kling for his tireless commitment and sage advice.

We also wish to thank numerous other collaborators who assisted in a variety of substantive and methodological components of the MTO study. A research team at the Urban Institute expertly developed the housing and neighborhood module for the long-term survey as well as a new methodology for using survey and administrative data to measure housing assistance receipt. Margery Turner, Susan Popkin, and Jennifer Comey provided feedback and guidance at various stages of design and analysis, and Megan Gallagher, David Price, and Kaitlin Franks supported measurement and analytic work. Numerous useful insights about the design of the long-term MTO study also came from discussions with the research team that has been carrying out qualitative data collection from MTO families, including Kathy Edin of Harvard University, Stefanie DeLuca of Johns Hopkins University, and Susan Clampet-Lundquist of St. Joseph’s University. We thank Debi McInnis and our colleagues at Abt Associates for their hard work tracking MTO families and for developing a method for constructing the participants’ address histories. Important insights about the analysis and interpretation of the medical findings in the long-term MTO data came from conversations with our collaborator Robert Whitaker of Temple University, as well as discussions with James Meigs of Harvard University. Don Rock assisted in developing a strategy for administering math and English achievement assessments to MTO

youth, and Michelle Najarian at Educational Testing Service helped create the scaled achievement scores. Nancy Sampson and her research team at the Harvard Medical School constructed the mental health outcomes. William Congdon at the Brookings Institution was instrumental in designing many of the modules for the MTO survey.

Outstanding assistance with the development of the survey instruments, data preparation, and analysis was provided by Matthew Sciandra, Fanghua Yang, Ijun Lai, Joe Amick, Ryan Gillette, Michael A. Zabek, Jordan Marvakov, Sabrina Yusuf, and Nicholas A. Potter and former research assistants and analysts Alessandra Del Conte Dickovick, Santhi Hariprasad, Plamen Nikolov, Brandi Coates, and Magali Fassiotto. Janet Stein at NBER, Michael Weis at Northwestern University, and Kari Beardsley, Sabrina Fisher, and Aida Cavalic at the Brookings Institution provided exceptional assistance in managing the numerous grants that supported this study. We thank Dan Feenberg for his technical advice, Mohan Ramanujan and Len McCain for helping to support the technological needs of the project, and Korland Simmons for his administrative support of the project. Barbara Ray, owner of Hiredpen, Inc., helped with editing the report.

The study would not have been possible without the incredible efforts of Nancy Gebler at the University of Michigan’s Survey Research Center (SRC) in leading the monumental task of directing, launching, and ensuring high-quality administration of the survey interview for thousands of MTO adults and youth. At SRC, we also thank senior project advisor Beth-Ellen Pennell; project manager Barbara Ward; production manager Margaret Hudson; data manager Lisa Wood; financial analyst Mary Kern; sampling statistician James Wagner; programmers James Hagerman and Pamela Swanson; project assistant Hongyu Johnson; the field team leaders Yvonne Ragland, Sima Ballinger, Ana Socarras, Beverly Hight, Susan Brown, Beth Babich, and Charlotte Morley; and the dedicated interviewers for their tremendous efforts.

We thank Jerry Yeo and Christine Lee at the University of Chicago for their assistance in carrying out separate analyses that helped validate our dried blood spot measures for health outcomes among MTO participants; John Rickford at Stanford University and Holly Craig at the University of Michigan for their assistance in developing measures of speech patterns; Michael Barr of the University of Michigan for his help designing measures of household savings behavior and access to credit and banking services; John List of the University of Chicago and Sendhil Mullainathan of Harvard University for assistance in developing measures of decision making processes; Heidi Guyer at the Health and Retirement Study for the dried blood spot protocols; Maryann Chandler at Flexsite Laboratories; and Eric Whitsel, leader of the biology core of the National Longitudinal Study of Adolescent Health, for guidance on dried blood spot measurement and assay.

We are also grateful to the large number of local, state, and federal government agencies that were willing to share the administrative data necessary for evaluating long-term MTO impacts.

But perhaps most important, we are grateful to the MTO families themselves for their willingness to share their lives with us.

We hope that the results of this study, one of the largest ever undertaken by HUD, will help guide government officials in their decisions about how best to improve the lives of some of our nation’s most disadvantaged residents.

Foreword

Begun in 1994, the Moving to Opportunity for Fair Housing (MTO) Demonstration Program is the most ambitious randomized social experiment ever conducted by HUD. The demonstration was designed to determine the benefits of offering mobility opportunities, with the goal of informing future voucher program policies. Over 4600 low-income families with children living in high-poverty central city public housing participated in this important demonstration.

I am pleased to present the final report of the demonstration, which describes the impacts of the program ten to fifteen years after families enrolled in the program. This study confirms that mobility opportunities and, by extension, neighborhood characteristics matter for family outcomes.

Three important themes emerge from the MTO demonstration:

1. Lower poverty and safer neighborhoods.

Mobility programs, which provide resources for families to move to a housing unit in a different location, result in families living in lower poverty neighborhoods. Both at the time of the move and at the final follow-up, families in the group that received rental housing vouchers without any restrictions (the “Section 8 group”) and in the group that received vouchers and mobility counseling but could only use those vouchers in neighborhoods with poverty rates below ten percent (the “experimental group”) lived in lower poverty neighborhoods than those in the group of families that enrolled in the demonstration but did not receive vouchers (the “control group”). The opportunity to move achieved an important goal of the participants: greater safety. Adults and female youths in both the Section 8 group and the experimental group felt safer in their neighborhoods than those in the control group.

2. Better health outcomes.

The study found that the opportunity to live in lower poverty neighborhoods was associated with better health outcomes. Women in the experimental group were less likely to have extreme obesity and diabetes compared to women in the control group. The women and their female children in the experimental group also experienced less psychological distress and major depression.

3. No better educational, employment, and income outcomes.

Families in the experimental group did not experience better employment or income outcomes than the other families.

The children in the Section 8 and experimental groups did not have better educational achievements than those in the control group and were not significantly less likely to engage in most forms of risky or criminal behavior. This finding leads to two important lessons.

• Mobility programs designed to give families access to greater opportunity may need to define opportunity more broadly than poverty rates or racial composition if improvements along these other dimensions are valued. Access to entry-level jobs, availability of high performing schools, and other neighborhood characteristics are additional factors that might need to be considered.

• A more comprehensive approach is needed to reverse the negative consequences of living in neighborhoods with heavily concentrated poverty. Housing is a platform for positive outcomes, but it is not sufficient alone for achieving these additional benefits.

HUD’s current programs support many of the MTO findings. For example, redevelopment programs like Choice Neighborhoods take a broader, more holistic approach to reforming a distressed, high poverty, high crime neighborhood into a safer one with lower poverty concentrations by linking housing improvements with appropriate services, schools, public assets, transportation, and access to jobs.

The MTO demonstration has contributed significantly to our knowledge of how neighborhoods affect families. It continues to provide an exceptionally rich dataset for understanding the role environment plays in individual outcomes. Moreover, in addition to the work done under HUD’s guidance, other complementary quantitative and qualitative studies have been undertaken, funded by a large group of federal agencies and philanthropic organizations. Thus, the MTO demonstration has served as a foundation for the pursuit of a much larger research agenda. Despite the considerable work already done with these data, much remains to be explored as HUD continues to support using data to drive policy.

Raphael W. Bostic, Ph.D.

Assistant Secretary for Policy

Development and Research

The contents of this report are the views of the contractor and do not necessarily reflect the views or policies of the U.S.

Department of Housing and Urban Development, the U.S. Government, or any state or local agency that provided data. The use of South Carolina Department of Social Services and the Florida Department of Juvenile Justice records in the preparation of this material is acknowledged, but it is not to be construed as implying official approval of either department of the conclusions presented. New York State Division of Criminal Justice Services (DCJS) provided de-identified arrest data for the study. DCJS is not responsible for the methods of statistical analysis, as well as any conclusions derived therefrom.

TABLE OF CONTENTS

EXECUTIVE SUMMARY

The Moving to Opportunity for Fair Housing Demonstration Program Final Impacts Evaluation XII

The Contributions of the MTO Demonstration

XII

T

Interim Findings: Four to Seven Years After Baseline

XIV

Long-Term Findings: 10 to 15 Years After Baseline

XIV

ABLE OF CONTENTS

CHAPTER 1 – The MTO Long-Term Study

1.1 How Housing Policies Might Affect Low-Income Families

3

1.2 Motivation for the Moving to Opportunity Experiment

8

1.3 Estimating the MTO Program Effects

14

1.4 MTO Mobility Outcomes

16

1.5 Previous MTO Findings

17

1.6 The Long-Term Study

24

1.7 Overview of the Report

30

CHAPTER 2 – Impacts on Mobility, Housing,

Neighborhoods, and Social Networks

2.1 Baseline and Control Group Context

40

2.2 Hypotheses About Effects on Mobility, Housing, Neighborhoods, and Social Networks 43

2.3 Data Sources and Measures

46

2.4 Long-Term Impacts on Mobility and Housing

51

2.5 Long-Term Impacts on Neighborhood Characteristics

57

2.6 Long-Term Impacts on Social Networks

58

2.7 Interpretation of Results

67

CHAPTER 3 – Impacts on Adult and Youth Physical Health

3.1 Hypotheses About Effects on Adult and Youth Physical Health

80

3.2 Earlier Research: Short-Term and Interim Impacts on Physical Health

84

3.3 Baseline and Control Group Context

84

3.4 Data Sources and Measures

84

3.5 Long-Term Impacts on Adult Physical Health

89

3.6 Long-Term Impacts on the Physical Health of Youth and Grown Children

95

3.7 Impacts on Hypothesized Mediators of Health

102

3.8 Interpretation of Physical Health Results

103

CHAPTER 4 – Impacts on Adult and Youth Mental Health

4.1 Hypotheses About Effects on Adult and Youth Mental Health

114

4.2 Earlier Research: Short-Term and Interim MTO Impacts on Mental Health

117

4.3 Data Sources and Measures

117

4.4 Context and Baseline Status of the Sample

121

4.5 Long-Term Impacts on Mental Health

121

4.6 Impacts on Hypothesized Mediators

133

4.7 Interpretation of Mental Health Results

134

VI

CHAPTER 5 – Impacts on Economic Self-Sufficiency

5.1 Baseline and Control Group Context

138

5.2 Hypotheses About Effects on Economic Self-Sufficiency

141

5.3 Data Sources for Measures of Employment and Earnings

145

5.4 Long-Term Impacts on Employment and Earnings of Adults

147

5.5 Long-Term Impacts on Employment and Earnings of Youth and Grown Children 154

5.6 Data Sources for and Measures of Income, Poverty, Food Consumption, and Social Assistance 158

5.7 Long-Term Impacts on Income, Poverty, Food Consumption, and Social Assistance 159

5.8 Data Sources for Measures of Savings and Assets

163

5.9 Long-Term Impacts on Savings and Assets

163

5.10 Interpretation of Economic Self-Sufficiency Results

163

CHAPTER 6 – Impacts on Risky and Criminal Behavior

6.1 Hypotheses About Effects on Risky and Criminal Behavior

186

6.2 Earlier Research: Short-Term and Interim Impacts on Risky and Criminal Behavior 193

6.3 Baseline and Control Group Context

194

6.4 Data Sources and Measures

195

6.5 Long-Term Impacts on Risky and Criminal Behavior

196

6.6 Interpretation of Results for Risky and Criminal Behavior

206

CHAPTER 7 – Impacts on Educational Outcomes

7.1 Baseline and Control Group Context

214

7.2 Hypotheses About Effects on Educational Outcomes

215

7.3 Earlier Research: On Neighborhoods and Educational Outcomes

Including Short-Term and Interim MTO Impacts

218

7.4 Data Sources and Measures

221

7.5 Long-Term Impacts on Hypothesized Mediators of Educational Effects

224

7.6 Long-Term Impacts on Achievement and Educational Outcomes

230

7.7 Interpretation of Education Results

238

CHAPTER 8 – Summary and Implications

8.1 Summary of Impact Estimates

254

8.2 Mobility, Housing, Neighborhoods, and Social Networks

254

8.3 Physical Health

256

8.4 Mental Health

257

8.5 Economic Self-Sufficiency

257

8.6 Risky and Criminal Behavior

258

8.7 Educational Outcomes

259

8.8 Can These MTO Results Be Trusted?

260

8.9 Implications for Social Science and Public Policy

261

References

267

List of Exhibits

EXECUTIVE SUMMARY

Exhibit ES–1. Allocation and Compliance of the Long-Term Evaluation Samples by Site and Treatment Group

XVII

Exhibit ES–2. Neighborhood Poverty Rates of Adults at Different Points in Time XVIII

Exhibit ES–3. Key Housing, Neighborhood, and Social Network Outcomes, Long-Term Evaluation XIX

Exhibit ES–4. Key Adult Physical Health Outcomes, Long-Term Evaluation

XXI

Exhibit ES–5. Key Physical Health Outcomes for Youth Ages 10–20, Long-Term Evaluation XXII

VII

Exhibit ES–6. Key Adult Mental Health Outcomes, Long-Term Evaluation

XXIII

Exhibit ES–7. Key Mental Health Outcomes for Youth Ages 13–20, Long-Term Evaluation XXIV

Exhibit ES–8. Key Adult Economic Self-Sufficiency Outcomes, Long-Term Evaluation XXVII

Exhibit ES–9. Key Economic Self-Sufficiency Outcomes for Youth Ages 15–20, Long-Term Evaluation XXVIII

Exhibit ES–10. Risky and Criminal Behavior Outcomes for Youth, Long-Term Evaluation XXX

Exhibit ES–11. Number of Post-Random Assignment Arrests for

Grown Children Ages 21–30 and Adults, Long-Term Evaluation

XXXIII

Exhibit ES–12. Achievement and Educational Outcomes for Youth, Long-Term Evaluation XXXV

CHAPTER 1

Exhibit 1.1. Hypothesized Pathways of MTO Impacts

5

Exhibit 1.2. Baseline Characteristics of the Adult Sample by Random Assignment Group 9

Exhibit 1.3. Baseline Characteristics of the Youth Sample by Random Assignment Group 12

Exhibit 1.4. MTO Voucher Lease-Up Rates for MTO Experimental and Section 8 Groups 16

Exhibit 1.5. Census Tract Characteristics for Key Addresses for

MTO Adults Surveyed in the Long-Term Evaluation

20

Exhibit 1.6. Impact of MTO on Selected Adult Outcomes, Interim Evaluation (4–7 Years After Baseline) 21

Exhibit 1.7. Impact of MTO on Selected Child and Youth Outcomes,

Interim Evaluation (4–7 Years After Baseline)

22

Exhibit 1.8. Allocation and Compliance of the Long-Term Evaluation Samples, By Site and Treatment Group

27

Exhibit 1.9. Content of Participant Data Collected for the MTO Long-Term Evaluation 28

Exhibit 1.10. MTO Adult and Youth Long-Term Survey Instrument Response Rates, by Random Assignment Group

32

Supplemental Exhibit 1.1. Baseline Characteristics of the Grown Child Sample by Random Assignment Group

32

Supplemental Exhibit 1.2. Mean, Standard Deviation, and Share Missing for Baseline Covariates by MTO Sample

33

CHAPTER 2

Exhibit 2.1. Selected Baseline Characteristics: Household and Neighborhood Characteristics 41

Exhibit 2.2. Adult Mobility and Homelessness

54

Exhibit 2.3. Adult Housing Tenure and Reasons for Moving

55

Exhibit 2.4. Adult Housing Assistance, Costs, and Stability

56

Exhibit 2.5. Adult Housing Quality

57

Exhibit 2.6. Average (Duration-Weighted) Neighborhood Census Tract Characteristics of All Adults’ Addresses From Random Assignment Through May 2008

60

Exhibit 2.7. Census Tract Characteristics of Adult’s Current Neighborhood (May 2008) 61

Exhibit 2.8. Adult Neighborhood Characteristics and Satisfaction

62

Exhibit 2.9. Interviewer Observations of the Adult’s Current Neighborhood

63

Exhibit 2.10. Adult and Youth Neighborhood Safety and Crime Victimization

64

Exhibit 2.11. Neighborhood Social Process and Adult Social Networks

66

Supplemental Exhibit 2.1. Adult Current Housing Condition

68

Supplemental Exhibit 2.2. Youth Neighborhood Census Tract

Characteristics for Current Address (May 2008)

69

Supplemental Exhibit 2.3. Adult Neighborhood Conditions

72

Supplemental Exhibit 2.4. Mediators: Adult Social Networks

73

Supplemental Exhibit 2.5. Mediators: Youth’s Social Network

73

Supplemental Exhibit 2.6. Mediators: Youth’s Peers

75

Supplemental Exhibit 2.7. Youth Self-Reflection

76

VIII

CHAPTER 3

Exhibit 3.1. Hypothesized Pathways Through Which MTO May Impact Physical Health 81

Exhibit 3.2. Adult Physical Health Outcomes

91

Exhibit 3.3. Adult Self-Rated Health and BMI Categories by Treatment Group

94

Exhibit 3.4. Youth Physical Health Outcomes

96

Exhibit 3.5. Female Youth Self-Rated Health and BMI Categories by Treatment Group 99

Exhibit 3.6. Male Youth Self-Rated Health and BMI Categories by Treatment Group 100

Exhibit 3.7. Parent-Reported Physical Health Outcomes of Grown Children Ages 21–30

101

Supplemental Exhibit 3.1. Physical Health Outcomes of Youth Ages 15–20

105

Supplemental Exhibit 3.2. Adult Health Behaviors: Exercise, Nutrition, and Sleep 108

Supplemental Exhibit 3.3. Youth Health Behaviors: Exercise, Nutrition, and Sleep 109

Supplemental Exhibit 3.4. Self-Reported Health Care Access for Adults and Youth 111

CHAPTER 4

Exhibit 4.1. Hypothesized Pathways Through Which MTO May Affect Mental Health 114

Exhibit 4.2. Adult Psychological Well-Being

116

Exhibit 4.3. Adult Mental Disorders

122

Exhibit 4.4. Youth Psychological Well-Being, Ages 13–20

125

Exhibit 4.5. Youth Mental Disorders, Ages 13–20

127

Exhibit 4.6. Summary Measures of Youth Mental Health, Ages 13–20

131

Exhibit 4.7. Parent-Reported Mental Health of Grown Children Ages 21–30

133

Supplemental Exhibit 4.1. Adult Mental Health Services in Past 12 Months

135

Supplemental Exhibit 4.2. Youth Psychological Well-Being, Ages 10–20 and Ages 15–20

135

CHAPTER 5

Exhibit 5.1. Selected Baseline Characteristics of Adults Surveyed in the Long-Term Evaluation: Work, Public Assistance, and Income

140

Exhibit 5.2. Hypothesized Pathways Through Which MTO May Impact Economic Self-Sufficiency 142

Exhibit 5.3. Adult Five-Site Quarterly Employment Rate

by Random Assignment Group and Calendar Quarter

147

Exhibit 5.4. Adult Five-Site Quarterly Employment Rate

by Random Assignment Group and Quarters Since Random Assignment

148

Exhibit 5.5. Adult Employment and Earnings, Administrative Data by Years Since Random Assignment 150

Exhibit 5.6. Adult Employment in 2007, Administrative Data by Site

152

Exhibit 5.7. Adult Self-Reported Employment and Earnings

153

Exhibit 5.8. Employment and School Enrollment of Grown Children:

Parent Reports and Administrative Data

155

Exhibit 5.9. Youth Employment and Earnings, Administrative Data

by Site for Fourth Quarter 2007 to Third Quarter 2008

156

Exhibit 5.10. Youth Self-Reported Employment, Earnings, and School Enrollment 157

Exhibit 5.11. Adult Self-Reported Household Income, Poverty Status, and Food Sufficiency 160

Exhibit 5.12. Welfare and Food Stamp Receipt of Grown Children, Administrative Data 161

Exhibit 5.13. Adult Government Assistance, Self-Reports and Administrative Data 162

Exhibit 5.14. Adult Self-Reported Family Savings and Assets

164

Supplemental Exhibit 5.1. Adult Employment, Administrative Data

by Site and Random Assignment (RA) Years

165

Supplemental Exhibit 5.2. Comparison of Self-Reported and Administrative Data, on Adult Current Employment, by Site

169

Supplemental Exhibit 5.3. Comparison of Self-Reported and Administrative Data on Adult Employment in Maryland, Illinois, and California (In-State Only)

170

Supplemental Exhibit 5.4. Adult Self-Reported Current Industry and Occupation 171

Supplemental Exhibit 5.5. Adult Self-Reported Current Employment Benefits

172

IX

Supplemental Exhibit 5.6. Four Mutually Exclusive Categories of Work and Schooling of Grown Children, Parent Reports

172

Supplemental Exhibit 5.7. Employment and Schooling Outcomes of Grown Children, Parent Reports and Administrative Data by Site

173

Supplemental Exhibit 5.8. Youth Employment and School Enrollment,

Self-Reported and Administrative Data by Site

177

Supplemental Exhibit 5.9. Self-Reported Youth Employment and School Enrollment 181

Supplemental Exhibit 5.10. Family Temporary Assistance for Needy Families (TANF) and Food Stamps Benefits in Massachusetts, Illinois, and Los Angeles County, Adult-Reported and Administrative Data, by Month Closest to Interview Month

182

Supplemental Exhibit 5.11. Family TANF and Food Stamps Benefits in Massachusetts, Illinois, and Los Angeles County, Adult-Reported and Administrative Data for the Same Month 183

Supplemental Exhibit 5.12. Adult Self-Reported Earned Income Tax Credit (EITC) 183

CHAPTER 6

Exhibit 6.1. The “Age-Crime Curve” for Criminal Behavior: National Data on Percent of Crime by Age Group 187

Exhibit 6.2. Developmental Pathways to Serious and Violent Offending

188

Exhibit 6.3. Hypothesized Pathways Through Which MTO May Impact Delinquency and Risky Behavior 190

Exhibit 6.4. National Trends in Substance Use and Teen Pregnancy

194

Exhibit 6.5. Risky Behavior Outcomes for Youth Ages 13–20

197

Exhibit 6.6. Behavior Problems and Delinquency for Youth Ages 13–20

199

Exhibit 6.7. Number of Post-Random Assignment Arrests for Youth Ages 15–20

200

Exhibit 6.8. Number of Post-Random Assignment Arrests for Grown Children Ages 21–30

202

Exhibit 6.9. Incarceration of Grown Children Ages 21–30

204

Exhibit 6.10. Number of Post-Random Assignment Arrests for Adults

205

Supplemental Exhibit 6.1. Prevalence of Post-Random Assignment Arrests for Youth Ages 15–20

207

Supplemental Exhibit 6.2. Prevalence of Post-Random Assignment Arrests for

Grown Children Ages 21–30

209

Supplemental Exhibit 6.3. Prevalence of Post-Random Assignment Arrests for Adults 211

CHAPTER 7

Exhibit 7.1. Baseline Characteristics of the Youth Sample by Random Assignment Group 217

Exhibit 7.2. Hypothesized Pathways Through Which MTO May Impact Achievement and Educational Outcomes

220

Exhibit 7.3. Characteristics of Schools Attended by Youth Ages 10–20

226

Exhibit 7.4. School Mobility among Youth Ages 10–20

228

Exhibit 7.5. Perceptions of School Climate among Youth Ages 10–17

229

Exhibit 7.6. Achievement Assessment Results for Youth Ages 13–20

231

Exhibit 7.7. Schooling Outcomes for Youth Ages 10–20

232

Exhibit 7.8. Post-Secondary Outcomes from National Student Clearinghouse Data for Youth Ages 15–20

234

Exhibit 7.9. Schooling Outcomes for Grown Children Ages 21–30

236

Supplemental Exhibit 7.1. Absenteeism, Tardiness, or Problems at School for Youth Ages 10–20

239

Supplemental Exhibit 7.2. Future Expectations of Youth Ages 10–20

241

Supplemental Exhibit 7.3. School Engagement and Parental Involvement in School 242

Supplemental Exhibit 7.4. Time Use and Access to Technology among Youth Ages 10–20

243

Supplemental Exhibit 7.5. Achievement Assessment Results by Baseline Age among Youth Ages 13–20

246

Supplemental Exhibit 7.6. Achievement Assessment Results by Site among Youth Ages 13–20

247

Supplemental Exhibit 7.7. Interviewer Observations of Youth Ages 13–20 during Achievement Assessment

248

Supplemental Exhibit 7.8. Post-Secondary Schooling Outcomes of Youth Ages 15–20, Self-Report versus Administrative Data

249

Supplemental Exhibit 7.9. Post-Secondary Schooling Outcomes of Grown Children Ages 21–30, Self-Report versus Administrative Data

251

X

XI

THE MOVING TO OPPORTUNITY FOR

Bill & Melinda Gates Foundation, the Russell Sage

FAIR HOUSING DEMONSTRATION

Foundation, and the Annie E. Casey Foundation.

PROGRAM FINAL IMPACTS EVALUATION

EXECUTIVE SUMMAR

This report presents the long-term impacts of a unique

The results show that MTO moves led to sustained

housing mobility demonstration, Moving to Opportunity improvements in housing quality and in many (MTO), on housing and neighborhood conditions,

aspects of the neighborhood’s environment, including

physical and mental health, economic self-sufficiency,

neighborhood socioeconomic composition and safety.

risky and criminal behavior, and educational outcomes.

These MTO-induced changes translate into a number

The MTO demonstration was authorized by the U.S.

of important improvements in mental and physical

Congress in section 152 of the Housing and Community health for adults, including lower rates of extreme Development Act of 1992. In 1994, the U.S. Department obesity, diabetes, psychological distress, and major of Housing and Urban Development (HUD) launched

depression. MTO had no detectable impacts on work,

MTO to test whether offering housing vouchers to

earnings, or other economic outcomes for adults.

families living in public housing projects in high-poverty

For youth, we see some signs of the same gender

neighborhoods of large inner cities could improve their

difference in responses to MTO as were found in the

lives and the lives of their children by allowing them

interim study, which reported on outcomes measured

to move to lower-poverty neighborhoods. The original

four to seven years after random assignment. One

authorizing legislation for MTO charged HUD with

outcome for which we see some hints of beneficial

describing “the long-term housing, employment, and

MTO impacts on male youth is a reduction in illegal

educational achievements of the families assisted under

drug selling.

the demonstration program.” This report discharges that

responsibility.

THE CONTRIBUTIONS OF THE MTO

DEMONSTRATION

Thanks to the generous support of other federal

There is a long-standing belief that living in a less

government agencies and private foundations, the scope

economically and socially distressed neighborhood

of our long-term study of MTO families was expanded

can improve people’s well-being and long-term

to include a number of outcome domains beyond

life chances, owing to some combination of better

those under HUD’s charge, most notably physical and

housing conditions, lower crime, less stress, greater

mental health. The additional funders that enabled this

job opportunities, more supportive schools, and

important expansion to the study’s scope included the

role models who are more affirming of pro-social

Y

National Science Foundation, the National Institute for

behaviors. However, empirically isolating the effect

Child Health and Human Development, the Centers

of neighborhoods on people’s outcomes from other

for Disease Control, the National Institute of Mental

possible influences has been difficult.

Health, the National Institute for Aging, the National

Opinion Research Center’s Population Research Center

MTO overcomes some of the empirical challenges

(supported by the National Institute of Child Health and of identifying neighborhood effects on people’s Human Development), University of Chicago’s Center

life outcomes because it was implemented as

for Health Administration Studies, the U.S. Department

an experiment. Akin to drug trials in medicine,

of Education’s Institute of Education Sciences, the John

families at the beginning of the experiment were

D. and Catherine T. MacArthur Foundation, the Smith

randomly assigned to a control group or one of two

Richardson Foundation, the Spencer Foundation, the

treatment groups. An MTO-type experiment enables

XII

us to determine whether moving to a lower-poverty

heads were on welfare, and fewer than 40 percent had

neighborhood itself, rather than some other characteristic graduated from high school. Most families had two or of the individuals or families that might be related to

three children. In more than 40 percent of enrolled

both their propensity to move and their behavioral

households, a household member had been victimized by

outcomes, directly caused improvements in health,

a crime during the previous six months.

economic security, or some other outcome of interest.

Because of random assignment, the control group’s

Not all families offered a housing voucher through MTO

experience shows, on average, what would have happened actually used it to move to a different neighborhood.

to the families in the treatment groups had they not been Those who did move with an MTO housing voucher offered a voucher through MTO.

(that is, those who “complied” with their MTO

treatment assignment) were on average relatively younger,

From 1994 to 1998, the MTO demonstration enrolled

more dissatisfied with their original neighborhoods,

4,604 low-income households in Baltimore, Boston,

and had fewer children than those who did not use the

Chicago, Los Angeles, and New York. Eligibility for

voucher.

MTO was limited to households with children in public

or other government-subsidized, project-based housing

Accordingly, a useful starting point for our analysis is to

in selected high-poverty areas. Enrolled families were

compare the average outcomes of all families assigned

assigned at random to one of three groups:

to the experimental group, regardless of whether they

relocated with an MTO voucher, with the average

1. The experimental group received Section 8 rental

outcomes of those assigned to the control group. The

assistance certificates or vouchers that they could

same comparison was calculated for the Section 8 and

use only in census tracts with 1990 poverty rates

control groups. These differences are called “intention to

below 10 percent. The families received mobility

treat” (ITT) effects. They capture the effect of offering

counseling and help in leasing a new unit. Forty-

a family the chance to move with an MTO voucher as

eight percent of families assigned to this group

part of the experimental or Section 8 group. Our design

moved to a lower-poverty neighborhood with

also enables us to estimate treatment/control group

an MTO voucher. One year after relocating,

differences for the subset of families who actually moved

families could use their voucher to move again if

with an MTO voucher. These are known as the effects of

they wished, without any special constraints on

“treatment on the treated” (or TOT).

location.

2. The Section 8 only group received regular

MTO is thus the first random-assignment social

Section 8 certificates or vouchers that they could

science experiment designed to identify the causal

use anywhere; these families received no special

effects of moving from high-poverty to lower-poverty

mobility counseling. Sixty-three percent moved

neighborhoods on the social, economic and educational

using a voucher obtained through MTO.

prospects, risky and criminal behavior, health, and

3. The control group received no certificates or

well-being of low-income families. Other unique

vouchers through MTO, but continued to be

characteristics of the demonstration project include:

eligible for project-based housing assistance and

whatever other social programs and services to

• A large study sample.

which they would otherwise be entitled.

• Multiple program sites drawn from different

regions across the United States.

The MTO program population was very economically

• Long-term survey and administrative data follow-

disadvantaged at baseline. Almost all the households that

up of program participants.

signed up for MTO were headed by women. Nearly two-

• Exhaustive tracking of program participants over

thirds were African-American, and most of the remaining

time and several sophisticated survey follow-ups of

one-third were Hispanic. Three-quarters of household

participants that produced high response rates.

XIII

• Direct measurement of key physical health

measured by arrests for violent crime. MTO had few

outcomes such as height, weight, blood pressure,

detectable effects on child physical health or risky

and biomarkers based on blood samples.

behaviors aside from an increase in nonsports injuries

• Multiple administrative data sources for

for male youth. MTO also had no detectable impacts on

independent measurements that complement

educational achievement as measured by standardized test

information from the surveys.

scores.1

• Examination of a large set of outcome domains.

LONG-TERM FINDINGS: 10 TO 15

INTERIM FINDINGS: FOUR TO SEVEN

YEARS AFTER BASELINE

YEARS AFTER BASELINE

The final evaluation, which is the topic of the present

An interim multisite evaluation of MTO’s effects

report, is an opportunity to answer questions about the

was conducted four to seven years after families

longer-term effects of housing mobility on poor families,

entered the program. At that point, families who had

measured 10 to 15 years after families enrolled in the

moved to lower-poverty neighborhoods were doing

program. The study’s populations of interest were all

significantly better than those in the control group in

members of the 4,604 households at the beginning of the

some important respects. On many other dimensions,

program. Subsets include:

however, the different groups had virtually identical

outcomes, on average. Since previous theoretical work

• 4,604 adults who were heads of those households

and observational studies had led to expectations of large

at the beginning of the program

neighborhood effects, this overall pattern of interim

• 6,308 youth who were ages 10–20 as of December

results was disappointing for many in the research and

31, 20072

policy communities.

• 4,643 grown children (ages 20–30 at the end of

2007).

Adults assigned to either the experimental or Section 8

groups were safer and more satisfied with their housing

Executive Summary (ES) Exhibit 1 shows how these

and neighborhoods compared with adults in the control

groups of adults and youth are distributed across

group, and were also less likely to report a household

demonstration sites and groups. It also shows the fraction

member being a victim of a crime or seeing illicit drugs

of adults and youth assigned to the experimental and

sold. Compared with the control group, adults in

Section 8 groups who moved with an MTO housing

the experimental group had better mental health and

voucher, that is, the “compliance rate.”

some better physical health outcomes and behaviors,

such as lower rates of obesity together with some signs

To learn more about the outcomes of this study, we draw

of improved diet and exercise. However, the MTO

on survey interviews with 3,273 of the adult household

“treatment” led neither to better labor market outcomes

heads as well as 5,105 youth who were age 10–20 at the

nor reductions in social program participation.

end of 2007. The effective response rate for the long-

term follow-up survey was 90 percent for the adult

MTO’s impacts on children in participating families

household heads and 89 percent for youth selected for

differed by gender, with generally beneficial effects for

the survey. These response rates were generally similar

female youth and, on balance, some deleterious effects

across randomly assigned MTO mobility groups, with

for male youth. Female youth in the experimental

the exception of a slightly lower response rate for adults

group had better mental health outcomes than their

in the Section 8 group (around 87 percent). We also

control group counterparts, whereas male youth in the

experimental group were more likely to engage in risky

1 These are the overall results covering all five MTO sites. There were

some detected improvements in individual sites. Youth saw some gains

behavior than control group males. Moving also reduced

in test scores in Baltimore and Chicago, for example.

violent behavior for both male and female youth, as

2 This group only partially overlaps with the set of youth who were ages

8–20 at the end of 2001 and interviewed as part of the interim survey.

XIV

relied on a variety of government administrative data

8 groups were 9 and 7 percentage points lower than

sources. This report examines the impact of moving to

this, respectively. (This intention to treat effect is

lower-poverty neighborhoods on residential mobility,

labeled “ITT” in the exhibits). For those families in the

housing conditions, neighborhood conditions, and social experimental and Section 8 groups that moved through networks of participating families. It also reports on

MTO, average neighborhood poverty rates were about

physical health, mental health, economic self-sufficiency,

18 and 11 percentage points lower, respectively.3 (This

risky and criminal behavior, and educational outcomes.

“treatment on the treated” effect is labeled “TOT” in the

These findings are outlined by topic below.

exhibits.) Median household income was almost $19,000

higher in the census tracts where experimental group

MOBILITY, HOUSING, NEIGHBORHOOD

movers lived. MTO moves also made participants feel

CONDITIONS, AND SOCIAL NETWORKS

safer in their new neighborhoods, although this was less

MTO had sizable positive impacts on housing and

clearly evident for male youth in the program.

neighborhood conditions and the social networks with

which participating families interacted. As Exhibit ES-2

MTO moves only modestly reduced neighborhood racial

and 3 show, the experimental and Section 8 groups were

segregation. For control group members, 88 percent

more likely than the control group to:

of their neighbors were members of racial and ethnic

minority groups. Families in the experimental group

• Live in lower-poverty neighborhoods

saw a decline of nearly 6 percentage points in the share

• Live in higher-quality homes

minority, while the decline was smaller for those in the

• Reside in slightly less racially segregated

Section 8 group. Families in both treatment groups, even

neighborhoods (although most families even in

those who moved with an MTO voucher, were still living

the experimental and Section 8 groups were still in

in census tracts that were three-quarters minority.

majority-minority neighborhoods)

• Have more social ties with relatively more affluent

MTO also changed the social environments of the

people

neighborhoods in which families were living. MTO

• Feel safer in their neighborhoods.

helped families move into neighborhoods where

neighbors were more willing to work together to support

MTO enabled many families to move to lower-poverty

shared norms, a measure of informal social control that

neighborhoods. In the census tracts in which MTO

previous research suggests may be particularly important

participants lived at baseline, some 53 percent of tract

in improving the lives of neighborhood residents. MTO

residents had family incomes below the poverty line.

increased social connections of adults to other people

Experimental and Section 8 families who moved with an

who were employed full-time or had completed college.

MTO voucher (the “compliers”) lived in neighborhoods

MTO moves did not appear to increase social isolation.

with mean tract poverty rates of 11 and 29 percent,

respectively, immediately after their initial program

A majority of families in all three groups were still

moves (see Executive Summary Exhibit 2). Over time,

receiving some form of housing assistance, with slightly

though, the differences between the treatment and

higher rates of assistance among the Section 8 group.

control groups narrowed, in large part because some

However, fewer in the experimental and Section 8 groups

control group families moved on their own to better

than in the control group were living in public housing,

neighborhoods and treatment group families moved

and relatively more were instead using vouchers. Given

on to neighborhoods with somewhat higher poverty

that most families were still receiving some form of

rates. When averaged over the entire 10 to 15-year

housing assistance, it is perhaps not surprising that MTO

study period, the control group’s neighborhood poverty

had only modest effects on out-of-pocket expenditures

rates were roughly 40 percent (see Executive Summary

Exhibit 2). The average neighborhood poverty rates for

3 This is the duration-weighted poverty rate; that is, the poverty rate

all families assigned to the experimental and Section

for each of a family’s addresses weighted by the amount of time the

family lived in a particular census tract.

XV

on housing. Compared with the control group adults,

lower among those in the experimental group. The share

those in the experimental group were less likely to report

of experimental group adults whose BMI was 40 kg/

being late with their rent or mortgage payments, but

m2 or greater, a commonly used definition of extreme

were more likely to have had trouble paying their utility

obesity, was about 3 percentage points lower than

bills on time.

those in the control group. There were no significant

differences between the Section 8 and control groups

Finally, MTO improved the quality of housing in

in the prevalence of extreme obesity. Diabetes was 3 to

which families were living. Adults in the experimental

6 percentage points less prevalent for those in the two

group were about 5 percentage points more likely than

MTO treatment groups than in the control group as

control group adults to report that their current housing

measured from blood samples. The effect is somewhat

conditions were excellent or good.

smaller for the experimental group if we use respondent

self-reports about whether their doctors had told them

ADULT AND YOUTH PHYSICAL HEALTH

they had diabetes. An advantage of the blood-based

Living in disadvantaged neighborhoods has been

biomarkers over self-reports of diabetes is that they are

hypothesized to worsen physical health. Previous research free from the underreporting caused by respondents has found strong correlations between living in poor

being unaware they have the condition.

neighborhoods and prevalence rates of mortality, heart

disease, obesity, depression, and substance abuse, even

Adult health outcomes, such as self-rated overall health,

after controlling for observable individual-level health

asthma, hypertension, chronic pain, and substance use,

risk factors. These previous findings suggest that moving

were largely unaffected by moving to lower-poverty

to a less disadvantaged neighborhood could conceivably

neighborhoods. Among youth, moving to a lower-poverty

improve health, and indeed there is some indication

neighborhood had little to no measured effect on the

that this is the case for MTO participants on several

health outcomes measured (see Exhibit ES-5).

important health outcomes.

ADULT AND YOUTH MENTAL HEALTH

As Exhibit ES-4 shows, a little more than one-half of

As with physical health, living in disadvantaged

the adults across all groups, including the control group,

neighborhoods has long been thought to increase the risk

rated their health as good or excellent. Evidence from

for mental health problems. Research has documented

self-reports, physical biomarkers, and blood samples

large correlations between rates of neighborhood poverty

indicate that at the time of the long-term follow-

or crime and depression, anxiety, and other mental health

up, compared with the control group, adults in the

problems. Exposure to violence, for example, is associated

experimental and Section 8 group have:

with long-term behavioral and psychological harm

for both youth and adults. Males, who are more often

• a lower prevalence of extreme obesity

exposed to violence than females, may be particularly

• a lower prevalence of diabetes

vulnerable.

• fewer self-reported physical limitations

• similar self-reported health status, as well as similar Data from a battery of psychological measures show rates of hypertension and health-related risk

that, compared with the control group, adults in the

behaviors.

experimental or Section 8 group have:

Although there were no significant differences across

• lower levels of psychological distress

groups in obesity (as measured by body mass index

• lower prevalence of depression

(BMI) of greater than or equal 30, where BMI is defined

• lower prevalence of anxiety

as weight in kilograms divided by height in meters

• similar rates of most other mental health problems.

squared (kg/m2)), the share of adults with BMI of 35

kg/m2 or greater was approximately 5 percentage points

XVI

As Exhibit ES-6 shows, MTO lowered the prevalence

as shown in the table and in Chapter 4, relative to female

of depression among adults in both the experimental

youth in the control group, females in the experimental

and Section 8 group by 3 and 5 percentage points,

group have:

respectively (although the experimental group impact

is just marginally significant). MTO did not have

• a lower prevalence of any lifetime mood disorder

statistically significant effects on other mental health

• fewer serious emotional or behavioral difficulties

measures, such as levels of mental calm, normal sleep

• fewer panic attacks in the past year

patterns, having any mood disorder, panic attacks,

• less psychological distress

post-traumatic stress, or intermittent explosive disorder.

• lower prevalence of oppositional defiant disorder in

However, MTO’s impacts on almost all of these measures

the past year

were in the direction of improved mental health for

• similar rates of other mental health problems.

movers relative to controls. One exception is that moves

to lower-poverty neighborhoods seemed to slightly

For male youth ages 10–20, however, moving to lower-

increase drug or alcohol abuse or dependence among

poverty neighborhoods may have increased lifetime

adults in the experimental group compared with the

post-traumatic stress disorder; prevalence of this disorder

control group.

among male youth in the Section 8 group were about

3 percentage points higher than in the control group.

Exhibit ES-7 shows selected mental health outcomes for

Although not reaching statistical significance, many of

male and female youth. For female youth ages 10–20,

the other mental health indicators had worsened for male

moving to lower-poverty neighborhoods had an overall

youth after moving.

positive effect on their mental health. More specifically,

EXHIBIT ES–1. ALLOCATION AND COMPLIANCE OF THE LONG-TERM EVALUATION SAMPLES BY SITE AND

TREATMENT GROUP

ALL

CONTROL EXPERIMENTAL GROUP

SECTION 8 GROUP

GROUPS

GROUP

N

N

N

COMPLIANCE N

COMPLIANCE

RATE (%)

RATE (%)

ADULTS

Baltimore

572

197

252

53.5

123

79.8

Boston

868

326

366

43.6

176

51.1

Chicago

825

232

460

33.4

133

67.4

Los Angeles

929

389

340

60.5

200

71.6

New York City

948

295

401

46.4

252

45.2

All sites

4,142

1,439

1,819

47.4

884

61.6

YOUTH, AGES 10–20

Baltimore

762

240

268

59.1

254

79.2

Boston

1,267

440

475

38.2

352

54.6

Chicago

1,363

328

701

31.7

334

72.0

Los Angeles

1,539

592

502

62.2

445

78.2

New York City

1,377

418

471

49.6

488

49.2

All sites

6,308

2,018

2,417

47.6

1,873

66.4

Notes: Percentages are weighted to reflect the randomization ratios and sampling of Section 8 adults and up to three youth per family. "Compliance" is defined as leasing a unit using a housing voucher provided by the Moving To Opportunity (MTO) program.

Data source and sample: MTO data system. The samples are N = 4,142 adults and N = 6,308 youth ages 10 to 20 as of December 2007 selected for the long-term survey. Excluded from the samples are the N = 462 Section 8 group adults and youth from households with greater than three youth ages 10–20 who were not randomly selected for the long-term survey.

XVII

EXHIBIT ES–2. NEIGHBORHOOD POVERTY RATES OF ADULTS AT DIFFERENT POINTS IN TIME

CONTROL EXPERIMENTAL GROUP

SECTION 8 GROUP

GROUP

ALL

ALL

COMPLIERS

NON-

ALL COMPLIERS

NON-

COMPLIERS

COMPLIERS

SHARE POOR IN

CENSUS TRACT OF

RESIDENCE FOR MTO

FAMILIES BY TIME SINCE

RANDOM ASSIGNMENT

Baseline address

0.531

0.527

0.530

0.524

0.526

0.540

0.505

Initial MTO-assisted move

0.107

0.287

address

Address at time of MTO

0.395

0.299

0.193

0.402

0.326

0.286

0.391

interim evaluation (4–7

years after baseline)

Address at time of MTO

0.313

0.274

0.210

0.334

0.283

0.244

0.345

long-term evaluation (10–15

years after baseline)

Average (duration-

0.396

0.306

0.200

0.407

0.329

0.285

0.400

weighted) of all addresses

since Random Assignment

Notes: Characteristics for the census tracts of addresses at which Moving To Opportunity (MTO) families were living at different points in time were linearly interpolated from the 1990 and 2000 decennial Censuses as well as the 2005–09 American Community Survey. Addresses at the time of the interim and long-term evaluations are as of the beginning of the fielding period for each study (December 31, 2001, for the interim evaluation and May 31, 2008, for the long-term evaluation).

Sample: Adults interviewed as part of the long-term evaluation (N = 3,273).

XVIII

EXHIBIT ES–3. KEY HOUSING, NEIGHBORHOOD, AND SOCIAL NETWORK OUTCOMES, LONG-TERM EVALUATION

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

ADULT

HOUSING

Rates current housing as

0.570

0.053*

0.109*

0.031

0.050

3,267

excellent or good [SR]

(0.021)

(0.044)

(0.029)

(0.046)

Currently receiving any

0.620

0.026

0.054

0.045~

0.072~

3,273

housing assistance [SR, HA]

(0.021)

(0.043)

(0.027)

(0.044)

Total housing cost (2009

$678.73

19.50

39.67

– 6.26

– 10.00

3,180

dollars), monthly [SR]

(23.30)

(47.39)

(30.73)

(49.10)

NEIGHBORHOOD

Median household income

$27,808.85

9,148.91*

18,848.48*

5,600.18*

9,027.10*

3,270

(2009 dollars) [CEN]

(544.97)

(1,122.75)

(660.90)

(1,065.32)

Average (duration-weighted)

0.396

– 0.089*

– 0.184*

– 0.069*

– 0.111*

3,270

census tract share persons

(0.006)

(0.012)

(0.007)

(0.011)

who are poor [CEN]

Average (duration-weighted)

0.880

– 0.061*

– 0.125*

– 0.018*

– 0.029*

3,270

census tract share minority

(0.007)

(0.014)

(0.009)

(0.015)

[CEN]

Feels safe during the day [SR]

0.804

0.036*

0.074*

0.045*

0.072*

3,262

(0.016)

(0.034)

(0.021)

(0.034)

Feels safe at night [SR]

0.596

0.043*

0.088*

0.073*

0.117*

3,246

(0.021)

(0.043)

(0.027)

(0.043)

SOCIAL NETWORK

Has at least one close friend

0.532

0.071*

0.145*

0.007

0.010

3,203

who graduated from college

(0.021)

(0.044)

(0.029)

(0.046)

[SR]

Has three or more close

0.432

0.006

0.013

– 0.026

– 0.042

3,265

friends [SR]

(0.021)

(0.044)

(0.028)

(0.045)

XIX

EXHIBIT ES–3. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

YOUTH AGES 10–20

NEIGHBORHOOD

FEELS SAFE DURING THE

DAY [SR]

All

0.801

0.018

0.037

– 0.012

– 0.018

4,863

(0.016)

(0.033)

(0.018)

(0.026)

Female

0.784

0.045*

0.090*

0.019

0.030

2,478

(0.022)

(0.045)

(0.025)

(0.039)

Male

0.817

– 0.009

– 0.019

– 0.043~

– 0.062~

2,385

(0.022)

(0.047)

(0.025)

(0.035)

FEELS SAFE AT NIGHT [SR]

All

0.540

0.035~

0.074~

0.019

0.028

4,862

(0.021)

(0.043)

(0.022)

(0.033)

Female

0.486

0.052~

0.104~

0.067*

0.105*

2,478

(0.028)

(0.056)

(0.030)

(0.048)

Male

0.591

0.020

0.043

– 0.030

– 0.043

2,384

(0.028)

(0.060)

(0.031)

(0.043)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered a Moving To Opportunity (MTO) housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report, CEN = 1990 and 2000 decennial census data as well as the 2005–09 ACS.

Model: Experimental and Section 8 impacts were estimated jointly using an Ordinary Least Squares (OLS) regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed; Youth long-term survey. Interviewed youth ages 10 to 20 as of December 31, 2007.

Measures: Baseline census tract is based on participant's address at baseline. Interpolated census tract characteristics are linearly interpolated from the 1990 and 2000

decennial census. American Community Survey (ACS) refers to the 2005–09 5-year average ACS data.

XX

EXHIBIT ES–4. KEY ADULT PHYSICAL HEALTH OUTCOMES, LONG-TERM EVALUATION

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

SELF-RATED HEALTH

Currently good or better health [SR]

0.564

0.002

0.004

– 0.005

– 0.009

3,269

(0.020)

(0.042)

(0.027)

(0.044)

ASTHMA

Asthma or wheezing attack during the past

0.293

– 0.018

– 0.038

– 0.042

– 0.066

3,267

year [SR]

(0.020)

(0.040)

(0.026)

(0.041)

OBESITY

Currently obese: Body Mass Index (BMI) ≥ 30

0.584

– 0.012

– 0.025

– 0.011

– 0.018

3,221

[M, SR]

(0.022)

(0.044)

(0.029)

(0.046)

BMI ≥ 35 [M, SR]

0.351

– 0.046*

– 0.095*

– 0.053*

– 0.086*

3,221

(0.020)

(0.042)

(0.027)

(0.043)

BMI ≥ 40 [M, SR]

0.175

– 0.034*

– 0.071*

– 0.029

– 0.048

3,221

(0.016)

(0.032)

(0.021)

(0.034)

DIABETES

Had diabetes or treated for it during the past

0.160

– 0.024

– 0.049

– 0.061*

– 0.098*

3,251

year [SR]

(0.015)

(0.030)

(0.018)

(0.029)

HbA1c test detected diabetes (HbA1c ≥

0.204

– 0.052*

– 0.108*

– 0.011

– 0.017

2,737

6.5%) [DBS]

(0.018)

(0.038)

(0.024)

(0.038)

HbA1c test detected diabetes or had or

0.234

– 0.034~

– 0.071~

– 0.008

– 0.013

2,732

treated for diabetes during the past year

(0.019)

(0.040)

(0.025)

(0.039)

[DBS, SR]

LIMITATIONS AND CHRONIC PAIN

Health limits respondent's ability to climb

0.510

– 0.048*

– 0.100*

– 0.023

– 0.038

3,270

several flights of stairs/lifting or carrying

(0.021)

(0.043)

(0.028)

(0.045)

groceries [SR]

HYPERTENSION

Currently has high blood pressure (systolic ≥

0.315

0.007

0.015

– 0.026

– 0.041

3,102

140 mm Hg or diastolic ≥ 90 mm Hg) [M]

(0.020)

(0.042)

(0.027)

(0.043)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered a Moving To Opportunity (MTO) housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of

"0.250" for "Working" would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report, M = direct measurement, DBS = dried blood spot assays.

Model: Experimental and Section 8 impacts were estimated jointly using an Ordinary Least Squares (OLS) regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.

Measures: For obesity inputs (height and weight), only a very small percent of the sample self-reported their height or weight. Body Mass Index (BMI) is measured as weight in kilograms divided by height in meters squared. For diastolic and systolic blood pressure, data are the average of two readings, if available; otherwise, data are from one reading. HbA1c = glycosylated hemoglobin.

XXI

EXHIBIT ES–5. KEY PHYSICAL HEALTH OUTCOMES FOR YOUTH AGES 10–20, LONG-TERM EVALUATION

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

GENERAL HEALTH

CURRENTLY GOOD OR BETTER HEALTH [SR]

All

0.883

0.005

0.009

0.000

– 0.001

5,100

(0.013)

(0.027)

(0.015)

(0.022)

Female

0.862

0.003

0.007

0.006

0.010

2,600

(0.019)

(0.038)

(0.021)

(0.034)

Male

0.903

0.006

0.012

– 0.007

– 0.010

2,500

(0.016)

(0.035)

(0.019)

(0.027)

ASTHMA

ASTHMA OR WHEEZING ATTACK DURING THE

PAST YEAR [SR]

All

0.190

0.006

0.013

– 0.013

– 0.019

5,092

(0.015)

(0.032)

(0.016)

(0.024)

Female

0.206

– 0.009

– 0.017

– 0.021

– 0.032

2,595

(0.021)

(0.043)

(0.023)

(0.037)

Male

0.174

0.021

0.045

– 0.006

– 0.008

2,497

(0.020)

(0.043)

(0.022)

(0.031)

OBESITY

CURRENTLY OBESE [M, SR]

All

0.229

– 0.010

– 0.022

– 0.010

– 0.014

5,034

(0.017)

(0.035)

(0.018)

(0.028)

Female

0.274

– 0.028

– 0.057

– 0.020

– 0.031

2,560

(0.025)

(0.051)

(0.026)

(0.041)

Male

0.187

0.007

0.016

0.000

0.000

2,474

(0.021)

(0.045)

(0.023)

(0.033)

ACCIDENTS AND INJURIES

HAD ACCIDENTS OR INJURIES REQUIRING

MEDICAL ATTENTION IN THE PAST YEAR [SR]

All

0.178

0.006

0.013

0.008

0.012

5,097

(0.014)

(0.030)

(0.016)

(0.024)

Female

0.164

– 0.023

– 0.046

– 0.024

– 0.037

2,597

(0.018)

(0.037)

(0.020)

(0.031)

Male

0.192

0.035

0.076

0.039

0.056

2,500

(0.022)

(0.047)

(0.024)

(0.034)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered a Moving To Opportunity (MTO) housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of

"0.250" for "Working" would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report, M = direct measurement.

Model: Experimental and Section 8 impacts were estimated jointly using an Ordinary Least Squares (OLS) regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 10 to 20 as of December 31, 2007.

Measures: Obesity is defined according to the International Obesity Task Force.

XXII

EXHIBIT ES–6. KEY ADULT MENTAL HEALTH OUTCOMES, LONG-TERM EVALUATION

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

Psychological Distress Index (K6)

0.000

– 0.107*

– 0.221*

– 0.097~

– 0.156~

3,273

Z-score, past month (higher score

(0.042)

(0.087)

(0.056)

(0.091)

indicates greater distress) [SR]

Major Depression with Hierarchy,

0.203

– 0.032~

– 0.066~

– 0.048*

– 0.077*

3,269

Lifetime [SR]

(0.017)

(0.035)

(0.021)

(0.034)

Any Mood Disorder, Lifetime [SR]

0.255

– 0.028

– 0.058

– 0.036

– 0.058

3,270

(0.018)

(0.038)

(0.024)

(0.039)

Generalized Anxiety Disorder with

0.065

– 0.003

– 0.005

– 0.020~

– 0.033~

3,273

Hierarchy, Lifetime [SR]

(0.010)

(0.021)

(0.011)

(0.017)

Any Anxiety Disorder, Lifetime [SR]

0.308

– 0.020

– 0.042

– 0.005

– 0.008

3,269

(0.020)

(0.041)

(0.026)

(0.042)

Panic Attacks, Lifetime [SR]

0.407

0.004

0.009

– 0.022

– 0.035

3,269

(0.021)

(0.044)

(0.028)

(0.045)

Post-Traumatic Stress Disorder, Lifetime

0.219

– 0.012

– 0.024

0.004

0.006

3,269

[SR]

(0.018)

(0.037)

(0.024)

(0.038)

Calm and peaceful most of the time,

0.487

0.018

0.037

– 0.025

– 0.040

3,272

past month [SR]

(0.022)

(0.045)

(0.029)

(0.047)

Dependence on drugs or alcohol, past

0.055

0.029*

0.060*

0.015

0.024

3,269

month [SR]

(0.011)

(0.022)

(0.015)

(0.023)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered a Moving To Opportunity (MTO) housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of

"0.250" for "Working" would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an Ordinary Least Squares (OLS) regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.

Measures: Psychological distress consists of 6 items (sadness, nervousness, restlessness, hopelessness, feeling that everything is an effort, worthlessness) scaled on a score from 0 (no distress) to 24 (highest distress) and then converted to z-scores using the mean and standard deviation of control group adults. Disorders with hierarchy take into account the comorbidity of other disorders: Depression with hierarchy takes into account Mania, and Hypomania; Generalized Anxiety Disorder (GAD) takes into account Depression and Mania; Intermittent Explosive Disorder (IED) takes into account Depression, Mania and Hypomania. Any Anxiety Disorder includes Panic Disorder, Generalized Anxiety Disorder (GAD) (without hierarchy), and Post-Traumatic Stress Disorder (PTSD). Any Mood Disorder includes Depression (without hierarchy), Bipolar I/II/Subthreshold, and Mania/Hypomania/Hypomania Subthreshold. Any Disorder includes all disorders from Any Anxiety Disorder and Any Mood Disorder as well as Intermittent Explosive Disorder (IED) (without hierarchy). Index of mental health problems is the average of depression (past year), anxiety (past year), mental calm, distress (K6) and normal sleep (7 to 8 hours last night) after standardizing by the control mean and standard deviation.

Substance dependence consists of 5 items about drug and/or alcohol use (use out of control, anxiety or worry about missing fix or drink, worry about use, frequency of desire to end use, difficulty of going without use) scaled on a score from 0 (no dependence) to 15 (highest level of dependence). A score of 3 or higher indicates dependence. See Section 4.3 for additional details.

XXIII

EXHIBIT ES–7. KEY MENTAL HEALTH OUTCOMES FOR YOUTH AGES 13–20, LONG-TERM EVALUATION

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

PSYCHOLOGICAL DISTRESS INDEX (K6)

Z-SCORE, PAST MONTH (HIGHER SCORE

INDICATES GREATER DISTRESS) [SR]

All

0.000

– 0.040

– 0.084

0.036

0.054

4,644

(0.041)

(0.085)

(0.047)

(0.070)

Female

0.115

– 0.119*

– 0.241*

– 0.013

– 0.021

2,371

(0.058)

(0.116)

(0.066)

(0.104)

Male

– 0.110

0.040

0.085

0.084

0.120

2,273

(0.054)

(0.116)

(0.060)

(0.086)

MAJOR DEPRESSION WITH HIERARCHY,

LIFETIME [SR]

All

0.105

– 0.015

– 0.031

– 0.014

– 0.021

4,639

(0.012)

(0.025)

(0.013)

(0.019)

Female

0.128

– 0.032~

– 0.065~

– 0.029

– 0.045

2,367

(0.017)

(0.035)

(0.019)

(0.029)

Male

0.084

0.003

0.006

0.000

0.000

2,272

(0.016)

(0.034)

(0.017)

(0.025)

ANY MOOD DISORDER, LIFETIME [SR]

All

0.178

– 0.013

– 0.027

0.003

0.004

4,644

(0.015)

(0.031)

(0.016)

(0.024)

Female

0.218

– 0.048*

– 0.096*

– 0.032

– 0.050

2,371

(0.022)

(0.044)

(0.024)

(0.038)

Male

0.140

0.022

0.047

0.038~

0.054~

2,273

(0.020)

(0.043)

(0.022)

(0.032)

GENERALIZED ANXIETY DISORDER WITH

HIERARCHY, LIFETIME [SR]

All

0.019

– 0.002

– 0.004

0.010~

0.015~

4,644

(0.005)

(0.010)

(0.006)

(0.009)

Female

0.022

– 0.002

– 0.003

0.010

0.016

2,371

(0.007)

(0.014)

(0.008)

(0.013)

Male

0.016

– 0.002

– 0.005

0.010

0.015

2,273

(0.006)

(0.013)

(0.009)

(0.013)

ANY ANXIETY DISORDER, LIFETIME [SR]

All

0.121

0.001

0.002

0.005

0.007

4,639

(0.013)

(0.027)

(0.014)

(0.020)

Female

0.149

– 0.011

– 0.023

– 0.014

– 0.021

2,367

(0.019)

(0.038)

(0.020)

(0.031)

Male

0.095

0.013

0.028

0.023

0.033

2,272

(0.017)

(0.037)

(0.019)

(0.027)

XXIV

EXHIBIT ES–7. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

SERIOUS BEHAVIORAL OR EMOTIONAL

PROBLEMS [SR]

All

0.103

– 0.022~

– 0.046~

0.019

0.029

4,644

(0.011)

(0.024)

(0.014)

(0.021)

Female

0.127

– 0.033*

– 0.068*

0.030

0.047

2,371

(0.017)

(0.034)

(0.021)

(0.032)

Male

0.081

– 0.010

– 0.021

0.007

0.010

2,273

(0.015)

(0.032)

(0.017)

(0.024)

PANIC ATTACKS, LIFETIME [SR]

All

0.387

– 0.017

– 0.035

0.014

0.021

4,639

(0.019)

(0.040)

(0.021)

(0.032)

Female

0.423

– 0.039

– 0.079

– 0.005

– 0.008

2,367

(0.027)

(0.054)

(0.029)

(0.045)

Male

0.354

0.006

0.012

0.033

0.047

2,272

(0.027)

(0.057)

(0.030)

(0.043)

POST-TRAUMATIC STRESS DISORDER,

LIFETIME [SR]

All

0.066

0.006

0.012

0.005

0.007

4,639

(0.010)

(0.020)

(0.011)

(0.016)

Female

0.092

0.002

0.003

– 0.019

– 0.030

2,367

(0.016)

(0.032)

(0.016)

(0.026)

Male

0.041

0.010

0.021

0.030*

0.043*

2,272

(0.012)

(0.026)

(0.015)

(0.021)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered a Moving To Opportunity (MTO) housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an Ordinary Least Squares (OLS) regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 13 to 20 as of December 31, 2007.

Measures: Psychological distress consists of 6 items (sadness, nervousness, restlessness, hopelessness, feeling that everything is an effort, worthlessness) scaled on a score from 0 (no distress) to 24 (highest distress) and then converted to z-scores using the mean and standard deviation for of control group youth. Serious mental illness is defined as a raw score of 13 or higher on the K6. Strengths and Difficulties consists of 5 behavioral and emotional items (obedience, worry/anxiety, unhappiness, getting along better with adults than peers, attention span) scaled on score from 0 (no behavioral/emotional problems) to 12 (severe behavioral or emotional problems). A score of 6 or higher indicates serious behavioral/emotional problems. See Section 4.3 for additional details.

XXV

ECONOMIC SELF-SUFFICIENCY

Those in the experimental group were slightly less likely

A primary motivation of MTO was to determine whether to have incomes below the poverty line than control moving to lower-poverty neighborhoods could boost

group members, but the differences were not statistically

earnings and labor force participation and ease reliance

significant. In contrast, the Section 8 group saw a

on welfare among vulnerable families. The study began

modest, but again not statistically significant, rise in

shortly before the implementation of welfare reform,

poverty relative to the control group. The study finds

and only one-fourth of adults reported being employed.

higher rates of food stamp use among the experimental

Three-fourths were receiving Aid to Families with

group—and less food insufficiency—but few differences

Dependent Children (AFDC), and eight in ten were

in use of Temporary Assistance for Needy Families

receiving food stamps. The average annual household

(TANF, the program that replaced AFDC after welfare

income at baseline was $12,827 (in 2009 dollars), well

reform). TANF receipt was on the decline nationally

below the official poverty line for a family of three.

throughout this time period owing to welfare reform

in 1996 and a robust economy in the latter half of the

Ten to fifteen years later, many more families were

1990s. There are no significant differences in welfare

employed, incomes were higher, and welfare receipt had

receipt among the Section 8 and control groups.

plummeted, but these changes were roughly equal for

treatment and control-group families. Overall, MTO

For adults, it appears that training, education, and

generated few systematic, detectable long-term effects on

employment services that directly enhance marketable

economic self-sufficiency for adults, youth, and grown

skills and changes in work incentives more directly

children.

affect labor market and economic outcomes of low-

income adults than do the indirect effects of changes

As Exhibit ES-8 shows, compared with control group

in neighborhood environments, at least in the range

members, experimental and Section 8 group adults have:

observed in the MTO demonstration.

• similar employment levels and earnings

Likewise, unlike early childhood education programs

• similar incomes

or early school quality improvements, which have

• less food insufficiency

been shown to improve economic self-sufficiency later

• somewhat higher use of food stamps (experimental

in life, moving to lower-poverty neighborhoods had

group only).

little systematic detectable effects on the economic

outcomes—including being idle, defined as neither

Earnings and employment trends fluctuated with

working nor in school—for youth and grown children

macroeconomic and policy shifts, with rapidly rising

compared with those in the control group

employment rates during economic booms and a recent

(see Exhibit ES-9).

fall-off during the severe recession. One distinction is a

short-term decline in employment during the first two

years after moving for those in the experimental and

Section 8 groups compared to the control group, perhaps

indicating difficulties finding work in a new location or

in securing child care. Their employment rates recovered

shortly thereafter and matched the ebbs and flows

evident in the control group. Ten years after moving,

employment outcomes were not statistically different

across groups. The types of jobs the participants held also

differed little across groups.

XXVI

EXHIBIT ES–8. KEY ADULT ECONOMIC SELF-SUFFICIENCY OUTCOMES, LONG-TERM EVALUATION

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

EMPLOYMENT

Currently employed [SR]

0.525

– 0.007

– 0.014

– 0.077*

– 0.124*

3,264

(0.021)

(0.042)

(0.028)

(0.045)

Calendar year 2007 [UI]

0.465

– 0.004

– 0.009

0.000

0.000

4,194

(0.017)

(0.036)

(0.019)

(0.030)

EARNINGS AND INCOME

Annual individual earnings (previous

$12,288.51

326.94

677.92

– 613.60

– 982.43

3,141

calendar year, 2009 dollars) [SR]

(583.44)

(1,209.79)

(807.20)

(1,292.40)

Calendar year 2007 (2009 dollars) [UI]

$11,325.14

– 347.83

– 731.73

112.93

180.50

4,194

(523.80)

(1,101.92)

(580.69)

(928.11)

Total Household Income

$20,025.90

607.58

1,255.56

– 41.67

– 67.33

3,258

(2009 dollars) [SR]

(727.58)

(1,503.54)

(1,009.18)

(1,630.66)

Household income is at or below 100% of

0.590

– 0.032

– 0.067

0.036

0.059

3,258

poverty line [SR]

(0.021)

(0.043)

(0.028)

(0.045)

Adult reported that their household

0.336

– 0.035~

– 0.072~

– 0.067*

– 0.106*

3,266

sometimes/often did not have enough to

(0.020)

(0.042)

(0.026)

(0.042)

eat in the past 12 months [SR]

FOOD STAMPS

Currently receiving food stamps [SR]

0.470

0.016

0.032

0.029

0.046

3,253

(0.021)

(0.043)

(0.028)

(0.045)

Total food stamps benefits received, July

$3,074.08

309.94*

664.54*

171.07

261.80

2,708

2007–June 2009 (2009 dollars) [FS]

(156.50)

(335.54)

(184.98)

(283.09)

TEMPORARY ASSISTANCE FOR NEEDY FAMILIES

Currently receiving TANF [SR]

0.158

0.010

0.021

0.026

0.041

3,262

(0.015)

(0.031)

(0.021)

(0.033)

Total TANF benefits received, July 2007–

$1,402.33

56.10

120.29

– 94.47

– 144.57

2,708

June 2009 (2009 dollars) [TANF]

(114.48)

(245.44)

(123.82)

(189.49)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered a Moving To Opportunity (MTO) housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of

"0.250" for "Working" would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report, UI = Unemployment Insurance administrative records, TANF = Temporary Assistance for Needy Families records, FS = Food Stamps records.

Model: Experimental and Section 8 impacts were estimated jointly using an Ordinary Least Squares (OLS) regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: For self-reports, data source is the adult long-term survey and the sample is all adults interviewed. Unemployment Insurance data uses individual level data from Maryland, Illinois, California, and Florida (representing individuals whose random assignment sites are Baltimore, Chicago, and Los Angeles) and aggregate data from New York and Massachusetts (representing individuals whose random assignment sites are New York City and Boston). FS and TANF analyses use individual data from Massachusetts, Illinois, Los Angeles County, and South Carolina and represent individuals whose random assignment sites are: Boston, Chicago and Los Angeles. We received TANF and food stamps data from Maryland, but the data quality is suspect. Sample for UI, TANF, and FS is all sample adults with baseline consent.

XXVII

EXHIBIT ES–9. KEY ECONOMIC SELF-SUFFICIENCY OUTCOMES FOR YOUTH AGES 15–20, LONG-TERM

EVALUATION

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

CURRENTLY EMPLOYED [SR]

All

0.395

– 0.046*

– 0.095*

– 0.033

– 0.050

3,604

(0.021)

(0.043)

(0.022)

(0.034)

Female

0.410

– 0.051~

– 0.104~

– 0.042

– 0.066

1,838

(0.029)

(0.059)

(0.031)

(0.049)

Male

0.381

– 0.041

– 0.087

– 0.025

– 0.036

1,766

(0.030)

(0.064)

(0.033)

(0.049)

CURRENTLY IDLE (NEITHER EMPLOYED

NOR ENROLLED IN SCHOOL) [SR]

All

0.215

0.007

0.014

0.026

0.039

3,604

(0.018)

(0.038)

(0.020)

(0.031)

Female

0.194

0.024

0.049

0.031

0.048

1,838

(0.024)

(0.048)

(0.027)

(0.043)

Male

0.235

– 0.011

– 0.023

0.022

0.032

1,766

(0.027)

(0.058)

(0.031)

(0.045)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered a Moving To Opportunity (MTO) housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of

"0.250" for "Working" would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an Ordinary Least Squares (OLS) regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 15 to 20 as of December 31, 2007.

RISKY AND CRIMINAL BEHAVIOR

Ten to fifteen years after random assignment we found

Previous nonexperimental studies suggest that risky

few statistically significant impacts of MTO on risky and

and criminal behavior is the outcome domain that has

criminal behavior, although we did find some signs of a

among the strongest associations with neighborhood

similar sort of gender difference in how youth respond

conditions (Sampson, Morenoff, and Gannon-Rowley,

to MTO moves as was found at interim, and some

2002). Neighborhoods may affect risky and criminal

indication of potentially beneficial impacts on arrests for

behavior through peer influences, the ability of local

drug selling.

adults to monitor and support pro-social behavior in

the community, or other factors that make pro-social

More specifically, as shown in exhibits ES-10 and ES-11,

alternatives to crime (such as school and work) more

we find:

or less attractive. The quality of policing services may

also vary across areas. If less economically distressed

• Male youth in the Section 8 group engage in

areas have higher-quality policing, that could help deter

more problem behavior, and those in both the

criminal behavior but might also lead our analysis of

experimental and Section 8 groups are more likely

administrative arrest records to understate any beneficial

to smoke compared with those in the control

effects of MTO that reduce crime, and overstate any

group.

adverse effects towards increased criminality.

• Female youth in the experimental group are less

likely to have tried alcohol than those in the

control group.

XXVIII

• Few statistically significant differences in the

for most crimes peak during late adolescence or early

number of arrests for violent crimes, property

adulthood.

crimes, or other (non-violent, non-property, non-

drug) crimes across treatment groups.

EDUCATIONAL OUTCOMES

• Some indication that the number of arrests

We had anticipated that MTO might generate more

for drug-selling declined for male youth in the

pronounced positive impacts on educational outcomes in

experimental group and for male grown children

the long-term follow up than in the interim study, in part

in the Section 8 group, and that the likelihood

because more families may have been willing to send their

of ever having been arrested for drug-selling may

children to schools in lower-poverty areas rather than

have declined for adults in the experimental group

remain in their original baseline schools. In addition,

compared with those in the control group.

many social scientists believe that early childhood is a

time in which cognitive, socio-emotional, and behavioral

The estimated effects of MTO on risky and criminal

skills may be particularly “plastic” and susceptible to

behavior of those who were teens at the long term follow

intervention, which led us to expect that MTO might

up were more muted compared to MTO’s effects on those have relatively larger effects on the long-term schooling who were teens at the time of the interim study. The

outcomes of participants who were preschool age at

long term data do provide a few indications of the same

baseline.

type of gender difference in youth responses to MTO

moves as were found in the interim data, with male youth Ten to fifteen years after random assignment, we found who moved through MTO engaging in relatively more

few statistically significant impacts on educational

of some risky behaviors (smoking, behavior problems)

outcomes, including for children who were very young

compared with controls, and female youth who moved

at baseline, and mixed impacts of MTO on different

through MTO experiencing declines in some risky

measures of school quality.

behaviors (drinking) compared to controls.

More specifically, in comparing children assigned to the

The fact that the effects of MTO on risky and criminal

experimental and Section 8 groups with control group

behavior are generally more muted in the long-term

children, we found:

data compared to the interim data suggests that

contemporaneous neighborhood environments may

• Similar average scores on reading and math

matter more for these outcomes than does accumulated

achievement tests across groups.

exposure to less distressed areas. The fact that we see signs

• Similar grades in school and rates of grade

of a gender difference in responses to residential mobility

retention across groups.

in a sample of teens in the long term data that is mostly

• Indications of slightly worse outcomes in some

non-overlapping with the youth who were studied in the

respects for Section 8 males, who were less likely

interim follow up suggests that the gender difference in

to be on track educationally and less likely to have

MTO impacts documented at interim may not be just a

attended college than control group males.

statistical artifact unique to that particular sample.

• No evidence that impacts on educational outcomes

varied systematically with the child’s age at the time

The one outcome for which we do see at least some hints

of random assignment.

of more pronounced impacts in the long-term data than

• A mixed pattern of differences across randomly

in the interim data is with declining arrest rates for drug

assigned groups with respect to school measures

distribution among the MTO treatment groups compared

typically associated with “quality.” Youth in the

to controls. These results are more pronounced for teens

experimental and Section 8 groups attended schools

in the long-term follow-up than for grown children or

with lower shares of students who are low-income

adults and are consistent with the widely-documented

or members of racial and ethnic minority groups

“age-crime curve” showing that aggregate arrest rates

and for the experimental group schools that ranked

XXIX

slightly higher on state exams; but these youth also and safety. MTO-induced improvements in housing and attended schools that were larger, in terms of the

neighborhood conditions appear to improve some aspects

total size of the student body.

of physical and mental health. Most noteworthy here are

reductions in extreme obesity and diabetes and better

These findings do not mean that neighborhood

mental health for adults. MTO moves may also reduce

environments never matter for educational outcomes.

involvement in some forms of risky or criminal behavior

The MTO mobility intervention generated more

of youth, most notably drug selling. However, moving

pronounced impacts on neighborhood conditions than

to lower-poverty neighborhoods as part of an MTO-

on school conditions. As with the interim follow up, the

type residential mobility program does not appear to

long term data show that MTO had modest and mixed

improve educational outcomes, employment, or earnings.

impacts on school quality. A majority of MTO children

The cause of high rates of dropout and unemployment

were still attending majority-minority, overwhelmingly

and low earnings found in many disadvantaged urban

low-income public schools located in the districts serving neighborhoods may rest with barriers that cannot be the five original MTO cities.

captured by neighborhood demographics, socio-economic

composition, and social processes, at least within the

THE CAUSES OF POVERTY ARE

range of neighborhood variation that mobility programs

COMPLEX AND EXTEND BEYOND

like MTO are able to modify. Put differently, MTO

NEIGHBORHOOD EFFECTS

was more successful in improving mental and physical

The findings of the long-term study of MTO suggest that health in poor families that signed up to participate in housing mobility programs can improve the quality of

the program than in bringing about the improvements

the immediate environments that families experience -in

in education and labor market outcomes needed to boost

particular living conditions related to housing quality,

family incomes.

neighborhood poverty and other aspects of disadvantage,

EXHIBIT ES–10. RISKY AND CRIMINAL BEHAVIOR OUTCOMES FOR YOUTH, LONG-TERM EVALUATION

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

RISKY AND DELINQUENT

BEHAVIOR FOR YOUTH

AGES 13–20

RISKY BEHAVIOR INDEX [SR]

All

0.467

– 0.001

– 0.002

0.007

0.010

4,623

(0.014)

(0.029)

(0.015)

(0.022)

Female

0.442

– 0.027

– 0.054

– 0.017

– 0.026

2,358

(0.019)

(0.037)

(0.020)

(0.031)

Male

0.491

0.025

0.053

0.029

0.042

2,265

(0.018)

(0.039)

(0.020)

(0.028)

BEHAVIOR PROBLEMS INDEX [SR]

All

0.379

0.004

0.008

0.009

0.013

4,629

(0.010)

(0.022)

(0.011)

(0.017)

Female

0.371

– 0.007

– 0.014

– 0.010

– 0.015

2,361

(0.014)

(0.028)

(0.015)

(0.024)

Male

0.387

0.015

0.032

0.027~

0.038~

2,268

(0.015)

(0.032)

(0.016)

(0.023)

XXX

EXHIBIT ES–10. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

RISKY AND DELINQUENT

BEHAVIOR FOR YOUTH

AGES 13–20 (CONTINUED)

DELINQUENCY INDEX [SR]

All

0.146

– 0.002

– 0.004

0.008

0.012

4,625

(0.008)

(0.017)

(0.009)

(0.014)

Female

0.110

– 0.006

– 0.011

– 0.005

– 0.008

2,360

(0.009)

(0.019)

(0.010)

(0.016)

Male

0.181

0.002

0.004

0.021

0.030

2,265

(0.013)

(0.029)

(0.016)

(0.022)

EVER SMOKED [SR]

All

0.312

0.042*

0.088*

0.043*

0.064*

4,618

(0.019)

(0.039)

(0.020)

(0.030)

Female

0.297

0.022

0.044

0.016

0.026

2,355

(0.025)

(0.051)

(0.028)

(0.043)

Male

0.327

0.062*

0.134*

0.069*

0.098*

2,263

(0.025)

(0.054)

(0.028)

(0.040)

EVER HAD ALCOHOLIC DRINK [SR]

All

0.534

– 0.032

– 0.067

– 0.017

– 0.026

4,618

(0.020)

(0.041)

(0.021)

(0.032)

Female

0.541

– 0.061*

– 0.124*

– 0.032

– 0.050

2,355

(0.026)

(0.053)

(0.029)

(0.045)

Male

0.528

– 0.003

– 0.006

– 0.004

– 0.005

2,263

(0.027)

(0.058)

(0.029)

(0.041)

NUMBER OF ARRESTS BY

CRIME TYPE FOR YOUTH AGES

15–20

VIOLENT CRIME ARRESTS [CJR]

All

0.325

0.043

0.091

– 0.062

– 0.094

4,717

(0.037)

(0.078)

(0.039)

(0.059)

Female

0.155

0.027

0.055

– 0.048

– 0.074

2,300

(0.033)

(0.069)

(0.033)

(0.050)

Male

0.481

0.060

0.128

– 0.076

– 0.115

2,417

(0.064)

(0.138)

(0.068)

(0.102)

PROPERTY CRIME ARRESTS [CJR]

All

0.239

0.065*

0.136*

– 0.013

– 0.019

4,717

(0.031)

(0.064)

(0.034)

(0.051)

Female

0.091

0.044~

0.090~

– 0.010

– 0.015

2,300

(0.026)

(0.053)

(0.023)

(0.035)

Male

0.375

0.086

0.183

– 0.016

– 0.025

2,417

(0.054)

(0.117)

(0.060)

(0.090)

XXXI

EXHIBIT ES–10. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

NUMBER OF ARRESTS BY

CRIME TYPE FOR YOUTH AGES

15–20 (CONTINUED)

DRUG POSSESSION ARRESTS [CJR]

All

0.117

0.010

0.020

– 0.034~

– 0.052~

4,717

(0.019)

(0.039)

(0.019)

(0.028)

Female

0.013

0.005

0.009

– 0.011

– 0.017

2,300

(0.010)

(0.021)

(0.010)

(0.016)

Male

0.213

0.015

0.032

– 0.057

– 0.085

2,417

(0.035)

(0.075)

(0.035)

(0.052)

DRUG DISTRIBUTION ARRESTS [CJR]

All

0.091

– 0.025

– 0.052

– 0.014

– 0.021

4,717

(0.017)

(0.036)

(0.022)

(0.033)

Female

0.006

0.011

0.023

– 0.006

– 0.009

2,300

(0.009)

(0.020)

(0.010)

(0.015)

Male

0.169

– 0.059~

– 0.127~

– 0.020

– 0.031

2,417

(0.032)

(0.069)

(0.042)

(0.063)

OTHER CRIME ARRESTS [CJR]

All

0.306

0.000

– 0.001

– 0.048

– 0.072

4,717

(0.034)

(0.071)

(0.037)

(0.055)

Female

0.090

0.015

0.030

0.021

0.032

2,300

(0.023)

(0.047)

(0.026)

(0.040)

Male

0.503

– 0.014

– 0.029

– 0.113~

– 0.170~

2,417

(0.064)

(0.136)

(0.067)

(0.100)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered a Moving To Opportunity (MTO) housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report. CJR = criminal justice records.

Model: Experimental and Section 8 impacts were estimated jointly using an Ordinary Least Squares (OLS) regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: For the self-reported measures, the data source is the youth long-term survey, and the sample is interviewed youth ages 13 to 20 as of December 31, 2007. For the arrest measures, the data source is individual criminal justice system arrest data (adult and juvenile data from California, Illinois, and Maryland; de-identified adult data from New York State; juvenile data from New York City; and adult or juvenile records from 8 additional states in which participants have lived), and the sample is all core household members ages 15 to 20 as of December 31, 2007.

Measures: The Risky Behavior Index is the fraction of 4 risky behaviors (smoking, alcohol use, marijuana use, and sex) that the youth reports ever having exhibited.

The Behavior Problems Index is the fraction of 11 problem behaviors (for example, difficulty concentrating and having a strong temper) that the youth reported as true or sometimes true at present or in the past 6 months. The Delinquency Index is the fraction of 8 delinquent behaviors (for example, carrying a gun and destroying property) that the youth reported ever having exhibited. Violent crime arrests involve charges of force or threat of force including homicide, rape, robbery, assault, kidnapping, and weapons charges. Property crime arrests involve taking money or property and include burglary, motor vehicle theft, larceny, trespassing, and receiving stolen property. Drug crime arrests are classified as either possession or distribution arrests. "Other" arrest charges cover any charges not classified as violent, property, or drug crimes and include disorderly conduct and moving violations.

XXXII

EXHIBIT ES–11. NUMBER OF POST-RANDOM ASSIGNMENT ARRESTS FOR GROWN CHILDREN AGES 21–30 AND

ADULTS, LONG-TERM EVALUATION

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

GROWN CHILDREN

NUMBER OF…

ANY CRIME ARRESTS [CJR]

All

2.862

– 0.090

– 0.212

– 0.116

– 0.202

4,641

(0.171)

(0.404)

(0.192)

(0.332)

Female

0.967

– 0.054

– 0.126

– 0.037

– 0.063

2,277

(0.131)

(0.306)

(0.157)

(0.269)

Male

4.673

– 0.124

– 0.296

– 0.192

– 0.335

2,364

(0.303)

(0.726)

(0.347)

(0.607)

VIOLENT CRIME ARRESTS

[CJR]

All

0.626

– 0.055

– 0.129

– 0.042

– 0.072

4,641

(0.047)

(0.112)

(0.054)

(0.093)

Female

0.279

– 0.038

– 0.089

– 0.047

– 0.080

2,277

(0.045)

(0.105)

(0.051)

(0.087)

Male

0.958

– 0.071

– 0.169

– 0.038

– 0.066

2,364

(0.082)

(0.196)

(0.092)

(0.161)

PROPERTY CRIME ARRESTS

[CJR]

All

0.633

– 0.006

– 0.014

– 0.042

– 0.072

4,641

(0.054)

(0.128)

(0.061)

(0.105)

Female

0.297

– 0.057

– 0.132

– 0.016

– 0.027

2,277

(0.046)

(0.107)

(0.065)

(0.112)

Male

0.953

0.044

0.105

– 0.064

– 0.112

2,364

(0.093)

(0.222)

(0.100)

(0.175)

DRUG POSSESSION ARRESTS

[CJR]

All

0.461

– 0.042

– 0.100

– 0.011

– 0.020

4,641

(0.044)

(0.104)

(0.053)

(0.093)

Female

0.069

0.001

0.002

0.015

0.026

2,277

(0.030)

(0.070)

(0.035)

(0.060)

Male

0.835

– 0.084

– 0.202

– 0.037

– 0.065

2,364

(0.082)

(0.196)

(0.103)

(0.179)

DRUG DISTRIBUTION ARRESTS

[CJR]

All

0.338

– 0.047

– 0.111

– 0.063~

– 0.110~

4,641

(0.035)

(0.082)

(0.037)

(0.064)

Female

0.058

– 0.023

– 0.055

0.018

0.032

2,277

(0.024)

(0.056)

(0.028)

(0.049)

Male

0.605

– 0.069

– 0.165

– 0.140*

– 0.245*

2,364

(0.063)

(0.151)

(0.067)

(0.116)

XXXIII

EXHIBIT ES–11. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

GROWN CHILDREN (CONTINUED)

OTHER CRIME ARRESTS

[CJR]

All

0.804

0.060

0.141

0.042

0.072

4,641

(0.066)

(0.155)

(0.074)

(0.128)

Female

0.263

0.063

0.147

– 0.008

– 0.013

2,277

(0.052)

(0.121)

(0.054)

(0.092)

Male

1.321

0.056

0.134

0.087

0.153

2,364

(0.116)

(0.278)

(0.137)

(0.239)

ADULTS

NUMBER OF…

Any crime arrests [CJR]

0.567

0.052

0.110

– 0.016

– 0.026

4,376

(0.064)

(0.135)

(0.060)

(0.096)

Violent crime arrests [CJR]

0.149

0.001

0.002

– 0.009

– 0.014

4,376

(0.020)

(0.043)

(0.022)

(0.035)

Property crime arrests [CJR]

0.133

0.043~

0.091~

– 0.002

– 0.002

4,376

(0.025)

(0.052)

(0.022)

(0.036)

Drug possession arrests

0.080

0.006

0.013

– 0.004

– 0.006

4,376

[CJR]

(0.018)

(0.038)

(0.019)

(0.030)

Drug distribution arrests

0.035

– 0.007

– 0.016

0.001

0.001

4,376

[CJR]

(0.008)

(0.017)

(0.009)

(0.015)

Other crime arrests [CJR]

0.171

0.009

0.020

– 0.003

– 0.005

4,376

(0.027)

(0.056)

(0.025)

(0.040)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered a Moving To Opportunity (MTO) housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: CJR = criminal justice records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS (Ordinary Least Squares) regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Individual criminal justice system arrest data. Adult and juvenile data from California, Illinois, Massachusetts, and Maryland; de-identified adult data from New York State; juvenile data from New York City; and adult or juvenile records from 8 additional states in which participants have lived. The grown children sample is all core household members who are now grown children (under age 18 at baseline and ages 21 to 30 as of December 31, 2007). The adult sample is all long-term survey sampling frame adults with a baseline consent form.

Measures: Violent crime arrests involve charges of force or threat of force including homicide, rape, robbery, assault, kidnapping, and weapons charges. Property crime arrests involve taking money or property and include burglary, motor vehicle theft, larceny, trespassing, and receiving stolen property. Drug crime arrests are classified as either possession or distribution arrests. "Other" arrest charges cover any charges not classified as violent, property, or drug crimes and include disorderly conduct and moving violations.

XXXIV

EXHIBIT ES–12. ACHIEVEMENT AND EDUCATIONAL OUTCOMES FOR YOUTH, LONG-TERM EVALUATION

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

ACHIEVEMENT ASSESSMENT

RESULTS FOR YOUTH AGES

13–20

READING ASSESSMENT

SCORE [ECLS-K]

All

0.000

0.003

0.006

0.040

0.058

4,432

(0.041)

(0.085)

(0.044)

(0.064)

Female

0.051

– 0.020

– 0.040

0.054

0.081

2,286

(0.055)

(0.110)

(0.061)

(0.093)

Male

– 0.050

0.027

0.058

0.025

0.035

2,146

(0.056)

(0.118)

(0.058)

(0.081)

MATH ASSESSMENT

SCORE [ECLS-K]

All

0.000

– 0.025

– 0.052

0.000

0.000

4,420

(0.044)

(0.090)

(0.048)

(0.069)

Female

– 0.004

– 0.034

– 0.069

– 0.033

– 0.050

2,280

(0.055)

(0.112)

(0.061)

(0.093)

Male

0.004

– 0.016

– 0.034

0.034

0.047

2,140

(0.060)

(0.128)

(0.067)

(0.093)

CHARACTERISTICS OF

THE AVERAGE SCHOOL

ATTENDED BY YOUTH

AGES 10–20

Share minority [CCD, PSS]

0.904

– 0.037*

– 0.077*

– 0.016~

– 0.023~

5,077

(0.008)

(0.016)

(0.008)

(0.012)

Share eligible for free lunch

0.701

– 0.048*

– 0.101*

– 0.026*

– 0.039*

5,043

[CCD]

(0.007)

(0.015)

(0.008)

(0.012)

Number of students [CCD,

927.1

25.4~

53.0~

41.4*

61.9*

5,077

PSS]

(14.9)

(31.1)

(17.5)

(26.1)

Pupil-teacher ratio [CCD,

17.834

– 0.103

– 0.214

– 0.061

– 0.091

5,076

PSS]

(0.103)

(0.215)

(0.113)

(0.170)

School-level percentile

18.684

3.070*

6.430*

1.218~

1.810~

4,884

ranking on state exam [SLAD]

(0.651)

(1.364)

(0.661)

(0.983)

SCHOOLING OUTCOMES

FOR OLDER YOUTH

EDUCATIONALLY ON-TRACK

[SR]

All (ages 15–20)

0.814

– 0.014

– 0.028

– 0.029

– 0.044

3,614

(0.018)

(0.037)

(0.021)

(0.031)

XXXV

EXHIBIT ES–12. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

EDUCATIONALLY ON-TRACK

[SR] (CONTINUED)

Female

0.827

– 0.007

– 0.015

0.008

0.012

1,842

(0.023)

(0.046)

(0.026)

(0.040)

Male

0.801

– 0.019

– 0.041

– 0.066*

– 0.096*

1,772

(0.026)

(0.055)

(0.029)

(0.042)

HIGH SCHOOL DIPLOMA [SR]

All (ages 19–20)

0.622

– 0.073~

– 0.141~

– 0.056

– 0.092

1,125

(0.038)

(0.073)

(0.042)

(0.068)

Female

0.708

– 0.087~

– 0.169~

– 0.054

– 0.089

576

(0.048)

(0.094)

(0.054)

(0.088)

Male

0.536

– 0.058

– 0.110

– 0.058

– 0.094

549

(0.058)

(0.109)

(0.062)

(0.102)

POST-SECONDARY

OUTCOMES FOR OLDER

YOUTH

SINCE 2007, ATTENDED

ANY COLLEGE [NSC]

All (ages 15–20)

0.262

– 0.014

– 0.029

– 0.021

– 0.033

4,717

(0.016)

(0.033)

(0.017)

(0.025)

Female

0.305

– 0.011

– 0.023

0.000

0.001

2,300

(0.023)

(0.048)

(0.025)

(0.039)

Male

0.222

– 0.016

– 0.033

– 0.042*

– 0.064*

2,417

(0.021)

(0.044)

(0.021)

(0.032)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered a Moving To Opportunity (MTO) housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working). Average school characteristics are weighted by the amount of time youth spent at each school.

Square brackets indicate the source of the outcome information: ECLS-K = achievement assessment from the Early Childhood Longitudinal Study-Kindergarten cohort study, CCD = Common Core of Data, PSS = Private School Universe Survey, SLAD = School-level assessment data from the National Longitudinal School-Level State Assessment Score Database, SR = self-report, NSC =

Model: Experimental and Section 8 impacts were estimated jointly using an Ordinary Least Squares (OLS) regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Executive Summary Exhibit 12. (continued)

Data source and sample: For ECLS-K scores and self-reported measures, the data source is the youth long-term survey. In some cases, the youth school histories on which the school characteristics measures are based combine self-reports from the long-term survey with parent reports from the interim survey. The sample is comprised of long-term survey interviewed youth of varying age ranges (as of December 31, 2007), with details included above. The sample for the NSC measure is all core household members ages 15 to 20 as of December 31, 2007.

Measures: The reading and math achievement assessment scores are theta scores transformed into z-scores via standardization on the mean and standard deviation for control group youth ages 13 to 20. The school-level percentile ranking on state exam measure includes schools through 8th grade only for New York and Massachusetts. On-track youth are those who were currently in school or received a high school diploma or GED (certificate of General Educational Development).

XXXVI

MOVING TO OPPORTUNITY

FOR FAIR HOUSING

DEMONSTRATION PROGRAM

FINAL IMPACTS EVALUATION

XXXVII

XXXVIII

THE MTO LONG-TERM STUDY

1

Chapter 1: The MTO Long-Term Study

THE MTO LONG-TERM STUDY

The neighborhoods of almost every American

individuals’ earnings, test scores, criminal involvement,

city provide dramatic contrasts in the well-being

health, and other outcomes to information about the

of their residents. Children and adults in high-

characteristics of their residential neighborhoods,

CHAPTER 1

poverty neighborhoods fare worse on a wide range of

which are usually measured at the census tract or ZIP

socioeconomic and health outcomes than do residents

code level. Studies typically find that neighborhood

of more affluent areas. These disparities have prompted attributes help predict the outcomes of adults and concern about the geographic concentration of poverty

children, even after adjusting for a large number of

in the United States and about the possibility that

family background and individual characteristics. (For

neighborhood environments themselves may directly

excellent reviews, see Dietz, 2002; Ellen and Turner,

affect people’s life chances (Jargowsky, 1997; Wilson,

1997; Kawachi and Berkman, 2003a; Leventhal and

1987).

Brooks-Gunn, 2000; and Sampson, Morenoff, and

Gannon-Rowley, 2002.) A primary concern with

Why might social problems be much more prevalent

these observational or “nonexperimental” studies is

in some neighborhoods than in others? Some social

that families often have at least some degree of choice

scientists emphasize differences in the quality of the

over where they live. If, as seems likely, the same

local housing stock and in the formal institutions that

attributes directly determine both family well-being

serve neighborhood residents, such as schools, police,

and selection of residential location, then what looks

or health care providers. Others point to differences

like a “neighborhood effect” might instead be caused

in the capacity of residents to develop the informal

by hard-to-observe individual attributes associated with

social institutions that provide local public goods,

both outcomes and residential decisions—what social

as well as differences in the presence of positive peer

scientists call “selection bias.”

influences and adult role models (Durlauf, 2004;

Jencks and Mayer, 1990; Wilson, 1987). For example,

To better understand the causal effects of neighborhood

a core group of middle-income residents with high

environments on the lives of low-income families, in

school diplomas and stable employment may be crucial

the early 1990s the U.S. Department of Housing and

for serving as role models who reinforce (and signal)

Urban Development (HUD) launched a randomized

the value of education and work, and to support a

housing mobility experiment known as Moving to

neighborhood’s capacity to develop well-functioning

Opportunity (MTO). From 1994 to 1998, the MTO

informal local networks that may help monitor and

demonstration program randomly assigned families the

support local children. Neighborhoods may also vary

opportunity to receive housing vouchers and assistance

in the degree to which peer norms support prosocial

in moving to low-poverty neighborhoods (census tracts

rather than antisocial behaviors. Gauging the role

with 1990 poverty rates below 10 percent). Eligibility

that neighborhood environments play in determining

for MTO was limited to families with children who

the well-being of poor families is vital for housing

were living in public housing or in project-based

and other public policies that affect the degree of

Section 8 assisted housing located in high-poverty areas

geographic concentration of poverty in America.

of five cities (Baltimore, Boston, Chicago, Los Angeles,

and New York). It is the first randomized social-science

Yet it is difficult in practice to determine the causal

experiment designed to identify the effects of moving

effects of neighborhood contexts on the long-term

from high-poverty to lower-poverty neighborhoods on

life chances of poor families. The social and medical

the social and economic prospects, schooling, health,

sciences have used data from population surveys to link and well-being of low-income families.

2

This report summarizes the results of the long-term

Although public support for government housing

follow-up study of MTO families, which used in-person

assistance for the poor remains strong, policymakers

surveys, physical health measures, and government

have become increasingly interested in alternatives to

administrative records to examine outcomes 10 to 15

public housing that may help deconcentrate poverty in

years after families entered the study. This introductory

America.1 Of particular concern have been conditions

chapter provides background on the motivation and

inside the nation’s most distressed public housing

design of MTO and discusses how the randomized

projects, such as the Henry Horner Homes on Chicago’s

experimental design helps overcome concerns with

West Side that Alex Kotlowitz chronicled in his book,

selection bias. It also discusses the baseline characteristics There Are No Children Here (Kotlowitz, 1992; see also and basic mobility outcomes of MTO families, the key

Jones and Newman, 1998). Over the last several decades,

results from previous waves of MTO research, and the

housing vouchers and related types of “tenant-based”

design of the long-term MTO study. The remaining

subsidies have accounted for a growing share of all new

chapters present the main findings.

federal commitments for low-income housing (Quigley,

2000).2 More recently, the 1998 Housing Act requires

1.1 HOW HOUSING POLICIES MIGHT

local public housing authorities (PHAs) to provide

AFFECT LOW-INCOME FAMILIES

families in “distressed” public housing with housing

The MTO study contributes to debates on housing

vouchers, which provide subsidies to rent units in the

policy strategies as well as to the large body of scientific

private housing market. The 1998 Housing Act also

and policy research on neighborhood effects. The effects

makes it easier for PHAs to convert other projects to

of moving families from one type of neighborhood

vouchers.

environment to another need not be identical to

what would happen if we changed the neighborhood

Because tenant-based subsidies provide families with

environments in which people were living without

more choice over where they live, recipients tend to

their having to move because, for example, the act of

reside in lower-poverty census tracts than do families

moving itself could create some temporary disruption

with project-based subsidies (Newman and Schnare,

in a family’s life. But evidence from MTO about the

1997; Khadduri, Shroder, and Steffen, 1998; Devine et

aspects of residential mobility that affect people’s well-

al., 2003; Olsen, 2003).3 Other housing policy decisions

being and life chances is still relevant to the research on

also influence the geographic concentration of poverty,

neighborhood effects, and might help inform the design

such as decisions about where to locate project-based

of “place-based” community-level interventions as well as units, how much funding to dedicate to developing residential mobility programs.

mixed-income housing projects, and how to fund and

operate tenant-based subsidy programs. If neighborhood

The federal government’s intervention in the housing

context exerts an independent effect on family behavior,

market began in earnest during the New Deal in the

then housing programs could have cascading influences

1930s, motivated by concerns about the failure of the

on different aspects of family life, such as earnings,

private housing market to provide decent shelter for

schooling, health, and child development. This is

low-income families (Hunt, 2009). Inadequate housing

important because the goal of federal housing policy

quality—for example, units without windows or adequate

plumbing or ventilation—was thought to contribute to

1 A 2001 survey by National Public Radio, the Kaiser Family Founda-

infant mortality and other adverse health outcomes, as

tion, and Harvard University found that 75 percent of respondents

support more spending “for housing for poor people” by government

well as other social problems such as delinquency (Hunt,

(see www.npr.org/programs/specials/poll/poverty/staticresults3.html,

2009: 17; see also Newman, 1999; Patterson et al., 2004; accessed May 5, 2010).

Harkness and Newman, 2005). These concerns resulted

2 A third possibility is government subsidies to private providers of

specific housing units. Such policies impose locational constraints on

in the U.S. Housing Act of 1937, which led to a system

recipients as do other project-based subsidies.

of public housing that peaked in size in the mid-1990s at 3 A number of studies provide suggestive evidence that per-unit costs around 1.4 million units (Olsen, 2003).

may be lower for tenant-based than for project-based subsidy programs

(GAO, 2001; HUD, 2000; Olsen, 2000; Shroder and Reiger, 2000).

3

Chapter 1: The MTO Long-Term Study

since at least the Housing Act of 1949 has been to

improve family well-being, broadly defined.

A large theoretical literature in the social and medical

sciences suggests several mechanisms through which

neighborhood characteristics, above and beyond a

family’s own economic and housing conditions, may

affect family outcomes. Our own theoretical orientation

leads us to believe that a model recognizing that people

respond to incentives can be useful for understanding

and predicting behavior in many circumstances. But

unlike some “rational choice” approaches, we recognize

that aspects of the social environment can also shape the

way people respond to incentives, or even directly shape

the incentives and opportunities that people face for

engaging in different activities.

Exhibit 1.1 presents a general model depicting the

MTO intervention’s hypothesized pathways that lead

from community-level mediators, to individual- and

family-level mediators, to outcomes (as originally

proposed in Exhibit 1.1 of Orr et al., 2003). The

influential typology of Jencks and Mayer (1990) defines

four different types of models or theories about why

neighborhood environments might affect people’s

behavioral outcomes and well being—epidemic models,

collective socialization, institutional models, and relative

deprivation or competition models. Constructing strong

non-experimental empirical tests of these models has

been challenging for the research community, and new

theoretical work has elaborated on many of the types of

models mentioned by Jencks and Mayer (see for example,

Sampson, Morenoff and Gannon-Rowley, 2002). But this

typology nevertheless remains useful for characterizing

alternative hypotheses for how residential mobility

through MTO, which alters neighborhood context, may

affect the outcomes for MTO adults and children.

4

EXHIBIT 1.1. HYPOTHESIZED PATHWAYS OF MTO IMPACTS

MTO INTERVENTION

RELOCATION TO LOW-POVERTY NEIGHBORHOODS

COMMUNITY-LEVEL

PERSON- AND FAMILY-LEVEL

MEDIATORS

MEDIATORS

OUTCOMES

Housing Market

Housing Mobility

Adult Social

Conditions

and Assistance

Networks

Adult Education,

Economic

Child/Youth

Employment, and Earnings

Opportunities

Peer Networks,

Peer Behavior

Household Income and

Public Assistance

Social and Physical

Home Environment

Environment

(including Parenting Behavior)

Adult/Youth/Child

Mental and Physical Health

Social Norms

Parent Attitudes Towards

and Values

Own and Children’s

Youth/Child Social

Education, Achievement

Well-Being

(including Delinquency and Risky Behavior)

Child/Youth Attitudes

Schools

Child/Youth

Toward Own Education,

Educational Performance

Achievement

MTO = Moving To Opportunity

MTO = Moving T

Exhibit 1.1 is a reformatted version of Exhibit 1.1 in the MTO interim impacts evaluation (see Orr et al., 2003) Exhibit 1.1 is a r

5

Epidemic models emphasize the roles played by peer

Moves to lower-poverty areas may also affect the extent

processes, and predict that MTO moves into less

to which neighborhood adults influence young people

disadvantaged and safer neighborhoods will improve

who are not their children, what Jencks and Mayer

outcomes by exposing people to different peer groups.

(1990) call collective socialization models. For example,

Epidemic models suggest that peers can spread behaviors

children’s language development and other learning

through learning, imitating their peers, stigma effects

outcomes may benefit if children live in areas with more

(that is, the negative stigma from antisocial behaviors

highly educated adults (Borjas, 1995). Neighborhood

declines when more people do them), and physical

adults who have completed high school and work in the

circumstances by which, for example, higher rates of

formal labor market might also help serve as role models

crime reduce the chances of getting arrested (Cook and

who signal the value of prosocial activities (Wilson,

Goss, 1996; Glaeser and Scheinkman, 1999; Manski,

1987). Neighborhoods may also vary in the degree to

2000; Moffitt, 2001). For example, living near other

which local adults are willing or able to work together to

employed people can increase one’s chances of gaining

enforce shared values, what Sampson, Raudenbush, and

employment through increased social support of work, or Earls (1997) call “collective efficacy” (see also Coleman, by receiving job information or references from neighbors 1988).

(Kain, 1968; Wilson, 1987, 1996). MTO could affect

health outcomes by changing exposure to different types

Moves might also enable families to access more

of what Fitzpatrick and LaGory (2000) call “health-

productive institutional resources. Families may gain

related subcultures,” which may influence the availability access to better schools or a higher quality (or quantity) of information about healthy behaviors or social support

of policing services (Sherman, 2003; Rivkin, Hanushek,

for such behaviors (Cubbin and Winkleby, 2005), or

and Kain, 2005). Adults and teenagers moving out

affect the likelihood of feelings of anomie (Sampson and

of the central city may be closer to low-skilled job

Bartusch, 1998; Browning and Cagney, 2003).

opportunities, as in the “spatial mismatch” hypothesis

(Kain, 1968). MTO might also help families move into

Some epidemic models predict that peer influences on

places with higher quality health clinics, safe places

behavior will vary with the prevalence of the behavior

for exercise, or grocery stores that sell fresh fruits and

within a community, which can lead to “tipping points”

vegetables (Blair Lewis et al., 2005; Browning and

in which the prevalence of the behavior can surge

Cagney, 2003; Huston, Evenson, and Bors, 2003;Kirby

dramatically once the share of people engaging in that

and Kaneda, 2005;Morland et al., 2002; Saelens et al.,

behavior crosses some threshold (Crane, 1991; Schelling, 2003; Subramanian et al., 2006; Zenk et al., 2003).

1971, 1978). If there are no tipping points, and if all

The move may also reduce exposure to pollution, noise,

people respond to a given type of neighborhood or peer

tobacco and alcohol ads, density of fast food restaurants

environment in the same way, then many epidemic

or bars, and crime (Browning and Cagney, 2003; Cubbin

models—those known as linear-in-the-means models

and Winkleby, 2005; Dowd, Sisson, and Kern, 1981;

where individuals respond to the average behavior of

Krause, 1993; Morland et al., 2002; Ponce et al., 2005;

their neighborhood peers—predict that policies to reduce Skogan, 1990; Thompson and Krause, 2000).

the concentration of disadvantaged families within

highly distressed neighborhoods may simply redistribute

In addition, models from behavioral economics suggest

the volume of anti-social behavior across different

that MTO could affect behavior by reducing people’s

neighborhoods (Manski, 2000). But if there are tipping

exposure to daily stressors that sap mental energy and

points, so that for example the risk that any given teen

willpower, and make it harder for people to resist

engages in delinquency is only influenced by their social

temptations (Lowenstein and O’Donoghue, 2004). The

environment if more than (say) a third of neighborhood

local context of neighborhoods can also affect self-control

youth engage in delinquency, then policies that help

and the salience of temptations to buy or use drugs or

reduce the concentration of disadvantage can help reduce engage in crime.

the overall volume of anti-social behavior in a city.

6

Mobility could also improve children’s life chances

science study. This makes it difficult to disentangle the

through changes in their household environments by,

effects of neighborhoods on outcomes from those of

for example, reducing parental stress in ways that lead to

hard-to-measure individual- or family-level attributes

less harsh and more supportive or cognitively stimulating that are associated with residential choices. Suppose parenting. Neighborhoods could also vary in the

a low-income child growing up in the racially and

availability of “marriageable men,” which could increase

economically integrated Chicago neighborhood of Hyde

marriage rates and family income for the mostly female

Park (home to the University of Chicago and President

heads of household and reduce the likelihood that their

Obama’s private residence) does better in school than

children grow up in a single-parent household (Neal,

a low-income child growing up in Washington Park (a

2004; Wilson, 1987).

very economically distressed, almost entirely African-

American neighborhood directly adjacent to Hyde Park).

Not every social science theory predicts that moves will

In this case, it is difficult to know whether to attribute

reduce involvement with risky or antisocial behaviors.

the Hyde Park child’s better performance in school to the

Competition models emphasize the potential for

community per se or to whatever it was about her family

increased competition for scarce resources like jobs or

that motivated or enabled them to live in Hyde Park.

grades (if teachers grade on a curve) that MTO movers

may face in more affluent neighborhoods. The failure

This difficulty, called selection bias, is a challenge to

to successfully compete for prosocial rewards could

observational (nonexperimental) studies that interview

lead some people to instead reverse course and try

families at their current residence. MTO solves this

competing for resources or social standing by engaging

concern by randomly assigning some families but not

in antisocial behaviors. Comparisons with the status

others the chance to move from high- to low-poverty

and accomplishments of new neighbors in more affluent

areas. By virtue of random assignment, the two groups

areas could also have a negative psychological impact,

of families should start out identical on average in

as predicted in relative deprivation models (Luttmer,

every way (up to sampling error) except that only one

2005). Finally, moving per se could be disruptive even if

group receives assistance to move to lower-poverty

MTO adults and children choose social “micro-climates”

communities. Thus, we can attribute any differences in

similar to those in their original neighborhoods (Jencks

average outcomes across the groups to the causal effects

and Mayer, 1990).4 Because these competing theories

of moving to low-poverty areas.

make different predictions about how moves could affect

the behavior and well-being of parents and their children, Although the design allows us to disentangle causal MTO’s net effect on the well-being and life chances of

effects of neighborhoods from selection biases, the

families is ultimately an empirical question.

fact that MTO-induced moves change so many

environmental characteristics at once makes it difficult

All of these theories of how neighborhood environments

to pinpoint the specific aspects of neighborhood

might affect the behavior and well-being of low-income

environments that influence family outcomes. But as we

families are plausible. Yet determining the degree to

discuss below, experimental estimates can help us rule

which these theoretical mechanisms operate in real life

out the importance of certain mediating mechanisms

is complicated by the fact that most people have at

as explanations for the observed impacts. A review of

least some degree of choice over where they live. Those

the overall pattern of results can provide some support

residential choices are influenced by an individual’s

for our hypotheses to validate (or invalidate), in part,

own characteristics, background, and other factors, not

the theoretical relationship between mechanisms and

all of which can be adequately measured in any social

outcomes. Analyses of the impact of MTO-induced

moves on potential mediating variables can help us

4 For example, it is possible that any benefits from moving to less

distressed areas through MTO could be lost if youth simply sort them-

narrow the possible neighborhood aspects that are most

selves back into peer groups that engage in and support antisocial be-

important in accounting for MTO’s impacts on key

haviors, even if these types of antisocial peer groups are somewhat less

common in their new neighborhood than in their old neighborhood.

socioeconomic and health outcomes. For example, if

7

we observe an impact on obesity, and we also observe

educational attainment. Compared with the surveyed

that MTO substantially improves the safety of the

students who remained in the city of Chicago, suburban

neighborhoods in which families reside but has few

movers were one-quarter as likely to have dropped out

detectable effects on diet and exercise, this pattern

of school (5 percent versus 20 percent); more likely to

would suggest that something about safety (such as

be in a college track in high school (40 percent versus 24

psychological stress) might be more important in

percent); more than twice as likely to attend any college

explaining the obesity results than explanations that

(54 percent versus 21 percent); and almost seven times

revolve around changing diet and exercise, such as

as likely to attend a four-year college (27 percent versus

availability of exercise opportunities or food stores.

4 percent). The only educational attainment measure for

which the suburban students did not appear to be doing

1.2 MOTIVATION FOR THE MOVING TO significantly better than the city students was grade point OPPORTUNITY EXPERIMENT

average, which could reflect higher grading standards in

HUD sponsored MTO to identify the causal effects

suburban schools (Rubinowitz and Rosenbaum, 2000:

of neighborhood environments on the well-being and

134–136). However more recent studies of Gautreaux

life outcomes of low-income families. It was motivated

show fewer differences between city and suburban movers

in part by an influential series of studies of the

(Deluca et al., 2010).

Chicago-based Gautreaux residential mobility program

(Rubinowitz and Rosenbaum, 2000), which was named

Although the Gautreaux program has been extremely

after the plaintiff (Dorothy Gautreaux) in a 1966 racial

influential in housing policy circles, the study was

discrimination lawsuit filed against the Chicago Housing nevertheless not a true randomized experiment. Families Authority (CHA) and HUD. The lawsuit charged

may have had some degree of choice about whether to

discrimination on the basis of the heavy concentration

accept the first apartment offered to them, and indeed

of African-American families in public housing projects

there is some evidence that the baseline characteristics of

located in high-poverty areas. The U.S. Supreme Court

families that ended up in the suburbs are systematically

agreed in 1976 and ordered CHA to provide housing

different from those who stayed in the city (Mendenhall,

vouchers to African-American public housing residents

Duncan, and Deluca, 2006; Votruba and Kling, 2009).

that they could use only in neighborhoods in the city or

This has made researchers nervous that the Gautreaux

suburbs that were less than 30 percent African-American. city and suburban movers may have differed in Units were assigned to families on a wait list who were

preexisting unobserved characteristics as well, which

either living in or eligible for public housing (Rubinowitz could lead analysts to confound the causal effects of and Rosenbaum, 2000). Some of those apartments

suburban moves with the influence of these unmeasured

were in areas of Chicago that were poor and segregated,

attributes that may affect both the likelihood of moving

but improving, whereas others were in low-poverty,

to the suburbs and the resulting outcomes.

predominantly white or integrated suburban areas (Keels

et al., 2005). Some 7,100 African-American families were THE MTO DEMONSTRATION

offered portable Section 8 vouchers. The nonprofit in

MTO was authorized by the U.S. Congress in the

charge of implementing the Gautreaux program helped

Housing and Community Development Act of 1992 (for

identify rental units for families on the program wait list, more background on MTO, see Goering et al., 1999; and most families are believed to have taken the first or

Goering, Feins, and Richardson, 2003). Families were

second unit offered to them.

enrolled in MTO between 1994 and 1998 in five cities:

Baltimore, Boston, Chicago, Los Angeles, and New York.

A 1988 follow-up survey of 342 families who used

To be eligible, families had to have children under age 18

Gautreaux vouchers found that moving to the Chicago

and live in public housing developments or project-based

suburbs instead of other parts of the city was associated

assisted housing in high-poverty areas (census tracts in

with significant improvements in children’s eventual

which more than 40 percent of the population was living

in poverty in 1990). The PHAs in each city conducted

8

outreach to all eligible households through fliers, tenant

housing projects, enrolled in MTO from 1994 to 1998

associations, and other means, and all those interested

(Goering, Feins, and Richardson, 2003; Goering et al.,

received the opportunity to apply for this special

1999, Table 5). Almost all of these households were

program. Interested households joined waiting lists of

female-headed. Nearly two-thirds were African-American,

the local PHAs. Families on the waiting list attended

and most of the rest were Hispanic (see Exhibit 1.2

group orientation sessions where they learned about the

for adult characteristics and Exhibit 1.3 for youth

demonstration program, its experimental design, and

characteristics).5 Three-quarters of household heads were

specifically the fact they would be randomly assigned to

on welfare and the average annual household income was

one of three groups (see below). Heads of households

$12,827 (in 2009 dollars), well below the poverty line.

that remained interested after the briefing completed

Fewer than 40 percent had graduated from high school.

the baseline survey and signed an enrollment agreement.

These households had an average of 2.5 children under

Before formal acceptance into the program, families were age 18. Although we do not know their immigration screened for Section 8 eligibility.

status, most spoke English.

A total of 4,604 households, or around one-quarter of

5 Supplemental Exhibit 1.1 shows characteristics of the MTO partici-

the eligible families living within the designated eligible

pants who were under 18 when enrolled but adults (aged 21 or older) at

the time prior to the initiation of the long-term MTO study in Decem-

ber 2007, a group that we call “grown children” in this report.

EXHIBIT 1.2. BASELINE CHARACTERISTICS OF THE ADULT SAMPLE BY RANDOM ASSIGNMENT

GROUP

EXPERIMENTAL SECTION 8

CONTROL

ALL GROUPS

GROUP

GROUP

GROUP

SAMPLE ADULT

CHARACTERISTICS (SR)

Female

98.2%

97.5%

97.4%

97.8%

AGE

As of December 2007

45.2

45.3

45.0

45.2

At baseline

33.6

33.7

33.3

33.5

RACE/ETHNICITY

African-American non-Hispanic

61.1%

60.7%

62.5%

61.4%

Hispanic

30.9%

31.9%

30.2%

31.0%

White non-Hispanic

2.9%

2.5%

2.4%

2.6%

American Indian non-Hispanic

0.5%

0.3%

0.2%

0.4%

Asian/Pacific Islander non-Hispanic

1.6%

2.1%

1.1%

1.6%

Other non-Hispanic

1.8%

1.9%

2.2%

1.9%

MARITAL STATUS

Never married

61.3%

62.0%

63.3%

62.1%

Married

11.5%

11.7%

10.4%

11.2%

Divorced

9.3%

8.8%

8.7%

9.0%

Widowed or separated

16.8%

16.5%

16.5%

16.6%

EDUCATION

High school diploma

38.9%

37.1%

36.1%

37.5%

GED

16.5%

17.8%

18.9%

17.6%

Currently in School

15.9%

16.7%

16.4%

16.3%

9

EXHIBIT 1.2. (CONTINUED)

EXPERIMENTAL SECTION 8

CONTROL

ALL GROUPS

GROUP

GROUP

GROUP

SAMPLE ADULT

CHARACTERISTICS (SR)

EMPLOYMENT STATUS AND INCOME

Full-time

14.5%

14.0%

14.1%

14.2%

Part-time

11.5%

10.5%

9.8%

10.7%

Not working

70.4%

72.6%

72.5%

71.7%

Household Income

$12,916.87

$12,703.70

$12,827.91

$12,826.81

GOVERNMENT BENEFITS

AFDC/TANF

75.0%

75.7%

74.6%

75.1%

Supplemental Security Income

17.7%

17.0%

17.0%

17.2%

Food Stamps

80.7%

81.1%

79.9%

80.6%

Women, Infants, and Children

35.4%

36.0%

35.1%

35.5%

TAKES OVER 30 MINUTES

TO GET TO…

Grocery store

21.0%

22.5%

21.2%

21.5%

Doctor

43.2%

44.1%

44.7%

43.9%

HOUSEHOLD CHARACTERISTICS [SR]

Number of family members

3.7

3.8

3.7

3.7

Teens in household

39.8%

39.3%

37.4%

38.9%

Disabled household member

15.7%

14.4%

15.5%

15.2%

Female head of household

90.9%

90.8%

91.6%

91.1%

CENSUS TRACT CHARACTERISTICS [CEN]

Poor

52.3%

51.8%

52.7%

52.3%

Minority

91.0%

91.1%

91.1%

91.1%

Female-headed households

62.7%

62.2%

63.1%

62.7%

Adults with high school degree

90.9%

90.8%

91.6%

91.1%

Adults with college degree

10.3%

10.4%

10.3%

10.3%

Adults employed

74.4%

74.8%

74.4%

74.5%

Adults in professional job

30.1%

29.8%

29.9%

30.0%

MTO SITE DISTRIBUTION [SR]

Baltimore

13.8%

13.8%

13.8%

13.8%

Boston

20.8%

20.8%

20.8%

20.8%

Chicago

19.4%

19.4%

19.4%

19.4%

Los Angeles

22.5%

22.5%

22.5%

22.5%

New York City

23.5%

23.5%

23.5%

23.5%

10

EXHIBIT 1.2. (CONTINUED)

EXPERIMENTAL SECTION 8

CONTROL

ALL GROUPS

GROUP

GROUP

GROUP

MOTIVATIONS FOR SIGNING UP TO

MOVE THROUGH MTO [SR]

Primary or secondary reason for moving was…

To get away from drugs and gangs

76.9%

76.3%

78.2%

77.1%

Better schools for the children

48.7%

52.4%

47.3%

49.4%

To get a bigger or better apartment

44.9%

45.2%

46.3%

45.4%

To get a job

6.4%

5.2%

6.2%

6.0%

NEIGHBORHOOD CHARACTERISTICS

AND MOBILITY EXPERIENCES [SR]

Household member was crime victim in

43.2%

42.6%

41.5%

42.5%

past 6 months

Streets very unsafe at night

49.4%

50.8%

50.4%

50.2%

Very dissatisfied with neighborhood

46.8%

47.0%

46.3%

46.7%

Very confident about finding new apartment

46.7%

49.5%

45.2%

47.1%

Lived in neighborhood 5 or more years

59.5%

62.0%

61.3%

60.8%

Moved more than three times in 5 years

9.0%

9.1%

10.5%

9.5%

prior to baseline

Applied for Section 8 previously

40.7%

38.8%

43.0%

40.8%

No family in neighborhood

64.7%

63.2%

64.0%

64.1%

No friends in neighborhood

41.4%

38.8%

40.9%

40.5%

Chatted with neighbors at least once per

51.7%

49.9%

54.0%

51.9%

week

Very likely to report neighbor’s child getting

55.0%

53.6%

56.6%

55.1%

into trouble

SAMPLE SIZE

1,819

1,346

1,439

4,604

Notes: Percentages are percent distributions and may not sum to 100 because of rounding and missing information. Data are weighted to reflect randomization ratios.

Square brackets indicate the source of the outcome information: SR = self-report. CEN = 1990 and 2000 decennial census data.

Data source and sample: Moving To Opportunity (MTO) Participant Baseline Survey. “Sample adult” refers to the 4,604 adults included in the interview sampling frame for the long-term evaluation (see section 1.6 for details). Selection of one adult from each household gave priority to female adults who were more likely to be the children’s caretakers. The priority order for selecting both the interim and final evaluation samples was: female heads of the core household, female spouses of the core household head, female baseline heads, female spouses of the baseline head, and finally, non-female heads of the core households. The baseline head is often but not always the same person as the sample adult. The baseline head completed the baseline survey, providing information on both the household and its individual members.

Measures: Characteristics for the census tracts of addresses at which MTO families were living at baseline were linearly interpolated from the 1990 and 2000

decennial Censuses.

11

EXHIBIT 1.3. BASELINE CHARACTERISTICS OF THE YOUTH SAMPLE BY RANDOM ASSIGNMENT GROUP

EXPERIMENTAL SECTION 8

CONTROL

ALL GROUPS

GROUP

GROUP

GROUP

AGE [PR]

As of December 2007

16.1

16.2

16.1

16.1

At baseline

4.6

4.7

4.7

4.6

SEX OF YOUTH [PR]

Female

50.3%

49.3%

47.9%

49.2%

Male

49.7%

50.7%

52.1%

50.8%

OLDER YOUTH CHARACTERISTICS [PR]

Gifted student or did advanced coursework

13.0%

12.4%

12.4%

12.6%

Suspended or expelled from school in past

3.8%

4.3%

3.3%

3.8%

two years

School called about behavior in past two

19.9%

20.7%

19.6%

20.0%

years

Behavioral or emotional problems

5.5%

7.0%

5.1%

5.8%

Learning problem

11.8%

13.1%

12.3%

12.3%

YOUNGER YOUTH CHARACTERISTICS [PR]

In hospital before first birthday

18.1%

18.0%

19.4%

18.5%

Weight at birth (pounds)

7.1

7.1

7.0

7.1

Weighed less than 6 pounds at birth

13.3%

14.5%

14.8%

14.1%

Adult read to youth more than once per day

24.1%

21.5%

26.9%

24.3%

ALL YOUTH CHARACTERISTICS [PR]

Health problems that limited activity

6.1%

5.8%

5.4%

5.8%

Health problems that required special

9.1%

9.3%

8.9%

9.1%

medicine or equipment

Sample Size

2,544

1,965

2,136

6,645

Notes: Percentages are percent distributions. Data are weighted to reflect randomization ratios. At baseline, older youth were ages 6 to 11 and younger youth were ages 0 to 5.

Square brackets indicate the source of the outcome information: PR = parent report.

Data source and sample: Moving To Opportunity (MTO) Participant Baseline Survey. N = 6,645 youth ages 10 to 20 as of December 2007 included in the interview sampling frame for the long-term evaluation (see section 1.6 for details) as reported by the baseline head of household.

12

Perhaps the most striking finding from the baseline

Section 8 certificate and voucher programs, including

surveys is that more than 40 percent of households that

having a limited amount of time to search for

applied had a household member victimized by a crime

housing and sign a lease before they lost the rights to

during the previous six months. Prior qualitative work

their subsidy, being required to contribute 30 percent

by several members of our present research team showed

of their adjusted income toward rent (the same rent

that the fear of violence among adults was “so deep-

requirement as in public housing), and prohibitions

seated that their entire daily routine was focused on

on rental assistance to households that engage in

keeping their children safe” (Kling, Liebman, and Katz,

certain types of criminal activity.6

2005, p. 244). One applicant, describing her previous

experiences living in Boston public housing, reported

The Section 8 only group received regular Section 8

that, “Every time I looked out my window, there was

certificates or vouchers that they could use anywhere;

dead bodies. So I didn’t want my kids to grow up in that

these families received no mobility counseling.

atmosphere…. They would see the dead bodies. And if

they didn’t die on the scene, they would see the blood”

The control group received no certificates or

(p. 252). A Hispanic woman in the Boston site noted

vouchers, but continued to be eligible for project-

that, “In this entryway, a woman was raped. People have

based assistance and whatever other social programs

been robbed, beaten, and stabbed right here” (p. 253).

and services to which families would otherwise be

Her son noted, “On the rooftops there were empty crack

entitled.

vials everywhere. It was pretty violent. Gun shots, fights

every day. I saw someone die over there. Some guy was

It is important to keep in mind that the control group,

shot in the neck”(p. 253).

to which the experimental and Section 8 only groups

are being compared, was not required to stay in public

It is probably not surprising, then, that fully three-

housing during the entire study period. In fact, during

quarters of MTO families report that getting away from

the course of the 1990s, HUD sponsored another large-

gangs and drugs—that is, crime—was the first or second

scale program called HOPE VI that ran in parallel to

most important reason for enrolling in the program.

MTO and provided assistance to local PHAs to demolish

About half of the households wanted to move so that

tens of thousands of the nation’s most distressed public

their children could attend a better school.

housing units, including many of the housing projects in

which MTO families were living (see Popkin et al., 2004

Eligible applicant households were randomly assigned to

for more details about HOPE VI). Families displaced as a

one of three groups:

result of HOPE VI sometimes received housing vouchers

through that alternative program, or they received

The experimental group received Section 8 rental

assistance in moving into other housing projects or

assistance certificates or vouchers that they could use

mixed-income housing developments in other areas.

only in census tracts with 1990 poverty rates below

10 percent. In each city, a nonprofit organization

Exhibits 1.2 and 1.3 show that the observable baseline

under contract to the PHA provided mobility

characteristics of families are on average very similar

counseling to help families locate and lease suitable

across the three randomly assigned MTO groups,

housing in a low-poverty area. After one year,

suggesting that the randomization in MTO was carried

families were able to use their voucher to relocate

out correctly. Because of random assignment, we

without any special MTO-imposed constraints

on their moves. Families who stayed in their new

6 HUD rules require PHAs to deny housing assistance to households

low-poverty neighborhoods less than a year did not

that include someone currently engaged in illegal drug use or in alcohol

abuse that affects other residents of the building, or someone who was

receive a new voucher. Aside from this requirement,

evicted from federally assisted housing for drug-related criminal activity

experimental group families were required to abide

during the past three years. Local PHAs are also left with some discre-

tion about the degree to which other types of criminal activity affect

by all of the regular rules and requirements of the

eligibility; see for example Kooklan (2008) at www.lahsa.org/docs/poli-

cyandplanning/section8.pdf.

13

expect even the unobserved attributes of families at

and α . However, controlling for baseline characteristics

2

baseline to be similar across MTO groups on average.

accounts for additional variation in the outcomes in our

Any differences in average outcomes across the three

data, and thus can improve the precision of our estimates

randomly assigned MTO groups can thus be attributed

for the ITT effects. The term e in equation (1) is a

i

to the MTO intervention, as we discuss in greater detail

residual or error term, and reflects the fact that outcomes

in the next section.

for MTO program participants will also be affected by

variables that are not included in our analysis. However

1.3 ESTIMATING THE MTO PROGRAM

any unmeasured background characteristics of families

EFFECTS

that affect outcomes should be uncorrelated with the

The great strength of MTO is the demonstration

indicators for treatment group assignment in equation

project’s randomized experimental design, which as

(1) by virtue of random assignment.

we noted above helps overcome concerns that previous

nonexperimental studies may confound the causal effects

We use ordinary least squares to estimate equation (1)

of neighborhoods with those of difficult-to-measure

for dichotomous (binary) outcome variables as well

individual- or family-level attributes that affect both

as continuous outcome variables. In practice, least

outcomes and residential selection. For this reason, we

squares estimates with dichotomous dependent variables

focus in this report on results that fully exploit MTO’s

yield results quite similar to the average marginal

experimental design.7 Estimates of MTO’s effects

effects that come from probit or logit models (see, for

correspond to two different questions. The first is the

example, Angrist and Pischke, 2009). All estimates

effect of the intervention on the average outcomes of

weight individuals by the inverse of their probability

those assigned to one of the MTO mobility groups (the

of assignment to their group to account for changes in

experimental group or Section 8 only group), regardless

the random assignment ratios over time, sampling of

of whether they relocated through the MTO program.

youth from large families and Section 8 adults, and, for

This is known as the intention to treat (ITT) effect. The those interviewed in the phase 2 stage, by the inverse ITT effect is estimated by applying ordinary least squares of the likelihood of selection for phase 2 subsampling.

to equation (1), where Yi is some outcome for MTO

Standard errors were adjusted for family clustering and

program participant (i); Exp and S8 are binary indicator heteroskedasticity using the Huber-White robust method.

i

i

variables equal to 1 if participant (i) was randomly

Analyses were performed using Stata 11.0 Special

assigned to the experimental or Section 8 groups. The

Edition.

control group is the omitted reference group.

If one is interested in the effectiveness of a program such

(1) Y = α + α Expi + α S8 + α X + e

as MTO in improving the situation of the entire class of

i

0

1

2

i

3

i

i

families to whom it is offered, the ITT estimates are the

Our estimates for the ITT effects for the experimental

appropriate results to examine. The overall effectiveness

and Section 8 treatments equal α and α in equation

of such a policy depends on both the effects on families

1

2

(1). In our analysis, we also control for a set of baseline

of using a voucher and the proportion of families in

characteristics, X , which are basically the same as those

one of the MTO mobility groups who use a voucher.

i

used in the interim MTO study (Orr et al., 2003) and are For continuous outcome variables, the ITT estimate is described in Supplemental Exhibit 1.2. Under random

essentially equal to the difference in average outcomes

assignment, the weighted distribution of the baseline

between everyone assigned to the experimental or Section

variables should be similar across the three MTO groups; 8 only group and everyone assigned to the control therefore, controlling for baseline characteristics should

group. If the dependent variable is dichotomous, the

not have much effect on the estimated coefficients α

ITT estimate is essentially the difference in prevalence

1

rates for that outcome between those assigned to the

7 For different perspectives about the value of the MTO experimental

experimental or Section 8 only group versus the control

design versus its nonexperimental alternatives, see Clampet-Lundquist

and Massey (2008), Ludwig et al. (2008), and Sampson (2008).

group. Note that this ITT estimate is not biased by the

14

fact that only some families relocate through the MTO

in response to the experimental group voucher offer may

program because we derive the ITT by comparing the

differ from those who signed leases in response to the

average outcomes of everyone assigned to one of the

Section 8 group voucher. Differences in the TOT impacts

mobility groups, whether they relocate through the

between the two groups may reflect differences in the

program or not, with the average outcomes of everyone

composition of families who relocate in the experimental

assigned to the control group.

versus Section 8 only group, rather than differences only

in the types of changes in residential environments that

If instead one is interested in our second question about

families experience.

the effects on family outcomes from actually moving,

one should consult the treatment on the treated (TOT)

We must also bear in mind that even among the

results, which are the effect on those who actually

experimental group families who signed leases and

leased up and moved through MTO. Under some

initially moved to a low-poverty area with their MTO

assumptions, we can estimate the TOT effect by using

vouchers, not all of them spent the entire follow-up

information about the proportion of families assigned to

period in low-poverty areas. As we show in the next

the experimental or Section 8 only group that actually

section, some experimental group families made a second

moved through the MTO program. More specifically, the or third move, sometimes to areas with higher poverty TOT estimate will be approximately equal to the ITT

rates. And in some cases, even if the family stayed

effect divided by the share of the experimental or Section in the same neighborhood, the poverty level of that 8 only group that relocates using an MTO voucher

neighborhood increased over the course of the follow-up

(Angrist, Imbens, and Rubin, 1996; Bloom, 1984). Our

period. It is true, however, that experimental and Section

analytic methods are similar to those in Orr et al. (2003)

8 only participants spent significantly more time in low-

and Kling, Liebman, and Katz (2007), which included

poverty areas than did the control group families.

more detailed discussions.

Finally, we must recognize that MTO is most informative

With both the TOT and ITT effects, we reiterate that

about small-scale or incremental voluntary mobility

we are comparing the outcomes of the experimental and

programs serving families in very distressed areas. A

Section 8 only groups with the average outcomes of a set

much larger-scale voluntary mobility intervention may

of families (the control group) that started out living in

generate much different effects. For example, if a large

public housing. As we show in the next section, many

number of public housing families receive vouchers, then

control group families left public housing on their own,

it may be more difficult for families to find affordable

without any assistance from the MTO demonstration.

apartments under the housing voucher program payment

Some control group families moved to the same kinds

standards, at least in the short run until housing market

of neighborhoods as some of the treatment group

supply responds. A much larger scale voucher mobility

families, and some even received vouchers through the

program also could potentially increase the general

regular Section 8 program. The estimates we present

dispersion of poor families, making it harder for any

here represent the incremental effects of offering families

given voucher recipient to find and lease an apartment

MTO housing vouchers, relative to what was available to

located in a very low (less than 10 percent) poverty tract.

the control group, whose outcomes reflect what would

Furthermore, MTO may not be a good guide to the

have happened to the treatment group families in the

impacts of potentially involuntary moves with vouchers

absence of the MTO demonstration.

such as those following HOPE VI public housing

demolitions.8

Although we can compare the ITT estimates for the

experimental group with those of the Section 8 group,

the TOT estimates for the two groups are not directly

comparable. This is because families who signed leases

8 See Jacob (2004) for a quasi-experimental analysis of the impact of

moves induced by public housing demolitions in Chicago on children’s

schooling outcomes.

15

EXHIBIT 1.4. MTO VOUCHER LEASE-UP RA

Exhibit 1.4. MTO V

TES FOR MTO EXPERIMENTAL AND SECTION 8 GROUPS

oucher Lease-Up Rates for MTO Experimental and Section Groups

100

63

77

52

67

77

47

80

47

54

44

33

60

46

Percent

60

Experimental

Percent 40

Section 8

20

0

Los

New York

All Sites

Baltimore

Boston

Chicago

Angeles

City

Sample: All adults in the experimental and Section groups included in the interview Sample: All adults in the experimental and Section gr

sampling frame for the long-term evaluation (N = 3,165). See section 1.6 for details.

[MTO = Moving to Opportunity]

1.4 MTO MOBILITY OUTCOMES

53.1 percent of all tract residents had incomes below

Of those households assigned to the experimental

the federal poverty line.9 The average census tract

group, 47 percent relocated using an MTO voucher

poverty rate for the experimental group families’ initial

(the “compliance rate”), whereas 63 percent of those

MTO-assisted move was 10.7 percent. These differences

assigned to the Section 8 group relocated through MTO.

in census tract poverty rates narrow considerably by

The compliance rate is higher for the Section 8 group

the time of the long-term follow-up survey. By 10–15

presumably in part because their vouchers (unlike the

years after random assignment, the average control

vouchers offered to experimental group families) had no

group family is living in a census tract with a poverty

geographic restriction.

rate of 31.3 percent, while the average family in the

experimental group is in a tract with a poverty rate of

As Exhibit 1.4 shows, the lease-up or compliance rates

27.4 percent, and the average family who moved with an

differed substantially across the five cities, from a low

MTO experimental group voucher was in a tract with a

of 33 percent in the Chicago experimental group to a

21.0 percent poverty rate.10

high of 77 percent in the Los Angeles and Baltimore

Section 8 groups. Compared with noncompliers, those

families who moved with MTO vouchers tend to be

9 For addresses at which MTO families were living during intercensal

years (between 1990 and 2000 or between 2000 and 2010), we interpo-

younger, be relatively more dissatisfied with their original late the characteristics of the census tract in which the MTO family was living using data from the 1990 and 2000 decennial censuses together

neighborhoods, and have fewer children (for details see

with census tract-level data from the 2005–09 American Community

Feins and Shroder, 2005; Shroder, 2002a).

Survey, which we describe in more detail later in this chapter.

10 Most of the convergence in census tract characteristics across the

three randomly assigned MTO groups over time seems to occur because

MTO’s explicit goal was to help move families into less

of residential mobility, rather than change over time in the neighbor-

economically distressed communities. As Exhibit 1.5

hoods due to gentrification or neighborhood decline. When we reana-

lyzed the data in Exhibit 1.5 holding the characteristics of the census

shows, by this measure MTO was successful. For the

tracts constant by linking each address in a family’s residential history

baseline census tracts in which families were residing,

to census tract characteristics measured in the 1990 or 2000 decennial

census, we see qualitatively similar findings.

16

Despite this convergence, the difference is still sizable in the

share during the study period was 88.0 percent minority

average tract poverty rate that families experience over the

for controls, 82.1 percent for all families assigned to the

entire study period. Specifically, for the control group, the

experimental group, and 75.0 percent for those in the

average duration-weighted tract poverty rate (which weights

experimental group who used an MTO voucher to move.

the tract poverty rate for each of a family’s addresses by the

amount of time the family spends at that address)11 is equal

Chapter 2 will describe the mobility patterns of families

to 39.6 percent compared with 30.6 percent for the average

over time in more detail, as well as the resulting

experimental group family and 20.0 percent for those who

characteristics of the neighborhoods in which they have

moved with an MTO voucher as part of the experimental

resided. Naturally occurring mobility of families in the

group. The exhibit also shows that the effect of the Section

control group reduces somewhat the effect of the MTO

8 group on family neighborhood conditions is in the same

mobility intervention on the differences in neighborhood

general direction of the experimental treatment, but is

environments across randomly assigned groups. But

generally more modest in magnitude.

the moves out of the poorest neighborhoods by many

Although MTO was explicitly designed to help public

in the control group may also make the MTO results

housing families move into lower-poverty census tracts,

more policy relevant: The neighborhood conditions that

these MTO-induced moves changed other aspects of

families in the experimental and Section 8 groups would

the socioeconomic composition of census tracts as

have experienced had they not been assigned to the

well. For example, Exhibit 1.5 shows that the average

MTO experimental and Section 8 treatment groups are

duration-weighted tract share of adults with a college

not restricted to just the highest poverty census tracts in

education was 16.1 percent for controls, 20.3 percent

which a relatively small share of public housing families

for all families assigned to the experimental group, and

reside.

24.7 percent for those in the experimental group who

moved through MTO. Previous research by Duncan et

1.5 PREVIOUS MTO FINDINGS

al. (1994) suggests that the presence of affluent, college-

Initial studies of the MTO program examined its short-

educated neighbors may have distinct effects above

term effects (two to three years after assignment), and

and beyond the presence or absence of low-income

individual research teams each focused on just one of

families. On the other hand, unlike the findings from

the MTO cities. These initial short-term findings are

the Gautreaux program, which was designed explicitly

generally consistent with the predictions of previous

to reduce racial segregation rather than just economic

research suggesting that moving to a less distressed

segregation, MTO engendered relatively modest changes

community generally improves adult and child

in neighborhood racial composition. The average tract

outcomes.12

11 The duration-weighted tract poverty rate is calculated from random as-

Several years later, HUD sponsored a multisite “interim

signment through May 31st, 2008. We chose to use the same endpoint for

MTO study” to examine outcomes for adults and youth

everyone rather than the actual date that a family was surveyed because differences in exactly when families were surveyed may be systematically related four to seven years after random assignment, gathered

to other characteristics that influence outcomes (if, for example, more dis-uniformly across the five MTO sites (Fortson and

advantaged families were harder to find and therefore were interviewed later during the survey fieldwork period). We use linear interpolations between

Sanbonmatsu, 2010; Kling, Ludwig, and Katz, 2005;

1990 and 2000 decennial censuses and from 2000 forward using Census

Kling, Liebman, and Katz, 2007; Orr et al., 2003;

2000 and the 2005–09 ACS five-year averages to calculate a census tract’s

percent poverty at a specific point in time. To simplify the calculation, we divide each family’s residential history by the time periods they lived at different addresses (or address “spells”), and we split any addresses that span April 2000 into the period before and after 2000 to allow for different interpolation slopes. Let T be the total amount of time between when family i was

12 Results from the site-by-site evaluations are described in Katz,

i

randomly assigned and May 31st, 2008; let k index the different addresses

Kling, and Liebman (2001) for Boston; Ludwig, Duncan, and

that families reported during the study period, up to K total addresses; let Hirschfield (2001), Ludwig, Ladd, and Duncan (2001), and Ludwig,

i

Tk be the amount of time that family i spent at address k according to our Duncan, and Pinkston (2005) for Baltimore; Hanratty, McClanahan,

i

address tracking data; and let Pk be our estimate for the census tract poverty and Pettit (1998) for Los Angeles; Rosenbaum, Harris, and Denton

i

rate for that address measured at the midpoint of the relevant address spell.

(1999) and Rosenbaum and Harris (2003) for Chicago; and Leven-

Our calculation for the duration-weighted tract poverty rate (Pi) for each

thal and Brooks-Gunn (2003, 2004) for New York. Syntheses of these

MTO family is then equal to: P = ∑K (Tk /T )*Pk .

results appear in Goering and Feins (2003).

i

i

i

i

i

17

Sanbonmatsu et al., 2006).13 Results from the interim

impacts on mental health outcomes and some physical

evaluation yielded a more complicated pattern than the

health outcomes, such as obesity, but had few detectable

“better neighborhoods, better outcomes” of previous

impacts on labor market outcomes or social program

research.

participation.14

Exhibits 1.6 and 1.7 summarize selected results from the

Exhibit 1.7 shows that MTO’s impacts on youth differed

MTO interim evaluation. The subsequent exhibits in this substantially by gender, with generally beneficial effects report all use the same format. The first column shows

on female youth and deleterious impacts for male

the “Control Mean” or average outcomes of everyone

youth. Female youth in the experimental group had

assigned to the MTO control group, which (because of

better mental health outcomes than those in the control

the randomized experimental design) tells us what would group and were less likely to engage in risky behaviors have happened to families in the experimental or Section

such as use of marijuana, whereas male youth in the

8 group had they not been randomly assigned to receive

experimental group had higher scores than control

mobility assistance. The next two columns present the

group males on an abbreviated index of 11 behavior

ITT and TOT effects for the experimental treatment and problems including difficulty concentrating, cheating then the following two columns present these estimates

or lying, bullying, getting in trouble, etc. The MTO

for the Section 8 treatment. For example, at the time of

treatments also reduced violent behavior for male and

the interim evaluation, 45 percent of the control group

female youth, as measured by arrests for violent crime.

adults were working and not on Temporary Assistance

MTO had few detectable effects on child health aside

for Needy Families (TANF). The experimental group

from an increase in non-sports injuries for male youth

ITT estimate is 1.8 percentage points. This implies that

(Fortson and Sanbonmatsu, 2009). MTO also had no

46.8 percent of the experimental group was working. As

detectable impacts on educational achievement test scores

we noted above, the TOT effect will essentially be equal

(Sanbonmatsu et al., 2006), although there were some

to the ITT effect divided by the proportion of families

limitations to the cognitive assessments that the interim

in the experimental or Section 8 only group that moves

study used, as well as some differences in MTO impacts

as a result of MTO. Because approximately 47 percent

across cities, with some potential signs of test score gains

of families assigned to the experimental group moved

in Baltimore and Chicago (Burdick-Will et al., 2011).

through MTO, the experimental TOT effect is about

twice the size of the ITT effect.

Another goal of the interim study was to learn more

about the mechanisms through which neighborhood

The interim MTO data suggest that adults assigned

environments influence youth behavior. The disruption

to either of the mobility groups were safer and more

of moving by itself does not appear to explain the gender

satisfied with their housing and neighborhoods and

differences in effects for youth. Mobility rates were

were less likely to report a household member being a

actually slightly higher among households with female

victim of a crime or seeing illicit drugs sold (see also

youth than among those with male youth, and the

Kling, Liebman, and Katz., 2007; and Orr et al., 2003,

adverse effects of MTO on male risky behaviors begin to

Exhibit 3.5). Compared with the control group, adults

manifest themselves starting several years after the initial

in the experimental group also had large beneficial

move, not right after as a simple mobility disruption

model would predict (Kling, Liebman, and Katz,

13 The sample universe for the interim MTO study included the house-

hold head of the 4,248 families randomly assigned in the MTO dem-

2007;Kling, Ludwig, and Katz, 2005).

onstration through December 31, 1997, and up to two children (n=

6,683) per household between the ages of 5 and 19 as of May 31, 2001.

Surveys were conducted between January and September 2002 with a

total of 3,526 adults, 1,780 children ages 8 to 11, and 2,829 youth ages

12 to 19 and achievement tests were administered to a total of 5,264

children and youth ages 5 to 19 (see Orr et al., 2003, Exhibit A.4, p.

14 See Ludwig et al. (2008), Cove et al. (2008), and Clampet-Lun-

A-8). Effective survey response rates equaled 90 percent for adults and

dquist and Massey (2008) for additional discussions of MTO’s interim

89 percent for children and youth, and were quite similar across MTO

effects on labor market outcomes; and see Comey, Briggs, and Weisman

treatment groups.

(2008) for a discussion of subsequent mobility among MTO families.

18

The observed pernicious effects of MTO moves on

homes in part because some parents placed girls on a

male youth are consistent with both relative deprivation

“shorter leash” than they did boys (see, for example,

and competition models that Jencks and Mayer (1990)

Popkin, Leventhal, and Weismann, 2008). Boys often

discuss, which emphasize the potential adverse effects

hung out in public spaces, which put them at elevated

on behavior from experiencing a decline relative to one’s

risk for conflict with neighbors and police, and

peers in socio-economic status or academic achievement.

increased their exposure to delinquent peer groups as

However, the existing theoretical literature does not

well as opportunities to engage in delinquent activities

explain why the effects of the relative deprivation and

themselves (see also Clampet-Lundquist et al., 2006;

competition models should dominate for male but

Clampet-Lundquist, 2007). MTO may also have other

not female youth. MTO families with male youth did

impacts on youth interactions with families beyond the

not move into different types of neighborhoods than

stringency of parental monitoring. For example, boys

those with female youth, at least as can be measured by

who moved were less likely to report a nonbiological

standard administrative data for census tracts and police

father figure in their life than boys in the control group,

beats and from survey questions about neighborhoods

a difference across groups that did not appear for girls.

developed by the Project on Human Development in

And, despite strong preferences to move so that their

Chicago Neighborhoods (PHDCN) (Kling, Ludwig,

children could attend better schools, MTO parents did

and Katz, 2005). Even within households, brothers and

not subsequently feel informed or empowered to make

sisters exhibit different responses to similar neighborhood decisions about enrolling their children in academically changes. The interim data suggest that MTO reduced

promising schools. Thus, many MTO children ended up

violent-crime arrests for both male and female youth; the in schools that were very similar to those in their initial reduction in violence seems to be more closely associated

neighborhood (Ferryman et al., 2008).

with lower rates of racial segregation in the treatment

neighborhoods than with changes in other neighborhood

attributes such as poverty or overall levels of violent

crime, or sociological process measures such as disorder

(Ludwig and Kling, 2007).

To further illuminate the underlying processes through

which MTO affects youth outcomes, and in particular

the source of the gender difference in program impacts,

MTO researchers later conducted qualitative interviews

with 14- to 19-year-old youth (see, for example,

Clampet-Lundquist et al., 2006; Popkin, Leventhal,

and Weismann, 2008).15 These interviews uncovered

subtle gender differences in how MTO affects youth

interactions with families, peers, and neighborhood

institutions such as schools and police that were not

readily apparent in the interim MTO survey data

analysis.

For example, the nature of how boys and girls interact

socially with their peers enables girls to more successfully

adapt to life in low-poverty areas. Girls were more likely

to visit with friends on their porches or inside their

15 See also the Urban Institute’s Three City Study of the Moving to

Opportunity program at www.urban.org/projects/mto.cfm.

19

EXHIBIT 1.5. CENSUS TRACT CHARACTERISTICS FOR KEY ADDRESSES FOR MTO ADULTS

SURVEYED IN THE LONG-TERM EVALUATION

CONTROL EXPERIMENTAL GROUP

SECTION 8 GROUP

GROUP

OUTCOME

ALL

ALL

COMPLIERS

NON-

ALL

COMPLIERS NON-

COMPLIERS

COMPLIERS

SHARE POOR [CEN]

Baseline address

0.531

0.527

0.530

0.524

0.526

0.540

0.505

Initial MTO-assisted move

0.107

0.287

address

Address at time of MTO

interim evaluation

0.395

0.299

0.193

0.402

0.326

0.286

0.391

(4–7 years after baseline)

Address at time of MTO

long-term evaluation

0.313

0.274

0.210

0.334

0.283

0.244

0.345

(10–15 years after baseline)

Average (duration-weighted)

of all addresses since

0.396

0.306

0.200

0.407

0.329

0.285

0.400

Random Assignment

SHARE COLLEGE EDUCATED [CEN]

Baseline address

0.103

0.101

0.095

0.106

0.105

0.103

0.110

Initial MTO-assisted move

0.284

0.173

address

Address at time of MTO

interim evaluation

0.154

0.197

0.243

0.152

0.175

0.186

0.157

(4–7 years after baseline)

Address at time of MTO

long-term evaluation

0.209

0.229

0.244

0.215

0.224

0.234

0.208

(10–15 years after baseline)

Average (duration-weighted)

of all addresses since

0.161

0.203

0.247

0.162

0.182

0.194

0.163

Random Assignment

SHARE MINORITY [CEN]

Baseline address

0.912

0.916

0.912

0.920

0.915

0.906

0.930

Initial MTO-assisted move

0.624

0.835

address

Address at time of MTO

interim evaluation

0.883

0.836

0.768

0.901

0.875

0.860

0.900

(4–7 years after baseline)

Address at time of MTO

long-term evaluation

0.841

0.804

0.771

0.834

0.826

0.813

0.846

(10–15 years after baseline)

Average (duration-weighted)

of all addresses since

0.880

0.821

0.750

0.888

0.860

0.841

0.892

Random Assignment

Notes: Characteristics for the census tracts of addresses at which Moving To Opportunity (MTO) families were living at different points in time were linearly interpolated from the 1990 and 2000 decennial Censuses as well as the 2005–09 American Community Survey (ACS). Addresses at the time of the interim and long-term evaluations are as of the beginning of the fielding period for each study (December 31, 2001 for the interim evaluation and May 31, 2008 for the long-term evaluation). Square brackets indicate the source of the outcome information: CEN = 1990 and 2000 decennial census data as well as the 2005–09 ACS.

Data source and sample: Adult long-term survey. All adults interviewed (N = 3,273).

20

EXHIBIT 1.6. IMPACT OF MTO ON SELECTED ADULT OUTCOMES, INTERIM EVALUATION (4–7 YEARS

AFTER BASELINE)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

NEIGHBORHOOD OUTCOMES

Share persons in poverty [CEN]

0.385

– 0.078*

– 0.167

– 0.062*

– 0.102*

3,675

(0.008)

(0.017)

(0.008)

(0.014)

Saw illicit drugs sold in past 30

0.445

– 0.117*

– 0.248*

– 0.103*

– 0.171*

3,480

days [SR]

(0.022)

(0.046)

(0.024)

(0.039)

Household member victim of crime

0.209

– 0.040*

– 0.085*

– 0.053*

– 0.089*

3,499

in past six months [SR]

(0.017)

(0.036)

(0.018)

(0.030)

Felt safe during the day [SR]

0.750

0.093*

0.198*

0.096*

0.161*

3,514

(0.018)

(0.039)

(0.019)

(0.032)

Satisfied or very satisfied with

0.475

0.138*

0.293*

0.108*

0.180*

3,524

neighborhood [SR]

(0.022)

(0.047)

(0.024)

(0.040)

ADULT OUTCOMES [SR]

Working and not on TANF

0.452

0.018

0.038

0.019

0.032

3,472

(0.020)

(0.043)

(0.023)

(0.038)

Psychological distress index for

0.329

– 0.034*

– 0.073*

– 0.012

– 0.020

3,521

past month

(0.015)

(0.032)

(0.016)

(0.028)

Good or better health

0.669

– 0.016

– 0.033

– 0.007

– 0.013

3,523

(0.020)

(0.042)

(0.022)

(0.036)

Obese, Body Mass Index ≥ 30

0.471

– 0.051*

– 0.108*

– 0.047

– 0.079

3,405

(0.022)

(0.047)

(0.025)

(0.042)

Notes: * = p < 0.05 on two-tailed t-test. Robust standard errors are shown in parentheses. Square brackets indicate the source of the outcome information. The control mean is unadjusted. Intention-to-Treat estimate (ITT), or estimated impact of being offered an Moving To Opportunity (MTO) housing voucher; estimated effect of Treatment-on-the-Treated (TOT), or estimated impact of moving using an MTO housing voucher.

Square brackets indicate the source of the outcome information: CEN = 2000 Census, SR = self-report.

Date source and sample: MTO Interim Impacts Evaluation (Orr et al., 2003). Tract poverty measure: Exhibit D2.1. Other neighborhood measures: Exhibit 3.5. Employment/TANF measure: Exhibit 8.8. Physical and mental health measures: Exhibit 4.2.

21

EXHIBIT 1.7. IMPACT OF MTO ON SELECTED CHILD AND YOUTH OUTCOMES, INTERIM EVALUATION

(4–7 YEARS AFTER BASELINE)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

WOODCOCK-JOHNSON REVISED

BROAD READING SCORE [M]

All youth (ages 5–19)

497.31

0.92

2.04

0.45

0.74

5,169

(0.93)

(2.06)

(1.06)

(1.73)

Female

499.69

1.84

3.96

– 0.19

– 0.30

(1.14)

(2.45)

(1.24)

(1.99)

Male

494.94

– 0.03

– 0.06

1.13

1.89

(1.38)

(3.14)

(1.59)

(2.66)

WOODCOCK-JOHNSON REVISED

BROAD MATH SCORE [M]

All youth (ages 5–19)

501.23

0.22

0.49

– 1.07

– 1.74

5,187

(0.78)

(1.75)

(0.85)

(1.39)

Female

502.30

0.81

1.73

– 1.21

– 1.92

(1.06)

(2.26)

(1.09)

(1.73)

Male

500.18

– 0.36

– 0.82

– 0.91

– 1.52

(1.05)

(2.42)

(1.17)

(1.95)

PSYCHOLOGICAL DISTRESS INDEX

[SR]

All youth (ages 12–19)

0.256

– 0.006

– 0.013

– 0.006

– 0.010

2,803

(0.015)

(0.032)

(0.016)

(0.028)

Female

0.304

– 0.045*

– 0.098*

– 0.043

– 0.072

(0.020)

(0.043)

(0.023)

(0.037)

Male

0.208

0.034

0.077

0.031

0.057

(0.020)

(0.045)

(0.022)

(0.040)

VERY GOOD OR BETTER HEALTH

2,822

[SR]

All youth (ages 12–19)

0.680

0.001

0.001

– 0.006

– 0.010

(0.024)

(0.054)

(0.027)

(0.048)

Female

0.635

0.030

0.065

0.008

0.013

(0.034)

(0.075)

(0.038)

(0.062)

Male

0.725

– 0.029

– 0.066

– 0.020

– 0.036

(0.034)

(0.076)

(0.036)

(0.067)

EVER USED MARIJUANA [SR]

All youth (ages 15–19)

0.344

– 0.031

– 0.071

– 0.020

– 0.036

1,569

(0.034)

(0.079)

(0.038)

(0.069)

22

EXHIBIT 1.7. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

EVER USED MARIJUANA [SR]

(CONTINUED)

Female

0.342

– 0.129*

– 0.276*

– 0.079

– 0.132

(0.044)

(0.095)

(0.050)

(0.085)

Male

0.348

0.068

0.169

0.042

0.083

(0.049)

(0.122)

(0.052)

(0.104)

ABBREVIATED BEHAVIOR

PROBLEMS INDEX [SR]

All youth (ages 12–19)

0.343

0.036*

0.080*

0.023

0.039

2,810

(0.014)

(0.032)

(0.016)

(0.028)

Female

0.352

– 0.002

– 0.004

– 0.007

– 0.012

(0.019)

(0.042)

(0.021)

(0.035)

Male

0.336

0.075*

0.169*

0.052*

0.095*

(0.020)

(0.045)

(0.022)

(0.040)

FRACTION OF CALENDAR

QUARTERS WITH VIOLENT CRIME

ARREST [ADMIN]

All youth (ages 15–19)

0.0138

– 0.0038*

– 0.0091*

– 0.0010

– 0.0019

2,532

(0.0019)

(0.0045)

(0.0022)

(0.0040)

Female

0.0088

– 0.0022

– 0.0053

– 0.0030

– 0.0054

(0.0020)

(0.0049)

(0.0026)

(0.0047)

Male

0.0190

– 0.0054

– 0.0129

0.0006

0.0011

(0.0030)

(0.0071)

(0.0034)

(0.0061)

EVER ARRESTED FOR PROPERTY

CRIME [ADMIN]

All youth (ages 15–19)

0.118

0.022

0.051

0.014

0.026

2,646

(0.015)

(0.036)

(0.017)

(0.031)

Female

0.087

– 0.006

– 0.013

– 0.007

– 0.013

(0.018)

(0.042)

(0.021)

(0.037)

Male

0.150

0.049*

0.115*

0.035

0.062

(0.024)

(0.055)

(0.027)

(0.048)

Notes: * = p < 0.05 on two-tailed t-test. Robust standard errors are shown in parentheses. The control mean is unadjusted. Intention-to-Treat estimate (ITT), or estimated impact of being offered a Moving To Opportunity (MTO) housing voucher; estimated effect of Treatment-on-the-Treated (TOT), or estimated impact of moving using an MTO housing voucher.

Square brackets indicate the source of the outcome information: M = direct measurement, SR = self-report, ADMIN = administrative records.

Date source and sample: MTO Interim Impacts Evaluation (Orr et al., 2003). Test score measures: Exhibits 6.5 (overall) and 6.6 (by gender). Mental health measure: Exhibit 4.5. Health measure: Exhibit 4.4. Behavior measures: Exhibits 5.2–5.5.

Measures: The fraction of calendar quarters with violent crime arrest is based on the first through fourth year after random assignment.

23

1.6 THE LONG-TERM STUDY

The final impact evaluation was designed to exploit the

This report presents results from following up with

MTO’s randomized experimental design to best address

MTO families over the long-term: 10–15 years after the

the following key questions:

time of random assignment. This evaluation is the first

opportunity to answer questions about the longer term

What are the long-term effects of a housing mobility

effects of being offered a housing voucher, and resulting

program intervention on participating families and

changes in housing and neighborhood conditions, on

their children, and how did these effects evolve over

subsequent well-being and life outcomes in the following time?

domains: housing and neighborhood conditions; physical

health; mental health; economic outcomes; educational

If differences in average neighborhood characteristics

outcomes; and risky and criminal behavior.

across groups persist over time, MTO’s effects on well-

being and behavior may increase over time. Ties to old

What can we conclude about the effects of MTO on

social networks will diminish, whereas social ties to new

low-income families so far? It generates persistent

communities and use of new neighborhood institutional

differences in housing and neighborhood satisfaction

resources will increase. For example, the benefits of

and characteristics across randomized groups, despite

greater exposure to more prosocial and affluent social

subsequent mobility among all three groups. Its impacts

networks may improve if families become more socially

on other behaviors through four to seven years after

integrated into their new communities, more attuned

assignment are more complex than previous research

to local social norms, and thus more responsive to the

would predict.

peer and adult social influences that are central to the

epidemic and collective socialization models. Social

But of primary importance for public policy are the

integration itself might require families to learn new

long-term effects on participants. Predicting these long-

modes of dress, language, or interactions to “fit in,”

term impacts on the basis of the interim MTO results

and families may also learn how to fully use better local

is a perilous business, as highlighted by the surprising

services such as schools. Families may also learn how to

differences in MTO impacts between the short-term

better navigate the potential opportunities and pitfalls

evaluations in each of the sites, which supported the

in low-poverty areas. To the extent that exposure to new

“better neighborhoods, better outcomes” hypotheses,

resources and opportunities makes MTO adults and

versus the more mixed pattern of results found with

youth more competitive over time for prosocial rewards,

the interim evaluation. Social science is filled with

any deleterious effects from competition or declines in

examples of long-term impacts that would have been

relative position may diminish over time. Any deleterious

hard to predict based on short- or medium-term impacts. “moving effects” may also attenuate.

Consider that the Head Start early childhood program

yields short-term test score impacts that seem to fade

The long-term evaluation is also an opportunity to

out after a few years, yet Head Start nonetheless seems

address a number of important open questions that

to generate long-term lasting benefits for participants

were raised by the interim study’s findings. For example,

such as higher rates of high school graduation (Currie

the long-term MTO study enables us to investigate

and Thomas, 1995;Garces, Thomas, and Currie, 2002;

whether differences in the effects of MTO on male

Ludwig and Miller, 2007; Deming, 2009). In hindsight

and female youth hold up over time, or if they emerge

the operative mechanism or causal pathway behind these

for the younger cohort of youth that the long-term

long-term “sleeper effects” is commonly thought to be

study examines, and better understand why male and

Head Start impacts on social-cognitive skills that are

female youth may respond so differently to changing

not captured by reading and math tests. But this pattern

neighborhoods. Another example is determining whether

of long-term benefits despite fadeout of short-term test

MTO impacts on basic screening indicators of adult

score impacts was certainly not anticipated prior to the

mental health found in the interim MTO study translate

collection and analysis of long-term outcome data.

into clinically important changes in mental health, which

impose the greatest costs to individuals and society at

24

large, and so are of greatest relevance for public health

of some MTO-like program, children in eligible families

and public policy. A final example comes from the

would have the benefits of MTO early in life instead

possibility that the interim mental health improvements

of experiencing the stresses of relocation that the older

among mothers in MTO translate into improved labor

MTO youth experienced.

market outcomes over time.

What are the mechanisms through which MTO affects

What are MTO’s long-term effects on those children

long-term outcomes?

who had not yet entered school when the study began?

Information about the mechanisms through which MTO

MTO included many participants who were fairly young

affects the behavior and life chances of participants

at the point of randomization, and so were too young at

is important for the design of tenant-based subsidy

the time of the interim study to provide developmentally programs as well as place-based interventions to modify meaningful measures for a variety of key outcomes such

specific elements of community environments. As

as school dropout, mental health, or risky behavior. The

noted, because MTO changes multiple neighborhood

long-term evaluation offers an important opportunity to

attributes simultaneously, isolating the effects of specific

study effects on this group of children (aged 10–20 as

mechanisms is complicated. However, the study design

of December 2007), including their math and reading

nonetheless can help us at least rule out some possible

achievement, school completion and employment,

mediating mechanisms. If, for example, MTO has long-

mental and physical health, and engagement in risky

term beneficial effects on the mental health of female

behavior. Those children who are very young at baseline

adults, yet has no detectable effect on access to health

and now teenagers at the time of our long-term (10–15

care services including mental health care, the pattern

year follow up) might exhibit even more pronounced

of findings would provide some evidence against the

responses to the MTO intervention than those who

importance of that mechanism.

were teens at the time of the interim (4–7) year study

for several reasons. One reason is that for children

SAMPLE DEFINITION AND DESCRIPTION

relatively younger at baseline and assessed now, MTO

The sample we used in the long-term evaluation includes

has generated longer-term exposure to less economically-

all 4,604 families randomly assigned in the MTO

distressed neighborhoods. Children who are relatively

demonstration.16 This group includes the 356 families

younger at baseline will also experience relatively

in the Los Angeles site who were randomized after

more pronounced MTO-induced changes in exposure

December 31, 1997, and were excluded from the interim

to violence, which has been shown to be potentially

evaluation’s sample frame because the corresponding

important for child development (Sharkey, 2010). In

increase in sample size was deemed insufficient to justify

addition a growing body of research in developmental

shortening the follow-up period. The 4,604 families

psychology, economics, neuroscience, and even animal

include 19,932 baseline household members and 17,133

studies suggests that younger children may be more

core household members (family members whom the

responsive to social policy interventions than older

head of household believed would move with the family

people given the possibility of declining developmental

if they received an MTO voucher). As in the interim

“plasticity” as people age (Becker and Murphy, 2000;

MTO study, our analysis focuses on the core household

Carneiro and Heckman, 2003; Knudsen et al., 2006;

members.

Shonkoff and Phillips, 2000).

The adult survey sampling frame for the long-term

Children relatively young at baseline are a particularly

evaluation included one adult from each of the original

important group to study because they most closely

MTO families. We prioritized female adults from the

parallel the groups to which study effects would most

reasonably be extrapolated if an MTO-like intervention

16 The interim MTO report indicated a total sample size of 4,608

families in the demonstration, but in the process of data collection it

were implemented universally. Under a universal version

was discovered that four of the applicants in MTO were actually mem-

bers of other MTO households.

25

core household when selecting our sample. The interview The adult surveys collected information about young sample included all sample adults from the experimental

adults who are not in the long-term survey sample

and control groups, and (because of funding constraints)

frame (those who were over age 20 at the time of survey

a random 66 percent subsample of adults from the

fieldwork) via adult proxy reports. That is, we ask the

Section 8 group, or a total of 4,142 adults.17

adults in the survey sample frame (who are almost all

The youth sampling frame consisted of all core

female) to report briefly on a few key outcomes for each

household members from all three MTO groups who

member in the core MTO household who was not in

were ages 10–20 on December 31, 2007. Up to three

the survey sample frame. We also asked MTO adults to

youth per household were eligible for interviews. In those provide proxy reports on other people who were currently families that contained more than three eligible youth,

living in the home at the time of the long-term survey,

we randomly selected three youth for the interview

regardless of whether they were in the baseline core

sample. The total number of people in our long-term

household or not. Administrative data are also available

survey sample was 6,308. We also collected parental

to measure outcomes for those not in the survey sample

reports on all core young adult household members who

frame.

were not in the MTO long-term survey sample frame

(under age 18 at baseline, age 21–30 as of December

CONTENT OF PARTICIPANT DATA

2007). We refer to this group as MTO grown children.

COLLECTION

Like the interim evaluation, the final impact evaluation

The allocation of this sample among the treatment

focuses on measuring and assessing MTO’s long-term

groups, by site and overall, is shown in Exhibit 1.8.

effects on the following key outcome domains:

The number of adults in each site ranges from 572 in

Baltimore to 948 in New York City. Somewhat more

• Housing and mobility

were assigned to the experimental group than to the

• Neighborhoods and social networks

Section 8 group to ensure that enough families signed

• Adult and youth physical and mental health

leases with an MTO voucher in the experimental group.

• Adult and youth economic self-sufficiency

The expectation was that the lease-up rate would be

• Adult education and youth schooling and

lower for experimental families whose vouchers initially

educational achievement

required them to live in a low-poverty tract compared

• Adult and youth risky and criminal behavior.

with families in the Section 8 only group, whose

vouchers were not subject to any additional mobility

These outcomes, as well as a variety of key possible

constraints. Assignment rates within sites were further

mediating mechanisms, are measured through in-person

adjusted during implementation to compensate for the

surveys of MTO adults and youth, direct measurement

fact that the lease-up rate for the experimental group

of health biomarkers, achievement assessments, and

turned out to be higher than had been anticipated. (The

administrative records. Exhibit 1.9 summarizes the

sample weights used in the quantitative analyses adjusted topics about which data were collected, by method, and for differences among sites and over time in the random

according to the age of sample members.

assignment ratio, and our regression models control for

site indicators; see the Technical Appendices for more

details).

17 Because of funding constraints, the original survey sample field-

ing design excluded the sample of Section 8 group adults. Additional

funding to expand the adult survey sample frame to include 66 percent

of the eligible Section 8 adult sample came through in December

2008. The Section 8 adult sample was released in February 2009,

eight months after survey fielding began. A small randomly-selected

subsample of experimental and control group adults was released to the

field survey sample at the same time. Our methods for analyzing data

from the MTO Section 8 group will be described in more detail in the

technical appendices, forthcoming.

26

EXHIBIT 1.8. ALLOCATION AND COMPLIANCE OF THE LONG-TERM EVALUATION SAMPLES BY SITE AND

TREATMENT GROUP

ALL GROUPS

CONTROL

EXPERIMENTAL GROUP

SECTION 8 GROUP

GROUP

N

N

N

COMPLIANCE N

COMPLIANCE

RATE (%)

RATE (%)

ADULTS

Baltimore

572

197

252

53.5

123

79.8

Boston

868

326

366

43.6

176

51.1

Chicago

825

232

460

33.4

133

67.4

Los Angeles

929

389

340

60.5

200

71.6

New York City

948

295

401

46.4

252

45.2

All sites

4,142

1,439

1,819

47.4

884

61.6

YOUTH, AGES

10–20

Baltimore

762

240

268

59.1

254

79.2

Boston

1,267

440

475

38.2

352

54.6

Chicago

1,363

328

701

31.7

334

72.0

Los Angeles

1,539

592

502

62.2

445

78.2

New York City

1,377

418

471

49.6

488

49.2

All sites

6,308

2,018

2,417

47.6

1,873

66.4

Notes: Percentages are weighted to reflect the randomization ratios and sampling of Section 8 adults and up to three youth per family. "Compliance" is defined as leasing a unit using a housing voucher provided by the MTO program.

Data source and sample: MTO data system. The samples are N = 4,142 adults and N = 6,308 youth ages 10 to 20 as of December 2007 selected for the long-term survey. Excluded from the samples are the N = 462 Section 8 group adults and youth from households with greater than three youth ages 10 to 20 who were not randomly selected for the long-term survey.

27

EXHIBIT 1.9. CONTENT OF PARTICIPANT DATA COLLECTED FOR THE MTO LONG-TERM EVALUATION

ADULT

YOUTH

GROWN CHILDREN

Survey

One adult per household (female

Up to three youth ages 10–20 as

Parent reports of household

sampling plan

caregivers prioritized)

of December 2007 per household members under age 18 at

Up to 4,142 adults total

Up to 6,308 youth total

baseline and up to age 30 as of

December 2007

Survey content Household roster

Neighborhoods

Adult respondent reports on

Housing and mobility

Social networks

eight items measuring education,

Neighborhoods

Education and schooling

employment, mental health, and

Social networks

Employment and earnings

risky behavior

Education

Risky behavior and behavior

Employment and earnings

problems

Income, public assistance

Mental and physical health

Savings and assets

Decision making

Mental and physical health

Youth reports on parenting

Decision making

Relationships and parenting

Reports on household outcomes

Direct

Height, weight, waist

Height and weight

measurement

circumference, and blood

pressure

Biomarkers

Dried Blood Spots to measure

total cholesterol, high-density

lipoproteins, C-reactive protein

(CRP), glycosylated hemoglobin

(HbA1c), and Epstein-Barr virus

(EBV)

Achievement

Math and reading achievement

assessments

tests based on ECLS-K 5th and

8th grade assessments

Language

Audio taping of open-ended

Audio taping of open-ended

assessments

questions and reading passage

question and reading passage for

for pre-selected random

pre-selected random subsample

subsample

Administrative

Addresses, housing program

Elementary and secondary school Earnings, social program

data

participation, earnings, social

records, post-secondary school

participation, arrests, post-

program participation, arrests,

enrollment, social program

secondary school enrollment, and

post-secondary school

participation, arrests, earnings,

mortality

enrollment, and mortality

and mortality

Neighborhood

Census tract data

Census tract data

indicators

Interviewer observations from

Interviewer observations from

neighborhood walk-around

neighborhood walk-around

28

The long-term surveys draw as much as possible (and

measurement. For adults, SRC also measured waist

where appropriate) from the survey instruments used

circumference and blood pressure and collected

in the interim MTO study. This facilitates comparisons

blood spots from finger pricks and sent them to

of impacts from interim to final evaluation. In the

a lab for analysis of various long-term disease

long-term evaluation, interviewers administered a

precursors;

different reading and math achievement test18 and

• Language assessments to measure speech patterns

different survey measurement of mental and physical

of adults and youth through audiotaping of open-

health, criminal offending, and victimization. They also

ended survey questions and a reading passage;

collected additional key mediating factors, including

• Observational data on the characteristics of the

basic features of decision-making and audio recordings of

respondent’s immediate neighborhood block

speech patterns. Most of the survey items in the interim

through neighborhood “walk-arounds” by trained

study, as well as the new additions to the long-term

interviewers.

MTO study, come from extensively pre- and field-tested

national surveys, which has the advantage of facilitating

Trained interviewers using Computer-Assisted Personal

comparisons between MTO and national samples. The

Interviewing (CAPI) on laptop computers administered

final MTO survey instruments are located at

the two surveys, household and youth, primarily in

www.mtoresearch.org.

the respondents’ homes, with the session scheduled

at the respondent’s convenience.19 Field interviewers

SURVEY DATA COLLECTION

also recorded their observations of the neighborhood

Between June 2008 and April 2010, the Survey Research

environments.

Center (SRC) at the University of Michigan collected the

following information for the long-term study:

As in the interim study, the survey fielding design

employed two-phase sampling to obtain responses from

• An adult respondent survey to gather data on

a representative subsample of hard-to-locate respondents

outcomes and mediating factors for the respondent (Groves et al., 2004). In the main sample phase, SRC

and other members of the household;

sought to contact and interview all of the adults and

• A youth survey to gather information on outcomes

youth who were in the survey sample frame. They

and mediating factors for youth who were ages

offered $50 to everyone selected for the survey sample

10–20 at the end of 2007, just prior to the start of

frame to complete the surveys. They offered adults an

the survey data collection period, and who resided

additional $25 to provide biomarkers (like blood spot

with MTO families at time of enrollment;

samples) and $10 for the audio recording. They offered

• Educational achievement data from standardized

youth an extra $25 to complete the reading and math

reading and math tests administered to MTO

achievement tests. Once SRC reached a response rate of

youth;

approximately 75 percent for the adult or youth survey

• Biomarker data, including height and weight

for a site, it selected a random subset of the 35 percent

of the remaining cases for more intensive interviewing

18 For the interim MTO study, children were administered standardized

Woodcock-Johnson-Revised (WJ-R) reading and math tests. However, the

efforts. In the data analysis, they “weighted up” the

average test scores on the WJ-R did not vary much among MTO adolescents

interviews they conducted as part of the second phase by

by age; for example, the average scores of 13-year-olds were not very different from those of 18-year-olds. This pattern made us nervous for the long-term

1/.35 = 2.856 to represent the other hard-to-reach cases

study about whether the WJ-R assessments adequately capture the sort of

academic content that adolescents will (or at least should) be learning in

that they did not try to interview. The “effective response

school. Given these concerns, for the long-term MTO study we replaced the

rate” for the study was 90 percent for the adult sample

WJ-R with the reading and math tests developed for the U.S. Department of

Education’s Early Childhood Longitudinal Survey of Kindergarteners in 1998

and 89 percent for the youth sample.20 Exhibit 1.10

(ECLS-K) for the fifth and eighth grade waves. To avoid “ceiling effects” (cases where some children know everything that is covered on the test and get every-19 A small number of adult and youth survey interviews were com-

thing right, so that their actual achievement level cannot be pinned down), the pleted by telephone.

ECLS assessments were supplemented with additional reading and math items

from the 10th grade test that was used in the U.S. Department of Education’s 20 For example, if the phase one response rate was 74 percent, and a

National Education Longitudinal Study of 1988 (NELS).

3.5-in-10 subsample of hard-to-locate cases were worked in phase two

with a response rate of, say, 50 percent, then the effective response rate

would be .74+.26*.5=.87, or 87 percent.

29

shows response rates for the adult and youth survey, by

MTO families. The details of the sampling scheme will

treatment status. Response rates are statistically similar

appear in the forthcoming Technical Appendices.

between the experimental and control groups for adults

and youth, and the Section 8 and control group youth.

We used census-tract-level data from the 1990 and

The Section 8 group adult response rate is slightly lower,

2000 decennial censuses and the 2005–09 waves of the

and statistically differs from the control group.

American Community Survey (ACS) to measure MTO

changes in the social and institutional environments

ADMINISTRATIVE DATA COLLECTION

in which families are living. A census tract is a small

The long-term study also includes an extensive effort

continuous geographic area comprising between 1,500

to collect administrative data from everyone who was

and 8,000 residents (600 to 3,200 housing units) that

a member of the core MTO household at baseline,

is delineated by observable physical objects (road, river,

regardless of whether they were included in the long-term train tracks, state or county line).22 Census tracts are survey sampling frame. At baseline, all household heads

often used in the social science research literature as a

signed a waiver granting HUD-authorized researchers

geographic measure of “neighborhood.”

permission to access government administrative data on

We are also currently in the process of collecting data on

themselves and their families for purposes of evaluating

local-area crime rates from police departments serving

MTO.

the five main MTO demonstration cities.

The study collected administrative data on:21

1.7 OVERVIEW OF THE REPORT

MTO research provides direct evidence on the impact

• Housing assistance from HUD administrative data;

of housing mobility interventions on low-income

• Earnings and employment from state

families. The findings are critical to improving our

unemployment insurance records;

understanding of the impacts of neighborhood context

• Social program participation (welfare, food stamps) more generally on the contemporaneous well-being from state data records;

and longer-term outcomes for poor households. An

• Elementary and secondary school outcomes from

experiment such as MTO that alters residential mobility

student-level school records from local school

and places families in different neighborhoods also can

districts and in some cases state education agencies;

be useful for simulating the potential effects of wholesale

• Post-secondary enrollment data from the National

neighborhood changes on individuals from disadvantaged

Student Clearinghouse;

backgrounds. It may be possible to identify specific

• Adult and juvenile arrest histories from state

mechanisms that will help target issues that can be

criminal justice agencies (and in some cases

directly addressed in today’s high-poverty communities.

county- or city-level juvenile justice agencies).

MTO findings are also central to a number of ongoing

We collected administrative data for the five MTO sites

housing-policy debates. The contrast in youth outcomes

and for a random sample of the other jurisdictions to

between the Section 8 and the control groups will be

which participants have moved. Sampling is necessary

directly relevant to debates about whether to increase the

because participants have now resided in 40 states

number of Section 8 vouchers, and particularly about

and attended schools in more than 300 public school

whether to offer vouchers to current public housing

districts. Sampling enables us to provide a representative

residents rather than renovate the aging public housing

picture of the outcomes of everyone in all three of the

stock (Olsen, 2003; Quigley, 2000).

randomized MTO groups, but without having to collect

administrative data from every agency that includes

22 See the U.S. Census Bureau’s Participant Statistical Area Program

21 The study also collected mortality records from the National Center

Guidelines at www.census.gov/geo/www/psapage.html and Participant

for Health Statistics’ National Death Index, although those outcomes

Statistical Area Program Criteria available at www.census.gov/geo/www/

are not included in this report.

statarea.pdf. (accessed May 5, 2010).

30

In extrapolating these results to other housing programs,

including employment, earnings, and income from

it is important to keep in mind that different people may government social programs and other sources. Chapter respond differently to the same policy intervention—a

6 examines impacts of MTO on the risky and criminal

possibility that researchers refer to as “treatment

behavior of participating adults and youth, while Chapter

heterogeneity.” We might be particularly concerned that

7 examines impacts on educational outcomes. Chapter 8

people who volunteer for a housing mobility programs

concludes and discusses the implications of the long-term

may be respond differently than would people who are

results of MTO for social science and public policy.

involuntarily moved, for example because their public

housing project is scheduled for demolition. MTO

estimates will be most directly relevant to other voluntary

mobility programs that involve similarly disadvantaged

minority families living in very distressed public housing

communities. In fact for MTO-type programs and

populations, these MTO findings will be particularly

valuable because most housing mobility programs are not

implemented in a way that includes a valid control group,

and so cannot be reliably evaluated directly themselves.

The treatment that the experimental group received does

not correspond precisely to particular policies now under

consideration, but this group may provide information

about the value of more intensive counseling initiatives

that could expand the number of neighborhoods into

which voucher families move. Remember, though, MTO

is a relatively small program, and the lessons from it are

most directly applicable to strategies to incrementally

expand or revise current programs, such as adding several

hundred Section 8 vouchers in a city. For the most part,

there are too few families who moved to a given low-

poverty neighborhood in MTO to really change the

character of the new place.23 A large-scale program, such

as eliminating all public housing and issuing vouchers to

former tenants, may have different effects than a smaller-

scale program.

This report is organized into eight chapters. Chapter

2 provides more detailed information about the MTO

experiment’s effect on mobility patterns for families,

housing conditions and receipt of means-tested housing

assistance from the government, and neighborhood

characteristics and social networks. Chapters 3 and

4 provide results on the physical and mental health

outcomes, respectively, of MTO adults and youth.

Chapter 5 provides results on economic outcomes,

23 Measuring the effects that MTO families might have on the areas to

which they move is not one of the goals of this evaluation.

31

EXHIBIT 1.10. MTO ADULT AND YOUTH LONG-TERM SURVEY INSTRUMENT RESPONSE RATES BY RANDOM

ASSIGNMENT GROUP

ADULT (%)

YOUTH (%)

Experimental Group

90.8

90.1

Section 8 Group

86.6

86.9

Control Group

90.0

88.9

All Groups

89.6

88.7

Notes: The response rate calculations presented above account for the change over time in the MTO random assignment ratios as well as the two-phase survey sampling design of the long-term evaluation. The weights equal the product of the random assignment ratio weight (described previously) and the sampling weight (equal to 1 for families interviewed in Phase 1, equal to 1/0.35 for families who were randomly selected for the Phase 2 survey sample, and equal to 0 for families who were not randomly selected for the Phase 2 survey sample. The effective response rate is equal to the weighted number of interviews divided by the weighted survey sample frame total minus the weighted number of decedents.

Data source and sample: MTO long-term evaluation survey. The survey sampling frame included 4,604 adults and 6,645 youth (ages 10 to 20 as of December 2007), and interviews were conducted with 3,273 adults and 5,101 youth.

SUPPLEMENTAL EXHIBIT 1.1. BASELINE CHARACTERISTICS OF THE GROWN CHILD SAMPLE BY RANDOM

ASSIGNMENT GROUP

EXPERIMENTAL SECTION 8

CONTROL

ALL

GROUP

GROUP

GROUP

GROUPS

AGE [PR]

As of December 2007

24.6

24.4

24.4

24.5

At baseline

12.7

12.6

12.6

12.7

GENDER [PR]

Female

50.3%

49.3%

47.9%

49.2%

Male

49.7%

50.7%

52.1%

50.8%

OTHER CHARACTERISTICS [PR]

Gifted student or did advanced coursework

13.0%

12.4%

12.4%

12.6%

Suspended or expelled from school in past 2 years

3.8%

4.3%

3.3%

3.8%

School called about behavior in past 2 years

19.9%

20.7%

19.6%

20.0%

Behavioral or emotional problems

5.5%

7.0%

5.1%

5.8%

Learning problem

11.8%

13.1%

12.3%

12.3%

Health problems that limited activity

6.1%

5.8%

5.4%

5.8%

Health problems that required special medicine/

9.1%

9.3%

8.9%

9.1%

equipment

SAMPLE SIZE

1,887

1,336

1,420

4,643

Notes: Percentages are percent distributions. Data are weighted to reflect randomization ratios. At baseline, older youth were ages 6 to 11 and younger youth were ages 0 to 5.

Square brackets indicate the source of the outcome information: PR = parent report.

Data source and sample: MTO Participant Baseline Survey. N = 4,643 long-term survey sample grown children (ages 21 to 30 as of December 31, 2007, and ages 7

to 17 at baseline) from all 4,604 MTO families as reported by the baseline head of household.

32

SUPPLEMENTAL EXHIBIT 1.2. MEAN, STANDARD DEVIATION, AND SHARE MISSING FOR BASELINE COVARIATES

BY MTO SAMPLE

ADULTS

YOUTH AGES 10–20

DESCRIPTION

MEAN

STANDARD SHARE

MEAN

STANDARD SHARE

DEVIATION

MISSING

DEVIATION

MISSING

CHARACTERISTICS OF

THE ADULT/YOUTH'S

PARENT

ADULT/PARENT'S AGE

AS OF 12/31/07 (OMITTED

CATEGORY: > 50)

≤ 35

0.141

0.348

0

0.184

0.387

0

36–40

0.224

0.417

0

0.305

0.460

0

41–45

0.231

0.422

0

0.247

0.431

0

46–50

0.187

0.390

0

0.149

0.356

0

RACE (OMITTED

CATEGORY: WHITE)

African-American

0.646

0.474

0.019

0.662

0.469

0.016

Other race

0.279

0.444

0.019

0.273

0.441

0.016

HISPANIC ETHNICITY

0.318

0.464

0.009

0.306

0.459

0.010

MALE ADULT/PARENT

0.018

0.133

0

0.018

0.132

0

EDUCATIONAL

ATTAINMENT AT BASELINE

Adult had a GED

0.191

0.393

0.065†

0.196

0.397

0.063†

Adult reported having

0.391

0.488

0.065†

0.373

0.484

0.063†

completed high school

Adult is missing information on

0.065

0.247

0.063

0.243

GED and high school diploma

status

Adult was enrolled in school

0.167

0.366

0.036

0.182

0.380

0.034

NEVER MARRIED

0.628

0.476

0.031

0.663

0.465

0.032

UNDER AGE 18 AT BIRTH OF

0.256

0.426

0.044

0.296

0.447

0.043

FIRST CHILD

WORKING

0.262

0.433

0.032

0.220

0.407

0.033

SITE (OMITTED

CATEGORY: NEW YORK

CITY)

Baltimore

0.136

0.343

0

0.128

0.334

0

Boston

0.204

0.403

0

0.183

0.387

0

Chicago

0.206

0.404

0

0.231

0.422

0

Los Angeles

0.225

0.418

0

0.238

0.426

0

FLAG INDICATING ADULT

0.358

0.479

0

INCLUDED IN FIRST

SURVEY RELEASE

33

SUPPLEMENTAL EXHIBIT 1.2. (CONTINUED)

ADULTS

YOUTH AGES 10–20

DESCRIPTION

MEAN

STANDARD SHARE

MEAN

STANDARD SHARE

DEVIATION

MISSING

DEVIATION

MISSING

CHARACTERISTICS

OF THE BASELINE

HOUSEHOLD

HOUSEHOLD MEMBER HAD

0.152

0.358

0.008

0.143

0.348

0.009

A DISABILITY

HOUSEHOLD STRUCTURE

No teen (ages 13–17) children

0.621

0.485

0

0.754

0.431

0

in core household at baseline

Core household size is two or

0.210

0.408

0

0.086

0.280

0

smaller

Core household size equals

0.308

0.462

0

0.247

0.432

0

three

Core household size equals

0.231

0.421

0

0.259

0.438

0

four

HOUSEHOLD MEMBER

0.422

0.492

0.007

0.426

0.493

0.008

WAS BEATEN/ASSAULTED;

THREATENED WITH A

GUN OR KNIFE; OR HAD

THEIR PURSE, WALLET,

OR JEWELRY SNATCHED

DURING THE 6 MONTHS

PRIOR TO BASELINE

RECEIVING AFDC

0.756

0.429

0.005

0.815

0.387

0.005

OWNED A CAR

0.183

0.387

0.004

0.188

0.390

0.005

HEAD OF HOUSEHOLD AT

BASELINE…

Had lived in their neighborhood

0.606

0.483

0.025

0.557

0.490

0.028

for 5 or more years

Stopped to chat with neighbor

0.521

0.497

0.009

0.527

0.497

0.009

in street or hallway at least

once a week

Was very dissatisfied with his/

0.474

0.497

0.009

0.486

0.497

0.01

her neighborhood

Reported he/she would be very

0.546

0.497

0.005

0.550

0.496

0.005

likely to tell neighbor if saw

neighbor's child getting into

trouble

Did not have any family living in

0.631

0.481

0.005

0.638

0.479

0.005

their neighborhood

Did not have any friends in the

0.401

0.488

0.008

0.402

0.488

0.008

their neighborhood

34

SUPPLEMENTAL EXHIBIT 1.2. (CONTINUED)

ADULTS

YOUTH AGES 10–20

DESCRIPTION

MEAN

STANDARD SHARE

MEAN

STANDARD SHARE

DEVIATION

MISSING

DEVIATION

MISSING

Felt streets near home were

0.506

0.498

0.008

0.507

0.499

0.006

very unsafe at night

Was very sure he/she would

0.477

0.498

0.007

0.499

0.498

0.008

find an apartment in a different

area of the city

Had moved more than three

0.097

0.294

0.009

0.112

0.314

0.009

times in the past 5 years

Had already previously applied

0.402

0.489

0.008

0.382

0.484

0.01

for a Section 8 voucher or

certificate at baseline

HEAD OF HOUSEHOLD'S

PRIMARY OR SECONDARY

REASON FOR WANTING

TO MOVE WAS…

To get away from gangs or

0.773

0.414

0.023

0.766

0.418

0.024

drugs

To have access to better

0.505

0.494

0.027

0.527

0.492

0.03

schools for children

YOUTH CHARACTERISTICS

(AGES 10–20 IN 2007)

YOUTH'S AGE AS OF

12/31/07 (OMITTED

CATEGORY: 15)

10

0.009

0.094

0

11

0.030

0.170

0

12

0.048

0.214

0

13

0.093

0.291

0

14

0.102

0.303

0

16

0.128

0.334

0

17

0.128

0.335

0

18

0.125

0.330

0

19

0.110

0.314

0

20

0.112

0.315

0

MALE YOUTH

0.495

0.500

0

FLAG INDICATING AGE 6–17

0.396

0.489

0

AT BASELINE (OMITTED

CATEGORY: UNDER 6)

HEALTH PROBLEMS

Problems requiring special

0.084

0.269

0.059

medicine and/or equipment

35

SUPPLEMENTAL EXHIBIT 1.2. (CONTINUED)

ADULTS

YOUTH AGES 10–20

DESCRIPTION

MEAN

STANDARD SHARE

MEAN

STANDARD SHARE

DEVIATION

MISSING

DEVIATION

MISSING

HEALTH PROBLEMS (CONTINUED)

Health problems limiting

0.056

0.230

0.068†

activity

Flag indicating missing

0.068

0.252

information on health problems

(see preceding item)

CHARACTERISTICS

APPLYING ONLY TO

YOUTH AGES 0–5 AT

BASELINE

HOSPITALIZATION

Hospitalized before first

0.191

0.393

0.057†

birthday

Flag indicating missing

0.057

0.232

information on hospitalization

(see preceding item)

BIRTHWEIGHT

Weighed less than 6 pounds

0.150

0.357

0.093†

at birth

Flag indicating missing

0.093

0.291

information on birthweight (see

preceding item)

READ TO BY AN ADULT

Read to by an adult more than

0.264

0.441

0.091†

once a day

Flag indicating missing

0.091

0.287

information on being read to

(see preceding item)

CHARACTERISTICS

APPLYING ONLY TO

YOUTH AGES 6–17 AT

BASELINE

BEHAVIORAL/EMOTIONAL

PROBLEMS

Behavioral/emotional problems

0.067

0.250

0.157†

in 2 years prior to baseline

Flag indicating missing

0.157

0.364

information on behavioral/

emotional problems (see

preceding item)

36

SUPPLEMENTAL EXHIBIT 1.2. (CONTINUED)

ADULTS

YOUTH AGES 10–20

DESCRIPTION

MEAN

STANDARD SHARE

MEAN

STANDARD SHARE

DEVIATION

MISSING

DEVIATION

MISSING

SUSPENSIONS/

EXPULSIONS

Suspended or expelled from

0.036

0.187

0.061†

school in 2 years prior to

baseline

Flag indicating missing

0.061

0.239

information on suspensions/

expulsions (see preceding item)

GIFTED STUDENT

Gifted student or did advanced

0.143

0.351

0.079†

course work

Flag indicating missing

0.079

0.269

information on gifted student

(see preceding item)

LEARNING AND BEHAVIORAL PROBLEMS

Learning problem in 2 years

0.129

0.336

0.050†

prior to baseline

Flag indicating missing

0.050

0.217

information on learning

problems (see preceding item)

School called about youth’s

0.204

0.396

0.040

behavioral problems in 2 years

prior to baseline

Notes: Weighted means, standard deviations, and share missing are shown for the interviewed adults and youth (ages 10–20). For baseline information that only applied to children age 0–5 or 6–17 at baseline, we exclude children to whom the item did not apply before calculating the means and other statistics. Missing values are replaced with mean imputed values for covariates that were missing for less than 5 percent of the full sampling frame. The imputed means for adult and household covariates are conditional on site and randomization in 1998 versus earlier years. Youth imputations are conditional on site, randomization in 1998

versus earlier years, age at baseline, and gender. Missing values for covariates with at least 5 percent missing for the full sample are excluded from the statistics above and are flagged as having missing data and set to zero in the analysis.

Data source and sample: MTO Participant Baseline Survey and MTO data system. Adults interviewed at final survey (N = 3,273); Youth interviewed at final survey (N

= 5,101).

† Indicates that baseline information (for the applicable sample) was missing on this covariate for at least 5 percent of the full sample. Due to the level of missing information, a missing flag was included in the analysis and the mean and standard deviation shown in this table are conditional on the baseline information being nonmissing (for example, the covariate is set to zero for individuals with missing information on this item however these zeros are not included in the means shown).

37

38

IMPACTS ON MOBILITY,

HOUSING, NEIGHBORHOODS,

AND SOCIAL NETWORKS

39

Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks IMPACTS ON MOBILITY, HOUSING,

NEIGHBORHOODS, AND SOCIAL NETWORKS

This chapter describes the long-term impacts of

racial segregation that families experienced, but the

MTO on the residential mobility patterns, housing

program did have sustained effects on a variety of other

conditions, neighborhood conditions, and social

measures of neighborhood socioeconomic composition.

CHAPTER 2

networks of participating families. These outcomes

Despite concerns about potential social isolation of

are measured 10 to 15 years after random assignment

MTO families who moved to more affluent areas,

and are derived from survey self-reports by MTO

program moves increased the likelihood of having close

participants, geocoded address histories of families

friends who had a college degree. MTO also made adult

linked to tract-level data from the Census Bureau,

and female youth participants, but not male youth, feel

and interviewer observations about the respondents’

safer in their new neighborhoods.

housing units and surrounding neighborhoods.

2.1 BASELINE AND CONTROL

We find that, on average, control group families made

GROUP CONTEXT

about two moves over the course of the 10–15-year

The MTO demonstration recruited families from

study period; families in the experimental or Section

public housing or assisted housing projects in some

8 groups who moved with a program voucher made,

of the poorest census tracts in the study’s five cities.

on average, approximately one extra move over the

About 90 percent of families interviewed for the long-

course of the study. MTO improved the quality of the

term evaluation lived in public housing at baseline.

housing units in which families were living but had

The other 10 percent lived in housing run by private

no detectable impacts on housing costs for families,

operators with government assistance.1

although the confidence intervals around these latter

estimates do not allow us to rule out modest impacts.

Exhibit 2.1 shows baseline characteristics of the

MTO had no detectable impacts on being “literally

long-term survey respondents and the census tracts

homeless” (i.e., living at a shelter, on the street, in

where they lived. At the point of enrollment in MTO,

an abandoned building, in a car or van, or similar

families expressed considerable dissatisfaction with

circumstances). However, being in the Section 8 group

their housing. Only one-quarter of adults rated the

increased the chances of “doubling up” with friends or

condition of their housing as excellent or good. At

relatives. Families in the experimental and Section 8

baseline, families lived in census tracts with poverty

groups were less likely than controls to report having

rates above 53 percent, and in which 91 percent of all

trouble paying their rent on time, but were more likely

residents were members of racial or ethnic minority

to have trouble paying their utility bills.

groups. The tracts had low labor force participation and

adult education levels. They also had high proportions

MTO allowed many families to move to lower-poverty

of families headed by a single parent or on welfare.

neighborhoods. During the 10 to 15 years after families

were randomly assigned, the poverty rate of the census

tracts in which the control group lived averaged

approximately 40 percent. For those families that

1 Private developers built rental housing under a number of differ-

ent federal programs from the 1960s to the 1980s. The developers

moved with MTO vouchers in the experimental and

received subsidies in various forms (such as below-market interest

rates on mortgages) in exchange for providing some units affordable

Section 8 groups, average tract poverty rates were about to low-income renters. Such developments were built under a number 18 and 11 percentage points lower, respectively. MTO

of programs (rent supplement, 221(d)(3), BMIR, Section 202, Sec-

tion 236, Section 8 new construction or substantial or moderate

had more modest effects on the degree of neighborhood rehabilitation). Units in such developments are typically referred to as private, “assisted” housing.

40

EXHIBIT 2.1. SELECTED BASELINE CHARACTERISTICS FOR ADULTS SURVEYED IN THE LONG-TERM

EVALUATION: HOUSEHOLD AND NEIGHBORHOOD CHARACTERISTICS

OUTCOME

CONTROL

EXPERIMENTAL SECTION 8

ALL

GROUP

GROUP

GROUP

GROUPS

HOUSING CONDITION

Rated baseline housing as excellent or good [SR]

26.3%

29.3%

25.0%

27.1%

BASELINE TRACT CHARACTERISTICS INTERPOLATED

USING 1990 AND 2000 CENSUS DATA TO THE FAMILY'S

DATE OF ENROLLMENT BETWEEN 1994–1998

Persons who are poor [CEN]

53.1%

52.7%

52.6%

52.8%

Persons above 200% of the poverty line [CEN]

24.8%

24.9%

24.9%

24.9%

Minority [CEN]

91.2%

91.6%

91.5%

91.4%

Employed, civilian [CEN]

74.2%

73.9%

74.3%

74.1%

College graduates [CEN]

10.3%

10.1%

10.6%

10.3%

More than high school education [CEN]

22.1%

21.7%

22.3%

22.0%

Single female-headed households [CEN]

63.4%

63.1%

63.3%

63.3%

Households on public assistance [CEN]

33.4%

33.8%

33.1%

33.4%

BASELINE TRACT CHARACTERISTICS MEASURED USING

AMERICAN COMMUNITY SURVEY DATA (2005–09)

Persons who are poor [CEN]

41.5%

41.5%

42.1%

41.7%

Persons above 200% of the poverty line [CEN]

28.5%

28.7%

28.2%

28.5%

Minority [CEN]

90.4%

90.5%

91.0%

90.6%

Employed, civilian [CEN]

79.5%

79.9%

79.6%

79.7%

College graduates [CEN]

18.6%

18.2%

18.6%

18.4%

More than high school education [CEN]

34.2%

33.7%

34.2%

34.0%

Single female-headed households [CEN]

60.0%

59.4%

61.0%

60.1%

Households on public assistance [CEN]

14.0%

13.6%

13.5%

13.7%

NEIGHBORHOOD SAFETY

Any household member was a victim of crime in

41.6%

43.4%

41.4%

42.2%

past 6 months [SR]

Streets are very unsafe at night [SR]

51.2%

49.3%

51.7%

50.6%

MOTIVATIONS FOR SIGNING UP TO MOVE THROUGH MTO

PRIMARY OR SECONDARY REASON FOR MOVING WAS…

To get away from drugs or gangs [SR]

77.9%

78.6%

74.9%

77.3%

For better schools for the children [SR]

48.1%

49.1%

55.3%

50.5%

To get a bigger or better apartment [SR]

45.7%

44.1%

43.8%

44.5%

HOPE VI PROJECT

Housing project has been demolished through

41.6%

39.5%

44.8%

41.6%

HOPE VI or another housing initiative [HUD]

INTERVIEWED N

1,139

1,456

678

3,273

Notes: Percentages are percent distributions and may not sum to 100 because of rounding and missing information. Data are weighted to reflect randomization ratios and the selection of adults into the interview sample and the two-phase sampling design of the long-term evaluation.

Square brackets indicate the source of the outcome information: SR = self-report from the MTO Participant Baseline Survey, CEN = 1990 and 2000 decennial census data as well as the 2005–09 American Community Survey, HUD = Administrative data on demolished housing projects through 2009.

Data source and sample: MTO Participant Baseline Survey. Adult long-term survey. All adults interviewed.

Measures: Baseline census tract is based on participant's address at baseline. Interpolated census tract characteristics are linearly interpolated from the 1990 and 2000 decennial census.

41

Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks Chapter 2: Impacts on Mobility

The third panel of Exhibit 2.1 shows the characteristics

moderate rents declined, both because the overall rent

of the same baseline census tracts but measured more

distribution shifted and because units were removed

recently using the 2005 to 2009 American Community

from the stock (demolition, conversion to for-sale status,

Survey’s (ACS) 5-year averages. Comparing the

or landlords opting out of the voucher programs) faster

demographic composition of these tracts at baseline

than affordable replacement units were built (U.S.

(interpolated to the date families enrolled in the

Department of Housing and Urban Development,

program, between 1994 and 1998, using 1990 and 2000

1997, 2007). The cities where MTO families are living

census data) with the 2005 to 2009 demographics of

are particularly expensive. The majority of low-income

these tracts suggests that, over time, these tracts have

renters in the five MTO sites—ranging from nearly 74

become slightly less poor and have higher proportions of

percent of low-income renters in California to 64 percent

college-educated residents. The share of residents who

in Massachusetts—spend more than 30 percent of their

are members of a racial or ethnic minority group has

income on rent, which is a commonly-used definition

remained about the same over time in these census tracts.

of “rent burdened” (see Center on Budget and Policy

Priorities, 2011). More than one-third of low-income

The baseline neighborhoods in which families were living renters in these same states spend more than 50 percent were also quite dangerous. Exhibit 2.1 shows that 42

of their income on rent (“severely rent burdened”).

percent of MTO household heads reported that someone

in the home had been the victim of a crime during

Housing markets in Boston, Chicago, Los Angeles,

the six months prior to the survey. Around 50 percent

and New York were strongly affected by the recent

reported that the streets in their neighborhood were

housing market collapse. The resulting foreclosure of

unsafe at night. Perhaps not surprisingly, 77 percent said

rental properties, tenant evictions, and the flood of new

that “getting away from drugs and gangs” was the first

renters who were former homeowners appear to have

or second most important reason for wanting to move.

increased competition in the rental market (Briggs,

The next most common reason for wanting to move was

Popkin, and Goering, 2010). Increasing poverty rates

better schools (approximately 50 percent reported this as

during the 2000s presented further challenges to low-

the first or second most important reason for enrolling),

income families. The net result of these two trends is

followed by wanting a bigger or better apartment

that between 2007 and 2009, the number of low-income

(approximately 45 percent said this was the first or

renters who devoted more than 50 percent of their

second reason).

income to rent rose 20 percent, from 5.91 million to

7.10 million renters (U.S. Department of Housing and

As MTO was taking place, the urban landscape itself was

Urban Development, 2011).

shifting. The overall share of people who were poor in

the United States declined between 1990 and 2000, but

Housing policies themselves changed in important ways

then increased again slightly during the 2000s (Anderson, as well. Cities demolished and redeveloped many of the 2011; Bishaw and Macartney, 2010; Bishaw and

most severely distressed public housing developments,

Renwick, 2011). The geographic concentration of low-

mostly under the HOPE VI program operated by

income people (that is, residential segregation by income) HUD. Some of the public housing projects in which was increasing from 1970 through 2000, the last year

MTO families were living at baseline were among

for which decennial census data are available (Watson,

those demolished as part of HOPE VI and other local

2009). At the time of this writing, trends beyond 2000

and federal housing initiatives. The share of the MTO

were unavailable, but there are reasons to suspect that

sample members from all three groups who at baseline

residential segregation by income increased further.

were living in housing projects that were eventually

demolished varied from 91.5 percent in Chicago to 6

Throughout the MTO study period, rents rose faster

percent in New York City. Overall, approximately 42

than wages for low-skilled people in metropolitan areas

percent of the sample was living at baseline in a project

nationwide. The stock of housing units with low to

that was eventually affected by a demolition program.

42

Families living in projects targeted for demolition

2.2 HYPOTHESES ABOUT

were typically either relocated to other public housing

EFFECTS ON MOBILITY, HOUSING,

properties or offered housing vouchers similar to those

NEIGHBORHOODS, AND SOCIAL

that were offered to the Section 8 group. Most families

NETWORKS

who relocated through these programs seem to now be

in better housing and safer neighborhoods (see Popkin

This section reviews the different pathways through

et al., 2004, for a review). Only a small share of families

which the MTO offer of a housing voucher might

returned to the redeveloped projects, but those who

influence the housing and neighborhood conditions of

did now live in dramatically better housing and lower-

families in the demonstration. Subsequent chapters of

poverty environments (Turner and Kingsley, 2008).

this report each include some discussion of the different

theoretical pathways through which the housing and

Even housing projects untouched by HOPE VI have seen neighborhood changes described in this chapter might improvements in housing and neighborhood conditions

lead to changes in health, earnings, or other outcomes.

over the last decade, owing to changes in HUD policy

that encouraged income mixing and the exclusion of

In thinking about the potential effects of housing

residents with criminal records. Crime rates in the

vouchers on housing and neighborhood conditions, it

United States also declined dramatically during the

is crucial to keep in mind that the MTO study sample

1990s with the largest proportional drops in crime often

consists of families who were all living in public housing

coming in the highest-crime places including places like

at baseline. Setting aside for the moment the changes

the baseline housing projects from which MTO families

described above in other housing policies over the course

were drawn (Harcourt and Ludwig, 2006; Cook and

of the study period, the MTO experiment essentially

Laub, 2002; and Levitt, 2004).

compares the effects of being offered a housing voucher

against the chance to stay in public housing. This is a

These demographic, housing market, and policy

very different intervention or “treatment” from what is

changes may have influenced the impacts of the MTO

examined by the HUD Welfare to Work (WtW) voucher

demonstration on housing and neighborhood conditions

study (Mills et al., 2006) or the Chicago housing voucher

by changing the housing and neighborhood options

study by Jacob and Ludwig (2011), which compare the

available to families in the two treatment groups, and

effects of being offered a voucher versus living in the

by changing the conditions that control group families

private-housing market without a subsidy. We return

experienced. For example, HOPE VI may have led

to the issue of how MTO compares with other voucher

to more mobility among control group families than

experiments at the end of this chapter.

would otherwise have been expected of public housing

HOUSING UNIT QUALITY

residents. This does not bias our estimates, given that

Whether the MTO comparison of housing vouchers

the experience of the control group is still an accurate

versus public housing should have any impact at all

picture of what would have happened to families assigned on housing-unit quality is unclear, given that both the to the treatment groups in the absence of MTO. But

public housing and housing voucher programs share

it is important to bear in mind that we are comparing

the same ostensible goal articulated by Title II of the

treatment families with a set of control families who

Housing Act of 1949, to ensure “a decent home and

were likely affected by substantial changes in housing

suitable living environment for every American family.”

policy. Although housing-market-induced changes in

If both public housing and housing vouchers provided

housing units available to voucher holders do not bias

families with “a decent home,” then MTO vouchers

our comparisons, they are relevant in thinking about the

could in principle have no net effect on housing unit

conditions under which we would expect to see similar

quality.

impacts from receiving housing vouchers.

43

Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks Any MTO impact on housing-unit quality will depend

and neighborhoods without having to give up a large

on the specifics of how public housing and housing

housing subsidy. Therefore, an MTO housing voucher

voucher programs are implemented in practice. Some

substantially reduces the costs to families of relocating.

economists worry that the lack of financial incentives

And economic models predict that a lower cost of

for government housing authorities to operate public

moving should lead families to make more moves.

housing projects efficiently could lead them to provide

low-quality housing units relative to private-market

The net effect of MTO on residential mobility rates over

housing (Olsen, 2003; see also the historical account of

the longer term could be different from the program’s

Chicago public housing by Hunt, 2009). On the other

short-term mobility effects. In the short-term, MTO

hand, the quality of the nation’s public housing stock

could increase the number of “good” (voluntary) moves

may be improving over time (Fischer and Sard, 2008).

made by families in the experimental and Section 8

The private housing market could itself also be operating groups. Over time, control group families may enter into inefficiently because of regulatory and other constraints

the private housing market on their own even without

on the available supply of low-income housing, or

receiving a housing voucher subsidy. Control group

because the high costs of moving reduce the competitive

families thereby could be placed at increased risk for

pressure on landlords to provide high-quality housing.

housing instability if income fluctuations make them

The degree to which housing vouchers actually improve

unable to meet monthly rent or utility expenses without

the unit quality of housing for families will then depend

a subsidy and if private-market landlords are quicker to

in part on the degree of enforcement in the study sites

evict families for nonpayment than are public housing

of housing quality standards and inspections, which

authorities. The result could be additional involuntary, or

are required as part of the standard housing voucher

“bad,” moves by the control group, serving to offset the

program.

higher initial mobility of the experimental and Section 8

groups. 3

MOBILITY

If people respond to incentives, at least in the aggregate

HOMELESSNESS

as standard rational choice theory predicts, then MTO

The prospect of housing instability among control

should increase the number of moves that families make

group families who move into the private housing

in the short-term. Families in public housing or other

market without a subsidy also raises the prospect that

government-assisted housing projects receive large rental

MTO vouchers could reduce homelessness. But even

subsidies that help reduce their housing-cost burden,

the net effect of MTO on homelessness is ambiguous

but only if they agree to live in the public housing

from the perspective of social science theory. It could be

unit offered to them. Normally, if a family in public

the very fact of relying on the private housing market

housing would like to move to a different apartment or

that puts a family at elevated risk for homelessness,

neighborhood, their only option would be to move into

regardless of whether the family has a voucher subsidy,

a private-market housing unit and give up their public

because of the increased risk of landlord difficulties

housing subsidy. 2 We might therefore expect somewhat

or sale or foreclosure of their rental property. Given

higher mobility rates among the MTO control families

that, by design, a larger share of the experimental and

than among more representative samples of low-income

Section 8 groups versus controls will wind up in the

families. MTO housing vouchers relaxed the location

private housing market, MTO could increase rather than

constraints that families faced in exchange for receiving a decrease homelessness.

rental subsidy. Put differently, families who are offered an

MTO housing voucher are able to move to different units

3 Previous research shows that, in general, American families tend

2 In principle some public housing families might be able to relocate

to move quite a bit. Among black and Hispanic households living in

to a different public housing project located in a different area, or they

poverty, fully 23 percent change residences each year (Schachter, 2004).

might be able to access a housing voucher through some other voucher

Most of these moves are local moves to similar types of neighborhoods;

program besides MTO. In practice in most cities there are long wait-

poor families in particular are likely to move from one poor neighbor-

lists for both public housing and housing voucher subsidies.

hood to another (Crowder and South, 2005).

44

NEIGHBORHOOD ENVIRONMENTS

poverty census tracts after their initial one-year lease was

By relaxing the locational constraint that public housing

up, MTO designers hoped that many experimental group

families face to receive a housing subsidy, MTO vouchers families would end up liking their new areas and choose should also increase the chances that families wind up

to stay. The costs of moving might also contribute to

living in lower-poverty neighborhoods. Public housing

the “stickiness” of experimental group voucher holders

offers families limited choice over where they live. In

in their initial low-poverty census tracts. Consistent

exchange for a large housing subsidy, families must

with the hope of MTO program designers, previous

live in whatever public housing units are offered by

research of families in the Gautreaux mobility program

the local housing authority. Given the politics of how

in Chicago found that many families who were initially

local housing authorities first selected public housing

assigned to relocate to low-poverty, mostly white Chicago

locations, and given that the design and tenant selection

suburbs wound up staying there (Keels et al., 2005). 5

policies often created high concentrations of poor

families, many housing project residents wind up living

We expect MTO moves to lead to changes in a

in very poor, racially segregated neighborhoods (Olsen,

wide range of other neighborhood attributes that

2003; Hunt, 2009; Schill and Wachter, 1995). Housing

families experience as well, given the correlation

vouchers enable public-housing families to move into

between neighborhood poverty and a variety of other

lower-poverty neighborhoods without having to give

neighborhood attributes such as prevalence of high

up their subsidy, and as such they reduce the costs of

school dropout and joblessness, crime, and quality of

changing their neighborhood conditions.

local public services (Briggs, 2008). The one exception

might be neighborhood racial composition, if the

The relatively greater locational flexibility afforded

mostly minority program population in MTO has a

to voucher families has its limits, however. Many

preference for living in mostly minority neighborhoods,

housing units will be unaffordable to voucher holders. 4

or encounters racial discrimination in the housing

Affordable housing units will be even more difficult to

market. Another potential exception could be access

find in low-poverty areas, given that standard economic

to public transportation if higher-poverty inner-city

theory predicts that any amenity associated with a

neighborhoods are closer to train and bus lines than

housing unit—including the “quality” of the surrounding lower-poverty areas that may be concentrated more along neighborhood—should be reflected in relatively higher

the periphery of the demonstration cities.

monthly rents. For a given rent level, families must trade

housing unit quality for neighborhood quality.

SOCIAL ISOLATION

A large body of research in social psychology and

We expect the constraint that MTO imposes on

sociology has documented a tendency of people to

experimental group families, which limits them to

associate with other people who share the same social

using their housing voucher in a low-poverty area at

characteristics or preferences, known as “homophily”

least initially, to lead to more pronounced changes

(see, for example, McPherson, Smith-Lovin, and Cook,

in tract poverty for families who move with an MTO

2001). One potential concern with MTO is that the

experimental group voucher compared with those who

families who moved with a housing voucher might wind

move with a Section 8 group voucher. Even though

up feeling socially isolated in their new low-poverty

experimental group families could move out of low-

areas if the moves reduce their access to their baseline-

neighborhood social ties. At the same time, families

4 The voucher subsidy amount is essentially equal to something like

can have difficulty establishing new connections with

the difference between the HUD-defined “fair market rent,” often set

between the 40th and 50th percentile of the metropolitan area’s rent

their new, more affluent neighbors (see, for example,

distribution, and 30 percent of the family’s adjusted income (total in-

come minus some deductions allowed by the program rules; see Olsen,

2003). Voucher program rules limit the ability of families to spend

5 Keels (2008) found remarkable persistence in low rates of neighbor-

their own money to live in units with rents that are much above the fair

hood poverty among Gautreaux children who are now adults, although

market rent, which means that many housing units will be unaffordable

many Gautreaux children have moved on to more racially balanced

to voucher holders.

neighborhoods.

45

Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks Briggs, 1998). The hope would be that, at the very

We supplemented these sources with other tracking

least, experimental and Section 8 group movers would

activities that HUD supported over the course of the

manage to make new social connections over time as they study period, including periodic canvasses of families by acculturate into their new neighborhood environments

Abt Associates, and the interim survey that Abt, together

(Briggs, Popkin, and Goering, 2010). The risk of social

with several members of our current National Bureau of

isolation for MTO movers could be declining over our

Economic Research (NBER) research team, carried out

study period as technological advances have reduced the

four to seven years after baseline (see Orr et al., 2003, for

costs of staying in close contact with the social networks

more details).

from their origin neighborhoods.

We used the reconciled administrative and survey-

2.3 DATA SOURCES AND MEASURES

based address histories to construct a “spell” file that

From June 2008 through April 2010, we carried out

incorporated information on the amount of time

long-term follow-up surveys with adults and up to three

that MTO families spent at different addresses. Our

youth from each family who were between 10 and 20

information about the sequence of families’ addresses

years old at the end of 2007. These follow-up surveys

is probably more accurate than our data on the specific

capture outcomes on average 12 years after baseline

amounts of time that families spent at each address,

(range from 10 to 15 years). We collected and geocoded

given that it is difficult for people to accurately recall

address histories and linked each address to census tract

the timing of episodic events (Bound, Brown, and

characteristics, and also asked survey interviewers to

Mathiowetz, 2001). In cases of conflicting information

record their observations of the neighborhoods in which

about where a family was living at a given point in time,

families were living. In what follows we discuss each of

we prioritized address information that came from actual

these data sources and measures in detail.

contact with the family. We built on the algorithm that

Abt Associates developed for the interim MTO study to

PARTICIPANT ADDRESS HISTORY

determine which addresses were reliable and which dates

Any effort to understand the neighborhood conditions

should be associated with each address.

requires that we reconstruct each family’s residential

history from random assignment onward. Our strategy

We created address histories for youth using previously

was to assemble a best guess of the family’s residential

collected administrative and survey data and asking

history from administrative records and previous

adult survey respondents about whether the youth had

canvasses and surveys of MTO families and then ask

ever lived apart from the adult (and if so, when). If the

MTO adults to confirm or correct their full history,

adult reported that the youth had always lived with him

beginning with their current address and moving back in

or her, or that the youth had lived away just once and

time to the point of random assignment. We also asked

for less than one year, we applied all of the addresses the

adults to indicate where they stayed during any gaps

adult supplied during the final survey to the youth (in

in their residential history in order to measure spells of

addition to the other addresses we already had for the

homelessness, as described in more detail below.

youth). If, however, the youth had lived away from the

adult for more than one year or more than once, or if

In constructing the histories, we drew from

the youth did not live with the adult at the long-term

administrative data from housing agency data used to

survey, then we only applied the adult’s address history to

track families receiving any form of housing assistance.

the estimated point when the youth was no longer living

These include several HUD data systems, including the

with the adult.

Public and Indian Housing Information Center (PIC),

the Tenant Rental Assistance Certification System

HOUSING TENURE

(TRACS), and the Multifamily Tenant Characteristics

Tenure refers to whether the respondent occupies housing

System (MTCS). In addition, we accessed the U.S. Postal as a renter, as an owner, is living doubled-up with others, Service’s National Change of Address (NCOA) system.

or is homeless. We asked adult heads of households

46

whether they presently owned, rented, lived with family

been threatened with foreclosure.6 To measure the

or friends, or if they lived in a group shelter. A small

stability of households’ utility service, we asked renters

number of people did not fit into any of these categories

who paid utilities if they were 15 days late in paying their

and specified another living situation. We collected

bill for gas, electricity, or water in any month during the

information on the top reason for moving out of the

past year.7 Of renters who were late at least one month,

second most recent address as well as reasons for moving

we asked if they received a notice that their utilities

into the current neighborhood.

would be shut off and also if their utilities were in fact

shut off.

If respondents did not have their own place to stay, we

determined the exact type and duration of homelessness.

HOUSING CONDITIONS

Respondents who reported staying with a friend or a

Adult household heads reported the conditions of their

relative were classified as “doubled-up.” We classified

current homes or apartments, including an overall

respondents as “literally homeless” if they did not have a

assessment of their housing and whether a set of specific

place to stay and were not living in a hotel or doubled-

problems was big or small, or no problem at all. The

up but were instead living at a shelter, on the street, in

list of potential housing problems that we asked about

an abandoned building, in a car or van, or other similar

comes from the American Housing Survey (U.S. Census

circumstance. Respondents who reported staying at

Bureau, 2004) and included different types of pests,

a hotel or motel were not classified in either of those

poorly maintained paint or plaster, and broken heating

categories. This question also allowed for open-ended

or cooling systems. In addition, we created a measure of

answers. Many of the open-ended answers were later

the number of people living in the household divided by

coded as “doubled-up” or “literally homeless.” There

the number of rooms in the home (excluding bathrooms

are five final outcome variables reported in the analysis.

and kitchens). We defined a situation as overcrowded if

Respondents are flagged as “doubled-up” or “literally

there was more than one person per room, and severely

homeless” on the basis of whether they fell into the

overcrowded if there were more than 1.5 people per

respective category at least once after randomization.

room.8

They were “without a home of their own” if they were

flagged as “doubled-up” or “literally homeless.” “Number HOUSING COSTS AND ASSISTANCE

of literally homeless spells” and “number of doubled-up

Housing assistance refers to any financial subsidy

spells” are the counts of respective spells that respondents for rental costs, such as whether the sample member reported.

received help in paying rent through one of the federal

housing assistance programs. At the study’s outset,

HOUSING STABILITY AND UTILITY

every participant was receiving housing assistance. At

PAYMENTS

the time of the long-term survey, we used a multistep

The adult survey also asked current renters if they had

“triangulation” method developed by the Urban Institute

been more than 15 days late in paying their rent in the

that compares two types of administrative data against

past 12 months and if they had been threatened with

6 Our survey questions on evictions and foreclosures focused on the

eviction in the past 12 months. Current homeowners

type of housing situation that the respondent was currently living in.

were asked if they were more than 15 days late paying

Thus, current renters were asked about evictions and current homeown-

ers were asked about foreclosures. Under this survey design, we cannot

their mortgage in the past 12 months and if they had

identify a current renter who may have had his or her home foreclosed

on during the past 12 months. Thus, the eviction and foreclosure

questions can most easily be thought of as reflecting housing stability

at respondent’s current address, but it also allows respondents to report

occurrences at other addresses with the same type of housing situation

in the past 12 months.

7 Like renters, homeowners can also be late on their utilities payments.

However, because of a skip error in the survey instrument, we did not

ask homeowners if they were late on their utility payments.

8 For more information on measures of overcrowding, see Blake, Keller-

son, and Simic (2007).

47

Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks the MTO survey responses. The results slot families into

NEIGHBORHOOD CENSUS TRACT

the following categories: public housing; tenant-based

CHARACTERISTICS

federal rental assistance; project-based non-public-

We measured the sociodemographic composition of

housing federal rental assistance; and no federal rental

the “neighborhoods” in which MTO families were

assistance (including owners, unassisted renters, the

living by linking their address to census tract-level data

homeless, and those with other statuses).9

from the Census Bureau. As the Census Bureau notes,

census tracts “usually have between 2,500 and 8,000

Housing cost burden is the ratio of monthly housing

persons and, when first delineated, are designed to be

costs (of current rent or mortgage plus utilities) to

homogeneous with respect to population characteristics,

household monthly income (primarily based on reports

economic status, and living conditions.”11 The census

of last year’s income). Households are cost-burdened if

tract characteristics we examined included median

they devote more than 30 percent of their income to

income, poverty and thresholds of poverty, household

housing costs. They are severely cost-burdened if the

composition, homeownership, race, ethnicity,

ratio exceeds 50 percent. We assessed monthly housing

employment, occupation, and education.

costs from survey data on adults’ rent or mortgage

payments and their most recent gas and electric bills.

The tract-level data we used are from the 1990 and 2000

If a respondent did not know or refused to provide the

decennial censuses, together with data from the 2005–09

amount of his or her utility bills, we used an average for

ACS. The ACS was designed to replace the census long

that site and treatment group.10 Although the survey did form, which was discontinued after 2000, although the not directly ascertain current monthly income, we were

ACS has a smaller sample. The ACS contains many of

able to estimate it using survey data on the components

the same questions as the census long form, although

of the previous year’s household income, adjusted for

some variables are measured slightly differently between

changes in employment status and receipt of government the two data sources.12 We can estimate census tract aid. For example, if a respondent had earnings the prior

characteristics at a specific point in time assuming that

year but was currently not working, we excluded his or

tract characteristics changed in a constant way in between

her earnings from our income estimate.

census data collection points, that is, by assuming a

9 First, we use respondents’ self-reports to determine each of eight pos-

sible housing assistance categories: renter with tenant-based assistance

(TBA renter); renter in public housing (public housing); renter with

project-based assistance (PBA renter); renter without housing assistance

(unassisted renter); homeowner; homeless individual; individuals who

11 www.census.gov/geo/www/cen_tract.html, Accessed April 16, 2011.

live with family or friends and do not pay rent (doubled up); and

individuals with another housing arrangement (other). Next, we analyze

12 For example, Census 2000 asked for income in the previous calendar

administrative data (PIC/MTCS and TRACS/MultiFamily) separately

year whereas the ACS asks for income over the previous 12 months.

to determine the housing assistance status for any head of household in

Estimates relying on measures of income in the last 12 months may be

the administrative file. If the status in the administrative data coincides slightly lower on average than measures of income from the prior calen-with that in the survey data, it is assigned to the respondent. For re-

dar year. (See, for example, Beaghan and Stern, 2009, available at www.

spondents whose statuses are not identical, we match (based on ZIP+4

amstat.org/sections/srms/Proceedings/y2009/Files/303932.pdf ). This

location) against a set of locations of public and project-based housing

difference in reported income could contribute to differences in poverty

obtained from HUD. If the status in HUD data is the same as in survey

measures for certain neighborhoods. In addition, the ACS and Census

or administrative data, then we use that status. If no match is found,

define residency in different ways. In the ACS, someone is counted as

we use the administrative data status when the administrative records

living in a census block if he or she presently resides there. However, for match the respondent with certainty; otherwise, we use the survey data

the census, people are only counted as presently residing if they “usually

status. Fully 86 percent of the survey analysis results agree with the

live” there. This may change the population of “residents” in areas with

administrative analysis results and 14 percent disagree. Of those that

significant populations that are transitory, such as areas with colleges,

disagree, results for 3.6 percent of the respondents disagree on type of

or communities with many summer homes. The Census Bureau in 2000

federal housing assistance, not the fact that they had federal housing

changed its questions on race and ethnicity to allow for identification

assistance at all. Thus, receipt of housing assistance is in agreement for

of multiple races. Another significant change is how the Census defined

89.5 percent of the respondents. Both rates are much higher than the

professions and careers, which changed with revisions to the Standard

78 percent agreement from the interim survey.

Occupational Classification and to the North American Industry Clas-

sification System. As a result, many of the major occupation groups

10 Reported utility costs in the MTO survey data are higher than docu-

in Census 2000 are not comparable to earlier censuses. For example,

mented average utility costs in comparable locations over a relatively

people who work for travel agencies are classified as part of the trans-

similar time period. We find that MTO’s effects on the housing burden

portation industry in the 1990 census but are classified as “administra-

do not qualitatively differ under a variety of sensitivity checks that

tive, support, and waste management” for the 2000 census. (See www.

impose caps or minimum values on utility costs.

census.gov/hhes/www/ioindex/pdfio/techpaper2000.pdf ).

48

linear trend for the evolution of tract characteristics.13

respondents to describe how likely it was (very likely,

We present census tract characteristics for different

likely, unsure, unlikely, or very unlikely) that their

points in time after random assignment for families, as

neighbors would act if they saw a group of children or

well as the average duration-weighted tract characteristics youth skipping school and “hanging out on the street,” of all the addresses that families lived at from the time of

or if they saw youth spray-painting graffiti on local

random assignment until just before the start of survey

buildings.

fielding (through May 31, 2008). We selected the start of

survey fielding in order to have a common endpoint for

As a measure of the strength and breadth of available

all families who were interviewed at different points in

social support, we also asked respondents about their

time from June 2008 through April 2010. We duration-

close friends using modified questions from the Social

weighted the characteristics of each census tract in which Capital Community Benchmark Survey (Saguaro a family lived during this time period by the number

Seminar, 2002). Interviewers asked adults to report how

of days they lived at each address. This ensures that

many close friends they had and how many of them

addresses where they lived longer count more heavily in

(all, most, some, a few, or none) were of a different race

the average.

or ethnicity than the respondent. Additional questions

asked how many of their close friends had college degrees

NEIGHBORHOOD SATISFACTION AND

or were employed full-time.

CHARACTERISTICS

To gauge satisfaction with their neighborhoods of

FAMILY AND NEIGHBORHOOD SAFETY

residence, we asked adults and youth to describe how

As noted above, one of the key reasons MTO families

much of a problem they have in their neighborhoods

signed up for the demonstration was their desire to

with things like litter or trash on the streets or sidewalks, live in safer neighborhoods. A greater sense of safety graffiti or writing on walls, abandoned buildings, people

can affect a family’s quality of life and behaviors in

drinking in public, and groups of people hanging

a variety of ways, including direct effects on mental

out in their neighborhood. As a measure of access to

health, physical health outcomes (for example, because

neighborhood essentials (for example, shopping and

of changes in the willingness to walk around in a

parks) and employment, respondents also reported if

neighborhood), and economic outcomes (for example,

they owned a working car or if they lived fewer than 15

increased willingness to use public transportation to

minutes from public transportation.

commute to jobs, or to leave children unattended or in

the company of other caregivers after school).

NEIGHBORHOOD SOCIAL PROCESSES

Social science research has increasingly focused on the

We asked respondents how safe they felt in the streets

role of neighborhood social processes in individual

around their neighborhood during the day and at night

outcomes, above and beyond the role of neighborhood

(very safe, safe, unsafe, or very unsafe). Interviewers also

structural characteristics such as social and demographic

asked about how responsive the local police were to calls

composition (see, for example, Sampson, Morenoff, and

(police not coming when called is a big problem, small

Gannon-Rowley, 2002). Our long-term survey asked

problem, or no problem) and whether the respondent

adults to report on social processes such as “collective

saw people selling or using illegal drugs in the past

efficacy,” defined by Sampson, Raudenbush, and

30 days. As a measure of actual crime victimization

Earls (1997) as the willingness of local adults to work

experiences, the survey asked if any household members

together to enforce shared norms. Specifically, we asked

had been victims of crime (purse, wallet, or jewelry was

snatched; threatened with a knife or gun; beaten up or

13 For this purpose, we assumed that the ACS characteristics repre-

assaulted; was stabbed or shot; and/or there was a break-

sent the midpoint of fielding of the ACS, which is July 1, 2007. ACS

fielding occurred continuously from 2005 to 2009. It is possible that

in/attempted break-in to their house) in the past six

characteristics in each census tract changed in a nonlinear way from the

months.

time of the ACS to the time of the interview, particularly because of the

most recent recession.

49

Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks We asked youth about the prevalence of gangs in their

and buildings on the block (storefront church,

neighborhood or by their school. This question is

abandoned/burned buildings, boarded up housing or

modified from the National Longitudinal Survey of

building, green grocer/delicatessen, supermarket/grocery

Youth 1997 (Moore et al., 1999) and aims to measure the store, fast-food and take-out place, or none of the above).

possibility of gang incidences in the neighborhood.

In addition to buildings, interviewers rated the condition

of the streets on the block (very good: recent resurfacing

Previous studies have found that female youth in high-

and smooth; moderate: evidence of keeping in good

poverty urban areas may be affected (mentally and in

repair; fair: minor repairs needed but not rough surface;

overall well-being) by sexual harassment. In an effort to

and poor: potholes and other evidence of neglect).

understand the level and type of such harassment that

They reported on the amount of garbage (including

female participants in MTO might have experienced, we

litter or broken glass) and density of trees on the street.

asked questions drawn from the Maryland Adolescent

Finally, interviewers noted the types (if any) of signage

Development in Context Study (Goldstein, Davis-Kean,

(neighborhood or crime watch, security warnings,

and Eccles, 2005) about how often (never, a couple of

advertising for beer/whiskey/other alcohol, “for sale”

times each year, one or more times a month, once or

signs, or none of the above).

twice a week, or every day) they faced unwanted or rude

comments, unwanted sexual attention, or were afraid to

ADULT SOCIAL NETWORKS AND CIVIC

go places because of unwanted attention or pressure.

ENGAGEMENT

We asked respondents a series of questions to determine

INTERVIEWER OBSERVATIONS OF THE

social connectedness, such as the number of friends the

NEIGHBORHOOD BLOCK

adults have in their neighborhood and how many times

In principle, different people might view conditions in

they visit their friends (whether or not they are living

the same neighborhood differently, and so ideally we

in the same neighborhood) at their homes (every day,

would obtain reports about neighborhood conditions

several days a week, twice a week, once a week, two or

from other neighborhood residents as well, as was done

three times in the past month, once in the past month,

in the Project on Human Development in Chicago

or not at all in the past month). To gain a sense of the

Neighborhoods (PHDCN; Sampson, Raudenbush, and

respondent’s community involvement, we asked how

Earls, 1997; Raudenbush and Sampson, 1999). This

often they chat with their neighbors (almost every day,

was not feasible in the MTO study, so instead we asked

once a week, once a month, a few times a year, or almost

interviewers to complete a checklist about different

never) and go to church or religious services (never in

attributes of the respondent’s block (defined as both

the past 12 months, several times in the past 12 months,

sides of the street that the respondent’s house is on or

once a month, once a week, more than once a week, or

faces, from one street corner to the other). Most of the

no church/place of worship).

observations were gathered in a walking tour, although

some were completed by driving around the block.14

YOUTH SOCIAL NETWORKS AND

SELF-ESTEEM

Specifically, the interviewers reported on the condition

The MTO youth survey draws on questions from

of most of the buildings on the block, as well as the

National Education Longitudinal Study of 1988 (NELS)

existence of window bars and any graffiti that was

(Ingels et al., 1994) and National Comorbidity Survey

painted over. They also noted the types of institutions

(Kessler et al., 2009) and asks youth about their friends’

involvement in school activities, use of illegal drugs, and

14 For PHDCN, interviewers walked all around the block, while for

whether they are school dropouts. The goal is to gain

logistical reasons, MTO interviewers only walked up and down the

street (block face) on which the MTO respondent was living. In some

an understanding of their peers’ views on studying and

unpublished analyses that were carried out for us by Michael Bader, a

graduate student at the time at the University of Michigan, we find the

continuing education beyond high school (whether it is

correlations for variables that come from observing a single block-face

very important, somewhat important, or not important

versus walking around the entire block typically ranged from .5 to .8,

depending on the measure.

at all). MTO youth were additionally asked about the

50

number of close friends they have (none, one, two,

squares regression of the outcome of interest against

three to five, six to ten, or more than ten), how many

dichotomous indicators for random assignment to

of their close friends live in their neighborhood, and

either the experimental group or the Section 8 group,

whether these friendships are new or holdovers from

controlling for a set of baseline control variables to

their neighborhoods of origin. Another aspect of a

improve statistical precision and are calculated using

youth’s social connections can come from church or other sample weights that account for changes over time in religious activities, so we asked them about how often

random-assignment ratios and for the random selection

they participated in activities such as youth groups, Bible of some families but not others into the survey sample classes, or choir (once a week or more, once a month or

frame (see Section 1.3 for details). The estimated effects

more, less than once a month, or never). Using items

of treatment on the treated (TOT) capture the effects of

from the Ferguson Tripod Study (Ferguson, 2010) and

MTO moves on those who initially moved with an MTO

NELS, we asked youth about speaking “proper” English

voucher. For each MTO treatment group the TOT is

(even when they are with their friends) and how often

equal to the ITT effect for that treatment group divided

people would describe their behavior as “ghetto” (very

by the share of that group that moved initially with an

often, somewhat often, or not at all), as well as a battery

MTO voucher.

of items about their perception of themselves and their

future.15

Because the remainder of the report uses tables with a

similar structure, we spend a moment here to review

2.4 LONG-TERM IMPACTS ON

how the table is laid out. Each row presents the results

MOBILITY AND HOUSING

of running a regression for the variable listed on the left-

Of those families randomly assigned to the experimental

hand side of the row, which is the outcome variable in

group, 48 percent “complied” or moved with an MTO

the regression, on the two treatment-group assignment

voucher. Of those assigned to the Section 8 group, the

dummy variables as well as the baseline covariates listed

compliance rate is 63 percent.

in Supplemental Exhibit 1.2. The first number in each

row is the control mean for that variable, that is, the

Exhibits 2.2 through 2.5 present MTO’s effects on the

average value of the outcome for the control group,

residential mobility patterns and housing assistance of

calculated using the sample weights described in Chapter

program participants, as well as their housing conditions. 1. The coefficients on the treatment-group assignment These estimates are calculated using the methods

variables together with their standard errors are listed

described in Chapter 1 (Section 1.3). The intention to

under the ITT columns for the experimental and Section

treat (ITT) estimates reflect the effects of being offered

8 treatments respectively. Next to the ITT estimates are

the chance to relocate with an MTO housing voucher.

the estimated effects of the TOT and accompanying

For example, the ITT estimate for the experimental

standard errors for that treatment group.

treatment compares the average outcome for everyone

assigned to the experimental group (regardless of whether The ITT estimates capture the effect of what is most they moved or not with an MTO voucher) with the

directly under the control of policymakers—offering

average outcome for everyone assigned to the control

families the opportunity to relocate with an MTO

group. The ITT estimate for the Section 8 treatment

voucher. The TOT estimate is of additional interest

group is analogously defined. Mechanically, the ITT

because the share of families that would relocate if

estimates are calculated by running an ordinary least

offered an MTO-like voucher could differ across settings.

The ITT estimates require no statistical assumptions

15 Respondents were also asked how much they agree with the follow-

other than that random assignment was carried out

ing statements: I feel good about myself, I am able to do things as well

correctly. The TOT estimates require some additional

as most other people, and chance and luck are very important for what

happens in my life. Possible answers are strongly agree, agree, disagree,

assumptions, the most important of which is that

or strongly disagree. Finally, youth were asked how they think other

assignment to either of the treatment groups has no

people see them in terms of popularity, athleticism, and academically

(possible responses were: never, sometimes, or all the time).

effect on those who do not move through MTO (the

51

Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks noncompliers). All of the tables that follow in the report

the course of the study period, underscoring the extreme

show both the ITT and TOT effects, allowing readers to

level of economic disadvantage of the study sample.

choose their preferred estimate. The text in most of the

Typically, control group families would deal with the

chapters focuses on the ITT effects. In this chapter, we

problem by doubling up with another family (19 percent

devote additional discussion to the TOT effects as well,

of control families did this at least once during the study

which are particularly relevant for thinking about the

period), although around 5 percent of control group

strength of MTO’s “treatment dose” on the housing and

families were literally homeless at some point. Most

neighborhood conditions of participating families.

families who were homeless were homeless just once, as

revealed by the close correspondence between the share of

Over the course of the study period, which follows

control families who had been homeless and the average

families on average for 12 years after baseline, adults in

number of homelessness spells among the control group.

the control group moved just over two (2.17) times on

There were no statistically significant differences between

average (as shown in Exhibit 2.2). For many of control

the control and treatment groups in the likelihood of

group households, at least one of these moves was

being literally homeless; however, the Section 8 group

triggered by the demolition of their initial public housing was more likely to report having been doubled-up than through HOPE VI or a related program. Nonetheless,

the control group (ITT effect of 4.5 percentage points).

the total number of moves we observe for MTO control

The lack of detectable MTO impacts on literal

group families over the study period is, if anything,

homelessness might initially seem contradictory with the

slightly lower than what we would predict from national

findings from the Welfare to Work (WtW) voucher study,

data, which indicate that around 23 percent of low-

which found that housing vouchers reduced the risk of

income African-American and Hispanic families move

homelessness by around one-third of the control group

each year (Schachter, 2004). If 23 percent of control

mean (Mills et al., 2006). But it is important to keep

group families in MTO moved each year, then over the

in mind that the “treatment” studied in WtW—giving

course of the 12-year follow-up period, we would expect

housing vouchers to families who were initially living in

2.76 moves. Mobility rates might be somewhat lower for

the private housing market without a subsidy—is quite

the MTO control group than for national samples given

different from that examined in MTO, which involved

that, as noted above, control families may be relatively

offering housing vouchers to families who already had a

more tied in place by their project-based housing

housing subsidy, in the form of a public housing or other

subsidies.

project-based assisted housing unit. The importance

of the different control group conditions can be seen

Assignment to either the experimental or Section 8

in the much higher rate of homelessness among the

group (the ITT effect) increases the average number of

(unsubsidized) control group in the WtW study. There,

moves for families by about .6 during the 10–15 year

approximately one-quarter of respondents did not have a

study period. Among those who actually moved through

place to stay during the last year prior to the final survey,

MTO, the TOT effect of the experimental treatment

which is higher than the rate of homelessness that MTO

was 1.16 additional moves for the experimental group

control-group families experienced cumulatively over the

compliers and 1.01 additional moves for Section 8

entire 10–15-year follow-up period.

compliers (both statistically significant at the standard 5

percent cut-off ). Learning more about the impacts of the Exhibit 2.3 shows that at the time of the long-term MTO intervention on distances moved by families is an

survey, 85 percent of adults in the control group

important question for future research.

were living in rental housing (including both public

housing or private market) and 12 percent had become

Perhaps the most striking finding in Exhibit 2.2 is that

homeowners, which is more than double the prevalence

approximately 20 percent of control group families

of homeownership reported at the interim study from

report having been without a home of their own (that is,

four to seven years after baseline (Orr et al., 2003,

doubled-up or literally homeless) at some time during

Exhibit C3.2, p. C–16). Being in the Section 8 treatment

52

group increased the likelihood of renting (the ITT effect

using a voucher or certificate (25 percent of the total control

was 4 percentage points, the TOT effect was 6 percentage group). Another 7.2 percent of the total control group was points, compared with a control mean of 85 percent) and

receiving other forms of housing assistance, such as the

reduced the likelihood of owning a home compared with

Rental Assistance Payment and Moderate Rehabilitation

the control group (ITT and TOT effects of – 4 and – 6

program. Families assigned to the experimental and Section

percentage points, respectively, compared with a control group

8 groups were 16 and 19 percentage points more likely than

homeownership rate of 12 percent). The effects of being

controls to be receiving a housing voucher at the time of

assigned to the experimental group on renting and owning are

the long-term follow-up survey (the TOT effects for both

qualitatively similar to those of the Section 8 group, but are not groups are nearer 30 percentage points).

statistically significant at the 5 percent level.

Exhibit 2.4 shows that monthly housing costs (rent or

Exhibit 2.3 also shows the reasons that survey respondents

mortgage plus utilities) as reported by families in the long-

left their previous address and moved to their current

term surveys averaged around $680 for controls. Exhibit 2.4

neighborhood. These results are somewhat complicated to

also shows that nearly 68 percent of control group adults

interpret because for most families their most recent move

had a housing-cost burden greater than 30 percent, in line

was a secondary move (see Exhibit 2.2). Therefore, for

with the national statistics cited above. Given that MTO

example, most experimental group families will have already did not have statistically significant effects on the likelihood made their initial MTO move into a safer neighborhood

of receiving any sort of housing assistance (public housing

(discussed further below), and so their most recent move

plus housing vouchers), it is perhaps not surprising that

will use that safer address as the starting location. Among

MTO had no detectable impacts on the amount of money

the control group, 16 percent of families reported that

that families spent out-of-pocket on housing or on housing

getting away from gangs or drugs was the most important

burdens. However, the confidence intervals around these

reason they moved from their previous address. Those in the estimates do not allow us to rule out effects of MTO moves experimental group, in contrast, were nearly 3 percentage

on housing costs that may be as large as $100 per month.

points less likely to say they left their most recent address

We do find that, compared with the control group adults,

for reasons of safety (the TOT effect is around 7 percentage

those in the experimental group were less likely to report

points). Exhibit 2.3 also shows that when voucher-holders

being late with their rent or mortgage payments, but they

made their most recent move, they were more likely than

were more likely to have had trouble paying their utility

control group members to have moved because of problems

bills on time while renting.

navigating the private housing market, such as landlord

problems or their house or apartment was sold.

Although MTO may not have had detectable effects on

housing costs for families, the program did improve the

About 62 percent of the control group adults were receiving quality of housing that families found. Exhibit 2.5 shows housing assistance at the time of the long-term follow-up.

that adults assigned to the experimental group were about

This estimate does not seem to be sensitive to whether we

5 percentage points more likely than controls to report

measure receipt of housing assistance from a combination

that their housing conditions were excellent or good (TOT

of administrative and survey data, which is what we report

effect of approximately 11 percentage points compared

in Exhibit 2.4 using the methods described above, or if

with 57 percent of control group adults). Adults in the two

we calculate it from only survey self-reports (not shown).

treatment groups also reported fewer specific problems with

Receipt of any sort of housing assistance did not differ

their housing units. For example, 52 percent of control

statistically at the 5 percent level between the controls and

group adults reported that their current housing units

the experimental group or between the controls and the

had problems with vermin. Adults in the experimental or

Section 8 group. However, MTO did change the type of

Section 8 group were 6 and 9 percentage points less likely

housing program in which families were participating. Of

than controls to report having problems with vermin (TOT

those in the control group who were receiving housing

effects of 13 or 14 percentage points). Results for additional

assistance, about one-half were currently in public housing

housing quality measures are shown in Supplemental

(30 percent of the total control group) and 40 percent were

Exhibit 2.1.

53

Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks EXHIBIT 2.2. ADULT MOBILITY AND HOMELESSNESS

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

MOBILITY

Number of moves since

2.165

0.561*

1.157*

0.629*

1.014*

3,273

Random Assignment [SR]

(0.073)

(0.151)

(0.096)

(0.155)

Leased an apartment using

0.000

0.484*

0.639*

3,273

an MTO voucher

(0.014)

(0.022)

HOMELESSNESS

AFTER RANDOM

ASSIGNMENT

Without a home of their own

0.214

0.020

0.040

0.056*

0.090*

3,273

at least once ("doubled-up"

(0.017)

(0.036)

(0.024)

(0.039)

or "literally homeless") [SR]

"Doubled-up" at least

0.191

0.008

0.017

0.045*

0.073*

3,273

once (stayed with family or

(0.017)

(0.034)

(0.023)

(0.037)

friends) [SR]

"Literally homeless" at least

0.049

0.009

0.018

0.018

0.030

3,273

once [SR]

(0.009)

(0.019)

(0.014)

(0.022)

Number of times "doubled-

0.257

0.014

0.028

0.074*

0.120*

3,273

up" [SR]

(0.026)

(0.053)

(0.034)

(0.056)

Number of times "literally

0.061

0.003

0.006

0.024

0.038

3,273

homeless" [SR]

(0.012)

(0.025)

(0.020)

(0.032)

Notes: * = p < 0 .05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.

Measures: "Doubled up" is defined as staying with friends or family when respondents were without a home of their own. "Literally homeless" is defined as staying at a shelter, on the street, abandoned building, car/van, movie theater/laundromat, and so on, when respondents were without a home of their own.

54

EXHIBIT 2.3. ADULT HOUSING TENURE AND REASONS FOR MOVING

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

CURRENT HOUSING TENURE

Currently rents house or apartment [SR]

0.850

0.024

0.049

0.040*

0.064*

3,265

(0.015)

(0.032)

(0.020)

(0.032)

Currently owns house or apartment [SR]

0.120

– 0.024~

– 0.050~

– 0.039*

– 0.063*

3,265

(0.014)

(0.029)

(0.018)

(0.029)

Currently doubled-up and pays part of rent/

0.015

– 0.003

– 0.006

– 0.001

– 0.002

3,266

mortgage [SR]

(0.005)

(0.010)

(0.008)

(0.012)

Currently doubled-up and does NOT pay part

0.009

0.005

0.010

– 0.003

– 0.005

3,267

of rent/mortgage [SR]

(0.004)

(0.009)

(0.005)

(0.009)

Currently lives in a group shelter [SR]

0.005

– 0.003

– 0.006

0.000

0.000

3,268

(0.002)

(0.005)

(0.004)

(0.007)

REASONS FOR MOVING FROM

PREVIOUS ADDRESS

To get a bigger or better apartment [SR]

0.259

– 0.058*

– 0.116*

– 0.034

– 0.054

3,168

(0.018)

(0.037)

(0.024)

(0.038)

To get away from drugs or gangs [SR]

0.159

– 0.033*

– 0.067*

– 0.051*

– 0.080*

3,168

(0.015)

(0.030)

(0.020)

(0.031)

Due to problems with landlord [SR]

0.073

0.022~

0.045~

0.022

0.035

3,168

(0.012)

(0.025)

(0.015)

(0.024)

Because house or apartment was sold [SR]

0.051

0.036*

0.074*

0.043*

0.068*

3,168

(0.011)

(0.022)

(0.015)

(0.024)

REASONS FOR MOVING TO CURRENT

NEIGHBORHOOD

For better or bigger living space [SR]

0.201

– 0.034*

– 0.071*

0.005

0.008

3,261

(0.017)

(0.034)

(0.023)

(0.036)

Because it is safer [SR]

0.156

0.010

0.020

0.036

0.057

3,261

(0.016)

(0.033)

(0.022)

(0.035)

Because unit is affordable [SR]

0.134

– 0.037*

– 0.076*

– 0.048*

– 0.076*

3,257

(0.015)

(0.030)

(0.018)

(0.029)

Because it has fewer problems with gangs or

0.085

– 0.010

– 0.020

– 0.013

– 0.021

3,261

drugs [SR]

(0.011)

(0.023)

(0.015)

(0.025)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.

55

Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks EXHIBIT 2.4. ADULT HOUSING ASSISTANCE, COSTS, AND STABILITY

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

CURRENT HOUSING ASSISTANCE

Any housing assistance [SR, HA]

0.620

0.026

0.054

0.045~

0.072~

3,273

(0.021)

(0.043)

(0.027)

(0.044)

Currently in public housing [SR, HA]

0.296

– 0.107*

– 0.220*

– 0.110*

– 0.177*

3273

(0.017)

(0.035)

(0.022)

(0.036)

Receiving housing voucher or certificate

0.252

0.159*

0.328*

0.194*

0.312*

3,273

[SR, HA]

(0.019)

(0.040)

(0.026)

(0.042)

Using other types of housing assistance

0.072

– 0.026*

– 0.054*

– 0.039*

– 0.063*

3,273

[SR, HA]

(0.010)

(0.020)

(0.013)

(0.020)

CURRENT HOUSING COSTS

Total housing cost (2009 dollars), monthly

$678.73

19.50

39.67

– 6.26

– 10.00

3,180

[SR]

(23.30)

(47.39)

(30.73)

(49.10)

Cost of rent or mortgage (2009 dollars),

$493.04

3.16

6.43

– 24.44

– 39.05

3,180

monthly [SR]

(20.16)

(41.01)

(26.14)

(41.76)

Household is housing burdened (monthly

0.676

0.011

0.022

0.020

0.032

3,169

housing cost/income > 30%) [SR]

(0.020)

(0.041)

(0.027)

(0.043)

Household is severely housing burdened

0.426

– 0.004

– 0.007

0.017

0.027

3,169

(monthly housing cost/income > 50%) [SR]

(0.021)

(0.043)

(0.029)

(0.046)

HOUSING STABILITY

More than 15 days late in paying rent/

0.258

– 0.055*

– 0.112*

– 0.067*

– 0.106*

3,242

mortgage at least once during past 12

(0.018)

(0.037)

(0.024)

(0.038)

months [SR]

Received eviction/foreclosure threat due

0.124

– 0.017

– 0.035

– 0.033~

– 0.053~

3,261

to nonpayment at least once during the

(0.014)

(0.028)

(0.018)

(0.028)

past 12 months [SR]

LATE UTILITY PAYMENTS (RENTERS)

Rents and was more than 15 days late in

0.333

0.052*

0.108*

0.033

0.053

3,236

paying utilities at least once during the

(0.020)

(0.041)

(0.027)

(0.043)

past 12 months [SR]

Rents and received shut-off notice due to

0.235

0.054*

0.112*

0.050*

0.081*

3,236

nonpayment at least once in the past 12

(0.018)

(0.038)

(0.025)

(0.040)

months [SR]

Rents and utilities have been shut off

0.052

0.021*

0.043*

0.016

0.026

3,236

for nonpayment at least once in past 12

(0.010)

(0.022)

(0.014)

(0.022)

months [SR]

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report, HA = Housing Assistance data from HUD.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.

56

EXHIBIT 2.5. ADULT HOUSING QUALITY

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

OVERALL RATING OF CURRENT

HOUSING CONDITION

Rates current housing as excellent or

0.570

0.053*

0.109*

0.031

0.050

3,267

good [SR]

(0.021)

(0.044)

(0.029)

(0.046)

CURRENT HOUSING PROBLEMS

Vermin (mice, rats, and/or cockroaches) is

0.517

– 0.063*

– 0.130*

– 0.088*

– 0.141*

3,266

a big or small problem [SR]

(0.021)

(0.043)

(0.028)

(0.045)

Heating or plumbing that does not work is

0.374

– 0.028

– 0.059

– 0.052~

– 0.083~

3,258

a big or small problem [SR]

(0.020)

(0.042)

(0.027)

(0.043)

Peeling paint or plaster is a big or small

0.466

– 0.091*

– 0.187*

– 0.120*

– 0.192*

3,265

problem [SR]

(0.021)

(0.044)

(0.028)

(0.044)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.

2.5 LONG-TERM IMPACTS ON

poverty rates were 9 and 7 percentage points lower,

NEIGHBORHOOD CHARACTERISTICS

respectively. The TOT effects of the experimental and

As Chapter 1 noted, the initially large differences

Section 8 treatments on average tract poverty rates

between treatment and control groups in census tract

equaled 18 and 11 percentage points.

poverty rates narrowed over time in large part because of

declines in the control group’s average tract poverty rate.

Exhibit 2.6 shows that MTO changed a variety of other

Exhibit 2.6 shows the effects of MTO on the average

measures of neighborhood advantage and disadvantage as

(duration-weighted) census tract characteristics that

well. For example, 54 percent of households in the census

families experienced during the entire 10- to 15-year

tracts where control group families lived were headed

study period. As noted above, we calculate each family’s

by females; the figure was 8 percentage points lower

average tract characteristics by first estimating the

for those in the experimental group (and 16 percentage

characteristics of the census tract when the family was

points lower for those in the experimental group who

living there, using information from the 1990 and 2000

moved with an MTO voucher). Median household

decennial census together with data from the 2005–09

income was almost $19,000 higher in the census tracts

ACS, and then average over all the family’s addresses

where experimental group compliers lived, a sizable

during the study period, weighting each address by

change compared with the average control group census

the share of the study period the family spends at that

tract median income of $27,800.

address. (Results for the youth sample are presented in

Supplemental Exhibit 2.2.)

On the other hand, MTO moves had more modest

impacts on neighborhood racial segregation. Among

The average poverty rate of the census tracts in which the control group members, 88 percent of residents in their control group lived over the study period was about 40

census tracts were members of racial and ethnic minority

percent poor. For families assigned to the experimental

groups. The figure was slightly lower for those in the

and Section 8 groups, duration-weighted average tract

Section 8 group. Families in the experimental group

57

Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks experienced a statistically significant decline in census

in their neighborhoods during the day (TOT effects of

tract minority share equal to around 6 percentage points

around 7 percentage points), compared with a control

(TOT effect of nearly 13 percentage points). However,

mean of 80 percent. Adults in the experimental and

these families were still living in census tracts that were

Section 8 groups were 7 percentage points less likely

overwhelmingly populated by minority residents.

than controls to say that police do not come when called

compared with 42 percent of controls and were also less

Exhibit 2.7 reports results for MTO’s impacts on the

likely to see drugs sold or used in the neighborhood

same set of census tract characteristics shown in Exhibit

(TOT effects of 14 and 12 percentage points). Although

2.6, but now measured at the point in time just before

MTO did not change feelings of safety among male

we started the long-term survey data collection (May

youth, relocation did increase feelings of safety among

2008) rather than duration-weighted averages in Exhibit

female youth. MTO moves also reduced the exposure

2.6. Most of these MTO effects on neighborhood

of female youth in the experimental group to unwanted

sociodemographic characteristics are still statistically

sexual attention (Exhibit 2.10). The qualitative

significant, particularly in the experimental group.

interviews with MTO families done at three of the

However, given the gradual convergence over time

MTO demonstration sites also found important impacts

between the two treatment groups and controls in their

of MTO moves in reducing “female fear,” the concern

average neighborhood conditions, the treatment-control

that many female youth have about sexual harassment,

differences in tract characteristics for each family’s May

coercion and rape (Popkin, Leventhal and Weismann,

2008 address tend to be smaller in magnitude than the

2010; see also Briggs, Popkin, and Goering, 2010).

duration-weighted averages.

Despite the improved feelings of safety, MTO had no

statistically significant effects on self-reported crime

The MTO families themselves report being more satisfied victimization rates in the long-term surveys among with their neighborhoods at the time of the long-term

adults, or among all youth pooled together. These results

survey (as was also the case in the interim survey data).

differ from what was found in the interim survey data,

Exhibit 2.8 shows that approximately 52 percent of

which showed declines in crime victimization (Orr et

adults in the control group said they were satisfied or

al., 2003). Somewhat surprisingly, the experimental

very satisfied with their current neighborhood; the

group female youth—unlike adults or male youth—

figure was 9 percentage points higher for adults in

reported lower levels of household crime victimization,

the experimental group (TOT effect of 19 percentage

which raises the possibility that female youth may not

points), and 8 percentage points higher for adults in the

share information about their own crime victimization

Section 8 group (TOT effect of 13 percentage points).

experiences with others in the home.

Adults in the experimental and Section 8 group also

tended to report fewer signs of disorder than adults in the 2.6 LONG-TERM IMPACTS ON SOCIAL

control group, particularly for problems like trash and

NETWORKS

graffiti (Exhibit 2.8). The interviewer observations for

MTO also helped families move into neighborhoods

these neighborhoods tend to confirm the reports (Exhibit where neighbors were more willing to work together 2.9). However, MTO did not seem to have any detectable to support shared norms, a measure of informal impact on access to private or public transportation (last

social control that previous research suggests may

row of Exhibit 2.8). (See also Supplemental Exhibit 2.3

be particularly important in improving the lives of

for additional results.)

neighborhood residents (Sampson, Raudenbush, and

Earls, 1997; Sampson, Morenoff, and Gannon-Rowley,

MTO also led to persistent improvements in

2002). Exhibit 2.11 shows that 59 percent of control

neighborhood safety, as measured by self-reports in

group adults reported that neighbors were likely or very

our long-term surveys (Exhibit 2.10). Adults in the

likely to do something if neighborhood children were

experimental and Section 8 groups were around 4

spray painting buildings with graffiti. The share is nearly

percentage points more likely than controls to feel safe

8 points higher for those adults in the experimental

58

group (and approximately 16 percentage points higher

Supplemental Exhibits 2.5–2.7. More than 96 percent

for the experimental group compliers, that is, the TOT

of control group youth reported having at least one close

effect). Adults in the Section 8 group were more likely

friend. Experimental and Section 8 youth (particularly

than controls to say neighborhood adults would be

male youth) were slightly less likely than controls to

likely or very likely to do something about local children

report having at least one close friend. Experimental

skipping school.

and Section 8 youth were also less likely than

controls to report visiting a friend from their original

The MTO experimental intervention increased the

neighborhoods. Female youth in the experimental group

social connections to people who had completed

were less likely to have friends who have ever used

college and may have increased connections to people

drugs than are girls in the control group, but otherwise,

employed full-time, with no net adverse impacts on

peers of MTO youth had similar characteristics across

social isolation. For example, 53 percent of controls said

treatment groups (Supplemental Exhibit 2.6). Youth

they have a friend who graduated from college, a figure

across treatment groups shared similar reports on how

that was 7 percentage points higher for those in the

good they felt about themselves and whether they

experimental group (see Supplemental Exhibit 2.4 for

considered themselves as speaking proper English or

additional results). These findings are consistent with

if others would describe their behavior as “ghetto”

those from the MTO interim study and with qualitative

(Supplemental Exhibit 2.7). We also found suggestive

investigations of housing mobility programs in Yonkers,

evidence (though somewhat inconsistent across treatment

New York (Orr et al., 2003; Briggs, 1998).

groups and gender) that youth in the two treatment

groups may be less likely than controls to say that other

MTO effects on the social connections of youth

people see them as popular or as good students.

differed somewhat for males and females, as shown in

59

Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks EXHIBIT 2.6. AVERAGE (DURATION-WEIGHTED) NEIGHBORHOOD CENSUS TRACT CHARACTERISTICS OF ALL

OF THE ADULTS’ ADDRESSES FROM RANDOM ASSIGNMENT THROUGH MAY 2008

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

Share of persons in tract who are

0.396

– 0.089*

– 0.184*

– 0.069*

– 0.111*

3,270

poor [CEN]

(0.006)

(0.012)

(0.007)

(0.011)

Share of persons who are above

0.361

0.108*

0.223*

0.068*

0.110*

3,270

200% of the poverty line [CEN]

(0.006)

(0.013)

(0.008)

(0.012)

Share minority (which is, non-

0.880

– 0.061*

– 0.125*

– 0.018*

– 0.029*

3,270

White or Hispanic) [CEN]

(0.007)

(0.014)

(0.009)

(0.015)

Share Hispanic [CEN]

0.315

– 0.038*

– 0.078*

– 0.003

– 0.005

3,270

(0.006)

(0.011)

(0.007)

(0.012)

Share Black [CEN]

0.535

– 0.034*

– 0.069*

– 0.019~

– 0.031~

3,270

(0.008)

(0.016)

(0.011)

(0.017)

Share of households that are

0.541

– 0.077*

– 0.159*

– 0.058*

– 0.093*

3,270

single female-headed families

(0.006)

(0.012)

(0.007)

(0.011)

[CEN]

Share college graduates [CEN]

0.161

0.042*

0.087*

0.018*

0.029*

3,270

(0.004)

(0.008)

(0.005)

(0.008)

Share with more than high school

0.318

0.063*

0.130*

0.034*

0.055*

3,270

education [CEN]

(0.005)

(0.010)

(0.006)

(0.009)

Share employed, civilian [CEN]

0.808

0.036*

0.075*

0.031*

0.050*

3,270

(0.003)

(0.006)

(0.003)

(0.005)

Share workers in managerial or

0.236

0.035*

0.072*

0.012*

0.019*

3,270

professional occupations [CEN]

(0.004)

(0.008)

(0.005)

(0.008)

Median household income (2009

$27,808.85

9,148.91*

1,8848.48*

5,600.18*

9,027.10*

3,270

dollars) [CEN]

(544.97)

(1122.75)

(660.90)

(1065.32)

Per capita income (2009 dollars)

$14,779.62

3,267.36*

6,731.38*

1,492.56*

2,405.91*

3,270

[CEN]

(266.65)

(549.35)

(340.51)

(548.88)

Share owner-occupied housing

0.225

0.103*

0.213*

0.072*

0.115*

3,270

units [CEN]

(0.007)

(0.015)

(0.009)

(0.014)

Share households on public

0.176

– 0.047*

– 0.096*

– 0.039*

– 0.064*

3,270

assistance [CEN]

(0.003)

(0.007)

(0.004)

(0.006)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: CEN = 1990 and 2000 decennial census data as well as the 2005–09 American Community Survey (

ACS).

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.

Measures: Census tract characteristics are linearly interpolated from the 1990 decennial census, 2000 decennial census, and 2005–09 ACS. Each census outcome is also duration-weighted by the amount of time the respondent spent at each address.

60

EXHIBIT 2.7. CENSUS TRACT CHARACTERISTICS OF ADULT'S CURRENT NEIGHBORHOOD (MAY 2008) CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

Share of persons in tract who

0.313

– 0.038*

– 0.079*

– 0.029*

– 0.046*

3,206

are poor [CEN]

(0.007)

(0.014)

(0.009)

(0.014)

Share of persons who are above

0.444

0.046*

0.095*

0.026*

0.043*

3,206

200% of the poverty line [CEN]

(0.008)

(0.017)

(0.011)

(0.017)

Share minority (which is non-

0.841

– 0.037*

– 0.075*

– 0.003

– 0.005

3,206

White or Hispanic) [CEN]

(0.010)

(0.021)

(0.014)

(0.022)

Share Hispanic [CEN]

0.303

– 0.015~

– 0.030~

– 0.001

– 0.002

3,206

(0.008)

(0.017)

(0.011)

(0.018)

Share Black [CEN]

0.498

– 0.024*

– 0.049*

– 0.005

– 0.007

3,206

(0.011)

(0.022)

(0.015)

(0.024)

Share of households which are

0.487

– 0.042*

– 0.086*

– 0.021*

– 0.034*

3,206

single female-headed families

(0.008)

(0.017)

(0.010)

(0.017)

[CEN]

Share college graduates [CEN]

0.209

0.021*

0.043*

0.009

0.014

3,206

(0.006)

(0.012)

(0.007)

(0.012)

Share with more than high

0.388

0.028*

0.059*

0.017*

0.027*

3,206

school education [CEN]

(0.007)

(0.013)

(0.008)

(0.013)

Share employed, civilian [CEN]

0.848

0.016*

0.033*

0.011*

0.018*

3,206

(0.003)

(0.006)

(0.004)

(0.006)

Share workers in managerial or

0.241

0.016*

0.033*

0.001

0.002

3,206

professional occupations [CEN]

(0.005)

(0.011)

(0.007)

(0.011)

Median household income in

$33,520.69

4,416.64*

9,099.20*

2,544.68*

4,121.36*

3,206

2009 dollars [CEN]

(754.05)

(1553.50)

(955.14)

(1546.94)

Per capita income (2009 dollars)

$17,134.84

1474.30*

3,037.36*

581.65

942.04

3,206

[CEN]

(389.69)

(802.84)

(499.28)

(808.63)

Share owner-occupied housing

0.329

0.048*

0.099*

0.030*

0.049*

3,206

units [CEN]

(0.010)

(0.021)

(0.013)

(0.021)

Share households on public

0.100

– 0.019*

– 0.039*

– 0.014*

– 0.023*

3,206

assistance [CEN]

(0.003)

(0.006)

(0.004)

(0.006)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: CEN = 1990 and 2000 decennial census data as well as the 2005–09 American Community Survey (ACS).

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.

Measures: Census tract characteristics are linearly interpolated from the 1990 decennial census, 2000 decennial census, and 2005–09 ACS.

61

Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks EXHIBIT 2.8. ADULT NEIGHBORHOOD CHARACTERISTICS AND SATISFACTION

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

NEIGHBORHOOD SATISFACTION

Very satisfied or satisfied with current

0.515

0.094*

0.194*

0.082*

0.131*

3,265

neighborhood [SR]

(0.022)

(0.044)

(0.028)

(0.045)

NEIGHBORHOOD

CHARACTERISTICS

Litter/trash/graffiti/abandoned

0.720

– 0.073*

– 0.151*

– 0.081*

– 0.128*

3,266

buildings are a big or small problem in

(0.020)

(0.041)

(0.027)

(0.043)

neighborhood [SR]

Public drinking/loitering are a big or

0.625

– 0.051*

– 0.105*

– 0.020

– 0.031

3,258

small problem in neighborhood [SR]

(0.021)

(0.044)

(0.028)

(0.045)

TRANSPORTATION

Has working car or lives less than 15

0.942

– 0.001

– 0.002

– 0.002

– 0.003

3,254

minutes to public transportation [SR]

(0.010)

(0.021)

(0.014)

(0.023)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.

62

EXHIBIT 2.9. INTERVIEWER OBSERVATIONS OF THE ADULT'S CURRENT NEIGHBORHOOD

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

BUILDINGS ON RESPONDENT'S BLOCK

Most buildings on this block are well kept

0.348

0.024

0.049

0.007

0.011

3,199

[NWA]

(0.021)

(0.044)

(0.028)

(0.045)

There is evidence of graffiti that has been

0.206

– 0.028~

– 0.057~

– 0.040~

– 0.063~

3,100

painted over [NWA]

(0.017)

(0.035)

(0.021)

(0.033)

There are metal bars on windows above

0.436

– 0.022

– 0.045

0.015

0.025

3,203

the basement level on at least one other

(0.020)

(0.042)

(0.027)

(0.044)

building on this block [NWA]

There are abandoned buildings on this

0.172

– 0.005

– 0.011

– 0.020

– 0.032

3,245

block [NWA]

(0.016)

(0.033)

(0.021)

(0.034)

There is a green grocer, delicatessen,

0.210

– 0.012

– 0.025

– 0.022

– 0.036

3,245

supermarket, or grocery store on this

(0.017)

(0.035)

(0.021)

(0.033)

block [NWA]

There are no store-front churches,

0.592

0.013

0.028

0.035

0.056

3,245

green grocers or delis, supermarkets or

(0.021)

(0.044)

(0.028)

(0.044)

grocery stores, fast-food or take-out, or

abandoned, burned out, or boarded up

houses or buildings on this block [NWA]

STREET CONDITION

Street is in very good/moderate condition

0.654

0.012

0.026

0.015

0.024

3,207

[NWA]

(0.021)

(0.043)

(0.027)

(0.044)

Street is in poor condition [NWA]

0.084

– 0.010

– 0.021

– 0.009

– 0.014

3,207

(0.011)

(0.023)

(0.016)

(0.026)

Moderate-heavy amount of litter on the

0.217

– 0.041*

– 0.085*

– 0.023

– 0.036

3,194

streets [NWA]

(0.017)

(0.036)

(0.023)

(0.036)

Trees are on most/all of the block [NWA]

0.422

0.011

0.022

0.044

0.071

3,198

(0.021)

(0.044)

(0.028)

(0.045)

No signs visible for neighborhood or

0.540

– 0.015

– 0.031

0.001

0.002

3,245

crime watch, security warnings, alcohol

0.022

0.045

0.029

0.047

advertisements, or houses “For Sale” on

(0.022)

(0.045)

(0.029)

(0.047)

this block [NWA]

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: NWA = neighborhood walk-around conducted by interviewers.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.

63

Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks EXHIBIT 2.10. ADULT AND YOUTH NEIGHBORHOOD SAFETY AND CRIME VICTIMIZATION

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

ADULT RESPONSES

Feels safe or very safe during the day

0.804

0.036*

0.074*

0.045*

0.072*

3,262

[SR]

(0.016)

(0.034)

(0.021)

(0.034)

Feels safe or very safe during the night

0.596

0.043*

0.088*

0.073*

0.117*

3,246

[SR]

(0.021)

(0.043)

(0.027)

(0.043)

Police don't respond when called [SR]

0.420

– 0.067*

– 0.138*

– 0.075*

– 0.118*

3,146

(0.021)

(0.044)

(0.028)

(0.045)

Saw drugs being sold or used in the

0.310

– 0.062*

– 0.128*

– 0.057*

– 0.090*

3,249

neighborhood in the past 30 days [SR]

(0.019)

(0.039)

(0.025)

(0.040)

Any household member was a crime

0.184

– 0.022

– 0.046

0.025

0.040

3,241

victim in the last 6 months [SR]

(0.016)

(0.033)

(0.022)

(0.035)

YOUTH RESPONSES

Feels safe or very safe during the day [SR]

All

0.801

0.018

0.037

– 0.012

– 0.018

4,863

(0.016)

(0.033)

(0.018)

(0.026)

Female

0.784

0.045*

0.090*

0.019

0.030

2,478

(0.022)

(0.045)

(0.025)

(0.039)

Male

0.817

– 0.009

– 0.019

– 0.043~

– 0.062~

2,385

(0.022)

(0.047)

(0.025)

(0.035)

Feels safe or very safe during the night [SR]

All

0.540

0.035~

0.074~

0.019

0.028

4,862

(0.021)

(0.043)

(0.022)

(0.033)

Female

0.486

0.052~

0.104~

0.067*

0.105*

2,478

(0.028)

(0.056)

(0.030)

(0.048)

Male

0.591

0.020

0.043

– 0.030

– 0.043

2,384

(0.028)

(0.060)

(0.031)

(0.043)

Saw drugs being sold or used in the neighborhood in the past 30 days [SR]

All

0.388

– 0.056*

– 0.116*

– 0.041*

– 0.062*

4,879

(0.020)

(0.040)

(0.021)

(0.031)

Female

0.393

– 0.087*

– 0.174*

– 0.085*

– 0.134*

2,486

(0.026)

(0.053)

(0.029)

(0.046)

Male

0.384

– 0.026

– 0.056

0.002

0.003

2,393

(0.026)

(0.057)

(0.028)

(0.040)

Any household member was a crime victim in the last 6 months (only asked of youth ages 13–20) [SR]

All

0.246

– 0.027

– 0.056

– 0.020

– 0.030

4,618

(0.018)

(0.038)

(0.020)

(0.029)

Female

0.255

– 0.059*

– 0.119*

– 0.044~

– 0.068~

2,358

(0.024)

(0.047)

(0.026)

(0.040)

Male

0.238

0.005

0.011

0.002

0.003

2,260

(0.025)

(0.053)

(0.027)

(0.038)

64

EXHIBIT 2.10. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

Gangs are present in neighborhood or school (only asked of youth ages 13–20) [SR]

All

0.672

– 0.022

– 0.045

0.000

0.000

4,597

(0.019)

(0.040)

(0.021)

(0.031)

Female

0.673

– 0.025

– 0.051

– 0.051~

– 0.079~

2,347

(0.026)

(0.052)

(0.029)

(0.045)

Male

0.671

– 0.019

– 0.041

0.052~

0.074~

2,250

(0.028)

(0.059)

(0.030)

(0.042)

FEMALE YOUTH ONLY

People have made unwanted or rude

0.749

0.008

0.017

0.026

0.041

2,584

comments to youth at least a few times

(0.023)

(0.046)

(0.024)

(0.038)

a year [SR]

People have given unwanted sexual

0.596

– 0.052*

– 0.104*

– 0.045

– 0.070

2,577

attention to youth at least a few times

(0.025)

(0.051)

(0.028)

(0.044)

a year [SR]

Is afraid to go places because of

0.401

– 0.003

– 0.005

– 0.030

– 0.047

2,582

unwanted attention or pressure at least

(0.026)

(0.051)

(0.028)

(0.044)

a few times a year [SR]

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed; Youth long-term survey. Interviewed youth ages 10 to 20 as of December 31, 2007.

Measures: "Any household member was a crime victim in the past 6 months" refers to a series of questions in which the youth is asked if anyone in their household: had their purse, wallet, or jewelry snatched from them, threatened with a gun or knife, was beaten or assaulted, was stabbed or shot, or if anyone attempted to break into their home.

65

Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks EXHIBIT 2.11. NEIGHBORHOOD SOCIAL PROCESS AND ADULT SOCIAL NETWORKS

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

COLLECTIVE EFFICACY

Likely or very likely neighbors would do

0.346

0.029

0.059

0.075*

0.119*

3,250

something if neighborhood kids were

(0.021)

(0.043)

(0.028)

(0.045)

skipping school and hanging out on a

street corner [SR]

Likely or very likely neighbors would do

0.589

0.076*

0.156*

0.042

0.067

3,255

something if they saw kids spray-painting

(0.021)

(0.043)

(0.028)

(0.045)

graffiti on a local building [SR]

FRIENDS

Has three or more close friends [SR]

0.432

0.006

0.013

– 0.026

– 0.042

3,265

(0.021)

(0.044)

(0.028)

(0.045)

Has at least one close friend who

0.532

0.071*

0.145*

0.007

0.010

3,203

graduated from college [SR]

(0.021)

(0.044)

(0.029)

(0.046)

Has at least one close friend who works

0.742

0.033~

0.068~

– 0.020

– 0.032

3,261

full-time [SR]

(0.019)

(0.038)

(0.025)

(0.041)

Has at least one close friend who

0.413

0.022

0.046

– 0.017

– 0.027

3,266

is a different race or ethnicity from

(0.021)

(0.043)

(0.027)

(0.043)

respondent [SR]

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.

66

2.7 INTERPRETATION OF RESULTS

instead were involved in the housing search process with

MTO offered housing vouchers to families living in

assistance from a local non-profit. What we cannot tell

public housing at baseline, with the primary goal of

from the MTO data is the degree to which MTO families

helping them move into lower-poverty areas. The results

moved to low-poverty, mostly-minority neighborhoods

in Chapter 1 showed that in that regard MTO was clearly because that was their preference or instead because of a success. Families in the experimental group used the

racial discrimination they encountered searching for

MTO voucher to move to tracts with poverty rates that

housing in more racially integrated communities. Both of

were on average 42 percentage points lower than their

these explanations fit the MTO data equally well.

baseline tracts (Exhibit 1.5). Section 8 families used

their vouchers to move to tracts with poverty rates 25

MTO did generate sizable and persistent differences in

percentage points lower. These findings are consistent

the quality of housing units as well, even though many

with the hypothesis that many families living in public

families who moved with an MTO voucher were still

housing are basically tied to high-poverty areas if they

living in units with problems such as vermin, peeling

wish to receive a housing subsidy, and (absent gaining

paint or plaster, or problems with their heating or

access to a housing voucher or another housing program) plumbing. An even larger share of the control group must give up their rent subsidy to move into a lower-families lived in units with these types of problems.

poverty area. The MTO voucher reduces the cost to

families of moving into a lower-poverty area.

The findings reported in this chapter suggest that at least

among the set of families who volunteered for MTO,

These effects on neighborhood conditions attenuate over

changing the form of housing assistance they receive

time for a combination of reasons, but primarily because

from public housing to vouchers leads to improved

some control families moved to lower-poverty areas over

housing quality and changes in a range of neighborhood

time even without benefit of an MTO housing voucher.

conditions as well. Our findings for MTO effects on

In addition, many treatment group families did not stay

neighborhood quality are quite different from studies

in the very low-poverty neighborhoods into which they

such as HUD’s Welfare to Work voucher experiment

made their initial MTO moves, and the fact that poverty

(Mills et al. 2006) or the Chicago housing voucher

rates declined over time in the baseline neighborhoods.

lottery study of Jacob and Ludwig (2011), which

Nonetheless as we showed in this chapter, poverty rates

compare outcomes of families that receive housing

averaged over the entire study period—as well as other

vouchers with those who are living unsubsidized in the

indicators of neighborhood socioeconomic composition,

private housing market. Those studies find that providing

and neighborhood safety as well—are still quite different

housing vouchers to families who are already living

between the MTO treatment groups and the control

(unsubsidized) in private-market housing leads to very

group.

modest if any changes in neighborhood conditions. The

limited effect of vouchers on neighborhood conditions

MTO did not generate sizable changes in neighborhood

for families already in private-market housing is not

racial composition, unlike the Gautreaux mobility

what we would predict from standard economic theory,

program (Rubinowitz and Rosenbaum, 2000). In

which suggests that families receiving generous voucher

Gautreaux, moving families to less racially segregated

subsidies would want to spend more on higher unit

neighborhoods was a key objective of the program. The

quality and higher neighborhood “quality.” Learning

non-profit organization that helped carry out Gautreaux

more about why vouchers change neighborhood

was responsible for identifying rental apartments

conditions for public housing residents but not for

for families, which eliminated the need for program

families already in private-market housing is an

participants to search themselves for housing and at the

important question for future research.

same time also gave participants limited choice over

where they would move. In MTO, program participants

Whether these MTO-induced changes in housing and

neighborhood conditions translate into changes in

67

Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks outcomes such as labor market earnings, schooling,

different housing and neighborhood conditions. But,

health or delinquency will depend on which aspects of

at the very least, these moves have left the families

housing and neighborhoods are most relevant to these

better off, as judged by the growing share of families

outcomes and the degree to which these outcomes are

that report being satisfied with their housing units and

affected by immediate versus accumulated exposure to

neighborhoods.

SUPPLEMENTAL EXHIBIT 2.1. ADULT CURRENT HOUSING CONDITION

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

OVERCROWDING IN HOUSING

Overcrowded household (ratio of more

0.229

– 0.008

– 0.017

– 0.021

– 0.034

3,260

than one person per room not including

(0.017)

(0.034)

(0.021)

(0.033)

bathrooms and hallways) [SR]

Severely overcrowded household (ratio

0.080

0.012

0.024

0.019

0.030

3,260

of more than one and one-half people

(0.011)

(0.024)

(0.015)

(0.023)

per room not including bathrooms and

hallways) [SR]

SPECIFIC HOUSING PROBLEMS

Walls with peeling paint or broken

0.466

– 0.091*

– 0.187*

– 0.120*

– 0.192*

3,265

plaster are a big or small problem [SR]

(0.021)

(0.044)

(0.028)

(0.044)

Plumbing not working is a big or small

0.327

– 0.058*

– 0.119*

– 0.053*

– 0.085*

3,265

problem [SR]

(0.020)

(0.040)

(0.026)

(0.041)

Rats or mice are a big or small problem

0.347

– 0.038~

– 0.079~

– 0.045~

– 0.072~

3,265

[SR]

(0.020)

(0.041)

(0.027)

(0.043)

Cockroaches are a big or small

0.356

– 0.053*

– 0.110*

– 0.089*

– 0.142*

3,267

problem [SR]

(0.019)

(0.040)

(0.025)

(0.039)

Broken or no locks on door to the unit

0.144

– 0.029~

– 0.059~

– 0.019

– 0.031

3,267

are a big or small problem [SR]

(0.015)

(0.030)

(0.019)

(0.030)

Broken windows or missing screens are

0.233

– 0.065*

– 0.134*

– 0.078*

– 0.124*

3,267

a big or small problem [SR]

(0.017)

(0.036)

(0.023)

(0.036)

Heating system that does not work is a

0.179

– 0.013

– 0.027

– 0.037~

– 0.059~

3,252

big or small problem [SR]

(0.016)

(0.033)

(0.020)

(0.033)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.

68

SUPPLEMENTAL EXHIBIT 2.2. YOUTH NEIGHBORHOOD CENSUS TRACT CHARACTERISTICS FOR CURRENT

ADDRESS (MAY 2008)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

Share of persons in tract who are poor [CEN]

All

0.309

– 0.032*

– 0.067*

– 0.032*

– 0.049*

4,974

(0.008)

(0.017)

(0.008)

(0.012)

Female

0.311

– 0.033*

– 0.065*

– 0.032*

– 0.050*

2,543

(0.009)

(0.019)

(0.010)

(0.016)

Male

0.306

– 0.032*

– 0.068*

– 0.033*

– 0.047*

2,431

(0.010)

(0.022)

(0.010)

(0.014)

Share of persons who are above 200% of the poverty line [CEN]

All

0.450

0.034*

0.070*

0.030*

0.044*

4,974

(0.010)

(0.020)

(0.010)

(0.015)

Female

0.446

0.034*

0.069*

0.031*

0.048*

2,543

(0.012)

(0.023)

(0.013)

(0.020)

Male

0.454

0.033*

0.072*

0.029*

0.041*

2,431

(0.012)

(0.026)

(0.012)

(0.018)

Share minority (non-White or Hispanic) [CEN]

All

0.832

– 0.016

– 0.034

– 0.019

– 0.028

4,974

(0.012)

(0.026)

(0.014)

(0.021)

Female

0.831

– 0.018

– 0.035

– 0.023

– 0.036

2,543

(0.015)

(0.029)

(0.017)

(0.027)

Male

0.834

– 0.015

– 0.033

– 0.014

– 0.020

2,431

(0.016)

(0.033)

(0.017)

(0.025)

Share Hispanic [CEN]

All

0.299

– 0.003

– 0.007

0.015

0.023

4,974

(0.009)

(0.019)

(0.009)

(0.014)

Female

0.306

– 0.004

– 0.008

0.012

0.019

2,543

(0.011)

(0.022)

(0.012)

(0.019)

Male

0.292

– 0.003

– 0.007

0.019

0.027

2,431

(0.011)

(0.024)

(0.011)

(0.016)

Share Black [CEN]

All

0.497

– 0.017

– 0.035

– 0.041*

– 0.062*

4,974

(0.013)

(0.027)

(0.015)

(0.022)

Female

0.488

– 0.019

– 0.039

– 0.045*

– 0.071*

2,543

(0.015)

(0.031)

(0.019)

(0.029)

Male

0.506

– 0.014

– 0.031

– 0.037*

– 0.053*

2,431

(0.015)

(0.033)

(0.019)

(0.026)

Share of households that are single female-headed families [CEN]

All

0.483

– 0.036*

– 0.075*

– 0.041*

– 0.061*

4,974

(0.010)

(0.020)

(0.010)

(0.015)

69

Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks SUPPLEMENTAL EXHIBIT 2.2. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

Share of households that are single female-headed families [CEN] (CONTINUED) Female

0.479

– 0.032*

– 0.065*

– 0.039*

– 0.062*

2,543

(0.011)

(0.022)

(0.012)

(0.019)

Male

0.486

– 0.040*

– 0.085*

– 0.043*

– 0.061*

2,431

(0.012)

(0.025)

(0.012)

(0.017)

Share college graduates [CEN]

All

0.209

0.015*

0.031*

0.015*

0.023*

4,974

(0.007)

(0.014)

(0.007)

(0.011)

Female

0.208

0.015*

0.031*

0.021*

0.033*

2,543

(0.008)

(0.016)

(0.009)

(0.014)

Male

0.209

0.015~

0.033~

0.009

0.013

2,431

(0.008)

(0.018)

(0.009)

(0.013)

Share with more than high school education [CEN]

All

0.391

0.021*

0.044*

0.017*

0.026*

4,974

(0.007)

(0.016)

(0.008)

(0.012)

Female

0.391

0.020*

0.041*

0.021*

0.033*

2,543

(0.009)

(0.018)

(0.010)

(0.015)

Male

0.392

0.022*

0.048*

0.013

0.019

2,431

(0.010)

(0.021)

(0.010)

(0.014)

Share employed, civilian [CEN]

All

0.848

0.012*

0.025*

0.014*

0.021*

4,974

(0.004)

(0.008)

(0.004)

(0.006)

Female

0.849

0.010*

0.020*

0.011*

0.018*

2,543

(0.004)

(0.008)

(0.005)

(0.008)

Male

0.846

0.014*

0.030*

0.017*

0.024*

2,431

(0.005)

(0.010)

(0.005)

(0.007)

Share workers in managerial or professional occupations [CEN]

All

0.240

0.009

0.018

0.007

0.011

4,974

(0.006)

(0.012)

(0.006)

(0.009)

Female

0.238

0.011~

0.023~

0.015*

0.024*

2,543

(0.007)

(0.014)

(0.007)

(0.012)

Male

0.243

0.006

0.014

– 0.001

– 0.001

2,431

(0.008)

(0.017)

(0.008)

(0.012)

Median household income (2009 dollars) [CEN]

All

$34,189.96

3,627.81*

7,526.02*

3,035.12*

4,543.18*

4,974

(895.53)

(1,857.81)

(903.84)

(1,352.93)

Female

$34,130.44

3,440.25*

6,920.75*

2,792.55*

4,385.01*

2,543

(1,050.07)

(2,112.42)

(1,109.29)

(1,741.86)

Male

$34,246.61

3,815.73*

8,193.22*

3,277.41*

4,677.22*

2,431

(1,118.32)

(2,401.28)

(1,126.04)

(1,606.98)

70

SUPPLEMENTAL EXHIBIT 2.2. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

Per capita income (2009 dollars) [CEN]

All

$17,257.40

918.75*

1,905.98*

622.10

931.21

4,974

(451.78)

(937.24)

(461.82)

(691.28)

Female

$17,150.40

945.79~

1,902.65~

897.02

1,408.54

2,543

(543.00)

(1,092.34)

(585.07)

(918.70)

Male

$17,359.24

897.28

1,926.66

339.91

485.08

2,431

(563.61)

(1,210.20)

(545.94)

(779.11)

Share owner-occupied housing units [CEN]

All

0.338

0.042*

0.088*

0.041*

0.062*

4,974

(0.012)

(0.025)

(0.013)

(0.019)

Female

0.335

0.040*

0.081*

0.034*

0.054*

2,543

(0.014)

(0.029)

(0.015)

(0.024)

Male

0.342

0.045*

0.096*

0.048*

0.069*

2,431

(0.015)

(0.032)

(0.015)

(0.022)

Share of households on public assistance [CEN]

All

0.098

– 0.015*

– 0.031*

– 0.014*

– 0.021*

4,974

(0.004)

(0.008)

(0.003)

(0.005)

Female

0.100

– 0.016*

– 0.031*

– 0.015*

– 0.024*

2,543

(0.004)

(0.008)

(0.004)

(0.007)

Male

0.097

– 0.015*

– 0.032*

– 0.013*

– 0.019*

2,431

(0.005)

(0.010)

(0.004)

(0.006)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: CEN = 1990 and 2000 decennial census data as well as the 2005–09 American Community Survey (ACS).

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 10 to 20 as of December 31, 2007.

Measures: Census tract characteristics are linearly interpolated from the 1990 decennial census, 2000 decennial census, and 2005–09 ACS.

71

Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks SUPPLEMENTAL EXHIBIT 2.3. ADULT NEIGHBORHOOD CONDITIONS

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

DRUGS

Saw drugs being used or sold in

0.310

– 0.062*

– 0.128*

– 0.057*

– 0.090*

3,249

neighborhood in the past 30 days [SR]

(0.019)

(0.039)

(0.025)

(0.040)

Saw someone using drugs in

0.198

– 0.050*

– 0.103*

– 0.050*

– 0.078*

3,247

neighborhood in past 30 days [SR]

(0.016)

(0.033)

(0.021)

(0.034)

Saw someone selling drugs in

0.264

– 0.047*

– 0.096*

– 0.051*

– 0.081*

3,249

neighborhood in past 30 days [SR]

(0.018)

(0.037)

(0.024)

(0.038)

NEIGHBORHOOD PROBLEMS

Litter or trash on the sidewalk is a big

0.646

– 0.075*

– 0.155*

– 0.069*

– 0.111*

3,265

or small problem [SR]

(0.021)

(0.042)

(0.028)

(0.045)

Graffiti on the walls is a big or small

0.466

– 0.061*

– 0.126*

– 0.041

– 0.065

3,261

problem [SR]

(0.021)

(0.042)

(0.028)

(0.044)

People drinking in public is a big or

0.513

– 0.056*

– 0.114*

– 0.020

– 0.031

3,246

small problem [SR]

(0.022)

(0.045)

(0.029)

(0.046)

Abandoned buildings is a big or small

0.388

– 0.047*

– 0.096*

– 0.040

– 0.064

3,259

problem [SR]

(0.020)

(0.042)

(0.027)

(0.043)

Groups of people hanging out is a big

0.557

– 0.049*

– 0.101*

– 0.012

– 0.019

3,258

or small problem [SR]

(0.021)

(0.044)

(0.029)

(0.045)

HOUSEHOLD VICTIMIZATION (PAST 6 MONTHS)

Household member had their purse,

0.077

– 0.005

– 0.010

0.008

0.012

3,234

wallet, or jewelry snatched [SR]

(0.011)

(0.023)

(0.014)

(0.023)

Household member was threatened

0.066

– 0.008

– 0.016

0.001

0.001

3,236

with a knife or gun [SR]

(0.010)

(0.021)

(0.013)

(0.021)

Household member was beaten up or

0.074

– 0.014

– 0.030

– 0.005

– 0.008

3,238

assaulted [SR]

(0.011)

(0.022)

(0.014)

(0.023)

Household member was stabbed or

0.029

– 0.006

– 0.013

– 0.008

– 0.013

3,241

shot [SR]

(0.007)

(0.015)

(0.008)

(0.013)

Someone attempted to break into the

0.043

– 0.003

– 0.007

0.015

0.025

3,242

home [SR]

(0.009)

(0.019)

(0.013)

(0.021)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed

72

SUPPLEMENTAL EXHIBIT 2.4. MEDIATORS: ADULT SOCIAL NETWORKS

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

Has no friends in the neighborhood [SR]

0.571

0.009

0.019

0.009

0.014

3,267

(0.021)

(0.044)

(0.028)

(0.045)

Visits friends in their home at least once

0.239

0.015

0.031

0.018

0.028

3,266

a week [SR]

(0.018)

(0.038)

(0.024)

(0.039)

Stops to chat with neighbor in street or

0.532

0.019

0.039

0.030

0.048

3,269

hallway at least once a week [SR]

(0.021)

(0.044)

(0.028)

(0.045)

Attended church or religious services at

0.460

– 0.028

– 0.057

0.013

0.020

3,264

least once a month in the past year [SR]

(0.021)

(0.044)

(0.028)

(0.045)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.

SUPPLEMENTAL EXHIBIT 2.5. MEDIATORS: YOUTH SOCIAL NETWORK

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

Youth has at least one close friend [SR]

All

0.963

– 0.018*

– 0.037*

– 0.019*

– 0.028*

5,094

(0.008)

(0.016)

(0.009)

(0.014)

Female

0.967

-0.009

– 0.019

– 0.021~

– 0.034~

2,595

(0.010)

(0.021)

(0.013)

(0.020)

Male

0.959

– 0.027*

– 0.057*

– 0.016

– 0.022

2,499

(0.012)

(0.026)

(0.014)

(0.020)

Youth has at least three close friends [SR]

All

0.640

– 0.023

– 0.048

– 0.032

– 0.047

5,094

(0.019)

(0.039)

(0.020)

(0.030)

Female

0.616

– 0.040

– 0.080

– 0.054~

– 0.085~

2,595

(0.026)

(0.051)

(0.028)

(0.045)

Male

0.661

– 0.006

– 0.013

– 0.009

– 0.013

2,499

(0.026)

(0.057)

(0.029)

(0.041)

Youth has at least one close friend in their neighborhood [SR]

All

0.624

– 0.014

– 0.030

– 0.028

– 0.041

5,089

(0.019)

(0.040)

(0.021)

(0.031)

Female

0.608

– 0.009

– 0.018

– 0.052~

– 0.082~

2,594

(0.026)

(0.053)

(0.029)

(0.046)

Male

0.640

– 0.021

– 0.045

– 0.002

– 0.003

2,495

(0.027)

(0.058)

(0.029)

(0.042)

73

Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks SUPPLEMENTAL EXHIBIT 2.5. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

Youth has at least one friend that was also a friend at random assignment [SR]

All

0.487

– 0.027

– 0.055

– 0.039~

– 0.058~

5,006

(0.020)

(0.041)

(0.022)

(0.032)

Female

0.496

– 0.024

– 0.049

– 0.037

– 0.059

2548

(0.027)

(0.055)

(0.030)

(0.047)

Male

0.479

– 0.029

– 0.062

– 0.040

– 0.057

2,458

(0.027)

(0.058)

(0.029)

(0.042)

Youth visits baseline friends or they visit youth at least a few times a week [SR]

All

0.280

– 0.035*

– 0.073*

– 0.038*

– 0.057*

5,002

(0.017)

(0.036)

(0.018)

(0.027)

Female

0.257

– 0.030

– 0.060

– 0.016

– 0.025

2,546

(0.023)

(0.047)

(0.025)

(0.040)

Male

0.302

– 0.040~

– 0.086~

– 0.060*

– 0.086*

2,456

(0.024)

(0.052)

(0.026)

(0.037)

Youth attended youth activities at church at least once a month during past year [SR]

All

0.360

– 0.023

– 0.049

0.001

0.002

5,086

(0.019)

(0.039)

(0.020)

(0.030)

Female

0.356

– 0.031

– 0.062

– 0.003

– 0.005

2,589

(0.025)

(0.051)

(0.028)

(0.044)

Male

0.363

– 0.016

– 0.035

0.005

0.007

2,497

(0.026)

(0.056)

(0.028)

(0.040)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 10 to 20 as of December 31, 2007.

74

SUPPLEMENTAL EXHIBIT 2.6. MEDIATORS: YOUTH'S PEERS

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

Close friends think that studying is very important [SR]

All

0.428

– 0.022

– 0.046

– 0.016

– 0.024

5,083

(0.019)

(0.040)

(0.021)

(0.032)

Female

0.474

– 0.009

– 0.019

– 0.029

– 0.046

2,589

(0.026)

(0.053)

(0.029)

(0.045)

Male

0.385

– 0.035

– 0.076

– 0.002

– 0.003

2,494

(0.027)

(0.058)

(0.030)

(0.042)

Close friends think that it is very important to continue their education past high school [SR]

All

0.732

0.006

0.013

0.003

0.005

5,082

(0.017)

(0.034)

(0.018)

(0.028)

Female

0.787

0.024

0.048

0.010

0.016

2,589

(0.021)

(0.042)

(0.024)

(0.037)

Male

0.680

– 0.012

– 0.026

– 0.003

– 0.005

2,493

(0.025)

(0.054)

(0.028)

(0.041)

Close friends have ever been involved in school activities [SR]

All

0.810

– 0.014

– 0.029

– 0.018

– 0.027

5,041

(0.015)

(0.030)

(0.017)

(0.025)

Female

0.820

– 0.016

– 0.033

– 0.027

– 0.042

2,568

(0.020)

(0.041)

(0.023)

(0.036)

Male

0.800

– 0.012

– 0.026

– 0.009

– 0.012

2,473

(0.022)

(0.047)

(0.025)

(0.035)

Close friends have ever used marijuana or other drugs [SR]

All

0.357

– 0.045*

– 0.094*

– 0.025

– 0.038

4,955

(0.019)

(0.039)

(0.021)

(0.031)

Female

0.315

– 0.063*

– 0.126*

– 0.034

– 0.053

2,541

(0.024)

(0.048)

(0.027)

(0.043)

Male

0.397

– 0.028

– 0.059

– 0.017

– 0.024

2,414

(0.026)

(0.057)

(0.028)

(0.041)

Close friends have ever dropped out of school [SR]

All

0.214

– 0.005

– 0.010

– 0.005

– 0.008

5,082

(0.015)

(0.031)

(0.017)

(0.026)

Female

0.200

– 0.018

– 0.037

– 0.040~

– 0.063~

2,591

(0.020)

(0.040)

(0.022)

(0.035)

Male

0.227

0.008

0.018

0.030

0.043

2,491

(0.022)

(0.048)

(0.025)

(0.036)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250"" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 10 to 20 as of December 31, 2007.

75

Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks SUPPLEMENTAL EXHIBIT 2.7. YOUTH SELF-REFLECTION

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

Youth speaks proper English very often or somewhat often (versus never) [SR]

All

0.894

0.001

0.001

0.004

0.005

5,092

(0.011)

(0.024)

(0.012)

(0.018)

Female

0.896

0.011

0.022

0.006

0.009

2,595

(0.015)

(0.031)

(0.017)

(0.027)

Male

0.891

– 0.010

– 0.021

0.002

0.002

2,497

(0.017)

(0.036)

(0.018)

(0.026)

Youth thinks people would never describe his or her behavior as "ghetto" (versus very often or somewhat often) [SR]

All

0.613

0.000

0.000

– 0.012

– 0.018

5,077

(0.019)

(0.039)

(0.020)

(0.030)

Female

0.638

– 0.016

– 0.031

– 0.007

– 0.011

2,586

(0.025)

(0.050)

(0.027)

(0.043)

Male

0.589

0.016

0.034

– 0.018

– 0.026

2,491

(0.026)

(0.056)

(0.029)

(0.041)

Youth strongly agrees that they feel good about themselves (versus agree, disagree, or strongly disagree) [SR]

All

0.675

0.013

0.027

– 0.011

– 0.016

5,095

(0.018)

(0.037)

(0.020)

(0.029)

Female

0.667

0.028

0.057

– 0.024

– 0.037

2,596

(0.024)

(0.049)

(0.027)

(0.043)

Male

0.683

– 0.003

– 0.007

0.003

0.004

2,499

(0.025)

(0.054)

(0.027)

(0.039)

Youth strongly agrees that they can do things as well as other people (versus agree, disagree, or strongly disagree) [SR]

All

0.584

0.017

0.035

– 0.007

– 0.010

5,093

(0.019)

(0.039)

(0.020)

(0.030)

Female

0.590

0.032

0.065

0.005

0.008

2,594

(0.026)

(0.052)

(0.028)

(0.044)

Male

0.579

0.002

0.004

– 0.018

– 0.026

2,499

(0.026)

(0.055)

(0.029)

(0.041)

Youth strongly disagrees or disagrees that chance and luck are very important in life (versus strongly agree or agree) [SR]

All

0.309

– 0.015

– 0.030

0.017

0.025

5,082

(0.018)

(0.037)

(0.019)

(0.029)

Female

0.308

– 0.070*

– 0.141*

– 0.013

– 0.020

2,590

(0.024)

(0.048)

(0.026)

(0.041)

Male

0.311

0.041

0.088

0.045

0.064

2,492

(0.025)

(0.054)

(0.028)

(0.040)

Youth thinks other people always see them as popular (versus some of the time or never) [SR]

All

0.323

– 0.028

– 0.057

– 0.030

– 0.045

5,087

(0.017)

(0.035)

(0.018)

(0.028)

Female

0.315

– 0.038~

– 0.077~

– 0.057*

– 0.089*

2,593

(0.023)

(0.047)

(0.025)

(0.040)

76

SUPPLEMENTAL EXHIBIT 2.7. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

Youth thinks other people always see them as popular (versus some of the time or never) [SR] (CONTINUED) Male

0.330

– 0.017

– 0.037

– 0.003

– 0.004

2,494

(0.024)

(0.052)

(0.027)

(0.038)

Youth thinks other people always see them as athletic (versus some of the time or never) [SR]

All

0.318

– 0.020

– 0.041

0.002

0.003

5,089

(0.017)

(0.036)

(0.019)

(0.028)

Female

0.207

– 0.035~

– 0.071~

– 0.003

– 0.005

2,593

(0.021)

(0.043)

(0.024)

(0.038)

Male

0.422

– 0.004

– 0.009

0.006

0.008

2,496

(0.027)

(0.058)

(0.030)

(0.043)

Youth thinks other people always see them as a good student (versus some of the time or never) [SR]

All

0.404

0.002

0.004

– 0.016

– 0.024

5,092

(0.019)

(0.039)

(0.020)

(0.031)

Female

0.455

0.027

0.055

0.014

0.022

2,595

(0.026)

(0.052)

(0.029)

(0.045)

Male

0.356

– 0.023

– 0.050

– 0.046~

– 0.066~

2,497

(0.026)

(0.055)

(0.027)

(0.039)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 10 to 20 as of December 31, 2007.

77

Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks 78

IMPACTS ON ADULT AND YOUTH

PHYSICAL HEALTH

79

Chapter 3: Impacts on Adult and Youth Physical Health

IMPACTS ON ADULT AND

YOUTH PHYSICAL HEALTH

In this chapter, we summarize MTO’s impacts on the

• Have fewer self-reported physical limitations; and

physical health of adults and youth. At the end of

• Show no statistically significant differences on

2007, the adults were on average 45.2 years old, with

other health outcomes such as self-rated overall

CHAPTER 3

29 percent over age 50, while the youth in our study

health, asthma, or blood pressure.

sample ranged in age from 10 to 20. We relied in part

on adult and youth self-reports of health status, health

Results for adults in the Section 8 group tend to point

behaviors, and access to health care. Because differing

in the same direction, but the effects in general were

access to health services across neighborhoods could

smaller and not statistically significant.

affect self-reported health outcomes, we collected

physical measurements of health outcomes as well.

We detect no significant differences across treatment

Specifically, we measured height and weight, and

groups in the physical health of youth. Similarly, we

for adults we also measured waist circumference and

see few effects on grown children, with the possible

blood pressure and collected blood samples to measure

exception of an increased prevalence of physical

physiological indicators of long-term disease risk. We

limitations among male grown children in the Section

also asked adults to report on the health outcomes of

8 group compared with those in the control group.

their grown children (those who were under age 18

We find few detectable differences across randomized

at baseline but over age 20 at the end of 2007), who

groups on health-related behaviors, such as diet and

were too old to have been included in our youth survey moderate exercise, although we did find that a higher sample frame.

share of adults in the experimental group compared

with the control group appear to engage in vigorous

Although MTO does not have detectable impacts

exercise. We also see signs of increased access to health

on many of the physical health outcomes that we

care services for people in the Section 8 group versus

examined, particularly those focused on the health

those in the control group.

of the average adult in each randomized mobility

group (central tendencies), we did find impacts on

3.1 HYPOTHESES ABOUT EFFECTS

several measures of the prevalence of adverse health

ON ADULT AND YOUTH PHYSICAL

outcomes that are relevant for people with the most

HEALTH

distressed health outcomes (those at the tails of the

Previous epidemiological research has found strong

health outcome distribution). Specifically, we find

associations between living in disadvantaged

that, compared with the control group, adults in the

neighborhoods and adverse health outcomes for adults,

experimental group:

such as mortality, heart disease, obesity, depression,

substance abuse, and poor overall health, even after

• Have a lower prevalence of extreme obesity

controlling for individual health risk factors and

(defined as a body mass index (BMI)1 of greater

protective factors. For example, Waitzman and Smith

than or equal to 35);

(1998) find that people living in federally designated

• Have a lower prevalence of diabetes (measured by poverty areas have higher rates of mortality even after glycosylated hemoglobin [HbA1c] greater than or controlling for individual characteristics. Ross and equal to 6.5 percent);

Mirowsky (2001) find that living in disadvantaged

neighborhoods is associated with poorer self-reported

1 Body mass index (BMI) is defined as weight in kilograms divided

health and physical functioning. Browning and Cagney

by height in meters squared.

80

(2002) find that individuals residing in neighborhoods

estimates of neighborhood effects on health tend to

with greater collective efficacy report better overall

be smaller in studies employing a richer set of control

health. Diez-Roux et al. (2001) find that adults living in

variables for the individual’s own (as opposed to

disadvantaged neighborhoods are at significantly greater

neighborhood) socioeconomic condition. Because

risk of developing coronary heart disease, even after

nonexperimental studies cannot adequately measure

controlling for income, occupation, and education (see

and control for all of the individual differences

Pickett and Pearl, 2001; Kawachi and Berkman, 2003b;

between people in different neighborhoods, the causal

Macintyre and Ellaway, 2003, for excellent reviews of

relationships between neighborhood and health remain

this literature). More recently, Bird et al. (2010) find that unclear. The design of the MTO study overcomes these

“good cholesterol” (high-density lipoprotein or HDL)

limitations and makes the estimation of causal effects

and lower blood pressure are associated with higher

possible.

neighborhood socioeconomic status after controlling for

other factors. For children, living in a high-poverty urban MTO-assisted moves to lower-poverty neighborhoods setting or unsafe neighborhood is also associated with

could affect health by altering a participant’s physical

adverse health outcomes (Curtis, Dooley, and Phipps,

and social environments and access to health-related

2004; Lumeng et al., 2006).

community resources. Exhibit 3.1 shows how these

changes might affect health, directly or indirectly, by

People living in high-poverty neighborhoods differ

changing health-related behaviors, including stress and

in many ways from those living in lower-poverty

health care access.

neighborhoods. Pickett and Pearl (2001) observe that

Exhibit 3.1. Hypothesized Pathways Through Which MTO May Impact Physical Health EXHIBIT 3.1. HYPOTHESIZED PATHWAYS THROUGH WHICH MTO MAY IMPACT PHYSICAL HEALTH

MTO INTERVENTION

RELOCATION TO LOW-POVERTY NEIGHBORHOODS

COMMUNITY-LEVEL

PERSON- AND FAMILY-LEVEL

PHYSICAL HEALTH

MEDIATORS

MEDIATORS

OUTCOMES

Physical Environment

· Cleaner air, fewer allergens

Physical Health

· More walkable

· General health status

· Fewer hazards

Access to Care

· Difficulties with daily

· Health insurance

activities

· Access to medical care

· Asthma

Community Resources

· Injuries

· Health care facilities

· Hypertension

· Access to jobs with health

· Obesity

Exercise and Nutrition

benefits

· Diabetes

· Healthier eating

· Access to fast food, grocery

· Chronic pain

· More exercise

stores, and liquor stores

· Exercise facilities

Unhealthy Behaviors

· Smoking

Safety and Stress

· Drinking

Social Environment

· Less stress

· Reduced crime and violence

· Collective efficacy

· Peers who eat healthier and

Social Supports

exercise more

· More/less isolation

· Different neighbors

· More/less support

Exhibit 3.1 is a modified version of Exhibit 4.1 in the MTO interim impacts evaluation (see Orr et al., 2003).

81

Chapter 3: Impacts on Adult and Youth Physical Health

Chapter 3: Impacts on Adult and Y

PHYSICAL ENVIRONMENT

that may affect health behaviors and outcomes. The

A drive through any major American city clearly shows

presence of grocery stores could affect diet by reducing

that the physical environments of neighborhoods vary

the purchase price or transportation costs associated

widely. The environment—both indoors and out—

with accessing fresh fruits and vegetables (Morland

may affect health by exposing individuals to pollution,

et al., 2002). The availability of liquor stores and fast

contaminants, allergens, and physical hazards like broken food restaurants can also affect diet, by reducing the glass. Poor housing quality, which can increase exposure

transportation costs associated with accessing inexpensive

to household dangers such as vermin, toxins, mold,

forms of alcohol or fast food (Zenk et al., 2005; Franco

and poor ventilation, may lead to a higher incidence

et al., 2008; Inagami et al., 2006; Dubowitz et al., 2008).

of asthma (Bryant-Stephens, 2009). Carpeting can

harbor dust mites, and its removal or cleaning is often

MTO’s potential effects on access to quality health care

recommended as an asthma remediation strategy (Crain

are more difficult to predict in advance. Low-income

et al., 2002; van der Heide et al., 1997). Low-income

families may have difficulty accessing health care in part

neighborhoods may also have compromised air quality,

because they lack health insurance. MTO might increase

which has been linked to coronary heart disease (Kan

access to health care if families move to areas with

et al., 2008) and poor health for infants (Currie and

more opportunities for employment that offers health

Walker, 2011). Poor children living in disadvantaged

insurance benefits. MTO might also help families move

urban areas may be at higher risk of lead exposure

to areas with better transportation or more local primary

as a result of lead dust from peeling paint and soil

care physicians, making it easier to receive preventive

contamination. This can impair brain development

care and other services. On the other hand, the MTO

and may be exacerbated by poor nutrition (Filippelli

demonstration took place in cities with some of the top-

and Laidlaw, 2010). Exposure to secondhand smoke

ranked hospitals in the country, many of them academic

may also be greater in low-income areas because of a

medical centers that for historical reasons are located near

higher prevalence of smoking (Bombard et al., 2010).

economically disadvantaged inner-city neighborhoods.2

The prevalence of accidents and injuries may be higher

These academic medical centers may be more experienced

among children living in distressed urban communities

in meeting the linguistic and cultural needs and daily

owing to unsafe playgrounds and other features of the

realities of low-income patients than are the health care

environment (Scharfstein and Sandel, 1998; Quinlan,

facilities located in more affluent areas.

1996). Accidents may be a particularly important way in

which neighborhoods affect child health, since accidents

SOCIAL ENVIRONMENT

are the most common cause of death among children ages Exposure to crime, social supports, and peer influences 1 to 14 in the United States.

are all parts of the social environment that may affect

health. Dangerous neighborhoods can have deleterious

The “built environment” of a neighborhood—buildings,

direct effects on health from injuries from assaults.

parks, and streets—may also influence health by affecting Exposure to violence can be traumatic. Living in a exercise patterns. Studies have found that neighborhood

dangerous neighborhood can produce chronic stress that

greenness is associated with lower BMI for youth (Bell,

in turn may be associated with asthma attacks (Wright,

Wilson, and Liu, 2008), and the “walkability” of a

1998; Bloomberg and Chen, 2005), hypertension

neighborhood may be associated with more physical

(Joint National Committee on Prevention, Detection,

activity and lower rates of overweight and obesity

Evaluation, and Treatment of High Blood Pressure,

(Mujahid et al., 2008; Lovasi et al., 2009; Sallis et al.,

1997; Kornitzer, Dramaix, and De Backer, 1999), and

2009).

a lower ability of the immune system to respond to

foreign substances, such as viruses and bacteria. Medical

COMMUNITY RESOURCES

Community resources such as stores, employers, and

2 Four of the five MTO cities are home to at least one of the top 14

honor-roll hospitals as ranked by U.S. News and World Report on the

transportation are another part of the built environment

basis of specialty rankings (Comarow, 2010).

82

researchers also speculate that exposure to trauma and

to neighbors with healthier lifestyles, including better

other stressors may lead to dysregulation across multiple

diet and exercise habits and less smoking (Lee and

physiological systems (Merkin et al., 2009). Stress

Cubbin, 2002; Berkman and Breslow, 1983). On the

may affect eating patterns, for example, by leading to

other hand, alcohol consumption may be higher in the

increased intake of dense-calorie foods (Torres and

more affluent areas into which MTO families move,

Nowson, 2007), and stress is associated with increased

since alcohol consumption has a positive association

use of alcohol, tobacco, and other substances. In

with socioeconomic status (SES) over at least part of the

addition, living in a dangerous neighborhood may limit

SES distribution (Substance Abuse and Mental Health

walking and other forms of outdoor exercise and could

Services Administration, 2010). The net effect on health

even influence access to health care facilities, especially in could be positive or negative given that previous research areas with inadequate or dangerous public transportation suggests that alcohol consumption may have a u-shaped systems. Reduced outdoor time in more dangerous

relationship with mortality (Lee et al., 2008).

neighborhoods could also increase exposure to indoor

pollutants.

The degree to which exposure to people with different

health habits translates into changes in health-related

MTO-assisted moves could help improve people’s

behavior by participants may depend on how socially

health if they relocate to less distressed neighborhoods

connected participants are with their new neighbors

with higher levels of “collective efficacy,” that is, the

(Christakis and Fowler, 2007). This exposure may be

level of trust among neighborhood residents and their

less helpful for Hispanic women in our study sample,

willingness to work together to support shared values

given previous research indicating that greater levels

(Sampson, Raudenbush, and Earls, 1997). Living in a

of acculturation (often associated with higher income)

neighborhood with higher levels of collective efficacy

may actually lead to less healthy lifestyles (see Lara et

could help families build social supports that buffer

al., 2005 for a review of this literature). This previous

the effects of stress (Cohen, 2004; Berkman and Glass,

research implies MTO moves for Hispanic women into

2000). Of course, this assumes that families are willing

neighborhoods with relatively larger shares of non-

and able to connect with their new neighbors. Moving

Hispanic residents could lead to additional acculturation

to a lower-poverty area may not be helpful if families are

and potentially to increased prevalence of health-risk

unable to connect to their new neighbors, and could even behaviors.

potentially harm families’ mental or physical health if

families feel deprived relative to the material wealth and

We hypothesize that moves to lower-poverty

social status of their new neighbors (Jencks and Mayer,

neighborhoods may improve physical health because the

1990; Luttmer, 2005; Lawler and Thye, 1999).

new neighborhoods may be safer and less stressful, have

greater community resources, have residents who tend

Peer influences are a final mechanism through which

to smoke less, eat healthier, and exercise more, and have

MTO-assisted moves may affect health behaviors and

fewer environmental hazards. However, it is possible that

subsequent outcomes (Sallis, Prochaska, and Taylor,

health might not be affected at all or that it could be

2000; Raudsepp and Viira, 2000). Epidemiological

negatively affected if housing quality has deteriorated,

studies find a socioeconomic gradient in diets, with

participants feel socially isolated, new neighborhoods are

higher socioeconomic groups tending to eat a higher-

less walkable, or access to health care—at least the sort

quality diet that includes more fresh fruits and

that is responsive to the needs of low-income minority

vegetables, whole grains, fish, and lower-fat meats and

families—is more difficult. Most of these mechanisms

dairy products and with lower socioeconomic groups

suggest that the effects on health from MTO will be

eating on average higher-calorie, lower-nutrient diets

greater if families spend more time in low-poverty

(Darmon and Drewnoski, 2008). As a result, moves to

neighborhoods.

lower-poverty neighborhoods may expose participants

83

Chapter 3: Impacts on Adult and Youth Physical Health

3.2 EARLIER RESEARCH: SHORT-

make it more difficult to eliminate asthma triggers, in the

TERM AND INTERIM IMPACTS ON

homes of the experimental and Section 8 groups.

PHYSICAL HEALTH

3.3 BASELINE AND CONTROL GROUP

Previous studies of the MTO demonstration’s impacts on CONTEXT

various physical health outcomes for adults and youth

have yielded a mixed pattern of results. The short-term

The baseline survey that HUD administered to families

study (on average two years after baseline) of MTO at

when they applied to MTO included only a handful of

the Boston site found that adults in the experimental

questions about health status. From these questions, we

group were more likely than control subjects to rate

know that approximately 15 percent of families had a

their health as good or better. Short-term data from

disabled household member (Exhibit 1.2), 8.4 percent

Boston also showed a lower incidence of asthma and

were receiving Social Security Disability Insurance

nonsports injuries (for example, physical injuries from

(SSDI), and 16 percent were receiving Supplemental

falls, fights, needles, and broken glass) for children in the Security Income (SSI) (Exhibit 5.1). Roughly 6 percent experimental group relative to the control group (Katz,

of the youth sample had a health problem that limited

Kling, and Liebman, 2001).

their activities, and 9 percent had a health problem

requiring special medicine or equipment. Among youth

Findings from the interim evaluation (four to seven years under age 6 at baseline, parents reported that 19 percent after baseline, pooling data from all five sites) show a

had been hospitalized before their first birthday (Exhibit

lower prevalence of obesity (BMI ≥ 30) for adults in

1.3). At the time they entered the program, the majority

the experimental group than those in the control group

of households were receiving Temporary Assistance for

(42.0 percent vs. 47.1 percent), together with some signs Needy Families (TANF), and about 25 percent of the of increased rates of exercise and improved diet (Orr et

adults indicated they were currently working. Families

al., 2003, Exhibit 4.2, p. 77 and Exhibit E4.2, p. E–2).

also expressed concerns about safety. Around 77 percent

However, the interim study did not detect statistically

of adults said getting away from gangs or drugs was their

significant effects for most other adult physical health

primary or secondary reason for signing up for MTO,

outcomes, including self-rated health, hypertension,

while 43 percent indicated someone in the household

physical limitations, asthma, or a summary health index

had been a victim of crime in the last six months (Exhibit

combining different health measures (Orr et al., 2003;

1.2).

Kling, Liebman, and Katz, 2007).

One of the main changes affecting adults’ health between

Among youth ages 15–20 at the time of the interim

the interim and final evaluations is that the adults are at

study, the overall health index revealed worse health for

least six years older at the final evaluation. A comparison

males in the experimental group relative to those in the

of self-reports at interim data collection (2002) with

control group, but beneficial effects on female youth

the final collection (2008–2010) shows an apparent

(Kling, Liebman, and Katz, 2007). However, the interim

decline in the health of the control group. For example,

study found no effects on any single health measure

the prevalence of obesity and self-reported physical

examined separately for children and youth ages 5 and

limitations increased by 11 percentage points, the rate

up (Orr et al., 2003). More detailed analyses of impacts

of asthma increased by 7 percentage points, and the

on children’s health by age confirmed the lack of overall

likelihood that a control group adult rated his or her

health effects and revealed sporadic adverse effects for

health as good or excellent declined by 10 percentage

specific health measures and age groups, despite some

points.

beneficial effects on children’s diet, exercise, and safety

3.4 DATA SOURCES AND MEASURES

(Fortson and Sanbonmatsu, 2010). The only health

mediator with a significant adverse impact was the

We used self-reported data from the final adult and

greater presence of wall-to-wall carpeting, which can

youth surveys, physical measurements of different health

indicators, and dried blood spot samples (drops of whole

84

blood collected on filter paper following a simple finger

measures such as mortality and functional limitations

stick; McDade et al., 2000) to measure impacts on

may differ by the education level of the self-reporter.

physical health. Using objective physical and biological

Some researchers have questioned whether self-rated

measures of health outcomes (called “biomarkers”)

health categories represent the same objective levels of

addresses potential concerns that neighborhood

health among respondents of different socioeconomic

environments themselves may affect self-reporting errors. status (Delpierre et al., 2009; Dowd and Zajacova, For example, people may be less willing to self-report

2010). Thus, one potential challenge in comparing

some health problem when that problem is relatively

self-rated health across MTO groups is that the families

less common in the local community, they may be more

living in more affluent neighborhoods may judge their

likely to find out about some health problem if they live

own health by higher standards (for example, more

in a community with better access to health care, or their negatively relative to others), and this might lead to assessment of whether their health is good or excellent

impact estimates that understate any benefits on health

may depend in part on the average health conditions

status.4

of other people in their community.3 Another value

of biomarkers as a complement to self-reported health

Diabetes: We asked respondents if a doctor had ever

outcomes is the ability to measure effects on hidden or

told them that they have diabetes or high blood sugar, if

undiagnosed health problems and precursors to long-

they were currently taking medication to treat diabetes

term health problems that could be asymptomatic at the

or to lower their blood sugar, and if in the past 12

time of the final data collection. Previous research shows

months they had diabetes or high blood sugar or received

that nearly one-third of all diabetes cases are undiagnosed treatment for it. We present two self-reported measures (Cowie et al., 2006); such cases, therefore, are unlikely to of diabetes: (1) ever being told by a doctor they had be detected through self-report but would be evident in

diabetes/high blood sugar, and (2) having diabetes or

biomarker data obtained from blood spot samples.

having been treated for diabetes in the past 12 months.

As noted above, underreporting of health problems could

ADULT AND YOUTH SURVEY RESPONSES present a challenge in relying on self-reports. Moreover, Interviewers asked respondents a variety of questions

the amount of the underreporting could vary across

about their health status, including physical limitations,

groups if, for example, experimental or Section 8 group

health-related behaviors, morbidity, injuries, dental

families had more opportunities to interact with a doctor

health, and access to health care. We selected questions

because of the program. This could lead to a larger share

from large national surveys (in particular, from the

of experimental and Section 8 group than control group

National Health Interview Survey) to measure, for

adults who have diabetes to know they have diabetes,

example, general health status, dental health, injuries,

thereby leading us to understate any beneficial impacts

asthma, height and weight, and smoking. The complete

of MTO on these health outcomes. Partly to address

list of survey questions (and their sources) is available at

this potential problem, we also collected blood spots

mtoresearch.org.

from respondents and assayed them for diabetes and

other health risks. (Similar issues arise with self-reported

SELF-REPORTED HEALTH OUTCOMES

hypertension, which is why we also directly measure

Self-Rated Health: Self-reported health is strongly

blood pressure as discussed below).

related to life expectancy among adults (Idler and Kasl,

1995; Idler and Benyamini, 1997). We measured self-

reported health by asking respondents, “In general,

how is your health: excellent, very good, good, fair, or

4 It is possible that Hispanics interpret questions about self-reported

poor?” Recent studies have noted that the strength of the health differently from African-Americans on average, or choose dif-relationship between self-rated health and other health

ferently from available response options. These sorts of self-reporting

issues should balance out in our experimental analysis since, by virtue

of random assignment, the share of MTO program participants who are

3 See, for example, Mango and Park (forthcoming) for a discussion of

Hispanic vs. African-American are similar across randomized mobility

local context and self-reported health status.

groups.

85

Chapter 3: Impacts on Adult and Youth Physical Health

Asthma: We asked adults and youth if they have ever

and we used this information to create a measure of

been told by a doctor or other health professional that

nonsports injuries (relocation can affect opportunities

they had asthma and if they had experienced wheezing or for involvement with extracurricular or other athletic whistling sounds in their chest in the past 12 months and activities). Isolating the causal effect of neighborhood how many attacks they had. We asked youth additional

context on serious nonsports injuries is particularly

follow-up questions about missed days of work and

important, given that injuries account for the two leading

school due to wheezing or whistling in their chest.

causes of death (accidents and homicide) in the United

States among 15- to 19-year-olds (Anderson and Smith,

Health limitations, chronic pain, and chronic health

2003).

problems: Measures of activities of daily living (ADLs)

are used in a number of national surveys to assess an

Smoking and drinking (adults): We asked adults if they

individual’s functional status and quality of life (Wiener

had ever smoked a cigarette and, if so, on how many

et al., 1990). We focused on ADLs that were likely to

of the past 30 days they had smoked and how many

be relevant for our sample of largely middle-aged adults

cigarettes they usually smoke each day. We also asked

and asked them if their health limited them “in climbing

them if they ever had an alcoholic beverage (defined as

several flights of stairs or lifting or carrying groceries”

beer, wine, a mixed drink, or a shot of liquor) and on

and considered them to have limitations if they indicated how many of the past 30 days they had one or more.

they were limited “a lot” or “a little” rather than “not

These questions were adapted from the 1997 National

limited at all.” We also asked adults if they ever had

Longitudinal Survey of Youth (analyses of impacts on

arthritis or rheumatism. To gain a better understanding

youth smoking and drinking are presented in Chapter 6).

of the severity of illness and injuries, we also asked

adults “how many days did illness or injury keep you

Dental health (youth): Similar to self-reports of overall

in bed more than half of the day?” We drew questions

health, we asked youth to describe the condition of their

on chronic pain from the National Comorbidity Survey

teeth (excellent, very good, good, fair, or poor). This

(Kessler et al., 1994, 2005) and asked adults about severe question was adapted from the National Health and or frequent headaches and about back, neck, or other

Nutrition Examination Survey.

chronic pain in the past year or ever. We asked youth

between ages 13 and 20 if they ever had chronic back or

SELF-REPORTED MEASURES OF

neck problems, frequent or very bad headaches, or other

CANDIDATE MEDIATORS

chronic pain. We also asked them if a doctor had ever

Exercise and sedentary behavior: We asked adults how

told them they have diabetes or high blood sugar or a

many times in a usual week they engaged in moderate

serious stomach or bowel problem, such as an ulcer or

activities for at least 10 minutes at a time (such as brisk

colitis. We also asked the parents if their grown children

walking, bicycling, vacuuming, gardening, or anything

had a physical health problem that kept them from

else that causes small increases in breathing or heart rate)

doing normal activities such as walking, getting dressed,

and how many times they did vigorous activities for at

housework, or working, and if their grown children had

least 10 minutes at a time (such as running, aerobics,

chronic physical health problems such as cancer, a heart

heavy yard work, or anything else that causes large

problem, or any other serious health problem.

increases in breathing or heart rate). We asked youth how

many days within the past seven days they engaged in

Accidents and injuries: We asked youth about both

aerobic exercise (physical activity for at least 20 minutes

serious accidents or injuries requiring medical care in

that made you sweat and breathe hard, such as basketball,

the past 12 months and, in the event they did not seek

soccer, running, swimming, fast bicycling, fast dancing,

treatment for injuries, about any other serious accidents

or similar aerobic activities) and how many days they

or injuries during the past 12 months that limited their

engaged in moderate exercise (physical activity for at least

usual activities but did not require medical attention.

30 minutes that did not make you sweat and breathe

Youth categorized the causes of accidents or injuries,

hard, such as fast walking, slow bicycling, skating,

86

pushing a lawn mower, or mopping floors).

of health status (if respondents use their new, more

We gauged the amount of television adults and youth

affluent neighbors as their benchmark) or affect diagnosis

watch in a typical week by having them select from the

of specific health problems if respondents are more likely

following categories: less than 2 hours per week, 3–10

to see a doctor. Physical health measures can also help

hours, 11–20 hours, 21–30 hours, 31–40 hours, or 40

detect the early stages of health problems that may not

hours or more.

yet be symptomatic.

Diet: We asked adults how many times they eat fruits

Height, weight, and obesity: We used protocols

and vegetables in a typical week. The possible responses

developed for the Health and Retirement Survey

were: I do not typically eat fruit; one to three times per

(Institute for Social Research, 2008) to measure adult

week; four to six times per week; one time per day; two

and youth height and weight. For height, respondents

times per day; three times per day; and four times or

removed their shoes and stood on a smooth surface with

more per day. Adults also reported the number of days

their heels and shoulders against a wall. Interviewers

per week that they typically ate fast food. The youth

placed a rafter square on the respondent’s head, marked

answered the same questions about eating fruits and

the height on a wall, and then measured it in inches

vegetables, as well as questions about the number of

(to the nearest quarter inch) using a tape measure.

times each week they drank soda or other carbonated

For weight, respondents removed their shoes and

drinks, soft drinks, or juices that contained sugar (diet

heavy objects from their pockets, as well as any heavy

drinks are excluded); ate sweet snacks like cookies,

outer layers of clothing before stepping on the scale.

chocolate bars, or candy; and ate fast food. The possible

Interviewers used a digital floor scale to measure weight

responses for these questions were: I do not typically eat/

to the nearest half pound. If weight or height could not

drink [item]; one to three times per week; four to six

be measured or if quality checks revealed an unusual

times per week; one time per day; two times per day;

value, we obtained self-reports from the respondents.

three times per day; and four times or more per day.

Using their height and weight, we calculated respondents’

BMI equal to their weight in kilograms divided by height

Health care access: We asked adults to specify the types

in meters-squared (BMI=kg/m2).5

of health insurance or health care coverage they currently

have for themselves (they could select multiple types of

In addition to examining impacts on mean BMI,

health insurance) and where they usually go (a clinic,

overweight (defined as a BMI ≥25), and obesity

doctor’s office, emergency room, or some other place)

(BMI ≥30), we also look at different parts of the BMI

when they need routine or preventive care. We asked

distribution, given previous evidence that having very

how long it had been since they last saw a doctor about

high BMI values (≥35 or ≥40) may be strongly associated

their own health, and where their children usually go for

with subsequent adverse health outcomes (National

health care. In addition, we asked how many times they

Institutes of Health, National Heart, Lung and Blood

had been to the emergency room in the past 12 months

Institute, and Obesity Education Initiative, 1998).

because of their own health, and whether in the past 12

months they or their children did not get needed medical The standard BMI measure tends to work less well for care for any of the following reasons: can’t afford it,

children and adolescents because BMI tends to increase

didn’t have transportation, didn’t know whom to see, or

from birth through about age 20 as a natural byproduct

had to take care of family or friends. We also asked youth of physical development. Instead, we define overweight if they had a routine physical exam or a dental exam in

and obesity for youth using criteria developed by the

the past year.

International Obesity Task Force (IOTF) (Cole et

PHYSICAL BIOMARKERS

We directly measured various health outcomes in part to

5 Additional details about the measurement protocols, as well as a

address concerns that MTO moves may affect self-reports description of the handling of outlier values, are in the technical appendices (forthcoming) of this report.

87

Chapter 3: Impacts on Adult and Youth Physical Health

al., 2000).6 The IOTF uses growth curves from an

the average of each measure.7 If only one valid reading

international sample to create youth obesity measures

was obtained, we used the single-reading values. We use

by age and gender that align with the adult standards.

the definitions suggested by the National Institutes of

Curves are further broken down by gender because while

Health, National Heart, Lung and Blood Institute, and

BMI tends to follow a linear trend for males, the trend

Obesity Education Initiative (2004) for pre-hypertension

line has more of a concave shape for females, and also

(systolic blood pressure of 120 mmHg or higher or

because puberty tends to affect female bodies at different

diastolic blood pressure of 80 mmHg or higher) and

ages than males (Cole et al., 2000).

hypertension (systolic pressure of 140 mmHg or higher,

or diastolic pressure of 90 mmHg or higher; see also

Waist circumference: Waist circumference is a measure

Chobanian et al., 2003). Because our self-reported

of central obesity (abdominal or central fat) and has also

hypertension measure does not distinguish between

been shown to be an important anthropometric correlate

having hypertension and being treated for hypertension,

of cardiovascular risk, even after controlling for BMI.

a limitation of our study is our inability to distinguish

How weight is carried matters for health (Srinivasan et

between people who are being effectively treated for

al., 2009; Wildman et al., 2008; Schneider et al., 2006;

hypertension versus those with no hypertension.

Depres and Lemieux, 2006; Lee et al., 2008). Central

obesity has also been shown to be a major risk factor for

DRIED BLOOD SPOT ASSAYS

type II diabetes (for more information, see discussion

Blood assays can potentially capture otherwise undetected

in National Institutes of Health, National Heart, Lung

health problems, such as undiagnosed diabetes, as well

and Blood Institute, and Obesity Education Initiative,

as longer-term risks of disease, such as cardiovascular

1998). Respondents removed bulky outer clothing, and

disease (CVD). Interviewers asked participants if they

interviewers took waist measurements over their regular

could collect a small sample of their blood by pricking

clothing. Interviewers asked the respondents to wrap

their fingers with a small instrument and then collecting

a flexible measuring tape around their waist at navel

enough blood to fill six small circles on a collection card.

level, inhale and exhale normally, and, while holding the

Respondents were offered an additional $25 incentive

exhale, hold the tape measure where the two ends met

(on top of the incentive provided for completing the

and then hand it to the interviewer, who recorded the

interview) for this blood sample collection. To avoid

measurement in inches.

risks to participants, anyone who indicated that they

had hemophilia or were taking anticoagulants or blood

Blood pressure: Interviewers took respondents’ blood

thinners was excluded from the blood spot collection.

pressure using a large-sized automated blood pressure

Among those interviewed and eligible for blood spot

cuff (Omron automated sphygmomanometer model

collection, the consent rate was over 90 percent.

HEM-711DLX) designed to accommodate arm

circumferences from 22 to 42 centimeters. Respondents

Interviewers used a sterile, single-use lancet to prick the

sat at a table with both feet flat on the floor and their

finger of the participant and collected up to six drops

arm resting, palm up, on the table. The cuff was placed

of blood on specimen paper, with one spot pretreated

on the respondent’s upper left arm, about half an inch

for the analysis of glycosylated hemoglobin (HbA1c).

above the elbow. Interviewers tried to collect two separate Interviewers allowed the blood spots to dry for 15

readings from each adult respondent. If two valid

minutes and then placed them in a foiled envelope with

diastolic and systolic readings were obtained, we used

a desiccant packet. Interviewers mailed the samples once

a week to a central location for proper freezer storage

at – 20°C to – 30°C. Because the interviews for Section

6 The Centers for Disease Control and Prevention has developed an

8 adults started about eight months after experimental

alternative set of growth curves that are typically used to assess whether

children are at risk of obesity. However, the growth curves apply only

up to age 20 and thus would not apply to about 25 percent of our

7 We considered a reading as valid if diastolic blood pressure was over

youth respondents who were over age 20 at the time of interview. As a

40 mmHg, the systolic blood pressure was over 60 mmHg, and the

result, we chose to use the IOTF criteria instead.

systolic was at least 10 points higher than the diastolic.

88

and control interviews, more of the Section 8 assays were physical health. Fifty-six percent of adults in the control done in the later batches by the laboratory.8

group rate their health as good, very good, or excellent;

the others rate their health as only fair or poor. There

HbA1c and diabetes: In addition to our survey

is little difference in self-rated health across randomly

questions about diabetes that were discussed above,

assigned mobility groups. The estimated intention-to-

we measured the concentration of HbA1c from the

treat effect (ITT) for the experimental group treatment

dried blood spot specimens. This assessment provides

is just 0.2 percentage points, with a standard error of

an integrated measure of average blood glucose levels

2 percentage points. These estimates enable us to rule

over several months prior to blood collection without

out an experimental ITT effect of any more than 4

requiring the respondent to fast. The American Diabetes

percentage points in either direction around zero. The

Association (2010) recently recommended guidelines for

top panel of Exhibit 3.3 shows that the distribution of

use of HbA1c to diagnose and monitor diabetes, with a

adults across each of the different possible self-reported

proposed HbA1c threshold of 6.5 percent for diagnosis.

health categories (poor, fair, good, very good, and

In addition to our two self-reported measures of diabetes, excellent) is similar for the treatment and control groups.

we created two additional measures using the HbA1c

As noted above, this health measure has the potential

assays: diabetes as indicated by an elevated HbA1c

limitation of confounding the effects of MTO moves on

level (≥ 6.5 percent) and a combined survey and blood

health status with effects on the benchmarks that families

measure of diabetes, defined as having either an elevated

use to think about what counts as good or excellent

HbA1c level or self-reporting either having had diabetes

health.

or having been treated for diabetes during the past year.

The combined measure is defined only for respondents

OBESITY AND WAIST CIRCUMFERENCE

with both survey and dried blood spot information.

Although we do not see statistically significant

Because the self-reported diabetes measure does not

experimental treatment effects on waist circumference, we

distinguish between having diabetes and being treated for estimate an experimental group ITT effect on average BMI diabetes, a limitation of our study is the inability to fully

of – 0.64, (p value, .07), which is equal to about 2 percent

differentiate between well-controlled diabetes (defined as of the control group’s mean BMI (32.8). The experimental a diagnosed case with normal HbA1c concentrations due

treatment had no detectable impact at long-term follow-

to medication or other factors) and no diabetes.

up on the likelihood of having a BMI ≥ 30, although we

observed significant differences between the experimental

Hs-CRP: The dried blood spot specimens were also

and control groups at more severe levels of obesity.

assayed for high-sensitivity C-reactive protein (hs-CRP),

Estimates show that those in the experimental group were

a biomarker of inflammation that has recently emerged

4.6 percentage points less likely than control subjects to

as an important predictor of cardiovascular disease

have a BMI of ≥ 35 (an effect that is equal to about 13

(McDade, 2003; Ridker et al., 2002, 2003; Arima et al.,

percent of the control group’s prevalence of 35.1 percent),

2008).9

and they were 3.4 percentage points less likely to have a

BMI of ≥ 40 (an effect equal to around 19 percent of the

3.5 LONG-TERM IMPACTS ON ADULT

control group’s prevalence of 17.5 percent).

PHYSICAL HEALTH

For the Section 8 group, impacts on extreme obesity are

SELF-RATED HEALTH STATUS

of a similar magnitude, but only the effect on BMI ≥ 35

Exhibit 3.2 shows estimates for long-term effects on adult is statistically significant. The bottom panel of Exhibit 3.3

shows the implied distribution of each treatment group by

8 More details about the dried blood spot data collection protocol and

laboratory assays and calibration are in technical appendices, forthcom-

obesity category. There are no statistically significant impacts

ing.

of either group membership on waist circumference. In

9 Additional details on the assays and recalibration of hs-CRP using the

results not presented here, we find significantly fewer

results of a validation study will be described in technical appendices,

forthcoming.

89

Chapter 3: Impacts on Adult and Youth Physical Health

Section 8 adults than control group (27 percent versus 33

they had diabetes (ITT of – 5.7 percentage points, which

percent) with a waist circumference above 43 inches (the

represents a 30 percent relative decline compared with

90th percentile for women based on the third National

the control group prevalence). Although we observe

Health and Nutrition Examination Survey; Ford, Mokdad,

no significant increase within the Section 8 group of

and Giles, 2003). However, we do not detect any significant elevated HbA1c levels, we cannot statistically rule out effects for the experimental group.

that the impacts using the survey measures are different

(at a statistically significant level) from the impacts

DIABETES AND HbA1c

estimated using HbA1c.12 In addition, the timing of the

Compared with the control group, the experimental

dried blood spot assays differed between the Section 8

group had statistically significantly lower levels of

group and the control group; interviews with adults in

diabetes as measured by blood assays for HbA1c ≥ 6.5

the Section 8 group occurred, on average, two months

percent (ITT effect of – 5.2 percentage points versus

after the control group interviews.

a control mean of 20.4 percent; see Exhibit 3.2).

They were also lower when diabetes is measured by a

HYPERTENSION AND hs-CRP

combination of blood sample results and self-reported

We detect no statistically significant differences between

data (ITT of – 3.4 percentage points, versus a control

the treatment and control groups on systolic or diastolic

mean prevalence of 23.4 percent; p value, .073). From

blood pressure, prehypertension, or hypertension.

self-reports of ever having been diagnosed with diabetes

However, we observe a nearly significant difference of –

by a doctor, we find a nearly significant ITT effect of –

4.2 percentage points ( p value, .08) for the experimental

2.8 percentage points for the experimental group (control group on hs-CRP levels >3 mg/L, relative to a control mean of 19.3 percent). Our analyses of self-reports of

mean of 58.6 percent. Previous research suggests that

having had diabetes, currently taking medication for

the relative risk of a major coronary event may be twice

it, or having been treated for it during the past year

as high for people with hs-CRP level of greater than

showed a nonsignificant experimental ITT effect of – 2.4 3 mg/L, relative to those with a level of <1.0 mg/L

percentage points versus a control mean of 16 percent.10

(Pearson et al., 2003). For the Section 8 group, we do

Exhibit 3.2 provides evidence of under-reporting with

not observe any statistically significant effects of MTO

the self-reported diabetes measure, given that 16 percent

on hypertension or hs-CRP.

of controls report having diabetes or being treated for

diabetes, while over 20 percent have HbA1c of 6.5

ASTHMA

percent or higher as measured from blood samples.11

We detect no statistically significant effects on self-

reported asthma for either treatment group. Some of

Adults in the Section 8 group were less likely than those

our estimates have fairly wide 95 percent confidence

in the control group to report that a doctor told them

intervals. For example, the Section 8 group ITT interval

for having an asthma or wheezing attack during the past

10 Our combined measure using self-reported and HbA1c data is

restricted to individuals with information from both sources. Alterna-

year ranges from – 9 to +1 percentage points, relative to

tively, if we were to include everyone who had either self-reported or

a control mean of 29 percent. Thus, we cannot rule out

HbA1c data, the ITT estimates are -.036 for the experimental group (p

< .05) and -.011 for the Section 8 group (not statistically significant) moderate effects.

compared with a control mean of .259.

11 There is even more under-reporting of uncontrolled diabetes.

HEALTH LIMITATIONS AND

Exhibit 3.2 shows that 16 percent of controls report having diabetes or

being treated for diabetes, 20 percent of controls have HbA1c of 6.5

CHRONIC PAIN

percent or higher, and 23 percent have either self-reported diabetes or

We observe no statistically significant difference across

treatment for diabetes or have HbA1c of 6.5 percent or higher. These

figures imply that around 3 percent of the control group say they have

treatment groups on reports of health limitations, arthritis

diabetes or are being treated for it, but do not have HbA1c of 6.5

percent or higher– these are presumably control group adults who are

being treated for diabetes. So 13 percent of the control group says they

12 Note that the impact estimates using our survey measure of diabetes

have diabetes and have HbA1c of 6.5 percent or higher as measured by

are similar regardless of whether we use the full set of respondents

blood samples, equal to about two-thirds of the total share of controls

interviewed or restrict the sample to only respondents with HbA1c data

(20 percent) who have HbA1c of 6.5 percent or higher.

(results not shown).

90

or rheumatism, headaches, chronic pain, or bed days,

SMOKING AND DRINKING

with two exceptions. The experimental group reported

Roughly one-third of adults in the control group

fewer health limitations than the control group, and the

reported that they currently smoked, and about

Section 8 group reported a higher prevalence of arthritis

44 percent reported having had an alcoholic drink

or rheumatism. Whether this estimated adverse Section

during the past month. The estimate points toward

8 effect on arthritis / rheumatism represents a real MTO

greater smoking for the experimental group, but is not

impact, a statistical fluke, or is because Section 8 adults are statistically significant. Respondents in the experimental more likely to see a doctor and be diagnosed with arthritis

group reported slightly more days in which they had an

or rheumatism is not known, although below we report

alcoholic drink. The frequency of alcohol consumption

results indicating that the Section 8 treatment did seem to

is low across all three groups: less than two days a

improve people’s access to medical care.

month, on average. (In Chapter 4, we present analyses of

dependence on alcohol or drugs for the adult sample, and

in Chapter 6, we present results for youth smoking and

alcohol use.)

EXHIBIT 3.2. ADULT PHYSICAL HEALTH OUTCOMES

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

GOOD OR BETTER HEALTH

0.564

0.002

0.004

– 0.005

– 0.009

3,269

CURRENTLY

(0.020)

(0.042)

(0.027)

(0.044)

(VERSUS FAIR OR POOR) [SR]

OBESITY AND WAIST

CIRCUMFERENCE

Height, in feet [M, SR]

5.315

0.009

0.019

– 0.001

– 0.002

3,242

(0.010)

(0.020)

(0.013)

(0.021)

Weight, in pounds [M, SR]

189.985

– 3.163

– 6.521

– 2.852

– 4.615

3,222

(2.077)

(4.281)

(2.915)

(4.717)

Body Mass Index (BMI) [M, SR]

32.803

– 0.636~

– 1.311~

– 0.489

– 0.792

3,221

(0.351)

(0.724)

(0.491)

(0.796)

Currently obese (BMI ≥ 30) [M, SR]

0.584

– 0.012

– 0.025

– 0.011

– 0.018

3,221

(0.022)

(0.044)

(0.029)

(0.046)

BMI ≥ 35 [M, SR]

0.351

– 0.046*

– 0.095*

– 0.053*

– 0.086*

3,221

(0.020)

(0.042)

(0.027)

(0.043)

BMI ≥ 40 [M, SR]

0.175

– 0.034*

– 0.071*

– 0.029

– 0.048

3,221

(0.016)

(0.032)

(0.021)

(0.034)

Waist circumference, in inches [M, SR]

40.868

– 0.273

– 0.563

– 0.543

– 0.867

3,177

(0.302)

(0.623)

(0.401)

(0.641)

DIABETES

Ever told by a doctor they had diabetes

0.193

– 0.028~

– 0.058~

– 0.057*

– 0.091*

3,251

or high blood sugar [SR]

(0.016)

(0.033)

(0.021)

(0.033)

Had diabetes or treated for it during the

0.160

– 0.024

– 0.049

– 0.061*

– 0.098*

3,251

past year [SR]

(0.015)

(0.030)

(0.018)

(0.029)

HbA1c test detected diabetes (HbA1c ≥

0.204

– 0.052*

– 0.108*

– 0.011

– 0.017

2,737

6.5%) [DBS]

(0.018)

(0.038)

(0.024)

(0.038)

91

Chapter 3: Impacts on Adult and Youth Physical Health

EXHIBIT 3.2. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

DIABETES (CONTINUED)

HbA1c test detected diabetes or had or

0.234

– 0.034~

– 0.071~

– 0.008

– 0.013

2,732

treated for diabetes during the past year

(0.019)

(0.040)

(0.025)

(0.039)

[DBS, SR]

HYPERTENSION AND RISK OF

CARDIOVASCULAR DISEASE

Systolic blood pressure, mm Hg [M]

123.482

0.629

1.292

0.456

0.723

3,102

(0.877)

(1.802)

(1.121)

(1.778)

Diastolic blood pressure, mm Hg [M]

82.600

0.551

1.132

0.364

0.577

3,102

(0.577)

(1.186)

(0.744)

(1.180)

Currently has high blood pressure

0.315

0.007

0.015

– 0.026

– 0.041

3,102

(systolic ≥ 140 mm Hg or diastolic ≥ 90

(0.020)

(0.042)

(0.027)

(0.043)

mm Hg) [M]

Currently has prehypertension or higher

0.616

0.019

0.039

0.020

0.032

3,102

(systolic ≥ 120 mm Hg or diastolic ≥ 80

(0.021)

(0.043)

(0.027)

(0.043)

mm Hg) [M]

Ever prescribed high blood pressure

0.382

0.013

0.027

0.018

0.029

3,261

medicine [SR]

(0.019)

(0.040)

(0.026)

(0.042)

Currently has high blood pressure or

0.479

0.009

0.018

– 0.012

– 0.019

3,158

taking blood pressure medicine [M, SR]

(0.021)

(0.042)

(0.027)

(0.043)

High sensitivity C-reaction Protein at

0.586

– 0.042~

– 0.087~

0.010

0.016

2,693

high risk level (> 3 mg/L) [DBS]

(0.024)

(0.049)

(0.030)

(0.048)

ASTHMA

Has ever been told by a doctor that they

0.275

– 0.022

– 0.044

– 0.022

– 0.036

3,268

had asthma [SR]

(0.019)

(0.039)

(0.025)

(0.040)

Asthma or wheezing attack during the

0.293

– 0.018

– 0.038

– 0.042

– 0.066

3,267

past year [SR]

(0.020)

(0.040)

(0.026)

(0.041)

LIMITATIONS AND CHRONIC PAIN

Health limits respondent's ability to climb

0.510

– 0.048*

– 0.100*

– 0.023

– 0.038

3,270

several flights of stairs or lift or carry

(0.021)

(0.043)

(0.028)

(0.045)

groceries [SR]

Ever had arthritis or rheumatism [SR]

0.304

0.017

0.036

0.051*

0.081*

3,259

(0.019)

(0.039)

(0.025)

(0.040)

Ever had severe or frequent headaches

0.410

– 0.006

– 0.013

– 0.039

– 0.063

3,267

[SR]

(0.021)

(0.044)

(0.028)

(0.045)

Had severe or frequent headaches in

0.233

0.001

0.002

– 0.032

– 0.052

3,267

past year [SR]

(0.019)

(0.038)

(0.025)

(0.039)

Ever had back, neck, or other chronic

0.440

– 0.024

– 0.050

– 0.010

– 0.016

3,266

pain [SR]

(0.021)

(0.043)

(0.028)

(0.045)

92

EXHIBIT 3.2. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

LIMITATIONS AND CHRONIC PAIN (CONTINUED)

Back, neck, or other chronic pain in the

0.295

– 0.024

– 0.050

– 0.003

– 0.004

3,265

past year [SR]

(0.019)

(0.040)

(0.026)

(0.042)

Number of days injury/illness kept adult

9.698

0.573

1.188

2.024

3.215

3,234

in bed more than half the day [SR]

(1.575)

(3.266)

(2.251)

(3.575)

SMOKING AND DRINKING

Currently a smoker [SR]

0.341

0.020

0.042

– 0.007

– 0.011

3,235

(0.020)

(0.042)

(0.027)

(0.043)

Ever drank an alcoholic beverage [SR]

0.602

0.046*

0.095*

– 0.037

– 0.058

3,239

(0.021)

(0.043)

(0.028)

(0.044)

Had alcoholic beverage in past 30 days

0.435

0.024

0.049

– 0.013

– 0.021

3,238

[SR]

(0.021)

(0.044)

(0.028)

(0.045)

Fraction of days adult had alcoholic drink

0.053

0.011*

0.022*

0.004

0.006

3,238

in past 30 days [SR]

(0.005)

(0.010)

(0.006)

(0.010)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report, M = direct measurement, DBS = dried blood spot assays.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.

Measures: For obesity inputs (height and weight), only a very small percent of the sample self-reported their height or weight. BMI is measured as weight in kilograms divided by height in meters squared. For diastolic and systolic blood pressure, data are the average of two readings, if available; otherwise, data are from one reading.

93

Chapter 3: Impacts on Adult and Youth Physical Health

EXHIBIT 3.3. ADULT SELF-RATED HEALTH AND BMI CATEGORIES BY TREATMENT GROUP

Exhibit 3.3. Adult Self-Rated Health and BMI Categories by Treatment Group

A. Self-Rated Health of Adults by Treatment Group

0.40

0.35

0.30

Control Share

0.25

Adjusted Experimental

0.20

Adjusted Section 8

0.15

SHARE OF GROUP

0.10

0.05

0.00

Excellent

Very Good

Good

Fair

Poor

HEALTH CATEGORY

B. BMI Categories for Adults by Treatment Group

0.30

Control Share

0.25

Adjusted Experimental

0.20

Adjusted Section 8

0.15

SHARE OF GROUP

0.10

0.05

0.00

Underweight/

Overweight

BMI ≥ 30

BMI ≥ 35

BMI ≥ 40

normal

but not obese

and < 35

and < 40

BMI CATEGORY

Notes: Share controls represents the unadjusted control mean. Adjusted experimental share represents the control mean plus the experimental ITT effect.

Adjusted Section 8 share represents the control mean plus the Section 8 ITT effect. For obesity inputs (height and weight), only a very small percent of the sample self-reported their height or weight. BMI is measured as weight in kilograms divided by height in meters squared. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates and field release, weighted, and clustering on family. Youth impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All interviewed adults.

94

3.6 LONG-TERM IMPACTS ON THE

treatment and control groups on accidents and injuries in

PHYSICAL HEALTH OF YOUTH AND

the past year, or on the prevalence of chronic pain, bad

GROWN CHILDREN

headaches, or serious illnesses such as diabetes or stomach

problems. More than one-third of youth ages 13–20

YOUTH AGES 10–20

in the control group reported chronic pain or frequent

Exhibit 3.4 shows the estimated effects of MTO on the

headaches. Although not statistically significant, MTO

physical health of youth.

moves tend to reduce the prevalence of accidents and

injuries for female youth relative to those in the control

Self-rated health status: Eighty-six percent of female

group. However, they tend to increase the prevalence for

youth and 90 percent of male youth in the control group male youth, as we found in the interim study.

rated their health as good, very good, or excellent. We

detected no statistically significant differences between

Dental health: We detect no differences across treatment

the treatment groups and control groups on this measure. groups in terms of whether youth report that their teeth As Exhibits 3.5 and 3.6 show, the distribution of

are in “very good” or “excellent” condition rather than

categorical responses across treatment groups is similar

“good,” “fair,” or “poor” condition. We detect no impacts

for both females and males.

by gender on this measure. Across the full range of

cut-points, we generally do not detect effects. However,

Obesity: Forty-five percent of youth in the control

a statistically significantly higher proportion of female

group met the IOTF criteria for overweight (designed

youth in the experimental group reported that their teeth

to correspond to an adult BMI ≥25), and 23 percent

were in excellent condition than do controls.

met the criteria for obesity (designed to correspond to

adult BMI ≥30). Control group females (27.4 percent)

exhibited higher levels of obesity than do control

group males (18.7 percent). There were no statistically

significant effects of either treatment group overall or

by gender on these outcomes, although we cannot rule

out modest effects. For example, for female youth, we

are only 95 percent confident that the experimental

impact on being overweight is within the range of – 3.3

to +7.7 percentage points, and we are only 95 percent

confident that the impact of obesity is between – 7.7 to

+2.1 percentage points (that is, we cannot rule out that

the treatment reduces obesity by as much as 28 percent

relative to the control prevalence nor that it increases

obesity by as much as 8 percent of the control mean).

Asthma: Nineteen percent of youth in the control group

reported an asthma or wheezing attack during the past

year, and 7 percent reported that wheezing limited their

activities or led them to miss school or work. We find

no statistically significant effects of either treatment on

asthma and wheezing attacks, overall or by gender.

Accidents, injuries, chronic pain, and serious illness:

We detect no significant differences between the

95

Chapter 3: Impacts on Adult and Youth Physical Health

EXHIBIT 3.4. YOUTH PHYSICAL HEALTH OUTCOMES

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

GENERAL HEALTH

Good or better health currently (versus fair or poor) [SR]

All

0.883

0.005

0.009

0.000

– 0.001

5,100

(0.013)

(0.027)

(0.015)

(0.022)

Female

0.862

0.003

0.007

0.006

0.010

2,600

(0.019)

(0.038)

(0.021)

(0.034)

Male

0.903

0.006

0.012

– 0.007

– 0.010

2,500

(0.016)

(0.035)

(0.019)

(0.027)

ASTHMA

Asthma or wheezing attack during the past year [SR]

All

0.190

0.006

0.013

– 0.013

– 0.019

5,092

(0.015)

(0.032)

(0.016)

(0.024)

Female

0.206

– 0.009

– 0.017

– 0.021

– 0.032

2,595

(0.021)

(0.043)

(0.023)

(0.037)

Male

0.174

0.021

0.045

– 0.006

– 0.008

2,497

(0.020)

(0.043)

(0.022)

(0.031)

Wheezing caused limited activities or missed school or work in the past year [SR]

All

0.071

0.003

0.007

– 0.012

– 0.018

5,091

(0.010)

(0.021)

(0.010)

(0.015)

Female

0.079

– 0.001

– 0.002

– 0.008

– 0.012

2,594

(0.014)

(0.028)

(0.015)

(0.023)

Male

0.064

0.008

0.017

– 0.017

– 0.025

2,497

(0.014)

(0.029)

(0.013)

(0.018)

OBESITY

Currently overweight [M, SR]

All

0.449

0.015

0.031

0.009

0.013

5,034

(0.020)

(0.042)

(0.022)

(0.032)

Female

0.482

0.022

0.045

0.039

0.061

2,560

(0.028)

(0.056)

(0.030)

(0.048)

Male

0.418

0.008

0.016

– 0.022

– 0.031

2,474

(0.027)

(0.059)

(0.029)

(0.042)

Currently obese [M, SR]

All

0.229

– 0.010

– 0.022

– 0.010

– 0.014

5,034

(0.017)

(0.035)

(0.018)

(0.028)

Female

0.274

– 0.028

– 0.057

– 0.020

– 0.031

2,560

(0.025)

(0.051)

(0.026)

(0.041)

Male

0.187

0.007

0.016

0.000

0.000

2,474

(0.021)

(0.045)

(0.023)

(0.033)

96

EXHIBIT 3.4. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

ACCIDENTS AND INJURIES

Had accidents or injuries requiring medical attention in the past year [SR]

All

0.178

0.006

0.013

0.008

0.012

5,097

(0.014)

(0.030)

(0.016)

(0.024)

Female

0.164

– 0.023

– 0.046

– 0.024

– 0.037

2,597

(0.018)

(0.037)

(0.020)

(0.031)

Male

0.192

0.035

0.076

0.039

0.056

2,500

(0.022)

(0.047)

(0.024)

(0.034)

Had serious accidents or injuries that required medical attention or limited activities in the past year [SR]

All

0.273

0.001

0.002

0.006

0.009

5,092

(0.017)

(0.035)

(0.019)

(0.028)

Female

0.233

– 0.024

– 0.049

– 0.011

– 0.017

2,595

(0.022)

(0.044)

(0.024)

(0.038)

Male

0.309

0.026

0.056

0.022

0.031

2,497

(0.025)

(0.054)

(0.028)

(0.040)

Had a nonsports accident or injury requiring medical attention in the past year [SR]

All

0.170

0.011

0.024

0.016

0.024

5,074

(0.014)

(0.030)

(0.016)

(0.024)

Female

0.176

– 0.015

– 0.030

0.001

0.002

2,584

(0.020)

(0.039)

(0.022)

(0.034)

Male

0.165

0.038~

0.082~

0.030

0.042

2,490

(0.020)

(0.044)

(0.023)

(0.032)

CHRONIC PAIN AND SERIOUS ILLNESS

AMONG YOUTH 13–20

Have ever had chronic back, neck, or other pain or frequent/very bad headaches [SR]

All

0.371

– 0.002

– 0.004

0.017

0.025

4,628

(0.019)

(0.039)

(0.021)

(0.032)

Female

0.432

– 0.019

– 0.039

– 0.010

– 0.015

2,362

(0.027)

(0.054)

(0.031)

(0.048)

Male

0.313

0.015

0.033

0.043

0.062

2,266

(0.027)

(0.057)

(0.030)

(0.042)

Have ever had serious illness like diabetes, high blood sugar, and serious stomach problem [SR]

All

0.044

– 0.013

– 0.026

– 0.010

– 0.015

4,627

(0.008)

(0.016)

(0.009)

(0.014)

Female

0.056

– 0.016

– 0.033

– 0.020

– 0.031

2,359

(0.012)

(0.025)

(0.014)

(0.021)

Male

0.033

– 0.009

– 0.019

0.000

0.001

2,268

(0.009)

(0.020)

(0.012)

(0.017)

97

Chapter 3: Impacts on Adult and Youth Physical Health

EXHIBIT 3.4. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

DENTAL

Teeth are in very good or better condition [SR]

All

0.400

– 0.004

– 0.008

0.000

0.000

5,095

(0.019)

(0.039)

(0.020)

(0.030)

Female

0.379

0.021

0.042

0.026

0.040

2,596

(0.025)

(0.051)

(0.028)

(0.043)

Male

0.419

– 0.028

– 0.060

– 0.025

– 0.036

2,499

(0.026)

(0.057)

(0.029)

(0.041)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report, M = direct measurement.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 10 to 20 as of December 31, 2007.

Measures: Overweight and obesity are defined according to the International Obesity Task Force.

SUBSET OF YOUTH AGES 15–20

sample of youth ages 10–14 (not shown) are imprecise,

Supplemental Exhibit 3.1 presents impacts on youth ages with some estimates having the opposite sign from the 15–20 at the end of 2007. This allows us to compare

older youth.

current results with interim results in 2001 reported

by Kling, Liebman, and Katz (2007). This comparison

GROWN CHILDREN AGES 21–30

holds constant the ages at which we measure the health

Exhibit 3.7 presents our results on the physical health

outcomes, and examines whether the impact is different

of grown children. Parents in the control group reported

for children who were relatively younger at the time of

that 4.5 percent of their grown children had physical

MTO random assignment (than the sample analyzed

health problems that kept them from normal activities,

by Kling, Liebman, and Katz) and thus had spent a

and 7.9 percent had a chronic health problem, such

relatively larger share of their lives in lower-poverty

as cancer or a heart problem. We do not detect any

neighborhoods before we measured health outcomes. In

significant effects for the experimental group or for

principle, this comparison will confound differences in

females in either group. However, we do observe a

how MTO effects vary by someone’s age at baseline with

significantly higher prevalence of health limitations

differences in how effects vary by cohort and the calendar among Section 8 males relative to males in the control years in which the health outcomes are measured.

group (ITT of 4.9 percent relative to a control mean of

4.3 percent).

With that last caveat in mind, we detect no effects on

overall health or self-reported asthma among this older

age group, but the results indicate less serious or chronic

illness among youth in the experimental group ( p value,

.03), more accidents and injuries requiring medical

attention for Section 8 males ( p value, .03), and hint at

possibly lower obesity (ITT of – 4.9 percentage points,

p value, .10) for female youth in the experimental group

compared with female controls. Estimates for the smaller

98

Exhibit 3.5. Female Youth Self-Rated Health and BMI Categories by Treatment Group EXHIBIT 3.5. FEMALE YOUTH SELF-RATED HEALTH AND BMI CATEGORIES BY TREATMENT GROUP

A. Female Youth Self-Rated Health by Treatment Group

0.40

0.35

0.30

Control Share

0.25

Adjusted Experimental

0.20

Adjusted Section 8

SHARE OF GROUP

0.15

0.10

0.05

0.00

Excellent

Very Good

Good

Fair/Poor

HEALTH CATEGORY

B. Female Youth BMI Categories by Treatment Group

0.70

0.60

Control Share

0.50

Adjusted Experimental

0.40

Adjusted Section 8

0.30

SHARE OF GROUP

0.20

0.10

0.00

Underweight/

Overweight

Obese

normal

but not obese

BMI CATEGORY

Notes: Share controls represents the unadjusted control mean. Adjusted experimental share represents the control mean plus the experimental ITT effect. Adjusted Section 8 share represents the control mean plus the Section 8 ITT effect. For obesity inputs (height and weight), only a very small percent of the sample self-reported their height or weight. BMI is measured as weight in kilograms divided by height in meters squared. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher.

Data source and sample: Youth long-term survey. Interviewed female youth ages 10 to 20 as of December 31, 2007.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates and field release, weighted, and clustering on family. Youth impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and technical appendices (forthcoming) for details.

99

Chapter 3: Impacts on Adult and Youth Physical Health

Exhibit 3.6. Male Youth Self-Rated Health and BMI Categories by Treatment Group EXHIBIT 3.6. MALE YOUTH SELF-RATED HEALTH AND BMI CATEGORIES BY TREATMENT GROUP

A. Male Youth Self-Rated Health by Treatment Group

0.40

0.35

0.30

Control Share

0.25

Adjusted Experimental

0.20

Adjusted Section 8

SHARE OF GROUP

0.15

0.10

0.05

0.00

Excellent

Very Good

Good

Fair/Poor

HEALTH CATEGORY

B. Male Youth BMI Categories by Treatment Group

0.70

0.60

Control Share

0.50

Adjusted Experimental

0.40

Adjusted Section 8

0.30

SHARE OF GROUP

0.20

0.10

0.00

Underweight/

Overweight

Obese

normal

but not obese

BMI CATEGORY

Notes: Share controls represents the unadjusted control mean. Adjusted experimental share represents the control mean plus the experimental ITT effect. Adjusted Section 8 share represents the control mean plus the Section 8 ITT effect. For obesity inputs (height and weight), only a very small percent of the sample self-reported their height or weight. BMI is measured as weight in kilograms divided by height in meters squared. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher.

Data source and sample: Youth long-term survey. Interviewed female youth ages 10 to 20 as of December 31, 2007.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates and field release, weighted, and clustering on family. Youth impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and technical appendices (forthcoming) for details.

100

EXHIBIT 3.7. PARENT-REPORTED PHYSICAL HEALTH OUTCOMES OF GROWN CHILDREN AGES 21–30

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

PHYSICAL LIMITATIONS

Physical health problem keeps from normal activities like walking, housework, or working [PR]

All

0.045

0.014

0.031

0.026~

0.047~

3,096

(0.010)

(0.023)

(0.014)

(0.025)

Female

0.048

0.013

0.029

0.001

0.001

1,525

(0.014)

(0.032)

(0.018)

(0.031)

Male

0.043

0.014

0.034

0.049*

0.093*

1,571

(0.014)

(0.034)

(0.022)

(0.041)

CHRONIC HEALTH PROBLEM

Has a chronic physical health problem such as cancer, a heart problem, or any other serious health problem [PR]

All

0.079

0.003

0.006

– 0.017

– 0.030

3,096

(0.013)

(0.030)

(0.015)

(0.028)

Female

0.080

0.008

0.018

– 0.023

– 0.040

1,526

(0.018)

(0.040)

(0.021)

(0.036)

Male

0.078

– 0.003

– 0.006

– 0.011

– 0.021

1,570

(0.017)

(0.039)

(0.020)

(0.039)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: PR = parent report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. Grown children (who were under age 18 at baseline and over age 20 as of December 31, 2007) of interviewed adults.

101

Chapter 3: Impacts on Adult and Youth Physical Health

3.7 IMPACTS ON HYPOTHESIZED

vegetables four times per week (a nonsignificant effect

MEDIATORS OF HEALTH

suggesting less healthy eating), but male youth may

be less likely to eat sweets four times per week (a

PHYSICAL ENVIRONMENT

nonsignificant effect suggesting more healthy eating).

Exhibits 2.2 through 2.9 in Chapter 2 showed

statistically significant impacts on a range of housing

ACCESS TO CARE

and neighborhood conditions that could affect health.

Supplemental Exhibit 3.4 shows how MTO affects access

Adults in the experimental group rated the condition

to health care for adults (top panel) and youth (bottom

of their housing more favorably than did control

panel). Although there are no statistically significant

subjects, and both treatment groups reported fewer

effects of the experimental treatment on adult access to

problems with vermin (a problem that can aggravate

health care, we see some signs of improved access for

asthma) and peeling paint or plaster, as well as greater

Section 8 adults, relative to control adults. For example,

satisfaction with their neighborhoods than their control

the Section 8 ITT effect on whether the adult and his

group counterparts. Interviewers’ observations of the

or her children have health insurance is 3.5 percentage

neighborhoods suggest persons in the experimental group points (p value, .096), relative to a control mean of 82.7

lived in neighborhoods with less litter than did control

percent. The Section 8 group did not differ significantly

subjects, which was also statistically significant.

from the control group in terms of whether they got

medical care when needed. However, they were less likely

EXERCISE AND NUTRITION

to report not receiving health care during the past 12

Supplemental Exhibit 3.2 presents estimated effects of

months specifically because they could not afford it (ITT

the MTO treatments on adult health behaviors related to of – 2.6 percentage points, versus a control mean of 6.1

exercise, television viewing, sleeping, and consumption

percent). For female youth, being in the experimental

of fruits and vegetables, soda, and sweets. We detect no

group seems to have lessened the likelihood of receiving

statistically significant impacts on these mediators, with

a routine physical exam in the past year. Even though

the exceptions that adults in the experimental group were most of the estimates on access to health care are not 4.6 percentage points more likely to exercise vigorously at statistically significant for adults or youth, our statistical least once a week compared with controls, and adults in

power is somewhat limited, and so the 95 percent

the Section 8 group were 4.8 percentage points less likely confidence interval of some of the ITTs encompasses than control subjects to report long sleep (defined as

moderate-sized effects.

>10 hours in the previous night). This is a large change

compared with the control mean of 9.9 percent. There

SOCIAL SUPPORTS AND SOCIAL

is no consistent pattern in the effects on healthy eating.

ISOLATION

In results not shown, we looked at effects for different

The results presented in Chapter 2 (and Supplemental

cut points on the food consumption questions. In the

Exhibits 2.4 and 2.5) suggest that the experimental group

rare instances that we detect differences across groups,

adults could have increased exposure to more affluent

the effects tend to be toward less healthy eating for the

peers, but that youth may be more socially isolated as a

experimental group relative to the control group.

result of their move. Specifically, for adults, assignment

to the experimental group rather than control group

Supplemental Exhibit 3.3 shows little evidence of a clear

did not change the likelihood of having three or more

effect of moving to different neighborhoods on youth

friends, but did increase the share of adults who reported

exercise levels, eating habits, and sleep patterns. To the

that they had a friend who was a college graduate. For

extent to which any of these impacts are statistically

youth, those assigned to the experimental group were

robust at 90 percent significance, they point in

more likely than controls to report they had no close

contradictory directions. For example, compared with

friends.

those in the control group, youth in the experimental

group may be somewhat less likely to eat fruits and

102

SAFETY AND STRESS

(BMI ≥ 30) for adults in the experimental group relative

Exhibit 2.10 suggests that adults in both treatment

to the control group (Kling, Liebman, and Katz, 2007),

groups feel safer in their current neighborhoods than do

results at 10–15 years show statistically significant

control subjects. They are also less likely to have seen

experimental group impacts only at more severe levels

people selling or using drugs in their neighborhood

of obesity (BMI ≥ 35 and BMI ≥ 40). This may be

in the past month. Similarly, female youth assigned to

because obesity is now so common across the MTO adult

the treatment groups rather than to the control group

sample—fully 58 percent of adults in the control group

reported improved neighborhood safety and less crime

were obese at the time of our long-term data collection,

victimization, and those assigned to the experimental

whereas 47 percent of controls were obese at the time of

group reported less unwanted sexual attention. In

the interim follow-up (Orr et al., 2003). We know that

contrast, there is no statistically significant effect on

obesity is associated with other chronic health problems,

safety for male youth. In fact, the two effects with

including heart disease, cancer, and, in particular,

borderline significance ( p values < .10) for males

diabetes. These comorbidities were not measured at

in the Section 8 group indicate that they are less

interim, but the blood spots collected as part of our

safe and experience more gang activity in their new

long-term study now enable us to confirm that reducing

neighborhoods and schools compared with control group extreme obesity is accompanied by reductions in the males.

prevalence of diabetes as well.

3.8 INTERPRETATION OF PHYSICAL

Our ability to physically measure a variety of health

HEALTH RESULTS

outcomes as part of the long-term study is an important

We have examined the effects of MTO on the health

enhancement over previous waves of MTO research.

outcomes of adults, youth, and grown children. For

In general, we might prefer measured health outcomes

adults, our analyses of the long-term survey data and

to self-reported outcomes because of the possibility of

biomarkers suggest somewhat better health among the

self-report error. In the case of MTO, we might also

experimental group adults relative to control subjects in

be concerned that neighborhoods could affect self-

terms of fewer physical limitations and lower prevalence

report error. One issue is that access to doctors might

of extreme obesity and diabetes. We also observe higher

vary across neighborhoods and lead to differences in

levels of drinking for the experimental group adults

diagnosis. In part to address this problem, we collected

compared with control subjects.13 We do not detect any

biomarker data and for self-reported measures such as

statistically significant effects on other health outcomes,

asthma and injuries. We also asked respondents about

such as self-rated health, hypertension, asthma, or

symptoms rather than simply medical diagnoses in an

chronic pain. In interpreting these null effects, it is

effort to minimize any potential bias. Another concern

important to bear in mind the size of effects that can

with self-reports is that neighborhoods could affect one’s

be ruled out, which varies depending on the measure.

frame of reference or expectations regarding one’s health.

For some measures, our 95 percent confidence intervals

In theory, a person’s rating of her health status may

do not allow us to rule out moderately sized impacts in

reflect both her objective health status and the health

either direction (toward better or worse health as a result

level she could expect to achieve (Adams and White,

of MTO moves).

2006). Although adults in the experimental group are

more likely than control subjects to report having a close

Whereas at the interim evaluation, four to seven years

friend who is a college graduate, it seems unlikely that

after random assignment, results revealed lower obesity

these types of changes in peer networks are large enough

to explain the lack of differences observed between

treatment and control groups on self-rated health.

13 The control group’s reports of ever drinking alcohol (60 percent)

and drinking in the past month (44 percent) are low but consistent

with published rates for minorities and for people with lower levels

The possibility of differing mortality or other health-

of education (for example, see Substance Abuse and Mental Health

Services Administration, 2010).

related attrition across groups is another source of

103

Chapter 3: Impacts on Adult and Youth Physical Health

potential bias. We think it is unlikely that mortality

behaviors of youth across treatment groups. Female youth

affects our impact estimates because our survey response

in the treatment groups felt greater safety relative to

rates are high, our extensive tracking efforts reveal

control subjects, and the experimental group experienced

similar rates of mortality across the treatment groups,

less psychological distress. For male youth in the Section

and interviewers traveled to participants rather than

8 group, some of our estimates point toward lower

requiring participants to travel to them (making it easier

perceptions of safety. (See Chapter 2 for the safety results

for participants with health limitations to participate in

and Chapter 4 for the mental health results.)

the follow-up).

In conclusion, the effects of MTO on adult or youth

In our analyses of health outcomes for youth ages

health outcomes are mixed, with relatively few

10–20, we do not detect statistically significant effects

statistically significant health impacts across a range

on self-rated health, asthma, obesity, injuries, chronic

of outcomes. However, the impacts that we do observe

pain, serious illness, or very good dental health. Echoing

for adults involve some of the most important health

the interim results, analyses restricted to only the older

outcomes—specifically, obesity and diabetes—and as

youth (ages 15–20) suggest some beneficial effects for the such speak to the potential public health importance of females in the experimental group and negative effects

these findings.

for the males in the Section 8 group, although these

results should be interpreted with caution, as some of the

results are in the opposite direction of those found for

youth ages 10–14. In our analyses of parental reports on

grown children who are now ages 21–30, we observe no

detectable effects on chronic health problems of moving

to a lower-poverty neighborhood, but we do observe a

higher rate of health limitations for males in the Section

8 group relative to control-group males.

We find little effect on most health mediators, except for

safety and stress, which makes us think that safety and

stress could potentially be the key mechanisms for the

effects on obesity and diabetes that we observe for adults.

At the interim report, we saw various effects on exercise

and eating of fruits and vegetables of moving to lower-

poverty neighborhoods, whereas at the final evaluation

we only see an impact on vigorous exercise for the

experimental group. Of note, the wording and response

categories on some of the exercise and diet questions

differ between the interim and final surveys.14

As with the adult health mediators, we detect few

differences on the eating habits, exercise, or sedentary

14 At interim, we asked adults and youth, “In a typical week, how

many days do you eat at least some green vegetables or fruit?” At final,

we asked adults, “How many times do you eat fruit and vegetables other

than french fries or potato chips” (from the National Longitudinal

Survey of Youth 1997). The possible responses were “I do not typically

eat fruit,” “1 to 3 times per week,” “4 to 6 times per week,” “1 time per

day,” “2 times per day,” “3 times per day,” and “4 times or more per

day.”

104

SUPPLEMENTAL EXHIBIT 3.1. PHYSICAL HEALTH OUTCOMES OF YOUTH AGES 15–20

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

GENERAL HEALTH

Good or better health currently (versus fair or poor) [SR]

All

0.871

0.010

0.020

0.008

0.012

3,621

(0.016)

(0.033)

(0.017)

(0.026)

Female

0.851

0.015

0.031

0.016

0.025

1,845

(0.022)

(0.045)

(0.025)

(0.039)

Male

0.890

0.004

0.009

0.001

0.001

1,776

(0.020)

(0.043)

(0.023)

(0.033)

ASTHMA

Asthma or wheezing attack during the past year [SR]

All

0.187

0.000

0.000

– 0.017

– 0.025

3,616

(0.018)

(0.037)

(0.019)

(0.029)

Female

0.217

– 0.025

– 0.051

– 0.031

– 0.048

1,842

(0.025)

(0.050)

(0.027)

(0.043)

Male

0.159

0.025

0.054

– 0.003

– 0.005

1,774

(0.024)

(0.051)

(0.026)

(0.038)

Wheezing caused limited activities or missed school/work for youth in past year [SR]

All

0.067

– 0.003

– 0.006

– 0.018

– 0.027

3,616

(0.012)

(0.024)

(0.011)

(0.017)

Female

0.077

– 0.009

– 0.019

– 0.013

– 0.020

1,842

(0.016)

(0.032)

(0.017)

(0.026)

Male

0.057

0.004

0.009

– 0.023

– 0.033

1,774

(0.016)

(0.034)

(0.015)

(0.021)

OBESITY

Currently overweight [M, SR]

All

0.463

0.012

0.026

– 0.001

– 0.002

3,580

(0.023)

(0.049)

(0.026)

(0.039)

Female

0.495

0.025

0.050

0.031

0.047

1,819

(0.032)

(0.065)

(0.036)

(0.056)

Male

0.432

0.000

0.001

– 0.032

– 0.047

1761

(0.032)

(0.070)

(0.035)

(0.051)

Currently obese [M, SR]

All

0.246

– 0.016

– 0.034

– 0.013

– 0.020

3,580

(0.020)

(0.041)

(0.022)

(0.033)

Female

0.301

– 0.049

– 0.098

– 0.032

– 0.050

1,819

(0.030)

(0.060)

(0.032)

(0.049)

Male

0.193

0.017

0.036

0.005

0.007

1,761

(0.025)

(0.054)

(0.028)

(0.040)

105

Chapter 3: Impacts on Adult and Youth Physical Health

SUPPLEMENTAL EXHIBIT 3.1. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

ACCIDENTS AND INJURIES

Had accidents/injuries requiring medical attention in the past year [SR]

All

0.166

0.012

0.024

0.025

0.038

3,618

(0.017)

(0.035)

(0.019)

(0.028)

Female

0.156

– 0.022

– 0.045

– 0.014

– 0.022

1,842

(0.022)

(0.044)

(0.024)

(0.037)

Male

0.175

0.045~

0.097~

0.064*

0.094*

1,776

(0.025)

(0.054)

(0.028)

(0.041)

Had serious accidents/injuries that required medical attention or limited activities in the past year [SR]

All

0.247

0.014

0.029

0.034

0.050

3,615

(0.019)

(0.040)

(0.022)

(0.034)

Female

0.210

– 0.015

– 0.031

0.020

0.032

1,841

(0.025)

(0.051)

(0.029)

(0.045)

Male

0.282

0.044

0.094

0.046

0.066

1,774

(0.029)

(0.062)

(0.032)

(0.046)

Had a non-sports accident/injury requiring medical attention in the past year [SR]

All

0.159

0.015

0.032

0.035~

0.053~

3,603

(0.017)

(0.034)

(0.019)

(0.029)

Female

0.164

– 0.006

– 0.012

0.020

0.031

1,832

(0.023)

(0.046)

(0.026)

(0.040)

Male

0.153

0.036

0.078

0.050~

0.072~

1,771

(0.023)

(0.050)

(0.027)

(0.039)

CHRONIC PAIN AND SERIOUS ILLNESS

Ever had chronic back, neck or other pain or frequent/very bad headaches [SR]

All

0.366

– 0.001

– 0.003

0.011

0.016

3,612

(0.021)

(0.044)

(0.024)

(0.036)

Female

0.432

– 0.028

– 0.057

– 0.027

– 0.043

1,839

(0.031)

(0.062)

(0.035)

(0.054)

Male

0.305

0.025

0.054

0.049

0.070

1,773

(0.029)

(0.063)

(0.033)

(0.047)

Ever had serious illness like diabetes, high blood sugar, and serious stomach problem [SR]

All

0.051

– 0.021*

– 0.043*

– 0.018

– 0.026

3,610

(0.009)

(0.019)

(0.011)

(0.016)

Female

0.066

– 0.027~

– 0.054~

– 0.030~

– 0.047~

1,836

(0.015)

(0.029)

(0.016)

(0.025)

Male

0.037

– 0.015

– 0.031

– 0.005

– 0.007

1,774

(0.011)

(0.024)

(0.014)

(0.020)

106

SUPPLEMENTAL EXHIBIT 3.1. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

DENTAL

Teeth are in very good or better condition [SR]

All

0.390

0.004

0.009

0.023

0.034

3,617

(0.022)

(0.046)

(0.024)

(0.036)

Female

0.374

0.018

0.036

0.035

0.055

1,842

(0.030)

(0.060)

(0.033)

(0.052)

Male

0.404

– 0.010

– 0.020

0.010

0.015

1,775

(0.032)

(0.068)

(0.034)

(0.050)

Notes: * = p < 0.05, ~ = p <0 .10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report, M = direct measurement.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 15 to 20 as of December 31, 2007.

Measures: Overweight and obesity are defined according to the International Obesity Task Force.

107

Chapter 3: Impacts on Adult and Youth Physical Health

SUPPLEMENTAL EXHIBIT 3.2. ADULT HEALTH BEHAVIORS: EXERCISE, NUTRITION, AND SLEEP

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

EXERCISE AND INACTIVITY

Number of times per week engages

3.877

– 0.144

– 0.298

– 0.292

– 0.465

3,253

in moderate physical activity [SR]

(0.143)

(0.294)

(0.189)

(0.302)

Engages in moderate physical

0.487

– 0.014

– 0.029

– 0.012

– 0.020

3,253

activity ≥ four times a week [SR]

(0.022)

(0.045)

(0.029)

(0.046)

Number of times per week engages

1.483

0.010

0.021

0.006

0.010

3,263

in vigorous physical activity [SR]

(0.098)

(0.203)

(0.133)

(0.212)

Engages in 10 or more minutes of

0.386

0.046*

0.094*

0.010

0.015

3,263

vigorous physical activity at least

(0.021)

(0.043)

(0.028)

(0.045)

one time per week [SR]

Watches ≥ 11 hours of television per

0.483

– 0.002

– 0.003

– 0.037

– 0.059

3,250

week [SR]

(0.022)

(0.045)

(0.029)

(0.046)

NUTRITION

Eats fruit or vegetables ≥ four times

0.566

0.016

0.034

0.038

0.061

3,268

per week [SR]

(0.021)

(0.044)

(0.028)

(0.045)

Eats fast food ≥ one times per week

0.614

0.005

0.011

0.004

0.007

3,262

[SR]

(0.020)

(0.042)

(0.027)

(0.043)

SLEEP

Short sleep the previous night

0.194

– 0.017

– 0.035

– 0.004

– 0.006

3,241

(< 6 hours) [SR]

(0.017)

(0.034)

(0.022)

(0.036)

7–8 hours of sleep the previous

0.291

0.015

0.031

0.020

0.033

3,241

night ("normal sleep") [SR]

(0.020)

(0.042)

(0.027)

(0.044)

Long sleep the previous night

0.099

– 0.013

– 0.026

– 0.048*

– 0.077*

3,241

(> 10 hours) [SR]

(0.013)

(0.026)

(0.015)

(0.024)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.

108

SUPPLEMENTAL EXHIBIT 3.3. YOUTH HEALTH BEHAVIORS: EXERCISE, NUTRITION, AND SLEEP

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

EXERCISE AND INACTIVITY

Fraction of past 7 days engaged in physical activity for ≥ 20 minutes that made youth sweat/breathe hard [SR]

All

0.440

0.006

0.012

0.013

0.019

5,082

(0.012)

(0.025)

(0.014)

(0.020)

Female

0.350

0.010

0.021

0.019

0.030

2,589

(0.017)

(0.034)

(0.019)

(0.030)

Male

0.525

0.001

0.003

0.006

0.009

2,493

(0.018)

(0.038)

(0.020)

(0.028)

Fraction of past 7 days engaged in physical activity for ≥ 30 minutes that did not make youth sweat/breathe hard [SR]

All

0.393

– 0.005

– 0.010

0.006

0.010

5,076

(0.014)

(0.029)

(0.015)

(0.023)

Female

0.397

– 0.009

– 0.018

– 0.006

– 0.009

2,587

(0.019)

(0.038)

(0.021)

(0.032)

Male

0.390

0.000

– 0.001

0.019

0.027

2,489

(0.020)

(0.042)

(0.022)

(0.031)

Watches ≥ 11 hours of television per week [SR]

All

0.317

0.003

0.006

– 0.009

– 0.014

5,052

(0.018)

(0.038)

(0.020)

(0.030)

Female

0.302

0.014

0.029

– 0.012

– 0.018

2,577

(0.024)

(0.049)

(0.027)

(0.042)

Male

0.330

– 0.009

– 0.019

– 0.007

– 0.009

2,475

(0.026)

(0.057)

(0.029)

(0.041)

NUTRITION

Eats fruit or vegetables ≥ four times per week [SR]

All

0.536

– 0.036~

– 0.075~

– 0.017

– 0.026

5,091

(0.020)

(0.041)

(0.021)

(0.032)

Female

0.528

– 0.039

– 0.078

0.018

0.028

2,595

(0.027)

(0.055)

(0.030)

(0.047)

Male

0.544

– 0.032

– 0.069

– 0.054~

– 0.077~

2,496

(0.028)

(0.060)

(0.030)

(0.042)

Drinks soda or other sugary drinks like juice (diet soda excluded) ≥ four times per week [SR]

All

0.639

– 0.017

– 0.035

0.018

0.027

5,094

(0.019)

(0.040)

(0.020)

(0.030)

Female

0.627

0.001

0.001

0.011

0.017

2,596

(0.026)

(0.053)

(0.028)

(0.044)

Male

0.650

– 0.035

– 0.075

0.027

0.038

2,498

(0.026)

(0.056)

(0.028)

(0.040)

Eats sweet snacks ≥ 4 times per week [SR]

All

0.598

– 0.018

– 0.038

– 0.010

– 0.015

5,093

(0.019)

(0.039)

(0.021)

(0.031)

109

Chapter 3: Impacts on Adult and Youth Physical Health

SUPPLEMENTAL EXHIBIT 3.3. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

Eats sweet snacks ≥ four times per week [SR] (CONTINUED)

Female

0.581

0.009

0.017

0.010

0.015

2,596

(0.026)

(0.052)

(0.028)

(0.044)

Male

0.614

– 0.045~

– 0.097~

– 0.029

– 0.041

2,497

(0.026)

(0.056)

(0.029)

(0.042)

Eats fast food ≥ four days per week [SR]

All

0.259

– 0.012

– 0.024

0.010

0.015

5,093

(0.018)

(0.037)

(0.019)

(0.028)

Female

0.233

0.000

– 0.001

– 0.003

– 0.005

2,595

(0.023)

(0.046)

(0.025)

(0.040)

Male

0.283

– 0.023

– 0.050

0.024

0.034

2,498

(0.025)

(0.054)

(0.027)

(0.039)

SLEEP

Hours of sleep last night [SR]

All

8.479

– 0.011

– 0.023

0.053

0.080

5,073

(0.081)

(0.169)

(0.091)

(0.135)

Female

8.597

0.065

0.131

0.072

0.113

2,587

(0.111)

(0.223)

(0.128)

(0.200)

Male

8.368

– 0.087

– 0.189

0.038

0.054

2,486

(0.117)

(0.253)

(0.125)

(0.177)

Short sleep (less than < seven hours or less than < six hours if over age 17) [SR]

All

0.129

– 0.015

– 0.031

– 0.011

– 0.016

5,073

(0.012)

(0.026)

(0.014)

(0.020)

Female

0.121

– 0.027

– 0.055

– 0.001

– 0.002

2,587

(0.017)

(0.034)

(0.019)

(0.030)

Male

0.136

– 0.002

– 0.003

– 0.021

– 0.029

2,486

(0.019)

(0.041)

(0.019)

(0.027)

Long sleep (> 11 hours or > 10 hours if over age 17) [SR]

All

0.129

– 0.012

– 0.026

– 0.002

– 0.002

5,073

(0.012)

(0.025)

(0.014)

(0.021)

Female

0.141

0.002

0.004

0.016

0.025

2,587

(0.018)

(0.037)

(0.020)

(0.032)

Male

0.117

– 0.027~

– 0.058~

– 0.019

– 0.027

2,486

(0.016)

(0.035)

(0.018)

(0.026)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report, M = direct measurement.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 10 to 20 as of December 31, 2007.

110

SUPPLEMENTAL EXHIBIT 3.4. SELF-REPORTED HEALTH CARE ACCESS FOR ADULTS AND YOUTH

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

ADULTS

Adult and children have health

0.827

0.019

0.039

0.035~

0.056~

3,253

insurance [SR]

(0.017)

(0.034)

(0.021)

(0.034)

Adult or children did not get medical

0.101

– 0.002

– 0.005

– 0.022

– 0.035

3,261

care when needed during the past 12

(0.013)

(0.027)

(0.016)

(0.025)

months [SR]

Adult or children did not get needed

0.061

– 0.002

– 0.005

– 0.026*

– 0.041*

3,261

medical care in the past 12 months

(0.010)

(0.022)

(0.012)

(0.019)

because they could not afford it [SR]

Adult has been to emergency room

0.446

0.036~

0.074~

– 0.002

– 0.003

3,262

for own health reasons at least once

(0.022)

(0.045)

(0.029)

(0.046)

in the past 12 months [SR]

Adult usually goes to emergency

0.051

0.015

0.031

– 0.011

– 0.018

3,264

room for routine care [SR]

(0.010)

(0.020)

(0.012)

(0.019)

Adult usually goes to a clinic or

0.548

– 0.021

– 0.043

– 0.008

– 0.012

3,264

health center for routine care [SR]

(0.021)

(0.044)

(0.028)

(0.045)

Adult usually goes to a doctor's

0.298

0.027

0.055

0.027

0.043

3,264

office or HMO for routine care [SR]

(0.020)

(0.040)

(0.026)

(0.042)

Adult saw or talked to a health

0.792

0.023

0.048

0.014

0.023

3,263

professional about their own health

(0.017)

(0.036)

(0.022)

(0.036)

in the past 6 months [SR]

Adult has children in the household

0.020

0.005

0.010

– 0.004

– 0.007

3,250

and they usually go to emergency

(0.006)

(0.012)

(0.007)

(0.011)

room for routine care [SR]

Adult has children in the household

0.412

– 0.010

– 0.020

0.009

0.014

3,250

and they usually go to a clinic or

(0.020)

(0.042)

(0.027)

(0.044)

health center for routine care [SR]

Adult has children in the household

0.210

0.014

0.029

0.014

0.023

3,250

and they usually go to a doctor's

(0.017)

(0.035)

(0.022)

(0.035)

office or HMO for routine care [SR]

111

Chapter 3: Impacts on Adult and Youth Physical Health

SUPPLEMENTAL EXHIBIT 3.4. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

YOUTH

Youth had routine physical exam in the past 12 months [SR]

All

0.719

– 0.036*

– 0.075*

0.002

0.002

5,060

(0.017)

(0.035)

(0.019)

(0.028)

Female

0.795

– 0.064*

– 0.129*

– 0.019

– 0.029

2,578

(0.022)

(0.044)

(0.024)

(0.037)

Male

0.647

– 0.008

– 0.017

0.021

0.030

2,482

(0.026)

(0.055)

(0.029)

(0.041)

Youth had dental exam in the past 12 months [SR]

All

0.610

– 0.012

– 0.026

0.000

0.000

5,086

(0.019)

(0.040)

(0.021)

(0.032)

Female

0.640

– 0.023

– 0.046

– 0.003

– 0.005

2,592

(0.025)

(0.050)

(0.029)

(0.045)

Male

0.582

– 0.002

– 0.003

0.003

0.004

2,494

(0.027)

(0.059)

(0.029)

(0.042)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed. Youth long-term survey. Interviewed youth ages 10 to 20 as of December 31, 2007.

112

IMPACTS ON ADULT AND YOUTH

MENTAL HEALTH

113

Chapter 4: Impacts on Adult and Youth Mental Health

IMPACTS ON ADULT AND

YOUTH MENTAL HEALTH

This chapter discusses MTO’s effects on the mental

4.1 HYPOTHESES ABOUT EFFECTS

health of participating adults and youth. For adults,

ON ADULT AND YOUTH MENTAL

we find that MTO produces statistically significant

CHAPTER 4

HEALTH

improvements in important mental health outcomes,

A substantial mental health literature documents that

including general psychological distress and depression. both adults and children who live in high-poverty, We also find some evidence for an increase in substance high-crime urban settings have elevated rates of a wide dependence among adults in the MTO experimental

range of adverse mental health outcomes (for example,

group compared with controls.

Bagley, Jacobson, and Palmer, 1973; Rezaeian et al.,

2005; Whitley et al., 1999). There is continuing

We detect few significant effects on the mental health

debate, however, about the relative importance of

of youth ages 13–20. However, we observe statistically

“geographic drift,” that is, differential selection of

significant beneficial effects for female youth in the

people with a predisposition to mental illness into

experimental group relative to controls on a subset of

these environments rather than causal influences of

our measures of mental health: lower prevalence of any

the environment on mental illness (van Kamp et al.,

lifetime mood disorder, lower prevalence of lifetime

2004). To the extent that these associations reflect

oppositional defiant disorder, fewer serious emotional

causal effects of the environment, we would expect

or behavioral difficulties, less psychological distress,

the MTO demonstration to improve mental health

and fewer panic attacks in the past year. For male

outcomes. Exhibit 4.1 describes some of the suggested

youth in the Section 8 group, our analyses suggest that

mechanisms by which relocating to a lower-poverty

MTO may be associated with a higher prevalence of

neighborhood might lead to improved mental health.

post-traumatic stress disorder. Parental reports on their

grown children’s mental health show no significant

differences across treatment groups.

Exhibit 4.1. Hypothesized Pathways Through Which MTO May Affect Mental Health EXHIBIT 4.1. HYPOTHESIZED PATHWAYS THROUGH WHICH MTO MAY AFFECT MENTAL HEALTH

MTO INTERVENTION

RELOCATION TO LOW-POVERTY NEIGHBORHOODS

COMMUNITY-LEVEL

PERSON- AND FAMILY-LEVEL

HEALTH

MEDIATORS

MEDIATORS

OUTCOMES

Physical Environment

· Less noise, light pollution

Mental Health

· Safer environment

· Psychological distress

Exercise

· Mental calm

· More exercise

· General happiness

· Major depression

Community Norms

· Bipolar Disorder

and Values

· Generalized Anxiety

· More positive peer groups,

Access to Care

Disorder

adult role models

· Health insurance

· Panic Attacks/Disorder

· Access to medical care

· Intermittent-Explosive

Disorder

Community Resources

· Oppositional-Defiant

· Mental health care facilities

Disorder

Safety and Stress

· Access to job with health

· Post-Traumatic Stress

· Increased sense of

benefits

Disorder

security, confidence

· Less stress, anxiety

Substance Use

· Drinking

Social Environment

· Reduced crime and violence

· Drug use

Social Isolation

· Different neighbors

· Feelings of isolation

Exhibit 4.1 is a modified version of Exhibit 4.1 in the MTO interim impacts evaluation (see Orr et al., 2003).

114

CHANGES IN PHYSICAL ENVIRONMENT

males may have higher exposure to neighborhood risk

A wide range of childhood adversities associated with the factors. Adolescent males tend to be subject to less physical environment, such as poverty, poor nutrition,

parental supervision than females and also tend to

abuse, and neglect, have been linked to a wide range

be greater risk-takers (Block, 1983; Bottcher, 2001;

of mental disorders (Anda et al., 2006; Green et al.,

LaGrange and Silverman, 1999). Male and female youth

2010). Animal studies suggest that the effects of early

also appear to have different coping styles and capacities.

environments on the body’s stress response system and

Psychosocial stress sometimes can have more pronounced

brain development mediate these associations to some

effects on males than females in part because males are

extent (Weaver, 2009). However, the social environments more likely to use confrontational techniques to deal of neighborhoods also seem to be important by varying

with stress, particularly stress involving interpersonal

exposure to stressful experiences that can provoke mental problems, whereas females are more likely to turn to disorders as well as in access to supportive resources for

supportive adults (Coleman and Hendry, 1999; Zaslow

mental disorders (Kloos and Shah, 2009).

and Hayes, 1986). The gender difference in the use of

adult supports could be magnified in situations like

CHANGES IN THE SOCIAL ENVIRONMENT MTO where households are disproportionately headed by Exposure to violence can cause long-term behavioral

females.

and psychological harm for both youth and adults

(Famularo et al., 1996; Groves et al., 1993; Zapata et al., CHANGES IN COMMUNITY RESOURCES

1992). This is especially true in predicting depression,

MTO-assisted moves to lower-poverty neighborhoods

generalized anxiety disorder, and post-traumatic stress

could improve access to mental health service providers,

disorder (PTSD). As a result, to the extent that MTO

or improve access to job opportunities that include

reduces exposure to crime and violence, we would expect

health benefits that would cover mental health services.

it to improve overall well-being and reduce psychological

On the other hand, free or very low-cost mental health

distress, depression, and anxiety (Aneshensel and Sucoff,

services may be concentrated in high-poverty areas, and

1996; Ross and Mirowsky, 2001; Silver, Mulvey, and

so access to low-cost mental health care services could

Swanson, 2002).

decrease with moves to better neighborhoods.

It is also possible that moving to lower-poverty

EARLIER RESEARCH

neighborhoods could influence externalizing disorders,

Some nonexperimental studies suggest a possible

including oppositional-defiant disorder, intermittent-

relationship between neighborhood of residence and

explosive disorder, conduct disorder, and substance

mental health status (for a recent review, see Diez-

use disorder among youth, as these are strongly

Roux and Mair, 2010). Although almost entirely cross-

related to peer environments and norms regarding the

sectional, these studies have consistently found that

appropriateness of violence and antisocial behaviors

important aspects of neighborhood environment are

(Deater-Deckard, 2001; Gifford-Smith et al., 2005).

significantly related to the mental health of residents.

The direction of these effects, however, is unknown, as

Gidlow et al. (2010), for example, found that consensus

there is no necessarily positive association between a

perceptions of neighborhood quality were significantly

decrease in neighborhood poverty levels and a decrease in related to the self-reported mental health of respondents exposure to maladaptive peer environments. It is possible in a sample of neighborhoods in the United Kingdom, that moves could have adverse effects by leaving MTO

with perceived social support the most important aspect

family members socially or culturally isolated in their

of the neighborhood. Van den Berg et al. (2010) find

new neighborhoods.

evidence consistent with the idea that the presence of

green space may buffer the effects of stressful experiences

Previous research suggests that the local social

on mental health.

environment could act differently on the mental health

outcomes of male and female youth, in part because

115

Chapter 4: Impacts on Adult and Youth Mental Health

Chapter 4: Impacts on Adult and Y

EXHIBIT 4.2. ADULT PSYCHOLOGICAL WELL-BEING

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

PSYCHOLOGICAL DISTRESS INDEX (K6), PAST MONTH [SR]

K6 Z-score (higher score

0.000

– 0.107*

– 0.221*

– 0.097~

– 0.156~

3,273

indicates greater distress)

(0.042)

(0.087)

(0.056)

(0.091)

Serious mental illness (K6 raw

0.133

0.001

0.001

– 0.015

– 0.024

3,273

score > =13)

(0.015)

(0.030)

(0.019)

(0.031)

SUBSTANCE DEPENDENCE, PAST MONTH [SR]

Severity of dependence

0.329

0.117*

0.241*

0.048

0.076

3,269

scale raw score (higher score

(0.055)

(0.114)

(0.077)

(0.122)

indicates greater dependence)

Dependence on drugs or

0.055

0.029*

0.060*

0.015

0.024

3,269

alcohol (severity of dependence

(0.011)

(0.022)

(0.015)

(0.023)

score of 3 or higher)

MENTAL CALM [SR]

Calm and peaceful most of the

0.487

0.018

0.037

– 0.025

– 0.040

3,272

time, past month

(0.022)

(0.045)

(0.029)

(0.047)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.

Measures: Psychological distress consists of 6 items (sadness, nervousness, restlessness, hopelessness, feeling that everything is an effort, worthlessness) scaled on a score from 0 (no distress) to 24 (highest distress) and then converted to z-scores using the mean and standard deviation of control group adults.

Serious mental illness is defined as a raw score of 13 or higher on the K6. Substance dependence consists of 5 items about drug and/or alcohol use (use out of control, anxiety or worry about missing fix or drink, worry about use, frequency of desire to end use, difficulty of going without use) scaled on a score from 0 (no dependence) to 15 (highest level of dependence). A score of 3 or higher indicates dependence. See Section 4.3 for additional details.

116

4.2 EARLIER RESEARCH:

none of the estimates were statistically significant, and

SHORT-TERM AND INTERIM MTO

all estimates were in the direction of small declines in

IMPACTS ON MENTAL HEALTH

mental health. A qualitative follow-up to the interim

Consistent with evidence from nonexperimental studies,

survey suggests that these gender differences could be

short-term evidence from the MTO sites showed that

related to differences in how female and male youth

adults in both treatment groups in the Boston site were

in the experimental group navigated their new social

more likely to feel calm and peaceful (Katz, Kling, and

environments, with distance from father figures and

Liebman, 2001). Short-term data from both Boston

increased exposure to neighborhood public spaces being

and New York showed that experimental group children

perhaps particularly important for male youth (Clampet-

experienced less fearfulness than the control group (Katz, Lundquist et al., 2011).

Kling, and Liebman, 2001; Leventhal and Brooks-Gunn,

4.3 DATA SOURCES AND MEASURES

2003). Younger children in the Section 8 group also

All of the mental health outcomes we describe were

experienced fewer unhappy, sad, or depressed feelings

measured with data from the long-term evaluation

(Leventhal and Brooks-Gunn, 2003).

surveys. These measures have all been used before in large

national surveys. The data on adult and youth health

The MTO interim study four to seven years after random outcomes are self-reported, whereas the data on grown assignment found that among adults, assignment to the

children (ages 21–30) are from parental or primary

experimental group rather than control group reduced

caregiver reports.

psychological distress (as measured by the Kessler 6 [K6]

measure of psychological distress) and increased reports

Adults responded to questions about their mental health

of mental calm (Orr et al., 2003, Exhibit 4.2, p. 77).

that included the same set of questions from the MTO

The point estimate of the impact on the incidence of

interim survey (K6 indicator of distress and a measure

depression for the experimental group was substantial but of calm). The K6 scale (Furukawa et al., 2003; Kessler only statistically significant when boundary cases were

et al., 2003) consists of six questions and is the most

included, and the impact on anxiety (measured using

widely used scale of nonspecific psychological distress

two questions) was undistinguishable from zero. The

in the literature. It is a core part of all three of the

estimates for the Section 8 group were not statistically

major ongoing national health tracking surveys in the

distinguishable from zero for any mental health measure

United States: the National Health Interview Survey

for adults.

(NHIS), the Behavioral Risk Factor Surveillance Survey

(BRFSS), and the Substance Abuse and Mental Health

For youth, the interim study found few statistically

Services Administration’s National Household Survey

significant changes in mental health when examining

on Drug Use and Health. We also asked adults to

male and female youth together. The main mental health

complete disorder-specific mental health measures (for

measures on the interim surveys for respondents ages

example, measures of depression, anxiety, intermittent

12–19 included the K6 and scales to measure lifetime

explosive disorder) from the World Health Organization’s

depression and generalized anxiety disorder developed

(WHO) Composite International Diagnostic Interview

for the National Comorbidity Survey Replication:

(CIDI) (Kessler and Üstün, 2004), the most widely used

Adolescent Supplement (NCSR-AS). There was some

epidemiological interview for mental disorders in the

sign of a decline in generalized anxiety disorder for the

world. The CIDI is used in the WHO World Mental

Section 8 group relative to controls. However, for all

Health Survey Initiative (Kessler and Üstün, 2008), the

three mental health measures, the point estimates for

world’s largest series of coordinated epidemiological

females in both treatment groups suggest substantively

surveys of mental disorders, as well as in the U.S.

large improvements in mental health, with four of the

National Comorbidity Surveys (Kessler et al., 1994,

six estimates (three outcomes for each of two treatment

2005). MTO adults completed the CIDI sections

groups) statistically significant. In contrast, for males,

designed to generate diagnoses of specific disorders

117

Chapter 4: Impacts on Adult and Youth Mental Health

defined by the Diagnostic and Statistical Manual of

than with peers, and attention span. The raw score can

Mental Disorders, 4th edition (DSM-IV), such as major

range from 0 (no behavioral or emotional problems) to

depression, generalized anxiety disorder, and post-

10 (severe emotional or behavioral problems). A score of

traumatic stress disorder.

6 or higher is considered indicative of serious behavioral

or emotional problems.

Interviews with youth ages 10–20 included questions

about their mental calm, distress, and strengths and

MEASURES OF MENTAL CALM

difficulties. The latter questions come from the Strengths Calm and peaceful: We replicated the question from the and Difficulties Questionnaire (SDQ) (Goodman and

MTO interim evaluation that asked adults how much of

Goodman, 2009), a widely used screening scale to detect

the time in the past 30 days they felt calm and peaceful.

clinically significant mental disorders among youth. The

Response categories match those for the K6 items. Our

MTO screening version of the SDQ is identical to the

measure reflects being calm and peaceful most of the

screening scale developed for use in the Health Interview

time during the past month.

Survey to assess DSM-IV “serious mental illness”

(Simpson et al., 2005). Interviews with youth 13 or older MEASURES OF SUBSTANCE

also included the CIDI diagnostic scales. We use parent

DEPENDENCE

reports on whether any of their grown children (ages

Severity of Dependence Scale (SDS): The scale consists

21–30) suffer from serious mental health problems or an

of five items about the use of drugs or alcohol: use out

alcohol or drug problem. We focus our primary analyses

of control, anxiety/worry about missing a fix or drink,

on youth ages 13–20 because they were administered the

worry about use, frequency of desire to end use, and

CIDI diagnostic scales as well as the other mental health

difficulty of going without use. The raw score on the

measures.1

SDS can range from 0 (no dependence) to 15 (highest

level of dependence). A score of 3 or higher indicates that

MEASURES OF DISTRESS AND

the respondent is likely to meet DSM-IV criteria for a

DIFFICULTIES

diagnosis of substance dependence.

Kessler 6 (K6) measure of psychological distress: We

asked adult and youth respondents how often in the past

MEASURES OF DSM-IV DISORDERS

30 days they felt sad, nervous, restless, hopeless, that

We used a fully structured diagnostic interview to

everything was an effort, or worthless.2 The raw score

assess the prevalence of disorders such as depression,

on the K6 can range from 0 (no distress) to 24 (highest

bipolar disorder, anxiety, panic disorder, PTSD, and

distress). We report K6 scores transformed to z-scores,

intermittent-explosive disorder (IED) among MTO

standardized using the control group’s mean and standard participants. Our diagnostic instrument was the CIDI, deviation. K6 scores of 13 or higher have been shown to

which was revised to make diagnoses according to the

be strongly predictive of serious mental illness (Kessler et definitions and criteria of the DSM-IV (American al., 2003).

Psychiatric Association, 1994) and expanded and

updated for the WHO World Mental Health Survey

Strengths and Difficulties Questionnaire (SDQ): We

Initiative (2004). The CIDI was developed with

administered youth ages 10–20 a brief version of the

careful attention to instruction, comprehension, and

SDQ developed by Robert Goodman. It consists of

motivation to increase the accuracy of reports (Kessler

five behavioral and emotional items: obedience, worry/

and Üstün, 2004) and has been validated through clinical

anxiety, unhappiness, getting along better with adults

reappraisals (Haro et al., 2006). The CIDI forms the

basis for the National Comorbidity Survey (NCS) for

adults and the NCS-Adolescent Supplement for youth,

1 In Supplemental Exhibit 4.2, we report results expanding the sample

to include 10- to 12-year-olds on the limited set of mental health mea-

which allows us to compare MTO participant responses

sures that apply to youth of all ages (10–20 years old).

to nationally representative samples. In addition to

2 Response categories on the K6 items are: all of the time; most of the

assessing lifetime occurrence of disorders, we also

time; some of the time; a little of the time; and none of the time.

118

assessed the 12-month prevalence and collected data on

Generalized anxiety disorder (GAD): GAD includes

age of onset and recency. We administered the structured excessive anxiety about multiple events or activities that diagnostic interview only to older youth, ages 13–20.

the respondent finds difficult to control on more days

than not over the course of at least six months. The

Major depression: Major depression is diagnosed if the

anxiety must also be associated with at least three of

respondent has experienced a major depressive episode,

the following six symptoms: restlessness, easy fatigue,

defined as a two-week or longer period where at least

difficulty concentrating, irritability, muscle tension,

one symptom is depressed mood or loss of interest or

and sleep disturbance. Furthermore, the anxiety

pleasure and where the respondent had at least five of

must cause clinically significant distress or impair

the following nine symptoms: depressed mood, markedly social, occupational, or other functioning. Finally, the diminished interest or pleasure, significant weight loss

disturbance cannot occur exclusively during a mood

or gain (unrelated to dieting), insomnia, psychomotor

disorder such as bipolar or depression. GAD with

agitation (for example, physical restlessness, pacing) or

hierarchy is diagnosed when neither major depressive

retardation (for example, being physically slowed down),

disorder nor mania is also diagnosed or where the onset

fatigue or loss of energy, feelings of worthlessness or

of GAD is earlier than the onset of major depressive

excessive or inappropriate guilt, diminished ability to

disorder and mania.

think or concentrate or indecisiveness, and recurrent

thoughts of death. In addition, the symptoms must

Panic attacks: These occur when a respondent

cause clinically significant distress or impair social,

experiences a discrete period of intense fear or discomfort

occupational, or other functioning. Depression with

where at least four of the following 13 symptoms

hierarchy is diagnosed where mania or hypomania is not

developed abruptly and reached a peak within 10

also diagnosed.

minutes: palpitations, pounding heart, or accelerated

heart rate; sweating; trembling or shaking; sensation of

Bipolar disorders (BPD) I and II: These are diagnosed

shortness of breath or smothering; feeling of choking;

if the respondent has experienced a manic (BPD I) or

chest pain, or discomfort; nausea or abdominal distress;

hypomanic (BPD II) episode, a distinct period in which

feeling dizzy, unsteady, or lightheaded; feelings of

the respondent has an abnormally and persistently

unreality or depersonalization; fear of losing control

elevated, expansive, or irritable mood. During that

or going crazy; fear of dying; numbing or tingling

period, the respondent must experience at least three

sensations; and chills or hot flushes.

(or four if the mood is irritable only) of the following

seven symptoms: inflated self-esteem or grandiosity,

Panic disorder: This requires recurrent, unexpected

decreased need for sleep, more talkative than usual or

panic attacks where at least one of the attacks is followed

pressure to keep talking, flight of ideas or subjective

by a month or more of persistent concern about having

sense of racing thoughts, distractibility, increase in goal-

additional attacks, worry about the implications of the

oriented activity (often in the form of marathon writing

attack or its consequences, or a significant change in

sessions or other very intensive work-related activities) or behavior related to the attacks. Finally, the panic attacks psychomotor agitation (for example, inability to sit still

cannot be better accounted for by another mental

without constant fidgeting), and excessive involvement

disorder, such as PTSD, which we operationalize in the

in pleasurable but risky activities. In addition, a manic

MTO study by focusing on “out of the blue” attacks.

episode requires the mood disturbance to impair normal

functioning, require hospitalization to prevent self-harm, Intermittent-explosive disorder: IED requires or have psychotic features. The length and severity of the at least three discrete episodes of failure to resist episode distinguish the manic episode from the less severe aggressive impulses that result in serious assaultive hypomania episode. Bipolar II also requires a history of

acts or destruction of property in which the degree of

at least one lifetime major depressive episode.

aggressiveness expressed is grossly out of proportion to

any precipitating psychosocial stressors. Additionally,

119

Chapter 4: Impacts on Adult and Youth Mental Health

it must not be the case that the aggressive episodes are

future (for example, does not expect to have a career,

better accounted for by another mental disorder (such

marriage, children, or a normal life span). Finally, the

as major depressive disorder or mania) or are due to the

respondent must indicate difficulty falling or staying

direct physiological effects of a substance or a general

asleep and an exaggerated startle response, and the

medical condition. The IED with hierarchy diagnosis

disturbance in behavior must cause clinically significant

takes into account major depressive disorder and mania/

impairment in social, academic, or occupational

hypomania, where the anger attacks cannot only have

functioning. The MTO version of the CIDI PTSD

occurred when depressive or manic symptoms were also

module contained fewer questions than the full CIDI

present and where the age of onset and recency for IED

battery, so we used responses to the included questions to

is compared with the age of onset and recency for major

generate a predicted probability of meeting the disorder

depressive disorder and mania/hypomania.

among respondents who meet the disorder in a nationally

representative sample.

Oppositional defiant disorder (ODD): ODD, which

is diagnosed only for youth, requires a pattern of

Summary measures: In addition to the disorders

negative, hostile, and defiant behavior lasting at least six

listed above, we include four summary measures of

months, during which at least four of the following eight disorders. Any mood disorder includes depression symptoms are often present: loses temper, argues with

(without hierarchy), bipolar I/II/subthreshold, and

adults, actively defies or refuses to comply with adult’s

mania/hypomania/hypomania subthreshold. Any

requests or rules, deliberately annoys people, blames

anxiety disorder includes panic disorder, GAD (without

others for his or her mistakes or misbehavior, touchy

hierarchy), and PTSD. Any disorder includes any mood

or easily annoyed by others, angry and resentful, and

disorder, any anxiety disorder, IED (without hierarchy),

spiteful and vindictive. Furthermore, the disturbance in

and for youth ODD (without hierarchy). Number of

behavior must cause clinically significant impairment in

disorders is a count of the following disorders: depression

social, academic, or occupational functioning, and the

(without hierarchy), bipolar I/II/subthreshold, panic

behaviors must not occur exclusively during the course of disorder, GAD (without hierarchy), PTSD, IED (without a mood disorder.

hierarchy), and for youth ODD (without hierarchy).

Post-traumatic stress disorder: PTSD occurs when

Absence of mental health problems: This measure is

a respondent has been exposed to and subsequently

based on an index we constructed for adults and youth

suffers because of a traumatic event in which the

as developed by Kling, Liebman, and Katz (2007).4 The

person experienced, witnessed, or was confronted with

adult version includes the K6; CIDI diagnosis of major

an event or events that involved actual or threatened

depressive disorder and GAD for the past year; mental

death or serious injury to the respondent or to others.3

calm; and a measure of normal sleep (between seven and

Furthermore, the respondent must exhibit three of the

eight hours per night). The youth version includes the

following five behaviors: avoiding activities, places, or

K6, lifetime CIDI diagnosis of major depressive disorder,

people that arouse recollections of the trauma; markedly

and CIDI lifetime diagnosis of GAD. We standardized

diminished interest or participation in significant

each item in the index by the control group mean and

activities; feeling of detachment or estrangement from

standard deviation. We standardized items separately

others; restricted range of affect (for example, unable

for male and female youth. We reversed measures of

to have loving feelings); and a sense of a foreshortened

problems so that higher scores indicate an absence of

4 We measure some of the mental health items included in the index

3 The MTO survey asked about the following events: beaten up as a

differently than Kling, Liebman, and Katz (2007) did. As we described

child by a primary caregiver, a spouse/romantic partner, or someone

above, the adult long-term survey included the full CIDI modules for

else; raped or sexually assaulted; mugged or threatened with a weapon;

depression and generalized anxiety disorder, whereas the interim survey

unexpected death of a loved one; traumatic event experienced by a

contained a CIDI short-form for depression and only two questions

loved one; witnessed physical fights at home; witnessed death or saw a

for anxiety. Also, the interim sleep question asked about how much the

dead body or someone seriously hurt; and an unspecified other event

adult typically slept at night, whereas the long-term measure is based on

(open-ended response option).

how much the adult slept last night.

120

problems, replaced missing values on individual measures adults have scores on the K6 that are, on average, a with the mean for the treatment group, and then

tenth of a standard deviation lower than the control

averaged standardized scores to create the index score.

group; however, the prevalence of serious mental illness,

as measured by a K6 score of at least 13, is similar for

MEASURES OF MENTAL HEALTH

both groups. The prevalence of dependence on drugs

SERVICES

or alcohol for the experimental group is an estimated

We asked adults a series of questions about any services

2.9 percentage points higher than among controls.

that they have received in the past year for problems with On mental calm (Exhibit 4.2) and normal sleep mental health or substance abuse. We asked those who

(Supplemental Exhibit 3.2), we detect no statistically

reported receiving any services how many sessions they

significant differences between adults in the experimental

received. We also asked all respondents whether they

and control groups. We observe no statistically significant

had taken any prescription medicine for these problems.

differences between the Section 8 group and the control

Finally, we asked all respondents who neither received

group on the K6 measure of serious mental illness,

services nor took prescription medicine if there was a

substance dependence, mental calm, or normal sleep and

time in the past year when they felt they needed to see a

only a marginally statistically significant decrease on the

medical professional for mental health or substance abuse K6 z-score.

issues.

Exhibit 4.3 shows the estimated impacts on adult mental

4.4 CONTEXT AND BASELINE STATUS

disorders that we assessed using the CIDI diagnostic

OF THE SAMPLE

interview. The lifetime prevalence of depression for the

We do not have baseline survey information on mental

experimental and Section 8 adults is lower by 3.2 (p <

health status. However, the baseline surveys did indicate

.10) and 4.8 percentage points, respectively, than the

that half of all MTO heads of household reported feeling control mean of 20.3 percent. We observe no statistically very unsafe in their neighborhoods at night. Fully 43

significant treatment impacts for either the experimental

percent reported that someone in the home had been the

or Section 8 groups on panic attacks, IED, PTSD, our

victim of a crime during the six months before enrolling.

measure of “any mental health disorder,” the number

As noted in prior chapters, more than three-quarters of

of disorders, or the mental health index. However, the

all household heads reported that safety (getting away

estimated MTO treatment effect on the mental health

from gangs and drugs) was the first or second most

index for the experimental group is in the direction of

important reason for wanting to enroll in MTO and

improved mental health and on the margin of statistical

move out of their baseline neighborhoods.

significance with a p value of .095 (Exhibit 4.3). Some

of the impact estimates have fairly wide confidence

4.5 LONG-TERM IMPACTS ON MENTAL intervals. For example, the 95 percent confidence interval HEALTH

for “any lifetime mood disorder” for the Section 8 group

ranges from – 8 to +1 percentage points compared with a

ADULTS

control mean of 26 percent.

We assessed the intention-to-treat (ITT) effects and

treatment-on-the-treated (TOT) effects on adult mental

health proxied by our measures of psychological well-

being and mental disorders using the regression models

described in Chapter 1. Exhibit 4.2 shows the estimated

impacts of the MTO treatments on psychological distress

(K6), substance dependence, and mental calm.

We observe a few statistically significant impacts on

the mental health of adults. The experimental group

121

Chapter 4: Impacts on Adult and Youth Mental Health

EXHIBIT 4.3. ADULT MENTAL DISORDERS

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

MAJOR DEPRESSION WITH HIERARCHY [SR]

Lifetime

0.203

– 0.032~

– 0.066~

– 0.048*

– 0.077*

3,269

(0.017)

(0.035)

(0.021)

(0.034)

Past year

0.119

– 0.021

– 0.043

– 0.013

– 0.021

3,269

(0.013)

(0.028)

(0.017)

(0.027)

BIPOLAR I [SR]

Lifetime

0.013

– 0.003

– 0.007

– 0.001

– 0.002

3,269

(0.004)

(0.009)

(0.006)

(0.010)

Past year

0.009

– 0.003

– 0.006

0.001

0.001

3,269

(0.004)

(0.007)

(0.006)

(0.009)

BIPOLAR II [SR]

Lifetime

0.009

0.006

0.012

0.000

– 0.001

3,269

(0.005)

(0.010)

(0.007)

(0.011)

Past Year

0.006

0.006

0.013

0.004

0.007

3,269

(0.004)

(0.009)

(0.006)

(0.010)

GENERALIZED ANXIETY DISORDER WITH HIERARCHY [SR]

Lifetime

0.065

– 0.003

– 0.005

– 0.020~

– 0.033~

3,273

(0.010)

(0.021)

(0.011)

(0.017)

Past year

0.033

0.006

0.012

– 0.012

– 0.019

3,273

(0.008)

(0.016)

(0.008)

(0.012)

PANIC DISORDER [SR]

Lifetime

0.081

0.003

0.007

0.002

0.003

3,269

(0.012)

(0.024)

(0.016)

(0.025)

Past year

0.063

0.001

0.001

0.000

0.000

3,269

(0.010)

(0.021)

(0.014)

(0.022)

PANIC ATTACKS [SR]

Lifetime

0.407

0.004

0.009

– 0.022

– 0.035

3,269

(0.021)

(0.044)

(0.028)

(0.045)

Past year

0.087

0.004

0.009

0.004

0.006

3,269

(0.012)

(0.026)

(0.016)

(0.026)

INTERMITTENT-EXPLOSIVE DISORDER WITH HIERARCHY [SR]

Lifetime

0.062

0.001

0.003

0.004

0.007

3,269

(0.010)

(0.022)

(0.015)

(0.024)

Past year

0.051

– 0.008

– 0.015

– 0.008

– 0.013

3,269

(0.009)

(0.019)

(0.013)

(0.021)

POST-TRAUMATIC STRESS DISORDER [SR]

Lifetime

0.219

– 0.012

– 0.024

0.004

0.006

3,269

(0.018)

(0.037)

(0.024)

(0.038)

Past year

0.164

– 0.016

– 0.032

– 0.023

– 0.036

3,269

(0.015)

(0.032)

(0.020)

(0.032)

122

EXHIBIT 4.3 (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

ANY ANXIETY DISORDER [SR]

Lifetime

0.308

– 0.020

– 0.042

– 0.005

– 0.008

3,269

(0.020)

(0.041)

(0.026)

(0.042)

Past year

0.220

– 0.012

– 0.024

– 0.021

– 0.034

3,269

(0.018)

(0.036)

(0.023)

(0.037)

ANY MOOD DISORDER [SR]

Lifetime

0.255

– 0.028

– 0.058

– 0.036

– 0.058

3,270

(0.018)

(0.038)

(0.024)

(0.039)

Past year

0.153

– 0.015

– 0.032

– 0.001

– 0.001

3,269

(0.015)

(0.031)

(0.020)

(0.033)

ANY DISORDER [SR]

Lifetime

0.412

– 0.026

– 0.053

– 0.022

– 0.036

3,270

(0.021)

(0.044)

(0.028)

(0.045)

Past year

0.286

– 0.021

– 0.043

– 0.019

– 0.031

3,269

(0.019)

(0.040)

(0.026)

(0.041)

NUMBER OF DISORDERS [SR]

Lifetime

0.786

– 0.049

– 0.101

– 0.062

– 0.099

3,269

(0.049)

(0.100)

(0.065)

(0.104)

Past year

0.535

– 0.031

– 0.064

– 0.041

– 0.066

3,269

(0.042)

(0.087)

(0.056)

(0.090)

MENTAL HEALTH INDEX (Z-SCORE) [SR]

Absence of mental health problems:

0.000

0.042~

0.086~

0.040

0.065

3,273

depression, anxiety, calm, distress

(0.025)

(0.052)

(0.034)

(0.055)

and sleep (higher score indicates

fewer mental health problems)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.

Measures: Disorders with hierarchy take into account the comorbidity of other disorders: Depression with hierarchy takes into account Mania and Hypomania; GAD

takes into account Depression and Mania; IED takes into account Depression, Mania, and Hypomania. Any Anxiety Disorder includes Panic Disorder, GAD (without hierarchy), and PTSD. Any Mood Disorder includes Depression (without hierarchy), Bipolar I/II/Subthreshold, and Mania/Hypomania/Hypomania Subthreshold. Any Disorder includes all disorders from Any Anxiety Disorder and Any Mood Disorder as well as IED (without hierarchy). Index of mental health problems is the average of the z-scores for depression (past year), anxiety (past year), mental calm, distress (K6), and normal sleep (7 to 8 hours last night) after standardizing by the control mean and standard deviation. A higher mental health index score indicates better mental health. See Section 4.3 for additional details.

123

Chapter 4: Impacts on Adult and Youth Mental Health

YOUTH

lifetime PTSD. The estimated impact is a prevalence

Exhibit 4.4 shows the estimated impacts on psychological that is 3.0 percentage points higher than the controls, well-being for youth ages 13–20. Supplemental Exhibit

who have a prevalence of 4.1 percent. And, although

4.2 shows these same impacts after expanding the age

not statistically significant, the estimated MTO impacts

range to youth ages 10–20. Impacts on mental disorders

for males in the Section 8 groups (and to some extent

such as depression and generalized anxiety are shown in

the experimental group) were in the direction of worse

Exhibits 4.5 and 4.6. All the exhibits provide separate

mental health than controls, and the Section 8 males

estimates for all, female, and male youth.

showed marginally significant impacts towards higher

prevalences of bipolar, any mood disorder, and any

We detect very few significant treatment impacts for

mental health disorder.

all youth taken together in either the experimental or

Section 8 groups. However, we see some significant

GROWN CHILDREN

effects for experimental group females compared with

As reported by the sample adults, the mental health of

control group females. Females in the experimental

MTO grown children (ages 21–30) appears to be similar

group have K6 scores that are over a tenth of a standard

across treatment groups (see Exhibit 4.7). Approximately

deviation lower than the control group, are 4.8

11 percent of the grown children in the control group

percentage points less likely than controls to have had

suffer from depression or another serious mental illness,

a mood disorder during their lifetime (control mean

and about 5 percent have an alcohol or drug problem.

of 21.8 percent), have fewer serious behavioral and

Adults report higher rates of alcohol and drug problems

emotional problems as indicated by the SDQ, reported

for male children (8.2 percent) than female children (2.6

fewer panic attacks, and had a lower prevalence of ODD

percent).

during the past year. We find no statistically significant

differences between the experimental and control group

females’ mental calm, bipolar, panic disorder, PTSD,

or IED and only marginally significant effects (towards

better mental health) on our absence of mental health

problems index, depression, and a K6 score indicative

of serious mental illness and a marginally significant

effect towards greater past year GAD.5 For females in

the Section 8 group, we detect no statistically significant

differences from controls on any mental health measure,

and the only impact that is marginally statistically

significant is a higher level of past year GAD.

For male youth, we detect no statistically significant

effects on our mental health index, the K6, strengths

and difficulties, mental calm, depression, bipolar, panic

disorder, panic attacks, IED, or ODD. While males in

the experimental group appear to have similar levels

of lifetime and past-year PTSD as males in the control

group, Section 8 group males have higher levels of

5 However, the bottom of Supplemental Exhibit 4.2 shows that,

when limited to youth ages 15–20, we find a positive and statistically

significant effect on the mental health index for female youth in the ex-

perimental group relative to those in the control group, a result similar

to findings for female youth aged 15–20 at the interim survey by Kling,

Liebman, and Katz (2007).

124

EXHIBIT 4.4. YOUTH PSYCHOLOGICAL WELL-BEING, AGES 13–20

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

PSYCHOLOGICAL DISTRESS INDEX

(K6), PAST MONTH [SR]

K6 Z-SCORE (HIGHER SCORE

INDICATES GREATER DISTRESS)

All

0.000

– 0.040

– 0.084

0.036

0.054

4,644

(0.041)

(0.085)

(0.047)

(0.070)

Female

0.115

– 0.119*

– 0.241*

– 0.013

– 0.021

2,371

(0.058)

(0.116)

(0.066)

(0.104)

Male

– 0.110

0.040

0.085

0.084

0.120

2,273

(0.054)

(0.116)

(0.060)

(0.086)

SERIOUS MENTAL ILLNESS

(K6 RAW SCORE ≥ 13)

All

0.067

– 0.012

– 0.025

0.009

0.013

4,644

(0.010)

(0.021)

(0.011)

(0.017)

Female

0.085

– 0.026~

– 0.053~

0.001

0.002

2,371

(0.015)

(0.030)

(0.017)

(0.026)

Male

0.050

0.002

0.005

0.016

0.023

2,273

(0.013)

(0.028)

(0.015)

(0.021)

BRIEF STRENGTHS AND DIFFICULTIES

QUESTIONNAIRE (SDQ) [SR]

SDQ RAW SCORE (HIGHER SCORE

INDICATES MORE DIFFICULTIES)

All

3.149

– 0.102

– 0.211

0.071

0.106

4,644

(0.071)

(0.147)

(0.079)

(0.119)

Female

3.245

– 0.174~

– 0.351~

0.150

0.234

2,371

(0.100)

(0.202)

(0.114)

(0.178)

Male

3.057

– 0.026

– 0.057

– 0.012

– 0.018

2,273

(0.093)

(0.200)

(0.101)

(0.145)

SERIOUS BEHAVIORAL OR

EMOTIONAL PROBLEMS (SDQ RAW

SCORE ≥ 6)

All

0.103

– 0.022~

– 0.046~

0.019

0.029

4,644

(0.011)

(0.024)

(0.014)

(0.021)

Female

0.127

– 0.033*

– 0.068*

0.030

0.047

2,371

(0.017)

(0.034)

(0.021)

(0.032)

Male

0.081

– 0.010

– 0.021

0.007

0.010

2,273

(0.015)

(0.032)

(0.017)

(0.024)

125

Chapter 4: Impacts on Adult and Youth Mental Health

EXHIBIT 4.4. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

CALM AND PEACEFUL MOST OF THE

TIME OR MORE OFTEN IN THE PAST

MONTH [SR]

All

0.595

0.017

0.034

– 0.010

– 0.014

4,642

(0.019)

(0.040)

(0.021)

(0.032)

Female

0.512

0.043

0.087

0.026

0.040

2,371

(0.028)

(0.057)

(0.031)

(0.048)

Male

0.675

– 0.010

– 0.022

– 0.045

– 0.064

2,271

(0.026)

(0.056)

(0.030)

(0.042)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 13 to 20 as of December 31, 2007.

Measures: Psychological distress consists of 6 items (sadness, nervousness, restlessness, hopelessness, feeling that everything is an effort, worthlessness) scaled on a score from 0 (no distress) to 24 (highest distress) and then converted to z-scores using the mean and standard deviation for of control group youth (with male and female youth standardized separately). Serious mental illness is defined as a raw score of 13 or higher on the K6. Strengths and Difficulties consists of 5 behavioral and emotional items (obedience, worry/anxiety, unhappiness, getting along better with adults than peers, attention span) scaled on score from 0

(no behavioral/emotional problems) to 10 (severe behavioral or emotional problems). A score of 6 or higher indicates serious behavioral/emotional problems. See Section 4.3 for additional details.

126

EXHIBIT 4.5. YOUTH MENTAL DISORDERS, AGES 13–20

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

MAJOR DEPRESSION WITH HIERARCHY [SR]

LIFETIME

All

0.105

– 0.015

– 0.031

– 0.014

– 0.021

4,639

(0.012)

(0.025)

(0.013)

(0.019)

Female

0.128

– 0.032~

– 0.065~

– 0.029

– 0.045

2,367

(0.017)

(0.035)

(0.019)

(0.029)

Male

0.084

0.003

0.006

0.000

0.000

2,272

(0.016)

(0.034)

(0.017)

(0.025)

PAST YEAR

All

0.059

– 0.005

– 0.010

– 0.004

– 0.005

4,639

(0.010)

(0.020)

(0.011)

(0.016)

Female

0.080

– 0.024~

– 0.049~

– 0.021

– 0.033

2,367

(0.014)

(0.029)

(0.015)

(0.024)

Male

0.038

0.015

0.033

0.014

0.019

2,272

(0.012)

(0.026)

(0.014)

(0.019)

BIPOLAR I [SR]

LIFETIME

All

0.014

0.004

0.009

0.004

0.006

4,639

(0.005)

(0.011)

(0.005)

(0.008)

Female

0.010

0.010

0.020

– 0.001

– 0.001

2,367

(0.006)

(0.013)

(0.005)

(0.008)

Male

0.017

– 0.002

– 0.004

0.008

0.012

2,272

(0.008)

(0.017)

(0.009)

(0.013)

PAST YEAR

All

0.009

0.004

0.008

0.001

0.001

4,639

(0.004)

(0.008)

(0.004)

(0.006)

Female

0.008

0.006

0.013

– 0.002

– 0.004

2,367

(0.005)

(0.011)

(0.004)

(0.007)

Male

0.011

0.001

0.002

0.004

0.005

2,272

(0.006)

(0.013)

(0.007)

(0.011)

BIPOLAR II [SR]

LIFETIME

All

0.006

0.001

0.002

0.008~

0.011~

4,639

(0.003)

(0.006)

(0.004)

(0.006)

Female

0.010

– 0.001

– 0.003

0.008

0.012

2,367

(0.005)

(0.011)

(0.007)

(0.012)

Male

0.001

0.004

0.008

0.007~

0.010~

2,272

(0.002)

(0.005)

(0.004)

(0.005)

127

Chapter 4: Impacts on Adult and Youth Mental Health

EXHIBIT 4.5. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

BIPOLAR II [SR] (CONTINUED)

PAST YEAR (CONTINUED)

All

0.006

0.000

0.000

0.007

0.010

4,639

(0.003)

(0.006)

(0.004)

(0.006)

Female

0.010

– 0.003

– 0.006

0.007

0.011

2,367

(0.005)

(0.010)

(0.007)

(0.012)

Male

0.001

0.003

0.006

0.006~

0.009~

2,272

(0.002)

(0.005)

(0.004)

(0.005)

GENERALIZED ANXIETY

DISORDER WITH

HIERARCHY [SR]

LIFETIME

All

0.019

– 0.002

– 0.004

0.010~

0.015~

4,644

(0.005)

(0.010)

(0.006)

(0.009)

Female

0.022

– 0.002

– 0.003

0.010

0.016

2,371

(0.007)

(0.014)

(0.008)

(0.013)

Male

0.016

– 0.002

– 0.005

0.010

0.015

2,273

(0.006)

(0.013)

(0.009)

(0.013)

PAST YEAR

All

0.007

0.005

0.010

0.008~

0.012~

4,644

(0.003)

(0.007)

(0.005)

(0.007)

Female

0.007

0.009~

0.019~

0.010~

0.015~

2,371

(0.005)

(0.010)

(0.006)

(0.009)

Male

0.007

0.001

0.002

0.006

0.008

2,273

(0.005)

(0.010)

(0.008)

(0.011)

PANIC DISORDER [SR]

LIFETIME

All

0.047

0.000

0.000

0.001

0.002

4,639

(0.008)

(0.017)

(0.009)

(0.013)

Female

0.052

– 0.013

– 0.027

– 0.004

– 0.006

2,367

(0.010)

(0.021)

(0.012)

(0.018)

Male

0.042

0.014

0.030

0.006

0.008

2,272

(0.012)

(0.027)

(0.013)

(0.018)

PAST YEAR

All

0.031

– 0.004

– 0.008

0.001

0.002

4,639

(0.007)

(0.014)

(0.007)

(0.011)

Female

0.031

– 0.011

– 0.022

0.002

0.003

2,367

(0.008)

(0.016)

(0.009)

(0.015)

Male

0.031

0.004

0.008

0.000

0.000

2,272

(0.011)

(0.023)

(0.011)

(0.015)

128

EXHIBIT 4.5. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

PANIC ATTACKS [SR]

LIFETIME

All

0.387

– 0.017

– 0.035

0.014

0.021

4,639

(0.019)

(0.040)

(0.021)

(0.032)

Female

0.423

– 0.039

– 0.079

– 0.005

– 0.008

2,367

(0.027)

(0.054)

(0.029)

(0.045)

Male

0.354

0.006

0.012

0.033

0.047

2,272

(0.027)

(0.057)

(0.030)

(0.043)

PAST YEAR

All

0.058

– 0.020*

– 0.042*

– 0.007

– 0.010

4,639

(0.009)

(0.019)

(0.010)

(0.015)

Female

0.060

– 0.029*

– 0.059*

– 0.003

– 0.005

2,367

(0.012)

(0.025)

(0.014)

(0.021)

Male

0.056

– 0.010

– 0.022

– 0.011

– 0.016

2,272

(0.013)

(0.029)

(0.014)

(0.020)

INTERMITTENT-

EXPLOSIVE DISORDER

WITH HIERARCHY [SR]

LIFETIME

All

0.164

– 0.001

– 0.003

0.006

0.009

4,639

(0.015)

(0.032)

(0.017)

(0.025)

Female

0.162

– 0.008

– 0.016

– 0.006

– 0.009

2,367

(0.021)

(0.042)

(0.022)

(0.034)

Male

0.166

0.005

0.011

0.017

0.025

2,272

(0.021)

(0.045)

(0.023)

(0.033)

PAST YEAR

All

0.133

– 0.012

– 0.025

0.012

0.018

4,639

(0.014)

(0.029)

(0.016)

(0.023)

Female

0.142

– 0.020

– 0.041

– 0.001

– 0.002

2,367

(0.019)

(0.038)

(0.021)

(0.032)

Male

0.125

– 0.004

– 0.008

0.025

0.036

2,272

(0.018)

(0.039)

(0.022)

(0.031)

OPPOSITIONAL-DEFIANT

DISORDER WITH

HIERARCHY [SR]

LIFETIME

All

0.120

– 0.001

– 0.002

0.009

0.013

4,639

(0.012)

(0.025)

(0.014)

(0.021)

Female

0.118

– 0.014

– 0.028

0.001

0.001

2,367

(0.016)

(0.032)

(0.018)

(0.028)

129

Chapter 4: Impacts on Adult and Youth Mental Health

EXHIBIT 4.5. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

OPPOSITIONAL-

DEFIANT DISORDER

WITH HIERARCHY [SR]

(CONTINUED)

LIFETIME (CONTINUED)

Male

0.122

0.012

0.026

0.016

0.023

2,272

(0.018)

(0.038)

(0.021)

(0.030)

PAST YEAR

All

0.061

– 0.008

– 0.017

– 0.009

– 0.013

4,639

(0.009)

(0.018)

(0.010)

(0.015)

Female

0.067

– 0.023*

– 0.046*

– 0.010

– 0.015

2,367

(0.012)

(0.023)

(0.013)

(0.021)

Male

0.055

0.006

0.014

– 0.008

– 0.012

2,272

(0.014)

(0.030)

(0.015)

(0.022)

POST-TRAUMATIC

STRESS DISORDER [SR]

LIFETIME

All

0.066

0.006

0.012

0.005

0.007

4,639

(0.010)

(0.020)

(0.011)

(0.016)

Female

0.092

0.002

0.003

– 0.019

– 0.030

2,367

(0.016)

(0.032)

(0.016)

(0.026)

Male

0.041

0.010

0.021

0.030*

0.043*

2,272

(0.012)

(0.026)

(0.015)

(0.021)

PAST YEAR

All

0.045

0.010

0.021

0.000

0.000

4,639

(0.009)

(0.018)

(0.009)

(0.013)

Female

0.063

0.007

0.013

– 0.019

– 0.030

2,367

(0.014)

(0.027)

(0.013)

(0.020)

Male

0.028

0.014

0.029

0.020

0.028

2,272

(0.011)

(0.024)

(0.013)

(0.018)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 13 to 20 as of December 31, 2007.

Measures: Disorders with hierarchy take into account the comorbidity of other disorders: Depression with hierarchy takes into account Mania and Hypomania; GAD

and ODD take into account Depression and Mania; and IED takes into account Depression, Mania, and Hypomania. See Section 4.3 for additional details.

130

EXHIBIT 4.6. SUMMARY MEASURES OF YOUTH MENTAL HEALTH, AGES 13–20

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

ANY ANXIETY DISORDER [SR]

LIFETIME

All

0.121

0.001

0.002

0.005

0.007

4,639

(0.013)

(0.027)

(0.014)

(0.020)

Female

0.149

– 0.011

– 0.023

– 0.014

– 0.021

2,367

(0.019)

(0.038)

(0.020)

(0.031)

Male

0.095

0.013

0.028

0.023

0.033

2,272

(0.017)

(0.037)

(0.019)

(0.027)

PAST YEAR

All

0.083

0.007

0.014

0.003

0.004

4,639

(0.012)

(0.024)

(0.012)

(0.018)

Female

0.098

0.002

0.003

– 0.006

– 0.010

2,367

(0.016)

(0.032)

(0.016)

(0.026)

Male

0.069

0.011

0.024

0.011

0.016

2,272

(0.016)

(0.034)

(0.017)

(0.024)

ANY MOOD DISORDER [SR]

LIFETIME

All

0.178

– 0.013

– 0.027

0.003

0.004

4,644

(0.015)

(0.031)

(0.016)

(0.024)

Female

0.218

– 0.048*

– 0.096*

– 0.032

– 0.050

2,371

(0.022)

(0.044)

(0.024)

(0.038)

Male

0.140

0.022

0.047

0.038~

0.054~

2,273

(0.020)

(0.043)

(0.022)

(0.032)

PAST YEAR

All

0.104

0.003

0.005

0.006

0.008

4,643

(0.012)

(0.026)

(0.014)

(0.020)

Female

0.141

– 0.025

– 0.051

– 0.020

– 0.030

2,370

(0.018)

(0.037)

(0.020)

(0.031)

Male

0.070

0.030~

0.065~

0.030~

0.043~

2,273

(0.016)

(0.034)

(0.017)

(0.025)

ANY MENTAL HEALTH

DISORDER [SR]

LIFETIME

All

0.370

– 0.001

– 0.002

0.011

0.017

4,640

(0.020)

(0.041)

(0.021)

(0.032)

Female

0.402

– 0.039

– 0.080

– 0.025

– 0.038

2,368

(0.027)

(0.055)

(0.030)

(0.046)

Male

0.339

0.037

0.080

0.047

0.066

2,272

(0.026)

(0.057)

(0.030)

(0.042)

131

Chapter 4: Impacts on Adult and Youth Mental Health

EXHIBIT 4.6. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

ANY MENTAL HEALTH

DISORDER [SR] (CONTINUED)

PAST YEAR

All

0.259

– 0.001

– 0.003

0.019

0.028

4,639

(0.018)

(0.037)

(0.020)

(0.030)

Female

0.298

– 0.032

– 0.065

– 0.012

– 0.019

2,367

(0.025)

(0.050)

(0.027)

(0.042)

Male

0.222

0.029

0.062

0.050~

0.070~

2,272

(0.024)

(0.051)

(0.027)

(0.039)

NUMBER OF MENTAL

HEALTH DISORDERS [SR]

LIFETIME

All

0.661

– 0.007

– 0.015

0.042

0.062

4,639

(0.044)

(0.091)

(0.048)

(0.071)

Female

0.738

– 0.070

– 0.141

– 0.033

– 0.052

2,367

(0.063)

(0.127)

(0.069)

(0.107)

Male

0.587

0.055

0.118

0.117~

0.166~

2,272

(0.057)

(0.123)

(0.063)

(0.090)

PAST YEAR

All

0.425

0.000

0.001

0.020

0.030

4,639

(0.037)

(0.076)

(0.039)

(0.057)

Female

0.504

– 0.061

– 0.122

– 0.026

– 0.041

2,367

(0.051)

(0.104)

(0.055)

(0.086)

Male

0.351

0.062

0.133

0.065

0.092

2,272

(0.048)

(0.103)

(0.050)

(0.070)

MENTAL HEALTH INDEX

(Z-SCORE) [SR]

ABSENCE OF MENTAL HEALTH PROBLEMS:

DEPRESSION, ANXIETY, AND DISTRESS

(HIGHER SCORE INDICATES

FEWER MENTAL HEALTH PROBLEMS)

All

0.000

0.031

0.065

– 0.016

– 0.024

4,644

(0.027)

(0.056)

(0.030)

(0.045)

Female

0.000

0.069~

0.139~

0.016

0.025

2,371

(0.036)

(0.073)

(0.042)

(0.065)

Male

0.000

– 0.006

– 0.013

– 0.048

– 0.068

2,273

(0.038)

(0.081)

(0.042)

(0.060)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 13 to 20 as of December 31, 2007.

Measures: Any Anxiety Disorder includes Panic Disorder, GAD (without hierarchy), and PTSD. Any Mood Disorder includes Depression (without hierarchy), Bipolar I/

II/Subthreshold, and Mania/Hypomania/Hypomania Subthreshold. Any Disorder includes all disorders from Any Anxiety Disorder and Any Mood Disorder as well as IED (without hierarchy) and ODD (without hierarchy). Index of mental health problems is the average of the z-scores for depression (lifetime), anxiety (lifetime), and distress (K6) after standardizing by the control mean and standard deviation (with male and female youth standardized separately). A higher mental health index score indicates better mental health. See Section 4.3 for additional details.

132

EXHIBIT 4.7. PARENT-REPORTED MENTAL HEALTH OF GROWN CHILDREN, AGES 21–30

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

DEPRESSION OR OTHER SERIOUS

MENTAL HEALTH PROBLEM [PR]

All

0.110

– 0.001

– 0.001

0.014

0.026

3,066

(0.014)

(0.033)

(0.020)

(0.036)

Female

0.096

– 0.013

– 0.029

0.015

0.026

1,518

(0.019)

(0.041)

(0.027)

(0.046)

Male

0.123

0.012

0.028

0.015

0.028

1,548

(0.021)

(0.050)

(0.028)

(0.052)

ALCOHOL OR DRUG PROBLEM [PR]

All

0.054

– 0.012

– 0.027

0.017

0.031

3,056

(0.010)

(0.023)

(0.015)

(0.028)

Female

0.026

– 0.002

– 0.005

0.010

0.017

1,514

(0.011)

(0.024)

(0.017)

(0.030)

Male

0.082

– 0.022

– 0.050

0.023

0.043

1,542

(0.016)

(0.038)

(0.024)

(0.046)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: PR = parent report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. Grown children (who were under age 18 at baseline and over age 20 as of December 31, 2007) of interviewed adults.

4.6 IMPACTS ON HYPOTHESIZED

randomly assigned treatment groups. Male youth in

MEDIATORS

the experimental group are less likely to have at least

This section reviews effects on the mediating factors

one close friend and have less contact with friends from

most directly relevant to the hypotheses we described in

their baseline neighborhoods. Youth across treatment

Section 4.1. The exhibits containing these results are in

groups have similar reports on how good they feel about

supplemental tables and Chapter 2.

themselves or how others see them, except for some

scattered, marginally significant effects toward others

SOCIAL ISOLATION

being less likely to see them as popular, athletic, or good

We analyzed a variety of measures of social networks,

students.

friendships, and social capital (Exhibit 2.11,

Supplemental Exhibits 2.4–2.7). MTO has no

SAFETY AND EXPOSURE TO VIOLENCE

detectable effect on adult reports of having friends

The MTO intervention appears to have produced

in the neighborhood, visitations in their home, or

improvements in safety and reductions in exposure

casual chatting with neighbors, although experimental

to violence (see Exhibit 2.10). Compared with the

group adults are more likely to report having at least

control group, experimental and Section 8 adults report

one friend who graduated from college. Effects on

improved feelings of safety, a lower likelihood of seeing

youth differ somewhat by gender. Female youth in the

drugs sold or used, and a higher likelihood of police

experimental group are less likely to have friends who

response to 911 emergency calls for service.

have ever used drugs than controls, but otherwise, peers

have similar characteristics for MTO youth across the

133

Chapter 4: Impacts on Adult and Youth Mental Health

An apparent gender difference seems to emerge for youth Results such as this are consistent with evidence from in the extent to which MTO moves shape perceptions of

other community-level mental health interventions

safety in the local neighborhood, which could potentially that low-intensity universal interventions are often help explain some of the gender differences in impacts

successful in preventing relatively mild disorders that

on mental health. Assignment to the MTO experimental

are comparatively easy to prevent but are ineffective in

or Section 8 groups rather than to the control group

preventing more severe disorders (Offord, 2000). The

improves feelings of safety and reduces the likelihood

standard approach to address this problem in community

of seeing drugs in the neighborhood for female youth,

mental health intervention research is to use tiered

but generally not for male youth. Female youth in the

interventions that add to the universal component

experimental group also report less unwanted sexual

a targeted component that provides more intensive

attention than those in the control group, a finding

intervention for people with high risk of disorders

that is consistent with qualitative research collected

(Merry et al., 2004; Multisite Violence Prevention

during the interim study. These findings are mirrored by

Project, 2004).

results suggesting that MTO moves are more likely to

shift female youth than male youth into more prosocial

The results are somewhat different for youth, where

peer groups. Whatever potential benefits MTO might

the beneficial mental health effects of the intervention

generate from moving into areas with a lower prevalence

are limited to females and there is an adverse effect

of antisocial peers could be partially offset for male youth on Section 8 males with regard to PTSD. In contrast if they wind up in antisocial cliques in their new areas.

to the adults, female youth in the experimental group

experienced reductions not only in mild distress but

MENTAL HEALTH SERVICES

also for serious disorders. As a result, the same basic

Supplemental Exhibit 4.1 shows MTO’s estimated

considerations apply in interpreting the results for

impacts on mental health services. We observe no

female youth as for adults, with a special emphasis on

statistically significant effects on receiving services,

the importance of finding individual-level indicators

the number of counseling sessions, being prescribed

that can distinguish between females who are likely to

medicine, or feeling the need for services for mental

profit from a universal MTO-like intervention and those

health or substance use issues. Approximately 30 percent

who need more targeted interventions in conjunction

of control group adults report receiving or needing

with a universal intervention to prevent mental illness

mental health services or prescription medicines; this

from occurring. Developing reliable algorithms to

number is 3.6 percentage points lower for adults in the

target indicated interventions is a major challenge in

experimental group. This difference is large as a share of

implementing effective multi-tiered community mental

the control mean but not quite statistically significant at

health interventions (Dodge, 2009).

the conventional 5 percent cutoff ( p value of .063).

Finally, the finding that MTO had no aggregate long-

4.7 INTERPRETATION OF MENTAL

term effect on the mental health of grown children is

HEALTH RESULTS

unsurprising. We would expect a universal intervention

The results for adults show that MTO is associated

such as this one to have no dramatic effect on the

with significant or nearly significant effects in reducing

mental health of youth who had already spent a good

recent psychological distress and improving overall

part of their developmental years in more disadvantaged

mental health, but largely by having effects on mild

neighborhoods. Indeed, one might even expect long-term

manifestations of mental disorder rather than on serious

adverse mental health effects on these young adults, as

mental disorders. This is clearest in the K6 results,

they had established ties with their old neighborhoods

where the mean score in both intervention groups is

before the MTO moves and were probably too old to

significantly or close to significantly lower than the

benefit from the relocations.

control group but the percentage of respondents with

extremely high scores does not differ between groups.

134

SUPPLEMENTAL EXHIBIT 4.1. ADULT MENTAL HEALTH SERVICES IN PAST 12 MONTHS

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

Received any professional mental health or

0.160

– 0.019

– 0.039

0.003

0.006

3,265

substance use services [SR]

(0.016)

(0.032)

(0.021)

(0.033)

Number of sessions of mental health or

2.478

– 0.128

– 0.263

– 0.680

– 1.078

3,256

substance use counseling or therapy [SR]

(0.379)

(0.780)

(0.456)

(0.723)

Prescribed medicine for mental health or

0.162

– 0.021

– 0.043

– 0.011

– 0.018

3,264

substance use problems [SR]

(0.015)

(0.031)

(0.021)

(0.033)

Felt the need for services for mental health or

0.088

– 0.003

– 0.007

– 0.022

– 0.035

3,265

substance use problems but did not get it [SR]

(0.012)

(0.025)

(0.016)

(0.026)

Received therapy, prescribed medicine, or

0.297

– 0.036~

– 0.075~

– 0.022

– 0.036

3,264

felt the need for services for mental health or

(0.019)

(0.040)

(0.026)

(0.041)

substance use problems [SR]

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.

SUPPLEMENTAL EXHIBIT 4.2. YOUTH PSYCHOLOGICAL WELL-BEING, AGES 10–20 AND AGES 15–20

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

YOUTH AGES 10–20

PSYCHOLOGICAL DISTRESS INDEX (K6),

PAST MONTH [SR]

K6 Z-SCORE (HIGHER SCORE INDICATES

GREATER DISTRESS)

All

0.000

– 0.022

– 0.046

0.039

0.059

5,101

(0.040)

(0.083)

(0.045)

(0.068)

Female

0.101

– 0.080

– 0.161

0.010

0.016

2,600

(0.056)

(0.112)

(0.064)

(0.100)

Male

– 0.096

0.036

0.077

0.067

0.096

2,501

(0.052)

(0.113)

(0.058)

(0.082)

SERIOUS MENTAL ILLNESS (K6 RAW

SCORE ≥ 13)

All

0.064

– 0.008

– 0.016

0.009

0.013

5,101

(0.010)

(0.020)

(0.011)

(0.016)

Female

0.081

– 0.021

– 0.042

0.004

0.006

2,600

(0.014)

(0.028)

(0.016)

(0.025)

Male

0.048

0.005

0.012

0.013

0.019

2,501

(0.012)

(0.027)

(0.013)

(0.019)

135

Chapter 4: Impacts on Adult and Youth Mental Health

SUPPLEMENTAL EXHIBIT 4.2. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

BRIEF STRENGTHS AND DIFFICULTIES

QUESTIONNAIRE (SDQ) [SR]

SDQ RAW SCORE (HIGHER SCORE

INDICATES MORE DIFFICULTIES)

All

3.123

– 0.092

– 0.191

0.083

0.125

5,101

(0.070)

(0.145)

(0.077)

(0.115)

Female

3.211

– 0.141

– 0.284

0.166

0.260

2,600

(0.097)

(0.197)

(0.111)

(0.174)

Male

3.040

– 0.039

– 0.085

– 0.004

– 0.006

2,501

(0.090)

(0.195)

(0.096)

(0.138)

SERIOUS BEHAVIORAL OR EMOTIONAL

PROBLEMS (SDQ RAW SCORE ≥ 6)

All

0.102

– 0.018

– 0.037

0.017

0.026

5,101

(0.011)

(0.024)

(0.013)

(0.020)

Female

0.123

– 0.027

– 0.054

0.031

0.048

2,600

(0.016)

(0.033)

(0.020)

(0.032)

Male

0.081

– 0.008

– 0.018

0.004

0.005

2,501

(0.015)

(0.032)

(0.016)

(0.023)

CALM AND PEACEFUL MOST OF THE TIME

OR MORE OFTEN IN THE PAST MONTH [SR]

All

0.599

0.020

0.042

– 0.007

– 0.010

5,099

(0.018)

(0.038)

(0.020)

(0.030)

Female

0.527

0.035

0.070

0.021

0.032

2,600

(0.027)

(0.053)

(0.029)

(0.046)

Male

0.667

0.006

0.013

– 0.035

– 0.050

2,499

(0.025)

(0.054)

(0.028)

(0.041)

YOUTH AGES 15–20

MENTAL HEALTH INDEX (Z-SCORE) [SR]

ABSENCE OF MENTAL HEALTH PROBLEMS:

DEPRESSION, ANXIETY, AND DISTRESS

(HIGHER SCORE INDICATES FEWER MENTAL HEALTH PROBLEMS)

All

0.000

0.051~

0.106~

– 0.003

– 0.005

3,621

(0.030)

(0.062)

(0.033)

(0.050)

Female

0.000

0.096*

0.193*

0.048

0.075

1,845

(0.041)

(0.082)

(0.045)

(0.071)

Male

0.000

0.007

0.014

– 0.054

– 0.078

1,776

(0.042)

(0.090)

(0.047)

(0.069)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 10 to 20 as of December 31, 2007.

Measures: Psychological distress consists of 6 items (sadness, nervousness, restlessness, hopelessness, feeling that everything is an effort, worthlessness) scaled on a score from 0 (no distress) to 24 (highest distress) and then converted to z-scores using the mean and standard deviation for control group youth (with male and female youth standardized separately). Serious mental illness is defined as a raw score of 13 or higher on the K6. Strengths & Difficulties consists of 5 behavioral and emotional items (obedience, worry/anxiety, unhappiness, getting along better with adults than peers, attention span) scaled on score from 0 (no behavioral/emotional problems) to 10 (severe behavioral or emotional problems). A score of 6 or higher indicates serious behavioral/emotional problems. Index of mental health problems is the average of the z-scores for depression (lifetime), anxiety (lifetime), and distress (K6) after standardizing by the control mean and standard deviation (with male and female youth standardized separately). A higher mental health index score indicates better mental health. See Section 4.3 for additional details.

136

IMPACTS ON ECONOMIC

SELF-SUFFICIENCY

137

Chapter 5: Impacts on Economic Self-Sufficiency

IMPACTS ON ECONOMIC SELF-SUFFICIENCY

This chapter summarizes the MTO demonstration’s

5.1 BASELINE AND CONTROL

impacts on employment, earnings, income and poverty, GROUP CONTEXT

receipt of social assistance, and savings and assets. We

Exhibit 5.1 presents baseline characteristics for

CHAPTER 5

examine economic self-sufficiency outcomes derived

adults interviewed in the long-term survey, overall

from the long-term survey based on adult and older

and separately by randomized treatment group. This

youth self-reports and parental reports on their grown

exhibit (which is analogous to Exhibit 1.2) focuses

children. We also use administrative data from state

on several baseline economic measures. At the time

unemployment insurance (UI) earnings records,

of random assignment, 25 percent (24 percent for the

Temporary Assistance for Needy Families (TANF)

control group) of adults reported being employed. This

records, and food stamp records to capture quarterly

low employment rate is not surprising given that the

earnings and employment levels, and receipt of public

sample consisted largely of female household heads

assistance.

with limited education, most of whom were receiving

public assistance (Aid to Families with Dependent

Generally, we find no persistent systematic MTO

Children [AFDC]; now called TANF) and living in

impacts on employment, earnings, income, and

public housing projects in high-poverty inner-city

other economic outcomes in our comparisons of the

neighborhoods. Seventy-six percent of sample members

treatment groups. Our analysis of administrative UI

were receiving AFDC at baseline and 81 percent

data finds that experimental and Section 8 adults had

were receiving food stamps (now called Supplemental

significantly lower employment and earnings than

Nutrition Assistance Program or SNAP). Further,

the control adults in the first two years following

nearly all adults (93 percent) had received AFDC at

random assignment, which suggests that moving may

some point in the past. The average annual household

have had an initially disruptive effect. The effects on

income at baseline was $12,709 (in 2009 dollars),1

adult employment reversed and became positive—but

well below the official poverty line of $17,285 for a

not statistically significant—by five to six years after

single parent with two children (Denavas-Walt, Proctor,

random assignment (seen in the interim follow-up).

and Smith, 2010). Moreover, median annual income at

baseline was even lower—approximately $10,614.

The overall earnings and employment impacts

measured using UI data remain small and positive (but

The labor market conditions, tax-transfer system

still not statistically significant) for 10 years following

incentives, and constraints facing sample members

random assignment. The experimental and Section 8

have changed dramatically since they were enrolled

groups experience few detectable effects on income,

in the demonstration in the mid-1990s. The national

social assistance receipt, savings, and assets, although

unemployment rate, which was 5.6 percent at the

compared with controls, adults in both of the treatment start of the demonstration in the last quarter of 1994, groups report higher levels of food sufficiency in the

declined during the sustained 1990s boom, falling to

long-term survey. There are no strong systematic

3.9 percent in the last quarter of 2000. Labor market

impacts on the employment and earnings of older

improvements during the late 1990s, which were

youth (15–20 years old as of the end of 2007) or grown strong in some large U.S. cities, including Boston, children (under 18 years at baseline, but 21–30 years

were particularly great for disadvantaged workers. But

old at the end of 2007).

1 All dollar values reported in this chapter are in constant 2009 dol-

lars, unless otherwise noted.

138

the labor market deteriorated in the early 2000s, with

The changing economic and policy environment from

the national unemployment rate rising to 6.0 percent in

the mid-1990s to the beginning of the 2000s improved

2003, recovering to 4.6 percent in 2007, skyrocketing

labor market opportunities for single mothers and

during the recession of 2008–2009, reaching 10.0

disadvantaged workers with concurrent large reductions

percent in the last quarter of 2009 and remaining at 9.4

in welfare caseloads. The recession of 2008–2009 and

percent as of December 2010. (The median household

its aftermath (including a tightening of state and local

income followed a generally similar pattern over this

government budgets) are expected to have created

period.)2 The recent economic downturn, occurring

labor market difficulties and substantial fluctuations in

at about the time of long-term survey data collection

labor market participation for adults during the long-

in 2008 and 2009, may have hit those at the lowest

term survey period. The weakening of labor market

end of the income distribution the hardest. Data from

opportunities at the time of long-term follow-up in the

the Census Bureau show that the official poverty rate

late 2000s coincides with many of the MTO adults—

increased from 12.5 percent in 2007 to 14.3 percent

largely women from disadvantaged backgrounds—aging

in 2009, with nearly 43.6 million people living below

into their 40s and 50s and “naturally” cutting back on

the federal poverty threshold in 2009 (Denavas-Walt,

employment and hours worked, particularly in the face of

Proctor, and Smith, 2010).

mounting health problems.

State welfare systems also changed substantially over the

course of the study period, starting with state welfare

waivers in the early 1990s and then federal welfare

reform in 1996. These changes, combined with the

expansion of the Earned Income Tax Credit from 1993

to 1996, increased the financial and social incentives for

female heads of household to move off public assistance

and into employment. National data on single female

household heads indicate large declines in welfare receipt

and large increases in employment rates and labor market

earnings from the mid-1990s to 2000 (Blank, 2002).

Much of the decline in TANF income support was the

result of a decrease in the share of eligible families who

were receiving assistance, rather than the result of any

decrease in the share of families eligible for TANF. In

2005, only 40 percent of eligible families received TANF

cash assistance. By comparison, about 80 percent of

eligible families received AFDC during the two decades

prior to welfare reform (Schott, 2009). Food stamp

caseloads increased dramatically during the recent

economic downturn (by 37 percent through February

2010). In contrast, TANF caseloads have increased

only by 10 percent overall, and 23 states have caseloads

that either increased by less than 5 percent or declined

(Pavetti and Rosenbaum, 2010).

2 Median income increased in the mid- to late-1990s, peaked in 1999,

declined in the early 2000s, recovered modestly from 2004 to 2007,

and declined again through 2009, during the recent recession.

139

Chapter 5: Impacts on Economic Self-Sufficiency

EXHIBIT 5.1. SELECTED BASELINE CHARACTERISTICS OF ADULTS SURVEYED IN THE LONG-TERM

EVALUATION: WORK, PUBLIC ASSISTANCE, AND INCOME

OUTCOME

CONTROL

EXPERIMENTAL SECTION 8

ALL GROUPS

GROUP

GROUP

GROUP

ADULT EMPLOYMENT STATUS

Working at baseline [SR]

23.8%

25.7%

26.5%

25.3%

Not working, but previously worked [SR]

56.4%

52.5%

55.1%

54.5%

Never worked for pay [SR]

19.8%

21.8%

18.4%

20.2%

HOUSEHOLD PUBLIC ASSISTANCE

RECEIPT

Receiving aid to families with dependent

76.3%

76.3%

73.6%

75.6%

Children (AFDC) [SR]

Ever received AFDC [SR]

93.6%

94.3%

92.1%

93.4%

Working and not receiving AFDC [SR]

14.7%

15.9%

17.4%

15.9%

Receiving food stamps [SR]

81.8%

81.6%

80.7%

81.4%

Receiving Women, Infants and Children

36.7%

36.6%

37.7%

36.9%

(WIC) Nutrition Program [SR]

Receiving Supplemental Security Income

16.4%

16.1%

17.0%

16.4%

(SSI) [SR]

Receiving Social Security Disability

8.4%

9.0%

7.3%

8.4%

Insurance (SSDI) [SR]

HOUSEHOLD INCOME (2009 DOLLARS)

Average income [SR]

$12,438.64

$12,865.83

$12,788.32

$12,709.32

Median income [SR]

$10,353.36

$10,629.45

$10,891.88

$10,613.94

MISCELLANEOUS

Owned a car at baseline [SR]

17.0%

19.0%

19.0%

18.3%

Very sure would like living in a neighborhood

45.5%

41.5%

46.4%

44.2%

with neighbors who earn more than them

[SR]

INTERVIEWED N

1,139

1,456

678

3,273

Notes: The numbers represent mean values on baseline characteristics for each treatment group and overall, weighted to reflect randomization ratios and the selection of adults into the interview sample and the two-phase sampling design of the long-term evaluation.

Square brackets indicate the source of the outcome information: SR = self-report.

Data source and sample: MTO Participant Baseline Survey. Adult long-term survey. All adults interviewed.

140

5.2 HYPOTHESES ABOUT EFFECTS

therefore, increased employment and earnings

ON ECONOMIC SELF-SUFFICIENCY

and lower welfare dependence. Reduced stress and

One primary motivation for the demonstration is to

anxiety due to less crime and violence in a person’s

measure the impacts of neighborhood environments on

neighborhood may also free up cognitive space for

the economic self-sufficiency outcomes of low-income

evaluating financial decisions and thinking about

families. These outcomes include employment and

long-term financial planning.

earnings, income and public assistance receipt, and

• Community members in low-poverty areas may

savings and assets. Residential mobility might directly

be more supportive of work and less accepting

affect these outcomes in several ways (see Exhibit 5.2),

of welfare than those in public housing projects.

described below.3

If sample members are surrounded by social

norms that encourage work over welfare, this

• Lower-poverty areas are likely to have lower

might increase job-search effort, employment,

unemployment rates and faster job growth than

and earnings, and may also reduce welfare

higher-poverty areas. Improved opportunities may

participation.

generate higher employment and earnings, an

• Social networks in less economically disadvantaged

increase in reservation wages (the lowest wage at

locations may provide access to new job

which an individual will accept a job), and better

opportunities. Although relocation may lead to an

jobs in terms of wages and benefits. Increased

initial disruption of social networks, these effects

employment options for the experimental and

may diminish as networks are established in new

Section 8 groups may also reduce welfare eligibility

neighborhoods. Recent research (for example,

and participation and free up financial resources

Bayer, Ross, and Topa, 2008; Ioannides and Loury,

for savings.

2004; and Maurin and Moschion, 2009) points

• Access to housing vouchers through MTO increases

to potentially important effects of neighborhood

the ability to relocate nearer potential jobs, rather

social networks on labor market outcomes.

than being tied to the location of a public housing

• Relocation may result in improved physical health

project. This in turn reduces job-search costs and,

potentially through fewer environmental hazards,

once employed, commuting costs. This may lead

more opportunities to walk or exercise in public

to increased employment and earnings, lower

spaces, or improved access to health care. Improved

reservation wages, and less dependence on public

health can reduce rates of absenteeism and raise

assistance.4 If relocation leads to a broader range

job productivity, leading to enhanced job prospects

of employment opportunities, it may also lead to

and earnings.

increased wages and fringe benefits (Weinberg,

• Relocation to new lower-poverty neighborhoods

Reagan, and Yankow, 2004).

may lead to increased access to and reliance on

• Relocation to a safer neighborhood may reduce

formal banks, as opposed to high-cost alternatives

stress and anxiety and, more generally, may

more prevalent in high-poverty areas, such as check

improve mental health and sense of control over

cashers and payday lenders.

one’s life. This in turn could result in more active

• The disruptions of social networks could make it

job searching, lower absenteeism, improved

more difficult for MTO movers to find work and

workplace relations and productivity, and,

arrange informal and affordable child care in the

short run and thus lead to higher public assistance

3 See Orr et al. (2003) for the original conception of this logic model.

receipt. However, the impact of moves on access

Shroder (2002b) and Corcoran and Heflin (2003) provide a discussion

to job search assistance, affordable child care, and

and hypotheses on the more specific effects on labor supply of federal

housing programs, compared with neighborhood environments more

transportation is ambiguous over the long term.

generally.

4 This is a formulation of the “spatial mismatch” hypothesis, for which

an extensive literature exists (for example, see Ihlanfeldt and Sjoquist,

1998).

141

Chapter 5: Impacts on Economic Self-Sufficiency

EXHIBIT 5.2. HYPOTHESIZED PATHWAYS THROUGH WHICH MTO MAY IMPACT ECONOMIC SELF-SUFFICIENCY

Exhibit 5.2. Hypothesized Pathways Through Which MTO May Impact Economic Self-Sufficiency MTO INTERVENTION

RELOCATION TO LOW-POVERTY NEIGHBORHOODS

COMMUNITY-LEVEL

PERSON- AND FAMILY-LEVEL

ECONOMIC

MEDIATORS

MEDIATORS

SELF-SUFFICIENCY

Community Norms and

Positive Effects

· Earnings

Values

· Improved mental health

· Employment

· Shared community value of

· Improved control over lives

· Decrease/stop monthly

work

· Reduced stress/anxiety

receipt of TANF, AFDC,

· Peer pressure to work/not

· Improved outlook

Food Stamps/SNAP,

depend on welfare

· Development of new

SSI benefits

· Welfare agency attitudes

social networks capable

· Bank account

toward work

of reliable financial

· Car ownership

· Shared community value of

support

· Less debt

saving

· More active job search

· Credit card ownership

· Peer pressure to build assets

· More use of networks for

labor market information

· Feelings of safety and

security

· Higher wages

Social and Physical

Environment

Negative Effects

· Reduced crime and violence

· Short-run disruption of

· Lower delinquency rate

existing social networks

· Welfare agency resources

· Less informal childcare

in the short run

· Social connections

Economic Opportunities

· Lower unemployment rates

· Better access to the labor

market

· Better wages and fringe

benefits

· Greater proximity to jobs

· Change in state welfare

system policies

· Access to formal banks

· Cheaper financial services

Exhibit 5.1 is a modified version of Exhibit 7.1 in the MTO interim impacts evaluation (see Orr et al., 2003) 142

EMPLOYMENT

At the interim evaluation, analysts found no systematic,

The existing evidence about the effects on adult

significant effects on adult employment, earnings, or

employment from residential mobility programs is

welfare participation, even though experimental and

somewhat mixed. Rosenbaum and Popkin’s (1991)

Section 8 household heads moved to and remained in

quasi-experimental analysis of female household heads

neighborhoods with higher employment rates than those

in Chicago’s Gautreaux Assisted Housing Program found

of control families (Orr et al., 2003; Kling, Liebman

substantially higher employment rates (14 percentage

and Katz, 2007; Ludwig et al., 2008). In a subsequent

points) for those who moved to the suburbs than for

nonexperimental analysis of survey data from the interim

those who moved within the city. The longer-term and

evaluation, Clampet-Lundquist and Massey (2008) re-

more comprehensive follow-up analysis of Gautreaux’s

estimate MTO impacts on the basis of the cumulative

impacts on low-income, black female household heads by time families spent in low-poverty neighborhoods and Mendenhall and colleagues (2006) finds little systematic

find that families with longer stays in new neighborhoods

impact on employment of moves to suburbs versus the

saw positive impacts on employment and earnings. These

city but persistent positive employment effects of being

results are likely subject to significant selection biases

initially placed in less segregated neighborhoods (a lower

because within each treatment group families were not

percentage of black residents) with greater neighborhood

randomly assigned to different types of neighborhoods

resources. A recent study by Anil and colleagues (2010)

and since persistence in low-poverty neighborhoods

also finds that moves through HOPE VI and similar

also is likely to be correlated with unobserved family

programs had significant positive effects on employment

attributes that may lead to improved outcomes (as

for residents who moved, relative to other public housing discussed by Ludwig et al., 2008). Moreover, the interim tenants in Atlanta, Georgia.5

evaluation found no evidence of effects on average

household income, poverty, or food insecurity.

However, early analyses of MTO on adult labor market

outcomes for Boston and Baltimore found no significant

It is possible that living in a less-disadvantaged

effects on adult household head employment in the first

neighborhood during childhood could have more

few years after randomization (Katz, Kling and Liebman, pronounced effects on labor market outcomes. A large 2001; Ludwig, Duncan and Pinkston, 2005). A survey of body of nonexperimental research finds that children early enrollees in Los Angeles found no significant short-who grow up in poor neighborhoods have worse

term employment effects but did find modest evidence

economic outcomes during adolescence and adulthood

of increases in hours of work and weekly earnings

than children who grow up around more affluent

for the experimental and Section 8 group household

neighbors, although the effects are somewhat attenuated

heads, relative to those in the control group (Hanratty,

when controlling for other aspects of family background

McLanahan and Pettit, 2003).

(see, for example, Corcoran et al., 1992; Page and

Solon, 2003; Sharkey, 2008). Research using quasi-

experimental designs (natural experiments) to evaluate

5 In related research, Jacob and Ludwig (2011) find negative effects of

the long-term impacts of housing mobility interventions

access to means-tested housing vouchers on adult labor supply (earn-

ings and employment) using data from a randomized housing voucher

on child economic outcomes yields mixed results.

wait-list lottery in Chicago. Their study sample consists of adults who

Oreopolous (2003) finds no systematic impacts of child

were already living in private-market housing at the time they applied

for a housing voucher. Those families (unlike MTO households) do

neighborhood environments on adult labor market

not experience very much change in neighborhood “types,” and—also

unlike MTO—do end up experiencing large increases in marginal tax

outcomes for children assigned to public housing projects

rates on earnings and large increases in disposable income as a result of

in substantially different neighborhoods of Toronto.

receiving a housing voucher. So the “treatment” in that study is quite

different from the one examined in MTO. However, they also present

Rosenbaum’s (1995) analysis of a 1988 follow-up survey

results for the subset of Chicago housing voucher applicants who live in

of families who moved with Gautreaux vouchers finds

public housing at baseline, essentially a replication of our comparisons

of the MTO Section 8 and control groups. For this subsample, Jacob

strong positive gains in educational and economic

and Ludwig find no significant impacts of access to means-tested hous-

outcomes for the children of suburban movers relative to

ing vouchers on either employment or public assistance receipt in an

analysis based on Illinois administrative UI and TANF data.

city movers, but longer-term follow-ups find less striking

143

Chapter 5: Impacts on Economic Self-Sufficiency

contrasts between suburban and city movers (DeLuca

MTO impacts on household income depend primarily

et al., 2010). Gould and colleagues (2009) find positive

on earnings and public assistance outcomes, because

impacts on long-term adult economic outcomes for

these are the main components of income for most

Yemenite refugees to Israel who as children were initially

sample members. An increase in earnings can lead to a

placed in more prosperous neighborhoods with better

decrease in welfare benefits, depending on the thresholds

infrastructure.

set for earned income disregards; the overall effect on

cash income is expected to be positive. However, the

At the interim evaluation, the MTO experimental

size of the effect on overall household income depends

treatment was associated with a substantial increase

on the reduction in welfare benefits for a given increase

in female youth school enrollment and a substantial

in earnings and the proportion of sample members that

decrease in female youth idleness (defined as being

receives cash assistance.

neither employed nor enrolled in school) compared with

the controls. The school enrollment rate increased by 16

SAVINGS AND ASSETS

percentage points (p=.096) and the idleness rate declined The MTO demonstration’s impacts on savings and assets by 16 percentage points (p=.074) for female youth in

are influenced by impacts on employment, earnings, and

experimental group families who moved with an MTO

income, as well as access to community-based financial

voucher (Orr et al., 2003, p. 132, Exhibit 7.6, p. 133,

institutions, new social norms, and the ways in which

and Exhibit D7.1b, p. D-11). MTO had no comparable

these changes alter an individual’s financial and savings

treatment effects on males in the interim evaluation.

decision-making. The initial MTO moves may disrupt

informal networks available for instrumental support and

Other factors that vary across neighborhoods can also

financial assistance in emergencies. If the demonstration

affect youth economic outcomes. School-to-work

improves economic conditions for families, this may lead

programs administered by local public schools may

to changes in their financial behavior or net worth simply

help youth secure internships while still enrolled in

by increasing their capacity to save or acquire assets.

high school and help non-college-bound youth secure

employment after high school. Local social norms about

The demonstration might also affect savings and

the stigma surrounding entry-level jobs or local criminal

asset outcomes conditional on earnings or income

activity, the level of violence associated with the local

levels if moves to low-poverty neighborhoods provide

illegal economy and the quality of local policing, and

increased access to formal banking opportunities, better

the level of difficulty in competing for jobs and related

information or knowledge about returns on investments

positive awards for behavior that supports schooling

through social networks and local institutions, or

or employment can also affect youth decisions about

exposure to neighborhood residents who utilize

whether to participate in the formal labor market or

mainstream financial services. Formal financial services

pursue underground or informal work.

might also be more readily available in communities

where a larger proportion of residents are employed in

PUBLIC ASSISTANCE AND INCOME

the formal labor market (Vermilyea and Wilcox, 2002).

The MTO demonstration’s impacts on welfare receipt

High-poverty areas may be less likely to have local bank

are likely to mirror the impacts on employment and

branches, although the evidence on this point is mixed

earnings. Moves from public housing to private housing

(Caskey, 1997; Vermilyea and Wilcox, 2002). However,

in lower-poverty areas may affect welfare participation

high-poverty areas are more likely to have check cashing

(TANF, food stamps, and Supplemental Security

services, payday or auto title lenders, pawnshops, and

Income). To the extent that the demonstration increases

rent-to-own stores that charge very high interest rates and

employment and income, it can be expected to reduce

may, as Carr and Schuetz argue, “undermine the ability

eligibility for and reliance on social assistance. Changes

of individual households to accumulate assets and build

in norms and attitudes toward the use of public assistance wealth” (2001, p. 5; see also Temkin and Sawyer, 2004; in new neighborhoods may also play a role.

Rhine et al., 2001). Moves to a low-poverty neighborhood

144

may affect financial decisions, because movers are exposed to and improvements in neighborhood safety, compared with information about the importance of saving. Social values in reports from control group adults. Observed improvements destination neighborhoods may encourage saving; moreover, in safety (Chapter 2) and increases in potential adult role individuals may realize they can save more using formal

models with stable jobs in the new neighborhoods provide

financial institutions, compared with informal institutions

a potentially favorable context for improving labor market

like check cashers and payday lenders, and use them more

opportunities and the attitudes of youth. Gender differences

as a result. New surroundings may lead movers to see future for youth on some mental health outcomes (Chapter 4) consumption through a different psychological frame, and

and delinquency behaviors (Chapter 6) lead one to expect

decreases in stress may free up some mental space that could more beneficial impacts of the demonstration on the labor be used for making financial decisions (Loewenstein and

market experiences of female youth than male youth. The

O’Donoghue, 2004; Gennetian, Mullainathan, and Shafir,

MTO demonstration did not significantly strengthen social

forthcoming).

networks or improve adult access to transportation.

MOVES AND ECONOMIC

5.3 DATA SOURCES FOR MEASURES

SELF-SUFFICIENCY

OF EMPLOYMENT AND EARNINGS

The mobility experiences of the experimental and Section

Employment and earnings outcomes are derived from

8 groups can help us understand how the demonstration

respondent self-reports from the long-term evaluation

might affect economic self-sufficiency. Treatment group

survey and quarterly earnings data of core household

differences in economic behavior over the long-term may

members come from state UI records. Administrative data

not be detectable at a shorter-term follow-up. MTO moves

is available for core adult household heads, children who are

may initially be disruptive, so any improvements in labor

now grown adults, and older youth.

market behavior may not be observed until the effects of

this initial disruption dissipate. The impact of an initial

The long-term survey includes a series of questions to

disruption will also be sensitive to the overall number

adults about their current employment status, hours of

of moves and whether the initial move precipitated high

work, earnings from their current main job, total earnings

subsequent mobility, potentially including a move back

for the most recent past calendar year, employee benefits,

to the participant’s neighborhood of origin. Labor market

job-search behavior, informal work, and the duration

outcomes may also be responsive to threshold effects based

and characteristics of their current main job.6 These

on the cumulative environmental and social improvements

employment questions are the same as the standard

that result from moves to lower-poverty neighborhoods and

employment status questions from the Current Population

not be apparent until after a critical mass of exposure to

Survey used by the Bureau of Labor Statistics to measure

improved neighborhood environments.

employment, unemployment, and labor force participation.

Respondent descriptions of their industry and occupation

At the long-term follow-up, the experimental and Section

are coded using Census 2000 three-digit industry and

8 groups on average had moved into neighborhoods that

occupation codes. Older youth are similarly asked a battery

appear to have more favorable employment opportunities,

of standard employment status questions, as well as a further

as well as norms more supportive of work, than do the

series of questions on earnings, informal employment

neighborhoods of the control group. The experimental

arrangements, and enrollment status and participation in

and Section 8 group members reside in neighborhoods

job training programs. A host of items on the long-term

with substantially lower poverty rates, higher adult

survey cover potential mediators such as social networks,

employment rates, and higher proportions of two-parent

families (Exhibit 2.7). Adults in the experimental group

6 All survey and administrative records outcomes are adjusted for ex-

tremely high and unreasonably low outliers and any logical inconsisten-

are significantly more likely to have at least one close

cies. Outliers on survey outcomes are adjusted based on a case-by-case

friend who graduated from college and, at a marginally

review and then any value above the 99th percentile is set to missing.

Zero values of total income are assigned the fifth percentile value.

significant level, more likely to have a close friend who

Sensitivity analyses show that these adjustments to outliers do not

works full-time (Exhibit 2.11). Experimental and Section

qualitatively alter the results. We’ve also conducted sensitivity analyses

limiting analyses of survey data outcomes to the available sites with UI

8 adults report high satisfaction with their neighborhoods

data, and, separately for the sites with available TANF/food stamp data

(not shown).

145

Chapter 5: Impacts on Economic Self-Sufficiency

neighborhood satisfaction, and household composition.

cell was required to have 10 or more matches, resulting

Census data on the characteristics of current neighborhoods in larger cell sizes to ensure a reportable match rate.

provide information on mediating factors for employment,

The most recent quarter of data varies by state: quarter

which can be used to test alternative hypotheses about

4 of 2008 for Maryland, quarter 3 of 2008 for Illinois,

MTO impacts on labor market outcomes.

quarter 1 of 2009 for California, and quarter 4 of 2009

for both Massachusetts and New York.

Administrative data from state UI records collected

and processed from each of the states of the five main

We focus on two primary labor market outcomes using

demonstration sites—California (for the Los Angeles site),

the UI administrative data: annual earnings and the

Illinois (for the Chicago site), Maryland (for the Baltimore

proportion of quarters that individuals were employed.

site), Massachusetts (for the Boston site), and New York (for Annual earnings were calculated by adding quarterly the New York City site)—and the state of Florida provide

earnings data. For adults, annual earnings were then

quarterly earnings information for core household members averaged across two-year periods.8 The share of quarters (adults, youth, and grown children). Agencies matched

employed indicates the portion of the period for which

these administrative records to core members by social

a person had recorded earnings more than zero. The

security numbers. Adult data are collapsed into years since

number of quarters with positive earnings is divided by

random assignment and grouped into two-year periods. The eight (the number of quarters in the two-year period).

results are annualized to illustrate the impact over time from

1 to 10 years since random assignment.

More generally, the administrative data serve an

important complement to the survey data on

Three of the five main site states provided individual-level

employment by allowing us to analyze the evolution

earnings information on each sample member who matched of employment and earnings impacts over time, to the UI records. Data from Massachusetts and New York

since random assignment for the full sample of core

are not available at the individual level but rather collapsed

participants (whether or not they were eligible for or

at a group level.7 For Massachusetts, cell sizes were

responded to the long-term survey). However, UI data

restricted to 10 or more individuals; for New York, each

does have some disadvantages. Specifically, informal and

uncovered employment are not measured, errors in social

7 To maximize use of the individual and aggregate cell data, UI analyses

security numbers may lead to incorrect matches, earnings

use the weighted share of participants in each site to create the combined

impact estimates. To create the combined estimates, we use the weighted

are not captured for out-of-state movers, information is

average of the estimate from each site. To calculate the combined standard

not provided about job quality and employee benefits,

error, we used the formula SE = sqrt(se1^2*share1^2 + se2^2*share2 ^2+…+

se4^2*share4^2) where “se1” is the standard error of the ITT estimate of the and it is not possible to disentangle the contributions of

first state, “share1” is the weighted share of the sample for the first state, etc.

weekly hours, quarterly weeks worked, and hourly wages

In our adult analyses for sites with individual-level data (i.e., Baltimore, Chicago, and Los Angeles) we control for our standard set of covariates (except to quarterly earnings.

for the survey release flag) and also include dummies for quarter of random assignment, and earnings in the year prior to random assignment and

weighted to account for changes in randomization ratios. The analyses with

Traditional measures of employment, labor force

Boston aggregate adult data control for the weighted cell means for baseline work status, baseline high school completion status, and dummies for quarter participation, and earnings gauge the labor market

of random assignment. The New York City aggregate analyses do not control

success of sample adults. But the interpretation of

for any baseline covariates. Youth analyses for sites with individual-level data include youth ages 15 to 20 as of December 31, 2007 and control for our

employment outcomes for youth also needs to take into

standard set of covariates. The Boston aggregate youth analyses include youth account the importance of schooling as an alternative

ages 12 to 20 as of December 2007 (because our request file to Massachu-

setts did not split out ages 15-20) and include only the following covariates: use of time that can improve eventual adult labor market

gender, age as of December 2007, and the following baseline characteristics outcomes. We use a summary measure of “idleness”—

of the youth: required special medicine and/or equipment and had health

problems that limited activity. The New York site youth analyses focus on

being out of work and not enrolled in school—to provide

youth ages 15 to 20 and do not control for any baseline covariates other than a more accurate picture of youth labor market outcomes.

gender. In the aggregate youth analyses we are unable to cluster on family. All adult and youth UI analyses use only participants for whom we had a social

security number from prior to random assignment that met a minimum

level of verification by the Social Security Administration (a “best SSN” de-8 Any quarterly wages over $45,000 are considered outliers and first

termined based on an algorithm developed by Jeffrey Liebman for the MTO

checked by examining the two surrounding quarters to try to rectify any

interim evaluation). Participants without best SSNs were omitted from the

outliers caused by single-quarter data entry error, and otherwise top coded main results, but included in sensitivity tests.

at $45,000.

146

5.4 LONG-TERM IMPACTS ON

employment gains in the late 1990s may have reflected life-

EMPLOYMENT AND EARNINGS

cycle work patterns that ceased as the typical MTO adult

OF ADULTS

entered her 40s in the 2000s.

Exhibit 5.3 plots the employment rate of sample adults

by random assignment group from 1995 to 2008 using

The experimental and Section 8 adults show broad

data from state UI records covering all five demonstration patterns quite similar to the control group, with rapidly sites. For adults in the control group, the employment

rising employment rates from 1995 to 2000 and modest

rate (the percentage with positive quarterly earnings)

employment declines in the 2000s. By 2000, higher

more than doubled, from 23.7 in the first quarter of

employment rates are apparent for experimental and

1995 to 51.5 in 2000, in the face of welfare reform and the

especially Section 8 adults, relative to control group adults.

rapid economic expansion of the second half of the 1990s.

However, this pattern of treatment group employment

However, the employment gains for adults in the control

advantage eroded by 2003. Adults in all three random

group somewhat eroded in the weaker labor market of the

assignment groups had similar employment trends and

2000s, with some employment decline in the 2001 recession ended up with much higher employment rates by the final and at the start of the recession of 2008–2009. Part of the

survey in 2008 (around 45 percent) than at the time of

random assignment in 1995 (around 25 percent).

EXHIBIT 5.3. ADULT FIVE-SITE QUARTERLY EMPLOYMENT RATE BY RANDOM ASSIGNMENT GROUP AND

Exhibit 5.3. Adult Five-Site Quarterly Employment Rate by Random Assignment Group and Calendar Quarter CALENDAR QUARTER

60

50

40

30

Experimental group mean

Section 8 group mean

Control group mean

PERCENT EMPLOYED

20

10

1995 Q1

1996 Q1

1997 Q1

1998 Q1

1999 Q1

2001 Q1

2002 Q1

2003 Q1

2004 Q1

2005 Q1

2006 Q1

2007 Q1

2008 Q1

CALENDAR QUARTER

Data source and sample: These analyses use individual data from Unemployment Insurance records from Maryland, Illinois, California, and Florida for those whose random assignment sites are Baltimore, Chicago, or Los Angeles. It also incorporates aggregated Unemployment Insurance data from Massachusetts and New York, which represent individuals whose random assignment sites are Boston or New York City. All sample adults with baseline consent (N = 4,194).

147

Chapter 5: Impacts on Economic Self-Sufficiency

Exhibit 5.4 plots the group employment rates by time

since random assignment using quarterly data for

all five sites. The patterns suggest a small disruption

effect of moves on employment during the first two

years of follow-up, as evidenced by the slightly higher

employment rate for the control group (shown in the

solid gray line) than for either the experimental or

Section 8 groups. The adult employment rates for the

experimental and Section 8 groups increased relative

to the control group in the second year after random

assignment and were slightly higher than the control

group roughly four years after random assignment. The

employment rates for all three groups followed the same

trend and remain generally similar over the remaining

follow-up period through 43 quarters, or roughly 10.5 to

11 years after random assignment.

EXHIBIT 5.4. ADULT FIVE-SITE EMPLOYMENT RATE BY RANDOM ASSIGNMENT GROUP AND THE NUMBER OF

Exhibit 5.4. Adult Five-Site Employment Rate by Random Assignment Group and Quarters Since Random Assignment QUARTERS SINCE RANDOM ASSIGNMENT

60

50

40

PERCENT

EMPLOYED

30

Experimental group mean

Section 8 group mean

PERCENT EMPLOYED

20

Control group mean

10

1

3

5

7

9

11

13

15

17

19

21

23

25

27

29

31

33

35

37

39

41

43

NUMBER OF QUARTERS SINCE RANDOM ASSIGNMENT

Data source and sample: These analyses use individual data from Unemployment Insurance records from Maryland, Illinois, California, and Florida for those whose random assignment sites are Baltimore, Chicago, or Los Angeles. It also incorporates aggregated Unemployment Insurance data from Massachusetts and New York, which represent individuals whose random assignment sites are Boston or New York City. All sample adults with baseline consent (N = 4,194).

148

Exhibit 5.5 more formally presents estimates of the

are no sizable or significant differences in employment

demonstration impacts on employment and earnings of

and earnings in 2007 by random assignment group.10

sample adults using the state UI records from a pre-

random assignment period to the ninth and tenth year

Exhibit 5.7 shows estimates of impacts on employment

of follow-up for the four sites—Baltimore, Boston, Los

outcomes based on adult self-reports from the long-term

Angeles, and Chicago—with complete data. The UI

survey. The survey data mirror the administrative data in

administrative data indicate modest negative impacts

that there are no significant differences in employment

on employment and earnings for the experimental

or earnings outcomes for the experimental and control

and Section 8 groups (marginally significant for the

groups. But the long-term survey-based estimates do show

experimental group) in the first two years following

a significant negative impact on current employment

random assignment.9 This pattern may reflect initial

and a sizable negative (but not significant) impact on

disruption effects of MTO moves on employment

annual earnings for the Section 8 adults—findings not

relations and child care arrangements.

corroborated by the patterns observed in the UI data.

The experimental and Section 8 groups gain on the

The observed negative effects on work and earnings in

control group in employment over the next several years

the self-reported survey data among those in the Section

with slightly beneficial (especially for Section 8 adults),

8 group is a likely artifact that results from the slightly

but not statistically significant, employment impact

later timing of when those adults were interviewed,

estimates by nine to ten years after random assignment.

compared with the experimental and control groups.

The impacts on average annual earnings over the first

The supplemental exhibits examine, in more detail,

ten years from random assignment are modestly negative

the differences in employment impact estimates for the

but insignificant for both the experimental and Section

Section 8 adults between the long-term survey and UI

8 groups, relative to the control group. Supplemental

administrative data. By the time the Section 8 adults

Exhibit 5.1 presents these same estimates separately

were interviewed (starting February 2009), they were

by site, and suggests beneficial (but generally not

facing higher national unemployment compared with the

significant) impacts on employment for the Los Angeles

experimental and control group adults.

and Chicago sites, and more negative impacts for the

Baltimore site and especially for the experimental group

A number of additional analyses examine possible

from the Boston site. The bottom line is one of little

explanations for the varying results by data source related

systematic MTO impact on employment and earnings

to calendar timing of data collection and differences

averaged across sites during the first ten years following

in the type and location of employment captured by

random assignment.

source. Supplemental Exhibit 5.2 compares intention-

to-treat (ITT) and treatment-on-the-treated (TOT)

Exhibit 5.6 incorporates UI data on employment and

effects for survey data and UI administrative data for

earnings from all five demonstration sites for 2007,

current employment around the time of the long-term

the most recent calendar year for which comparable

survey. The estimated impacts are quite similar for the

information is available for all demonstrations sites. There two data sources for the experimental group relative to the control group, but the Section 8 group impacts are

9 This initial disruption effect is also described in Orr et al. (2003,

substantially more negative for current employment in

p. 128): “The employment rate (fraction of quarters employed) of

the experimental group moves from being modestly (but statistically

10 UI data from Boston and New York were delivered as aggregate cell

significantly) lower than the control group (by 2.5 percentage points)

data. Data from Boston included means and standard deviations for

in the first two years after random assignment to being only slightly

each quarter, as well as annualized. The New York aggregated cell data

lower (by 0.9 percentage points) and statistically indistinguishable from

did not include annualized standard deviations. We estimated an upper

the control group on average in the third and fourth years after random

bound to employment levels using the individual level data from Balti-

assignment.” The estimates in Exhibit 5.5 slightly differ from Orr et

more, Chicago, and Los Angeles to calculate the annualized New York

al. in that data are available for only four sites—New York is excluded.

share employed outcome, and took the average correlations of earnings

Exhibit 5.6 suggests that adding data from New York does not qualita-

from these same individual UI sources to calculate the standard devia-

tively alter the main conclusions.

tion for the New York annual earnings measure.

149

Chapter 5: Impacts on Economic Self-Sufficiency

the survey than in the UI data. Supplemental Exhibit

Supplemental Exhibit 5.4 further shows the industry and

5.3 explores this discrepancy by examining employment

occupation of employed adults, measured at the time of

in the previous calendar year and including only the

the survey interview.11 Adults in the experimental and

sample of respondents who were living in the state of

control groups have similar distributions of employment

the initial demonstration site at the time of the survey

across the retail, health care, education, and social service

interview (that is, out-of-state movers were excluded).

sectors and have similar broad occupational distributions.

The gap between the survey and UI data results is

Section 8 adults, however, generally show patterns of

modestly reduced overall, and specifically in California

lower employment rates in the long-term survey than

and Illinois. In fact, for Illinois, the Section 8 results

the control group, across all sectors including retail and

for survey and UI employment are quite similar in

social services, and occupation levels. And, as shown in

Supplemental Exhibit 5.3, excluding out-of-state

Supplemental Exhibit 5.5, experimental group adults

movers, as opposed to Supplemental Exhibit 5.2 where

were slightly (but not significantly) more likely than

the direction of the effect on employment measured

control group adults to be employed at the time of

via survey is opposite the direction of the effect on

the long-term survey in jobs offering health insurance,

employment measured via UI data. As it turns out,

vacation, and paid sick leave, while the Section 8 adults

Chicago has the highest number of out-of-state movers

were less likely to be employed in jobs with such benefits.

compared with the other sites. Thus, this closer aligning

of survey and administrative records points to a similar

negative effect on current employment among Section 8

adults.

11 Industry and occupation are coded using 1990 census codes.

EXHIBIT 5.5. ADULT EMPLOYMENT AND EARNINGS, ADMINISTRATIVE DATA BY YEARS SINCE RANDOM

ASSIGNMENT (RA)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

EMPLOYED [UI]

THREE-SITE WEIGHTED AVERAGE

(MD, IL, CA ONLY)

Pre-RA year 1

0.276

– 0.001

– 0.003

0.005

0.006

2,365

(0.015)

(0.031)

(0.017)

(0.024)

FOUR-SITE WEIGHTED AVERAGE

(MD, IL, CA, MA)

RA years 1 and 2

0.408

– 0.023~

– 0.049~

– 0.017

– 0.025

3,250

(0.012)

(0.026)

(0.014)

(0.020)

RA years 3 and 4

0.499

– 0.003

– 0.006

0.005

0.008

3,250

(0.016)

(0.033)

(0.017)

(0.025)

RA years 5 and 6

0.519

0.005

0.010

0.015

0.022

3,250

(0.016)

(0.034)

(0.018)

(0.027)

RA years 7 and 8

0.485

0.002

0.005

0.029

0.043

3,250

(0.017)

(0.036)

(0.020)

(0.029)

RA years 9 and 10

0.465

0.007

0.014

0.026

0.038

3,250

(0.017)

(0.036)

(0.019)

(0.028)

RA years 1–10

0.475

– 0.003

– 0.005

0.012

0.017

3,250

(0.012)

(0.026)

(0.013)

(0.020)

150

EXHIBIT 5.5. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

ANNUAL WAGES (2009 DOLLARS) [UI]

THREE-SITE WEIGHTED AVERAGE

(MD, IL, AND CA ONLY)

Pre-RA Year 1

$3,163.83

94.20

191.41

56.61

76.72

2,365

(240.38)

(488.46)

(274.91)

(372.61)

FOUR-SITE WEIGHTED AVERAGE

(MD, IL, CA, MA)

RA years 1 and 2

$5,781.17

– 377.39~

– 786.86~

– 214.97

– 317.03

3,250

(221.62)

(462.10)

(255.44)

(376.71)

RA years 3 and 4

$8,398.43

– 422.45

– 880.83

– 541.97

– 799.28

3,250

(348.14)

(725.88)

(368.47)

(543.40)

RA years 5 and 6

$10,111.07

– 274.30

– 571.93

– 24.84

– 36.63

3,250

(436.74)

(910.63)

(459.28)

(677.33)

RA years 7 and 8

$10,526.24

– 358.94

– 748.41

– 131.96

– 194.60

3,250

(518.30)

(1,080.68)

(570.49)

(841.34)

RA years 9 and 10

$10,643.49

-179.70

– 374.68

90.45

133.39

3,250

(550.89)

(1,148.63)

(617.70)

(910.96)

RA years 1–10

$9,092.08

– 322.56

– 672.54

– 164.66

– 242.83

3,250

(343.66)

(716.54)

(369.25)

(544.56)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: UI = Unemployment Insurance administrative records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Individual level data from Maryland, Illinois, California, and Florida (representing individuals whose random assignment sites are Baltimore, Chicago, and Los Angeles) and aggregate data from Massachusetts (representing individuals whose random assignment site is Boston). All sample adults with baseline consent.

151

Chapter 5: Impacts on Economic Self-Sufficiency

EXHIBIT 5.6. ADULT EMPLOYMENT IN 2007, ADMINISTRATIVE DATA BY SITE

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

EMPLOYED [UI]

Maryland

0.593

– 0.097*

– 0.181*

– 0.090~

– 0.115~

629

(0.044)

(0.082)

(0.048)

(0.061)

Illinois

0.471

0.021

0.064

0.020

0.030

883

(0.038)

(0.112)

(0.045)

(0.067)

California

0.424

0.059

0.095

0.064

0.082

853

(0.037)

(0.060)

(0.039)

(0.050)

Massachusetts

0.444

– 0.033

– 0.074

– 0.003

– 0.006

885

(0.036)

(0.081)

(0.044)

(0.085)

New York

0.431

0.004

0.010

– 0.015

– 0.033

944

(0.040)

(0.087)

(0.040)

(0.088)

Five-site weighted average

0.465

– 0.004

– 0.009

0.000

0.000

4,194

(0.017)

(0.036)

(0.019)

(0.030)

ANNUAL WAGES

(2009 DOLLARS) [UI]

Maryland

$14,420.03

– 3,090.98*

– 5,770.22*

– 1472.88

– 1,884.70

629

(1,316.30)

(2,457.27)

(1,607.95)

(2,057.55)

Illinois

$9,733.91

696.73

2,070.28

180.33

267.88

883

(1,121.28)

(3,331.79)

(1,297.35)

(1,927.19)

California

$9,649.18

709.15

1,141.41

971.73

1,256.06

853

(1,127.95)

(1,815.48)

(1,125.39)

(1,454.68)

Massachusetts

$12,245.56

– 755.34

– 1692.58

745.68

1429.99

885

(1,397.82)

(3,132.25)

(1,599.76)

(3,067.87)

New York

$11,448.08

– 73.08

– 158.73

– 260.76

– 580.57

944

(887.41)

(1,927.62)

(880.92)

(1,961.30)

Five-site weighted average

$11,325.14

– 347.83

– 731.73

112.93

180.50

4,194

(523.80)

(1101.92)

(580.69)

(928.11)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: UI = Unemployment Insurance administrative records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Individual level data from Maryland, Illinois, California, and Florida (representing individuals whose random assignment sites are Baltimore, Chicago, and Los Angeles) and aggregate data from New York and Massachusetts (representing individuals whose random assignment sites are New York City and Boston). All sample adults with baseline consent.

152

EXHIBIT 5.7. ADULT SELF-REPORTED EMPLOYMENT AND EARNINGS

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

Currently employed [SR]

0.525

– 0.007

– 0.014

– 0.077*

– 0.124*

3,264

(0.021)

(0.042)

(0.028)

(0.045)

Currently employed full time [SR]

0.367

– 0.016

– 0.033

– 0.047~

– 0.076~

3,232

(0.020)

(0.042)

(0.027)

(0.043)

Currently employed at a job with

0.267

0.014

0.029

0.009

0.015

3,068

weekly earnings above the poverty line

(0.019)

(0.038)

(0.026)

(0.042)

[SR]

Currently employed at a job with

0.345

0.013

0.026

– 0.025

– 0.040

3,068

weekly earnings above the federal

minimum wage [SR]

(0.020)

(0.042)

(0.028)

(0.044)

Currently employed at a job with

0.243

0.005

0.010

– 0.010

– 0.016

3,068

weekly earnings above $10/hr [SR]

(0.018)

(0.037)

(0.024)

(0.038)

Annual individual earnings (previous

$12,288.51

326.94

677.92

– 613.60

– 982.43

3,141

calendar year, 2009 dollars) [SR]

(583.44)

(1,209.79)

(807.20)

(1,292.40)

Current weekly earnings at main job

$222.98

0.56

1.15

– 21.03

– 33.54

3,068

(2009 dollars) [SR]

(11.69)

(24.03)

(15.82)

(25.23)

Employed over 1 year at current main

0.469

– 0.007

– 0.014

– 0.063*

– 0.101*

3,225

job [SR]

(0.021)

(0.043)

(0.028)

(0.045)

In past 2 years, worked for pay [SR]

0.677

0.011

0.023

– 0.048~

– 0.077~

3,270

(0.019)

(0.038)

(0.026)

(0.041)

Currently working and household is

0.499

– 0.020

– 0.041

– 0.071*

– 0.115*

3,260

not receiving Temporary Assistance for

(0.021)

(0.043)

(0.028)

(0.045)

Needy Families [SR]

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All interviewed adults.

Measures: Weekly earnings are first adjusted to 2009 dollars and then applied to the appropriate statistic. Poverty thresholds use the 2009 values and adjusts for head of household's age, household size, and number of kids. For the federal minimum wage, the 2009 value of $7.25 was used to measure whether respondents earned more than the minimum wage. All comparisons with hourly wage rates assumed a 40 hour work week at the hourly wage rate. Job tenure looks at the year in which respondent started their current job. If the interview year is a different year than the year they started their job, we assume that they have been at their job for more than 1 year.

153

Chapter 5: Impacts on Economic Self-Sufficiency

5.5 LONG-TERM IMPACTS ON

MTO impacts on the labor market activities of youth

EMPLOYMENT AND EARNINGS OF

are summarized in Exhibits 5.9 and 5.10. Exhibit 5.9

YOUTH AND GROWN CHILDREN

shows employment and annual earnings impacts for

Exhibit 5.8 shows the employment and school

youth based on state UI administrative data covering the

enrollment rates of grown children in the control group

time from quarter 4, 2007, to quarter 3, 2008, for the

and summarizes the estimated impacts on the labor

four sites with complete administrative data: Baltimore,

force status and earnings of these grown children. The

Boston, Chicago, and Los Angeles. The UI data indicate

top panel of the exhibit displays parental reports in the

that youth in the experimental group have lower

long-term survey on the current situation of their grown

employment rates and earnings than youth in the control

children. The bottom panel presents demonstration

group (this finding is statistically significant overall

impact estimates based on administrative UI data

and for males). There were no significant differences

covering grown children for quarter 4, 2007, to quarter

between the Section 8 and control group youth. Exhibit

3, 2008, for four sites.

5.10 presents estimates based on youth self-reports of

employment, earnings, and enrollment outcomes. The

Compared with the control group, adult respondents

survey data indicate that experimental group youth are

in the experimental group report that their male grown

significantly less likely to be employed than control

children are more likely to be currently employed and

group youth. The impact estimates on employment for

less likely to be idle at the time of the long-term survey

female and male youth are similarly negative, although

than those in the control group. Female grown children

gender-specific estimates are not statistically significant.

in the Section 8 group are less likely to be employed and

Site-by-site analyses presented in Supplemental Exhibit

more likely to be idle than those in the control group.

5.8 suggest that experimental group youth, especially

Supplemental Exhibit 5.6 provides further detail on these in Baltimore, are more likely to be currently enrolled in patterns by presenting treatment group impact estimates

school, compared with the control group. There are no

for four mutually exclusive categories of work and

statistically significant differences by random assignment

schooling for grown children.

group in the long-term survey in idleness, as seen in

Exhibit 5.10. A further decomposition of impacts on four

In contrast to the parental reports in the long-term

mutually exclusive youth labor market and enrollment

survey, the UI data show that male grown children in

categories is given in Supplemental Exhibit 5.9.

the experimental group have negative employment and

earnings outcomes, compared with the control group.

Supplemental Exhibit 5.7 shows that the positive

employment outcomes in the survey data are driven

by the New York site, which unfortunately had to be

excluded from the UI analyses because of the aggregate

nature of the data and our inability to specifically

examine the grown children subgroup. Los Angeles and

Chicago have consistent estimated effects on male grown

children from UI and parental reports. However, for the

Boston site, the UI and survey data yield substantially

different experimental employment impacts of opposite

sign. Unfortunately, the Massachusetts UI agency did not

provide individual level data for the Boston site limiting

further analysis of this discrepancy.

154

EXHIBIT 5.8. EMPLOYMENT AND SCHOOL ENROLLMENT OF GROWN CHILDREN: PARENT REPORTS AND

ADMINISTRATIVE DATA

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

SURVEY DATA

CURRENTLY EMPLOYED [PR]

All

0.562

0.017

0.039

– 0.049

– 0.089

2,953

(0.023)

(0.051)

(0.031)

(0.056)

Female

0.615

– 0.030

– 0.066

– 0.086*

– 0.149*

1,480

(0.031)

(0.067)

(0.040)

(0.070)

Male

0.505

0.066*

0.156*

– 0.009

– 0.018

1,473

(0.032)

(0.076)

(0.041)

(0.078)

CURRENTLY ENROLLED IN

SCHOOL [PR]

All

0.158

– 0.007

– 0.016

– 0.001

– 0.002

3,089

(0.016)

(0.036)

(0.023)

(0.042)

Female

0.205

– 0.015

– 0.033

0.006

0.010

1,527

(0.025)

(0.054)

(0.036)

(0.062)

Male

0.110

0.000

0.000

– 0.007

– 0.013

1,562

(0.020)

(0.047)

(0.025)

(0.047)

CURRENTLY IDLE (NEITHER

ENROLLED IN SCHOOL NOR WORKING) [PR]

All

0.372

– 0.012

– 0.028

0.026

0.047

2,958

(0.022)

(0.049)

(0.029)

(0.054)

Female

0.299

0.036

0.079

0.063

0.109

1,483

(0.029)

(0.063)

(0.039)

(0.067)

Male

0.451

– 0.062~

– 0.147~

– 0.015

– 0.028

1,475

(0.032)

(0.075)

(0.040)

(0.076)

UNEMPLOYMENT INSURANCE

RECORDS (FROM THE FOURTH

QUARTER OF 2007 THROUGH

THE THIRD QUARTER OF 2008

FROM MD, MA, IL, AND CA)

EMPLOYED [UI]

All

0.484

– 0.027

– 0.063

– 0.010

– 0.016

3,052

(0.020)

(0.047)

(0.024)

(0.037)

Female

0.551

– 0.004

– 0.008

– 0.002

– 0.002

1,507

(0.017)

(0.038)

(0.027)

(0.042)

Male

0.418

– 0.040*

– 0.095*

– 0.018

– 0.029

1,545

(0.016)

(0.039)

(0.023)

(0.037)

ANNUAL WAGES (2009

DOLLARS) [UI]

All

$9,732.33

– 967.89

– 2,256.02

– 381.43

– 595.62

3,052

(634.09)

(1,477.98)

(718.02)

(1,121.24)

155

Chapter 5: Impacts on Economic Self-Sufficiency

EXHIBIT 5.8. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

UNEMPLOYMENT INSURANCE

RECORDS (FROM THE FOURTH

QUARTER OF 2007 THROUGH

THE THIRD QUARTER OF 2008

FROM MD, MA, IL, AND CA) (CONTINUED)

ANNUAL WAGES (2009

DOLLARS) [UI] (CONTINUED)

Female

$10,661.72

335.84

760.18

285.00

441.40

1,507

(682.66)

(1,545.23)

(922.52)

(1,428.76)

Male

$8,781.12

– 1,839.40*

– 4,434.65*

– 872.72

– 1,375.28

1,545

(533.73)

(1,286.78)

(735.46)

(1,158.97)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: PR = parent report, UI = Unemployment Insurance administrative records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: For parent reports, data source is the adult long-term survey. Grown children (who were under age 18 at baseline and over age 20 as of December 31, 2007) of interviewed adults. Unemployment Insurance data use individual level data from Maryland, Illinois, California, and Florida (representing individuals whose random assignment sites are Baltimore, Chicago, and Los Angeles) and aggregate data from Massachusetts (representing individuals whose random assignment site is Boston). All core grown children whose parent has a baseline consent.

EXHIBIT 5.9. YOUTH EMPLOYMENT AND EARNINGS, ADMINISTRATIVE DATA BY SITE FOR THE FOURTH

QUARTER OF 2007 THROUGH THE THIRD QUARTER 0F 2008

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

EMPLOYED [UI]

All

0.256

– 0.036*

– 0.076*

0.004

0.006

3,490

(0.015)

(0.032)

(0.018)

(0.025)

Female

0.272

– 0.030

– 0.063

0.012

0.017

1,691

(0.024)

(0.051)

(0.026)

(0.037)

Male

0.241

– 0.045*

– 0.093*

– 0.002

– 0.003

1,799

(0.021)

(0.044)

(0.023)

(0.032)

ANNUAL WAGES (2009 DOLLARS) [UI]

All

$2,371.26

– 487.12*

– 1,025.87*

30.26

42.34

3,490

(225.21)

(474.29)

(247.74)

(346.68)

Female

$2,174.63

– 291.02

– 616.18

370.47

528.51

1,691

(297.80)

(630.55)

(326.36)

(465.58)

Male

$2,552.76

– 681.06*

– 1,426.76*

– 293.28

– 403.26

1,799

(323.22)

(677.12)

(358.88)

(493.46)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: UI = Unemployment Insurance administrative records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Individual level data from Maryland, Illinois, California, and Florida (representing individuals whose random assignment sites are Baltimore, Chicago, and Los Angeles) and aggregate data from Massachusetts (representing individuals whose random assignment site is Boston). All core youth ages 15 to 20 as of December 31, 2007, whose parent has a baseline consent. Massachusetts includes all youth ages 12 to 20. Since our request file did not split by age, we cannot look separately at those who were 15 to 20 in Massachusetts.

156

EXHIBIT 5.10. YOUTH SELF-REPORTED EMPLOYMENT, EARNINGS, AND SCHOOL ENROLLMENT

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

CURRENTLY EMPLOYED [SR]

All

0.395

– 0.046*

– 0.095*

– 0.033

– 0.050

3,604

(0.021)

(0.043)

(0.022)

(0.034)

Female

0.410

– 0.051~

– 0.104~

– 0.042

– 0.066

1,838

(0.029)

(0.059)

(0.031)

(0.049)

Male

0.381

– 0.041

– 0.087

– 0.025

– 0.036

1,766

(0.030)

(0.064)

(0.033)

(0.049)

CURRENTLY ENROLLED IN SCHOOL [SR]

All

0.615

0.018

0.037

– 0.019

– 0.029

3,621

(0.020)

(0.041)

(0.022)

(0.034)

Female

0.637

0.001

0.002

0.001

0.001

1,845

(0.027)

(0.054)

(0.030)

(0.047)

Male

0.594

0.036

0.077

– 0.039

– 0.057

1,776

(0.028)

(0.061)

(0.030)

(0.044)

CURRENTLY IDLE (NEITHER EMPLOYED

NOR ENROLLED IN SCHOOL) [SR]

All

0.215

0.007

0.014

0.026

0.039

3,604

(0.018)

(0.038)

(0.020)

(0.031)

Female

0.194

0.024

0.049

0.031

0.048

1,838

(0.024)

(0.048)

(0.027)

(0.043)

Male

0.235

– 0.011

– 0.023

0.022

0.032

1,766

(0.027)

(0.058)

(0.031)

(0.045)

CURRENT WEEKLY EARNINGS (2009

DOLLARS) [SR]

All

$88.98

– 11.81~

– 24.59~

– 8.55

– 12.74

3,463

(6.51)

(13.55)

(7.17)

(10.68)

Female

$88.76

– 14.31~

– 28.89~

– 12.16

– 18.74

1,767

(8.44)

(17.05)

(9.59)

(14.78)

Male

$89.19

– 9.35

– 20.16

– 5.00

– 7.21

1,696

(9.72)

(20.95)

(10.84)

(15.63)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 15 to 20 as of December 31, 2007.

Measures: "Currently enrolled in school" also includes youth who are on summer vacation.

157

Chapter 5: Impacts on Economic Self-Sufficiency

5.6 DATA SOURCES FOR AND

construct the proportion of months receiving benefits

MEASURES OF INCOME, POVERTY,

and the amount received from both AFDC/TANF and

FOOD CONSUMPTION, AND SOCIAL

food stamps.

ASSISTANCE

Administrative records were collected and analyzed

Information about public assistance receipt and income

from seven state agencies and one county agency:15

is available from respondent self-reports on the long-

Massachusetts, Illinois, and Los Angeles County, plus

term survey and administrative records from state TANF

five additional states (Indiana, Georgia, South Carolina,

agencies. In the long-term survey, we asked adults about

North Carolina, and Florida) where participants

current receipt of TANF, food stamps, Supplemental

have moved since enrolling in the demonstration.

Security Income, and Medicaid; the Earned Income Tax

Participants were matched to administrative records using

Credit (EITC),12 and to recall their total household

different personal information, depending on the state.

income for the most recent calendar year preceding

Massachusetts residents were matched strictly on social

the survey—2007, 2008, or 2009 depending on their

security number, Los Angeles County residents on social

interview date. The long-term survey used an innovative

security number and date of birth, and Illinois residents

technique for collecting retrospective reports of total

on social security number, date of birth, and name. Once

household income by embedding an income calculator.

matched, government agencies returned both benefit

amounts and a status variable indicating whether a case

Respondents are first asked about specific sources of

was active for TANF and food stamps.

income in the most recent calendar year for earnings,

government sources, and then income from other

We present results for MTO participants from the

sources. If a respondent does not know the total value

Boston, Chicago and Los Angeles site for the 24-month

of these income sources, the interviewer asks a series

interval from July 2007 through June 2009. We construct

of yes-no questions about different income amounts to

variables for two units of analysis: the entire household

determine the income category.13 These income sources

and grown children. The household unit variables are

are then added up to calculate for the respondent a

designed to capture all TANF and food stamps received

total household income level. The respondent then has

by all core members in each family by summing all

the opportunity to agree or adjust the total household

distinct monthly benefit amounts received by family

income. Moreover, survey data is available on food

members, whether that person was active or not. This

sufficiency,14 constructed measures of self-sufficiency

creates an upper bound on welfare receipt. This measure

(the proportion of adults who were working and not

includes members who have started their own households

receiving TANF at the time of the survey), and food

and captures benefit amounts transferred to children

expenditure. Individual-level administrative data on

born after randomization under any core member’s care.

monthly TANF and food stamp receipt are used to

The subgroup of grown children captures those who

12 EITC refunds were ascertained using the following wording: Work-

enter adulthood and receive TANF and food stamps as

ers sometimes receive a tax refund check- early in the year- from the

Earned Income Tax Credit or because they overpaid taxes in the previ-

household heads. Food stamp and TANF receipt are

ous year. Did you receive a tax refund check from the federal govern-

ment early in [year of survey]? How much was your tax refund?

examined at the individual level. These results present

13 The “unfolding scale” (see Duncan and Petersen, 2001) for income

two measures for grown children: the share of months

consisted of the following questions: Would it amount to $10,000

actively on food stamps and TANF, and the total amount

or more? Would it amount to $20,000 or more? Would it amount to

$25,000 or more? Would it amount to $15,000 or more? Would it

of food stamps and TANF benefits received while active

amount to $5,000 or more?

on their public assistance case. We limit this analysis to

14 Food sufficiency is measured using a modified version of a single

only those grown children who are actively on TANF

item used by the U.S. Department of Agriculture (see Bickel et al.,

2000).We asked respondents: Which of these statements best describes

to prevent attributing benefits that are received in their

the foot eaten in your household in the last 12 months: I/we always had

enough to eat, sometimes I/we did not have enough to eat, or often I/

we did not have enough to eat.

15 The analyses currently exclude New York and Maryland.

158

parent’s household to their newly formed household.

Adults in the experimental and Section 8 groups report

This also implies that information might be missing

decreased food insufficiency (roughly 3.5 percentage

about child-only cases for young adults with children.

points lower for experimental adults and 7 percentage

points lower for Section 8 adults) compared with the

5.7 LONG-TERM IMPACTS ON

control group rate of 34 percent. Experimental and

INCOME, POVERTY, FOOD

Section 8 adults also report lower food expenditures—

CONSUMPTION, AND SOCIAL

roughly $7 per week less than control group adults. These

ASSISTANCE

effects are difficult to explain or have confidence in

Exhibits 5.11, 5.12, and 5.13 present estimates of MTO

without further investigation. The results may be chance

effects on income, TANF, and food stamp receipt.

findings or may be in part the result of a reduction in

Exhibit 5.11 shows household self-reported responses on

grocery expenditure as measured in the long-term survey

income, food security, and poverty status. Exhibit 5.12

or lower food prices in the new neighborhoods among

shows welfare receipt information for grown children

experimental and Section 8 families.

culled from administrative data from July 2007 to

June 2009. Exhibit 5.13 shows self-reported data on

The administrative data show that experimental group

receipt of public assistance at the time of interview and

adults have significantly higher rates of food stamp

administrative data indicating the share of months that

receipt but similar TANF receipt, compared with control

households received public assistance during the same

group adults (see Exhibit 5.13). Rates of TANF receipt

two-year time period.

generally follow national patterns of declines over time,

with nearly 75 percent of the sample reporting receipt of

We find no systematic, detectable MTO treatment effects welfare at baseline (measured during the earlier 1990s), on total reported income or social assistance receipt.

28.6 percent on average as reported at the interim

This is further shown in an analysis that more closely

survey interview (roughly during the early 2000s), and

aligns the timing of social assistance receipt as reported

16 percent reporting TANF receipt at the time of the

in the survey and documented in the social assistance

long-term survey interview (in the later 2000s). The

administrative data in Supplemental Exhibits 5.10 and

long-term survey data reveals a qualitatively similar but

5.11. Experimental households are less likely to be living

statistically insignificant pattern of slightly higher TANF

in poverty—defined as having an income at or below

and food stamp receipt for the experimental group. There

the poverty line16—than those in the control group in

are no significant differences between Section 8 and

the calendar year before the long-term survey, but this

control group adults in terms of social assistance receipt

finding is not statistically significant. The opposite

in either the administrative or survey data. In addition,

pattern of a modest (also statistically insignificant) rise in the administrative data indicate that grown children poverty is found for the Section 8 group, relative to the

in the experimental group receive more total TANF

control group (Exhibit 5.11). However, we worry here

benefits than the controls (this is marginally statistically

about the later timing of the survey for the Section 8

significant). There is a similar (but statistically

group. Supplemental Exhibit 5.12 shows similar rates of

insignificant) pattern for the Section 8 grown children

earned income tax credit receipt in the previous calendar

(Exhibit 5.12).

year among the experimental, Section 8, and control

group adults, although Section 8 adults claimed higher

total refund amounts (above $2,500) compared with the

control group.

16 Poverty status was determined by comparing household income in

the previous year to the U.S. Census Bureau’s poverty thresholds for the

adult respondent’s current household size, number of children under

18, and age of the head of household (Denavas-Walt, Proctor, and

Smith, 2010).

159

Chapter 5: Impacts on Economic Self-Sufficiency

EXHIBIT 5.11. ADULT SELF-REPORTED HOUSEHOLD INCOME, POVERTY STATUS, AND FOOD SUFFICIENCY

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

HOUSEHOLD INCOME,

LAST CALENDAR YEAR

Total earnings of sample adult and

$14,518.70

– 483.91

– 998.61

– 1,752.38~

– 2,826.29~

3,266

spouse/partner (2009 dollars) [SR]

(744.03)

(1,535.41)

(1,015.66)

(1,638.08)

Earnings from other household members

$2,672.24

– 17.56

– 36.16

427.21

675.34

3,079

(2009 dollars) [SR]

(302.68)

(623.35)

(465.34)

(735.61)

Government income (2009 dollars) [SR]

$3,542.62

244.44

505.49

323.53

511.54

3,139

(217.77)

(450.33)

(303.01)

(479.08)

Other sources of income (2009 dollars)

$464.94

57.05

117.67

212.05*

339.02*

3,176

[SR]

(72.58)

(149.71)

(103.04)

(164.74)

Total household income (2009 dollars)

$20,025.90

607.58

1,255.56

– 41.67

– 67.33

3,258

[SR]

(727.58)

(1,503.54)

(1,009.18)

(1,630.66)

POVERTY STATUS,

LAST CALENDAR YEAR

Household income is at or below 50% of

0.298

– 0.012

– 0.024

0.036

0.058

3,258

poverty line [SR]

(0.019)

(0.040)

(0.026)

(0.043)

Household income is at or below 100% of

0.590

– 0.032

– 0.067

0.036

0.059

3,258

poverty line [SR]

(0.021)

(0.043)

(0.028)

(0.045)

Household income is at or below 150% of

0.756

– 0.007

– 0.014

0.008

0.013

3,258

poverty line [SR]

(0.018)

(0.038)

(0.024)

(0.039)

FOOD SUFFICIENCY

Adult reported that their household

0.336

– 0.035~

– 0.072~

– 0.067*

– 0.106*

3,266

sometimes/often did not have enough to

(0.020)

(0.042)

(0.026)

(0.042)

eat in the past 12 months [SR]

Current total weekly food expenditure

$113.05

– 7.01*

– 14.39*

– 7.27~

– 11.59~

3,165

(2009 dollars) [SR]

(2.88)

(5.92)

(3.82)

(6.09)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All interviewed adults.

Measures: Poverty levels are determined by household size and age of household head. Government income includes welfare, SSI, unemployment benefits, and social security. "Other sources of income" includes money from any businesses, help from friends or relatives, child support, and any other money income. "Food Insufficiency" reflects the adult's response that in the past 12 months, there are some times or always not enough to eat.

160

EXHIBIT 5.12. WELFARE AND FOOD STAMP RECEIPT OF GROWN CHILDREN, ADMINISTRATIVE DATA CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

TEMPORARY ASSISTANCE FOR NEEDY

FAMILIES (TANF) BENEFITS,

JULY 2007–JUNE 2009 [TANF]

FRACTION OF MONTHS ACTIVELY

RECEIVING TANF

All

0.046

0.012

0.029

0.010

0.017

2,833

(0.008)

(0.019)

(0.009)

(0.015)

Female

0.083

0.021

0.046

0.016

0.026

1,398

(0.016)

(0.034)

(0.017)

(0.027)

Male

0.010

0.004

0.009

0.004

0.007

1,435

(0.006)

(0.014)

(0.007)

(0.012)

TOTAL TANF BENEFITS WHILE ACTIVE ON

CASE (2009 DOLLARS)

All

$270.13

98.32~

230.41~

88.60

145.10

2,833

(55.54)

(130.15)

(59.30)

(97.12)

Female

$499.05

139.69

308.15

127.25

204.59

1,398

(103.06)

(227.36)

(109.64)

(176.28)

Male

$43.11

57.62

143.69

50.16

83.62

1,435

(38.61)

(96.28)

(41.26)

(68.78)

FOOD STAMPS BENEFITS,

JULY 2007–JUNE 2009 [FS]

FRACTION OF MONTHS ACTIVELY

RECEIVING FOOD STAMPS

All

0.228

0.004

0.010

0.009

0.014

2,833

(0.016)

(0.038)

(0.018)

(0.029)

Female

0.322

0.013

0.029

0.006

0.010

1,398

(0.026)

(0.057)

(0.028)

(0.045)

Male

0.134

– 0.004

– 0.010

0.011

0.018

1,435

(0.019)

(0.047)

(0.021)

(0.034)

TOTAL FOOD STAMPS BENEFITS WHILE

ACTIVE ON CASE (2009 DOLLARS)

All

$879.05

13.20

30.93

0.21

0.35

2,833

(76.64)

(179.59)

(82.65)

(135.36)

Female

$1,388.81

– 20.51

– 45.24

– 82.65

– 132.89

1,398

(134.74)

(297.23)

(140.46)

(225.83)

Male

$373.56

46.19

115.18

79.65

132.78

1,435

(74.85)

(186.66)

(78.66)

(131.13)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: TANF = Temporary Assistance for Needy Families, FS = Food Stamps.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: FS and TANF analyses use individual data from Massachusetts, Illinois, Los Angeles County, and South Carolina and represent individuals whose random assignment sites are: Boston, Chicago and Los Angeles. We received TANF and food stamps data from Maryland, but the data quality is suspect.

Sample for UI, TANF, and FS are all sample adults with baseline consent. All core grown children.

Measures: "Active" means the family is receiving welfare benefits directly through the grown child. The measures presented in this table include only payments made to the grown child while they were active on the case. We restrict this measure to "active" status to avoid picking up payments made to a youth's parent even though the youth is no longer part of the case (due to their age). This measure would not capture "child-only" payments made to children of the grown child.

161

Chapter 5: Impacts on Economic Self-Sufficiency

EXHIBIT 5.13. ADULT GOVERNMENT ASSISTANCE, SELF REPORTS AND ADMINISTRATIVE DATA CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

SURVEY DATA [SR]

Currently receiving Temporary Assistance for

0.158

0.010

0.021

0.026

0.041

3,262

Needy Families (TANF)

(0.015)

(0.031)

(0.021)

(0.033)

Currently receiving food stamps

0.470

0.016

0.032

0.029

0.046

3,253

(0.021)

(0.043)

(0.028)

(0.045)

Currently receiving Supplemental Security

0.293

0.004

0.008

0.019

0.030

3,252

Income

(0.019)

(0.039)

(0.026)

(0.041)

Currently receiving Medicaid

0.367

– 0.003

– 0.005

0.037

0.060

3,273

(0.019)

(0.040)

(0.026)

(0.043)

ADMINISTRATIVE DATA

FAMILY TANF BENEFITS [TANF]

Fraction of months received TANF, July 2007–

0.238

0.003

0.005

– 0.027

– 0.042

2,708

June 2009

(0.017)

(0.037)

(0.018)

(0.028)

Total TANF benefits received, July 2007–June

$1,402.33

56.10

120.29

– 94.47

– 144.57

2,708

2009 (2009 dollars)

(114.48)

(245.44)

(123.82)

(189.49)

FAMILY FOOD STAMPS BENEFITS [FS]

Fraction of months received food stamps (July

0.594

0.046*

0.098*

– 0.007

– 0.011

2,708

2007–June 2009)

(0.019)

(0.040)

(0.020)

(0.031)

Total food stamps benefits received, July 2007–

$3,074.08

309.94*

664.54*

171.07

261.80

2,708

June 2009 (2009 dollars)

(156.50)

(335.54)

(184.98)

(283.09)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: TANF = Temporary Assistance for Needy Families, FS = Food Stamps records, SR = self report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: FS and TANF analyses use individual data from Massachusetts, Illinois, Los Angeles County, and South Carolina and represent individuals whose random assignment sites are: Boston, Chicago and Los Angeles. We received TANF and food stamps data from Maryland, but the data quality is suspect.

Sample for UI, TANF, and FS are all sample adults with baseline consent. All sample adults with a baseline consent.

Measures: The measures represented in this table are at the family level. The outcome reflects the fraction of months during the 12 month period that any family members' case received TANF or Food Stamps benefits. The benefit amount represents the total amount of benefits received during those 12 months for all family members' cases. This family level measure does not restrict to "active" months in order to capture all benefits received by the family and dependents. For example, a payment could have been made to a child even though the adult on the case was sanctioned.

162

5.8 DATA SOURCES FOR MEASURES

and Section 8 adults are less likely to experience food

OF SAVINGS AND ASSETS

insufficiency than control group adults. Ownership of

The inclusion of savings and assets measures in the

assets, such as cars and homes, are the same or lower

long-term survey is a new addition, compared with

among experimental and Section 8 families, compared

measures available from the interim survey. The long-

with those in the control group.

term survey items are drawn from common sources

of savings and asset questions, such as the Survey of

Yet debt levels are slightly higher for the experimental

Consumer Finances, the Health and Retirement Survey,

and Section 8 groups. Youth idleness rates are similar

and the Panel Study of Income Dynamics. Information

among random assignment groups at the long-term

is collected on savings or checking accounts, home or

survey. Parental reports are suggestive of beneficial

vehicle ownership, credit cards, and debt on any of these

impacts on employment and reduction in idleness for

assets and also unpaid medical bills. Detailed questions

male grown children, but the limited administrative UI

about matters related to portfolio composition or use

data do not provide support for this finding.

of more sophisticated savings vehicles are not included,

given the low incomes of the demonstration participants

On average, experimental and Section 8 households

and the first-order hypothesized effect of reducing the

moved to neighborhoods with a greater share of “middle-

share of adults who are unbanked.

class” (higher income, more educated, and steadily

employed) neighbors and greater safety than control

5.9 LONG-TERM IMPACTS ON

households. The advantages of these neighborhood

SAVINGS AND ASSETS

attributes felt by the treatment groups eroded a bit from

Exhibit 5.14 shows that the demonstration had few

the time of initial moves to interim to the long-term

detectable effects on measures of savings, assets, and

survey. Still, these changes in environment are associated

debt—including owning a car or having a credit card or

with persistent improvements in some mental health and

bank account—for both the experimental and Section 8

physical health outcomes for adults in the experimental

groups, relative to the control group. The experimental

and Section 8 groups, relative to the control group adults

group adults are more likely to report having any

(Chapters 3 and 4).But such neighborhood changes

household debt, and, at marginal statistical significance,

initially are associated with (if anything) an adverse

the Section 8 adults are somewhat more likely to report

initial disruption effect on labor market outcomes and

having any household debt and to report owing more

do not appear to generate any sustained improvement

than $5,000 in credit card debt, compared with control

in labor market outcomes or other measures of adult

group adults. As shown in Chapter 2, experimental and

economic self-sufficiency.

Section 8 participants are less likely to be homeowners

than control group adults.

The lack of impacts of a housing mobility intervention

on economic outcomes for adult residents of housing

developments contrasts with recent evaluations showing

5.10 INTERPRETATION OF ECONOMIC sustained positive impacts on earnings of disadvantaged SELF-SUFFICIENCY RESULTS

adults from the direct provision of high-quality

training and employment services. Examples of such

MTO has few systematic, detectable long-term effects

successful employment programs include the Jobs-Plus

on economic self-sufficiency outcomes for adults, youth,

demonstration, which provides training, outreach, and

and grown children, as measured through survey and

improved work incentives for public housing residents

administrative records data up to the long-term follow-up (Riccio, 2010), and sectoral employment programs, survey period. Experimental and Section 8 group adults

which provide training with strong links to local

have similar employment, earnings, and income levels

employer demands (Maguire et al., 2010). Training,

as control group adults. Experimental group adults are

education, and employment services that directly enhance

more likely to receive TANF benefits. Both experimental

marketable skills and changes in work incentives appear

163

Chapter 5: Impacts on Economic Self-Sufficiency

to more directly impact labor market and economic

impacts on youth economic outcomes of some early

outcomes of low-income adults than do the indirect

childhood education programs (Almond and Currie,

effects of changes in neighborhood environments, at least 2010) and improvements in school quality in early grades in the range observed in the MTO demonstration.

(Chetty et al., 2010). Housing mobility interventions

that affect neighborhood environments by a similar

Furthermore, the demonstrations have not generated

magnitude as MTO may not be powerful enough to

systematic detectable gains in economic outcomes for

greatly affect youth long-term economic outcomes

youth and grown children in the experimental and

without being combined with further direct services for

Section 8 groups, compared with those in the control

the children and training and education programs to

group. These results contrast with the long-term positive

improve parental earnings opportunities.

EXHIBIT 5.14. ADULT SELF-REPORTED FAMILY SAVINGS AND ASSETS

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

Household currently owns a car

0.436

– 0.023

– 0.047

– 0.033

– 0.053

3,239

[SR]

(0.020)

(0.041)

(0.026)

(0.041)

Adult currently has a bank account

0.559

0.014

0.029

– 0.039

– 0.062

3,191

[SR]

(0.021)

(0.043)

(0.028)

(0.044)

Any household debt [SR]

0.410

0.055*

0.113*

0.056~

0.089~

3,209

(0.021)

(0.044)

(0.029)

(0.045)

Household owes ≥ $5,000 on

0.058

0.001

0.003

0.014

0.023

3,218

medical bills (2009 dollars) [SR]

(0.010)

(0.020)

(0.016)

(0.025)

Adult has a credit card [SR]

0.259

0.025

0.051

0.002

0.004

3,238

(0.019)

(0.038)

(0.024)

(0.038)

Household owes ≥ $5,000 in credit

0.065

0.016

0.033

0.028~

0.044~

3,211

card debt (2009 dollars) [SR]

(0.011)

(0.022)

(0.015)

(0.023)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All interviewed adults.

164

SUPPLEMENTAL EXHIBIT 5.1. ADULT EMPLOYMENT, ADMINISTRATIVE DATA, BY SITE AND RANDOM

ASSIGNMENT (RA) YEARS

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

SHARE EMPLOYED [UI]

PRE-RA YEAR 1

Maryland

0.286

– 0.037

– 0.070

– 0.036

– 0.046

629

(0.031)

(0.057)

(0.033)

(0.042)

Illinois

0.270

0.009

0.026

0.018

0.027

883

(0.026)

(0.076)

(0.030)

(0.045)

California

0.274

0.017

0.027

0.005

0.007

853

(0.025)

(0.041)

(0.027)

(0.035)

Three-site weighted average

0.276

– 0.001

– 0.003

0.005

0.006

2,365

(0.015)

(0.031)

(0.017)

(0.024)

RA YEARS 1 AND 2

Maryland

0.414

– 0.013

– 0.024

– 0.050

– 0.064

629

(0.028)

(0.053)

(0.032)

(0.041)

Illinois

0.408

– 0.058*

– 0.173*

– 0.012

– 0.017

883

(0.025)

(0.073)

(0.029)

(0.043)

California

0.401

– 0.021

– 0.034

– 0.042

– 0.054

853

(0.025)

(0.041)

(0.028)

(0.036)

Massachusetts

0.410

0.001

0.003

0.024

0.047

885

(0.021)

(0.047)

(0.022)

(0.042)

Four-site weighted average

0.408

– 0.023~

– 0.049~

– 0.017

– 0.025

3,250

(0.012)

(0.026)

(0.014)

(0.020)

RA YEARS 3 AND 4

Maryland

0.549

– 0.011

– 0.021

– 0.025

– 0.032

629

(0.035)

(0.065)

(0.038)

(0.049)

Illinois

0.476

0.021

0.062

0.051

0.076

883

(0.030)

(0.090)

(0.036)

(0.054)

California

0.455

0.035

0.056

0.000

0.000

853

(0.031)

(0.050)

(0.033)

(0.042)

Massachusetts

0.530

– 0.058~

– 0.131~

– 0.013

– 0.025

885

(0.030)

(0.067)

(0.029)

(0.055)

Four-site weighted average

0.499

– 0.003

– 0.006

0.005

0.008

3,250

(0.016)

(0.033)

(0.017)

(0.025)

RA YEARS 5 AND 6

Maryland

0.615

0.012

0.022

– 0.055

– 0.070

629

(0.035)

(0.065)

(0.039)

(0.050)

Illinois

0.501

0.025

0.075

0.040

0.060

883

(0.033)

(0.099)

(0.039)

(0.058)

California

0.454

0.052

0.083

0.039

0.050

853

(0.033)

(0.053)

(0.034)

(0.044)

165

Chapter 5: Impacts on Economic Self-Sufficiency

SUPPLEMENTAL EXHIBIT 5.1. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

SHARE EMPLOYED [UI] (CONTINUED)

RA YEARS 5 AND 6 (CONTINUED)

Massachusetts

0.534

– 0.066*

– 0.149*

0.017

0.034

885

(0.030)

(0.067)

(0.032)

(0.062)

Four-site weighted average

0.519

0.005

0.010

0.015

0.022

3,250

(0.016)

(0.034)

(0.018)

(0.027)

RA YEARS 7 AND 8

Maryland

0.610

– 0.039

– 0.072

– 0.037

– 0.047

629

(0.038)

(0.070)

(0.042)

(0.054)

Illinois

0.462

0.050

0.149

0.041

0.061

883

(0.034)

(0.101)

(0.041)

(0.060)

California

0.436

0.027

0.044

0.062~

0.080~

853

(0.035)

(0.056)

(0.035)

(0.046)

Massachusetts

0.468

– 0.041

– 0.092

0.032

0.062

885

(0.032)

(0.071)

(0.039)

(0.075)

Four-site weighted average

0.485

0.002

0.005

0.029

0.043

3,250

(0.017)

(0.036)

(0.020)

(0.029)

RA YEARS 9 AND 10

Maryland

0.579

– 0.058

– 0.108

– 0.040

– 0.051

629

(0.041)

(0.076)

(0.044)

(0.057)

Illinois

0.445

0.047

0.140

0.059

0.088

883

(0.034)

(0.100)

(0.040)

(0.060)

California

0.425

0.042

0.067

0.060~

0.077~

853

(0.035)

(0.056)

(0.036)

(0.046)

Massachusetts

0.443

– 0.021

– 0.048

0.006

0.011

885

(0.032)

(0.073)

(0.033)

(0.063)

Four-site weighted average

0.465

0.007

0.014

0.026

0.038

3,250

(0.017)

(0.036)

(0.019)

(0.028)

RA YEARS 1–10

Maryland

0.553

– 0.022

– 0.041

– 0.041

– 0.053

629

(0.028)

(0.053)

(0.031)

(0.040)

Illinois

0.458

0.017

0.051

0.036

0.053

883

(0.025)

(0.075)

(0.029)

(0.044)

California

0.434

0.027

0.043

0.024

0.031

853

(0.025)

(0.040)

(0.026)

(0.034)

Massachusetts

0.477

– 0.037~

– 0.083~

0.013

0.026

885

(0.022)

(0.049)

(0.021)

(0.041)

Four-site weighted average

0.475

– 0.003

– 0.005

0.012

0.017

3,250

(0.012)

(0.026)

(0.013)

(0.020)

166

SUPPLEMENTAL EXHIBIT 5.1. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

ANNUAL WAGES (2009 DOLLARS) [UI]

PRE-RA YEAR 1

Maryland

$2,885.21

– 180.81

– 337.54

– 335.68

– 429.54

629

(402.44)

(751.27)

(423.82)

(542.32)

Illinois

$3,095.51

371.97

1,105.29

404.78

601.29

883

(400.14)

(1,188.98)

(480.48)

(713.75)

California

$3,432.36

293.02

471.62

– 211.87

– 273.86

853

(425.58)

(684.98)

(453.71)

(586.47)

Three-site weighted average

$3,163.83

94.20

191.41

56.61

76.72

2,365

(240.38)

(488.46)

(274.91)

(372.61)

RA YEARS 1 AND 2

Maryland

$4,786.21

96.59

180.31

– 346.50

– 443.38

629

(402.81)

(751.96)

(449.83)

(575.60)

Illinois

$5,232.02

– 603.87

– 1,794.35

– 268.39

– 398.68

883

(417.76)

(1,241.33)

(490.36)

(728.42)

California

$5,670.21

– 924.22*

– 1,487.57*

– 1,032.71~

– 1,334.88~

853

(435.28)

(700.61)

(527.91)

(682.38)

Massachusetts

$7,161.93

44.67

100.10

723.97

1,388.36

885

(480.79)

(1,077.37)

(528.41)

(1,013.34)

Four-site weighted average

$5,781.17

– 377.39~

– 786.86~

– 214.97

– 317.03

3,250

(221.62)

(462.10)

(255.44)

(376.71)

RA YEARS 3 AND 4

Maryland

$8,125.47

– 166.07

– 310.02

– 353.63

– 452.51

629

(765.92)

(1,429.82)

(792.12)

(1,013.60)

Illinois

$7,504.97

– 124.31

– 369.38

– 481.40

– 715.11

883

(660.57)

(1,962.83)

(762.10)

(1,132.08)

California

$7,477.82

– 88.18

– 141.93

– 997.51

– 1,289.38

853

(639.05)

(1,028.57)

(718.58)

(928.83)

Massachusetts

$10,418.11

– 1,230.45~

– 2,757.22~

– 295.14

– 565.99

885

(725.29)

(1,625.24)

(675.26)

(1,294.95)

Four-site weighted average

$8,398.43

– 422.45

– 880.83

– 541.97

– 799.28

3,250

(348.14)

(725.88)

(368.47)

(543.40)

RA YEARS 5 AND 6

Maryland

$10,509.31

332.50

620.72

– 284.52

– 364.07

629

(874.49)

(1,632.50)

(1,019.74)

(1,304.87)

Illinois

$9,011.70

492.27

1,462.74

27.38

40.67

883

(804.95)

(2,391.85)

(902.33)

(1,340.39)

California

$8,824.30

– 85.27

– 137.24

– 652.90

– 843.94

853

(834.91)

(1,343.82)

(855.17)

(1,105.39)

Massachusetts

$12,222.01

– 1,658.96~

– 3,717.42~

715.98

1,373.03

885

(946.40)

(2,120.72)

(911.08)

(1,747.18)

Four-site weighted average

$10,111.07

– 274.30

– 571.93

– 24.84

– 36.63

3,250

(436.74)

(910.63)

(459.28)

(677.33)

167

Chapter 5: Impacts on Economic Self-Sufficiency

SUPPLEMENTAL EXHIBIT 5.1. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

ANNUAL WAGES (2009 DOLLARS) [UI]

(CONTINUED)

RA YEARS 7 AND 8

Maryland

$11,947.92

– 586.02

– 1,093.97

– 358.35

– 458.55

629

(1,033.78)

(1,929.86)

(1,193.35)

(1,527.02)

Illinois

$9,264.16

699.30

2,077.93

– 181.69

– 269.89

883

(911.22)

(2,707.61)

(1,070.05)

(1,589.53)

California

$9,489.59

– 282.26

– 454.30

– 670.89

– 867.20

853

(997.39)

(1,605.34)

(980.19)

(1,266.99)

Massachusetts

$11,812.85

– 1,329.14

– 2,978.36

600.70

1,151.96

885

(1,155.54)

(2,589.36)

(1,280.68)

(2,455.95)

Four-site weighted average

$10,526.24

– 358.94

– 748.41

– 131.96

– 194.60

3,250

(518.30)

(1,080.68)

(570.49)

(841.34)

RA YEARS 9 AND 10

Maryland

$13,334.45

– 2,569.26*

– 4,796.29*

– 931.10

– 1,191.45

629

(1,170.38)

(2,184.85)

(1,402.79)

(1,795.02)

Illinois

$9,048.23

1,227.64

3,647.83

395.85

588.03

883

(1,010.41)

(3,002.36)

(1,205.51)

(1,790.75)

California

$9,415.62

203.63

327.76

93.96

121.45

853

(1,036.58)

(1,668.42)

(1,059.42)

(1,369.40)

Massachusetts

$11,543.13

– 252.62

– 566.08

508.26

974.69

885

(1,178.90)

(2,641.70)

(1,290.40)

(2,474.59)

Four-site weighted average

$10,643.49

– 179.70

– 374.68

90.45

133.39

3,250

(550.89)

(1,148.63)

(617.70)

(910.96)

RA YEARS 1–10

Maryland

$9,740.67

– 578.45

– 1,079.85

– 454.82

– 581.99

629

(704.70)

(1,315.52)

(806.73)

(1,032.30)

Illinois

$8,012.22

338.21

1,004.95

– 101.65

– 151.00

883

(633.52)

(1,882.45)

(720.37)

(1,070.10)

California

$8,175.51

– 235.26

– 378.66

– 652.01

– 842.79

853

(644.21)

(1,036.88)

(684.88)

(885.27)

Massachusetts

$10,631.61

– 885.30

– 1,983.79

450.75

864.41

885

(747.31)

(1,674.60)

(747.08)

(1,432.67)

Four-site weighted average

$9,092.08

– 322.556

– 672.542

– 164.660

– 242.834

3,250

(343.663)

(716.550)

(369.255)

(544.564)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO

housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" would indicate that 25

percent of the control group was working).

Square brackets indicate the source of the outcome information: UI = Unemployment Insurance administrative records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family.

Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Individual level data from Maryland, Illinois, California, and Florida (representing individuals whose random assignment sites are Baltimore, Chicago, and Los Angeles) and aggregate data from Massachusetts (representing individuals whose random assignment site is Boston). All sample adults with baseline consent.

168

SUPPLEMENTAL EXHIBIT 5.2. COMPARISON OF SELF-REPORTED AND ADMINISTRATIVE DATA ON ADULT

CURRENT EMPLOYMENT BY SITE

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

"CURRENTLY EMPLOYED," BY SITE AND SOURCE

MASSACHUSETTS

Self-report

0.502

– 0.046

– 0.107

– 0.086

– 0.153

678

(0.045)

(0.105)

(0.059)

(0.106)

Unemployment Insurance records

0.413

– 0.068~

– 0.152~

– 0.020

– 0.039

885

(0.039)

(0.087)

(0.049)

(0.094)

THREE SITE (MARYLAND, ILLINOIS, CALIFORNIA)

Self-report

0.533

– 0.005

– 0.011

– 0.078*

– 0.109*

1,856

(0.027)

(0.054)

(0.037)

(0.052)

Unemployment Insurance records

0.467

– 0.013

– 0.026

0.008

0.010

2,365

(0.024)

(0.049)

(0.027)

(0.037)

MARYLAND

Self-report

0.610

– 0.115*

– 0.211*

– 0.138*

– 0.180*

450

(0.055)

(0.100)

(0.070)

(0.091)

Unemployment Insurance records

0.582

– 0.121*

– 0.226*

– 0.082

– 0.105

629

(0.046)

(0.087)

(0.052)

(0.066)

ILLINOIS

Self-report

0.545

0.011

0.033

– 0.074

– 0.109

670

(0.046)

(0.138)

(0.068)

(0.101)

Unemployment Insurance records

0.436

0.033

0.099

0.060

0.088

883

(0.041)

(0.122)

(0.049)

(0.072)

CALIFORNIA

Self-report

0.478

0.061

0.097

– 0.028

– 0.039

736

(0.043)

(0.068)

(0.054)

(0.075)

Unemployment Insurance records

0.415

0.046

0.074

0.033

0.043

853

(0.041)

(0.066)

(0.042)

(0.054)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report, UI = Unemployment Insurance administrative records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: For self-reports, data source is the adult long-term survey and the sample is all adults interviewed. Unemployment Insurance data analyses use individual level data from Maryland, Illinois, California, and Florida (representing individuals whose random assignment sites are Baltimore, Chicago, and Los Angeles) and aggregate data from Massachusetts (representing individuals whose random assignment site is Boston). All sample adults with baseline consent.

Measures: Time period for administrative data is June 2008–December 2009.

169

Chapter 5: Impacts on Economic Self-Sufficiency

SUPPLEMENTAL EXHIBIT 5.3. COMPARISON OF SELF-REPORTED AND ADMINISTRATIVE DATA ON ADULT

EMPLOYMENT IN MARYLAND, ILLINOIS, AND CALIFORNIA (IN-STATE ONLY)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

EMPLOYMENT IN THE PREVIOUS CALENDAR

YEAR, BY SITE AND SOURCE

ALL THREE SITES

Self-report

0.664

0.021

0.040

– 0.077*

– 0.106*

1,546

(0.028)

(0.054)

(0.039)

(0.054)

Unemployment Insurance records

0.607

0.004

0.009

– 0.047

– 0.064

1,546

(0.029)

(0.057)

(0.041)

(0.056)

MARYLAND

Self-report

0.731

– 0.100~

– 0.187~

– 0.190*

– 0.243*

408

(0.055)

(0.102)

(0.079)

(0.101)

Unemployment Insurance records

0.711

– 0.119*

– 0.220*

– 0.116

– 0.149

408

(0.055)

(0.103)

(0.074)

(0.094)

ILLINOIS

Self-report

0.667

0.060

0.172

– 0.075

– 0.109

576

(0.045)

(0.130)

(0.065)

(0.094)

Unemployment Insurance records

0.610

0.036

0.102

– 0.082

– 0.120

576

(0.049)

(0.140)

(0.071)

(0.104)

CALIFORNIA

Self-report

0.613

0.118*

0.182*

0.067

0.091

562

(0.047)

(0.072)

(0.057)

(0.078)

Unemployment Insurance records

0.529

0.122*

0.188*

0.105~

0.144~

562

(0.049)

(0.075)

(0.061)

(0.083)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report, UI = Unemployment Insurance administrative records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Sample is restricted to adults with annual earnings available in both survey and administrative data for the year prior to the interview and who are currently living in their random assignment state. For self-reports, data source is the adult long-term survey and the sample is all adults interviewed.

Unemployment Insurance data use individual level data from Maryland, Illinois, California, and Florida (representing individuals whose random assignment sites are Baltimore, Chicago, and Los Angeles) and aggregate data from Massachusetts (representing individuals whose random assignment site is Boston). All sample adults with baseline consent.

Measures: Time period for administrative data is from June 2008-December 2009.

170

SUPPLEMENTAL EXHIBIT 5.4. ADULT SELF-REPORTED CURRENT INDUSTRY AND OCCUPATION

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

INDUSTRY

Retail [SR]

0.060

– 0.003

– 0.006

– 0.022~

– 0.036~

3,250

(0.010)

(0.021)

(0.013)

(0.021)

Medical/health care [SR]

0.122

0.010

0.020

– 0.005

– 0.008

3,250

(0.014)

(0.029)

(0.018)

(0.029)

Education [SR]

0.059

0.002

0.004

– 0.012

– 0.019

3,250

(0.011)

(0.023)

(0.013)

(0.021)

Social services [SR]

0.075

– 0.002

– 0.005

– 0.017

– 0.027

3,250

(0.011)

(0.023)

(0.014)

(0.023)

Industry other than retail,

0.207

– 0.013

– 0.026

– 0.025

– 0.040

3,250

medical, education, or social

(0.017)

(0.036)

(0.024)

(0.038)

services [SR]

OCCUPATION

Blue collar [SR]

0.062

– 0.009

– 0.018

– 0.015

– 0.024

3,253

(0.010)

(0.021)

(0.013)

(0.020)

White collar [SR]

0.202

0.001

0.003

– 0.032

– 0.050

3,253

(0.016)

(0.033)

(0.021)

(0.034)

Professional or managerial [SR]

0.061

0.002

0.005

– 0.001

– 0.001

3,253

(0.010)

(0.020)

(0.013)

(0.021)

Service [SR]

0.260

– 0.001

– 0.003

– 0.034

– 0.055

3,253

(0.019)

(0.040)

(0.025)

(0.040)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All interviewed adults.

"Measures: Industry and occupation outcomes indicate that the adult respondent was currently working and was working in that industry or occupation.

Occupation categories are not mutually exclusive. Industry coding is based on CPS codes: Retail trade 580–691; Medical trade 812–840, Education trade 842–860, and Social services 861–871. Occupation is coded as follows: Blue collar 473–889 (production, craft, repair, machine operators, assemblers, inspectors, transportation, handlers, cleaners, helpers, and laborers); White collar 3–389 (executive/managerial, professional specialty, technical/administrative support, or sales), Managerial/professional 3–199 (executive, administrative, and managerial or professional specialty), and Service occupation 403–469 (private household service, protective service, and all other services).

171

Chapter 5: Impacts on Economic Self-Sufficiency

SUPPLEMENTAL EXHIBIT 5.5. ADULT SELF-REPORTED CURRENT EMPLOYMENT BENEFITS

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

EMPLOYMENT BENEFITS

Health insurance [SR]

0.342

0.013

0.027

– 0.044~

– 0.070~

3,229

(0.020)

(0.041)

(0.026)

(0.042)

Paid sick leave [SR]

0.307

0.016

0.034

– 0.035

– 0.056

3,220

(0.019)

(0.040)

(0.025)

(0.040)

Paid vacation [SR]

0.350

0.004

0.007

– 0.039

– 0.062

3,227

(0.020)

(0.041)

(0.027)

(0.043)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All interviewed adults.

SUPPLEMENTAL EXHIBIT 5.6. FOUR MUTUALLY EXCLUSIVE CATEGORIES OF WORK AND SCHOOLING OF

GROWN CHILDREN, PARENT REPORTS

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

SURVEY DATA

CURRENTLY EMPLOYED ONLY [PR]

All

0.456

0.021

0.047

– 0.020

– 0.037

2,958

(0.023)

(0.051)

(0.031)

(0.056)

Female

0.486

– 0.024

– 0.052

– 0.062

– 0.108

1,485

(0.032)

(0.069)

(0.041)

(0.071)

Male

0.424

0.067*

0.158*

0.024

0.046

1,473

(0.032)

(0.076)

(0.041)

(0.078)

CURRENTLY ENROLLED IN SCHOOL

ONLY [PR]

All

0.059

– 0.006

– 0.014

0.024

0.043

3,084

(0.010)

(0.022)

(0.016)

(0.029)

Female

0.080

– 0.008

– 0.018

0.024

0.042

1,522

(0.016)

(0.036)

(0.025)

(0.044)

Male

0.037

– 0.004

– 0.009

0.024

0.045

1,562

(0.011)

(0.026)

(0.018)

(0.034)

CURRENTLY EMPLOYED AND

ENROLLED IN SCHOOL [PR]

All

0.097

– 0.004

– 0.009

– 0.028

– 0.050

3,084

(0.013)

(0.031)

(0.018)

(0.032)

Female

0.122

– 0.008

– 0.018

– 0.020

– 0.034

1,524

(0.020)

(0.046)

(0.028)

(0.048)

Male

0.071

0.000

0.001

– 0.035~

– 0.066~

1,560

(0.017)

(0.040)

(0.019)

(0.035)

172

SUPPLEMENTAL EXHIBIT 5.6. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

SURVEY DATA

CURRENTLY IDLE (NEITHER

EMPLOYED NOR ENROLLED IN

SCHOOL) [PR]

All

0.372

– 0.012

– 0.028

0.026

0.047

2,958

(0.022)

(0.049)

(0.029)

(0.054)

Female

0.299

0.036

0.079

0.063

0.109

1,483

(0.029)

(0.063)

(0.039)

(0.067)

Male

0.451

– 0.062~

– 0.147~

– 0.015

– 0.028

1,475

(0.032)

(0.075)

(0.040)

(0.076)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: PR = parent report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. Grown children (who were under age 18 at baseline and over age 20 as of December 31, 2007) of interviewed adults.

SUPPLEMENTAL EXHIBIT 5.7. EMPLOYMENT AND SCHOOLING OUTCOMES OF GROWN CHILDREN, PARENT

REPORTS AND ADMINISTRATIVE DATA BY SITE

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

CURRENTLY EMPLOYED [PR]

ALL

Maryland

0.594

0.010

0.021

– 0.063

– 0.089

430

(0.059)

(0.130)

(0.076)

(0.108)

Massachusetts

0.662

– 0.050

– 0.133

– 0.111

– 0.223

553

(0.056)

(0.148)

(0.071)

(0.144)

Illinois

0.511

– 0.072

– 0.255

– 0.055

– 0.095

631

(0.048)

(0.170)

(0.059)

(0.102)

California

0.554

0.026

0.042

0.007

0.009

662

(0.050)

(0.078)

(0.061)

(0.085)

New York

0.513

0.099~

0.233~

– 0.013

– 0.039

677

(0.052)

(0.121)

(0.063)

(0.194)

FEMALE

Maryland

0.610

0.089

0.189

– 0.032

– 0.041

215

(0.076)

(0.162)

(0.102)

(0.129)

Massachusetts

0.739

– 0.152*

– 0.378*

– 0.159~

– 0.280~

297

(0.072)

(0.180)

(0.085)

(0.150)

Illinois

0.556

– 0.093

– 0.311

– 0.098

– 0.182

307

(0.070)

(0.233)

(0.091)

(0.169)

California

0.636

– 0.066

– 0.091

– 0.072

– 0.093

326

(0.067)

(0.092)

(0.076)

(0.098)

New York

0.537

0.031

0.077

– 0.045

– 0.154

335

(0.069)

(0.171)

(0.087)

(0.298)

173

Chapter 5: Impacts on Economic Self-Sufficiency

SUPPLEMENTAL EXHIBIT 5.7. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

CURRENTLY EMPLOYED [PR] (CONTINUED)

MALE

Maryland

0.580

– 0.075

– 0.172

– 0.089

– 0.143

215

(0.089)

(0.204)

(0.117)

(0.186)

Massachusetts

0.560

0.076

0.215

– 0.035

– 0.083

256

(0.079)

(0.225)

(0.094)

(0.225)

Illinois

0.469

– 0.051

– 0.196

– 0.017

– 0.028

324

(0.076)

(0.292)

(0.079)

(0.127)

California

0.465

0.120~

0.212~

0.094

0.147

336

(0.069)

(0.122)

(0.084)

(0.130)

New York

0.487

0.173*

0.384*

0.025

0.069

342

(0.069)

(0.155)

(0.084)

(0.236)

CURRENTLY ENROLLED IN

SCHOOL [PR]

ALL

Maryland

0.106

– 0.010

– 0.023

– 0.032

– 0.044

457

(0.037)

(0.086)

(0.051)

(0.071)

Massachusetts

0.182

0.003

0.007

– 0.038

– 0.076

577

(0.039)

(0.106)

(0.057)

(0.113)

Illinois

0.118

0.011

0.037

0.058

0.100

659

(0.032)

(0.114)

(0.044)

(0.076)

California

0.196

– 0.012

– 0.020

– 0.058

– 0.082

688

(0.038)

(0.060)

(0.051)

(0.073)

New York

0.172

0.017

0.039

0.053

0.160

708

(0.035)

(0.082)

(0.049)

(0.147)

FEMALE

Maryland

0.158

– 0.005

– 0.011

– 0.061

– 0.077

228

(0.055)

(0.124)

(0.065)

(0.081)

Massachusetts

0.227

– 0.069

– 0.177

– 0.071

– 0.125

305

(0.052)

(0.135)

(0.077)

(0.135)

Illinois

0.188

0.006

0.021

0.129~

0.242~

311

(0.049)

(0.164)

(0.072)

(0.136)

California

0.203

0.025

0.034

– 0.011

– 0.014

336

(0.055)

(0.077)

(0.073)

(0.094)

New York

0.232

0.012

0.031

0.006

0.021

347

(0.059)

(0.146)

(0.069)

(0.232)

MALE

Maryland

0.059

– 0.017

– 0.040

– 0.003

– 0.005

229

(0.053)

(0.122)

(0.069)

(0.106)

Massachusetts

0.124

0.089

0.250

0.010

0.023

272

(0.055)

(0.155)

(0.067)

(0.154)

Illinois

0.057

0.014

0.053

0.003

0.005

348

(0.039)

(0.147)

(0.046)

(0.074)

174

SUPPLEMENTAL EXHIBIT 5.7. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

CURRENTLY ENROLLED IN

SCHOOL [PR] (CONTINUED)

FEMALE (CONTINUED)

California

0.189

– 0.049

– 0.087

– 0.109~

– 0.173~

352

(0.050)

(0.090)

(0.060)

(0.096)

New York

0.109

0.023

0.051

0.097~

0.267~

361

(0.044)

(0.096)

(0.056)

(0.155)

CURRENTLY IDLE (NEITHER

EMPLOYED NOR ENROLLED

IN SCHOOL) [PR]

ALL

Maryland

0.367

0.006

0.014

0.021

0.030

431

(0.056)

(0.125)

(0.071)

(0.101)

Massachusetts

0.284

0.071

0.189

0.101

0.202

556

(0.052)

(0.139)

(0.069)

(0.137)

Illinois

0.435

0.055

0.197

0.012

0.021

630

(0.050)

(0.177)

(0.061)

(0.106)

California

0.351

– 0.014

– 0.021

0.035

0.049

665

(0.046)

(0.073)

(0.057)

(0.081)

New York

0.416

– 0.113*

– 0.264*

– 0.063

– 0.191

676

(0.050)

(0.116)

(0.061)

(0.185)

FEMALE

Maryland

0.306

– 0.054

– 0.115

0.047

0.059

215

(0.068)

(0.144)

(0.103)

(0.130)

Massachusetts

0.204

0.185*

0.467*

0.176*

0.310*

299

(0.067)

(0.168)

(0.083)

(0.147)

Illinois

0.360

0.075

0.253

– 0.002

– 0.003

305

(0.072)

(0.240)

(0.092)

(0.172)

California

0.268

0.044

0.061

0.091

0.117

329

(0.065)

(0.088)

(0.071)

(0.092)

New York

0.359

– 0.041

– 0.102

– 0.016

– 0.054

335

(0.064)

(0.158)

(0.081)

(0.273)

MALE

Maryland

0.420

0.072

0.169

– 0.005

– 0.009

216

(0.087)

(0.205)

(0.112)

(0.179)

Massachusetts

0.389

– 0.072

– 0.204

– 0.006

– 0.013

257

(0.076)

(0.218)

(0.091)

(0.210)

Illinois

0.506

0.037

0.140

0.021

0.033

325

(0.077)

(0.291)

(0.080)

(0.129)

California

0.441

– 0.072

– 0.127

– 0.027

– 0.043

336

(0.065)

(0.116)

(0.080)

(0.127)

New York

0.480

– 0.190*

– 0.421*

– 0.115

– 0.322

341

(0.070)

(0.154)

(0.083)

(0.233)

175

Chapter 5: Impacts on Economic Self-Sufficiency

SUPPLEMENTAL EXHIBIT 5.7. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

UNEMPLOYMENT INSURANCE

DATA, FOURTH QUARTER OF 2007

THROUGH THIRD QUARTER OF 2008 [UI]

EMPLOYED

ALL

Maryland

0.540

– 0.055

– 0.120

0.018

0.024

650

(0.043)

(0.093)

(0.052)

(0.070)

Illinois

0.405

– 0.021

– 0.074

– 0.052

– 0.088

826

(0.038)

(0.135)

(0.048)

(0.081)

California

0.486

– 0.009

– 0.015

– 0.010

– 0.013

829

(0.039)

(0.066)

(0.045)

(0.062)

Massachusetts

0.513

– 0.028

– 0.071

0.010

0.019

747

(0.039)

(0.099)

(0.043)

(0.085)

All four sites

0.484

– 0.027

– 0.063

– 0.010

– 0.016

3,052

(0.020)

(0.047)

(0.024)

(0.037)

FEMALE

Maryland

0.606

– 0.071

– 0.162

– 0.004

– 0.005

316

(0.059)

(0.134)

(0.072)

(0.093)

Illinois

0.445

0.032

0.108

0.005

0.009

404

(0.056)

(0.188)

(0.067)

(0.121)

California

0.594

– 0.090

– 0.140

– 0.073

– 0.097

401

(0.058)

(0.089)

(0.062)

(0.081)

Massachusetts

0.564

0.090

0.183

0.060

0.079

386

(0.084)

(0.172)

(0.159)

(0.211)

All four sites

0.551

– 0.004

– 0.008

– 0.002

– 0.002

1,191

(0.017)

(0.038)

(0.027)

(0.042)

MALE

Maryland

0.478

– 0.039

– 0.081

0.039

0.054

334

(0.063)

(0.129)

(0.075)

(0.102)

Illinois

0.368

– 0.072

– 0.277

– 0.109~

– 0.174~

422

(0.055)

(0.210)

(0.063)

(0.101)

California

0.388

0.066

0.123

0.057

0.081

428

(0.051)

(0.094)

(0.060)

(0.086)

Massachusetts

0.458

– 0.152

– 0.537

– 0.056

– 0.209

361

(0.108)

(0.384)

(0.177)

(0.652)

All four sites

0.418

– 0.040*

– 0.095*

– 0.018

– 0.029

1,545

(0.016)

(0.039)

(0.023)

(0.037)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: PR = parent report, UI = Unemployment Insurance administrative records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: For parent reports, data source is the adult long-term survey. Grown children (who were under age 18 at baseline and over age 20 as of December 31, 2007) of interviewed adults. Unemployment Insurance data use individual level data from Maryland, Illinois, California, and Florida (representing individuals whose random assignment sites are Baltimore, Chicago, and Los Angeles) and aggregate data from Massachusetts (representing individuals whose random assignment site is Boston). All core grown children whose parent has a baseline consent.

Measures: The administrative data only look at the last four common calendar quarters (fourth quarter of 2007 through the third quarter of 2008).

176

SUPPLEMENTAL EXHIBIT 5.8. YOUTH EMPLOYMENT AND SCHOOL ENROLLMENT, SELF-REPORTED AND

ADMINISTRATIVE DATA BY SITE

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

CURRENTLY EMPLOYED [SR]

ALL

Maryland

0.450

– 0.158*

-0.271*

– 0.116~

– 0.155~

486

(0.062)

(0.107)

(0.060)

(0.080)

Massachusetts

0.456

– 0.032

– 0.081

– 0.032

– 0.057

716

(0.046)

(0.117)

(0.047)

(0.084)

Illinois

0.339

0.002

0.006

– 0.006

– 0.009

757

(0.046)

(0.159)

(0.050)

(0.068)

California

0.321

– 0.035

– 0.054

– 0.069~

– 0.088~

874

(0.039)

(0.059)

(0.041)

(0.052)

New York

0.439

– 0.070

– 0.142

– 0.041

– 0.080

771

(0.046)

(0.094)

(0.050)

(0.098)

FEMALE

Maryland

0.430

– 0.148~

– 0.247~

– 0.109

– 0.146

244

(0.087)

(0.145)

(0.086)

(0.115)

Massachusetts

0.453

– 0.004

– 0.008

0.004

0.008

376

(0.063)

(0.136)

(0.065)

(0.127)

Illinois

0.368

– 0.051

– 0.180

– 0.018

– 0.025

392

(0.063)

(0.225)

(0.072)

(0.100)

California

0.371

– 0.057

– 0.086

– 0.151*

– 0.187*

428

(0.055)

(0.082)

(0.060)

(0.074)

New York

0.439

– 0.029

– 0.058

– 0.023

– 0.049

398

(0.067)

(0.135)

(0.069)

(0.148)

MALE

Maryland

0.468

– 0.168*

– 0.297*

– 0.122

– 0.163

242

(0.085)

(0.151)

(0.085)

(0.113)

Massachusetts

0.459

– 0.061

– 0.189

– 0.073

– 0.117

340

(0.069)

(0.213)

(0.072)

(0.115)

Illinois

0.313

0.056

0.187

0.003

0.004

365

(0.067)

(0.224)

(0.075)

(0.103)

California

0.274

– 0.011

– 0.017

0.010

0.013

446

(0.050)

(0.079)

(0.060)

(0.079)

New York

0.439

– 0.112

– 0.230

– 0.058

– 0.102

373

(0.070)

(0.142)

(0.071)

(0.126)

CURRENTLY ENROLLED IN SCHOOL [SR]

ALL

Maryland

0.423

0.143*

0.245*

0.040

0.053

486

(0.057)

(0.098)

(0.062)

(0.082)

177

Chapter 5: Impacts on Economic Self-Sufficiency

SUPPLEMENTAL EXHIBIT 5.8. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

CURRENTLY ENROLLED IN SCHOOL

[SR] (CONTINUED)

ALL (CONTINUED)

Massachusetts

0.685

– 0.023

– 0.059

– 0.007

– 0.012

722

(0.046)

(0.117)

(0.046)

(0.083)

Illinois

0.666

– 0.031

– 0.108

0.043

0.060

760

(0.041)

(0.143)

(0.048)

(0.065)

California

0.624

0.051

0.078

– 0.046

– 0.059

877

(0.037)

(0.057)

(0.042)

(0.054)

New York

0.609

0.022

0.044

– 0.038

– 0.074

776

(0.043)

(0.086)

(0.045)

(0.088)

FEMALE

Maryland

0.501

0.107

0.179

– 0.081

– 0.108

244

(0.073)

(0.121)

(0.077)

(0.103)

Massachusetts

0.730

– 0.056

– 0.123

– 0.030

– 0.059

380

(0.061)

(0.134)

(0.064)

(0.125)

Illinois

0.673

– 0.023

– 0.082

0.068

0.093

394

(0.051)

(0.183)

(0.063)

(0.086)

California

0.636

0.043

0.065

0.032

0.039

428

(0.051)

(0.076)

(0.054)

(0.067)

New York

0.598

– 0.005

– 0.011

0.032

0.069

399

(0.062)

(0.124)

(0.066)

(0.141)

MALE

Maryland

0.355

0.172*

0.303*

0.156~

0.207~

242

(0.081)

(0.144)

(0.083)

(0.111)

Massachusetts

0.636

0.013

0.040

0.020

0.032

342

(0.063)

(0.194)

(0.063)

(0.101)

Illinois

0.659

– 0.039

– 0.129

0.020

0.028

366

(0.064)

(0.214)

(0.067)

(0.092)

California

0.613

0.056

0.088

– 0.118~

– 0.155~

449

(0.053)

(0.083)

(0.061)

(0.081)

New York

0.619

0.055

0.111

– 0.117*

– 0.209*

377

(0.056)

(0.113)

(0.056)

(0.100)

CURRENTLY IDLE (NEITHER EMPLOYED

NOR ENROLLED IN SCHOOL) [SR]

ALL

Maryland

0.297

– 0.039

– 0.066

0.022

0.029

486

(0.059)

(0.101)

(0.059)

(0.078)

Massachusetts

0.167

0.030

0.076

0.009

0.017

716

(0.038)

(0.097)

(0.038)

(0.068)

178

SUPPLEMENTAL EXHIBIT 5.8. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

CURRENTLY IDLE (NEITHER EMPLOYED

NOR ENROLLED IN SCHOOL) [SR]

(CONTINUED)

ALL (CONTINUED)

Illinois

0.206

0.028

0.097

– 0.020

– 0.028

757

(0.037)

(0.128)

(0.043)

(0.059)

California

0.201

0.018

0.027

0.071~

0.091~

874

(0.036)

(0.055)

(0.040)

(0.051)

New York

0.230

– 0.039

– 0.079

0.002

0.004

771

(0.038)

(0.077)

(0.042)

(0.082)

FEMALE

Maryland

0.278

0.002

0.003

0.102

0.136

244

(0.072)

(0.121)

(0.078)

(0.104)

Massachusetts

0.133

0.053

0.114

0.045

0.088

376

(0.052)

(0.112)

(0.052)

(0.100)

Illinois

0.191

0.030

0.108

– 0.024

– 0.033

392

(0.050)

(0.178)

(0.061)

(0.084)

California

0.158

0.043

0.065

0.040

0.050

428

(0.047)

(0.071)

(0.050)

(0.062)

New York

0.239

– 0.031

– 0.063

– 0.013

– 0.029

398

(0.052)

(0.106)

(0.060)

(0.129)

MALE

Maryland

0.315

– 0.076

– 0.133

– 0.054

– 0.072

242

(0.088)

(0.154)

(0.086)

(0.115)

Massachusetts

0.202

0.007

0.021

– 0.031

– 0.050

340

(0.055)

(0.170)

(0.054)

(0.087)

Illinois

0.220

0.026

0.086

– 0.017

– 0.024

365

(0.061)

(0.203)

(0.065)

(0.089)

California

0.241

– 0.006

– 0.010

0.099~

0.131~

446

(0.049)

(0.077)

(0.059)

(0.079)

New York

0.222

– 0.048

– 0.099

0.020

0.035

373

(0.055)

(0.113)

(0.061)

(0.108)

179

Chapter 5: Impacts on Economic Self-Sufficiency

SUPPLEMENTAL EXHIBIT 5.8. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

UNEMPLOYMENT INSURANCE DATA, FOURTH QUARTER OF 2007 THROUGH THIRD QUARTER OF 2008 [UI]

EMPLOYED

All

Maryland

0.338

– 0.042

– 0.069

– 0.031

– 0.039

588

(0.043)

(0.071)

(0.047)

(0.060)

Illinois

0.134

0.016

0.047

0.040

0.055

896

(0.027)

(0.083)

(0.036)

(0.049)

California

0.293

– 0.036

– 0.056

0.001

0.002

923

(0.031)

(0.048)

(0.034)

(0.043)

Massachusetts

0.278

– 0.075*

– 0.198*

– 0.004

– 0.006

1,083

(0.027)

(0.070)

(0.030)

(0.053)

Four-site average

0.256

– 0.036*

– 0.076*

0.004

0.006

3,490

(0.015)

(0.032)

(0.018)

(0.025)

FEMALE

Maryland

0.394

– 0.019

– 0.034

– 0.038

– 0.050

284

(0.058)

(0.102)

(0.062)

(0.081)

Illinois

0.183

0.020

0.063

0.032

0.042

440

(0.042)

(0.132)

(0.051)

(0.067)

California

0.282

– 0.009

– 0.015

0.031

0.039

423

(0.042)

(0.066)

(0.047)

(0.059)

Massachusetts

0.276

– 0.091~

– 0.219~

0.009

0.017

544

(0.049)

(0.117)

(0.047)

(0.091)

Four-site average

0.272

– 0.030

– 0.063

0.012

0.017

1,691

(0.024)

(0.051)

(0.026)

(0.037)

MALE

Maryland

0.250

– 0.043

– 0.066

– 0.022

– 0.027

353

(0.047)

(0.071)

(0.053)

(0.064)

Illinois

0.073

0.008

0.022

0.046

0.064

545

(0.027)

(0.079)

(0.037)

(0.051)

California

0.232

– 0.047

– 0.072

– 0.012

– 0.015

646

(0.033)

(0.051)

(0.034)

(0.044)

Massachusetts

0.280

– 0.064~

– 0.184~

– 0.015

– 0.024

539

(0.038)

(0.111)

(0.044)

(0.070)

Four-site average

0.207

– 0.037*

– 0.075*

0.001

0.001

2,083

(0.018)

(0.037)

(0.020)

(0.027)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report, UI = Unemployment Insurance administrative records.

Model: Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: For self-reports, data source is youth long-term survey. Interviewed youth ages 15 to 20 as of December 31, 2007. Unemployment Insurance data use individual level data from Maryland, Illinois, California, and Florida (representing individuals whose random assignment sites are Baltimore, Chicago, and Los Angeles) and aggregate data from Massachusetts (representing individuals whose random assignment site is Boston). All core youth ages 15 to 20 as of December 31, 2007 whose parent has a baseline consent. Massachusetts includes all youth ages 12 to 20. Since our request file did not split by age, we cannot look separately at those who were 15 to 20 in Massachusetts.

Measures: The administrative data only looks at the last four common calendar quarters (fourth quarter of 2007 through the third quarter of 2008).

180

SUPPLEMENTAL EXHIBIT 5.9. SELF-REPORTED YOUTH EMPLOYMENT AND SCHOOL ENROLLMENT

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

CURRENTLY EMPLOYED ONLY [SR]

All

0.169

– 0.023

– 0.048

– 0.005

– 0.008

3,604

(0.015)

(0.031)

(0.018)

(0.027)

Female

0.165

– 0.023

– 0.046

– 0.028

– 0.043

1,838

(0.021)

(0.043)

(0.023)

(0.037)

Male

0.173

– 0.024

– 0.051

0.017

0.025

1,766

(0.022)

(0.047)

(0.026)

(0.038)

CURRENTLY ENROLLED IN SCHOOL

ONLY [SR]

All

0.390

0.039*

0.081*

0.007

0.011

3,604

(0.019)

(0.040)

(0.021)

(0.031)

Female

0.396

0.027

0.055

0.011

0.017

1,838

(0.027)

(0.054)

(0.028)

(0.044)

Male

0.384

0.052~

0.111~

0.003

0.004

1,766

(0.028)

(0.061)

(0.031)

(0.045)

CURRENTLY ENROLLED IN SCHOOL

AND EMPLOYED [SR]

All

0.226

– 0.023

– 0.048

– 0.028

– 0.042

3,604

(0.019)

(0.040)

(0.021)

(0.031)

Female

0.245

– 0.029

– 0.058

– 0.014

– 0.022

1,838

(0.027)

(0.054)

(0.029)

(0.046)

Male

0.208

– 0.017

– 0.036

– 0.042

– 0.061

1,766

(0.026)

(0.057)

(0.028)

(0.040)

CURRENTLY IDLE (NEITHER EMPLOYED

NOR ENROLLED IN SCHOOL) [SR]

All

0.215

0.007

0.014

0.026

0.039

3,604

(0.018)

(0.038)

(0.020)

(0.031)

Female

0.194

0.024

0.049

0.031

0.048

1,838

(0.024)

(0.048)

(0.027)

(0.043)

Male

0.235

– 0.011

– 0.023

0.022

0.032

1,766

(0.027)

(0.058)

(0.031)

(0.045)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 15 to 20 as of December 31, 2007.

Measures: "Currently enrolled in school" also includes youth who are on summer vacation.

181

Chapter 5: Impacts on Economic Self-Sufficiency

SUPPLEMENTAL EXHIBIT 5.10. FAMILY TANF AND FOOD STAMPS BENEFITS IN MASSACHUSETTS, ILLINOIS, AND LOS ANGELES COUNTY, ADULT REPORTED AND ADMINISTRATIVE DATA BY MONTH CLOSEST TO

INTERVIEW MONTH

CONTROL

EXPERIMENTAL VERSUS

SECTION 8

MEAN

CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

SURVEY DATA

Currently receiving TANF

0.174

0.020

0.042

0.020

0.032

1,985

[SR]

(0.020)

(0.043)

(0.028)

(0.044)

Currently receiving food

0.498

0.008

0.017

0.010

0.016

1,977

stamps [SR]

(0.027)

(0.056)

(0.036)

(0.055)

ADMINISTRATIVE DATA

Received TANF in the month

0.244

– 0.009

– 0.019

– 0.004

– 0.006

1,985

closest to the survey month

(0.023)

(0.048)

(0.030)

(0.047)

[TANF]

Active and received TANF

0.187

0.011

0.024

0.006

0.009

1,985

in the month closest to the

(0.020)

(0.043)

(0.027)

(0.041)

survey month [TANF]

Received food stamps in

0.632

0.029

0.060

0.040

0.061

1,977

the month closest to the

(0.025)

(0.052)

(0.033)

(0.051)

survey month [FS]

Active and received food

0.557

0.022

0.045

0.033

0.051

1,977

stamps in the month closest

(0.027)

(0.056)

(0.037)

(0.056)

to the survey month [FS]

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report, TANF = Temporary Assistance for Needy Families records, FS = Food Stamps records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: For self-reports, data source is the adult long-term survey and the sample is all adults interviewed. FS and TANF analyses use individual data from Massachusetts, Illinois, Los Angeles County, and South Carolina and represent individuals whose random assignment sites are: Boston, Chicago and Los Angeles. We received TANF and food stamps data from Maryland, but the data quality is suspect. Samples for TANF, and FS are all sample adults with baseline consent.

Measures: The time period is the month closest to the month of interview. "Active" means the family is receiving welfare benefits directly through the adult.

Otherwise, general receipt of the welfare benefits means that the family is receiving welfare benefits either through the adult or another family member.

182

SUPPLEMENTAL EXHIBIT 5.11. FAMILY TANF AND FOOD STAMPS BENEFITS IN MASSACHUSETTS, ILLINOIS, AND LOS ANGELES COUNTY, ADULT REPORTED AND ADMINISTRATIVE DATA FOR THE SAME MONTH

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

SURVEY DATA

Currently receiving TANF [SR]

0.178

0.034~

0.071~

– 0.007

– 0.011

1,875

(0.021)

(0.043)

(0.027)

(0.042)

Currently receiving food stamps [SR]

0.501

0.029

0.061

0.003

0.005

1,871

(0.027)

(0.056)

(0.037)

(0.057)

ADMINISTRATIVE DATA

Received TANF in the month of survey

0.253

– 0.006

– 0.013

– 0.011

– 0.016

1,875

[TANF]

(0.023)

(0.048)

(0.031)

(0.047)

Active and received TANF in the month

0.194

0.011

0.023

0.013

0.020

1,875

of survey [TANF]

(0.021)

(0.043)

(0.028)

(0.043)

Received food stamps in the month of

0.650

0.034

0.070

0.028

0.044

1,871

survey [FS]

(0.025)

(0.051)

(0.034)

(0.051)

Active and received food stamps in the

0.575

0.023

0.049

0.021

0.032

1,871

month of survey [FS]

(0.027)

(0.055)

(0.038)

(0.058)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report, TANF = Temporary Assistance for Needy Families, FS = Food Stamps.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: For self-reports, data source is the adult long-term survey and the sample is all adults interviewed. FS and TANF analyses use individual data from Massachusetts, Illinois, Los Angeles County, and South Carolina and represent individuals whose random assignment sites are: Boston, Chicago and Los Angeles. We received TANF and food stamps data from Maryland, but the data quality is suspect. Samples for TANF and FS are all sample adults with baseline consent.

Measures: The time period is the month of survey. "Active" means the family is receiving welfare benefits directly through the adult. Otherwise, general receipt of welfare benefits means that the family is receiving welfare benefits either through the adult or another family member.

SUPPLEMENTAL EXHIBIT 5.12. ADULT SELF-REPORTED EITC

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

Received EITC for the previous calendar

0.421

0.011

0.023

0.020

0.033

3,242

year [SR]

(0.021)

(0.043)

(0.028)

(0.045)

Received EITC and amount was more than

0.194

0.007

0.014

0.042~

0.067~

3,119

$2,500 (2009 dollars) [SR]

(0.017)

(0.035)

(0.024)

(0.038)

Received EITC and used it to pay bills

0.303

– 0.007

– 0.015

– 0.029

– 0.047

3,231

(credit card, housing costs, tuition) [SR]

(0.020)

(0.040)

(0.026)

(0.041)

Received EITC and put money into

0.019

– 0.001

– 0.002

0.014

0.023

3,231

savings account [SR]

(0.006)

(0.012)

(0.009)

(0.015)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working"

would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All interviewed adults.

183

Chapter 5: Impacts on Economic Self-Sufficiency

184

IMPACTS ON RISKY AND

CRIMINAL BEHAVIOR

185

Chapter 6: Impacts on Risky and Criminal Behavior

IMPACTS ON RISKY AND

CRIMINAL BEHAVIOR

This chapter discusses the reasons why moving to

6.1 HYPOTHESES ABOUT EFFECTS

better neighborhoods might affect the risky or criminal ON RISKY AND CRIMINAL BEHAVIOR

behavior of youth and adults. We present data on risky

This section begins with a brief review of age and

CHAPTER 6

and criminal behavior from combinations of survey

developmental patterns of risky and criminal behavior.

self ‑reports, proxy reports, and administrative arrest

These age patterns are relevant for thinking about

records for those who were youth at the time of our

what sort of specific anti ‑social behaviors may be

long ‑term follow ‑up (ages 13 to 20 or 15 to 20), grown most susceptible to influence from MTO and for children (ages 21 to 30), or adults (mostly household

which particular age sub ‑groups within the study

heads).

sample. We then discuss what previous theories in the

“neighborhood effects” literature predict regarding

The long ‑term follow ‑up data show slightly more

MTO’s impacts on risky and delinquent behaviors.

favorable treatment impacts for females than for

males. These gender differences in long ‑term MTO

DEVELOPMENTAL PATTERNS OF RISKY

impacts are more muted than the interim evaluation

AND CRIMINAL BEHAVIOR1

results, but are qualitatively similar. Assignment to

Criminal behavior is disproportionately concentrated

the experimental and Section 8 groups increased the

during adolescence and early adulthood. Exhibit

prevalence of smoking for male youth in the long ‑term

6.1 provides an example of the “age ‑crime curve,”

MTO survey. Section 8 males also exhibited increased

which shows the fraction of all arrests for the Federal

scores on a behavior problems index by a marginally

Bureau of Investigation’s Uniform Crime Report “part

significant amount equal to about 7 percent of the

1” violent crime offenses (murder, rape, robbery,

control group’s mean. For female youth, assignment to

aggravated assault) and property offenses (burglary,

the experimental group seemed to reduce the prevalence motor vehicle theft, larceny) committed by different of drinking by around 11 percent of the control mean.

age groups using national data from 2009. Rates of

We found few statistically significant impacts for most

criminal involvement increase during adolescence,

other measures of risky or criminal behavior among

starting around middle ‑school age, and peak during

the different age groups we examined. One potentially

late adolescence and early adulthood (depending on

important exception is arrests for drug distribution,

the exact type of crime being examined). This age

for which we find mixed but suggestive indications of

pattern is observed for both males and females, and has

lower arrest rates among youth, grown children, and

been documented across different countries and times

adults assigned to the MTO treatment groups relative

(Hirschi and Gottfredson, 1983).

to those assigned to the control group.

1 This section draws heavily on material from Ander et al. (2010).

186

EXHIBIT 6.1. THE “AGE ‑CRIME CURVE” FOR CRIMINAL BEHAVIOR: NATIONAL DATA ON PERCENT OF CRIME

Exhibit 6.1. The "Age-Crime Curve" for Criminal Behavior: National Data on Percent of Crime Committed by Age Group COMMITTED BY AGE GROUP

35

30

Violent Crime

25

Murder and Non-Negligent Manslaughter

Property Crime

20

15

PERCENT

OF ARRESTS

10

05

00

Under 10

10-14

15-19

20-24

25-29

30-34

35-39

40-44

45-49

50-54

55-59

60-64

65 +

AGE GROUP

Notes: Percentages represent the fraction of arrests involving different age groups based on 2009 data from the Federal Bureau of Investigation’s Uniform Crime Report. Violent offenses include murder, rape robbery, and aggravated assault. Property crimes include burglary, motor vehicle theft, and larceny.

Data source: Bureau of Justice Statistics (2011).

More generally, there is a substantial concentration of

argues that their behavior stems from ill health, difficult

anti‑social behavior within the population—particularly

temperament, and subtle neuropsychological and

the most serious forms of anti‑social behavior. In a

cognitive deficits, which are likely to be compounded

landmark longitudinal study of 10,000 boys born

by challenging family backgrounds (in part because

in Philadelphia in 1945, University of Pennsylvania

many of these traits are heritable). More common are

criminologist Marvin Wolfgang found that 6 percent of

adolescent‑limited offenders, who engage in delinquency

all boys accounted for one‑half of all arrests and fully

because of “social mimicry.” For some youth in this latter

two ‑thirds of all arrests for violent crimes (Wolfgang,

group, adolescent offending can be “ensnaring”—as when

Figlio, and Sellin, 1972; Wolfgang, 1983). This pattern

a youthful offense contributes to school dropout and

has also been widely replicated in other places and time

harms future employment prospects, thereby making

periods (see, for example, Tracy, Wolfgang, and Figlio,

persistent offending more likely.

1985; Shannon, 1982; Tillman, 1987; Farrington, 2002).

Gender is one important predictor of involvement in risky

A growing body of research building on Wolfgang’s

or criminal behavior, whether of the adolescent‑limited or

finding suggests that the age‑crime curve shown in

life‑course persistent varieties. Male youth are much more

Exhibit 6.1 reflects the behavior of two largely distinct

likely than females to engage in most anti ‑social behaviors,

“types” of people. For a small share of all youth,

particularly delinquency and violence. For example,

anti‑social and aggressive behavior begins during early

national data from 1994 show that for youth under age 18,

childhood, and persists through adolescence into

males are arrested more than four times as often as females

adulthood, although the manifestations of this behavior

for aggravated assault, and seven or eight times as often for

change over time. Psychologist Terrie Moffitt (1993)

robbery (Cook and Laub, 2002).

calls this group the life‑course‑persistent offenders, and

187

Chapter 6: Impacts on Risky and Criminal Behavior

A variety of individual‑ and family‑level characteristics

Dodge, 2009; Steinberg, Blatt‑Eisengart, and Cauffman,

help distinguish life‑course persistent and

2006). Low levels of parental monitoring are also

adolescent‑limited offenders from people who never

predictive of the risk of involvement in drug selling

engage in crime. These characteristics include poor

(Little and Steinberg, 2006). Since so many of these

academic performance, particularly on verbal tasks

family‑level risk factors are correlated with economic

(Maguin and Loeber, 1996), and levels of warmth and

disadvantage, rates of anti‑social behavior in the

consistent discipline within the home as opposed to

low‑income MTO study sample are likely to exceed what

hostility and conflict (Widom, 1989; Gonzalez and

we see in more nationally representative populations.

Exhibit 6.2. Developmental Pathways to Serious and Violent Offending

EXHIBIT 6.2. DEVELOPMENTAL PATHWAYS TO SERIOUS AND VIOLENT OFFENDING

AGE OF ONSET:

PERCENTAGE OF BOYS:

Late

Few

VIOLENCE SERIOUS

rape, attack, DELINQUENCY

strong-arm, auto theft,

homicide burglary

PHYSICAL FIGHTING

MODERATELY SERIOUS

physical fighting, gang fighting

DELINQUENCY

fraud, pick pocketing

PROPERTY DAMAGE

AUTHORITY

vandalism, fire setting

AVOIDANCE

MINOR AGGRESSION

truancy, running away,

annoying others,

staying out late

MINOR COVERT BEHAVIOR

bullying

shoplifting, frequent lying

DEFIANCE/DISOBEDIENCE

Overt Pathway

Covert Pathway

STUBBORN BEHAVIOR

(before age 15)

Early

Authority Conflict Pathway

Many

(before age 12)

Exhibit 6.2 is reproduced from Thornberry, Huizinga, and Loeber (2004), Figure 1.

Criminal behavior and particularly violence are

previously—suggesting a “developmental ordering to

perhaps of greatest public policy concern, given their

seriousness” of anti ‑social behavior. Exhibit 6.2 also

disproportionately large social costs (Anderson, 1999;

makes clear that more youth engage in less ‑serious risky

Cook and Ludwig, 2000; Cohen, 2005; Ludwig, 2006).

or anti ‑social behaviors than in more ‑serious forms of

But serious forms of anti ‑social behavior are almost

delinquency such as violence.2 One important additional

always preceded by less ‑serious risky or anti ‑social

behavioral marker for youth ‑violence risk not reflected

behaviors. Exhibit 6.2 (reproduced from Thornberry,

Huizinga, and Loeber, 2004) shows that most youth

2 Criminal offenders in general “tend to engage in a diversity of crime

who wind up engaging in violent behavior have a

types [but] with a somewhat greater tendency to repeat the same crime

history of engaging in less ‑serious problem behavior

or to repeat within the group of property crimes or the group of violent

crimes” (Piquero, Farrington, and Blumstein, 2003).

188

in Exhibit 6.2 is substance use, including levels of use for

intoxicating substances that fall below clinical thresholds

for a substance ‑abuse disorder.

One implication of these developmental patterns is

that less ‑serious forms of anti ‑social behavior by youth

(such as disobeying parents and teachers or hanging out

with other youth who tend to get into trouble), even

if not of great social policy concern for their own sake,

are important outcomes to examine because they are

indicative of developmental trajectories that may lead to

more serious and socially costly behaviors in the future.

These patterns also help guide our decisions about how

to analyze the data from the MTO long ‑term study.

The large differences in base rates for risky and criminal

behavior by gender help motivate our decision to present

results separately for male and female youth. Given the

age pattern to anti ‑social behavior, we will tend to focus

our analysis of more serious forms of criminal behavior

on youth who are in their peak offending ages at the time

of our survey (15 to 20 at the end of 2007) and also look

separately at grown children in MTO who were under 18

at baseline but have now reached young adulthood (ages

21 to 30 at the end of 2007). Our outcomes of interest

include measures that are primarily related to onset (for

example, ever arrested or lifetime prevalence of drug use)

as well as those that capture duration and desistance of

the behavior of interest (for example, total number of

arrests).

HYPOTHESES ABOUT POTENTIAL

NEIGHBORHOOD INFLUENCES

Neighborhood context (and by extension neighborhood

mobility) may influence youth involvement with

risky and criminal behavior through the physical

and institutional environments, as well as through

the social environment (Exhibit 6.3). Most of the

same mechanisms may be relevant for the behavior of

MTO grown children and adults as well, although for

convenience in this sub section we just refer to “youth.”

189

Chapter 6: Impacts on Risky and Criminal Behavior

Exhibit 6.3. Hypothesized Pathways Through Which MTO May Impact Delinquency and Risky Behavior EXHIBIT 6.3. HYPOTHESIZED PATHWAYS THROUGH WHICH MTO MAY IMPACT DELINQUENCY AND RISKY

BEHAVIOR

MTO INTERVENTION

RELOCATION TO LOW-POVERTY NEIGHBORHOODS

COMMUNITY-LEVEL

PERSON- AND FAMILY-LEVEL

DELINQUENCY AND RISKY

MEDIATORS

MEDIATORS

BEHAVIOR OUTCOMES

Community Norms

Adults

and Values

Positive Effects

· Tobacco/alcohol use

· More pro-social peer group

· Increased sense of

· Use of marijuana and

· Better adult role models

safety

other drugs

· Better community

· Less stress, anxiety

· Self-reports of delinquent

monitoring of youth

and illegal behaviors

(collective efficacy)

Negative Effects

· Gang involvement

· Possible adverse effects

· Feelings of isolation

from increased competition

· Fighting/violence

· Depression caused by

with higher socio-economic

· Arrests/contact with the

change

status peers

criminal justice system

· Sexual activity/contra-

Social and Physical

ceptive use/pregnancies

Environment

Youth

· Reduced crime, violent

activity

Positive Effects

·

· Better school resources

Increased sense of

safety

· Increased recreational

activities for youth

· Increased perceived

returns to "legitimate"

· More or higher quality

activities (work, education)

policing

· Increased orientation

towards the future

Economic Opportunities

Negative Effects

· Better labor market

· Feelings of inadequacy,

opportunities

inability to compete

Exhibit 6.3 is a modified version of Exhibit 5.1 in the MTO interim impacts evaluation (Orr et al., 2003).

190

The physical features of a neighborhood might influence

learning criminal behavior from peers, pure preference

youth involvement with risky behavior or crime in a

externalities (individuals enjoy imitating their peers),

number of ways (Macintyre and Ellaway, 2003). For

stigma effects and social norms (the negative signal

decades urban planners and criminologists have been

from criminal behavior declines when more people do

concerned with the possibility that some building

them), and physical externalities (for example, higher

designs contribute to crime, for example, through the

rates of crime reduce the chances of getting arrested

construction of enclosed stairwells in public housing

because of congestion effects in law enforcement); see

buildings that are difficult for local residents to monitor.

Kleiman (1993), Cook and Goss (1996), Glaeser and

Other physical features of neighborhoods, such as access

Scheinkman (1999), Brock and Durlauf (2001), Manski

to public transportation and job opportunities, may

(2000), and Moffitt (2001). Some epidemic models

influence the legal labor market opportunities that are

predict peer influences on youth criminal behavior that

available to people and that represent a key alternative

varies with the prevalence of peer criminal behavior

to criminal behavior in the standard economic model of

within a community, which can lead to nonlinearities in

crime (Becker, 1968).

peer effects or “tipping points.” Collective socialization

models concentrate on the way adults in a neighborhood

The quality or availability of local institutions may also

influence young people who are not their children,

be relevant for youth involvement in risky or criminal

for example, through human capital externalities that

behavior. For example, previous research suggests that

increase the opportunity costs of involvement with

the quality of local public schools varies dramatically

crime (Borjas, 1995), by acting as role models (Wilson,

across areas (Rivkin, Hanushek, and Kain, 2005), which

1987), or the willingness of local adults to enforce shared

influences student success in school and consequently the values as in the “collective efficacy” model of Sampson, relative attractiveness of pro ‑ versus anti ‑social behavior.

Raudenbush, and Earls (1997) (see also Coleman, 1988).

It is also possible that high ‑crime neighborhoods might

have policing of lower quality or quantity compared to

More recently new models from behavioral economics raise

more affluent areas.3 If youth move to neighborhoods

the possibility that moving to a less distressed area could

with greater policing, MTO could increase the likelihood reduce youth involvement with risky or criminal behavior of a youth being arrested for any given level of actual

by influencing future orientation, attitudes towards risk,

criminal activity. Previous research raises the possibility

and other aspects of decision making. For example, work

that minorities may be at particularly elevated risk for

by Loewenstein and O’Donoghue (2004) suggests that

being arrested, even after controlling for frequency of

moves to less dangerous, less distressed areas could lead

involvement with criminal behavior and other risk factors to more future ‑oriented, less risky, or violent behavior such as neighborhood type, family socio ‑economic status, by reducing youth exposure to neighborhood stressors, and educational history (Huizinga et al., 2007).

which sap mental energy and reduce people’s ability to

control the more impulsive parts of their brains.

Most of the theoretical work in the public health

and social science literatures has been devoted to

MTO could have additional effects on risky or criminal

understanding the possible psychosocial mechanisms

behavior that are mediated through MTO’s impacts

through which the neighborhood social context may

on household environments. For example, parental

influence behavior, including risky or criminal behavior.

unemployment, substance use, poor mental health,

One influential typology of models in this literature

exposure to community violence, and inadequate housing

comes from Jencks and Mayer (1990). Epidemic models

may all be risk factors for child maltreatment, and each

are those that emphasize the power of peers to spread

of these risk factors could be affected by MTO. This

behaviors. Such contagion effects can arise from

possibility highlights the importance of measuring youth

violence victimization that occurs within as well as

3 Whether or not such variation in policing exists depends in part on

outside of the home.

the degree to which policymakers choose to allocate additional police

resources to the highest ‑crime neighborhoods (Sherman, 2002).

191

Chapter 6: Impacts on Risky and Criminal Behavior

Although most behavioral models predict that MTO

which raises the possibility that the behavioral impact

moves should reduce youth involvement with risky or

of assignment to an MTO mobility group could decline

criminal behavior, Jencks and Mayer’s discussion implies

over time.

that other outcomes are possible. Competition models

emphasize the competition between neighbors for scarce

On the other hand most of the psychosocial behavioral

resources like grades or jobs. Failure in the competition

models discussed here suggest that the benefits of a

for pro ‑social rewards may lead youth to compete instead given change in neighborhood environment may become for anti ‑social rewards, which could elevate risk for

more beneficial (or less detrimental) over time, as MTO

criminal involvement. Relative deprivation models focus

families spend more time in lower ‑poverty areas. The

on negative psychological impacts from experiencing a

benefits of exposure to higher ‑achieving peers, more pro‑

decline in one’s relative material or social standing in a

social adults, and higher ‑quality neighborhood schools,

community (Luttmer, 2005).

labor market options and policing environments may

accumulate over time and lead to more pronounced

Additional uncertainty about MTO’s net effects on youth positive effects on youth behavior through the epidemic, risky or criminal behavior comes from the possibility that collective socialization, and institutional models, as youth may choose their own social “micro ‑climates” from well as more pronounced changes in youth decision‑

within their new neighborhood social “macro ‑climates”

making processes. To the extent to which exposure to

(Jencks and Mayer, 1990; Sharkey, 2006; Cicala, Fryer,

the opportunities available in lower ‑poverty areas makes

and Spenkuch, 2011). For example, it is possible that

MTO youth more competitive over time for getting good

any benefits from moving to less distressed areas through

grades or jobs, any deleterious effects from competition

MTO could be lost if youth simply sort themselves back

or declines in relative position may also decline over

into peer groups that engage in and support anti ‑social

time. We may also expect that any deleterious “moving

behaviors that put youth at elevated risk for violent

effects” on families attenuate over time.

victimization or offending. However “fitting in” to new

lower ‑poverty areas for MTO youth may entail learning

Moreover theories about developmentally “sensitive”

new modes of language, dress, expected emotional

or “critical” periods (Shonkoff and Phillips, 2000;

responses (Lawler and Thye, 1999), and other aspects

Knudsen et al., 2006) predict that MTO impacts on

of social interactions, which could vary by gender, given

risky or criminal behavior should be more pronounced

evidence that boys and girls value different attributes and for program participants who are adolescents at the personal characteristics (Ferguson, 2001). Boys may have long‑term follow ‑up (and so were ages 10 or younger more difficulty integrating into their new neighborhoods, early in the study period, when MTO’s impacts on given evidence that boys are more likely than girls to “use neighborhood conditions were particularly large) aggressive or confrontational techniques to deal with

compared to those who were adolescents at the interim

interpersonal difficulties” (Coleman and Hendry, 1999,

follow ‑up (most of whom would already have been 10

p. 218). Compared to girls, boys might also have more

or older at baseline). Sensitive periods are hypothesized

reputational or social capital to lose from moving to a

to be ages in which development of certain skills or

new neighborhood, as suggested by Anderson’s (1999)

developmental processes are particularly susceptible to

findings that many youth growing up in dangerous

the social environment, but are not necessarily the only

neighborhoods may develop violent social networks or

times in which those skills or processes can be modified.

strategic involvement in violence to help deter violence

Critical periods are thought to be ages at which some

against themselves in the future.

skills or developmental processes are shaped, after which

they are largely fixed.

Chapters 1 and 2 of this report show that the difference

in average neighborhood environments between the

The non ‑experimental empirical literature reveals

MTO mobility groups (experimental and Section 8) and

mixed results on the importance of these theoretical

the MTO control group declines somewhat over time,

neighborhood mechanisms in affecting risky and

192

criminal behaviors. Case and Katz (1991) found strong

al., 2001; Simons et al., 2004; for an excellent summary, see

relationships between one’s own risky and delinquent

Sampson, Morenoff, and Gannon ‑Rowley, 2002).4

behaviors and that of one’s peers for illegal drug use,

alcohol use, and criminal offending in the Boston Youth

6.2 EARLIER RESEARCH:

Survey. However, Esbensen and Huizinga (1990) found

SHORT‑TERM AND INTERIM IMPACTS

that the level of disorganization of the neighborhood did

ON RISKY AND CRIMINAL BEHAVIOR

not affect neighborhood ‑level prevalence or frequency of

Initial short ‑term findings from MTO drawing on data

drug use. Studies of a sample of young black women in

collected for individual demonstration sites showed

Chicago found some relationship between pregnancy risk that during the first few years after random assignment, and low neighborhood socioeconomic status (Hogan and

males in both the experimental and Section 8 groups

Kitagawa, 1985) and evidence that this risk was related to exhibited fewer behavior problems (disobedience, lower contraceptive use (Hogan, Astone, and Kitagawa,

bullying, depression) than those in the control group in

1985). The proportion of managerial workers in a census

the Boston MTO site (Katz, Kling, and Liebman, 2001)

tract has been shown to be related to teen childbearing

and in the New York MTO site as well (Leventhal and

(Crane, 1991; Brooks ‑Gunn et al., 1993), but Case and

Brooks‑Gunn, 2003). Evidence from the Baltimore MTO

Katz did not find direct evidence of peer influences on

site suggested that the experimental group males had

out ‑of ‑wedlock childbearing.

fewer arrests for violent crimes relative to the control

group (Ludwig, Duncan, and Hirschfield, 2001).

The non ‑experimental research seems to provide stronger

support for an association between neighborhood attributes

Results from the MTO interim evaluation, which

and involvement with crime or violence specifically.

measured outcomes for youth in a consistent way across all

One of the best ‑known non ‑experimental studies on this

five MTO demonstration sites 4 to 7 years after random

question is Sampson, Raudenbush, and Earls’ (1997)

assignment, suggested that the impacts of moving to a

analysis of data from the Project on Human Development

less dangerous and distressed neighborhood on youth

in Chicago Neighborhoods (PHDCN). Their analysis finds

behavior might be more complex than the previous

that one of the best predictors for variation in violence

empirical literature would suggest. Although the results

was the neighborhood’s degree of informal social control

indicated that neighborhood influence on risky and

together with social cohesion and trust, what they term

criminal behavior by female youth follows a pattern

“collective efficacy.” Collective efficacy is found to have

predicted by most prior theoretical and non ‑experimental

a strong association with violence even after controlling

empirical studies, the pattern of program impacts is more

for a rich set of individual ‑level characteristics, and seems

complicated for male youth (Kling, Ludwig, and Katz,

to mediate the effects of other neighborhood attributes

2005). In the first few years after random assignment,

such as socioeconomic composition (see also Sampson

male youth in the experimental group had fewer violent‑

and Raudenbush, 1999, and Morenoff, Sampson, and

crime arrests and behavior problems than those in the

Raudenbush, 2001). Qualitatively similar findings for

control group.5 However by three to four years after

violence offending rather than victimization that also draw

randomization males in the experimental group were

on data from the PHDCN have been reported by Sampson, arrested more often than those in the control group, Morenoff, and Raudenbush (2005). Similar patterns of

primarily for property crimes, and were also more likely to

results for exposure to violence and violent behavior have

engage in a variety of other delinquent and risky behaviors.

been reported in a number of other studies (for example,

Hirschfield and Bowers, 1997; Warner and Rountree, 1997; 4 A more recent observational study drawing on PHDCN data finds some evidence of a non ‑linear relationship between youth violence

Rountree and Warner, 1999; Bellair, 2000; Peterson, Krivo,

involvement and exposure to violent peers, where the effect of exposure

and Harris, 2000; Wikström and Loeber, 2000; Beyers et

to additional violent peers declines at higher levels of peer violence

(Zimmerman and Messner, 2011).

5 These short ‑term findings are consistent with those reported for the

Boston and Baltimore sites by Katz, Kling, and Liebman (2001) and

Ludwig, Duncan, and Hirschfield (2001).

193

Chapter 6: Impacts on Risky and Criminal Behavior

Evidence of a similar gender difference in how youth

6.3 BASELINE AND CONTROL

outcomes change in response to moving out of public

GROUP CONTEXT

housing into less distressed areas comes from a recent

At baseline, three ‑quarters of control group families

analysis of mortality records matched to an entirely

reported that their primary or secondary reason for

separate sample of public housing families who applied

moving was to get away from drugs and gangs and over

for housing vouchers in Chicago in the late 1990s (Jacob, 40 percent indicated that a member of their household Ludwig, and Miller, 2011). That study found housing

had been victimized during the past 6 months (see

voucher offers produced declines in mortality rates for

Exhibit 1.2). Exhibit 6.4 shows that most forms of risky

female youth compared to those who were not offered a

behaviors in the United States (other than marijuana use)

voucher, but did not have the same protective effect on

have held steady or declined in prevalence since the MTO

male youth.

demonstration was planned in the early 1990s. The top

panels report data from the Monitoring the Future survey

Kling, Ludwig, and Katz (2005) argue that the most

on self‑reported use rates over the past 30 days, and show

likely explanation for the unexpected gender difference

that smoking and drinking have declined over time, and

in how youth risky and criminal behavior responds

use of illicit drugs other than marijuana has been fairly

to residential mobility rests with a variant of the

constant. Marijuana use, in contrast, has increased over

competition model discussed by Jencks and Mayer

time. The bottom panel of Exhibit 6.4 shows that rates of

(1990). Over time some MTO males may have realized

teen pregnancy, births, and abortions have also declined

that they had a comparative advantage in the competition over this period for 15‑ to 19‑year‑olds; this pattern holds for anti ‑social rather than pro ‑social rewards within their for minority as well as white teens (not shown).

new, lower ‑poverty neighborhoods.

EXHIBIT 6.4. NATIONAL TRENDS IN SUBSTANCE USE AND TEEN PREGNANCY

YEAR

SUBSTANCE USE IN THE LAST 30 DAYS

1991

2006

ANY ILLICIT DRUG (%)

8th graders

5.7

9.5

12th graders

16.4

23.8

ANY ILLICIT DRUG OTHER THAN MARIJUANA (%)

8th graders

3.8

3.5

12th graders

7.1

8.6

MARIJUANA/HASHISH (%)

8th graders

3.2

8.0

12th graders

13.8

21.4

ALCOHOL (%)

8th graders

25.1

13.8

12th graders

54.0

41.2

SMOKING (%)

8th graders

14.3

7.1

12th graders

28.3

19.2

TEEN PREGNANCY (RATES PER 1,000 FEMALES AGES 15–19)

1991

2010

PREGNANCY RATE

115.3

71.5

BIRTHRATE

61.8

41.9

ABORTION RATE

37.4

19.3

Data source: Johnston et al. (2011) and Guttmacher Institute (2010).

Crime rates, particularly for violent crime, were also declining dramatically over the MTO study period—although 194

rates of violence had been surging in the MTO

in Baltimore and 25.6 in Chicago, compared with 6.1 in

demonstration cities in the years during which MTO

Boston, 11.8 in Los Angeles, and 8.6 in New York City.

was being planned. While the overall homicide rate in

the U.S. increased only modestly from 1985 to 1991

6.4 DATA SOURCES AND MEASURES

(from 8.2 to 10.4 per 100,000), the rate at which teen

A variety of survey ‑based and administrative data sources

ages 13 to 17 were arrested for murder more than tripled

were used to measure MTO participants’ involvement in

over this time period (Cook and Laub, 2002). Most of

risky or criminal behavior.

this increase was driven by gun homicides, committed

disproportionately by and against minority youth

DATA SOURCES

(Cook and Laub, 1998, 2002; Blumstein, 2000). Many

One source of information about risky and criminal

criminologists believe that this surge in youth crime was

behavior is the long ‑term follow ‑up surveys we collected.

driven by violence associated with the growth of crack

Youth who were ages 13 to 20 at the end of 2007 were

cocaine, which may have contributed to growing gun use asked to self ‑report on their own risky behavior (youth by teens involved in crack distribution and eventually

ages 10 to 12 in our survey sample frame were asked a

other youth as well (Blumstein, 1995). The rate at which

smaller subset of questions that were developmentally

teens age 13 to 17 committed murder proceeded to drop

appropriate). For MTO participants who were children

by more than half over the next several years (Cook and

(under 18) at baseline but ages 21 to 30 at the time of

Laub, 2002). The most likely explanations seem to be

our survey, and so not eligible for our survey sample

the ebbing of the crack epidemic, as well as increased

frame, we asked MTO adult household heads to answer

spending on police and prisons (Levitt, 2004).

a few survey questions that provide proxy information

about whether grown children in the study have been in

The declines in crime observed over the 1990s were

prison or jail.

most pronounced in those places that had the highest

crime rates at the peak of the crack epidemic (Blumstein, Our second major source of information about 2000; Raphael and Ludwig, 2003; Levitt, 2004;

involvement in risky or criminal behavior is from

Harcourt and Ludwig, 2006). The largest cities in

official administrative arrest histories maintained by

particular experienced unusually pronounced declines

government agencies. We have obtained individual

in crime during the 1990s, and even within cities the

adult arrest histories from state ‑level criminal justice

highest ‑crime areas experienced above ‑average declines.

agencies in the five states that contain the original MTO

This pattern will serve to narrow the differential in

demonstration sites (California, Illinois, Maryland,

neighborhood crime rates between families assigned

Massachusetts, and individual ‑level de ‑identified data

to the MTO experimental or Section 8 groups and the

from New York) as well as from eight other states in

control group, because control group families were more

which MTO families have lived (Connecticut, Florida,

likely to stay behind in the highest ‑crime neighborhoods, Georgia, Iowa, Pennsylvania, South Carolina, Tennessee, which subsequently experienced the largest drops in their and Virginia). These adult arrest histories capture 98

crime rates.

percent of all person‑years for adults in the MTO study

sample. We also obtained juvenile arrest histories with

Although crime dropped almost everywhere in the

state‑wide coverage for four of the five original site states

U.S. over the 1990s, the size of the drop was somewhat

(California, Illinois, Maryland, and Massachusetts),

larger in some of our MTO demonstration sites than

city‑wide coverage for New York City, and state‑wide

others, leading to some difference in crime levels across

coverage for two other states in which MTO families

cities during our study period. In particular, Baltimore

have lived (Florida and Iowa). Combined with the data

and Chicago have greatly elevated levels of crime and

from the adult arrest agencies, the juvenile arrest histories

violence compared with the other three MTO cities. For

capture 97 percent and 95 percent of all person‑years for

example, 1998 homicide rates per 100,000 equaled 47.1

grown children and youth, respectively, in the sample.

195

Chapter 6: Impacts on Risky and Criminal Behavior

MEASURES

(since these youth were relatively young prior to random

Most of the relevant survey questions about youth risky

assignment) and approximately the same in prevalence in

and criminal behavior are taken from the National

all random assignment groups.

Longitudinal Survey of Youth and replicate items used in

the interim survey. These measures have been linked in

We used our administrative arrest records to construct

the literature to many neighborhood characteristics, and

measures of whether the person was ever arrested (and

they have been shown to be highly correlated with other

the number of times arrested) between the time of MTO

measures of behavior problems (see Moore et al., 1999).

random assignment and December 31, 2007, which was

Following standard practice in the research literature, we

the last date for which we could obtain arrest records

aggregate information on specific outcomes into several

from the major states containing most of the MTO

key indices:

families.6 We also construct measures of ever arrested

and number of arrests for specific types of crimes, namely

• Abbreviated behavior problems index—Fraction

violence (most commonly for aggravated or simple

of 11 behavior problems that youth respondent

assault, and, less frequently, for more serious crimes

self ‑reported as “often or sometimes true” of

like murder, rape, robbery, and kidnapping), property

themselves: has difficulty concentrating, cheats

offenses (motor vehicle theft, larceny, burglary), drug

or lies, bullies or is cruel or mean to others, is

offenses (drug possession and drug distribution), and all

disobedient at home, has trouble getting along with other offenses (a category that is dominated by relatively other children, is restless or overactive, has a very

less serious crimes like disorderly conduct). If more than

strong temper, is withdrawn/does not get involved

one charge was tied to that arrest date, each arrest was

with others, hangs around with kids who get into

categorized using the most serious charge from that arrest

trouble, is disobedient at school, and has trouble

event.7 Arrests that occurred prior to randomization

getting along with teachers.

were controlled for in the statistical analysis (that is, used

• Delinquency index—Fraction of 8 delinquent

as explanatory variables), but were not included in the

behaviors that the youth self ‑reported ever having

outcome measures.

engaged in: carrying a hand gun, belonging to a

gang, purposely damaging or destroying property,

6.5 LONG ‑TERM IMPACTS ON RISKY

stealing something worth less than $50, stealing

AND CRIMINAL BEHAVIOR

something worth more than $50, engaging in other This section discusses the main results for the impacts property crimes, attacking someone with idea of

of the MTO mobility experiment on risky and criminal

hurting them, having a situation end in serious

behavior by MTO youth, grown children, and adult

fight or assault, and selling drugs.

household heads.

• Risky behavior index—Fraction of 4 risky

behaviors that a youth self ‑reported ever having

engaged in: alcohol use, cigarette smoking,

6 Accessing administrative arrest records was more straightforward in

marijuana use, and sexual intercourse.

some jurisdictions than in others, and so the point in calendar time at

which we received data varied across jurisdictions—which means the

last calendar year for which we have arrest data varies across areas. We

chose to use administrative arrest records through December 31, 2007

Both the delinquency and risky behavior indices measure as a common endpoint, which we have available for all jurisdictions having ever engaged in certain behaviors. This reference

mentioned in the text and is consistent with the original study design

we planned before beginning data collection.

period has the advantage of encompassing events

7 We select the most serious charge based on how New York State

throughout the entire period since random assignment.

criminal law classifies offenses. We use New York state law because the

Most youth were randomly assigned prior to the ages

criminal justice agency in that state only provides us with the most

serious criminal charge per arrest, so using their selection criteria in the at which the behaviors tend to occur frequently. Some

other states (where we have all charges associated with each arrest) helps

of these behaviors may have occurred before random

improve consistency in the data across states. We have explored other

systems for selecting the most serious charge per arrest and obtain quite

assignment, but these should be both small in number

similar results, in part because in the large majority of cases an arrestee is charged with a single criminal offense.

196

YOUTH OUTCOMES

presented in the top panel of Exhibit 6.5, the variation

The first panel of Exhibit 6.5 shows that the estimated

in the MTO experimental and Section 8 intent‑to‑treat

effects of the MTO experimental and Section 8

(ITT) effects on the behavior problems index for all

treatments on the overall risky behavior index for all

youth does not permit us to detect an impact if the

youth (boys and girls together) are not statistically

difference from the control group is smaller than around

significant. Sometimes statistically insignificant results

.03, or roughly 6 percent of the control group’s mean

are hard to interpret because they are not very precisely

(.47). Looking at the individual components of this index

estimated—that is, the statistical uncertainty (confidence reveals some signs of the same gender difference in youth interval) around an estimate is so large that they cannot

responses to MTO mobility as were found in the interim

rule out even moderately large impacts. As a way to

study.

think about the precision of the insignificant estimates

EXHIBIT 6.5. RISKY BEHAVIOR OUTCOMES FOR YOUTH AGES 13–20

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

RISKY BEHAVIOR INDEX [SR]

All

0.467

– 0.001

– 0.002

0.007

0.010

4,623

(0.014)

(0.029)

(0.015)

(0.022)

Female

0.442

– 0.027

– 0.054

– 0.017

– 0.026

2,358

(0.019)

(0.037)

(0.020)

(0.031)

Male

0.491

0.025

0.053

0.029

0.042

2,265

(0.018)

(0.039)

(0.020)

(0.028)

COMPONENTS OF THE

RISKY BEHAVIOR INDEX

EVER SMOKED [SR]

All

0.312

0.042*

0.088*

0.043*

0.064*

4,618

(0.019)

(0.039)

(0.020)

(0.030)

Female

0.297

0.022

0.044

0.016

0.026

2,355

(0.025)

(0.051)

(0.028)

(0.043)

Male

0.327

0.062*

0.134*

0.069*

0.098*

2,263

(0.025)

(0.054)

(0.028)

(0.040)

EVER HAD ALCOHOLIC

DRINK [SR]

All

0.534

– 0.032

– 0.067

– 0.017

– 0.026

4,618

(0.020)

(0.041)

(0.021)

(0.032)

Female

0.541

– 0.061*

– 0.124*

– 0.032

– 0.050

2,355

(0.026)

(0.053)

(0.029)

(0.045)

Male

0.528

– 0.003

– 0.006

– 0.004

– 0.005

2,263

(0.027)

(0.058)

(0.029)

(0.041)

EVER USED MARIJUANA [SR]

All

0.363

– 0.008

– 0.017

0.003

0.005

4,609

(0.020)

(0.042)

(0.022)

(0.032)

Female

0.327

– 0.042

– 0.084

– 0.026

– 0.040

2,350

(0.027)

(0.054)

(0.029)

(0.045)

197

Chapter 6: Impacts on Risky and Criminal Behavior

EXHIBIT 6.5. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

EVER USED MARIJUANA [SR] (CONTINUED)

Male

0.397

0.025

0.054

0.031

0.044

2,259

(0.028)

(0.061)

(0.030)

(0.043)

EVER HAD SEX [SR]

All

0.662

– 0.003

– 0.005

– 0.001

– 0.001

4,580

(0.017)

(0.036)

(0.019)

(0.029)

Female

0.605

– 0.022

– 0.044

– 0.022

– 0.035

2,334

(0.025)

(0.050)

(0.027)

(0.042)

Male

0.716

0.017

0.036

0.020

0.029

2,246

(0.023)

(0.050)

(0.026)

(0.037)

Notes: * = p < 0.05, ~ = p < 0.10 on two ‑tailed t ‑test. Robust standard errors shown in parentheses. ITT= Intention‑to‑Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment‑on‑the‑Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self ‑report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long ‑term survey. Interviewed youth ages 13 to 20 as of December 31, 2007.

Measures: The Risky Behavior Index is the fraction of the 4 risky behaviors listed above that the youth reports ever having exhibited.

The second panel of Exhibit 6.5 shows that compared to

of 2001). The fact that a similar finding arises for two

male youth assigned to the control group, those assigned

largely different samples of MTO youth would seem

to either the MTO experimental or Section 8 groups

to increase our confidence that this gender difference

are about 13 percent more likely to smoke. In contrast,

in how youth respond to MTO ‑induced moves is not a

female youth assigned to the MTO experimental group

chance finding.

are 6 percentage points less likely than those assigned

to the control group to drink (about 11 percent of the

Exhibit 6.6 shows that there are no statistically

control prevalence). The estimated impact for female

significant impacts of assignment to either the MTO

youth assigned to the MTO Section 8 group is in the

experimental or Section 8 groups on more serious forms

same direction but slightly smaller in magnitude and not

of anti ‑social or criminal behavior, including a measure

statistically significant. And consistent with those gender

of 11 problem behaviors the youth self ‑reports ever

differences, although not statistically significant, are the

having engaged in (the behavior problems index) or 8

results for sexual activity and marijuana use, where the

delinquent behaviors the youth self ‑reports ever having

sign of the estimate for males in both treatment groups

engaged in (the delinquency index) when we pool male

is positive (more sex and marijuana use) and the sign for

and female youth together. However the estimated MTO

females in both treatment groups is negative (less sex and impacts are all negative for female youth and positive marijuana use).These hints in the long ‑term MTO data

for male youth, even if most of these estimates are not

of the same gender difference in youth response to MTO

statistically significant. (The one exception is the positive

moves that were found in the interim MTO data are

Section 8 impact on the behavioral problems index for

interesting in part because overlap between the two study male youth, which is marginally significant.) samples is limited. The interim MTO study focused on

delinquency and risky behavior outcomes among youth

ages 12 to 19 as of the end of 2001, while the long ‑term

youth survey examines youth ages 13 to 20 as of the end

of 2007 (who would have been ages 7 to 14 at the end

198

EXHIBIT 6.6. BEHAVIOR PROBLEMS AND DELINQUENCY FOR YOUTH AGES 13–20

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

BEHAVIOR PROBLEMS INDEX [SR]

All

0.379

0.004

0.008

0.009

0.013

4,629

(0.010)

(0.022)

(0.011)

(0.017)

Female

0.371

– 0.007

– 0.014

– 0.010

– 0.015

2,361

(0.014)

(0.028)

(0.015)

(0.024)

Male

0.387

0.015

0.032

0.027~

0.038~

2,268

(0.015)

(0.032)

(0.016)

(0.023)

DELINQUENCY INDEX [SR]

All

0.146

– 0.002

– 0.004

0.008

0.012

4,625

(0.008)

(0.017)

(0.009)

(0.014)

Female

0.110

– 0.006

– 0.011

– 0.005

– 0.008

2,360

(0.009)

(0.019)

(0.010)

(0.016)

Male

0.181

0.002

0.004

0.021

0.030

2,265

(0.013)

(0.029)

(0.016)

(0.022)

Notes: * = p < 0.05, ~ = p < 0.10 on two ‑tailed t ‑test. Robust standard errors shown in parentheses. ITT= Intention‑to‑Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment‑on‑the‑Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self ‑report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long ‑term survey. Interviewed youth ages 13 to 20 as of December 31, 2007.

Measures: The Behavior Problems Index is the fraction of 11 problem behaviors (for example, difficulty concentrating and having a strong temper) that the youth reported as true or sometimes true at present or in the past 6 months. The Delinquency Index is the fraction of 8 delinquent behaviors (for example, carrying a gun and destroying property) that the youth reported ever having exhibited.

Assignment to either of the MTO treatment groups did

not have any statistically significant impacts on overall

arrest rates of male or female youth ages 15 to 20, but

impacts on these outcomes are somewhat imprecisely

estimated (Exhibit 6.7). For example, the top panel of

Exhibit 6.7 shows that for the MTO experimental ITT

effect, for female youth we cannot rule out impacts on

number of arrests that range from – .03 to +.24, that is,

– 10 percent to +66 percent of the control group’s mean

number of arrests (0.36). For male youth, we cannot

rule out impacts that range from around – 16 percent

to +26 percent of the control group mean. Exhibit 6.7

suggests that the MTO experimental treatment may

have increased property crime arrest rates, with positive

point estimates for both male and female youth that are

not quite statistically significant when disaggregated by

gender. The MTO experimental treatment may have

reduced arrest rates for drug selling among male youth

(marginally significant), with an ITT effect that is equal

to over one ‑third of the control group mean.

199

Chapter 6: Impacts on Risky and Criminal Behavior

EXHIBIT 6.7. NUMBER OF POST ‑RANDOM ASSIGNMENT ARRESTS FOR YOUTH AGES 15–20

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

ARRESTS BY CRIME TYPE

NUMBER OF…

ANY CRIME ARRESTS [CJR]

All

1.078

0.093

0.195

– 0.170

– 0.259

4,717

(0.101)

(0.212)

(0.112)

(0.170)

Female

0.356

0.101

0.207

– 0.054

– 0.082

2,300

(0.069)

(0.142)

(0.071)

(0.109)

Male

1.741

0.087

0.187

– 0.283

– 0.425

2,417

(0.185)

(0.397)

(0.204)

(0.306)

VIOLENT CRIME ARRESTS [CJR]

All

0.325

0.043

0.091

– 0.062

– 0.094

4,717

(0.037)

(0.078)

(0.039)

(0.059)

Female

0.155

0.027

0.055

– 0.048

– 0.074

2,300

(0.033)

(0.069)

(0.033)

(0.050)

Male

0.481

0.060

0.128

– 0.076

– 0.115

2,417

(0.064)

(0.138)

(0.068)

(0.102)

PROPERTY CRIME ARRESTS [CJR]

All

0.239

0.065*

0.136*

– 0.013

– 0.019

4,717

(0.031)

(0.064)

(0.034)

(0.051)

Female

0.091

0.044~

0.090~

– 0.010

– 0.015

2,300

(0.026)

(0.053)

(0.023)

(0.035)

Male

0.375

0.086

0.183

– 0.016

– 0.025

2,417

(0.054)

(0.117)

(0.060)

(0.090)

DRUG CRIME ARRESTS [CJR]

All

0.208

– 0.015

– 0.032

– 0.048

– 0.073

4,717

(0.030)

(0.063)

(0.034)

(0.051)

Female

0.019

0.016

0.032

– 0.017

– 0.026

2,300

(0.016)

(0.033)

(0.016)

(0.025)

Male

0.381

– 0.044

– 0.094

– 0.077

– 0.116

2,417

(0.057)

(0.121)

(0.064)

(0.096)

OTHER CRIME ARRESTS [CJR]

All

0.306

0.000

– 0.001

– 0.048

– 0.072

4,717

(0.034)

(0.071)

(0.037)

(0.055)

Female

0.090

0.015

0.030

0.021

0.032

2,300

(0.023)

(0.047)

(0.026)

(0.040)

Male

0.503

– 0.014

– 0.029

– 0.113~

– 0.170~

2,417

(0.064)

(0.136)

(0.067)

(0.100)

200

EXHIBIT 6.7. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

DRUG CRIME ARRESTS

BY TYPE

NUMBER OF…

DRUG POSSESSION ARRESTS

[CJR]

All

0.117

0.010

0.020

– 0.034~

– 0.052~

4,717

(0.019)

(0.039)

(0.019)

(0.028)

Female

0.013

0.005

0.009

– 0.011

– 0.017

2,300

(0.010)

(0.021)

(0.010)

(0.016)

Male

0.213

0.015

0.032

– 0.057

– 0.085

2,417

(0.035)

(0.075)

(0.035)

(0.052)

DRUG DISTRIBUTION ARRESTS

[CJR]

All

0.091

– 0.025

– 0.052

– 0.014

– 0.021

4,717

(0.017)

(0.036)

(0.022)

(0.033)

Female

0.006

0.011

0.023

– 0.006

– 0.009

2,300

(0.009)

(0.020)

(0.010)

(0.015)

Male

0.169

– 0.059~

– 0.127~

– 0.020

– 0.031

2,417

(0.032)

(0.069)

(0.042)

(0.063)

Notes: * = p < 0.05, ~ = p < 0.10 on two ‑tailed t ‑test. Robust standard errors shown in parentheses. ITT= Intention‑to‑Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment‑on‑the‑Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: CJR = criminal justice records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Individual criminal justice system arrest data. Adult and juvenile data from California, Illinois, Maryland, and Massachusetts; de ‑identified adult data from New York State; juvenile data from New York City; and adult or juvenile records from 8 additional states in which participants have lived. All core household members ages 15 to 20 as of December 31, 2007.

Measures: Violent crime arrests involve charges of force or threat of force including homicide, rape, robbery, assault, kidnapping, and weapons charges. Property crime arrests involve taking money or property and include burglary, motor vehicle theft, larceny, trespassing, and receiving stolen property. Drug crime arrests are classified as either possession or distribution arrests. "Other" arrest charges cover any charges not classified as violent, property, or drug crimes and include disorderly conduct and moving violations.

GROWN CHILDREN OUTCOMES

some suggestive evidence for a decline in arrests for

Exhibit 6.8 presents results for administratively ‑recorded

drug selling that echoes what we find for MTO youth

arrest rates of grown children—MTO participants who

in the long ‑term follow ‑up. The last row of Exhibit 6.8

were ages 21 to 30 at the end of 2007 and would have

shows that the Section 8 ITT effect for males on number

been roughly ages 15 to 25 at the time of the interim

of arrests since random assignment for drug selling is

study. Data from the interim study showed that MTO

negative and equal to around 23 percent of the control

reduced violent ‑crime arrests for male and female

group mean. The experimental ITT effect is in the same

youth through 4 to 7 years after random assignment,

direction but half as large (in absolute value) and not

but increased property ‑crime arrests for male youth

statistically significant.

(Kling, Ludwig, and Katz, 2005). The administrative

arrest records that we have collected for the long ‑term

follow ‑up show no statistically significant impacts on

either violent or property crimes, although we do see

201

Chapter 6: Impacts on Risky and Criminal Behavior

EXHIBIT 6.8. NUMBER OF POST ‑RANDOM ASSIGNMENT ARRESTS FOR GROWN CHILDREN AGES 21–30

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

ARRESTS BY CRIME TYPE

NUMBER OF…

ANY CRIME ARRESTS [CJR]

All

2.862

– 0.090

– 0.212

– 0.116

– 0.202

4,641

(0.171)

(0.404)

(0.192)

(0.332)

Female

0.967

– 0.054

– 0.126

– 0.037

– 0.063

2,277

(0.131)

(0.306)

(0.157)

(0.269)

Male

4.673

– 0.124

– 0.296

– 0.192

– 0.335

2,364

(0.303)

(0.726)

(0.347)

(0.607)

VIOLENT CRIME ARRESTS [CJR]

All

0.626

– 0.055

– 0.129

– 0.042

– 0.072

4,641

(0.047)

(0.112)

(0.054)

(0.093)

Female

0.279

– 0.038

– 0.089

– 0.047

– 0.080

2,277

(0.045)

(0.105)

(0.051)

(0.087)

Male

0.958

– 0.071

– 0.169

– 0.038

– 0.066

2,364

(0.082)

(0.196)

(0.092)

(0.161)

PROPERTY CRIME ARRESTS [CJR]

All

0.633

– 0.006

– 0.014

– 0.042

– 0.072

4,641

(0.054)

(0.128)

(0.061)

(0.105)

Female

0.297

– 0.057

– 0.132

– 0.016

– 0.027

2,277

(0.046)

(0.107)

(0.065)

(0.112)

Male

0.953

0.044

0.105

– 0.064

– 0.112

2,364

(0.093)

(0.222)

(0.100)

(0.175)

DRUG CRIME ARRESTS [CJR]

All

0.799

– 0.089

– 0.211

– 0.075

– 0.129

4,641

(0.065)

(0.154)

(0.076)

(0.131)

Female

0.127

– 0.022

– 0.052

0.033

0.057

2,277

(0.044)

(0.103)

(0.051)

(0.087)

Male

1.440

– 0.153

– 0.366

– 0.177

– 0.310

2,364

(0.119)

(0.285)

(0.144)

(0.251)

OTHER CRIME ARRESTS [CJR]

All

0.804

0.060

0.141

0.042

0.072

4,641

(0.066)

(0.155)

(0.074)

(0.128)

Female

0.263

0.063

0.147

– 0.008

– 0.013

2,277

(0.052)

(0.121)

(0.054)

(0.092)

Male

1.321

0.056

0.134

0.087

0.153

2,364

(0.116)

(0.278)

(0.137)

(0.239)

202

EXHIBIT 6.8. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

DRUG CRIME ARRESTS BY TYPE

NUMBER OF…

DRUG POSSESSION ARRESTS [CJR]

All

0.461

– 0.042

– 0.100

– 0.011

– 0.020

4,641

(0.044)

(0.104)

(0.053)

(0.093)

Female

0.069

0.001

0.002

0.015

0.026

2,277

(0.030)

(0.070)

(0.035)

(0.060)

Male

0.835

– 0.084

– 0.202

– 0.037

– 0.065

2,364

(0.082)

(0.196)

(0.103)

(0.179)

DRUG DISTRIBUTION ARRESTS [CJR]

All

0.338

– 0.047

– 0.111

– 0.063

– 0.110~

4,641

(0.035)

(0.082)

(0.037)

(0.064)

Female

0.058

– 0.023

– 0.055

0.018

0.032

2,277

(0.024)

(0.056)

(0.028)

(0.049)

Male

0.605

– 0.069

– 0.165

– 0.140*

– 0.245*

2,364

(0.063)

(0.151)

(0.067)

(0.116)

Notes: * = p < 0.05, ~ = p < 0.10 on two ‑tailed t ‑test. Robust standard errors shown in parentheses. ITT= Intention‑to‑Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment‑on‑the‑Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: CJR = criminal justice records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Individual criminal justice system arrest data. Adult and juvenile data from California, Illinois, Maryland, and Massachusetts; de ‑identified adult data from New York State; juvenile data from New York City; and adult or juvenile records from 8 additional states in which participants have lived. All core household members who are now grown children (under age 18 at baseline and ages 21 to 30 as of December 31, 2007).

Measures: Violent crime arrests involve charges of force or threat of force including homicide, rape, robbery, assault, kidnapping, and weapons charges. Property crime arrests involve taking money or property and include burglary, motor vehicle theft, larceny, trespassing, and receiving stolen property. Drug crime arrests are classified as either possession or distribution arrests. "Other" arrest charges cover any charges not classified as violent, property, or drug crimes and include disorderly conduct and moving violations.

203

Chapter 6: Impacts on Risky and Criminal Behavior

Exhibit 6.9 shows that there are no statistically significant effects of the experiment based on household head proxy reports about whether grown children were ever in jail or prison.

EXHIBIT 6.9. INCARCERATION OF GROWN CHILDREN AGES 21–30

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

EVER IN JAIL/PRISON [PR]

All

0.205

0.007

0.016

– 0.008

– 0.014

3,087

(0.018)

(0.041)

(0.023)

(0.041)

Female

0.057

0.014

0.031

– 0.003

– 0.006

1,526

(0.017)

(0.038)

(0.022)

(0.038)

Male

0.357

– 0.001

– 0.001

– 0.013

– 0.024

1,561

(0.030)

(0.072)

(0.037)

(0.070)

Notes: * = p < 0.05, ~ = p < 0.10 on two ‑tailed t ‑test. Robust standard errors shown in parentheses. ITT= Intention‑to‑Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment‑on‑the‑Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: PR = parent report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long ‑term survey. Grown children (who were under age 18 at baseline and ages 21 to 30 as of December 31, 2007) of interviewed adults.

204

ADULT OUTCOMES

with an ITT effect that is marginally significant (p<.10)

Exhibit 6.10 presents results for number of arrests

and equal to around one ‑third of the control group mean.

following random assignment for the adults in our long‑

The experimental ITT effect for drug selling is negative

‑term follow ‑up survey sample. The vast majority of adults and not significant when we look at total number of in MTO are female heads of household, so the exhibit

post ‑randomization arrests (Exhibit 6.10), but marginally

does not present results separately by gender. The exhibit

significant (p<.10) when we look at a measure of “ever

shows that adults assigned to the experimental group are

arrested” for drug selling (Supplemental Exhibit 6.3).

arrested more often than controls for property crimes,

EXHIBIT 6.10. NUMBER OF POST ‑RANDOM ASSIGNMENT ARRESTS FOR ADULTS

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

ARRESTS BY CRIME TYPE

NUMBER OF…

Any crime arrests [CJR]

0.567

0.052

0.110

– 0.016

– 0.026

4,376

(0.064)

(0.135)

(0.060)

(0.096)

Violent crime arrests [CJR]

0.149

0.001

0.002

– 0.009

– 0.014

4,376

(0.020)

(0.043)

(0.022)

(0.035)

Property crime arrests [CJR]

0.133

0.043~

0.091~

– 0.002

– 0.002

4,376

(0.025)

(0.052)

(0.022)

(0.036)

Drug crime arrests [CJR]

0.114

– 0.001

– 0.002

– 0.003

– 0.005

4,376

(0.022)

(0.047)

(0.023)

(0.037)

Other crime arrests [CJR]

0.171

0.009

0.020

– 0.003

– 0.005

4,376

(0.027)

(0.056)

(0.025)

(0.040)

DRUG CRIME ARRESTS BY TYPE

NUMBER OF…

Drug possession arrests [CJR]

0.080

0.006

0.013

– 0.004

– 0.006

4,376

(0.018)

(0.038)

(0.019)

(0.030)

Drug distribution arrests [CJR]

0.035

– 0.007

– 0.016

0.001

0.001

4,376

(0.008)

(0.017)

(0.009)

(0.015)

Notes: * = p < 0.05, ~ = p < 0.10 on two ‑tailed t ‑test. Robust standard errors shown in parentheses. ITT= Intention‑to‑Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment‑on‑the‑Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: CJR = criminal justice records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult and juvenile data from California, Illinois, Maryland, and Massachusetts; de ‑identified adult data from New York State; and adult or juvenile records from 8 additional states in which participants have lived. All long ‑term survey sampling frame adults with a baseline consent form.

Measures: Violent crime arrests involve charges of force or threat of force including homicide, rape, robbery, assault, kidnapping, and weapons charges. Property crime arrests involve taking money or property and include burglary, motor vehicle theft, larceny, trespassing, and receiving stolen property. Drug crime arrests are classified as either possession or distribution arrests. “Other” arrest charges cover any charges not classified as violent, property, or drug crimes and include disorderly conduct and moving violations.

205

Chapter 6: Impacts on Risky and Criminal Behavior

6.6 INTERPRETATION OF RESULTS

youth respond to MTO in the long ‑term follow ‑up is not

FOR RISKY AND CRIMINAL BEHAVIOR

simply an artifact of varying policing quality across areas.

Overall the estimated effects of the MTO residential‑

mobility intervention on risky and criminal behavior

We now have at least suggestive evidence of gender

that we found in the long ‑term MTO data (measuring

differences in how youth respond to residential mobility

outcomes 10 to 15 years after baseline) were more muted from three different samples. This pattern was found very than what was found in the interim MTO study (4 to 7

strongly in the interim MTO youth sample, and now in

years after baseline). Because MTO treatment impacts

a more muted way for those program participants who

on neighborhood environments have been declining

were youth at the time of our long ‑term follow ‑up—a

over the course of the study period (Chapter 2), the

largely different cohort of youth compared to those

pattern of impacts on risky and criminal behavior over

examined in the interim study. In addition a qualitatively

time is consistent with the idea that contemporaneous

similar pattern has been found in an independent

neighborhood conditions may be more important than

sample of public housing families in Chicago who

accumulated exposure for outcomes in this domain.

applied for housing vouchers, using information from

mortality records (Jacob, Ludwig, and Miller, 2011).

We do not see signs of any sustained decline in violent

Understanding the mechanisms behind this pattern and

criminal behavior of the sort found in the interim data,

possible policy responses remain important priorities for

although the confidence intervals around our long‑

social science research.

term estimates are fairly sizable and so we cannot rule

out either moderately ‑sized declines (or increases) in

that outcome. Our long ‑term follow ‑up data also yield

suggestive evidence that arrests for drug selling may

have declined for male youth, male grown children,

and perhaps adults as well as a result of the MTO

intervention. It is not clear why a residential mobility

intervention would reduce this type of criminal behavior,

but perhaps drug selling is a crime that requires

cooperation among numerous people.

As with the interim MTO data, we find some signs

that assignment to the MTO experimental rather than

the control group might increase risky behaviors for

male youth (smoking) while at the same time decreasing

the prevalence of at least some of these behaviors

(drinking) for female youth. A concern in interpreting

the administrative arrest data is that the likelihood of

arrest could vary across neighborhoods. In particular,

if the quality of policing is relatively higher in lower‑

poverty areas, then any evidence of an increase in arrests

in administratively ‑recorded arrest records may overstate

any adverse effects of MTO in the direction of increasing

criminal behavior. However the fact that we see signs that

MTO moves increased self ‑reported smoking among male

youth as well as administratively ‑recorded property ‑crime

arrests suggests the (muted) gender difference in how

206

SUPPLEMENTAL EXHIBIT 6.1. PREVALENCE OF POST ‑RANDOM ASSIGNMENT ARRESTS

FOR YOUTH AGES 15–20

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

ARRESTS BY CRIME TYPE

EVER ARRESTED AFTER RANDOM

ASSIGNMENT FOR…

ANY CRIME [CJR]

All

0.297

0.013

0.027

– 0.016

– 0.024

4,717

(0.016)

(0.034)

(0.018)

(0.028)

Female

0.189

0.012

0.025

– 0.012

– 0.019

2,300

(0.021)

(0.044)

(0.023)

(0.035)

Male

0.397

0.014

0.029

– 0.019

– 0.029

2,417

(0.024)

(0.052)

(0.028)

(0.041)

VIOLENT CRIME [CJR]

All

0.167

0.015

0.032

– 0.023

– 0.035

4,717

(0.014)

(0.029)

(0.015)

(0.023)

Female

0.103

0.006

0.012

– 0.030~

– 0.046~

2,300

(0.017)

(0.034)

(0.017)

(0.026)

Male

0.226

0.025

0.053

– 0.016

– 0.025

2,417

(0.022)

(0.047)

(0.024)

(0.035)

PROPERTY CRIME [CJR]

All

0.132

0.019

0.040

– 0.001

– 0.001

4,717

(0.012)

(0.026)

(0.014)

(0.021)

Female

0.070

0.011

0.023

– 0.003

– 0.005

2,300

(0.014)

(0.029)

(0.015)

(0.023)

Male

0.189

0.027

0.057

0.001

0.002

2,417

(0.020)

(0.043)

(0.023)

(0.034)

DRUG CRIME [CJR]

All

0.091

0.004

0.008

– 0.014

– 0.021

4,717

(0.010)

(0.022)

(0.011)

(0.017)

Female

0.016

0.009

0.018

– 0.008

– 0.012

2,300

(0.008)

(0.016)

(0.007)

(0.011)

Male

0.161

– 0.001

– 0.002

– 0.020

– 0.030

2,417

(0.019)

(0.040)

(0.020)

(0.030)

OTHER CRIME [CJR]

All

0.159

– 0.010

– 0.020

– 0.016

– 0.025

4,717

(0.013)

(0.027)

(0.014)

(0.021)

Female

0.078

– 0.001

– 0.002

0.009

0.014

2,300

(0.015)

(0.030)

(0.016)

(0.025)

Male

0.234

– 0.017

– 0.037

– 0.041~

– 0.061~

2417

(0.021)

(0.045)

(0.023)

(0.034)

207

Chapter 6: Impacts on Risky and Criminal Behavior

SUPPLEMENTAL EXHIBIT 6.1. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

DRUG CRIME ARRESTS BY TYPE

EVER ARRESTED AFTER RANDOM

ASSIGNMENT FOR…

DRUG POSSESSION [CJR]

All

0.065

0.008

0.017

– 0.008

– 0.012

4,717

(0.009)

(0.019)

(0.009)

(0.014)

Female

0.012

0.004

0.008

– 0.007

– 0.010

2,300

(0.007)

(0.014)

(0.006)

(0.010)

Male

0.113

0.012

0.026

– 0.010

– 0.015

2,417

(0.016)

(0.034)

(0.017)

(0.026)

DRUG DISTRIBUTION [CJR]

All

0.048

– 0.004

– 0.009

– 0.005

– 0.008

4,717

(0.007)

(0.016)

(0.008)

(0.012)

Female

0.005

0.009

0.018

– 0.003

– 0.004

2,300

(0.005)

(0.011)

(0.005)

(0.007)

Male

0.087

– 0.016

– 0.035

– 0.007

– 0.011

2,417

(0.014)

(0.029)

(0.015)

(0.023)

Notes: * = p < 0.05, ~ = p < 0.10 on two ‑tailed t ‑test. Robust standard errors shown in parentheses. ITT= Intention‑to‑Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment‑on‑the‑Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: CJR = criminal justice records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Individual criminal justice system arrest data. Adult and juvenile data from California, Illinois, Maryland, and Massachusetts; de ‑identified adult data from New York State; juvenile data from New York City; and adult or juvenile records from 8 additional states in which participants have lived. All core household members ages 15 to 20 as of December 31, 2007.

Measures: Violent crime arrests involve charges of force or threat of force including homicide, rape, robbery, assault, kidnapping, and weapons charges. Property crime arrests involve taking money or property and include burglary, motor vehicle theft, larceny, trespassing, and receiving stolen property. Drug crime arrests are classified as either possession or distribution arrests. "Other" arrest charges cover any charges not classified as violent, property, or drug crimes and include disorderly conduct and moving violations.

208

SUPPLEMENTAL EXHIBIT 6.2. PREVALENCE OF POST ‑RANDOM ASSIGNMENT ARRESTS FOR GROWN

CHILDREN AGES 21–30

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

ARRESTS BY CRIME TYPE

EVER ARRESTED AFTER RANDOM

ASSIGNMENT FOR…

ANY CRIME [CJR]

All

0.511

– 0.003

– 0.006

– 0.017

– 0.030

4,641

(0.018)

(0.043)

(0.019)

(0.034)

Female

0.348

0.009

0.022

– 0.020

– 0.034

2,277

(0.025)

(0.059)

(0.027)

(0.047)

Male

0.666

– 0.014

– 0.034

– 0.015

– 0.026

2,364

(0.025)

(0.059)

(0.027)

(0.046)

VIOLENT CRIME [CJR]

All

0.279

– 0.007

– 0.017

– 0.017

– 0.030

4,641

(0.016)

(0.038)

(0.018)

(0.031)

Female

0.152

0.004

0.010

– 0.026

– 0.045

2,277

(0.019)

(0.045)

(0.021)

(0.036)

Male

0.400

– 0.018

– 0.044

– 0.009

– 0.016

2,364

(0.025)

(0.060)

(0.028)

(0.049)

PROPERTY CRIME [CJR]

All

0.274

0.008

0.019

– 0.016

– 0.027

4,641

(0.017)

(0.039)

(0.018)

(0.031)

Female

0.164

– 0.006

– 0.013

– 0.021

– 0.036

2,277

(0.020)

(0.047)

(0.022)

(0.037)

Male

0.379

0.022

0.052

– 0.010

– 0.018

2,364

(0.025)

(0.060)

(0.027)

(0.048)

DRUG CRIME [CJR]

All

0.255

– 0.012

– 0.029

– 0.034*

– 0.059*

4,641

(0.015)

(0.035)

(0.016)

(0.028)

Female

0.077

– 0.005

– 0.011

– 0.005

– 0.009

2,277

(0.015)

(0.035)

(0.016)

(0.028)

Male

0.425

– 0.020

– 0.047

– 0.061*

– 0.107*

2,364

(0.025)

(0.059)

(0.028)

(0.048)

OTHER CRIME [CJR]

All

0.311

0.024

0.056

0.008

0.014

4,641

(0.017)

(0.039)

(0.018)

(0.031)

Female

0.169

0.019

0.044

– 0.014

– 0.025

2,277

(0.020)

(0.047)

(0.022)

(0.037)

Male

0.447

0.029

0.068

0.029

0.050

2,364

(0.026)

(0.062)

(0.028)

(0.049)

209

Chapter 6: Impacts on Risky and Criminal Behavior

SUPPLEMENTAL EXHIBIT 6.2. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

DRUG CRIME ARRESTS BY TYPE

EVER ARRESTED AFTER RANDOM

ASSIGNMENT FOR…

DRUG POSSESSION [CJR]

All

0.198

– 0.011

– 0.026

– 0.015

– 0.026

4,641

(0.014)

(0.032)

(0.015)

(0.026)

Female

0.053

– 0.003

– 0.008

– 0.007

– 0.012

2,277

(0.013)

(0.030)

(0.014)

(0.024)

Male

0.336

– 0.019

– 0.044

– 0.023

– 0.040

2,364

(0.024)

(0.056)

(0.027)

(0.047)

DRUG DISTRIBUTION [CJR]

All

0.155

– 0.018

– 0.042

– 0.032*

– 0.055*

4,641

(0.013)

(0.030)

(0.013)

(0.023)

Female

0.037

– 0.009

– 0.021

0.001

0.002

2,277

(0.011)

(0.026)

(0.012)

(0.021)

Male

0.268

– 0.026

– 0.062

– 0.062*

– 0.108*

2,364

(0.022)

(0.053)

(0.024)

(0.041)

Notes: * = p < 0.05, ~ = p < 0.10 on two ‑tailed t ‑test. Robust standard errors shown in parentheses. ITT= Intention‑to‑Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment‑on‑the‑Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: CJR = criminal justice records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Individual criminal justice system arrest data. Adult and juvenile data from California, Illinois, Maryland, and Massachusetts; de ‑identified adult data from New York State; juvenile data from New York City; and adult or juvenile records from 8 additional states in which participants have lived. All core household members who are now grown children (under age 18 at baseline and ages 21 to 30 as of December 31, 2007).

Measures: Violent crime arrests involve charges of force or threat of force including homicide, rape, robbery, assault, kidnapping, and weapons charges. Property crime arrests involve taking money or property and include burglary, motor vehicle theft, larceny, trespassing, and receiving stolen property. Drug crime arrests are classified as either possession or distribution arrests. "Other" arrest charges cover any charges not classified as violent, property, or drug crimes and include disorderly conduct and moving violations.

210

SUPPLEMENTAL EXHIBIT 6.3. PREVALENCE OF POST ‑RANDOM ASSIGNMENT ARRESTS FOR ADULTS

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

ARRESTS BY CRIME TYPE

EVER ARRESTED AFTER RANDOM

ASSIGNMENT FOR…

Any crime [CJR]

0.235

0.004

0.009

– 0.016

– 0.026

4,376

(0.015)

(0.031)

(0.016)

(0.025)

Violent crime [CJR]

0.095

0.007

0.014

0.000

0.000

4,376

(0.010)

(0.022)

(0.012)

(0.018)

Property crime [CJR]

0.080

0.020~

0.041~

0.004

0.007

4,376

(0.010)

(0.022)

(0.011)

(0.017)

Drug crime [CJR]

0.061

– 0.008

– 0.017

– 0.002

– 0.003

4,376

(0.008)

(0.018)

(0.010)

(0.015)

Other crime [CJR]

0.109

– 0.005

– 0.010

– 0.008

– 0.012

4,376

(0.011)

(0.023)

(0.012)

(0.019)

DRUG CRIME ARRESTS BY TYPE

EVER ARRESTED AFTER RANDOM

ASSIGNMENT FOR…

Drug possession [CJR]

0.041

0.004

0.007

0.000

0.000

4,376

(0.007)

(0.015)

(0.008)

(0.013)

Drug distribution [CJR]

0.029

– 0.010~

– 0.020~

– 0.003

– 0.004

4,376

(0.006)

(0.012)

(0.006)

(0.010)

Notes: * = p < 0.05, ~ = p < 0.10 on two ‑tailed t ‑test. Robust standard errors shown in parentheses. ITT= Intention‑to‑Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment‑on‑the‑Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: CJR = criminal justice records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult and juvenile data from California, Illinois, Maryland, and Massachusetts; de ‑identified adult data from New York State; and adult or juvenile records from 8 additional states in which participants have lived. All long ‑term survey sampling frame adults with a baseline consent form.

Measures: Violent crime arrests involve charges of force or threat of force including homicide, rape, robbery, assault, kidnapping, and weapons charges. Property crime arrests involve taking money or property and include burglary, motor vehicle theft, larceny, trespassing, and receiving stolen property. Drug crime arrests are classified as either possession or distribution arrests. "Other" arrest charges cover any charges not classified as violent, property, or drug crimes and include disorderly conduct and moving violations.

211

Chapter 6: Impacts on Risky and Criminal Behavior

212

IMPACTS ON

EDUCATIONAL

OUTCOMES

213

Chapter 7: Impacts on Educational Outcomes

IMPACTS ON EDUCATIONAL OUTCOMES

By providing opportunities for children and their

In keeping with the results from the interim study (Orr

families to move to less-distressed neighborhoods,

et al., 2003; Sanbonmatsu et al., 2006), MTO has few

MTO was expected to provide access to superior

detectable effects on the school performance of youth.

CHAPTER 7

educational opportunities in the form of better schools

There are no statistically significant differences across

and increased exposure to peers, neighbors, and

randomized groups in achievement test scores. We see

communities that value academic achievement. This

signs of reduced rates of grade retention for Section

in turn could lead to improved educational outcomes

8 female youth compared with controls, but on a few

for these youth, including increased achievement test

other measures such as high school graduation we see

scores, completion rates of high school, and advanced

statistically significant or marginally significant declines

educational degrees, as well as higher earnings.

in educational outcomes for youth in one or both of

the treatment groups compared with controls. Given

This chapter describes the educational experiences and

the large number of comparisons made, these impacts

behaviors of the MTO youth and the long-term impacts

could potentially be chance findings. Disappointingly,

of the program on their educational outcomes. The results the generally null results also extended to the younger include effects on post-secondary school participation

children who had not yet entered school at baseline.

among children who were already in school when the

Results for grown children are mixed.

program started, as well as the first estimates of MTO’s

effects on the youngest children at baseline—those

7.1 BASELINE AND CONTROL

between infancy and age 5—all of whom were too young

GROUP CONTEXT

to provide reliable measures of educational success at the

The plight of children in urban schools is widely

MTO interim evaluation. These children are of particular

documented. Children in urban communities perform

interest because their opportunity to move through MTO

well below their counterparts in suburban schools

to better neighborhoods and potentially better schools

on standardized tests, are more likely to drop out

occurred at the earliest, and most malleable, stage of their

of school, and are much less likely to attend college

cognitive and socio-emotional development.

(Casserly, 2002; Swanson, 2009). Drop-out rates

among low-income students are almost three times

We find that relative to youth in the control group, youth

the rate of students in the middle three income

in the experimental and Section 8 groups experienced

quintiles (Chapman, Laird, and Kewal-Ramani,

mixed impacts on school quality—they attended schools

2010). Graduation rates for whites and Asians are

with lower proportions of low-income and minority

approximately 50 percent greater than those for blacks,

students and with slightly higher average test scores, but

Hispanics, and American Indians (Swanson, 2004).

the schools serving treatment-group youth also had larger

Ninth graders in the richest 20 percent of American

overall student bodies and the experimental group’s most

households are almost seven times more likely than

recent school had higher student-teacher ratios. School

those in the poorest 20 percent to place in the top fifth

mobility is similar across treatment and controls groups,

of math percentile scores (Ingels et al., 2011). These

although youth in the experimental group may have

poor educational outcomes have been linked to both

been distributed across a slightly larger number of school

low-quality schools and home environments that do

districts compared with the control group. Youth in the

not support educational achievement.

experimental group, especially females, reported more

hospitable school climates than the control group.

214

The experiences of MTO children are not unlike the

Section 8 children might have moved to lower-poverty

experiences of low-income children in urban communities

neighborhoods, the differences between the quality of the

nationwide. Although the measures used in MTO do not

public schools in their new and original neighborhoods

correspond exactly to those used in studies of nationally

may have been attenuated by urban school reform and

representative samples of children, available data indicate

any new public and private investments that came as a

elevated rates of problem behaviors among MTO children

result.

compared with the average child in America. Exhibit 7.1

presents summary statistics for the education-related

One example is the small high schools initiative that

variables collected at baseline for MTO children.1

occurred in one of the five MTO demonstration sites—

Approximately 6 percent of the older youth (ages 6 to

New York City—which together with other reforms

11 at baseline) had behavioral problems and 12 percent

boosted achievement test scores and graduation rates

had learning problems at baseline, and 3 percent had been

(Bloom, Thompson, and Unterman, 2010). Another

suspended or expelled from school in the two years prior

example comes from the accountability efforts in the

to baseline. Over 20 percent of parents indicated that

Chicago Public Schools, which has increased substantially

during the two years prior to baseline someone from their

the number of children performing at grade level in

child’s school had asked them to come in to talk about

reading and mathematics over the last ten years, with the

behavior or other problems the child was having at school. most dramatic increases occurring in some of the city’s lowest performing schools (Roderick et al., 1999). The

MTO children attended schools at baseline that were

performance of students in the Chicago Public Schools

characterized by both low achievement and high poverty.

—where a majority of MTO youth in the Chicago site

Exhibit 7.1 shows that 86 percent of their classmates

attended schools throughout their school careers—has

were eligible for free or reduced lunch and 94 percent

increased relative to other schools in the state (Jacob,

were members of minority groups. On average, students

2003). Boston saw similar improvements across all grades

at these schools ranked at the 15th percentile on state

and subject areas, including some narrowing of racial

assessments of academic skills. As discussed in Chapter 1, test-score gaps as well (Office of Research Assessment and over half of MTO families indicated that the primary or

Evaluation, 2010a). In Baltimore, from 2006 to 2010

secondary reason they wanted to move was to send their

the percentage of elementary school students achieving

children to better schools.

a level of proficient increased from 82 percent to 89

percent in math and from 78 to 88 percent in reading

The parents of MTO children also had relatively low

(Baltimore City Public Schools, 2011).

levels of school attainment themselves at baseline

compared with national samples, which is relevant given

7.2 HYPOTHESES ABOUT EFFECTS

the strong documented association between parental

ON EDUCATIONAL OUTCOMES

schooling and children’s learning outcomes (Jencks and

The explicit goal of MTO was to help families residing

Phillips, 1998). Only 35 percent of the sample adults had in public housing move to lower-poverty areas. And, completed a high school degree at the time of random

as demonstrated in Chapter 2, following random

assignment (another 18 percent had earned a GED).

assignment MTO experimental and Section 8 families

were substantially more likely than controls to live

The MTO demonstration was implemented during

in low-poverty neighborhoods—even measured 10 to

the mid- to late 1990s, a time of increased emphasis

15 years after baseline. A large literature in the social

on urban school reform. Since then, all five of the

sciences suggests multiple pathways through which

MTO cities have initiated high-profile efforts aimed

neighborhood socioeconomic composition could directly

at improving the educational outcomes for students

or indirectly influence the amount that students learn

in city schools. As a result, although experimental and

in school, or their persistence in school and ultimate

schooling attainment.

1 No information on standardized achievement test scores was collected

at baseline.

215

Chapter 7: Impacts on Educational Outcomes

Exhibit 7.2 presents a conceptualization of these

Children growing up in relatively more affluent

pathways. Four important community characteristics

communities may also be exposed to adults and peers

are associated with the level of affluence of the

who place relatively greater importance on educational

neighborhood: the quality of the schools, community

achievement, learning, and pro-social behavior. More

values and the community as a socializing agent for these affluent neighborhoods also tend to have relatively values, the safety of the community, and the economic

higher rates of professional and managerial employment;

opportunities available in the community. As the model

people in these jobs may act as positive role models

shows, one of these community characteristics—higher

who help signal the returns to educational completion

quality schools—may directly influence students’

and academic success. In addition, peers could directly

educational outcomes. In addition, in response to living

influence learning outcomes by providing children with

in more affluent communities that may (a) have better

opportunities to participate in more developmentally

educational institutions, (b) be safer and more cohesive

productive study groups, or by influencing the content or

communities, and (c) provide employment opportunities

pace of teaching within the classroom environment.

for adults, the attitudes and behavior of the children and

their families are expected to change in ways that better

A third community characteristic that may be relevant

support learning and achievement.

for children’s educational outcomes is the neighborhood’s

physical environment—including safety. Parents who

The first, and most direct, mediator of educational

believe that their children are safe may feel less stress

outcomes is school quality. Previous research typically

and anxiety, resulting in improved mental health and

finds that higher-income neighborhoods have better

improved parenting (McLoyd and Flanagan, 1990).

schools (Connell and Halpern-Felsher, 1997). Typically,

Children who feel safe in their physical surroundings

“better” schools are defined as having higher teaching

may be more likely to flourish academically and

quality, greater educational resources, more rigorous

personally. Previous observational research has shown a

course offerings, smaller class sizes, and a school climate

negative association between exposure to serious violence

that values learning and achievement and holds high

and children’s short-term academic outcomes (Sharkey,

expectations for students (Darling-Hammond, 1996).

2010).

These school characteristics are, in turn, hypothesized to

increase students’ commitment to academic achievement

Finally, the economic opportunities within a

and promote behaviors that produce achievement—

neighborhood could influence children’s schooling

studying, attendance, and engagement in school.

outcomes for several reasons. Communities with

improved economic opportunities may increase the

However, changing schools could have unintended

resources of parents and the local parent-teacher

negative consequences on school performance as well.

association enabling them to supplement public school

One reason stems from whatever initial disruption

resources or offering enrichment activities (for example,

may arise as a result of residential mobility induced by

by raising money to hire teacher’s aides or by offering

MTO. In addition, there may be increased competition

after school programs). Living in a community with

for grades and academic success in more affluent

improved job opportunities could also improve the

schools, which could lead children who move through

economic conditions of MTO families themselves,

MTO to develop low self-confidence in the midst of a

which may enable parents to increase their own private

more affluent, higher-achieving peer group. A recent

investments in their children’s learning. And the presence

survey found a consistent negative association between

of formal labor market opportunities in an area could

school mobility and school achievement or high school

influence perceptions of the economic returns to

graduation rates, especially for minority groups such as

schooling attainment and achievement.

Hispanics and African-Americans (Beatty, 2010).

216

EXHIBIT 7.1. BASELINE CHARACTERISTICS OF THE YOUTH SAMPLE BY RANDOM ASSIGNMENT GROUP

CONTROL

EXPERIMENTAL SECTION 8

ALL

N

GROUP

GROUP

GROUP

GROUPS

YOUTH CHARACTERISTICS

AT BASELINE

Age (years) [PR]

4.7

4.6

4.7

4.7

5,101

Gender (%) [PR]

Female

48.7

51.7

51.2

50.5

5,101

Male

51.3

48.3

48.8

49.5

5,101

YOUNGER YOUTH

CHARACTERISTICS–AGES 0-5

AT BASELINE (%)

Someone read to youth more than once

26.9

23.7

21.0

24.0

3,085

per day [PR]

Enrolled in Head Start [PR]

17.3

22.8

21.7

20.7

2,674

OLDER YOUTH

CHARACTERISTICS–AGES 6-11

AT BASELINE (%)

Gifted student/did advanced

14.5

12.3

12.9

13.2

2,016

coursework [PR]

Suspended or expelled from school in

3.2

3.1

4.1

3.4

2,016

past two years [PR]

School called about behavior or

19.6

20.0

21.8

20.4

2,016

schoolwork in past two years [PR]

Behavioral or emotional problems [PR]

6.1

5.1

5.9

5.7

2,016

Learning problem [PR]

13.4

10.1

13.7

12.3

2,016

OLDER YOUTHS' SCHOOL

CHARACTERISTICS AT BASELINE

Baseline grade [PR]

1.50

1.37

1.49

1.45

2,006

Percent free/reduced-price lunch [CCD]

85.4

85.9

86.5

85.9

1,534

Percent minority [CCD]

94.4

92.9

93.5

93.6

1,621

Number of students per teacher [CCD]

19.4

19.1

18.8

19.1

1,625

School's percentile rank on state exam

14.2

14.9

15.3

14.8

1,530

[SLAD]

SAMPLE ADULT/PARENT

CHARACTERISTICS AT BASELINE (%)

High school diploma [PR]

34.0

35.7

35.1

35.0

5,101

GED [PR]

21.9

15.1

18.7

18.4

5,101

Enrolled in school [PR]

18.6

17.0

19.2

18.2

5,101

PARENT SCHOOL INVOLVEMENT

AT BASELINE (%)

IN THE PAST 12 MONTHS, A

HOUSEHOLD ADULT HAD…

Attended a school meeting [PR]

80.2

79.2

78.9

79.4

5,078

Attended a school event [PR]

69.2

67.7

69.2

68.6

5,080

Volunteered at school [PR]

47.8

47.9

46.2

47.4

5,081

217

Chapter 7: Impacts on Educational Outcomes

EXHIBIT 7.1. (CONTINUED)

CONTROL

EXPERIMENTAL SECTION 8

ALL

N

GROUP

GROUP

GROUP

GROUPS

FAMILY'S PRIMARY OR SECONDARY

49.4

52.0

57.6

52.7

5,101

REASON FOR SIGNING UP FOR MTO

WAS BETTER SCHOOLS (%) [PR]

Notes: The numbers represent mean values on baseline characteristics for each treatment group and overall, weighted to reflect randomization ratios and the selection of youth into the interview sample and the two-phase sampling design of the long-term evaluation.

Square brackets indicate the source of the outcome information: PR = parent report. CCD = Common Core of Data. SLAD = School-level assessment data from the National Longitudinal School-Level State Assessment Score Database.

Data source and sample: Parent reported items were reported by the baseline head of household on the Moving to Opportunity Participant Baseline survey (age and gender were verified by the youth as part of the long-term survey). School characteristics come from the above-referenced data sources and are based on parent reports from the baseline survey or from the interim survey (if baseline school is missing in the baseline data).

Measures: The older youth (N = 2,016) and younger youth (N = 3,085) sample sizes are reflected in measures presented above that are also standard covariates in our regression analysis. Missing values for the covariates were imputed by site, treatment group, age, and gender. See Section 1.3 and the technical appendices (forthcoming) for details. Other measures, such as the Head Start measure and the school characteristics, have lower sample sizes because missing values were not imputed.

7.3 EARLIER RESEARCH

and their grades suffered as a result (Rosenbaum, 1995).

ON NEIGHBORHOODS AND

However, later findings from Gautreaux showed that

EDUCATIONAL OUTCOMES

children who moved to suburban neighborhoods were

INCLUDING SHORT-TERM AND

more likely to graduate from high school and attend

INTERIM MTO IMPACTS

four-year colleges compared with those whose families

moved to other parts of the city of Chicago (Rubinowitz

A number of observational (nonexperimental) studies

and Rosenbaum, 2000; Popkin et al., 2000; DeLuca and

have shown that neighborhood socio-economic status is

Rosenbaum, 2003).

correlated with the educational outcomes of children, even

after statistically adjusting for observable characteristics of

MTO was inspired by Gautreaux and designed as a

the children and their families. Previous research studies

true randomized experiment to overcome lingering

have found that the presence of affluent neighborhood

concerns about selection bias that arise with studies of

residents in particular is positively related to young

Gautreaux. As discussed in previous chapters, through

children’s IQ and verbal ability scores (e.g., Brooks-Gunn

the time of the interim follow-up (4 to 7 years after

et al., 1993; Duncan, Brooks-Gunn, and Klebanov, 1994;

baseline), MTO was successful in helping experimental

Klebanov et al., 1997); higher reading recognition (Chase-

and Section 8 group families move to neighborhoods that

Lansdale et al., 1997), math achievement (Entwisle,

are hypothesized to be more educationally “productive”

Alexander, and Olson, 1994); and adolescent school

than those serving controls with respect to some of the

completion and self-reported grades (Brooks-Gunn et al.,

community characteristics that the previous section

1993; Connell and Halpern-Felsher, 1997; Dornbusch,

hypothesizes may be relevant for educational outcomes

Ritter, and Steinberg, 1991; Duncan, 1994).

(Orr et al., 2003; Kling, Ludwig, and Katz, 2005;

Sanbonmatsu et al., 2006; Kling, Liebman, and Katz,

The most influential “natural experiment” study of the

2007). MTO helped families move into neighborhoods

relationship between neighborhood environments and

that had lower poverty rates and greater presence of

schooling outcomes is the Gautreaux mobility program

college graduates, employed people, and people employed

in Chicago. Starting in the 1970s, under Gautreaux

specifically in managerial or professional occupations.

thousands of African-American public housing residents

MTO adults in the experimental and Section 8 group

were offered the opportunity to use housing vouchers

were also more likely than controls to report feeling

to relocate to more racially and economically mixed

safe, and that their neighbors would work together to

neighborhoods in either the suburbs of Chicago or in

support shared pro-social norms—what sociologists call

other parts of the city itself. Students whose families

“collective efficacy” (Sampson, Raudenbush and Earls,

moved to the suburbs initially had difficulties adapting

1997).

to the higher academic expectations in suburban schools,

218

However MTO had much more muted impacts on the

members, and mothers’ jobs—with schools sometimes

characteristics of the schools that children attended

coming after that, if at all (Deluca and Rosenblatt,

(Sanbonmatsu et al., 2006; see also Ferryman et al., 2008 2010).

and Deluca and Rosenblatt, 2010). Even though children

in the experimental group were living in considerably

The interim report also observed that it might have been

safer and more affluent neighborhoods, some 80

too soon to observe impacts on educational outcomes

percent were attending schools in the same struggling

for these children, given the relatively small changes

urban school districts as they had been at baseline.

observed in the mediators. At the time of the interim

MTO was found to have had some small but positive

evaluation some MTO children had been exposed to

effects on children’s educational experiences based on

their new environment for as little as four years and

the characteristics of children’s schools, but few were

there was some evidence that they were having difficulty

attending schools that could even be considered above

adjusting to these new environments. It was hoped that

average for the states in which they resided.

more beneficial impacts would be observed in the long-

term follow-up.

Taken as a whole, the pattern of impacts on educational

outcomes for MTO children found at interim was

disappointing (Orr et al., 2003; Sanbonmatsu et al.,

2006). Across the five MTO sites, the changes in

neighborhood conditions associated with being assigned

to the MTO experimental group were not sufficient to

generate noteworthy impacts on the education-related

behaviors, attitudes, or achievement test scores of MTO

children. However, Burdick-Will and coauthors (2011)

find a positive impact on reading achievement scores for

the combined Baltimore and Chicago demonstration

sites.2

If the underlying MTO process model is correct, the

program’s lack of impacts on educational outcomes might

be attributed to the relatively small changes observed

in the characteristics of the schools attended by the

MTO children. MTO’s qualitative report (Popkin et

al., 2002) also suggested that increased opportunities

for school choice, the children’s desires to remain with

friends, negative experiences in more affluent schools,

and student special needs led some experimental group

children to remain in the same school or to attend

schools in their old neighborhoods. More recent

qualitative interviews with Baltimore families found that

parents’ decisions about schooling often had little to do

with academic quality. For many poor families, moving

priorities began with proximity to transportation, family

2 These results are consistent with the positive impact on reading scores

for African-Americans in the MTO sample reported by Sanbonmatsu et

al. (2006) and driven by results for the Chicago and Baltimore sites.

219

Chapter 7: Impacts on Educational Outcomes

EXHIBIT 7.2. HYPOTHESIZED PATHWAYS THROUGH WHICH MTO MAY IMPACT ACHIEVEMENT AND

Exhibit 7.2. Hypothesized Pathways Through Which MTO May Impact Achievement and Educational Outcomes EDUCATIONAL OUTCOMES

MTO INTERVENTION

RELOCATION TO LOW-POVERTY NEIGHBORHOODS

COMMUNITY-LEVEL

STUDENT- AND FAMILY-LEVEL

EDUCATIONAL

MEDIATORS

MEDIATORS

OUTCOMES

Quality of Schools

Student Attitudes

School Performance

and Behavior

· Higher student achievement

· Standardized achievement/

· Increased value on learning

proficiency test scores

· More resources in schools

· More positive feelings

· Academic track (honors/

· Lower student/teacher ratio

toward school

AP, regular, remedial)

· Lower counselor/student

· Greater sense of safety and

· Special education

ratio

security in and out of

· School environments that

school

· Academic honors, awards

support learning

· Better attendance

Educational Progress

· More time spent studying

· Retention in grade

Community Norms

· More participation in

· On-time high school

and Values

extracurricular activities

graduation

· Better adult/peer role

· Fewer disciplinary

· Post-graduation

models

sanctions from school

educational plans,

· Better community monitor-

enrollment

ing of youth (collective

Parent Attitudes and

efficacy)

Behavior

· Shared community value of

· Greater sense of

education

confidence, safety for self

and family

Social and Physical

· Less stress, anxiety

Environment

· Greater parent support for

· Lower crime and violence

education/achievement

· Increased recreational

· Increased wages, higher

activities for youth

employment

· Changed parenting

practices (less restrictive,

Economic Opportunities

authoritarian)

· More job openings

· Better wages

Exhibit 7.2 is a modified version of Exhibit 6.1 in the MTO interim impacts evaluation (Orr et al., 2003).

220

7.4 DATA SOURCES AND MEASURES

Surveys (available years between 1993-94 and 2007-08;

A variety of survey-based and administrative data sources U.S. Department of Education, 2011b). This allows us to were used to measure MTO youth’s school environments, construct a host of school-level characteristics depicting experiences in school, educational expectations and

sociodemographic composition of the student body

educational and achievement outcomes.

(racial/ethnic composition, free and reduced price lunch

eligibility), school size (the number of students as of

SCHOOL MOBILITY AND SCHOOL

October), pupil-teacher ratio, Title I status, and the type

CHARACTERISTICS

of school (magnet or charter).4

As part of our in-person survey data collection we

obtained measures of school-level characteristics and

In addition, we matched the schools to the National

mobility from an in-depth retrospective history of

Longitudinal School-Level State Assessment Score

schools that children attended. We asked youth to

Database (NLSLSASD) spanning 1999 to 2007 to

report all of the schools they attended for each grade

obtain school-level test score data that we then used

from their current or most recent school back through

to rank the schools within MTO’s five main states

to their grade at baseline (or back through kindergarten

(NLSLSASD, 2010).5 We use school-level statistics on

for youth who were not yet of elementary school age at

the share of students at each school performing at or

baseline). For approximately 60 percent of the youth

above proficiency in math and in reading (or language

sample, the school history is constructed by combining

arts scores when reading is not available) to rank schools

youth reports from their most recent school attended to

for a specific grade, academic year, and subject. We

the last school reported by their parents at the interim

then average the reading and math rankings to obtain a

survey point.3 One challenge in constructing the school

combined measure.

history was to identify all of the specific schools that

were mentioned by survey respondents. Most schools

We present measures of school characteristics for both the

were accurately identified by using a school-lookup to

most recent school (with data available) that the youth

identify the school during the interview. For schools

attended, and for the average across all of the schools

that could not be identified during the interview, the

attended by the youth from baseline to the interview

respondent was asked for the name and address of the

weighted by the number of years they attended a school.

school. The information provided was sometimes, but

If a youth repeated a grade at a school, we count that

not always, sufficiently detailed to identify the school. In school-grade twice. If a youth attended more than one some cases the respondent did not report the name of the school during the same year, we divide that year equally school. Across our sample, we were able to identify the

among the schools. To the extent possible, we link each

schools for approximately 92 percent of all student-years

year of a youth’s school history to the closest year of data

of elementary and secondary education attended from

available for that school and that measure.

baseline to interview date.

ACHIEVEMENT ASSESSMENTS

We use the school history to construct measures of school The youth in-person interviews included a 45-minute mobility such as counts of the total number of different

achievement assessment in math and reading as

schools and school districts attended. Additionally, we

designed for the 5th and 8th grade follow-up waves of

used the school history information to match the schools 4 A Title I eligible school is a school designated as being high poverty attended by the MTO youth to data from the National

and eligible to participate in federal Title I programs. Schoolwide Title

I programs apply to Title I schools with student bodies that consist of

Center for Education Statistics (NCES) Common Core

at least 40 percent low-income students. Schools designated by state

of Data (1993-94 to 2009-10 academic years; U.S.

legislatures as “charter schools” provide free public education under a

special charter granted by the legislature. Magnet schools and programs

Department of Education, 2011a) and Private School

are designed to either attract students of different racial and ethnic

backgrounds to reduce racial isolation or to focus on a particular aca-

3 For the long-term survey, these youth were asked to report on their

demic or social theme such as science or performing arts.

schools and grades going back only to the highest grade reported by

5 We limit our analyses of test score rankings to schools within the five

their parent at interim.

original MTO demonstration states.

221

Chapter 7: Impacts on Educational Outcomes

the U.S. Department of Education’s Early Childhood

youth ages 13-20 performed below the level of chance on

Longitudinal Study-Kindergarten Cohort (ECLS-K).6

the reading test and about 7 percent on the math test.

Youth ages 10 to 12 were administered the 5th grade

test and youth ages 13 to 20 were administered the 8th

The ECLS-K achievement assessments address many of

grade test. The ECLS-K assessments are adaptive—

the limitations documented from the administration

respondents first take a short “routing test” that then

of the Woodcock Johnson-Revised (WJ-R) assessment

directs the interviewer to provide them with test forms of at the MTO interim study, which included evidence different difficultly levels—to reduce the time required

of “interviewer effects” (where results systematically

to accurately measure a subject’s academic achievement

differed across children according to which interviewer

level.7 Because not all youth were asked every test

administered the tests) and average scores that did not vary

item, our assessment results were scored for us by the

much by age among adolescents. Equally important, the

Educational Testing Service (ETS) to provide us with

ECLS-K assessments are designed to more closely measure

estimates of each youth’s underlying academic ability

what children learn in school (as opposed to measuring

(known as a “theta score” in the testing literature) as

aptitude only), and to be more sensitive to capturing

estimated from a statistical model based on item response whether MTO moved children into improved schooling theory.

and learning environments. The ECLS-K assessments

have several other desirable features including appropriate

One concern was the possibility that some of the older

coverage of material that is relevant for the wide dispersion

youth in our survey sample would find the items on the

of ages of MTO youth in the long-term MTO study, good

tests too easy. If these youth correctly answered every

discriminating power across a wide range of ability levels.

item on the test, the assessment would lose its ability

They also have been extensively pre-tested and piloted

to provide information about which youth in the study

to ensure that, for example, the test items work equally

know more than others, known as a “ceiling effect” in

well for subgroups of youth such as those from racial and

the testing literature. To guard against ceiling effects, we

ethnic minority groups.

supplemented the ECLS-K 8th grade test with a small

set of math and reading items from the U.S. Department INTERVIEW OBSERVATIONS OF YOUTH

of Education’s National Educational Longitudinal

DURING ACHIEVEMENT ASSESSMENTS

Survey-1988 (NELS) assessment for high school

During the administration of the math and reading

students. Only about 8 percent of the MTO sample of

achievement assessments, the interviewers documented

13- to 20-year-olds performed well enough to take these

their perceived observations of youth attentiveness,

additional NELS items in math or reading. Another

cooperation and motivation using five predetermined

concern is the possibility of a “floor effect,” in which the

response categories. We constructed a series of binary

assessment is too difficult for some children and so loses

outcomes contrasting the two most positive responses

its ability to distinguish the achievement of students

with the three most negative responses. “Complete and

at the bottom of the distribution. About 14 percent of

full attention” and “very attentive” were contrasted

with “attentive”, “difficulty attending,” and “unable to

6 The ECLS-K 5th grade test is administered in the home via paper

attend”; high and very high motivation with average,

and pencil and an interviewer-controlled easel (both for MTO and for

ECLS-K itself ). The ECLS-K 8th grade national test is administered

low, and very low motivation; and “very cooperative”

in a group format in school whereas the MTO 8th grade assessment is

and “cooperative” with “matter of fact”, “uncooperative”,

individually administered to youth in their homes.

and “very uncooperative.” (We report these interviewer

7 The ECLS-K 5th test is administered via an easel. Both the 5th and

8th grade tests are two-stage tests. Nationally, the first stage is a short assessments in Supplemental Exhibit 7.7; they do not

routing test so that the combination of age, grade and performance help

determine the difficulty of the second stage of the test (as compared

appear to be affected by the MTO intervention.)

with age or grade alone). This routing test is scored on-site by an asses-

sor and is then used to direct children to the appropriate level for the

SELF-REPORTED YOUTH OUTCOMES

second stage. The second stage 5th grade test is available in three levels

and the second stage 8th grade test is available in two levels, where

The MTO youth survey additionally asked youth a

items in the highest level of the 5th grade test, for example, overlap

with some items in the lowest level of the 8th grade test.

series of questions about their school performance and,

222

as discussed below in the section on mediators, their

positive responses on the five items: strongly agree/

perceptions and experiences in school and where and how agree responses to the positive items (teachers interested they spent time outside of school.

in students, fair discipline, and safety) and disagree/

strongly disagree responses to the negative items (teasing

Schooling Outcomes: Youth reported their overall grades and feeling “put down”). These items were drawn from in their last year of school and, as measures of their

a variety of sources. The teacher interest in students,

educational track, whether they had ever been enrolled in discipline, and safety items replicate items asked in the an advanced math or Advanced Placement (AP) course or interim MTO study and are drawn from the National a program for the gifted and talented; whether they had

Longitudinal Survey of Youth-1997 (NLSY97). The

ever been enrolled in any special class or received special

teasing item comes from Harvard University professor

help for any emotional, physical or mental condition;

Ronald Ferguson’s Tripod Study, and the “put down” item

and whether they had ever repeated a grade. For older

comes from NELS.

youth (ages 15 to 20), we constructed a measure of being

educationally “on track”—currently in school or had

Problem behavior: We constructed tardiness and

received a high school degree or certificate of General

absenteeism outcomes to indicate whether the youth was

Educational Development (GED)—and as a measure of

late for school once or more per month, whether the

college preparation, we also asked whether youth had

youth was absent more than five percent of the school

taken the SAT or ACT.8

year, and the fraction of the school year that the youth

was absent. Youth also reported whether they had been

PARENT-REPORTED OUTCOMES FOR

suspended or expelled from school in the past two years.

GROWN CHILDREN

These items all replicate items used at interim.10

In the MTO adult or household head survey, parents

reported on their MTO grown children, who were ages

Educational expectations: Youth are asked to report the

21 to 30 as of December 31, 2007, and so too old for

highest degree they expect to complete ranging from less

our long-term follow-up youth survey sample, but who

than high school graduation to graduate or professional

were under age 18 at baseline. Specifically, parents were

school. We also asked youth a similar question about how

asked to report whether each of their grown children was

far in school they believed their mother wanted them to

currently enrolled in school, had attained a GED or high go (same response options). These items were borrowed school diploma, and had ever been enrolled in college.

from NELS.

SELF-REPORTED MEASURES OF

School engagement: The school engagement measures

CANDIDATE MEDIATORS

focus on homework and reading. We asked youth how

School Climate: Youth reported whether they strongly

much of their assigned homework that they usually

agreed, agreed, disagreed, or strongly disagreed with five

complete, with response options ranging from “All plus

statements about the climate of their most recent school:9 some extra” to “None” and including a “Never assigned” (1) teachers are interested in students; (2) students who

option. For youth who indicated that homework was

study hard are teased; (3) discipline is fair; (4) the youth

assigned, we also asked separate questions about how

feels safe at the school; and (5) the youth often feels

many hours per week they spent on homework in

“put down” by teachers. We calculated a school climate

school and out of school. Additionally, youth reported

index from these measures as the fraction of normatively

the number of hours per week they spent doing

additional reading on their own outside of school (not in

8 Most of these items were also asked at the interim study and are

borrowed from (or are original items similar to items from) NLSY97.

connection with schoolwork).11

Exceptions include the repeated grade item, asked at interim and

borrowed from the Los Angeles Family and Neighborhood Survey (LA-

10 Original source surveys for these adapted items are as follows: tardi-

FANS). The gifted/talented program and special education items are

ness—Survey of Program Dynamics-1998; absenteeism—NLSY97;

new to the long-term survey and come from NELS.

suspension/expulsion—LAFANS.

9 Youth who were enrolled in post-secondary school or who were no

11 Original source surveys for these adapted items are as follows: home-

longer in school were asked to report on their most recent year in high

work completion—Ronald Ferguson’s Tripod Study; homework hours—

school.

NELS; and additional reading—the 1979 wave of NLSY.

223

Chapter 7: Impacts on Educational Outcomes

Time use and access to technology: To measure where

verification. The NSC enrollment database covers over

and how youth spend their time outside of school,

3,000 colleges and over 90 percent of students in the

youth report on their participation in school sports or

United States (National Student Clearinghouse, 2011).

school clubs or groups and, if so, whether any leadership

We focus our analyses on enrollment data for the three-

positions were held in those school-related activities.

year period from January 2007 to January 2010.13 The

We also asked how often youth participate in organized

NSC data allowed us to construct measures of whether

group activity, hang out in their neighborhood or a

MTO participants had been enrolled in any post-

nearby basketball court, shop at a mall or a store, and

secondary institution in their database as well as the

hang out at their own house and at someone else’s house. types of institutions (two- and four-year colleges; private, We also determined access to technology by asking

public, and for-profit colleges) in which they were

whether they use a computer in their home and how

enrolled and the length of their enrollment. NSC data

often they use the internet.

coverage is generally even across the treatment groups

and there is little variation in coverage by randomization

PARENT-REPORTED MEASURES OF

site. For interviewed youth and grown children whose

CANDIDATE MEDIATORS

parents were interviewed, we also have survey reports of

Parents were asked to report on the schooling, health,

any college attendance and attendance at two- vs. four-

and behavior of one randomly selected youth ages 10

year colleges. Comparisons of the NSC data measures

to 17 as of December 2007 in their household. Parents

and the survey outcome measures appear in Supplemental

reported whether in the past two years someone from the Exhibits 7.8 and 7.9. The results from the NSC do not youth’s school had asked someone from the household

differ substantially from the results from self-reporting.

to “come in and talk about problems [the youth] was

having with school work or behavior.” To measure their

7.5 LONG-TERM IMPACTS ON

engagement with their child’s schooling, we asked parents HYPOTHESIZED MEDIATORS OF

whether in the past 12 months they or another household EDUCATIONAL EFFECTS

adult had attended a school meeting or event (such as

Exhibit 7.3 presents MTO’s impacts on the

a back-to-school night or parent/teacher organization

characteristics of the schools attended by MTO youth

meeting). We also asked whether they had volunteered

over the course of the long-term follow-up period.

or worked at the youth’s school or been on a school

Compared with schools attended by control group

committee or club outside of school. Finally, parents

youth, MTO experimental and Section 8 youth attended

reported whether they were very, somewhat, or not all

schools that differed across a variety of socioeconomic

satisfied with the education their child had received up to and demographic characteristics. Youth in the that point.12

experimental and Section 8 groups attended schools with

POST-SECONDARY ENROLLMENT

lower proportions of minority students and students

receiving free or reduced-price lunch, and youth in the

FROM THE NATIONAL STUDENT

experimental group attended schools that performed

CLEARINGHOUSE

better on state exams. Experimental and Section 8 youth

In addition to obtaining self-reported data on college

attended schools that were 8 to 11 percent larger on

enrollment from youth, we obtained post-secondary

average than those attended by control group students,

enrollment data on MTO youth and grown children

and for youth in the experimental group, the most recent

from a match through the StudentTracker service of

schools had more pupils per teacher as well. The ratio of

the National Student Clearinghouse (NSC). The NSC

pupils to teachers differs from the number of students

is a non-profit organization that works with colleges

per classroom because it includes reading specialists and

and universities to provide degree and enrollment

13 NSC data were available back through 2001, but it took until the

12 The satisfaction item was new to the long-term survey and came

end of 2006 for the NSC to be near complete (96% of schools had

from NELS, while the remaining items were originally asked on the

joined the NSC by then), so we have limited our analysis to data from

MTO baseline survey and were also asked as part of the interim study.

2007 forward.

224

other types of teachers. MTO did not affect the type of

experimental and Section 8 groups are more likely to

school youth attended in terms of whether the school was be suspended or expelled from school in the past two a charter or a magnet.14

years compared with controls. MTO experimental group

youth—especially males—also are less likely to report

Youth in the experimental group attended schools

that they expect to complete college. These experimental

that ranked slightly higher on statewide reading and

group youth are no more likely to report their mothers

math exams compared with controls. Youth in all three

want them to complete college than are the control group

treatment groups disproportionately attended low

youth (Supplemental Exhibit 7.2). The MTO treatments

performing schools, with average school rankings ranging had few detectable effects on time spent on homework or from the 19th to 22nd percentiles across the groups.

reading, or on parent involvement in the youth’s school

Only about one in six youth from either the treatment or (Supplemental Exhibit 7.3). MTO had little effect on control groups recently attended a school in the top half

parental satisfaction of their children’s education for the

of their state’s schoolwide test score distribution.

experimental group, however parents in the Section 8

group were less satisfied with their children’s education.

Exhibit 7.4 presents MTO treatment effects on school

mobility. At a marginally significant level, experimental

Youth reports of where their out-of-school time is spent,

group youth may have attended school in a slightly larger computer use and access to the internet are generally number of total different school districts compared with

similar for MTO experimental, Section 8 and control

the control group. The total number of schools attended

groups, except for some indication of higher rates of

was similar across the MTO treatment groups.

home computer use for male youth in the Section 8

group compared with controls (Supplemental Exhibit

Broad objective measures of school-level student

7.4). There is a small reduction in MTO experimental

demographics and standardized test score performance

and Section 8 youth spending time at someone else’s

provide one picture of school quality. This picture can

house compared with the control youth.

be complemented by the youth’s own perceptions of

school quality (Exhibit 7.5). Experimental group youth

reported marginally more favorable and hospitable

interactions and perceptions of the school environment

than the control group, as measured by our index of

school climate. More specifically, compared with their

control group peers, experimental group youth were less

likely to report feeling put down by teachers, and female

experimental group youth in particular were more likely

to report that teachers took interest in students.

Supplemental Exhibits 7.1 through 7.4 present various

other aspects of school participation and related activities

that could mediate the effects of MTO on educational

outcomes. Youth in the experimental and Section 8 group

have similar levels of tardiness, absenteeism, and behavior

problems in school as the control group (Supplemental

Exhibit 7.1). However, MTO male youth in both the

14 Only a small percentage of MTO youth attended private elementary

or secondary school; youth in all three random assignment groups spent

an average of about 4% of their school years since baseline attending

private schools.

225

Chapter 7: Impacts on Educational Outcomes

EXHIBIT 7.3. CHARACTERISTICS OF SCHOOLS ATTENDED BY YOUTH AGES 10–20

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

RACE/ETHNICITY OF STUDENTS

AT SCHOOL

MINORITY [CCD, PSS]

Most recent school

0.879

– 0.023*

– 0.047*

– 0.014

– 0.021

5,045

(0.010)

(0.021)

(0.011)

(0.016)

Average school

0.904

– 0.037*

– 0.077*

– 0.016~

– 0.023~

5,077

(0.008)

(0.016)

(0.008)

(0.012)

BLACK [CCD, PSS]

Most recent school

0.495

– 0.009

– 0.020

– 0.021~

– 0.031~

4,857

(0.010)

(0.022)

(0.012)

(0.019)

Average school

0.532

– 0.022*

– 0.046*

– 0.025*

– 0.037*

5,054

(0.007)

(0.016)

(0.009)

(0.013)

HISPANIC [CCD, PSS]

Most recent school

0.342

– 0.015~

– 0.032~

0.003

0.005

5,058

(0.009)

(0.019)

(0.010)

(0.014)

Average school

0.339

– 0.022*

– 0.046*

0.006

0.009

5,077

(0.007)

(0.015)

(0.007)

(0.011)

SOCIOECONOMIC STATUS OF

STUDENTS AT SCHOOL

SHARE ELIGIBLE FOR FREE

LUNCH [CCD]

Most recent school

0.598

– 0.025*

– 0.053*

– 0.021~

– 0.031~

4,766

(0.010)

(0.021)

(0.011)

(0.017)

Average school

0.701

– 0.048*

– 0.101*

– 0.026*

– 0.039*

5,043

(0.007)

(0.015)

(0.008)

(0.012)

SHARE ELIGIBLE FOR FREE OR

REDUCED-PRICE LUNCH [CCD]

Most recent school

0.668

– 0.026*

– 0.054*

– 0.021~

– 0.031~

4,749

(0.010)

(0.022)

(0.011)

(0.017)

Average school

0.752

– 0.040*

– 0.083*

– 0.019*

– 0.029*

5,043

(0.007)

(0.014)

(0.008)

(0.012)

SHARE SCHOOLS ELIGIBLE

FOR TITLE I [CCD]

Most recent school

0.731

– 0.069*

– 0.145*

– 0.052*

– 0.078*

5,036

(0.017)

(0.036)

(0.018)

(0.027)

Average school

0.774

– 0.047*

– 0.099*

– 0.020~

– 0.029~

5,077

(0.010)

(0.022)

(0.011)

(0.017)

226

EXHIBIT 7.3. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

SCHOOL SIZE

NUMBER OF STUDENTS

[CCD, PSS]

Most recent school

1235.1

139.4*

290.6*

104.5*

156.4*

5,058

(38.4)

(80.0)

(41.2)

(61.7)

Average school

927.1

25.6~

53.4~

41.8*

62.5*

5,077

(14.9)

(31.1)

(17.5)

(26.1)

PUPIL–TEACHER RATIO

[CCD, PSS]

Most recent school

16.463

0.377*

0.784*

0.230

0.344

4,997

(0.170)

(0.354)

(0.191)

(0.286)

Average school

17.834

– 0.103

– 0.214

– 0.061

– 0.091

5,076

(0.103)

(0.215)

(0.113)

(0.170)

SCHOOL TYPE

CHARTER SCHOOL [CCD]

Most recent school

0.061

– 0.009

– 0.019

– 0.013

– 0.019

5,038

(0.010)

(0.020)

(0.010)

(0.015)

Average school

0.025

– 0.006~

– 0.013~

– 0.005

– 0.008

5,077

(0.004)

(0.008)

(0.004)

(0.006)

MAGNET SCHOOL [CCD]

Most recent school

0.163

0.011

0.022

– 0.004

– 0.005

5,020

(0.015)

(0.031)

(0.016)

(0.024)

Average school

0.161

– 0.013

– 0.028

– 0.016

– 0.025

5,077

(0.010)

(0.020)

(0.010)

(0.015)

SCHOOL-LEVEL RANKING ON

STATE EXAM

PERCENTILE RANKING [SLAD]

(ONLY THROUGH 8TH GRADE

FOR NEW YORK AND MARYLAND)

Most recent school

24.848

1.204

2.523

0.178

0.265

4,884

(1.064)

(2.229)

(1.089)

(1.619)

Average school

18.684

3.070*

6.430*

1.218~

1.810~

4,884

(0.651)

(1.364)

(0.661)

(0.983)

SHARE SCHOOLS RANKING

ABOVE 50TH PERCENTILE [SLAD]

Most recent school

0.169

0.012

0.025

– 0.014

– 0.020

4,884

(0.017)

(0.035)

(0.017)

(0.025)

Average school

0.104

0.019*

0.040*

– 0.003

– 0.004

4,884

(0.009)

(0.019)

(0.009)

(0.014)

227

Chapter 7: Impacts on Educational Outcomes

EXHIBIT 7.3. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

SHARE SCHOOLS RANKING

ABOVE 75TH PERCENTILE [SLAD]

Most recent school

0.065

0.016

0.033

0.006

0.009

4,884

(0.011)

(0.023)

(0.011)

(0.017)

Average school

0.029

0.014*

0.030*

0.007

0.010

4,884

(0.005)

(0.011)

(0.005)

(0.007)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working). Average school characteristics are weighted by the amount of time youth spent at each school.

Square brackets indicate the source of the outcome information: CCD = Common Core of Data, PSS = Private School Universe Survey, SLAD = School-level assessment data from the National Longitudinal School-Level State Assessment Score Database.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: In some cases, youth school histories combine self-reports from the long-term survey with parent reports from the interim survey. Long-term survey interviewed youth ages 10 to 20 as of December 31, 2007.

Measures: The number of students measure is based on enrollment as of October. A Title I eligible school is a school designated as being high poverty and eligible to participate in federal Title I programs. Schoolwide Title I programs apply to Title I schools with student bodies that consist of at least 40 percent low-income students. Schools designated by state legislatures as “charter schools” provide free public education under a special charter granted by the legislature. Magnet schools and programs are designed to either attract students of different racial and ethnic backgrounds to reduce racial isolation or to focus on a particular academic or social theme such as science or performing arts.

EXHIBIT 7.4. SCHOOL MOBILITY AMONG YOUTH AGES 10–20

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

Number of schools attended [SR, PR]

4.022

0.072

0.150

0.070

0.105

5,078

(0.062)

(0.128)

(0.070)

(0.104)

Number of public school districts attended

1.540

0.071~

0.147~

0.055

0.082

5,078

[SR, PR]

(0.039)

(0.082)

(0.042)

(0.062)

Most recent school in 5 main districts

0.640

– 0.031

– 0.064

– 0.018

– 0.027

5,078

[SR, PR]

(0.021)

(0.045)

(0.024)

(0.036)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report, PR = parent report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: In some cases, youth school histories combine self-reports from the long-term survey with parent reports from the interim survey. Long-term survey interviewed youth ages 10 to 20 as of December 31, 2007.

Measures: The 5 main districts are Baltimore City Public Schools, Boston Public Schools, Chicago Public Schools, Los Angeles Unified School District, and the New York City Department of Education.

228

EXHIBIT 7.5. PERCEPTIONS OF SCHOOL CLIMATE AMONG YOUTH AGES 10–17

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

SCHOOL CLIMATE INDEX [SR]

All

0.797

0.020~

0.043~

– 0.002

– 0.003

3,328

(0.011)

(0.023)

(0.012)

(0.017)

Female

0.786

0.025~

0.052~

0.006

0.010

1,694

(0.015)

(0.032)

(0.016)

(0.025)

Male

0.807

0.016

0.034

– 0.011

– 0.015

1,634

(0.014)

(0.031)

(0.016)

(0.022)

STUDENTS GET TEASED IF THEY

STUDY HARD [SR]

All

0.294

– 0.016

– 0.034

– 0.001

– 0.002

3,323

(0.021)

(0.045)

(0.023)

(0.033)

Female

0.296

– 0.011

– 0.023

– 0.048

– 0.072

1,692

(0.030)

(0.063)

(0.031)

(0.047)

Male

0.292

– 0.022

– 0.047

0.047

0.065

1,631

(0.030)

(0.064)

(0.034)

(0.047)

DISCIPLINE IN SCHOOL IS FAIR [SR]

All

0.758

0.017

0.037

– 0.032

– 0.046

3,316

(0.020)

(0.043)

(0.023)

(0.034)

Female

0.740

0.027

0.055

– 0.019

– 0.028

1,685

(0.028)

(0.058)

(0.032)

(0.049)

Male

0.775

0.008

0.018

– 0.046

– 0.063

1,631

(0.028)

(0.061)

(0.032)

(0.044)

OFTEN FEELS PUT DOWN BY

TEACHERS [SR]

All

0.129

– 0.031*

– 0.065*

– 0.005

– 0.007

3,327

(0.015)

(0.031)

(0.017)

(0.025)

Female

0.124

– 0.033

– 0.067

0.005

0.008

1,693

(0.021)

(0.044)

(0.024)

(0.036)

Male

0.133

– 0.029

– 0.062

– 0.015

– 0.021

1,634

(0.020)

(0.043)

(0.024)

(0.033)

FEELS SAFE IN SCHOOL [SR]

All

0.831

– 0.001

– 0.003

– 0.001

– 0.001

3,325

(0.017)

(0.037)

(0.020)

(0.028)

Female

0.808

0.007

0.015

0.013

0.020

1,694

(0.026)

(0.053)

(0.028)

(0.043)

Male

0.853

– 0.010

– 0.021

– 0.015

– 0.020

1,631

(0.023)

(0.049)

(0.025)

(0.035)

TEACHERS INTERESTED IN

STUDENTS [SR]

All

0.816

0.040*

0.084*

0.015

0.022

3,325

(0.017)

(0.036)

(0.019)

(0.027)

229

Chapter 7: Impacts on Educational Outcomes

EXHIBIT 7.5. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

TEACHERS INTERESTED IN

STUDENTS [SR] (CONTINUED)

Female

0.801

0.051*

0.105*

– 0.004

– 0.006

1,692

(0.025)

(0.051)

(0.028)

(0.043)

Male

0.831

0.029

0.062

0.036

0.049

1,633

(0.023)

(0.050)

(0.025)

(0.034)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 10 to 17 as of December 31, 2007.

Measures: The school climate index is the fraction of positive responses on the five individual school climate items presented in this exhibit. If the youth agreed or strongly agreed with a positive school climate item (for example fair discipline) or if the youth disagreed or strongly disagreed with a negative school climate item (for example students teased if they study hard), then the response was counted as positive.

7.6 LONG-TERM IMPACTS ON

scores (at least for reading) for the Section 8 group in

ACHIEVEMENT AND EDUCATION

the Chicago site and a negative effect in New York City

OUTCOMES

but overall few signs of any consistent site differences

Exhibit 7.6 shows that MTO had no detectable effects

in program effects—unlike with the interim follow-up

on math or reading achievement test scores among youth data, where both Baltimore and Chicago seemed to show aged 13 to 20 at the end of 2007.

consistent large, positive effects on achievement test

15 Recall that we

have converted our achievement test score results into

scores (Burdick-Will et al., 2011).

“Z-scores,” which come from subtracting the control

group’s average test score from each child’s individual

MTO also has few detectable effects on youth self-reports

test score result, and then dividing by the standard

of their own schooling performance, as shown in Exhibit

deviation of the control group’s test score distribution.

7.7. Experimental and control group youth were similar

By construction the control group’s average test score in

in terms of reporting they were educationally on track;

this Z-score metric will equal 0.

however, male youth in the Section 8 group appear less

likely to be on track than their male counterparts in the

The null result on achievement test scores holds even

control group. Experimental youth appear less likely to

for the subset of children who were less than 6 years old

have a diploma or to get Bs or higher in school compared

at the time of study enrollment—those who had been

with control group youth (both of these differences are

hypothesized to benefit the most from moves to lower

only marginally significant with p<.10), and experimental

poverty neighborhoods (see Supplemental Exhibit 7.5 for youth were also less likely to take advanced coursework subgroup analyses by baseline age).

as compared with the control group. And Section 8

16 In Supplemental

Exhibit 7.6, we see some signs of a positive effect on test

youth, especially males, are less likely to take advanced

coursework and the SAT or ACT. However, female youth

15 Similarly, we do not detect any significant effects on reading or

in the Section 8 group are substantially less likely to have

math for the smaller sample of youth ages 10-12 who took the ECLS

repeated a grade than females in the control group.

5th grade test.

16 The standard errors on these estimates are quite large. A 95 percent

confidence interval allows us to rule out a beneficial (or detrimental)

Approximately one-quarter of youth ages 15 to 20 in

impact any larger than about .08 to .09 standard deviations for the

either the experimental or Section 8 groups. Analyses of reading and

the control group were enrolled in some type of post-

math achievement measured as proficiency probabilities at varying cut-

secondary institution since 2007; and, this is similar

points also show no statistically detectable effect.

230

across the MTO treatment groups. MTO generally

Exhibit 7.9 shows that female grown children in the

had few detectable effects on post-secondary school

section 8 group were somewhat more likely to graduate

enrollment and attendance for youth in our long-term

from high school than the control group. But female

follow up (Exhibit 7.8). Male youth in the Section 8

grown children in both the experimental and Section 8

group appear to be less likely to attend college than those groups were less likely than controls to enroll in college.

in control group.

EXHIBIT 7.6. ACHIEVEMENT ASSESSMENT RESULTS FOR YOUTH AGES 13–20

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

READING ASSESSMENT SCORE [ECLS-K]

All

0.000

0.003

0.006

0.040

0.058

4,432

(0.041)

(0.085)

(0.044)

(0.064)

Female

0.051

– 0.020

– 0.040

0.054

0.081

2,286

(0.055)

(0.110)

(0.061)

(0.093)

Male

– 0.050

0.027

0.058

0.025

0.035

2,146

(0.056)

(0.118)

(0.058)

(0.081)

MATH ASSESSMENT SCORE [ECLS-K]

All

0.000

– 0.025

– 0.052

0.000

0.000

4,420

(0.044)

(0.090)

(0.048)

(0.069)

Female

– 0.004

– 0.034

– 0.069

– 0.033

– 0.050

2,280

(0.055)

(0.112)

(0.061)

(0.093)

Male

0.004

– 0.016

– 0.034

0.034

0.047

2,140

(0.060)

(0.128)

(0.067)

(0.093)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: ECLS-K = achievement assessment from the Early Childhood Longitudinal Study-Kindergarten cohort study.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 13 to 20 as of December 31, 2007.

Measures: The reading and math achievement assessment scores are theta scores transformed into z-scores via standardization on the mean and standard deviation for control group youth ages 13 to 20.

231

Chapter 7: Impacts on Educational Outcomes

EXHIBIT 7.7. SCHOOLING OUTCOMES FOR YOUTH AGES 10–20

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

EDUCATIONALLY ON-TRACK [SR]

All (ages 15–20)

0.814

– 0.014

– 0.028

– 0.029

– 0.044

3,614

(0.018)

(0.037)

(0.021)

(0.031)

Female

0.827

– 0.007

– 0.015

0.008

0.012

1,842

(0.023)

(0.046)

(0.026)

(0.040)

Male

0.801

– 0.019

– 0.041

– 0.066*

– 0.096*

1,772

(0.026)

(0.055)

(0.029)

(0.042)

HIGHEST K-12 GRADE ENROLLED [SR]

All (ages 10–20)

10.482

– 0.053

– 0.109

– 0.006

– 0.009

5,087

(0.039)

(0.081)

(0.042)

(0.064)

Female

10.533

– 0.091

– 0.184

0.018

0.028

2,592

(0.059)

(0.118)

(0.064)

(0.101)

Male

10.435

– 0.012

– 0.027

– 0.033

– 0.047

2,495

(0.048)

(0.103)

(0.053)

(0.076)

HIGH SCHOOL DIPLOMA [SR]

All (ages 19–20)

0.622

– 0.073~

– 0.141~

– 0.056

– 0.092

1,125

(0.038)

(0.073)

(0.042)

(0.068)

Female

0.708

– 0.087~

– 0.169~

– 0.054

– 0.089

5,76

(0.048)

(0.094)

(0.054)

(0.088)

Male

0.536

– 0.058

– 0.110

– 0.058

– 0.094

549

(0.058)

(0.109)

(0.062)

(0.102)

MOSTLY BS OR HIGHER [SR]

All (ages 10–20)

0.438

– 0.035~

– 0.073~

0.002

0.002

5,066

(0.019)

(0.039)

(0.021)

(0.031)

Female

0.502

– 0.041

– 0.082

– 0.010

– 0.015

2,582

(0.027)

(0.054)

(0.029)

(0.045)

Male

0.379

– 0.030

– 0.064

0.013

0.018

2,484

(0.026)

(0.055)

(0.029)

(0.042)

ADVANCED COURSEWORK [SR]

All (ages 10–20)

0.889

– 0.027*

– 0.056*

– 0.041*

– 0.062*

5,092

(0.012)

(0.025)

(0.014)

(0.021)

Female

0.887

– 0.028~

– 0.056~

– 0.034~

– 0.053

2,596

(0.016)

(0.033)

(0.019)

(0.029)

Male

0.891

– 0.026

– 0.055

– 0.049*

– 0.070*

2,496

(0.017)

(0.036)

(0.019)

(0.028)

EVER RECEIVED SPECIAL EDUCATION

SERVICES [SR]

All (ages 10–20)

0.153

0.007

0.015

– 0.006

– 0.009

5,088

(0.014)

(0.028)

(0.015)

(0.023)

Female

0.115

0.028~

0.057~

0.013

0.020

2,592

(0.017)

(0.034)

(0.019)

(0.030)

232

EXHIBIT 7.7. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

EVER RECEIVED SPECIAL EDUCATION

SERVICES [SR] (CONTINUED)

Male

0.189

– 0.014

– 0.031

– 0.025

– 0.036

2,496

(0.020)

(0.043)

(0.022)

(0.031)

EVER REPEATED A GRADE [SR]

All (ages 10–20)

0.377

0.000

0.000

– 0.048*

– 0.072*

5,096

(0.018)

(0.038)

(0.020)

(0.030)

Female

0.349

– 0.029

– 0.059

– 0.085*

– 0.133*

2,598

(0.025)

(0.050)

(0.026)

(0.041)

Male

0.405

0.029

0.063

– 0.011

– 0.016

2,498

(0.026)

(0.056)

(0.028)

(0.041)

TOOK SAT/ACT [SR]

All (ages 15–20)

0.518

– 0.031

– 0.065

– 0.040~

– 0.060~

3,599

(0.022)

(0.046)

(0.024)

(0.036)

Female

0.569

– 0.049

– 0.098

– 0.024

– 0.037

1,833

(0.030)

(0.061)

(0.034)

(0.052)

Male

0.470

– 0.013

– 0.028

– 0.056~

– 0.082~

1,766

(0.032)

(0.069)

(0.033)

(0.048)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. All interviewed youth.

Measures: On-track youth are those who were currently in school or received a high school diploma or GED. The highest grade enrolled measure was top-coded at 12th grade. The Mostly Bs or Higher measure reflects the overall grades that the youth received in their most recent school year (youth in post-secondary school reported on their most recent year of high school). Advanced coursework that the youth reported ever having taken an advanced math or AP class or having been enrolled in a program for the gifted and talented. The special education services measure is based on a report of whether the youth had ever been enrolled in any special class or received special help for any emotional, physical or mental condition. Grade repetition is the proportion of youth who reported that they had ever repeated a grade, including kindergarten.

233

Chapter 7: Impacts on Educational Outcomes

EXHIBIT 7.8. POST-SECONDARY OUTCOMES FROM NATIONAL STUDENT CLEARINGHOUSE DATA FOR YOUTH

AGES 15–20

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

SINCE 2007, ATTENDED…

ANY COLLEGE [NSC]

All

0.262

– 0.014

– 0.029

– 0.021

– 0.033

4,717

(0.016)

(0.033)

(0.017)

(0.025)

Female

0.305

– 0.011

– 0.023

0.000

0.001

2,300

(0.023)

(0.048)

(0.025)

(0.039)

Male

0.222

– 0.016

– 0.033

– 0.042*

– 0.064*

2,417

(0.021)

(0.044)

(0.021)

(0.032)

TWO-YEAR COLLEGE [NSC]

All

0.184

– 0.015

– 0.032

– 0.015

– 0.023

4,717

(0.014)

(0.029)

(0.015)

(0.022)

Female

0.205

– 0.017

– 0.036

0.011

0.017

2,300

(0.021)

(0.042)

(0.022)

(0.034)

Male

0.165

– 0.013

– 0.027

– 0.041*

– 0.061*

2,417

(0.018)

(0.039)

(0.019)

(0.028)

FOUR-YEAR COLLEGE [NSC]

All

0.105

0.004

0.009

– 0.004

– 0.007

4,717

(0.011)

(0.024)

(0.012)

(0.018)

Female

0.130

0.015

0.031

0.005

0.008

2,300

(0.018)

(0.037)

(0.019)

(0.029)

Male

0.083

– 0.006

– 0.013

– 0.013

– 0.020

2,417

(0.013)

(0.029)

(0.014)

(0.021)

PRIVATE COLLEGE [NSC]

All

0.048

0.000

– 0.001

0.003

0.005

4,717

(0.008)

(0.016)

(0.008)

(0.013)

Female

0.060

0.000

– 0.001

0.012

0.018

2,300

(0.012)

(0.025)

(0.014)

(0.021)

Male

0.036

0.000

0.000

– 0.005

– 0.008

2,417

(0.009)

(0.019)

(0.009)

(0.014)

PUBLIC COLLEGE [NSC]

All

0.225

– 0.013

– 0.028

– 0.022

– 0.033

4,717

(0.015)

(0.032)

(0.016)

(0.024)

Female

0.261

– 0.013

– 0.026

– 0.004

– 0.006

2,300

(0.022)

(0.046)

(0.024)

(0.037)

Male

0.192

– 0.014

– 0.029

– 0.040*

– 0.059*

2,417

(0.020)

(0.042)

(0.020)

(0.030)

FOR-PROFIT COLLEGE [IES]

All

0.011

0.004

0.008

0.006

0.009

4,717

(0.004)

(0.009)

(0.005)

(0.007)

234

EXHIBIT 7.8. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

SINCE 2007, ATTENDED… (CONTINUED)

FOR-PROFIT COLLEGE [IES] (CONTINUED)

Female

0.014

0.003

0.006

0.003

0.004

2,300

(0.007)

(0.014)

(0.007)

(0.011)

Male

0.008

0.005

0.010

0.009

0.014

2,417

(0.005)

(0.010)

(0.006)

(0.009)

SINCE 2007, NUMBER OF MONTHS

ENROLLED IN….

TWO-YEAR COLLEGES [NSC]

All

1.267

0.103

0.216

– 0.066

– 0.100

4,717

(0.142)

(0.299)

(0.137)

(0.208)

Female

1.374

0.246

0.509

0.185

0.284

2,300

(0.218)

(0.451)

(0.213)

(0.326)

Male

1.169

– 0.031

– 0.066

– 0.304~

– 0.457~

2,417

(0.183)

(0.392)

(0.173)

(0.260)

FOUR-YEAR COLLEGES [NSC]

All

1.182

0.007

0.014

– 0.235

– 0.357

4,717

(0.158)

(0.333)

(0.152)

(0.230)

Female

1.564

– 0.019

– 0.039

– 0.268

– 0.411

2,300

(0.258)

(0.532)

(0.252)

(0.385)

Male

0.833

0.031

0.066

– 0.204

– 0.306

2,417

(0.176)

(0.378)

(0.170)

(0.255)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: NSC = National Student Clearinghouse. IES = Institute for Education Sciences.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: The sample for the NSC and IES measures is all core household members ages 15 to 20 as of December 31, 2007.

Measures: Number of days enrolled in college was calculated according to enrollment begin date and enrollment end date reported by NSC, and the number of days was then divided by 30 to create number of months enrolled in school.

235

Chapter 7: Impacts on Educational Outcomes

EXHIBIT 7.9. SCHOOLING OUTCOMES FOR GROWN CHILDREN AGES 21–30

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

CURRENT ENROLLMENT

AND SECONDARY

SCHOOLING OUTCOMES

CURRENTLY ENROLLED IN

SCHOOL [PR]

All

0.158

– 0.007

– 0.016

– 0.001

– 0.002

3,089

(0.016)

(0.036)

(0.023)

(0.042)

Female

0.205

– 0.015

– 0.033

0.006

0.010

1,527

(0.025)

(0.054)

(0.036)

(0.062)

Male

0.110

0.000

0.000

– 0.007

– 0.013

1,562

(0.020)

(0.047)

(0.025)

(0.047)

HAS HIGH SCHOOL DIPLOMA

[PR]

All

0.554

– 0.013

– 0.029

0.007

0.013

3,088

(0.024)

(0.054)

(0.031)

(0.055)

Female

0.624

– 0.004

– 0.008

0.002

0.003

1,530

(0.031)

(0.068)

(0.042)

(0.072)

Male

0.482

– 0.022

– 0.052

0.011

0.022

1,558

(0.032)

(0.076)

(0.039)

(0.074)

HAS HIGH SCHOOL DIPLOMA

OR GED [PR]

All

0.707

– 0.016

– 0.036

0.055*

0.099*

3,088

(0.022)

(0.050)

(0.027)

(0.049)

Female

0.751

– 0.001

– 0.003

0.069~

0.118~

1,530

(0.028)

(0.061)

(0.036)

(0.061)

Male

0.662

– 0.030

– 0.071

0.042

0.078

1,558

(0.031)

(0.073)

(0.038)

(0.071)

POST-SECONDARY

SCHOOLING OUTCOMES

SINCE 2007, ATTENDED…

ANY COLLEGE [NSC]

All

0.196

– 0.015

– 0.035

– 0.011

– 0.019

4,639

(0.014)

(0.034)

(0.016)

(0.027)

Female

0.282

– 0.046*

– 0.106*

– 0.052*

– 0.089*

2,275

(0.023)

(0.054)

(0.025)

(0.042)

Male

0.115

0.014

0.034

0.028

0.048

2,364

(0.017)

(0.040)

(0.019)

(0.033)

TWO-YEAR COLLEGE [NSC]

All

0.135

– 0.018

– 0.042

– 0.013

– 0.022

4,639

(0.012)

(0.029)

(0.014)

(0.024)

236

EXHIBIT 7.9. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

POST-SECONDARY

SCHOOLING OUTCOMES

(CONTINUED)

SINCE 2007, ATTENDED…

(CONTINUED)

TWO-YEAR COLLEGE [NSC]

(CONTINUED)

Female

0.198

– 0.040*

– 0.094*

– 0.044*

– 0.076*

2,275

(0.020)

(0.047)

(0.022)

(0.037)

Male

0.076

0.004

0.008

0.017

0.030

2,364

(0.014)

(0.033)

(0.015)

(0.027)

FOUR-YEAR COLLEGE [NSC]

All

0.083

– 0.003

– 0.007

0.006

0.010

4,639

(0.010)

(0.023)

(0.011)

(0.019)

Female

0.121

– 0.020

– 0.047

– 0.010

– 0.017

2,275

(0.016)

(0.037)

(0.018)

(0.031)

Male

0.047

0.013

0.032

0.020

0.035

2,364

(0.011)

(0.027)

(0.013)

(0.023)

PRIVATE COLLEGE [NSC]

All

0.053

0.001

0.002

– 0.006

– 0.010

4,639

(0.008)

(0.019)

(0.009)

(0.015)

Female

0.074

– 0.008

– 0.018

– 0.021

– 0.035

2,275

(0.013)

(0.030)

(0.014)

(0.024)

Male

0.033

0.009

0.022

0.008

0.014

2,364

(0.010)

(0.023)

(0.011)

(0.019)

PUBLIC COLLEGE [NSC]

All

0.153

– 0.010

– 0.025

– 0.004

– 0.006

4,639

(0.013)

(0.031)

(0.014)

(0.025)

Female

0.226

– 0.035~

– 0.082~

– 0.033

– 0.057

2,275

(0.021)

(0.049)

(0.023)

(0.039)

Male

0.085

0.013

0.032

0.024

0.043

2,364

(0.015)

(0.036)

(0.016)

(0.029)

FOR-PROFIT COLLEGE [IES]

All

0.031

– 0.002

– 0.005

– 0.001

– 0.002

4,639

(0.006)

(0.015)

(0.007)

(0.013)

Female

0.039

– 0.005

– 0.012

– 0.005

– 0.008

2,275

(0.010)

(0.023)

(0.011)

(0.019)

Male

0.023

0.001

0.003

0.002

0.004

2,364

(0.008)

(0.019)

(0.009)

(0.016)

237

Chapter 7: Impacts on Educational Outcomes

EXHIBIT 7.9. (CONTINUED)

CONTROL

EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

POST-SECONDARY

SCHOOLING OUTCOMES

(CONTINUED)

SINCE 2007, NUMBER OF

MONTHS ENROLLED IN…

TWO-YEAR COLLEGES [NSC]

All

1.052

– 0.196

– 0.462

– 0.198

– 0.344

4,639

(0.128)

(0.302)

(0.129)

(0.224)

Female

1.535

– 0.275

– 0.639

– 0.396~

– 0.679~

2,275

(0.221)

(0.515)

(0.219)

(0.375)

Male

0.592

– 0.122

– 0.293

– 0.012

– 0.022

2,364

(0.128)

(0.306)

(0.137)

(0.240)

FOUR-YEAR COLLEGES [NSC]

All

0.875

0.043

0.100

0.079

0.137

4,639

(0.133)

(0.314)

(0.146)

(0.253)

Female

1.275

– 0.108

– 0.252

– 0.023

– 0.039

2,275

(0.226)

(0.525)

(0.244)

(0.419)

Male

0.495

0.188

0.450

0.177

0.310

2,364

(0.148)

(0.354)

(0.166)

(0.290)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: PR = parent report. NSC = National Student Clearinghouse. IES = Institute for Education Sciences.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Parent report measures come from the adult long-term adult survey and are presented for grown children (under age 18 at baseline and ages 21 to 30 as of December 31, 2007) whose parents were interviewed as part of the long-term survey. The sample for the NSC and IES measures is all core household members who are grown children, regardless of whether their parent was interviewed.

Measures: Number of days enrolled in college was calculated according to enrollment begin date and enrollment end date reported by NSC, and the number of days was then divided by 30 to create number of months enrolled in school.

7.7 INTERPRETATION OF

chapter, and the fact that the sign of the impacts are

EDUCATIONAL RESULTS

somewhat mixed, the few statistically significant effects

The long-term follow-up data examined here found no

presented in this chapter may simply arise from chance.

detectable impacts on achievement test scores in reading

or math measured 10 to 15 years after baseline. We

We also found few statistically significant effects on

also found few statistically significant impacts on other

educational outcomes even for those children who

educational outcomes for youth. There is some indication were very young (preschool age) at baseline. This is that females in the Section 8 group might be less likely

perhaps surprising because Chapter 2 showed that MTO

than those in the control group to repeat a grade, but on

generated particularly large differences across randomized

several other measures such as high school graduation,

groups in average neighborhood conditions during the

grades, advanced course taking, and taking the SAT or

first few years after random assignment—exactly when

ACT, there were statistically significant or marginally

children who were of preschool age at baseline would

significant declines in outcomes for youth in one or both have been in what researchers believe to be a particularly of the treatment groups compared with controls. Given

“plastic” stage of human development.

the large number of impact estimates examined in this

238

These results should not be interpreted as implying that

within the large urban school districts that served the

neighborhoods never matter for academic achievement,

five MTO demonstration cities. While many of these

since school quality may be one especially important

cities were engaging in urban school reform during our

mediator through which neighborhoods influence

study period, which might have narrowed some of the

learning and MTO wound up generating very modest

difference in school quality found between very poor and

differences across randomized groups in the average

less poor neighborhoods, the school systems as a whole

characteristics of the schools children attended. For

were still not performing terribly well compared with

example all children across all three MTO groups

national norms. It is certainly possible that a mobility

attended schools that had overwhelmingly majority-

intervention that achieved larger impacts on school

minority student bodies, in which about two-thirds

quality—for example, a program that helped more

were eligible for free or reduced-price lunch and that

families move to affluent suburbs—could have more

ranked on average near the 20th to 25th percentile

pronounced impacts on educational outcomes. MTO

relative to other schools based on the achievement

is informative instead about the impact on educational

test performance of their students. Part of the issue

outcomes that neighborhood change can have without

could be that most children in our study sample stayed

much change in school quality.

SUPPLEMENTAL EXHIBIT 7.1. ABSENTEEISM, TARDINESS, OR PROBLEMS AT SCHOOL FOR YOUTH AGES 10–20

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

LATE FOR SCHOOL ONCE OR

MORE PER MONTH [SR]

All (ages 10–20)

0.626

0.000

0.001

– 0.010

– 0.015

5,085

(0.020)

(0.041)

(0.022)

(0.032)

Female

0.637

– 0.021

– 0.042

0.000

0.000

2,592

(0.027)

(0.054)

(0.028)

(0.044)

Male

0.616

0.022

0.048

– 0.022

– 0.031

2,493

(0.026)

(0.057)

(0.029)

(0.042)

ABSENT MORE THAN 5% OF THE

SCHOOL YEAR [SR]

All (ages 10–20)

0.392

– 0.020

– 0.041

0.019

0.028

4,951

(0.019)

(0.039)

(0.021)

(0.032)

Female

0.382

– 0.007

– 0.015

0.026

0.041

2,522

(0.026)

(0.051)

(0.030)

(0.046)

Male

0.402

– 0.032

– 0.069

0.012

0.017

2,429

(0.026)

(0.056)

(0.030)

(0.043)

FRACTION OF SCHOOL YEAR

ABSENT FROM SCHOOL [SR]

All (ages 10–20)

0.060

0.000

0.001

0.001

0.002

4,951

(0.003)

(0.007)

(0.003)

(0.005)

Female

0.059

– 0.003

– 0.005

0.001

0.001

2,522

(0.004)

(0.008)

(0.005)

(0.007)

Male

0.060

0.003

0.007

0.002

0.002

2,429

(0.005)

(0.012)

(0.005)

(0.007)

239

Chapter 7: Impacts on Educational Outcomes

SUPPLEMENTAL EXHIBIT 7.1. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

SCHOOL ASKED SOMEONE TO COME

IN TO DISCUSS YOUTH'S SCHOOLWORK

OR BEHAVIOR PROBLEMS IN PAST 2 YEARS [PR]

All (ages 10–17)

0.367

– 0.034

– 0.072

– 0.037

– 0.056

1,963

(0.030)

(0.063)

(0.038)

(0.057)

Female

0.309

– 0.036

– 0.078

– 0.014

– 0.022

998

(0.039)

(0.084)

(0.050)

(0.078)

Male

0.419

– 0.030

– 0.063

– 0.058

– 0.083

965

(0.044)

(0.092)

(0.053)

(0.076)

SUSPENDED OR EXPELLED

FROM SCHOOL IN PAST TWO

YEARS [SR]

All (ages 10–17)

0.308

0.021

0.045

0.029

0.042

3,322

(0.022)

(0.046)

(0.024)

(0.034)

Female

0.288

– 0.035

– 0.073

– 0.012

– 0.018

1,692

(0.028)

(0.058)

(0.032)

(0.048)

Male

0.327

0.078*

0.170*

0.069*

0.095*

1,630

(0.031)

(0.068)

(0.034)

(0.047)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report, PR = parent report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Self-reported items come from the youth long-term survey, while the parent report measure comes from the adult long-term survey. The sample for the parent report measure is all core household members ages 10 to 17 as of December 31, 2007. The sample for the suspension/expulsion measure is limited to interviewed youth ages 10 to 17 as of December 31, 2007. All other measures use the full interviewed youth sample (ages 10 to 20 as of December 31, 2007).

240

SUPPLEMENTAL EXHIBIT 7.2. FUTURE EXPECTATIONS OF YOUTH AGES 10–20

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

YOUTH EXPECTS TO AT LEAST…

COMPLETE TWO YEARS OF

COLLEGE [SR]

All

0.696

– 0.048*

– 0.100*

– 0.025

– 0.037

5,078

(0.018)

(0.038)

(0.020)

(0.030)

Female

0.746

– 0.031

– 0.062

– 0.020

– 0.032

2,591

(0.024)

(0.048)

(0.026)

(0.041)

Male

0.647

– 0.066*

– 0.143*

– 0.028

– 0.040

2,487

(0.027)

(0.058)

(0.029)

(0.041)

FINISH COLLEGE [SR]

All

0.559

– 0.035~

– 0.074~

– 0.029

– 0.043

5,078

(0.019)

(0.040)

(0.021)

(0.031)

Female

0.632

– 0.033

– 0.066

– 0.015

– 0.023

2,591

(0.026)

(0.052)

(0.028)

(0.045)

Male

0.490

– 0.038

– 0.082

– 0.043

– 0.061

2,487

(0.027)

(0.059)

(0.030)

(0.042)

YOUTH THINKS MOTHER WANTS

HIM/HER TO AT LEAST…

COMPLETE TWO YEARS OF

COLLEGE [SR]

All

0.870

– 0.009

– 0.018

– 0.002

– 0.003

5,039

(0.013)

(0.026)

(0.014)

(0.021)

Female

0.897

– 0.008

– 0.016

– 0.014

– 0.022

2,569

(0.016)

(0.032)

(0.018)

(0.028)

Male

0.845

– 0.010

– 0.021

0.010

0.014

2,470

(0.019)

(0.041)

(0.020)

(0.029)

FINISH COLLEGE [SR]

All

0.820

– 0.006

– 0.012

0.006

0.009

5,039

(0.015)

(0.032)

(0.016)

(0.024)

Female

0.854

– 0.003

– 0.006

– 0.016

– 0.025

2,569

(0.018)

(0.037)

(0.020)

(0.031)

Male

0.788

– 0.008

– 0.018

0.029

0.041

2,470

(0.023)

(0.050)

(0.024)

(0.034)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 10 to 20 as of December 31, 2007.

Measures: Completing two years of college includes a two-year degree. Finishing college indicates the completion of a four- or five-year degree. Youth who reported thinking that they will or that their mother wants them to attain education beyond completing two years of college or finishing college are included with the "complete two years of college" and "finish college" groups, respectively.

241

Chapter 7: Impacts on Educational Outcomes

SUPPLEMENTAL EXHIBIT 7.3. SCHOOL ENGAGEMENT AND PARENTAL INVOLVEMENT IN SCHOOL

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

SCHOOL ENGAGEMENT

ALWAYS COMPLETES HOMEWORK [SR]

All (ages 10–20)

0.478

– 0.005

– 0.010

– 0.014

– 0.020

5,095

(0.019)

(0.040)

(0.021)

(0.031)

Female

0.536

0.018

0.036

0.010

0.015

2,596

(0.026)

(0.052)

(0.028)

(0.044)

Male

0.424

– 0.027

– 0.058

– 0.036

– 0.052

2,499

(0.027)

(0.058)

(0.029)

(0.041)

HOURS PER WEEK SPENT ON

HOMEWORK [SR]

All (ages 10–20)

5.294

0.067

0.140

– 0.166

– 0.248

4,762

(0.204)

(0.424)

(0.220)

(0.329)

Female

5.800

– 0.158

– 0.317

– 0.032

– 0.050

2,377

(0.306)

(0.612)

(0.333)

(0.528)

Male

4.834

0.291

0.629

– 0.307

– 0.434

2,385

(0.275)

(0.595)

(0.290)

(0.410)

HOURS PER WEEK READING [SR]

All (ages 10–20)

3.234

– 0.027

– 0.056

0.034

0.051

5,093

(0.199)

(0.415)

(0.233)

(0.348)

Female

3.915

0.190

0.384

0.209

0.328

2,595

(0.310)

(0.625)

(0.346)

(0.542)

Male

2.595

– 0.244

– 0.526

– 0.136

– 0.193

2,498

(0.243)

(0.524)

(0.298)

(0.426)

PARENT INVOLVEMENT IN SCHOOL

ADULT ATTENDED AN EVENT OR A

MEETING AT YOUTH’S SCHOOL IN PAST

YEAR [PR]

All (ages 10–17)

0.706

0.026

0.056

0.008

0.012

1,969

(0.028)

(0.058)

(0.037)

(0.055)

Female

0.696

0.017

0.036

0.002

0.004

1,003

(0.039)

(0.082)

(0.051)

(0.079)

Male

0.715

0.036

0.076

0.013

0.019

9,66

(0.038)

(0.081)

(0.050)

(0.072)

ADULT VOLUNTEERED AT YOUTH’S

SCHOOL IN PAST YEAR [PR]

All (ages 10–17)

0.166

– 0.001

– 0.002

0.002

0.003

1,970

(0.023)

(0.049)

(0.027)

(0.041)

Female

0.178

– 0.013

– 0.028

– 0.001

– 0.002

1,004

(0.034)

(0.073)

(0.040)

(0.062)

Male

0.155

0.012

0.025

0.005

0.008

966

(0.031)

(0.064)

(0.036)

(0.052)

242

SUPPLEMENTAL EXHIBIT 7.3. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

PARENT INVOLVEMENT IN SCHOOL

(CONTINUED)

ADULT IS SOMEWHAT OR VERY

SATISFIED (VERSUS NOT AT ALL

SATISFIED) WITH YOUTH'S EDUCATION [PR]

All (ages 10–17)

0.920

– 0.015

– 0.031

– 0.054*

– 0.080*

1,966

(0.018)

(0.038)

(0.025)

(0.038)

Female

0.926

0.008

0.018

– 0.044

– 0.068

1,001

(0.022)

(0.047)

(0.032)

(0.050)

Male

0.915

– 0.038

– 0.080

– 0.063~

– 0.091~

965

(0.029)

(0.061)

(0.038)

(0.054)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report, PR = parent report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Self-reported items come from the youth long-term survey, while the parent report measures comes from the long-term adult survey. The sample for the self-reported measures is interviewed youth ages 10 to 20 as of December 31, 2007. The sample for the parent report measures is all core household members ages 10 to 17 as of December 31, 2007.

Measures: The sample size for the weekly homework hours measure is lower than other self-reported measures in this exhibit due to a survey instrument skip error that was corrected in the midst of the interview fielding period. Imputing missing values by randomization site and treatment group and by site, group, and using responses from youth who answered other homework questions similarly to those affected by the skip error yields qualitatively similar results. The weekly reading hours measure refers to reading outside of school that is not in connection with schoolwork.

SUPPLEMENTAL EXHIBIT 7.4. TIME USE AND ACCESS TO TECHNOLOGY AMONG YOUTH AGES 10–20

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

TIME USE: EXTRACURRICULAR ACTIVITY

PARTICIPATES IN SCHOOL SPORTS [SR]

All

0.445

– 0.016

– 0.033

– 0.001

– 0.002

5,088

(0.019)

(0.039)

(0.021)

(0.031)

Female

0.374

– 0.034

– 0.068

– 0.015

– 0.024

2,592

(0.025)

(0.051)

(0.028)

(0.043)

Male

0.511

0.002

0.004

0.012

0.018

2,496

(0.028)

(0.059)

(0.030)

(0.043)

PARTICIPATES IN SCHOOL CLUBS/

GROUPS [SR]

All

0.278

0.004

0.008

– 0.004

– 0.005

5,048

(0.017)

(0.035)

(0.019)

(0.028)

Female

0.321

0.015

0.030

– 0.008

– 0.012

2,573

(0.024)

(0.049)

(0.026)

(0.041)

Male

0.239

– 0.007

– 0.015

0.001

0.002

2,475

(0.023)

(0.049)

(0.025)

(0.036)

243

Chapter 7: Impacts on Educational Outcomes

SUPPLEMENTAL EXHIBIT 7.4. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

TIME USE: EXTRACURRICULAR ACTIVITY

(CONTINUED)

LEADERSHIP POSITION IN SCHOOL

SPORTS OR CLUBS/GROUPS [SR]

All

0.361

– 0.006

– 0.013

0.015

0.022

5,092

(0.018)

(0.038)

(0.020)

(0.030)

Female

0.351

– 0.014

– 0.028

– 0.024

– 0.038

2,596

(0.025)

(0.051)

(0.028)

(0.044)

Male

0.370

0.000

0.001

0.054~

0.077~

2,496

(0.026)

(0.055)

(0.029)

(0.042)

PARTICIPATES IN ORGANIZED

ACTIVITY ONCE OR MORE PER

WEEK [SR]

All

0.506

– 0.024

– 0.050

– 0.006

– 0.009

5,093

(0.019)

(0.039)

(0.020)

(0.030)

Female

0.390

– 0.038

– 0.077

– 0.014

– 0.022

2,596

(0.025)

(0.050)

(0.027)

(0.042)

Male

0.614

– 0.010

– 0.021

0.001

0.002

2,497

(0.026)

(0.057)

(0.029)

(0.041)

TIME USE: INFORMAL ACTIVITY

HANGS OUT IN NEIGHBORHOOD

OR AT BASKETBALL COURT ONCE

OR MORE PER WEEK [SR]

All

0.450

– 0.008

– 0.017

– 0.018

– 0.026

5,095

(0.019)

(0.039)

(0.020)

(0.030)

Female

0.327

– 0.017

– 0.034

– 0.038

– 0.060

2,596

(0.023)

(0.047)

(0.025)

(0.039)

Male

0.566

0.000

0.001

0.003

0.004

2,499

(0.027)

(0.059)

(0.030)

(0.042)

SHOPS AT MALL OR STORE ONCE

OR MORE PER WEEK [SR]

All

0.476

– 0.001

– 0.003

0.002

0.003

5,092

(0.019)

(0.040)

(0.021)

(0.031)

Female

0.515

– 0.009

– 0.018

0.014

0.022

2,596

(0.027)

(0.054)

(0.029)

(0.046)

Male

0.439

0.007

0.015

– 0.011

– 0.016

2,496

(0.027)

(0.057)

(0.029)

(0.042)

HANGS OUT AT SOMEONE ELSE'S

HOUSE ONCE OR MORE PER WEEK [SR]

All

0.561

– 0.037~

– 0.077~

– 0.042*

– 0.062*

5,071

(0.019)

(0.039)

(0.020)

(0.030)

244

SUPPLEMENTAL EXHIBIT 7.4. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

HANGS OUT AT SOMEONE ELSE'S

HOUSE ONCE OR MORE PER WEEK [SR]

(CONTINUED)

Female

0.512

– 0.031

– 0.062

– 0.030

– 0.047

2,586

(0.027)

(0.054)

(0.028)

(0.044)

Male

0.606

– 0.042

– 0.092

– 0.053~

– 0.076~

2,485

(0.026)

(0.056)

(0.029)

(0.041)

NEVER HANGS OUT AT HOME [SR]

All

0.077

– 0.004

– 0.008

– 0.007

– 0.010

5,095

(0.010)

(0.020)

(0.010)

(0.015)

Female

0.075

– 0.008

– 0.016

0.000

0.000

2,596

(0.014)

(0.028)

(0.015)

(0.024)

Male

0.079

0.000

0.001

– 0.014

– 0.020

2,499

(0.014)

(0.031)

(0.015)

(0.021)

ACCESS TO TECHNOLOGY

USES COMPUTER AT HOME [SR]

All

0.704

– 0.009

– 0.019

0.049*

0.073*

5,095

(0.020)

(0.041)

(0.020)

(0.030)

Female

0.720

– 0.027

– 0.055

0.029

0.045

2,596

(0.024)

(0.048)

(0.026)

(0.041)

Male

0.688

0.009

0.019

0.068*

0.097*

2,499

(0.026)

(0.056)

(0.027)

(0.039)

USES INTERNET AT LEAST 4 HOURS

PER WEEK [SR]

All

0.555

0.001

0.001

– 0.014

– 0.021

3,949

(0.021)

(0.043)

(0.024)

(0.035)

Female

0.571

– 0.020

– 0.039

– 0.017

– 0.027

2,028

(0.029)

(0.055)

(0.031)

(0.049)

Male

0.540

0.022

0.047

– 0.012

– 0.017

1,921

(0.030)

(0.064)

(0.033)

(0.047)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR = self-report.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 10 to 20 as of December 31, 2007.

Measures: The sample size for the weekly hours of internet use measure is lower than other self-reported measures in this exhibit due to a survey instrument skip error that was corrected in the midst of the interview fielding period. Imputing missing values by randomization site and treatment group yields qualitatively similar results.

245

Chapter 7: Impacts on Educational Outcomes

SUPPLEMENTAL EXHIBIT 7.5. ACHIEVEMENT ASSESSMENT RESULTS BY BASELINE AGE AMONG YOUTH AGES

13–20

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

BASELINE AGES 0–5

READING ASSESSMENT SCORE

[ECLS-K]

All

– 0.092

0.026

0.057

0.080

0.108

2,542

(0.054)

(0.115)

(0.056)

(0.076)

Female

– 0.031

0.021

0.045

0.087

0.122

1,309

(0.074)

(0.156)

(0.081)

(0.113)

Male

– 0.152

0.032

0.069

0.072

0.094

1,233

(0.072)

(0.158)

(0.073)

(0.095)

MATH ASSESSMENT SCORE [ECLS-K]

All

0.001

– 0.057

– 0.121

– 0.060

– 0.080

2,535

(0.058)

(0.123)

(0.060)

(0.081)

Female

0.019

– 0.067

– 0.141

– 0.066

– 0.092

1,306

(0.077)

(0.162)

(0.079)

(0.111)

Male

– 0.017

– 0.046

– 0.101

– 0.054

– 0.069

1,229

(0.078)

(0.171)

(0.086)

(0.112)

BASELINE AGES 6–11

READING ASSESSMENT SCORE [ECLS-K]

All

0.119

– 0.026

– 0.050

– 0.008

– 0.013

1,890

(0.058)

(0.113)

(0.065)

(0.105)

Female

0.160

– 0.061

– 0.118

0.017

0.029

977

(0.076)

(0.147)

(0.092)

(0.155)

Male

0.081

0.014

0.028

– 0.036

– 0.055

913

(0.083)

(0.167)

(0.090)

(0.138)

MATH ASSESSMENT SCORE [ECLS-K]

All

– 0.001

0.017

0.034

0.081

0.129

1,885

(0.060)

(0.119)

(0.070)

(0.112)

Female

– 0.035

0.014

0.026

0.024

0.042

974

(0.072)

(0.140)

(0.089)

(0.152)

Male

0.032

0.019

0.038

0.138

0.209

911

(0.090)

(0.182)

(0.103)

(0.155)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: ECLS-K = achievement assessment from the Early Childhood Longitudinal Study-Kindergarten cohort study.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 13 to 20 as of December 31, 2007.

Measures: The reading and math achievement assessment scores are theta scores transformed into z-scores via standardization on the mean and standard deviation for control group youth ages 13 to 20.

246

SUPPLEMENTAL EXHIBIT 7.6. ACHIEVEMENT ASSESSMENT RESULTS BY SITE AMONG YOUTH AGES 13–20

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

READING ASSESSMENT SCORE [ECLS-K]

All

0.000

0.003

0.006

0.040

0.058

4,432

(0.041)

(0.085)

(0.044)

(0.064)

Baltimore

– 0.020

– 0.025

– 0.043

– 0.071

– 0.093

589

(0.108)

(0.183)

(0.112)

(0.147)

Boston

0.107

– 0.081

– 0.206

0.128

0.225

878

(0.099)

(0.253)

(0.095)

(0.167)

Chicago

– 0.281

0.074

0.256

0.237*

0.330*

974

(0.088)

(0.302)

(0.099)

(0.138)

Los Angeles

– 0.057

0.090

0.136

0.050

0.061

1,069

(0.078)

(0.119)

(0.088)

(0.107)

New York City

0.266

– 0.074

– 0.147

– 0.188*

– 0.335*

922

(0.085)

(0.170)

(0.096)

(0.170)

MATH ASSESSMENT SCORE [ECLS-K]

All

0.000

– 0.025

– 0.052

0.000

0.000

4,420

(0.044)

(0.090)

(0.048)

(0.069)

Baltimore

– 0.084

– 0.164

– 0.276

– 0.111

– 0.144

591

(0.105)

(0.177)

(0.116)

(0.151)

Boston

0.204

– 0.126

– 0.321

0.040

0.070

875

(0.114)

(0.291)

(0.108)

(0.187)

Chicago

– 0.314

0.028

0.095

0.137

0.191

973

(0.101)

(0.349)

(0.125)

(0.174)

Los Angeles

– 0.061

0.080

0.122

0.044

0.053

1,063

(0.076)

(0.116)

(0.088)

(0.107)

New York City

0.262

– 0.021

– 0.042

– 0.166~

– 0.296~

918

(0.084)

(0.169)

(0.088)

(0.157)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: ECLS-K = achievement assessment from the Early Childhood Longitudinal Study-Kindergarten cohort study.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 13 to 20 as of December 31, 2007.

Measures: The reading and math achievement assessment scores are theta scores transformed into z-scores via standardization on the mean and standard deviation for control group youth ages 13 to 20.

247

Chapter 7: Impacts on Educational Outcomes

SUPPLEMENTAL EXHIBIT 7.7. INTERVIEWER OBSERVATIONS OF YOUTH AGES 13–20 DURING ACHIEVEMENT

ASSESSMENT

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

HIGHLY OR VERY HIGHLY MOTIVATED

DURING ASSESSMENT [OBS]

All

0.840

– 0.019

– 0.039

0.019

0.028

4,582

(0.015)

(0.032)

(0.015)

(0.022)

Female

0.851

– 0.008

– 0.017

0.019

0.030

2,335

(0.019)

(0.039)

(0.020)

(0.031)

Male

0.830

– 0.029

– 0.063

0.019

0.027

2,247

(0.022)

(0.047)

(0.022)

(0.031)

COOPERATIVE OR VERY COOPERATIVE

DURING ASSESSMENT [OBS]

All

0.914

– 0.001

– 0.003

0.008

0.012

4,580

(0.011)

(0.022)

(0.012)

(0.018)

Female

0.921

0.005

0.010

0.021

0.033

2,335

(0.014)

(0.028)

(0.014)

(0.022)

Male

0.908

– 0.008

– 0.017

– 0.006

– 0.008

2,245

(0.016)

(0.035)

(0.019)

(0.026)

VERY OR FULLY ATTENTIVE DURING

ASSESSMENT [OBS]

All

0.811

0.019

0.039

0.041*

0.060*

4,578

(0.016)

(0.033)

(0.016)

(0.024)

Female

0.826

0.024

0.048

0.031

0.047

2,334

(0.020)

(0.040)

(0.022)

(0.033)

Male

0.797

0.014

0.030

0.051*

0.072*

2,244

(0.022)

(0.048)

(0.024)

(0.033)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: OBS = interviewer observations.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 13 to 20 as of December 31, 2007.

Measures: For interviewer observations of motivation, high and very high motivation were two of five choices (the other three were average, low, and very low). For cooperation, cooperative and very cooperative were two of five choices (the other three were matter of fact, uncooperative, and very uncooperative). For attention, very attentive and complete and full attention were two of five choices (the other three were attentive, difficulty attending, and unable to attend).

248

SUPPLEMENTAL EXHIBIT 7.8. POST-SECONDARY SCHOOLING OUTCOMES OF YOUTH AGES 15–20, SELF-REPORT VERSUS ADMINISTRATIVE DATA

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

ANY COLLEGE

EVER ATTENDED ANY COLLEGE [SR]

All (ages 15–20)

0.243

0.011

0.022

– 0.009

– 0.014

3,610

(0.019)

(0.040)

(0.021)

(0.032)

Female

0.271

0.023

0.047

0.011

0.017

1,838

(0.026)

(0.053)

(0.031)

(0.048)

Male

0.218

– 0.002

– 0.004

– 0.029

– 0.042

1,772

(0.026)

(0.056)

(0.027)

(0.039)

ATTENDED ANY COLLEGE SINCE

2007 [NSC]

All

0.262

– 0.014

– 0.029

– 0.021

– 0.033

4,717

(0.016)

(0.033)

(0.017)

(0.025)

Female

0.305

– 0.011

– 0.023

0.000

0.001

2,300

(0.023)

(0.048)

(0.025)

(0.039)

Male

0.222

– 0.016

– 0.033

– 0.042*

– 0.064*

2,417

(0.021)

(0.044)

(0.021)

(0.032)

EVER ATTENDED ANY COLLEGE [SR]

OR ATTENDED ANY COLLEGE SINCE

2007 [NSC], OVERLAPPING SAMPLE

All

0.355

– 0.022

– 0.044

– 0.019

– 0.028

3,611

(0.019)

(0.038)

(0.021)

(0.031)

Female

0.408

– 0.025

– 0.051

– 0.001

– 0.002

1,841

(0.027)

(0.054)

(0.031)

(0.047)

Male

0.302

– 0.018

– 0.037

– 0.037

– 0.053

1,770

(0.026)

(0.053)

(0.027)

(0.039)

FOUR-YEAR COLLEGE

EVER ATTENDED FOUR-YEAR

COLLEGE [SR]

All (ages 15–20)

0.111

0.003

0.006

– 0.023

– 0.035

3,610

(0.014)

(0.030)

(0.016)

(0.023)

Female

0.130

0.012

0.024

– 0.017

– 0.026

1,838

(0.021)

(0.042)

(0.024)

(0.037)

Male

0.094

– 0.006

– 0.013

– 0.029

– 0.042

1,772

(0.019)

(0.040)

(0.020)

(0.029)

ATTENDED FOUR-YEAR COLLEGE

SINCE 2007 [NSC]

All

0.105

0.004

0.009

– 0.004

– 0.007

4,717

(0.011)

(0.024)

(0.012)

(0.018)

Female

0.130

0.015

0.031

0.005

0.008

2,300

(0.018)

(0.037)

(0.019)

(0.029)

249

Chapter 7: Impacts on Educational Outcomes

SUPPLEMENTAL EXHIBIT 7.8. (CONTINUED)

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

FOUR-YEAR COLLEGE (CONTINUED)

ATTENDED FOUR-YEAR COLLEGE

SINCE 2007 [NSC] (CONTINUED)

Male

0.083

– 0.006

– 0.013

– 0.013

– 0.020

2,417

(0.013)

(0.029)

(0.014)

(0.021)

EVER ATTENDED FOUR-YEAR

COLLEGE [SR] OR ATTENDED FOUR-

YEAR COLLEGE SINCE 2007 [NSC],

OVERLAPPING SAMPLE

All

0.148

– 0.001

– 0.002

– 0.013

– 0.020

3,611

(0.015)

(0.031)

(0.017)

(0.025)

Female

0.181

0.002

0.003

– 0.012

– 0.018

1,841

(0.022)

(0.045)

(0.025)

(0.039)

Male

0.115

– 0.004

– 0.008

– 0.015

– 0.021

1,770

(0.019)

(0.040)

(0.020)

(0.029)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: SR= self-report, NSC = National Student Clearinghouse.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Self-reported measures come from the youth long-term survey and are presented for interviewed youth ages 15 to 20 as of December 31, 2007. The sample for the NSC and IES measures is all core household members ages 15 to 20 as of December 31, 2007, regardless of whether they were interviewed.

Measures: Number of days enrolled in college was calculated according to enrollment begin date and enrollment end date reported by NSC, and the number of days was then divided by 30 to create number of months enrolled in school.

250

SUPPLEMENTAL EXHIBIT 7.9. POST-SECONDARY SCHOOLING OUTCOMES OF GROWN CHILDREN AGES 21–30, SELF-REPORT VERSUS ADMINISTRATIVE DATA

CONTROL EXPERIMENTAL

SECTION 8

MEAN

VERSUS CONTROL

VERSUS CONTROL

OUTCOME

ITT

TOT

ITT

TOT

N

EVER ENROLLED IN COLLEGE [PR]

All

0.399

– 0.039~

– 0.089~

– 0.020

– 0.035

3,081

(0.022)

(0.051)

(0.029)

(0.052)

Female

0.484

– 0.034

– 0.075

– 0.016

– 0.027

1,524

(0.031)

(0.068)

(0.041)

(0.071)

Male

0.311

– 0.044

– 0.104

– 0.024

– 0.045

1,557

(0.030)

(0.072)

(0.036)

(0.067)

ATTENDED ANY COLLEGE SINCE

2007 [NSC]

All

0.196

– 0.015

– 0.035

– 0.011

– 0.019

4,639

(0.014)

(0.034)

(0.016)

(0.027)

Female

0.282

– 0.046*

– 0.106*

– 0.052*

– 0.089*

2,275

(0.023)

(0.054)

(0.025)

(0.042)

Male

0.115

0.014

0.034

0.028

0.048

2,364

(0.017)

(0.040)

(0.019)

(0.033)

EVER ENROLLED IN COLLEGE [PR]

OR ATTENDED ANY COLLEGE SINCE

2007 [NSC], OVERLAPPING SAMPLE

All

0.415

– 0.033

– 0.076

– 0.006

-0.010

3,216

(0.021)

(0.047)

(0.028)

(0.049)

Female

0.522

– 0.043

-0.095

– 0.028

-0.048

1,570

(0.029)

(0.065)

(0.039)

(0.066)

Male

0.312

-0.024

– 0.056

0.015

0.028

1,646

(0.028)

(0.065)

(0.035)

(0.065)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would indicate that 25 percent of the control group was working).

Square brackets indicate the source of the outcome information: PR = parent report, NSC = National Student Clearinghouse.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Parent report measures come from the adult long-term survey and are presented for grown children (under age 18 at baseline and ages 21 to 30 as of December 31, 2007) whose parents were interviewed as part of the long-term survey. The sample for the NSC and IES measures is all core household members who are grown children, regardless of whether their parent was interviewed.

Measures: Number of days enrolled in college was calculated according to enrollment begin date and enrollment end date reported by NSC, and the number of days was then divided by 30 to create number of months enrolled in school.

251

Chapter 7: Impacts on Educational Outcomes

252

SUMMARY AND IMPLICATIONS

253

Chapter 8: Summary and Implications

SUMMARY AND IMPLICATIONS

In this chapter, we summarize the pattern of MTO

conditions themselves. On the other hand, the cause

impacts 10 to 15 years after random assignment and

of low earnings and high rates of unemployment,

discuss their implications both for social science and

criminal involvement, and high school dropout found

CHAPTER 8

for public policies designed to improve the well-being

in many disadvantaged urban neighborhoods may rest

of low-income families.

more with individual barriers to success in school,

the labor market and other pro-social settings than to

MTO provided very disadvantaged families living

purely neighborhood-level sources of disadvantage,

in public housing developments in high-poverty

at least within the range of neighborhood variation

neighborhoods a chance to move to a better life.

that mobility programs like MTO are able to modify.

We find that the offer of an MTO housing voucher,

Accordingly, interventions that directly and effectively

which enables families to move to lower-poverty

address individual-level barriers remain essential

neighborhoods, generated sustained reductions over

components in strategies to improve the long-term

the next 10 to 15 years in neighborhood (census tract)

life chances of low-income families in addition to any

poverty rates for families in both treatment groups

housing mobility and neighborhood-based policies.

(Section 8 and the experimental group) relative to

families in the control group. The differences in

8.1 SUMMARY OF IMPACT

neighborhood conditions between the MTO treatment

ESTIMATES

(experimental and Section 8) groups and the control

Chapters 2 through 7 of this report presented impact

group narrowed over time, in large part because of

estimates on the following outcomes for adults and

residential mobility and ongoing declines in the average youth:

poverty rates in the census tracts of families in the

control group. MTO moves also generated sustained

• Residential mobility, housing conditions,

improvements in housing conditions and in other

neighborhood environments, and social networks;

neighborhood attributes beyond economic segregation,

• Physical health;

such as safety; the latter is particularly important given

• Mental health;

that concern for safety was the most important reason

• Employment, earnings, income, receipt of social

families signed up for MTO.

assistance, and savings and assets;

• Risky and criminal behavior; and

Neighborhood improvements induced by MTO moves

• Educational outcomes.

boosted mental and physical health much more than

labor market outcomes, educational outcomes, and for

A Technical Appendix will provide additional technical

the most part more than for risky and criminal behavior details about our data collection and estimation as well. Moreover, the health effects in MTO seem

procedures, as well as the results of sensitivity analyses.

more pronounced for adult women than for youth. The

MTO study sample was very disadvantaged at baseline,

8.2 MOBILITY, HOUSING,

and so our results may not be fully applicable to more

NEIGHBORHOODS, AND SOCIAL

representative samples of families. But our results are

NETWORKS

certainly consistent with the idea that some of the

MTO provided help to families in the experimental

variation in health outcomes across neighborhoods

group to move out of public housing into private-

in current-day America is caused by neighborhood

market housing located in low-poverty census tracts

254

(those with 1990 poverty rates below 10 percent). To

group prevalence rate of 57 percent. MTO movers in

understand the consequences of such moves for the

both the experimental and Section 8 groups were also less

subsequent socioeconomic and health outcomes of these

likely than controls to report specific housing problems,

families, we compared their experiences with those of the such as vermin, broken windows or missing screens, families in the control group. The MTO demonstration

plumbing that is not working, or peeling paint and

also allowed us to analyze the impacts of moving

plaster.

using a standard Section 8 voucher through analogous

comparisons of the Section 8 group and the control

MTO moves did not seem to affect overall housing costs

group.

for families, although, as discussed below, the confidence

interval for this estimate was fairly wide. Compared

Among those assigned to the experimental group in

with control group families, those in the experimental

the long-term survey sample, 47 percent moved with

group were less likely to have problems paying their

an MTO housing voucher (the “compliers”), while 63

rent on time, but they were more likely to have trouble

percent of families in the Section 8 group moved with an paying utilities. Being in the Section 8 treatment group MTO housing voucher. Readers might be surprised that

increased the likelihood of renting and reduced the

so few families took advantage of what would initially

likelihood of homeownership; the experimental treatment

seem like such an appealing opportunity to move out

effects were qualitatively similar but not statistically

of public housing, and conclude that there must be

significant.

something wrong with how MTO was implemented.

But the voucher use rate in MTO is in line with what

Among families who moved as part of the experimental

other studies have found. The voucher “take-up”

group, MTO was clearly successful in helping them

or “compliance” rate in the Experimental Housing

experience substantially different neighborhood

Allowance Program was approximately 65 percent (Leger

environments compared with controls, at least in the

and Kennedy, 1990), and it was approximately 20

short term. The results presented in Exhibit 1.5 of

percent in the Gautreaux mobility program in Chicago

Chapter 1 showed that at baseline, the average MTO

(Rubinowitz and Rosenbaum, 2000, p. 67).

family was living in a census tract in which 53 percent of

all residents were poor. Among those in the experimental

Assignment to either the experimental or Section 8

and Section 8 groups who moved with an MTO voucher

groups increased the number of moves families made

(the “compliers”), the average tract poverty rates

compared with controls, but a perhaps surprising finding decreased to 11 and 29 percent respectively after the is that mobility rates for the control group were lower

initial move through the MTO program.

than might be expected on the basis of previous studies

of low-income households who were not receiving

Over time, these poverty differences narrow substantially.

government housing assistance. Over the 10 to 15-year

At the time of the long-term follow-up surveys, the

study period, the average control group family moved

difference in average census-tract poverty rates between

about twice compared with about three total moves for

experimental group compliers and control group families

families who relocated as part of the MTO experimental

had declined from around 35 or 40 percentage points

or Section 8 groups.

to about 10 percentage points. Overall the duration-

weighted average census-tract poverty rate for the control

MTO moves clearly improved the quality of the housing

group over the entire 10- to 15-year study period was

units in which families were living. Experimental group

about 40 percent. This compares with 20 percent for

adults were about 5 percentage points more likely than

experimental group compliers and 29 percent for Section

controls to rate their housing at the time of the long-

8 group compliers.

term survey as good or excellent (the effects of treatment

on the treated [TOT] was approximately 11 percentage

The convergence in neighborhood poverty rates over

points), a sizable change compared with the control

time occurred in part because some experimental group

255

Chapter 8: Summary and Implications

compliers moved after their first year in a low-poverty

The presumption in MTO was that the census tract poverty

area to neighborhoods with somewhat higher poverty

rate is in part an indicator of the constellation of living

rates. A majority of the convergence since interim,

conditions that may be important to the well-being of

however, occurred because of declines over time in the

low-income families. Consistent with this presumption, we

control group’s average census-tract poverty rate, which

found that MTO also generated large, persistent changes

is itself due mostly to the relocation of control group

in a range of other neighborhood attributes, including

families over time into lower-poverty areas (rather than

exposure to more affluent neighbors, less physical and social

from control families living in neighborhoods that were

disorder, and perceptions of greater safety. Despite concerns

gentrifying around them).

about the risk of social isolation for MTO families in lower-

poverty areas, moves increased the likelihood that families

A different approach to thinking about the size of the

were friends with someone who had a college degree.

MTO impact on neighborhood poverty is to ask how far

MTO shifted families within the national distribution of

Although MTO moves also led to statistically significant

neighborhood poverty rates. For example, Quigley and

declines in neighborhood racial composition, the

Raphael (2008) note that at the time of the interim MTO

experimental group compliers were still living in census

study, the impact for all experimental group families

tracts in which the large majority of all residents were

(regardless of whether they moved in conjunction with the members of racial and ethnic minority groups. This program) amounted to a shift from the 96th percentile

is one important way in which the MTO “treatment”

of the nationwide census tract poverty distribution to the

is different from that of the widely cited Gautreaux

88th percentile. Of course, the effect of MTO on those

mobility program. The latter program was more explicitly

who actually move via the program will be larger. But

focused on moves to reduce residential racial segregation

perhaps more important, and less obvious, is the fact that

(Rubinowitz and Rosenbaum, 2000). Another difference

there are just not that many census tracts in the United

is that Gautreaux was explicitly designed to help families

States with extremely high poverty rates of the sort in

move into affluent suburban areas, which MTO achieved

which MTO families are living (as Figure 4 in Quigley

at a much lower rate.

and Raphael, 2008, p. 22, makes clear). This means very

large changes in the absolute tract poverty level can lead to 8.3 PHYSICAL HEALTH

modest changes in a family’s rank in the nationwide tract

A large theoretical and empirical literature dating back

poverty distribution.

to the 1700s has hypothesized that neighborhood

environments may influence physical health outcomes

An alternative way to think about the relative magnitudes through exposure to environmental risk factors, social of MTO’s impacts on tract poverty rates within the

norms about health-related behaviors, and access to

nationwide tract-poverty distribution, which is less

health care providers. The interim MTO study found

sensitive to the fact that there are relatively few areas

that, four to seven years after random assignment, MTO

with extremely high poverty rates, is in terms of standard moves reduced the likelihood that adults were obese deviations within the national Census 2000 distribution

(as defined by a body mass index [BMI] of 30 kg/m2

for tract poverty rates. Experimental group moves

or higher). MTO had no detectable impacts on most

generate decreases in tract poverty rates equal to nearly

other measures of physical health collected as part of the

3 standard deviations measured one year after random

interim study. Given the potential importance of physical

assignment, 0.8 standard deviations measured 10 to

health impacts for both MTO participants themselves

12 years after random assignment, and 1.5 standard

and society as a whole, the long-term study included a

deviations when averaged over the entire post-study

detailed battery of health outcomes, including directly

period (results from analyses not shown in tables). Not

measured height and weight (rather than self-reported, as

many policy interventions induce changes of this size in

in the interim study), blood pressure, and blood samples

the social environments of families.

that were assayed for “biomeasures” that can be used to

diagnose disease or predict future risk.

256

The results showed that 10 to 15 years after random

were about 0.1 standard deviations lower for adults

assignment, there was no longer a statistically significant

assigned to the experimental group than to the control

impact on obesity (BMI ≥ 30 kg/m2), perhaps because

group. This is about the same size as found in the interim

such a large share of the sample became obese during

MTO study (Kling, Liebman, Katz, 2007, Table 3, p.

that interval (58 percent of control group families).

92). Given the importance of mental health for the

However, assignment to either the experimental or

well-being of MTO program participants, the long-term

Section 8 groups reduced the chances of more extreme

surveys included more detailed mental health measures.

obesity (BMI ≥ 35 kg/m2) by around 5 percentage points We found that those in the experimental group had a compared with a control group prevalence of around 35

3 percentage point reduction and those in the Section

percent, and reduced the chances of BMI ≥ 40 kg/m2

8 group a 5 percentage point reduction (marginally

by around 3 percentage points (although not statistically

significant) in lifetime prevalence of major depression

significant for the Section 8 group) compared with a

among adults compared with a control group prevalence

control group prevalence of about 18 percent.

of 20 percent. Although MTO had no statistically

significant effects on other specific disorders, almost all

Alongside these sizable differences in extreme obesity,

of the impact estimates are in the direction of improved

MTO also generates declines in diabetes prevalence of up mental health. One exception is that MTO moves may to 6 percentage points for the experimental group using

have increased rates of substance abuse or dependence.

our preferred measures from blood assays of glycosylated

hemoglobin (HbA1c), effects that were somewhat smaller Female youth (ages 15–20 at the time of the long-if we rely instead on self-reports of diabetes status or

term surveys) in the experimental group had improved

treatment. These impacts are sizable compared with

mental health (as measured by the mental health index)

the control group prevalence rate of 16 to 20 percent.

compared to controls. The changes were as large in

We also found that experimental group moves may

magnitude as adult improvements. For female youth ages

have reduced the likelihood of having high-risk levels

13–20 in the experimental group, MTO also reduced

(>4 mg/L) of high-sensitivity C-reactive protein (a

serious behavioral or emotional problems, as measured

measure of inflammation), which have been shown to be

by the commonly used “strengths and difficulties

predictive of cardiovascular disease risk.

questionnaire” (SDQ); reduced psychological distress;

and appears to have reduced the prevalence of panic

There were few impacts on most other physical health

attacks (past year). Male youth in the Section 8 group

measures for adults, with the exception of self-reported

may have experienced an increased prevalence of post-

functional limitations (e.g., difficulty climbing stairs or

traumatic stress disorder relative to controls. This general

carrying groceries). MTO had few significant impacts

pattern of generally beneficial impacts on female youth

on the physical health outcomes of youth in the

and some detrimental impacts on male youth echoes the

participating families. We discuss below possible ways in

pattern found in the interim report (Kling, Ludwig and

which these health impacts may have arisen.

Katz 2005, Kling, Liebman and Katz, 2007).

8.4 MENTAL HEALTH

8.5 ECONOMIC SELF-SUFFICIENCY

Given the importance of safety in motivating families to

The MTO program began in the mid-1990s, a time

sign up for the demonstration as well as MTO’s impacts

when the economy was booming and welfare reform

on neighborhood safety, mental health might be expected was increasing the number of single mothers who were to be one of the outcome domains most improved by

working in the formal labor market. Employment rates

MTO.

of mothers in both treatment groups and the control

group increased sharply as well from the mid-1990s to

The long-term follow-up found sustained MTO impacts

2000. The increases, however, were no higher for either

on a widely used index of psychological distress (Kessler’s the experimental or Section 8 groups than for the control

“K6” index). The long-term data showed that K6 scores

group. In fact, previous studies of MTO found that the

257

Chapter 8: Summary and Implications

first few years after random assignment, the experimental employment rates in jobs that are not covered by the UI and Section 8 group adults had somewhat lower average

system, but it is also possible these are chance findings.

earnings than controls, presumably from the disruptions

and transitions associated with moving (Orr et al., 2003). 8.6 RISKY AND CRIMINAL BEHAVIOR

We had anticipated that MTO might have even more

For the long-term follow-up, we had hypothesized that

pronounced impacts on the risky and criminal behavior

MTO moves might increase employment rates and

of those who were adolescents (age 15–20) at the time

earnings more for experimental than control mothers

of the long-term follow-up (10–15 years after random

for a variety of reasons. The disruptions associated with

assignment) compared to those who were age 15–20

moving would presumably have subsided. Additional

at the interim study (4–7 years after baseline). The

time in lower-poverty neighborhoods might have

interim study found declines in violent-crime arrests

increased the chances that movers had made new social

for both male and female youth who moved through

connections that could serve as sources of job referrals,

MTO relative to controls, but for most other forms

or would have internalized or responded to social norms

of risky behavior found that male youth did worse as

in low-poverty areas that were more supportive of work.

a result of moving through MTO while female youth

The children in these households were mostly adolescents did better. The effects of MTO might have been even by the long-term survey, thus limiting child-care

stronger for those who were teenagers at the time of the

constraints. We also speculated that the improved mental long-term study (compared to those who were teens at and physical health outcomes documented in the interim interim) because they would have been exposed for a report and in the long-term survey could eventually

longer period of time to less dangerous and economically

translate into improved labor market outcomes.

distressed neighborhood environments. Moreover those

who were teens at the time of the long-term follow-up

None of these hypotheses were evident in the long-term

would have lived in less disadvantaged communities early

data. The initial negative effect of MTO moves on labor

in childhood – a life stage in which cognitive, socio-

market outcomes attenuated over time, but there were no emotional and behavioral skills have been hypothesized statistically significant gains in longer-term employment

by many social scientists to be particularly amenable to

rates, earnings, or total family income as measured by

social policy intervention.

either survey self-reports or administrative records. We

found some signs of negative effects of the Section 8

However the estimated effects of MTO on risky and

treatment on self-reported employment rates in our long-

criminal behavior of those who were teens at the long-

term survey data, but we suspect this is likely an artifact

term follow-up were more muted compared to what the

of the slight differences in timing between when the

effects on teens were at the time of the interim study.

surveys were carried out for the Section 8 versus control

We find no statistically significant effects of MTO on

groups during the Great Recession in 2008–2009.

violent crime arrests in the long term data. The long term

data provide a few indications of the same type of gender

For the youth in MTO households, moves had no

difference in youth responses to MTO moves as were

detectable effects on idleness rates but seem to have had,

found in the interim data, with male youth who moved

if anything, adverse effects on employment rates and

through MTO engaging in relatively more of some risky

earnings. These youth were age 15–20 at the time of the

behaviors (smoking) compared to controls and female

long-term survey. For youth who were teenagers at the

youth who moved through MTO experiencing declines

time of the interim study and were grown children at the

in some risky behaviors (drinking) compared to controls.

long-term study, males in the experimental group were

more likely than controls to be employed, according

The fact that the effects of MTO on risky and

to the proxy reports by their parents. However, state

criminal behavior are generally more muted in the

unemployment insurance (UI) data showed they were

long-term data compared to the interim data suggests

less likely to be employed. MTO may have increased

that contemporaneous neighborhood environments

258

may matter more for this outcome domain than does

reading and math achievement tests, grades in school,

accumulated exposure to less distressed areas. The fact

and rates of grade retention. However, Section 8 males

that we see signs of a gender difference in responses to

were less likely to be educationally on track and to

residential mobility in a sample of teens in the long term

have attended college than control group males. There

data that is mostly non-overlapping with the youth who

was no evidence that impacts on educational outcomes

were studied in the interim follow-up, and also in an

varied systematically with a child’s age at time of random

entirely different set of public housing families examined assignment.

in a Chicago study by Jacob, Ludwig and Miller (2011),

suggests that the gender difference in MTO impacts

These findings do not mean that neighborhood

documented at interim may not be just a statistical

environments never matter for educational outcomes.

artifact unique to that particular study and sample.

The MTO mobility intervention generated more

pronounced impacts on neighborhood conditions than

The one outcome for which we do see at least some

on school conditions. As with the interim follow-up,

hints of more pronounced impacts in the long-term

the long term data show that MTO had modest and

data than in the interim data is with declining arrest

mixed impacts on school quality. Children assigned to

rates for drug distribution among the MTO treatment

the treatment groups rather than to the control group

groups compared to controls. The results are large and

were in schools that had slightly lower proportions of

statistically significant for male youth in the experimental low-income and minority students, and those assigned group, somewhat sizable for both the experimental and

to the experimental group were in schools that had

Section 8 effects for male grown children (but only

slightly higher test scores, but were also larger in terms

significant for the Section 8 group), and for adults is

of the total size of the student body. A majority of

sensitive to whether we focus on the extensive versus

MTO children were still attending majority-minority,

intensive margin of involvement in drug selling. The

overwhelmingly low-income public schools located in the

stronger results for adolescents than for older people is

districts serving the five original MTO cities.

consistent with the widely-documented “age-crime curve”

showing that aggregate arrest rates for most crimes peak

The potential contingent nature of neighborhood effects

during late adolescence or early adulthood. Evidence of

on educational outcomes also comes from considering

impacts on all age groups for drug distribution, even if

the pattern of MTO impacts across cities and over time

the results are somewhat mixed for the older cohorts,

from the interim to the long-term follow-up. Data from

would itself seem to provide some support for the idea

the interim study showed some signs of MTO impacts

that this might be a real effect and not a chance finding.

on achievement test scores for children in the Baltimore

and Chicago sites, the cities where baseline levels of

8.7 EDUCATIONAL OUTCOMES

both concentrated neighborhood disadvantage and

As with the interim study, MTO had few statistically

neighborhood crime and violence were more extreme

significant effects on educational outcomes in the long-

than in the other three MTO cities (Boston, Los Angeles,

term follow up. Disappointingly, the null results also

and New York City). That this might be a real effect

extended to children who had not yet enrolled in school

rather than a spurious finding is suggested by evidence

at baseline, who would have experienced particularly

of neighborhood effects on test scores drawn from two

large MTO-induced changes in neighborhood

entirely different Chicago study samples (see Burdick-

environments during a life stage in which cognitive,

Will et al., 2011). Over time from the interim to the

social-emotional and behavioral skills are hypothesized

long-term study in MTO, the neighborhood conditions

by many social scientists to be unusually “plastic” and

in which Baltimore and Chicago families lived became

susceptible to intervention.

more similar to what we see in the other three cities,

and any indication of cross-site differences in MTO

Compared to the control group, children assigned to the

impacts on test scores attenuates. This pattern seems

experimental and Section 8 groups had similar scores on

consistent with our results for the risky and criminal

259

Chapter 8: Summary and Implications

behavior chapter in suggesting that contemporaneous

A third threat to the MTO study is the possibility of

neighborhood conditions may be more important than

“interference” between program participants, called

accumulated exposure in affecting outcomes in these two the “stable unit treatment value assumption” (SUTVA) domains.

(Rubin, 1980; see also Sobel [2006] for a discussion

within the context of MTO). In MTO, this problem

8.8 CAN THESE MTO RESULTS

could arise if the neighbors of MTO participants

BE TRUSTED?

were offered MTO housing vouchers, or if there were

Although we have sought to maximize the scientific

other MTO participants living in their destination

quality of our long-term MTO follow-up, it remains

neighborhoods. The basic concern is that MTO

possible that we have missed some important MTO

participation by other people in the community could

impacts.

modify the impacts of MTO participation on a given

family, so that the effects of MTO moves might depend

One potential problem with any study such as ours is

on the share of other families in the community that also

sample attrition. If, for example, the most successful

participate in the program. If this were the case, then

MTO families could not be tracked or refused to be

our estimates would be relevant only for other mobility

interviewed, then our estimates likely understate MTO’s

programs with similar types of interactions among

benefits. We do not believe systematic differences across

residents and compliance rates.

randomized MTO groups in attrition from the study

sample are a major concern. Thanks to the efforts of the

Although this assumption cannot be tested directly,

University of Michigan’s Survey Research Center (SRC),

some suggestive evidence argues against its practical

the effective response rates for the long-term MTO

importance in the MTO context. First, most families

follow-up were 90 percent for adults and 89 percent for

that signed up for MTO were fairly socially isolated at

youth. Moreover, the response rates were quite similar

baseline: fully 41 percent of household heads reported

across randomized groups. High response rates such as

that they had no friends in the neighborhood, and

these limit the degree to which any differences between

65 percent reported that they had no family in the

respondents and nonrespondents can affect our estimates. neighborhood. Given that around one-quarter of eligible Moreover, the characteristics (measured when the study

families signed up for MTO, and some public housing

began) of those who responded to the long-term survey

residents would not have been eligible (for example,

appear to be quite similar across randomized groups.

because they did not have children), social interactions

among MTO families may have been limited. Second,

Another potential threat comes from survey misreporting. the MTO intervention deliberately aimed to avoid Of particular concern is the possibility that living in a

concentrating MTO families in new neighborhoods (as

more versus less disadvantaged neighborhood could affect can be seen in exhibits C2.2–C2.6 in Orr et al., 2003).

survey misreporting patterns, if, for example, people were In future work, we plan to explore this issue more by more embarrassed to admit being unemployed in more

taking advantage of exact address information that

affluent areas where relatively more people are working.

included apartment numbers for families at baseline.

We have tried to limit this problem by using multiple

These addresses will allow us to examine whether MTO’s

sources of data. For example, we directly measured health impacts vary according to how many other families in outcomes whenever possible (such as height, weight,

nearby units were also offered MTO vouchers.

blood pressure, and even using blood spots to measure

health outcomes like diabetes) rather than relying only

Perhaps the most serious scientific challenge with this

on self-reports. We also used administrative government

study concerns its statistical power, which, of course, is

records along with survey measures of labor market

an issue with almost any feasible social experiment. All

outcomes. The impacts as measured across these various

of the statistical tests presented in this report examine

sources of data are generally consistent.

whether the 95 percent confidence intervals for our

estimate of MTO’s impact on some outcome include zero

260

or not. When our confidence interval includes zero, we

assignment to one of the treatment groups changed

cannot reject the null hypothesis that there is no effect,

such a wide range of housing and neighborhood

but that does not mean the effect is actually zero. There

attributes for families. One useful step for assessing

are many estimated impacts that are not statistically

the potential mechanisms of action behind MTO’s

significant and where we can be confident that MTO

impacts is to examine those outcomes where MTO did

does not have large effects. However, we sometimes

generate statistically significant impacts, and consider

cannot rule out moderately-sized impacts. The size of

which potentially relevant measures of candidate

the effect that we can rule out varies across outcomes

mediating mechanisms were or were not also changed by

depending on the statistical properties of the outcome

MTO. Unchanged measures of a candidate mediating

variable itself. For example, for employment rates as

mechanism would indicate that this causal pathway

measured by administrative unemployment insurance

was unlikely to be the active ingredient in changing

(UI) records (Exhibit 5.5), our 95 percent confidence

outcomes.

interval ranges from –10 percent of the control mean

employment rate to +10 percent of the control mean.

For example, one striking feature of the results is that

The 95 percent confidence interval of our estimated

MTO seemed to have improved many aspects of health

effect of MTO on housing costs ranged from around

without generating detectable changes in many of the

–10 percent up to +20 percent of the control mean. It is

most obvious mediating pathways. For adults the MTO

important for readers to keep in mind that an effect that

experimental treatment reduced diabetes, extreme

is “not statistically significant” does not mean its effect is obesity prevalence, and the risk of having elevated rates necessarily zero.

of C-reactive protein (a predictor of cardiovascular

disease risk). Yet the experimental treatment did not

8.9 IMPLICATIONS FOR SOCIAL

have detectable impacts on diet (recognizing that our

SCIENCE AND PUBLIC POLICY

measures are limited for those health behaviors), or on

In this section, we address the following social science

the likelihood of having health insurance, or on other

and policy questions:

measures of access to medical care, and had at best mixed

impacts on our measures of physical exercise.

• What are the mechanisms through which MTO

improves long-run health outcomes, and perhaps

It is plausible that MTO improved health by reducing

some measures of risky and criminal behavior as

psychological and social stress. The baseline MTO

well?

surveys make clear that concerns about safety were the

• Why does MTO not have more pronounced effects most common motivation for signing up for MTO

on labor market or educational outcomes?

and moving to a new neighborhood. Our follow-up

• What do the MTO results imply for the scientific

surveys show that MTO moves succeeded in improving

study of “neighborhood effects”?

perceptions of neighborhood safety. Medical research

• What do the MTO results imply for housing policy has shown that stress may affect metabolism and other in particular and social policy in general?

physiological systems (Kuo et al., 2008; Merkin et al.,

2009), even independent of the effects of stress on diet,

such as the consumption of “comfort foods” that are

WHAT ARE THE MECHANISMS THROUGH high in fat and sugar. This stress pathway would also WHICH MTO IMPROVES HEALTH

be consistent with our findings of beneficial MTO

OUTCOMES AND PERHAPS RISKY AND

impacts on a measure of general psychological distress (as

CRIMINAL BEHAVIOR AS WELL?

measured by the “K6” index) and on the prevalence of

MTO’s evaluation design provides a strong platform for

major depression.

estimating the causal effects of moving as part of either

the experimental or Section 8 groups. Determining why

Learning more about the mediating mechanisms of

MTO affects outcomes is more challenging because

impacts on health is an important goal for future

261

Chapter 8: Summary and Implications

research. One way to push the experimental data is to

staying connected to father figures (Clampet-Lundquist

examine whether the patterns of MTO health impacts

et al., 2011). Compared to females, male youth may

are relatively larger in those sites where assignment to

also experience a larger decline in their relative academic

the treatment group generates relatively larger impacts

standing as a result of moving through MTO, and have

on health or related outcomes, which is the quasi-

more of a comparative advantage in anti-social behavior

experimental approach applied by Kling, Liebman and

as a means of securing status or resources (Kling, Ludwig,

Katz (2007) using the interim data.

and Katz, 2005). While the long-term MTO surveys were

designed to help test most of these candidate hypotheses,

We also see some hints in the long-term follow-up data

we have not yet found the “smoking gun” to definitively

that the MTO intervention may have reduced drug

explain why male and female youth seem to respond

selling among male youth, male grown children, and

differently to residential mobility interventions like

perhaps adults (household heads) as well. Drug selling—

MTO.

at least outdoor drug selling of the sort that is most

vulnerable to law enforcement discovery—may be even

WHY DID MTO FAIL TO INFLUENCE

more geographically concentrated across neighborhoods

LABOR MARKET AND EDUCATIONAL

than other types of crime in part because of differences

OUTCOMES?

in the capacity of community residents to control public

Given the large body of previous (nonexperimental)

spaces through informal social control or by marshaling

research suggesting that neighborhood environments

public sector resources. In addition, selling regularly

might play a role in affecting labor market and

from the same location—usually in economically

educational outcomes, it is puzzling that our long-term

disadvantaged areas—is a way to help solve the market

follow-up found few detectable beneficial impacts. One

coordination problem of buyers and sellers finding one

possible reason is that MTO seems to have had only

another, given that formal advertising is not possible in

modest impacts on some of the mechanisms through

this market (see for example Cook et al., 2007). Why

which neighborhood environments are thought to affect

we should see more pronounced signs of MTO effects

these outcome domains.

on drug selling in the long-term data compared to the

interim data is not entirely clear, but could be related to

For example, much of the previous research on labor

the large increase in marijuana use among young people

market outcomes has focused on a “mismatch” in

that occurred over the MTO study period.

geographic proximity to jobs among low-income

individuals. This theory argues that one reason

The long-term follow-up of MTO participants

employment rates are so low for many urban minority

also produced some muted echoes of the same

workers is because racial discrimination prevents them

gender difference in how youth respond to mobility

from relocating to suburbs and other locations where job

interventions as was found in the interim study (see

opportunities are becoming more plentiful. Yet MTO

Kling, Ludwig, and Katz, 2005; Kling, Liebman,

moves did not seem to have very large impacts on these

and Katz, 2007). The survey and administrative data

mediating mechanisms. Even at the interim study, there

collected for the interim study, together with subsequent

was little impact of moves on local job availability as

qualitative work, raised several hypotheses for this

measured by employment growth by residential ZIP code

gender difference. For example, previous work raised

(Kling, Liebman and Katz, 2007, p. 99). From a social

the possibility that male youth might spend more of

science perspective, one implication is that the MTO

their time hanging out in public spaces than do female

experiment provides a weak test of the spatial mismatch

youth, which could put them at elevated risk for getting

hypothesis (Kain, 1968). Yet the fact that MTO

into trouble or into conflict with community residents.

provides a weak test of the spatial mismatch idea is itself

Male youth who move through MTO might also have

informative. Because MTO is at least as intensive as any

more difficulty than do female youth in navigating their

residential mobility program that could be implemented

new neighborhood environments, making friends, or

at large scale, it suggests that mobility programs might

262

not be the most promising way to overcome any spatial

Another question is why the mental and physical health

mismatch problems and improve access to jobs for

impacts in both the interim and long-term follow-ups

disadvantaged urban workers.

did not translate into improved labor market outcomes,

given the connection between health and employment,

MTO did seem to have some effect on social connections or in children’s schooling outcomes, given that improved to people who could be good sources of job referrals,

mental health for parents should presumably translate

but the effect of those connections on labor market

into more developmentally productive parenting

outcomes is unclear. Chapter 2 showed that moving

practices. The answer may be that while MTO created

via the experimental group increased the likelihood of

changes in mental and physical health that are large

having at least one close friend who graduated from

enough to be important for policy, they are not large

college by nearly 15 percentage points (nearly one-third

enough by themselves to lead to impacts on educational

of the control group mean of 53 percent), and increased

or labor market outcomes that are sizable enough to be

the likelihood of having at least one close friend who

detected in the MTO data.1

worked full-time by 7 percentage points (around 10

percent of the control mean of 74 percent; p < .10). Yet

In sum, the challenges that MTO participants face in

in the interim survey data, only a very modest share of

schools and in the labor market do not appear to have

MTO participants reported that they found their jobs

been caused by the kinds of neighborhood-level risk

“through someone living in their neighborhood such as

factors that a typical MTO move could alleviate. Perhaps

a friend, relative or acquaintance” (Kling, Liebman, and

these problems arise from individual- or family-level

Katz, 2007). Some of the qualitative work with MTO

risk factors, or perhaps they are caused by neighborhood

families also suggests that neighbors may not have had

conditions that MTO did not address (for example,

information about the types of jobs that would be most

concentrations of minority neighbors or school quality).

relevant for MTO program participants (Turney et al.,

At any rate, these findings suggest that housing mobility

2006).

programs should be supplemented by more direct

individual- and family-level interventions to generate

With respect to educational outcomes, many parents

substantial improvements in schooling, work and

at least seem to believe that the quality of local public

earnings.

schools is an important mechanism through which

neighborhood location may influence children’s academic WHAT DO THE RESULTS IMPLY ABOUT

achievement and attainment. Yet MTO had more modest THE EXISTENCE OF NEIGHBORHOOD

impacts on school quality compared to the intervention’s

EFFECTS?

impacts on other neighborhood social conditions,

Because MTO is the first true randomized experiment

consistent with the trend of many urban school systems

that has been carried out to test the effects of residential

to provide families with more “public school choice”

mobility on low-income families, the study is often

that enables them to attend schools outside their local

looked to as a key source of information about the

neighborhoods (see for example Cullen, Jacob, and

effects of neighborhood environments on families

Levitt, 2006). Children assigned to the MTO treatment

(“neighborhood effects”). It is important to keep in

groups wound up attending schools that served students

who were slightly less likely to have very low test scores,

1 For example, Ettner, Frank, and Kessler (1997) estimated that mental

be poor, or be members of racial and ethnic minority

health disorders reduced labor force participation rates by between 14

and 34 percentage points for adult women. The effect of moving via

groups compared to the student bodies of the control

the experimental group on the lifetime prevalence of any mental health

group’s schools. But children in the MTO treatment

disorder is around 5 percentage points, which is not quite statistically

significant (p value of .22). But even if we were confident that this were

groups still wound up attending schools that were very

a “real” 5 percentage point impact, not a statistical fluke, a 5 percentage point change in prevalence of mental health disorders would imply an

low performing and served overwhelmingly poor and

MTO impact on labor force participation rates of no more than about

majority-minority student populations.

2 percentage points (5 percent times 34 percent). Our estimates for

MTO’s impacts on labor force participation would not enable us to rule

out an impact of about this size (see Exhibit 5.7).

263

Chapter 8: Summary and Implications

mind that the MTO “treatment” combines the effects

Finally, some social scientists have reacted with

of moving per se, changes in housing quality, and

surprise to the fact that MTO has not had more

neighborhood conditions, which in principle make it

pronounced impacts on outcomes like employment

difficult to disentangle the effects of these different

rates or earnings. They have argued that MTO is

changes. In practice, we suspect that the effects of

not a fair test of the neighborhood effects hypothesis

moving per se may not be such an important determinant because the demonstration did not generate changes of differences in outcomes across MTO groups since the

in neighborhood conditions that are “large enough”

effects of the extra moves that treatment-group families

(Clampet-Lundquist and Massey, 2008). Of course,

make are likely to dissipate over time. Moreover, the

with any social experiment it is always the case that a

control group itself on average moved twice over this

more intensive intervention can be imagined. MTO

time period as well, so most families in MTO in all

does not have anything to say about, for example, the

three groups have experienced the disruptions of moving

effects of moving a family from the distressed, crime-

several times during the study period.

ridden South Side Chicago neighborhood of Washington

Park into a condominium in Chicago’s Gold Coast or a

If we do not believe that differences in move rates

house in the upscale Chicago suburb of Oak Park. But

across MTO groups are a key determinant of differences

the range of neighborhood variation induced by MTO

in outcomes, to what degree are the impacts that we

is about as large as what we could possibly imagine any

find owing to changes in housing conditions versus

feasible housing policy achieving. In fact, it is hard to

neighborhood conditions? Some suggestive evidence that

imagine an elected official in contemporary America

our results might be driven by changes in neighborhoods

ever pushing to implement anything on a large scale

rather than housing quality comes from the analysis by

that was even remotely like the experimental treatment,

Kling, Liebman and Katz (2007) of the interim data.

which required families to move at least initially into a

They find that the pattern across MTO demonstration

low-poverty census tract. Therefore, we think the proper

sites and randomized treatment groups in effects on

interpretation of the neighborhood effects question that

adult mental health is strongly related to the pattern

can be answered by MTO is: What are the effects on the

across sites and groups in changes in tract poverty rates

outcomes of this very disadvantaged inner-city sample

specifically, much more so than to the site-group pattern

of public housing families from helping them make

in MTO impacts on the likelihood of changing housing

moves that change neighborhood conditions by about as

units with a housing voucher.

much as what we could imagine any actual policy ever

achieving in the real world?2

Also relevant in thinking about what we can learn about

neighborhood effects from MTO is that the study

WHAT DO THE RESULTS IMPLY FOR

enrolled a particularly disadvantaged group of public

HOUSING POLICY IN PARTICULAR AND

housing families who volunteered to participate in this

SOCIAL POLICY GENERALLY?

program. If the effects of MTO-like moves are different

MTO tells us most about the effects of a modestly

for different types of families, then MTO’s impacts

scaled, voluntary housing-mobility intervention on the

may not tell us what would happen if we changed the

outcomes of low-income families. MTO offered housing

neighborhoods of other, more representative samples

vouchers to no more than a few thousand families in

of American families. The possibility that MTO-like

five of the nation’s largest cities, and so was unlikely to

moves might have distinctive effects on different types

of families also implies that MTO does not necessarily

2 It is true that the Gautreaux mobility program in Chicago gener-

ated even more dramatic changes in neighborhood conditions than the

inform us about the effects of involuntary moves across

MTO results (see Rubinowitz and Rosenbaum, 2000). Similarly large

neighborhoods, as occurs when local housing authorities

changes in neighborhood conditions can be found in a recent Baltimore

mobility program as well (see DeLuca and Rosenblatt, undated).

demolish housing projects for example. If families sign

But, crucially, both of these mobility programs were initiated at the

up for MTO because they believe they have relatively

instigation of judges, rather than elected policymakers. Gautreaux and

the Baltimore mobility program also differ from MTO in many ways

more to gain from moving, then MTO findings would

with respect to their target populations, which is a separate point from

the political feasibility for elected officials to push for Gautreaux-like

overstate the effects of an involuntary mobility program.

mobility programs.

264

have had any important effect on the way the overall

neighborhoods, are outweighed by the intangible benefits

private housing market operated in these cities. A larger

to MTO families themselves of improved quality of life

mobility program might have different impacts on

from better housing and neighborhood conditions.

families from those estimated in MTO if efforts to move

relatively more low-income families cause changes in the

The MTO findings suggest that housing mobility

overall private housing market and the options available

programs alone are unlikely to be a panacea for the

to families, what sociologists often call “system-level

schooling problems and labor market difficulties faced by

effects” and economists call “general-equilibrium effects.” disadvantaged families living in public housing projects A larger mobility program could, for example, increase

and other high-poverty, inner-city neighborhoods.

rents in the private housing market, at least in the short

Policies to increase skills and directly address other

term, or lead to the re-concentration of former public

individual barriers to work remain essential if we are

housing families in new neighborhoods.

to improve the long-term life chances and economic

self-sufficiency of disadvantaged families living in

Some additional calculations show that an MTO-type,

high-poverty areas. Several recent random-assignment

small-scale mobility intervention for disadvantaged

evaluations indicate that high-quality education,

families may well generate more benefits than costs.

training, and employment services programs can improve

Our rough estimate is that the costs of the extra

schooling outcomes and produce sustained earnings

housing assistance and counseling provided to the

increases for inner-city, disadvantaged youth and adults.

MTO experimental treatment group might add up to

For example, Success for All, a comprehensive reading

around $800 per treatment-group “complier” per year, as

intervention that involves extra time for reading,

averaged over the 10 to 15 year follow-up study period.3 ability grouping, frequent assessment, and remediation On the benefit side of the ledger, it seems possible that

(including tutoring), has been found to improve

MTO’s impacts on diabetes and depression alone could

reading scores for elementary school children and

be enough to approximately equal the tangible costs

perhaps middle schoolers as well (Borman et al., 2007,

of the intervention.4 Whether MTO passes a benefit-

Chamberlain et al., 2007). The Jobs-Plus demonstration

cost test then would depend on whether any intangible

produced sustained (seven-year) earnings gains for

costs of the program, in the form of negative spillovers

adult, nondisabled public housing residents through

of MTO moves on residents of origin or destination

employment and training services, changes in rent rules

to increase work incentives, and neighbor-to-neighbor

3 The cost of the housing vouchers themselves in the short term are

negligible, or even negative, since the alternative is public housing ini-

outreach centering on work (Riccio, 2010). Sectoral

tially for families. Many housing economists believe that public housing

employment programs—industry-specific training

might be even more expensive than housing vouchers (Olsen, 2003).

Exhibit 2.4 shows that the effect of experimental moves on the likeli-

programs to prepare underskilled workers for skilled

hood of receiving any housing assistance 10–15 years after baseline was

+.05 (not statistically significant). If the average housing subsidy cost

positions and connect them with employers—have

for families is around $8,000 per year, then the increased persistence on

produced substantial earnings gains for disadvantaged

housing subsidies might cost $400 per year. The other tangible financial

cost would be the additional counseling services provided to families,

adults in large U.S. cities (Maguire et al., 2010). And

which averaged approximately $4,500 per family (Goering et al., 1999,

high schools organized as Career Academies integrating

Table 4, which we assume is 1995 dollars). If we amortize this over 12

years of post-study data, on average, this would be approximately $400

academic and technical curricula and work-based

per year per experimental group mover.

learning opportunities with local employers produce

4 In Chapter 3, we showed that moving via the experimental group

sizable long-term (11-year) earnings improvements for

reduced the prevalence of diabetes by nearly 11 percentage points.

Previous research suggests that diabetes increases annual medical

youth in low-income urban settings (Kemple, 2008).

expenditures by around 240 percent per person, equal to at least $4,500

per year in 2011 dollars (Trogdon and Hyland, 2008). Therefore, an 11

percentage point decline in diabetes prevalence would generate benefits

The MTO results point to the possible value in paying

of approximately $500 per year per MTO adult assigned to the experi-

mental group. In Chapter 4, we found that the effect on depression

increased attention to community-level interventions

prevalence during the past year is 4.3 percentage points, which is not

for improving health outcomes of disadvantaged

quite statistically significant. A review by Berto et al. (2000) suggests

the total costs of depression in the United States could be on the order

families, such as those in MTO, who live in some of

of $110 billion per year. If 7 percent of American adults have clinical

our most distressed inner-city areas. In principle, the

depression in a given year, this implies $7,500 per case in social costs,

so that the estimated experimental TOT effect on depression implies

benefits of around $340.

265

Chapter 8: Summary and Implications

effects of a community-level intervention that changed

neighborhood environments without moving families

could be different from the effects of an MTO-like

mobility program that moves families into different

types of neighborhoods. This type of difference in the

effects of a mobility intervention versus community-level

intervention could arise if, for example, moving disrupts

important social ties and social support that would

otherwise help families take full advantage of changes

in neighborhood environments. But as a general rule,

poor families in the United States tend to move often.5

In practice, the effects of an intervention that changed

all severely distressed urban neighborhoods in some way

might wind up being little different from interventions

that helped very disadvantaged families, such as those in

MTO, move out of such neighborhoods.

Perhaps the more difficult challenge for drawing

inferences about community-level interventions from

MTO is the difficulty of determining which aspects

of neighborhood environments should be the highest

priority targets for such programs. The MTO findings do

not suggest that access to health care or opportunities,

or neighborhood racial segregation, are the key ways

in which community environments influence health

outcomes like diabetes, extreme obesity, psychological

distress and depression. More likely intervention

priorities are neighborhood socioeconomic composition,

informal social control as suggested by the “collective

efficacy” theory of Sampson, Raudenbush, and Earls

(1997), and, perhaps most important, the neighborhood

attribute that the MTO families themselves have cared

the most about since the beginning—safety.

5 For example, data from the Current Population Survey show that

between 2002 and 2003, 14 percent of people changed residences;

the figure was 18 percent for African-Americans and Hispanics, and

23 to 24 percent for minorities with incomes below the poverty line

(Schachter, 2004).

266

REFERENCES

267

References

Adams, J., and M. White. 2006. Is the disease risk

Aneshensel, Carol S., and Clea A. Sucoff. 1996. The

associated with good self-reported health constant

neighborhood context of adolescent mental

across the socio-economic spectrum? Public Health

health. Journal of Health and Social Behavior

REFERENCES

120: 70-75.

37(4): 293-310.

Almond, Douglas, and Janet Currie. 2010. Human

Angrist, Joshua D., Guido W. Imbens, and Donald

capital development before age 5. NBER Working

B. Rubin. 1996. Identification of causal effects

Paper no. 15827. Cambridge, MA: National Bureau

using instrumental variables. Journal of the

of Economic Research.

American Statistical Association 91(434): 444-55.

Altman, David G. 1995. Strategies for community

Angrist, Joshua D., and Jorn-Steffen Pischke. 2009.

health intervention: promises, paradoxes, pitfalls.

Mostly harmless econometrics. Princeton, NJ:

Psychosomatic Medicine 57: 226-33.

Princeton University Press.

American Diabetes Association. 2010. Standards of

Anil, Bulent, David L. Sjoquist, and Sally Wallace.

medical care in diabetes – 2010. Diabetes Care

2010. The effect of a program-based housing

33(1): S11-61.

move on employment: Hope VI in Atlanta.

American Psychiatric Association. 1994. Diagnostic

Southern Economic Journal 77(1): 138-60.

and statistical manual of mental disorders (4th ed.).

Arima, Hisatomi, Michiaki Kubo, Koji Yonemoto,

Washington, DC: Author.

Yasufumi Doi, Toshiharu Ninomiya, Yumihiro

Anda, Robert F., Vincent J. Felitti, J. Douglas Bremner,

Tanizaki, Jun Hata, Kiyoshi Matsumura, Mitsuo

John D. Walker, Charles Whitfield, Bruce D.

Lida, and Yutaka Kiyohara. 2008. High-

Perry, Shanta R. Dube, and Wayne H. Giles. 2006.

sensitivity C-reactive protein and coronary heart

The enduring effects of abuse and related adverse

disease in a general population of Japanese: The

experiences in childhood: A convergence of evidence

Hisayama study. Arteriosclerosis, Thrombosis, and

from neurobiology and epidemiology. European

Vascular Biology 28(7): 1385-91.

Archives of Psychiatry and Clinical Neuroscience 256:

Aud, Susan, William Hussar, Grace Kena, Kevin

174-86.

Bianco, Lauren Frohlich, Jana Kemp, Kim

Ander, Roseanna, Philip J. Cook, Jonathan Guryan, Jens

Tahan, Thomas Nachazel, and Gretchen Hannes.

Ludwig, Harold Pollack, and Laurence Steinberg.

2011. “The Condition of Education 2011.” U.S

2010. “Preventing Youth Violence.” Chicago:

Department of Education, National Center for

University of Chicago Crime Lab.

Education Statistics.

Anderson, Elijah. 1999. Code of the Streets: Decency,

Bagley, Christopher, Solomon Jacobson, and Clare

Violence, and the Moral Life of the Inner City. New

Palmer. 1973. Social structure and the ecological

York: W.W. Norton & Company.

distribution of mental illness, suicide, and

Anderson, Robin J. 2011. Dynamics of economic well-

delinquency. Psychological Medicine 3: 177-87.

being: Poverty, 2004-2006. Current Population

Baltimore City Public Schools. 2011. “Blueprint

Report P70-123. Washington, DC: U.S. Census

for Progress: Report on Results 2009-2010.”

Bureau.

Accessed August 15, 2011. http://www.bcps.org/

Anderson Robert N., and Betty L. Smith. Smith. 2003.

SYSTEM/reports/ResultsReport2009-2010.pdf.

Deaths: Leading causes for 2001. National Vital

Banerjee, Abhijit, and Sendhil Mullainathan. 2010.

Statistics Reports 52(9).

The shape of temptation: Implications for

the lives of the poor. Working paper, Harvard

University.

268

Bayer, Patrick, Steve Ross, and Giorgio Topa. 2008. Place Bird, Chloe E., Teresa Seeman, José J. Escarce, Ricardo of work and place of residence: Informal hiring

Basurto-Dávila, Brian K. Finch, Tamara Dubowitz,

networks and labor market outcomes. Journal of

Melonie Heron, et al. 2010. Neighbourhood

Political Economy 116(6): 1150-96.

socioeconomic status and biological ‘wear and tear’

Beaghan, Michael, and Sharon Stern. 2009. Usability

in a nationally representative sample of US adults.

of the American Community Survey Estimates

Journal of Epidemiology and Community Health

of the Group Quarters Population for Substate

64(10): 860-65.

Geographies. Washington, DC: U.S. Census Bureau. Bishaw, Alemayehu, and Suzanne Macartney. 2010.

Beatty, Alexandra. 2010. “Committee on the Impact

Poverty: 2008 and 2009. American Community

of Mobility and Change on the Lives of Young

Survey Brief ACSBR/09-1. Washington, DC: U.S.

Children, Schools, and Neighborhoods.” Prepared

Census Bureau.

for the National Research Council and Institute

Bishaw, Alemayehu, and Trudi J. Renwick. 2011.

of Medicine. The National Academies Press,

Poverty: 2007 and 2008 American Community

Washington, D.C.

Surveys. American Community Survey Report

Becker, Gary S. 1968. “Crime and Punishment: An

ACSBR/08-1. Washington, DC: U.S. Census

Economic Approach.” Journal of Political Economy

Bureau.

76(2): 169-217.

Blair Lewis, Lavonna, David C. Sloane, Lori Miller

Becker, Gary S., and Kevin M. Murphy. 2000. Social

Nascimento, Allison L. Diamant, Joyce Jones

economics: Market behavior in a social environment.

Guinyard, Antronette K. Yancey, and Gwendolyn

Cambridge, MA: Belknap Press.

Flynn. 2005. African Americans’ access to healthy

Bell, Janice, Jeffrey S. Wilson, and Gilbert C. Liu. 2008.

food options in South Los Angeles restaurants.

Neighborhood greenness and 2-year changes in body

American Journal of Public Health 95(4): 668-73.

mass index of children and youth. American Journal

Blake, Kevin S., Rebecca L. Kellerson, and Aleksandra

of Preventative Medicine 35(6): 547-53.

Simic. 2007. Measuring overcrowding in housing.

Bellair, Paul E. 2000. “Informal Surveillance and Street

Washington, DC: U.S. Department of Housing and

Crime: A Complex Relationship.” Criminology 38:

Urban Development.

137-170.

Blank, Rebecca. 2002. Evaluating welfare reform in the

Berkman, Lisa F., and Lester Breslow. 1983. Health and

United States. Journal of Economic Literature 40(4):

ways of living. New York: Oxford University Press.

1105-66.

Berkman, Lisa F., and Thomas Glass. 2000. Social

Block, Jeanne H. 1983. Differential premises arising

integration, social networks, social support and

from differential socialization of the sexes: Some

health. In Social Epidemiology. 137-173. New York:

conjectures. Child Development 54: 1335-54.

Oxford University Press.

Bloom, Howard. 1984. Accounting for no-shows in

Berto, Patrizia, Daniele D’Ilario, Pierfrancesco Ruffo,

experimental evaluation designs. Evaluation Review

Roberto Di Virgilio, and Fortunata Rizzo.

8 (April): 225-46.

2000. Depression: Cost-of-illness studies in the

Bloom, Howard S., Saskia Levy Thompson, and

international literature, a review. Journal of Mental

Rebecca Unterman. 2010. Transforming the High

Health Policy and Economics 3: 3-10.

School Experience: How New York City’s New

Beyers, Jennifer M., Rolf Loeber, Per-Olof H. Wikstrom,

Small Schools are Boosting Student Achievement

Magda Stouthamer-Loeber. 2001. “What predicts

and Graduation Rates. Manpower Demonstration

adolescent violence in better-off neighborhoods?”

Research Corporation, June. http://www.mdrc.org/

Journal of Abnormal Child Psychology. 29(5): 369-81.

publications/560/overview.html.

Bickel, Gary, Mark Nord, Cristofer Price, William

Bloomberg, Gordon R., and Edith Chen. 2005. The

Hamilton, and John Cook. 2000. Guide to

relationship of psychologic stress with childhood

Measuring Household Food Security, Revised 2000.

asthma. Immunology and Allergy Clinics of North

Alexandria, VA: U.S. Department of Agriculture,

America 25(1): 83-105.

Food and Nutrition Service.

269

References

Refer

Blumstein, Alfred. 2000. “Disaggregating the violence

Brooks-Gunn, Jeanne, Greg J. Duncan, Pamela

trends.” In The Crime Drop in America, edited by

K. Klebanov, and Naomi Sealand. 1993.“Do

Alfred Blumstein and Joel Wallman, 13-44. NY:

Neighborhoods Influence Child and Adolescent

Cambridge University Press.

Development?” American Journal of Sociology 99:

———. 1995. “Youth Violence, Guns, and the Illicit-

353-95.

Drug Industry.” Journal of Criminal Law and

Browning, Christopher R., and Kathleen A. Cagney.

Criminology 86: 10-36.

2002. Neighborhood structural disadvantage,

Bombard, Jennifer, Patricia Dietz, Christine Galavotti,

collective efficacy, and self-rated physical health

Lucinda England, Van T. Tong, Donald K. Hayes,

in an urban setting. Journal of Health and Social

and Brian Morrow. 2010. Chronic diseases and

Behavior 43(4): 383-99.

related risk factors among low-income mothers.

———. 2003. Moving beyond poverty: Neighborhood

Maternal and Child Health Journal December 14.

structure, social processes, and health. Journal of

Borjas, George J. 1995. Ethnicity, neighborhoods and

Health and Social Behavior 44(4): 552-71.

human-capital externalities. American Economic

Bryant-Stephens, Tyra. 2009. Asthma disparities in

Review 85: 365-90.

urban environments. Journal of Allergy and Clinical

Borman, Geoffrey D., Robert E. Slavin, Alan Cheung,

Immunology 123(6): 1199-206.

Anne Chamberlain, Nancy Madden, and Bette

Burdick-Will, Julia, Jens Ludwig, Stephen Raudenbush,

Chambers. 2007. Final reading outcomes of the

Robert Sampson, Lisa Sanbonmatsu, and Patrick

national randomized field trial of Success For All.

Sharkey. 2011. “Converging Evidence for

American Educational Research Journal. 44(3): 701-

Neighborhood Effects on Children’s Test Scores:

731.

An Experimental, Quasi-Experimental and

Bottcher, Jean. 2001. Social practices of gender: How

Observational Comparison.” Social Inequality and

gender relates to delinquency in the everyday lives of

Educational Disadvantage. Greg Duncan and Richard

high-risk youths. Criminology 39: 893-931.

Murnane, eds. New York: Russell Sage Foundation.

Bound, John, Charles C. Brown, and Nancy Mathiowetz. Bureau of Justice Statistics. 2011. Sourcebook of Criminal 2001. Measurement error in survey data. In

Justice Statistics, Table 4.7.2009. Accessed September

Handbook of econometrics, ed. E.E. Learner and J.J.

1. http://www.albany.edu/sourcebook/pdf/t472009.

Heckman. 3705-3843. New York: North Holland.

pdf.

Briggs, Xavier de Souza. 2008. More pluribus, less unum? Carneiro, Pedro, and James J. Heckman. 2003. Human The changing geography of race and opportunity.

capital policy. In Inequality in America: What role

In Neighbourhood Renewal and Housing Markets:

for human capital policies? ed. by James J. Heckman

Community Engagement in the US & UK, ed. H.

and Alan B. Krueger. 77-239. Cambridge, MA: MIT

Beider. Oxford, UK: Blackwell.

Press.

__________. 1998.Brown kids in white suburbs:

Carr, James H., and Jenny Schuetz. 2001. Financial

Housing mobility and the many facets of social

services in distressed communities: Issues and answers.

capital. Housing Policy Debate 9(1): 177-221.

Washington, DC: Fannie Mae Foundation.

Briggs, Xavier de Souza, Susan J. Popkin, and John

Case, A., and L. Katz. 1991. “The Company You

Goering. 2010. Moving to Opportunity: The story of

Keep: The Effects of Family and Neighborhood

an American experiment to fight ghetto poverty. New

on Disadvantaged Youths.” National Bureau of

York: Oxford University Press.

Economic Research Working Paper No. 3705.

Brock, W. A., and S. N. Dulauf. 2001. “Interactions-

Caskey, John. 1997. Lower-income Americans, higher

Based Models.” In Handbook of Econometrics, Vol. 5,

cost financial services. University of Wisconsin–

edited by J. J. Heckman and E. Leamer, 3297–3380.

Madison, School of Business: Filene Research

Amsterdam: North-Holland.

Institute, Center for Credit Union Research.

270

Casserly, Michael. 2002. “Beating the Odds II: A

Cicala, Steven, Roland Fryer, and Jörg Spenkuch. 2011.

City-by-City Analysis of Student Performance and

“A Roy Model of Social Interactions.” Harvard

Achievement Gaps on State Assessments, Spring

University, February.

2001 Results.” Prepared for the Council of the Great Clampet-Lundquist, Susan. 2007. No more ‘Bois ball: City Schools. Washington, D.C.

The impact of relocation from public housing on

Center on Budget and Policy Priorities. 2011. State-by-

adolescents. Journal of Adolescent Research 22(3):

state fact sheets: Federal rental assistance. Washington,

298-323.

DC: author.

Clampet-Lundquist, Susan, Kathryn Edin, Jeffrey R.

Chamberlain, Anne, Cecelia Daniels, Nancy A. Madden,

Kling, and Greg J. Duncan. 2006. Moving at-risk

and Robert E. Slavin. 2007. A randomized

kids to better neighborhoods: Why girls fare better

evaluation of the Success for All middle school

than boys. Working Paper 888. Princeton, NJ:

reading program. Middle Grades Research Journal

Princeton University, Industrial Relations Section.

(MGRJ). 2(1): 1-21.

———. 2011. Moving teenagers out of high-risk

Chapman , Chris, Jennifer Laird, and Angelina Kewal-

neighborhoods: How girls fare better than boys.

Ramani. 2010. “Trends in High School Dropout

American Journal of Sociology 116: 1154-89.

and Completion Rates in the United States:

Clampet-Lundquist, Susan, and Douglas S. Massey.

1972–2008.” Prepared for the National Center for

2008. Neighborhood effects on economic self-

Education Statistics, Washington, D.C.

sufficiency: A reconsideration of the Moving to

Chase-Lansdale, P. Lindsay, Rachel A. Gordon, Jeanne

Opportunity experiment. American Journal of

Brooks-Gunn, and Pamela K. Klebanov. 1997.

Sociology. 114(1): 107-43.

“Neighborhood and familial influences on the

Cohen, Mark A. 2005. The Costs of Crime and Justice.

intellectual and behavioral competence of preschool

New York: Routledge.

and early school-age children.” In Neighborhood

Cohen, Sheldon. 2004. Social relationships and health.

poverty: Context and consequences for development,

American Psychologist 59(8): 676-84.

eds. Jeanne Brooks-Gunn, Greg J. Duncan, and J.

Cole, Tim J., Mary C. Bellizzi, Katherine M. Flegal,

Lawrence Aber. 79-118. New York: Russell Sage

and William H. Dietz. 2000. Establishing a

Foundation.

standard definition for child overweight and obesity

Chetty, Raj, John Friedman, Nathaniel Hilger,

worldwide: International survey. British Medical

Emmanuel Saez, Diane Whitmore Schanzenbach,

Journal 320:1240.

and Danny Yagan. 2010. How does your

Coleman, James S. 1988. Social capital in the creation

kindergarten classroom affect your earnings?

of human capital. American Journal of Sociology 94:

Evidence from Project STAR. NBER Working Paper

S95-S120.

no. 1638. Washington, DC: National Bureau of

Coleman, John C., and Leo B. Hendry. 1999. The nature

Economic Research.

of adolescence (3rd ed.). London: Routledge.

Chobanian, Aram V., George L. Bakris, Henry R. Black,

Comarow, Avery. 2010. Best Hospitals 2010-11: The

William C. Cushman, Lee A. Green, Joseph L. Izzo,

Honor Roll. U.S. News & World Report. Accessed

Jr., Daniel W. Jones, et al., and the National High

February 11, 2001. http://health.usnews.com/

Blood Pressure Education Program Coordinating

health-news/best-hospitals/articles/2010/07/14/best-

Committee. 2003. Seventh report of the Joint

hospitals-2010-11-the-honor-roll

National Committee on Prevention, Detection,

Comey, Jennifer, Xavier De Souza Briggs, and Gretchen

Evaluation, and Treatment of High Blood Pressure.

Weismann. 2008. Struggling to stay out of high-

Hypertension 42(6): 1206-52.

poverty neighborhoods: Lessons from the Moving to

Christakis, Nicholas, and James Fowler. 2007. The

Opportunity experiment. Washington, DC: Urban

spread of obesity in a large social network over 32

Institute.

years. New England Journal of Medicine 357:370-79.

271

References

Connell, James P. and Bonnie L. Halpern-Felsher. 1997.

Crain, Ellen F., Michelle Walter, George T. O’Connor,

“How neighborhoods affect educational outcomes in

Herman Mitchell, Rebecca S. Gruchalla, Meyer

middle childhood and adolescence: conceptual issues

Kattan, George S. Malindzak, et al. 2002. Home

and an empirical example.” In Neighborhood poverty:

and allergic characteristics of children with asthma

Context and consequences for development, eds. Jeanne

in seven U.S. urban communities and design of an

Brooks-Gunn, Greg J. Duncan, and J. Lawrence

environmental intervention: The Inner-city asthma

Aber. 174-99. New York: Russell Sage Foundation.

study. Environmental Health Perspectives 110(9):

Cook, Philip J., and Kristen Goss. 1996. A selective

939-45.

review of the social-contagion literature. Working

Crane, Jonathan. 1991. The epidemic theory of ghettos

Paper. Sanford Institute of Public Policy, Duke

and neighborhood effects on dropping out and

University.

teenage childbearing. American Journal of Sociology

Cook, Philip J., and John H. Laub. 2002. After the

96(5): 1226-59.

epidemic: recent trends in youth violence in the

Crowder, Kyle, and Scott J. South. 2005. Race, class,

United States. In Michael Tonry, ed., Crime and

and changing patterns of migration between poor

justice: a review of research. Pp. 117-153. Chicago:

and nonpoor neighborhoods. American Journal of

University of Chicago Press.

Sociology, 110(6): 1715-76.

———. 1998. “The Unprecedented Epidemic in Youth

Cubbin, Catherine, and Marilyn A. Winkleby. 2005.

Violence.” In Youth Violence, edited by Michael

Protective and harmful effects of neighborhood-

Tonry and Mark H. Moore, 101-138. University of

level deprivation on individual health knowledge,

Chicago Press.

behavior changes, and risk of coronary heart disease.

Cook, Philip J., Jens Ludwig, Sudhir A. Venkatesh, and

American Journal of Epidemiology 162: 559-68.

Anthony A. Braga. 2007. Underground gun markets. Cullen, Julie Berry, Brian A. Jacob and Steven D. Levitt.

Economic Journal. 117: F558-588.

2006. The effect of school choice on participants:

Cook, Philip J., and Jens Ludwig. 2000. Gun Violence:

Evidence from randomized lotteries. Econometrica.

The Real Costs. New York: Oxford University Press.

74(5): 1191-1230.

Corcoran, Mary, Roger Gordon, Deborah Laren, and

Currie, Janet, and Duncan Thomas. 1995. Does Head

Gary Solon. 1992. The association between men’s

Start make a difference? American Economic Review

economic status and their family and community

80: 341-364.

origins. Journal of Human Resources 27:575–601.

Currie, Janet, and Reed Walker. 2011. Traffic congestion

Corcoran, Mary, and Colleen Heflin. 2003. Barriers

and infant health: Evidence from E-Z pass. American

to work among recipients of housing assistance.

Economic Journal: Applied Economics 3(1): 65-90.

Cityscape 6(2): 73-87.

Curtis, Lori J., Martin D. Dooley, and Shelley A. Phipps.

Cove, Elizabeth, Xavier De Souza Briggs, Margery Austin

2004. Child well-being and neighbourhood quality:

Turner, and Cynthia Duarte. 2008. Can escaping

Evidence from the Canadian National Longitudinal

from poor neighborhoods increase employment and

Survey of Children and Youth. Social Science and

earnings? Washington, DC: The Urban Institute.

Medicine 58(10): 1917-27.

Cowie, Catherine C., Keith F. Rust, Danita D. Byrd-

Darling-Hammond, Linda. 1996. “Restructuring Schools

Holt, Mark S. Eberhardt, Katherine M. Flegal,

for High Performance.” In Rewards and Reform:

Michael M. Engelgau, Sharon H. Saydah, Desmond

Creating Educational Incentives that Work. Susan H.

E. Williams, Linda S. Geiss, and Edward W. Gregg.

Fuhrman and Jennifer A. O’Day, eds. 144-194. New

2006. Prevalence of diabetes and impaired fasting

York: Jossey-Bass.

glucose in adults in the U.S. population. Diabetes

Darmon, Nicole, and Adam Drewnoski. 2008. Does

Care 29:1263–68.

social class predict diet quality? American Journal of

Clinical Nutrition 87(5): 1107-17.

272

Deater-Deckard, Kirby. 2001. Annotation: Recent

Department of Data Management (DDM). 2006. “Iowa

research examining the role of peer relationships

Tests of Basic Skills Citywide Results Over Time--

in the development of psychopathology. Journal of

1997 to 2005.” Prepared for the Office of Research,

Child Psychology and Psychiatry 42: 565-79.

Evaluation, and Accountability. Chicago Public

Delpierre, C., V. Lauwers-Cances, G.D. Datta, T. Lang,

Schools.

and L. Berkman. 2009. Using self-rated health for

Depres, Jean-Pierre, and Isabelle Lemieux. 2006.

analysing social inequalities in health: A risk for

Abdominal obesity and metabolic syndrome. Nature

underestimating the gap between socioeconomic

444(14): 881-87.

groups? Journal of Epidemiology and Community

Devine, Deborah J., Robert W. Gray, Lester Rubin, and

Health 63(6): 426-32.

Lydia B. Taghavi. 2003. Housing choice voucher

Deluca, Stefanie, Greg J. Duncan, Micere Keels, and

location patterns: Implications for participant and

Ruby Mendenhall. 2010. Gautreaux mothers and

neighborhood welfare. Washington, DC: U.S.

their children: An update. Housing Policy Debate

Department of Housing and Urban Development.

20(1): 7-25.

Dietz, Robert D. 2002. The estimation of neighborhood

DeLuca, Stefanie and James E. Rosenbaum. 2003.

effects in the social sciences. Social Science Research

“Do Blacks Prefer Integrated Neighborhoods?

31: 539-75.

Testing Survey Opinions with Quasi-Experimental

Diez-Roux, Ana V., and Christina Mair. 2010.

Residential Mobility Data.” Housing Policy Debate

Neighborhoods and health. Annals of the New York

14: 305-346.

Academy of the Sciences 1186: 125-45.

———. 2002. “If Low-Income Blacks Are Given a

Diez-Roux, Ana V., Sharon Stein Merkin, Donna Arnett,

Chance to Live in White Neighborhoods, Will

Lloyd Chambless, Mark Massing, F. Javier Nieto,

They Stay? Examining Mobility Patterns with

Paul Sorlie, Moyses Szklo, Herman A. Tyroler, and

Quasi-Experimental Data.” Working Paper WP-

Robert L. Watson. 2001. Neighborhood of residence

02-28. Evanston, IL: Institute for Policy Research,

and incidence of coronary heart disease. New

Northwestern University.

England Journal of Medicine 345(2): 99-106.

DeLuca, Stefanie and Peter Rosenblatt. 2010. “Does

Dodge, Kenneth A. 2009. Community intervention

Moving To Better Neighborhoods Lead to Better

and public policy in the prevention of antisocial

Schooling Opportunities? Parental School Choice

behavior. Journal of Child Psychology and Psychiatry

in an Experimental Housing Voucher Program.”

50: 194-200.

Teachers College Record. 112(5): 1443–1491.

Dornbusch, Sanford M., Philip L. Ritter, and Laurence

———. 2009. Leaving the Ghetto: Residential Mobility

Steinberg. 1991. “Community influences on

and Opportunity in Baltimore. Paper presented at

the relations of family statuses to adolescent

the annual meeting of the American Sociological

school performance: Differences between African

Association Annual Meeting, San Francisco, CA,

Americans and Non-Hispanic Whites.” American

Aug 08.

Journal of Education 38: 543-67.

Deming, David. 2009. Early childhood intervention and

Dowd, James J., Rose P. Sisson, and Dennis M. Kern.

life-cycle skill development: Evidence from Head

1981. Socialization to violence among the aged.

Start. American Economic Journal: Applied Economics.

Journal of Gerontology 36: 350-61.

1(3): 111-34.

Dowd Jennifer B., and Anna Zajacova. 2010. Does

Denavas-Walt, Carmen, Bernadette Proctor, and Jessica

self-rated health mean the same thing across

Smith. 2010. Income, poverty, and health insurance

socioeconomic groups? Evidence from biomarker

coverage in the United States: 2009. U.S. Census

data. Annals of Epidemiology 20(10): 743-49.

Bureau, Current Population Reports: Consumer

Income, P60-238. Washington, DC: Government

Printing Office.

273

References

Dubowitz, Tamara, Melonie Heron, Chloe E. Bird,

Farrington, David. 2002. “Key Results from the First

Nicole Lurie, Brian K. Finch, Ricardo Basurto-

Forty Years of the Cambridge Study in Delinquency

Dávila, Lauren Hale, and José J. Escarce. 2008.

Development.” In Taking Stock of Delinquency:

Neighborhood socioeconomic status and fruit and

An Overview of Findings from Contemporary

vegetable intake among whites, blacks, and Mexican

Longitudinal Studies, edited by T. Thornberry and

Americans in the United States. American Journal of

M. Krohn. New York: Kluwer/Plenum.

Clinical Nutrition 87(6): 1883-91.

Feins, Judith D., and Mark D. Shroder. 2005. Moving

Duncan, Greg J. 1994. “Families and Neighbors as

to opportunity: The demonstration’s design and its

Sources of Disadvantage in the Schooling Decisions

effects on mobility. Urban Studies 42(8): 1275-99.

of Black and White Adolescents.” American Journal

Ferguson, Ronald. 2010. Tripod Student Perceptions

of Education 103(1): 20-53.

Survey (Version 6). Tripod Project for School

Duncan, Greg J., Jeanne Brooks-Gunn, and Pamela K.

Improvement. Available at http://tripodproject.org.

Klebanov. 1994. Economic deprivation and early

———. 2001. “A Diagnostic Analysis of Black-White

childhood development. Child Development 65(2):

GPA Disparities in Shaker Heights, Ohio.” In

296-318.

Brookings Papers on Education Policy, edited by Diane

Duncan, Greg J. and Eric Petersen. 2001.The Long and

Ravitch, 347-414. Washington, DC: Brookings

Short of Asking Questions about Income, Wealth

Institution Press.

and Labor Supply. Social Science Research, 30(2):

Ferryman, Kadija, Xavier De Souza Briggs, Susan

248-263.

J. Popkin, and Maria Rendon. 2008. Do better

Durlauf, Steven N. 2004. Neighborhood effects. In

neighborhoods for MTO families mean better schools?

Handbook of regional and urban economics, ed. J.V.

Washington, DC: Urban Institute.

Henderson and J.V. Thisse.2173-2242. Amsterdam:

Fields, Jason, Kristin Smith, Loretta E. Bass, and Terry

North Holland.

Lugaila. (2001). A Child’s Day: Home, School and

Ellen, Ingrid D., and Margery A. Turner. 1997. Does

Play (Selected Indicators of Child Well-Being).

neighborhood matter? Housing Policy Debate 8(4):

CENSUS-P70-68. Washington D.C.: Bureau of the

833-66.

Census (DOC).

Entwisle, Doris R., Karl L. Alexander, and Linda

Filippelli, Gabriel M., and Mark A.S. Laidlaw. 2010.

Steffel Olson. 1994. “The gender gap in math: Its

The elephant in the playground: Confronting lead-

possible origins in neighborhood effects.” American

contaminated soils as an important source of lead

Sociological Review 59: 822-38.

burdens to urban populations. Perspectives in Biology

Esbensen, F., and D. Huizinga. 1990. “Community

and Medicine 53(1): 31-45.

Structure and Drug Use: From a Social

Fischer, Will, and Barbara Sard. 2008. Preserving safe,

Disorganization Perspective.” Justice Quarterly 7(4):

high quality public housing should be a priority of

691-709.

federal housing policy. Washington, DC: Center on

Ettner, Susan L., Richard G. Frank and Ronald C.

Budget and Policy Priorities.

Kessler. 1997. The impact of psychiatric disorders

Fitzpatrick, Kevin, and Mark Lagory. 2000. Unhealthy

on labor market outcomes. Industrial and Labor

places: The ecology of risk in the urban landscape. New

Relations Review 51(1): 64-81.

York: Routledge.

Famularo, Richard, Terence Fenton, Robert Kinscherff,

Ford, Earl S., Ali H. Mokdad, and Wayne H. Giles,

and Marilyn Augustyn. 1996. Psychiatric

2003. Trends in waist circumference among U.S.

comorbidity in childhood post traumatic stress

adults. Obesity Research 11:1223–31.

disorder. Child Abuse and Neglect 20: 953-61.

Fortson, Jane G., and Lisa Sanbonmatsu. 2010. Child

health and neighborhood conditions: Evidence from

a randomized housing voucher experiment. Journal

of Human Resources 45(4): 840-64.

274

Franco, Manuel, Ana V. Diez-Roux, Thomas A. Glass,

———. 2002. A cross-site analysis of initial Moving

Benjamin Caballero, and Frederick L. Brancati.

to Opportunity demonstration results. Journal

2008. Neighborhood characteristics and availability

of Housing Research 13(1). Available at www.

of healthy foods in Baltimore. American Journal of

fanniemaefoundation.org/programs/jhr/pdf/

Preventive Medicine 35(6): 561-67.

jhr_1301_goering.pdf.

Furukawa, T.A., R.C. Kessler, T. Slade, and G. Andrews.

Goering, John, Joan Kraft, Judith Feins, Debra McInnis,

2003. The performance of the K6 and K10 screening

Mary Joel Holin, and Huda Elhassan. 1999.

scales for psychological distress in the Australian

Moving to Opportunity for fair housing demonstration

National Survey of Mental Health and Well-Being.

program: Current status and initial findings.

Psychological Medicine 33: 357-62.

Washington, DC: U.S. Department of Housing and

Garces, Eliana, Duncan Thomas, and Janet Currie.

Urban Development.

2002. Longer term effects of Head Start. American

Goldstein, Sara, Pamela Davis-Kean, and Jacquelynne

Economic Review 92: 999-1012.

Eccles. 2005. Parents, peers, and problem behavior:

General Accounting Office. 2001. Federal housing

A longitudinal investigation of the impact of

assistance programs: Costs and housing

relationship perceptions and characteristics on the

characteristics. Report GAO-01-901R. Washington,

development of adolescent problem behaviors.

DC: GAO.

Developmental Psychology 41(1): 401-13.

Gennetian, Lisa, Sendhil Mullainathan, and Eldar

Gonzalez, Nancy, and Kenneth A. Dodge. 2010. “Family

Shafir (forthcoming).Economic instability, mental

and Peer Influences on Adolescent Behavior and

resources and poverty traps. In The Behavioral

Risk-Taking.”

Foundations of Policy, ed. E. Shafir. Princeton and

Goodman, Anna, and Robert Goodman. 2009. Strengths

New York: Princeton University and Russell Sage

and difficulties questionnaire as a dimensional

Foundation.

measure of child mental health. Journal of the

Gidlow, Christopher, Thomas Cochrane, Rachel C.

American Academy of Child and Adolescent Psychiatry

Davey, Graham Smith, and Jon Fairburn. 2010.

48: 400-03.

Relative importance of physical and social aspects

Gould, Eric D., Victor Lavy, and M. Daniele Paserman

of perceived neighborhood environment for self-

2009. Sixty years after the magic carpet ride: The

reported health. Preventive Medicine 51(2): 157-63.

long-run effect of the early childhood environment

Gifford-Smith, Mary, Kenneth A. Dodge, Thomas J.

on social and economic outcomes. NBER Working

Dishion, and Joan McCord. 2005. Peer influence in

Paper no. 14884. Cambridge: National Bureau of

children and adolescents: Crossing the bridge from

Economic Research.

developmental to intervention science. Journal of

Green, Jennifer G., Katie A. McLaughlin, Patricia A.

Abnormal Child Psychology 33: 255-65.

Berglund, Michael J. Gruber, Nancy A. Sampson,

Glaeser, Edward L., and José A. Scheinkman. 1999.

Alan M. Zaslavsky, and Ronald C. Kessler. 2010.

Measuring social interactions. Unpublished paper,

Childhood adversities and adult psychiatric disorders

Harvard University.

in the national comorbidity survey replication I:

Goering, John, and Judith D. Feins, eds. 2003. Choosing

associations with first onset of DSM-IV disorders.

a better life? Evaluating the Moving to Opportunity

Archives of General Psychiatry 67: 113-23.

social experiment. Washington DC: Urban Institute.

Groves, Betsy M., Barry Zuckerman, Steven Marans, and

Goering, John, Judith D. Feins, and Todd M.

Donald J. Cohen. 1993. Silent victims: Children

Richardson. 2003. What have we learned about

who witness violence. Journal of the American

housing mobility and poverty deconcentration?

Medical Association 269: 262-64.

In Choosing a better life? Evaluating the Moving to

Groves, Robert M., Floyd J. Fowler, Mick P. Couper,

Opportunity social experiment, ed. John Goering

James M. Lepkowski, Eleanor Singer, and Roger

and Judith D. Feins. 3-36. Washington, DC: Urban

Tourangeau. 2004. Survey Methodology. New York:

Institute.

John Wiley & Sons.

275

References

Guttmacher Institute. 2010. U.S. Teenage Pregnancies,

Hogan, Dennis P., and Evelyn M. Kitagawa. 1985.

Births, and Abortions: National and State Trends

“The Impact of Social Status, Family Structure,

and Trends by Race and Ethnicity. www.guttmacher.

and Neighborhood on the Fertility of Black

org (accessed August 28, 2011).

Adolescents.” American Journal of Sociology. 90(4):

Hanratty, Maria H., Sara A. McLanahan, and Becky

825-855.

Pettit. 2003. Los Angeles site findings. In Choosing

Huizinga, David, Terence Thornberry, Kelly Knight, and

a better life? Evaluating the Moving to Opportunity

Peter Lovegrove. 2007. Disproportionate Minority

social experiment, ed. John Goering and Judith Feins.

Contact in the Juvenile Justice System: A Study of

245-74. Washington, DC: Urban Institute.

Differential Minority Arrest/Referral to Court in Three

———. 1998. The impact of the Los Angeles Moving

Cities. Washington, DC: National Criminal Justice.

to Opportunity program on residential mobility,

Available at https://www.ncjrs.gov/pdffiles1/ojjdp/

neighborhood characteristics, and early child and

grants/219743.pdf.

parent outcomes. Working Paper Number 98-

Hunt, D. Bradford. 2009. Blueprint for disaster: The

18. Los Angeles: Bendheim-Thoman Center for

unraveling of Chicago public housing. Chicago:

Research on Child Wellbeing.

University of Chicago Press.

Harcourt, Bernard, and Jens Ludwig. 2006. Broken

Huston, Sara L., Kelly R. Evenson, and Philip Bors.

windows: New evidence from New York City and a

2003. Neighborhood environment, access to places

5-city social experiment. University of Chicago Law

for activity, and leisure-time physical activity in a

Review.

diverse North Carolina population. American Journal

Harkness, Joseph, and Sandra J. Newman. 2005.

of Health Promotion 18: 58-69.

Housing affordability and children’s wellbeing:

Idler, Ellen, and Yael Benyamini. 1997. Self-rated health

Evidence from the National Survey of America’s

and mortality: A review of twenty-seven community

Families. Housing Policy Debate 16(2): 223-55.

studies. Journal of Health and Social Behavior 38(1):

Haro, Josep M., Saena Arbabzadeh-Bouchez, Traolach S.

21-37.

Brugha, Giovanni De Girolamo, Margaret E. Guyer, Idler, Ellen, and Stanislav V. Kasl. 1995. Self-ratings Robert Jin, Jean Pierre Lepine, Fausto Mazzi, Blanca

of health: Do they also predict changes in

Reneses, Gemma Vilagut, Nancy A. Sampson,

functionality? Journal of Gerontology: Social Sciences

and Ronald C. Kessler. 2006. Concordance of the

50(6): S344-53.

Composite International Diagnostic Interview

Ihlanfeldt, Keith, and David Sjoquist. 1998. The spatial

Version 3.0 (CIDI 3.0) with standardized clinical

mismatch hypothesis: A review of recent studies and

assessments in the WHO World Mental Health

their implications for welfare reform. Housing Policy

Surveys. International Journal of Methods in

Debate 9(4): 849-92.

Psychiatric Research 15(4): 167-80.

Inagami, Sanae, Deborah Cohen, Brian Karl Finch,

Hirschfield, Alex, and Kate J. Bowers. 1997. “The effect

and Steven Asch. 2006. You are where you shop:

of social cohesion on levels of recorded crime in

Grocery store locations, weight, and neighborhoods.

disadvantaged areas.” Urban Studies. 34(8): 1275–

American Journal of Preventive Medicine 31(1):

95.

10–17.

Hirschi, Travis, and Michael R. Gottfredson. 1983. “Age

Ingels, Steven J., Ben Dalton, Tommy E. Holder,

and the Explanation of Crime.” American Journal of

Jr., Erich Lauff, Laura J. Burns, Laura LoGerfo.

Sociology 89(3): 552-584.

2011. “The High School Longitudinal Study of

Hogan, Dennis P., Nan Marie Astone, and Evelyn M.

2009 (HSLS:09) A First Look at Fall 2009 Ninth-

Kitagawa. 1985. “Social and Environmental Factors

Graders.” Prepared for the National Center for

Influencing Contraceptive Use Among Black

Education Statistics.

Adolescents.” Family Planning Perspectives. 17(4):

165-169.

276

Ingels, Steven J., Kathryn L. Dowd, John D. Baldridge,

Johnston, L. D., P.M. O’Malley, J.G. Bachman, and

James L. Stipe, Virginia H. Bartot, and Martin R.

J.E. Schulenberg. 2011. Monitoring the Future

Frankel. 1994. National education longitudinal study

national results on adolescent drug use: Overview of

of 1998 second follow-up: student component data file

key findings, 2010. Ann Arbor: Institute for Social

user’s manual. Washington, DC: U.S. Department of

Research, University of Michigan.

Education.

Joint National Committee on Prevention, Detection,

Institute for Social Research. 2008. Health and retirement

Evaluation, and Treatment of High Blood Pressure.

study: Physical measures and biomarkers 2008. Ann

1997. The sixth report of the Joint National

Arbor: University of Michigan. Available at http://

Committee on Prevention, Detection, Evaluation,

hrsonline.isr.umich.edu/modules/meta/2008/core/

and Treatment of High Blood Pressure. Archives of

qnaire/online/2008PhysicalMeasuresBiomarkers.pdf.

Internal Medicine 157(21): 2413-46.

Ioannides, Yannis M., and Linda Datcher Loury. 2004.

Jones, LeAlan, and Loyd Newman. 1998. Our America:

Job information networks, neighborhood effects,

Life and death on the south side of Chicago. New

and inequality. Journal of Economic Literature

York: Washington Square.

42:1056-93.

Kain, John F. 1968. Housing segregation, Negro

Jacob, Brian. 2004. Public housing, housing vouchers

employment, and metropolitan decentralization.

and student achievement: Evidence from public

Quarterly Journal of Economics 82(2): 175-97.

housing demolitions in Chicago. American Economic Kan, Haidong, Gerardo Heiss, Kathryn M. Rose, Eric A.

Review 94(1): 233-58.

Whitsel, Fred Lurmann, and Stephanie J. London.

———. 2003. “Getting Inside Accountability: Lessons

2008. Prospective analysis of traffic exposure as a

from Chicago.” In William G. Gale and Janet

risk factor for incident coronary heart disease: The

Rothenberg Pack, eds., Brookings-Wharton Papers

Atherosclerosis Risk in Communities (ARIC) Study.

on Urban Affairs 2003. Washington, D.C.: The

Environmental Health Perspectives 116(11): 1463-68.

Brookings Institution, pp. 41-70.

Katz, Lawrence F., Jeffrey R. Kling, and Jeffrey B.

Jacob, Brian A., and Jens Ludwig. 2011. The effects of

Liebman. 2001. Moving to Opportunity in Boston:

housing assistance on labor supply: Evidence from a

Early results of a randomized mobility experiment.

voucher lottery. American Economic Review.

Quarterly Journal of Economics 116: 607-54.

Jacob, Brian A., Jens Ludwig, and Douglas L. Miller.

Kawachi, Ichiro, and Lisa F. Berkman, eds. 2003a.

2011. “The effects of housing and neighborhood

. Neighborhoods and health. New York: Oxford

conditions on child mortality.” National Bureau of

University Press.

Economic Research Working Paper No. 17369.

———. 2003b. Introduction, In Neighborhoods and

Jargowsky, Paul A. 1997. Poverty and place: Ghettos,

health, ed. Ichiro Kawachi and Lisa F. Berkman. Pp.

barrios, and the American city. New York: Russell

1-19. New York: Oxford University Press.

Sage Foundation.

Keels, Micere. 2008. Residential attainment of now-

Jencks, Christopher, and Susan E. Mayer. 1990. The

adult Gautreaux Children: Do they gain, hold or

social consequences of growing up in a poor

lose ground in neighborhood ethnic and economic

neighborhood. In Inner-city poverty in the United

segregation? Housing Studies 23(4): 541-64.

States, ed. L.E. Lynn, Jr., and M.G.H. McGeary.

Keels, Micere, Stefanie DeLuca, Greg J. Duncan, Ruby

111-86. Washington, DC: National Academy Press.

Mendenhall, and James Rosenbaum 2005. Fifteen

Jencks, Christopher and Meredith Phillips, eds. 1998.

years later: Can residential mobility programs

The Black-White Test Score Gap. Washington, D.C.:

provide a long-term escape from neighborhood

The Brookings Institution.

segregation, crime, and poverty? Demography 42(1):

51-73.

277

References

Kemple, James J. 2008. Career Academies: Long-term

Khadduri, Jill, Mark Shroder, and Barry Steffen.

impacts on labor market outcomes, educational

1998. Welfare reform and HUD-assisted housing:

attainment, and transitions to adulthood. New York:

Measuring the extent of needs and opportunities.

MDRC.

Paper presented at Conference on Managing

Kessler, Ronald, Shelli Avenevoli, E. Jane Costello,

Affordable Housing Under Welfare Reform:

Jennifer Green, Michael Gruber, Steven Heeringa,

Reconciling Competing Demands. Washington,

Kathleen Merikangas, Beth-Ellen Pennell, Nancy

DC, June 26.

Sampson, and Alan Zaslavsky. 2009. National

Kirby, James B., and Kaneda, Toshiko 2005.

Comorbidity Survey Replication Adolescent

Neighborhood socioeconomic disadvantage and

Supplement (NCS-A): II. Overview and design.

access to health care. Journal of Health and Social

Journal of the American Academy of Child and

Behavior 46(1): 15-31.

Adolescent Psychiatry 48(4): 380-85.

Klebanov, Pamela K., Jeanne Brooks-Gunn, P. Lindsay

Kessler, Ronald C., Peggy R. Barker, Lisa J. Colpe,

Chase-Lansdale, and Rachel A. Gordon. 1997. “Are

Joan F. Epstein, Joseph C. Gfroerer, Eva Hiripi,

neighborhood effects on young children mediated by

Mary J. Howes, Sharon-Lise T. Normand, Ronald

features of the home environment?” In Neighborhood

W. Manderscheid, Ellen E. Walters, and Alan M.

poverty: Context and consequences for development,

Zaslavsky. 2003. Screening for serious mental

eds. Jeanne Brooks-Gunn, Greg J. Duncan, and J.

illness in the general population. Archives of General

Lawrence Aber. 119-145. New York: Russell Sage

Psychiatry 60: 184-89.

Foundation.

Kessler, R.C., W.T. Chiu, O. Demler, K.R. Merikangas,

Kleiman, Mark A. R. 1993. “Enforcement swamping:

and E.E. Walters. 2005. Prevalence, severity, and

A positive-feedback mechanism in rates of illicit

comorbidity of 12-month DSM-IV disorders in the

activity.” Mathematical Computational Modeling

National Comorbidity Survey Replication. Archives

17(2): 65-75.

of General Psychiatry 62:617-27.

Kling, Jeffrey R., Jeffrey B. Liebman, and Lawrence F.

Kessler, Ronald C., Katherine A. McGonagle, Shanyang

Katz. 2007. Experimental analysis of neighborhood

Zhao, Christopher B. Nelson, Michael Hughes,

effects. Econometrica 75(1): 83-119.

Suzann Eshleman, Hans-Ulrich Wittchen, and

———.2005. “Bullets don’t got no name: Consequences

Kenneth S. Kendler. 1994. Lifetime and 12-month

of fear in the ghetto.” In Discovering successful

prevalence of DSM-III-R psychiatric disorders

pathways in children’s development: Mixed methods in

in the United States: Results from the National

the study of childhood and family life, ed. Thomas S.

Comorbidity Survey. Archives of General Psychiatry

Weisner. 243-281. Chicago: University of Chicago

51: 8-19.

Press.

Kessler, R.C., and T.B. Üstün, eds. 2008. The WHO

Kling, Jeffrey R., Jens Ludwig, and Lawrence F. Katz.

world mental health surveys: Global perspectives on

2005. Neighborhood effects on crime for female and

the epidemiology of mental disorders. New York:

male youth: Evidence from a randomized housing

Cambridge University Press.

voucher experiment. Quarterly Journal of Economics

__________. 2004. The World mental health (WMH)

120(1): 87-130.

survey initiative version of the World Health

Kloos, Bret, Seema Shah. (2009). A social ecological

Organization (WHO) composite international

approach to investigating relationships between

diagnostic interview (CIDI). International Journal of

housing and adaptive functioning for persons

Methods in Psychiatric Research 13: 93-121.

with serious mental illness. American Journal of

Community Psychology 44: 316-26.

278

Knudsen, Eric I., James J. Heckman, Judy Cameron,

Lee, Rebecca E., and Catherine Cubbin. 2002.

and Jack P. Shonkoff. 2006. Building America’s

Neighborhood context and youth cardiovascular

future workforce: Economic, neurobiological and

health behaviors. American Journal of Public Health

behavioral perspectives on investment in human skill

92(3): 428-36.

development. Proceedings of the National Academy of

Leger, Mireille L. and Stephen D. Kennedy (1990) Final

Sciences. 103(27): 10155-10162.

comprehensive report of the freestanding housing

Kooklan, Paria. 2008. The Section 8 Housing Choice

voucher demonstration. Washington, DC: U.S.

Voucher Program: Federal Rules and Local

Department of Housing and Urban Development,

Policies Affecting Individuals with Criminal

Office of Policy Development and Research.

Histories. Los Angeles, CA: Los Angeles Homeless

Leventhal, Tama, and Jeanne Brooks-Gunn. 2004. A

Services Authority. http://www.lahsa.org/docs/

randomized study of neighborhood effects on

policyandplanning/section8.pdf.

low-income children’s educational outcomes.

Kornitzer, M., Dramaix, M., and G. De Backer. 1999.

Developmental Psychology 40(4): 488-507.

Epidemiology of risk factors for hypertension:

———. 2003. The early impacts of Moving to

Implications for prevention and therapy. Drugs

Opportunity on children and youth in New York

57(5): 695-712.

City. In Choosing a better life: Evaluating the Moving

Kotlowitz, Alex. 1992. There are no children here. New

to Opportunity social experiment, ed. John Goering

York: Anchor.

and Judith Feins. Washington, DC: Urban Institute

Krause, Neal. 1993. Neighborhood deterioration and

Press.

social isolation in later life. International Journal of

———.2000. The neighborhoods they live in: The

Aging and Human Development 36: 9-38.

effects of neighborhood residence of child and

Kuo, Lydia E., Magdalena Czarnecka, Joanna B.

adolescent outcomes. Psychological Bulletin 126:

Kitlinska, Jason U. Tilan, Richard Kvetnanský, and

309-37.

Zofia Zukowska. 2008. Chronic stress, combined

Levitt, Steven D. 2004. “Understanding Why Crime

with a high-fat/high-sugar diet, shifts sympathetic

Fell in the 1990s: Four Factors That Explain the

signaling toward neuropeptide Y and leads to obesity

Decline and Six That Do Not.” Journal of Economic

and the metabolic syndrome. Annals of the New York

Perspectives 18(1): 163-190.

Academy of Sciences 1148: 232-37.

Little, Michelle and Laurence Steinberg. 2006.

LaGrange, Teresa C., and Robert A. Silverman. 1999.

“Psychosocial correlates of adolescent drug dealing

Low self-control and opportunity: Testing the

in the inner city.” Journal of Research in Crime and

general theory of crime as an explanation for the

Delinquency 43(4): 357-386.

gender differences in delinquency. Criminology 37:

Loewenstein, George, and Ted O’Donoghue. 2004.

41-72.

Animal spirits: Affective and deliberative processes

Lara, Marielena L., Christina Gamboa, M. Iya

in human behavior. Working Paper. Carnegie

Kahramanian, Leo S. Morales, and David E. Hayes

Mellon University, Department of Social and

Bautista. 2005. Acculturation and Latino health

Decision Sciences.

in the United States: A review of the literature and

Lovasi, Gina S., Kathryn M. Neckerman, James W.

its sociopolitical context. Annual Review of Public

Quinn, Christopher C. Weiss, and Andrew Rundle.

Health 46: 367-97

2009. Effect of individual or neighborhood

Lawler, Edward J., and Shayne R. Thye. 1999. Bringing

disadvantage on the association between

emotions into social exchange theory. Annual Review

neighborhood walkability and body mass index.

of Sociology 25: 217-44.

American Journal of Public Health 99: 279-84.

Lee, Crystal, Rachel R. Huxley, Rachel P. Wildman,

Ludwig, Jens. 2006. “The Costs of Crime.” Testimony to

and Mark Woodward. 2008. Indices of abdominal

the U.S. Senate Judiciary Committee. September 19,

obesity are better discriminators of cardiovascular

2006.

risk factors than BMI: A meta-analysis. Journal of

Clinical Epidemiology 61:646-53.

279

References

Ludwig, Jens, Greg J. Duncan, and Paul Hirschfield.

Mango, Eiji and Albert Park. Forthcoming. Relative

2001. Urban poverty and juvenile crime: Evidence

deprivation and health: Which reference groups

from a randomized housing-mobility experiment.

matter? Journal of Human Resources.

Quarterly Journal of Economics 116(2): 655-80.

Maguin, Eugene, and Rolf Loeber. 1996. “Academic

Ludwig, Jens, Greg Duncan, and Joshua Pinkston.

performance and delinquency.” Crime and Justice.

2005. Housing mobility programs and economic

20: 145-264.

self-sufficiency: Evidence from a randomized

Manski, Charles F. 2000. Economic analysis of social

experiment. Journal of Public Economics 89(1): 131-

interactions. Journal of Economic Perspectives 14:

56.

115-36.

Ludwig, Jens, and Jeffrey R. Kling. 2007. Is crime

Maurin, Eric, and Julie Moschion. 2009. The social

contagious? Journal of Law and Economics 50: 491-

multiplier and labor market participation of

518.

mothers. American Economic Journal: Applied

Ludwig, Jens, Helen Ladd, and Greg J. Duncan. 2001.

Economics 1(1): 251-72.

The effects of urban poverty on educational

McDade, Thomas W. 2003. Life history theory and the

outcomes: Evidence from a randomized experiment.

immune system: Steps toward a human ecological

In Urban poverty and educational outcomes, ed.

immunology. Yearbook of Physical Anthropology

William Gale and Janet Rothenberg Pack. 147-201.

46:100-25.

Washington, DC: Brookings Institution.

McDade, Thomas W., Joy F. Stallings, Adrian Angold,

Ludwig, Jens, Jeffrey B. Liebman, Jeffrey R. Kling, Greg

E. Jane Costello, Mary Burleson, John T. Cacioppo,

J. Duncan, Lawrence F. Katz, Ronald C. Kessler,

Ronald Glaser, and Carol M. Worthman. 2000.

and Lisa Sanbonmatsu. 2008. What can we learn

Epstein-Barr virus antibodies in whole blood spots:

about neighborhood effects from the Moving to

A minimally invasive method for assessing an aspect

Opportunity experiment? American Journal of

of cell-mediated immunity. Psychosomatic Medicine

Sociology 114(1): 144-88.

62:560-67.

Ludwig, Jens, and Douglas L. Miller. 2007. Does Head

McLoyd, Vonnie C. and Constance A. Flanagan, eds.

Start improve children’s life chances? Evidence

1990. Economic Stress: Effects on Family Life and

from a regression discontinuity approach. Quarterly

Child Development. New York: Jossey-Bass.

Journal of Economics. 122(1): 159-208.

McPherson, Miller, Lynn Smith-Lovin, and James M.

Lumeng, Julie C., Danielle Appugliese, Howard J.

Cook (2001) Birds of a feather: Homophily in social

Cabral, Robert H. Bradley, and Barry Zuckerman.

networks. Annual Review of Sociology. 27: 415-444.

2006. Neighborhood safety and overweight status

Mendenhall, Ruby, Greg Duncan, and Stefanie DeLuca.

in children. Archives of Pediatrics and Adolescent

2006. Neighborhood resources, racial segregation

Medicine 160(1): 25-31.

and economic mobility: Results from the Gautreaux

Luttmer, Erzo F.P. 2005. Neighbors as negatives: Relative

program. Social Science Research 35(4): 892-923.

earnings and well-being. Quarterly Journal of

Merkin, Sharon S., Ricardo Basurto-Davila, Arun

Economics 120(3): 963-1002.

Karlamangla, Chloe Bird, Nicole Lurie, Jose Escarce,

Macintyre, Sally, and Anne Ellaway. 2003.

and Teresa Seeman. 2009. Neighborhoods and

Neighborhoods and health: An overview. In

cumulative biological risk profiles by race/ethnicity

Neighborhoods and Health, ed. I. Kawachi and L.

in a national sample of us adults: NHANES III.

Berkman. Pp. 20-42. New York: Oxford University

Annals of Epidemiology 19(3): 194-201.

Press.

Merry, Sally, Heather McDowell, Sarah Hetrick, Juillet

Maguire, Sheila, Joshua Freely, Carol Clymer, Maureen

Bir, and N. Muller. 2004. Psychological and/or

Conway, and Deena Schwartz. 2010. Tuning in

educational interventions for the prevention of

to local labor markets: Findings from the sectoral

depression in children and adolescents. Cochrane

employment impact study. New York: Public/Private

Database of Systematic Reviews CD003380.

Ventures.

280

Mills, Gregory, Daniel Gubits, Larry Orr, David Long,

National Institutes of Health, National Heart, Lung and

Judie Feins, Bulbul Kaul, Michelle Wood, and Amy

Blood Institute, and Obesity Education Initiative.

Jones. 2006. Effects of housing vouchers on welfare

2004. Seventh report of the Joint National Committee

families. Washington, DC: U.S. Department of

on Prevention, Detection, Evaluation, and Treatment

Housing and Urban Development, Office of Public

of High Blood Pressure. Washington, DC: National

Development and Research.

Institutes of Health.

Moffitt, Terrie E. 1993. “Adolescent-limited and

———. 1998. Clinical guidelines on the identification,

life-course-persistent antisocial behavior: A

evaluation, and treatment of overweight and obesity

developmental taxonomy.” Psychological Review

in adults. Washington, DC: National Institutes of

100(4): 674-701.

Health.

Moffitt, Robert A. 2001. Policy interventions, low-

National Longitudinal School-Level State Assessment

level equilibria, and social interactions. In Social

Score Database (NLSLASD) 2010. Retrieved Jan 13,

dynamics, ed. Steven Durlauf and Peyton Young.

2011 from http://www.schooldata.org.

Cambridge, MA: MIT Press.

National Student Clearinghouse (NSC). 2011. http://

Moore, Kristin A., Sharon McGrode., Elizabeth C. Hair,

www.studentclearinghouse.org/about/aboutus.htm.

and Marjorie L. Gunnoe. 1999. NLSY97 codebook

Accessed on August 22, 2011.

supplement main file round 1, appendix 9, family

Neal, Derek A. 2004. The relationship between marriage

process and adolescent outcome measures. Washington,

market prospects and never-married motherhood.

DC: U.S. Department of Labor.

Journal of Human Resources 39(4): 938-57.

Morenoff, Jeffrey D. , Robert J. Sampson, and Stephen

Newman, Sandra J., ed. 1999. The home front:

W. Raudenbush. 2001. “Neighborhood Inequality,

Implications of welfare reform for housing policy.

Collective Efficacy, and the Spatial Dynamics of

Washington, DC: Urban Institute.

Urban Violence.” Criminology 39(3): 517-59.

Newman, Sandra J., and Ann B. Shnare. 1997. “... and a

Morland, Kimberly, Steve Wing, Ana Diez Roux, and

suitable living environment”: The failure of housing

Charles Poole. 2002. Neighborhood characteristics

programs to deliver on neighborhood quality.

associated with the location of food stores and

Housing Policy Debate 8(4): 703-41.

food service places. American Journal of Preventive

Office of Research, Assessment and Evaluation (ORAE).

Medicine 22: 23-29.

2010a. “Boston Public Schools: 4-Year Graduation

Mujahid, Mahasin S., Ana Diez Roux, Mingwu Shen,

Rate.” Accessed August 15, 2011. http://www.

Deepthiman Gowda, Brisa Sanchez, Steven Shea,

bostonpublicschools.org/files/Cohort%202010%20

David Jacobs, Jr., and Sharon Jackson. 2008.

4-Y%20Graduation%20Rate%20Report%5B1%5D.

Relationship between neighborhood environments

pdf.

and obesity in the multi-ethnic study of

———. 2010b. “Massachusetts Comprehensive

atherosclerosis. American Journal of Epidemiology.

Assessment System Results of Spring 2010.”

167 (11): 1349-1357.

Accessed August 15, 2011. http://www.

Multisite Violence Prevention Project. 2004. The

bostonpublicschools.org/files/2010_mcas_final.pdf.

multisite violence prevention project: Background

Offord, David R. 2000. Selection of levels of prevention.

and overview. American Journal of Preventive

Addictive Behaviors 25: 833-42.

Medicine 26: 3-11.

Olsen, Edgar O. 2003. Housing programs for low-

Murnane, Richard J. 2007. “Improving the Education of

income households. In Means-tested transfer programs

Children Living in Poverty.” The Future of Children,

in the United States, ed. Robert Moffitt. Chicago:

17(2), pp. 161-182.

University of Chicago Press.

———. 2000. The cost-effectiveness of alternative

methods of delivering housing subsidies. Working

paper. Charlottesville: University of Virginia,

Department of Economics.

281

References

Oreopoulos, Philip. 2003. The long-run consequences of Ponce, Nancy A., Katherine J. Hoggatt, Michelle living in a poor neighborhood. Quarterly Journal of

Wilhelm, and Beate Ritz. 2005. Preterm birth:

Economics 118(4): 1533-75.

The interaction of traffic-related air pollution with

Orr, Larry, Judith D. Feins, Robin Jacob, Erik Beecroft,

economic hardship in Los Angeles neighborhoods.

Lisa Sanbonmatsu, Lawrence Katz, Jeffrey B.

American Journal of Epidemiology 162(2): 140-48.

Liebman, and Jeffrey R. Kling. 2003. Moving to

Popkin, Sue, Larry F. Buron, Diane K. Levy, and Mary K.

Opportunity interim impacts evaluation. Washington,

Cunningham. 2000. “The Gautreaux Legacy: What

DC: U.S. Department of Housing and Urban

Might Mixed- Income and Dispersal Strategies Mean

Development, Office of Policy Development and

for the Poorest Public Housing Tenants?” Housing

Research.

Policy Debate 11(4): pp. 9-11.

Page, Marianne, and Gary Solon. 2003. Correlations

Popkin, Susan J., Laura E. Harris and Mary K.

between brothers and neighboring boys in their

Cunningham. 2002. Families in Transition: A

adult earnings: The importance of being urban.

Qualitative Analysis of the MTO Experience.

Journal of Labor Economics 21(4): 831-55.

Washington, DC: The Urban Institute. http://www.

Patterson, Rhiannon, Michelle Wood, Ken Lam,

huduser.org/publications/pdf/mtoqualf.pdf.

Satyendra Patrabansh, Gregory Mills, Steven

Popkin, Sue, Bruce Katz, Mary Cunningham, Karen

Sullivan, Hiwotte Amare, and Lily Zandniapour.

Brown, Jeremy Gustafson, and Margery Turner.

2004. Evaluation of the welfare to work voucher

2004. A decade of HOPE VI: Research findings and

program: Report to Congress. Washington, DC: U.S.

policy challenges. Washington, DC: Urban Institute.

Department of Housing and Urban Development.

Popkin, Susan J., Tama Leventhal, and Gretchen

Pavetti, LaDonna, and Dottie Rosenbaum. 2010.

Weismann. 2008. Girls in the ‘hood: The importance

Creating a safety net that works when the economy

of feeling safe. Washington, DC: Urban Institute.

doesn’t: The role of the food stamp and TANF

Popkin, Susan J., Tama Leventhal, and Gretchen

programs. Washington, DC: Center on Budget and

Weismann. 2010. Girls in the ‘hood: How safety

Policy Priorities.

affects the life chances of low-income girls. Urban

Pearson, Thomas A., George A. Mensah, R. Wayne

Affairs Review.

Alexander, Jeffrey L. Anderson, Richard O. Cannon

Quigley, John M. 2000. A decent home: Housing

III, Michael Criqui, Yazid Y. Fadl, et al. 2003.

policy in perspective. In Brookings-Wharton Papers

Markers of inflammation and cardiovascular disease:

on Urban Affairs, ed. William G. Gale and Janet

Application to clinical and public health practice:

Rothenberg Pack. Pp. 53-100. Washington, DC:

A statement for healthcare professionals from the

Brookings Institution.

Centers for Disease Control and Prevention and the

Quigley, John M., and Steven Raphael. 2008.

American Heart Association. Circulation 107: 499-

Neighborhoods, economic self-sufficiency, and the

511.

MTO Program. Brookings-Wharton Papers on Urban

Peterson, Ruth D., Lauren J. Krivo, and Mark A. Harris.

Affairs. Washington, DC: Brookings.

2000. Disadvantage and neighborhood violent

Quinlan, Kyran P. 1996. Injury control in practice.

crime: Do local institutions matter? Journal of

Archives of Pediatrics Adolescent Medicine 150: 954-

Research in Crime and Delinquency. 37(1): 31-63.

57.

Pickett, Kate E., and Michelle Pearl. 2001. Multilevel

Raphael, Steven, and Jens Ludwig. 2003. “Do prison

analyses of neighborhood socioeconomic context

sentence enhancements reduce gun crime? The

and health outcomes: A critical review. Journal of

case of Project Exile.” In Evaluating Gun Policy,

Epidemiology and Community Health 55(2): 111-22.

edited by Jens Ludwig and Philip J. Cook, 251-286.

Piquero, Alex R., David P. Farrington, and Alfred

Washington, DC: Brookings Institution Press.

Blumstein. 2003. “The Criminal Career Paradigm.”

Crime and Justice 30: 359-506.

282

Raudenbush, Stephen W., and Robert J. Sampson. 1999.

Roderick, Melissa, Anthony S. Bryk, Brian A. Jacob, John

‘Ecometrics’: Toward a science of assessing ecological

Q. Easton, and Elaine Allensworth. 1999. Ending

settings, with application to the systematic

Social Promotion: Results from the First Two Years.

social observation of neighborhoods. Sociological

Chicago: Consortium on Chicago School Research.

Methodology 29: 1-41.

December 1999.

Raudsepp, Lennart, and Roomet Viira. 2000.

Rosenbaum, James. 1995. Changing the geography

Sociocultural correlates of physical activity in

of opportunity by expanding residential choice:

adolescents. Pediatric Exercise Science 12(1): 51-60.

Lessons from Gautreaux. Housing Policy Debate 6(1):

Research and Policy Support Group (RPSG). 2010.

6231-69.

“NYC 2010 Mathematics and English Language

Rosenbaum, James E. and Stefanie DeLuca. 2008. “Does

Arts Citywide Test Results.” Accessed August

Changing Neighborhoods Change Lives? The

15, 2011. http://schools.nyc.gov/accountability/

Chicago Gautreaux Housing Program.” In David

Reports/Data/TestResults/2010_MATH_ELA_

Grusky (Ed.), Social Stratification: Race, Class and

NYC_FULL%20DECK.pdf.

Gender in Sociological Perspective. Westview Press.

Rezaeian, Mohsen, Graham Dunn, Selwyn St. Leger, and

pp. 393-399.

Louis Appleby. 2005. The ecological association

Rosenbaum, Emily, and Laura E. Harris. 2003. Short-

between suicide rates and indices of deprivation

term impacts of moving for children: Evidence

in English local authorities. Social Psychiatry and

from the Chicago MTO program. In Choosing a

Psychiatric Epidemiology 40: 785-91.

better life: Evaluating the moving to opportunity social

Rhine, Sherrie L.W., Maude Toussaint-Comeau, Jeanne

experiment, ed. William Gale and Janet Rothenberg

M. Hogarth, and William H. Greene. 2001. The

Pack. Washington, DC: Urban Institute.

role of alternate financial service providers in serving Rosenbaum, Emily, Laura Harris, and Nancy A. Denton.

LMI neighborhoods. In Changing Financial Markets

1999. New places, new faces: An analysis of

and Community Development, A Federal Reserve

neighborhoods and social ties among MTO movers

System Community Affairs Research Conference,

in Chicago. Unpublished manuscript. Available at

Proceedings of a Conference held in Washington,

http://www.nber.org/mtopublic/chicago/mto_chi1.

D.C., April 5-6, 2001: 59-80. Chicago, IL: Federal

pdf

Reserve Bank of Chicago.

Rosenbaum, James, and Susan Popkin. 1991.

Riccio, James A. 2010. Sustained earnings gains for

Employment and earnings of low-income blacks

residents in a public housing jobs program: Seven-

who move to middle-class suburbs. In The urban

year findings from the jobs-plus demonstration.

underclass, ed. Christopher Jencks and Paul

Policy Brief. New York: MDRC.

Peterson. pp. 342-256. Washington, DC: Brookings

Ridker, Paul M., Julie E. Buring, Nancy R. Cook, and

Institution.

Nadar Rifai. 2003. C-reactive protein, the metabolic Ross, Catherine E., and John Mirowsky. 2001.

syndrome, and risk of incident cardiovascular

Neighborhood disadvantage, disorder, and health.

events. Circulation 107:391-97.

Journal of Health and Social Behavior 42(3): 258-76.

Ridker, Paul M., Nadar Rifai, Lynda Rose, Julie E.

Rountree, Pamela Wilcox, and Barbara D. Warner. 1999.

Buring, and Nancy R. Cook. 2002. Comparison

“Social ties and crime: Is the relationship gendered?”

of C-reactive protein and low-density lipoprotein

Criminology. 37(4): 789-813.

cholesterol levels in the prediction of first

Rubin, Donald B. 1980. Comment on ‘Randomization

cardiovascular events. New England Journal of

analysis of experimental data in the Fisher

Medicine 347(20): 1557-65.

randomization test’ (by D. Basu). Journal of the

Rivkin, Steven G., Eric A. Hanushek, and John F. Kain.

American Statistical Association 75: 591-593

2005. Teachers, schools, and academic achievement. Rubinowitz, Leonard S., and James E. Rosenbaum.

Econometrica 73(2): 417-58.

2000. Crossing the Class and Color Lines: From Public

Housing to White Suburbia. Chicago: University of

Chicago Press.

283

References

Saelens, Brian E., James F. Sallis, Jennifer B. Black,

Sanbonmatsu, Lisa, Jeffrey R. Kling, Greg J. Duncan,

and Diana Chen. 2003. Neighborhood-based

and Jeanne Brooks-Gunn. 2006. Neighborhoods

differences in physical activity: An environment

and academic achievement: Results from the Moving

scale evaluation. American Journal of Public Health

to Opportunity experiment. Journal of Human

93(9): 1552-58.

Resources 41(4): 649-91.

Saguaro Seminar, Kennedy School of Government,

Schachter, Jason P. 2004. Geographical mobility: 2002 to

Harvard University. 2002. The social capital

2003. Population characteristics. Current Population

benchmark survey (data codebook). Horsham, PA:

Report P20-549. Washington, DC: U.S. Bureau of

TNS Intersearch [producer]. Storrs. CT: Roper

the Census.

Center for Public Opinion Research [distributor].

Scharfstein, Joshua, and Megan Sandel. 1998. Not safe

Sallis, James F., Judith J. Prochaska, and Wendell C.

at home: How America’s housing crisis threatens the

Taylor. 2000. A review of correlates of physical

health of its children. Boston: Boston Medical Center

activity of children and adolescents. Medicine and

Children’s Hospital and the Doc4Kids Project.

Science in Sports and Exercise 32(5): 963-75.

Schelling, Thomas C. 1978. Micromotives and

Sallis, James, Brian Saelens, Lawrence Frank, Terry

macrobehavior. New York: Norton.

L. Conway, Donald J. Slymen, Kelli L. Cain,

———. 1971. Dynamic models of segregation. Journal of

James E. Chapman, and Jacqueline Kerr. 2009.

Mathematical Sociology 1: 143-86.

Neighborhood built environment and income:

Schill, Michael, and Susan Wachter. 1995. The spatial

Examining multiple health outcomes. Social Science

bias of federal housing law and policy: Concentrated

and Medicine 68:1285-93.

poverty in urban America. University of Pennsylvania

Sampson, Robert J. 2008. Moving to inequality:

Law Review 143(5): 1285-1342.

Neighborhood effects and experiments meet social

Schneider, Harald, Heide Glaesmer, Jens Klotsche,

structure. American Journal of Sociology 114(1): 189-

Steffen Bohler, Hendrik Lehnert, Andreas Zeiher,

231.

Winfried Marz, David Pittrow, Gunter K. Stalla,

Sampson, Robert J., and Dawn Jeglum Bartusch. 1998.

and Hans-Ulrich Wittchen. 2006. Accuracy of

Legal cynicism and (subcultural?) tolerance of

anthropometric indicators of obesity to predict

deviance: The neighborhood context of racial

cardiovascular risk. Journal of Clinical Endocrinology

differences. Law and Society Review 32: 777-804.

and Metabolism 92(2): 589-94.

Sampson, Robert J., Jeffrey D. Morenoff, and Thomas

School Information Branch Planning, Assessment,

Gannon-Rowley. 2002. Assessing ‘neighborhood

and Research (SIBPAR). No Date. “District

effects’: Social processes and new directions in

Accountability Report Card 2006-2007.” Prepared

research. Annual Review of Sociology 28: 443-78.

for the Los Angeles Unified School District.

Sampson, Robert J., Jeffrey D. Morenoff, and Stephen

Accessed August 15, 2011. www.lausd.k12.ca.us/

Raudenbush. 2005. “Social Anatomy of Racial and

lausd/offices/bulletins/ldstldtz.html.

Ethnic Disparities in Violence.” American Journal of

Schott, Liz. 2009. An introduction to TANF. Washington,

Public Health 95: 224-232.

DC: Center for Budget and Policy Priorities.

Sampson, R.J., and S.W. Raudenbush. 1999. “Systematic Shannon, Lyle. 1982. “Assessing the Relationship of social observation of public spaces: a new look at

Adult Criminal Careers to Juvenile Careers: A

disorder in urban neighborhoods.” American Journal

Summary.” Washington, DC: U.S. Department of

of Sociology 105(3): 603-651.

Justice.

Sampson, Robert J., Stephen W. Raudenbush, and Felton Sharkey, Patrick. 2010. The acute effect of local homicide Earls. 1997. Neighborhoods and violent crime: A

on children’s cognitive performance. Proceedings of

multilevel study of collective efficacy. Science 277:

the National Academy of Sciences. 107(26): 11733-

918-924.

38.

284

———. 2008. The intergenerational transmission of

Srinivasan, Sathanur R., Ren Wang, Wei Chen, Christine

context. American Journal of Sociology 113(4): 931-

Y. Wei, Jihua Xu, and Gerald S. Berenson. 2009.

69.

Utility of waist-to-height ratio in detecting central

———. 2006. “Navigating dangerous streets: The

obesity and related adverse cardiovascular risk

sources and consequences of street efficacy.”

profile among normal weight younger adults (from

American Sociological Review 71: 826-846.

the Bogalusa Heart Study). American Journal of

Sherman, Lawrence. 2002. Fair and effective policing. In

Cardiology 104(5): 721-24.

Crime: Public policies for crime control, ed. James Q.

Steinberg, Laurence, Ilana Blatt-Eisengart, and Elizabeth

Wilson and Joan Petersilla. Pp. 383-412. Oakland,

Cauffman. 2006. “Patterns of competence and

CA: ICS Press.

adjustment among adolescents from authoritative,

Shonkoff, Jack P., and Deborah A. Phillips. 2000.

authoritarian, indulgent, and neglectful homes: A

From neurons to neighborhoods: The science of early

replication in a sample of serious juvenile offenders.”

childhood development. Washington, DC: National

Journal of Research on Adolescence 16(1): 47-58.

Academy Press.

Subramanian S.V., J. T. Chen, D. H. Rehkopf, P. D.

Shroder, Mark. 2002a. Locational constraint, housing

Waterman, and N. Krieger. 2006. Comparing

counseling, and successful lease-up in a randomized

individual- and area-based socioeconomic measures

housing voucher experiment. Journal of Urban

for the surveillance of health disparities: A multilevel

Economics 51(2): 315-38.

analysis of Massachusetts births 1989-1991.

———. 2002b. Does housing assistance perversely affect

American Journal of Epidemiology 164(9): 823-34.

self-sufficiency? A review essay. Journal of Housing

Substance Abuse and Mental Health Services

Economics 11(4): 381-417.

Administration. 2010. Results from the 2009

Shroder, Mark, and Arthur Reiger. 2000. Vouchers versus

national survey on drug use and health. Volume

production revisited. Journal of Housing Research

1: Summary of national findings. NSDUH Series

11(1): 91-107.

H-38A, HHS publication no. SMA 10-4856.

Silver, Eric, Edward P. Mulvey, and Jeffrey W. Swanson.

Rockville, MD: Office of Applied Studies.

2002. Neighborhood structural characteristics and

Swanson, Christopher. 2004. “Who Graduates? Who

mental disorder: Faris and Dunham revisited. Social

Doesn’t? A Statistical Portrait of Public High School

Science & Medicine 55(8): 1457-70.

Graduation, Class of 2001.” Prepared for the Urban

Simons, L.G., R.L. Simons, R.D. Conger, and G.H.

Institute’s Education Policy Center. Urban Institute:

Brody. 2004 “Collective socialization and child

Washington, DC.

conduct problems: A multilevel analysis with an

Swanson, Christopher. 2009. Cities in Crisis 2009:

African American sample.” Youth and Society 35(3):

Closing the Graduation Gap – Educational and

267-292.

Economic Conditions in America’s Largest Cities.

Simpson, Gloria A., Barbara Bloom, Robin A. Cohen,

Educational Projects in Education, Bethesda, MD.

Stephen Blumberg, and Karen H. Bourdon. 2005.

www.edweek.org/co/copies.

U.S. children with emotional and behavioral

Temkin, Kenneth, and Noah Sawyer. 2004. Analysis of

difficulties: Data from the 2001, 2002, and 2003

alternative financial service providers. Washington,

national health interview surveys. Advance Data

DC: Fannie Mae Foundation.

from Vital and Health Statistics 360: 1-13.

Thompson, Estina E., and Neal Krause. 2000. Living

Skogan, Wesley G. 1990. Disorder and decline: Crime and

alone and neighborhood characteristics as predictors

the spiral of decay in American neighborhoods. Berkley

of social support later in life. The Journals of

and Los Angeles, CA: University of California Press.

Gerontology Series B: Psychological Sciences and Social

Sobel, Michael E. 2006. What do randomized studies

Sciences 55: S245-53.

of housing mobility demonstrate? Causal inference

Thornberry, Terence P., David Huizinga, and Rolf

in the face of interference. Journal of the American

Loeber. 2004. “The causes and correlates studies:

Statistical Association. 101(476): 1398-1407.

Findings and policy implications.” Juvenile Justice 9:

3-19.

285

References

Tillman, Robert. 1987. “The Size of the ‘Criminal

———. 2011b. National Center for Education Statistics.

Population’: The Prevalence and Incidence of Adult

Private School Universe Survey (PSS). 1993–2010.

Arrest.” Criminology 25(3): 561- 579.

Retrieved May 27, 2011, from http://nces.ed.gov/

Torres, Susan, and Caryl Nowson. 2007. Relationship

surveys/pss/.

between stress, eating behavior, and obesity.

van den Berg, Agnes E., Jolanda. Maas, Robert A.

Nutrition 23: 887-94.

Verheij, and Peter P. Groenewegen. 2010. Green

Tracy, Paul, Marvin Wolfgang, and Robert Figlio. 1985.

space as a buffer between stressful life events and

“Delinquency in Two Birth Cohorts: Executive

health. Social Science and Medicine 70: 1203-10.

Summary.” Rockville, MD: Office of Juvenile Justice van der Heide, S., H.F. Kauffman, A.E.J. Dubois and and Delinquency Prevention.

J.G.R. de Monchy. 1997. Allergen-avoidance

Trogdon Justin G., and Thomas Hylands. 2008.

measures in homes of house-dust-mite-allergic

Nationally representative medical costs of diabetes

asthmatic patients: Effects of acaricides and mattress

by time since diagnosis. Diabetes Care 31(12): 2307-

encasings. Allergy 52(9): 921-27.

2311.

van Kamp, Irene, Jeanne van Loon, Mariel Droomers,

Turner, Margery, and Kingsley G. Thomas. 2008. Federal

and Augustinus de Hollander. 2004. Residential

Programs for Addressing Low-Income Housing

environment and health: A review of methodological

Needs. Washington, DC: Urban Institute.

and conceptual issues. Reviews on Environmental

Turney, Kristin, Susan Clampet-Lundquist, Kathryn

Health 19: 381-401.

Edin, Jeffrey R. Kling, and Greg J. Duncan. 2006.

Vermilyea, Todd, and James A. Wilcox. 2002. Who is

Neighborhood effects on barriers to employment:

unbanked and why: Results from a large, new survey

Results from a randomized housing mobility

of low-and-moderate income adults. Chicago:

experiment in Baltimore. Brookings-Wharton Papers

Federal Reserve Bank of Chicago, Conference on

on Urban Affairs. Washington, DC: Brookings.

Bank Structure and Competition.

U.S. Census Bureau. 2004. Census Special Reports,

Votruba, Mark E., and Jeffrey R. Kling. 2009. Effects

AHS/R/04-1, Housing Data Between the Censuses:

of neighborhood characteristics on the mortality of

The American Housing Survey. Washington, DC.

black male youth: Evidence from Gautreaux. Social

U.S. Department of Housing and Urban Development

Science and Medicine 68(5): 814-23.

(HUD). 2011. Affordable Housing needs 2009:

Waitzman, Norman J., and Ken R. Smith. 1998.

Report to Congress. Washington DC: HUD.

Phantom of the area: Poverty-area residence and

———. 2003. Choice voucher location patterns:

mortality in the United States. American Journal of

Implications for participant and neighborhood

Public Health 88: 973-76.

welfare. Washington, DC: HUD, Office of Policy

Warner, Barbara D., and Pamela Wilcox Rountree.

Development and Research.

1997. “Local Social Ties in a Community and

———. 2000. Economic cost analysis of different forms

Crime Model: Questioning the Systematic Nature

of assisted housing. Issue Brief 11. Washington, DC:

of Informal Social Control.” Social Problems 44(4):

HUD, Office of Policy Development and Research.

520-536.

———. 1997. Characteristics of HUD-assisted renters and

Watson, Tara, 2009. Inequality and the measurement of

their units in 1993. Washington, DC: HUD.

residential segregation by income. Review of Income

U.S. Department of Education. 2011a. National Center

and Wealth 55: 820-844.

for Education Statistics. Common Core of Data

Weaver, Ian C. G. 2009. Shaping adult phenotypes

(CCD). Public Elementary/Secondary School

through early life environments. Birth Defects

Universe Survey. 1993-2010. Retrieved May 18,

Research Part C: Embryo Today 87: 314-26.

2011 from http://nces.ed.gov/ccd.

Weinberg, Bruce, Patricia B. Reagan, and Jeffrey J.

Yankow. 2004. Do neighborhoods affect work

behavior? Evidence from the NLSY79. Journal of

Labor Economics 22(4): 891-924.

286

Whitley, Elise, David Gunnell, Daniel Dorling, and

Zapata, B. Cecilia, Annabella Rebolledo, Eduardo Atalah,

George D. Smith. 1999. Ecological study of social

Beth Newman, and Mary-Claire King. 1992. The

fragmentation, poverty, and suicide. British Medical

influence of social and political violence on the risk

Journal 319: 1034-37.

of pregnancy complications. American Journal of

WHO World Mental Health Survey Consortium.

Public Health 82(5): 685.

2004. Prevalence, severity, and unmet need for

Zaslow, M.J., and C.D. Hayes. 1986. Sex differences in

treatment of mental disorders in the World Health

children’s response to psychosocial stress: Toward a

Organization world mental health surveys. Journal of

cross-context analysis. In Advances in Developmental

the American Medical Association 291(21): 2581-90.

Psychology, vol. 4, ed. M. Lamb, A. Brown, and B.

Widom, Cathy S. 1989. “Child Abuse, Neglect, and

Rogoff. Hillsdale, NJ: Lawrence Erlbaum.

Violent Criminal Behavior.” Criminology 27(2):

Zenk, Shannon N., Amy J. Schulz, Barbara A. Israel,

251-271.

Sherman A. James, Shuming Bao, and Mark L.

Wiener, Joshua M., Raymond J. Hanley, Robert Clark,

Wilson. 2005. Neighborhood racial composition,

and Joan F. Van Nostrand. (1990). Measuring

neighborhood poverty, and the spatial accessibility

the activities of daily living—Comparisons across

of supermarkets in metropolitan Detroit. American

national surveys. Journal of Gerontology 45(6): S229-

Journal of Public Health 95(4): 660-67.

37.

Zimmerman, Gregory M. and Steven F. Messner. 2011.

Wildman, Rachel P. Paul Muntner, Kristi Reynolds,

“Neighborhood context and nonlinear peer effects

Aileen P. McGinn, Swapnil Rajpathak, Judith Wylie-

on adolescent violent crime.” Criminology 49(3):

Rosett, and MaryFran R. Sowers. 2008. The obese

873-904.

without cardiometabolic risk factor clustering and

the normal weight with cardiometabolic risk factor

clustering: Prevalence and correlates of 2 phenotypes

among the US population. NHANES 1999-2004.

Archives of Internal Medicine 168: 1617-24.

Wilson, William Julius. 1996. When Work Disappears:

The World of the New Urban Poor. New York: Alfred

A. Knopf.

———. 1987. The truly disadvantaged: The inner city, the

underclass, and public policy. Chicago: University of

Chicago Press.

Wright, Rosalind J. 1998. Review of psychosocial

stress and asthma: An integrated biopsychosocial

approach. Thorax 53: 1066-74.

Wikström, Per-Olof., and Rolf Loeber. 2000. “Do

disadvantaged neighborhoods cause well-adjusted

children to become adolescent delinquents? A

study of male juvenile serious offending, risk and

protective factors, and neighborhood context.”

Criminology. 38: 1109-1142.

Wolfgang, Marvin E. 1983. “Delinquency in two birth

cohorts.” American Behavioral Scientist 27: 75-86.

Wolfgang, Marvin E., Robert M. Figlio, and Thorsten

Sellin. 1972. Delinquency in a Birth Cohort.

Chicago: University of Chicago Press.

287

References

288

U.S. Department of Housing and Urban Development

Office of Policy Development and Research

Washington, DC 20410-6000

November 2011

Document Outline

- 00_HUD-393 Cover Art(jp)Final_3

- 01_Front Matter_(jp)1.6

- 02_Chapter 1_(jp)1.5

- 03_Chapter 2_(jp)1.5

- 04_Chapter 3_(jp)1.5

- 05_Chapter 4_(jp)1.4

- 06_Chapter 5_(jp)1.5

- 07_Chapter 6_(jp)1.4

- 08_Chapter 7_(jp)1.5

- 09_Chapter 8_(jp)1.4

- 10_References_(jp)1.5

- 11_Back_Cover_(jp)1.5