Opportunity Neighborhoods

for Latino and African American Children

FINAL REPORT

U.S. Department of Housing and Urban Development | Office of Policy Development and Research

Opportunity Neighborhoods

for Latino and African-

American Children

Prepared for:

U.S. Department of Housing

and Urban Development

Washington, D.C.

Prepared by:

Anna Maria Santiago

George C. Galster

Jessica L. Lucero

Karen J. Ishler

Eun Lye Lee

Georgios Kypriotakis

Lisa Stack

Case Western Reserve University

Wayne State University

March 2014

FINAL REPORT

Acknowledgments

This report was prepared under U.S. Department of Housing and Urban Development (HUD)

grant H-12612-CA. We deeply appreciate the support, advice, and thoughtful reviews on an

earlier draft of this report received from our Government Technical Representative Michael T.

Morse as well as Government Technical Monitor Brent D. Mast. We also thank Ronald Wilson

at HUD, who provided valuable comments on an earlier version of our chapter on exposure to

violence.

Additional support for the Denver Child Study came from the National Institute of Child and Human Development (5R01 HD47786-2), the William K. Kellogg Foundation (PO126495), the

MacArthur Foundation (08-92652-000-HCD), and the Annie E. Casey Foundation (GA-2012

X1654). This study was also supported by funding from Wayne State University and the Jack,

Joseph and Morton Mandel School of Applied Social Sciences at Case Western Reserve

University.

Outstanding advice about the design and implementation of the Denver Child Study was

provided by Tama Leventhal (Tufts University) and Xiaoming Li (Wayne State University).

Advice about our analytical strategy and interpretation of study findings was provided by

Stefanie A. DeLuca (Johns Hopkins University), Greg J. Duncan (University of California,

Irvine), Lisa A. Gennetian (National Bureau of Economic Research), David J. Harding

(University of California, Berkeley), Jens Ludwig (University of Chicago), Jeffrey D. Morenoff

(University of Michigan), and seminar participants in the Quantitative Methodology program at

the University of Michigan and Glasgow University.

This study would not have been possible without the long-time project management of Jackie

Cutsinger; the programming assistance of Albert F. Anderson (Public Data Queries, Inc.) and

Liquan Xu; and the research assistance of Kristen Berg, Marjorie Edguer, Rosalind Jackson,

Jung-Eun Kim, Kim Kostaroff, Sharon Lindhorst, Andrew Linn, Tina McCullar, Rob Mehregan,

Amy Roberts, Ana Santiago-Sanroman, Gabriela Sehinkman, Rebecca Grace Stokan, Tanisha

Tate, Leigh Taylor, Noah Urban, Porsche Van Brocklin-Fischer, Sarah Van Zoeren, and Rebecca

Wiersma. We also thank our interviewers for their dedication to the project: Chris Alexander,

Janet Genuise, Dawn Gibson, Adriana Giraldo, Andrea Heimonen, Holly Hill-Kvicala, Kristy

Horen, Amy Kuras, Melissa Mancevich, Lori Morrish, Jean Murphy, Melissa Overton, Kai Paul,

Tameka Ramsey, José Sanroman, Robert Sprecher, Patricia Tauber, and Roxana Zuniga.

We are grateful to the Denver, Colorado, Housing Authority (DHA) for their willingness to

participate in this study and share the administrative data necessary to conduct this research and

evaluate study findings. Special thanks to Salvadore Carpio, Ismael Guerrero, Stella Madrid,

Renee Nicolosi, and Karen Spruce. We thank the Piton Foundation (Denver) and especially Matt

Barry for sharing unpublished neighborhood indicator data.

We are especially grateful to all of the families who participated in the Denver Child Study for their willingness to share their lives and experiences with us. We hope that the results of this

study will help guide government officials in their decisions about how best to support the

development of opportunity neighborhoods for all children.

The contents of this report are the views of the contractors and do not necessarily reflect the

views or policies of HUD, the U.S. government, DHA, our sponsoring foundations and

universities, or any state or local agency that provided data, nor are they responsible for the

methods of statistical analysis or any conclusions derived therefrom.

CONTENTS

EXECUTIVE SUMMARY ....................................................................................................... xiv

I.

INTRODUCTION..............................................................................................................1

Policy and Scholarly Context for Neighborhood Effects on Children ................................1

Purpose and Contributions of Our Denver Child Study.......................................................3

Structure of the Report.........................................................................................................4

II.

HOW NEIGHBORHOOD CONTEXT MAY INFLUENCE CHILDREN

AND HOW CAN WE MEASURE IT? .......................................................................... 6

Potential Causal Mechanisms of Neighborhood Context ................................................... 6

Social-Interactive Mechanisms.................................................................................... 6

Environmental Mechanisms ........................................................................................ 7

Geographical Mechanisms........................................................................................... 7

Institutional Mechanisms............................................................................................. 8

Summary of Evidence About Potential Neighborhood Effect Mechanisms ............... 8

Temporal Dimensions of Neighborhood Effects ................................................................ 9

Gender and Ethnic Differences in Neighborhood Effects ............................................... 11

Methodological Challenges of Quantifying Neighborhood Effects ................................. 11

Implications for the Denver Child Study .......................................................................... 13

III.

DATA AND METHODOLOGY ................................................................................... 14

The Natural Experiment in Denver................................................................................... 14

Denver Child Study Household Survey ............................................................................ 16

Characteristics of Caregivers and Households .......................................................... 16

Characteristics of Children and Youth ...................................................................... 17

Characteristics of Neighborhoods Experienced by Children and Youth................... 19

The Issue of Neighborhood Scale.............................................................................. 21

Creation of Analytical Databases .............................................................................. 23

Statistical Modeling Approaches ...................................................................................... 24

Dichotomous Outcome Models ................................................................................. 24

Hazard Models............................................................................................................25

iii

Temporal Aspects of Neighborhood Effects ............................................................. 26

Gender and Ethnic Differences in Neighborhood Effects ......................................... 27

Reduced-Form Estimates of Neighborhood Effects .................................................. 27

IV.

PHYSICAL AND BEHAVIORAL HEALTH OUTCOMES ..................................... 28

Introduction....................................................................................................................... 28

Physical Health Outcomes Analysis .......................................................................... 28

Behavioral Health Outcomes Analysis ...................................................................... 30

Estimated Neighborhood Effects on Physical and Behavioral Health Outcomes ............ 31

Asthma ....................................................................................................................... 31

Neurodevelopmental Disorders ................................................................................. 35

Obesity....................................................................................................................... 38

Internalizing Behaviors.............................................................................................. 42

Behavioral Health Service Utilization ....................................................................... 45

Gender and Ethnic Differences in Neighborhood Effects ................................................ 48

Asthma ....................................................................................................................... 48

Neurodevelopmental Disorders ................................................................................. 49

Obesity....................................................................................................................... 49

Internalizing Behaviors.............................................................................................. 50

Behavioral Health Service Utilization ....................................................................... 51

Nonlinear Neighborhood Effects ...................................................................................... 51

Discussion ......................................................................................................................... 53

Neighborhood Safety ................................................................................................. 53

Neighborhood Ethnic and Nativity Composition ...................................................... 54

Neighborhood Social Status ...................................................................................... 55

Neighborhood Institutional Resources ...................................................................... 56

Neighborhood Physical Environment........................................................................ 57

Geographic Selection Bias Revisited......................................................................... 57

Conclusion ........................................................................................................................ 58

V.

EXPOSURE TO VIOLENCE OUTCOMES ............................................................... 59

Introduction.................................................................................................................59

iv

Exposure to Neighborhood Violence Analysis.......................................................... 59

Exposure to School Violence Analysis...................................................................... 60

Exposure to Violence in the Home Analysis ............................................................. 60

Model Estimation....................................................................................................... 61

Estimated Neighborhood Effects on Exposure to Violence Outcomes ............................ 62

Witnessing Violence in the Neighborhood................................................................ 62

Victim of Neighborhood Violence ............................................................................ 65

Witnessing Violence at School .................................................................................. 69

Victim of School Violence ........................................................................................ 73

Witnessing Violence in the Home ............................................................................. 78

Gender and Ethnic Differences in Neighborhood Effects ................................................ 81

Witnessing Neighborhood Violence.......................................................................... 81

Victim of Neighborhood Violence ............................................................................ 82

Witnessing School Violence...................................................................................... 83

Victim of School Violence ........................................................................................ 84

Witnessing Violence at Home ................................................................................... 85

Nonlinear Neighborhood Effects ...................................................................................... 86

Discussion ......................................................................................................................... 88

Neighborhood Safety ................................................................................................. 88

Neighborhood Ethnic and Nativity Composition ...................................................... 89

Neighborhood Social Status ...................................................................................... 90

Neighborhood Physical Environment........................................................................ 91

Geographic Selection Bias Revisited......................................................................... 91

Conclusion ........................................................................................................................ 92

VI.

RISKY BEHAVIOR OUTCOMES............................................................................... 93

Introduction....................................................................................................................... 93

Estimated Neighborhood Effects on Behavioral Outcomes ............................................. 94

Smoking Cigarettes.................................................................................................... 94

Drinking Alcohol ....................................................................................................... 97

Smoking Marijuana ..................................................................................................100

v

Running Away From Home..................................................................................... 103

Engaging in Violent Behavior ................................................................................. 106

Gender and Ethnic Differences in Neighborhood Effects .............................................. 109

Smoking Cigarettes.................................................................................................. 109

Drinking Alcohol ..................................................................................................... 110

Smoking Marijuana ................................................................................................. 110

Running Away From Home..................................................................................... 111

Engaging in Violent Behaviors................................................................................ 112

Nonlinear Neighborhood Effects .................................................................................... 113

Discussion ....................................................................................................................... 114

Neighborhood Safety ............................................................................................... 114

Neighborhood Social Status .................................................................................... 116

Neighborhood Ethnic and Nativity Composition .................................................... 116

Neighborhood Housing Stock and Environment ..................................................... 117

Neighborhood Peers and Social Capital .................................................................. 118

The Curious Case of Smoking ................................................................................. 119

Geographic Selection Bias Revisited....................................................................... 119

Conclusion ...................................................................................................................... 120

VII.

EDUCATIONAL OUTCOMES .................................................................................. 121

Introduction..................................................................................................................... 121

Estimated Neighborhood Effects on Educational Outcomes.......................................... 122

Academically Advanced Classes and Gifted Programs........................................... 123

Special Education Placement................................................................................... 126

Suspensions and Expulsions .................................................................................... 129

Repeating a Grade.................................................................................................... 129

Dropping Out of School Before Graduating............................................................ 134

Gender and Ethnic Differences in Neighborhood Effects .............................................. 134

Academically Advanced Classes and Gifted Programs........................................... 137

Special Education Placement................................................................................... 137

Suspensions and Expulsions .....................................................................................138

vi

Repeating a Grade.................................................................................................... 139

Dropping Out of School Before Graduating............................................................ 139

Nonlinear Neighborhood Effects .................................................................................... 140

Discussion ....................................................................................................................... 141

Neighborhood Safety ............................................................................................... 142

Neighborhood Housing Stock and Environment ..................................................... 143

Neighborhood Social Status .................................................................................... 143

Neighborhood Ethnic and Nativity Composition .................................................... 144

Neighborhood Peers................................................................................................. 145

Geographic Selection Bias Revisited....................................................................... 146

Conclusion ...................................................................................................................... 146

VIII. LABOR MARKET OUTCOMES ............................................................................... 147

Introduction..................................................................................................................... 147

Teen Labor Market Analysis ................................................................................... 147

Young Adult Labor Market Analysis ...................................................................... 148

Estimated Neighborhood Effects on Labor Market Outcomes....................................... 149

Teens........................................................................................................................ 150

Young Adults........................................................................................................... 154

Gender and Ethnic Differences in Neighborhood Effects .............................................. 158

Teens........................................................................................................................ 158

Young Adults........................................................................................................... 159

Nonlinear Neighborhood Effects .................................................................................... 160

Discussion ....................................................................................................................... 160

Neighborhood Safety ............................................................................................... 160

Neighborhood Ethnic and Nativity Composition .................................................... 162

Neighborhood Social Status .................................................................................... 163

Neighborhood Housing Stock and Institutions........................................................ 165

Geographic Selection Bias Revisited....................................................................... 165

Conclusion ................................................................................................................166

vii

IX.

MARRIAGE AND CHILDBEARING ....................................................................... 167

Introduction..................................................................................................................... 167

Estimated Neighborhood Effects on Marriage and Childbearing................................... 169

Marriage and Cohabitation ...................................................................................... 169

Teen Childbearing and Fathering ............................................................................ 172

Nonmarital Childbearing in Young Adulthood ....................................................... 175

Gender and Ethnic Differences in Neighborhood Effects .............................................. 175

Marriage/Cohabitation During Adolescence and Young Adulthood ...................... 175

Teen Childbearing and Fathering ............................................................................ 178

Nonmarital Childbearing in Young Adulthood ....................................................... 179

Nonlinear Neighborhood Effects .................................................................................... 179

Discussion ....................................................................................................................... 180

Neighborhood Safety ............................................................................................... 180

Neighborhood Ethnic and Nativity Composition .................................................... 181

Neighborhood Social Status .................................................................................... 182

Neighborhood Physical Environment...................................................................... 182

Geographic Selection Bias Revisited....................................................................... 182

Conclusion ...................................................................................................................... 183

X.

EXTENSIONS AND VARIATIONS ON THE CORE APPROACH ...................... 184

Introduction..................................................................................................................... 184

Experiments With Differential Effects Across Developmental Stages .......................... 184

Our Developmental Stage-Specific Approach......................................................... 184

Results From Our Developmental Stage Neighborhood Context Experiments ...... 185

Experiments With Cumulative Exposure Measures ....................................................... 186

Our Cumulative Neighborhood Context Approach ................................................. 186

Results From Our Cumulative Neighborhood Context Experiments ...................... 187

Conclusion .......................................................................................................................190

viii

XI.

CONCLUSIONS AND IMPLICATIONS .................................................................. 191

Quantifying Neighborhood Effects on the Development of Low-Income

Latino and African American Children and Youth......................................................... 191

Discussion of Effects From Residential Context ............................................................ 193

Neighborhood Safety ............................................................................................... 193

Neighborhood Social Status .................................................................................... 193

Neighborhood Ethnic and Nativity Composition .................................................... 194

Neighborhood Physical Characteristics ................................................................... 194

Contrasting Findings With the Moving To Opportunity Demonstration........................ 195

Implications..................................................................................................................... 196

APPENDICES ........................................................................................................................... 198

Appendix A: Investigating Quasi-Random Assignment in Our Natural Experiment.... 198

Appendix B: Child Impacts Study Questionnaire.......................................................... 230

Appendix C: Model Results by Gender and Ethnicity ............................... (electronic only)

Appendix D: Results of Non-Linear (Spline) Analyses ............................ (electronic only)

Appendix E: Core Model Results .............................................................. (electronic only)

Appendix F: Results of Bayesian Modeling of Health Outcomes ............. (electronic only)

REFERENCES...........................................................................................................................284

ix

LIST OF EXHIBITS

Exhibit III-1. Characteristics of Caregivers and Households ........................................................18

Exhibit III-2. Characteristics of Children and Youth.....................................................................19

Exhibit III-3. Characteristics of Neighborhoods Experienced by Children and Youth ................22

Exhibit IV-1. Standardized Logit Models Predicting Childhood Diagnosis of Asthma................33

Exhibit IV-2. Standardized Cox Models Predicting Hazard of Childhood Diagnosis

of Asthma...........................................................................................................................34

Exhibit IV-3. Standardized Logit Models Predicting Childhood Diagnosis of

Neurodevelopmental Disorders .........................................................................................36

Exhibit IV-4. Standardized AFT Frailty Models Predicting Timing of Childhood Diagnosis

of Neurodevelopmental Disorders .....................................................................................37

Exhibit IV-5. Standardized Logit Models Predicting Childhood Diagnosis of Obesity................40

Exhibit IV-6. Standardized Cox Models Predicting Hazard of Childhood Diagnosis

of Obesity...........................................................................................................................41

Exhibit IV-7. Standardized Logit Models Predicting Childhood Diagnosis of

Internalizing Behaviors......................................................................................................43

Exhibit IV-8. Standardized AFT Frailty Models Predicting Timing of Childhood

Diagnosis of Internalizing Behaviors.................................................................................44

Exhibit IV-9. Standardized Logit Models Predicting Childhood Behavioral

Health Service Use.............................................................................................................46

Exhibit IV-10. Standardized Cox Models Predicting Hazard of Childhood Behavioral

Health Service Use.............................................................................................................47

Exhibit V-1. Standardized Logit Models Predicting Witnessing Neighborhood Violence

During Childhood ..............................................................................................................63

Exhibit V-2. Standardized AFT Frailty Models Predicting Timing of Witnessing

Neighborhood Violence During Childhood.......................................................................64

Exhibit V-3. Standardized Logit Models Predicting Ever Being Victimized in

Neighborhood During Childhood ......................................................................................67

Exhibit V-4. Standardized AFT Frailty Models Predicting Timing of Neighborhood

Victimization During Childhood .......................................................................................68

Exhibit V-5. Standardized Logit Models Predicting Witnessing School

Violence During Childhood...............................................................................................70

x

Exhibit V-6. Standardized AFT Frailty Models Predicting Timing of Witnessing

School Violence During Childhood...................................................................................71

Exhibit V-7. Standardized Logit Models Predicting Ever Being Victimized

at School During Childhood ..............................................................................................75

Exhibit V-8. Standardized AFT Frailty Models Predicting Timing of School

Victimization During Childhood .......................................................................................76

Exhibit V-9. Standardized Logit Models Predicting Witnessing Violence at Home

During Childhood ..............................................................................................................79

Exhibit V-10. Standardized AFT Frailty Models Predicting Timing of Witnessing

Violence at Home During Childhood ................................................................................80

Exhibit VI-1. Standardized Logit Models Predicting Childhood Onset of Smoking ....................95

Exhibit VI-2. Standardized Cox Models Predicting Hazard of Smoking

During Childhood ..............................................................................................................96

Exhibit VI-3. Standardized Logit Models Predicting Childhood Onset of

Alcohol Consumption ........................................................................................................98

Exhibit VI-4. Standardized Cox Models Predicting Hazard of Alcohol Consumption

During Childhood ..............................................................................................................99

Exhibit VI-5. Standardized Logit Models Predicting Childhood Onset of

Marijuana Use..................................................................................................................101

Exhibit VI-6. Standardized Cox Models Predicting Hazard of Marijuana Use

During Childhood ...........................................................................................................102

Exhibit VI-7. Standardized Logit Models Predicting Childhood Onset of Running

Away From Home............................................................................................................104

Exhibit VI-8. Standardized AFT Frailty Models Predicting Timing of Running Away

From Home During Childhood .......................................................................................105

Exhibit VI-9. Standardized Logit Models Predicting Childhood Onset of Violent Behavior ....107

Exhibit VI-10. Standardized Cox Models Predicting Hazard of Engaging in

Violent Behavior During Childhood ...............................................................................108

Exhibit VII-1. Standardized Multilevel Mixed-Effects Logistic Models Predicting

Placement Into Gifted/Advanced Classes ........................................................................124

Exhibit VII-2. Standardized AFT Frailty Models Predicting Timing of Placement

Into Gifted/Advanced Classes..........................................................................................125

Exhibit VII-3. Standardized Multilevel Mixed-Effects Logistic Models Predicting

Placement Into Special Education Classes ......................................................................127

xi

Exhibit VII-4. Standardized AFT Frailty Models Predicting Timing of Placement

Into Special Education Classes ........................................................................................128

Exhibit VII-5. Standardized Multilevel Mixed-Effects Logistic Models Predicting

Suspensions and Expulsions From School ......................................................................130

Exhibit VII-6. Standardized AFT Frailty Models Predicting Timing of Suspensions

and Expulsions from School ............................................................................................131

Exhibit VII-7. Standardized Multilevel Mixed-Effects Logistic Models Predicting

Repeating a Grade............................................................................................................132

Exhibit VII-8. Standardized Cox Models Predicting Hazard of Repeating a Grade ...................133

Exhibit VII-9. Standardized Multilevel Mixed-Effects Logistic Models Predicting

Leaving School Without a Diploma ................................................................................135

Exhibit VII-10. Standardized Cox Models Predicting Hazard of Leaving School

Without a Diploma...........................................................................................................136

Exhibit VIII-1. Standardized Logit Models Predicting Teen Employment .................................151

Exhibit VIII-2. Standardized Logit Models Predicting Teen Employment for

20+ Hours per Week ........................................................................................................152

Exhibit VIII-3. Standardized Tobit Models Predicting Teen Weekly Hours Worked ................153

Exhibit VIII-4. Standardized Multilevel Mixed-Effects Logistic Models Predicting

Full-Time Employment Among Young Adults ..............................................................155

Exhibit VIII-5. Standardized Multilevel Mixed-Effects Logistic Models Predicting

Post-Secondary Education Among Young Adults ..........................................................156

Exhibit VIII-6. Standardized Logit Models Predicting Young Adult Was Neither

in School Nor Employed..................................................................................................157

Exhibit IX-1. Standardized Logit Models Predicting Marriage or Cohabitation

Between the Ages of 15 and 24 .......................................................................................170

Exhibit IX-2. Standardized Cox Models Predicting Hazard of Marriage or

Cohabitation Between the Ages of 15 and 24..................................................................171

Exhibit IX-3. Standardized Logit Models Predicting Giving Birth or Fathering a

Child Between the Ages of 15 and 18 .............................................................................173

Exhibit IX-4. Standardized Cox Models Predicting Hazard of Giving Birth or

Fathering a Child Between the Ages of 15 and 18 ..........................................................174

Exhibit IX-5. Standardized Logit Models Predicting Nonmarital Births Occurring

Between the Ages of 18 and 24 .......................................................................................176

xii

Exhibit IX-6. Standardized Cox Models Predicting Hazard of Nonmarital Births

Occurring Between the Ages of 18 and 24 ......................................................................177

Exhibit X-1. Average Difference in Neighborhood Indicator Odds Ratios Across

Child Developmental Stages ...........................................................................................188

Exhibit X-2. Average Difference in Estimated Neighborhood Indicator Parameters

Between Contemporaneous and Cumulative Measures of Context ................................189

xiii

Executive Summary

EXECUTIVE SUMMARY

The Denver Child Study explores the extent to which multiple dimensions of neighborhood

context affect the physical and behavioral health, exposure to violence, risky behaviors,

education, youth and young adult labor market outcomes, and marriage and childbearing of

Latino and African-American children and youth from low-income families. The study uses a

natural experiment involving the Denver, Colorado, Housing Authority (DHA), which since

1969 has operated public housing units located in a wide range of neighborhoods throughout the

city and county of Denver. Because the initial assignment of households on the DHA waiting list

to vacant public housing units (and, thus, to neighborhoods) mimics a random process, this

program represents an unusual opportunity for reducing parental geographic selection bias and

observing the unusual combination of low-income, minority youths raised for extended periods

in advantaged (as well as disadvantaged) neighborhoods.

In this study, we analyze data from several administrative sources and data we have collected

from telephone and in-person surveys with Latino or African-American current and former DHA

tenants whose children were the appropriate ages when they lived in DHA housing. Our surveys

provide retrospective information on a battery of youth outcomes, family characteristics, and

residential histories. By merging this information we have created a pseudo-longitudinal panel

providing for each year of children’s lives detailed characteristics about their families,

neighborhoods, and outcomes in many domains.

Research Questions

We analyze the Denver Child Study dataset with a variety of multivariate statistical models in an effort to answer the following research questions:

 Among Latino and African-American children and youth who spent at least two years

living in DHA public housing, are there statistically and economically significant

differences in their outcomes in six domains (behavioral and physical health, exposure to

violence, risky behaviors, education, employment, marriage and childbearing) that can

be attributed to differences in their neighborhood environments (controlling for family

and individual characteristics)?

 Does the answer depend on gender, ethnicity, or developmental stage?

 Does the answer depend on whether neighborhood environment is measured

concurrently with the outcome or cumulatively throughout childhood prior to the

outcome?

 Are the relationships between neighborhood context and child outcomes linear or

nonlinear—that is, suggestive of thresholds past which neighborhood effects differ in

magnitude?

xiv

Executive Summary

Research Methods

The Natural Experiment in Denver

In addition to its large-scale, conventional public housing developments, DHA has operated

since 1969 a program providing approximately 1,500 low-income families with opportunities to

live in scattered-site, single-family and small-scale, multi-family units. These units are located in a wide range of neighborhoods throughout the congruent city and county of Denver, whereas the

conventional developments are typically located in less-advantaged neighborhoods. From 1987

onward, as applicants (who met certain basic eligibility criteria) came to the top of the public

housing waiting list, they were offered a vacant DHA unit (in either conventional or scattered-

site programs), with the number of bedrooms appropriate for their family size and gender of

children. If they did not accept this unit, they were offered the next similarly sized unit that

became available (typically after a nontrivial wait). If applicants did not accept this second unit, they dropped to the bottom of the queue, creating a wait of a year or more.

We conducted a variety of statistical tests to ascertain whether the initial assignment of

households to a DHA dwelling unit (and neighborhood thereby) mimicked random assignment of

household to neighborhood. These tests were based on the intuitively appealing notion that in a

quasi-random assignment there would be few statistically significant correlations among

observed DHA tenant characteristics and neighborhood characteristics, no more than might occur

through chance. We found that only DHA tenant ethnicity generated associations with

neighborhood conditions (in particular, aspects of neighborhood disadvantage). This indicates

that, conditioned on ethnicity, the DHA allocation process produced a quasi-random initial

assignment of households across neighborhood characteristics. Because we control for ethnicity

in all our models, we are confident that unobserved tenant characteristics that might affect both

neighborhood of residence and child outcomes are not seriously biasing our estimated

neighborhood effect parameters.

The quasi-randomness of this initial DHA assignment potentially erodes over time as some

residents selectively leave their initial locations while others stay. To investigate the degree to

which selective moves subsequent to DHA residence and selective remaining in DHA residence

may affect our measurement of neighborhood effects, we replicate our analyses using multiple

(typically three) overlapping samples of children and youth (about whom we gained information

through our survey) that differ in when they lived in DHA and the duration for which they did

so. We report only those results that are robust across multiple samples.

A further important feature of our natural experiment is the comparatively long exposures

children in DHA households had to their assigned neighborhoods, in part because we were

considering site-based assisted housing, not vouchers. Our sample of households had a six-year

mean (five median) DHA residential duration—approximately twice as long as reported for the

seminal Moving To Opportunity (MTO) experimental group that used vouchers in low-poverty

neighborhoods (mean = 2.7 years; median = 3.3 years). Recent scholarly work stresses the

importance of taking into account the length of time children are exposed to particular

neighborhood contexts, lest one underestimate the true effects that neighborhoods have on them.

Natural experiments have become widely accepted among social scientists as a valid means of

obtaining unbiased estimates of neighborhood effects. Yet their use inevitably raises questions

xv

Executive Summary

about the generality of results. We believe that our findings can fairly be generalized to low-

income, Latino, and African-American families who apply for and remain on the waiting list

long enough to obtain public housing. As such, it may not be fully generalizable to the

population of minority families who obtain subsidized rental housing or to the larger population

of minority families who qualify for housing assistance. Nevertheless, it is similar to—yet

considerably more general than—the populations forming the samples for the oft-cited MTO-

based scholarly studies. We believe that our findings are generalizable for low-income minority

households who have traditionally been the focus of subsidized housing policies in the United

States.

Data Sources

Household and Child Information

We developed and fielded during 2006–2008 the Denver Child Study telephone survey, which

collected retrospective and current information about the household, adults, and children.

Detailed information related to multiple domains of outcomes was gathered for all eligible

children associated with each household. Each household’s residential mobility history was

obtained so it could be associated with neighborhood developmental context for children. Study

eligibility criteria were (1) presence of children in the home between 0 and 18 years of age, when

they moved into DHA; (2) family remained in DHA housing for at least two years; (3) family

first entered DHA in 1987 or later (when DHA’s current quasi-random assignment process came

into operation); and (4) Latino or African-American ethnicity identified. Ultimately, 711

households that were interviewed and whose surveys met our quality standards for reliable and

complete information remained in the study. Children from these households constitute the

subjects in the Denver Child Study.

Our Denver Child Study survey collected information on a wide variety of parental/caregiver (“caregiver” hereafter) and household characteristics that we employed as controls. The survey

asked caregivers to supply information about all of their children with whom they had lived in

DHA public housing for at least one year. In this manner, we collected detailed information

about the children’s gender, ethnicity, birth order, residential histories, health, exposure to

violence, risky behaviors and activities, education, and (for older children) marriage and

childbearing histories and labor market outcomes during adolescence and young adulthood.

Neighborhood Information

We obtained a wide variety of neighborhood data from four sources. The first source was the

decennial U.S. Census, where we used census tract geographic scales from the 1970, 1980, 1990,

and 2000 censuses. We employed the Neighborhood Change Database (NCDB) for this

information, because it adjusts data to account for changes in tract boundaries between decennial

censuses. For estimates of non-census year data, we used linear interpolation or extrapolation.

We gathered indicators that have been widely employed in prior research on neighborhood

effects, including percentages of households moving in during the prior year, female-headed

households, families below the poverty line, unemployed adults, Latino population, non-Latino

xvi

Executive Summary

African-American population, 1 foreign-born population, homes that are renter occupied, homes that were built during various periods, and mean occupational prestige (based on the General

Social Survey prestige score weighted by the observed proportional distribution of occupations

of employees in the tract). We used principal components analyses to derive an indicator of

neighborhood social vulnerability, comprised of the equally weighted sum of census tract

percentages of poor, unemployed, renters, and female-headed households.

The second source was subjective indicators based on responses of the caregivers interviewed in

our Denver Child Study. For each neighborhood in which they lived while they were raising

children, we asked the primary caregiver to respond to a battery of questions related to the

location’s assets and liabilities. From the responses, we devised three composite neighborhood

indicators (social capital, social problems, and institutional resources) and a dichotomous

measure of the presence of negative peer influences in the neighborhood. The social capital

index (range: 0–6) was incremented by “one” for each of the following respondent descriptions

of people in the neighborhood—(1) could get together to solve neighborhood problems; (2)

would watch out for their children and property; (3) knew them and their children by name; (4)

they and their children could look up to them; or (5) could be counted on in times of trouble—

and whether the respondent participated in any organizations located in the neighborhood (for

example, block clubs, tenant groups, religious organizations). The social problems index (range:

0–5) was incremented by a factor of “one” for each of the following neighborhood conditions:

(1) people selling drugs; (2) gang activity; (3) homes broken into by burglars; (4) people being

robbed or mugged; and (5) people being beaten or raped. We used Item Response Theory

analysis to generate a latent factor score of neighborhood resources present during childhood.

Resources included parks, recreation centers, mentoring or counseling centers for children,

subsidized day care facilities, and good police protection. All of these composite indicators

proved reliable.

The third source of neighborhood information was the Denver-based Piton Foundation’s

Community Facts database, which provided small area-based, annually measured information

culled from Denver administrative databases that are not provided by the Census. We employed

violent crimes reported to police per 1,000 population, property crimes reported to police per

1,000 population, and confirmed cases of child abuse and neglect per 1,000 children. The Piton

Foundation data are aggregated to 77 named areas consisting of two census tracts, on average,

and thus are measured at a larger spatial scale than our census-based data. Piton series data are

available only for the city and county of Denver.

The fourth source for data on toxic airborne pollutants coded to census tracts was the U.S.

Environmental Protection Agency. Specifically, we employed their summary index of respiratory

health risk associated with the combined concentrations of 124 toxic airborne compounds as well

as their neurological risk index, identifying the concentrations of lead pollutants.

1 The ethnic makeup of Denver in 2000 was 52 percent non-Latino whites, 11 percent non-Latino African Americans, and 32 percent Latinos.

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Executive Summary

Statistical Modeling Approaches

Our core modeling approach employs two complementary empirical strategies. The first explores

the predictors of whether a child ever experienced a certain outcome (either by the time of our survey or by 18 years of age, whichever came first). It employs various techniques for modeling

dichotomous outcomes: logit with clustered robust standard errors, multilevel mixed-effects

logit, and mixed effects Bayesian analyses. The second explores the predictors affecting the

timing when the onset of a particular outcome occurred. It employs Cox proportional hazard

models with clustered robust standard errors or accelerated failure time frailty analyses. For our

core modeling efforts in both approaches, we measure time-varying predictors

contemporaneously with the onset of outcome being modeled. We also explore how results differ

when we measure cumulative exposures to neighborhood context. Moreover, we investigate

whether relationships observed across the full sample are robust across males and females and

across Latino and African-American ethnic groups. Finally, we investigate potential nonlinear

neighborhood effects using spline regression analysis.

All of our analytical strategies yield “reduced form” estimates of the degree to which

neighborhood indicators correlate with the particular developmental outcome being investigated

through unspecified intervening causal pathways. We intentionally omit from our models any

endogenous or predetermined covariates that may themselves be affected by neighborhood

environment. In this fashion, we avoid “overcontrolling” and thus minimizing the apparent

relationships between neighborhood indicators and the particular outcome.

Primary Findings

Overview

Many aspects of neighborhood context proved statistically and substantively important predictors

of child and youth outcomes in all domains, though sometimes in unexpected ways. Aspects of

the neighborhood’s safety, physical environment, social status, ethnic mix, and nativity mix were

associated with large differences in the odds and timing of virtually all outcomes investigated. In

particular, neighborhoods with higher occupational prestige and percentages of foreign-born

populations as well as lower property crime rates and scores on a social problems index had

more favorable outcomes for children across the board. The consequences of higher

neighborhood percentages of Latino and African-American ethnic composition and lower

percentages of pre-1940 vintage housing also were generally favorable though more mixed

depending on the outcome. Particular indicators seemed to exert their influence only on selected

child outcomes: Higher respiratory risk index predicted poorer health outcomes, more risky

behaviors and inferior education outcomes; negative peers in the neighborhood predicted more

exposure to violence and risky behaviors.

The magnitudes of most of the aforementioned apparent neighborhood influences typically

appeared to be contingent on the gender and ethnicity of the child or youth. The evidence did not

suggest, however, that any particular gender or ethnicity was generally more sensitive to

neighborhood context. Instead, the relative sensitivity depended on the outcome in question.

Differences in magnitudes of neighborhood effects across developmental stages were exhibited

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Executive Summary

for several outcomes and could be substantial. At which stage neighborhood effects appear

stronger varied both by outcome in question and sometimes whether neighborhood context was

measured contemporaneously or cumulatively. We thus caution against making broad

generalizations about “for whom and at which developmental stage are neighborhood influences

most important,” given the apparent multicontingent nature of the answer.

Neighborhood effects on health measured as cumulative exposures appeared stronger, on

average, than those measured contemporaneously, but only when the outcome in question was

observed during the middle school developmental stage. Quite a different pattern emerged for

educational outcomes. With these outcomes, there was no clear pattern of cumulative measures

being stronger; indeed, if anything, for the high school stages the contemporaneous measures

appeared marginally stronger for some outcomes. Our results suggest that no general conclusion

can be reached about the comparative strength of contemporaneous and cumulative measures of

context; it appears to depend on outcome.

Nonlinear neighborhood effects did not appear to be the norm, though for some indicators

(especially violent crime) they were consistently manifested. Observed nonlinear patterns were

often dissimilar across indicators, although a few (respiratory risk, occupational prestige, social

vulnerability) often exhibited theoretically supported minimum thresholds. Others (of particular

note, violent crime) exhibited V-shaped or inverse V-shaped relationships with particular

outcomes. Once again, no generalizations can be made: Nonlinear relationships appear to be

contingent on neighborhood indicator and outcome in question.

Physical and Behavioral Health

We investigated five outcomes: diagnoses of asthma, neurodevelopmental disorders, obesity,

internalizing behaviors, and behavioral health service utilization. Aspects of the neighborhood’s

safety, ethnic and nativity mix, social status, resources, and environmental quality all provided

substantial predictive power for these outcomes. We caution, however, that whether these

relationships were manifested by causal links, though the probability of a child having a health

problem or the probability of having a set of symptoms medically diagnosed was sometimes not

entirely clear. We believe that the most convincing way to interpret the neighborhood property

crime, social problems index, occupational prestige, resources, environmental pollution, and

housing stock vintage relationships is that they represent causal forces that directly affect child

health. Thus, we conclude that low-income Latino and African-American children will

demonstrate one or more comparatively superior health outcomes if they live in a neighborhood

with a lower property crime rate, social problems index, and respiratory and neurological

pollution risk and with a higher occupational prestige score, public resource factor score, and

degree of walkability and land use mixes. Further, we believe that results for violent crime; child

abuse and neglect rates; neighborhood social vulnerability score; local medical facility; and

foreign-born, Latino, and African-American population percentages can best be interpreted as

influences on the odds of a given set of adverse child symptoms generating parental actions

leading to a medical diagnosis. Thus, we conclude that potential health problems of low-income

Latino and African-American children will be less likely to be diagnosed if they live in a

neighborhood with a higher violent crime rate; child abuse and neglect rate; social vulnerability

score; and foreign-born, Latino, or African-American population percentage and one in which

there are no medical facilities.

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Executive Summary

Exposure to Violence

We investigated five outcomes: witnessing violence in the neighborhood, school, and home and

being victimized by violence in the neighborhood and school. Aspects of the neighborhood’s

safety, ethnic and nativity mix, social status, and housing stock all provided substantial

predictive power for these outcomes. Exposure to violence in the neighborhood, at school, or at

home was generally less likely in neighborhoods with lower rates of property crime, social

problems, and pre-1940–vintage housing stock and higher rates of violent crime (up to a point),

child abuse and neglect rates, occupational prestige, and neighborhood social vulnerability. We

believe that relationships observed for child abuse rates and social vulnerability were likely

reflecting neighborhood effects that yield systematic underreporting. Higher percentages of

immigrants, Latinos, and African Americans in the neighborhood were also linked to lower odds

of witnessing violence, although the effects of neighborhood composition depended on the

outcome in question and again may be more suggestive of forces associated with underreporting

of such violence. The magnitudes of most of these apparent influences (especially property

crime), however, appeared to be only modestly contingent on gender and ethnicity of youth,

although for some aspects of context cross-strata differences were substantial. Nonlinear

neighborhood effects appeared often; several indicators exhibited minimum thresholds, and

others demonstrated V-shaped relationships.

Risky Behaviors

We investigated five outcomes: smoking tobacco, drinking alcohol, using marijuana or other

drugs, running away from home, and engaging in violent or aggressive behaviors. Aspects of the

neighborhood’s safety, social status, ethnic and nativity mix, physical environment, and peer and

social capital dimensions exhibited substantial predictive power for these outcomes. One or more

risky behaviors were generally less likely in neighborhoods with higher violent crime rates (up to

a point); foreign-born, African-American, and Latino residential percentages; and occupational

prestige and lower property crime rates, social problems index and social vulnerability,

percentages of pre-1940–vintage dwellings, and respiratory risks from air pollution and negative

peer influences. The magnitudes of most of these apparent influences were only modestly

contingent on gender and ethnicity, although for some aspects of context cross-strata differences

were substantial. Nonlinear neighborhood effects appeared often and were often dissimilar across

indicators, although several exhibited minimum thresholds that can be easily interpreted

theoretically.

Educational Outcomes

We investigated five outcomes placement in special education classes, participating in advanced

or gifted classes, repeating a grade, being suspended or expelled, and dropping out of school

before earning a diploma. Aspects of the neighborhood’s violent and property crime rates,

physical environment, social status, and ethnic and nativity mix exhibited substantial predictive

power in predicting these outcomes. Educational outcomes were generally more favorable in

neighborhoods that had higher occupational prestige and percentages of foreign-born and Latino

residents as well as lower rates of property crime and pre-1940–vintage dwellings. Outcomes

generally were better in neighborhoods that had moderate rates of violent crime than with none

but grew progressively worse as violent crime rates exceeded average levels. The magnitudes of

most of these apparent influences typically were contingent on gender and ethnicity of the

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Executive Summary

student. Nonlinear neighborhood effects did not appear to be the norm, though violent crime

consistently manifested a V-shaped relationship, with the odds of educational outcomes and

respiratory risk exhibiting a theoretically defensible minimum threshold before negative

outcomes were manifested.

Labor Market Outcomes

We investigated six outcomes: being employed, employed more than 20 hours weekly, hours

worked before 18 years of age, young adult full-time employment, postsecondary education, and

neither working nor attending school. Aspects of the neighborhood’s safety, ethnic and nativity

mix, social status, and housing stock all exhibit substantial predictive power for these outcomes.

In general, teen employment will be more likely when living in neighborhoods with lower

violent crime rates and occupational prestige, higher percentages of pre-1940–vintage housing,

and higher property crime and child abuse rates. Young adult full-time employment will be more

likely for those raised during high school in neighborhoods with higher percentages of foreign-

born residents and lower percentages of Latino residents. Postsecondary education will be more

likely for those raised during high school in neighborhoods with lower property crime and child

abuse rates but higher shares of socially vulnerable populations and higher violent crime rates.

These apparent influences appeared more complicated and nuanced than conventionally posited,

however. Especially noteworthy is the typical contingency of the neighborhood effect magnitude

based on gender and ethnicity. Indeed, virtually no neighborhood indicator employed had

consistently significant predictive power across more than two strata. We also note that the

importance for young adults of contexts experienced while they were in high school speaks to

the temporal durability of these neighborhood effects during the teenage developmental stage.

Marriage and Childbearing

We investigated three outcomes: cohabiting or marrying as a teen or young adult, giving birth to

or fathering a child as a teen, and childbearing before marriage as a young adult. Aspects of the

neighborhood’s safety, social status, ethnic and nativity mix, and physical environment exhibited

substantial predictive power for these outcomes. Risks for one or more of these outcomes (for at

least one stratum or more) diminish when living in neighborhoods that have higher violent crime

rates; occupational prestige; and percentages of foreign-born, Latino, or African-American

residents. The risks increase when living in neighborhoods with higher rates of property crime,

caregiver reports of neighborhood social problems, and percentages of dwellings built before

1940. These relationships were manifested particularly strongly and generally for African-

American youth, and no noteworthy nonlinear relationships emerged.

Supplemental Investigations

We first investigated whether context played a more powerful role during certain developmental

stages in the period before onset by considering a variety of health and educational outcomes that

frequently occurred in our sample during more than one developmental stage. Our exploration

confirmed the conventional wisdom that such differences exist and can be substantial. Moreover,

we found that at which stage neighborhood effects appear stronger varied both by outcome in

question and sometimes whether neighborhood context is measured contemporaneously or

cumulatively.

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Executive Summary

Second, we investigated the degree to which neighborhood context had stronger impacts on child

and youth development if exposure persisted over a sustained period by computing cumulative

measures of exposure to neighborhood conditions through the time of onset using the same

selected health and educational indicators. A consistent pattern emerged for our health outcomes.

Neighborhood effects measured as cumulative exposures appeared stronger, on average, than

those measured contemporaneously but only when the outcome in question was observed during

middle school. Quite a different pattern emerged for our educational outcomes. With these

outcomes, there was no clear pattern of cumulative measures being stronger; indeed, if anything,

the contemporaneous measures appeared marginally stronger for some outcomes during high

school or late adolescence. Our results suggest that no general conclusion can be reached about

the comparative strength of contemporaneous and cumulative measures of context; it appears to

depend on outcome.

Discussion of Effects From Residential Context

Neighborhood Safety

Indicators of neighborhood safety provided the most consistent explanatory power across our

domains of child and youth well-being. Some of the relationships manifested were to be

expected; others were surprising but revealing. As expected, our social problems index (a

caregiver assessment of disorder, property, and especially violent crime in the immediate

environs) and property crime rate (measured at the approximate scale of two encompassing

census tracts) were strongly associated with a wide range of negative outcomes in virtually every

domain investigated. Unexpectedly, violent crime rates (also measured at the approximate scale

of two encompassing census tracts) exhibited the opposite associations, especially in places with

below-average violent crime rates. We think that this finding reflects the net effects produced by

the conflicting forces impinging on children arising from violent crime in the broader

neighborhood, controlling for crime in the immediate environs: negative direct effects from

crime and alterations in caregiver actions in response to such that are intended to ameliorate

them. Caregivers may respond in several ways in an effort to shield their children from violent

crime in the wider environs, such as limiting youths’ activity spaces closer to home and

expanding caregiver monitoring activities. So long as violent crime stays below average, these

compensatory actions apparently yield net positive outcomes for children that manifest

themselves as reduced exposure to violence (as caregivers would hope), fewer risky behaviors,

and improved educational performance (as caregivers would like but perhaps not have expected).

Unfortunately, our findings suggest that the efficacy of such compensatory caregiver responses

will be overwhelmed in neighborhoods with above-average violent crime rates. In such cases

more crime is, as conventionally predicted, associated with poorer child outcomes in health,

exposure to violence, risky behaviors, and educational performance. Our results here provide

implicit testimony to the importance of both measuring neighborhood characteristics at different

geographic scales and probing for nonlinear relationships.

Neighborhood Social Status

Residing in a higher-occupational-prestige neighborhood was one of the most consistent

predictors of favorable child outcomes in almost every domain. These results have intuitive

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Executive Summary

appeal and are consistent with prior scholarship on the importance of local networks, norms, and

role models in transmitting neighborhood effects. Neighborhoods that surround their children

with higher-prestige workers likely expose them to norms and role models and provide access to

networks of richer information that ultimately promote better health, less exposure to violence,

fewer risky behaviors, better educational performance, and less nonmarital childbearing. There

are theoretical reasons why neighborhood social status could directly affect each of these

outcomes; many mediated causal pathways are also possible. For example, better child health

outcomes, less exposure to violence, and fewer risky behaviors should provide clear educational

payoffs for children and youth; better secondary educational achievement, in turn, might deter

nonmarital childbearing as young adults.

Another measure of neighborhood status, our social vulnerability score (summed percentages of

poor, unemployed, renter, and female-headed households) also proved a consistently predictive

aspect of context. As would be expected, our evidence suggests that a more socially vulnerable

neighborhood will generate (through potentially a variety of mechanisms) several negative

outcomes for children and youth: more risky behaviors and less likelihood of marriage (for

African Americans). The evidence also supports the notion that in places that have above-

average concentrations of vulnerable populations, caregivers are less likely to seek medical

treatment when their children present with symptoms and less likely to know about and report

their children’s exposure to violence.

Neighborhood Nativity and Ethnic Composition

Our evidence implies that higher percentages of foreign-born residents create a collective

socialization context that supports the positive development of low-income minority children and

youth in many ways: less likelihood of being victimized by neighborhood violence (for boys),

fewer risky behaviors (with the exception of smoking), superior educational performance, better

employment rates as young adults, and increased chances of marriage (for young women). Less

positively, our findings also suggest that high immigrant concentrations can discourage parents

from seeking diagnoses of adverse health symptoms, raise the chances of boys witnessing

neighborhood violence, and reduce the chances that young adult African Americans will marry.

A similar portrait emerges for the Latino percentage in the neighborhood that we also believe can

best be explained by their distinctive normative and cultural structures. Low-income minority

children raised among more Latino neighbors experienced better outcomes in terms of

witnessing neighborhood violence, risky behaviors, educational performance, and teen

childbearing. As in the case of immigrants, however, the portrait of neighborhood effects is not

uniformly positive. Our findings suggest that high Latino concentrations can discourage parents

from seeking diagnoses of adverse health symptoms, raise the chances of being victimized by

neighborhood violence or witnessing school violence, and reduce the chances that young adults

will be employed full time.

By contrast, the percentage of African-American neighbors rarely predicted child outcomes, and

when it did the results again were mixed. Higher concentrations of African-American residents

apparently reduced the chances of running away and women having children as teenagers but

decreased the chances of young women and African Americans getting married and discouraged

parents from seeking diagnoses of their children’s adverse health symptoms.

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Executive Summary

Neighborhood Physical Characteristics

We believe that our findings offer persuasive evidence that neighborhoods built before 1940 in

Denver have distinctive design, structural, and land use features that independently engender a

variety of effects on resident children and youth. It appears that most of these effects are

detrimental for children: greater exposure to violence, larger likelihood of risky behaviors,

weaker educational performance, and higher odds of bearing children outside of marriage as

young adults. Some outcomes, however, are more positive: lower incidence of obesity, greater

chance of working as a teen, and greater chance of being married as a young adult.

The quality of the ambient environment also seems to have a powerful impact on several child

outcomes, at least after pollution concentration thresholds have been surpassed. This strongly

suggests a biological mechanism through which this neighborhood effect is transmitted. High

levels of neighborhood respiratory risk pollutants apparently led to substantially heightened

chances of asthma exacerbations, smoking, and weak educational performance. High levels of

neurological risk pollutants also apparently produced several detrimental health outcomes for

female and African-American youth.

Contributions of the Denver Child Study

Our study contributes to the measurement of neighborhood effects in at least four ways. First,

because parents of our sampled children were quasi-randomly assigned to neighborhoods, the

challenge of parental geographic selection bias is largely overcome. We believe that our

observed statistical associations can be treated as indications of causal effects. Second, we

evaluate a wide variety of measures of neighborhood environment, both objective and subjective,

measured at different spatial scales. Third, because of the unusual nature of the site-based

housing assistance provided by DHA, we are able to observe how low-income minority children

and youth respond to a wide range of contexts after often extensive degrees of exposure. Fourth,

ours is one of the few studies to examine neighborhood impacts on the outcomes of low-income

Latino children and youth.

Perhaps because of these innovative features of our study, we have observed dramatic and

consistent evidence of powerful neighborhood effects on a wide range of outcomes for low-

income Latino and African-American children and youth. These results stand in contrast to some

of those produced by the recently completed MTO demonstration. We believe that the

differences can be explained through one or more of the following reasons:

 There are differences in the samples of low-income families investigated.

 The neighborhood “treatments” differ substantially on several grounds.

 They have different (though overlapping) sets of outcome indicators that are sometimes

measured differently.

 The study sites are different metropolitan contexts demographically and geographically.

Our study also contributes to the formulation and reform of assisted housing and community

development policy. Our findings suggest that well-formulated assisted housing and urban

revitalization programs can yield substantial payoffs in multiple outcome domains by changing

the developmental context of low-income minority children and youth, either by changing the

character of neighborhoods or by changing the neighborhoods in which these children reside.

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Executive Summary

Our study has pinpointed particular attributes of the residential environment that seem most

predictive for a wide variety of positive outcomes, thus giving a strategic guide to policymakers

as to what directions and investments are likely to yield the greatest social gains.

xxv

I. Introduction

I.

INTRODUCTION

Policy and Scholarly Context for Neighborhood Effects on Children

Rarely has the attention of scholars and housing policymakers alike been so simultaneously

focused on the same topic: neighborhood effects on children. To what degree are the life chances

of children and youth influenced by the environs in which they are raised?

Since seminal writings a quarter-century ago (Wilson, 1987; Jencks and Mayer, 1990; Galster

and Killen, 1995), a veritable explosion of scholarly publications devoted to probing this topic

from multiple disciplinary perspectives has occurred. Compare the reviews in Gephart (1997);

Leventhal and Brooks-Gunn (2000); Sampson, Morenoff, and Gannon-Rowley (2002); Ellen and

Turner (2003); Newburger, Birch, and Wachter (2011); Galster (2012); and Foster and Brooks-

Gunn (2013). Despite the impressive volume of investigations, heated scholarly debates over the

nature and quantitative importance of neighborhood effects persist (Van Ham et al., 2012;

Ludwig, 2012). Undoubtedly, much of this controversy stems from disagreements over which

studies sufficiently surmount the daunting obstacles impeding the accurate measurement of

neighborhood effects on individual residents’ outcomes. In Chapter II, we will discuss these

obstacles and how our study overcomes them.

Despite disagreements among scholars, official federal, state, and local pronouncements suggest

that many policymakers believe that neighborhood effects are important. Illustrative is this

January 10, 2014, statement by President Barack Obama, made while announcing new “Promise

Zones” in five cities:

“[Our goal is that] a child’s success be determined not by the ZIP code she

lives in but by the strength of her work ethic and the scope of her dreams.”

(cited in Shear, 2014)

Numerous programmatic initiatives have emerged that aim to encourage or require changes

within neighborhoods or where assisted households live as an antidote to the perceived social

evils associated with “concentrated disadvantage” (Briggs, 2005). Examples include:2

 The HOPE VI Program, which replaces dilapidated concentrations of public housing with

more diverse housing stocks occupied by a broader mix of income groups and tenures.

 Public housing management and tenant allocation reforms promulgated by legislation and

regulations that are designed to encourage a greater diversity of income mixes within the

developments.

 Supportive services for those receiving tenant-based assistance through the federal

Housing Choice Voucher program that are aimed at helping voucher holders move into

superior neighborhood environments.

2 See the special issues of Evidence Matters (2013) and Cityscape: A Journal of Policy Development and Research,

2013, volume 15(2) , both published by the U.S. Department of Housing and Urban Development Office of Policy Development and Research.

1

I. Introduction

 The Promise Neighborhoods federal initiative, which intends to develop a robust set of

educational, recreational, training, and other supportive institutions in previously

disadvantaged neighborhoods where subsidized housing was located.

 Local and state land-use planning rules requiring mixed-income developments.

This U.S. policy direction and programmatic particulars have been challenged on conceptual and

empirical grounds (for example, Goetz, 2003; Joseph, 2006; Joseph, Chaskin, and Webber, 2006;

Galster, 2013)3. Perhaps most fundamental to this critique is the argument that disadvantaged

households and their children economically fare about the same, regardless of their residential

environments. Perhaps the most widely cited evidence buttressing this critique was provided by

the recently released final report of the Moving To Opportunity (MTO) demonstration

(Sanbonmatsu et al., 2011).4 The MTO research design randomly assigned public housing

residents in extremely disadvantaged neighborhoods in Baltimore, Boston, Chicago, Los

Angeles, and New York who volunteered to participate to one of three experimental groups: (1)

controls that did not receive a rental housing voucher but could remain in public housing in

disadvantaged neighborhoods if they wished, (2) recipients of rental vouchers, and (3) recipients

of rental vouchers and relocation assistance who had to move to neighborhoods with less than

10 percent poverty rates and remain for at least a year. Analyses of MTO data collected over a

decade uncovered some mental and physical health benefits to parents and children who moved

to low-poverty neighborhoods but no substantial neighborhood effects on adult labor market

outcomes, youths’ educational attainments, or a variety of other behaviors (Ludwig, 2012).

Based on these modest findings, it has been claimed that “MTO is the gold standard . . . [and] its

results . . . have proven discouraging . . . neighborhood quality . . . [therefore has] little effect on desirable and measurable outcomes” (Smolensky, 2007, p. 1016).

Such a sweeping conclusion is inappropriate given the substantial debate over the power of MTO

as a test of neighborhood effects (compare Clampet-Lundquist and Massey, 2008; Sampson,

2008; Burdick-Will et al., 2010; Briggs, Popkin, and Goering, 2010; Briggs et al., 2008, 2011;

Sanbonmatsu et al. 2011; Ludwig, 2012). The debate focuses on five domains relevant to child

and youth outcomes. First, although MTO randomly assigns participants to treatment groups, it

neither randomly assigns characteristics of neighborhoods initially occupied by voucher holders

(except maximum poverty rates for the experimental group) nor characteristics of neighborhoods

in which participants in all three groups may move subsequently. Thus, there remains

considerable question about the degree to which geographic selection on unobservable household

characteristics persists. Second, MTO may not create adequate duration of exposure to

neighborhood conditions by any group at any location to observe much treatment effect from the

new neighborhood context.5 Third, MTO overlooks the potentially long-lasting and indelible

effects that disadvantaged neighborhoods had upon older youth in the experimental group who

spent their early childhoods in such places. Fourth, it appears that even experimental MTO

movers rarely moved out of predominantly African American–occupied neighborhoods near

those of concentrated disadvantage and achieved only modest changes in school quality, social

3 Also see the special issue of Cityscape, 2013, 15(2).

4 See also Orr et al. (2003); Goering and Feins (2003); Kling, Liebman, and Katz (2007); Ludwig et al. (2008); Briggs, Popkin, and Goering (2010); and the special issue of Cityscape, 2012, 14(2) devoted to MTO.

5 For example, nonexperimental analysis focusing on MTO families who resided for a majority of the study period in low-poverty or higher education neighborhoods revealed their substantially better adult employment and earnings than in the control group (Turner et al., 2012).

2

I. Introduction

networks, and job accessibility. As a result, they may not have experienced sizable enhancements

in their neighborhood context. Fifth, MTO involves vague and heterogeneous treatments within

and among the three groups; besides initial poverty rate, the rest of the residential environment

remains an unmeasured, unstandardized “bundle.” It is impossible to discern when groups’

outcomes differ or do not and which particular aspects of neighborhood context are responsible.

Thus, its theoretical promise and conventional wisdom notwithstanding, MTO may not have

provided definitive evidence about the potential effects on low-income children from prolonged

residence in multiply advantaged neighborhoods.

Purpose and Contributions of Our Denver Child Study

The Denver Child Study explores the extent to which multiple dimensions of neighborhood

context affect the physical and behavioral health, exposure to violence, risky behaviors,

education, youth and young adult labor market, educational outcomes, and marriage and fertility

behaviors of Latino and African-American children and youth from low-income families. The

study takes advantage of a natural experiment involving the Denver, Colorado, Housing

Authority (DHA), which since 1969 has operated public housing units located in a wide range of

neighborhoods throughout the city and county of Denver. Because the initial assignment of

households on the DHA waiting list to vacant public housing units (and, thus, to neighborhoods)

mimics a random process, this program represents an unusual opportunity for reducing parental

geographic selection bias and observing the unusual combination of low-income, minority

youths raised for extended periods in advantaged (as well as disadvantaged) neighborhoods.

In this study, we analyze data from several administrative sources and data we have collected

from telephone surveys with Latino or African-American current and former DHA tenants whose

children were the appropriate ages when they lived in DHA housing. Our surveys provide

retrospective information on a battery of youth outcomes, family characteristics, and residential

histories. By merging this information, we have created a pseudo-longitudinal panel providing

for each year of children’s lives detailed characteristics about their families, neighborhoods, and

outcomes in many domains.

We analyze this dataset with a variety of multivariate statistical methods in an effort to answer

the following research questions:

 Among Latino and African-American children and youth who spent at least two years

living in DHA public housing, are there statistically and economically significant

differences in their outcomes in six domains (behavioral and physical health, exposure to

violence, risky behaviors, education, employment, marriage and childbearing) that can

be attributed to differences in their neighborhood environments (controlling for family

and individual characteristics)?

 Does the answer depend on gender, ethnicity, or developmental stage?

 Does the answer depend on whether neighborhood environment is measured

concurrently with the outcome or cumulatively throughout childhood prior to the

outcome?

3

I. Introduction

 Are the relationships between neighborhood context and child outcomes linear or

nonlinear—that is, suggestive of thresholds past which neighborhood effects differ in

magnitude?

Our study contributes to advancing the scientific measurement of neighborhood effects in at least

four ways. First, because parents of our sampled children were quasi-randomly assigned to

neighborhoods, the challenge of parental geographic selection bias is largely overcome. Second,

we evaluate an unprecedented variety of measures of neighborhood context, both objective and

subjective, measured at different spatial scales. Third, because of the unusual nature of the site-

based housing assistance that DHA provided, we are able to observe how low-income, minority

children and youth respond to a wide range of contexts after often-extensive durations of

exposure. 6 Fourth, ours is one of the few studies to examine neighborhood impacts on the outcomes of Latino youth.

Our study also contributes to the formulation and reform of assisted housing and community

development policy. We are implicitly investigating the degree to which housing and urban

revitalization programs can yield substantial payoffs in multiple outcome domains by changing

the geographic developmental context of low-income, minority children and youth.

Structure of the Report

Our report is organized into 11 chapters. Chapter II discusses the theoretical and empirical

foundation for the current study. It reviews the contemporary scholarly understandings of the

numerous causal mechanisms through which neighborhood context may influence the

development of children and youth. Then, it delineates the major challenges that empirical

research faces in trying to obtain an accurate measure of how much neighborhood context affects

a variety of child and youth outcomes. Finally, it explains how our Denver Child Study offers important advantages in overcoming these challenges.

Chapter III describes the data that we gathered for this study and how we analyze it. It explains

the nature of the natural experiment involving DHA and how it offers a rare opportunity to

investigate neighborhood effects. It discusses in detail the household survey that we conducted

and the other, secondary sources of data we drew on to provide a rich and comprehensive set of

neighborhood indicators. Descriptive statistics of our analysis sample are provided here. Finally,

this chapter introduces our primary statistical models (mixed-effect logistic regression and

hazard and accelerated failure time models) employed to discern predictors of if outcomes ever

occur for children and youth and, if so, how soon they occur in their lives.

Chapters IV through IX present our findings related to six domains of outcomes for children and

youth: physical and behavioral health, exposure to violence, risky behaviors, education,

employment, and marriage and childbearing. In each chapter, we explore the degree to which our

results seem general across gender and ethnic strata and whether they exhibit any important

nonlinear relationships.

6 In our Denver Child Study sample, we observe a six-year mean (five-year median) residential duration in DHA housing, approximately twice as long as reported for the MTO experimental group (mean: 2.7 years; median: 3.3 years).

4

I. Introduction

Chapter XI provides a holistic summary of our major findings and their significance. It compares

and contrasts our results to other major studies of neighborhood effects (especially MTO) and

offers potential explanations for any differences.

5

II. How Neighborhood Context May Influence Children and How Can We Measure It?

II. HOW NEIGHBORHOOD CONTEXT MAY INFLUENCE CHILDREN

AND HOW CAN WE MEASURE IT

Potential Causal Mechanisms of Neighborhood Context

Our theoretical framework for understanding links between neighborhood contexts and

children’s and youths’ outcomes draws from widely accepted ecological models of child

development. As explicated by Bronfenbrenner and Morris (1998), children’s development is

shaped by both the proximal (for example, family) and distal (for example, neighborhood)

contexts in which children live and interact. There is broad theoretical agreement about potential

causal pathways connecting neighborhood context and various outcomes for children and youth.

We therefore list these mechanisms and describe them only briefly here. Our synthesis of the

social science and public health literatures suggests that 15 distinctive linkages can be identified.

It is useful to group these 15 mechanisms of neighborhood effects under four broad rubrics:

social–interactive, environmental, geographical, and institutional.7

Social–Interactive Mechanisms

This set of mechanisms refers to social processes endogenous to neighborhoods. These processes

include:

 Social Contagion. Behaviors, aspirations, and attitudes may be changed by contact with

peers who are neighbors. Under certain conditions, these changes can take on contagion

dynamics that are akin to “epidemics.”

 Collective Socialization. Individuals may be encouraged to conform to local social norms conveyed by neighborhood role models and other social pressures. This socialization

effect is characterized by a minimum threshold or critical mass being achieved before a

norm can produce noticeable consequences for others in the neighborhood.

 Social Networks. Individuals may be influenced by the interpersonal communication of

information and resources of various kinds transmitted through neighbors. These

networks can involve either “strong ties” or “weak ties.”

 Social Cohesion and Control. The degree of neighborhood social disorder and its

converse—“collective efficacy”—may influence a variety of behaviors and psychological

reactions of residents.

 Competition. Under the premise that certain local resources are limited and not pure

public goods, this mechanism posits that groups within the neighborhood will compete

for these resources among themselves. Because the outcome is a zero-sum game,

residents’ access to these resources (and their resulting opportunities) may be influenced

by the ultimate success of their group in “winning” this competition.

7 By contrast, Manski (1995) groups mechanisms into endogenous, exogenous, and correlated categories. Ellen and Turner (1997) group them into five categories: concentration, location, socialization, physical, and services.

Leventhal and Brooks-Gunn (2000) use the rubrics “institutional resources,” “relationships,” and “norms/collective efficacy.” For multiple perspectives on how neighborhood may affect children and youth, see Jencks and Mayer (1990); Brooks-Gunn, Duncan, and Aber (1997); and Booth and Crouter (2001).

6

II. How Neighborhood Context May Influence Children and How Can We Measure It?

 Relative Deprivation. This mechanism suggests that residents who have achieved some

socioeconomic success will be a source of disamenities for their less well-off neighbors.

The latter, it is argued, will view the successful with envy or will make them perceive

their own relative inferiority as a source of dissatisfaction.

 Parental Mediation. The neighborhood may affect (through any of the mechanisms listed

under all categories here) parents’ physical and mental health, stress, coping skills, self-

efficacy, behaviors, and material resources. All of these, in turn, may affect the home

environment in which children are raised.

Environmental Mechanisms

Environmental mechanisms refer to natural and human-made attributes of the local space that may directly affect the mental or physical health of residents without affecting their behaviors.

As in the case of social–interactive mechanisms, the environmental category can also assume

distinct forms:

 Exposure to Violence. If people sense that their property or person is in danger, they may suffer psychological and physical responses that may impair their functioning or

perceived well-being. These consequences are likely to be even more pronounced if the

person has been victimized.

 Physical Surroundings. Decayed physical conditions of the built environment (for

example, deteriorated structures and public infrastructure, litter, graffiti) may impart

psychological effects on residents, such as a sense of powerlessness. Noise may create

stress and inhibit decisionmaking through a process of “environmental overload.”

 Toxic Exposure. People may be exposed to unhealthy levels of air-, soil-, or water-borne pollutants because of current and historical land uses and other ecological conditions in

the neighborhood.

Geographical Mechanisms

Geographic mechanisms refer to aspects of spaces that may affect residents’ life courses yet do not arise within the neighborhood but rather purely because of the neighborhood’s location

relative to larger scale political and economic forces, such as:

 Spatial Mismatch. Certain neighborhoods may have limited accessibility (in either spatial proximity or as mediated by transportation networks) to job opportunities appropriate to

the skills of their residents, thereby restricting their employment opportunities. Teen job-

seekers who lack their own vehicle may be especially affected.

 Public Services. Some neighborhoods may be located within local political jurisdictions that offer inferior public services and facilities because of their limited tax base resources,

incompetence, corruption, or other operational challenges. These, in turn, may adversely

affect the personal development and educational opportunities of residents.

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II. How Neighborhood Context May Influence Children and How Can We Measure It?

Institutional Mechanisms

The last category of mechanisms involves actions by individuals or organizations (typically not

located in the given neighborhood) that control important institutional resources in the

neighborhood or points of interface between neighborhood residents and vital markets:

 Stigmatization. Neighborhoods may be stigmatized on the basis of public stereotypes held by powerful institutional or private actors about its current residents. In other cases, this

may occur regardless of the neighborhood’s current population because of its history,

environmental or topographical disamenities, style, scale and type of dwellings, or

condition of their commercial districts and public spaces. Such stigma may reduce the

opportunities and perceptions of residents of stigmatized areas in a variety of ways, such

as job opportunities and self-esteem.

 Local Institutional Resources. Some neighborhoods may have access to few high-quality

private, nonprofit, or public institutions and organizations, such as social services, day

care facilities, schools, and medical clinics. The lack of the same may adversely affect the

personal development opportunities of residents.

 Local Market Actors. There may be substantial spatial variations in the prevalence of

certain private market actors that may encourage or discourage certain behaviors by

neighborhood residents, such as liquor stores, fresh food markets, fast food restaurants,

and illegal drug markets.

Summary of Previous Evidence About Potential Neighborhood Effect Mechanisms

Scholars have reviewed the empirical literature related to causal processes potentially connecting

neighborhood contexts with child outcomes (see especially Leventhal and Brooks-Gunn, 2000;

Galster, 2012; and Foster and Brooks-Gunn, 2013). With the caveat that firm conclusions are

elusive here, given the incomplete and sometimes inconsistent state of scholarship and the

complexity of the topic, this previous work provisionally suggests the following.

First, the fact that neighborhood poverty rates appear consistently related to a range of outcomes

in a nonlinear, threshold-like fashion further suggests that the social contagion (peers) and the

collective socialization (roles models, norms) forms of causal linkages are transpiring. There also

may be some selectivity involved, as some socially weaker groups in the United States seem

more vulnerable to these contexts than stronger ones.

Second, the presence of affluent neighbors appears to provide positive externalities to their less

well-off neighbors, seemingly working via social controls and collective socialization. Social

networks and peer influences between affluent and poor neighbors, by contrast, do not appear as

important in this vein. There is evidence to suggest thresholds here as well, though the precise

threshold is unclear and likely varies by outcome being considered. Finally, most evidence

indicates that the influence on vulnerable individuals of advantaged neighbors is smaller in

absolute value than the influence of disadvantaged neighbors, whatever the mechanism(s) at

play.

Third, studies have consistently found that there is relatively little social networking between

lower and higher socioeconomic status households or children in the same neighborhood, and

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II. How Neighborhood Context May Influence Children and How Can We Measure It?

this lack is compounded if there are also racial differences involved. Thus, there is little to

support the version of neighborhood effects that advantaged neighbors create valuable “weak

ties” for disadvantaged ones.

Fourth, local environmental differences appear substantial and likely produce important

differentials in physical and behavioral health. There are huge differences in exposure to

violence across neighborhoods, and this undoubtedly produces important and durable

psychological consequences for children that, in turn, likely have numerous but difficult-toquantify added effects. Exposure to environmental pollutants, at least past some threshold concentrations, undoubtedly produces significant consequences for the health of children and

youth through biological processes.

Fifth, there is probably a substantial indirect effect on children and youth than transpires through the combined effects of the social–interactive, environmental, geographic, and institutional

dimensions of the neighborhood context on their parents. This mediation of neighborhood effects

through parents is likely to affect a broad range of outcomes for their offspring, though there

have been no attempts to measure comprehensively such effects.

Finally, there is a contingent aspect to the foregoing conclusions. Different neighborhood

mechanisms likely play a more or less salient role depending on the gender, ethnicity, and

developmental stage of the children in question. Moreover, certain mechanisms may be the

predominant vehicles for transmitting context to particular outcomes but not others.

Temporal Dimensions of Neighborhood Effects

The temporal dimensions of neighborhood effects must also be considered, because it is likely

that different mechanisms operate distinctively in terms of how quickly an effect transpires after

exposure, whether a minimum duration of exposure is required before any impact ensues, and the

degree to which prior exposures create durable impacts that are not easily altered by current

environments (Galster, 2012). First, consider how quickly a neighborhood effect might occur

after a child has been exposed to it (either by moving into a new neighborhood or by having the

current neighborhood change substantially). Socialization processes, for example, likely take

time before wielding influence. Therefore, it might be deduced that those who are exposed only

briefly to an environment that is trying to re-shape their behaviors will experience little if any

effect from it compared with those who are exposed to the same socializing environment for a

longer period. A similar deduction holds for the impacts that operate through local social

networks; it takes time for these networks to develop after an individual moves in (or evolve if

the neighborhood is changing around the individual). It thus follows that some minimum

duration of exposure to this new context will be required before new local social networks

produce any measurable differences in educational, employment, or other information conveyed

by them. Finally, effects of local institutions like job placement agencies, counseling centers, and health centers will be felt only after some period elapses, insofar as the services provided have

slow, cumulative impacts on the human capital of those assisted. This implies that recent, short-

term neighborhood exposures will yield smaller impacts compared with sustained durations

producing substantial cumulative exposure, as has been argued before (Leventhal and Brooks-

Gunn, 2000; Wheaton and Clarke, 2003).

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II. How Neighborhood Context May Influence Children and How Can We Measure It?

However, whereas socialization processes, the development of social networks and local

institutions likely takes some time before a noticeable effect can be expected; the impacts of

contextual changes in stigmatization, social disorder, and accessibility may manifest themselves

more rapidly. A person’s move to a stigmatized neighborhood may imply that the image of the

neighborhood will be immediately connected by external decisionmakers to the person

concerned. Similarly, the psychological and behavioral impacts from social disorder may be felt

quickly. Finally, geographic challenges for unemployed and underemployed youths in gaining

information about and easily commuting to higher paying jobs should manifest themselves

almost immediately if the accessibility characteristics of a neighborhood in which they reside

change. Yet, even through these faster acting mechanisms, a stronger cumulative effect may be

expected from sustained, longer term exposure.

The final consideration relates to the persistence or durability of impact. Another way to frame

this issue is whether the neighborhood effect mechanism is reversible. This seems especially

plausible with some mechanisms—namely, socialization, networks, accessibility, and

stigmatization. It is reasonable to posit that a change in any of these contextual dimensions could

produce a comparable (in absolute value) change in outcome, regardless of the starting value and

the direction of change. This implies that the impact from any given environment will not persist

if that environment changes in a radically different direction. However, for other mechanisms,

this symmetric reversibility is less likely. For example, if one replaces a weak institutional

education-training infrastructure that had retarded resident youths’ economic opportunities with a

far superior one, one would expect (likely after a lag) an improvement in their human capital,

thus rendering the initial impact transitory. By contrast, the opposite situation of a superior

institutional structure producing strong human capital is likely to produce persistent effects,

because a hypothetical, new, inferior set of institutions may do little to erode the human capital

previously attained. As another example, the benefits to behavioral health produced by a

violence-free environment will quickly dissolve if the context turns violent. In contrast, the

psychological harms caused by exposure to a violent environment can persist for a considerable

period, even when the individual is placed in a safe environment. Of course, we recognize that

even if in principle the mechanism is reversible (either symmetrically or asymmetrically), the

impact may not be reversible if the initial context triggered behavioral changes that were durable.

Should an initial neighborhood context result in individuals making decisions that adversely

affected their education, job training, or criminal record, for instance, the economic

consequences could be long lasting, even when the current neighborhood environment had

changed dramatically.

Unfortunately, there have been few rigorous investigations of the above temporal dimensions of

neighborhood effects. Nevertheless, a consensus has emerged that several sorts of outcome:

neighborhood indicator relationships appear stronger when measured as cumulative exposures

instead of contemporaneous exposures (see Aaronson,1998; Wheaton and Clarke, 2003; Turley,

2003; Sampson, Sharkey, and Raudenbush, 2008; and Musterd, Galster, and Andersson, 2012).

The evidence on whether some minimum exposure duration is required for an effect to occur and

whether effects are durable over the long run seem more contingent on the particular

relationships being investigated (cf. Turley, 2003; Kaupinnen, 2007; Sampson, Sharkey, and

Raudenbush, 2008; and Musterd, Galster, and Andersson, 2012). We thus concur with the recent

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II. How Neighborhood Context May Influence Children and How Can We Measure It?

admonition by Briggs and Keys (2009: 451) that more research on the temporal aspects of

neighborhood effects is required. We hope to contribute to this effort in this report.

Gender and Ethnic Differences in Neighborhood Effects

Recent work not only suggests that there is no uniformly “dominant” neighborhood effect

mechanism producing many sorts of consequences for children, but the influence of each

mechanism may vary across residential groups within any given neighborhood depending on

their social identity and the degree to which they are embedded in local social life (Pinkster,

2012). Several of the above mechanisms suggest that effects are heterogeneous by gender and

ethnic group, though not necessarily in unambiguous ways (Galster, Andersson, and Musterd,

2010). The key linkages rely on the notion that intraneighborhood mechanisms have effects only

to the extent that children and youth (1) spend a substantial amount of time in the neighborhood,

(2) are locally oriented in their social interactions, and (3) do not marshal sufficient resources to insulate themselves from these effects. Gender and ethnic characteristics may be related to each

of these three conditions. We would expect, for example, that local social control in ethnic areas

with more traditional, patriarchal norms would limit the geographic scale of girls’ interactions

(Pinkster, 2008). However, these same social controls may produce strict monitoring of the

behaviors of girls, thus potentially insulating them from neighborhood peer effects and negating

their greater time spent in the neighborhood (Pinkster, 2008). We would predict that girls would

be more vulnerable to neighborhood social disorder; though in certain ethnic groups young

males’ “coming of age” rituals may expose them to serious risks of violence. The evidence from

nonexperimental and experimental quantitative studies indeed suggests that different

mechanisms may have varying salience across different groups (Crowder and South, 2003;

Turley, 2003; Burdick-Will et al., 2010; Galster, Andersson and Musterd, 2010; Clampet-

Lundquist et al., 2011; Ludwig, 2012; Musterd, Galster, and Andersson, 2012).

Methodological Challenges of Quantifying Neighborhood Effects

The methodological concerns associated with empirical investigation of the behavioral and

psychological impacts of neighborhoods have been the subject of several excellent treatises; see

especially Manski (1993, 1995, 2000); Duncan, Connell, and Klebanov (1997); Duncan and

Raudenbush (1999); Sampson, Morenoff, and Gannon-Rowley (2002); Durlauf and Cohen-Cole

(2004); Oakes (2004); Galster (2008); and Foster and Brooks-Gunn (2013). Perhaps four of the

most vexing obstacles identified are (1) measuring neighborhood context, (2) measuring

exposure to neighborhood context, (3) geographic selection bias, and (4) endogeneity.

As noted above, there are numerous potential mechanisms through which neighborhood context

may exert causal influence on children and youth. The challenge is directly measuring the

attributes associated with these mechanisms. Rarely, if ever, do investigators have access to the

appropriate data for doing so in a comprehensive fashion. As a result, the standard (if not wholly

satisfying) practice is to employ neighborhood indicators that are readily available (most often

from the Census) and argue that they serve as proxies for one or more of the underlying causal

processes.

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II. How Neighborhood Context May Influence Children and How Can We Measure It?

The second issue relates to the intensity, duration, and consistency with which children are

exposed to neighborhood context. Researchers can readily identify the neighborhoods in which

subjects reside, but it is a far greater challenge to identify the degree to which they are exposed

to the processes thought to convey neighborhood effects, whether these processes work

instantaneously to generate outcomes for individuals or only after substantial cumulative impact.

As is the case with so much of research design in the context of neighborhood effects, what is

appropriate depends on which underlying process is assumed to operate. If, for example,

stigmatization were the predominant mechanism through which neighborhood effects transpired,

one could reasonably posit that the effect would apply equally to all youths residing in the

stigmatized place and that the effect would occur almost immediately upon a new resident’s

arrival. If socialization via peers were the predominant mechanism, however, the intensity of

exposure to such an influence would depend on the degree to which youths’ social networks and

routine activity spaces were contained within the neighborhood. Moreover, the degree to which

such a socialization process would change the youths’ behavior would be directly related to the

duration of their exposure to a consistent set of peers. Thus, within the context of the

socialization mechanism, we would expect neighborhood effects to be strongest for those who

have mainly intraneighborhood social relationships, undertake most of their activities there, and

have lived there an extended time. The empirical challenge is to operationalize these exposure

effects and allow for the measured neighborhood effect to be contingent upon them.

The geographic selection issue is that different types of parents who have distinct (sometimes

unmeasured) characteristics will be more or less likely to move from or to certain types of

neighborhoods. It is conceivable that several of these unmeasured characteristics of parents not

only affect their residential mobility behavior but also affect the outcomes for children that are

being investigated. This raises the possibility that an observed statistical relationship between

individual child outcomes and neighborhood context is not indicative of a neighborhood’s

independent effect but may be merely spurious in the extreme. Unmeasured (and not controlled

statistically) parental characteristics may be affecting both children’s outcomes and their

observed neighborhood characteristics as well.8

Finally, the methodological challenge related to endogeneity is that some household

characteristics and associated neighborhood characteristics may be mutually causal, in which

case the independent impacts of neighborhood may be obscured if the endogenous household

characteristics affect the same child outcome being investigated. To be more specific, individuals

jointly make decisions about neighborhood characteristics, whether to own or rent their dwelling,

and how long they plan on residing there. For example, those who wish to buy a home and

remain in it an extended time will, to the extent feasible, try to avoid neighborhoods with a poor

quality of life and gloomy prospects for home appreciation. So, if neighborhood, tenure, and

household residential mobility are simultaneously determined and all have some impact on the

given child outcome, to what extent is the measured relationship for neighborhood an unbiased

estimate of its independent impact?

8 The direction of this bias has been the subject of debate, with Jencks and Mayer (1990) and Tienda (1991) arguing that measured neighborhood impacts are biased upwards and Brooks-Gunn, Duncan, and Aber (1997) arguing the opposite. The challenge is to overcome this geographic selection bias, whatever its direction.

12

II. How Neighborhood Context May Influence Children and How Can We Measure It?

Implications for the Denver Child Study

The Denver Child Study takes seriously the implications of the prior summary of scholarship related to mechanisms and quantification of neighborhood effects. Although details are provided

in the next chapter, we offer an overview here. First, in an effort to measure virtually all of the

foregoing potential causal mechanisms, we employ an unusually wide variety of neighborhood

indicators. Some indicators come from administrative data and are objectively measured; others

represent subjective assessments by parents and caregivers (“caregivers,” hereafter) of the

neighborhoods in which they were raising children. Our battery of neighborhood indicators

includes multiple proxies for causal processes in each of the social–interactive, geographic,

environmental and institutional domains described above. Many of these offer direct measures

for such processes as peers, social networks, exposure to violence, institutional facilities, and

pollutants.

Second, we confront the issue of temporal exposure to neighborhood by measuring it in two

ways. We first conduct all our analyses based on contemporaneous measures of neighborhood—

that is, exposure to conditions measured at the time when a particular child or youth outcome

occurred (for the first time, if repeat events are possible), such as being diagnosed with a

particular condition or disease, engaging in a particular behavior, dropping out of school, or

having a baby. We then conduct analyses based on cumulative measures of neighborhood—that

is, exposure to conditions measured as averages over the entire period from birth to onset of a

particular outcome.

Third, we investigate the potential heterogeneity of neighborhood effects by conducting stratified

versions of our analyses. We replicate models for males, females, African Americans, and

Latinos, comparing patterns of parameter magnitudes and statistical significance.

Fourth, we deal with potential geographic selection bias through the quasi-random assignment

process embodied in our Denver Housing Authority (DHA) natural experiment, a method that

has been touted for the study of neighborhood effects (Oakes, 2004) and has been widely

employed internationally in this vein; see, for example, Oreopoulos (2003); Edin, Fredricksson,

and Åslund (2003); Piil Damm (2009; 2014); and DeLuca et al. (2010). We also analyze

alternative samples in an effort to bound the “true” neighborhood effect estimate.

Fifth, our natural experiment also helps us overcome potential endogeneity bias. The

simultaneity of decisions regarding location, tenure, and mobility expectations is effectively

broken by the eligibility and assignment processes that DHA employs. All DHA housing is

rental, so homeownership options are removed from consideration. Length of residency is less

influenced by tenants while they reside in DHA housing, because their leases can be terminated

involuntarily for a variety of lease infractions or as the result of economic success that renders

them ineligible for continued housing assistance.

13

III. Data and Methodology

III. DATA AND METHODOLOGY

The Natural Experiment in Denver

In addition to its large-scale, conventional public housing developments, the Denver Housing

Authority (DHA) has operated since 1969 a program providing approximately 1,500 low-income

families with opportunities to live in scattered-site, single-family and small-scale, multi-family

units. These units are located in a wide range of neighborhoods throughout the congruent city

and county of Denver. By contrast, DHA conventional developments are typically located in less

advantaged neighborhoods. From 1987 onward, as applicants (who met certain basic eligibility

criteria) came to the top of the public housing waiting list, they were offered a vacant DHA unit

(in either conventional or scattered-site programs) with the number of bedrooms appropriate for

their family size and gender of children. If they did not accept this unit, they were offered the

next similarly sized unit that became available (typically after a nontrivial wait). If applicants did not accept this second unit, they dropped to the bottom of the queue, creating a wait of at least a

year before a subsequent offer.9

As detailed in Appendix A, we conducted a variety of statistical tests to ascertain whether the

initial assignment of households to a DHA dwelling unit (and neighborhood thereby) mimicked

random assignment of household to neighborhood. These tests were based on the intuitively

appealing notion that in a quasi-random assignment there would be few statistically significant

correlations among observed DHA tenant characteristics and neighborhood characteristics, no

more than might occur through chance. Were this to prove the case, we would be secure in

assuming that unobserved DHA tenant characteristics would also be uncorrelated with

neighborhood characteristics. We found that only DHA tenant ethnicity generated associations

with neighborhood conditions (in particular, aspects of neighborhood disadvantage). This

indicates that, conditioned on ethnicity, the DHA allocation process produced a quasi-random initial assignment of households across neighborhood characteristics. The empirical implication

is that our models reported here control for ethnicity to avoid geographic selection bias. We also

carried out a test that gives us added confidence that there are unlikely to be any unobserved

DHA tenant characteristics that are both highly correlated with neighborhood characteristics

initially assigned and strongly predict child and youth outcomes. This test involved a Monte

Carlo simulation of the correlations that would be observed between neighborhood

characteristics and typically unobserved household characteristics based purely on chance and

compared these with actual correlations observed in our dataset; see Appendix A for details.

The quasi-randomness of this initial DHA assignment potentially erodes over time, as some

residents selectively leave their initial locations while others stay. Three potential sources of

geographic selection based on caregiver unobservables might arise after initial assignment. First,

DHA households can voluntarily transfer between scattered-site and conventional public housing

9 Our independent evaluation of DHA records showed that 88.3 percent of the applicants accepted their first or second offers; 7.9 percent ended up rejecting both offers and taking a third offer; 3.8 percent rejected three or more offers.

14

III. Data and Methodology

developments, although this occurred rarely.10 Second, a substantial part of our information comes from households no longer residing in DHA housing, and their subsequent locations were

likely not randomly chosen.11 In these cases, cumulative contextual exposures will be a combination of randomly assigned and (to some degree) selectively chosen neighborhood

characteristics. To the extent that the former contexts are sufficient to rupture the correlation

between unobservable caregiver characteristics affecting child outcomes and neighborhood

characteristics they experienced, estimates of neighborhood effects will not be substantially

biased. A third potential source of selection relates to those who do not move out of their DHA housing for an extended period. Perhaps their unwillingness or inability to move out of DHA is

related to some unobservable caregiver characteristics that may also be connected to child

outcomes being investigated.

To investigate the degree to which selective moves subsequent to DHA residence and selective

remaining in DHA residence may affect our measurement of neighborhood effects, we replicate

our analyses using three overlapping samples of children and youth about whom we gained

information through our survey (described below), what we label “ever,” “mostly,” and

“currently” in DHA:

 “Ever in DHA” Sample. This sample includes children and youth whose onset of the

outcome being investigated occurred since their family was assigned to its first randomly

assigned DHA dwelling.

 “Mostly in DHA” Sample. This sample includes children and youth who spent a majority of the years between onset of the outcome being investigated and when their family was

first randomly assigned to its DHA dwelling.

 “Currently in DHA” Sample. This sample includes children and youth whose onset of the

outcome being investigated occurred while they were living in their first randomly

assigned DHA dwelling.

Most of the contextual exposure that the “mostly in DHA” sample of “stayers” had accumulated

involved the randomly assigned neighborhood; this is not necessarily true in the “ever in DHA”

sample, because it includes some “movers” who selected out of the DHA-assigned location

before the neighborhood exposure period under investigation. The “currently in DHA” sample

encompasses children both from households that have remained in DHA for long periods prior to

the time of observation as well as those whose families may have been assigned as recently as

two years prior to observation. We believe that the “true” neighborhood effect parameters likely

fall within the range of estimates for these samples based on the above arguments. We will

emphasize results that are robust across multiple samples.

A further important feature of our natural experiment is the comparatively long exposures

children in DHA households had to their assigned neighborhoods. Our sample of households had

a six-year mean (median: 5 years) DHA residential duration, approximately twice as long as

10 Of the post-1986–vintage tenants residing in conventional public housing developments at the time of the Denver Child Study interviews, 99 percent were originally placed in such; only 1 percent moved in from dispersed housing.

Of the post-1986–vintage tenants residing in dispersed housing at that time, 94 percent were originally placed in such, while 6 percent moved in from the conventional developments. Moreover, an unknown number of these transfers were involuntary, required by regulations after changes in family size or composition.

11 Slightly more than one-third of all caregivers interviewed in the study were former DHA residents.

15

III. Data and Methodology

reported for the Moving To Opportunity (MTO) experimental group (mean: 2.7 years; median:

3.3 years). Recent work by Wodtke et al. (2011); Crowder and South (2011); and Moulton, Peck,

and Dillman (2012) stresses the importance of taking into account the length of time children are

exposed to particular neighborhood contexts, lest one underestimate the true effects that

neighborhoods have on them.

The use of natural experiments inevitably raises questions about the generality of results. We

believe that our findings can fairly be generalized to low-income, Latino, and African-American

families that apply for and remain on the waiting list long enough to obtain public housing. As

such, it may not be fully generalizable to the population of minority families that obtain

subsidized rental housing and certainly may not be to the larger population of minority families

that qualify for housing assistance. Nevertheless, it is similar to—yet considerably more general

than—the populations forming the samples for the oft-cited MTO-based scholarly studies noted

above. Finally, we believe that our findings are generalizable to low-income minority households

that have traditionally been the focus of subsidized housing policies in the United States.

Denver Child Study Household Survey

We developed and fielded during 2006–8 the Denver Child Study telephone survey (conducted in person for about 20 percent of the sample, who had no landline phones) that collected

retrospective and current information about the household, adults, and children. Detailed

information related to multiple domains of outcomes was gathered for all eligible children

associated with each household (see Appendix B). Each household’s residential mobility history

was obtained so it could be associated with neighborhood developmental context for children.

Study eligibility criteria were (1) presence of children in the home between birth and 18 years of

age when they moved into DHA housing, (2) family remained in DHA housing for at least two

years, (3) family first entered DHA in 1987 or later (when DHA’s current quasi-random

assignment process came into operation), and (4) were of Latino or African-American ethnicity.

Attempts to recruit subjects for the study were made by mail and phone in both English and

Spanish, when appropriate. Compensation for participation took the form of either a cash or gift

card. We estimate an overall participation rate of 56.5 percent (85 percent for those still residing in DHA housing), with most nonparticipation the result of our inability to locate the household;

less than 6 percent refused to participate when contacted. Our team successfully completed 711

interviews with the primary caregivers of eligible households, whose surveys subsequently

passed our rigorous data verification and reliability checks. Children and youth analyzed in our

study were current or past members of these 711 households, who spent two or more years

residing in DHA housing before reaching 19 years of age.

Characteristics of Caregivers and Households

Our Denver Child Study survey collected information on a wide variety of parental/caregiver (“caregiver,” hereafter) and household characteristics that we employed as controls; these are

listed in Exhibit III-1. This included information about caregiver national origin, education,

economic status, disability status, marital status, fertility and employment histories, and access to health insurance. Less conventionally, our survey asked respondents whether they had used

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III. Data and Methodology

alcohol, marijuana, or other illegal drugs since becoming a parent and, if so, how often. The

survey also asked questions that permitted us to compute a reliable indicator of depressive

symptomatology (Center for Epidemiologic Studies Depression [CES-D]).12 We were also able to measure a series of household events (like eviction, inability to pay bills, insufficient food),

from which we created a “household economic stressors index.” All of these time-varying

characteristics were measured for the period during which the observed youth resided in the

household.13 We recorded the birth order of the focal child, number of siblings, and other behaviors of older siblings. Finally, residential history information permitted us to compute the

number of moves the household had undertaken during the childhood of the observed youth. We

believe that this battery of characteristics controls for key dimensions of household context

related to economic and intellectual resources; caregiver and sibling role modeling; supervision

and monitoring of children; and parenting behaviors, attitudes, and norms that would likely

affect a variety of outcomes experienced as children and youth.

Children and youth in the Denver Child Study live in households that have many characteristics reflecting their disadvantaged household circumstances. For illustrative purposes, we present the

characteristics of caregivers and households for our “ever in DHA” analysis sample used to

model the diagnosis of asthma, because this outcome may occur throughout childhood. For these

descriptive analyses, we restrict our sample of children to those who resided in DHA housing at

the time of diagnosis and for whom we had complete information on all core covariates. The

average age of caregivers at time of diagnosis was 39 but ranged from 19 to 79 years of age.14

Nearly one out of seven caregivers was an immigrant. One 1 of 10 caregivers was disabled.

Eleven percent of caregivers were married or cohabiting, and the average number of siblings

present in households was 1.9. Prior to asthma diagnosis, children had moved, on average,

2.6 times. Approximately 38 percent of caregivers had no diploma, 39 percent had only a high

school diploma or General Education Development (GED) certification, and the remaining

23 percent had completed some postsecondary education at the time of asthma diagnosis.

Slightly more than half (54.9 percent) of all caregivers were employed full time. Average annual

caregiver earnings were $12,069. About 39 percent of caregivers were able to monitor their

children on a full-time basis; another 6 percent could monitor their children part time, while the

remaining 55 percent were not available because of full-time work responsibilities. Three-

quarters of all households had access to some form of health insurance at the time of diagnosis.

Nonetheless, many of these households faced challenges: 13 percent reported regular alcohol,

marijuana, or drug use since becoming caregivers; 24 percent reported depressive

symptomatology at the time of survey; and they faced on average 1.4 incidents of acute financial

crisis while raising their children.

Characteristics of Children and Youth

Our Denver Child Study survey asked caregivers to supply information about all their children with whom they had lived in DHA public housing for at least one year. In this manner, we

12 We use a dummy variable indicating whether the parent exhibited subclinical or clinical depressive symptomatology (that is, scored at least 16 on the CES-D scale).

13 The exception was the caregiver depressive symptomatology scale, which was measured at the time of survey and serves as a control for affect when responding to survey questions, not necessarily caregiver emotional state during onset of a particular child outcome.

14 Many of the caregivers whom we interviewed were grandparents or guardians of the child, not biological parents.

17

III. Data and Methodology

collected detailed information about the children’s gender, ethnicity, birth order, residential

histories, health, exposure to violence, behaviors and activities, education, and (for older

children), marital or fertility histories and labor market outcomes during early adolescence and

young adulthood.

Exhibit III-1. Characteristics of caregivers and households*

Mean or

Percent

SD

Min

Max

Caregiver reported depressive symptomatology

24.1

0.43

0

1

Caregiver age

39.1

9.84

19.54

79.13

Caregiver immigrant status

13.9

0.35

0

1

Caregiver history of substance abuse

13.1

0.34

0

1

Caregiver disability status

9.8

0.30

0

1

Caregiver educational attainment

No degree or certification

33.7

0.47

0

1

Technical certificate (no high school diploma)

4.2

0.20

0

1

GED (high school equivalency)

13.0

0.34

0

1

High school diploma

25.5

0.44

0

1

Technical certificate (post–high school)

13.7

0.34

0

1

Two-year college degree (A.A., A.S., A.A.S.)

7.4

0.26

0

1

Four-year college degree (B.A, B.S.)

2.5

0.16

0

1

Married or cohabiting

10.8

0.31

0

1

Average caregiver earnings (in dollars)

12,069

12,935

0 66,352

Caregiver not available to monitor or supervise children

54.9

0.50

0

1

Caregiver available to monitor or supervise children full time

39.3

0.49

0

1

Caregiver available to monitor or supervise children part time

5.8

0.23

0

1

Household stressor scale score

1.41

1.19

0

5

Household had health insurance

76.8

0.42

0

1

Total number of moves from birth through onset

2.64

2.39

0

14

Number of siblings in household

1.91

1.36

0

7

* For this table, all time-varying household characteristics were measured contemporaneously to time of diagnosis of asthma or 18 years of age (or time of survey, whichever younger) if no such diagnosis.

N = 814

We will present descriptive statistics for the various outcomes that we analyze in subsequent

chapters, because the analysis samples are often considerably different because of the age range

that would be relevant for a specific outcome (for example, including only school age children in

educational outcome analyses but all ages for health outcomes and exposure to violence in the

neighborhood and home analyses).

The descriptive statistics for child and youth characteristics used as control variables are listed in Exhibit III-2. The children and youth in the Denver Child Study reflect the overall composition of children residing in DHA public housing: 31.4 percent are Latino males, 28.9 percent are

Latina females, 20.9 percent are African-American males, and 18.8 percent are African-

American females. Approximately 31 percent were the first-born children in their households.

18

III. Data and Methodology

Exhibit III-2. Characteristics of children and youth

Percent

SD

Min

Max

Gender and ethnicity

Latina female

28.9

0.45

0

1

Latino male

31.4

0.46

0

1

African-American female

18.8

0.39

0

1

African-American male

20.9

0.41

0

1

First born in family

31.2

0.46

0

1

N = 814

Characteristics of Neighborhoods Experienced by Children and Youth

It is generally accepted that “neighborhood” has both objective “space” dimensions (that is,

economic, demographic, social indicators associated with geographies) and subjective “place”

dimensions (that is, the human experience of territory; Fitzpatrick and LaGory, 2000). We

obtained a wide variety of neighborhood data about both dimensions from four sources.

The first source was the decennial U.S. Census, where we used census tract geographic scales

from 1970, 1980, 1990, and 2000 censuses. We employed the Neighborhood Change Database

(a Geolytics proprietary product) for this information, because it adjusts data to account for

changes in tract boundaries between decennial censuses. For estimates of non–census-year data,

we used linear interpolation or extrapolation. We gathered indicators that have been widely

employed in prior research on neighborhood effects, including percentages of households

moving in during the prior year, female-headed households, families below the poverty line,

unemployed adults, Latino population, non-Latino African-American population, 15 foreign-born

population, homes that are renter occupied, homes that were built during various periods, and

mean occupational prestige based on the General Social Survey prestige score weighted by the

observed proportional distribution of occupations of employees in the tract. Given high

correlations among several of these variables, we conducted four principal components analyses,

one for a comparable set of variables for each of the 1970–2000 censuses.16 For each census year, the analysis produced a single component (with an eigenvalue greater than unity) that

consistently consisted of the roughly equally weighted sum of census tract percentages of poor,

unemployed, renters, and female household heads. We call this our neighborhood social

vulnerability score.

The second source was subjective indicators based on responses of the caregivers interviewed in

15 The ethnic makeup of Denver in 2000 was 52 percent non-Latino whites, 11 percent non-Latino African Americans, and 32 percent Latinos.

16 The creation of our linked database occurred prior to the release of the 2010 Census and the five-year average American Community Survey data.

19

III. Data and Methodology

our Denver Child Study.17 For each neighborhood in which they lived while they were raising children, we asked the caregivers to respond to a battery of questions related to the location’s

assets and liabilities.18 From the responses, we devised three indicators (neighborhood social capital, social problems, and institutional resources) and a dichotomous measure of the presence

of bad peer influences in the neighborhood. The social capital index (range: 0–6) was

incremented by “one” for each of the following respondent descriptions of people in the

neighborhood: could get together to solve neighborhood problems; would watch out for their

children and property; knew them and their children by name; they and their children could look

up to them or could be counted on in times of trouble; and whether the respondent participated in

any organizations located in the neighborhood (for example, block clubs, tenant groups, religious

organizations). The social problems index (range: 0–5) was incremented by a factor of “one” for

each of the following neighborhood conditions: people selling drugs; gang activity; homes

broken into by burglars; people being robbed or mugged; and people getting beaten or raped. We

used Item Response Theory analysis to generate a latent factor score denoting neighborhood

resources present during childhood. Resources included parks, recreation centers, mentoring or

counseling centers for children, subsidized day care facilities, and good police protection. Higher

values indicate a higher probability of having these resources within the neighborhood. These

indicators proved reliable; additional details about their properties are available from the authors.

The third source of neighborhood information was the Denver-based Piton Foundation’s

Neighborhood Facts Database, which provided small area-based, annually measured

information culled from Denver administrative databases on characteristics that the Census does

not provide. These included violent crimes reported to police per 1,000 population, property

crimes reported to police per 1,000 population, and confirmed cases of child abuse and neglect

per 1,000 children. The Piton Foundation data are aggregated to 77 named community areas

consisting of two census tracts, on average, and thus are measured at a larger spatial scale than

our Census-based data. Moreover, Piton series are available only for the city and county of

Denver, which produced shrinkage in our analysis sample because some former DHA

households interviewed had moved out of the county.

The fourth source for data on toxic airborne pollutants coded to census tracts was the U.S.

Environmental Protection Agency. In particular, we employed their summary index of

respiratory health risk associated with the combined concentrations of 124 toxic airborne

compounds as well as their neurological risk index, identifying the concentrations of lead

pollutants.19

Descriptive statistics for all these neighborhood indicators are presented in Exhibit III-3. At the

17 Recent research has shown that such subjective information based on residents’ perceptions of neighborhoods provide important additional explanatory power in modeling a variety of economic outcomes (Furtado, 2011).

18 This similar to the oft-used approach to obtain subjective neighborhood indicators; see Muhajarine et al. (2008).

19 The respiratory and neurological risk indices are generated from tract-level estimates of 124 air toxics (listed at

http://www.epa.gov/ttn/atw/nata2002/02pdfs/2002polls.pdf). These estimates are then aggregated based on a “risk per million” index, with the number representing the likelihood that one person out of 1 million equally exposed people would develop the respiratory or neurological health issue if exposed continuously to the specific concentration over a 70-year lifetime. Source and further information:

http://www.epa.gov/ttn/atw/nata2002/natafaq.html#A6. In our analyses, we have rescaled the neurological risk index by a factor of 100.

20

III. Data and Methodology

time of the reported asthma diagnosis, the typical child was residing in a neighborhood that was

59 percent Latino and 13 percent African American. Approximately 28 percent of the residents

were foreign born with the majority coming from Mexico. Children lived in neighborhoods

characterized by a high degree of residential instability—one out of four residents had moved

into the neighborhood in the preceding 12 months—as well as moderate levels of social

vulnerability (mean: 128). They also resided in neighborhoods populated with adults working in

less prestigious occupations (mean: 37). Children also tended to live in neighborhoods with older

housing: Approximately one-quarter of the neighborhood housing stock was built before 1940

and slightly less than one-half was built between 1940 and 1970. Caregiver subjective measures

of neighborhood quality suggest that children lived in neighborhoods with moderate levels of

social capital (mean: 3.4), modest levels of resources (mean: 0.18), but good access to medical

facilities. At the same time, children lived in neighborhoods that experienced, on average, 2.2

problems and were exposed to negative peers. When compared with the city of Denver as a

whole, the typical child in our study was exposed to higher than average child abuse and neglect

rates (mean: 11.4 per 1,000), violent crime rates (mean: 11.5 per 1,000), and property crime rates

(mean: 51.65 per 1,000). The average neurological hazards index was 8.01, while the average

respiratory hazards index was 5.29, suggesting that children in our study were exposed to

nontrivial levels of air and lead pollutants.

The Issue of Neighborhood Scale

There has never been a scholarly consensus on how neighborhood should be defined

conceptually or operationalized empirically (Galster, 2001; Coulton, 2012; Taylor, 2012). Many

scholars have employed a purely ecological or geographic perspective, some a purely perceptual

or social–interactive perspective, while others have attempted to integrate both perspectives. The

upshot is that, whatever neighborhood is, it undoubtedly has distinct social, economic, and psychological meanings to residents at various geographic scales, as was first observed by

Suttles (1972) and Birch et al. (1979). Moreover, the various causal processes presented in

Chapter II that transmit neighborhood effects on residents undoubtedly operate across different

spatial scales (Galster, 2012).

21

III. Data and Methodology

Exhibit III-3. Characteristics of neighborhoods Experienced by children

and youth*

Mean or

Percentage

SD

Min

Max

Census neighborhood indicators

Percentage of African-American residents

13.4

15.67

0.16

77.3

Percentage of Latino residents

59.4

20.24

6.60

91.9

Percentage of foreign-born residents

27.9

13.72

4.42

62.41

Social vulnerability score (range: 0–400)

127.90

63.87

26.50

288.97

Occupational prestige score (range: 0–100)

37.23

3.58

31.36

47.90

Percentage of residents who moved in the preceding

12 months

25.3

10.37

2.44

55.11

Age of housing stock

Percentage of housing built before 1940

25.9

19.42

0

85.08

Percentage of housing built between 1940 and 1969

47.5

22.53

0.86

97.16

Denver Child Study neighborhood indicators

Social capital index (range: 0–6)

3.40

1.73

0

6

Social problems index (range: 0–5)

2.16

1.91

0

6

Living in neighborhood with negative peers

54.1

0.50

0

1

Living in neighborhood with hospitals and clinics

86.1

0.35

0

1

Resource factor score

0.18

0.70

−1.74

1.02

Piton neighborhood indicators

Violent crime rate per 1,000

11.49

7.30

1.01

34.59

Property crime rate per 1,000

51.65

30.10

9.09

153.74

Child abuse and neglect rate per 1,000

11.39

7.38

1.92

30.14

Environmental Protection Agency neighborhood indicators

Neurological hazards index (rescaled by 100)

8.01

1.38

4.66

14.77

Respiratory hazards index

5.29

0.53

3.83

6.91

* For this table, all time-varying household characteristics were measured contemporaneously to time of diagnosis of asthma or 18 years of age (or time of survey, whichever is younger) if no such diagnosis.

N = 814

22

III. Data and Methodology

The implication for empirical researchers of neighborhood effects that logically follows from the

above is that neighborhood context should be operationalized at multiple scales. This proves challenging for several reasons, however. First, data are typically available only for a few

neighborhood geographies (often just one: census tracts) that have been defined

administratively.20 These geographies may not correspond well with boundaries either perceived by residents or the scales over which causal processes imparting neighborhood effects vary.

Second, even if researchers can in principle generate their own boundaries through geographic

information system technologies (Coulton, 2012), the appropriate bounding is unclear. There is a

great deal of interpersonal variance in resident-defined boundaries of neighborhoods, and there

may be multiple causal processes at work at distinctive spatial scales to produce the observed

neighborhood effect. Finally, variables measuring similar aspects of neighborhood context but at

different scales can easily be too highly correlated to produce distinct statistical estimates of

neighborhood-effect parameters.

The most direct way of answering the question, “What scale(s) of neighborhood matter most in

generating individual resident outcomes,” is to conduct parallel analyses of a particular outcome,

where neighborhood context is measured at different scales and their parameter estimates are

compared. Several studies have taken this tack: Buck (2001); Bolster et al. (2007); and

Andersson and Musterd (2010). All find statistically significant relationships at various scales

but stronger correlations between economic outcomes and neighborhood variables when the

latter are measured at smaller spatial scales.

Our Denver Child Study addresses the issue of neighborhood scale in the following ways. As

noted above, we employ a battery of neighborhood indicators measured at three spatial scales:

the Denver community area (about two census tracts on average), census tracts, and survey

respondent–defined neighborhoods. Because the former two geographies are typically considered

“too large” from the standpoint of residents’ intense neighborly social interactions (Suttles,

1972), we believe that estimated parameters of neighborhood indicators measured at these larger

scales will be biased downward. Unfortunately, because few indicators are available at more than

one spatial scale, we cannot conduct parallel analyses of a particular outcome where

neighborhood context is measured at different scales.

Creation of Analytical Databases

We spent considerable effort cleaning, reconciling, and augmenting the survey data. When our

audits revealed inconsistencies or omissions in the responses, we attempted to contact

respondents again and seek clarifications. Information respondents provided on their residential

histories was cross-checked with residential location information contained in the DHA

administrative databases, U.S. Postal Service, Lexis-Nexis, Intelius address files, and several

additional online search engines.

20 The neighborhood effects literature is replete with alternative specifications of neighborhood geography, because data are collected at various scales by different institutions (Galster, 2008). The United States–based studies typically employ the census tract, an area bounded by local planners who employ transportation routes or topographical features to create as demographically homogeneous an area as possible, containing roughly 4,000

inhabitants, on average.

23

III. Data and Methodology

After residential history information obtained on the survey was verified for accuracy, we

geocoded each address, using the U.S. Census Bureau’ American FactFinder Web site utility. In cases where respondents could not recall specific addresses but only proximate cross-streets, we

verified these locations using MapQuest, and then identified the corresponding census tract using

the aforementioned Census Web site showing tract boundaries. This procedure provided the

census tract corresponding to each location in respondents’ residential histories, which, in turn,

permitted us to match each location to the aforementioned battery of neighborhood indicators for

census tract neighborhoods. We were able to successfully link 92 percent of the residential

locations identified by respondents.

We then transformed these data for households and neighborhoods into the format of a child-

year unit of observation. For each child-year, there are variables associated with (1) fixed child characteristics, (2) fixed caregiver characteristics, (3) temporally varying child characteristics,

(4) temporally varying caregiver-household characteristics, (5) temporally varying neighborhood

characteristics, and (6) temporally varying outcomes.

Statistical Modeling Approaches

Our core modeling approach employs two complementary empirical strategies. The first explores

the predictors of whether a child ever experienced a certain outcome (either by the time of our survey or 18 years of age, whichever came first). It employs various techniques for modeling

such dichotomous outcomes: logit, multilevel, mixed-effects logit, and Bayesian analyses. The

second explores the predictors of the timing when the onset of a particular outcome occurred. It employs Cox or accelerated failure time (AFT) analyses. For our core modeling efforts in both

approaches, we measure time-varying predictors contemporaneously with the onset of outcome

being modeled. We also explore how results differ when we measure cumulative exposures to

neighborhood context. Moreover, we investigate whether relationships observed across the full

sample are robust across males and females and across Latino and African-American ethnic

groups. We explicate these approaches further below.

Dichotomous Outcome Models

In our first statistical approach, we employ both standard and multilevel, mixed-effects logistic

regression models to estimate the odds of a child or youth experiencing a particular outcome,

based on time-invariant predictors and time-varying predictors measured at age of onset or first

occurrence (or time of survey or 18 years of age, whichever is earlier, if the given outcome never

occurred).

For our preferred specification, we estimate a multilevel, mixed-effects logit model specified as

one level conditional on a set of family random effects 𝒖𝒊:

Pr(𝑦𝑖𝑗 =

+

1|𝒖𝒊) = 𝐻(𝛽𝑥𝑖𝑗 𝑧𝑖𝑗𝒖𝒊) + εij

where 𝐻 is the logistic cumulative distribution function, 𝑦𝑖𝑗 is the binary outcome, 𝑖 is the

number of families, 𝑗 is the number of children observed within each family, χ are predictors, 𝛽

are their associated coefficients, and εij is a random error. Because this is a random intercept only model, 𝑧𝑖𝑗 is a scalar of 1. When the number of observations within each cluster (that is, family) is small and unbalanced across clusters, as it is in our study, the random-effects model above

24

III. Data and Methodology

likely provides less biased parameter estimates than standard logit models (Cheah, 2009). We

also observed that it produced somewhat more precise estimates, although the point estimates

were typically similar.

Unfortunately, multilevel, mixed-effects logistic regression models are considerably more

sensitive to small sample sizes, sometimes failing to converge and excluding variables they

determine are perfectly predictive. We therefore also estimate a standard logit model employing

robust standard errors to account for clustering of children in the same family.21 In this study, we report the multilevel, mixed-effects logit model whenever possible; otherwise, the standard logit

model employing robust standard errors is reported. In any event, the point estimates produced

by the two types of models do not differ substantially.

Some of our physical and behavioral health outcomes occurred rarely, involved small analysis

samples, or had highly skewed distributions, thus rendering one or more of the above logistic

modeling approaches unfeasible or unstable. In such cases, we also employed Bayesian analysis

with noninformative priors.22 In contrast to maximum likelihood estimation, Bayes does not rely on large-sample theory or meeting assumptions of normality. Instead, Bayesian analysis uses

Markov chain Monte Carlo algorithms with Gibbs sampling to iteratively replicate the observed

data by obtaining approximations of the posterior distributions of the parameters. For each

parameter, a 95 percent confidence interval is produced. In Bayesian inference, this interval is

interpreted as follows: based on the observed data, there is a 95 percent chance that this credible

interval contains the true value of the parameter. A posterior predictive p-value (PPP) of model fit can be obtained via a fit statistic f and is based on the usual chi-square test of H0 against H1.

Lower PPP values indicate weaker fit. For a positive estimate, the p-value is the proportion of the posterior distribution that is below zero. For a negative estimate, the p-value is the proportion of the posterior distribution that is above zero.

Hazard Models

Our second, complementary analytical approach models the hazard function for the given

outcome. We start by estimating a Cox proportional hazards model with clustered robust

standard errors:

λ(t| χ ) = λ

χ + … + β χ

= λ

β

ij

0(t) exp( β1 1ij

n nij ) + εij

0(t) exp( χij ) + εij

where λ(t| χij ) is the observed time of the given outcome (or the censoring time of 18 years of age) for youth, ij and λ0(t) are the baseline hazard; other symbols are defined as above. We then conduct a global chi-square test to ascertain whether the residuals of the Cox model violate the

assumption of proportionality. If they do (as was often the case), we estimate an accelerated

21 For the two logistic models, we used Stata logit and xtmelogit algorithms. We do not need to worry about clustering at the neighborhood level here: Children who live in the same neighborhood are typically experiencing a different value of the neighborhood indicator, because they are experiencing such for different years of their lives and different calendar years. There is no commonly experienced “higher spatial scale,” as is typically the case in hierarchical data structures.

22 We used the Bayes Estimator model algorithm in Mplus and included a cluster adjustment to account for the clustering of the children within families. We also tested several Bayesian models using prior distributions estimated from the prevalence rates for asthma and obesity among children residing in Denver. See Muthén (2010), “Bayesian analysis in Mplus: A brief introduction,” available at

http://www.statmodel.com/download/IntroBayesVersion%203.pdf).

25

III. Data and Methodology

failure time model of the age at which the outcome occurred.23 In the AFT model, the outcome is the natural logarithm of the survival time t, which is expressed as a linear function of the covariates:

ln(t

β + ε

ij) = χij

ij

where all symbols are defined as above. In AFT models, values greater than zero mean a longer

spell prior to the occurrence of the outcome; values below zero depict shorter spells.

The AFT model is generally preferred to the Cox proportional hazard model with data that

violate the assumptions of proportionality, because it is more robust to omitted covariates and

less sensitive to choice of probability distribution. Specifically, we used the frailties version of the AFT model to address the clustering of siblings within families.

Temporal Aspects of Neighborhood Effects

In Chapter II, we discussed the issue of how temporal aspects of exposure to neighborhood

context might be investigated. We confront this issue by measuring exposure timing in two ways.

We first conduct all our analyses based on contemporaneous measures of neighborhood—that is, exposure to conditions measured at the time when a particular child or youth outcome occurred

(for the first time, if repeat events are possible), such as being diagnosed with a particular

condition or disease, engaging in a particular behavior, dropping out of school, or having a baby.

We then replicate several analyses based on cumulative measures of neighborhood—that is,

exposure to conditions measured as averages over the entire period from birth to onset of a

particular outcome. Given our theoretical discussions in Chapter II, we would expect these

alternative measures to perform differently depending on both the outcome being investigated

and the particular causal processes we are measuring with our neighborhood indicators.

The cumulative measure of exposure raises a new methodological challenge. Computing

exposures for each year of our sample children’s lives raises no technical problems, but the

question arises which years are appropriate to analyze. The complication emerges, because for most children, their families’ quasi-random assignment to a neighborhood by DHA occurred at

some point during childhood, not before they were born. On one hand, their experiences prior to

this date involve potential correlations between neighborhood contexts and unmeasured

caregiver characteristics that could introduce geographic selection bias into the results. On the

other hand, their experiences prior to this date may have produced some indelible effects than

should not be ignored when interpreting relationships observed during the postassignment years

of childhood. Our response to this dilemma is to compute our cumulative exposure measures for

all years of childhood leading up to onset of the given outcome but to limit our analysis sample

to children who spent the majority of their lives before onset living in DHA housing. We think

the potential for reintroducing geographic selection bias is minimal for this sample.

23 We used the Stata streg algorithm with a lognormal model for AFT; for estimating the parameters, we used maximum likelihood.

26

III. Data and Methodology

Gender and Ethnic Differences in Neighborhood Effects

In Chapter II, we discussed the theory and evidence indicating that a given neighborhood context

may not have identical consequences for all resident children and youth. We investigate the

potential heterogeneity of neighborhood effects by conducting stratified versions of our

statistical analyses described above. We replicate models for males, females, Latinos, and

African Americans, comparing patterns of parameter magnitudes and statistical significance.

Reduced-Form Estimates of Neighborhood Effects

Both of our analytical strategies yield “reduced form” estimates of the degree to which

neighborhood indicators correlate with the particular developmental outcome being investigated

through unspecified intervening causal pathways. We intentionally omit from our models any

endogenous or predetermined covariates that may themselves be affected by neighborhood

environment. In this fashion, we avoid “overcontrolling” and thus minimizing the apparent

relationships between neighborhood indicators and the particular outcome. As an illustration, we

suspect that labor market success as a young adult will be a function of obtaining a high school

diploma as well as other neighborhood conditions experienced as a teen. Yet, obtaining a

diploma itself is related to a different set of neighborhood conditions as well as academic

performance in secondary school (as we demonstrate in Chapter VII). But academic performance

in secondary school itself may be related to a different set of neighborhood conditions as well as

a youth’s exposure to violence—yet another endogenous variable—and so on. In our model of

labor market success as a young adult, we thus do not control for high school diploma, academic

performance, or exposure to violence, instead allowing neighborhood effects that might impinge

on any or all of these intervening outcomes through complex causal pathways to emerge in

summary fashion.

27

IV. Physical and Behavioral Health Outcomes

IV. PHYSICAL AND BEHAVIORAL HEALTH OUTCOMES

Introduction

In this chapter, we examine neighborhood influences on a several health-related outcomes for

low-income, minority youth in our Denver Child Study. For all children, we analyze whether

they were diagnosed with asthma, neurodevelopmental disorders, and/or obesity during

childhood. For children between 7 and 18 years of age, we also examine diagnosis of

internalizing behaviors and behavioral health service utilization during the period between

middle childhood and late adolescence. As noted below, we find evidence of strong

neighborhood effects on both physical and behavioral health outcomes, although sometimes with

dimensions of these neighborhood contexts operating in unexpected ways.

Physical Health Outcomes Analysis

Study participants in our three physical health analysis samples range from 2 to 30 years of age

at time of the survey, although we only examine these outcomes occurring during childhood

(through 18 years of age). The average age of the children and youth across these analysis

samples varied between 12.3 and 12.8 years. The resultant sample sizes for these “ever in Denver

Housing Authority (DHA)” groups were 896 (obesity), 841 (neurodevelopmental disorders), and

814 (asthma). In these analysis samples, we have a slight over-representation of Latino males

(31 percent) compared with the other gender-ethnic groups: Latina females comprise 28 percent,

African-American males 22 percent, and African-American females 19 percent.24

We analyze three physical health outcomes for the period prior to turning 18 years of age: (1)

ever diagnosed with asthma; (2) ever diagnosed with neurodevelopmental disorders (any one or

more of mental retardation, developmental delay, learning disability, attention deficit

hyperactivity disorder, or autism), and (3) ever diagnosed as obese. We ascertain these outcomes

on the basis of the Denver Child Study caregiver survey respondents’ answers to the questions,

“Has a doctor or medical professional ever diagnosed your child with the following . . . . If so, at what age was this first diagnosed?” Approximately 1 in 10 of our sampled children and youth

were diagnosed with asthma as a child, with a median age of onset of six years. Nearly 7 percent

of children and youth in the sample were diagnosed with one or more neurodevelopmental

disorders during childhood; the median age of onset was 7.5 years of age. Caregivers reported

that 5 percent of children in the study had been diagnosed as obese, with a median age of onset

of 11.5 years.25

We recognize that there is some inherent ambiguity in our health indicators. A diagnosis

outcome results from the joint probabilities that a child health problem is present and that the

24 These statistics apply to the “ever in DHA” sample but are comparable in the other three analysis samples, as well. Complete descriptive statistics for all variables related to these samples are available from the authors.

25 We are well aware that the caregiver reports of diagnosed obesity are substantially lower than what is reported for the city of Denver. We make adjustments for this under-reporting in our Bayesian analysis models, which allows for the introduction of priors. Given the time span across which children resided in Denver neighborhoods, we set these priors at 6 percent at the beginning of our study period in 1970 and 27 percent at the end of our study period in the mid-2000s.

28

IV. Physical and Behavioral Health Outcomes

caregiver will seek medical advice given the problem is present. The latter probability, in turn, is a function of the caregiver’s physical and behavioral health, personal efficacy, etc.,26 and the

institutional structure that the caregiver can access easily, like proximity to medical facilities.

Neighborhood context can potentially affect some or all of the aforementioned components

leading to a diagnosis. Unfortunately, we are unable to discern these mechanisms, though of

course we do attempt to measure neighborhood institutional resources and medical facilities.

Thus, we emphasize that our estimated statistical relationships represent a “net impact” of the

given neighborhood indicator on the odds of a diagnosis, not necessarily on the odds that the child has a health problem. Indeed, this relationship may be obscured by another component of

the above causal chain, leading to a diagnosis. For example, neighborhood indicator X may cause a resident child to be sicker but may also cause (1) the caregiver to be sicker and thus less likely to seek a diagnosis for the child and/or (2) fewer medical facilities to be accessible to provide a

potential diagnosis even if the caregiver sought one. These countervailing neighborhood effects

could well yield no association (or even a negative association) between the indicator and the

observed odds of diagnosis, even though in this hypothetical example it actually was causally

associated with children’s health.

The implication is that our findings here need to be interpreted with care: An observed statistical

relationship should not be viewed as unambiguously good or bad normatively, regardless of its

sign. If certain neighborhood attributes are, for example, associated with higher odds of a child

health problem diagnosis, this may be “bad,” because it indicates that these places are less

healthy for children. Yet, this finding may be “good,” because it indicates that children are not

more likely to have the health problem, but those who have it are more likely to be diagnosed

with it.

Because all three of the physical health outcomes are dichotomous measures, we employ logistic

regression and Cox proportional hazards models to estimate the odds or hazards of ever being

diagnosed with a given health problem as well as accelerated failure time (AFT) frailty models,

when appropriate, to estimate the age at which a child is first diagnosed.27 Given that our health diagnoses occur rarely, we also employ complex, mixed-effects Bayesian analyses.

In this chapter, we estimate these models for the previously defined “ever in DHA,” “currently in

DHA,” and “mostly in DHA” samples to assess the robustness of our results. Further, we add a

fourth analysis sample, “majority in DHA,” as an additional robustness check. Children who

spent the majority of their childhood in DHA housing (measured in terms of time of survey or

18 years of age for older children and youth) and whose health conditions were diagnosed after

initial random assignment constitute the study population in this sample. This would be the most

restrictive of the analysis samples used in the study.

26 It will also depend on the caregiver’s economic resources and insurance, but we control for these in our models.

27 We used the Stata logit models with robust standard errors to adjust for clustering of children within families and stcox for estimating the proportional hazards models. We employed AFT instead of Cox proportional hazards models in the developmental disorders analyses when the global chi-square test rejected the null hypothesis of proportionality—that is, that the effect of a covariate is to multiply the hazard by some constant. By contrast, the AFT model assumes that the effect of a covariate is to multiply the predicted event time by some constant. As an additional robustness check we also ran complex, mixed-effects Bayesian models in Mplus to address issues of small sample sizes, non-normality in distributions, and the need for more flexible estimation procedures than available in maximum-likelihood procedures.

29

IV. Physical and Behavioral Health Outcomes

The logistic, Cox, AFT, and Bayesian models use the same core covariates common to all our

analyses. Here, we measure “contemporaneous” family and neighborhood context at the time of

the diagnosis of health condition or at either age at the time of survey or 18 years of age

(whichever is younger) if the health condition never occurred during childhood. Thus, these

analyses can be interpreted as investigating the degree to which health outcomes diagnosed

during childhood have any relationship with the neighborhood conditions to which they were

exposed at the point when they were diagnosed. We use the full set of neighborhood covariates

described in Chapter III. Our physical health outcome analyses also controlled for low birth

weight or extreme prematurity as well as residence in public housing at the time of diagnosis.

The former was intended to control for preexisting health conditions that are often correlated

with childhood health outcomes, while the latter indicator is intended as an additional control for

housing quality.

Behavioral Health Outcomes Analysis

Study participants in our behavioral health analysis samples range in age from 7 to 35 years at

the time of survey, although we only examine these outcomes occurring during childhood

(through 18 years of age). The average age of the children and youth across these analysis

samples varied between 14.8 and 16.6 years. The resultant sample sizes for these “ever in DHA”

groups were 691 (internalizing behaviors) and 584 (behavioral health service utilization).28

Approximately one-third of both samples comprises Latino males; the remainder of the sample

consists of Latinas (27–28 percent), African-American males (20–22 percent), and African-

American females (19 percent).

We analyze two behavioral health outcomes for the period prior to turning 18 years of age: (1)

ever diagnosed with an internalizing behavior (any one or more of depression, anxiety, or post-

traumatic stress disorder [PTSD] and (2) behavioral health service utilization. We ascertain

diagnosis of an internalizing behavior on the basis of the Denver Child Study caregiver survey respondent’s answers to the questions, “Has a doctor or medical professional ever diagnosed

your child with the following . . . . If so, at what age was this first diagnosed?” Approximately

5 percent of children in the study had been diagnosed with one or more of these internalizing

behaviors during childhood. The median age of diagnosis was 12 years. Behavioral health

service utilization was estimated using the Denver Child Study caregiver survey respondents’

first (mutually exclusive) categorical response to this question, asked in reference to all children residing in the household between 8 and 18 years of age: “Has your child ever seen a

psychiatrist, psychologist, or a counselor? . . . If so, when was the first time [date]?” In our

analysis sample, we found that 16 percent of the children and youth had received behavioral

health services during childhood. The median age when children first received these services was

12 years.

Because both of our behavioral health measures are dichotomous, we employ logistic regression

to estimate the odds of ever being diagnosed with an internalizing behavior or using behavioral

health services during childhood and Cox proportional hazards models or AFT models to

examine issues of timing of such diagnosis or utilization. As was the case with the physical

28 These statistics apply to the “ever in DHA” sample but are comparable in the other three analysis samples, as well. Complete descriptive statistics for all variables related to these samples are available from the authors.

30

IV. Physical and Behavioral Health Outcomes

health outcomes, we also estimated complex, mixed-effects Bayesian models using Mplus to test

the robustness of our results. For all analyses, we measure predictors contemporaneously with

the onset of first diagnosis and employ robust standard errors to account for clustering of

children within the same family.29 These models use the same core covariates common to our physical health analyses.

We replicated our analyses using four samples of children and youth 7–18 years of age: “ever in

DHA,” “currently in DHA,” “mostly in DHA,” and “majority in DHA.” All analysis samples

required (1) family quasi-random assignment to DHA housing prior to onset of internalizing

behavior or behavioral health service use and (2) covariates observed for the time of onset. Most

of the contextual exposure in these latter analysis samples had accumulated while children

resided in the randomly assigned neighborhood; this is not necessarily true in the “ever in DHA”

sample, which includes some families who selected out of the DHA-assigned location.

Estimated Neighborhood Effects on Physical and Behavioral Health Outcomes

Tables below present nondichotomous predictor variables that are normalized to aid cross-

variable comparability of coefficients. As before, we consider only those results that are

statistically significant in two or more of the analysis samples for the given model type.

Typically, the logit, Cox proportional hazard or AFT, and Bayesian models provided reinforcing

results, so they will be discussed concurrently. Ranges of parameter estimates reported below

reflect the variation across the four analysis samples. Instead of interpreting each individual

correlation reported, we provide a holistic discussion of results at the end of the chapter.

Asthma

Results for our models of an asthma diagnosis during childhood are presented in Exhibits IV-1

and IV-2. The first shows results for each of four alternative analysis samples from our logistic

regression models, with clustered robust standard results predicting ever having been diagnosed

with asthma. The second shows the corresponding Cox proportional hazard models with robust

standard errors estimating the hazards of experiencing this diagnosis.

The models revealed several statistically significant individual-level or household-level

predictors. Our logit, Cox, and Bayesian analyses demonstrated that children had a lower

probability of being diagnosed with asthma during childhood if they were Latino; were full term

and normal weight at birth; and/or had caregivers who were older, single parents, not disabled, or

had lower levels of schooling. Compared with African-American male youth in our samples,

Latino or Latina youth had 66–88 percent lower odds of being diagnosed with asthma during

childhood; the hazard of being diagnosed with asthma was 60–70 percent lower for Latino and

Latina youth than otherwise-identical African-American male counterparts. Children who

weighed less than 5 pounds at birth or were born prematurely were 2.8–10.1 times more likely to

be diagnosed with asthma relative to children who were full term or normal weight at birth; the

hazard rates were approximately 2.5 times higher. Similar reductions in both the odds and the

29 As noted in Chapter III, we do not need to worry about clustering at the neighborhood level, because children who live in the same neighborhood are experiencing a different value of the neighborhood indicator: They are experiencing such for different years of their lives and different calendar years.

31

IV. Physical and Behavioral Health Outcomes

hazard of an asthma diagnosis were noted among older caregivers: a one-standard-deviation

increase in caregiver age was associated with a 74– 88 percent reduction in the odds and a 76–

86 percent reduction in the hazard of being diagnosed with asthma. Children residing with two

adult caregivers had between 8.2 and 57 times higher odds and between 3.3 and 6.9 times greater

hazard of being diagnosed with asthma relative to those residing with one caregiver. Children

whose caregivers were disabled were 5.2 to 23.2 times more likely to be diagnosed with asthma

that those with nondisabled caregivers; the comparable hazard rate was 3.8 to 9.6 times higher.

Children whose parent or primary caregiver achieved a high school diploma had from 2.9 and

4.9 times higher odds of being diagnosed with asthma compared with children whose caregivers

did not have a diploma; the comparable hazard rate was 2.7 to 3.3 times greater.

Many contemporaneous neighborhood indicators related to demographic, status, resources,

safety, stability, and physical context were statistically significant predictors of being diagnosed with asthma during childhood across our statistical models. In a one-standard deviation-higher

neighborhood, the:

 Percentage of African-American residents was associated with 60–67 percent lower odds

of an asthma diagnosis.

 Percentage of foreign-born residents was associated with 65–92 percent lower odds and

66–73 percent lower hazards of an asthma diagnosis.

 Social vulnerability score was associated with 81–96 percent lower odds and 74–

76 percent lower hazards of an asthma diagnosis.

 Occupational prestige scale was associated with 81–99 percent lower odds and 62–

85 percent lower hazards of an asthma diagnosis.

 Neighborhood resource factor score was associated with 46–69 percent lower odds of an

asthma diagnosis.

 Violent crime rate was associated with 40–96 percent lower odds of an asthma diagnosis.

 Property crime rate was associated with at least a four times higher odds and 1.9–to

2.7 times higher hazards of an asthma diagnosis.30

 Confirmed child abuse and neglect rate was associated with 77–98 percent lower odds

and 56–58 percent lower hazards of an asthma diagnosis.

 Percentage of residents who moved into the neighborhood during the past year was

associated with 2.8–3.1 times higher odds and 1.6–3.2 times higher hazards of an asthma

diagnosis.

 Neurological risk index was associated with 1.6–7.9 times higher odds and 1.5–3.2 times

higher hazards of an asthma diagnosis.

With the exceptions of the percentage of African-American residents, social vulnerability score,

and our indicator of residential instability, the Bayesian analyses also found the remaining

neighborhood indicators above to be statistically significant predictors of ever being diagnosed

with asthma during childhood.

30 In some smaller samples, the frequency of asthmas diagnosis is so low and the corresponding paucity of observations in many cells that we view the parameter estimated by our maximum likelihood models to be unreliably large. In such cases, we will not report the point estimate, only the minimum value estimated across the samples.

32

IV. Physical and Behavioral Health Outcomes

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34

IV. Physical and Behavioral Health Outcomes

Neurodevelopmental Disorders

Results for our models predicting a diagnosis of neurodevelopmental disorders during childhood

are presented in Exhibits IV-3 and IV-4. The first exhibit summarizes results for each of four

alternative analysis samples from our logistic regression models, with clustered robust standard

errors predicting ever having been diagnosed with a neurodevelopmental disorder; the second

shows the corresponding AFT frailty models, with robust standard errors predicting the timing of

this diagnosis.

As for statistically significant individual-level or household-level predictors, our logit and

Bayesian analyses demonstrated that children had a lower probability of being diagnosed with a

neurodevelopmental disorder during childhood if they were Latina or had caregivers who were

older; comparable AFT models found significantly longer spells before diagnosis, as well.

Compared with African-American male youth in our samples, Latina youth had 82–92 percent

lower odds of being diagnosed with a neurodevelopmental disorder during childhood and

between 1.6 and 21.3 times longer spells before diagnosis. Similar reductions in the odds of

diagnosis and increasing duration prior to diagnosis were observed among older caregivers: A

one-standard-deviation increase in caregiver age was associated with 43–76 percent lower odds

and a 1.5–2.3 times increase in the time prior to diagnosis of a neurodevelopmental disorder.

Further, our AFT models revealed additional individual and household-level predictors of the

timing of diagnosis of a neurodevelopmental disorder. Children were more likely to be diagnosed

with a neurodevelopmental disorder sooner if their caregivers were disabled (22–83 percent) or if

they were born prematurely or weighed less than 5 pounds at birth (22–27 percent). Children

residing in households that had health insurance had 40–60 percent longer spells prior to

diagnosis. A one-standard-deviation increase in the number of moves that children made prior to

diagnosis was associated with 26–49 percent longer spells to a diagnosis of neurodevelopmental

disorders.

As in the case of asthma, a number of contemporaneous neighborhood indicators related to

demographic, status, resources, safety, and physical context were statistically significant

predictors of being diagnosed with neurodevelopmental disorders during childhood across our

statistical models. In a one-standard deviation-higher neighborhood, the:

 Percentage of foreign-born residents was associated with 68–88 percent lower odds of

being diagnosed as well as 34–48 percent longer spells prior to diagnosis with a

neurodevelopmental disorder.

 Occupational prestige score was associated with 67–97 percent lower odds of being

diagnosed and a 39–73 percent longer spell to diagnosis of a neurodevelopmental

disorder.

 Violent crime rate was associated with 91–100 percent lower odds of being diagnosed

with and a 1.9–2.2 times longer spell to diagnosis of a neurodevelopmental disorder.

 Property crime rate was associated with at least a 2.3 times higher odds of being

diagnosed and 20–31 percent shorter spell to diagnosis of a neurodevelopmental

disorder.

 Social problems index was associated with 13–17 percent shorter time to diagnosis of a

neurodevelopmental disorder.

35

IV. Physical and Behavioral Health Outcomes

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37

IV. Physical and Behavioral Health Outcomes

 Confirmed child abuse and neglect rate was associated with 70–91 percent lower odds of

being diagnosed and 18–28 percent longer spell to diagnosis of a neurodevelopmental

disorder.

 Neurological risk index was associated with 4.5–5.7 times higher odds of being

diagnosed with a neurodevelopmental disorder.

The aforementioned neighborhood indicators also emerged as significant predictors of a

neurodevelopmental disorder diagnosis in our Bayesian analyses.

Obesity

Results for our models predicting a diagnosis of obesity during childhood are presented in

Exhibits IV-5 and IV-6. The first exhibit summarizes results for each of four alternative analysis

samples from our logistic regression models, with clustered robust standard results predicting

ever having been diagnosed as obese during childhood; the second shows the corresponding Cox

robust standard error models estimating the hazard of experiencing this diagnosis.

The models revealed several statistically significant individual-level or household-level

predictors. Our logit and Cox analyses indicated that children had lower odds or hazards of being

diagnosed as obese during childhood if they had more siblings or had caregivers who were older.

Conversely, children had greater odds or hazards of being diagnosed as obese if they lived with

two caregivers. A one-standard-deviation increase in the number of siblings was associated with

50–55 percent lower odds or hazards of being diagnosed as obese. Similar reductions in the odds

or hazard of diagnosis were observed among older caregivers: A one-standard-deviation increase

in caregiver age was associated with 67–92 percent lower odds or hazards of being diagnosed as

obese. Living with two caregivers significantly increased the odds or hazard of being diagnosed

obese by a factor of at least 8.7, although the odds ratios varied widely across analysis samples.31

Educational attainment was a third variable that was significant in multiple logit models: The

odds of children being diagnosed as obese were at least 4.2 times higher if the child’s caregiver

had attained additional school post–high school compared with children whose caregivers had

not completed a high school diploma. Further, children whose caregivers were immigrants had at

least 5.9 times higher odds of being diagnosed as obese.

Several contemporaneous neighborhood indicators related to ethnic, status, and safety contexts

were statistically significant predictors of being diagnosed with neurodevelopmental disorders

during childhood across our statistical models. Only one exhibited a positive association: living

in a neighborhood with medical facilities was associated with at least a 4.2 times higher odds of

being diagnosed as obese. In contrast, in a one-standard-deviation higher neighborhood, the:

 Percentage of African-American residents was associated with 67–72 percent lower odds

of being diagnosed as obese.

 Percentage of Latino residents was associated with 73–86 percent lower odds of being

diagnosed as obese.

31 We have no confidence in the extremely large point estimates from the obesity models estimated for some of our smaller samples because of the rarity of the diagnosis and the corresponding paucity of observations in many cells.

38

IV. Physical and Behavioral Health Outcomes

 Occupational prestige scale was associated with 83–88 percent lower odds of being

diagnosed as obese.

39

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41

IV. Physical and Behavioral Health Outcomes

 Violent crime rate was associated with 94–96 percent lower odds of being diagnosed as

obese.

 Confirmed child abuse and neglect rate was associated with 64–89 percent lower odds of

being diagnosed as obese.

Only violent crime rates and child abuse and neglect rates emerged as significant predictors of an

obesity diagnosis in our Bayesian analyses, while none of the neighborhood indicators were

robust across two or more samples when estimating Cox models.

Internalizing Behaviors

Results for our models predicting a diagnosis of internalizing behaviors during childhood are

presented in Exhibits IV-7 and IV-8. We summarize the results for each of four alternative

analysis samples from our logistic regression models, with clustered robust standard errors

predicting ever being diagnosed with depression, anxiety, or PTSD during childhood in our first

exhibit; the second shows the corresponding Cox robust standard error models.

Our logit, AFT, and Bayesian analyses consistently revealed several statistically significant

individual-level and household-level predictors of a diagnosis of internalizing behaviors.

Children had lower odds or hazards of being diagnosed with internalizing behaviors during

childhood if they had caregivers who were older. Children who lived with two caregivers were at

least 8 times more likely to be diagnosed with internalizing behaviors and have 33–45 percent

shorter spells to diagnosis. They also experienced 37–52 percent longer spells to diagnosis if

their caregivers reported depressive symptomatology at the time of survey. Further, children

living in households that had one-standard-deviation higher levels of household stressors had 2–

3 times higher odds of exhibiting these internalizing behaviors as well as 13–20 percent shorter

spells prior to diagnosis. Our AFT models revealed that relative to younger siblings, children

who were first born in their families had 25–49 percent longer spells prior to diagnosis.

Multiple contemporaneous neighborhood indicators related to nativity, status, safety, and

stability contexts were statistically significant predictors of being diagnosed with an internalizing behavior during childhood across our logit and AFT statistical models. In a one-standard

deviation-higher neighborhood, the:

 Percentage of foreign-born residents was associated with 43–63 percent longer spells

prior to an internalizing behaviors diagnosis.

 Social vulnerability score was associated with 33–45 percent longer spells prior to an

internalizing behaviors diagnosis.

 Social problems index was associated with 2.2–3.3 times higher odds of an internalizing

behaviors diagnosis as well as 16–27 percent shorter spells prior to diagnosis.

 Property crime rate was associated with at least 3.2 times higher odds of an internalizing

behaviors diagnosis and 17–41 percent shorter spells to diagnosis.

42

IV. Physical and Behavioral Health Outcomes

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IV. Physical and Behavioral Health Outcomes

 Abuse and neglect rate was associated with 14–24 percent longer spells prior to

diagnosis.

 Percentage of residents who moved into the neighborhood in the previous 12 months was

associated with 18–20 percent shorter spells prior to diagnosis

All of the aforementioned neighborhood indicators also emerged as significant predictors of

being diagnosed with an internalizing behavior in our Bayesian analyses.

Behavioral Health Service Utilization

Results for our models predicting behavioral health service utilization during childhood are

presented in Exhibits IV-9 and IV-10. The first exhibit summarizes results for each of four

alternative analysis samples from our logistic regression models with clustered robust standard

errors predicting ever having seen a psychiatrist, psychologist, or counselor during childhood;

the second shows the corresponding Cox robust standard error models estimating the hazard of

using these services.

As for individual-level or household-level predictors, our logit, Cox, and Bayesian analyses

revealed that children had lower odds or hazard of using behavioral health services during

childhood if they were Latino, had caregivers who were older, or had caregivers with histories of

substance abuse. Conversely, children had a greater odds or hazard of using behavioral health

services if they lived with two caregivers, had caregivers with higher levels of schooling, or if

their caregivers were disabled. Latino male youth had 59–79 percent lower odds or hazards of

using behavioral health services than their counterparts in the other strata. Similar reductions in

the odds or hazards of behavioral health service use were associated with older caregivers: a onestandard-deviation increase in caregiver age was associated with 58–90 percent lower odds or hazard of using behavioral health services. For children whose caregivers reported histories of

substance abuse, the odds of using behavioral health services were 63–86 percent higher. Living

with two caregivers was associated with at least 2.5 times higher odds or hazard of behavioral

health service utilization, although the odds or hazards ratios varied widely across analysis

samples. Educational attainment was also a statistically significant predictor across multiple logit and Cox models: the odds or hazards of children using behavioral health services were between

2.3 and 5.2 times higher if the child’s caregiver had attained additional school post–high school

compared with children whose caregivers had not completed a diploma. Compared with children

who had nondisabled caregivers, children who had disabled caregivers had at least 2.5 times

higher odds of using behavioral health services during childhood.

Not surprisingly, many of the same neighborhood indicators related to nativity composition,

status, safety, and stability were statistically significant predictors of using behavioral health

services, as in the case of internalizing behaviors. In a one-standard-deviation higher

neighborhood, the:

 Percentage of foreign-born residents was associated with 70–84 percent lower odds or

hazards of using behavioral health services.

 Social vulnerability score was associated with 72–93 percent lower odds or hazards of

using behavioral health services.

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IV. Physical and Behavioral Health Outcomes

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47

IV. Physical and Behavioral Health Outcomes

 Occupational prestige score was associated with 68–96 percent lower odds or hazards of

using behavioral health services.

 Property crime rate was associated with at least 1.9 times higher odds of using behavioral

health services.

 Percentage of residents who moved into the neighborhood in the preceding 12 months

was associated with a 1.4–2.9 times higher odds or hazards of using behavioral health

services.

All of the aforementioned neighborhood indicators also emerged as significant predictors of

behavioral health service utilization in our Bayesian analyses.

Gender and Ethnic Differences in Neighborhood Effects

Estimated parameters for our models stratified by gender and ethnicity are presented in

Appendix C. In our discussion of stratified results, we employ results from our analyses of the

“ever in DHA” sample for each of the physical and behavioral health outcomes. In our overview,

we find substantial heterogeneity in apparent neighborhood effects. Indeed, in rare cases—

property crime rates, violent crime rates, and child abuse and neglect rates—there were

statistically significant relationships in the aggregate sample, replicated consistently across three or more strata.

Asthma

The aforementioned aggregate relationships between asthma diagnosis and neighborhood

safety—measured in terms of neighborhood property crimes as well as child abuse and neglect

rates—were the only statistically significant predictors across all four strata in our logit models.

All groups experienced significantly higher odds (3.8–8.0 times) of being diagnosed with asthma

with higher neighborhood property crime rates yet significantly lower odds of diagnosis (77–

91 percent) with higher neighborhood child abuse and neglect rates. Further, neighborhood

safety relationships were strongest for female youth.

Several significant associations between neighborhood context and asthma diagnosis that

emerged in the aggregated models were produced almost exclusively from relationships

emerging from the African-American stratum. African-American children and youth were less

likely to be diagnosed with asthma if they resided in neighborhoods that had higher fractions of

African-American residents, higher levels of occupational prestige, or lower levels of

neurological risk (lead pollutants). The remaining predictors in the aggregate models indicated

particularly strong relationships in particular strata. If residing in a neighborhood with a one-

standard-deviation-higher, the:

 Percentage of foreign-born residents was associated with substantially lower odds and

hazards of being diagnosed with asthma for Latinos (86–90 percent) as well as for female

youth (83 percent).

 Social vulnerability score was associated with 81 percent lower odds and hazard of being

diagnosed with asthma for male youth.

48

IV. Physical and Behavioral Health Outcomes

 Violent crime rate was associated with 91 percent lower odds of female youth being

diagnosed with asthma.

One neighborhood indicator emerged as a statistically significant predictor of asthma diagnosis

for females was not significant in the aggregate model. Residing in a neighborhood that had a

one-standard-deviation-higher social capital score was associated with 2.1 times higher odds of

female youth being diagnosed with asthma.

Neurodevelopmental Disorders

The aforementioned aggregate relationship between diagnosis of a neurodevelopmental disorder

and neighborhood safety was generally a statistically significant predictor across strata. All

groups experienced significantly lower odds of being diagnosed and longer spells prior to

diagnosis of a neurodevelopmental disorder in neighborhoods that had higher violent crime rates.

Higher child abuse and neglect rates were associated with significantly lower odds of being

diagnosed with a neurodevelopmental disorder for Latinos and for male youth.

The predictive power of the nativity composition in the aggregate models proved to be strongest

for the Latino stratum. Residing in a neighborhood that had a one-standard-deviation-higher

percentage of foreign-born residents was associated with 85 percent lower odds and 89 percent

longer spells before Latino children were diagnosed with a neurodevelopmental disorder.

One other neighborhood indicator emerged as a statistically significant predictor of a

neurodevelopmental disorder diagnosis in males, though not in the aggregate sample. Residing in

a neighborhood that had a one-standard-deviation-higher social vulnerability score was

associated with 8.3 times higher odds of male youth being diagnosed with a neurodevelopmental

disorder.

Obesity

The aforementioned significant aggregate relationships between a diagnosis of obesity and

neighborhood safety—measured in terms of violent crime rates as well as child abuse and

neglect rates—was not observed in our stratified models. Indeed, the only significant

neighborhood predictor across three of the four strata was living in a neighborhood that had

medical facilities. Female youth and African-American and Latino children residing in these

neighborhoods had more than six times higher odds of being diagnosed as obese during

childhood.

Several significant associations between neighborhood context and obesity diagnosis that

emerged in the aggregated models were produced primarily from relationships emerging from

the African-American stratum. African-American children and youth were less likely to be

diagnosed as obese if they resided in neighborhoods that had higher percentages of African-

American or Latino residents, higher levels of social vulnerability, and lower levels of

occupational prestige.

The percentage of pre-1940–vintage neighborhood housing stock was a strong predictor of

obesity diagnosis in the aggregate sample but proved to be so only for the female stratum.

49

IV. Physical and Behavioral Health Outcomes

Residing in a neighborhood that had a one-standard-deviation-higher percentage of the housing

stock built before 1940 was associated with 2.1–2.5 times higher odds or hazards of being

diagnosed as obese for female youth.

Three other neighborhood indicators emerged as a statistically significant predictor of an obesity

diagnosis in only one stratum, even though they were not predictive of patterns in the aggregate

sample. Residing in a neighborhood that had a one-standard-deviation-higher:

 Percentage of the housing stock built between 1940 and 1970 was associated with 2.7–

3.0 times higher odds or hazards of being diagnosed as obese for female youth.

 Social capital score was associated with 84 percent higher odds of being diagnosed for

Latinos only.

 Resources factor score was associated with 74 percent lower odds of female youth being

diagnosed as obese.

Our Bayesian analyses, which can be found in Appendix F, revealed similar statistically

significant neighborhood indicators across the gender and ethnic strata.

Internalizing Behaviors

Property crime rate was the only significant neighborhood predictor across all four strata in the

logit models and two strata in the AFT models. Children residing in neighborhoods that had a

one-standard-deviation-higher property crime rate had 22–43 percent shorter spells to diagnosis

of internalizing behaviors. Males and Latinos were 4.6 and 11.4 times, respectively, more likely

to be diagnosed with internalizing behaviors if they resided in neighborhoods that had higher

property crime rates.

Many neighborhood indicators proved most (or only) predictive in the Latino stratum. Latino

youth were at least five times more likely to be diagnosed with internalizing behaviors if they

resided in neighborhoods that had higher fractions of Latino and African-American residents but

had 97 percent lower odds of diagnosis if they resided in neighborhoods that had more foreign-

born residents. Latino children also experienced 47 percent shorter spells prior to diagnosis if

they resided in neighborhoods that had one-standard-deviation-higher fractions of both Latino

and African-American residents. However, spells prior to diagnosis were 72 percent longer for

Latinos who lived in neighborhoods that had higher percentages of foreign-born residents as well

as 35 percent longer in neighborhoods that had higher child abuse and neglect rates.

Latinos and boys had significantly higher odds (4–5 times) and shorter spells (12–20 percent)

prior to diagnosis if they resided in neighborhoods that had more neighborhood social problems,

while African Americans and girls experienced 37–47 percent longer spells prior to diagnosis if

they resided in neighborhoods that had one-standard-deviation-higher levels of social

vulnerability.

Of interest, the presence of higher levels of neighborhood resources exhibited the opposite

relationships for boys and girls. Greater neighborhood resources was associated with 4.7 times

higher odds and 20 percent reduced time to diagnosis of internalizing behaviors for boys; among

girls, this was associated with 80 times lower odds and 29 percent increased time to diagnosis.

50

IV. Physical and Behavioral Health Outcomes

Behavioral Health Service Utilization

Occupational prestige and the percentage of foreign-born residents were significant

neighborhood predictors across three of the four strata in both logit and Cox models. With the

exception of the Latino stratum, children residing in neighborhoods that had a one-standarddeviation-higher occupational prestige score had 65–98 percent lower odds or hazards of using behavioral health services during childhood. Latinos, female youth, and male youth had

significantly lower odds and hazards (66–82 percent) of using behavioral health services if they

resided in neighborhoods that had higher percentages of foreign-born residents. Neighborhood

stability proved a strong predictor in two strata. African-American children and female youth had

significantly higher odds (greater than 2.1 times) of using behavioral health services if they

resided in neighborhoods with standard-deviation-higher percentages of neighborhood residents

who moved in during the previous year. Neighborhood social vulnerability proved an especially

strong predictor among males. Residing in a neighborhood that had a one-standard-deviationhigher social vulnerability score was associated with 92 percent lower odds of behavioral health service use for male youth.

Two other neighborhood indicators emerged as statistically significant predictors of behavioral

health service utilization in a particular stratum, though not in the aggregate sample. Residing in

a neighborhood that had a one-standard-deviation-higher:

 Neurological risk index was associated with at least a 3.9 times higher odds or hazards of

using behavioral health services for female youth and African-American children.

 Violent crime rate was associated with 86 percent lower odds of behavioral health service

use for African-American youth.

Our Bayesian analyses found comparable statistically significant relationships between

behavioral health service utilization and the aforementioned neighborhood indicators.

Nonlinear Neighborhood Effects

Results for our nonlinear investigations employing spline regressions are presented in

Appendix D. Several noteworthy nonlinear relationships between neighborhood indicators and

physical or behavioral health outcomes were uncovered that were robust across models.

Violent crime rates consistently demonstrated for asthma, neurodevelopmental disorder, and

internalizing behavior diagnoses a nonlinear pattern indicating an asymmetric V-shaped pattern

of marginal impacts (with the downward-sloping left side of the V longer).32 Illustrated in the case of neurodevelopmental disorders, a standard-deviation increase in violent crime rates in a

neighborhood remaining below the mean of such rates would be expected to reduce the odds of

such a diagnosis by 98 percent, but such a change in a neighborhood remaining above the mean

violent crime rate would be expected to increase the odds by 72 percent.33 This nonlinear pattern 32 Our aggregate results reported in Exhibits IV-3 and IV-4 clearly showed a strong inverse relationship overall, implying that there must be relatively little upslope to the above-mean segment of the spline relationship.

33 These and other reported estimates are based on xtmelogit results for the “ever in DHA” sample but are consistent with those from the logit model and in most cases the Cox hazard/AFT models. The effect for the above-mean range 51

IV. Physical and Behavioral Health Outcomes

was confirmed in the Cox and AFT models. Indeed, as we will show in forthcoming chapters,

this nonlinear result for violent crime rates is quite general across child outcomes analyzed. We

think that this finding reflects the net effects produced by the conflicting forces impinging on

children arising from violent crime in the broader neighborhood, controlling for crime in the

immediate environs as we do. These forces are the negative direct effects from violent crime and

alterations in caregiver actions in response that are intended to ameliorate such effects. We

discuss this important finding more fully below.

Several neighborhood indicators—occupational prestige, percentage of foreign residents, and

respiratory risk index—exhibited distinct threshold relationships—that is, they only had

predictive power when they exceeded sample mean values. In the cases of asthma and behavioral

health service usage outcomes, a standard-deviation-higher value of prestige in a neighborhood

remaining above the mean prestige would be expected to decrease the odds of asthma diagnosis

and behavioral health service usage by 99 percent. In the cases of obesity and behavioral health

service usage outcomes, a standard-deviation-higher value of the percentage of foreign born in a

neighborhood remaining above the mean percentage would be expected to decrease the odds of

obesity diagnosis and behavioral health service usage by 72–93 percent, respectively. Finally, a

standard-deviation-higher value of respiratory risk in a neighborhood remaining above the mean

risk would be expected to increase the odds of asthma diagnosis by a substantial percentage,

though we do not have confidence in the precise parameter estimated.34 These threshold relationships have strong theoretical grounding in sociology and medicine, respectively, as

discussed further below.

Two less expected threshold relationships also emerged. Greater neighborhood social

vulnerability and turnover proved strongly inversely associated with the odds of an internalizing

behavior diagnosis in neighborhoods that have above-average values for these indicators.

Analogous nonlinear patterns were revealed in our AFT models of this outcome. To illustrate, in

a neighborhood that has above-average vulnerability and residents moving in during the prior

year, standard-deviation-higher values for these indicators would be predicted to yield 98 percent

and 99 percent lower odds of diagnosis and 123 percent and 89 percent longer spells before

diagnosis, respectively. We think this reflects the reduced likelihoods of parents in such

vulnerable, unstable neighborhoods seeking medical attention for children who have

internalizing behavioral symptoms or for disclosing such behaviors because of the stigmatization

for reasons discussed more fully below.

Finally, the percentage of pre-1940–vintage housing exhibited different nonlinear patterns in

predicting obesity and neurodevelopmental disorders. In the former case, it showed a threshold-

like pattern, only being positively associated with obesity diagnosis odds when it exceeded

sample mean. In the latter case, it manifested diminishing marginal positive impacts, switching

from a strongly positive association with the odds of neurodevelopmental disorders to a modestly

negative association at extremely high percentages of older housing.

is computed by adding the estimated logit coefficients (not odds ratios), and then exponentiating the value to return the “net” odds ratio for the spline segment.

34 We suspect that this was the result of sparse cell sizes and the resulting sensitivity of our xtmelogit algorithm.

52

IV. Physical and Behavioral Health Outcomes

Discussion

The results reported above clearly show that many aspects of neighborhood context are

statistically and substantively important predictors of our physical and behavioral health

outcomes. Below, we organize the discussion around thematic categories of neighborhood

context.

Neighborhood Safety

In understanding impacts on physical and behavioral health outcomes, our results suggest that

“neighborhood safety” needs to be viewed as a multidimensional construct, components of

which have differential impacts. We have found that property crime rates are generally

associated with higher odds of having an adverse health diagnosis or use of behavioral health

services, whereas violent crime and child abuse and neglect rates are generally associated with

the opposite. More specifically, living in neighborhoods that had higher property crime rates is

associated for all or most strata of our sample children with substantially greater chances of

being diagnosed with asthma or neurodevelopmental disorders and using mental health services.

However, living in a neighborhood that had higher violent crime rates is associated for the

sample overall with reduced chances of being diagnosed with neurodevelopmental disorders; for

female youth being diagnosed with asthma; and use of behavioral health services by African-

American, male, and female youth. Residence in neighborhoods that have higher child abuse and

neglect rates was also related to lower chances that low-income children were diagnosed with

asthma (especially for males), neurodevelopmental disorders (especially for Latinos and males),

obesity (all except for male), and internalizing behaviors (especially for Latinos).

The observed positive relationship between property crime and our physical and behavioral

health outcomes is expected. There are several plausible links between more property crime in

the environment and adverse health consequences for children. In neighborhoods that have

higher rates of property crime, there will be higher incidences of children witnessing and being

victimized by violence (as we will demonstrate in Chapter V) and as a result reacting in ways

that put their physical and behavioral health at risk. There may also be greater fear among

children and their caregivers that restricts more child activities to indoor spaces that may involve more health risks resulting from intensified exposure to indoor toxins such as lead, mold, dust

mites, and vermin. Another link may be through intensification of risky behaviors that harm

health (such as smoking, drinking, using drugs, and engaging in violence), as we will

demonstrate in Chapter VI.

The observed asymmetric V-shaped relationship between violent crime and several physical and

behavioral health outcomes was unexpected, but we believe that is can be explained in two ways

that are not mutually exclusive. The first explanation is that the asymmetric V-shaped

relationship observed here is mimicked in violent crime’s relationships with exposure to violence

(see Chapter V) and engaging in risky behaviors (see Chapter VI). We offer possible

explanations for those relationships in those chapters, so we will not repeat them here. Suffice it

to say that the pattern between violent crime and health may solely be replicating analogous

patterns between violent crime and the other agents generating the adverse health impacts.

53

IV. Physical and Behavioral Health Outcomes

Our second explanation suggests that there are offsetting effects of violent crime on the

probabilities of children having health problems and the probabilities of caregivers obtaining

confirmatory medical diagnoses of such problems.35 As for the former effect, there is ample evidence that exposure to violence generates adverse behavioral and physical health outcomes

for children (see Chapter II). Moreover, it is likely that children’s exposure to violence is

statistically greater in Denver neighborhoods that have higher officially reported violent crime

rates. If this were the only neighborhood effect mechanism operative, we would observe a

positive relationship between violent crime rates and odds of diagnosed health problems. But in

our case, this relationship must be offset by the negative relationship between violent crime and

the likelihood of a diagnosis given that the child indeed has the health problem in question.

Underlying causal pathways may be that higher levels of violence:

 Erode the willingness or ability of caregivers to recognize adverse health symptoms of

their children, perhaps because of the stress associated with caregivers’ own or their

children’s potential or past victimization.

 Erode the willingness or ability of caregivers to seek medical care for their children,

perhaps because of fear of their own or their children’s victimization when seeking such

care.

 Reduce the likelihood that facilities appropriate for diagnosing such health problems are

located proximate to the low-income household.36

We think the first two items above more plausible, given the Moving To Opportunity evidence

about how neighborhood violence can intensify parental stress and exacerbate their physical and

behavioral problems (Ludwig, 2012; Sanbonmatsu et al., 2012).

Our findings about the inverse relationship between neighborhood child abuse and neglect rates

and the odds of adverse health diagnoses can also be understood through the same lens of

caregiver perceptions and behaviors related to their children’s health. Neighborhoods in which

children are often treated poorly by their caregivers are unlikely to provide a normative

collective context where children’s symptomatic health problems are treated with sympathy,

concern, or affirmative responses like seeking medical attention.

Neighborhood Ethnic and Nativity Composition

We have identified several important relationships between the foreign-born, African-American,

and Latino composition of the neighborhood’s population and children’s health outcomes. For

the full sample, higher percentages of foreign-born residents were associated with lower odds of

asthma diagnosis (especially for females and Latinos), neurodevelopmental disorder diagnoses

(especially for Latinos), internalizing behavior diagnoses (especially for Latinos), and behavioral

health service utilization (at least past a threshold concentration of foreign-born residents).

Similarly, higher percentages of African-American residents were associated with lower odds of

both asthma and obesity diagnoses (especially for African Americans). Higher percentages of

35 The violent crime relationship is replicated by the observed inverse relationship between our neighborhood social problems index (which is heavily weighted toward perceived violence and disorder) and the odds of a

developmental disorder diagnosis.

36 Although we try to control for this in our models.

54

IV. Physical and Behavioral Health Outcomes

Latino population were associated with lower likelihoods of obesity diagnosis (in the full

sample) and internalizing behavior diagnosis (for Latinos only).

We see no persuasive theoretical bases to suggest why higher concentrations of these groups

would improve the health of neighborhood children, controlling as we do for the child’s own

ethnicity and caregiver nativity. Instead, we think the above relationships more likely reflect the

following (not mutually exclusive) factors related to the probability that a caregiver will obtain a medical diagnosis, given particular child adverse health symptoms:

 Collective norms and values related to what standards define “problematic health

symptoms.”37

 Collective norms and values related to caregivers’ appropriate help-seeking behaviors.

 Local information networks offering limited information about children’s health risks and

appropriate parental responses.

 Difficulty accessing and interacting with the health care system because of cultural, class

or linguistic barriers.

This last argument is consistent with the health literature on the “epidemiological paradox of

immigrants.” The paradox is that those groups who may have the least familiarity, cultural

resonance, or ability to communicate with the U.S. health care system have “better” health

outcomes, as (erroneously) indicated by lower rates of disease diagnosis.

Neighborhood Social Status

Two indices related to neighborhood social status often proved predictive of children’s physical

and behavioral health: occupational prestige and neighborhood social vulnerability. Residing in a

higher prestige neighborhood was associated with a reduced likelihood of using behavioral

health services and diagnoses of neurodevelopmental disorders, obesity, and asthma (the latter

two especially for African Americans). For several of these relationships, a distinct threshold

was observed. These results have intuitive appeal for several reasons. First, higher prestige

neighborhoods may have distinctive local information networks, norms, and role models related

to encouraging a variety of pro-health behaviors of neighboring caregivers and their children.

Such mechanisms likely come into play only after a threshold of prestige has been surmounted,

because only then are the aforementioned forces likely to be the dominant ones in the

neighborhood. Second, beneficial health results may arise from lower exposure to violence and

lower incidences of risky behaviors, fully consistent with findings we will present in Chapters V

and VI that higher prestige neighborhoods are strongly negatively associated with these child

outcomes. In contrast, we doubt that higher prestige neighbors would dampen the willingness or

ability of low-income caregivers to acknowledge adverse health symptoms of their children and

seek appropriate care; if anything, we would predict the opposite. We thus are persuaded that the

occupation prestige result provides evidence of an unambiguously pro-health (not just pro-

diagnosis) neighborhood effect.

37 Explanations based on collective socialization are especially persuasive, given the observed threshold relationship for percentage of foreign-born residents.

55

IV. Physical and Behavioral Health Outcomes

The findings for neighborhood social vulnerability were less expected, however, given the

conventionally observed inverse relationship between similarly conceived “neighborhood

disadvantage” variables and healthy outcomes. We begin by emphasizing that our results are not

strictly comparable with those in prior scholarly works for two reasons. First, our index sums

neighborhood percentages of unemployment, poverty, and female-headed households and

renters; it does not include ethnic, racial, or nativity measures, as do most others. Second, our

models control for a host of other neighborhood characteristics that are often associated with

“disadvantaged neighborhoods” but for which other studies have no direct measures, notably

crime, child abuse, institutional resources, bad peer influences, social problems, social capital,

and occupational prestige. Thus, other studies’ “neighborhood disadvantage” variables serve as

ambiguous proxies for a wide range of other attributes besides social status and should not be

used as precedents for results using our social vulnerability measure.

Nevertheless, it is not obvious why our social vulnerability indicator should be associated with

decreased likelihoods of asthma diagnosis (especially for males), use of behavioral health

services (again, especially for males), internalizing behavior diagnosis (in more vulnerable

neighborhoods), and (in the case of African Americans only) obesity diagnoses. We find it

implausible that such neighborhoods constitute intrinsically healthier environments in which

children have lower incidences of such health problems. Instead, we think that the relationship is

founded on altering the likelihood that health problems generate diagnoses. Several possible (not

mutually exclusive) alternative explanations are that more vulnerable, lower status

neighborhoods have:

 Collective norms and values that establish higher standards defining “problematic health

symptoms”; if such norms suggest that “real men don’t get sick,” for example, it could

explain why the relationships are especially strong for male youth.

 Collective norms and values that establish higher standards of symptomatology, defining

when caregivers should seek medical attention for their children.

 Local information networks that supply limited information and other resources about

children’s health risks and appropriate parental responses.

 Limited community resources that could be employed to assist the caregiver in accessing

medical facilities, such as vehicles to borrow.

Neighborhood Institutional Resources

We found that our institutional resources index was inversely related to the odds of an asthma

diagnosis and (in the case of females) an obesity diagnosis. Given that our index includes the

availability of parks and recreation centers, this finding is interpretable in a straightforward way as a pro-health consequence of providing such facilities. We can think of no plausible reasons

why the presence of such would deter caregivers from obtaining a diagnosis given certain

symptomatology. In contrast, the presence of medical facilities in the neighborhood was

associated with higher odds of an obesity diagnosis for virtually all strata. We interpret this as a relationship working though the probability of obtaining a medical diagnosis given a high body

mass index, instead of one influencing a child’s weight.

56

IV. Physical and Behavioral Health Outcomes

Neighborhood Physical Environment

Results for our two indices of air pollutants supported the conventional medical wisdom

regarding the deleterious consequences of pollution for healthy child development (see, for

example, Acevedo-Garcia et al., 2003). Children raised in neighborhoods that have higher

neurological risks exhibited substantially higher odds of being diagnosed with asthma (especially

if they were African American) and neurodevelopmental disorders and (in the case of females

and African Americans) using behavioral health services. Children raised in neighborhoods that

have higher-than-average respiratory risk also exhibited substantially higher odds of being

diagnosed with asthma. Given that we can find no persuasive reasons why these environmental

conditions should influence the likelihood of diagnosis given certain symptomatology, we

believe they reveal another neighborhood force that directly impinges on children’s health. This

interpretation is buttressed by our frequent finding of threshold relationships here.

We also found intriguing results related to the age of a neighborhood’s housing stock and obesity

diagnoses. Higher percentages of both pre-1940– and 1940–1970–vintage housing were

associated with higher odds of female youth being diagnosed as obese. The percentage of

pre-1940–vintage dwellings indicator exhibited a minimum threshold before this relationship

became manifest. We think it unlikely that these relationships emerged because of characteristics

of older dwellings themselves (such as higher rates of lead, mold, mildew, vermin infestations,

inadequate heating, and ventilation systems,); otherwise, they should have been stronger

predictors of other health indicators. Rather, we think it reasonable to posit that they serve as

proxies for the design, density, and land uses of the neighborhood. If older neighborhoods in

Denver encourage more walking because they are indeed denser and typically offer a mix of

residential and nonresidential land uses, they well could manifest payoffs in lower child obesity

rates.

Geographic Selection Bias Revisited

In Chapter III, we argued that the estimated value of the “true” neighborhood effect likely lies

within the range of estimates garnered from our various analysis samples, which consider

different potential types of geographic selection post-initial assignment by DHA. For the

physical and behavioral health outcomes reported in Exhibits IV-1 to IV-8, a number of the

estimated neighborhood indicator parameters were substantially different between the four

analysis samples, so our likely “true” estimate is less circumscribed than we would like. One

likely reason for this variation is that some of our analysis samples are small and the number of

observed diagnoses even smaller, producing sometimes exaggerated point estimates from some

of our maximum likelihood estimators. We must also acknowledge the possibility, however, that

there may be unmeasured differences between the parents of those who raised their children in

DHA housing for most of their childhood until time of diagnosis and those who did not.38 We of

course do not know whether these unmeasured differences operated to bias the observed

neighborhood effects upward or downward, and there is no general cross-sample pattern to the

size of estimated parameters.39

38 We remind the reader that those who left DHA comprise a heterogeneous group: both the economically successful and those who may have been evicted for lease violations.

57

IV. Physical and Behavioral Health Outcomes

Conclusion

Many aspects of neighborhood context are statistically and substantively important predictors of

diagnoses of asthma, neurodevelopmental disorders, obesity, internalizing behaviors, and

behavioral health service use by low-income Latino and African-American children. Aspects of

the neighborhood’s safety, ethnic and nativity mix, social status, resources, and environmental

quality all provide substantial predictive power for children’s physical and behavioral health

outcomes, although the relationships’ magnitudes are often contingent on gender and ethnicity.

We caution, however, that whether these relationships are manifested by causal links though the

probability of a child having a health problem or the probability of having a set of symptoms

medically diagnosed is sometimes not entirely clear. We believe that the most convincing way to

interpret the neighborhood property crime, social problems index, occupational prestige,

resources, environmental pollution, and housing stock vintage relationships is that they represent

causal forces that directly affect child health. Thus, we conclude that low-income Latino and

African-American children will demonstrate one or more comparatively superior health

outcomes if they live in a neighborhood that has a lower property crime rate, social problems

index, and respiratory and neurological pollution risk and that have a higher occupational

prestige index, public resource factor score, and degree of walkability and land use mixes. On the

contrary, we believe that results for violent crime; child abuse and neglect rates; neighborhood

social vulnerability; local medical facility; and foreign-born, Latino, and African-American

population percentages can best be interpreted as neighborhood influences on the odds of a given

set of adverse child symptoms generating parental actions leading to a medical diagnosis. Thus,

we conclude that potential health problems of low-income Latino and African-American children

will be less likely to be diagnosed if they live in a neighborhood that has a higher violent crime

rate; child abuse and neglect rate; social vulnerability; and foreign-born, Latino, or African-

American population percentage and one in which there are no medical facilities.

58

V.

Exposure to Violence Outcomes

V.

EXPOSURE TO VIOLENCE OUTCOMES

Introduction

In this chapter, we examine neighborhood influences on five indicators of exposure to violence

for low-income, minority youth in our Denver Child Study. For all children, we analyze whether they were a witness to or victim of neighborhood violence and whether they witnessed violence

at home during childhood. For children between 5 and 18 years of age, we also examine whether

they were a witness to or victim of violence at school. As noted below, we find evidence of

strong neighborhood effects emanating from several dimensions of the residential environment,

especially those related to neighborhood safety, social status, ethnic composition, and physical

environment on children’s witnessing and experiencing violence in their neighborhoods, schools,

and homes.

Exposure to Neighborhood Violence Analysis

Over the course of childhood, children in our study could have been exposed to violence in their

neighborhood as witnesses or victims. Therefore, we examine the extent to which neighborhood

factors contributed to the likelihood that a child was a witness or victim of violence. Study

participants in our core analysis samples range in 2 to 35 years of age at the time of survey,

although we only measure exposure to neighborhood violence outcomes occurring through

18 years of age (or at the time of survey). The average age of the children and youth across these

analysis samples varied between 11.2 and 12.9 years of age. The resultant sample sizes for these

“ever in DHA” groups were 932 (victim of neighborhood violence) and 781 (witnessed

neighborhood violence). In these analysis samples, we have a slight overrepresentation of Latino

males (32 percent) compared with the other gender-ethnic groups: Latinas comprise between 29

and 31 percent, African-American males 20 percent, and African-American females between 18

and 19 percent.40

In this chapter, we assess two measures of exposure to neighborhood violence during childhood:

(1) ever witnessed neighborhood violence and (2) ever a victim of neighborhood violence. We

ascertain these outcomes on the basis of the Denver Child Study caregiver survey respondent’s answers to the questions, “Has your child ever witnessed violence in or around the

neighborhood? If so, at what age?” Caregivers were asked about experiences of victimization in

the neighborhood, as well: “Has your child ever been beaten up, chased, threatened, or robbed in

or around the neighborhood? If so, how old was he or she the first time and last time it

happened?” Approximately 37 percent of our sampled children and youth witnessed

neighborhood violence as a child, with a median age of onset of 8 years of age (although this

ranged from 2 to 18 years of age, with 72 percent witnessing violence before 12 years of age).

Eleven percent of children and youth in the sample had been victims of neighborhood violence

during childhood; the median age of onset was 12 years of age, although it ranged from 2 to

17 years of age.

40 These statistics apply to the “ever in DHA” sample but are comparable in the other three analysis samples, as well. Complete descriptive statistics for all variables related to these samples are available from the authors.

59

V.

Exposure to Violence Outcomes

We recognize that all our measures of child exposure to violence (whether witness or victim, in

neighborhood, school, or home) have shortcomings. First, they are subject to underreporting.

Caregivers may not know about or, perhaps, wish to divulge all instances of their children’s

exposure to violence. Second, there may be variation among caregivers about what constitutes

“witnessing”, “getting beaten up,” and so on. Both of these shortcomings will add error to our

dependent variables, but so long as they remain uncorrelated with our neighborhood indicators,

they will not introduce bias. Third, there is likely endogeneity with our measures of exposure and

two of our neighborhood indicators: social problems index and negative peers. Caregivers who

know that their children have been exposed are more likely to draw upon that fact when

subjectively assessing the extent to which the corresponding neighborhood had social problems

(many components of which involved crime) and negative peer influences.

Exposure to School Violence Analysis

Over the course of their school careers, children in our study could have been exposed to

violence in their school settings as witnesses or victims. Therefore, we also examine the extent to

which neighborhood factors contributed to the likelihood that a child was either a witness or

victim of school violence. Study participants in our two neighborhood exposure to violence

analysis samples range from 2 to 34 years of age at the time of survey, although we only

examine these outcomes occurring during childhood (through 18 years of age or the time of

survey). The average age of the children and youth across these analysis samples varied between

12.3 and 13.9 years of age. The resultant sample sizes for these “ever in DHA” groups were 913

(victim of school violence) and 814 (witnessed school violence). In these analysis samples, we

have a slight overrepresentation of Latino males (32 percent) compared with the other gender-

ethnic groups: Latinas comprise between 27 and 28 percent, African-American males 21 percent,

and African-American females between 19 and 20 percent.41

We estimate models for two indicators of exposure to school violence during childhood: (1) ever

witnessed school violence and (2) ever a victim of school violence. We determined these

outcomes on the basis of the Denver Child Study caregiver survey respondent’s answers to the questions, “Has your child ever witnessed violence in or around school? If so, at what age?”

Caregivers were asked about experiences of victimization at school, as well: “Has your child

ever been beaten up, chased, threatened, or robbed in or around school? If so, how old was he or

she the first time and last time it happened?” Approximately 28 percent of our sampled children

and youth witnessed violence at school as a child, with a median age of onset of 12 years of age,

although this ranged from 3 to 18 years of age, with 40 percent witnessing violence at school

before 12 years of age. Seven percent of children and youth in the sample had been victims of

violence at school during childhood; the median age of onset was 12 years of age.

Exposure to Violence in the Home Analysis

Over the course of childhood, children in our study could have been exposed to violence within

their homes, as well. Therefore, we examine the extent to which neighborhood factors

41 These statistics apply to the “ever in DHA” sample but are comparable with the other three analysis samples, as well. Complete descriptive statistics for all variables related to these samples are available from the authors.

60

V.

Exposure to Violence Outcomes

contributed to the likelihood that a child was a witness of violence at home.42 Study participants

in our exposure to violence in the home analysis sample range from 2 to 34 years of age at the

time of survey, although we only examine these outcomes occurring during childhood (through

18 years of age). The overall sample size for the “ever in DHA” analysis sample was 745; the

average age of the children and youth in this analysis sample is 13.2 years of age. In our analysis

samples, we have a slight overrepresentation of Latino males (33 percent) compared with the

other gender-ethnic groups: Latinas comprise 28 percent, African-American males 20 percent,

and African-American females 20 percent.43

We estimate models for one outcome: ever witnessed violence in the home during childhood. We

derive this outcome based on Denver Child Study caregiver survey respondents’ answers to the questions, “Has your child ever witnessed violence in the home? If so, at what age?”

Approximately 9 percent of our sampled children and youth witnessed violence at home as a

child, with a median age of onset of 6 years of age, although this ranged from 2 to 17 years of

age, with 81 percent witnessing violence at home before 12 years of age.44

Model Estimation

Because all five measures of exposure to violence are dichotomous, we employ logistic

regression (with clustered robust standard errors) models to estimate the odds of ever witnessing

violence in the neighborhood, at school, or at home or being victimized in the neighborhood or at

school. We use accelerated failure time (AFT) frailty models to estimate the timing of first

witnessing of the three forms of violence as well as timing to first victimization.45 In this chapter, we estimate these models for the previously defined “ever in DHA,” “currently in DHA,” and

“mostly in DHA” samples to assess the robustness of our results. Further, we add a fourth

analysis sample, “majority in DHA” as an additional robustness check, because age of onset is

most likely to occur during early and middle childhood, when children also were more likely to

be residing in Denver, Colorado, Housing Author (DHA) housing. Children who spent the

majority of their childhood in DHA housing (measured in terms of time of survey or 18 years of

age for older children and youth) and whose exposure to violence or victimization occurred after

initial random assignment constitute the study populations in these analyses.

42 We have additional information about the extent to which children were victims of violence at home via our questions about out-of-home placements during childhood; however, the reporting of this victimization by caregivers was too low to conduct separate analyses on this outcome.

43 These statistics apply to the “ever in DHA” sample but are comparable with the other three analysis samples, as well. Complete descriptive statistics for all variables related to these samples are available from the authors.

44 We recognize that caregivers may be reluctant to report that their children had witnessed violence in the home.

We would note, however, that many of our caregivers were not biological parents, and the children under their care may have witnessed violence in their prior rather than current homes. Nevertheless, we must assume that there is no systematic pattern of underreporting associated with neighborhood context.

45 We used Stata logit models with robust standard errors to adjust for clustering of children within families, because our xtmelogit models failed to converge for one or more of our analysis samples or stratum. We used Stata streg to estimate the AFT frailty models to adjust for the same issues of clustering of children. Further, we estimated AFT

models instead of Cox proportional hazards models in our analyses when the global chi-square test rejected the null hypothesis of proportionality. The AFT model assumes that the effect of a covariate is to multiply the predicted event time by some constant.

61

V.

Exposure to Violence Outcomes

The logistic and AFT models use the same core child and household covariates common to all of

our analyses, with the exceptions of caregiver disability status, which was perfectly predicted in

many of our models. Here, we measure “contemporaneous” family and neighborhood context at

time of exposure to violence or victimization or at either age at time of survey or 18 years of age

(whichever is younger) if such exposure or victimization never occurred during childhood. Thus,

these analyses can be interpreted as investigating the degree to which childhood exposure to

violence or victimization has any relationship with the neighborhood conditions to which

children were exposed at the point of exposure. We use the full set of neighborhood covariates

described in Chapter III, with the exception of our indicator for the presence of medical facilities in the neighborhood and two environmental quality indicators (neurorisk and respiratory risk

indices) because of sparse cell counts or excessive attrition of cases from our analysis samples.

Estimated Neighborhood Effects on Exposure to Violence Outcomes

Exhibits V-1 through V-10 present nondichotomous predictor variables that are normalized to

aid cross-variable comparability of coefficients. As before, we consider only those results that

are statistically significant in two or more of the analysis samples for the given model type.

Typically, the logit and AFT models provided similar results, so they will be discussed

concurrently. Ranges of parameter estimates reported below reflect the variation across the four

analysis samples. We will initially present the findings without comment; we will discuss them

holistically later to minimize redundant explanations.

Witnessing Violence in the Neighborhood

Results for our models predicting exposure to neighborhood violence during childhood are

presented in Exhibits V-1 and V-2. The first shows results for each of four alternative analysis

samples from our logistic regression models, with clustered robust standard results predicting

ever having witnessed neighborhood violence during childhood. The second shows the

corresponding AFT frailty models estimating the timing of first exposure.

The models reveal several statistically significant individual-level or household-level predictors.

Our logit and AFT analyses suggest that children had a lower probability of witnessing

neighborhood violence during childhood and longer duration to first exposure if they lived with

caregivers who were older or lived in larger families. A one-standard-deviation increase in

caregiver age was associated with a 48–83 percent reduction in the odds of being a witness and

43–69 percent longer spell before witnessing neighborhood violence. A one-standard-deviation

increase in the number of siblings in the household was associated with a 31–65 percent

reduction in the odds of being a witness and a corresponding 10–13 percent longer spell before

witnessing neighborhood violence for the first time.

Conversely, the odds of children witnessing neighborhood violence were significantly higher in

households experiencing high levels of economic stress: a one-standard-deviation-higher level of

household stressors was associated with 31–60 percent higher odds of witnessing violence in the

neighborhood. Moreover, the AFT models suggest additional statistically significant child and

household factors that influence the age of first witnessing neighborhood violence. Children who

were first born had spells before witnessing neighborhood violence that were 22–41 percent

longer than those for children who were born later. Likewise, children who moved more

62

V.

Exposure to Violence Outcomes

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64

V.

Exposure to Violence Outcomes

frequently during childhood had spells prior to witnessing neighborhood violence that were 16–

30 percent longer, with each standard-deviation-higher increase in the number of childhood

moves. However, the spell before first witnessing neighborhood violence was significantly

shortened (19 percent) if the household had health insurance.

Multiple contemporaneous neighborhood indicators related to ethnic, social status, safety, and

physical context were statistically significant predictors of being a witness to neighborhood

violence during childhood across our statistical models. Consider first ethnic composition. A

one-standard deviation-higher neighborhood in the percentage of Latino residents was associated

with 59–65 percent lower odds of being a witness to neighborhood violence.

Two indicators of the social status context of neighborhood also were predictive of witnessing

neighborhood violence. A one-standard-deviation-higher occupational prestige scale was

associated with 79–93 percent lower odds of being a witness as well as 16–26 percent longer

spells prior to first witnessing neighborhood violence. A similar increase in the social

vulnerability score was associated with 58–83 percent lower odds of being a witness to

neighborhood violence. We will interpret this surprising latter result below.

As would be expected, two of our neighborhood safety indicators were predictive of individual-

level witnessing of neighborhood violence. A one-standard-deviation higher:

 Social problems index was associated with 2.1–2.7 times higher odds of being a witness

as well as 15–17 percent shorter spell prior to first witnessing such violence.

 Property crime rate was associated with at least 7 times higher odds of being a witness as

well as 11–15 percent shorter spells prior to first witnessing neighborhood violence.46

Surprisingly, two indicators of neighborhood violence proved to be inversely related to caregiver

reports that their children witnessed violence in the neighborhood. We probe these results further

below. A one-standard-deviation-higher:

 Violent crime rate was associated with 58–76 percent lower odds of being a witness to

neighborhood violence.

 Confirmed child abuse and neglect rate was associated with 53–77 percent lower odds of

being a witness to neighborhood violence.

Finally, one aspect of the physical environment of the neighborhood was a significant predictor

of the odds of childhood exposure to violence. A one-standard-deviation-higher percentage of

housing built prior to 1940 was associated with 1.6–2.3 times higher odds of being a witness to

neighborhood violence.

Victim of Neighborhood Violence

Results for our models predicting becoming a victim of neighborhood violence during childhood

are presented in Exhibits V-3 and V-4. The first exhibit summarizes results for each of four

46 In some smaller samples, the frequency of witnessing neighborhood violence is so low and the corresponding paucity of observations in many cells that we view the parameter estimated by our maximum likelihood models to be unreliably large. In such cases, we will not report the point estimate, only the minimum value estimated across the samples.

65

V.

Exposure to Violence Outcomes

alternative analysis samples from our logistic regression (with clustered robust standard errors)

models predicting ever becoming a victim of neighborhood violence; the second shows the

corresponding AFT frailty models predicting the timing of neighborhood victimization.

The models reveal several statistically significant individual-level or household-level predictors.

Our logit analyses suggest that children had lower probabilities of becoming a victim of

neighborhood violence during childhood if they were not African-American males; comparable

AFT models suggest that these same children had longer spells prior to first victimization.

Compared with African-American male youth in our samples, Latina females have 82–

90 percent lower odds of becoming victims of neighborhood violence and between 45 and

70 percent longer spells prior to first victimization. In addition, African-American females

experienced 78–87 percent lower odds of becoming a victim of neighborhood violence as well as

26–42 percent longer spells prior to first victimization compared with African-American males.

Although Latino males were as likely as African-American males to become victims of

neighborhood violence, Latino males experienced significantly longer spells (21–37 percent)

prior to first victimization. One additional child characteristic was a significant predictor of

neighborhood victimization: Children who were first born had spells before becoming a victim

of neighborhood violence that were approximately 21 percent longer than those for children who

were born later.

Several caregiver and household variables were predictive of ever becoming a victim of

neighborhood violence. The odds of becoming a victim as well as extending the spell prior to

first victimization were associated with older caregivers: A one-standard-deviation increase in

caregiver age was associated with 61–71 percent lower odds of becoming a victim and 37–

68 percent increase in the time prior to first victimization. Also, compared with children whose

caregivers did not complete high school, children of caregivers holding a high school diploma

had 14–19 percent shorter spells prior to becoming victims of neighborhood violence. Children

residing in larger families also experienced longer spells prior to victimization: A one-standarddeviation-higher number of siblings in the household was associated with approximately 10 percent longer spells. Residential instability for children demonstrated an ambiguous

relationship with neighborhood victimization. On one hand, a one-standard-deviation-higher

increase in the number of moves experienced during childhood increased the odds of becoming a

victim of neighborhood violence by 35–54 percent. On the other hand, that same increase in the

number of moves also was associated with 11 percent longer spell prior to first victimization.

Finally, an increase in household economic stressors was associated with higher odds of

becoming a victim of neighborhood violence. A one-standard-deviation increase in the level of

household stressors increased the odds of becoming a victim by 44–79 percent.

Multiple contemporaneous neighborhood indicators related to demographic, social status, safety,

and physical context were statistically significant predictors of becoming a victim of

neighborhood violence during childhood across our statistical models. Regarding the

66

V.

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V.

Exposure to Violence Outcomes

ethnic and social mix of the neighborhood, we found that higher shares of immigrant and highoccupational-prestige neighbors were associated with lower chances of victimization. A onestandard-deviation-higher:

 Percentage of foreign-born residents was associated with 53–79 percent lower odds of

becoming a victim of neighborhood violence.

 Occupational prestige was associated with 66–96 percent lower odds of becoming a

victim of neighborhood violence as well as 21–39 percent longer spells prior to first

victimization.

We continued to see strong (although seemingly contradictory) relationships between different

aspects of neighborhood safety and neighborhood victimization. A one-standard-deviationhigher:

 Property crime rate was associated with at least 4 times higher odds of becoming a

victim of neighborhood violence as well as 16–21 percent shorter spells prior to first

victimization.47

 Social problems index was associated with 1.7–2.4 times–higher odds of becoming a

victim of neighborhood violence as well as 9–15 percent–shorter spells prior to first

victimization.

 Violent crime rate was associated with 51–86 percent–lower odds of becoming a victim

of neighborhood violence as well as 16–19 percent–longer spells prior to first

victimization.

 Confirmed child abuse and neglect rate was associated with 50–84 percent–lower odds

of becoming a victim of neighborhood violence as well as 10 percent longer spells prior

to first victimization.

As was the case with witnessing neighborhood violence, the odds of becoming a victim were

significantly higher for children residing in neighborhoods that had higher percentages of older

homes. A one-standard-deviation increase in the percentage of the housing stock built prior to

1940 was associated with 1.5–3.5 times–higher odds of becoming a victim of neighborhood

violence as well as an 11 percent shorter spell prior to first victimization.

Witnessing Violence at School

Results for our models predicting exposure to violence at school are presented in Exhibits V-5

and V-6. The first shows results for each of four alternative analysis samples from our logistic

regression models, with clustered robust standard results predicting ever witnessing violence at

school. The second shows the corresponding AFT frailty models estimating the timing of first

witnessing school violence.

The empirical models reveal that many of the same individual-level and household-level

predictors proved significant as in the models related to neighborhood exposure to violence.

47 In some smaller samples, the frequency of witnessing neighborhood violence is so low and the corresponding paucity of observations in many cells that we view the parameter estimated by our maximum likelihood models to be unreliably large. In such cases, we will not report the point estimate, only the minimum value estimated across the samples.

69

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71

V.

Exposure to Violence Outcomes

Our logit and AFT analyses suggest that children had a lower probability of witnessing school

violence or longer duration to first exposure to such violence if they were Latino or the first born within their families. Compared with African-American males, Latino youth had significantly

lower odds of witnessing violence at school: for Latina females, the odds were reduced by 60–

71 percent; for Latino males, the odds were reduced by 69–81 percent. Moreover, the duration to

first witnessing school violence was increased by 18–31 percent for Latina females and 25–

49 percent for Latino males. Children who were first born experienced 15–26 percent–longer

spells prior to first witnessing school violence compared with siblings who were born later.

Several caregiver and household characteristics predicted witnessing school violence: caregiver

age, number of siblings, mobility, presence of health insurance, and families that had two

caregivers. A one-standard-deviation increase in:

 Caregiver age was associated with 13–25 percent longer spells prior to witnessing school

violence.

 The number of siblings in the household was associated with 6 percent longer spells

before witnessing school violence.

 The number of childhood moves was associated with 7–14 percent–longer spells prior to

witnessing school violence.

When compared with children living with one caregiver, children living with two caregivers had

2.1–2.2 times–higher odds of witnessing school violence as well as 10–13 percent–shorter spells

prior to witnessing school violence.48 Finally, children living in households that had health insurance had approximately 2.5 times–higher odds of witnessing school violence compared with

children without insurance.

Of more relevance to our study, many contemporaneous neighborhood indicators related to

demographic, social status, safety, and physical dimensions of neighborhood context were

statistically significant predictors of being a witness to school violence during childhood across

our statistical models, generally in analogous patterns they exhibited in the realm of exposure to

neighborhood violence. First, children raised in neighborhoods that had greater immigrant

concentrations experienced significantly reduced odds of witnessing school violence. A one-

standard-deviation-higher neighborhood percentage of foreign-born residents was associated

with 41–66 percent–lower odds of being a witness to school violence.

As in the case of neighborhood violence, higher social-status neighborhoods, as measured by

occupational prestige, seemed to provide environments in which children’s exposure to violence

in schools was lower. Children residing in neighborhoods that had a standard-deviation-higher

value of occupational prestige experienced 58–89 percent–lower odds of being a witness to

school violence as well as 13–17 percent–longer spells prior to first witnessing of such violence.

Neighborhood safety indicators remained strong predictors of exposure to school violence, as

they had in the case of neighborhood violence, although once again, the relationships involving

48 We think that this result reflects a difference in the likelihood of caregivers reporting a given child’s exposure to violence, not a difference in actual exposure. Households that had two caregivers are more likely to find out about their children’s exposures.

72

V.

Exposure to Violence Outcomes

rates of violent crime and child abuse and neglect appeared counterintuitive on their face. A one-

standard-deviation-higher:

 Property crime rate was associated with at least 2 times–higher odds of being a witness

as well as 9–14 percent–shorter spells prior to first witnessing of such violence.49

 Social problems index was associated with 1.5–2.0 times–higher odds of being a witness

to school violence as well as 5–11 percent–shorter spells prior to first witnessing of such

violence.

 Violent crime rate was associated with 53–79 percent–lower odds of being a witness to

school violence as well as 8–13 percent–longer spells prior to first witnessing of such

violence.

 Confirmed child abuse and neglect rate was associated with 43–77 percent–lower odds

of being a witness to school violence.

Also echoing earlier findings, housing vintage within the neighborhood continued to predict

exposure to school violence. A one-standard-deviation increase in the percentage of housing

stock built prior to 1940 was associated with 1.4–2.2 times–higher odds of being a witness to

school violence.

Victim of School Violence

Results for our models predicting becoming a victim of school violence are presented in

Exhibits V-7 and V-8 in a format consistent with those preceding. The sole individual-level

predictor of school victimization was the child’s birth order. Children who were first born had

21 percent–longer spells before becoming a victim of school violence than children who were

born into their families subsequently. However, a number of caregiver and household

characteristics were statistically significant. Children had a lower probability of becoming a

victim of school violence if they were living with caregivers who were older or if they had

histories of substance abuse. A one-standard-deviation increase in caregiver age was associated

with 48–68 percent–lower odds of becoming a victim and a 14–53 percent increase in the spell

prior to first school victimization. Compared with children whose caregivers did not have a

history of substance abuse, children whose parents had such a history had 73–94 percent–lower

odds of becoming a victim of school violence as well as 30–74 percent–longer spells prior to

victimization.50 Further, the likelihood of reporting school victimization was higher if caregivers reported depressive symptomatology.51 Children whose parents reported depressive symptomatology had 2.7–2.9 times–higher odds of becoming victims of school violence as well

as 19–22 percent–shorter spells prior to victimization compared with children whose parents did

49 In some smaller samples, the frequency of witnessing neighborhood violence is so low and the corresponding paucity of observations in many cells that we view the parameter estimated by our maximum likelihood models to be unreliably large. In such cases, we will not report the point estimate, only the minimum value estimated across the samples.

50 This result is likely a reporting issue; substance abusers may be less aware that their children have been victimized, perhaps because they have fewer communication lines open with their children.

51 We caution that causation here may be ambiguous, inasmuch as caregivers’ mental state may have been affected by their children’s past exposure to violence.

73

V.

Exposure to Violence Outcomes

not report those symptoms.52 Finally, compared with children whose caregivers did not complete high school, children of caregivers holding a high school diploma had 14–19 percent–shorter

spells prior to becoming victims of neighborhood violence.53

52 Please note, however, that we are unable to estimate the causal sequencing of this relationship, because we had information only about parental depressive symptomatology reported at the time of survey, which may not coincide with the timing of children’s past exposure to violence.

53 As with several prior results, we think this can best be interpreted as a reporting issue. Better educated caregivers are more likely to be aware of their children’s exposure to violence and perhaps more likely to register particular events involving their children as “violent acts.”

74

V.

Exposure to Violence Outcomes

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* p

76

V.

Exposure to Violence Outcomes

Further, our AFT models suggested additional household predictors of the timing of first

violence victimization in school. Children residing in larger families experienced longer spells

prior to victimization: A one-standard-deviation-higher number of siblings was associated with

approximately 10–18 percent–longer spells. Surprisingly, although greater residential instability

during childhood proved positively associated with the risk of witnessing school violence, its

relationship with school victimization was the opposite.54 A one-standard-deviation-higher increase in the number of moves experienced during childhood was associated with 12–

15 percent–longer spells prior to first victimization at school.

Multiple contemporaneous neighborhood indicators related to social status, resources, safety, and

physical context were statistically significant predictors of becoming a victim of school violence

across our statistical models. Two of our indicators of neighborhood social status proved strongly

predictive. A one-standard-deviation-higher occupational prestige score was associated with 85–

99 percent–lower odds of becoming a victim of school violence as well as 36–67 percent–longer

spells prior to first school victimization.

Our social vulnerability measure produced the same unexpected results, as it did in the case of

witnessing neighborhood violence: A one-standard-deviation increase in neighborhood social

vulnerability was associated with 91–94 percent–lower odds of becoming a victim of school

violence.

The availability of neighborhood resources (for example, parks, playgrounds, recreation centers)

was positively related to the spell prior to first school victimization. A one-standard-deviationhigher resource factor score was associated with 11–17 percent–longer spells prior to victimization at school.

As in our previous exposure to violence models, both property crime rate and the index of social

problems were predictive of school victimization. A one-standard-deviation-higher property

crime rate was associated with at least two times–higher odds of becoming a victim of school

violence.55 A comparable variation in the social problems index was associated with 1.8–

2.5 times–higher odds of becoming a victim of school violence.

Finally, the observed relationship between vintage of the neighborhood housing stock and

exposure to violence continued to be a significant predictor of school victimization. A one-

standard-deviation-higher percentage of housing built prior to 1940 was associated with 1.8–

4.4 times–higher odds of becoming a victim of school violence as well as 12–17 percent–shorter

spells prior to first victimization.

54 What may be happening is that as students experience a larger number of schools, they are more likely to experience a violent one. Yet, as a newcomer, they may be less likely to be embedded in longer standing interpersonal relationships or gang-related activities that would put them at higher risk for being victimized in school.

55 In some smaller samples, the frequency of witnessing neighborhood violence is so low and the corresponding paucity of observations in many cells that we view the parameter estimated by our maximum likelihood models to be unreliably large. In such cases, we will not report the point estimate, only the minimum value estimated across the samples.

77

V.

Exposure to Violence Outcomes

Witnessing Violence in the Home

Results for our models predicting witnessing violence in the home are presented in Exhibits V-9

and V-10 in comparable formats as above.

Compared with our other analyses, relatively few individual-level or household-level predictors

were associated with witnessing violence at home. Children who were the first born within their

families experienced 1.4–1.6 times–longer spells before witnessing violence at home compared

with siblings who were born later. Lower odds of witnessing violence at home or having longer

spells prior to first exposure were associated with living with caregivers who were older, had

lower levels of educational attainment, and did not report depressive symptomatology or having

resided in households that had greater levels of residential stability. A one-standard-deviation

increase in caregiver age was associated with a 84–98 percent reduction in the odds of being a

witness as well as 2.0–2.8 times–longer spells before witnessing violence at home. Compared

with children whose caregivers did not complete a degree, children living with caregivers

holding a high school diploma had 23–28 percent–shorter spells before witnessing violence at

home. If they lived with caregivers who held hold postsecondary degrees, the spell before

witnessing violence at home was 29–34 percent shorter. Children who moved more frequently

during childhood had spells prior to witnessing violence at home that were 22–42 percent longer

with each standard deviation–higher increase in the number of childhood moves.

In contrast, several contemporaneous neighborhood indicators related to demographic, social

status, resources, safety, and physical context were statistically significant predictors of being a witness to violence in the home. Our logit and AFT models suggest that the ethnic mix of the

neighborhood significantly predicted both the odds of witnessing violence in the home and time

to first exposure. A one-standard-deviation-higher:

 Percentage of African-American residents was associated with a 65–100 percent

reduction in the odds of witnessing violence in the home as well as 24–32 percent–longer

spells prior to first exposure.

 Percentage of Latino residents was associated with 49–66 percent–longer spells prior to

first exposure to violence in the home.

Two indicators of neighborhood social status—occupational prestige and social vulnerability—

also predicted exposure to violence at home, although in opposite directions. A one-standarddeviation-higher occupational prestige scale was associated with 94–98 percent–lower odds of witnessing violence in the home as well as 1.3–2.2 times–longer spells prior to first witnessing of

such violence. A similar increase in neighborhood social vulnerability was associated with 88–

96 percent–lower odds of witnessing violence in the home as well as 45–95 percent–longer spells

prior to witnessing such. This latter surprising result replicates what we observed in our other

types of exposures to violence and will be discussed below.

78

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Exposure to Violence Outcomes

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V.

Exposure to Violence Outcomes

The scope and accessibility of neighborhood resources was also a significant predictor of ever

witnessing violence at home. A one-standard-deviation–higher resource factor score was

associated with 2.5–3.1 times–higher odds of witnessing violence at home as well as a 16–

25 percent–shorter time to first witnessing of violence at home.

As with all previous models, the neighborhood safety indicators of property crime rate and social

problems index were positively associated with exposure to violence. A standard deviation

increase in property crime was associated with at least 12 times–higher odds of being a witness

to violence in the home as well as 31–36 percent–shorter spells prior to first witnessing such.56 A

one-standard-deviation-higher social problems index was associated with 16–18 percent–shorter

spell prior to first witnessing violence in the home.

Finally, the housing vintage dimension of the physical environment predicted exposure to

violence in the home, again echoing a result found in all our models. A one-standard-deviation

increase in percentage of housing built prior to 1940 was associated with 12–18 percent–shorter

spells prior to witnessing violence at home.

Gender and Ethnic Differences in Neighborhood Effects

Estimated parameters for our models stratified by gender and ethnicity are presented in

Appendix C. In our discussion of stratified results, we employ results from our analyses of the

“ever in DHA” sample for each of the exposure to violence outcomes. In overview, although

there was some heterogeneity in apparent neighborhood effects on exposure to violence, several

predictors were robust across strata and alternative aspects of violence. In particular, property

crime rates, child abuse and neglect rates, occupational prestige, and pre-1940–vintage housing

stock were statistically significant predictors in the aggregate samples and consistently across

three or more strata in several outcomes.

Witnessing Neighborhood Violence

The aforementioned aggregate relationships between witnessing neighborhood violence and

neighborhood safety (measured in terms of neighborhood property crime rates, social problems

index, and child abuse and neglect rates), occupational prestige, and pre-1940–vintage housing

stock were statistically significant predictors across at least three of the four strata in our logit models. All groups experienced significantly higher odds (at least 5.3 times) of witnessing

neighborhood violence with higher neighborhood property crime rates. However, only female

and Latino youth experienced significantly (19–22 percent) shorter spells prior to first exposure.

The social problems index predicted 10–20 percent–shorter spells prior to witnessing

neighborhood violence for male, female, and Latino youth as well as 2.9–3.2 percent–higher

odds of witnessing such violence for Latino and female youth only. Female, Latino, and African-

American youth experienced significantly lower odds (between 50 and 72 percent reduction) of

witnessing violence with one-standard-deviation-higher child abuse and neglect rates. For all

youth, regardless of gender or ethnicity, a one-standard-deviation-higher occupational prestige

56 In some smaller samples, the frequency of witnessing neighborhood violence is so low and the corresponding paucity of observations in many cells that we view the parameter estimated by our maximum likelihood models to be unreliably large. In such cases, we will not report the point estimate, only the minimum value estimated across the samples.

81

V.

Exposure to Violence Outcomes

score was associated with 71– 89 percent–lower odds of as well as 23–29 percent–longer spells

prior to witnessing neighborhood violence. Higher fractions of older housing in the

neighborhood were related to increased odds of witnessing neighborhood violence by a factor of

1.6 to 3.4.

Other significant associations between neighborhood context and witnessing neighborhood

violence that emerged in the aggregate models were almost exclusively produced from

relationships emerging from the female and African-American strata. Female youth were less

likely to witness neighborhood violence and experience longer spells prior to first exposure if

they resided in neighborhoods that had higher fractions of African-American and Latino

residents. Only female youth had 66 percent–lower odds of witnessing neighborhood violence if

they resided in neighborhoods that had higher levels of social vulnerability. They were more

likely to witness neighborhood violence (3.1 times higher) if they lived in neighborhoods that

had higher levels of social problems and have 9 percent–shorter spells prior to witnessing such

behavior if they resided in neighborhoods with higher levels of resources.

African-American youth were less likely to witness neighborhood violence and experience

longer spells prior to exposure if they resided in neighborhoods that had higher fractions of

Latino residents (78 percent reduction in odds, 33 percent longer spell). However, they were

1.6 times more likely to witness neighborhood violence if they lived in neighborhoods that had

higher levels of neighborhood social capital.

Two other neighborhood indicators emerged as statistically significant predictors of witnessing

neighborhood violence for particular strata, even though they were not significant in the

aggregate analyses. The presence of negative peers in the neighborhood was associated with

substantially higher odds of witnessing neighborhood violence for African-American and male

youth (4.1 and 2.8 times, respectively) as well as shortened spells prior to first exposure by

21 percent for African-American youth. Residing in a neighborhood with a one-standarddeviation-higher percentage of neighborhood residents who moved in the previous year was associated with 9 percent shorter spells prior to witnessing neighborhood violence for Latino

youth only.

Victim of Neighborhood Violence

The aforementioned aggregate relationships between becoming a victim of neighborhood

violence and neighborhood safety (measured in terms of neighborhood property crimes as well

as child abuse and neglect rates) were the only statistically significant predictors across three or more of the strata in our logit or AFT models. All groups experienced significantly higher odds

(5.1–8.3 times) of becoming a victim of neighborhood violence with rising neighborhood

property crime rates; a corresponding reduction of 14–20 percent in the length of the spell prior

to victimization was experienced by male, female, and Latino youth in the sample. Significantly

lower odds of victimization (39–81 percent) were associated with higher neighborhood child

abuse and neglect rates for all groups; however, only female and African-American youth

experienced significantly longer spells (24 percent and 13 percent, respectively) prior to

victimization.

82

V.

Exposure to Violence Outcomes

The aggregate relationships revealed for occupational prestige and the social problems index

proved strong predictors only for male and Latino youth. A one-standard-deviation-higher value

of occupational prestige was associated with 69–77 percent–lower odds of male and Latino

youth, respectively, becoming victims of neighborhood violence as well as 19–20 percent–longer

spells prior to victimization for both groups. A one-standard-deviation-higher social problems

index was associated with 1.9–2.2 times–higher odds of becoming a victim of neighborhood

violence for male and Latino youth, respectively; the corresponding spell prior to victimization

was reduced by 11–12 percent for both groups.

Other significant associations between neighborhood context and witnessing neighborhood

violence that emerged in the aggregated models were almost exclusively produced from

relationships emerging from the male youth stratum. Male youth were less likely (67 percent

reduction in odds) to become victims of neighborhood violence if they resided in neighborhoods

that had higher fractions of foreign-born residents as well as experience longer (23 percent)

spells prior to victimization. They also were less likely to become victims if they resided in

neighborhoods that had higher levels of social vulnerability (76 percent reduction in odds) but

1.6 times more likely to become victims if they resided in neighborhoods that had a one-

standard-deviation-higher percentage of housing built prior to 1940.

Two other neighborhood indicators emerged as statistically significant predictors of being a

victim of neighborhood violence in particular strata, even though they were not significant in the

aggregate analyses. Residing in neighborhoods with negative peers was associated with at least

3 times higher odds of becoming a victim of neighborhood violence for male and African-

American youth as well as a corresponding 39 percent decrease in the spell prior to victimization

for African-American youth.57 Residing in a neighborhood that had a one-standard-deviationhigher percentage of residents who moved into neighborhood during the previous year was associated with 1.7 percent–higher odds of becoming a victim for African-American youth only.

Witnessing School Violence

As in the case of witnessing neighborhood violence, the aggregate relationships between

witnessing school violence and neighborhood property crimes, social problems, child abuse and

neglect rates, and occupational prestige were statistically significant predictors across at least

three of the four strata in our logit models. All groups experienced significantly higher odds

(2.6–3.7 times) of witnessing school violence with higher neighborhood property crime rates.

Female and Latino youth experienced significantly (10–11 percent) shorter spells prior to first

exposure. Male, Latino, and African-American youth experienced 6–9 percent shorter spells

prior to witnessing school violence, with a one-standard-deviation increase in the social

problems score; however, the corresponding 1.9 times increase in the odds ratio was significant

only for female and African-American youth. Female, Latino, and African-American youth

experienced significantly lower odds (between 38 and 62 percent reduction) of witnessing school

violence, with a one-standard-deviation-higher child abuse and neglect rate. For male, female,

and African-American youth, a one-standard-deviation–higher occupational prestige score was

57 In some smaller samples, the frequency of witnessing neighborhood violence is so low and the corresponding paucity of observations in many cells that we view the parameter estimated by our maximum likelihood models to be unreliably large. In such cases, we will not report the point estimate, only the minimum value estimated across the samples.

83

V.

Exposure to Violence Outcomes

associated with 55–75 percent–lower odds of witnessing school violence; however the

corresponding 15 percent increase in the spell prior to witnessing such violence was significant

only for African-American youth.

The strong relationships in the aggregate models between witnessing school violence and

immigrant composition, violent crime rates, and age of housing were manifested only in one or

two strata. Residing in a neighborhood with a one-standard-deviation-higher:

 Percentage of foreign-born residents was associated with 47 percent–lower odds of

witnessing school violence for male youth only.

 Violent crime rate was associated with 56 percent and 63 percent lower odds of

witnessing school violence for male youth and African-American youth, respectively, as

well as 13 percent–longer spells prior to witnessing school violence for African-

American youth only.

 Percentage of housing stock built prior to 1940 was associated with 1.5 times– and

2.6 times–higher odds of witnessing school violence for male and African-American

youth, respectively, as well as 8 percent–shorter spells prior to witnessing school violence

for African-American youth only.

Three other neighborhood indicators emerged as statistically significant predictors of witnessing

school violence for particular strata, even though they were not significant in the aggregate

analyses. Residing in a neighborhood with a one-standard-deviation-higher:

 Percentage of African-American residents was associated with 18 percent–longer spells

prior to witnessing school violence for Latino youth.

 Resource factor score was associated with an 8 percent–shorter spell prior to witnessing

school violence for African-American youth.

 Social capital index score was associated with 77 percent–higher odds of witnessing

school violence for female youth.

Victim of School Violence

The neighborhood property crime rate was the only statistically significant predictor from the

aggregate model that also proved so across all four strata in our logit models. All groups

experienced significantly higher odds (2.1–4.3 times) of becoming victims of school violence

with rising neighborhood property crime rates. However, the corresponding 11 percent and

17 percent decrease in spells prior to school victimization were significant only for female and

Latino youth, respectively.

Neighborhood occupational prestige was statistically significant in three of the strata. A onestandard-deviation increase in occupational prestige was associated with 83–95 percent–lower odds of school victimization as well as 23–50 percent–longer spells prior to victimization for

male, female, and Latino youth.

84

V.

Exposure to Violence Outcomes

Three neighborhood indicators that were strong predictors of school violence victimization in the

aggregate models proved to be so in only one or two strata.58 Residing in a neighborhood that had a one-standard-deviation-higher:

 Social problems index was associated with 1.8–3.2 times–higher odds of becoming a

victim of school violence for male and Latino youth, respectively, as well as a

16 percent–shorter spell prior to becoming a victim of school violence for Latino youth.

 Percentage of housing stock built before 1940 was associated 2.6 times– and 2.4 times–

higher odds of becoming a victim and 12–17 percent–shorter spells prior to school

victimization for male and African-American youth, respectively.

 Resource factor score was associated with a 15 percent–longer spell prior to becoming a

victim of school violence for female youth.

Four other significant associations emerged as statistically significant predictors of becoming a

victim of school violence in particular strata, though not in the aggregate: peers, residential

stability, rates of violent crime, and rates of child abuse and neglect. The presence of negative

peers in the neighborhood was associated with 4.5 times–higher odds of female youth becoming

victims of school violence as well as a 31 percent decrease in the spell prior to victimization.

Residing in a neighborhood with a one-standard-deviation-higher:

 Percentage of residents moving in the previous year was associated with 2.6 times–higher

odds of female youth becoming a victim of school violence.

 Violent crime rate was associated with 22 percent–longer spells prior to becoming a

victim of school violence for African-American youth.

 Child abuse and neglect rate was associated with 19 percent–longer spells prior to

becoming a victim of school violence for African-American youth.

Witnessing Violence at Home

The aforementioned aggregate relationships between ever witnessing violence at home and

neighborhood property crime rates and percentage of African-American residents were the only

predictors that proved statistically significant across all strata. All groups experienced

significantly higher odds (at least 5.8 times) of witnessing violence at home as well as 21–

65 percent–shorter spells prior to first witnessing such violence with a standard-deviation-higher

neighborhood property crime rate. A one-standard-deviation increase in the percentage of

African-American residents in the neighborhood was associated with a lengthened spell prior to

witnessing violence at home by a factor of 1.3 to 2.7 for all strata of youth analyzed.

Other indicators of occupational and ethnic composition of neighborhoods that were strong

predictors in the aggregate also proved so across several strata. Residence in a standarddeviation-more prestigious neighborhood was associated with 95–100 percent–lower odds of witnessing violence at home for male and female youth. Such a difference was also associated

with 1.7–6.5 times–longer spells for male, female, and African-American youth.59 Further, a 58 Moreover, neighborhood social vulnerability, which was significant in the aggregate models, was not a statistically significant predictor of school victimization in the stratified models.

59 In some smaller samples, the frequency of witnessing neighborhood violence is so low and the corresponding paucity of observations in many cells that we view the parameter estimated by our maximum likelihood models to 85

V.

Exposure to Violence Outcomes

corresponding increase in the percentage of Latino residents was associated with 79–91 percent–

lower odds of ever becoming a witness and spells prior to witnessing violence at home that were

lengthened by a factor of at least 1.5 for male and female youth.

All other neighborhood indicators that were strong predictors in the aggregate models proved to

be so only in selected strata. Residing in a neighborhood with a one-standard-deviation-higher:

 Social vulnerability score was associated with a 33 percent–shorter spell prior to

witnessing violence at home for Latino youth but a 42 percent–longer spell for female

youth.

 Social problems index was associated with 18–23 percent–shorter spells prior to

witnessing violence at home for male and Latino youth.

 Resource factor score was associated with 4.4 times–higher odds of ever witnessing

violence at home for female youth and a 25 percent–shorter spell prior to witnessing

violence at home for African-American youth.

 Percentage of housing stock built prior to 1940 was associated with 11.8 times–higher

odds of ever witnessing violence at home for female youth and 27–38 percent–shorter

spells prior to witnessing violence at home for female and African-American youth,

respectively.

Two other associations emerged as statistically significant predictors of witnessing violence at

home in particular strata, though they were not so in the aggregate models. Residing in a

neighborhood with a one-standard-deviation-higher:

 Percentage of foreign-born residents was associated with 64 percent–shorter spell prior to

witnessing violence at home for African-American youth.

 Violent crime rate was associated with 85 percent–lower odds of ever witnessing

violence at home for female youth as well as 1.4–2.0 times–longer spells prior to

witnessing violence at home for Latino and African-American youth.

Nonlinear Neighborhood Effects

Results for our nonlinear investigations employing spline regressions are presented in

Appendix D. Several noteworthy nonlinear relationships between neighborhood indicators and

exposure to violence outcomes were uncovered. Here, we emphasize statistically significant

nonlinear findings that were generally robust across models and various outcomes related to

violence.

First, several context measures exhibited strong associations only after a minimum threshold

value had been exceeded. There was strong evidence that the occupational prestige of the

neighborhood exhibited such a threshold.60 In neighborhoods that had above-mean values of prestige, a standard-deviation increase in prestige was associated with an 89–95 percent

reduction in the odds of being exposed to violence, but no relationship was manifested for

be unreliably large. In such cases, we did not report the point estimate; instead, we reported the minimum value estimated across the samples.

60 This outcome was robust across both logit and hazard/survival models.

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V.

Exposure to Violence Outcomes

prestige values in the below-mean range. This threshold relationship manifested itself in three

measures of exposure: witnessing and being victimized by violence in the neighborhood and

witnessing violence at school. Our index of social problems showed the opposite threshold. In

neighborhoods that had above-mean values of this index, a standard-deviation increase in

problems was associated with a 335 percent increase in the odds of witnessing violence at

school. Finally, our measure of neighborhood social vulnerability exhibited a threshold but in an

unexpected fashion. In neighborhoods that had above-mean values of the social vulnerability

score, a standard-deviation increase in this index is associated with a 95–99 percent decrease in

the odds of being victimized by violence in the neighborhood or witnessing violence at home,

respectively.

Second, three neighborhood indicators—violent crime rates, percentage of households moving in

during the prior year, and percentage of Latino residents—demonstrated a V-shaped relationship,

with several measures of exposure to violence. A standard-deviation increase in the rate of

violent crime in a neighborhood that remains below (above) the mean of such rates would be

expected to manifest an:

 98 percent decrease (72 percent increase) in the odds of witnessing violence in the

neighborhood.

 99 percent decrease (73 percent increase) in the odds of being victimized by violence in

the neighborhood.

 99 percent decrease (62 percent increase) in the odds of witnessing violence in the home.

 93 percent decrease (6 percent increase) in the odds of witnessing violence at school.

A standard-deviation increase in the percentage of households moving in during the prior year in

a neighborhood that remains below (above) the mean of such percentage would be expected to

manifest an:61

 89 percent decrease (475 percent increase) in the odds of witnessing violence at home.

 72 percent decrease (51 percent increase) in the odds of witnessing violence in the

neighborhood.

A standard-deviation increase in the percentage of Latino residents in a neighborhood that

remains below (above) the mean of such percentage would be expected to manifest a:

 78 percent decrease (183 percent increase) in the odds of being victimized by violence in

the neighborhood.

 74 percent decrease (22 percent increase) in the odds of witnessing violence at school.

 92 percent decrease (39 percent decrease; diminished marginal negative effect) in the

odds of witnessing violence in the neighborhood.

61 Oddly, for the victimization in school model, the only nonlinear relationship observed exhibited an inverse V-shaped relationship for neighborhood turnover rates.

87

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Exposure to Violence Outcomes

Discussion

In overview, the results reported above clearly show that many aspects of neighborhood context

are statistically and substantively important predictors of our exposure to violence outcomes.

Below, we organize the discussion around thematic categories of neighborhood context.

Neighborhood Safety

In understanding impacts on our exposure to violence outcomes, our results suggest that

“neighborhood safety” needs to be viewed as a multidimensional construct, components of

which have differential impacts. Property crime rates and our neighborhood social problems

index (at least past a threshold) are generally associated with higher odds of (and shorter spell

before) witnessing or being a victim of crime in neighborhood, school, and home settings,

whereas violent crime and child abuse and neglect rates are generally associated with the

opposite. More specifically, living in neighborhoods that have higher property crime rates is

powerfully predictive for all or most strata of our sample children of greater chances of

witnessing violence in the neighborhood, at school, or at home during childhood and becoming a

victim of neighborhood and school violence. Further, these residential contexts also are

predictive of shorter spells prior to first witnessing or experiencing violent behavior. A similar

pattern manifests itself in the case of our social problems index, though slightly less robustly

across all strata and, in the case of witnessing violence at school, only past a threshold.

In contrast, living in a neighborhood that has higher violent crime rates is associated with lower

odds of exposure to neighborhood and school violence for the sample overall and for African-

American youth most powerfully, although this relationship only occurs up to a point until a

threshold is reached. Such a context is also associated with lower odds of witnessing violence at

home for male and female youth and longer spells to first witnessing of such violence for Latino

and African-American youth. Residence in neighborhoods that have higher child abuse and

neglect rates is also inversely related to the chances that low-income children were exposed to

neighborhood or school violence as witnesses or victims, particularly African-American

children.

The observed positive relationships between property crime and social problems index (which

includes three components related to violent crime and one related to property crime) and our

exposure to violence outcomes are expected. It is conventional to posit a direct link between

more crime in the distal environment and increasing opportunities for children to not only see

these crimes but also to be victimized by them. More surprising superficially are the results for

rates of violent crime and child abuse and neglect. Further reflection reveals, however, plausible

explanations.

To interpret the violent crime results correctly, one must recall that in the Denver Child Study violent crime is measured at the Piton neighborhood scale (roughly two census tracts in size), but

our social problems index is measured for the neighborhood as perceived by the caregiver survey

respondent, which we presume is much smaller in size. Thus, the current findings should be

interpreted as consequences of variations in officially reported violent crime at a larger spatial

scale while holding constant perceived violent crime at the smaller scale surrounding the youth’s home.

88

V.

Exposure to Violence Outcomes

In this context, we believe that a plausible explanation is that fear of violence in the wider

geographic context induces more caregiver or self-imposed restrictions on children’s movements

outside of the home, immediate environs, or school. The consequence of this change in youths’

routine activity spaces may be reduced chances of their being exposed to violence (and having

their own behavioral problems, which may expose them to violence, as we will demonstrate in

Chapter VI). The nonlinear, V-shaped relationship evinced in the case of violent crime clearly

suggests, however, that there are limits to the efficacy of these defensive, compensatory

responses to violence in the wider neighborhood. Past the mean level of violent crime rates, the

relationship turns positive with exposure to violence, as we would expect a priori.

Our findings about the inverse relationship between neighborhood child abuse and neglect rates

and exposure to violence are more challenging to explain. We think that the explanation lies with

the likelihood of caregivers reporting that their children have been exposed to violence, given a certain degree of “objectively measured” exposure. We made a similar argument in Chapter IV

in the context of the observed relationship between higher neighborhood abuse and neglect rates

and fewer reported diagnoses of child health problems. Neighborhoods in which children are

often treated poorly by their caregivers are unlikely to provide a normative context where

children’s exposure to violence is treated with much notice or concern; thus, caregivers may

underreport such. For example, high abuse and neglect rates may reflect extremely tolerant

standards of what constitutes “violence.”

Neighborhood Ethnic and Nativity Composition

We have identified several important associations between the foreign-born, Latino, and African-

American composition of the neighborhood’s population and children’s exposure to violence

outcomes. Higher percentages of foreign-born residents are associated with (1) lower odds of

being a victim of neighborhood violence in the full sample, especially for males; (2) lower odds

of males witnessing neighborhood violence; and (3) lower odds of African-American youth

witnessing violence in the home. On the one hand, we think that these results could be the result

of limitations and adaptations of youths’ activities and the spaces in which they occur that are

collectively enforced by immigrants’ cultural traditions and norms. For example, immigrant

communities may be more likely to share in communal responsibilities, accompanying children

to and from school and monitoring them when they play outside. These kinds of adaptations and

supplemental resources invested in youths’ routine activity spaces might make them less

vulnerable to violence, though our evidence suggests perhaps less strongly so for female youth.

On the other hand, these results may reflect immigrant communities’ power to enforce cultural

norms related to the definition of violence or fear of stigmatization that might make all

caregivers residing there less likely to report their children’s exposure to violence to our

interviewers (Warner and Rountree, 1997).

Higher percentages of Latino residents also are associated with lower odds of witnessing

neighborhood violence (for the full sample) and lower odds of and shorter spells prior to

witnessing violence at home (for males and females). Analogous arguments to those above

related to immigrant communities can again be forwarded here as potential explanations. Recall,

however, that this variable also demonstrates a V-shaped relationship (that is, with being

victimized by violence in the neighborhood and witnessing violence in schools), which cannot be

easily explained by underreporting. Perhaps what is occurring in neighborhoods that have above89

V.

Exposure to Violence Outcomes

average concentrations of Latinos is that the aforementioned protective behaviors are being

overwhelmed by associated upsurges in gang- and drug-related activities.

Higher percentages of African-American residents predict lower odds of and longer spells prior

to witnessing violence in the home for the full sample (especially strongly for females) and

witnessing violence at school for Latino youth. We think these results are explained by the

following (not mutually exclusive) factors related to whether youth reveal their exposures to

their caregivers or caregivers reveal exposures to the interviewers:

 Collective norms and values related to what standards define “violent behavior” within

the contexts of neighborhood, school, and home62 and how such behaviors are shared

with others outside of the community (perhaps related to oppositional cultural attributes

held by some African-American communities).

 Collective norms and values related to youths’ appropriate help-seeking behaviors and

reporting their exposure to violence to their caregivers.

 Local information networks offering limited information about the risks to children

associated with exposure to violence and appropriate caregiver responses to potential

cases of their children’s exposure to violence.

Neighborhood Social Status

Two indices related to neighborhood social status often prove predictive of children’s exposure

to violence: occupational prestige and neighborhood social vulnerability. Residing in a higher

prestige neighborhood is associated with a reduced likelihood of and longer spells before

witnessing or experiencing childhood violence in the neighborhood, at school, or at home for all

youth (but especially for African-American youth). For several of these relationships, a distinct

threshold was observed. These results have strong intuitive appeal from the perspective of local

information networks, norms, and role models related to parents encouraging safer environments

for their children. Higher prestige neighbors might also provide safer environments in and

around their homes that not only redound to the benefit of their own children but also

neighboring children with whom their children associate. We thus are persuaded that the

occupational prestige result provides evidence of an unambiguously protective neighborhood

effect.

The findings for neighborhood social vulnerability were less expected, however, given the

conventionally observed inverse relationship between similarly conceived “neighborhood

disadvantage” variables and exposure to violence outcomes (see, for example, the review in

Leventhal and Brooks-Gunn, 2000). As we explained in our previous chapter on health

outcomes, prior research provides little precedent here, because their measure of

“disadvantaged” neighborhoods is constructed differently, and we control for many other aspects

of neighborhood. Nevertheless, it is not obvious why our social vulnerability indicator should be

associated with decreased likelihoods of witnessing neighborhood violence (especially female

youth), being a victim of neighborhood or school violence (particularly male youth), and

witnessing violence at home (only Latino youth). We find it implausible that such neighborhoods

constitute intrinsically safer environments in which children have lower exposure to violence in

62 Explanations based on collective socialization are especially persuasive given the observed threshold relationship for percentage of foreign-born residents.

90

V.

Exposure to Violence Outcomes

all three settings. Instead, we think that the relationship is founded on altering the likelihood that safety issues generate identification and reporting of the same to our interviewers. In other

words, we think that our social vulnerability indicator is reflecting underreporting of exposure to

violence, given all other contextual variables we have controlled in our models. Several possible

(not mutually exclusive) alternative explanations are that more vulnerable, lower status

neighborhoods have:

 Collective norms and values that establish higher standards defining “violent behavior”;

if such norms suggest that “boys will be boys” in terms of violent behaviors, for example,

it could explain why the relationships are especially strong for male youth.

 Collective norms and values that establish the boundaries within which youth and

caregivers operate in the reporting of exposure to violence—that is, norms that sanction

“airing the family’s dirty linen” to outsiders.

 Fear of stigmatization or retaliation for exposing violent behaviors to outsiders, including

interviewers.

Neighborhood Physical Environment

We also found intriguing results related to the consistently positive relationship between the age

of a neighborhood’s housing stock and exposure to violence in several dimensions. Higher

percentages of pre-1940–vintage homes are associated with higher odds of youth witnessing or

experiencing violence in their neighborhoods, schools, and homes. This association was

especially strong for African Americans and their increased risk of victimization at school. We

believe that these relationships emerged because of the street configurations and land uses

distinguishing older Denver neighborhoods as well as the characteristics of older dwellings

themselves—that is, greater incidences of vacant, abandoned, or poorly maintained properties.

Neglected, vacant, or abandoned properties may provide visible signs of social disorder that

symbolize withering of collective efficacy. In extreme case, such properties may become centers

of gang and drug activities within the neighborhood. All such forces likely generate more

opportunities for exposure to witnessing or being victimized (Raleigh and Galster, forthcoming).

Further, the walkable, mixed-use nature of such traditionally designed neighborhoods may

enhance opportunities for “street interactions” with peers (thereby maximizing the potential for

contagious social processes) while simultaneously degrading their opportunities to be monitored

and supervised. Ancillary consequences, as we show in Chapters IV and VI, are poorer physical

and mental health as well as heightened engagement in risky behaviors.

Geographic Selection Bias Revisited

In Chapter III, we argued that the estimated value of the “true” neighborhood effect likely lies

within the range of estimates garnered from our various analysis samples, which consider

different potential types of geographic selection postinitial assignment by DHA. For the

exposure to violence outcomes reported in Exhibits V-1 to V-10, a number of the estimated

neighborhood indicator parameters were substantially different between the four analysis

samples, so our likely “true” estimate is less circumscribed than we would like. One likely

reason for this variation is that some of our analysis samples are small and the number of

observed outcomes even smaller, producing sometimes exaggerated point estimates from some

of our maximum likelihood estimators. We must also acknowledge the possibility, however, that

91

V.

Exposure to Violence Outcomes

there may be unmeasured differences between the caregivers of those who raised their children

in DHA housing for most of their childhood until time of violence exposure and those who did

not.63 We of course do not know whether these unmeasured differences operated to bias the

observed neighborhood effects upward or downward, and there is no general cross-sample

pattern to the size of estimated parameters.

Conclusion

Many aspects of neighborhood context are statistically and substantively important predictors of

exposure to violence for low-income Latino and African-American children. Aspects of the

neighborhood’s safety, ethnic and nativity mix, social status, and housing stock all provide

substantial predictive power for children’s exposure to violence outcomes in multiple domains.

Exposure to violence in the neighborhood, at school, or at home is generally less likely in

neighborhoods that have lower rates of property crime, social problems, and pre-1940–vintage

housing stock and higher rates of violent crime (up to a point), child abuse and neglect rates,

occupational prestige, and social vulnerability. We believe that relationships observed for child

abuse rates and social vulnerability are likely reflecting neighborhood effects that yield

systematic underreporting. Higher percentages of immigrants, Latinos, and African Americans in

the neighborhood are also linked to lower odds of witnessing violence, although the effects of

neighborhood composition depend on the outcome in question and may again be more

suggestive of forces associated with underreporting of such violence. The magnitudes of most of

these apparent influences (especially property crime), however, appear to be only modestly

contingent on the gender and ethnicity of the youth, although for some aspects of context cross-

strata, differences are substantial. Nonlinear neighborhood effects appeared often; several

indicators exhibited minimum thresholds, and others demonstrated V-shaped relationships.

63 We remind the reader that those who left DHA comprise a heterogeneous group: both the economically successful and those who may have been evicted for lease violations.

92

VI. Risky Behavior Outcomes

VI. RISKY BEHAVIOR OUTCOMES

Introduction

In this chapter, we consider a variety of outcomes for low-income, minority youth in our Denver

Child Study that conventionally are viewed as risky behaviors. We analyze whether they smoked cigarettes, drank alcohol, smoked marijuana, ran away from home, or engaged in aggressive or

violent behavior during childhood. For all of these outcomes, we find evidence suggesting strong

neighborhood effects emanating from several dimensions of the residential environment,

especially those related to neighborhood safety, social status, ethnic composition, and physical

environment.

In the Denver Child Study, caregivers were asked about a variety of risky behaviors affecting their children who were eight years of age or older at the time of the survey. The questions were

phrased, “Before turning 18, has your child ever ____? If so, how old was your child when this

first occurred?” The dependent variables of interest here are whether a child had engaged in one

of five behaviors before 18 years of age that our survey described as smoked cigarettes; drunk

alcohol; smoked marijuana, or “pot”; run away from home; and used aggressive or violent

behavior such as hitting, slapping, or punching. There are between 733 and 810 children in our

64

65

behavioral analysis samples with complete information.

The incidence and mean age of onset

of the above behaviors were as follows:

 13.2 percent smoked cigarettes starting at 15.5 years of age.

 11.2 percent drank alcohol starting at 16.1 years of age.

 10.5 percent smoked marijuana starting at 15.7 years of age.

 6.9 percent ran away starting at 14.8 years of age.

 18.4 percent used aggressive or violent behavior starting at 12.0 years of age.

We recognize the potential shortcomings of these behavioral indicators. First, they are subject to

recall error by the caregiver survey respondent, though we intentionally chose outcomes for

which this likely would be minimal. Second, they are based on caregiver perceptions of the

behaviors. Although caregivers may have first-hand knowledge or child reports as the basis of

these perceptions, we note that their perceptions may not always be accurate, because children

may deliberately hide some of these behaviors from them. Third, they are subject to caregivers’

willingness to reveal socially sensitive behaviors of their children to the interviewer. Although

all three concerns likely create considerable noise in our dependent variables, we assume that

there is no systematic pattern in these errors related to neighborhood indicators.

Because the occurrence of all our youth behavioral outcomes can be expressed as dichotomous

measures, we employ logit models with robust clustered standard errors for parameter

64 The descriptive statistics here apply to the “ever in DHA” group; sample sizes vary depending on the outcome; see Exhibits VI-1 to VI-10 for details of sample sizes by outcome and alternative analysis samples.

65 The age of onset could have been earlier than eight years of age, even though the survey only asked the question for children eight years of age and older.

93

VI. Risky Behavior Outcomes

estimation.66 We also estimate Cox proportional hazard models employing robust standard errors 67 for all behaviors except running away from home. For that specific outcome, we estimate

accelerated failure time (AFT) frailty models for time to onset of running away from home.68 As

with the outcomes reported in earlier chapters, we estimate these models for the previously

defined “ever in DHA,” “currently in DHA,” and “mostly in DHA” samples to assess the

robustness of our results and bound potential degrees of geographic selection bias following

Denver, Colorado, Housing Authority (DHA) assignment.

All logistic, hazard, and AFT models use the same neighborhood indicators and core covariates

common to all our analyses in this report. Our behavioral outcome analyses add one covariate:

whether the child reached puberty earlier than normal (the reference category is puberty on time or late reaching). Ten percent of our sample did so.

Estimated Neighborhood Effects on Behavioral Outcomes

The exhibits present nondichotomous predictor variables that are normalized to aid cross-

variable comparability of coefficients. As before, we consider only those results that are

statistically significant in two or more of the analysis samples for the given model type.

Typically, the logistic regression, Cox proportional hazard, and AFT frailty models provided

reinforcing results, so they will be presented concurrently. Ranges of parameter estimates

reported below reflect the variation across the three analysis samples. We defer interpreting the

estimates until later in this chapter so that we can provide a more holistic discussion, especially

when it comes to explaining unexpected results.

Smoking Cigarettes

Results for our models of smoking are presented in Exhibits VI-1 and VI-2. The first shows (for

each of three alternative analysis samples) clustered robust standard error logit model results for

ever having smoked before 18 years of age; the second shows the corresponding Cox robust

standard error hazard models for the timing of first instance of this behavior.

The models generally revealed few consistently significant individual-level or household-level

predictors of smoking. Youths who had more siblings had a 36–42 percent–lower hazard of

smoking per standard-deviation increase in siblings. Youths whose caregivers were one standard

deviation older exhibited similar reductions of 34–43 percent in their hazards of smoking.

66 Because our multilevel, mixed-effects models either did not converge or did not yield plausible point estimates because of sparse cell sizes in some samples for some outcomes, we report results from our logit models with clustered robust standard errors. Clustered standard errors are based on multiple siblings within the same household.

67 For each hazard model, we used the global chi-square test to determine whether the proportionality assumption of the Cox model was violated; except for running away from home, this assumption was never violated in the other behavioral analyses.

68 The proportionality assumption of the Cox model was violated in our analyses predicting running away from home. Therefore, we employed AFT frailty models to account for clustering with families.

94

VI. Risky Behavior Outcomes

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VI. Risky Behavior Outcomes

Only one neighborhood indicator related to safety proved a consistently statistically significant

predictor of smoking across samples.69 A one-standard-deviation-higher neighborhood property crime rate was associated with a 60–72 percent–greater hazard of smoking and 167–296 percent–

greater odds of ever having smoked before 18 years of age.70

Drinking Alcohol

Results for our models of underage drinking are presented in Exhibits VI-3 and VI-4. The first

shows (for each of three alternative analysis samples) clustered robust standard error logit

model71 results for ever having drunk alcohol before 18 years of age; the second shows the

corresponding Cox robust standard error hazard models for the timing of this behavior.

Normalized versions of all continuous variables are employed.

Examination of these exhibits reveals once again that only caregiver age and depressive

symptomatology demonstrated explanatory power among the control variables.72 Youths whose caregivers were one standard deviation older exhibited reductions in the range of 51–78 percent

in their hazards of drinking. Caregivers who self-reported depressive symptoms at the time of the

interview had 281–351 percent–higher odds of saying their child drank while underage.

As in the case of smoking, alternative aspects of neighborhood safety exhibit distinct

relationships with youth drinking behavior, with property crime having a positive association

with drinking but violent crime and abuse rates having negative associations. A one-standarddeviation-higher neighborhood:

 Property crime rate was associated with at least 124 percent–greater odds and 35–

29 percent–greater hazard of ever drinking before 18 years of age.

 Violent crime rate was associated with 81–99 percent–lower odds of ever drinking

before 18 years of age and 68–75 percent reductions in the hazard of doing so.

 Child abuse and neglect rate was associated with 36–90 percent lower odds of ever

drinking before 18 years of age.

Superior social status dimensions of neighborhood context also proved predictive of lower

likelihoods of drinking. A one-standard-deviation-higher neighborhood social vulnerability score

was associated with 174–436 percent–greater odds of ever drinking before 18 years of age. Our

neighborhood occupational prestige score exhibited the opposite association, with the

corresponding range of impact being 62–94 percent–lower odds of underage drinking.

69 Note that a higher social problems index was associated with a greater hazard of smoking but only in the “ever in DHA” sample.

70 These estimated odds ratios were for the logit model with clustered robust standard errors. In this case, we deem these estimates more reliable than those produced by the xtmelogit model, which were the orders of magnitude we deemed suspicious in the smaller samples.

71 We report logit results, because the xtmelogit model did not converge.

72 Having siblings who drank reduced the hazard of drinking but only in the “ever in DHA” sample.

97

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* p

99

VI. Risky Behavior Outcomes

Smoking Marijuana

Results for our models of marijuana use are presented in Exhibits VI-5 and VI-6. The first shows

(for each of three alternative analysis samples) clustered robust standard error logit model results for ever having smoked marijuana; the second shows the corresponding Cox robust standard

error models estimating the hazard of first marijuana use. Normalized versions of all continuous

variables are employed.

Compared with African-American males, Latino males had 189–360 percent–higher odds of

smoking marijuana. Children who were first born were less likely to have used marijuana (47–

64 percent–lower odds) before 18 years of age, all else being equal. Several family covariates

also proved predictive of this behavior.73 As with smoking and drinking, older caregivers evinced lowers odds (60–64 percent per standard deviation increase) and reduced hazards (78–

91 percent) of reporting their child used marijuana. Families that have health insurance revealed

lower odds and reduced hazards of child marijuana use (27–39 percent per standard deviation

and 61–78 percent, respectively). Children in families experiencing higher levels of residential

instability had higher odds of using marijuana (35–60 percent per standard deviation).

As with the other behavioral outcomes investigated, neighborhood rates of violent and property

crime proved strongly predictive of marijuana use before 18 years of age but again in opposite

directions. All else being equal, a youth being raised in a neighborhood that had a one-standarddeviation-higher property crime rate would be predicted to have at least 100 percent–greater odds and 72–207 percent–higher hazards of smoking marijuana. Conversely, a youth being

raised in a neighborhood that had a one-standard-deviation-higher violent crime rate would have

74–100 percent–lower odds and 70–86 percent–lower hazards of this behavior. Child abuse and

neglect rates echoed once more the violent crime results, with a one-standard-deviation-higher

rate associated with 42–94 percent–lower odds of marijuana use.74

Two findings were related to the potential impacts of the neighborhood physical environment. A

one-standard-deviation-higher percentage of the neighborhood housing stock built prior to 1940

was associated with 70–90 percent–higher odds and 46–62 percent–higher hazards of using

marijuana before 18 years of age. Youth residing in a neighborhood that had a one-standarddeviation-higher respiratory risk index were predicted to have 31–34 percent–lower odds and 32–38 percent–lower hazards of this behavior.

73 In addition, having more siblings reduced the hazard of the focal child smoking marijuana but only in the “ever in DHA” sample.

74 Even the most conservative logit estimates indicate changes in odds of 100 percent, 74 percent, and 94 percent associated with standard deviation increases in property crime, violent crime, and child abuse rates, respectively.

100

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VI. Risky Behavior Outcomes

The social environment also appears to be important here. Higher levels of neighborhood social

capital were associated with 82–84 percent–higher odds of marijuana use during childhood.

More neighborhood social problems (an index that includes teens smoking marijuana and doing

drugs) proved strongly predictive of the odds of marijuana use (2.2–3.3 times higher per standard

deviation increase in the social problems index). Finally, at least for youth who are currently

living in DHA housing, a one-standard-deviation-higher neighborhood occupational prestige

index was associated with 84 percent–lower odds and a 61 percent–lower hazard of marijuana

75

use.

Running Away From Home

Results for our models of running away during childhood are presented in Exhibits VI-7 and

VI-8. The first shows (for each of three alternative analysis samples) robust clustered standard

error logit model results for ever having run away; the second shows the corresponding AFT

frailty models for the timing of this action. Normalized versions of all continuous variables are

employed.

Several covariates are predictive in these models. Compared with their younger siblings, children

who were first born had 7 percent–longer spells prior to running away. As we have found with

other behaviors, older caregivers are less likely to report that their children had run away before

18 years of age (63–64 percent–lower odds per standard-deviation increase). In contrast to prior

behaviors, having more siblings was associated with 90–117 percent–greater odds of the focal

child running away. Several other predictors emerged for the first time here. Children of

immigrants had 15–23 percent–longer spells prior to running away. Children from families that

had better educated caregivers, higher incomes, caregivers who used alcohol or drugs while they

were raising children, or caregivers who did not work outside of the home experienced

significantly greater (order of magnitude in the hundreds of percent) odds of running away.76

Numerous aspects of neighborhood safety, social status, demographics, and physical

environment predict running away before 18 years of age. Once again, neighborhood crime rates

were notable predictors in opposite directions. A school child being raised in a neighborhood that

had a one-standard-deviation-higher violent crime rate would have 93–100 percent–lower odds

of running away and a 20–30 percent–longer spell prior to onset of the behavior. In contrast, a

child being raised in a neighborhood that had a one-standard-deviation-higher property crime

rate would have at least 2.2 times–higher odds of running away and 6–7 percent–shorter spells

prior to onset. Unlike in the cases of drinking and smoking marijuana, however, child abuse and

neglect rates were associated with 5–6 percent–shorter spells prior to running away per standard

deviation increase.

75 This finding applies only to the “currently in DHA sample” and so should be treated with caution.

76 Several of these parameter estimates are unreliably large because of sparse cell counts, so we do not note them in the text.

103

VI. Risky Behavior Outcomes

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105

VI. Risky Behavior Outcomes

Neighborhoods that had higher shares of immigrants and adults employed in prestigious

occupations were less likely to have youths running away. A one-standard-deviation increase in

the:

 Percentage of foreign-born residents was associated with 72–85 percent–lower odds of

and 8–9 percent–longer spells prior to running away.

 Occupational prestige index was associated with 80–99 percent–lower odds of and 14–

17 percent–longer spells prior to running away.

Social dimensions of context also proved important predictors of this behavior. A one-standarddeviation increase in the:

 Neighborhood problems index was associated with 85–303 percent–higher odds of and

3–5 percent–shorter spells prior to running away.

 Social capital index was associated with 3–5 percent–shorter spells prior to running

away.

Finally, one aspect of the physical environment predicted running away. Having medical

facilities in the neighborhood was associated with 83–93 percent–lower odds of and 14 percent–

longer spells prior to running away.

Engaging in Violent Behavior

Results for our models of violent behaviors are presented in Exhibits VI-9 and VI-10. The first

shows (for each of three alternative analysis samples) robust clustered standard errors logit

model results for ever having engaged in violent behaviors before 18 years of age; the second

shows the corresponding Cox proportional hazard (robust standard error) models for the timing

of this behavior. Normalized versions of all continuous variables are employed.

As in several previous behavioral models, caregivers who were younger, had depressive

symptoms, or were disabled were more likely to report that their child had engaged in violent

behaviors. Such was also the case for those who had moved less. In particular, children whose

caregiver:

 Was a standard deviation older had 54–83 percent–lower odds and 71–89 percent–

smaller hazards of engaging in violence.

 Reported depressive symptomatology at the time of our survey exhibited 67–93 percent–

greater hazards and substantially higher odds77 of engaging in violence.

 Was disabled exhibited 147–220 percent–greater hazards of engaging in violence.

 Moved the household a standard deviation more often had 18–22 percent–smaller

hazards of engaging in violence.

77 We view the point estimates as unreliably large.

106

VI. Risky Behavior Outcomes

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108

VI. Risky Behavior Outcomes

Once again, the same pattern of results for neighborhood property crime emerged, but violent

crime and child abuse did not, surprisingly, prove predictive of youth violence.78 A youth being raised in a neighborhood that had a one-standard-deviation-higher property crime rate would

have 59–182 percent–higher hazards of engaging in violence and much greater odds of ever

doing so before 18 years of age.79

The ethnic and occupational composition of the neighborhood demonstrated strong statistical

significance, with higher shares apparently reducing youths’ use of violence. A one-standarddeviation increase in the:

 Percentage of Latino residents was associated with 66–86 percent–lower odds and a

45 percent–smaller hazard of a youth engaging in violence.

 Occupational prestige score was associated with 57–94 percent–lower odds and 55–

78 percent–smaller hazards of a youth engaging in violence.

The same two neighborhood physical context variables that proved predictive of running away

revealed the same patterns here. A one-standard-deviation-higher percentage of the

neighborhood housing stock built prior to 1940 was associated with at least 1.4 times–higher

odds and 41–64—percent–greater hazards of violent behaviors. Having a hospital in the

neighborhood was associated with 52–56 percent–smaller hazards of youth engaging in violence.

Gender and Ethnic Differences in Neighborhood Effects

Estimated parameters for our behavioral outcome models stratified by gender and ethnicity are

presented in Appendix C. As is the case in all our discussions of stratified results, we employ the

“ever in DHA” sample results and normalized continuous covariates. Here again we find

substantial heterogeneity in apparent neighborhood effects on behavioral outcomes. Violent and

property crime rates proved the exception, exhibiting statistically significant (though opposite-indirection) relationships in the aggregate sample that were replicated consistently across most strata for multiple behavioral outcomes.

Smoking Cigarettes

The aggregate results for property and violent crime were replicated across several youth strata.

We observed for male, female, and Latino youths residing in a neighborhood with a one-

standard-deviation-higher rate of property crime produced several-hundred percent–higher odds

of smoking; the impact on female youth was the strongest.80 For a similar difference in violent crime rates, the figures were 84 percent– and 71 percent–lower odds of smoking for male and

Latino youth, respectively.

Stratified Cox hazard models revealed for the female and African-American strata relationships

for neighborhood problems and nativity composition that did not appear in the aggregate results

78 The only exception was for the “currently in DHA” sample, where the Cox models showed negative associations between these two rates and youth violent behaviors.

79 We view the point estimates as unreliably large.

80 Estimates from logit models ranged from 176 percent to 396 percent for these genders.

109

VI. Risky Behavior Outcomes

above. Youths in a neighborhood that had a one-standard-deviation-higher:

 Neighborhood social problems index had a 70 (126) percent–greater hazard of smoking if

they were female (African American).

 Percentage of foreign-born residents had a 183 percent–greater hazard of smoking if they

were African American.

Drinking Alcohol

The inverse relationship between violent crime and youth drinking was remarkably similar

across all four strata, with 77 percent and 87 percent reductions in the in odds ratios associated

with a standard-deviation change in violent crime. Property crime rates proved significant

predictors only for male and Latino youth, with the point estimate for the latter almost three

times larger.

The two measures of neighborhood related to social status that were significant in the aggregate

samples also proved predictive in two (but different) gender and ethnic strata. A standard-

deviation-higher:

 Occupational prestige scale was associated with 75 (70) percent lower odds of drinking

for male and Latino youth.

 Social vulnerability score was associated with at least 4.2 times–higher odds of drinking

for female youth and even greater odds increments for African-American youth.

Social capital emerged as a differential predictor of drinking across three strata, explaining why

it did not appear significant in the aggregate samples. A standard-deviation-higher social capital

index was associated with identical 52 percent–lower odds of drinking for both female and

African-American youth but a 54 percent– greater hazard of drinking for male youth.

Finally, neighborhood social problems emerged as a predictor of more drinking among male

youth. A standard-deviation-higher social problems index was associated with 90 percent–higher

odds of drinking by underage male youth.

Smoking Marijuana

As in the case of drinking, the inverse relationship between violent crime and youth drinking was

remarkably similar across all four strata, with 78–99 percent reductions in the odds ratios

associated with a standard-deviation change in violent crime. Property crime rates again proved

significant predictors only for male and Latino youth, with the point estimate for the latter almost twice as large.

The measures of neighborhood social vulnerability and social problems that were significant in

the aggregate samples proved predictive in one or two gender or ethnic strata. The same patterns

associated with physical context measures emerged relative to the age of the housing stock and

air pollution. A standard-deviation-higher:

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VI. Risky Behavior Outcomes

 Social vulnerability score was associated with substantially higher odds of smoking

marijuana but only for African-American youth.

 Social problems index was associated with 202 percent–higher odds and a 221 percent–

greater hazard of smoking marijuana for male youth and an even larger increase in these

odds for African-American youth.

 Percentage of dwellings built before 1940 was associated with between 81 percent– and

89 percent–higher odds of male and female youth smoking marijuana.

Finally, several neighborhood indicators emerged as predictors of marijuana use for a particular

stratum, though they were not significant in the aggregate samples. A standard-deviation-higher

occupational prestige scale was associated with 67 percent–lower odds and a 43 percent–smaller

hazard of Latino youth smoking marijuana. A standard-deviation-higher social capital index was

associated with 100 percent–higher odds and a 145 percent–greater hazard of male youth

smoking marijuana. The presence of caregiver-assessed negative peers in the neighborhood was

associated with a 294 percent–greater hazard of this behavior by female youth but was associated

with a 92 percent reduction in the odds of such among African-American youth. Perhaps most

unexpectedly, larger values of our neighborhood resources factor score predicted lower odds and

hazards of marijuana use for male youth but just the opposite (and more strongly) for female

youth.81

Running Away From Home

Neighborhood crime rates maintained their notable (and opposite-direction) predictive power

across many if not all strata. Violent crime maintained the notable homogeneity of impacts

across all strata, with a standard-deviation increase predicting a narrow range of 81–99 percent

reductions in the odds of running away. Property crime was only strongly associated with female

and Latino youths’ running away propensities. Also as before, the point estimates of odds ratio

changes are almost twice as large for Latinos as for female youth.

Neighborhood social status produced significant differences across strata. Occupational prestige

was statistically significant across all strata and associated with a 86–94 percent reduction in the odds of and 12–17 percent–longer spells prior to running away from home. Neighborhood social

problems and immigrant share appear to have strong influences primarily in the female and

Latino strata. A standard-deviation-higher:

 Social problems index was associated with 125 (237) percent–higher odds of female

(Latino) youth running away.

 Percentage of foreign-born residents was associated with 83 (82) percent–lower odds of

female (Latino) youth running away but 14 percent–longer spells for African-American

youth.

81 This association may be reflective of successfully getting males off of the streets and away from sources of marijuana through youth involvement in these recreational resources. However, the opposite might occur for female youth: More resources in the neighborhood opens up greater access to being outside and in contact with people or places that might have access to marijuana.

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VI. Risky Behavior Outcomes

Neighborhood physical context related to housing and hospitals and social capital aspects—all of

which were statistically significant in the aggregate models—only proved significant predictors

for some strata. A standard-deviation-higher value in percentage of pre-1940–vintage dwellings

was associated with 2.8–3.0 times–higher odds of running away for African-American and

female youth, respectively; it also shortened the spell prior to running away for female youth.

The presence of neighborhood medical facilities lengthened the spell prior to running away by

25 percent for female and African-American youth. Although higher levels of social capital

increased the odds of running away for female youth, it was also associated with 4–5 percent

reductions in the spells prior to running away not only for females but also for African-American

and Latino youth.

Ethnic composition, social vulnerability, and peers emerged as predictors for some strata, even

though they were not consistent predictors in the aggregate models. A standard-deviation-higher:

 Percentage of African-American residents was associated with 80 percent reductions in

the odds of male youth running away.

 Percentage of Latino residents was associated with 10 percent–longer spells prior to

running away for female youth.

 Social vulnerability score was associated with 6 percent–longer spells prior to running

away for female youth.

Finally, having negative peers in the neighborhood was associated with 18 percent–shorter spells

prior to running away for African-American youth.

Engaging in Violent Behaviors

Our stratified analyses confirmed that our aggregate results indicating the strong predictive

power of property crime was manifested widely across groups but were especially strong for

female and Latino youth. A standard-deviation-higher property crime rate would be expected to

increase the hazards of youth engaging in violent behaviors by 146 percent, 86 percent, and

42 percent for Latino, female, and male youth, respectively; the corresponding increases on the

odds would be 375 percent, 711 percent, and 143 percent, respectively. The opposite relationship

was manifested for all youth in the case of neighborhood child abuse and neglect rates. A

standard-deviation-higher rate would be predicted to lower the odds of engaging in violent

behavior by 49–74 percent. Violent crime was not a statistically significant predictor in any

stratum.

Two other strong relationships in the aggregate sample proved so widely across strata, with

virtual identical strength. Occupational prestige proved to be a consistently strong predictor of

fewer violent acts by youth. A standard-deviation-higher occupational prestige score would be

expected to lower the hazards of engaging in violent behavior by a remarkably similar

59 percent, 60 percent, and 60 percent for Latino, African-American, and male youth,

respectively; comparable reductions in the odds were noted for Latino and male youth. A

standard-deviation-higher percentage of dwellings built before 1940 would be expected to

heighten the hazard of youth engaging in violent acts by 58 percent, 56 percent, and 55 percent

for Latino, African-American, and male youth, respectively.

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VI. Risky Behavior Outcomes

Few other predictors emerged in the stratified models. Living in a neighborhood where youth got

into trouble seemed most influential for females and African-American youth, raising their

respective hazards of engaging in violent behavior by 450 percent and 143 percent, respectively.

Living among a higher percentage of Latino neighbors reduced the hazard and odds (59 percent

and 71 percent, respectively) of violent behavior per standard-deviation increase for African-

American youth. Living in a neighborhood that had higher social capital increased this hazard by

36 percent and 65 percent per standard-deviation increase for male and Latino youth, respectively.

Nonlinear Neighborhood Effects

Results for our nonlinear investigations employing spline regressions are presented in

Appendix D. Many noteworthy nonlinear relationships between neighborhood indicators and

youth behavioral outcomes were uncovered that were robust across models.82

Three neighborhood indicators—occupational prestige, percentage of foreign-born residents, and

percentage of Latino residents—exhibited distinct threshold relationships—that is, they only had

predictive power when they exceeded sample mean values. In the cases of smoking marijuana

and running away outcomes, a standard deviation–higher value of prestige in a neighborhood

remaining above the mean prestige would be expected to decrease the odds of engaging in these

behaviors by 92 percent and 99 percent, respectively. In the cases of underage drinking and

running away outcomes, a standard deviation–higher value of the percentage of foreign-born

residents in a neighborhood remaining above the mean percentage would be expected to decrease

the odds of both by 99 percent. Finally, a standard deviation–higher value of Latino residents in a

neighborhood remaining above the mean percentage would be expected to decrease the odds of

committing violent acts by 81 percent but increase the odds of running away by a substantial percentage, though we do not have confidence in the precise parameter estimate.

Diminishing marginal size of relationship also was exhibited by three neighborhood indicators.

The negative association between violent crime rates and the odds of either running away from

home or smoking marijuana grew progressively weaker at higher ranges of violent crime. A

standard-deviation increase in violent crime rates in a neighborhood remaining below the mean

of such rates would be expected to reduce the odds of running away and smoking marijuana by

99 percent and 97 percent, respectively; such a change in a neighborhood remaining above the

mean violent crime rate would be expected to reduce the odds by only 62 percent and 32 percent,

respectively. A declining marginal impact was also exhibited in both cases of the positive

associations between neighborhood property crime rates and smoking marijuana and the

percentage of pre-1940–vintage housing and running away.

The diminishing marginal effects of violent crime were not evident for all behavioral outcomes,

however. For example, violent crime and respiratory risk demonstrated for several behavioral

outcomes a nonlinear pattern indicating a V-shaped pattern of marginal impacts. We summarize

these results in terms of first how much a standard-deviation increase in violent crime rates in a

82 These and other reported estimates are based on logit model results for the “ever in DHA” sample, though the nonlinear findings are also consistent with those from the Cox hazard/AFT frailty models in most cases. The effect for the above-mean range is computed by adding the estimated logit coefficients (not odds ratios), and then exponentiating the value to return the “net” odds ratio for the spline segment.

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VI. Risky Behavior Outcomes

neighborhood remaining below the mean of such rates would be expected to reduce the odds of a

particular outcome, and then equivalently how much such a change in a neighborhood remaining

above the mean violent crime rate would be expected to increase the odds:

 Smoking: 91 percent decrease, 30 percent increase.

 Violent behaviors: 91 percent decrease, 26 percent increase.

The corresponding figures for a standard-deviation increase in the respiratory risk index are:

 Smoking: 52 percent decrease, 235 percent increase.

 Smoking marijuana: 72 percent decrease, 132 percent increase.

Respiratory risk also demonstrated a statistically significant V-shaped pattern of marginal

impacts on the odds of running away from home, though we are not sufficiently confident in the

parameter estimates to report them.83

Finally, three indicators—occupational prestige, percentage of foreign-born residents, and the

neighborhood resources factor score—demonstrated an inverted V-shaped relationship with the

odds of smoking. We summarize these results in terms of first how much a standard-deviation

increase in a particular indicator in a neighborhood remaining below the mean of this indicator

would be expected to increase the odds of smoking, and then equivalently how much such a

change in a neighborhood remaining above the mean would be expected to decrease the odds of

smoking:

 Prestige: 256 percent increase, 82 percent decrease.

 Foreign born: 505 percent increase, 79 percent decrease.

 Resources factor score: 567 percent increase, 64 percent decrease.

Discussion

The results reported above clearly show that several aspects of the neighborhood safety; social

status; and demographic, physical, peer, and social capital context are statistically and

substantively important predictors of risky child and youth behaviors. Below, we organize the

discussion around these thematic categories of neighborhood context. We note at the outset that

some of our results were unexpected and challenging to explain, though lack of empirical

consensus around the determinants of youths’ risky behaviors has long characterized this field of

study; see reviews in Leventhal and Brooks-Gunn (2000) as well as Foster and Brooks-Gunn

(2013).

Neighborhood Safety

Our most consistent finding was that neighborhood property crime rates exhibited statistically

significant and substantively large positive relationships in the aggregate sample that were

replicated consistently across most strata for all risky behavioral outcomes analyzed. These

findings were buttressed by those related to the neighborhood social problems index, which

83 The percentage of Latino residents also manifested a V-shaped relationship with the odds of smoking.

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VI. Risky Behavior Outcomes

showed this to be a general predictor of marijuana use (especially for male youth and African

Americans) and running away (especially for female youth and Latinos) and a predictor of

smoking for female youth and African-Americans and drinking for male youth. These observed

relationships were expected, inasmuch as several underlying (not mutually exclusive) causal

pathways are plausible. In neighborhoods that have more property crime and other socially

problematic behaviors84 there may be:

 Less collective efficacy, with an environment in which not only crime but also risky

youth behaviors to go unchallenged in public spaces.

 Weaker collective social norms proscribing risky behaviors by youth.

 Potentially more youth role models of risky behaviors to emulate.

 Higher incidences of youth being victimized by crimes (as we found in Chapter V),

which creates psychological reactions leading to risky behaviors.

 Greater access to cigarettes, alcohol, and marijuana through peer or adult networks or

gangs that may be connected to the economic payoffs from property crime.

 More incentives for older youth to seek employment (as we document and explain in

Chapter VIII), which may provide more disposable income for youths’ purchases of

cigarettes, alcohol, and marijuana.

What was unexpected was that this aspect of neighborhoods appeared less predictive of the

behaviors of African-American youth, although the neighborhood social problems index

predicted greater odds of their smoking cigarettes and marijuana.85 It may be the case that Denver African-American families have distinctive caregiver monitoring strategies or arrange for

more of their children’s time to spent outside of the neighborhood (such as enrolling in schools

outside of the neighborhood, which is permitted in Denver). Whatever its source, this same

pattern will emerge for the educational outcomes discussed in the next chapter.

Neighborhood violent crime rates also were associated with risky behaviors but, surprisingly, in

opposite ways from the above indicators of neighborhood safety. Violent crime rates exhibited

statistically significant and substantively large relationships in the aggregate sample that were

replicated consistently across at least two strata for all behavioral outcomes. Youth living in

places with more violent crime were, all else being equal, less likely to drink, smoke marijuana, run away, and (at least up to a point) smoke cigarettes and engage in violent behaviors. As we

explained in Chapter V, these findings should be interpreted as consequences of variations in

violent crime at a larger spatial scale while holding constant violent crime at the smaller scale surrounding the youth’s home.

In this context, we believe that a plausible explanation may follow the same lines as we

advanced in the previous chapter. Fear of violence in the wider geographic context may induce

more caregiver or self-imposed restrictions on children’s movements outside of the home or

immediate environs (including not being employed, as we will demonstrate in Chapter VIII).

84 Recall that our neighborhood social problems index summarizes the following activities: people selling drugs, gang activity, homes broken into by burglars, people being robbed or mugged, and people getting beaten or raped.

85 Most dramatically, the positive relationship between property crime rates and every risky behavior was statistically and substantively strong for Latino and male youth and weak for African-American youth. It was strong for female youth regarding smoking and violent behaviors.

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VI. Risky Behavior Outcomes

Such geographic restrictions in activity spaces (and working) could result in (1) more intensive

parental monitoring of behaviors or (2) less disposable income of youths available to purchase

cigarettes, alcohol, or marijuana. The consequence may be reduced chances of youth engaging in

risky behaviors. The nonlinear relationships evinced in the cases of smoking and violent

behaviors clearly suggest, however, that there may be limits to the efficacy of these defensive,

compensatory responses to violence in the wider neighborhood. In wider neighborhoods that

have above-average violent crime rates, the associations between violent crime and smoking and

violent behaviors are strongly positive, suggesting that past some threshold some negative

behavioral effects of violent crime can no longer be held in check by such limitations in activity

spaces. These apparent negative behavioral effects likely are manifestations of psychological and

physical reactions associated with intense stress related to potential and actual exposure to

violence in these more dangerous places, as we explored in Chapter V.

Neighborhood Social Status

Our results clearly showed that neighborhoods inhabited by higher status, less vulnerable

residents were associated with much lower incidences of risky behaviors by our low-income,

minority youth, with the possible exception of smoking.86 Our occupational prestige measure exhibited consistently strong predictive power, indicating lower hazards of all behavioral

outcomes across the full sample and most individual strata; in the cases of smoking marijuana

and running away, it exhibited a minimum threshold before the apparently salutary effects were

manifested. Our neighborhood social vulnerability score was associated with substantially

greater likelihoods of drinking (especially for female and African-American youth) and smoking

marijuana (especially African-American youth); for male youth, this pattern was evinced for

running away from home.

We would posit that higher status neighbors may be associated with several mechanisms that

could produce the observed inverse relationships with risky behaviors, including collective

socialization, role modeling, and collective efficacy and social control of public spaces in the

neighborhood. As several of these causal mechanisms are theoretically expected to operate only

after a critical mass has been achieved, the finding of thresholds in some of the neighborhood

prestige relationships is particularly supportive. Higher status environments may also be

associated with stronger norms supporting educational performance (which we explore in

Chapter VII), which may further circumscribe motives for engaging in risky behavior.

Neighborhood Ethnic and Nativity Composition

There were several important relationships between the foreign-born, Latino, and African-

American composition of the neighborhood’s population and several behavioral outcomes. With

one exception, we found that higher percentages of these groups in the neighborhood were

associated (typically after exceeding a threshold) with a lower likelihood of risky behaviors.87

Higher percentages of Latino residents were associated with a lower likelihood of smoking (at

86 This is one of the few consistent findings in the empirical literature related to neighborhood effects and youths’

risky behaviors; see the reviews in Leventhal and Brooks-Gunn (2000) and Matheson et al. (2011).

87 It is worth recalling that these relationships were observed after controlling for the immigrant and ethnic status of the sample children’s parents (which never proved consistently statistically significant covariates, however, in any behavioral outcomes).

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VI. Risky Behavior Outcomes

least for females) and engaging in violent behaviors (especially for African Americans and past

the threshold)88 but (past the threshold) a higher likelihood of running away. Higher percentages

of African-American residents were associated with a lower likelihood of running away, for

females and males alike. Higher percentages of foreign-born residents were associated (past the

threshold) with a lower likelihood of running away (especially for females and Latinos) and

drinking. The exception to the pattern was that higher percentages of foreign-born residents were

associated a higher likelihood of smoking (especially for African Americans).

We think our results are consistent with the notion that a dominant immigrant or ethnic group in

the neighborhood can play powerful normative, role-modeling, and behavioral monitoring

functions whose impacts extend to other youth beyond those in the given group. For example,

groups with multigenerational households and extended family networks (more likely immigrant

and Latino in Denver) may more heavily monitor the behavior of all children residing in the neighborhood. A dominant group of neighbors may serve as adult role models and make

resources available to all resident low-income children, thereby enhancing collective

socialization in the neighborhood. Immigrant families who maintain values and behaviors from

their countries of origin may experience reduced intergenerational conflict, which is often linked

to initiation of adolescent risky or delinquent behaviors (like drinking and running away).

Further, these families may continue to enforce strong cultural proscriptions regarding such

behaviors with their second-generation children. These children, in turn, may serve as agents of

“positive behavioral contagion” for neighboring peers who are not from immigrant families. This

interpretation is also consistent with our findings regarding teen fertility in Chapter IX. Implicit in all these explanations is the notion that a critical mass of the given group must be exceeded

before these externalities will extend beyond the given group to the larger resident population.

This notion is strongly supported by our finding that both immigrant and Latino indicators

exhibited minimum thresholds at which relationships began to be manifested: drinking and

running away in the case of immigrant percentage and violence and running away in the case of

Latino percentage.

Neighborhood Housing Stock and Environment

The neighborhood’s housing stock that was built before 1940 generally demonstrated a pattern of

apparent encouragement for several risky behaviors. Sample youth living in older Denver

neighborhoods exhibited much higher likelihoods of smoking marijuana (both males and females

alike), running away (especially female youth), and engaging in violent behaviors (especially

males, Latinos, and African Americans). We think this is likely the result of the external

configuration and land use mix of older Denver neighborhoods, not the internal environments

associated with older housing per se. This is consistent with the explanation we provided in

Chapter IV, where we found that older neighborhoods apparently provided health benefits in the

form of reduced chances of obesity, potentially by encouraging more walking activity. Here we

are perhaps getting a hint of the downside of walkable, mixed-use neighborhoods: adverse

behavioral consequences for youth. The distinctive routine activity spaces inhabited by youth in

such neighborhoods may enhance their interactions with peers (thereby maximizing the potential

for contagious social processes) while degrading their opportunities to be monitored and

88 This is contrary to some previous research, but those studies did not use as many neighborhood indicators as we; see the review in Leventhal and Brooks-Gunn (2000).

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VI. Risky Behavior Outcomes

supervised. An ancillary consequence, as we have shown in Chapter V, is greater exposure to

violence.

A different revelation emerged from the respiratory risk index results, which exhibited V-shaped

relationships with smoking cigarettes. This relationship was asymmetric, with stronger positive

relationships manifested in neighborhoods with above-mean pollution levels. This is consistent

with the notion that we were observing a form of threshold relationship here, though we cannot

be sure what mechanisms were operating. Perhaps in heavily polluted environments, youth are

more likely to smoke, because they see little negative marginal health cost or because they seek

relief through smoking from the other degraded health consequences from the pollution. This

would be consistent with our finding from Chapter IV that children raised in neighborhoods with

higher-than-average respiratory risk exhibited substantially higher odds of being diagnosed with

asthma. Whatever the cause, there apparently are air pollution–smoking–poor health synergisms

that work in mutually reinforcing ways to the detriment of low-income, minority youth.

Neighborhood Peers and Social Capital

Interesting results emerged related to our measure of bad peer influences in the neighborhood:

caregivers who perceived that many youth in their neighborhoods “get into trouble.” This

neighborhood indicator proved to be a consistent and significant predictor only for African–

American youth, with one exception.89 African-American youth living in neighborhoods with

“negative peers” were substantially more likely to drink, run away from home, and engage in

violent behaviors.90 These results offer some tantalizing indications that peer effects may indeed be a vital mechanism for creating a causal link between neighborhood context and youth

behaviors, but this may not be as powerful a force for Latino youth. Latino families try to

regulate peer networks heavily, and their children are more likely to be involved with family and

close friendship (fictive kin) networks. Thus, Latino youth may be intimately engaged with

fewer peers and consequently be less influenced by peer pressures.

Finally, our neighborhood social capital indicator exhibited a heterogeneous pattern of apparent

impacts contingent on gender and ethnicity. Residing in a neighborhood that had higher levels of

social capital was associated with female youth drinking less, male youth drinking and smoking

marijuana more, and African-American youth drinking and running away more. We would

expect that youth embedded in a neighborhood that had greater social capital would be less likely

to engage in risky behaviors because of positive role modeling and collective social control

mechanisms, but here this seems supported only in the case of female youth. The opposite results

may be arising through the stronger collective socialization force that is also associated with

social capital. For male and African-American youth, more social capital may translate into an

increased likelihood that norms supportive of risky behaviors are conveyed consistently and

more powerfully through the neighborhood context.

89 The one exception was for female youths’ violent behaviors, which were made more likely by negative peers.

90 Oddly, however, they were less likely to smoke marijuana.

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VI. Risky Behavior Outcomes

The Curious Case of Smoking

We single out one particular behavior—smoking cigarettes—for special attention here, because it

consistently exhibited a strong and provocative pattern across two distinctive dimensions of

residential context: occupational prestige and institutional resources. In each case, the

relationship was as an inverse V shape: a direct (inverse) relationship for neighborhoods below

(above) mean values for the given indicator. We are unsure of the causal processes that might

rest behind these observations but forward some plausible possibilities.

Consider first the relationship with prestige. We would first observe that our finding of an

inverse V–shaped relationship offers an explanation for the inconsistent findings in the scholarly

literature.91 We offer the following explanation. Compared with low-prestige neighborhoods, those with a modicum of youth from more prestigious backgrounds may have more resources

available through local social networks accessible to low-income youth that encourages their

consumption of tobacco. In higher prestige neighborhoods, however, the availability of network

resources to obtain cigarettes may be offset by predominant antismoking collective norms that

influence the preferences of low-income youth.

In the case of the inverse V–shaped relationship with neighborhood resources, a different

explanation is required. Compared with neighborhoods that had no public institutional resources,

youth in those with a modicum of such (which are primarily parks and playgrounds in Denver)

may find that they have more opportunities to occupy activity spaces where they can more easily

gather with peers for unsupervised activities like smoking. Denver neighborhoods with the

highest score for public institutional resources will not only have parks and playgrounds but also

indoor recreation centers and youth counseling facilities. In these latter types of settings, youths are likely better supervised, are not permitted to smoke, and may be discouraged by staff from

smoking elsewhere.

Geographic Selection Bias Revisited

Recall in Chapter III that we argued that the estimated value of the “true” neighborhood effect

likely lies within the range of estimates garnered from our various analysis samples, which

consider different potential types of geographic selection postinitial assignment by DHA. In

typical cases of behavioral outcomes reported in Exhibits VI-1 to VI-10, the estimated

neighborhood indicator parameters were substantially similar among the “ever in DHA,”

“currently in DHA,” and “mostly in DHA” samples. Thus, we are less inclined to worry here

about distortions caused by postassignment geographic selection when considering these

behavioral outcomes.

91 Earlier studies (for example, Ennett et al., 1997; Allison et al., 1999) found that the risks of adolescent smoking were higher in more advantaged neighborhoods, perhaps as a result of lower costs associated with smoking.

However, more recent studies have found higher levels of adolescent smoking in more disadvantaged neighborhoods (for example, Matheson et al., 2011; Picket and Pearl, 2001; Xue, Zimmerman, and Caldwell, 2007). Others report no significant neighborhood socioeconomic status relationship with smoking (for example, Wen, Van Duker, and Olsen, 2009; Mistry et al., 2011).

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VI. Risky Behavior Outcomes

Conclusion

Many aspects of the neighborhood’s safety, social status, ethnic and nativity mix, physical

environment, and peer and social capital dimensions exhibit substantial predictive power for the

odds of risky behaviors by youth ever occurring and the temporal hazard of outcomes occurring.

One or more risky behaviors are generally less likely in neighborhoods that have higher violent

crime rates (up to a point), foreign-born residents, African-American and Latino residential

percentages, and occupational prestige and lower property crime rates, social problems index,

social vulnerability, percentages of pre-1940–vintage dwellings, respiratory risks from air

pollution, and bad peer influences. The magnitudes of most of these apparent influences are only

modestly contingent on gender and ethnicity, although for some aspects of context cross-strata

differences are substantial. Nonlinear neighborhood effects appear often; observed nonlinear

patterns are inconsistent across indicators, although several exhibit minimum thresholds that can

be easily interpreted theoretically.

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VII. Educational Outcomes

VII. EDUCATIONAL OUTCOMES

Introduction

In this chapter, we consider a variety of primary and secondary school–related outcomes for low-

income, minority youth in our Denver Child Study. We analyze whether they ever attended

advanced or gifted classes, were placed in special education programs, were suspended or

expelled from school, had to repeat a grade, or dropped out of school before receiving their

diploma. For all these educational outcomes, we find evidence suggesting strong neighborhood

effects emanating from several dimensions of the residential environment.

The approximately 750 children92 in our educational analysis range in age from 6 to 18 years of

age; we confine our analysis to those 12 years of age and older when estimating school dropout

behaviors. We ascertain these outcomes on the basis of the Denver Child Study caregiver survey respondent’s responses. It was beyond the scope of this study to gather performance data based

on school administrative records. Participation in high-performance programs was determined on

the basis of caregiver responses to the question, “Did __ [youth] __ ever attend a special or

advanced class or school for gifted students? [if yes, at what age?].” Eleven percent of our

sample had such high academic designations, with mean age of such designation at 12.1 years of

age. Placement in special education classes was based on the question, “Has __ [youth] __ ever

been classified by school personnel as needing special education? [if yes, at what age?].”

Fourteen percent of our sample had such special education designations, with mean age of

designation at 10.6 years of age. Disciplinary problems were measured by caregivers’ responses

to the question, “Was __ [youth] __ ever suspended/expelled from elementary/middle/high

school [if yes, which grades?].”93 In our sample, 23 percent of the youth had been suspended or

expelled, with the mean age at such disciplinary actions being 13.2 years of age. Grade repetition

was determined on the basis of the caregiver question “Has __ [youth] __ ever repeated a grade?

[If yes, which grades repeated?].” Ten percent of our sample repeated at least one grade, with an

average age of 10.8 years of age at the time of repetition. Dropping out of school was determined

if the caregiver said that the youth (18 years of age or less only) “was not attending school and

had not graduated;” 13 percent of youth 12 years of age and older were so designated, with the

mean age of those dropping out being 16.4 years of age.

We recognize the potential shortcomings of these educational indicators. They are subject to

recall error by the caregiver survey respondent, though we intentionally chose outcomes for

which this likely would be minimal. They cannot distinguish unambiguously among outcomes

that are produced by the youth’s academic abilities and behaviors (which are what we hope to

measure) and those that are produced by school programs, facilities, policies, and actions by

92 This applies to the “ever in DHA” group; sample sizes vary somewhat depending on the outcome. The dropping-out outcome was confined to older children, so their sample size was only 571; see Exhibits VII-1 through VII-10

for details.

93 Separate questions were asked about suspensions and expulsions for each level of school experience; we collapsed this information into one summary indicator of the first occurrence of either a suspension or expulsion at any age.

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VII. Educational Outcomes

teachers, counselors, and administrators. As an illustration, a student may be sufficiently high

achieving to warrant selection into advanced classes, but a school may not offer such as part of

its curriculum; a student may be sufficiently underachieving to warrant repeating a grade but is

not required to do so because of an institutional culture of “social promotions.” This confounding

is probably strongest for outcomes occurring at the elementary school level, because there will

be the strongest potential correlation between neighborhood indicators and school policy

resulting from the smaller size of catchment areas. By implication, the suspension or expulsion

and dropout outcomes will be the least ambiguous to interpret, given the aforementioned

statistics on mean age of onset.

Because all of our educational outcomes are dichotomous measures, we employ multilevel

mixed-effects logistic models for parameter estimation. We also estimate either Cox proportional

hazards models with robust standard errors or accelerated failure time (AFT) frailty models. As

with outcomes reported in earlier chapters, we estimate these models for the previously defined

“ever in DHA,” “currently in DHA,” and “mostly in DHA” samples to assess the robustness of

our results and bound potential degrees of geographic selection bias post–Denver, Colorado,

Housing Authority (DHA) assignment.

All logistic, Cox, and AFT models use the same neighborhood indicators and core covariates

common to all our analyses. Our educational outcome analyses add three more covariates in an

effort to control for school context.94 We control for whether the child was attending a neighborhood school (roughly 8 of 10 were) and whether the school was public (roughly 7 of 8

cases). We also employ an index of teachers’ influence. Caregivers were asked, “Please tell me if

teachers’ influence on your child[ren] has been very positive, somewhat positive, somewhat

negative, very negative, or no influence at all.” We scored the responses 4, 3, 1, 0, and 2,

respectively. We also asked an analogous question about school counselors and scored it in the

same fashion. We created a composite index of teacher and counselor influence by summing the

two scores. In our educational analyses, we measure “contemporaneous” household and

neighborhood context as that experienced at age of first occurrence; if an event never occurred,

context is measured at 18 years of age (or age at the time of survey if less than 18 years of age).

Estimated Neighborhood Effects on Educational Outcomes

The tables below present nondichotomous predictor variables that are normalized to aid cross-

variable comparability of coefficients. As before, we consider only those results that are

statistically significant in two or more of the analysis samples for the given model type.

Typically, the multilevel mixed-effects logit, Cox proportional hazards, or AFT models provided

reinforcing results, so they will be discussed concurrently. Ranges of parameter estimates

reported below reflect the variation across the three analysis samples. Instead of interpreting each individual correlation reported, we provide a holistic discussion of results at the end.

94 Unfortunately, we neither have information on the school attendance history of sampled children nor access to school records. Thus, we can apply only crude proxies for school environment.

122

VII. Educational Outcomes

Academically Advanced Classes and Gifted Programs

Results for our models of placement in advanced classes or gifted programs are presented in

Exhibits VII-1 and VII-2. The first exhibit shows (for each of three alternative analysis samples)

the multilevel mixed-effects logistic model results for ever having been so placed; the second

exhibit shows the corresponding AFT robust standard error models for the timing of this

placement.95

The models generally reveal few consistently significant individual-level, household-level, or

school-level predictors. Compared with African-American male youth in our samples, African-

American females had 172–537 percent–greater odds of ever being placed in advanced or gifted

classes, depending on the analysis sample. They were placed in such classes 17–27 percent

sooner during their school years than otherwise-identical African-American male counterparts.

Youth whose primary caregivers achieved a high school diploma had 174–361 percent–higher

odds of ever being in these high-achievement classes and were placed in such 22–26 percent

more quickly than comparable colleagues whose caregivers did not have a diploma. Finally,

youth in neighborhood-based schools were substantially less likely to be placed in advanced or

gifted classes. Youth in neighborhood schools had 64–90 percent–lower odds of and 21–

38 percent–longer spells before such placements.96

Several neighborhood indicators related to social, status, safety, and physical context proved

consistently statistically significant predictors of being placed in advanced or gifted classes. A

one-standard deviation-higher neighborhood:

 Social capital scale was associated with 61–80 percent–greater odds of placement.

 Occupational prestige scale was associated with 55–98 percent–lower odds of and 27–

52 percent–longer spells before placement.

 Violent crime rate was associated with a 25–45 percent–longer period before placement

and 57–95 percent–lower odds of placement.

 Share of dwellings built before 1940 was associated with a 10–14 percent–shorter period

before placement.

95 We employed AFT instead of Cox models here when the global chi-square test rejected the null hypothesis of proportionality—that is, that the effect of a covariate is to multiply the hazard by some constant. In contrast, the AFT model assumes that the effect of a covariate is to multiply the predicted event time by some constant.

96 Neighborhood-based schools probably are less likely to have specialized classes available for exceptionally well-performing students. We recognize that there may be some endogeneity in this relationship, however. Students who bring special academic talents to school or develop them while in school may be more likely to be reassigned to schools that are not neighborhood based (such as magnet schools).

123

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125

VII. Educational Outcomes

Special Education Placement

Results for our models of placement in special education programs are presented in

Exhibits VII-3 and VII-4. The first summarizes (for each of three alternative analysis samples)

the multilevel mixed-effects logistic model results for ever having been so placed; the second

shows the corresponding AFT frailty models for the timing of this placement. Normalized

versions of all continuous variables are employed.

Examination of these exhibits reveals that ethnicity, nativity, and mobility of the household were

major predictors of placement into special education classes. Latinas had 74–87 percent–lower

odds than African-American males to be in special education, and the period prior to such

placement was 50–86 percent longer. Youth whose primary caregiver immigrated to the United

States had 43–52 percent–longer periods before special education placement. Children from

households that moved a standard deviation more often experienced a 17–40 percent–longer

spell before being assigned to special education.

Both violent crime rates and property crime rates were strongly associated with special education

placement but in opposite directions. A child growing up in a neighborhood that had a one-

standard-deviation-higher rate of violent crime exhibited 58–93 percent–lower odds of school

personnel designating them for special education and a 33–73 percent increase in the spell in

school before this occurs. A similar situation in the case of a variation in rate of property crime

evinced 131 percent or higher97 increases in the odds of special education classification and a

17–31 percent decrease in the duration of the spell before such placement.

Several other neighborhood context variables also proved robustly predictive of special

education placement across two or more samples. All else being equal, children in a

neighborhood that had one-standard-deviation-higher:

 Occupational prestige score had 72–83 percent–lower odds of ever being placed in

special education classes and 49 percent–longer spells before placement.

 Foreign-born resident percentage had 43–73 percent–lower odds of placement in special

education classes.

 Percentage of residents moving in during the past year had 35–52 percent–lower odds of

placement in special education classes and 16–18 percent–longer duration before

assignment.

 Child abuse and neglect rate had 52–83 percent–lower odds of placement in special

education classes.

97 The coefficient for the “currently in DHA” sample we view as unreasonably large and likely a spurious product of the estimation algorithm.

126

VII. Educational Outcomes

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128

VII. Educational Outcomes

Suspensions and Expulsions

Results for our models of suspensions and expulsions from school are presented in

Exhibits VII-5 and VII-6. The first shows (for each of three alternative analysis samples)

multilevel mixed-effects logistic model results for ever having had such a disciplinary action; the

second shows the corresponding AFT frailty models for the timing of the first suspension or

expulsion. Normalized versions of all continuous variables are employed.

Although all other gender and ethnic groups tended toward a lower likelihood of experiencing

disciplinary actions at school than African-American males, only Latinas had significantly longer

(14–32 percent) spells before such actions occurred. Youth whose caregivers reported depressive

symptomatology at the time of survey exhibited 117–171 percent–higher odds of being

suspended or expelled and 13–18 percent–shorter spells before such disciplinary actions were

taken. Household mobility was associated with slightly longer spells before these actions took

place.

As with many other educational outcomes investigated, neighborhood rates of violent and

property crime proved strongly predictive (albeit in opposite directions). All else being equal,

youth being raised in a neighborhood with a one-standard-deviation-higher violent crime rate

would be predicted to have 57–91 percent–lower odds of being suspended or expelled. In

contrast, a child being raised in a neighborhood that had a one-standard-deviation-higher

property crime rate would have 112 percent or higher odds of suspension or expulsion.98

Other findings were related to the potential impacts of the neighborhood physical and social

environments. A one-standard-deviation-higher percentage of the neighborhood housing stock

built prior to 1940 was associated with 6–10 percent–shorter spells prior to being suspended or

expelled from school. Greater neighborhood social capital was associated with an increased

likelihood of being suspended or expelled (a 47–55 percent differential for every standard-

deviation difference).

Repeating a Grade

Results for our models of repeating a year in school are presented in Exhibits VII-7 and VII-8.

The first shows (for each of three alternative analysis samples) multilevel mixed-effects logistic

model results for ever having to repeat a grade; the second shows the corresponding Cox

proportional hazards (robust standard error) models for the timing of this repetition. Normalized

versions of all continuous variables are employed. Characteristics of the youth, household, or

school were not consistently predictive of this outcome; only neighborhood safety characteristics

were.

Neighborhood rates of violent and property crime proved strongly predictive (again, in opposite

directions). All else being equal, youth being raised in a neighborhood that had a one-standarddeviation-higher violent crime rate would be predicted to have 83–100 percent–lower odds of repeating a grade and a 71–94 percent decrease in the hazards of such. In contrast, a child being

98 The coefficient for the “currently in DHA” sample we view as unreasonably large.

129

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133

VII. Educational Outcomes

raised in a neighborhood that had a one-standard-deviation-higher property crime rate would

have 161–419 percent–higher odds99 of repeating a grade and a 68–155 percent increase in the

hazards of such.

Nativity composition of neighborhood also proved predictive. A one-standard-deviationhigher percentage of foreign-born residents in the neighborhood was associated with 86–

98 percent–lower odds of repeating a grade.

Dropping Out of School Before Graduating

Results for our models of leaving school without a diploma are presented in Exhibits VII-9 and

VII-10. The first shows (for each of three alternative analysis samples) multilevel mixed-effects

logistic model results for dropping out of school; the second shows the corresponding Cox

proportional hazards (robust standard error) models for the timing of school exits. Normalized

versions of all continuous variables are employed.

Two household covariates proved predictive across two or more samples. Children whose parent

or caregiver reported depressive symptomatology at the time of our survey exhibited a 113–

141 percent–greater hazard of dropping out.100 Youth of immigrant parents or caregivers

exhibited 59–61 percent–lower hazards of dropping out.

Once again, the same pattern of contrary results for neighborhood crime types emerged. A school

child being raised in a neighborhood that had a one-standard-deviation-higher violent crime rate

would have 75–93 percent–lower odds of dropping out. In contrast, a child being raised in a

neighborhood that had a one-standard-deviation-higher property crime rate would have 226–

472 percent–higher odds of dropping out and an increase in the hazards of such of 54–

73 percent.

Two additional neighborhood variables related to the physical environment and social status

proved predictive. A one-standard-deviation-higher percentage of the neighborhood housing

stock built prior to 1940 was associated with 114–206 percent–higher odds of dropping out. A

one-standard-deviation-higher percentage of the neighborhood occupational prestige score was

associated with 84–92 percent–lower odds of a resident youth dropping out and a 52–59 percent

decrease in the hazards of such.

Gender and Ethnic Differences in Neighborhood Effects

Estimated parameters for our educational outcome models stratified by gender and ethnicity are

presented in Appendix C. As is the case in all our discussions of stratified results, we employ the

“ever in DHA” sample results and normalized continuous covariates. Here again we find substantial

heterogeneity in apparent neighborhood effects on educational outcomes. Violent and property

crime rates proved the exception, exhibiting statistically significant (though opposite-in-direction) relationships in the aggregate sample that were replicated consistently across strata for multiple

educational outcomes, though this pattern was least consistent for the African-American stratum.

99 The coefficient for the “currently in DHA” sample we view as unreasonably large.

100 We recognize the potential of bidirectional causality here.

134

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VII. Educational Outcomes

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136

VII. Educational Outcomes

Academically Advanced Classes and Gifted Programs

Only two of the neighborhood indicators that were statistically significant, strong predictors in

the aggregate samples described above proved to be so in more than two of the gender-ethnic

strata; the others in only one stratum. A one-standard-deviation-higher neighborhood:

 Percentage of dwellings built before 1940 was associated with a 16 percent–shorter spell

before placement in advanced classes or gifted programs for Latino youth; an 11 percent–

shorter spell for female youth; and 13 percent–shorter spell and 117 percent–higher odds

for African-American youth.

 Occupational prestige was associated with 76 percent–lower odds of and 33 percent–

longer spells before placement for African-American youth and 72 percent–lower odds

for Latino youth.

 Social capital score was associated with a 190 percent–greater odds of placement and a

15 percent–shorter spell prior to placement for African-American youth only.

 Violent crime rate was associated with 91 percent–lower odds of placement and a

50 percent–longer spell before such placement for females only.

Additional neighborhood indicators emerged as powerful predictors of placement in advanced

courses or gifted programs for certain strata only and not the entire sample. We found that a one-

standard-deviation-higher neighborhood:

 Property crime rate was associated with 115 percent–higher odds of placement for Latino

youth.

 Percentage of foreign-born residents was associated with 71 percent–lower odds and

31 percent–longer spells for African-American youth.

 Percentage of African-American residents was associated with 60 percent–lower odds for

male youth and 22 percent increases in spell prior to such placement; corresponding

figures for African-American youth were 67 percent and 24 percent, respectively.

Special Education Placement

The violent crime and property crime rates that were strongly associated with special education

placement in the aggregate samples maintained their power only in the female and Latino strata.

Female youth growing up in a neighborhood that had a one-standard-deviation-higher rate of

violent crime exhibited 90 percent–lower odds of school personnel placing them into special

education classes and an 89 percent increase in the spell before this occurs. The comparable

estimates for Latinos were 89 percent and 98 percent lower, respectively. A similar variation in

the case of property crime rate evinced a 569 percent increase in the odds of special education

placement and a 40 percent decrease in the duration of the spell before such placement occurred

for females; for Latinos, there was a 123 percent increase in the odds of placement.

One other neighborhood context variable that was robustly predictive of special education

placement across aggregate samples proved so in two strata. All else being equal, youth in a

neighborhood that had a one-standard-deviation-higher foreign-born resident percentage had

61 percent– and 67 percent–lower odds of placement for Latino and female youth, respectively.

The inverse aggregate relationship between percentage of neighbors moving in during the prior

137

VII. Educational Outcomes

year and special education placement was significant for the Latino stratum only: each standard-

deviation increase in the percentages of neighbors moving in during the prior year was associated

with a 22 percent–longer spell prior to placement in special education.

Intriguing neighborhood indicators emerged as powerful predictors of participation in special

education programs for certain strata only. We found that a one-standard-deviation-higher

neighborhood:

 Percentage of Latino residents exhibited 293 percent–higher odds of being placed into

special education classes if they were Latinos themselves.

 Percentage of dwellings built before 1940 exhibited 75 percent–lower odds of assignment

and 43 percent–longer spells before assignment if they were females.

 Social vulnerability score exhibited 274 percent–greater odds of special education

placement and a 38 percent reduction in time to placement if they were Latino.

 Proportions of negative peers exhibited 198 percent–higher odds of assignment and

28 percent–shorter spells before assignment if they were African American.

Suspensions and Expulsions

Neighborhood violent crime rates were equally powerful predictors in both Latino and African-

American strata, an exceptional pattern across the educational outcomes we investigated. Youth

raised in a neighborhood that had a one-standard-deviation-higher violent crime rate would have

64 percent– and 67 percent–lower odds of suspension or expulsion, respectively, in these two

ethnic strata. A modestly larger relationship (71 percent–lower odds and 21 percent–longer

spells) was observed for males, but violent crime was not a statistically significant predictor in

the female stratum. Property crime rates only proved most strongly predictive in the male and

Latino strata, with a one-standard-deviation-higher property crime rate being associated with 203

(162) percent higher odds of male (Latino) youth facing such disciplinary actions. Higher

property crime rates also shortened the spell prior to suspension or expulsion for male youth.

Two other predictors that were statistically significant in the aggregate samples proved so in only

one stratum. The percentage of housing built before 1940 was predictive of suspensions and

expulsions in the African-American strata. African-American youth would have 105 percent–

higher odds of and 10 percent–shorter spells prior to school suspension or expulsion living in a

neighborhood that had a standard-deviation-higher percentage of housing built before 1940. The

positive relationship between social capital and suspensions and expulsions was manifested only

in the female stratum. Female youth being raised in a neighborhood with a one-standarddeviation-higher social capital index would have 77 percent–higher odds of being suspended or expelled from school.

The only neighborhood indicator predictive of suspensions and expulsions of a stratum that was

not significant in the aggregate models was the presence of negative peers. If caregivers assessed

the neighborhood as having many youth who got into trouble, their female youth exhibited a

15 percent–shorter period until they were suspended or expelled from school.

138

VII. Educational Outcomes

Repeating a Grade

Neighborhood crime rates maintained their notable (and opposite direction) predictive power

across many strata, with violent (property) crime negatively (positively) associated with grade

repetition. Violent crime rates were equally powerful predictors in all but the African-American

strata. Property crime rates held the greatest predictive power for female youth but were also

significant in the male and Latino strata. A school child being raised in a neighborhood that had a

one-standard-deviation-higher property crime rate would have a 309 percent–, 102 percent–, and

110 percent–higher odds of repeating a grade if the youth were female, male, or Latino,

respectively. The only other predictive contextual variable in the aggregate samples was the

nativity composition. It remained statistically significant only in the Latino stratum, predicting

62 percent–lower odds of repeating for a standard-deviation-higher percentage of foreign-born

residents.

Models of repeating a grade revealed four other neighborhood context predictors for particular

strata when they were disaggregated. Youth residing in neighborhoods that had a one-standarddeviation-higher:

 Social vulnerability score had 651 percent– and 448 percent–higher odds of repeating a

grade if they were male or Latino, respectively; males also had their hazard of such rise

by 495 percent; Latinos had their hazard of such rise by 348 percent.

 Percentage Latino residents reduced the hazard of repeating a grade by 82 percent for

African Americans.

 Percentage African-Americans residents increased the hazard of repeating a grade by

142 percent for Latinos.

Dropping Out of School Before Graduating

As in the case of repeating a grade, the same pattern of contrary and statistically significant

results for neighborhood crime types consistently emerged across male, female, and Latino

strata. Again, the variation in reduction of odds of dropping out associated with a standard-

deviation increase in violent crime rate across all four strata is small: 70–80 percent. In contrast, the variation in the higher odds associated with living in a neighborhood that had a one-standarddeviation-higher property crime rate are similar for males and Latinos (340 percent and 402 percent, respectively) but less for females (162 percent). This is the opposite of the pattern in the repeated grade models, where the impact of property crime was largest for females.

Two additional neighborhood context variables related to housing stock age and resident status

proved predictive in both the aggregate samples and in several strata. A one-standard-deviationhigher percentage of pre-1940 housing stock was predictive of 349 percent–, 202 percent–, and 132 percent–higher odds of dropping out for females, African Americans, and Latinos,

respectively. A one-standard-deviation-higher neighborhood occupational prestige score was

associated with 87 percent–, 94 percent–, 83 percent–, and 93 percent–lower odds of a resident

male, female, African American, and Latino dropping out, respectively; the associated decreases

in hazards is also similar across these strata (73–82 percent).

139

VII. Educational Outcomes

Several other neighborhood indicators proved to be strong predictors of dropping out in

particular strata. Residing in a neighborhood that had caregiver-assessed negative peers was

associated with a 278 percent–greater hazard of female youth dropping out. Living in a

neighborhood that had a one-standard-deviation-higher:

 Percentage of Latino residents was associated with 91 (86) percent–lower odds and 71

(79) percent lower hazards of dropping out for males (Latinos).

 Social problems index was associated with 192 percent–higher odds and 89 percent–

greater hazard of dropping out for males.

 Percentage of foreign-born residents was associated with 85 percent–lower odds and

64 percent–lower hazard of dropping out for females.

Nonlinear Neighborhood Effects

Results for our nonlinear investigations employing spline regressions are presented in

Appendix D. A few noteworthy nonlinear relationships were uncovered that were robust across

models. We discuss these results further in the following section.

Violent crime rates consistently demonstrated a negative linear relationship with all educational

outcomes in our aggregate models. Our nonlinear explorations revealed that these relationships

are more accurately described (most clearly for special education, repeating grade, and dropping

out outcomes) as diminishing negative marginal effects or perhaps an asymmetric, V-shaped

pattern of marginal impacts. In the case of special education assignment, a standard-deviation

increase in violent crime rates in a neighborhood remaining below the mean of such rates would

be expected to reduce the odds by 86 percent, but such a change in a neighborhood remaining

above the mean violent crime rate would be expected to increase the odds by 3 percent.101 In the case of repeating a grade, the corresponding marginal figures for the two segments of the

relationship were an 88 percent reduction for below the mean and a 15 percent increase for above

the mean. In the case of dropping out, the corresponding marginal figures for the two segments

of the relationship were even more dramatic: a 97 percent reduction for below the mean and a

28 percent increase for above the mean.102

There is also a hint that the opposite nonlinearity (that is, diminishing marginal positive effects) may characterize the generally positive relationship between property crime rates and

educational outcomes exhibited across many outcomes in the aggregate samples. In the case of

special education placement, a standard-deviation increase in property crime rates in a

neighborhood remaining below the mean of such rates would be expected to increase the odds

and the hazard of placement by more than 20 times over an equivalent change in a neighborhood

remaining above the mean property crime rate.

101 These estimates are based on xtmelogit results for the “ever in DHA” sample, though, are consistent with those from the logit model. The effect for the above-mean range is computed by adding the estimated coefficients (not odds ratios), and then exponentiating the value to return the “net” odds ratio for the spline.

102 This nonlinear relationship was strongly echoed by the Cox proportional hazards model results.

140

VII. Educational Outcomes

Three predictors—the foreign-born population, pre-1940 housing stock composition, and social

problems score of a neighborhood—did not appear to be related to the odds of repeating a grade

in the linear model results. In the spline models, however, they exhibited dramatic, inverted

V-shaped relationships with these odds that apparently were masked in the linear model. A

standard-deviation increase in percentage of foreign-born residents in a neighborhood remaining

below the mean of such percentages would be expected to increase the odds of repeating a grade

by 737 percent, but such a change in a neighborhood remaining above the mean percentage of

foreign-born residents would be expected to decrease the odds by 89 percent. The corresponding estimates for the percentage of pre-1940 housing stock were even larger: 1,770 percent increase

and 49 percent decrease, respectively. A standard-deviation increase in the social problems index

in a neighborhood remaining below the mean of such would be expected to increase the hazard

of repeating a grade by 219 percent, but such a change in a neighborhood remaining above the

mean percentage of foreign-born residents would be expected to decrease the hazard by

29 percent. The foreign-born population exhibited a similar nonlinear relationship with being

suspended or expelled. A standard-deviation increase in percentage of foreign-born residents in a

neighborhood remaining below the mean of such percentages would be expected to increase the

odds of suspension by 412 percent, but such a change in a neighborhood remaining above the

mean percentage of foreign-born residents would be expected to decrease the odds by

77 percent.103

The neighborhood percentage of Latino residents did not appear to be related to the odds of

suspension or expulsion in the linear model results. It exhibited a V-shaped relationship in the

spline models, however. A standard-deviation increase in the percentage of Latino residents in a

neighborhood remaining below the mean of such percentages would be expected to decrease the

odds of suspension or expulsion by 73 percent, but such a change in a neighborhood remaining

above the mean percentage of Latino residents would be expected to increase the odds by

119 percent.

Finally, one nonlinear relationship indicated the presence of minimum threshold before any

strong relationship emerged. In the cases of repeating a grade and dropping out, the respiratory

hazards index appeared to have no relationship when it remained below average; only when the

respiratory hazards index assumed values above its mean was there any predictive power. A

standard-deviation increase in this index occurring in a neighborhood remaining above the mean

of this index would be expected to increase the odds (hazard) of repeating a grade by a

substantial 357 (238) percent and the odds (hazard) of dropping out by 478 (363) percent.

Discussion

The results reported above clearly show that several aspects of the neighborhood safety,

physical, social, and demographic context are statistically and substantively important predictors

of many outcomes related to educational performance. Below, we organize the discussion around

thematic categories of neighborhood context.

103 The aforementioned patterns for foreign-born and pre-1940 characteristics were echoed in the Cox model results, as well.

141

VII. Educational Outcomes

Neighborhood Safety

The most dramatic and consistent finding was that neighborhood violent and property crime rates

apparently had opposite effects on educational outcomes. Violent and property crime rates

exhibited statistically significant and substantively large relationships in the aggregate sample

that were replicated consistently across at least two strata for multiple educational outcomes,

though this pattern was least consistent for the African-American stratum. As might be expected,

youth living in more violent neighborhoods had a lower likelihood of taking advanced or gifted

classes. Unexpectedly, places that had more violent crime were associated with reduced

likelihood of being placed in special education, being suspended or expelled, repeating a grade,

or dropping out before receiving a diploma. Precisely the opposite relationships were manifested

for property crime rates (except in the case of advanced or gifted classes). For both types of

crime, relationships exhibited with:

 Special education outcomes were strongest for female and Latino youth.

 Suspensions and expulsions were strongest for male and Latino youth.

 Grade repetition and dropping out were strongest for male, female, and Latino youth.

The unexpected direct relationships between violent crime rates and multiple positive

educational performance measures suggest what might be termed a “compensatory effect.” As

we introduced in Chapters V and VI, one possible explanation may be that fear of violence

induces more caregiver or self-imposed restrictions on youths’ movements outside of home,

immediate environs, and school (including not being employed, as we explore in the next

chapter). Analogous sorts of reactions may occur in schools located in more violent

neighborhoods that, though directly aimed at protecting children from violence, indirectly yield

proeducation spillovers. The ironic consequence of these compensatory personal and institutional

behaviors may be superior school performance and reduced chances of having disciplinary

problems. This relationship may be mediated by risky behaviors that yield negative

consequences for educational performance, fully consistent with the findings reported in

Chapter VI. The nonlinear relationships evinced in the cases of special education assignment,

grade repetition, and dropping out clearly suggest, however, that there are limits to the efficacy

of these defensive, compensatory responses to neighborhood violence by parents, students, and

schools. In neighborhoods with above-average violent crime rates the associations between

violent crime and these three outcomes are strongly negative, suggesting that past some threshold

the corrosive educational effects of neighborhood violent crime can no longer be held in check.

These negative effects could represent manifestations of psychological and physical reactions

associated with intensified stress related to potential and actual exposure to violence (as we

explored in Chapter V) or the upsurge in likelihood of risky behaviors (as we observed in

Chapter VI).

Several underlying causal pathways are plausible for understanding the observed inverse

relationships between property crime rates and multiple measures of educational performance. In

neighborhoods with more property crime there may be more incentives to engage in risky

behaviors (as we demonstrated in Chapter VI) and for older youth to seek employment (as we

142

VII. Educational Outcomes

document in the next chapter), which may work to the detriment of their school performance.104

In such neighborhoods, there also will be higher incidences of youth witnessing and being

victimized by violence (as we demonstrated in Chapter V), which creates distractions at least and

psychological trauma at worst that can impede academic achievement. Finally, in Chapter IV we

demonstrated that property crime is strongly predictive of higher risks of asthma,

neurodevelopmental disorders, internalizing behavior, and use of behavioral health services, all

of which could interfere with children’s academic performance.

Why the aforementioned relationships are distinctly weaker for African-American children

remains a subject for further explorations, especially because the same finding emerged in

Chapter VI regarding risky behaviors and neighborhood safety. It may be the case that Denver

African-American families have more success in defending their children against the potential

harms of neighborhood crime, either by distinctive caregiver monitoring strategies or by

arranging for more of their children’s time to be spent outside of more crime-ridden

neighborhoods (such as enrolling in schools outside of the neighborhood, which is permitted in

Denver).105

Neighborhood Housing Stock and Environment

Youth living in older Denver neighborhoods that have higher percentages of pre-1040–vintage

dwellings exhibited several inferior educational outcomes: much higher rates of suspensions and

expulsions (especially if they were African American), repeating a grade (though with strong

diminishing marginal impacts), and dropping out (especially if they were not males).106 Our

explanation is that older Denver neighborhoods serve as a proxy for traditional street patterns,

mixed land uses, and associated routine activity spaces, as we have amplified in prior chapters.

We have previously found that growing up in such neighborhoods is associated with greater

exposure to violence (Chapter V) and higher likelihoods of risky behaviors (Chapter VI), both of

which may generate negative impacts on educational performance.107

Neighborhood Social Status

A neighborhood’s superior occupational status often proved predictive of multiple positive

educational outcomes. Residing in a higher-prestige neighborhood was associated with reduced

likelihoods of special education placement (especially for African Americans and females),

repeating a grade (only for females), dropping out (all strata), and perhaps being suspended or

104 Neighborhoods that have more property crime can create incentives to work for at least two reasons. There may be higher incidences of teens being victimized by property crimes, which creates a stronger need to replace stolen or damaged goods. There also may be increased status competition from perpetrators of property crime involving the ostentatious display of personal consumption items. In addition, there may be a correlation between property crime and greater amounts of nonresidential land uses, which may serve as a proxy for locally available employment opportunities (which we cannot measure in our models).

105 This distinction is not the result of insufficient variation in violent crime rates across the African-American stratum; on the contrary, the variation is larger than for the Latino stratum.

106 The exception to this pattern is that older housing was associated with a slightly shorter period before placement into advanced or gifted classes and female students’ lower likelihood of being placed in special education classes.

107 We do not interpret these results as implying that older housing offers a less healthy environment for youth cognitive and behavioral development, given that our findings in Chapter IV revealed no relationships between this variable and asthma, developmental disorders, or mental health outcomes.

143

VII. Educational Outcomes

expelled (at least for those currently living in DHA). These results have intuitive appeal from the

perspective of local networks, norms, and role models related to these educational behaviors.

Neighborhoods that surround their youth with higher prestige workers likely expose them to

norms and role models that encourage educational success and perhaps provide access to

networks of information about postsecondary school opportunities, prerequisites, and payoffs.108

Mediated causal pathways are also possible. We demonstrated in Chapters IV, V, and VI that

higher status neighborhoods strongly predicted better child health outcomes, less exposure to

violence, and fewer risky behaviors, any or all of which could provide clear educational payoffs

for children and youth.

The one unexpected result was that neighborhood occupational prestige was inversely related to

being in advanced or gifted classes for African-American and Latino youth alike as well as the

full sample. This might be attributed to our sample youth from such prestigious neighborhoods

attending more competitive schools with a higher achieving student body, thus reducing their

chances of being selected for the advanced programs or being designated as “gifted” relatively,

even if their performance was enhanced in absolute terms. Alternatively, schools in higher

prestige Denver neighborhoods may hold stereotypes about Latino and African-American

students being less talented.

Neighborhood Ethnic and Nativity Composition

We identified several important, direct relationships between the foreign-born composition of the

neighborhood’s population and positive educational outcomes. For the full sample, higher

percentages of foreign-born residents were associated with several positive outcomes: lower

odds of either being placed in special education classes (especially for female and Latino youth)

or repeating a grade of school (especially for Latinos); for female youth, this variable also

predicted significantly lower odds of dropping out. In the cases of suspensions or expulsions and

repeating grades, nonlinear results indicate the apparent marginal benefits are strongest when the

immigrant population constitutes a larger share of the neighborhood. These results are consistent

with the notion that immigrant populations can play powerful normative, role-modeling, and

behavioral monitoring functions that are proeducational when they become a culturally

significant force in the neighborhood. The fact that the majority of such immigrants in Denver

originated from Mexico can also explain why these educational results typically appear strongest

for Latino and female youth, who might be expected to be most influenced by the

aforementioned collective socialization and monitoring forces. It is worth noting that the

apparent positive educational externalities stemming from foreign-born neighbors were observed

even after controlling for the immigrant status of the sample youths’ parents (which only rarely

proved a statistically significant covariate, however). The pathway(s) from immigrant neighbors

to superior educational outcomes may also be mediated, as in the case of occupational prestige.

We demonstrated in Chapter VI that neighborhoods that have larger immigrant concentrations

strongly predicted fewer risky behaviors, which could provide clear educational payoffs for

children and youth. One less felicitous outcome—less likelihood of African-American youth

taking advanced or gifted classes—was associated with higher percentages of foreign-born

neighbors. This may be the result of schools in immigrant-dense areas diverting limited

108 This mimics results from Gautreaux, which showed how higher economic expectations in advantaged neighborhoods positively influenced lower income teen in-movers (Rosenbaum, DeLuca, and Tuck, 2005).

144

VII. Educational Outcomes

curricular resources away from advanced courses to more basic, remedial, and English as a

Second Language classes.

Although never statistically significant consistently across the full analysis samples, the ethnic

composition of the neighborhood revealed some intriguing and asymmetric relationships for

Latino and African-American youth. Higher percentages of Latino residents were associated with

both favorable and unfavorable educational consequences for Latino youth: reduced odds of

dropping out and (at least up to a point) being suspended or expelled109 and increased odds of

special education placement. These findings imply that Latino community norms and behavioral

monitoring may operate in a similar fashion as those described above for immigrants to create

proeducation outcomes. These positive externalities are understandably stronger for Latino

youth, who are most immersed in and vulnerable to such neighborhood collective processes.

Because a majority of foreign-born residents of Denver are primarily of Mexican origin, this

raises the prospect of substantial proeducation effects—especially for Latino but also for

African-American youth—in neighborhoods inhabited by greater shares of Latino immigrants,

all else being equal. This effect may be mediated through risky behaviors, consistent with our

findings in Chapter V. The result related to special education may be a function of language and

cultural competency. Latino youth more embedded within Latino communities may develop

fewer skills in mainstream English and culture, which may penalize them in the perceptions of

school personnel making decisions about who is assigned to gifted and advanced programs.

Different results emerged for the African-American composition of the neighborhood. Stratified

analyses revealed that higher percentages of African-American residents were associated with

lower odds of taking advanced or gifted classes for resident African-American and male youth

and higher odds of repeating a grade for resident Latino youth. We cannot forward a strong case

for why these results emerged. It may be related to different collective norms and behavioral

monitoring strategies in the African-American communities in Denver. It may also be explained

by idiosyncrasies in the schools in such areas. For example, schools in heavily African-American

neighborhood may be less likely to offer advanced classes. Such schools may also be less likely

to provide bilingual or English as a Second Language classes, thereby increasing the likelihood

of grade repetition by Latino students who are not native English speakers.

Neighborhood Peers

A final interesting result emerged related to our measure of bad peer influences in the

neighborhood: caregivers who perceived that many youth in their neighborhoods “get into

trouble.” Although this neighborhood indicator never proved to be a consistent and significant

predictor across the three analysis samples, it did emerge as important in some strata for some

outcomes. Female youth living in neighborhoods that had “negative peers” were much more

likely to be suspended or expelled and to drop out before graduating; African-American youth in

such places were more likely placed in special education classes. These results offer some

tantalizing indications that peer effects may indeed be a vital mechanism for creating a causal

link between neighborhood context and individual educational outcomes.

109 The last relationship was nonlinear, however, and suggested that high concentrations of Latino residents may have harmful behavioral consequences for students.

145

VII. Educational Outcomes

Geographic Selection Bias Revisited

Recall in Chapter III that we argued that the estimated value of the “true” neighborhood effect

likely lies within the range of estimates garnered from our various analysis samples, which

consider different potential types of geographic selection postinitial assignment by DHA. In

many cases of educational outcomes reported in Exhibits VII-1 to VII-10, the estimated

neighborhood indicator parameters were substantially different among the “ever in DHA,”

“currently in DHA,” and “mostly in DHA” samples, so unfortunately, our likely “true” estimate

was not narrowly circumscribed. Fortunately, however, there was no pattern of one particular

sample consistently producing the largest or smallest set of parameter estimates for the

neighborhood indicators. Thus, we are less inclined to worry here about distortions caused by

postassignment geographic selection. Instead, we suspect that the wide variance of point

estimates was the result of the sensitivity of our maximum-likelihood estimators when key

multivariate combinations exhibited small cell counts in particular samples.

Conclusion

Many aspects of neighborhood context are statistically and substantively important predictors of

a battery of school-related outcomes, though sometimes in unexpected ways. Aspects of the

neighborhood’s violent and property crime rates, physical environment, social status, ethnic mix,

and nativity mix exhibit substantial predictive power in both models predicting the odds of

educational outcomes ever occurring and the duration before such outcomes occur. Educational

outcomes generally are more favorable in neighborhoods that have higher occupational prestige

and percentages of foreign-born and Latino residents and lower rates of property crime and pre1940–vintage dwellings. Outcomes generally are better in neighborhoods that have moderate rates of violent crime than with none but grow progressively worse as violent crime rates exceed

average levels. The magnitudes of most of these apparent influences typically appear to be

contingent on the gender and ethnicity of the youth. Nonlinear neighborhood effects do not

appear to be the norm, though violent crime consistently manifests a V-shaped relationship with

the odds of educational outcomes. The few nonlinear patterns observed are inconsistent across

indicators, although respiratory risk exhibits a theoretically defensible minimum threshold before

negative outcomes are manifested.

146

VIII. Labor Market Outcomes

VIII. LABOR MARKET OUTCOMES

Introduction

In this chapter, we consider a variety of labor market–related outcomes for two older groups of

low-income, minority youth in our Denver Child Study: teens 14–17 years of age and young

adults 18–33 years of age. For the teen group, we analyze whether they were gainfully employed

before reaching adulthood, whether they were employed more than 20 hours per week, and a

continuous measure of how many hours they were employed weekly during this period. For the

young adult group, we analyze whether since turning 18 years of age they primarily worked full

time, attended school after high school, and neither worked nor attended postsecondary school.

For both groups, we find evidence of strong neighborhood effects, though sometimes with

unexpected aspects of context operating in surprising ways.

Teen Labor Market Analysis

The subjects in our teen analysis range from 14 to 33 years of age at the time of survey (average

age: 20), though here we examine only their labor outcomes when they were younger than

18 years of age. In this sample, we have a slight overrepresentation of Latino males (32 percent)

compared with the other gender-ethnic groups: Latina females comprise 26 percent, African-

American females 24 percent, and African-American males 18 percent.110 We analyze three teen

labor market outcomes for the period prior to turning 18 years of age: (1) whether they were ever

employed, (2) whether they worked an average of 20 or more hours per week when employed,111

and (3) the number of hours worked weekly on average (including zero). We ascertain these

outcomes on the basis of the Denver Child Study caregiver survey respondent’s responses to the questions, “Were any of your children employed before age 18? If yes, on average how many

hours per week did __ [youth] __work before age 18?” Forty-four percent of our sampled teens

worked prior to 18 years of age, and 27 percent worked more than 20 hours per week during

their high school years, on average. Sample teens worked 8.8 hours weekly, averaged across

workers and nonworkers.

Because the first two teen labor outcomes are dichotomous measures, we employ logit models

with clustered robust standard errors for parameter estimation.112 The last measure for weekly

hours worked is highly positively skewed, because 56 percent of the sample worked zero hours.

We therefore employ the well-known Tobit estimation procedure using clustered standard errors

110 These statistics apply to the “ever in DHA” sample but are comparable in the “majority of high school in DHA sample,” as well. Complete descriptive statistics for all variables related to these samples are available from the authors.

111 We model this dichotomous outcome in addition to a continuous measure of work hours because of the sizable scholarly literature indicating that 20 hours of work weekly is a threshold that separates high schoolers based on a variety of academic performance measures, with those working more intensively being associated with poorer performance (D’Amico, 1984; Steinberg and Dornbusch, 1991;Steinberg, Fegley, and Dornbusch, 1993; Warren, 2002; Warren, LePore, and Mare, 2000).

112 In one or more of our strata, the multilevel mixed-effects logistic models failed to converge, so for consistency, we employ logits throughout.

147

VIII. Labor Market Outcomes

(Tobin, 1958).113 As with outcomes reported in earlier chapters, we estimate these models for the

previously defined “ever in DHA,” “currently in DHA,” and “mostly in DHA” samples to assess

the robustness of our results.

Both logistic and Tobit models use the same core covariates common to all our analyses. Here,

we measure “contemporaneous” family and neighborhood context as the average experienced

from 14 years of age through the year in which a teen begins working or through 17 years of age

if the teen never worked (or age at the time of survey if before 17 years of age).114 Thus, these

analyses can be interpreted as investigating the degree to which employment outcomes evinced

before turning 18 years of age have any relationship with the average neighborhood conditions to

which they were exposed during high school up to the point where they started working. Our

labor market outcome analyses add two more covariates. The age of the caregiver when she or he

gave birth or fathered the focal child is included as a control of the caregiver’s experience and

sophistication in coping with life challenges while raising a child, potentially affecting the

child’s life trajectory. We also add the annual growth rate in U.S. gross domestic product (GDP)

during the year in which the youth first worked (or time of survey or 17 years of age, whichever

is earlier). This is intended as a control for the strength of the macro economy and thus the job

market prospects confronting the youth during high school.115 We employ the same set of

neighborhood indicators as before, with the exception of omitting the respiratory risk and

neurological risk indicators. Unfortunately, these indicators were not available during the period

when many of our sample youth were in high school, so their inclusion in the model forced an

unacceptable diminution of sample size.

Young Adult Labor Market Analysis

The young adults in our analysis range in age from 18 to 33 years of age at the time of survey

(average age: 22) and are almost evenly divided by gender and ethnicity: Latinas(os) comprise

24 (29) percent and African-American females (males) 24 (23) percent, respectively.116 For this

group, we define three labor market–related outcomes. The first outcome is “primarily working

full time,” ascertained on the basis of the Denver Child Study caregiver survey respondent’s first (mutually exclusive) categorical response to the question, “Since turning 18, has _[youth]_

primarily been working full time, working part time, not working but attending school, or neither working nor attending school?” We define the second outcome as “not being employed (either

part or full time) or enrolled in postsecondary education or training,” assessed by the same question above. The third outcome is “attended school past grade 12,” which we assess by a

caregiver response to the question, “How many years of schooling did [youth] complete?” being

113 The coefficient of a covariate in a Tobit model should be interpreted as the net effect of (1) the change in the dependent variable for those with positive values, weighted by the probability of having such values; and (2) the change in the probability of having positive values, weighted by the expected value of the dependent variable when it is positive (McDonald and Moffit, 1980).

114 If the teen ever worked, the parent or caregiver was asked the teen’s age at this point. We did not use hazard models here to probe the timing of first work because of the narrow span of feasible years involved.

115 Recall that localized labor market prospects are implicit in our neighborhood social vulnerability score, which includes within it census tract unemployment rate. Unfortunately, tract-specific information on the location of jobs was unavailable for most of the years relevant for our study sample.

116 These statistics apply to the “ever in DHA” sample but are comparable in the “majority of high school in DHA sample,” as well. Complete descriptive statistics for all variables related to these samples are available from the authors.

148

VIII. Labor Market Outcomes

greater than 12 years. Note that the first two outcomes are mutually exclusive responses to the

same question, but the third is not because it is based on a different question.117 In our analysis

sample, 42 percent primarily worked full time, 13 percent completed at least a year of

postsecondary education, and 17 percent neither primarily worked nor attended postsecondary

school as young adults.

Because we have no information about the residential locations of our sample young adults after

18 years of age, we must employ a different means of operationalizing “contemporaneous

contexts” compared with the labor outcome models for teens or any of the other child outcomes

considered in previous chapters of this report. Here, we compute the average contexts

experienced by the young adult during 14-18 years of age, both circumstances in their families

and neighborhood contexts. So, instead of measuring context for a given year of onset, we use

the average context for an entire developmental stage. Thus, these analyses can be interpreted as

investigating the degree to which average neighborhood conditions to which youth were exposed

during high school have any relationship with the likelihoods of their employment and education

outcomes evinced as young adults.

All of our young adult labor market outcomes are dichotomous, so we employ multilevel mixed-

effects logistic models (or logits when the mixed-effects models did not converge) as our

primary analytical procedure. These models use the same core covariates common to all our

analyses but add the same two covariates as above: caregiver age at time of focal child’s birth

and annual growth rate in U.S. GDP during the year in which the youth turned 18 years of age.

We employ the same set of neighborhood indicators as before, again omitting the respiratory risk

and neurological risk indicators because of sample size considerations, as explained above.

The nature of this age group also requires us to modify slightly the specification of the various

analysis samples that we have previously compared to test the robustness of our findings. Here,

we will replicate our analyses using two samples118 of young adults that we label “ever in DHA”

and “mostly in DHA.” Both samples required (1) family quasi-random assignment to Denver,

Colorado, Housing Authority (DHA) housing before the youth reached 14 years of age and (2)

covariates observed for the majority of years during 14 to 18 years of age. The “mostly in DHA”

category includes the prior criteria plus the youth spent the majority of 14–18 years of age living in DHA housing. Most of the contextual exposure this latter sample had accumulated as

adolescents involved the randomly assigned neighborhood; this is not necessarily true in the

former sample, because it includes some families who selected out of the DHA-assigned location

before the child reached adolescence.

Estimated Neighborhood Effects on Labor Market Outcomes

As with other chapters, we first highlight the main findings without comment, reserving

interpretation and explanations for a final section of the chapter so we can be more holistic in our discussion.

117 A subject could have acquired some postsecondary education but still “primarily” worked full time.

118 Unlike prior analyses, we cannot specify a “currently in DHA” sample, because we do not know the residential location of young adults from our survey.

149

VIII. Labor Market Outcomes

Teens

Results for our models of teen labor market outcomes are presented in Exhibits VIII-1 to VIII-3.

The first two show logit model results for ever having been employed and being employed for

more than 20 hours weekly before turning 18 years of age, respectively; the third shows the

Tobit model results for average weekly hours worked during this period. All present

nondichotomous variables that are normalized to aid cross-variable comparability of coefficients.

As before, we consider only those results that are statistically significant in two or more of the

analysis samples.

The models generally reveal few consistently significant individual- or household-level

predictors. Younger teens clearly were less likely to work. Compared with those 18 years of age

and older at the time of survey, 15-year-olds had 90–93 percent–lower odds of ever working and

never were employed more than 20 hours per week; on average, they worked 25–27–fewer hours

weekly, depending on the analysis sample. Compared with those 18 years of age and older at the

time of survey, 16-year-olds had 65–71 percent–lower odds of ever working and on average

worked 10–12–fewer hours weekly. Latino males had 66–75 percent–lower odds of working

20 hours per week or more, compared with African-American males. Teens whose families

experienced a one-standard-deviation-higher stressor index had 38 percent–lower odds of being

employed and would be predicted to work 3–4 hours less, on average. Finally, as expected, a

one-standard-deviation-higher annual growth rate in GDP during a youth’s teen years was

associated with between 37 percent and 61 percent–higher odds of working 20 or more hours

weekly.

Neighborhood crime indicators proved statistically significant predictors, though in opposite

directions. Higher rates of property crime were associated with greater teen employment

prospects, but the opposite association was exhibited by violent crime rates. A one-standarddeviation-higher neighborhood property crime rate was associated with 78–109 percent–higher odds of being employed. A one-standard-deviation-higher neighborhood violent crime rate was

associated with 59–60 percent–lower odds of being employed 20 or more hours per week and (in

the “ever in DHA” sample) 5.5 fewer hours worked weekly.

Several other neighborhood context indicators proved consistently statistically significant

predictors of teen employment, though in surprising directions. Teens in a neighborhood that had

a one-standard-deviation-higher household turnover rate had 119 percent–higher odds of

working 20 or more hours weekly. Neighborhoods that had a one-standard-deviation-higher

percentage of pre-1940 housing stock were associated with 49–56 percent–higher odds of ever

working as a teen and about 4 more weekly hours of work. Teens experiencing a one-standarddeviation-higher neighborhood child abuse and neglect rate during their high school years would be predicted to work 4.7–5.3 more hours weekly, depending on the analysis sample.

150

VIII. Labor Market Outcomes

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VIII. Labor Market Outcomes

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* p

153

VIII. Labor Market Outcomes

Young Adults

Results for our multilevel logit (or logit) models of young adult labor market outcomes are

presented in Exhibits VIII-4 through VIII-6. All present nondichotomous variables that are

normalized to aid cross-variable comparability of coefficients. Consistent with our discussion of

other outcomes earlier in this report, below we will emphasize results that are robust across both

samples applicable to this age group.

As in the case of teen labor market outcomes, few individual- or household-level predictors

proved statistically significant across samples. Those who were one standard deviation older at

the time of the survey were substantially more likely (231–556 percent–higher odds, depending

on the sample) to have primarily worked full time and less likely (54–76 percent–lower odds) to

have primarily neither worked nor attended school since turning 18 years of age. Latino males

had 235–556 percent–higher odds than African-American males of primarily working full time,

all else being equal. If the young adult’s household had a one-standard-deviation-higher log of

income during high school, their odds of neither working full time nor attending school were

reduced by 61–75 percent.

Neighborhood nativity and ethnic composition proved predictive. Higher percentages of

neighborhood foreign-born populations were strongly associated with more felicitous young

adult outcomes, whereas the opposite was the case for percentages of Latino population. Young

adults who spent their high school years in a neighborhood that had a one-standard-deviationhigher percentage of foreign-born residents had, all else being equal, 169–458 percent–higher odds of primarily working full time and 64–85 percent–lower odds of neither working nor

attending school. In contrast, young adults who spent their high school years in a neighborhood

with one-standard-deviation-higher percentage of Latino residents have, all else being equal, 69–

82 percent–lower odds of primarily working full time and at least 3 times–higher119 odds of

neither working nor attending school.

Two other neighborhood context variables proved predictive but in unexpected ways. Our

composite indicator of neighborhood social vulnerability proved positively associated with

educational achievements as a young adult. Those who spent their high school years in a

neighborhood that had one-standard-deviation-higher social vulnerability score would be

predicted to have 291–529 percent–higher odds of obtaining some postsecondary schooling.

Higher rates of violent crime were associated with 81–92 percent–lower odds of neither working

nor attending school and (in the “ever in DHA” sample) 5 times–higher odds of obtaining

postsecondary education.

119 The point estimate for the “mostly in DHA” sample is unrealistically large, reflecting an idiosyncrasy in the maximum likelihood algorithm in both the logit and multilevel logit models.

154

VIII. Labor Market Outcomes

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157

VIII. Labor Market Outcomes

Gender and Ethnic Differences in Neighborhood Effects

Estimated parameters for our models stratified by gender and ethnicity are presented in

Appendix C. As is the case in all our prior discussions of stratified results, we employ the “ever

in DHA” sample results, with the same estimation procedure employed across all strata as in the

aggregate analyses to ensure comparability. Here again, we find substantial heterogeneity in

apparent neighborhood effects. Indeed, in rare cases—most notably the violent crime rate—

statistically significant relationships in the aggregate sample were replicated consistently across

more than one stratum.

Teens

In the case of teens 14–17 years of age, the aforementioned aggregate relationships between

neighborhood crime rates and teen labor market outcomes were strongly observed only in the

male and African-American strata. Violent crime rates proved to be a consistent, statistically

significant predictor for African Americans in all three teen outcomes. A standard-deviation

increase in violent crime was associated with 83 percent–lower odds of working, 74 percent–

lower odds of working 20 hours or more, and 9.7 fewer hours worked weekly, on average, for

African Americans. For males, the corresponding figures were almost as large: 61 percent–lower

odds of working 20 hours or more and 7.5 fewer hours worked weekly, on average. The positive

relationship between neighborhood property crime rates and ever working was strong only for

the African-American sample.

The associations between neighborhood pre-1940–vintage dwellings and teen labor force

outcomes that emerged as significant in the aggregate models were almost exclusively produced

from relationships emerging from the Latino stratum. For this group, a standard-deviation

increase in the percentage of older dwellings was associated with 109 percent–higher odds of

working and 7.1 more hours worked weekly, on average.

The aggregate positive relationship between neighborhood child abuse and neglect rates and

hours worked manifested itself for females (7.9 more hours per standard-deviation increase) and

African Americans (14.3 more hours). This indicator also predicted substantially higher odds of

African Americans ever working as teens and working more than 20 hours weekly.

Prestige emerged as a statistically and economically significant predictor of fewer teen hours

worked in two strata. A one-standard-deviation-higher neighborhood prestige score was

associated with 7.6 fewer hours worked weekly by Latino teens and 8.0 fewer hours by male

teens.

Finally, two additional aspects of context proved important for African-American teens. Access

to medical facilities was associated with 94 percent–higher odds of ever working before 18 years

of age, 254 percent–higher odds of working 20 hours or more, and 5.8 more hours worked

weekly. For males, such access was associated with 4.0 more hours worked. African-American

teens residing during high school in a neighborhood that had a one-standard-deviation-higher

social vulnerability score would be predicted to have 80 percent–lower odds of ever working as

teens and 10 fewer hours worked, on average.

158

VIII. Labor Market Outcomes

Young Adults

The percentage of foreign-born residents proved to be a robustly powerful predictor of all young

adult labor market outcomes, though in quite dissimilar magnitudes across several strata. Those

spending their high school years in a neighborhood that had a one-standard-deviation-higher

value of this indicator would be predicted to have their odds of primarily working full time

boosted by roughly a factor of three for males and Latinos, twice the magnitude measured for the

aggregate sample. The identical situation would be predicted to boost the odds of undertaking

postsecondary education by 910 percent and 1,270 percent for females and Latinos, respectively;

again, this is far larger than the magnitude for the aggregate sample. This variable’s association

with the odds of neither working nor undertaking postsecondary education was virtually identical

across the four strata.

The percentage of Latino residents also evinced several divergent patterns of significance across

strata. Those spending their high school years in a neighborhood that had a one-standarddeviation-higher value of this indicator would be predicted to have their odds of primarily working full time reduced by 78 percent and 94 percent for males and African Americans,

respectively. The identical situation would be predicted to reduce the odds of undertaking

postsecondary education by 94 percent for Latinos.

The aforementioned unexpected result for neighborhood social vulnerability and postsecondary

schooling was manifested only in the Latino stratum. For Latinos, high school exposure to

greater levels of neighborhood social vulnerability was associated with substantially higher

(about 1,200 percent) odds of undertaking postsecondary education. However, the violent crime

results were more broadly represented across strata. High school exposure to higher violent

crime rates was strongly associated with higher odds of postsecondary education for both

females and African Americans, and with lower odds of neither working nor attending school for

both females and males.

Other neighborhood indicators emerged as statistically and economically significant predictors of

young adult outcomes in a particular stratum, even though they were not so in the aggregate

sample. Residing during high school years in a neighborhood that had a one-standard-deviationhigher:

 Social problems scale was associated with 141 percent–higher odds of primarily working

full time after 18 years of age for females, 303 percent–higher odds of postsecondary

education for Latinos, and 75 percent–lower odds of neither working nor attending school

for Latinos.

 Property crime rate was associated with 92 percent–lower odds of females acquiring

postsecondary education.

 Child abuse and neglect rate was associated with 100 percent–lower odds of African

Americans acquiring postsecondary education and vastly greater odds of females neither

primarily working nor going to school.

 Social capital index was associated with 113 percent–higher odds of primarily working

full time after 18 years of age for Latinos and 770 percent–higher odds of postsecondary

education for African Americans.

159

VIII. Labor Market Outcomes

 Percentage of African-American residents was associated with 73 percent–lower odds of

primarily working full time after 18 years of age and substantially higher odds of neither

working nor attending school for African Americans only.

 Percentage of residents moving in during the past year was associated with vastly higher

odds of African Americans neither primarily working nor going to school.

 Occupational prestige score was associated with 98 percent–lower odds of females

neither primarily working nor going to school.

 Pre-1940 housing stock percentage was associated with a 101 percent–higher odds of

primarily working full time after 18 years of age for males and a 89 percent–lower odds

of acquiring some postsecondary education for African Americans.

Nonlinear Neighborhood Effects

Results for our nonlinear investigations employing spline regressions are presented in

Appendix D. Only one noteworthy nonlinear relationship was uncovered. Social capital

exhibited an inverted V-shaped relationship with teens’ likelihood of working more than

20 hours per week. This unusual relationship undoubtedly precluded it appearing statistically

significant in the core models. A standard-deviation increase in the social capital index in a

neighborhood remaining below the mean of this index would be expected to increase the odds of

employment over 20 hours by 81 percent, but such an increase in a neighborhood remaining

above the mean social capital index would be expected to decrease the odds by 39 percent. 120

This implies that neighborhoods with either extremely low or high social capital may impede

teens finding full-time employment; in the former case because there may be no local social

networks to pass on job-related information and in the latter case because most of the networks

are local and perhaps bereft of “bridging social capital.”

Discussion

The results reported above clearly show that several aspects of neighborhood context are

statistically and substantively important predictors of teen and young adult outcomes related to

work and postsecondary education, though not necessarily identically for all groups. Below, we

organize the discussion around thematic categories of neighborhood context.

Neighborhood Safety

In understanding impacts on labor market outcomes, our results indicate that “neighborhood

safety” should not be viewed as a homogeneous, unidimensional construct. On the contrary, we

have found that property crime and child abuse indicators on the one hand and violent crime and

social problem indicators on the other hand appear to generate distinctive consequences. For

low-income (especially male and African-American) teens, living in a neighborhood during high

school with higher violent crime rates seems to reduce their teen employment prospects, but

living in one with higher property crime or child abuse rates seems to have the opposite effect

(especially for African-American and female youth). For low-income (especially female) young

120 The effect for the above-mean range is computed by adding the estimated coefficients (not odds ratios), and then exponentiating the value to return the “net” odds ratio for the spline.

160

VIII. Labor Market Outcomes

adults, living during high school in a neighborhood that has higher violent crime rates or social

problems seems to increase their chances of acquiring postsecondary education, but living in one

with higher property crime or child abuse rates seems to have the opposite effect.

The observed inverse relationship between violent crime and teen employment is expected,

though the underlying causal pathways may be numerous. In violent neighborhoods, there may

be:

 Fewer teen job opportunities within them or nearby (which we cannot measure in our

models).

 Higher incidences of teens being victimized (see Chapter V) and reacting in ways that

render them less willing or able to secure employment.

 Greater fear among teens to seek employment in places and times that might make them

more vulnerable to being victimized.

 Greater reluctance among caregivers to allow teens to seek employment for fear that it

might make them more vulnerable to being victimized.

Although we can rule out none of the above mechanisms, we think that the latter two are more

consistent with our finding of stronger effects among African-American males, who are much

more likely to be victimized.

The observed inverse relationship between the odds of acquiring postsecondary education and

property crime or child abuse rates is to be expected. Prior research (Coulton et al., 2007) has

suggested that high child abuse and neglect rates are emblematic of neighborhoods that have

weak collective norms and social structures for supporting the healthy, holistic development of

children and youth. This interpretation is buttressed by our finding related to residential

instability, which also has been shown to degrade neighborhood intergenerational closure

(Sampson, Morenoff, and Earls, 1999). Higher property crime may also indicate that these are

places where the underground economy may be most likely to draw teens into activities

anathema to young adults seeking postsecondary education. It is also possible that the impact of

property crime on the odds of acquiring postsecondary education is mediated by one or more

pathways through health, exposure to violence, risky behaviors, and primary and secondary

education outcomes. We demonstrated in earlier chapters that higher property crime is strongly

associated with a greater likelihood of neurodevelopmental disorders, internalizing behaviors,

behavioral health service utilization, exposure to violence, engaging in risky behaviors, and

exhibiting poor educational performance as a youth, all of which could constrain a young adult’s

willingness and ability to acquire postsecondary education.

The observed direct relationship between indicators of violence and young adult female

postsecondary educational attainments and (possibly) full-time employment121 were unexpected.

One possible explanation works through the indirect effect of family responses to neighborhood

violence that (perhaps unwittingly) enhance educational performance and prosocial behaviors

during high school. If fear of violence induces more caregiver monitoring or self-imposed

restrictions on teens’ movements outside of home and school (including not being employed as a

121 Our neighborhood social problems index, which is comprised heavily of items reflecting violent crime perceptions, was strongly predictive of full-time work for young adult females.

161

VIII. Labor Market Outcomes

teen), a consequence may be superior high school performance and reduced incidences of

antisocial and risky behaviors (such a teen childbearing, as we document in Chapter IX), which

in turn could lead to greater odds of postsecondary schooling and employment. Considerable

ethnographic research documents the efforts of low-income parents to protect their children from

exposure to violence (Furstenberg et al., 1999; Anderson, 1999; Galster and Santiago, 2006).

Another explanation may be spurious correlation. It might be that schools and public or private

agencies of all kinds offered compensatory services and facilities in more violent Denver

neighborhoods, enhancing educational performance in secondary schools and thereby boosting

early adult chances for employment and education.

The observed direct relationships between property crime and abuse rates and teen employment

prospects (especially for African-American and female youth) are also unexpected. Several

underlying causal pathways are plausible, however. In neighborhoods that have more property

crime, there may be:

 Higher incidences of teens being victimized by property crimes, which creates a stronger

need to replace stolen or damaged goods that can create incentives to work.

 Increased competition from perpetrators of property crime involving the ostentatious

display of personal consumption items, which can create incentives to work.

 A correlation between property crime and greater amounts of nonresidential land uses,

which may serve as a proxy for locally available employment opportunities (which we

cannot measure in our models).

In neighborhoods that have higher child abuse and neglect rates, there may be stronger incentives

for teens to escape from unpleasant home environments via work. The relationship may also be

spurious. Such neighborhoods may be places of intensified scrutiny from low-income families by

welfare agency staff who are potential reporters of maltreatment (Cancian et al, 2010). Children

there may not be subjected to greater incidences of maltreatment, but there is greater likelihood

of official reporting of such treatment when it occurs. The other activities of welfare agency staff may yield benefits for teen employment in these neighborhoods.

Neighborhood Ethnic and Nativity Composition

We have identified several important though offsetting relationships between Latino and foreign-

born composition of the neighborhood’s population and young adult outcomes. For the full

sample, higher percentages of foreign-born residents are associated with higher odds of working

full time and acquiring postsecondary education and lower odds of neither working nor attending

school. We think that these results likely reflect immigrant communities’ proeducation and pro-

work values and their ability to more closely monitor teen activities (such as drinking, using

drugs, and unprotected sex, as we demonstrated in Chapters V and IX) that might risk future

educational and employment prospects. Of interest, we could detect no significant statistical

differences in these aforementioned relationships between African-American and Latino teens.

This suggests that the mechanism(s) behind the observed relationship transcends intragroup

culture. Norms and values might well spread from immigrant families and students to others in

the neighborhood or classroom, of course. Moreover, immigrant households may have more

adults (from multiple generations) who are not in the workforce, on average. This cadre of home-

based adults may provide more opportunities for supervised study and recreation in homes for

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VIII. Labor Market Outcomes

not only their own children but also neighboring children. All these ruminations are consistent

with our finding in Chapter IX that foreign-born residents apparently convey positive

externalities to all neighborhood youth in discouraging teen parenting.

Higher percentages of Latino residents are strongly associated with the opposite outcomes as

immigrant communities noted above. Moreover, the neighborhood’s percentage of residents who

are African American also appears to matter for African-American teens living there.

Neighborhoods that have greater African-American predominance are associated with inferior

young adult economic prospects: less likelihood of working full time and greater likelihood of

neither working nor attending school. These results for Latino and African-American

neighborhoods may have multiple causes. We think a persuasive potential cause may be that

minority neighborhoods are more likely to have active underground or informal economies, thus

providing networks and role models that more easily lead teens into young adulthoods of little

steady employment or higher education. Our results that higher property crime rates are

associated with lower odds of postsecondary education (for women) are consistent with this

explanation. Another may be that minority youth from more identifiable minority neighborhoods

in Denver are stigmatized and thereby have more opportunities foreclosed. Yet another may be

that such neighborhoods are less healthy (we could not control for environmental pollutants),

yielding inferior health outcomes that erode prospects for postsecondary education and

employment. Finally, we cannot discount the possibility that such neighborhoods convey the

opposite set of norms as those described above for immigrant-dense neighborhoods.

Because a majority of foreign-born residents of Denver are Latino (primarily of Mexican origin),

this raises the issue of net effects in neighborhoods inhabited by Latino immigrants. The answer

is contingent on outcome, sample, and stratum considered. In the case of full-time employment,

results indicate a small, net negative association for the full “ever in DHA” sample but zero

relationship in the “mostly in DHA” sample. For Latino teens in the “ever in DHA” sample,

there is a strong net positive association.122 In the case of postsecondary education, opposite

results emerge. There is a substantial net positive association between Latino immigrant

percentage and odds of postsecondary education for the full “ever in DHA” sample but a net

negative association in the Latino stratum. In the case of neither working nor attending school,

there is a strong positive net association for the full samples and for Latinos. Holistically, these results are consistent with the argument that Denver neighborhoods that have higher Latino

(Mexican) immigrant shares culturally promote full-time work at the cost of acquiring

postsecondary education.

Neighborhood Social Status

Two indices related to neighborhood social status proved predictive of teen and young adult

labor outcomes in several strata: occupational prestige and neighborhood social vulnerability.

Residing in a higher prestige neighborhood during high school was associated with a

substantially reduced likelihood of young adult females’ neither working nor attending school.

Residing in a socially vulnerable neighborhood was associated with much lower teen

122 We calculated this using parameters from the “ever in DHA” sample xtmelogit model. We added the coefficients (not odds ratios) of the Latino and foreign-born variables, and then exponentiated the result to secure the “net”

estimate.

163

VIII. Labor Market Outcomes

employment prospects for African-American youth. These results have intuitive appeal from the

perspective of local networks, norms, and role models related to these outcomes. Less socially

vulnerable neighborhoods that surround their teens with high-prestige workers likely intensely

expose these youth to norms and role models that durably encourage educational achievements

and adult labor force participation and to networks of information about these productive

opportunities and the “soft skills” required to exploit them. Higher prestige, less socially

vulnerable neighborhoods also seem to discourage risky behaviors (as we demonstrated in

Chapter V) that might impede postsecondary employment and educational success.

Our finding that higher prestige neighborhoods apparently lead Latino and male teens to work

fewer hours is consistent with this longer term emphasis on education and work in such

neighborhoods. Given the longstanding evidence on the deleterious effects of too-intensive teen

labor force participation (generally more than 20 hours weekly) on high school academic

performance (D’Amico, 1984; Steinberg and Dornbusch, 1991;Steinberg, Fegley, and

Dornbusch, 1993; Warren, 2002; Warren, LePore, and Mare, 2000), it follows that

neighborhoods imparting stronger norms supporting educational performance might discourage

work during high school. Another factor may also be at play here. Higher prestige neighborhoods

may represent more competitive localized labor markets in which low-income males are likely to

be less attractive job applicants than their better heeled teen neighbors. Our results are also

consistent with those produced by recent qualitative research on both the Moving To

Opportunity (MTO) and Gautreaux programs (as well as many prior statistical analyses).123

Some low-income MTO caregivers in advantaged (presumably less-vulnerable, higher prestige

than originally occupied) neighborhoods stressed during interviews the value of adult role

modeling of work habits for their teens and the “soft skill” enhancement that improved their

employment prospects (Briggs, Popkin, and Goering, 2010; Briggs et al., 2011). This mimics

results from Gautreaux that showed how higher economic expectations in advantaged

neighborhoods positively influenced lower income teen in-movers (Rosenbaum, DeLuca, and

Tuck, 2005).

The finding that neighborhood social vulnerability was associated with an increased likelihood of

gaining postsecondary education (especially among Latinos) was less expected, however. We

would posit that collective proeducation norms assume increased potency in more distressed

Latino neighborhoods, because education is seen as a means of escaping the hardships they have

witnessed.

123 Although we note that the prior literature has a range of results on similarly conceived “neighborhood disadvantage” variables, we stress that our results are not strictly comparable for two reasons. First, our index sums neighborhood percentages of unemployment, poverty, female-headed households and renters; it does not include ethnic, racial, or nativity measures, as do most others. Second, our models control for a host of other neighborhood characteristics that are often associated with “disadvantaged neighborhoods” but for which other studies have no direct measures, notably crime, child abuse, institutional resources, bad peer influences, social problems, social capital, and occupational prestige. Thus, other studies’ “neighborhood disadvantage” variables serve as ambiguous proxies for a wide range of other attributes besides social status and should not be used as precedents for results using our social vulnerability indicator.

164

VIII. Labor Market Outcomes

Neighborhood Housing Stock and Institutions

The share of the neighborhood’s housing stock that was built before 1940 had an unexpected

pattern of inconsistent impacts across the strata. On the negative side, for African Americans,

older housing was associated with a lower likelihood of acquiring postsecondary education. On

the positive side, for males it was associated with a higher likelihood of full-time work as a

young adult; for the full sample (and especially Latinos), it was associated with greater odds of

working and more hours worked as a teen.

The negative results are consistent with the notion that older housing offers a less healthy

environment for youth educational development.124 Inferior health outcomes may flow into

reduced chances for postsecondary education, both directly if the health problems are chronic

(thereby affecting the ability to attend postsecondary school) and indirectly through inferior

primary and secondary educational achievements (which are consistent with the findings for the

pre-1940 housing variable in Chapter VII). Another potential causal mechanism here could be

related to risky behaviors as well as exposure to violence, which we found to be higher in older

neighborhoods (see Chapters V and VI), likely because of associated differences in

neighborhood physical characteristics. Many of the risky behaviors in question would make it

more difficult for youth to perform well academically and to build credentials that would permit

their enrolling in higher education. Exposure to neighborhood and school violence as witnesses

and victims could similarly inhibit youths’ academic foundation for higher education.

In contrast, for other youth, this older housing characteristic was associated with improved

employment outcomes for both teens and young adults. The foregoing concerns over health may

still be relevant, so there must be some offsetting factor at work. Perhaps what is occurring is a

spurious correlation with job accessibility. Older (often heavily Latino-occupied) neighborhoods

are close to the Denver Central Business District and the Lo-Do entertainment district, both huge

generators of entry-level jobs during the period under investigation.

Finally, we think it intriguing that spending more of one’s high school years in a neighborhood

well served by a hospital or health clinic proved strongly predictive of superior performance in

all three teen employment measures for African Americans. This result is suggestive that, given

the well-documented inferior health status and health insurance coverage of African-American

youth as a group (Nazroo, 2003), their young economic success may crucially hinge on the local

proximity of care-giving institutions, especially in emergency situations.

Geographic Selection Bias Revisited

Recall from Chapter III that we argued that the estimated value of the “true” neighborhood effect

likely lies within the range of estimates garnered from our various analysis samples, which

consider different potential types of geographic selection postinitial assignment by DHA. In all

cases of teen labor outcomes reported in Exhibits VIII-1 through VIII-3, the estimated

neighborhood indicator parameters were not substantially different between the “ever in DHA”

and “mostly in DHA” (during high school) samples, so our likely “true” estimate is narrowly

124 Because in these models we cannot control for respiratory and neurological pollutants, it is possible that age of housing stock also serves as a proxy for environmental quality here, which we saw affected child health in Chapter IV.

165

VIII. Labor Market Outcomes

circumscribed. Unfortunately, this was not the case for the young adult outcome analyses, where

parameters were substantially larger in the sample that spent most of their high school years

living in DHA housing. It is thus possible for our older cohorts that there may be more

substantial unmeasured differences between the caregivers of those who raised their teens in

DHA housing most years during 14–18 years of age and those who did not (primarily had left

DHA by the time their children reached high school).125 We of course do not know whether these

unmeasured differences operated to bias the observed neighborhood effects upward or

downward.126

Conclusion

Many aspects of neighborhood context are statistically and substantively important predictors of

teen and young adult outcomes related to work and postsecondary education. Aspects of the

neighborhood’s safety, ethnic and nativity mix, social status, and housing stock all exhibit

substantial predictive power for teen and young adult employment and postsecondary

educational attainments. In general, teen employment will be more likely in neighborhoods that

have lower violent crime rates and occupational prestige, higher percentages of pre-1940–vintage

housing, and higher property crime and child abuse rates. Young adult full-time employment will

be more likely for those raised during high school in neighborhoods that have higher percentages

of foreign-born residents and lower percentages of Latino residents. Postsecondary education

will be more likely for those raised during high school in neighborhoods that have lower

property crime and child abuse rates but higher shares of socially vulnerable populations and

higher violent crime rates. These apparent influences appear more complicated and nuanced than

conventionally posited, however. Especially noteworthy is the typical contingency of the

neighborhood effect magnitude based on gender and ethnicity.127 Indeed, virtually no

neighborhood indicator employed had consistently significant predictive power across more than

two strata. We also note that the importance for young adults of contexts experienced while they

were in high school speaks to the temporal durability of these neighborhood effects during the

teenage developmental stage.128

125 We remind the reader that those who left DHA comprise a heterogeneous group: both the economically successful and those who may have been evicted for lease violations.

126 We can surmise, however, that the unmeasured differences among caregiver types considerably narrowed for the younger cohort. Recall that the teen sample includes all in the young adult sample plus others who were 14–17 years of age at the time of our survey. For the teen labor outcomes analysis, this meant adding 123 younger observations to the “ever in DHA” young adult sample; the comparable figure for the “mostly in DHA” sample was 117.

127 This has been observed previously in observational data models of neighborhood effects on economic outcomes; see, for example, Crowder and South (2003) and Galster, Andersson, and Musterd (2010).

128 This has been observed previously in observational data models of neighborhood effects on economic outcomes; see, for example, Wheaton and Clarke (2003); Sampson, Sharkey, and Raudenbush (2008); and Musterd, Galster, and Andersson (2012).

166

IX. Marriage and Childbearing

IX. MARRIAGE AND CHILDBEARING

Introduction

In this chapter, we consider marriage and cohabitation, teen childbearing and fathering, and

nonmarital childbearing or fathering in young adulthood for low-income, minority youth in our

Denver Child Study. We analyze whether these youth married or began living with a partner

between the 15 and 24 years of age inclusive, ever gave birth to or fathered a child from 15 to

19 years of age inclusive, or gave birth to or fathered a child outside of marriage between 18 and

24 years of age inclusive. We find evidence suggesting strong neighborhood effects emanating

from several dimensions of the residential environment for each of these outcomes.

The sizes of our analysis samples were 488 (marriage or cohabitation), 471 (teen childbearing

and fathering), and 367 (nonmarital childbearing and fathering in young adulthood).129 Study

participants in our three analysis samples ranged in age from 15 to 35 years of age at the time of

the survey. For our outcomes on marriage/cohabitation and teen childbearing/fathering, we

confine our analysis to those 15 years of age and older, as this is consistent with the majority of

the literature; we restrict our analysis of nonmarital childbearing/fathering in young adulthood to

those 18 years of age and older. Marriage/cohabitation indicators were derived from Denver

Child Study caregiver survey respondent’s answers to the question, “Has__ [youth] ever been married or lived with someone? (If yes, at what age?).” Nearly one 1 of 4 youth in our sample

had married or cohabitated with someone when they were between 15 and 24 years of age; the

mean age at the time of marriage or cohabitation was 19 years of age. We ascertain our

childbearing/fathering outcomes on the basis of caregiver survey respondents’ responses to the

question, “Has __ [youth] __ ever given birth to or fathered children of their own? (If yes, at

what age?).” Approximately 17 percent of our sample had birthed or fathered a child during ages

15–19 inclusive; the mean age of such an event was 17.2 years of age. One out of 4 young adults

in our sample had birthed or fathered a child outside of marriage between 18 and 24 years of age;

the mean age at the time of this nonmarital birth was 20 years of age. Between 26 percent and

32 percent of all three samples was comprised of Latino females; the remainder of the samples

was comprised of Latinos (23–31 percent), African-American males (21 percent), and African-

American females (18–20 percent).

We recognize the potential shortcomings of these indicators. All are subject to recall error by the

caregiver survey respondent, although we intentionally chose outcomes for which this error

would be minimal. Although caregivers would almost always be aware of whether their

daughters and sons were living with a spouse or partner or of their daughter giving birth, it is

quite possible that they could be unaware of whether their sons had fathered children. Given this

expected gender asymmetry, we will place particular emphasis here on the stratified childbearing

results for female youth.

129 This applies to the “ever in DHA” samples for each outcome; the corresponding sample sizes for the “mostly in DHA” samples were 245, 365, and 282, respectively.

167

IX. Marriage and Childbearing

Because our outcome measures are dichotomous, we employ logit models with robust standard

errors adjusting for clustering at the family level for our parameter estimations.130 We also

estimate Cox proportional hazards models with robust standard errors for estimating the hazards

of marriage/cohabitation and childbearing.131 As with outcomes reported in earlier chapters, we

estimate these models for the previously defined “ever in DHA” and “mostly in DHA” samples

to assess the robustness of our results and bound potential degrees of geographic selection bias

following Denver, Colorado, Housing Authority (DHA) assignment. The “currently in DHA”

sample was too small to examine these marriage and childbearing outcomes. The logistic and

Cox proportional hazards models use the same neighborhood indicators and core covariates

common to all our analyses, with the exception of a small number of theoretically important

additions. Our analyses of marriage and cohabitation control for whether youth in our sample

had birthed or fathered a child prior to living with a spouse or partner. The childbearing/fathering analyses include three additional variables in an effort to control for a number of individual- and

neighborhood-level indicators that are often cited in the literature as important predictors of teen or nonmarital childbearing and fathering. In addition to the core covariates, we control for

whether the youth:

 Reached puberty early (roughly 1 in 10 reached puberty early; on time/late is the

reference category).

 Had a caregiver who was a teen parent (roughly 1 in 4 had been; not a teen parent is the

reference category).

 Had a caregiver who gave birth to the sampled youth outside of marriage (roughly 6 out

of 10 did; not is the reference category).

In addition to the core neighborhood-level indicators, we incorporated another measure in the

teen childbearing/fathering models to account for the percentage of all births in the neighborhood

born to teens. Because of significant missing data, small cell counts, or problematic joint

distributions, we removed our indicators of neurological risk and respiratory risk from these

models as well as our indicators of child abuse and neglect rates and the presence of medical

facilities. We also removed percentage of Latino residents in the neighborhood from our teen

childbearing/fathering models because of the high correlation with the percentage of foreign-

born residents. Similar to our adolescent and young adult labor analyses, in this chapter we

operationalize family and neighborhood contexts as averages during high school starting with

15 years of age through the age of marriage/cohabitation or at birth or fathering of a child. If

these marriage or childbearing outcomes never occurred during childhood, our neighborhood

indicators are measured as averages from 15–18 years of age or age at the time of survey if

younger than 18 years of age for both logit and Cox models.

130 Because the xtmelogit models failed to converge in some model runs, we present logit models with robust standard errors for these analyses.

131 Global chi-square tests indicated that the Cox models were the appropriate specifications here rather than the accelerated failure time models.

168

IX. Marriage and Childbearing

Estimated Neighborhood Effects on Marriage and Childbearing

Exhibits IX-1 through IX-6 present standardized logit and Cox proportional hazards model

results for “ever in DHA” and “mostly in DHA” samples. We incorporate robust standard errors

for both types of models to account for clustering of children in families. As before, we consider

only those results that are statistically significant in both of the analysis samples for the given

model type. Ranges of parameter estimates reported below reflect the variation across the two

analysis samples. As with other chapters, we will first highlight the main findings without

comment, reserving interpretation and explanations for a final section of the chapter so we can be

more holistic in our discussion.

Marriage and Cohabitation

Results for our models of marriage/cohabitation during adolescence and young adulthood are

presented in Exhibits IX-1 and IX-2. The first exhibit shows logit models for ever having lived

with a spouse or partner between 15 and 24 years of age; the second exhibit shows the

corresponding Cox models estimating the hazards of this event occurring.

The models revealed only one significant individual-level or household-level predictor of living

with a spouse or partner between 15 and 24 years of age. Compared with youth in our samples

who did not bear or father a child prior to marriage or cohabitation, youth who had given birth or

fathered children were 13–87 times more likely to begin living with a spouse or partner.

Several neighborhood indicators related to social status, physical, and safety context consistently

proved statistically significant predictors of living with a spouse or partner during adolescence or young adulthood. A one-standard-deviation-higher neighborhood:

 Occupational prestige score was associated with 80–96 percent–lower odds and

59 percent–lower hazards of living with a spouse or partner.

 Percentage of pre-1940–vintage dwellings in the neighborhood was associated with 2.1–

4.6 times–higher odds of living with a spouse or partner but also with 37–112 percent–

greater hazards of living together.

 Property crime rate was associated with 4–15.8 times–higher odds of living with a spouse

or partner as well as 71–155 percent–greater hazards of living together.

 Violent crime rate was associated with a 68–96 percent reduction in the odds of living

with a spouse or partner but also with a 34–76 percent reduction in the hazard of the

same.

169

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171

IX. Marriage and Childbearing

Teen Childbearing and Fathering

Results for our models of teen childbearing and fathering are presented in Exhibits IX-3 and

IX-4. The first shows estimates produced by logit models for ever having birthed or fathered a

child as a teen; the second shows the corresponding Cox models for estimating the hazard of

becoming a teen parent. In both exhibits, we present findings for our two alternative analysis

samples. In Appendix C, we report findings for the “ever in DHA” female stratum, because they

represent the overwhelming number of observations of the dependent variable here. We

investigated this additional stratum as a robustness check to assuage concerns that the highly

asymmetric values of the dependent variable across genders might lead to unrepresentative

results in the aggregate samples.132

The models revealed few consistently significant individual-level or household-level predictors.

Compared with African-American males in our samples, Latino females were 4.4–5 times more

likely to become a teen parent. Youths whose caregivers had graduated from high school had 64–

71 percent–lower odds of ever becoming a teen parent, compared with youths whose parents did

not have a diploma.

Several neighborhood indicators related to demographic, status, and safety context proved

consistently statistically significant predictors of becoming a teen parent. A one-standarddeviation-higher neighborhood:

 Percentage of Latino individuals in the neighborhood was associated with 66–75 percent–

lower odds and 43–62 percent–lower hazards of becoming a teen parent.

 Occupational prestige score was associated with 56–60 percent–lower odds of becoming

a teen parent.

 Property crime rate was associated with 2.2–2.6 times–higher odds of becoming a teen

parent.

132 There was a nontrivial fraction (10 percent) of males in our sample who fathered children as teens. In comparison, 25 percent of females in our sample gave birth as teens.

172

IX. Marriage and Childbearing

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* p

174

IX. Marriage and Childbearing

Nonmarital Childbearing in Young Adulthood

Results for our models of nonmarital childbearing in young adulthood are presented in

Exhibits IX-5 and IX-6. The first exhibit shows (for each of two alternative analysis samples)

logit models with robust standard errors adjusting for clustering at the family level for ever

having birthed or fathered a child outside of marriage between 18 and 24 years of age; the

second exhibit shows the corresponding Cox robust standard error models for the hazard of

experiencing a nonmarital birth.

In contrast to our previous marriage and childbearing outcomes, there were no consistently

significant individual-level or household-level predictors of nonmarital childbearing/fathering in

our logistic regression models. Nonetheless, two neighborhood indicators related to status and

physical context proved statistically significant predictors of nonmarital births in our logistic

regression models only. A one-standard-deviation-higher neighborhood:

 Occupational prestige score in the neighborhood was associated with 50–54 percent–

lower odds of experiencing a nonmarital birth.

 Percentage of pre-1940–vintage housing stock was associated with 1.6 times–higher odds

of experiencing a nonmarital birth.

Gender and Ethnic Differences in Neighborhood Effects

Estimated parameters for our three models stratified by gender and ethnicity are presented in

Appendix C. As is the case in all our discussions of stratified results, we employ the “ever in

DHA” sample results and normalized continuous covariates.

Marriage/Cohabitation During Adolescence and Young Adulthood

We find considerable heterogeneity in size and significance of apparent neighborhood effects on

marriage/cohabitation between males and females and between Latino and African-American

youth and young adults. Among the four strong predictors in the aggregate samples, only the

neighborhood property crime rate yielded statistically significant coefficients across all four

strata. The range of variation associated with a standard-deviation-higher property crime rate was

3–21 times–higher odds, with the highest being manifested for African Americans. Residence in

neighborhoods that had higher occupational prestige was associated with 72–99 percent–lower

odds of marriage/cohabitation for Latino, female, and African-American youth. Similar

differences (48–98 percent) in the odds and hazards of marriage/cohabitation were observed for

male, Latino, and African-American youth residing in neighborhoods with one-standarddeviation-higher violent crime rates. Female and African-American youth living in neighborhoods that had one-standard-deviation-higher percentages of pre-1940 dwellings had

1.4–8 times–higher odds and hazards of living together with a spouse or partner.

175

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177

IX. Marriage and Childbearing

Two new neighborhood indicators emerged as strong predictors of marriage/cohabitation for

particular strata, though not in the aggregate samples. Most of these emerged as predictors

among African-American youth and young adults. Residing in a neighborhood that had a

standard-deviation-higher:

 Percentage of African-American neighbors was associated with 64 percent– and

85 percent–lower odds of living with a spouse or partner for females and African

Americans, respectively.

 Social problems index was associated with 2.2–4.5 times–higher odds of living with a

spouse or partner for males and African Americans and with a 2 times–higher hazard of

the same for males.

 Percentage of foreign-born individuals was associated with 94 percent–lower odds of

living with a spouse or partner for African Americans but a 2 times–higher hazard of the

same for males.

 Social vulnerability score was associated with 99 percent–lower odds of living with a

spouse or partner for African Americans.

 Social capital index was associated with a 41 percent–greater hazard of living with a

spouse or partner for females.

Teen Childbearing and Fathering

We find marked gender and ethnic heterogeneity in estimates of neighborhood indicator

relationships with teen childbearing and fathering. Nevertheless, the three neighborhood

indicators that were strong predictors in the aggregate samples proved to be so across several

strata, as well. The percentage of Latino residents in the neighborhood continued to be a strong

predictor of lower risks of teen childbearing and fathering for female and African-American

teens. For females in our study, a one-standard-deviation-increase in the occupational prestige

score was associated with a 69 percent reduction in the odds of becoming a teen parent. An

equivalent increase in the property crime rate was associated with 4.6 times–higher odds of

becoming a teen parent.

Two additional neighborhood indicators emerged as statistically significant predictors in only

one or two strata, though not in the aggregate samples. Residing in a neighborhood that had a

standard-deviation-higher:

 Social problems index was associated with 4.2 times–higher odds of becoming a teen

parent for Latino youth.

 Percentage of African-American individuals in the neighborhood was associated with

56 percent–lower odds of females becoming a teen parent.

Although we found significant gender and ethnic differences in estimated neighborhood

parameters in our Cox models, these occurred sporadically across the different groups, and each

one was significant for only one stratum. Moreover, these results did not augment what we found

in the logistic regression results.

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IX. Marriage and Childbearing

Nonmarital Childbearing and Fathering

We find relatively few gender or ethnic differences in apparent neighborhood effects on

nonmarital births during young adulthood, with all but one confined to the results of our logistic

regression analyses. Neither of the strong neighborhood indicators in the aggregate samples were

statistically significant predictors of nonmarital childbearing/fathering in more than two strata.

For males and African Americans only, a one-standard-deviation-higher neighborhood

occupational prestige score was associated with 56–75 percent–lower odds of experiencing a

nonmarital birth. Higher percentages of pre-1940–vintage housing stock increased the odds of

having a nonmarital birth by 70 percent but only for male youth.

Three additional neighborhood indicators emerge as statistically significant predictors in our

Latino stratum. For Latino teens, a one-standard-deviation-higher neighborhood:

 Percentage of African-American individuals in the neighborhood was associated with

81 percent–lower odds of experiencing a nonmarital birth.

 Social problems index was associated with 2.2 times–higher odds of experiencing a

nonmarital birth.

 Child abuse and neglect rate was associated with 60 percent–lower odds of experiencing

a nonmarital birth.

Nonlinear Neighborhood Effects

Results for our nonlinear investigations employing spline regressions are presented in

Appendix D. For all three outcomes investigated in this chapter, we found distinctive but

opposite nonlinear relationships with violent and property crime rates. The relationships among

teen parenting, nonmarital fertility, cohabiting outcomes, and violent crime exhibited

diminishing marginal negative effects; property crime exhibited diminishing marginal positive

effects. Logistic models consistently showed these relationships, which typically were replicated

by the Cox proportional hazard models.133 Higher rates of violent (property) crime in a

neighborhood lowered (raised) the odds and the hazard of all three outcomes much more strongly

in neighborhoods that had below-average values of violent (property) crime than in those with

above-average values. These differences were substantial. A standard-deviation increase in

violent crime was associated with a:

 99 (80) percent decrease in the odds (hazards) of marrying/cohabiting as a teen in ranges

below the mean violent crime rate; in ranges above the mean, the corresponding figures

were 44 (34) percent.

 96 percent decrease in the odds of giving birth/fathering a child as a teen in ranges below

the mean violent crime rate; in ranges above the mean, the corresponding figure was

26 percent.

133 The exceptions were that the Cox parameters were insignificant for both crime rates in the teen parenting model and for property crime in the out-of-wedlock childbearing model.

179

IX. Marriage and Childbearing

 96 (89) percent decrease in the odds (hazards) of experiencing a nonmarital birth in

ranges below the mean violent crime rate; in ranges above the mean, the corresponding

figures were 5 (8) percent.

A standard-deviation increase in property crime was associated with at least 9-fold–higher odds

of all three outcomes in ranges below the mean property crime rate; in ranges above the mean,

the odds only increased by 15 percent (nonmarital childbearing/fathering), 150 percent (giving

birth/fathering a child as a teen), and 213 percent (marrying/cohabiting as a teen).

Two other neighborhood characteristics exhibited nonlinear relationships with one outcome; both

suggested diminishing marginal positive impacts. A standard-deviation increase in the

percentage of pre-1940–vintage dwellings was associated with a 600 percent increase in the odds

of marrying/cohabiting as a teen in ranges below the mean rate; in ranges above the mean, the

corresponding figure was only 42 percent. A standard-deviation increase in child abuse and

neglect rates was associated with a 569 (308) percent increase in the odds (hazards) of giving

birth out of wedlock in ranges below the mean rate; in ranges above the mean, the corresponding

figures were 60 (45) percent decreases.

Discussion

The results reported above clearly show that several aspects of the neighborhood safety,

demographic, social status, and physical environment are statistically and substantively

important predictors of marriage and fertility during adolescence and young adulthood. Below,

we organize the discussion around thematic categories of neighborhood context.

Neighborhood Safety

As with our other analyses, the most dramatic and consistent finding was that neighborhood rates

of property crime and violent crime apparently have opposite relationships with

marriage/cohabitation and teen childbearing and fathering. Property crime rates exhibited

statistically significant and substantively large relationships in the aggregate sample that were

replicated for all strata (marriage/cohabitation) and for female and Latino youth (teen

childbearing and fathering). Precisely the opposite relationships were manifested for violent

crime rates. Places with more violent crime exhibited a reduced likelihood of living with a

spouse or partner (in the aggregate sample and most strata) or experiencing a nonmarital birth (in

the African-American stratum). The inverse relationships between violent crime rates,

marriage/cohabitation, and nonmarital childbearing/fathering were partially unexpected. In the

case of marriage/cohabitation, living in more violent neighborhoods might reduce the availability

of marriageable partners. In the case of nonmarital childbearing/fathering, one possible

explanation may again be one we have drawn on in earlier chapters. Increased fear of crime may

cause caregivers to impose geographic restrictions on youths’ movements outside of home and

their immediate environments. There also may be more intense monitoring by caregivers in

neighborhoods that have higher threats of violence. Both factors may lead to the situation where

youths are less likely to have unsupervised periods and places to engage in risky sexual activity

that could lead to a nonmarital births. This explanation is consistent with what we have discussed

180

IX. Marriage and Childbearing

in the context of exposure to violence in Chapter V, other risky behaviors in Chapter VI, and

educational outcomes in Chapter VII.

The observed direct relationship between property crime rates, marriage/cohabitation, and teen

childbearing and fathering is expected. We think that this finding can be interpreted consistently

with that we presented in Chapter VI in the context of the positive relationship between property

crime and other risky behaviors. Property crime, social disorder, and other risky behaviors may

be visible indicators of neighborhoods in which informal social control is diminished. As noted

above, increased social disorder has been consistently linked in the literature with higher

incidences of risky sexual behaviors among teens (Harding, 2003; Way, Finch, and Cohen,

2006). In turn, higher incidences of pregnancy resulting from these risky behaviors may produce

higher pressures for youth to get married or to live with the partners who birthed/fathered their

children. There may be another link between property crime and fertility that is transmitted

through educational performance, which we found in Chapter VII to be inversely related to

property crime. Weak educational performance indicators have been found elsewhere to be

predictive of risky sexual behaviors (Manlove, 1998).

Another aspect of neighborhood safety proved to be an important predictor of cohabitation and

childbearing outcomes for particular strata. The extent to which caregivers perceived the

existence of social problems in their immediate environs served as an important risk factor for

marriage/cohabitation (for males and African Americans), teen childbearing and fathering (for

males and Latinos), and nonmarital childbearing/fathering (for Latinos). Although these findings

were not replicated in the aggregate samples, they suggest that male and African-American youth

may marry or move in with partners as a way of coping with or escaping from the negative

neighborhood conditions of their adolescence. Further, Latino youth are at greater risk for

becoming parents when they live in these neighborhoods that caregivers perceive to be riddled

with a variety of social problems, many of which are associated with crime and disorder. This

finding is consistent with our finding above for property crime and as such may reflect analogous

causal mechanisms.

Neighborhood Ethnic and Nativity Composition

We identified several substantively important, inverse relationships between the minority ethnic

and nativity composition of the neighborhood’s population and a variety of marriage and fertility

outcomes. Both male and female youths raised in neighborhoods that had higher percentages of

Latino neighbors would be expected to have reduced chances of becoming teen parents. We

found that growing up in a neighborhood that has higher percentages of African-American

neighbors was predictive of a lower likelihood of marriage/cohabitation for female and African-

American youth, teen parenting for females, and nonmarital childbearing/fathering for young

adult Latinos. Finally, higher percentages of immigrant neighbors predicted less chance of

African-American youths cohabiting. Analogous to our earlier results regarding risky behaviors

(in Chapter VI) and educational outcomes (in Chapter VII), these results are consistent with the

notion that Latino and immigrant populations play powerful normative, role-modeling, and

behavioral monitoring functions that discourage both marriage and childbearing, especially

outside of marriage (Erickson, 1998). The apparent protective nature of the Latino composition

of the neighborhood for teen parenting extended to young African-American residents, as well,

suggesting that these mechanisms can reach beyond same-ethnic lines. There may be a different

181

IX. Marriage and Childbearing

normative mechanism at play generating our results for African-American composition of the

neighborhood. In places that have greater concentrations of African Americans, there may be

less pressure for girls or African-American youth to get married or to have children as teens,

controlling for the status and other characteristics of the neighborhood, because marriage is not

viewed as the primary rite of passage into adulthood within low-income African-American

communities.

Neighborhood Social Status

Living in a neighborhood that has superior occupational prestige apparently served as a

protective factor for marriage/cohabitation, teen childbearing and fathering, and nonmarital

childbearing across the aggregate samples. These results have intuitive appeal from the

perspective of local networks, norms, and role models related to marriage and childbearing.

Neighborhoods that surround their youths with higher prestige neighbors likely expose these

youth to norms and role models that discourage risky behaviors and encourage educational and

occupational success, thereby modeling alternative pathways to adulthood besides parenthood.

They also may expose youth to more role models who are married. These claims are fully

consistent with the relationships for neighborhood prestige we observed in Chapters VI, VII, and

VIII. This theory is also supported by Crane (1991), who found that the percentage of high-status

employees in the neighborhood was inversely related to teen childbearing, and Brewster (1994),

who found that higher female employment rates in the neighborhood protected against teen

pregnancy. Higher prestige neighborhoods could also be related to norms and information related

to unprotected intercourse, not sexual activity in general.

Neighborhood Physical Environment

We found intriguing results related to the age of a neighborhood’s housing stock,

marriage/cohabitation, and nonmarital childbearing/fathering. Higher percentages of pre-1940–

vintage homes were associated with higher odds of living with a spouse or partner (overall and

especially for female and African-American youth) as well as fathering a child out of wedlock in

young adulthood (overall and especially for males). We think it unlikely that these relationships

emerged because of physical characteristics of older dwellings themselves. Rather, we again

draw on an argument we have made earlier in Chapter IV: This indicator may serve as a proxy

for the design, density, and land use mix of the neighborhood. If older neighborhoods in Denver

encourage more interactions among youth, including relationships that might lead to sexual

activity, these interactions may result in higher rates of nonmarital births, which, in turn, may

lead to higher rates of marriage/cohabitation.

Geographic Selection Bias Revisited

In typical cases of the three marriage and fertility outcomes reported in Exhibits IX-1 through

IX-6, the estimated neighborhood indicator parameters were substantially similar between the

“ever in DHA” and “mostly in DHA” samples. Thus, we are less inclined to worry here about

major distortions caused by postassignment geographic selection.

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IX. Marriage and Childbearing

Conclusion

A number of aspects of neighborhood context are statistically and substantively important

predictors of teen and young adult cohabitation and childbearing behaviors, though sometimes in

unexpected ways. Aspects of the neighborhood’s safety, social status, ethnic and nativity mix,

and physical environment exhibit substantial predictive power models predicting the odds and

the temporal hazard of living with a spouse or partner, becoming a teen parent, or experiencing a

nonmarital birth during young adulthood. Risks for one or more of these outcomes (for at least

one stratum or more) diminish in neighborhoods that had higher violent crime rates; occupational

prestige; and percentages of foreign-born, Latino, or African-American residents. The risks

increase in neighborhoods that have higher rates of property crime, caregiver reports of

neighborhood social problems, and percentages of dwellings built before 1940. These

relationships are manifested particularly strongly and generally for African-American youth.

183

X.

Extensions and Variations on the Core Approach

X.

EXTENSIONS AND VARIATIONS ON THE CORE APPROACH

Introduction

In this chapter, we describe two experimental modifications of the core approach that we have

employed thus far in this report. The first probes how the power of neighborhood effects may

vary across developmental stages. The second probes the consequences of measuring

neighborhood context over a different time frame.

Experiments With Differential Effects Across Developmental Stages

Recent child-development theory predicts that the relative influence of distal contexts like

neighborhood should vary across developmental stages, primarily because caregivers will

perform stronger mediating roles for younger children and different causal mechanisms will have

different saliency at different ages (Leventhal and Brooks-Gunn, 2000; Booth and Crouter, 2001;

Foster and Brooks-Gunn, 2013). Our core analyses are unable to investigate this possibility

directly by focusing on whether and when a certain outcome ever occurred, because they do not

consider the degree to which context played a more powerful role during certain developmental

stages in the period before onset. Here, we explore this possibility by considering a variety of

health and educational outcomes that frequently occur in our sample during more than one

developmental stage.

Our Developmental Stage-Specific Approach

For our experiments, we used conventional specifications for developmental stages, labeling

them with their roughly corresponding phases in school:

 0–5 years of age: Preschool (PS).

 6–11 years of age: Elementary School (ES).

 12–14 years of age: Middle School (MS).

 15–18 years of age: High School (HS).

We employed two health indicators (asthma and neurodevelopmental disorders) and three

educational outcomes (attending gifted programs or advanced classes, placement in special

education classes, and being suspended or expelled from school). We then ran a series of mixed-

effects logistic regressions predicting whether the particular outcome occurred134 on samples

stratified by developmental stage—that is, for all youth in our sample who were of the

appropriate age at time of survey or older to be included in the given stage being analyzed. We

then compared the estimated magnitude of the neighborhood indicators’ odd ratios across the

developmental stages to conduct our test. We computed cross-indicator averages for all the

estimated parameters and replicated this only for comparisons in which one or both parameters

are significantly different from zero. We converted the estimated odds ratios into their

134 The educational outcomes could have occurred in non-mutually exclusive ways during ES, MS, or HS. For health outcomes, we model whether onset first occurred in the stage and also model whether it has been previously diagnosed and remains ongoing during the stage.

184

X.

Extensions and Variations on the Core Approach

corresponding percentage differences in the odds associated with a unit change in the indicator

before averaging. We also checked the sensitivity of our results with alternative developmental

stages.

In addition, we assessed the sensitivity of our results to alternative ways of measuring

neighborhood context. In one set of models, we measured the neighborhood context variables

and covariates as averages only for the particular developmental stage being analyzed,135 roughly corresponding to the contemporaneous measurements we have employed throughout our core

analyses. In another set of models, we measured the neighborhood context variables and

covariates as averages from birth cumulatively through the particular developmental stage being

analyzed.

Results From Our Developmental Stage Neighborhood Context Experiments

Results of our experiments comparing effects across developmental stages are summarized in

Exhibit X-1. It presents the average differences in the (converted) odds ratios between the

particular developmental stages portrayed.

The results for asthma clearly indicate that neighborhood indicators have the least powerful

predictive power during the PS stage and the most power during the MS stage. This outcome

appertains regardless of whether indicators are measured contemporaneously or cumulatively.

Results for developmental disorders also show that the comparative power of neighborhood

context is greater during the MS stage than during the ES stage. The magnitudes of average odds

ratios differences reported in Exhibit X-1 appear substantial, with the strength of the effect

during middle school registering at least in double-digit differences in percentage point impacts.

Emerging evidence from recent studies using the National Longitudinal Study of Adolescent

Health (for example, Matjasko, Needham, Grunden, and Farb, 2010) suggest that certain stages

within adolescence may be more susceptible to neighborhood contexts than others.

The three educational outcomes reveal distinct patterns of cross-stage differences depending on

which outcome is considered. When enrollment in either gifted/advanced classes or special

education placement is the outcome, it is clear that neighborhood effects during HS are generally

considerably larger than the equivalent indicators measured during ES. In contrast, the

relationships for the suspension/expulsion outcome are the opposite. The comparative power of

neighborhood effects between HS and MS stages for all three educational outcomes depends on

whether the neighborhood indicators are measured contemporaneously or cumulatively. In the

former situation, effects during HS are stronger; in the latter situation, the opposite is exhibited.

These findings suggest holistically that contemporaneous context most powerfully predicts

educational outcomes during HS, whereas cumulative context does so during MS.

Our exploration into potential variations in magnitudes of neighborhood effects across different

developmental stages confirms the conventional wisdom that such differences exist and can be

substantial.136 Moreover, we have found that at which stage neighborhood effects appear stronger

135 For youth whose age places them within a developmental stage, we compute the averages only over the years of the stage during which the child has been alive.

136 We note that our method of averaging odds ratios obscures potentially larger cross-stage variations in effects for particular neighborhood indicators.

185

X.

Extensions and Variations on the Core Approach

varies both by outcome in question and sometimes whether neighborhood context is measured

contemporaneously or cumulatively. We thus caution against making broad generalizations

about “at which stage in a child’s life are neighborhood influences most important,” given the

apparent multicontingent nature of the answer.

Experiments With Cumulative Exposure Measures

As noted in Chapter II, there is emerging evidence from several studies that neighborhood

context may have stronger impacts on child and youth development if exposure persists over a

sustained period; see Aaronson (1998); Wheaton and Clarke (2003); Turley (2003); Sampson,

Sharkey, and Raudenbush (2008); and Musterd, Galster, and Andersson (2012). We explore this

possibility with selected outcomes from the Denver Child Study.

Our Cumulative Neighborhood Context Approach

Recall that our core analyses presented in Chapters IV–IX measured neighborhood context and

other household covariates contemporaneously with the age of onset of the given outcome (or

time of survey or 18 years of age, whichever younger). In our experiments here, we altered these

variables so that they were measured cumulatively over the period prior to onset (or time of

survey or 18 years of age, whichever is younger). Specifically, for all covariates we computed

the averages over this period.

Employing such cumulative measures raises another methodological challenge, however. For

some youth in our sample, comparatively little of their childhood may have been spent residing

in Denver, Colorado, Housing Association (DHA) housing (or subsequent to the assignment to

the same). This means that the specter of geographic selection bias might unwittingly be

reintroduced for all experiences of pre-DHA assignment. To minimize such contamination, we

restricted our sample for these cumulative measure explorations to those youth who had resided

in DHA housing a majority of (1) the given developmental stage when context is measured

contemporaneously and (2) their childhood up through the given developmental stage when

context is measured cumulatively.137

Our quantitative approach here replicated the one we employed above to investigate cross-stage

differences, in this case comparing differences in estimated odds ratios for identical, multilevel,

mixed-effects logit models, except that in one the covariates are measured contemporaneously

and in the other cumulatively. We computed cross-indicator averages of all the estimated

parameters and replicated this only for comparisons where one or both parameters were

significantly different from zero. As before, we converted the estimated odds ratios into their

corresponding percentage differences in the odds associated with a unit change in the indicator,

and then averaged across all the indicators for the given measurement type. We also probed the

sensitivity of our results to alternative developmental stages. Again, we employed the same

selection of health and educational outcomes.

137 We used the same sample restrictions for the comparative contemporaneous measure models.

186

X.

Extensions and Variations on the Core Approach

Results From Our Cumulative Neighborhood Context Experiments

Results of our experiments comparing effects between contemporaneous and cumulative

neighborhood measures are summarized in Exhibit X-2. It presents the differences in the average

estimated parameters between contemporaneous and cumulative neighborhood measures for the

particular developmental stages portrayed.

A consistent pattern emerges for our health outcomes. Neighborhood effects measured as

cumulative exposures appear stronger, on average, than those measured contemporaneously but

only when the outcome in question is observed during the MS developmental stage. The typical

difference in odds ratios for this stage ranges from 17 to 31 percentage points when only

statistically significant parameters are compared.138 This finding is consistent with a growing

body of scholarly literature: See Aaronson (1998); Wheaton and Clarke (2003); Turley (2003);

Sampson, Sharkey, and Raudenbush (2008); and Musterd, Galster, and Andersson (2012). It is

interesting, however, that this conclusion is not supported when outcomes are modeled in ES.

One potential explanation may be a statistical artifact: The two measures may not differ

appreciably at this early stage in a child’s life. A behavioral reason may be that, at least for these outcomes, PS neighborhood context is less important than that to which youths have been

cumulatively exposed during later developmental stages; thus, including it in the cumulative

measure adds little.

Quite a different pattern emerges for our educational outcomes. With these outcomes, there is no

clear pattern of cumulative measures being stronger; indeed, if anything, during HS the

contemporaneous measures appear marginally stronger for gifted/advanced classes and

suspensions/expulsions outcomes. Our results suggest that no general conclusion can be reached

about the comparative strength of contemporaneous and cumulative measures of context; it

appears to depend on outcome.

138 The average difference calculated for developmental disorders was heavily influenced by three unrealistically large (but statistically insignificant) odds ratios, as detailed in the note above. We have confidence in the sign of the average differences for both ES and MS, however, when these outliers are excluded.

187

X.

Extensions and Variations on the Core Approach

Exhibit X-1. Average difference in neighborhood indicator odds ratios

across child developmental stages

Outcome: Asthma

When Neighborhood Context Is Measured:

Contemporaneous

Cumulative

PS–ES

PS–MS

ES–MS

ES–MS

All ORs

−0.08

−0.08

0.01

−0.02

Signif ORs

−0.05

−0.17

−0.22

−0.29

Outcome: Developmental Disorder

When Neighborhood Context Is Measured:

Contemporaneous

Cumulative

PS–ES

PS–MS

ES–MS

ES–MS

All ORs

N/A

N/A

−0.01*

−0.13*

Signif ORs

N/A

N/A

−0.11

−0.36

Outcome: Participated in Gifted/Advanced Classes

When Neighborhood Context Is Measured:

Contemporaneous

Cumulative

ES–MS

ES–HS

MS–HS

ES–MS

ES–HS

MS–HS

All ORs

<0*

−0.09

>0*

−0.03

−0.01

−0.02

Signif ORs

***

−0.11

−0.09

−0.2

***

0.14

Outcome: Assigned to Special Education Programs

When Neighborhood Context Is Measured:

Contemporaneous

Cumulative

ES–MS

ES–HS

MS–HS

ES–MS

ES–HS

MS–HS

All ORs

−0.03

−0.10

−0.07

−0.01

−0.09

−0.08

Signif ORs

0.08

−0.21

−0.09

0.29

−0.18

***

Outcome: Suspended or Expelled from School

When Neighborhood Context is Measured:

Contemporaneous

Cumulative

ES–MS

ES–HS

MS–HS

ES–MS

ES–HS

MS–HS

All ORs

0.03

0.00

−0.03

0.00

0.06

0.06

Signif ORs

0.34

0.08

−0.19

0.12

0.35

0.22

_______________

^ Values shown are average differences in the percentage differences in changes in odds ratios

associated with unit change in neighborhood indicators.

* Value uncertain because of three unusually large odds ratios; see text for explanation.

*** No statistically significant parameters estimated; N/A = no model could be estimated.

PS = preschool; ES = elementary school; MS = middle school; HS = high school; OR = odds ratio; N/A =

not applicable

188

X.

Extensions and Variations on the Core Approach

Exhibit X-2. Average difference in estimated neighborhood indicator parameters^

between contemporaneous and cumulative measures of context

Outcome: Asthma

Developmental Stage

ES

MS

HS

All ORs

0.01

−0.01

N/A

Signif

ORs

0.02

−0.17

N/A

Outcome: Developmental Disorder

Developmental Stage

ES

MS

HS

All ORs

−0.01*

−0.13*

N/A

Signif

ORs

−0.03

−0.31

N/A

Outcome: Gifted/Advanced Class

Developmental Stage

ES

MS

HS

All ORs

−0.03

>0*

0.03

Signif

ORs

***

−0.13

0.08

Outcome: Special Education

Developmental Stage

ES

MS

HS

All ORs

0.00

0.02

0.01

Signif

ORs

−0.03

***

−0.01

Outcome: Suspended or Expelled

Developmental Stage

ES

MS

HS

All ORs

−0.02

−0.05

0.05

Signif

ORs

−0.08

−0.03

0.12

_______________

^ Values shown are average differences in the percentage differences in changes in odds ratios

associated with unit change in neighborhood indicators

* Value uncertain due to three unusually large odds ratios; see text for explanation;

*** No statistically significant parameters estimated; N/A no model could be estimated

ES = elementary school; MS = middle school; HS = high school; OR = odds ratio; N/A = not applicable

189

X.

Extensions and Variations on the Core Approach

Conclusion

The experimental extensions of our core model have revealed several insights into the nuanced,

contingent-laden nature of neighborhood effects. First, we have found differences in the apparent

magnitude of neighborhood effects across developmental stages, although which stage appears

stronger varies both by the outcome in question and whether neighborhood context is measured

contemporaneously or cumulatively. Neighborhood context effects on health outcomes measured

during MS appear especially strong, however, compared with earlier stages. Second, we have

found that neighborhood context measured as cumulative lifetime exposure provides stronger

effects on health outcomes than when it is measured contemporaneously with the outcome,

although this relationship does not hold during early stages of children’s lives nor for educational outcomes. Our results suggest that no general conclusion can be reached about the comparative

strength of contemporaneous and cumulative measures of context; it appears to depend on

outcome.

190

XI. Conclusions and Implications

XI. CONCLUSIONS AND IMPLICATIONS

Quantifying Neighborhood Effects on the Development of Low-Income Latino and African-

American Children and Youth

Our Denver Child Study (DCS) explored the extent to which multiple dimensions of

neighborhood context affected the physical and behavioral health, exposure to violence, risky

behaviors, education, youth and young adult labor market and educational outcomes, and

marriage and fertility behaviors of Latino and African-American children and youth from low-

income families. Our study used a natural experiment involving the Denver, Colorado, Housing

Authority (DHA), which since 1969 has operated public housing units located in a wide range of

neighborhoods throughout the city and county of Denver. Because the initial assignment of

households on the DHA waiting list to vacant public housing units (and, thus, to neighborhoods)

mimics a random process, this program provided an unusual opportunity for reducing parental

geographic selection bias and observing the unusual combination of low-income, minority

youths raised for extended periods in advantaged (as well as disadvantaged) neighborhoods.

In this study, we analyzed data from several administrative sources and data we collected from

telephone and in-person surveys with Latino or African-American current and former DHA

tenants. Our surveys provided retrospective information on a battery of youth outcomes, family

characteristics, and residential histories. By merging this information, we created a pseudo-

longitudinal panel providing for each year of children’s lives detailed characteristics about their

families, neighborhoods, and outcomes in the domains noted above. We statistically analyzed

relationships between outcomes and neighborhood indicators while controlling for child and

family characteristics, employing logistic, hazard, and accelerated failure time models. We

estimated models for three overlapping samples of families who spent different periods in DHA

housing as a test for robustness and report only results that yield consistent patterns across

samples.

Based on these analyses, we can answer our research questions as follows.

Among Latino and African-American children and youth who spent at least two years living in

DHA public housing, are there statistically and economically significant differences in their

outcomes in six domains (behavioral and physical health, exposure to violence, risky behaviors,

education, employment, and marriage and childbearing) that can be attributed to differences in

their neighborhood environments (controlling for family and individual characteristics)?

The short answer is a resounding YES. Many aspects of neighborhood context proved to be

statistically and substantively important predictors of child and youth outcomes, though

sometimes in unexpected ways. Aspects of the neighborhood’s safety, physical environment,

social status, ethnic mix, and nativity mix were associated with large differences in the odds,

hazards, and timing of virtually all outcomes investigated. In particular, neighborhoods that had

higher occupational prestige and percentages of foreign-born populations and lower property

crime rates and scores on a social problems index had more favorable outcomes across the board.

The consequences of higher neighborhood percentages of Latino and African-American ethnic

191

XI. Conclusions and Implications

composition and lower percentages of pre-1940–vintage housing were generally favorable

though more mixed depending on the outcome. Particular indicators seemed to exert their

influence only on selected child outcomes: higher respiratory risk index predicting poorer health

outcomes, more risky behaviors, and inferior education outcomes; bad peers in the neighborhood

predicting more exposure to violence and risky behaviors.

Does the answer depend on gender, ethnicity, or developmental stage?

The magnitudes of most of the aforementioned apparent neighborhood influences typically

appeared to be contingent on the gender and ethnicity of the child or youth. The evidence did not

suggest, however, that any particular gender or ethnicity was generally more sensitive to

neighborhood context. Instead, the relative sensitivity depended on the outcome in question.

Differences in magnitudes of neighborhood effects across developmental stages were exhibited

for several outcomes and could be substantial. At which stage neighborhood effects appear

stronger varied by outcome in question and sometimes whether neighborhood context was

measured contemporaneously or cumulatively. We thus caution against making broad

generalizations about “for whom and at which developmental stage are neighborhood influences

most important,” given the apparent multicontingent nature of the answer.

Does the answer depend on whether neighborhood environment is measured concurrently with

the outcome or cumulatively throughout childhood prior to the outcome?

Neighborhood effects on health measured as cumulative exposures appeared stronger, on

average, than those measured contemporaneously but only when the outcome in question was

observed during the middle school developmental stage. Quite a different pattern emerged for

educational outcomes. With these outcomes, there was no clear pattern of cumulative measures

being stronger; indeed, if anything, for the high school stage, the contemporaneous measures

appeared marginally stronger for some outcomes. Our results suggest that no general conclusion

can be reached about the comparative strength of contemporaneous and cumulative measures of

context; it appears to depend on outcome.

Are the relationships between neighborhood context and child outcomes linear or nonlinear—

that is, suggestive of thresholds past which neighborhood effects differ in magnitude?

Nonlinear neighborhood effects did not appear to be the norm, though for some indicators

(especially violent crime) they were consistently manifested. Observed nonlinear patterns were

often dissimilar across indicators, although a few (respiratory risk, occupational prestige, social

vulnerability) often exhibited theoretically supported minimum thresholds. Others (of particular

note, violent crime) exhibited V-shaped or inverse V-shaped relationships with particular

outcomes. Once again, no generalizations can be made: Nonlinear relationships appear to be

contingent on neighborhood indicator and outcome in question.

192

XI. Conclusions and Implications

Discussion of Effects From Residential Context

Neighborhood Safety

Neighborhood indicators in the domain of safety provided the most consistent explanatory power

across our domains of child and youth well-being. Some of the relationships manifested were to

be expected; others were surprising but revealing. As expected, our social problems index (a

caregiver assessment of disorder, property, and especially violent crime in the immediate

environs) and property crime rate (measured at the approximate scale of two encompassing

census tracts) were strongly associated with a wide range of negative outcomes in virtually every

domain investigated. Unexpectedly, violent crime rates (measured at the approximate scale of

two encompassing census tracts) exhibited the opposite associations, especially in places that had

below-average violent crime rates. We think that this finding reflects the net effects produced by

the conflicting forces impinging on children arising from violent crime in the broader

neighborhood, controlling for crime in the immediate environs—negative direct effects from

crime and alterations in caregiver actions in response to such that are intended to ameliorate

them. Caregivers may respond in several ways in an effort to shield their children from violent

crime in the wider environs, such as limiting youths’ activity spaces closer to home and

expanding caregiver monitoring activities. So long as violent crime stays below average, these

compensatory actions apparently yield net positive outcomes for children that manifest

themselves as reduced exposure to violence (as caregivers would hope), fewer risky behaviors,

and improved educational performance (as caregivers would like but perhaps not have expected).

Unfortunately, our findings suggest that the efficacy of such compensatory caregiver responses

will be overwhelmed in neighborhoods with above-average violent crime rates. In such cases,

more crime is, as conventionally predicted, associated with poorer child outcomes in health,

exposure to violence, risky behaviors, and educational performance. Our results here provide

implicit testimony to the importance of both measuring neighborhood characteristics at different

geographic scales and probing for nonlinear relationships.

Neighborhood Social Status

Residing in a higher occupational prestige neighborhood was one of the most consistent

predictors of favorable child outcomes in almost every domain. These results have intuitive

appeal and are consistent with prior scholarship on the importance of local networks, norms, and

role models in transmitting neighborhood effects. Neighborhoods that surround their children

with higher prestige workers likely expose them to norms and role models and provide access to

networks of richer information that ultimately promote better health, less exposure to violence,

fewer risky behaviors, better educational performance, and less nonmarital childbearing. There

are theoretical reasons why neighborhood social status could directly affect each of these

outcomes; many mediated causal pathways are also possible. For example, better child health

outcomes, less exposure to violence, and fewer risky behaviors should provide clear educational

payoffs for children and youth; better secondary educational achievement, in turn, might deter

nonmarital childbearing as young adults.

Another measure of neighborhood status, our social vulnerability score (summed percentages of

poor, unemployed, renter, and female-headed households) also proved a consistently predictive

aspect of context. As would be expected, our evidence suggests that a more socially vulnerable

193

XI. Conclusions and Implications

neighborhood will generate (through potentially a variety of mechanisms) several negative

outcomes for children and youth: more risky behaviors and less likelihood of marriage (for

African Americans). The evidence also supports the notion that in places that have above-

average concentrations of vulnerable populations, caregivers are less likely to seek medical

treatment when their children present with symptoms and less likely to know about and report

their children’s exposure to violence.

Neighborhood Ethnic and Nativity Composition

Our evidence implies that higher percentages of foreign-born residents create a collective

socialization context that supports the positive development of low-income, minority children

and youth in many ways: less likelihood of being victimized by neighborhood violence (for

boys), fewer risky behaviors (with the exception of smoking), superior educational performance,

better employment rates as young adults, and increased chances of marriage (for young women).

Less positively, our findings also suggest that high immigrant concentrations can discourage

parents from seeking diagnoses of adverse health symptoms, raise the chances of boys

witnessing neighborhood violence, and reduce the chances that young adult African Americans

will marry.

A similar portrait emerges for the Latino percentage in the neighborhood that we also believe can

best be explained by their distinctive normative and cultural structures. Low-income, minority

children raised among more Latino neighbors experienced better outcomes in terms of

witnessing neighborhood violence, risky behaviors, educational performance, and teen

childbearing. As in the case of immigrants, however, the portrait of neighborhood effects is not

uniformly positive. Our findings suggest that high Latino concentrations can discourage parents

from seeking diagnoses of adverse health symptoms, raise the chances of being victimized by

neighborhood violence or witnessing school violence, and reduce the chances that young adults

will be employed full time.

In contrast, the percentage of African-American neighbors rarely predicted child outcomes, and

when it did the results again were mixed. Higher concentrations of African-American residents

apparently reduced the chances of running away and women having children as teenagers but

decreased the chances of young women and African Americans getting married and discouraged

parents from seeking diagnoses of their children’s adverse health symptoms.

Neighborhood Physical Characteristics

We believe that our findings offer persuasive evidence that neighborhoods built before 1940 in

Denver have distinctive design, structural, and land use features that independently engender a

variety of effects on resident children and youth. It appears that most of these effects are

detrimental for children: greater exposure to violence, larger likelihood of risky behaviors,

weaker educational performance, and higher odds of bearing children outside of marriage as

young adults. Some outcomes, however, are more positive: lower incidence of obesity, greater

chance of working as a teen, and greater chance of being married as a young adult.

The quality of the ambient environment also seems to have a powerful impact on several child

outcomes, at least after pollution concentration thresholds have been surpassed. This strongly

194

XI. Conclusions and Implications

suggests a biological mechanism through which this neighborhood effect is transmitted. High

levels of neighborhood respiratory risk pollutants apparently led to substantially heightened

chances of asthma attacks, smoking, and weak educational performance. High levels of

neurological risk pollutants also apparently produced several detrimental health outcomes for

female and African-American youth.

Contrasting Findings With the Moving To Opportunity Demonstration

Given its salience, the findings from the Moving To Opportunity (MTO) analysis should be

compared with those from the DCS, though we acknowledge at the outset that precise

comparisons are impossible due to fundamental differences in measurement and analytical

design. In the domains of behavioral and physical health, exposure to violence, and risky

behaviors, however, we see both studies finding similar neighborhood effects. We believe that

our study suggests larger impacts of neighborhood in these realms than MTO, however. Results

are quite different in the realms of educational, labor market, and marriage and childbearing

outcomes, where we continue to find strong neighborhood effects, whereas MTO found

essentially none. We think that there are several reasons for these differences between the

studies’ outcomes.

First, there are differences in the samples of low-income families investigated:

 Baseline conditions differ dramatically. In MTO, all families were selected from

dilapidated public housing located in extremely disadvantaged neighborhoods; in DCS,

all families were selected from well-maintained public housing located in a wide variety

of neighborhoods. If there are durable damaging effects on children from living in

concentrated disadvantage—as we found from our labor findings—the MTO design

reduces the potentially salutary impacts of subsequent environments.

 In the full DCS sample, 56 percent of the children are Latino; only 30 percent of MTO

families are Latino (Sanbonmatsu et al., 2011). The neighborhood effects measured by

DCS for many of our educational, labor market, and childbearing outcomes appear

stronger for Latinos.

Second, the neighborhood “treatments” differ substantially:

 MTO offers an uncontrolled, “bundled” treatment: a disadvantaged public housing

development neighborhood; a nonpublic housing development neighborhood; and a

census tract with less than 10 percent poverty (at least for a year), followed by whatever

neighborhood bundles of attributes voucher holders subsequently choose.139 DCS

disentangles variations in exposure to a wide variety of distinct attributes comprising the

neighborhood bundle. If particular neighborhoods contain two attributes that generate

countervailing effects on a given child outcome, they may be cancelled out by the MTO

design.

139 MTO disaggregates aspects of the neighborhood context in measuring baseline and final conditions and assesses cross-group differences in these differences but never tests whether these different neighborhood components relate differently to child and youth outcomes.

195

XI. Conclusions and Implications

 Treatment exposure (both in terms of consistency and duration) is lower in MTO,

because many control families were forced to move as their public housing was

demolished, and the two experimental groups used vouchers. In contrast, our sample

spent considerable time in public housing and did not participate in the voucher program.

As a consequence, the DCS sample of households had a 6-year mean (5-year median)

DHA residential duration, approximately twice as long as reported for the MTO

experimental group (mean: 2.7 years; median: 3.3 years). Theory suggests that several

neighborhood effect mechanisms require a minimum duration of exposure before their

impact will come into play. Moreover, even if the average context is the same during a

period of a child’s life, two places well above and below average may yield different

consequences for a child than the one that was consistently experienced. For instance,

two cases having the same mean but different variances of the given neighborhood

indicator may not create identical “exposure” to that indicator; longer duration exposure

thus creates an important difference in the consistency of exposure.

 In MTO, the neighborhood treatment is confounded with another treatment that generally

has deleterious effects on children: moving. On average, DCS children moved 2.9 times

during childhood (median: 2.0 moves). Unfortunately, we have no comparable data for

MTO children.

Third, MTO has a wider set of outcome measures than DCS. Moreover, although many measures

in MTO (like DCS) rely on self-reporting and parental reporting, MTO also has some direct

measurement of outcomes that use biometrics, school records, and other administrative records.

We would argue, however, that there is no reason why reliance on parental recall would bias

measured neighborhood effects upward. Moreover, we would note that parental perceptions of

child outcomes often shape their behaviors vis-à-vis their children and so have validity in their

own right.

Fourth, children and youth were living in quite different metropolitan contexts in MTO and DCS.

MTO sites were Boston, New York, Baltimore, Chicago, and Los Angeles; DCS was conducted

in Denver only. Denver has many demographic and geographic features that make it unlike any

of the MTO sites. Denver is a newer, faster growing (except for Los Angeles) metropolitan area.

It has no concentrated, impoverished, heavily disinvested African-American ghetto; the African-

American population represents a relatively small share of the overall population. In contrast,

Latinos are a substantial share. Ethnic residential segregation is lower. Denver has a unified city–

county government and thus has much less geographic variation in local fiscal capacity and

public services than in the other sites. All of these distinctions imply that Denver offers very

different opportunity structures, local cultural norms, public expectations, and institutional

supports than the MTO sites. They may play themselves out in complicated ways that manifest

themselves in greater power for neighborhood effects.

Implications

We think that the DCS can contribute to the formulation and reform of assisted housing and

community development policy. Our findings suggest that well-formulated and targeted assisted

housing and urban revitalization programs can yield substantial payoffs in multiple outcome

domains by changing the developmental context of low-income, minority children and youth,

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XI. Conclusions and Implications

either by changing the character of neighborhoods or by changing the neighborhoods in which

these children reside. Our study has pinpointed particular attributes of the residential

environment that seem most predictive for a wide variety of positive outcomes, thus giving a

strategic guide to policymakers as to which directions and investments are likely to yield the

greatest social gains.

197

Appendix A

APPENDIX A. INVESTIGATING QUASI-RANDOM

ASSIGNMENT IN OUR NATURAL EXPERIMENT

198

Appendix A

Introduction

Although often advocated (for example, Oakes, 2004), some question whether natural

experiments can be leveraged to draw convincing implications about causal neighborhood

effects. The main reservation from doing so has been the lack of assurance that they in fact

produce a quasi-random assignment of households and thus convincingly avoid geographic

selection bias. This appendix uses our natural experiment involving public housing in Denver,

Colorado, and investigates whether it convincingly produced an essentially random allocation of

households across neighborhoods.

Methods of Analyzing Randomness of Initial Assignment

A few investigations of neighborhood effects employing natural experiments have probed the

degree to which quasi-random assignment was achieved. Three methods have been employed.

First, the allocation processes employed in the natural experiments are described in detail in an

effort to uncover points at which nonrandom selections could occur (for example, Oreopoulos,

2003; Edin, Fredricksson, and Åslund, 2003; Jacob, 2004; Lyle, 2007; Piil Damm, 2009; 2014).

Second, the sample of individuals analyzed is divided across two or more locations, and their

mean characteristics are compared statistically. Third, regression is used to assess whether there

are any non-zero relationships between individual characteristics and neighborhood

characteristics. We employ all three strategies here and present a fourth, original approach

involving Monte Carlo simulation.

Possibilities for Tenant Self-Selections and Staff Selections in the Denver Housing Authority

Allocation Process

First, we explore the possibility of selection arising because the tenant can potentially choose

between two Denver Housing Authority (DHA) units that may be located in quite different

neighborhoods. Our independent evaluation of DHA records showed that 70 percent of the

applicants accepted their first offer and 19 percent rejected the first but accepted their second.

Eight percent rejected both offers and, after falling to the bottom of the waiting list and again

rising to the top, were given a third offer that they accepted. Only 3 percent rejected 3 or more

offers.

Perhaps more revealing than acceptance rates is probing whether applicants ended up in

neighborhoods they would have selected on their own. Before their initial assignment to a DHA

dwelling, clients were asked by DHA whether they had any geographic location preferences.

DHA administrative data show that 42.5 percent of the clients in our sample did not articulate

any locational preference, approximately one-third expressed general geographical areas (for

example, Southwest Denver), while the remaining 23.5 percent provided responses that ranged

from specific addresses to specific DHA developments (for example, North Lincoln Campus of

Learners). To assess whether those who stated a preference were assigned to a housing unit in

their specified area, a number of different approaches were taken. For those who specified a

particular address, we determined whether that address was the DHA unit to which the client was

initially assigned. For those who specified a preference for a particular DHA development, we

199

Appendix A

used the unit number reported by DHA (which has an abbreviation of the development

embedded in it) to assess whether the initial DHA unit was located within that development. For

those who specified a preference for a particular neighborhood, we relied on our survey data to

determine whether the original DHA unit was in the specified neighborhood. Finally, initially

assigned DHA units were mapped to identify where within the Denver metropolitan area they

were located for those who specified a preference for a particular part of the metro area. When

these assessments had been made, we were able to calculate frequencies and percentages for

those who specified a geographic preference and got it ( N = 190; 25.8 percent) and those who specified a geographic preference but did not get a housing unit that met that preference ( N =

233; 31.7 percent). If the vast majority of households in our sample had a strong geographic

preference and were granted this preference by the DHA assignment process, one would

challenge the process as one producing a quasi-random assignment, but our analyses indicate to

the contrary that the vast majority of the respondents to our survey (74.2 percent) were either

instances where there was no geographic preference articulated or where the client’s stated

preference was not honored. Because we are unable to ascertain the geographic location of all

potential DHA unit vacancies that arose during the times that each client was assigned to his or

her initial unit, we are unable to perform any formal statistical tests to determine whether the

frequencies we obtained for those who were assigned their expressed preference were any

different than what would be expected by chance.

A second potential source of selection can arise from the actions of the DHA staff. If the staff

have multiple vacancies to consider at one time, dwelling offers may be made on the basis of

observable characteristics of the applicants at the top of the waiting list. Though our interviews

with DHA staff uncovered no suggestions that this occurred, we nevertheless must acknowledge

this possibility.

In sum, a close examination of the DHA dwelling allocation process leaves some room for

selection. A nontrivial share of DHA applicants did not accept their first offer from DHA

(30 percent) or ended up in a neighborhood they said they preferred (26 percent). It also may be

possible that DHA staff practiced some selection in their dwelling offers, though we have no

direct evidence of this. The degree to which this potential for selection was manifested is tested

below.

Comparisons of Individual Characteristics Across Space

A second way we test the randomness of the DHA assignment process is by ascertaining the

degree to which there are any systematic patterns of where individuals with particular

characteristics end up residing in their first DHA units. In other words, we investigate whether

certain types of households end up disproportionately allocated to particular places, whether it be

because of DHA practices or to choices made by applicants regarding, for instance, refusing first

options. We parse space in two ways: across DHA housing developments and by census tracts.

In both variants, we examine a wide range of individual characteristics—26 variables in all—

measuring attributes that are typically gathered in surveys used in neighborhood effects research

200

Appendix A

and many others that are not (but we have acquired through our aforementioned survey). These

individual characteristics are listed in columns of Exhibit A-1.140

Our method involves regressing each individual characteristic on a series of dummy variables. In

one variant, these dummies signify different DHA developments; in the other, they signify

census tracts. 141 We stratify these regressions by family size (zero or one child, two children,

three or more children), because there is a distinct geographic pattern in Denver of where public

housing units of various bedroom configurations are located. Our test of quasi-random

assignment is whether the place-based dummy variables denoting where DHA households were

originally placed are significantly different from zero.142 If they are, we reject the null hypothesis of random assignment of applicants to DHA dwellings.

140 Note that in our study, we consider only Latino and African-American residents of DHA; thus, we measure only African-American ethnic status, with Latino ethnicity being the reference group.

141 The scattered-site DHA developments are not identified in their allocation process by individual address but rather by broader geographic area encompassing several census tracts (though we are aware of the tract of each development). This produces the seemingly anomalous situation shown in Exhibits A-1 and A-2, where apparently many more tracts are represented than “developments.”

142 Here, number of children in the household refers to the number of eligible children for our study and not the total number of all children in the household. So, it is possible for households with 0–1 eligible children to have other siblings with the same father.

201

Appendix A

Exhibit A-1A. Relationships between DHA resident characteristics and DHA developments: Households with 0–1 Child

P/C employment

P/C received TANF P/C receiving Food

P/C had checking

P/C had health

status at time of

P/C disability status at

P/C is single parent

P/C hourly wage at

at time of DHA

Stamps at time of

account at time of

insurance at time of

DHA move-in

time of survey (1=yes;

(1=yes, 0=no)

time of DHA move-in

move-in (1=yes, DHA move-in (1=yes, DHA move-in (1=yes,

DHA move-in

(1=employed, 0=not

0=no)

0=no)

0=no)

0=no)

(1=yes, 0=no)

employed)

DHA Development

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Arrowhead Townhouses

-0.111

0.638

-1.133

0.113

-2.653

0.607

-0.0889

0.652

-1.156

0.062

-1.044

0.146

-0.956

0.215

-0.911

0.209

Columbine Homes

-0.500

0.052

-0.300

0.698

-4.701

0.402

-0.200

0.351

-0.267

0.691

-0.267

0.732

0.1000

0.905

-0.133

0.865

Curtis Park Home

-0.100

0.667

-0.200

0.775

-2.984

0.556

-8.31e-15

1.000

0.1000

0.869

0.200

0.776

-7.11e-15 1.000

-9.46e-15 1.000

FHA Repossessed East

-7.78e-16 1.000

0.200

0.886

6.466

0.524

-0.200

0.606

-0.600

0.621

-0.600

0.670

-0.400

0.791

0.200

0.888

Goldsmith Village

-1.03e-15 1.000

-0.133

0.886

-0.251

0.970

-0.200

0.439

-0.267

0.741

0.0667

0.943

0.600

0.552

-0.467

0.623

South Lincoln

-0.240

0.248

-1.080

0.086

-6.033

0.184

-8.12e-15

1.000

-1.000

0.066

-0.760

0.228

-0.880

0.194

-0.640

0.315

North Lincoln COL

-0.278

0.170

-0.106

0.863

-0.594

0.893

-0.117

0.490

-0.294

0.577

-0.0167

0.978

0.1000

0.879

-0.0778

0.900

Quigg Newton Homes

-0.167

0.416

-0.467

0.450

-8.084

0.071

-0.133

0.435

-0.333

0.533

0.0333

0.957

-0.0667

0.920

-0.133

0.832

Sun Valley Annex

-0.111

0.604

-0.522

0.419

-10.08

0.032

-0.0889

0.619

-0.156

0.781

0.0111

0.986

-0.289

0.679

-0.189

0.774

Pacific Place

-3.08e-16 1.000

-0.800

0.568

-13.78

0.175

-0.200

0.606

-0.600

0.621

-0.600

0.670

-0.400

0.791

-0.800

0.574

T Bean Tower (Elderly & Disabled) -7.88e-16 1.000

-0.800

0.568

-13.78

0.175

-0.200

0.606

-0.600

0.621

-0.600

0.670

-0.400

0.791

-0.800

0.574

Platte Valley Homes

-0.333

0.282

-0.467

0.617

-7.451

0.271

-0.200

0.439

0.400

0.621

0.400

0.670

-0.400

0.692

-0.133

0.888

Westridge Homes

-0.227

0.280

-0.300

0.636

-5.520

0.229

-0.0182

0.917

-0.191

0.728

0.0364

0.954

-0.127

0.852

0.0182

0.977

Westwood Homes

-0.154

0.490

-1.031

0.126

-6.469

0.185

0.108

0.563

-0.369

0.526

-0.754

0.265

-0.862

0.236

-0.646

0.345

Stapleton Homes

-9.83e-16 1.000

-0.300

0.779

-6.284

0.417

-0.200

0.499

-0.100

0.914

0.400

0.710

-0.400

0.729

0.200

0.854

Thomas Connole (Elderly & Dis.)

-1.000

0.032

-0.800

0.568

-13.78

0.175

0.800

0.040

-0.600

0.621

0.400

0.776

-0.400

0.791

0.200

0.888

East Village

-0.200

0.456

-0.400

0.621

-7.034

0.230

0.600

0.008

-0.600

0.391

-0.400

0.622

0.200

0.819

-0.200

0.808

Combined Devel-Disp Housing S. -0.355

0.083

-0.284

0.645

-4.886

0.274

-0.103

0.545

-0.342

0.521

0.0129

0.983

-0.271

0.684

-0.0581

0.926

Combined Devel-Disp Housing E. -0.250

0.239

-0.250

0.696

-2.848

0.538

0.1000

0.572

-0.350

0.527

-0.150

0.815

0.200

0.772

0.0500

0.939

Combined Devel-Disp Housing W. -0.316

0.139

-0.116

0.857

-1.142

0.806

-0.0421

0.813

-0.337

0.545

-0.0737

0.909

0.232

0.739

-0.116

0.859

Observations

261

261

261

261

261

261

261

261

F-Test

0.898

0.917

1.491

1.842

0.930

0.891

0.985

0.531

p value

0.590

0.566

0.0850

0.0175

0.550

0.599

0.481

0.952

Pseudo R ²

0.0696

0.0710

0.111

0.133

0.0719

0.0691

0.0759

0.0424

Note: P/C = Parent or Caregiver;

reference group = Arapaho Cts.

bold = p <.05

202

Appendix A

Exhibit A-1A. Relationships between DHA resident characteristics and DHA developments: Households with 0–1 child (continued)

P/C had too little

P/C had difficulty

Frequency that P/C

Number of years

Number of years

Frequency that P/C

P/C ever seen a

P/C born in the

Spanish language

money for food at

paying all bills at

smoked marijuana

during childhood that during childhood that

drank alcohol since

psychiatrist (1=yes,

United States

interview (1=yes;

time of DHA move-

time of DHA move-

since becoming a

P/C lived in public

P/C lived in a home

becoming a parent

0=no)

(1=yes; 0=no)

0=no)

in (1=yes, 0=no)

in (1=yes, 0=no)

parent

housing

owned by parents

DHA Development

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Arrowhead Townhouses

-0.756

0.327

-1.378

0.128

0.533

0.649

0.889

0.356

0.356

0.177

-0.689

0.887

-6.889

0.287

0.0889

0.580

1.58e-15

1.000

Columbine Homes

0.133

0.873

-0.267

0.786

0.0333

0.979

0.667

0.524

0.300

0.294

10.37

0.050

-20.00

0.005

0.200

0.252

1.84e-15

1.000

Curtis Park Home

0.400

0.597

-1.200

0.177

-0.300

0.794

0.200

0.832

0.400

0.122

-0.500

0.916

-9.300

0.144

0.100

0.526

0.100

0.374

FHA Repossessed East

0.800

0.597

-0.600

0.735

5.200

0.025

-7.48e-15

1.000

-0.200

0.698

-5.800

0.543

7.000

0.581

0.200

0.526

1.51e-15

1.000

Goldsmith Village

0.467

0.644

-0.267

0.821

-3.467

0.025

0.333

0.791

0.467

0.176

-5.800

0.362

-4.667

0.581

-0.133

0.526

1.75e-15

1.000

South Lincoln

-0.720

0.288

-0.960

0.227

-0.200

0.846

0.280

0.741

8.05e-15 1.000

1.120

0.793

-14.96

0.009

0.120

0.395

0.0800

0.427

North Lincoln COL

-0.0889

0.893

-0.267

0.730

-0.189

0.851

0.194

0.813

0.0778

0.729

-1.244

0.764

-7.139

0.197

0.0889

0.518

0.0278

0.777

Quigg Newton Homes

0.0333

0.960

-0.133

0.865

-0.333

0.743

-0.300

0.719

0.267

0.242

-0.200

0.962

-9.900

0.078

0.133

0.338

0.0667

0.502

Sun Valley Annex

0.0778

0.911

-0.656

0.423

-0.356

0.738

0.111

0.899

0.0222

0.926

4.700

0.286

-8.611

0.142

0.144

0.321

1.56e-15

1.000

Pacific Place

-0.200

0.895

-0.600

0.735

1.200

0.602

1.000

0.597

-0.200

0.698

-5.800

0.543

-20.00

0.116

0.200

0.526

1.37e-15

1.000

T Bean Tower (Elderly & Disabled) -0.200

0.895

-0.600

0.735

0.200

0.931

1.000

0.597

0.800

0.122

-5.800

0.543

-8.000

0.528

0.200

0.526

1.51e-15

1.000

Platte Valley Homes

0.133

0.895

-0.267

0.821

1.867

0.224

-5.18e-15

1.000

0.467

0.176

3.200

0.615

-7.000

0.408

0.200

0.342

1.49e-15

1.000

Westridge Homes

0.164

0.811

-0.100

0.901

-0.527

0.613

-0.0455

0.958

0.209

0.371

0.336

0.938

-9.864

0.087

0.109

0.445

0.0909

0.372

Westwood Homes

-0.508

0.485

-0.908

0.287

0.200

0.856

0.769

0.397

0.108

0.664

-2.492

0.586

-6.077

0.319

0.0462

0.761

1.56e-15

1.000

Stapleton Homes

0.300

0.795

-0.600

0.658

1.700

0.334

-6.33e-15

1.000

-0.200

0.612

-0.300

0.967

4.500

0.642

0.200

0.407

1.47e-15

1.000

Thomas Connole (Elderly & Dis.)

-0.200

0.895

0.400

0.821

-0.800

0.728

-6.35e-15

1.000

-0.200

0.698

-5.800

0.543

7.000

0.581

-0.800

0.012

1.47e-15

1.000

East Village

0.200

0.819

-0.600

0.558

0.400

0.763

0.600

0.583

0.200

0.502

-2.000

0.716

-10.40

0.157

-0.200

0.273

0.200

0.124

Combined Devel-Disp Housing S.

0.0903

0.892

-0.213

0.785

0.0387

0.970

0.194

0.816

-0.00645 0.977

0.458

0.913

-5.968

0.286

0.135

0.329

1.25e-15

1.000

Combined Devel-Disp Housing E.

-1.55e-15 1.000

-0.250

0.757

-0.350

0.739

1.000

0.247

0.150

0.524

-3.200

0.462

-6.050

0.297

0.100

0.487

0.0500

0.626

Combined Devel-Disp Housing W. 0.274

0.693

-0.547

0.501

0.0421

0.968

0.421

0.627

0.168

0.477

-3.905

0.372

-7.053

0.226

0.200

0.168

0.0526

0.610

Observations

261

261

261

261

261

261

261

261

261

F-Test

0.713

0.676

1.117

0.573

1.169

1.315

1.436

1.179

0.525

p value

0.812

0.848

0.333

0.929

0.283

0.170

0.106

0.273

0.954

Pseudo R ²

0.0561

0.0534

0.0851

0.0456

0.0888

0.0987

0.107

0.0895

0.0419

Note: P/C = Parent or Caregiver;

reference group = Arapaho Cts.

bold = p <.05

203

Appendix A

Exhibit A-1A. Relationships between DHA resident characteristics and DHA developments: Households with 0–1 child (continued)

Biological father

Parent have HS

Parent have any

Parent Depressive

always lived in

Kids share same

Parenting Efficacy

Parenting Beliefs

Parent's age at time P/C African American

diploma at time of

higher education at

Symptomatology

household with

biological dad

Scale at time of

Scale at time of

of DHA move-in

(1=yes; 0=no)

DHA move-in (1=yes; time of DHA move-

Scale at time of

child(ren) (1=yes;

(1=yes; 0=no)

interview

interview

0=no)

in (1=yes; 0=no)

interview

0=no)

DHA Development

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Arrowhead Townhouses

-0.0889

0.657

5.756

0.338

0.289

0.269

0.133

0.611

-0.200

0.267

-0.0444

0.865

-0.0667

0.990

-0.0889

0.962

-1.911

0.332

Columbine Homes

0.133

0.540

6.200

0.342

-0.433

0.127

-0.0333

0.907

-0.200

0.306

0.0667

0.814

-6.233

0.295

0.467

0.819

0.367

0.864

Curtis Park Home

-0.100

0.611

0.900

0.879

0.100

0.697

0.100

0.697

-0.100

0.571

-0.300

0.242

5.100

0.343

-0.300

0.871

-1.100

0.570

FHA Repossessed East

-0.200

0.611

11.20

0.343

-0.600

0.243

-0.200

0.697

-0.200

0.571

0.400

0.435

-8.400

0.435

-4.200

0.257

3.200

0.409

Goldsmith Village

-0.200

0.446

11.87

0.132

0.0667

0.845

-0.200

0.560

0.133

0.571

0.0667

0.845

-3.400

0.635

0.467

0.850

-3.133

0.225

South Lincoln

0.0800

0.649

5.680

0.282

-0.240

0.296

0.360

0.118

-0.120

0.448

0.0800

0.727

1.600

0.739

-1.040

0.530

-0.920

0.595

North Lincoln COL

-0.0611

0.721

4.561

0.375

-0.0722

0.747

0.217

0.334

-0.0889

0.564

0.178

0.426

-1.844

0.694

-1.256

0.436

-0.828

0.623

Quigg Newton Homes

-0.167

0.337

4.300

0.408

-0.267

0.239

0.233

0.304

-0.167

0.285

0.133

0.555

2.033

0.668

-1.667

0.307

-1.400

0.412

Sun Valley Annex

-0.0889

0.624

0.311

0.954

-0.156

0.511

0.0778

0.743

-0.144

0.376

-0.0444

0.851

-1.067

0.830

-1.811

0.289

-0.578

0.746

Pacific Place

-0.200

0.611

30.20

0.011

0.400

0.436

-0.200

0.697

-0.200

0.571

0.400

0.435

8.600

0.424

-3.200

0.387

-0.800

0.836

T Bean Tower (Elderly & Disabled) -0.200

0.611

17.20

0.145

0.400

0.436

0.800

0.121

-0.200

0.571

0.400

0.435

8.600

0.424

1.800

0.627

2.200

0.570

Platte Valley Homes

-0.200

0.446

12.20

0.122

0.400

0.243

-0.200

0.560

0.133

0.571

0.0667

0.845

3.933

0.583

-2.200

0.373

0.533

0.836

Westridge Homes

-0.109

0.540

3.609

0.499

-0.327

0.159

0.0727

0.755

-0.0182

0.909

0.0364

0.875

-1.309

0.788

-1.291

0.441

0.473

0.787

Westwood Homes

-0.0462

0.807

8.123

0.152

-0.138

0.574

-0.123

0.618

0.185

0.277

0.0154

0.950

-0.785

0.879

0.0308

0.986

-0.415

0.823

Stapleton Homes

-0.200

0.506

5.200

0.564

0.400

0.308

0.800

0.043

-0.200

0.459

0.400

0.307

-6.900

0.401

1.800

0.524

0.700

0.813

Thomas Connole (Elderly & Dis.)

-0.200

0.611

29.20

0.014

-0.600

0.243

-0.200

0.697

-0.200

0.571

0.400

0.435

10.60

0.325

-11.20

0.003

-7.800

0.045

East Village

-3.54e-15 1.000

15.00

0.028

3.03e-15

1.000

6.26e-15

1.000

-4.03e-15 1.000

0.200

0.499

6.400

0.303

-3.600

0.093

-2.600

0.245

Combined Devel-Disp Housing S.

-0.0387

0.823

1.910

0.713

-0.342

0.131

0.123

0.588

-0.135

0.384

0.110

0.627

-1.787

0.706

-1.232

0.449

-0.574

0.736

Combined Devel-Disp Housing E.

-3.49e-15 1.000

7.850

0.146

0.250

0.286

0.100

0.670

0.0500

0.756

0.150

0.522

-2.850

0.562

-2.150

0.204

-1.750

0.322

Combined Devel-Disp Housing W. 0.0105

0.953

9.095

0.094

-0.337

0.153

0.116

0.624

-0.0947

0.559

0.189

0.421

-1.453

0.768

-1.095

0.519

-0.221

0.901

Observations

261

261

261

261

261

261

261

261

261

F-Test

0.619

1.677

2.800

1.260

1.165

0.787

0.955

1.209

0.865

p value

0.897

0.0378

0.000108

0.207

0.286

0.729

0.518

0.247

0.632

Pseudo R ²

0.0491

0.123

0.189

0.0950

0.0885

0.0615

0.0737

0.0915

0.0672

Note: P/C = Parent or Caregiver;

reference group = Arapaho Cts.

bold = p <.05

204

Appendix A

Exhibit A-1B. Relationships between DHA resident characteristics and DHA developments: Households with two children

P/C employment

P/C receiving

P/C had checking

P/C had health

status at time of DHA

P/C disability status at P/C received TANF at

P/C is single parent

P/C hourly wage at

Food Stamps at

account at time of

insurance at time

move-in

time of survey (1=yes; time of DHA move-in

(1=yes, 0=no)

time of DHA move-in

time of DHA move-

DHA move-in

of DHA move-in

(1=employed, 0=not

0=no)

(1=yes, 0=no)

in (1=yes, 0=no)

(1=yes, 0=no)

(1=yes, 0=no)

employed)

DHA Development

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Arrowhead Townhouses

0.333

0.383

0.667

0.600

9.167

0.266

3.61e-15

1.000

0.167

0.874

0.167

0.857

0.500

0.685

2.46e-14 1.000

Columbine Homes

0.333

0.298

0.417

0.695

4.542

0.509

0.500

0.043

0.417

0.637

0.417

0.590

2.73e-14

1.000

-0.250

0.816

Curtis Park Homes

0.0476

0.858

-0.476

0.591

0.792

0.890

0.143

0.486

-0.476

0.517

0.452

0.483

-0.286

0.739

-0.714

0.425

FHA Repossessed East

0.333

0.491

0.667

0.678

17.17

0.100

4.17e-15

1.000

-0.333

0.803

-0.333

0.776

1.000

0.521

2.38e-14 1.000

Goldsmith Village

0.333

0.491

-0.333

0.836

-7.333

0.481

4.18e-15

1.000

-0.333

0.803

-0.333

0.776

2.82e-14

1.000

2.38e-14 1.000

South Lincoln

0.175

0.500

-0.386

0.655

0.227

0.968

0.0526

0.793

0.140

0.845

0.404

0.522

-0.158

0.851

-0.842

0.335

North Lincoln COL

-0.0370

0.884

0.444

0.600

8.220

0.135

0.0741

0.706

0.0370

0.958

0.333

0.589

0.630

0.443

-0.333

0.697

220

0.333

0.491

-0.333

0.836

-7.333

0.481

4.58e-15

1.000

-0.333

0.803

-0.333

0.776

2.87e-14

1.000

-1.000

0.538

Quigg Newton Homes

0.222

0.395

0.111

0.898

0.146

0.979

0.167

0.407

0.167

0.817

0.333

0.598

0.222

0.792

-0.278

0.751

Sun Valley Annex

0.0833

0.758

0.333

0.710

3.164

0.586

4.70e-15

1.000

0.250

0.737

0.417

0.524

0.333

0.702

-0.333

0.713

Pacific Place

0.333

0.298

-1.833

0.085

5.042

0.464

4.33e-15

1.000

0.167

0.850

0.417

0.590

-2.000

0.053

-2.500

0.021

Platte Valley Homes

-3.83e-15

1.000

1.16e-14

1.000

-2.233

0.726

0.500

0.029

-1.333

0.104

0.500

0.485

0.167

0.861

-0.500

0.615

Westridge Homes

0.0333

0.904

-0.133

0.884

-3.933

0.507

0.100

0.637

0.267

0.726

0.267

0.689

0.300

0.736

-0.400

0.666

Westwood Homes

0.0333

0.904

0.167

0.856

0.552

0.926

0.100

0.637

0.367

0.630

0.467

0.484

0.200

0.822

-0.1000

0.914

Stapleton Homes

0.333

0.383

-4.333

0.001

-0.583

0.943

0.500

0.090

0.667

0.527

0.167

0.857

-4.500

0.000

-5.000

0.000

East Village

0.333

0.383

0.667

0.600

8.167

0.321

4.62e-15

1.000

-0.333

0.752

-0.333

0.718

0.500

0.685

-0.500

0.697

Combined Devel-Disp Hsing S.

0.0769

0.759

0.0256

0.975

3.107

0.565

0.128

0.507

-0.282

0.684

-0.128

0.833

0.154

0.849

-0.410

0.626

Combined Devel-Disp Hsing E.

0.194

0.440

0.500

0.550

8.579

0.114

0.139

0.473

-2.55e-16

1.000

0.0556

0.927

0.556

0.493

-0.278

0.742

Combined Devel-Disp Hsing W. 0.121

0.631

0.212

0.800

1.736

0.749

0.0909

0.640

-0.0606

0.931

0.242

0.691

0.333

0.682

-0.242

0.775

Observations

244

244

244

244

244

244

244

244

F-Test

0.699

2.087

2.329

1.193

0.899

0.578

2.352

1.840

p value

0.818

0.00616

0.00184

0.265

0.584

0.920

0.00164

0.0200

Pseudo R ²

0.0560

0.150

0.165

0.0919

0.0709

0.0467

0.166

0.135

Note: P/C = Parent or Caregiver;

reference group = Arapaho Cts.

bold = p <.05

205

Appendix A

Exhibit A-1B. Relationships between DHA resident characteristics and DHA developments: Households with two children (continued)

P/C had too little

P/C had difficulty

Frequency that P/C

Number of years

Number of years

Frequency that P/C

P/C ever seen a

P/C born in the

Spanish language

money for food at

paying all bills at time

smoked marijuana

during childhood during childhood that

drank alcohol since

psychiatrist (1=yes,

United States

interview (1=yes;

time of DHA move-in

of DHA move-in

since becoming a

that P/C lived in

P/C lived in a home

becoming a parent

0=no)

(1=yes; 0=no)

0=no)

(1=yes, 0=no)

(1=yes, 0=no)

parent

public housing

owned by parents

DHA Development

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Arrowhead Townhouses

-0.167

0.893

3.167

0.082

-0.500

0.846

-0.500

0.804

0.167

0.710

1.34e-13 1.000

17.83

0.106

7.02e-15 1.000

4.22e-15 1.000

Columbine Homes

-0.667

0.521

2.667

0.080

1.000

0.642

2.500

0.138

0.667

0.077

11.25

0.057

-1.917

0.835

6.93e-15 1.000

4.24e-15 1.000

Curtis Park Homes

-1.024

0.237

2.381

0.061

0.571

0.749

0.429

0.760

0.0238

0.939

6.000

0.222

4.262

0.578

-0.143

0.525

0.0714

0.655

FHA Repossessed East

0.333

0.832

2.667

0.246

-1.000

0.758

5.000

0.050

0.667

0.241

1.27e-13 1.000

10.33

0.458

6.36e-15 1.000

4.46e-15 1.000

Goldsmith Village

-0.667

0.671

2.667

0.246

-7.26e-14

1.000

-1.82e-15

1.000

0.667

0.241

16.00

0.073

-8.667

0.533

6.37e-15 1.000

4.45e-15 1.000

South Lincoln

-0.772

0.361

2.246

0.070

-0.474

0.786

-0.842

0.539

0.0351

0.909

6.579

0.171

-2.193

0.769

6.04e-15 1.000

4.67e-15 1.000

North Lincoln COL

-0.259

0.754

2.815

0.021

-0.481

0.778

-0.778

0.562

0.222

0.458

2.630

0.575

5.296

0.470

-0.259

0.229

0.0370

0.808

220

0.333

0.832

3.667

0.111

-1.000

0.758

-1.000

0.694

-0.333

0.557

1.22e-13 1.000

10.33

0.458

5.98e-15 1.000

4.67e-15 1.000

Quigg Newton Homes

-0.556

0.512

1.833

0.140

-0.722

0.680

-0.667

0.628

0.222

0.469

2.833

0.556

4.556

0.544

-0.222

0.314

0.167

0.287

Sun Valley Annex

-0.333

0.704

3.083

0.017

-0.667

0.713

-0.667

0.639

-0.167

0.600

6.333

0.204

2.667

0.732

-0.167

0.465

0.0833

0.607

Pacific Place

-2.417

0.021

0.917

0.546

0.750

0.727

-0.750

0.656

-0.0833

0.824

6.000

0.309

-1.917

0.835

-0.250

0.355

0.250

0.193

Platte Valley Homes

-0.333

0.729

2.833

0.045

-0.833

0.675

0.333

0.831

-4.96e-15

1.000

2.833

0.604

-0.833

0.922

5.69e-15 1.000

4.77e-15 1.000

Westridge Homes

-0.0667

0.941

3.267

0.013

-1.000

0.589

-1.100

0.448

0.0667

0.837

3.800

0.455

7.933

0.317

-0.1000

0.667

0.100

0.545

Westwood Homes

-0.567

0.527

2.767

0.035

-1.500

0.418

-0.400

0.783

0.0667

0.837

5.800

0.254

4.033

0.611

-0.300

0.198

4.69e-15 1.000

Stapleton Homes

-5.167

0.000

-1.833

0.312

0.500

0.846

-0.500

0.804

0.167

0.710

5.500

0.435

-8.667

0.431

5.97e-15 1.000

4.68e-15 1.000

East Village

-0.167

0.893

2.667

0.142

-1.000

0.697

-0.500

0.804

-0.333

0.458

1.21e-13 1.000

3.833

0.727

-0.500

0.122

4.69e-15 1.000

Combined Devel-Disp Hsing S.

-0.436

0.593

2.974

0.013

-1.051

0.533

-0.564

0.669

0.0513

0.862

5.077

0.273

3.513

0.626

-0.0769

0.716

0.0256

0.865

Combined Devel-Disp Hsing E.

-0.194

0.812

3.167

0.008

-0.528

0.755

-0.778

0.557

0.222

0.452

1.944

0.675

3.361

0.642

-0.139

0.513

0.0556

0.713

Combined Devel-Disp Hsing W. -0.303

0.712

2.879

0.017

-1.061

0.532

-0.394

0.767

-0.0303

0.919

4.394

0.345

0.758

0.917

-0.182

0.394

0.152

0.317

Observations

244

244

244

244

244

244

244

244

244

F-Test

2.138

1.514

0.447

1.044

1.226

0.952

0.879

0.874

0.744

p value

0.00481

0.0821

0.979

0.411

0.238

0.520

0.609

0.616

0.771

Pseudo R ²

0.153

0.114

0.0365

0.0814

0.0942

0.0747

0.0694

0.0690

0.0593

Note: P/C = Parent or Caregiver;

reference group = Arapaho Cts.

bold = p <.05

206

Appendix A

Exhibit A-1B. Relationships between DHA resident characteristics and DHA developments: Households with two children (continued)

Biological father

Parent have HS

Parent have any

Parent Depressive

always lived in

P/C African

Kids share same

Parenting Efficacy Parenting Beliefs

Parent's age at time

diploma at time of

higher education at

Symptomatology

household with

American (1=yes;

biological dad

Scale at time of

Scale at time of

of DHA move-in

DHA move-in (1=yes; time of DHA move-in

Scale at time of

child(ren) (1=yes;

0=no)

(1=yes; 0=no)

interview

interview

0=no)

(1=yes; 0=no)

interview

0=no)

DHA Development

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Arrowhead Townhouses

1.15e-14

1.000

-4.833

0.554

-0.500

0.174

1.000

0.015

-0.333

0.248

1.000

0.025

12.17

0.151

0.333

0.921

-7.833

0.030

Columbine Homes

1.14e-14

1.000

-7.583

0.267

-0.500

0.105

0.250

0.465

-0.0833

0.730

0.250

0.502

12.92

0.069

-5.917

0.037

-1.333

0.658

Curtis Park Homes

0.286

0.222

-9.619

0.092

-0.214

0.403

0.500

0.080

-0.262

0.193

0.286

0.357

3.881

0.510

-2.952

0.209

-2.619

0.297

FHA Repossessed East

1.10e-14

1.000

-6.333

0.540

5.98e-15

1.000

1.70e-14

1.000

-0.333

0.361

1.000

0.076

-4.333

0.685

-0.667

0.876

-4.333

0.342

Goldsmith Village

1.10e-14

1.000

-11.33

0.273

5.99e-15

1.000

1.70e-14

1.000

-0.333

0.361

1.33e-14 1.000

13.67

0.202

0.333

0.938

-1.333

0.770

South Lincoln

0.158

0.489

-10.07

0.071

-0.526

0.036

0.316

0.257

-0.281

0.153

0.316

0.297

2.140

0.710

-2.246

0.328

-1.386

0.572

North Lincoln COL

0.148

0.507

-7.222

0.185

-0.370

0.131

0.111

0.683

-0.185

0.336

0.407

0.170

6.815

0.227

-3.481

0.122

-0.889

0.711

220

1.07e-14

1.000

-3.333

0.747

-1.000

0.032

1.000

0.054

-0.333

0.361

1.000

0.076

-0.333

0.975

-5.667

0.184

0.667

0.884

Quigg Newton Homes

1.01e-14

1.000

-8.111

0.147

-0.722

0.004

0.167

0.550

-0.278

0.159

0.333

0.273

4.000

0.489

-1.833

0.426

-1.333

0.588

Sun Valley Annex

0.167

0.482

-10.42

0.072

-0.750

0.004

0.167

0.564

-0.250

0.221

0.417

0.186

6.583

0.271

-2.167

0.363

-0.500

0.844

Pacific Place

1.14e-14

1.000

-5.583

0.414

-1.000

0.001

0.250

0.465

-0.333

0.168

0.250

0.502

-3.083

0.663

-1.167

0.679

-2.583

0.392

Platte Valley Homes

0.167

0.521

-4.833

0.445

3.89e-15

1.000

0.167

0.598

-0.167

0.456

0.167

0.628

2.500

0.703

-1.167

0.655

-2.500

0.371

Westridge Homes

0.100

0.679

-10.33

0.080

-0.800

0.003

0.500

0.091

-0.133

0.521

0.400

0.213

9.267

0.129

-4.067

0.095

0.867

0.739

Westwood Homes

0.200

0.408

-7.733

0.190

-0.400

0.132

0.400

0.175

-0.333

0.110

0.200

0.533

3.167

0.604

-2.367

0.330

-1.433

0.581

Stapleton Homes

1.06e-14

1.000

-8.333

0.308

5.07e-15

1.000

1.63e-14

1.000

-0.333

0.248

1.33e-14 1.000

2.667

0.752

-5.667

0.093

-3.833

0.288

East Village

1.06e-14

1.000

4.167

0.610

-0.500

0.174

1.61e-14

1.000

-0.333

0.248

1.000

0.025

-1.333

0.875

-3.167

0.347

-0.833

0.817

Combined Devel-Disp Hsing S.

0.205

0.351

-4.333

0.419

-0.795

0.001

0.385

0.152

-0.256

0.176

0.385

0.188

3.923

0.480

-3.077

0.165

-1.590

0.501

Combined Devel-Disp Hsing E.

0.167

0.450

-5.076

0.346

-0.111

0.646

0.361

0.180

-0.167

0.380

0.472

0.108

3.306

0.553

-3.528

0.112

-3.111

0.190

Combined Devel-Disp Hsing W. 0.182

0.411

-4.606

0.393

-0.970

0.000

0.212

0.432

-0.212

0.266

0.424

0.149

4.333

0.438

-2.061

0.355

-0.667

0.779

Observations

244

243

244

244

244

244

244

244

244

F-Test

0.522

1.044

7.950

1.481

0.503

1.014

0.980

0.954

1.135

p value

0.951

0.411

1.31e-16

0.0938

0.960

0.446

0.486

0.517

0.318

Pseudo R ²

0.0424

0.0817

0.403

0.112

0.0409

0.0792

0.0767

0.0749

0.0878

Note: P/C = Parent or Caregiver;

reference group = Arapaho Cts.

bold = p <.05

207

Appendix A

Exhibit A-1C. Relationships between DHA resident characteristics and DHA developments: Households with three or more children

P/C employment

P/C received

P/C receiving

P/C had checking

P/C had health

P/C is single

status at time of DHA

P/C disability status

P/C hourly wage at

TANF at time of

Food Stamps at

account at time of

insurance at time

parent (1=yes,

move-in

at time of survey

time of DHA move-in

DHA move-in

time of DHA move-

DHA move-in

of DHA move-in

0=no)

(1=employed, 0=not

(1=yes; 0=no)

(1=yes, 0=no)

in (1=yes, 0=no)

(1=yes, 0=no)

(1=yes, 0=no)

employed)

DHA Development

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Arrowhead Townhouses

0.250

0.627

1.000

0.443

18.75

0.077

-3.82e-15

1.000

-0.750

0.631

0.250

0.833

4.70e-15

1.000

0.750

0.523

Columbine Homes

0.250

0.386

0.429

0.558

7.407

0.211

-3.45e-15

1.000

-0.179

0.838

-0.0357

0.957

0.143

0.850

0.464

0.481

Curtis Park Homes

0.0682

0.800

0.455

0.505

7.409

0.180

0.0909

0.604

-1.295

0.113

-0.205

0.741

-0.727

0.303

0.386

0.528

FHA Repossessed East

0.250

0.627

1.000

0.443

22.50

0.034

-3.79e-15

1.000

-0.750

0.631

0.250

0.833

1.000

0.459

-0.250

0.831

Goldsmith Village

0.250

0.627

1.000

0.443

20.00

0.059

-3.79e-15

1.000

-0.750

0.631

0.250

0.833

1.000

0.459

0.750

0.523

South Lincoln

5.54e-15 1.000

0.437

0.502

7.734

0.144

0.125

0.456

-0.125

0.873

0.188

0.752

0.188

0.781

0.500

0.394

North Lincoln COL

-0.150

0.562

0.200

0.760

15.88

0.003

0.0667

0.693

-1.617

0.041

-1.283

0.032

4.85e-15

1.000

-0.250

0.672

Quigg Newton Homes

-0.0658

0.795

0.474

0.460

8.752

0.093

0.158

0.339

-0.329

0.668

-0.171

0.769

0.105

0.874

0.539

0.351

Sun Valley Annex

-0.125

0.657

0.625

0.382

12.09

0.038

-3.53e-15

1.000

-1.375

0.109

-7.89e-16 1.000

0.625

0.398

0.375

0.560

Pacific Place

0.250

0.530

0.500

0.621

9.500

0.246

-3.58e-15

1.000

-0.750

0.535

-0.250

0.785

0.500

0.632

-0.250

0.783

Platte Valley Homes

-0.250

0.530

-4.500

0.000

-1.30e-13

1.000

-3.59e-15

1.000

-0.250

0.836

0.250

0.785

4.49e-15

1.000

-0.250

0.783

Westridge Homes

-0.114

0.672

0.727

0.286

12.40

0.025

0.182

0.300

-0.205

0.802

0.0682

0.912

0.182

0.796

0.477

0.436

Westwood Homes

0.107

0.710

-1.000

0.172

4.819

0.416

0.143

0.447

-0.464

0.596

-0.179

0.788

-1.286

0.090

-1.107

0.094

Stapleton Homes

-0.0833

0.812

0.333

0.708

5.383

0.455

0.333

0.147

-0.417

0.696

-0.0833

0.918

0.333

0.718

0.0833

0.917

East Village

0.250

0.627

-2.31e-14

1.000

-4.42e-14

1.000

-3.57e-15

1.000

-0.750

0.631

0.250

0.833

1.000

0.459

0.750

0.523

Combined Devel-Disp Hsing S.

-0.132

0.586

0.647

0.294

12.27

0.015

0.0882

0.578

-0.279

0.705

-0.103

0.854

0.676

0.290

0.544

0.327

Combined Devel-Disp Hsing E.

0.00758 0.975

0.576

0.351

10.54

0.036

0.121

0.445

-0.356

0.630

-0.235

0.675

0.455

0.477

0.417

0.454

Combined Devel-Disp Hsing W. -0.00926 0.970

0.630

0.314

11.64

0.022

0.0370

0.818

-0.231

0.757

-0.0463

0.935

0.370

0.567

0.528

0.348

Observations

203

203

203

203

203

203

203

203

F-Test

0.575

2.884

1.479

0.452

1.202

1.169

1.525

1.359

p value

0.914

0.000169

0.101

0.974

0.263

0.291

0.0852

0.157

Pseudo R ²

0.0533

0.220

0.126

0.0424

0.105

0.103

0.130

0.117

Note: P/C = Parent or Caregiver;

reference group = Arapaho Cts.

bold = p <.05

208

Appendix A

Exhibit A-1C. Relationships between DHA resident characteristics and DHA developments: Households with three or more Children (continued)

Number of years

P/C had too little

P/C had difficulty

Frequency that P/C

Number of years

Frequency that P/C

P/C ever seen a

during childhood

P/C born in the

Spanish language

money for food at paying all bills at time

smoked marijuana

during childhood

drank alcohol since

psychiatrist

that P/C lived in a

United States

interview (1=yes;

time of DHA move-

of DHA move-in

since becoming a

that P/C lived in

becoming a parent

(1=yes, 0=no)

home owned by

(1=yes; 0=no)

0=no)

in (1=yes, 0=no)

(1=yes, 0=no)

parent

public housing

parents

DHA Development

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Arrowhead Townhouses

-0.250

0.828

-0.250

0.916

1.92e-14

1.000

-6.57e-15

1.000

-0.250

0.649

-13.50

0.139

8.000

0.549

3.25e-15 1.000

-2.92e-16 1.000

Columbine Homes

0.464

0.471

0.179

0.893

1.286

0.420

-0.429

0.789

0.179

0.563

-6.071

0.235

1.286

0.863

-0.143

0.542

0.143

0.375

Curtis Park Homes

0.477

0.427

-0.795

0.522

0.0909

0.951

-0.364

0.808

0.114

0.692

-5.955

0.211

6.545

0.348

3.52e-15 1.000

-5.00e-16 1.000

FHA Repossessed East

0.750

0.514

0.750

0.752

1.93e-14

1.000

-6.47e-15

1.000

-0.250

0.649

-13.50

0.139

22.00

0.100

3.21e-15 1.000

-3.69e-16 1.000

Goldsmith Village

0.750

0.514

0.750

0.752

1.000

0.725

1.000

0.727

0.750

0.174

-13.50

0.139

4.000

0.764

3.18e-15 1.000

-3.81e-16 1.000

South Lincoln

-0.0625

0.913

-0.562

0.636

-0.250

0.860

-5.91e-15

1.000

0.0625

0.820

-9.375

0.040

7.750

0.246

-0.187

0.370

0.187

0.192

North Lincoln COL

-0.583

0.314

-0.383

0.749

0.667

0.641

-0.467

0.746

0.417

0.133

-8.700

0.059

5.867

0.382

-0.133

0.526

-6.40e-16 1.000

Quigg Newton Homes

0.118

0.834

-0.461

0.694

0.263

0.851

0.895

0.525

0.329

0.225

-8.342

0.064

5.684

0.387

-0.263

0.202

0.211

0.137

Sun Valley Annex

0.625

0.321

0.375

0.773

0.625

0.688

0.375

0.811

0.125

0.678

-10.12

0.043

7.250

0.321

2.85e-15 1.000

-6.93e-16 1.000

Pacific Place

0.250

0.779

0.250

0.892

2.500

0.257

3.000

0.177

0.250

0.557

-6.000

0.395

8.500

0.411

2.86e-15 1.000

-7.06e-16 1.000

Platte Valley Homes

0.250

0.779

-0.250

0.892

2.03e-14

1.000

-6.14e-15

1.000

-0.250

0.557

-13.50

0.057

4.000

0.699

2.82e-15 1.000

-7.14e-16 1.000

Westridge Homes

0.295

0.622

0.386

0.756

-0.545

0.713

-0.727

0.626

-0.0682

0.812

-8.682

0.069

12.73

0.069

-0.273

0.212

0.0909

0.544

Westwood Homes

-1.393

0.032

-1.393

0.296

1.857

0.244

1.286

0.423

0.179

0.563

-7.786

0.128

8.143

0.277

-0.143

0.542

-6.44e-16 1.000

Stapleton Homes

0.0833

0.915

-2.917

0.074

2.333

0.230

-2.667

0.173

0.417

0.268

-7.167

0.250

16.67

0.068

2.92e-15 1.000

-9.54e-16 1.000

East Village

-0.250

0.828

0.750

0.752

2.000

0.482

-6.07e-15

1.000

-0.250

0.649

12.50

0.170

22.00

0.100

2.82e-15 1.000

-6.52e-16 1.000

Combined Devel-Disp Hsing S.

0.0735

0.892

-0.515

0.647

-0.382

0.776

-0.941

0.487

0.103

0.692

-10.76

0.013

10.18

0.108

-0.206

0.298

0.0294

0.828

Combined Devel-Disp Hsing E.

0.0530

0.922

-0.371

0.742

-0.0909

0.946

0.455

0.737

0.205

0.432

-11.50

0.008

10.58

0.095

-0.152

0.444

0.0606

0.655

Combined Devel-Disp Hsing W. 0.157

0.775

0.269

0.814

0.963

0.480

0.630

0.646

0.0463

0.861

-7.093

0.105

5.407

0.398

-0.185

0.356

0.0741

0.590

Observations

203

203

203

203

203

203

203

203

203

F-Test

1.534

0.721

0.852

1.086

0.990

1.279

0.879

0.530

0.823

p value

0.0822

0.787

0.637

0.369

0.473

0.206

0.604

0.941

0.671

Pseudo R ²

0.131

0.0659

0.0769

0.0960

0.0883

0.111

0.0792

0.0493

0.0745

Note: P/C = Parent or Caregiver;

reference group = Arapaho Cts.

bold = p <.05

209

Appendix A

ExhibitA-1C. Relationships between DHA resident characteristics and DHA developments: Households with three or more children (continued)

Biological father

Parent have HS

Parent have any

Parent Depressive

always lived in

P/C African

Kids share same

Parenting Efficacy Parenting Beliefs

Parent's age at time

diploma at time of higher education at

Symptomatology

household with

American (1=yes;

biological dad

Scale at time of

Scale at time of

of DHA move-in

DHA move-in (1=yes; time of DHA move-

Scale at time of

child(ren) (1=yes;

0=no)

(1=yes; 0=no)

interview

interview

0=no)

in (1=yes; 0=no)

interview

0=no)

DHA Development

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Arrowhead Townhouses

-0.250

0.613

3.250

0.729

-1.28e-14

1.000

-2.63e-15

1.000

-5.40e-15 1.000

-6.77e-15 1.000

-3.250

0.763

3.500

0.337

0.750

0.865

Columbine Homes

-0.107

0.699

12.25

0.021

-0.571

0.031

0.571

0.061

-5.08e-15 1.000

0.143

0.625

8.321

0.170

-1.643

0.422

-1.679

0.498

Curtis Park Homes

-0.0682

0.792

2.795

0.568

-9.76e-15

1.000

0.455

0.110

-5.63e-15 1.000

0.273

0.317

7.659

0.175

0.500

0.793

-2.795

0.227

FHA Repossessed East

-0.250

0.613

4.250

0.650

-1.16e-14

1.000

-2.46e-15

1.000

-5.38e-15 1.000

-6.64e-15 1.000

13.75

0.204

-4.500

0.218

-9.250

0.037

Goldsmith Village

0.750

0.130

7.250

0.439

-1.15e-14

1.000

-2.54e-15

1.000

-5.34e-15 1.000

1.000

0.056

20.75

0.056

-5.500

0.132

-2.250

0.611

South Lincoln

-0.188

0.448

6.687

0.154

-0.500

0.034

0.375

0.168

0.125

0.417

0.375

0.151

2.687

0.619

0.688

0.706

-1.438

0.515

North Lincoln COL

0.150

0.547

4.250

0.368

-0.467

0.050

0.200

0.464

0.0667

0.667

0.400

0.129

-1.383

0.799

1.033

0.573

-1.983

0.373

Quigg Newton Homes

-0.145

0.552

6.566

0.155

-0.895

0.000

0.316

0.238

0.0526

0.728

0.316

0.219

2.803

0.598

0.974

0.587

-1.197

0.582

Sun Valley Annex

0.250

0.356

8.125

0.114

-0.375

0.146

0.375

0.208

-5.19e-15 1.000

0.125

0.662

2.625

0.657

0.250

0.900

-0.250

0.918

Pacific Place

0.250

0.514

13.25

0.069

-1.000

0.007

-1.50e-15

1.000

-5.19e-15 1.000

-6.01e-15 1.000

3.250

0.698

1.46e-14 1.000

0.250

0.942

Platte Valley Homes

-0.250

0.514

3.750

0.605

-9.39e-15

1.000

0.500

0.235

-5.19e-15 1.000

-6.04e-15 1.000

2.250

0.788

0.500

0.859

-4.750

0.166

Westridge Homes

-0.0682

0.792

9.068

0.065

-0.545

0.027

0.273

0.336

0.182

0.258

0.273

0.317

-0.886

0.875

0.318

0.867

-2.068

0.371

Westwood Homes

-0.107

0.699

1.393

0.791

-0.857

0.001

0.143

0.639

-5.13e-15 1.000

0.429

0.144

-0.250

0.967

1.786

0.383

-3.679

0.139

Stapleton Homes

0.417

0.218

13.58

0.035

-1.02e-14

1.000

0.333

0.369

-4.91e-15 1.000

-6.34e-15 1.000

8.417

0.255

-0.167

0.947

-2.250

0.456

East Village

-0.250

0.613

11.25

0.230

-1.39e-14

1.000

1.000

0.066

-5.22e-15 1.000

-6.05e-15 1.000

-3.250

0.763

2.500

0.493

0.750

0.865

Combined Devel-Disp Hsing S.

0.162

0.489

9.515

0.033

-0.647

0.004

0.412

0.110

0.0294

0.840

0.529*

0.033

1.956

0.702

0.206

0.905

-0.779

0.709

Combined Devel-Disp Hsing E.

-0.00758 0.974

7.098

0.111

-0.303

0.174

0.394

0.126

0.152

0.299

0.303

0.221

-0.0682

0.989

0.773

0.654

-1.311

0.531

Combined Devel-Disp Hsing W. 0.120

0.611

8.139

0.071

-0.963

0.000

0.370

0.155

0.148

0.315

0.259

0.300

0.194

0.970

0.352

0.840

-2.028

0.339

Observations

203

203

203

203

203

203

203

203

203

F-Test

1.349

1.183

5.652

0.705

0.627

1.101

1.111

0.715

0.740

p value

0.162

0.279

1.47e-10

0.803

0.876

0.354

0.344

0.793

0.767

Pseudo R ²

0.117

0.104

0.356

0.0645

0.0578

0.0972

0.0981

0.0654

0.0675

Note: P/C = Parent or Caregiver;

reference group = Arapaho Cts.

bold = p <.05

210

Appendix A

Results of these tests using DHA development dummies are presented in

Exhibit A-1(A-C), those using census tract dummies in Exhibit A-2(A-C). Exhibit A-1

shows that there were few statistically significant differences in individual characteristics

across the various DHA developments: Of 1,482 coefficients across all family size strata,

only 72 (5 percent) were so.143 A similar aggregate portrait emerges from Exhibit A-2: Of

3,640 coefficients across all family size strata, only 202 (5.5 percent) were significant.144

Examination of individual characteristics reveals, however, that African Americans who

have two or more children were not proportionally distributed across all DHA

developments or census tracts where such developments were located.145 We cannot be

sure whether any systematic actions by the DHA or African-American applicants to DHA

produced this result, but the outcome was clearly inconsistent with quasi-random

assignment across developments or neighborhoods. The second notable revelation was

that DHA residents with disabilities (most of whom had two or fewer children) were also

allocated nonrandomly to a relatively few developments, producing a distinct profile for

their census tract characteristics. This is not surprising, inasmuch as certain DHA

developments are designed especially for elderly and disabled residents, and other,

scattered-site developments are rendered off-limits to the disabled because of

expectations of tenant contributions to dwelling and grounds maintenance. Conditioning

on ethnic and disability status, however, we think this evidence offers a compelling case

that DHA allocations were quasi-random across developments and neighborhoods,

because only 3 percent of the remaining coefficients proved statistically significant in

both Exhibits A-1and A-2 and there was no pattern to these coefficients. This percentage

could have been generated by chance even if true random assignment had been

undertaken.

143 The percentages across the 0–1 child, 2 children, and 3+ children strata were 3 percent, 6 percent, and 6 percent, respectively.

144 The percentages across the 0–1 child, 2 children, and 3+ children strata were 4 percent, 6 percent, and 6 percent, respectively.

145 Seventeen of the 37 DHA site coefficients were significantly different from zero for the African-American characteristic combining both family size strata; the corresponding figure for the 97 tract coefficients was 44.

211

Appendix A

Exhibit A-2A. Relationships between DHA resident characteristics and census tracts: Households

with 0–1 child

P/C employment

P/C receiving Food

P/C had checking

P/C had health

status at time of DHA

P/C disability status

P/C received TANF

P/C is single parent

P/C hourly wage at

Stamps at time of

account at time of

insurance at time of

move-in

at time of survey

at time of DHA move-

(1=yes, 0=no)

time of DHA move-in

DHA move-in (1=yes,

DHA move-in

DHA move-in

(1=employed, 0=not

(1=yes; 0=no)

in (1=yes, 0=no)

0=no)

(1=yes, 0=no)

(1=yes, 0=no)

employed)

Census Tract

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

2.0100

-2.46e-14

1.000

-1.000

0.604

-17.00

0.190

-1.000

0.042

-1.000

0.547

-1.000

0.605

9.19e-14

1.000

-1.000

0.605

2.0200

-0.267

0.524

-0.733

0.602

-12.11

0.201

-0.933

0.009

-0.867

0.474

-0.600

0.671

0.333

0.826

-0.333

0.813

3.0100

-1.000

0.082

-1.000

0.604

-17.00

0.190

-5.22e-14

1.000

-7.97e-15

1.000

-3.06e-14

1.000

1.000

0.630

-1.84e-14

1.000

3.0200

-0.667

0.155

-0.333

0.832

-10.67

0.314

-1.000

0.013

-1.000

0.460

-0.667

0.672

0.333

0.844

-0.333

0.833

5.0200

-0.500

0.314

8.11e-15

1.000

2.350

0.834

-1.000

0.019

-1.000

0.486

-0.500

0.765

1.000

0.579

-1.85e-14

1.000

7.0100

-2.46e-14

1.000

-0.333

0.832

-2.000

0.850

-1.000

0.013

-0.667

0.623

-0.333

0.833

1.000

0.556

-1.79e-14

1.000

7.0200

-0.190

0.646

-0.476

0.733

-8.343

0.374

-0.810

0.023

-0.571

0.634

-0.333

0.811

0.238

0.874

-0.190

0.892

8.0000

-0.0645

0.875

-0.613

0.658

-11.32

0.225

-0.935

0.008

-0.581

0.626

-0.419

0.762

0.0323

0.983

-0.355

0.798

9.0200

-2.45e-14

1.000

6.88e-15

1.000

5.000

0.700

-1.000

0.042

-8.65e-15

1.000

-3.04e-14

1.000

1.000

0.630

-1.000

0.605

9.0300

-1.000

0.082

6.67e-15

1.000

2.000

0.877

-1.000

0.042

-1.000

0.547

-1.000

0.605

1.000

0.630

-1.90e-14

1.000

9.0400

-2.45e-14

1.000

6.62e-15

1.000

1.500

0.908

-5.37e-14

1.000

-1.000

0.547

-3.03e-14

1.000

1.000

0.630

-1.90e-14

1.000

10.0000

-0.500

0.254

-0.500

0.734

-7.917

0.424

-1.000

0.008

-0.667

0.599

-0.667

0.651

0.500

0.753

-0.333

0.821

11.0100

-0.0625

0.881

-0.625

0.656

-10.90

0.249

-0.938

0.009

-0.688

0.570

-0.250

0.859

0.375

0.804

-0.375

0.790

14.0100

-2.47e-14

1.000

-1.000

0.604

-17

0.190

-1.000

0.042

-1.000

0.547

-1.000

0.605

9.18e-14

1.000

-1.000

0.605

15.0000

-0.333

0.476

-0.667

0.672

-12.00

0.257

-1.000

0.013

-0.333

0.806

-3.14e-14

1.000

9.19e-14

1.000

-1.88e-14

1.000

16.0000

-0.0667

0.873

-0.333

0.813

-5.205

0.582

-0.800

0.026

-0.333

0.783

-0.267

0.850

0.400

0.792

-0.200

0.887

18.0000

-2.44e-14

1.000

-1.000

0.549

-17.00

0.131

-0.500

0.238

-0.500

0.728

-0.500

0.765

9.19e-14

1.000

-1.85e-14

1.000

19.0000

-0.305

0.455

-0.695

0.613

-5.769

0.533

-0.881

0.012

-0.983

0.406

-0.712

0.605

0.102

0.945

-0.508

0.712

21.0000

-2.45e-14

1.000

5.87e-15

1.000

2.000

0.877

-1.000

0.042

-9.41e-15

1.000

-3.01e-14

1.000

1.000

0.630

-1.98e-14

1.000

23.0000

-2.44e-14

1.000

-0.500

0.764

-9.500

0.398

-0.500

0.238

-9.48e-15

1.000

-0.500

0.765

9.24e-14

1.000

-1.98e-14

1.000

24.0300

-0.167

0.689

-1.000

0.475

-8.504

0.367

-0.722

0.043

-1.111

0.357

-1.000

0.476

-0.111

0.941

-0.778

0.580

31.0200

-1.000

0.082

-1.000

0.604

-17.00

0.190

-5.51e-14

1.000

-1.000

0.547

-3.00e-14

1.000

9.26e-14

1.000

-2.01e-14

1.000

35.0000

-2.43e-14

1.000

5.30e-15

1.000

-1.000

0.938

-1.000

0.042

-9.87e-15

1.000

-2.98e-14

1.000

9.27e-14

1.000

-2.00e-14

1.000

36.0200

-2.43e-14

1.000

-0.500

0.764

-7.750

0.490

-1.000

0.019

-1.000

0.486

-0.500

0.765

0.500

0.781

-2.02e-14

1.000

37.0200

-2.43e-14

1.000

-1.000

0.604

-17.00

0.190

-5.52e-14

1.000

-1.00e-14

1.000

-3.00e-14

1.000

9.27e-14

1.000

-2.02e-14

1.000

37.0300

-0.500

0.314

-0.500

0.764

-8.325

0.458

-0.500

0.238

-1.000

0.486

-1.000

0.550

1.000

0.579

-2.02e-14

1.000

40.0300

-2.44e-14

1.000

4.81e-15

1.000

7.000

0.589

-1.000

0.042

-1.000

0.547

-3.01e-14

1.000

1.000

0.630

-2.03e-14

1.000

41.0100

-2.44e-14

1.000

4.92e-15

1.000

0.250

0.985

-1.000

0.042

-1.000

0.547

-3.01e-14

1.000

9.26e-14

1.000

-2.03e-14

1.000

41.0200

-2.43e-14

1.000

-1.000

0.604

-17.00

0.190

-1.000

0.042

-1.03e-14

1.000

-3.02e-14

1.000

1.000

0.630

-2.03e-14

1.000

41.0300

-0.333

0.476

-0.333

0.832

-1.250

0.906

-0.667

0.096

-1.000

0.460

-1.000

0.526

0.667

0.694

-1.95e-14

1.000

41.0400

-0.500

0.314

4.70e-15

1.000

1.450

0.897

-1.000

0.019

-1.000

0.486

-0.500

0.765

1.000

0.579

-2.05e-14

1.000

43.0100

-0.500

0.314

-1.000

0.549

-17.00

0.131

-5.60e-14

1.000

-1.000

0.486

-0.500

0.765

0.500

0.781

-0.500

0.765

44.0400

-2.48e-14

1.000

-0.333

0.832

-4.763

0.653

-0.667

0.096

-1.000

0.460

-1.000

0.526

0.667

0.694

-0.667

0.673

45.0100

-0.333

0.476

-0.333

0.832

-3.000

0.777

-1.000

0.013

-1.000

0.460

-0.667

0.672

0.333

0.844

-0.667

0.673

45.0200

-0.200

0.633

-1.200

0.394

-10.66

0.261

-0.733

0.041

-0.733

0.545

-1.000

0.478

-0.400

0.792

-0.867

0.540

46.0100

-1.000

0.082

8.40e-15

1.000

3.000

0.817

-1.000

0.042

-1.000

0.547

-3.05e-14

1.000

9.19e-14

1.000

-1.83e-14

1.000

46.0200

-1.000

0.082

8.34e-15

1.000

1.000

0.938

-1.000

0.042

-1.000

0.547

-1.000

0.605

9.18e-14

1.000

-1.000

0.605

50.0100

-1.000

0.082

8.66e-15

1.000

4.000

0.758

-5.18e-14

1.000

-1.000

0.547

-3.06e-14

1.000

1.000

0.630

-1.82e-14

1.000

51.0200

-1.000

0.045

-0.500

0.764

-7.775

0.489

-1.000

0.019

-1.000

0.486

-0.500

0.765

9.18e-14

1.000

-1.82e-14

1.000

54.0000

-2.46e-14

1.000

-1.000

0.604

-17

0.190

-1.000

0.042

-1.000

0.547

-1.000

0.605

9.18e-14

1.000

-1.000

0.605

55.0300

-0.333

0.435

-0.556

0.699

-9.111

0.346

-0.889

0.015

-0.889

0.472

-0.444

0.757

0.333

0.830

-0.111

0.939

68.0900

-2.49e-14

1.000

-0.333

0.832

-3.467

0.743

-1.000

0.013

-0.667

0.623

-0.333

0.833

1.000

0.556

-0.667

0.673

83.0300

-1.000

0.082

4.36e-15

1.000

17.00

0.190

-1.000

0.042

-1.000

0.547

-3.05e-14

1.000

1.000

0.630

-2.06e-14

1.000

Observations

261

261

261

261

261

261

261

261

F-Test

1.524

0.242

1.352

1.709

0.306

0.260

0.287

0.216

p value

0.0276

1.000

0.0853

0.00711

1.000

1.000

1.000

1.000

Pseudo R ²

0.232

0.0458

0.211

0.253

0.0572

0.0489

0.0538

0.0411

Note: P/C = Parent

or Caregiver;

reference group is

Tract 1.0200

bold = p <.05

212

Appendix A

Exhibit A-2A. Relationships between DHA resident characteristics and census tracts: Households

with 0–1 child (continued)

Number of years

P/C had too little

P/C had difficulty

Frequency that P/C

Number of years

Frequency that P/C

P/C ever seen a

during childhood

P/C born in the

Spanish language

money for food at time paying all bills at time

smoked marijuana

during childhood that

drank alcohol since

psychiatrist (1=yes,

that P/C lived in a

United States

interview (1=yes;

of DHA move-in

of DHA move-in

since becoming a

P/C lived in public

becoming a parent

0=no)

home owned by

(1=yes; 0=no)

0=no)

(1=yes, 0=no)

(1=yes, 0=no)

parent

housing

parents

Census Tract

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

2.0100

3.54e-14

1.000

9.63e-14

1.000

4.03e-14

1.000

7.87e-14

1.000

2.98e-14

1.000

2.31e-13

1.000

1.17e-12

1.000

-2.37e-14

1.000

-1.36e-15

1.000

2.0200

0.333

0.825

0.467

0.789

0.133

0.950

-0.200

0.911

0.467

0.342

5.333

0.565

7.533

0.536

-0.133

0.660

0.133

0.525

3.0100

1.000

0.628

9.63e-14

1.000

4.06e-14

1.000

7.87e-14

1.000

1.000

0.138

12.00

0.345

1.17e-12

1.000

-2.37e-14

1.000

-1.36e-15

1.000

3.0200

0.667

0.692

0.667

0.732

1.667

0.484

7.88e-14

1.000

3.08e-14

1.000

2.02e-13

1.000

9.000

0.509

-2.35e-14

1.000

-1.22e-15

1.000

5.0200

0.500

0.780

1.000

0.628

-4.000

0.114

0.500

0.813

2.99e-14

1.000

2.33e-13

1.000

25.00

0.085

-2.37e-14

1.000

-1.36e-15

1.000

7.0100

0.333

0.843

0.667

0.732

1.333

0.575

0.333

0.867

0.333

0.544

1.98e-13

1.000

22.00

0.107

-2.35e-14

1.000

-1.22e-15

1.000

7.0200

0.333

0.823

0.524

0.762

0.238

0.910

-0.0952

0.957

0.429

0.379

6.429

0.484

10.62

0.379

-0.0952

0.751

0.0952

0.647

8.0000

0.290

0.845

0.0645

0.970

0.548

0.793

0.161

0.927

0.258

0.593

8.065

0.377

12.42

0.301

-0.0645

0.829

-1.62e-15

1.000

9.0200

3.43e-14

1.000

9.63e-14

1.000

4.20e-14

1.000

7.88e-14

1.000

1.000

0.138

2.41e-13

1.000

25.00

0.135

-2.38e-14

1.000

1.000

0.001

9.0300

1.000

0.628

1.000

0.675

8.000

0.006

4.000

0.103

1.000

0.138

2.41e-13

1.000

22.00

0.188

-2.38e-14

1.000

-1.35e-15

1.000

9.0400

1.000

0.628

9.62e-14

1.000

2.000

0.493

7.88e-14

1.000

3.03e-14

1.000

2.41e-13

1.000

18.00

0.281

-2.39e-14

1.000

-1.34e-15

1.000

10.0000

0.333

0.832

0.333

0.855

0.833

0.708

0.667

0.721

0.500

0.331

16.17

0.096

1.12e-12

1.000

-2.31e-14

1.000

-1.18e-15

1.000

11.0100

0.250

0.868

0.438

0.801

0.750

0.724

-0.312

0.861

0.375

0.444

3.813

0.680

10.19

0.402

-2.36e-14

1.000

-1.43e-15

1.000

14.0100

1.000

0.628

9.65e-14

1.000

3.94e-14

1.000

7.81e-14

1.000

2.99e-14

1.000

2.28e-13

1.000

1.17e-12

1.000

-2.36e-14

1.000

-1.31e-15

1.000

15.0000

0.333

0.843

0.333

0.864

2.000

0.401

7.86e-14

1.000

0.333

0.544

12.67

0.222

25.33

0.064

-2.36e-14

1.000

-1.25e-15

1.000

16.0000

0.467

0.757

-0.200

0.909

0.600

0.778

0.133

0.940

0.467

0.342

5.467

0.555

13.80

0.258

-0.133

0.660

0.0667

0.751

18.0000

0.500

0.780

1.000

0.628

0.500

0.843

0.500

0.813

0.500

0.391

2.28e-13

1.000

7.000

0.628

-2.36e-14

1.000

-1.34e-15

1.000

19.0000

-0.186

0.899

0.0508

0.976

0.678

0.744

0.203

0.907

0.237

0.621

6.169

0.496

10.20

0.391

-0.102

0.731

0.0508

0.804

21.0000

3.63e-14

1.000

9.65e-14

1.000

4.32e-14

1.000

7.66e-14

1.000

1.000

0.138

14.00

0.271

17.00

0.309

-2.37e-14

1.000

-1.27e-15

1.000

23.0000

3.64e-14

1.000

9.65e-14

1.000

3.000

0.235

5.000

0.019

0.500

0.391

2.43e-13

1.000

13.50

0.350

-2.37e-14

1.000

-1.26e-15

1.000

24.0300

-0.111

0.941

-0.333

0.847

1.500

0.479

0.667

0.708

0.556

0.256

5.111

0.579

12.06

0.320

-0.167

0.580

0.0556

0.790

31.0200

3.62e-14

1.000

1.000

0.675

4.38e-14

1.000

7.63e-14

1.000

3.11e-14

1.000

2.48e-13

1.000

27.00

0.107

-1.000

0.017

-1.25e-15

1.000

35.0000

3.62e-14

1.000

9.65e-14

1.000

4.38e-14

1.000

7.63e-14

1.000

3.11e-14

1.000

2.48e-13

1.000

27.00

0.107

-2.37e-14

1.000

-1.24e-15

1.000

36.0200

3.62e-14

1.000

9.64e-14

1.000

0.500

0.843

0.500

0.813

0.500

0.391

9.000

0.413

13.50

0.350

-2.38e-14

1.000

-1.23e-15

1.000

37.0200

3.62e-14

1.000

1.000

0.675

4.43e-14

1.000

7.63e-14

1.000

3.11e-14

1.000

15.00

0.238

1.19e-12

1.000

-2.37e-14

1.000

-1.25e-15

1.000

37.0300

3.62e-14

1.000

9.65e-14

1.000

4.45e-14

1.000

7.64e-14

1.000

3.12e-14

1.000

2.50e-13

1.000

8.500

0.556

-2.37e-14

1.000

-1.23e-15

1.000

40.0300

3.61e-14

1.000

9.64e-14

1.000

4.47e-14

1.000

7.63e-14

1.000

3.12e-14

1.000

2.51e-13

1.000

27.00

0.107

-1.000

0.017

-1.23e-15

1.000

41.0100

1.000

0.628

1.000

0.675

1.000

0.731

2.000

0.414

3.12e-14

1.000

2.51e-13

1.000

10.00

0.549

-2.38e-14

1.000

-1.23e-15

1.000

41.0200

3.61e-14

1.000

1.000

0.675

4.46e-14

1.000

7.64e-14

1.000

3.13e-14

1.000

2.51e-13

1.000

1.20e-12

1.000

-2.38e-14

1.000

-1.23e-15

1.000

41.0300

0.667

0.692

0.333

0.864

2.000

0.401

7.74e-14

1.000

0.667

0.225

5.000

0.630

18.00

0.187

-0.333

0.325

0.333

0.156

41.0400

0.500

0.780

9.64e-14

1.000

1.000

0.692

7.63e-14

1.000

3.13e-14

1.000

9.500

0.388

15.00

0.300

-2.38e-14

1.000

-1.22e-15

1.000

43.0100

3.61e-14

1.000

0.500

0.809

-3.500

0.166

3.000

0.158

3.13e-14

1.000

2.52e-13

1.000

21.00

0.147

-0.500

0.165

-1.22e-15

1.000

44.0400

0.333

0.843

0.333

0.864

0.333

0.889

7.87e-14

1.000

0.667

0.225

2.14e-13

1.000

15.00

0.271

-2.36e-14

1.000

-1.27e-15

1.000

45.0100

0.333

0.843

-2.333

0.232

1.667

0.484

0.667

0.739

0.667

0.225

3.333

0.748

7.000

0.607

-2.36e-14

1.000

-1.31e-15

1.000

45.0200

-0.267

0.859

-0.267

0.878

1.000

0.638

0.667

0.709

0.267

0.587

4.467

0.630

12.07

0.322

-0.133

0.660

-1.24e-15

1.000

46.0100

3.55e-14

1.000

1.000

0.675

4.00e-14

1.000

7.86e-14

1.000

1.000

0.138

2.30e-13

1.000

27.00

0.107

-2.36e-14

1.000

-1.35e-15

1.000

46.0200

1.000

0.628

9.64e-14

1.000

2.000

0.493

7.86e-14

1.000

2.99e-14

1.000

2.31e-13

1.000

21.00

0.209

-2.36e-14

1.000

-1.34e-15

1.000

50.0100

1.000

0.628

1.000

0.675

3.97e-14

1.000

7.85e-14

1.000

2.99e-14

1.000

2.29e-13

1.000

16.00

0.338

-2.36e-14

1.000

-1.34e-15

1.000

51.0200

3.59e-14

1.000

9.65e-14

1.000

3.96e-14

1.000

7.85e-14

1.000

2.99e-14

1.000

2.30e-13

1.000

5.500

0.703

-2.36e-14

1.000

-1.33e-15

1.000

54.0000

3.60e-14

1.000

9.66e-14

1.000

2.000

0.493

1.000

0.683

2.99e-14

1.000

2.29e-13

1.000

1.17e-12

1.000

-2.36e-14

1.000

-1.34e-15

1.000

55.0300

0.333

0.828

0.556

0.755

1.444

0.506

0.444

0.807

0.222

0.657

7.444

0.432

14.00

0.261

-2.37e-14

1.000

-1.31e-15

1.000

68.0900

0.667

0.692

0.333

0.864

-2.667

0.263

0.333

0.867

0.667

0.225

2.18e-13

1.000

15.33

0.261

-0.333

0.325

-8.30e-16

1.000

83.0300

3.60e-14

1.000

1.000

0.675

1.000

0.731

1.000

0.683

3.15e-14

1.000

2.56e-13

1.000

27.00

0.107

-2.38e-14

1.000

-1.21e-15

1.000

Observations

261

261

261

261

261

261

261

261

261

F-Test

0.259

0.383

1.299

0.777

0.972

0.773

0.981

0.865

0.905

p value

1.000

1.000

0.117

0.837

0.526

0.843

0.511

0.709

0.643

Pseudo R ²

0.0488

0.0706

0.205

0.133

0.162

0.133

0.163

0.146

0.152

Note: P/C = Parent

or Caregiver;

reference group is

Tract 1.0200

bold = p <.05

213

Appendix A

Exhibit A-2A. Relationships between DHA resident characteristics and census tracts: Households

with 0–1 child (continued)

Biological father

Parent have HS

Parent have any

Parent Depressive

always lived in

Kids share same

Parenting Efficacy

Parenting Beliefs

Parent's age at time P/C African American

diploma at time of

higher education at

Symptomatology

household with

biological dad (1=yes;

Scale at time of

Scale at time of

of DHA move-in

(1=yes; 0=no)

DHA move-in (1=yes; time of DHA move-in

Scale at time of

child(ren) (1=yes;

0=no)

interview

interview

0=no)

(1=yes; 0=no)

interview

0=no)

Census Tract

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

2.0100

3.92e-14

1.000

19.00

0.195

3.94e-14

1.000

-1.000

0.134

2.20e-14

1.000

-3.11e-14

1.000

1.51e-13

1.000

1.000

0.832

7.000

0.161

2.0200

0.0667

0.849

5.867

0.583

0.200

0.677

-0.533

0.273

0.0667

0.842

-0.267

0.580

11.87

0.245

-0.667

0.846

3.400

0.351

3.0100

1.000

0.038

24.00

0.102

3.92e-14

1.000

-1.000

0.134

2.20e-14

1.000

-1.000

0.130

31.00

0.027

-5.000

0.288

7.000

0.161

3.0200

0.333

0.396

14.33

0.231

3.87e-14

1.000

-1.000

0.067

2.20e-14

1.000

-3.12e-14

1.000

5.000

0.661

1.333

0.728

3.667

0.368

5.0200

3.92e-14

1.000

3.500

0.783

0.500

0.380

-0.500

0.386

2.19e-14

1.000

-3.09e-14

1.000

5.000

0.679

-2.000

0.623

4.500

0.298

7.0100

3.93e-14

1.000

7.333

0.540

1.000

0.063

-0.667

0.221

0.333

0.372

-3.12e-14

1.000

15.00

0.189

-1.667

0.664

1.667

0.682

7.0200

0.0952

0.784

0.619

0.953

0.286

0.548

-0.714

0.139

0.190

0.565

-0.381

0.425

8.571

0.396

-0.238

0.944

4.429

0.220

8.0000

0.0323

0.926

-2.194

0.835

0.387

0.413

-0.742

0.122

0.0323

0.922

-0.419

0.376

9.516

0.343

-0.581

0.864

2.548

0.477

9.0200

3.90e-14

1.000

-3.000

0.838

3.86e-14

1.000

-1.000

0.134

2.19e-14

1.000

-3.08e-14

1.000

5.000

0.720

-2.000

0.671

2.000

0.688

9.0300

3.90e-14

1.000

-7.000

0.633

3.86e-14

1.000

-1.000

0.134

2.19e-14

1.000

-1.000

0.130

18.00

0.198

-2.000

0.671

-1.000

0.841

9.0400

3.90e-14

1.000

5.03e-13

1.000

3.86e-14

1.000

-1.000

0.134

2.19e-14

1.000

-1.000

0.130

1.57e-13

1.000

3.000

0.524

7.000

0.161

10.0000

0.333

0.364

3.000

0.789

0.167

0.740

-0.833

0.102

2.19e-14

1.000

-0.333

0.508

4.167

0.696

1.667

0.643

4.167

0.274

11.0100

3.94e-14

1.000

-1.750

0.870

0.438

0.362

-0.563

0.247

2.21e-14

1.000

-0.188

0.696

12.19

0.231

0.437

0.898

1.375

0.705

14.0100

3.90e-14

1.000

16.00

0.275

4.01e-14

1.000

-9.49e-14

1.000

2.19e-14

1.000

-3.14e-14

1.000

5.000

0.720

3.000

0.524

4.000

0.423

15.0000

3.92e-14

1.000

0.667

0.956

0.667

0.215

-0.333

0.540

2.21e-14

1.000

-0.333

0.536

17.33

0.129

0.333

0.931

5.333

0.191

16.0000

0.133

0.704

-2.600

0.808

0.667

0.166

-0.733

0.132

0.133

0.689

-0.600

0.214

13.80

0.176

1.000

0.771

3.067

0.400

18.0000

3.87e-14

1.000

25.00

0.050

4.05e-14

1.000

-0.500

0.386

2.18e-14

1.000

-3.13e-14

1.000

5.500

0.649

-2.500

0.539

2.500

0.563

19.0000

0.220

0.520

0.576

0.956

0.458

0.329

-0.508

0.285

0.102

0.755

-0.288

0.540

10.02

0.314

-0.0169

0.996

3.068

0.388

21.0000

3.86e-14

1.000

-7.000

0.633

1.000

0.129

-1.000

0.134

2.18e-14

1.000

-3.15e-14

1.000

1.000

0.943

3.000

0.524

1.000

0.841

23.0000

3.86e-14

1.000

-2.500

0.844

1.000

0.080

-1.000

0.084

2.18e-14

1.000

-0.500

0.382

9.500

0.432

0.500

0.902

1.500

0.728

24.0300

0.111

0.750

6.833

0.521

0.833

0.082

-0.722

0.136

0.111

0.738

-0.333

0.487

13.28

0.191

-0.111

0.974

2.333

0.520

31.0200

3.84e-14

1.000

26.00

0.077

4.22e-14

1.000

-1.000

0.134

2.18e-14

1.000

-3.15e-14

1.000

21.00

0.133

-10.00

0.034

-4.000

0.423

35.0000

1.000

0.038

-3.000

0.838

1.000

0.129

-1.000

0.134

2.18e-14

1.000

-1.000

0.130

-1.000

0.943

3.000

0.524

7.000

0.161

36.0200

3.85e-14

1.000

27.00

0.034

1.000

0.080

-0.500

0.386

0.500

0.207

-3.16e-14

1.000

2.500

0.836

3.000

0.462

-3.000

0.487

37.0200

1.000*

0.038

-6.000

0.682

1.000

0.129

-9.88e-14

1.000

2.18e-14

1.000

-3.15e-14

1.000

-1.000

0.943

3.000

0.524

5.000

0.316

37.0300

3.85e-14

1.000

13.00

0.306

0.500

0.380

-9.90e-14

1.000

2.18e-14

1.000

-3.16e-14

1.000

4.500

0.709

-1.000

0.806

5.000

0.247

40.0300

3.85e-14

1.000

3.000

0.838

1.000

0.129

-1.000

0.134

2.19e-14

1.000

-1.000

0.130

5.000

0.720

-2.000

0.671

3.000

0.547

41.0100

3.85e-14

1.000

-6.000

0.682

1.000

0.129

-1.000

0.134

2.18e-14

1.000

-1.000

0.130

21.00

0.133

2.000

0.671

7.000

0.161

41.0200

3.85e-14

1.000

-9.000

0.539

1.000

0.129

-1.000

0.134

2.18e-14

1.000

-3.16e-14

1.000

30.00

0.032

-10.00

0.034

3.04e-13

1.000

41.0300

0.333

0.396

2.667

0.823

0.333

0.535

-1.000

0.067

0.333

0.372

-3.26e-14

1.000

3.333

0.770

-0.333

0.931

1.333

0.743

41.0400

3.85e-14

1.000

4.500

0.723

1.000

0.080

-0.500

0.386

0.500

0.207

-0.500

0.382

1.87e-13

1.000

2.500

0.539

3.05e-13

1.000

43.0100

0.500

0.230

2.000

0.875

1.000

0.080

-0.500

0.386

2.18e-14

1.000

-3.16e-14

1.000

11.00

0.363

-3.500

0.391

2.000

0.643

44.0400

3.91e-14

1.000

6.333

0.596

1.000

0.063

-1.000

0.067

0.667

0.075

-3.14e-14

1.000

7.667

0.501

-4.000

0.298

5.333

0.191

45.0100

0.667

0.090

-5.000

0.676

3.97e-14

1.000

-0.333

0.540

0.333

0.372

-0.333

0.536

6.667

0.558

2.000

0.603

4.333

0.288

45.0200

0.200

0.569

3.733

0.727

0.400

0.405

-0.933

0.056

0.333

0.318

-0.400

0.407

8.800

0.388

0.733

0.831

3.533

0.332

46.0100

3.91e-14

1.000

-8.000

0.585

3.94e-14

1.000

-1.000

0.134

2.20e-14

1.000

-3.12e-14

1.000

10.00

0.473

2.000

0.671

5.000

0.316

46.0200

1.000

0.038

7.000

0.633

3.95e-14

1.000

-9.52e-14

1.000

2.20e-14

1.000

-3.12e-14

1.000

7.000

0.616

-4.000

0.395

4.000

0.423

50.0100

3.90e-14

1.000

-1.000

0.946

1.000

0.129

-1.000

0.134

1.000

0.029

-3.13e-14

1.000

8.000

0.566

1.000

0.832

2.90e-13

1.000

51.0200

0.500

0.230

-3.500

0.783

1.000

0.080

-0.500

0.386

2.20e-14

1.000

-3.13e-14

1.000

4.500

0.709

3.000

0.462

7.000

0.106

54.0000

3.90e-14

1.000

27.00

0.066

1.000

0.129

-1.000

0.134

2.20e-14

1.000

-3.13e-14

1.000

19.00

0.174

-2.000

0.671

3.000

0.547

55.0300

0.111

0.756

-0.111

0.992

0.111

0.821

-0.667

0.180

0.111

0.744

-0.222

0.651

11.89

0.253

1.111

0.751

3.222

0.386

68.0900

4.00e-14

1.000

8.667

0.469

0.667

0.215

-1.000

0.067

0.333

0.372

-0.333

0.536

7.000

0.539

1.667

0.664

0.667

0.870

83.0300

1.000

0.038

12.00

0.413

4.23e-14

1.000

-1.000

0.134

2.18e-14

1.000

-3.17e-14

1.000

4.000

0.774

-7.000

0.138

2.000

0.688

Observations

261

261

261

261

261

261

261

261

261

F-Test

1.513

1.829

1.938

1.093

1.055

0.944

0.928

1.290

0.964

p value

0.0299

0.00277

0.00113

0.333

0.390

0.576

0.603

0.123

0.540

Pseudo R ²

0.231

0.266

0.277

0.178

0.173

0.158

0.155

0.204

0.160

Note: P/C = Parent

or Caregiver;

reference group is

Tract 1.0200

bold = p <.05

214

Appendix A

Exhibit A-2B. Relationships between DHA resident characteristics and census tracts: Households with two children

P/C employment

P/C had checking

P/C had health

P/C disability status at

P/C received TANF at

P/C receiving Food

P/C is single parent

status at time of DHA

P/C hourly wage at

account at time of

insurance at time of

time of survey (1=yes;

time of DHA move-in Stamps at time of DHA

(1=yes, 0=no)

move-in (1=employed, time of DHA move-in

DHA move-in (1=yes, DHA move-in (1=yes,

0=no)

(1=yes, 0=no)

move-in (1=yes, 0=no)

0=not employed)

0=no)

0=no)

Census Tract

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

2.0200

-0.167

0.601

-0.583

0.604

-12.13

0.086

0.0833

0.722

0.583

0.536

0.167

0.825

-0.417

0.700

-0.417

0.714

3.0100

3.29e-15

1.000

-0.667

0.620

-12.00

0.154

3.44e-15

1.000

-1.59e-15

1.000

-0.167

0.854

0.167

0.897

-0.333

0.806

4.0100

3.00e-15

1.000

-3.72e-14

1.000

0.900

0.936

3.01e-15

1.000

-8.42e-16

1.000

-0.500

0.680

0.500

0.773

-1

0.584

4.0200

2.96e-15

1.000

-3.71e-14

1.000

0.500

0.965

1.000

0.008

1.000

0.509

0.500

0.680

0.500

0.773

2.87e-15

1.000

5.0200

2.89e-15

1.000

-0.500

0.734

-7.750

0.400

3.39e-15

1.000

-1.27e-15

1.000

-2.82e-14

1.000

-0.500

0.724

-0.500

0.737

7.0100

-0.500

0.231

-0.500

0.734

-9.500

0.303

0.500

0.104

-8.29e-16

1.000

-2.83e-14

1.000

-0.500

0.724

-0.500

0.737

7.0200

-0.267

0.396

-0.733

0.508

-13.97

0.045

0.133

0.564

0.533

0.566

0.167

0.823

-0.300

0.778

-0.267

0.812

8.0000

-0.250

0.419

-0.400

0.714

-8.889

0.194

0.100

0.660

0.600

0.513

0.200

0.785

-0.100

0.924

-0.250

0.821

9.0200

-0.500

0.231

-0.500

0.734

-18.50

0.046

3.40e-15

1.000

0.500

0.686

0.500

0.613

-0.500

0.724

3.14e-15

1.000

9.0300

-0.500

0.167

-0.500

0.695

-10.37

0.194

3.38e-15

1.000

0.500

0.640

-2.82e-14

1.000

0.250

0.838

-0.250

0.846

9.0500

-1.000

0.051

-3.64e-14

1.000

-1.250

0.912

3.41e-15

1.000

-1.48e-15

1.000

0.500

0.680

-0.500

0.773

-1.000

0.584

10.0000

3.04e-15

1.000

-0.250

0.845

-6.625

0.406

0.500

0.061

0.750

0.483

0.250

0.770

-0.500

0.683

-0.250

0.846

11.0100

3.71e-15

1.000

-0.429

0.717

-8.190

0.268

0.286

0.246

0.286

0.773

0.0714

0.928

0.0714

0.950

-0.143

0.905

11.0200

-0.500

0.231

-0.500

0.734

-9.425

0.306

3.37e-15

1.000

-1.16e-15

1.000

0.500

0.613

0.500

0.724

2.93e-15

1.000

13.0100

-1.000

0.051

-1.000

0.579

-18.50

0.102

3.13e-15

1.000

-6.77e-16

1.000

0.500

0.680

-0.500

0.773

2.98e-15

1.000

13.0200

-1.000

0.051

-1.000

0.579

-18.50

0.102

3.07e-15

1.000

1.000

0.509

0.500

0.680

-0.500

0.773

2.91e-15

1.000

14.0200

-0.250

0.489

-0.250

0.845

-4.398

0.581

3.12e-15

1.000

0.250

0.815

-0.250

0.770

0.250

0.838

-0.250

0.846

14.0300

-1.000

0.051

-3.61e-14

1.000

8.500

0.451

3.10e-15

1.000

-6.14e-16

1.000

-0.500

0.680

0.500

0.773

-1.000

0.584

15.0000

2.81e-15

1.000

-5.000

0.001

-11.75

0.203

0.500

0.104

1.000

0.418

-2.82e-14

1.000

-5.000

0.001

-5.000

0.001

16.0000

-0.278

0.372

-1.056

0.337

-10.96

0.111

0.111

0.627

-2.09e-15

1.000

0.222

0.763

-0.722

0.494

-0.556

0.617

18.0000

3.47e-15

1.000

-0.667

0.620

-12.50

0.138

3.38e-15

1.000

0.333

0.767

-0.167

0.854

-0.167

0.897

-0.333

0.806

19.0000

-0.277

0.359

-0.574

0.589

-6.530

0.326

0.0638

0.773

0.383

0.668

0.160

0.823

-0.223

0.827

-0.532

0.621

21.0000

2.93e-15

1.000

-1.000

0.497

-18.50

0.046

3.35e-15

1.000

0.500

0.686

-2.81e-14

1.000

-0.500

0.724

-0.500

0.737

23.0000

2.90e-15

1.000

-3.71e-14

1.000

-5.075

0.524

3.16e-15

1.000

0.500

0.640

0.250

0.770

0.250

0.838

-0.250

0.846

24.0300

-0.111

0.733

-0.333

0.772

-7.989

0.268

0.333

0.165

-0.667

0.490

0.0556

0.943

-0.167

0.880

-0.333

0.775

31.0100

2.96e-15

1.000

-3.70e-14

1.000

6.500

0.564

3.11e-15

1.000

1.000

0.509

0.500

0.680

0.500

0.773

2.91e-15

1.000

35.0000

-0.143

0.669

-0.286

0.809

-2.757

0.709

3.56e-15

1.000

0.286

0.773

0.0714

0.928

0.0714

0.950

-0.286

0.811

36.0100

2.96e-15

1.000

-0.500

0.734

-7.000

0.447

3.10e-15

1.000

0.500

0.686

0.500

0.613

-0.500

0.724

-0.500

0.737

36.0200

2.97e-15

1.000

-0.500

0.734

-8.000

0.385

0.500

0.104

0.500

0.686

-2.83e-14

1.000

0.500

0.724

-0.500

0.737

36.0300

4.07e-15

1.000

-3.57e-14

1.000

0.333

0.968

3.30e-15

1.000

-2.52e-15

1.000

-3.167

0.001

-0.500

0.699

-0.333

0.806

37.0300

2.96e-15

1.000

-3.72e-14

1.000

1.100

0.922

3.07e-15

1.000

-9.69e-16

1.000

-0.500

0.680

0.500

0.773

2.89e-15

1.000

41.0100

3.94e-15

1.000

-3.57e-14

1.000

-3.390

0.687

0.333

0.235

0.333

0.767

0.167

0.854

-0.167

0.897

-0.333

0.806

41.0200

2.91e-15

1.000

-3.68e-14

1.000

6.000

0.595

3.27e-15

1.000

-1.07e-15

1.000

-0.500

0.680

0.500

0.773

2.94e-15

1.000

41.0300

4.14e-15

1.000

-3.56e-14

1.000

2.833

0.736

3.26e-15

1.000

0.333

0.767

0.167

0.854

0.167

0.897

2.60e-15

1.000

41.0400

2.95e-15

1.000

-3.69e-14

1.000

1.500

0.894

3.21e-15

1.000

-1.04e-15

1.000

0.500

0.680

0.500

0.773

2.93e-15

1.000

42.0100

2.94e-15

1.000

-3.71e-14

1.000

0.500

0.965

3.14e-15

1.000

-1.03e-15

1.000

0.500

0.680

0.500

0.773

2.87e-15

1.000

42.0200

-1.000

0.051

-3.70e-14

1.000

-0.480

0.966

3.10e-15

1.000

-9.44e-16

1.000

0.500

0.680

-0.500

0.773

-1

0.584

43.0100

-1.000

0.051

-3.72e-14

1.000

-1.250

0.912

1.000

0.008

1.000

0.509

-0.500

0.680

-0.500

0.773

2.88e-15

1.000

43.0400

-0.500

0.231

-0.500

0.734

-8.250

0.370

0.500

0.104

0.500

0.686

0.500

0.613

0.500

0.724

2.85e-15

1.000

44.0300

-0.500

0.231

-3.71e-14

1.000

2.375

0.796

3.08e-15

1.000

-9.93e-16

1.000

-0.500

0.613

-0.500

0.724

-1.000

0.502

44.0400

2.96e-15

1.000

-3.72e-14

1.000

-0.500

0.965

1.000

0.008

1.000

0.509

0.500

0.680

0.500

0.773

2.91e-15

1.000

45.0100

3.38e-15

1.000

-0.143

0.904

-3.214

0.663

3.68e-15

1.000

0.286

0.773

-0.0714

0.928

-0.357

0.753

-0.143

0.905

45.0200

-0.308

0.332

-0.462

0.680

-10.67

0.128

0.154

0.509

0.538

0.566

0.192

0.798

-0.346

0.748

-0.0769

0.946

46.0200

2.97e-15

1.000

-1.000

0.579

-18.50

0.102

3.15e-15

1.000

1.000

0.509

0.500

0.680

-0.500

0.773

3.06e-15

1.000

47.0000

2.94e-15

1.000

-3.62e-14

1.000

-18.50

0.102

1.000

0.008

-7.03e-16

1.000

-0.500

0.680

-0.500

0.773

2.88e-15

1.000

48.0200

2.97e-15

1.000

-1.000

0.579

-18.50

0.102

1.000

0.008

-7.14e-16

1.000

0.500

0.680

0.500

0.773

3.24e-15

1.000

50.0100

-0.500

0.231

-0.500

0.734

-7.500

0.415

3.31e-15

1.000

0.500

0.686

-2.85e-14

1.000

-0.500

0.724

-1.000

0.502

54.0000

-0.250

0.448

-2.750

0.019

-9.937

0.173

3.24e-15

1.000

-0.750

0.443

-1.250

0.111

-2.500

0.026

-2.500

0.035

55.0300

-0.200

0.567

-0.200

0.871

-4.400

0.568

3.22e-15

1.000

0.200

0.846

0.1000

0.904

0.1000

0.933

-0.200

0.873

68.0900

2.91e-15

1.000

-1.000

0.579

-18.50

0.102

3.29e-15

1.000

-1.19e-15

1.000

-0.500

0.680

-0.500

0.773

2.87e-15

1.000

69.0100

2.96e-15

1.000

-3.59e-14

1.000

9.500

0.400

3.14e-15

1.000

-6.83e-16

1.000

0.500

0.680

-0.500

0.773

3.21e-15

1.000

83.0300

2.93e-15

1.000

-1.000

0.579

-18.50

0.102

3.13e-15

1.000

-1.07e-15

1.000

0.500

0.680

0.500

0.773

3.17e-15

1.000

85.3400

2.92e-15

1.000

-3.61e-14

1.000

1.500

0.894

3.20e-15

1.000

-7.22e-16

1.000

-0.500

0.680

0.500

0.773

3.21e-15

1.000

Observations

244

244

244

244

244

244

244

244

F-Test

0.932

0.861

1.267

1.555

0.396

1.068

1.022

0.763

p value

0.610

0.735

0.127

0.0168

1.000

0.365

0.445

0.876

Pseudo R ²

0.206

0.194

0.261

0.302

0.0994

0.230

0.222

0.175

Note: P/C = Parent

or Caregiver;

reference group is

Tract 1.0200

bold = p <.05

215

Appendix A

Exhibit A-2B. Relationships between DHA resident characteristics and census tracts: Households with two children (continued)

P/C had too little

P/C had difficulty

Frequency that P/C

Number of years

Number of years

Frequency that P/C

P/C ever seen a

Spanish language

money for food at

paying all bills at time

smoked marijuana

during childhood that

during childhood that

P/C born in the United

drank alcohol since

psychiatrist (1=yes,

interview (1=yes;

time of DHA move-in

of DHA move-in

since becoming a

P/C lived in public

P/C lived in a home

States (1=yes; 0=no)

becoming a parent

0=no)

0=no)

(1=yes, 0=no)

(1=yes, 0=no)

parent

housing

owned by parents

Census Tract

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

2.0200

0.0833

0.939

-1.833

0.242

-1.250

0.536

-0.333

0.844

0.500

0.182

3.417

0.562

-0.333

0.971

0.333

0.234

-0.417

0.029

3.0100

0.667

0.610

0.500

0.789

-0.833

0.730

0.500

0.805

0.667

0.137

6.333

0.369

-2.500

0.822

0.167

0.618

-0.167

0.462

4.0100

1.000

0.568

0.500

0.842

-1.500

0.643

-1.500

0.581

1.000

0.096

-2.52e-13

1.000

-9.500

0.525

0.500

0.265

-0.500

0.101

4.0200

1.000

0.568

-0.500

0.842

-10.50

0.001

0.500

0.854

1.000

0.096

12.00

0.205

12.50

0.403

0.500

0.265

-0.500

0.101

5.0200

-4.02e-14

1.000

-0.500

0.807

1.05e-13

1.000

-1.500

0.499

-2.33e-14

1.000

-2.66e-13

1.000

-9.500

0.436

0.500

0.173

-0.500

0.045

7.0100

-4.11e-14

1.000

0.500

0.807

1.500

0.570

-0.500

0.822

0.500

0.308

-2.52e-13

1.000

10.000

0.412

-1.06e-14

1.000

2.22e-14

1.000

7.0200

0.600

0.578

0.0333

0.983

-1.433

0.471

-1.567

0.349

0.400

0.278

7.800

0.181

3.367

0.714

0.433

0.117

-0.433

0.021

8.0000

0.450

0.672

-0.200

0.895

-2.350

0.231

-1.500

0.363

0.300

0.409

5.050

0.378

3.600

0.691

0.300

0.270

-0.400

0.031

9.0200

-4.04e-14

1.000

0.500

0.807

-5.500

0.038

-1.000

0.652

0.500

0.308

10.000

0.196

4.000

0.743

0.500

0.173

-0.500

0.045

9.0300

0.250

0.840

-0.250

0.888

-3.750

0.102

-0.500

0.795

-2.35e-14

1.000

-2.66e-13

1.000

10.50

0.321

-9.60e-15

1.000

-0.250

0.245

9.0500

-4.02e-14

1.000

-0.500

0.842

0.500

0.877

-1.500

0.581

1.000

0.096

-2.70e-13

1.000

-9.500

0.525

0.500

0.265

-0.500

0.101

10.0000

-4.13e-14

1.000

-0.500

0.778

0.500

0.827

2.000

0.299

1.000

0.019

11.25

0.094

-2.750

0.795

0.500

0.116

-0.500

0.021

11.0100

0.286

0.803

-0.214

0.896

-0.929

0.661

-2.643

0.139

0.571

0.146

-2.93e-13

1.000

8.786

0.369

0.214

0.466

-0.214

0.282

11.0200

0.500

0.727

-1.49e-15

1.000

-1.500

0.570

-1.500

0.499

-2.36e-14

1.000

-2.64e-13

1.000

9.500

0.436

-9.66e-15

1.000

2.32e-14

1.000

13.0100

-4.06e-14

1.000

-0.500

0.842

-0.500

0.877

-0.500

0.854

-2.39e-14

1.000

-2.52e-13

1.000

14.50

0.332

0.500

0.265

-0.500

0.101

13.0200

-4.07e-14

1.000

0.500

0.842

-1.500

0.643

-1.500

0.581

1.000

0.096

19.00

0.046

-9.500

0.525

0.500

0.265

-0.500

0.101

14.0200

0.250

0.840

0.250

0.888

-0.250

0.913

0.250

0.897

0.250

0.556

15.50

0.021

-9.500

0.369

0.500

0.116

-0.500

0.021

14.0300

-4.06e-14

1.000

0.500

0.842

-0.500

0.877

-0.500

0.854

-2.38e-14

1.000

-2.52e-13

1.000

9.500

0.525

0.500

0.265

-0.500

0.101

15.0000

-4.500

0.002

-5.000

0.015

1.05e-13

1.000

-1.000

0.652

0.500

0.308

5.500

0.476

-9.500

0.436

0.500

0.173

-0.500

0.045

16.0000

-0.167

0.876

-1.167

0.445

-0.111

0.955

-0.222

0.893

0.333

0.361

5.222

0.365

2.000

0.826

0.389

0.155

-0.444

0.017

18.0000

0.333

0.799

-3.500

0.062

-1.167

0.629

-1.500

0.459

0.667

0.137

5.333

0.450

-9.500

0.394

0.500

0.136

-0.500

0.028

19.0000

0.191

0.853

-0.372

0.801

-0.968

0.612

-1.266

0.430

0.468

0.186

3.830

0.492

2.351

0.789

0.351

0.185

-0.479

0.008

21.0000

0.500

0.727

-4.500

0.029

-0.500

0.850

-1.500

0.499

-2.35e-14

1.000

7.500

0.332

-9.500

0.436

0.500

0.173

-0.500

0.045

23.0000

0.250

0.840

-0.250

0.888

-0.500

0.827

-0.500

0.795

0.250

0.556

-2.57e-13

1.000

9.000

0.394

0.500

0.116

-0.500

0.021

24.0300

0.444

0.691

-0.278

0.862

-1.278

0.536

-0.389

0.823

0.333

0.384

1.889

0.754

4.389

0.645

0.389

0.175

-0.500

0.011

31.0100

1.000

0.568

0.500

0.842

-1.500

0.643

-1.500

0.581

1.000

0.096

-2.55e-13

1.000

15.50

0.300

0.500

0.265

-0.500

0.101

35.0000

0.429

0.709

0.214

0.896

-2.071

0.329

-2.214

0.214

0.429

0.276

3.143

0.612

5.214

0.594

0.0714

0.808

-0.357

0.074

36.0100

1.000

0.485

-1.46e-15

1.000

1.000

0.705

-1.000

0.652

1.000

0.042

-2.56e-13

1.000

4.000

0.743

0.500

0.173

-0.500

0.045

36.0200

-4.06e-14

1.000

-0.500

0.807

2.000

0.449

2.000

0.368

1.000

0.042

-2.62e-13

1.000

2.000

0.870

0.500

0.173

-0.500

0.045

36.0300

-4.11e-14

1.000

-0.500

0.789

-1.167

0.629

-1.167

0.565

0.333

0.456

11.33

0.109

-9.500

0.394

0.500

0.136

-0.500

0.028

37.0300

-4.11e-14

1.000

0.500

0.842

2.500

0.440

-1.500

0.581

-2.31e-14

1.000

-2.53e-13

1.000

-9.500

0.525

0.500

0.265

-0.500

0.101

41.0100

0.333

0.799

0.167

0.929

-1.500

0.534

-1.500

0.459

0.667

0.137

4.667

0.508

3.833

0.731

0.500

0.136

-0.500

0.028

41.0200

1.000

0.568

-0.500

0.842

-1.500

0.643

4.500

0.099

1.000

0.096

-2.62e-13

1.000

9.500

0.525

0.500

0.265

-0.500

0.101

41.0300

1.000

0.444

0.167

0.929

-1.500

0.534

-1.500

0.459

0.667

0.137

-2.88e-13

1.000

1.167

0.917

0.167

0.618

-0.500

0.028

41.0400

1.000

0.568

0.500

0.842

-1.500

0.643

-1.500

0.581

1.000

0.096

-2.58e-13

1.000

4.500

0.763

0.500

0.265

-0.500

0.101

42.0100

1.000

0.568

-0.500

0.842

-10.50

0.001

-1.500

0.581

1.000

0.096

-2.56e-13

1.000

7.500

0.616

0.500

0.265

-0.500

0.101

42.0200

-4.09e-14

1.000

-0.500

0.842

-1.500

0.643

-1.500

0.581

-2.31e-14

1.000

-2.55e-13

1.000

-9.500

0.525

-0.500

0.265

0.500

0.101

43.0100

-4.10e-14

1.000

0.500

0.842

-1.500

0.643

-1.500

0.581

1.000

0.096

-2.54e-13

1.000

-9.500

0.525

0.500

0.265

-0.500

0.101

43.0400

0.500

0.727

-1.48e-15

1.000

0.500

0.850

-5.500

0.014

1.000

0.042

-2.54e-13

1.000

9.500

0.436

0.500

0.173

-0.500

0.045

44.0300

0.500

0.727

-1.33e-15

1.000

0.500

0.850

-1.000

0.652

-2.32e-14

1.000

-2.54e-13

1.000

-9.500

0.436

0.500

0.173

-0.500

0.045

44.0400

1.000

0.568

0.500

0.842

-1.500

0.643

4.500

0.099

-2.31e-14

1.000

-2.53e-13

1.000

17.50

0.242

0.500

0.265

-0.500

0.101

45.0100

0.429

0.709

0.0714

0.965

-0.929

0.661

-1.214

0.495

0.143

0.716

-2.90e-13

1.000

2.071

0.832

0.500

0.090

-0.500

0.013

45.0200

0.308

0.777

-0.192

0.902

-1.500

0.455

-0.962

0.569

0.385

0.302

5.385

0.359

5.654

0.542

0.269

0.333

-0.500

0.009

46.0200

-4.05e-14

1.000

-0.500

0.842

-1.500

0.643

-1.500

0.581

1.000

0.096

19.00*

0.046

-9.500

0.525

0.500

0.265

-0.500

0.101

47.0000

-4.07e-14

1.000

0.500

0.842

-10.50

0.001

0.500

0.854

-2.38e-14

1.000

-2.51e-13

1.000

-9.500

0.525

0.500

0.265

-0.500

0.101

48.0200

1.000

0.568

0.500

0.842

2.500

0.440

-0.500

0.854

-2.39e-14

1.000

-2.53e-13

1.000

6.500

0.663

0.500

0.265

-0.500

0.101

50.0100

0.500

0.727

-1.05e-15

1.000

-1.000

0.705

-1.000

0.652

-2.38e-14

1.000

-2.68e-13

1.000

-9.500

0.436

0.500

0.173

-0.500

0.045

54.0000

-1.875

0.099

-2.375

0.143

1.05e-13

1.000

-1.000

0.569

0.500

0.197

7.000

0.252

3.500

0.717

0.375

0.196

-0.375

0.057

55.0300

0.800

0.504

0.1000

0.953

-1.300

0.556

-1.300

0.484

0.400

0.329

3.600

0.577

0.300

0.977

0.300

0.328

-0.500

0.017

68.0900

-4.07e-14

1.000

-0.500

0.842

-0.500

0.877

-0.500

0.854

1.000

0.096

16.00

0.092

-9.500

0.525

0.500

0.265

-0.500

0.101

69.0100

-4.06e-14

1.000

0.500

0.842

-1.500

0.643

-1.500

0.581

-2.39e-14

1.000

-2.52e-13

1.000

3.500

0.815

0.500

0.265

-0.500

0.101

83.0300

1.000

0.568

-0.500

0.842

-0.500

0.877

-0.500

0.854

1.000

0.096

-2.56e-13

1.000

3.500

0.815

0.500

0.265

-0.500

0.101

85.3400

-4.05e-14

1.000

0.500

0.842

0.500

0.877

-0.500

0.854

-2.39e-14

1.000

11.00

0.245

-9.500

0.525

0.500

0.265

-0.500

0.101

Observations

244

244

244

244

244

244

244

244

244

F-Test

0.923

0.908

1.389

0.952

1.127

0.977

0.847

0.649

1.016

p value

0.625

0.653

0.0571

0.572

0.277

0.525

0.758

0.967

0.456

Pseudo R ²

0.205

0.202

0.279

0.210

0.239

0.214

0.191

0.153

0.221

Note: P/C = Parent

or Caregiver;

reference group is

Tract 1.0200

bold = p <.05

216

Appendix A

217

Exhibit A-2B. Relationships between DHA resident characteristics and census tracts: Households with two children (continued)

Biological father

Parent have any

Parent Depressive

always lived in

P/C African

Parent have HS diploma

Kids share same

Parenting Efficacy

Parenting Beliefs

Parent's age at time of

higher education at

Symptomatology

household with

American (1=yes;

at time of DHA move-in

biological dad (1=yes;

Scale at time of

Scale at time of

DHA move-in

time of DHA move-in

Scale at time of

child(ren) (1=yes;

0=no)

(1=yes; 0=no)

0=no)

interview

interview

(1=yes; 0=no)

interview

0=no)

Census Tract

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

2.0200

1.27e-14

1.000

-5.417

0.433

0.250

0.427

0.167

0.631

-1.93e-14

1.000

-0.333

0.373

7.750

0.271

-0.750

0.789

-1.750

0.561

3.0100

1.30e-14

1.000

-2.167

0.793

3.63e-15

1.000

0.333

0.421

0.333

0.230

0.167

0.709

10.33

0.220

1.667

0.619

1.000

0.781

4.0100

1.42e-14

1.000

-11.50

0.300

3.48e-15

1.000

3.52e-15

1.000

-1.97e-14

1.000

-0.500

0.404

5.000

0.658

-12.00

0.008

-6.000

0.214

4.0200

1.42e-14

1.000

0.500

0.964

1.000

0.049

1.000

0.073

-1.97e-14

1.000

-0.500

0.404

3.000

0.790

-3.000

0.505

-1.000

0.836

5.0200

1.34e-14

1.000

-6.000

0.507

4.86e-15

1.000

1.97e-15

1.000

-1.94e-14

1.000

0.500

0.307

-3.000

0.745

0.500

0.892

-0.500

0.899

7.0100

1.43e-14

1.000

4.000

0.658

3.55e-15

1.000

3.54e-15

1.000

-1.97e-14

1.000

-0.500

0.307

14.50

0.117

0.500

0.892

-0.500

0.899

7.0200

0.133

0.616

-4.167

0.541

0.133

0.667

0.400

0.242

0.133

0.560

0.0333

0.928

11.27

0.105

-1.800

0.515

0.933

0.753

8.0000

0.100

0.703

-8.650

0.198

0.250

0.413

0.250

0.458

0.1000

0.657

-0.200

0.582

6.150

0.368

-0.600

0.825

-1.050

0.719

9.0200

0.500

0.158

-10.50

0.246

4.95e-15

1.000

1.52e-15

1.000

-1.94e-14

1.000

-1.09e-15

1.000

4.500

0.625

9.63e-14

1.000

-1.500

0.703

9.0300

0.250

0.414

-1.000

0.898

4.77e-15

1.000

0.500

0.204

0.250

0.343

-1.29e-15

1.000

-3.750

0.638

1.000

0.753

0.250

0.942

9.0500

1.000

0.022

3.500

0.752

4.46e-15

1.000

1.61e-15

1.000

1.000

0.008

-0.500

0.404

27.00

0.017

-1.000

0.824

-4.000

0.407

10.0000

1.45e-14

1.000

-4.750

0.544

0.500

0.162

0.250

0.525

0.250

0.343

-0.250

0.555

14.25

0.075

-4.250

0.182

-1.000

0.769

11.0100

1.27e-14

1.000

-4.357

0.548

0.143

0.665

0.143

0.695

0.143

0.558

0.0714

0.856

1.429

0.847

0.857

0.771

1.143

0.717

11.0200

0.500

0.158

3.500

0.699

5.09e-15

1.000

0.500

0.271

-1.94e-14

1.000

-1.28e-15

1.000

12.50

0.176

1.500

0.683

-4.500

0.254

13.0100

1.43e-14

1.000

11.50

0.300

6.17e-15

1.000

1.000

0.073

-1.98e-14

1.000

-0.500

0.404

8.000

0.478

1.000

0.824

-2.000

0.678

13.0200

1.44e-14

1.000

-15.50

0.163

6.10e-15

1.000

1.000

0.073

-1.98e-14

1.000

-0.500

0.404

7.000

0.535

1.000

0.824

-1.000

0.836

14.0200

0.500

0.103

5.750

0.463

0.250

0.484

0.750

0.057

-1.98e-14

1.000

-1.46e-15

1.000

1.250

0.875

0.750

0.813

-4.250

0.214

14.0300

1.000

0.022

-2.500

0.821

1.000

0.049

1.000

0.073

-1.98e-14

1.000

-0.500

0.404

1.000

0.929

2.000

0.656

-3.000

0.534

15.0000

1.30e-14

1.000

-5.500

0.543

1.000

0.016

1.23e-15

1.000

-1.92e-14

1.000

-0.500

0.307

4.000

0.664

-4.000

0.276

-3.500

0.375

16.0000

0.222

0.399

-5.611

0.405

0.833

0.007

0.389

0.251

0.111

0.624

-0.278

0.447

4.167

0.544

-0.611

0.823

-1.778

0.545

18.0000

1.30e-14

1.000

-3.500

0.672

2.69e-15

1.000

5.80e-16

1.000

0.333

0.230

-0.167

0.709

2.333

0.781

-1.667

0.619

-0.333

0.926

19.0000

0.149

0.559

-5.585

0.393

0.553

0.064

0.213

0.516

0.0851

0.698

-0.138

0.695

5.915

0.374

-1.489

0.574

-0.851

0.765

21.0000

1.35e-14

1.000

3.000

0.740

4.92e-15

1.000

0.500

0.271

-1.94e-14

1.000

-0.500

0.307

4.500

0.625

-2.500

0.496

3.000

0.446

23.0000

1.39e-14

1.000

-2.000

0.798

1.000

0.006

0.750

0.057

-1.96e-14

1.000

0.500

0.239

8.250

0.302

-0.250

0.937

-5.750

0.093

24.0300

1.23e-14

1.000

-0.389

0.956

0.778

0.017

0.222

0.531

0.111

0.640

-0.0556

0.885

5.889

0.414

0.222

0.938

-3.333

0.280

31.0100

1.41e-14

1.000

-2.500

0.821

1.000

0.049

3.21e-15

1.000

1.000

0.008

-0.500

0.404

-3.000

0.790

2.000

0.656

1.000

0.836

35.0000

0.143

0.614

-4.000

0.588

0.714

0.032

0.143

0.695

0.143

0.558

0.0714

0.856

4.143

0.575

-3.286

0.265

-5.286

0.095

36.0100

0.500

0.158

2.500

0.782

1.000

0.016

0.500

0.271

0.500

0.101

-0.500

0.307

7.500

0.416

-6.000

0.103

-3.500

0.375

36.0200

1.37e-14

1.000

-3.500

0.699

1.000

0.016

0.500

0.271

0.500

0.101

-0.500

0.307

14.00

0.130

-5.000

0.174

-3.000

0.446

36.0300

1.27e-14

1.000

-2.500

0.762

0.667

0.077

1.000*

0.017

-1.94e-14

1.000

-0.167

0.709

3.000

0.721

-0.667

0.842

-3.000

0.404

37.0300

1.42e-14

1.000

12.50

0.260

1.000

0.049

3.30e-15

1.000

-1.97e-14

1.000

0.500

0.404

14.00

0.215

-2.000

0.656

-2.000

0.678

41.0100

1.26e-14

1.000

-4.833

0.558

1.000

0.008

0.333

0.421

0.333

0.230

0.167

0.709

3.333

0.692

-2.667

0.426

-1.667

0.643

41.0200

1.37e-14

1.000

-3.500

0.752

1.000

0.049

2.52e-15

1.000

-1.95e-14

1.000

0.500

0.404

-3.000

0.790

1.000

0.824

-4.000

0.407

41.0300

0.333

0.302

-1.500

0.856

1.000

0.008

0.667

0.109

-1.94e-14

1.000

-0.167

0.709

-1.667

0.843

-0.667

0.842

-1.000

0.781

41.0400

1.39e-14

1.000

1.500

0.892

1.000

0.049

2.87e-15

1.000

1.000

0.008

0.500

0.404

25.00

0.028

-3.000

0.505

-4.000

0.407

42.0100

1.40e-14

1.000

1.500

0.892

1.000

0.049

3.13e-15

1.000

-1.96e-14

1.000

0.500

0.404

8.000

0.478

2.000

0.656

-1.000

0.836

42.0200

1.000

0.022

0.500

0.964

3.37e-15

1.000

3.23e-15

1.000

-1.96e-14

1.000

0.500

0.404

-5.000

0.658

2.000

0.656

-12.00

0.014

43.0100

1.41e-14

1.000

-9.500

0.391

1.000

0.049

3.31e-15

1.000

-1.96e-14

1.000

-0.500

0.404

11.00

0.330

-8.000

0.076

-5.000

0.301

43.0400

0.500

0.158

-10.00

0.269

1.000

0.016

3.36e-15

1.000

-1.97e-14

1.000

-0.500

0.307

4.500

0.625

-2.000

0.586

3.000

0.446

44.0300

1.41e-14

1.000

-0.500

0.956

1.000

0.016

0.500

0.271

-1.96e-14

1.000

-1.17e-15

1.000

-2.000

0.828

2.000

0.586

2.000

0.612

44.0400

1.42e-14

1.000

4.500

0.685

1.000

0.049

3.43e-15

1.000

-1.97e-14

1.000

-0.500

0.404

-4.000

0.723

-3.000

0.505

1.15e-13

1.000

45.0100

0.143

0.614

1.214

0.867

3.94e-15

1.000

0.143

0.695

0.286

0.242

0.0714

0.856

2.429

0.742

-1.000

0.734

0.571

0.856

45.0200

0.154

0.566

-5.577

0.417

0.538

0.086

0.308

0.372

-1.92e-14

1.000

-0.192

0.605

4.923

0.482

-1.308

0.639

-0.923

0.758

46.0200

1.42e-14

1.000

2.500

0.821

5.87e-15

1.000

3.50e-15

1.000

-1.97e-14

1.000

0.500

0.404

13.00

0.250

-6.000

0.183

-5.000

0.301

47.0000

1.43e-14

1.000

7.500

0.498

6.03e-15

1.000

3.65e-15

1.000

-1.98e-14

1.000

-0.500

0.404

4.000

0.723

9.45e-14

1.000

2.000

0.678

48.0200

1.000

0.022

12.50

0.260

5.90e-15

1.000

1.000

0.073

-1.98e-14

1.000

-0.500

0.404

5.000

0.658

2.000

0.656

1.11e-13

1.000

50.0100

0.500

0.158

-1.500

0.868

1.000

0.016

0.500

0.271

-1.95e-14

1.000

-1.06e-15

1.000

-3.500

0.704

1.500

0.683

1.000

0.800

54.0000

0.250

0.371

-1.750

0.807

5.70e-15

1.000

0.375

0.296

-1.96e-14

1.000

-0.125

0.747

5.375

0.461

-0.250

0.931

-0.750

0.810

55.0300

0.400

0.177

3.700

0.625

0.200

0.562

0.200

0.598

-1.93e-14

1.000

-0.100

0.807

12.40

0.109

-2.400

0.435

-2.000

0.544

68.0900

1.37e-14

1.000

-8.500

0.443

1.000

0.049

2.57e-15

1.000

-1.95e-14

1.000

-0.500

0.404

15.00

0.185

2.000

0.656

-1.000

0.836

69.0100

1.43e-14

1.000

-7.500

0.498

1.000

0.049

3.64e-15

1.000

1.000

0.008

0.500

0.404

-1.000

0.929

-8.000

0.076

3.000

0.534

83.0300

1.000

0.022

-5.500

0.620

1.000

0.049

3.14e-15

1.000

-1.97e-14

1.000

-0.500

0.404

-3.000

0.790

1.000

0.824

3.000

0.534

85.3400

1.43e-14

1.000

-13.50

0.224

5.95e-15

1.000

1.000

0.073

-1.97e-14

1.000

0.500

0.404

-5.000

0.658

-6.000

0.183

1.000

0.836

Observations

244

243

244

244

244

244

244

244

244

F-Test

1.178

0.915

3.164

1.044

1.173

0.964

1.045

1.033

1.066

p value

0.213

0.641

4.14e-09

0.406

0.219

0.549

0.404

0.426

0.369

Pseudo R ²

0.247

0.204

0.469

0.226

0.247

0.212

0.226

0.224

0.229

Note: P/C = Parent

or Caregiver;

reference group is

Tract 1.0200

bold = p <.05

217

Appendix A

Exhibit A-2C. Relationships between DHA resident characteristics and census tracts: Households with three or more children

P/C employment

P/C receiving Food

P/C had checking

P/C had health

status at time of

P/C disability status

P/C received TANF

P/C is single parent

P/C hourly wage at

Stamps at time of

account at time of

insurance at time of

DHA move-in

at time of survey

at time of DHA move-

(1=yes, 0=no)

time of DHA move-in

DHA move-in

DHA move-in

DHA move-in (1=yes,

(1=employed, 0=not

(1=yes; 0=no)

in (1=yes, 0=no)

(1=yes, 0=no)

(1=yes, 0=no)

0=no)

employed)

Census Tract

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

3.0100

0.389

0.268

0.556

0.571

8.686

0.205

-0.167

0.460

0.111

0.922

0.444

0.612

-0.167

0.864

0.278

0.744

5.0200

-0.611

0.206

0.556

0.681

6.401

0.497

-0.167

0.592

0.611

0.696

0.444

0.713

-0.167

0.901

0.278

0.813

6.0000

0.389

0.421

-0.444

0.742

-7.849

0.406

-0.167

0.592

0.611

0.696

0.444

0.713

-0.167

0.901

0.278

0.813

7.0200

0.0812

0.635

0.325

0.498

5.418

0.106

-0.0128

0.907

0.150

0.787

0.291

0.497

0.141

0.766

0.0470

0.910

8.0000

-0.0111

0.946

0.222

0.629

4.327

0.179

-0.167

0.117

-1.122

0.036

0.178

0.665

0.433

0.341

-0.122

0.759

9.0200

0.389

0.421

0.556

0.681

11.34

0.230

-0.167

0.592

-0.389

0.804

0.444

0.713

-0.167

0.901

0.278

0.813

9.0300

-0.111

0.751

0.0556

0.955

2.401

0.726

-0.167

0.460

-0.389

0.732

-0.556

0.526

0.333

0.731

-0.722

0.396

9.0400

0.139

0.593

0.306

0.674

5.526

0.277

-0.167

0.320

-0.389

0.644

-0.306

0.638

0.583

0.418

0.278

0.659

9.0500

0.389

0.268

0.556

0.571

15.15

0.028

0.333

0.141

0.611

0.591

0.444

0.612

0.333

0.731

-0.722

0.396

10.0000

0.264

0.187

0.0556

0.921

0.601

0.877

-0.167

0.196

0.111

0.864

0.0694

0.889

0.0833

0.880

0.0278

0.954

11.0100

0.189

0.427

-0.0444

0.947

0.351

0.940

-0.167

0.277

0.211

0.784

0.244

0.681

-0.167

0.800

0.278

0.630

11.0200

0.389

0.268

0.0556

0.955

1.651

0.809

-0.167

0.460

0.611

0.591

0.444

0.612

-0.167

0.864

0.278

0.744

13.0100

0.389

0.421

0.556

0.681

14.15

0.135

0.833

0.008

0.611

0.696

0.444

0.713

0.833

0.533

0.278

0.813

14.0200

0.103

0.622

0.413

0.481

9.794

0.018

-0.0238

0.860

0.183

0.788

0.159

0.762

0.405

0.485

0.135

0.791

14.0300

-0.611

0.206

-0.444

0.742

-7.849

0.406

-0.167

0.592

0.611

0.696

0.444

0.713

-0.167

0.901

0.278

0.813

15.0000

-0.111

0.669

-0.194

0.789

-3.811

0.453

0.333

0.048

-0.139

0.869

-0.0556

0.932

0.0833

0.908

-0.472

0.454

16.0000

0.158

0.356

-0.0598

0.900

-1.580

0.636

-0.0897

0.416

-0.697

0.210

0.0598

0.889

-0.782

0.100

-0.261

0.530

19.0000

0.124

0.365

-0.150

0.695

2.824

0.292

-0.0784

0.375

-0.0948

0.831

-0.232

0.498

-0.0784

0.836

-0.310

0.351

21.0000

0.389

0.421

0.556

0.681

16.15

0.088

-0.167

0.592

0.611

0.696

-0.556

0.645

0.833

0.533

0.278

0.813

23.0000

0.389

0.268

0.556

0.571

10.15

0.139

-0.167

0.460

-0.389

0.732

-0.0556

0.949

0.833

0.391

-0.222

0.794

24.0300

0.189

0.427

-2.044

0.002

-4.099

0.377

-0.167

0.277

-0.189

0.806

0.244

0.681

0.0333

0.960

-0.122

0.832

35.0000

0.189

0.427

0.356

0.593

4.001

0.389

0.0333

0.828

0.211

0.784

0.244

0.681

0.0333

0.960

0.0778

0.893

36.0100

0.389

0.421

0.556

0.681

18.15

0.056

-0.167

0.592

-0.389

0.804

0.444

0.713

-0.167

0.901

0.278

0.813

36.0200

0.0253

0.888

-0.0808

0.872

-0.548

0.876

0.0152

0.896

0.0657

0.910

0.0808

0.857

0.379

0.447

0.00505

0.991

41.0100

0.389

0.136

0.306

0.674

2.929

0.564

-0.167

0.320

0.111

0.895

-0.0556

0.932

0.0833

0.908

-0.222

0.724

41.0300

-0.111

0.751

0.556

0.571

17.15

0.013

-0.167

0.460

-0.389

0.732

-0.556

0.526

0.833

0.391

0.278

0.744

41.0400

0.389

0.268

0.556

0.571

12.90

0.061

-0.167

0.460

-0.389

0.732

-0.0556

0.949

0.833

0.391

-0.222

0.794

42.0200

0.389

0.421

-0.444

0.742

-7.849

0.406

-0.167

0.592

0.611

0.696

0.444

0.713

-0.167

0.901

0.278

0.813

43.0400

0.389

0.421

-0.444

0.742

-7.849

0.406

-0.167

0.592

0.611

0.696

0.444

0.713

0.833

0.533

0.278

0.813

44.0300

0.389

0.421

-0.444

0.742

-7.849

0.406

-0.167

0.592

0.611

0.696

-0.556

0.645

-0.167

0.901

-0.722

0.538

44.0400

0.389

0.421

-0.444

0.742

-7.849

0.406

-0.167

0.592

-0.389

0.804

-0.556

0.645

0.833

0.533

0.278

0.813

45.0100

-0.278

0.344

-0.444

0.588

-7.849

0.171

-0.167

0.378

-0.389

0.682

0.111

0.879

-0.167

0.837

-0.0556

0.938

45.0200

0.0556

0.772

-1.111

0.040

-1.768

0.637

-0.0556

0.653

-0.167

0.789

-0.111

0.817

-1.056

0.048

-1.278

0.007

46.0200

0.389

0.421

-0.444

0.742

-7.849

0.406

-0.167

0.592

0.611

0.696

0.444

0.713

-0.167

0.901

0.278

0.813

47.0000

0.389

0.421

-0.444

0.742

-7.849

0.406

-0.167

0.592

0.611

0.696

-0.556

0.645

0.833

0.533

0.278

0.813

48.0200

0.389

0.421

0.556

0.681

10.25

0.278

-0.167

0.592

0.611

0.696

0.444

0.713

0.833

0.533

0.278

0.813

50.0100

-0.611

0.206

-0.444

0.742

-7.849

0.406

-0.167

0.592

-0.389

0.804

0.444

0.713

0.833

0.533

0.278

0.813

2.0000

-0.611

0.206

0.556

0.681

15.15

0.109

-0.167

0.592

-0.389

0.804

-0.556

0.645

0.833

0.533

-0.722

0.538

54.0000

0.0556

0.772

0.333

0.535

4.568

0.224

-0.0556

0.653

-0.167

0.789

7.63e-16

1.000

0.611

0.251

0.0556

0.905

55.0300

0.389

0.421

0.556

0.681

15.15

0.109

-0.167

0.592

0.611

0.696

0.444

0.713

0.833

0.533

0.278

0.813

68.0900

0.389

0.421

0.556

0.681

12.15

0.199

-0.167

0.592

-0.389

0.804

0.444

0.713

0.833

0.533

0.278

0.813

83.1100

-0.111

0.751

0.0556

0.955

1.151

0.866

-0.167

0.460

0.111

0.922

-0.0556

0.949

0.333

0.731

-0.722

0.396

83.1200

0.389

0.421

0.556

0.681

8.151

0.388

-0.167

0.592

-0.389

0.804

-0.556

0.645

-0.167

0.901

0.278

0.813

119.0200

0.389

0.421

0.556

0.681

16.15

0.088

-0.167

0.592

-0.389

0.804

-0.556

0.645

0.833

0.533

0.278

0.813

Observations

203

203

203

203

203

203

203

203

F-Test

0.647

0.628

1.476

0.697

0.332

0.202

0.549

0.424

p value

0.954

0.964

0.0433

0.919

1.000

1.000

0.989

0.999

Pseudo R ²

0.153

0.149

0.291

0.163

0.0847

0.0532

0.133

0.106

Note: P/C = Parent

or Caregiver;

reference group is

Tract 2.0200

bold = p <.05

218

Appendix A

Exhibit A-2C. Relationships between DHA resident characteristics and census tracts: Households with three or more children (continued)

P/C had too little

P/C had difficulty

Frequency that P/C

Number of years

Number of years

Frequency that P/C

P/C ever seen a

Spanish language

money for food at

paying all bills at

smoked marijuana

during childhood that during childhood that P/C born in the United

drank alcohol since

psychiatrist (1=yes,

interview (1=yes;

time of DHA move-in time of DHA move-

since becoming a

P/C lived in public

P/C lived in a home States (1=yes; 0=no)

becoming a parent

0=no)

0=no)

(1=yes, 0=no)

in (1=yes, 0=no)

parent

housing

owned by parents

Census Tract

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

3.0100

0.722

0.384

1.111

0.461

0.889

0.641

-0.833

0.661

-1.77e-15

1.000

1.056

0.865

0.333

0.969

-0.222

0.400

0.278

0.145

5.0200

-0.278

0.808

0.111

0.957

-0.111

0.966

-0.833

0.751

-0.500

0.331

21.56

0.012

-12.67

0.292

0.278

0.445

-0.222

0.396

6.0000

-0.278

0.808

1.111

0.593

-0.111

0.966

-0.833

0.751

-0.500

0.331

-5.444

0.524

12.33

0.304

-0.722

0.048

-0.222

0.396

7.0200

0.261

0.520

0.803

0.276

-0.419

0.653

-1.372

0.141

-0.269

0.140

-0.291

0.923

2.333

0.583

0.0470

0.715

-0.145

0.119

8.0000

0.389

0.318

0.511

0.470

0.222

0.804

-0.633

0.478

-0.100

0.567

-2.911

0.317

-1.733

0.671

0.144

0.244

-0.222

0.013

9.0200

0.722

0.528

1.111

0.593

1.889

0.473

-0.833

0.751

-0.500

0.331

-5.444

0.524

14.33

0.233

-0.722

0.048

-0.222

0.396

9.0300

0.222

0.789

0.611

0.685

0.389

0.838

-0.833

0.661

-1.85e-15

1.000

1.056

0.865

8.333

0.339

0.278

0.293

-0.222

0.243

9.0400

-0.278

0.652

0.111

0.921

2.639

0.064

0.417

0.768

-0.500

0.072

1.056

0.818

-5.917

0.360

0.278

0.157

-0.222

0.116

9.0500

0.222

0.789

0.611

0.685

-0.111

0.954

-0.833

0.661

-0.500

0.181

5.556

0.370

-7.667

0.379

0.278

0.293

-0.222

0.243

10.0000

0.472

0.319

0.611

0.477

1.139

0.296

-1.208

0.266

-1.79e-15

1.000

1.056

0.765

-7.167

0.150

0.153

0.310

-0.0972

0.370

11.0100

0.522

0.354

0.311

0.761

0.489

0.706

0.167

0.897

-0.100

0.692

-5.444

0.196

-3.467

0.557

0.278

0.122

-0.222

0.086

11.0200

-0.278

0.738

0.611

0.685

2.889

0.131

2.667

0.162

-1.59e-15

1.000

2.056

0.740

-12.67

0.147

0.278

0.293

-0.222

0.243

13.0100

0.722

0.528

1.111

0.593

1.889

0.473

-9.833

0.000

0.500

0.331

-5.444

0.524

14.33

0.233

0.278

0.445

-0.222

0.396

14.0200

-0.135

0.785

0.111

0.902

0.0317

0.978

-0.833

0.464

-0.0714

0.748

-5.444

0.143

3.048

0.558

-0.151

0.339

-0.222

0.052

14.0300

-0.278

0.808

1.111

0.593

-0.111

0.966

-0.833

0.751

0.500

0.331

15.56

0.070

-12.67

0.292

0.278

0.445

-0.222

0.396

15.0000

-0.0278

0.964

-4.139

0.000

1.639

0.248

-2.833

0.046

0.250

0.366

4.056

0.378

6.333

0.327

0.278

0.157

-0.222

0.116

16.0000

0.261

0.520

-0.274

0.710

-0.188

0.840

-1.218

0.191

-0.269

0.140

2.940

0.332

-2.359

0.579

0.278

0.032

-0.222

0.018

19.0000

-0.278

0.392

-0.0654

0.912

0.0948

0.899

-1.010

0.176

-1.50e-15

1.000

-0.121

0.960

-1.196

0.725

0.131

0.206

-0.134

0.073

21.0000

0.722

0.528

0.111

0.957

-0.111

0.966

-0.833

0.751

0.500

0.331

-5.444

0.524

-12.67

0.292

-0.722

0.048

-0.222

0.396

23.0000

0.222

0.789

0.611

0.685

-0.111

0.954

-0.833

0.661

-0.500

0.181

-5.444

0.380

14.33

0.101

-0.222

0.400

-0.222

0.243

24.0300

0.122

0.828

0.311

0.761

0.689

0.595

-0.633

0.623

-0.300

0.236

3.156

0.453

-1.067

0.857

0.278

0.122

-0.222

0.086

35.0000

0.122

0.828

0.711

0.487

0.289

0.823

0.567

0.660

0.1000

0.692

-3.044

0.469

-3.467

0.557

0.278

0.122

-0.222

0.086

36.0100

-0.278

0.808

1.111

0.593

-0.111

0.966

-0.833

0.751

0.500

0.331

-5.444

0.524

6.333

0.597

0.278

0.445

-0.222

0.396

36.0200

-0.00505

0.991

0.657

0.397

-0.202

0.837

-1.015

0.299

-0.136

0.476

-2.263

0.477

1.061

0.812

0.0960

0.479

-0.131

0.179

41.0100

0.222

0.718

0.611

0.585

-2.111

0.137

0.167

0.906

-1.59e-15

1.000

-5.444

0.237

-5.917

0.360

0.278

0.157

-0.222

0.116

41.0300

-0.278

0.738

0.111

0.941

-0.111

0.954

-0.833

0.661

-1.63e-15

1.000

-5.444

0.380

14.33

0.101

-0.222

0.400

-0.222

0.243

41.0400

0.222

0.789

0.611

0.685

0.389

0.838

-0.333

0.861

-0.500

0.181

-5.444

0.380

0.833

0.924

0.278

0.293

-0.222

0.243

42.0200

-0.278

0.808

0.111

0.957

-0.111

0.966

-0.833

0.751

-0.500

0.331

-5.444

0.524

14.33

0.233

0.278

0.445

-0.222

0.396

43.0400

-0.278

0.808

0.111

0.957

-0.111

0.966

-0.833

0.751

0.500

0.331

-5.444

0.524

-12.67

0.292

0.278

0.445

-0.222

0.396

44.0300

-0.278

0.808

-8.889

0.000

0.889

0.735

2.167

0.409

-0.500

0.331

-5.444

0.524

6.333

0.597

0.278

0.445

-0.222

0.396

44.0400

-0.278

0.808

0.111

0.957

-0.111

0.966

-0.833

0.751

-0.500

0.331

-5.444

0.524

14.33

0.233

0.278

0.445

-0.222

0.396

45.0100

-0.278

0.689

-2.889

0.023

-3.111

0.053

-3.833

0.017

-0.167

0.593

-5.444

0.294

5.333

0.464

-0.389

0.079

0.444

0.006

45.0200

-1.056

0.021

-1.667

0.045

1.889

0.072

0.389

0.709

-0.167

0.414

-1.000

0.768

1.778

0.709

0.167

0.249

-0.222

0.034

46.0200

0.722

0.528

1.111

0.593

-0.111

0.966

-0.833

0.751

-0.500

0.331

-5.444

0.524

-12.67

0.292

0.278

0.445

-0.222

0.396

47.0000

-0.278

0.808

0.111

0.957

-9.111

0.001

-0.833

0.751

-0.500

0.331

18.56

0.031

-12.67

0.292

0.278

0.445

-0.222

0.396

48.0200

-0.278

0.808

0.111

0.957

0.889

0.735

-0.833

0.751

-0.500

0.331

6.556

0.443

-12.67

0.292

0.278

0.445

-0.222

0.396

50.0100

0.722

0.528

1.111

0.593

1.889

0.473

-9.833

0.000

-0.500

0.331

-5.444

0.524

14.33

0.233

0.278

0.445

-0.222

0.396

2.0000

0.722

0.528

0.111

0.957

-0.111

0.966

-0.833

0.751

-0.500

0.331

-5.444

0.524

14.33

0.233

-0.722

0.048

-0.222

0.396

54.0000

0.0556

0.903

0.556

0.501

-0.444

0.671

-1.056

0.312

-0.0556

0.785

0.222

0.948

7.667

0.109

0.278

0.056

-0.222

0.034

55.0300

-0.278

0.808

0.111

0.957

-0.111

0.966

-0.833

0.751

-0.500

0.331

-5.444

0.524

14.33

0.233

0.278

0.445

-0.222

0.396

68.0900

0.722

0.528

1.111

0.593

0.889

0.735

0.167

0.949

0.500

0.331

-5.444

0.524

-3.667

0.760

0.278

0.445

-0.222

0.396

83.1100

0.222

0.789

0.111

0.941

0.389

0.838

-0.333

0.861

0.500

0.181

-5.444

0.380

13.33

0.127

-0.222

0.400

0.278

0.145

83.1200

0.722

0.528

1.111

0.593

0.889

0.735

0.167

0.949

0.500

0.331

-5.444

0.524

14.33

0.233

0.278

0.445

-0.222

0.396

119.0200

-0.278

0.808

0.111

0.957

1.889

0.473

0.167

0.949

-0.500

0.331

-5.444

0.524

14.33

0.233

0.278

0.445

-0.222

0.396

Observations

203

203

203

203

203

203

203

203

203

F-Test

0.511

1.359

0.876

1.061

0.855

0.920

1.153

1.323

0.989

p value

0.995

0.0887

0.690

0.386

0.723

0.617

0.261

0.109

0.500

Pseudo R ²

0.124

0.275

0.196

0.228

0.192

0.204

0.243

0.269

0.216

Note: P/C = Parent

or Caregiver;

reference group is

Tract 2.0200

bold = p <.05

219

Appendix A

Exhibit A-2C. Relationships between DHA resident characteristics and census tracts: Households with three or more children (continued)

Biological father

Parent have HS

Parent have any

Parent Depressive

always lived in

Kids share same

Parenting Efficacy

Parenting Beliefs

Parent's age at time P/C African American

diploma at time of

higher education at

Symptomatology

household with

biological dad

Scale at time of

Scale at time of

of DHA move-in

(1=yes; 0=no)

DHA move-in (1=yes; time of DHA move-in

Scale at time of

child(ren) (1=yes;

(1=yes; 0=no)

interview

interview

0=no)

(1=yes; 0=no)

interview

0=no)

Census Tract

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

3.0100

0.833

0.012

0.833

0.895

-0.111

0.719

0.167

0.657

-0.0556

0.760

-0.389

0.258

-2.222

0.758

1.333

0.578

1.444

0.622

5.0200

-0.167

0.713

7.333

0.399

-0.111

0.794

-0.333

0.520

-0.0556

0.824

-0.389

0.411

-3.222

0.746

3.333

0.313

-0.0556

0.989

6.0000

-0.167

0.713

1.333

0.878

-0.111

0.794

-0.333

0.520

-0.0556

0.824

-0.389

0.411

18.78

0.060

-7.667

0.021

-1.056

0.794

7.0200

0.0641

0.689

2.872

0.351

0.274

0.072

-0.0256

0.889

0.0983

0.268

-0.0812

0.628

-3.607

0.306

0.0256

0.982

0.406

0.776

8.0000

0.233

0.131

2.200

0.457

0.489

0.001

4.61e-16

1.000

-0.0556

0.514

-0.122

0.448

1.844

0.586

-0.333

0.767

0.678

0.622

9.0200

-0.167

0.713

7.333

0.399

-0.111

0.794

0.667

0.199

-0.0556

0.824

-0.389

0.411

-8.222

0.408

-0.667

0.840

2.944

0.466

9.0300

0.333

0.311

5.833

0.355

-0.111

0.719

0.167

0.657

-0.0556

0.760

-0.389

0.258

-2.222

0.758

2.333

0.330

-4.056

0.167

9.0400

0.333

0.172

-2.167

0.643

-0.111

0.628

0.167

0.549

0.444

0.001

-0.139

0.585

-5.222

0.329

0.0833

0.963

2.194

0.313

9.0500

-0.167

0.612

9.333

0.140

-0.111

0.719

-0.333

0.375

-0.0556

0.760

-0.389

0.258

-2.222

0.758

1.333

0.578

-0.0556

0.985

10.0000

0.0833

0.656

6.083

0.092

0.264

0.136

0.167

0.436

-0.0556

0.591

-0.264

0.178

4.778

0.246

-1.667

0.223

0.319

0.848

11.0100

0.0333

0.881

-0.667

0.876

-0.111

0.596

0.0667

0.793

-0.0556

0.652

-0.189

0.417

-0.622

0.899

2.133

0.190

2.544

0.201

11.0200

-0.167

0.612

1.833

0.771

-0.111

0.719

0.167

0.657

0.444

0.015

0.111

0.746

17.28

0.018

0.333

0.889

1.944

0.507

13.0100

-0.167

0.713

1.333

0.878

-0.111

0.794

-0.333

0.520

-0.0556

0.824

0.611

0.197

13.78

0.167

-4.667

0.159

2.944

0.466

14.0200

0.262

0.183

4.905

0.194

0.175

0.345

0.0952

0.671

-0.0556

0.609

0.468

0.023

-3.937

0.361

1.190

0.406

1.087

0.534

14.0300

0.833

0.067

7.333

0.399

-0.111

0.794

-0.333

0.520

-0.0556

0.824

0.611

0.197

1.778

0.858

-0.667

0.840

-0.0556

0.989

15.0000

0.333

0.172

4.583

0.328

0.889

0.000

0.167

0.549

-0.0556

0.680

-0.139

0.585

9.028

0.093

-0.667

0.707

-0.0556

0.980

16.0000

0.0641

0.689

-3.205

0.299

0.889

0.000

0.0513

0.780

-0.0556

0.531

-0.158

0.346

0.932

0.791

0.872

0.456

0.0214

0.988

19.0000

0.0392

0.760

-0.225

0.927

0.418

0.001

-0.0392

0.789

0.0327

0.645

-0.00654

0.961

-0.399

0.888

0.627

0.503

0.621

0.588

21.0000

-0.167

0.713

11.33

0.193

-0.111

0.794

-0.333

0.520

-0.0556

0.824

-0.389

0.411

10.78

0.279

-2.667

0.419

-1.056

0.794

23.0000

-0.167

0.612

-0.167

0.979

-0.111

0.719

-0.333

0.375

0.444

0.015

-0.389

0.258

-6.222

0.389

1.833

0.444

1.444

0.622

24.0300

-0.167

0.454

-2.067

0.629

0.889

0.000

0.0667

0.793

-0.0556

0.652

-0.389

0.096

-0.222

0.964

0.933

0.566

0.544

0.784

35.0000

0.0333

0.881

-2.667

0.533

0.489

0.021

-0.133

0.601

0.144

0.242

-0.389

0.096

3.778

0.440

0.733

0.652

3.144

0.115

36.0100

-0.167

0.713

6.333

0.466

0.889

0.038

0.667

0.199

-0.0556

0.824

0.611

0.197

-4.222

0.671

-2.667

0.419

1.944

0.630

36.0200

0.0152

0.928

3.788

0.243

0.434

0.007

0.0303

0.875

-0.0556

0.551

-0.0253

0.886

0.141

0.970

0.333

0.786

2.308

0.126

41.0100

0.0833

0.732

5.333

0.255

0.889

0.000

0.167

0.549

-0.0556

0.680

0.111

0.662

-6.222

0.245

1.083

0.542

2.194

0.313

41.0300

0.333

0.311

8.333

0.187

0.889

0.005

0.167

0.657

0.444

0.015

0.111

0.746

-9.722

0.179

3.333

0.165

-7.556

0.011

41.0400

-0.167

0.612

-1.167

0.853

0.889

0.005

0.167

0.657

-0.0556

0.760

-0.389

0.258

4.278

0.553

-0.667

0.781

-5.056

0.085

42.0200

0.833

0.067

-2.667

0.759

0.889

0.038

0.667

0.199

-0.0556

0.824

-0.389

0.411

-3.222

0.746

3.333

0.313

-5.056

0.211

43.0400

-0.167

0.713

9.333

0.284

0.889

0.038

0.667

0.199

-0.0556

0.824

-0.389

0.411

3.778

0.704

3.333

0.313

4.944

0.221

44.0300

-0.167

0.713

1.333

0.878

0.889

0.038

-0.333

0.520

-0.0556

0.824

-0.389

0.411

-7.222

0.468

-2.667

0.419

0.944

0.815

44.0400

-0.167

0.713

7.333

0.399

0.889

0.038

-0.333

0.520

0.944

0.000

0.611

0.197

-7.222

0.468

1.333

0.686

-5.056

0.211

45.0100

0.500

0.070

5.000

0.344

-0.111

0.668

-0.333

0.289

0.278

0.069

0.278

0.334

-5.222

0.387

2.667

0.184

-0.0556

0.982

45.0200

0.167

0.354

-4.667

0.178

0.111

0.512

4.64e-16

1.000

-0.0556

0.576

0.0556

0.767

-2.556

0.518

0.444

0.735

-0.389

0.808

46.0200

0.833

0.067

-1.667

0.848

-0.111

0.794

-0.333

0.520

-0.0556

0.824

-0.389

0.411

0.778

0.938

-9.667

0.004

0.944

0.815

47.0000

-0.167

0.713

-0.667

0.939

-0.111

0.794

0.667

0.199

-0.0556

0.824

0.611

0.197

-7.222

0.468

1.333

0.686

-1.056

0.794

48.0200

0.833

0.067

2.333

0.788

0.889

0.038

-0.333

0.520

-0.0556

0.824

-0.389

0.411

-10.22

0.304

2.333

0.480

2.944

0.466

50.0100

-0.167

0.713

2.333

0.788

0.889

0.038

-0.333

0.520

-0.0556

0.824

-0.389

0.411

-4.222

0.671

1.333

0.686

-5.056

0.211

2.0000

0.833

0.067

8.333

0.338

0.889

0.038

0.667

0.199

-0.0556

0.824

0.611

0.197

-11.22

0.260

3.333

0.313

-5.056

0.211

54.0000

-0.0556

0.757

4.667

0.178

-4.56e-15

1.000

0.111

0.589

-0.0556

0.576

-0.167

0.375

3.222

0.415

-0.444

0.735

3.389

0.036

55.0300

0.833

0.067

11.33

0.193

-0.111

0.794

-0.333

0.520

0.944

0.000

0.611

0.197

-11.22

0.260

3.333

0.313

4.944

0.221

68.0900

0.833

0.067

1.333

0.878

0.889

0.038

-0.333

0.520

-0.0556

0.824

0.611

0.197

18.78

0.060

-5.667

0.087

-0.0556

0.989

83.1100

0.333

0.311

-18.17

0.004

0.389

0.210

0.167

0.657

-0.0556

0.760

-0.389

0.258

-7.222

0.317

-0.167

0.944

-0.0556

0.985

83.1200

0.833

0.067

2.333

0.788

0.889

0.038

-0.333

0.520

0.944

0.000

-0.389

0.411

-8.222

0.408

-2.667

0.419

1.944

0.630

119.0200

0.833

0.067

2.333

0.788

-0.111

0.794

-0.333

0.520

-0.0556

0.824

-0.389

0.411

7.778

0.434

3.333

0.313

1.944

0.630

Observations

203

203

203

203

203

203

203

203

203

F-Test

1.179

0.988

3.075

0.562

2.076

1.168

1.035

1.021

0.954

p value

0.231

0.502

0.000000153

0.986

0.000555

0.242

0.425

0.448

0.559

Pseudo R ²

0.247

0.216

0.461

0.135

0.366

0.245

0.224

0.221

0.210

Note: P/C = Parent

or Caregiver;

reference group is

Tract 2.0200

bold = p <.05

220

Appendix A

Relationships Between Individual Characteristics and Neighborhood Characteristics

Even if (as we have found) there were nonrandom assignments to DHA developments or

neighborhoods on the basis of ethnic or disability status, it would not necessarily follow

that there would be a strong relationship between these statuses and a wide variety of

neighborhood characteristics. Thus, our third investigative strategy involves the use of

continuously measured neighborhood characteristics instead of dummy variables to probe

their potential systematic co-variation with characteristics of individual DHA families.

Specifically, we employed the same individual characteristics as above and 12

characteristics of census tracts’ population and housing (percentages of female-headed

households, poor families and individuals, unemployed adults, those with only

elementary school education, those with college degrees, employees in professional or

technical occupations, non-Latino African-American population, Latino population,

foreign-born population, housing vacancy rate, homes built prior to 1940, homes that are

owner-occupied) conventionally used in neighborhood effect studies. We employed

multivariate regression (again stratified by family size) to estimate the statistical

associations between 27 individual and 12 neighborhood characteristics. As before, a

quasi-random assignment would be reflected in coefficients approximating zero and an

insignificant F-test for the regression as a whole.

Results are shown in Exhibit A-3A-C. Overall, of the 36 regressions, 26 exhibited

insignificant F-tests. More convincingly, of the 972 regression estimates, 900

(92 percent) yielded coefficients that were statistically insignificant. Across the three

family-size strata, the percentages of insignificant coefficients were 91, 93, and 92,

respectively, suggesting that generally the outcomes corresponded to quasi-random

assignment. Further examination is required, however, to ascertain whether there was any

systematic sorting by a particular household characteristic as revealed by that

characteristic garnering the bulk of the statistically significant coefficients.

221

Appendix A

Exhibit A-3A. Relationships between DHA resident and neighborhood characteristics: Households with 0–1 child Percent of

Percent of

Percent of

Percent female

Percent

neighborhood

neighborhood

Percent foreign professional/ex

Percent Black

Neighborhood

Percent homes

headed

Neighborhood

Hispanic

Neighborhood Neighborhood

residents with

residents with

born in

ecutive workers

residents in

homeownership built before 1940

households in

poverty rate

residents in unemployment vacancy rate

less than 9th Bachelor's degree neighborhood

in

neighborhood

rate

in neighborhood

neighborhood

neighborhood

grade education

or higher

neighborhood

P

P

P

P

P

Coeff.

P value Coeff. P value Coeff.

P value Coeff. P value Coeff. P value Coeff. P value Coeff.

Coeff.

Coeff.

Coeff.

Coeff.

Coeff. P value

value

value

value

value

value

P/C had checking account at time of DHA move-in (1=yes, 0=no)

-1.465

0.367 -1.447

0.039

1.633

0.096

0.290

0.715 1.363 0.099 -2.595

0.099 1.302 0.303

0.000 0.869 -0.356 0.523 -0.521 0.196

2.156 0.248

1.834

0.244

P/C had health insurance at time of DHA move-in (1=yes, 0=no)

-2.293

0.403 -1.505

0.201

1.431

0.387

-2.150

0.111 1.679 0.229 -1.979

0.455 -0.042 0.984

0.000 0.934

0.389 0.679

0.770 0.258

4.141 0.189

3.237

0.224

P/C had too little money for food at time of DHA move-in (1=yes, 0=no)

-1.123

0.506 -0.100

0.891

-0.203

0.842

-0.313

0.705 -0.209 0.808 -1.573

0.335 0.968 0.461

0.000 0.249 -0.789 0.173

0.081 0.846

2.511 0.196 -1.870

0.254

P/C had dif iculty paying al bil s at time of DHA move-in (1=yes, 0=no)

-0.728

0.473 -0.091

0.835

0.353

0.564

0.814

0.102 0.152 0.768 -0.768

0.434 -1.109 0.160

0.000 0.968 -0.435 0.211 -0.700 0.006

0.186 0.873 -1.085

0.270

Frequency that P/C drank alcohol since becoming a parent

-0.683

0.351 -0.014

0.966

-0.461

0.297

0.170

0.635 -0.296 0.426 -0.840

0.236 0.008 0.989

0.000 0.326 -0.228 0.363 -0.137 0.449

1.692 0.045

0.890

0.211

Frequency that P/C smoked marijuana since becoming a parent

0.158

0.865 -0.613

0.124

0.763

0.173

-0.073

0.872 0.484 0.304 -0.252

0.779 0.368 0.609

0.000 0.069 -0.128 0.688 -0.181 0.431

-0.380 0.721 -1.548

0.086

Frequency that parent did other drugs since becoming a parent

1.390

0.176 0.545

0.216

-0.851

0.169

-0.186

0.711 -0.486 0.351 1.427

0.150 1.066 0.181

0.000 0.219

0.601 0.088

0.214 0.401

-1.113 0.345

0.902

0.364

P/C ever seen a psychiatrist (1=yes, 0=no)

-3.241

0.260 0.545

0.658

0.425

0.806

0.412

0.770 -0.459 0.753 -2.065

0.458 -0.866 0.698

0.000 0.342 -0.680 0.490

0.903 0.206

2.915 0.378

3.054

0.274

Number of years during childhood that P/C lived in public housing

0.304

0.059

0.116

0.092

-0.088

0.365

-0.058

0.463 -0.095 0.245 0.333

0.032 0.156 0.210 0.000 0.502 0.098 0.074 0.040 0.313

-0.309 0.094

0.024

0.879

Number of years during childhood that P/C lived in a home owned by parents

-0.036

0.770 0.065

0.215

-0.010

0.887

0.046

0.440 -0.053 0.391 0.050

0.669 0.001 0.991

0.000 0.999 -0.011 0.801

0.020 0.511

-0.068 0.626 -0.040

0.736

Biological father always lived in household with child(ren) (1=yes; 0=no)

-3.851

0.315 0.127

0.938

0.066

0.977

1.258

0.503 0.166 0.932 0.115

0.975 -2.046 0.492

0.000 0.854

0.952 0.469

0.041 0.966

0.554 0.900

1.565

0.673

Kids share same biological dad (1=yes; 0=no)

-0.337

0.839 -1.542

0.031

0.189

0.850

-0.858

0.290 0.878 0.296 -1.552

0.332 -0.251 0.845

0.000 0.861 -0.764 0.178 -0.323 0.431

1.293 0.496 -1.865

0.245

Parent Depressive Symptomatology Scale at time of interview

0.070

0.623 -0.025

0.677

-0.101

0.237

0.014

0.836 -0.045 0.530 0.012

0.929 0.089 0.420

0.000 0.746

0.064 0.187

0.006 0.859

0.043 0.793

0.161

0.242

Parenting Efficacy Scale at time of interview

-0.537

0.155 0.040

0.806

-0.105

0.645

0.010

0.957 -0.140 0.465 -0.381

0.296 -0.386 0.189

0.000 0.600 -0.082 0.524

0.002 0.979

0.975 0.025

0.236

0.519

Parenting Beliefs Scale at time of interview

-0.121

0.741 -0.031

0.842

-0.017

0.940

0.115

0.523 -0.042 0.820 0.061

0.863 -0.217 0.445

0.000 0.228

0.069 0.583

0.033 0.714

-0.303 0.472 -0.010

0.977

P/C is single parent (1=yes, 0=no)

1.645

0.604 3.279

0.017

-3.279

0.088

2.256

0.148 -4.004 0.014 4.985

0.105 -0.074 0.976

0.000 0.192

0.626 0.565

0.528 0.503

-7.325 0.046 -1.908

0.535

P/C employment status at time of DHA move-in (1=employed, 0=not employed)

6.530

0.075 2.469

0.117

-2.348

0.288

0.846

0.637 -2.339 0.209 6.466

0.069 -0.457 0.872

0.000 0.570

0.838 0.505 -0.200 0.826

-9.081 0.032 -2.762

0.437

P/C hourly wage at time of DHA move-in

-0.640

0.005 -0.277 0.004

0.180

0.185

-0.087

0.429 0.231 0.044 -0.588

0.007 0.067 0.702 0.000 0.800 -0.034 0.661 0.027 0.624

0.756 0.004

0.260

0.232

P/C disability status at time of survey (1=yes; 0=no)

-0.738

0.839 -1.978

0.205

4.828

0.028 -0.305 0.864 3.996 0.031 -0.549 0.876 2.138 0.449 0.000 0.132 -1.295 0.299 0.890 0.324

-3.872 0.354

4.809

0.173

P/C received TANF at time of DHA move-in (1=yes, 0=no)

0.321

0.888 2.610

0.008

-2.631

0.056

0.345

0.757 -2.701 0.020 3.058

0.165 -1.242 0.482

0.000 0.984

0.786 0.313

1.472 0.010

-1.748 0.503

0.661

0.764

P/C receiving Food Stamps at time of DHA move-in (1=yes, 0=no)

-1.343

0.432 -1.643

0.026

1.742

0.092

-0.036

0.965 1.658 0.057 -2.322

0.160 0.544 0.682

0.000 0.843 -0.408 0.486 -0.522 0.218

1.774 0.366

0.207

0.900

P/C born in the United States (1=yes; 0=no)

-0.990

0.845 3.038

0.163

-7.767

0.011

3.465

0.163 -6.404 0.013 -2.208

0.652 1.415 0.719

0.000 0.521 -2.072 0.233 -1.096 0.383

7.094 0.223 -0.893

0.856

Spanish language interview (1=yes; 0=no)

1.612

0.826 -0.352

0.911

-6.399

0.150

-0.433

0.904 -3.553 0.341 -2.183

0.758 10.140 0.077

0.000 0.514

0.473 0.851

0.133 0.942

9.686 0.252

6.997

0.326

Parent's age at time of DHA move-in

-0.222

0.064 -0.087

0.091

0.185

0.011

0.054

0.362 0.145 0.018 -0.299

0.010 0.022 0.813 0.000 0.613 -0.078 0.058 -0.037 0.210

0.180 0.193

0.166

0.154

P/C African American (1=yes; 0=no)

4.347

0.108 -0.870

0.453

1.633

0.316

-0.391

0.767 1.860 0.175 2.512

0.336 10.800 0.000

0.000 0.801

0.304 0.742

1.243 0.064

-5.315 0.087

3.701

0.158

Parent have HS diploma at time of DHA move-in (1=yes; 0=no)

0.321

0.904 -0.158

0.890

-0.189

0.907

0.690

0.599 0.267 0.844 0.676

0.794 -2.481 0.233

0.000 0.252

1.331 0.148 -0.403 0.544

-1.262 0.681

3.502

0.178

Parent have any higher education at time of DHA move-in (1=yes; 0=no)

-5.315

0.199 0.444

0.802

-1.570

0.529

6.147

0.003 -2.270 0.280 -3.063 0.443 -1.675 0.602 0.000 0.519 -1.019 0.472 -2.783 0.007

2.273 0.632 -6.210

0.122

Constant

67.950

0.000 20.090 0.000

17.810

0.013

8.444

0.147 27.540 0.000 51.180

0.000 18.670 0.044 0.000 0.448 16.960 0.000 7.765 0.009 20.080 0.142 18.850

0.102

F test

1.331

2.359

1.995

1.048

2.275

1.697

1.800

0.684

1.422

1.812

1.376

0.974

p value (bold <.05)

0.134

0.000

0.003

0.405

0.001

0.021

0.011

0.881

0.088

0.011

0.109

0.506

R²

0.132

0.212

0.185

0.107

0.206

0.162

0.170

0.072

0.139

0.171

0.136

0.100

Note: P/C = Parent or Caregiver; al neighborhood characteristics measured at

time of first DHA move-in; N=265

222

Appendix A

Exhibit A-3B. Relationships between DHA resident and neighborhood characteristics: Households with two children

Percent of

Percent of

Percent of

Percent female

neighborhood

neighborhood

Percent foreign

Percent Black

Percent Hispanic

Percent homes built

professional/execut Neighborhood

Neighborhood

Neighborhood

Neighborhood

headed households residents with less

residents with

born in

residents in

residents in

before 1940 in

ive workers in

poverty rate

unemployment

vacancy rate

homeownership rate

in neighborhood

than 9th grade

Bachelor's degree or

neighborhood

neighborhood

neighborhood

neighborhood

neighborhood

education

higher

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff. P value Coeff. P value Coeff. P value Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

Coeff.

P value

P/C had checking account at time of DHA move-in (1=yes, 0=no)

0.189

0.942

-2.028

0.086

2.391

0.127

-1.524 0.277

2.629

0.056

-1.215 0.630

-1.597

0.471

0.000

0.143

-1.026

0.226

-0.943

0.186

-1.098

0.714

0.092

0.973

P/C had health insurance at time of DHA move-in (1=yes, 0=no)

-5.529

0.050

-0.545

0.667

0.864

0.608

0.235 0.876

0.011

0.994

-4.222 0.121

-1.752

0.462

0.000

0.187

-1.004

0.270

0.648

0.399

4.517

0.162

-2.046

0.487

P/C had too lit le money for food at time of DHA move-in (1=yes, 0=no)

2.078

0.407

-0.296

0.794

1.806

0.230

1.160 0.390

1.506

0.252

2.530 0.297

2.321

0.276

0.000

0.509

0.997

0.220

-0.099

0.885

-4.907

0.089

0.952

0.717

P/C had dif iculty paying al bil s at time of DHA move-in (1=yes, 0=no)

-0.393

0.620

0.037

0.917

-0.449

0.344

0.083 0.845

-0.338

0.416

-0.579 0.449

0.219

0.745

0.000

0.313

-0.138

0.590

-0.446

0.040

0.830

0.361

-1.787

0.032

Frequency that P/C drank alcohol since becoming a parent

-0.391

0.470

0.147

0.547

-0.238

0.463

0.612 0.036

-0.213

0.454

0.000 1.000

0.183

0.690

0.000

0.772

0.115

0.512

0.007

0.961

-0.313

0.614

0.241

0.672

Frequency that P/C smoked marijuana since becoming a parent

0.115

0.869

0.080

0.799

-0.693

0.098

-0.305 0.417

-0.499

0.173

-0.054 0.936

-0.079

0.894

0.000

0.709

0.031

0.892

0.292

0.127

0.594

0.459

0.493

0.501

Frequency that parent did other drugs since becoming a parent

-0.095

0.921

-0.363

0.402

0.767

0.182

-0.826

0.110

0.743

0.140

-0.625 0.499

0.061

0.941

0.000

0.727

-0.455

0.144

-0.045

0.863

0.719

0.513

-0.594

0.554

P/C ever seen a psychiatrist (1=yes, 0=no)

-1.837

0.490

-0.802

0.504

0.546

0.732

-0.651 0.649

0.891

0.523

-2.149 0.403

1.862

0.410

0.000

0.109

-0.091

0.916

-0.347

0.633

4.827

0.115

0.650

0.816

Number of years during childhood that P/C lived in public housing

-0.065

0.700

0.102

0.185

-0.223

0.030

-0.007 0.943

-0.175

0.050

0.138 0.402

-0.224

0.121

0.000

0.865

0.047

0.389

0.059

0.205

-0.204

0.295

-0.299

0.095

Number of years during childhood that P/C lived in a home owned by parents

0.056

0.608

0.117

0.017

-0.191

0.004

0.028 0.635

-0.179

0.002

0.164 0.119

-0.012

0.895

0.000

0.152

0.053

0.135

0.053

0.074

-0.161

0.197

-0.087

0.446

Biological father always lived in household with child(ren) (1=yes; 0=no)

-5.611

0.109

-1.872

0.236

1.228

0.558

-2.060 0.273

1.176

0.521

-3.886 0.250

-1.142

0.700

0.000

0.011

-0.615

0.587

0.726

0.447

6.819

0.090

-4.365

0.234

Kids share same biological dad (1=yes; 0=no)

0.812

0.747

-0.054

0.962

0.827

0.584

0.743 0.583

0.732

0.579

0.000 1.000

2.941

0.169

0.000

0.280

-0.204

0.803

0.444

0.519

-1.280

0.658

2.286

0.387

Parent Depressive Symptomatology Scale at time of interview

0.073

0.598

0.094

0.132

-0.103

0.215

0.113 0.131

-0.074

0.312

0.083 0.536

-0.273

0.021

0.000

0.655

0.056

0.215

-0.033

0.389

-0.134

0.398

0.074

0.611

Parenting Efficacy Scale at time of interview

-0.218

0.549

-0.085

0.605

-0.012

0.956

-0.046 0.814

0.045

0.812

-0.434 0.219

-0.002

0.995

0.000

0.311

-0.145

0.220

-0.040

0.686

0.335

0.423

0.154

0.688

Parenting Beliefs Scale at time of interview

-0.381

0.260

-0.075

0.622

-0.045

0.824

0.237 0.193

-0.050

0.778

-0.142 0.664

-0.465

0.106

0.000

0.288

0.068

0.535

-0.002

0.987

-0.023

0.953

-0.146

0.681

P/C is single parent (1=yes, 0=no)

-0.119

0.969

0.298

0.832

-0.137

0.941

0.171 0.919

-0.413

0.800

-1.812 0.547

2.667

0.314

0.000

0.223

-1.061

0.294

-0.459

0.590

2.897

0.418

-1.666

0.610

P/C employment status at time of DHA move-in (1=employed, 0=not employed)

3.100

0.407

2.102

0.214

-3.590

0.110

-0.260 0.897

-2.740

0.163

1.779 0.622

2.003

0.528

0.000

0.882

0.265

0.827

0.375

0.714

1.578

0.713

1.961

0.617

P/C hourly wage at time of DHA move-in

-0.142

0.529

-0.208

0.042

0.310

0.022

-0.074 0.543

0.261

0.028

-0.185 0.394

0.077

0.689

0.000

0.703

-0.029

0.693

-0.051

0.408

0.094

0.716

-0.076

0.748

P/C disability status at time of survey (1=yes; 0=no)

1.511

0.697

-0.201

0.909

2.662

0.254

-1.059 0.612

0.640

0.753

1.628 0.665

2.611

0.429

0.000

0.496

-1.205

0.339

1.048

0.324

-7.744

0.083

-3.599

0.378

P/C received TANF at time of DHA move-in (1=yes, 0=no)

0.789

0.524

1.080

0.054

-1.157

0.119

0.035 0.958

-1.053

0.106

1.711 0.153

-0.346

0.742

0.000

0.272

0.793

0.049

0.250

0.459

-0.258

0.856

0.185

0.887

P/C receiving Food Stamps at time of DHA move-in (1=yes, 0=no)

1.317

0.312

0.299

0.611

-0.632

0.418

0.422 0.546

-0.562

0.411

1.618 0.199

-0.606

0.584

0.000

0.989

0.622

0.141

0.248

0.485

-1.562

0.296

2.041

0.136

P/C born in the United States (1=yes; 0=no)

0.564

0.904

-0.940

0.658

2.557

0.364

0.695 0.783

1.572

0.523

-0.462 0.919

6.171

0.122

0.000

0.519

-0.657

0.666

0.833

0.516

-3.571

0.508

3.292

0.504

Spanish language interview (1=yes; 0=no)

-5.352

0.430

0.030

0.992

3.595

0.376

-0.298 0.935

2.795

0.432

-3.032 0.643

4.686

0.416

0.000

0.942

-0.383

0.862

2.195

0.236

1.611

0.836

16.520

0.021

Parent's age at time of DHA move-in

-0.410

0.004

-0.111

0.082

0.082

0.334

0.091 0.229

0.049

0.508

-0.406 0.003

-0.039

0.742

0.000

0.446

-0.096

0.037

-0.053

0.171

0.403

0.013

-0.057

0.701

P/C African American (1=yes; 0=no)

3.588

0.177

-1.879

0.118

2.408

0.131

-1.240 0.385

2.693

0.054

2.783 0.278

14.490

0.000

0.000

0.611

1.997

0.021

2.166

0.003

-0.767

0.801

10.820

0.000

Parent have HS diploma at time of DHA move-in (1=yes; 0=no)

-1.866

0.501

0.524

0.675

-0.723

0.663

2.317 0.121

-1.942

0.182

-1.383 0.605

3.846

0.103

0.000

0.190

-0.718

0.424

-0.361

0.633

2.141

0.501

0.343

0.906

Parent have any higher education at time of DHA move-in (1=yes; 0=no)

1.676

0.686

2.753

0.142

-2.151

0.386

6.148 0.006

-3.587

0.100

0.096 0.981

3.614

0.304

0.000

0.371

-1.023

0.446

-1.943

0.087

-2.279

0.632

-2.183

0.615

Constant

69.240

0.000

26.490

0.000

7.737

0.274

8.193 0.197

18.440

0.003 58.680 0.000 10.360

0.301

0.000

0.633

17.450

0.000

8.223

0.011

23.270

0.086

22.400

0.071

F test

1.124

1.438

1.482

1.219

1.721

1.285

3.511

0.933

1.480

1.392

1.135

1.434

p value (bold <.05)

0.313

0.082

0.066

0.218

0.018

0.166

0.000

0.564

0.066

0.102

0.301

0.083

R²

0.118

0.146

0.150

0.127

0.170

0.133

0.295

0.100

0.150

0.142

0.119

0.146

Note: P/C = Parent or Caregiver; al neighborhood characteristics measured at

time of first DHA move-in; N=255

223

Appendix A

Exhibit A-3C. Relationships between DHA resident and neighborhood characteristics: Households with three or more children

Percent of

Percent of

Percent of

Percent female

Percent

neighborhood

neighborhood

Percent foreign professional/ex

Percent Black

Neighborhood

Percent homes

headed

Neighborhood

Hispanic

Neighborhood Neighborhood

residents with

residents with

born in

ecutive workers

residents in

homeownership built before 1940

households in

poverty rate

residents in unemployment vacancy rate

less than 9th Bachelor's degree neighborhood

in

neighborhood

rate

in neighborhood

neighborhood

neighborhood

grade education

or higher

neighborhood

P

P

P

P

P

Coeff.

P value Coeff. P value Coeff.

P value Coeff. P value Coeff. P value Coeff. P value Coeff.

Coeff.

Coeff.

Coeff.

Coeff.

Coeff. P value

value

value

value

value

value

P/C had checking account at time of DHA move-in (1=yes, 0=no)

-1.465

0.367 -1.447 0.039

1.633

0.096

0.290

0.715 1.363 0.099 -2.595

0.099 1.302 0.303

0.000 0.869 -0.356 0.523 -0.521 0.196

2.156 0.248 1.834

0.244

P/C had health insurance at time of DHA move-in (1=yes, 0=no)

-2.293

0.403 -1.505 0.201

1.431

0.387 -2.150

0.111 1.679 0.229 -1.979

0.455 -0.042 0.984

0.000 0.934 0.389 0.679

0.770 0.258

4.141 0.189 3.237

0.224

P/C had too lit le money for food at time of DHA move-in (1=yes, 0=no)

-1.123

0.506 -0.100 0.891

-0.203

0.842 -0.313

0.705 -0.209 0.808 -1.573

0.335 0.968 0.461

0.000 0.249 -0.789 0.173

0.081 0.846

2.511 0.196 -1.870

0.254

P/C had dif iculty paying al bil s at time of DHA move-in (1=yes, 0=no)

-0.728

0.473 -0.091 0.835

0.353

0.564

0.814

0.102 0.152 0.768 -0.768

0.434 -1.109 0.160

0.000 0.968 -0.435 0.211 -0.700 0.006

0.186 0.873 -1.085

0.270

Frequency that P/C drank alcohol since becoming a parent

-0.683

0.351 -0.014 0.966

-0.461

0.297

0.170

0.635 -0.296 0.426 -0.840

0.236 0.008 0.989

0.000 0.326 -0.228 0.363 -0.137 0.449

1.692 0.045 0.890

0.211

Frequency that P/C smoked marijuana since becoming a parent

0.158

0.865 -0.613 0.124

0.763

0.173 -0.073

0.872 0.484 0.304 -0.252

0.779 0.368 0.609

0.000 0.069 -0.128 0.688 -0.181 0.431

-0.380 0.721 -1.548

0.086

Frequency that parent did other drugs since becoming a parent

1.390

0.176 0.545 0.216

-0.851

0.169 -0.186

0.711 -0.486 0.351 1.427

0.150 1.066 0.181

0.000 0.219 0.601 0.088

0.214 0.401

-1.113 0.345 0.902

0.364

P/C ever seen a psychiatrist (1=yes, 0=no)

-3.241

0.260 0.545 0.658

0.425

0.806

0.412

0.770 -0.459 0.753 -2.065

0.458 -0.866 0.698

0.000 0.342 -0.680 0.490

0.903 0.206

2.915 0.378 3.054

0.274

Number of years during childhood that P/C lived in public housing

0.304

0.059 0.116 0.092

-0.088

0.365 -0.058

0.463 -0.095 0.245 0.333

0.032 0.156 0.210 0.000 0.502 0.098 0.074 0.040 0.313

-0.309 0.094 0.024

0.879

Number of years during childhood that P/C lived in a home owned by parents

-0.036

0.770 0.065 0.215

-0.010

0.887

0.046

0.440 -0.053 0.391 0.050

0.669 0.001 0.991

0.000 0.999 -0.011 0.801

0.020 0.511

-0.068 0.626 -0.040

0.736

Biological father always lived in household with child(ren) (1=yes; 0=no)

-3.851

0.315 0.127 0.938

0.066

0.977

1.258

0.503 0.166 0.932 0.115

0.975 -2.046 0.492

0.000 0.854 0.952 0.469

0.041 0.966

0.554 0.900 1.565

0.673

Kids share same biological dad (1=yes; 0=no)

-0.337

0.839 -1.542 0.031

0.189

0.850 -0.858

0.290 0.878 0.296 -1.552

0.332 -0.251 0.845

0.000 0.861 -0.764 0.178 -0.323 0.431

1.293 0.496 -1.865

0.245

Parent Depressive Symptomatology Scale at time of interview

0.070

0.623 -0.025 0.677

-0.101

0.237

0.014

0.836 -0.045 0.530 0.012

0.929 0.089 0.420

0.000 0.746 0.064 0.187

0.006 0.859

0.043 0.793 0.161

0.242

Parenting Efficacy Scale at time of interview

-0.537

0.155 0.040 0.806

-0.105

0.645

0.010

0.957 -0.140 0.465 -0.381

0.296 -0.386 0.189

0.000 0.600 -0.082 0.524

0.002 0.979

0.975 0.025 0.236

0.519

Parenting Beliefs Scale at time of interview

-0.121

0.741 -0.031 0.842

-0.017

0.940

0.115

0.523 -0.042 0.820 0.061

0.863 -0.217 0.445

0.000 0.228 0.069 0.583

0.033 0.714

-0.303 0.472 -0.010

0.977

P/C is single parent (1=yes, 0=no)

1.645

0.604 3.279 0.017

-3.279

0.088

2.256

0.148 -4.004 0.014 4.985

0.105 -0.074 0.976

0.000 0.192 0.626 0.565

0.528 0.503

-7.325 0.046 -1.908

0.535

P/C employment status at time of DHA move-in (1=employed, 0=not employed)

6.530

0.075 2.469

0.117

-2.348

0.288

0.846

0.637 -2.339 0.209 6.466

0.069 -0.457 0.872

0.000 0.570 0.838 0.505 -0.200 0.826

-9.081 0.032 -2.762

0.437

P/C hourly wage at time of DHA move-in

-0.640

0.005 -0.277 0.004

0.180

0.185 -0.087

0.429 0.231 0.044 -0.588

0.007 0.067 0.702 0.000 0.800 -0.034 0.661 0.027 0.624

0.756 0.004 0.260

0.232

P/C disability status at time of survey (1=yes; 0=no)

-0.738

0.839 -1.978 0.205

4.828

0.028 -0.305 0.864 3.996 0.031 -0.549 0.876 2.138 0.449 0.000 0.132 -1.295 0.299 0.890 0.324

-3.872 0.354 4.809

0.173

P/C received TANF at time of DHA move-in (1=yes, 0=no)

0.321

0.888 2.610 0.008

-2.631

0.056

0.345

0.757 -2.701 0.020 3.058

0.165 -1.242 0.482

0.000 0.984 0.786 0.313

1.472 0.010

-1.748 0.503 0.661

0.764

P/C receiving Food Stamps at time of DHA move-in (1=yes, 0=no)

-1.343

0.432 -1.643 0.026

1.742

0.092 -0.036

0.965 1.658 0.057 -2.322

0.160 0.544 0.682

0.000 0.843 -0.408 0.486 -0.522 0.218

1.774 0.366 0.207

0.900

P/C born in the United States (1=yes; 0=no)

-0.990

0.845 3.038 0.163

-7.767

0.011

3.465

0.163 -6.404 0.013 -2.208

0.652 1.415 0.719

0.000 0.521 -2.072 0.233 -1.096 0.383

7.094 0.223 -0.893

0.856

Spanish language interview (1=yes; 0=no)

1.612

0.826 -0.352

0.911

-6.399

0.150 -0.433

0.904 -3.553 0.341 -2.183

0.758 10.140 0.077

0.000 0.514 0.473 0.851

0.133 0.942

9.686 0.252 6.997

0.326

Parent's age at time of DHA move-in

-0.222

0.064 -0.087 0.091

0.185

0.011

0.054

0.362 0.145 0.018 -0.299

0.010 0.022 0.813 0.000 0.613 -0.078 0.058 -0.037 0.210

0.180 0.193 0.166

0.154

P/C African American (1=yes; 0=no)

4.347

0.108 -0.870 0.453

1.633

0.316 -0.391

0.767 1.860 0.175 2.512

0.336 10.800 0.000

0.000 0.801 0.304 0.742

1.243 0.064

-5.315 0.087 3.701

0.158

Parent have HS diploma at time of DHA move-in (1=yes; 0=no)

0.321

0.904 -0.158 0.890

-0.189

0.907

0.690

0.599 0.267 0.844 0.676

0.794 -2.481 0.233

0.000 0.252 1.331 0.148 -0.403 0.544

-1.262 0.681 3.502

0.178

Parent have any higher education at time of DHA move-in (1=yes; 0=no)

-5.315

0.199 0.444 0.802

-1.570

0.529

6.147

0.003 -2.270 0.280 -3.063 0.443 -1.675 0.602 0.000 0.519 -1.019 0.472 -2.783 0.007

2.273 0.632 -6.210

0.122

Constant

67.950

0.000 20.090 0.000 17.810 0.013

8.444

0.147 27.540 0.000 51.180

0.000 18.670 0.044 0.000 0.448 16.960 0.000 7.765 0.009 20.080 0.142 18.850

0.102

F test

1.331

2.359

1.995

1.048

2.275

1.697

1.800

0.684

1.422

1.812

1.376

0.974

p value (bold <.05)

0.134

0.000

0.003

0.405

0.001

0.021

0.011

0.881

0.088

0.011

0.109

0.506

R²

0.132

0.212

0.185

0.107

0.206

0.162

0.170

0.072

0.139

0.171

0.136

0.100

Note: P/C = Parent or Caregiver; al neighborhood characteristics measured at

time of first DHA move-in; N=265

224

Appendix A

Only two individual characteristics had a frequency of statistically significant coefficients

that were greater than average: African-American DHA tenant (15 percent) and

household wages (14 percent). It is noteworthy that although disability status generated a

nonrandom assignment to particular developments because of DHA rules (as shown in

Exhibit A-1) this apparently did not produce a strong association with particular

neighborhood characteristics, because the locations where the disabled were assigned

evinced considerable variation.

Of course, geographic selection bias arises to the extent that individual household

characteristics that are not observed (or controlled statistically) are correlated with both neighborhood characteristics and child outcomes. In this regard, it is revealing to separate

the individual characteristics listed in Exhibit A-3 into the first 15 (which were not

observable to DHA officials, because they were gleaned from our household survey) and

the last 12 (which likely were). Ninety-five percent of the former set’s coefficients were

not statistically significant, whereas only 88 percent of the later set’s were. This is

consistent with the notion that, although DHA’s assignment process may not have

produced a completely random assignment across neighborhood characteristics based on

household characteristics that DHA staff could observe, it nevertheless likely produced

such based on household characteristics that they could not observe.

We therefore conclude that this third piece of evidence suggests the DHA allocation

process produced a quasi-random assignment across geography, with the possible

exception of two individual characteristics observable by the DHA—African-American

ethnicity and household wages—that are easily controlled in our analyses. Even more

importantly, we conclude that the DHA allocation process produced a quasi-random

assignment across geography in terms of individual characteristics not observable by

DHA (but observable to us from our survey). This gives us some confidence that any

additional household characteristics we do not observe in our study are similarly quasi-

randomly allocated across neighborhood characteristics.

Relationships Between Typically Unobserved Individual Characteristics and

Neighborhood Characteristics Using Monte Carlo Simulation

Recall that the key issue at hand is whether DHA’s assignment of public housing tenants

to neighborhoods effectively removes the correlation between unobservable (that is,

cannot be controlled statistically) parental characteristics that might affect both

characteristics of location chosen and individual outcomes being investigated. We

investigated this by examining the degree to which a variety of characteristics of parents

or caregivers in our sample that typically are not observed in neighborhood effect studies

were correlated with multiple characteristics of their neighborhoods at the time of initial

assignment by DHA. The intuition guiding our analysis is as follows. An actual random

assignment of DHA applicants to DHA dwellings will likely produce by chance a few

non-zero pairwise correlations between DHA household characteristics and neighborhood

characteristics. A Monte Carlo simulation repeating such random assignments will

generate bootstrapped standard errors and distributions of such correlations for each pair.

This provides the benchmark against which we will compare the actual pairwise

correlations between DHA household characteristics and neighborhood characteristics. If

225

Appendix A

the pattern of the actual correlations does not differ significantly from that produced by

the simulation, we will fail to reject the null hypothesis that the DHA assignment process

yielded a quasi-random geographic assignment of households according to their

unobserved characteristics.

In particular, we implemented this strategy as follows. We considered here the

unobserved (by DHA and typically in other studies) characteristics of parents (listed in

Exhibit A-4) and the characteristics of census tracts considered above. For each of the

three aforementioned family sizes of DHA tenants we calculated the Pearsonian

correlation between each pairwise combination of parental characteristics and

neighborhood characteristics observed when the DHA first assigned our sample

households to their DHA units.

As a comparative benchmark for these correlations we conducted Monte Carlo

simulations in which each sample household was, indeed, randomly assigned to one of

the DHA units (for the appropriate family size) with its associated bundle of

neighborhood characteristics that we observed whenever the initial assignment of

household in our study actually occurred.146 In each iteration after all households were

randomly assigned, we calculated correlations for all pairwise combinations of parental

characteristics and neighborhood characteristics. We used 10,000 repetitions of these

simulations to produce distributions for all pairwise combinations of parental

characteristics and neighborhood characteristics and associated bootstrapped standard

errors. This allowed us to estimate (1) for each correlation a 95 percent confidence

interval and (2) across all pairwise correlations how many significantly different from

zero would be expected by chance when produced by a random assignment process.

The results are reported in Exhibit A-4. The parental characteristics are listed in the rows

and the three family-size strata in the columns. The cells show for how many of the

possible neighborhood characteristics the initial DHA assignment produced an actual

correlation with the given parental characteristic that was significantly different from

zero at the 5 percent level (two-tailed test); the actual correlation coefficient and the

neighborhood characteristic involved are reported in these cases. The exhibit shows that

for families that have no or one child and families with who have children, only 8

(5 percent of possible correlations) were statistically different from zero; the

corresponding figure for families that have three or more children was 12 (8 percent of

possible correlations). Our simulations showed that in more than 98 percent and

95 percent of the cases, respectively, a larger number of statistically significant

correlations were produced by a random assignment. This strongly indicated that the

relatively rare non-zero correlations we observed from initial DHA allocations of tenants

to neighborhoods (shown in Exhibit A-4) were consistent with those that would have

been generated by a pure process of random assignment. These results suggest that the

146 The programming and execution of these simulations was conducted by Dr. Albert Anderson of PDQ

Inc., whose contribution we gratefully acknowledge.

226

Appendix A

DHA natural experiment likely removes the correlation between parental characteristics

(which we do not observe and cannot control in our Denver study) that may potentially

affect both initial DHA neighborhood characteristics and subsequent individual

outcomes.

227

Appendix A

Exhibit A-4. Simulation results: Number of statistically significant

correlations between typically unobserved household characteristics and

neighborhood characteristics

Household Characteristic

Families with 0–1

Families with Two

Families with Three or

Child

Children

More Children

Ever not enough food for family while

0

1 (% black = 0.14)

0

reside in this location

Ever unable to pay all bills while

2 (% foreign-born =

2 (% elem. school

1 (% vacant = −0.12)

residing in this location

0.13; % vacant =

ed. = −0.17; %

−0.16)

vacant = −0.14)

Frequency of alcohol use since

2 (% unemployed =

0

1 (% black = −0.09)

becoming a parent

−0.16; % owner

=0.13)

Frequency of marijuana use since

1 (% black = 0.17)

0

0

becoming a parent

Frequency of drug use since becoming a

1 (% black = 0.13)

0

0

parent

Ever seen psychologist, psychiatrist, or

0

0

0

counselor

Did your parents ever live in public

1 (% female heads =

0

1 (% foreign born =

housing when you were growing up?

0.22)

−0.18)

Did your parents ever own their home

0

3 (% elem. school =

0

when you were growing up?

0.26; % college =

−0.26; % own =

0.20)

Born in the United States

1 (% college =

0

0

−0.16;)

Primary language is Spanish

0

0

0

Father of child always lived in the home

0

0

5 (% female heads =

while child was growing up

−0.11; % elementary

school = −0.10; % poor

= −0.10; % own = 0.09;

% pre-1940 homes =

−0.12)

Parental depression (Center for

0

1 (% Latino = 0.13)

2 (% elem. school =

Epidemiologic Studies Depression) scale

0.13; % Latino = 0.13)

Parental self-efficacy scale

0

0

0

Parental beliefs and practices scale

0

1 (% Latino = −0.21)

2 (% college = −0.09; %

black = −0.12)

Source: Authors’ calculations based on Monte Carlo simulations of Denver Child Study

survey data; statistically significant household–neighborhood characteristic correlations

shown parenthetically.

228

Appendix A

Conclusion

Natural experiments involving residential placements under the auspices of some public

program offer potentially powerful vehicles for measuring neighborhood effects, because

they can rupture the association between unobserved characteristics of the individuals

being studied and characteristics of their neighborhood. In this appendix, we have

investigated the extent to which a natural experiment involving public housing in Denver

offers such potential.

Our analysis of the DHA’s dwelling allocation procedures revealed considerable room

for tenant self-selection or DHA staff selection to enter. Nevertheless, we found that the

initial occupancy mimicked a quasi-random assignment process to DHA dwellings or

neighborhoods, with the exception of ethnicity and disability status. Only African-

American ethnicity (and to a lesser degree, household wages) exhibited above-average

frequencies of associations with neighborhood conditions, however. This suggests that,

conditioned on ethnicity and wages, the DHA allocation process produced a quasi-

random initial assignment across neighborhood characteristics. The empirical implication

is that models estimating neighborhood effects using the current data must control for

ethnicity and wages to avoid geographic selection bias. We, in fact, do so in all analyses

conducted in this report.

Even more importantly, two sorts of analyses indicate that the DHA allocation process

produced a quasi-random assignment across neighborhood conditions in terms of

individual characteristics not observable by DHA (but observable to us from our survey).

This gives us some confidence that any additional household characteristics we do not

observe in our study are similarly quasi-randomly allocated across neighborhood

characteristics.

229

Appendix B

APPENDIX B. CHILD IMPACTS SURVEY

229

For optimum accuracy, please print

in capital letters and avoid contact

with the edge of the box.

23469

CHILD IMPACTS SURVEY

ID NUMBER:

DATE OF INTERVIEW:

/

/ 2 0

First Name

MI

INTERVIEWER: Last Name

RECORD START TIME:

:

Hour Minutes

RECORD END TIME:

:

Hour Minutes

TIME OF DAY:

A.M.

P.M.

TOTAL NUMBER OF CALLS:

CURRENT STATUS:

Current DHA Resident - Conventional Housing

Current DHA Resident - Dispersed Housing

Former DHA Resident - Conventional Housing

Former DHA Resident - Dispersed Housing

Sponsored by the National Institute for Child Health and Human Development

1 of 53

DENVER CHILD IMPACTS SURVEY

Hello, my name is _____________. I'm an interviewer working on a research project conducted by professors from Wayne State University in Detroit. We are conducting a study sponsored by the National Institute of Child Health and Human Development to evaluate the impact of DHA housing programs on children. We recently sent you a letter that describes the study and the importance of your participation. It 23469

indicates that your participation in this study is completely voluntary, that all of your answers will be held in strict confidence, and that there are no known risks or benefits to your participation.

MOBILITY-NEIGHBORHOOD HISTORY MODULE

I would like to begin by asking you some questions about your experiences in the various places where you've lived since you've been a parent.

N1. When did you and your family FIRST move into a Denver Housing Authority (DHA) dwelling?

/

[check against our records from DHA, which show: ]

/

month year

month year

[INTERVIEWER: IF THERE IS A DIFFERENCE OF MORE THAN ONE MONTH, DISCUSS WITH

RESPONDENT AND RESOLVE DISCREPANCY]

N1A. Before you moved in, did DHA give you more than one neighborhood in which you could have chosen to live in a DHA unit?

Yes

No

Don't Know

[INTERVIEWER: If YES, confirm that multiple DHA units offered were located in different neighborhoods.]

N2pre. And how many children, IN TOTAL, do you have? [In this study, we ask you to consider as children your biological children, stepchildren, grandchildren, siblings, foster children, or any other children that you have cared for as the primary caregiver and guardian.]

total number of children

N2. At the time when you FIRST moved into DHA housing, what was the name of the oldest child living in the household who was under the age of 18 when you moved in and who lived with you for at least 1 year when you lived in DHA housing?

KEY NAME CHILD #1

Date of Birth

N2rel. What is your relation to CHILD?

/

/

month day year

other (specify)

code*

[NB: THIS name gets populated into Homelessness Module and is used as KEY NAME CHILD #1 in Question N5 below.

INTERVIEWER: Please reconfirm the name of the oldest child who lived with the R in DHA housing before moving to the next question.

Verify spelling of CHILD's name.]

* See RELATIONSHIP CODES on bottom of next page (pg. 3).

2 of 53

N3pre1. Did any of your other children live with you in DHA housing for at least a year when they were under the age of 18?

Yes

No

N3pre2. If yes, how many?

23469

N3. Starting with the oldest child after [KEY NAME CHILD #1],

N3age. What is the child's DOB?

N3rel. What is your relation to CHILD?

please tell me the names of these children.

month day year

code*

other (specify)

Child 2

/

/

Child 3

/

/

Child 4

/

/

Child 5

/

/

Child 6

/

/

[INTERVIEWER: Please check the list of additional children before moving to Question N4]

N4pre1. Do you have any other children that we have not yet mentioned?

Yes

No

N4pre2. If yes, how many?

N4. Starting with the oldest child please tell

N4age. What is child's

N4rel. What is your

me the names of these children.

current age?

relation to CHILD?

Non-DHA (other) child name

age

code*

other (specify)

1 Biological mother

2 Biological father

*RELATIONSHIP TO R INSTRUCTION TO INTERVIEWER:

3 Adoptive or step parent

4 Legal foster parent

Please use the following codes for relationship of

5 Informal foster parent

respondent to CHILD for questions N3rel and N4rel:

6 Grandparent

96 Other (specify)

98 Don't know

3 of 53

99 Refused

For the remainder of the study, we will be asking you questions about all of the children mentioned above that lived with you in DHA housing at some point while under the age of 18.

23469

N5pre. First, how many different places have you lived since [KEY NAME CHILD #1] was born?

total number of places

N5. Now I'd like you to think back about ALL the places where you've lived since [KEY NAME CHILD #1] was born, even if you only lived there a short time. Let's begin by listing the place where you lived when [KEY NAME CHILD #1] was born, giving me as much information as you can remember about the address. Then we'll talk about where you lived next, and so on, up to your current home. However, I only want you to list places where at least one of the children who lived with you in DHA housing were still residing with you.

What was the street number and name of the place you lived when [KEY NAME CHILD #1] was born?

[FILL IN AS MUCH ADDRESS INFORMATION AS POSSIBLE FOR EACH PLACE BEFORE ASKING QUESTIONS A-D FOR ANY LOCATION.

COMPLETE THE LOCATION TABLE BELOW FROM N5A THROUGH N5D BEFORE BEGINNING WITH THE NEXT SET OF QUESTIONS.]

Location 1

Location 2

Location 3

Location 4

Location 5

Location 6

Location 7

Location 8

Location 9

Location 10

Number

Street

What were the

nearest 2 cross

streets?

What was the

name of the

neighborhood?

What City

State

Zip?

When did you:

/

/

/

/

/

/

/

/

/

/

Move in?

Mo Year

Mo Year

Mo Year

Mo Year

Mo Year

Mo Year

Mo Year

Mo Year

Mo Year

Mo Year

Move out?

/

/

/

/

/

/

/

/

/

/

Mo Year

Mo Year

Mo Year

Mo Year

Mo Year

Mo Year

Mo Year

Mo Year

Mo Year

Mo Year

[CATI

COMPUTE/

interviewer

check if LT 12

Months

Months

Months

Months

Months

Months

Months

Months

Months

Months

months]

4 of 53

Location 1

Location 2

Location 3

Location 4

Location 5

Location 6

Location 7

Location 8

Location 9

Location 10

A. What was

your main

reason for

23469

moving here?

Interviewer:

Fill in text box

for each

location

B. What type of

building was it?

1. single-family,

1

1

1

1

1

1

1

1

1

1

detached

house

2. duplex w/ 2

2

2

2

2

2

2

2

2

2

2

living units

3. townhouse

other

other

other

other

other

other

other

other

other

3

other

3

3

3

3

3

3

3

3

3

w/ attached

units

4. mobile home

4

4

4

4

4

4

4

4

4

4

5. apartment in

5

5

5

5

5

5

5

5

5

5

building w/

3-6 units

6

6

6

6

6

6

6

6

6

6

6. apartment in

building w/

6+ units

96

96

96

96

96

96

96

96

96

96

96. other, specify

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

97. NA

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

98. DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

99 REF

C1. Did you rent

own

own

own

own

own

own

own

own

own

own

or own this

rent

rent

rent

rent

rent

rent

rent

rent

rent

rent

dwelling?

other

other

other

other

other

other

other

other

other

other

[IF RENT ASK

C2-C3]

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

C2. How much

did you pay per

$

$

$

$

$

$

$

$

$

$

month in rent?

C3. Did your

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

rent include

No

No

No

No

No

No

No

No

No

No

utilities?

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

5 of 53

Location 1

Location 2

Location 3

Location 4

Location 5

Location 6

Location 7

Location 8

Location 9

Location 10

D1.Was dwelling

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

run by public

No

No

No

No

No

No

No

No

No

No

housing authority?

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

23469

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

D2.Was dwelling

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

receiving subsidy

No

No

No

No

No

No

No

No

No

No

that reduced your

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

rent (Section 8)?

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

[If reside more than 12 mos., then ask E.-F.]

E. Try to recall the conditions in the NEIGHBORHOOD at the time you were living there. Tell me whether your neighborhood had the following...

[INTERVIEWER: ASK EACH QUESTION FOR EACH LOCATION BEFORE MOVING TO NEXT QUESTION]

Location

Location

Location

Location

Location

Location

Location

Location

Location

Location

1

2

3

4

5

6

7

8

9

10

1. People who could get together to

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

solve neighborhood problems.

No

No

No

No

No

No

No

No

No

No

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

2. Many neighbors who watch out for my

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

children and property.

No

No

No

No

No

No

No

No

No

No

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

3. Many neighbors who knew me and

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

my children by name.

No

No

No

No

No

No

No

No

No

No

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

4. Local health clinics or hospitals.

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

No

No

No

No

No

No

No

No

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

5. Many adult neighbors you and your

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

children could look up to.

No

No

No

No

No

No

No

No

No

No

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

6. Good police protection.

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

No

No

No

No

No

No

No

No

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

7. Many neighbors you could count on

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

for help in times of trouble.

No

No

No

No

No

No

No

No

No

No

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

6 of 53

(continued) E. Please try to recall conditions in the neighborhood where you were living at this time. Tell me whether your neighborhood had the following...

[INTERVIEWER: ASK EACH QUESTION FOR EACH LOCATION BEFORE MOVING TO NEXT QUESTION]

Location

Location

Location

Location

Location

Location

Location

Location

Location

Location

1

2

3

4

5

6

7

8

9

10

[Negative Neighborhood Conditions]

23469

8. People selling drugs.

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

No

No

No

No

No

No

No

No

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

9. Gang activity.

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

No

No

No

No

No

No

No

No

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

10. Homes that were broken into by

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

burglars.

No

No

No

No

No

No

No

No

No

No

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

11. People being robbed or mugged

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

often.

No

No

No

No

No

No

No

No

No

No

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

12. Many children or teens who got into

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

trouble.

No

No

No

No

No

No

No

No

No

No

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

13. People getting beaten or raped.

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

No

No

No

No

No

No

No

No

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

14. Neighbors who did not accept me or

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

my children because of our race,

No

No

No

No

No

No

No

No

No

No

ethnicity, or income.

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

15. [If YES, to any negative

conditions listed above, ask the

following]:

Please describe how your behavior

changed, if at all, to reduce the effects

of these neighborhood conditions.

Next, I'd like to talk to you about

resources. In your neighborhood

were there...

16. Parks or playgrounds where children

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

could play?

No

No

No

No

No

No

No

No

No

No

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

17. If Yes, did your child(ren) use the

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

parks or playgrounds?

No

No

No

No

No

No

No

No

No

No

NA

[If No, skip to E19]

NA

NA

NA

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

7 of 53

(continued) E. Please try to recall conditions in the neighborhood where you were living at this time. Tell me whether your neighborhood had the following...

[INTERVIEWER: ASK EACH QUESTION FOR EACH LOCATION BEFORE MOVING TO NEXT QUESTION]

Location

Location

Location

Location

Location

Location

Location

Location

Location

Location

1

2

3

4

5

6

7

8

9

10

18. If Yes, what impact did using the

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Negative

23469

parks or playgrounds have on your

None

None

None

None

None

None

None

None

None

None

child(ren)?

Positive

Positive

Positive

Positive

Positive

Positive

Positive

Positive

Positive

Positive

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

19. Indoor recreation center?

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

No

No

No

No

No

No

No

No

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

20. If Yes, did your child(ren) use the

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

indoor recreation center?

No

No

No

No

No

No

No

No

No

No

[If No, skip to E22]

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

21. If Yes, what impact did using the

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Negative

indoor recreation center have on your

None

None

None

None

None

None

None

None

None

None

child(ren)?

Positive

Positive

Positive

Positive

Positive

Positive

Positive

Positive

Positive

Positive

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

22. Mentoring or counseling centers for

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

children?

No

No

No

No

No

No

No

No

No

No

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

23. If Yes, did your child(ren) use the

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

mentoring or counseling centers?

No

No

No

No

No

No

No

No

No

No

[If No, skip to E25]

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

24. If Yes, what impact did using the

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Negative

mentoring or counseling centers have

None

None

None

None

None

None

None

None

None

None

on your child(ren)?

Positive

Positive

Positive

Positive

Positive

Positive

Positive

Positive

Positive

Positive

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

25. Were you active in any organizations

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

located in the neighborhood, such as

No

No

No

No

No

No

No

No

No

No

block clubs, tenant groups, religious

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

organizations, and the like?

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

8 of 53

F. Next I'd like to ask you about your employment, assets, and sources of income when you lived in this place.

[INTERVIEWER: ASK EACH QUESTION FOR EACH LOCATION BEFORE MOVING TO NEXT QUESTION]

Location

Location

Location

Location

Location

Location

Location

Location

Location

Location

1

2

3

4

5

6

7

8

9

10

23469

[repeat address info]

1. Were you employed?

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

No

No

No

No

No

No

No

No

[If No, skip to F5]

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

2. How many hours per week did you

usually work (total for all jobs)?

3. How many weeks per year did you

usually work (total for all jobs)?

4. How much did you usually earn, on

$

$

average, per hour?

.

.

$

.

$

.

$

.

$

.

$

.

$

.

$

.

$

.

5a. Was anyone else in the household

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

employed?

No

No

No

No

No

No

No

No

No

No

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

REF

REF

REF

5b. If Yes, number of others employed

in household. If DK enter -1; if REF

enter -2.

6. Did you or anyone in your household

receive:

a. SSI (Supplemental Security Income)? Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

b. Unemployment Compensation?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

c. Social Security?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

d. Alimony or child support?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

e. Food stamps?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

f. AFDC, TANF, or welfare?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

g. Death (survivor) benefits?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

h. Student grants/scholarships?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

i. Other income NOT from employment

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

(specify):?

9 of 53

[INTERVIEWER: ASK EACH QUESTION FOR EACH LOCATION BEFORE MOVING TO NEXT QUESTION]

Location

Location

Location

Location

Location

Location

Location

Location

Location

Location

[F. Income and assets continued]

1

2

3

4

5

6

7

8

9

10

23469

7. Did you have:

a. A checking account?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

b. A savings account?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

c. Individual Retirement Account?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

d. Retirement or pension plan?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

e. Stocks, bond, mutual funds?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

f. Life insurance?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

g. Business account or assets?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

h. Health insurance for you and your

child?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

i. Car, truck or van you owned free and

clear?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

8. During this time did you ever:

a. Lose a job and were unemployed for

a month or more?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

b. Lose your health insurance?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

c. Have a major illness or injury?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

d. Have too little money to buy enough

food for your family?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

e. Have your electricity, gas, or phone

service cut off?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

f. Get evicted from your home?

Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF Y N DK REF

10 of 53

Location

Location

Location

Location

Location

Location

Location

Location

Location

Location

[F. Income and assets continued]

1

2

3

4

5

6

7

8

9

10

23469

9. During this time, did you ever have

difficulty paying all your regular monthly

bills on time (e.g., rent or mortgage,

utilities, credit cards or other debts and

loans)?

1. Never

1

1

1

1

1

1

1

1

1

1

[IF NEVER SKIP PAST F10]

2

2

2

2

2

2

2

2

2

2

2. Some of the time

3

3

3

3

3

3

3

3

3

3

3. Most of the time

4. All of the time

4

4

4

4

4

4

4

4

4

4

DK. Don't know

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

10. Please describe the circumstances

surrounding the difficulty paying your

bills.

[INTERVIEWER FILL IN TEXT BOX]

[CONTINUE WITH HOMELESSNESS HISTORY MODULE ONCE YOU'VE COMPLETED THIS SECTION]

11 of 53

HOMELESSNESS HISTORY MODULE

HH1. Since [KEY NAME CHILD #1]: [the oldest child when you first moved into DHA public housing] was born, have you and your children ever stayed in a facility for the homeless, such as a shelter, hotel, church, or mission, because you had no place to live?

23469

No

Yes

DK

REF

[If NO, skip to next question HH2]

HH1num. How many times did you stay in such a facility?

number of times

[If YES, ask: When was the

How long were you in such a facility

first/next time this occurred?]

the first/next time this occurred?

Month Year

Incident #1:

/

months

days

Incident #2:

/

months

days

Incident #3:

/

months

days

Incident #4:

/

months

days

Incident #5:

/

months

days

HH2. Since [KEY NAME CHILD #1] was born, have you and your children ever stayed outdoors, in a car, or with friends or relatives because you had no place to live?

No

Yes

DK

REF

[If NO, skip to next module CHILD ROSTER]

HH2num. How many times did this happen to you?

number of times

[If YES, ask: When was the

first/next time this occurred?]

For how long were you in this

Month Year

situation?

Incident #1:

/

months

days

Incident #2:

/

months

days

Incident #3:

/

months

days

Incident #4:

/

months

days

Incident #5:

/

months

days

12 of 53

CHILD ROSTER AND FIXED CHARACTERISTICS MODULE

Now for the rest of this interview I'd like to focus only on the children who lived with you in DHA housing for at least a year when they were under age 18. Earlier you told me that these children were: [INTERVIEWER: REVERIFY WITH RESPONDENT the names listed in row A

23469

below; KEY CHILD should always be listed as #1. ASK EACH QUESTION ACROSS FOR ALL CHILDREN BEFORE MOVING ON TO

THE NEXT QUESTION]

C1.

I'd like to ask several things about each of these children.

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

a. Name of CHILD

b. Gender

Male

Male

Male

Male

Male

Male

Female

Female

Female

Female

Female

Female

c. Which of the following best

describes CHILD's ethnic

background?

1. African-American / Black

1

1

1

1

1

1

2. Latino / Hispanic

2

2

2

2

2

2

96. Other (specify)

other

other

other

other

other

other

98. DK

99. REF

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

d. Was CHILD born in the U.S.?

No

No

No

No

No

No

Yes

Yes

Yes

Yes

Yes

Yes

If NO: How long has CHILD lived in

DK

DK

DK

DK

DK

DK

U.S.?

REF

REF

REF

REF

REF

REF

years

years

years

years

years

years

e. When did CHILD live with you?

From (age at Starting Date in years;

age

age

age

age

age

age

birth age coded as "0")

To (age at Ending Date in years)

age

age

age

age

age

age

13 of 53

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

f. Was there any time when CHILD

No

No

No

No

No

No

23469

DID NOT live with you for 6 months or

Yes

Yes

Yes

Yes

Yes

Yes

more?

DK

DK

DK

DK

DK

DK

If NO, ask for next child.

REF

REF

REF

REF

REF

REF

If YES: When did CHILD NOT live

with you?

INTERVIEWER: IF FROM BIRTH

ENTER "0" FOR CHILD'S AGE

From (age at Starting Date in years)

age

age

age

age

age

age

To (age at Ending Date in years)

age

age

age

age

age

age

And WHERE was CHILD living during

this period?

1. with other parent

1

1

1

1

1

1

2. with friends or relatives

2

2

2

2

2

2

3. in a hospital

3

3

3

3

3

3

4. in a foster home

4

4

4

4

4

4

5. in a detention center or jail

5

5

5

5

5

5

6. child is deceased

6

6

6

6

6

6

96. other (specify)

98. DK

other

other

other

other

other

other

99. REF

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

[INTERVIEWER: after all children completed, continue with BIOLOGICAL FATHER/MOTHER MODULE]

14 of 53

BIOLOGICAL FATHER & MOTHER/FATHER FIGURE MODULE

[Interviewers, if biological father is the same for all children, ask question for each child before continuing with the next question.

If fathers are different, please ask questions in sequence for each child before continuing with next child.]

23469

Now I'd like to ask about the parents and other important adults in your children's lives.

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

F1. Who is CHILD's biological father?

[first name]

F2. Since CHILD was born, has

BIOLOGICAL FATHER always lived in

No

No

No

No

No

No

the household with you and your children?

Yes

Yes

Yes

Yes

Yes

Yes

DK

DK

DK

DK

DK

DK

[IF YES, skip to F7]

REF

REF

REF

REF

REF

REF

[IF NO, continue with F3-F9]

F3. When did FATHER live in the

No

No

No

No

No

No

household for the first time?

Yes

Yes

Yes

Yes

Yes

Yes

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

NO=Never lived in household [SKIP TO F4]

REF

REF

REF

REF

REF

REF

YES=Lived in household from:

Start

Start

Start

Start

Start

Start

Start date (mo/yr)

/

/

/

/

/

/

End

End

End

End

End

End

End date (mo/yr)

/

/

/

/

/

/

Second time lived in household?

Start

Start

Start

Start

Start

Start

Start date (mo/yr)

/

/

/

/

/

/

End date (mo/yr)

End

End

End

End

End

End

/

/

/

/

/

/

Third time lived in household?

Start

Start

Start

Start

Start

Start

Start date (mo/yr)

/

/

/

/

/

/

End

End

End

End

End

End

End date (mo/yr)

/

/

/

/

/

/

F4. What is the primary reason that

1

1

1

1

1

1

FATHER does/did not live in the

2

2

2

2

2

2

household?

3

3

3

3

3

3

1. Divorce or legal separation

4

4

4

4

4

4

2. Hospitalized or in mental institution

5

5

5

5

5

5

3. In jail or prison

6

6

6

6

6

6

4. Deceased

7

7

7

7

7

7

5. Violent or abusive behavior

6. Moved out of town

8

8

8

8

8

8

7. Alcohol or drug abuse

9

9

9

9

9

9

8. Served in the military

Other

Other

Other

Other

Other

Other

9. Father was not married to or cohabiting

with CHILD's mother.

96. Other (specify)

98. DK

DK

DK

DK

DK

DK

DK

99. REF

REF

REF

REF

REF

REF

REF

15 of 53

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

F5. When FATHER did not live in the

Never

Never

Never

Never

Never

Never

household, how often did he provide

Rarely, only a few times

Rarely, only a few times

Rarely, only a few times

Rarely, only a few times

Rarely, only a few times

Rarely, only a few times

financial support for CHILD?

Once a year

Once a year

Once a year

Once a year

Once a year

Once a year

Couple of times a year

Couple of times a year

Couple of times a year

Couple of times a year

Couple of times a year

Couple of times a year

23469

Most every month

Most every month

Most every month

Most every month

Most every month

Most every month

Weekly or more often

Weekly or more often

Weekly or more often

Weekly or more often

Weekly or more often

Weekly or more often

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

F6. Overall, how often did/does CHILD

Never

Never

Never

Never

Never

Never

see FATHER, on average?

Rarely, only a few times

Rarely, only a few times

Rarely, only a few times

Rarely, only a few times

Rarely, only a few times

Rarely, only a few times

Once a year

Once a year

Once a year

Once a year

Once a year

Once a year

Couple of times a year

Couple of times a year

Couple of times a year

Couple of times a year

Couple of times a year

Couple of times a year

Most every month

Most every month

Most every month

Most every month

Most every month

Most every month

Weekly or more often

Weekly or more often

Weekly or more often

Weekly or more often

Weekly or more often

Weekly or more often

DK

DK

DK

DK

DK

DK

NA

NA

NA

NA

NA

NA

REF

REF

REF

REF

REF

REF

F7. Overall, would you say that FATHER'S

Very positive

Very positive

Very positive

Very positive

Very positive

Very positive

influence on CHILD has been:

Somewhat positive

Somewhat positive

Somewhat positive

Somewhat positive

Somewhat positive

Somewhat positive

1. Very positive

No influence

No influence

No influence

No influence

No influence

No influence

2. Somewhat positive

Somewhat negative

Somewhat negative

Somewhat negative

Somewhat negative

Somewhat negative

Somewhat negative

3. Neither positive or negative/no influence

Very negative

Very negative

Very negative

Very negative

Very negative

Very negative

4. Somewhat negative

5. Very negative

DK

DK

DK

DK

DK

DK

(NA if not involved with child)

NA

NA

NA

NA

NA

NA

REF

REF

REF

REF

REF

REF

F8a. Who is the CHILD's BIOLOGICAL

MOTHER?

F8b. Since CHILD was born, has

No

No

No

No

No

No

BIOLOGICAL MOTHER always lived with

Yes

Yes

Yes

Yes

Yes

Yes

this child in the same household?

DK

DK

DK

DK

DK

DK

[IF YES, Skip to F12]

REF

[IF NO, Continue with F9-F11]

REF

REF

REF

REF

REF

No

F9. When did MOTHER live in the

No

No

No

No

No

Yes

household with CHILD for the first time?

Yes

Yes

Yes

Yes

Yes

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

No: Never lived with CHILD [SKIP TO F10]

REF

REF

REF

REF

REF

REF

Yes: Lived with CHILD from:

Start

Start

Start

Start

Start

Start

Start date (mo/yr)

/

/

/

/

/

/

End

End

End

End

End

End

End date (mo/yr)

/

/

/

/

/

/

Second time lived with CHILD:

Start

Start

Start

Start

Start

Start

Start date (mo/yr)

/

/

/

/

/

/

End

End

End

End

End

End

End date (mo/yr)

/

/

/

/

/

/

Third time lived with CHILD:

Start

Start

Start

Start

Start

Start

Start date (mo/yr)

/

/

/

/

/

/

End

End

End

End

End

End

End date (mo/yr)

/

/

/

/

/

/

16 of 53

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

F10. What is the primary reason that

1

1

1

1

1

1

23469

MOTHER does/did not live in the

2

2

2

2

2

2

household?

3

3

3

3

3

3

1. Divorce or legal separation

4

4

4

4

4

4

2. Hospitalized or in mental institution

5

5

5

5

5

5

3. In jail or prison

4. Deceased

6

6

6

6

6

6

5. Violent or abusive behavior

7

7

7

7

7

7

6. Moved out of town

8

8

8

8

8

8

7. Alcohol or drug abuse

8. Served in the military

9

9

9

9

9

9

9. Mother not married to or cohabiting with

Other

Other

Other

Other

Other

Other

CHILD's father

96. Other (specify)

98. DK

DK

DK

DK

DK

DK

DK

99. REF

REF

REF

REF

REF

REF

REF

F11. Overall, how often did/does CHILD

Never

Never

Never

Never

Never

Never

see MOTHER, on average?

Rarely, only a few times

Rarely, only a few times

Rarely, only a few times

Rarely, only a few times

Rarely, only a few times

Rarely, only a few times

Once a year

Once a year

Once a year

Once a year

Once a year

Once a year

Couple of times a year

Couple of times a year

Couple of times a year

Couple of times a year

Couple of times a year

Couple of times a year

Most every month

Most every month

Most every month

Most every month

Most every month

Most every month

Weekly or more often

Weekly or more often

Weekly or more often

Weekly or more often

Weekly or more often

Weekly or more often

DK

DK

DK

DK

DK

DK

NA

NA

NA

NA

NA

NA

REF

REF

REF

REF

REF

REF

F12. Overall, would you say that

Very positive

Very positive

Very positive

Very positive

Very positive

Very positive

MOTHER'S influence on CHILD has been:

Somewhat positive

Somewhat positive

Somewhat positive

Somewhat positive

Somewhat positive

Somewhat positive

1. Very positive

No influence

No influence

No influence

No influence

No influence

No influence

2. Somewhat positive

Somewhat negative

Somewhat negative

Somewhat negative

Somewhat negative

Somewhat negative

Somewhat negative

3. Neither positive or negative/no influence

Very negative

Very negative

Very negative

Very negative

Very negative

Very negative

4. Somewhat negative

5. Very negative

DK

DK

DK

DK

DK

DK

(NA if not involved with CHILD)

NA

NA

NA

NA

NA

NA

REF

REF

REF

REF

REF

REF

F13. Since CHILD was born, have there

No

No

No

No

No

No

been any men other than the

Yes

Yes

Yes

Yes

Yes

Yes

BIOLOGICAL FATHER who were very

NA

NA

NA

NA

NA

NA

important influences - either good or bad

DK

DK

DK

DK

DK

DK

- on CHILD?

REF

REF

REF

REF

REF

REF

[IF NO, skip to the next child or end module]

[IF YES, ask F14-F17]

17 of 53

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

F14. I'd like you to think of the adult man

other than the father who was the most

important influence on CHILD. How would

23469

you describe the relationship between this

man and CHILD?

1

1

1

1

1

1

1. Adoptive father

2

2

2

2

2

2

2. Stepfather

3

3

3

3

3

3

3. Foster care father

4

4

4

4

4

4

4. Grandfather

5

5

5

5

5

5

5. Uncle

6

6

6

6

6

6

6. Brother

7

7

7

7

7

7

7. Boyfriend/Partner of the mother

8. Godfather

8

8

8

8

8

8

9. Minister, priest, or religious leader

9

9

9

9

9

9

10. Teacher

10

10

10

10

10

10

11. Coach

11

11

11

11

11

11

12. Employer

12

12

12

12

12

12

13. Volunteer mentor

13

13

13

13

13

13

14. Policeman or court official

14

14

14

14

14

14

15. Neighbor

15

15

15

15

15

15

96. Other, specify

other

other

other

other

other

other

97. NA

NA

NA

NA

NA

NA

NA

98. DK

DK

DK

DK

DK

DK

DK

99. REF

REF

REF

REF

REF

REF

REF

F15. Did this man ever live in the

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

household with CHILD?

NO=Never lived with CHILD [SKIP TO F16]

Start

Start

Start

Start

Start

Start

YES=Lived with CHILD from:

FIRST TIME:

Start date (mo/yr)

/

/

/

/

/

/

End

End

End

End

End

End

End date (mo/yr)

/

/

/

/

/

/

Start

Start

Start

Start

Start

Start

SECOND TIME:

Start date (mo/yr)

/

/

/

/

/

/

End

End

End

End

End

End

End date (mo/yr)

/

/

/

/

/

/

THIRD TIME:

Start

Start

Start

Start

Start

Start

Start date (mo/yr)

/

/

/

/

/

/

End

End

End

End

End

End

End date (mo/yr)

/

/

/

/

/

/

F16. When this man was in contact with

Never

Never

Never

Never

Never

Never

CHILD, how often did he provide financial

Rarely, only a few times

Rarely, only a few times

Rarely, only a few times

Rarely, only a few times

Rarely, only a few times

Rarely, only a few times

support for CHILD?

Once a year

Once a year

Once a year

Once a year

Once a year

Once a year

Couple of times a year

Couple of times a year

Couple of times a year

Couple of times a year

Couple of times a year

Couple of times a year

Most every month

Most every month

Most every month

Most every month

Most every month

Most every month

Weekly or more often

Weekly or more often

Weekly or more often

Weekly or more often

Weekly or more often

Weekly or more often

DK

DK

DK

DK

DK

DK

NA

NA

NA

NA

NA

NA

REF

REF

REF

REF

REF

REF

18 of 53

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

F17. Overall, would you say that this man's

Very positive

Very positive

Very positive

Very positive

Very positive

Very positive

influence on CHILD has been:

Somewhat positive

Somewhat positive

Somewhat positive

Somewhat positive

Somewhat positive

Somewhat positive

1. Very positive

No influence

No influence

No influence

No influence

No influence

No influence

2. Somewhat positive

23469

Somewhat negative

Somewhat negative

Somewhat negative

Somewhat negative

Somewhat negative

Somewhat negative

3. Neither positive or negative/no

Very negative

Very negative

Very negative

Very negative

Very negative

Very negative

influence

4. Somewhat negative

NA

NA

NA

NA

NA

NA

5. Very negative

DK

DK

DK

DK

DK

DK

[NA if not involved with CHILD]

REF

REF

REF

REF

REF

REF

19 of 53

CHILD'S HEALTH OUTCOMES MODULE

In the next several sections of the survey, I will be asking you questions about your child(ren) in terms of their health, their experiences at school, behavior, employment, and their own marital and childbearing histories. Since we are speaking to many different families, some of the questions I will be asking may not apply to your child(ren). When that happens, you just need to let me know that it is not applicable and 23469

we will move on to the next question. I would like to begin by asking you some questions about your child(ren)'s health.

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

Name of Child

Age of Child

H1. Were any of your CHILDREN born

No

No

No

No

No

No

prematurely (less than 32 weeks)?

Yes

Yes

Yes

Yes

Yes

Yes

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H2. Did any of your CHILDREN weigh

No

No

No

No

No

No

less than five pounds at birth?

Yes

Yes

Yes

Yes

Yes

Yes

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H3. Did any of your CHILDREN have any

No

No

No

No

No

No

other health problems at birth?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO H5]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H4. If Yes, what were these other health

problems at birth?

[INTERVIEWER FILL IN TEXT BOX]

H5. While growing up, did all of your

No

No

No

No

No

No

CHILDREN receive regular, routine

Yes

Yes

Yes

Yes

Yes

Yes

medical care such as check-ups and

NA

NA

NA

NA

NA

NA

immunizations?

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H6. Has your doctor or health care

No

No

No

No

No

No

provider ever said that any of your

Yes

Yes

Yes

Yes

Yes

Yes

CHILDREN have asthma?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO H13]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H7. If Yes, how old was CHILD when

he/she was first diagnosed with asthma?

H8. If Yes, has CHILD ever been seen in

No

No

No

No

No

No

the emergency room because of his/her

Yes

Yes

Yes

Yes

Yes

Yes

asthma?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO H10]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H9. If Yes, how many times has CHILD

been seen in the emergency room

because of his/her asthma?

H10. If Yes, has CHILD ever been

No

No

No

No

No

No

hospitalized because of his/her asthma?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO H12]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H11. If Yes, how many times has CHILD

been hospitalized because of his/her

asthma?

20 of 53

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

Name/Age of Child

H12. How old was CHILD when his/her

23469

asthma was under control?

[IF NOT UNDER CONTROL, ENTER "97"]

H13. Has your doctor or health care

No

No

No

No

No

No

provider ever said that any of your

Yes

Yes

Yes

Yes

Yes

Yes

CHILDREN have elevated levels of lead

NA

NA

NA

NA

NA

NA

in the blood? [IF NO, SKIP TO H15]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H14. If Yes, how old was CHILD when

he/she was first diagnosed with elevated

levels of lead in the blood?

H15. Has your doctor or health care

No

No

No

No

No

No

provider ever said that any of your

Yes

Yes

Yes

Yes

Yes

Yes

CHILDREN have tuberculosis (TB)?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO H18]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H16. If Yes, how old was CHILD when

he/she was first diagnosed with

tuberculosis (TB)?

H17. If Yes, how old was CHILD when

his/her tuberculosis was under control?

[IF NOT UNDER CONTROL, ENTER "97"]

H18. Has your doctor or health care

No

No

No

No

No

No

provider ever said that any of your

Yes

Yes

Yes

Yes

Yes

Yes

CHILDREN were overweight or obese?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO H22]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H19. If Yes, how old was CHILD when

your doctor or health care provider first told

you he/she was overweight or obese?

H20. Has CHILD returned to and

No

No

No

No

No

No

maintained a normal, healthy weight?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO H22]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H21. If Yes, how old was CHILD when

he/she returned to a normal, healthy

weight?

[IF NOT YET RETURNED TO HEALTHY

WEIGHT, ENTER "97"]

H22. Has your doctor or health care

No

No

No

No

No

No

provider ever said that any of your

Yes

Yes

Yes

Yes

Yes

Yes

CHILDREN have mental retardation?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO H24]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H23. If Yes, how old was CHILD when

he/she was first diagnosed with mental

retardation?

21 of 53

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

Name/Age of Child

H24. Has your doctor or health care

No

No

No

No

No

No

provider ever said that any of your

Yes

Yes

Yes

Yes

Yes

Yes

23469

CHILDREN have cerebral palsy?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO H28]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H25. If Yes, how old was CHILD when

he/she was first diagnosed with cerebral

palsy?

H26. Has CHILD gotten his/her cerebral

No

No

No

No

No

No

palsy under control?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO H28]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H27. If Yes, how old was CHILD when

his/her cerebral palsy was under control?

[IF NOT UNDER CONTROL, ENTER "97"]

H28. Has your doctor or health care

No

No

No

No

No

No

provider ever said that any of your

Yes

Yes

Yes

Yes

Yes

Yes

CHILDREN have autism spectrum

NA

NA

NA

NA

NA

NA

disorder?

DK

DK

DK

DK

DK

DK

[IF NO, SKIP TO H30]

REF

REF

REF

REF

REF

REF

H29. If Yes, how old was CHILD when

he/she was first diagnosed with autism

spectrum disorder?

H30. Has your doctor or health care

No

No

No

No

No

No

provider ever said that any of your

Yes

Yes

Yes

Yes

Yes

Yes

CHILDREN have epilepsy?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO H34]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H31. If Yes, how old was CHILD when

he/she was first diagnosed with epilepsy?

H32. Has CHILD gotten his/her epilepsy

No

No

No

No

No

No

under control?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO H34]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H33. If Yes, how old was CHILD when

his/her epilepsy was under control?

[IF NOT UNDER CONTROL, ENTER "97"]

H34. Has your doctor or health care

No

No

No

No

No

No

provider ever said that any of your

Yes

Yes

Yes

Yes

Yes

Yes

CHILDREN have hyperactivity, ADHD, or

NA

NA

NA

NA

NA

NA

ADD?

DK

DK

DK

DK

DK

DK

[IF NO, SKIP TO H38]

REF

REF

REF

REF

REF

REF

H35. If Yes, how old was CHILD when

he/she was first diagnosed with

hyperactivity, ADHD, or ADD?

H36. Has CHILD gotten his/her

No

No

No

No

No

No

hyperactivity, ADHD, or ADD under

Yes

Yes

Yes

Yes

Yes

Yes

control?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO H38]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

22 of 53

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

Name/Age of Child

H37. If Yes, how old was CHILD when

his/her hyperactivity, ADHD, or ADD was

23469

under control?

[IF NOT UNDER CONTROL, ENTER "97"]

H38. Has your doctor or health care

No

No

No

No

No

No

provider ever said that any of your

Yes

Yes

Yes

Yes

Yes

Yes

CHILDREN have a developmental delay?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO H40]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H39. If Yes, how old was CHILD when

he/she was first diagnosed with a

developmental delay?

H40. Has your doctor or health care

No

No

No

No

No

No

provider ever said that any of your

Yes

Yes

Yes

Yes

Yes

Yes

CHILDREN have a learning disability?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO H42]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H41. If Yes, how old was CHILD when

he/she was first diagnosed with a learning

disability?

[Ask all remaining questions only if child is

age 9 or older, otherwise skip to next module.]

H42. Was CHILD early, on time, or late in

early

early

early

early

early

early

reaching puberty?

on time

on time

on time

on time

on time

on time

late

late

late

late

late

late

DK

DK

DK

DK

DK

DK

NA

NA

NA

NA

NA

NA

H43. Have any of your CHILDREN ever

No

No

No

No

No

No

seen a psychiatrist, psychologist, or a

Yes

Yes

Yes

Yes

Yes

Yes

counselor?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO NEXT CHILD OR END

DK

DK

DK

DK

DK

DK

HEALTH MODULE]

REF

REF

REF

REF

REF

REF

H44. If Yes, when was the first time; last

First (mo/yr)

First (mo/yr)

First (mo/yr)

First (mo/yr)

First (mo/yr)

First (mo/yr)

time CHILD saw a psychiatrist,

/

/

/

/

/

/

psychologist, or a counselor?

Last (mo/yr)

Last (mo/yr)

Last (mo/yr)

Last (mo/yr)

Last (mo/yr)

Last (mo/yr)

/

/

/

/

/

/

H45. Has a psychiatrist, psychologist, or

No

No

No

No

No

No

counselor ever said that any of your

Yes

Yes

Yes

Yes

Yes

Yes

CHILDREN have post-traumatic stress or

NA

NA

NA

NA

NA

NA

PTSD?

DK

DK

DK

DK

DK

DK

[IF NO, SKIP TO H49]

REF

REF

REF

REF

REF

REF

H46. If Yes, how old was CHILD when

he/she was first diagnosed with PTSD?

H47. Has CHILD gotten his/her PTSD

No

No

No

No

No

No

under control?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO H49]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H48. If Yes, how old was CHILD when

his/her PTSD was under control? [IF NOT

UNDER CONTROL, ENTER "97"]

23 of 53

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

Name/Age of Child

H49. Has a psychiatrist, psychologist, or

No

No

No

No

No

No

counselor ever said that any of your

Yes

Yes

Yes

Yes

Yes

Yes

23469

CHILDREN have problems with

NA

NA

NA

NA

NA

NA

depression?

DK

DK

DK

DK

DK

DK

[IF NO, SKIP TO H53]

REF

REF

REF

REF

REF

REF

H50. If Yes, how old was CHILD when

he/she was first diagnosed with depression?

H51. Has CHILD gotten his/her

No

No

No

No

No

No

depression under control?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO H53]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H52. If Yes, how old was CHILD when

his/her depression was under control?

[IF NOT UNDER CONTROL, ENTER "97"]

H53. Has a psychiatrist, psychologist, or

No

No

No

No

No

No

counselor ever said that any of your

Yes

Yes

Yes

Yes

Yes

Yes

CHILDREN have anxiety problems? [IF

NA

NA

NA

NA

NA

NA

NO, SKIP TO NEXT CHILD OR END

DK

DK

DK

DK

DK

DK

HEALTH MODULE]

REF

REF

REF

REF

REF

REF

H54. If Yes, how old was CHILD when

he/she was first diagnosed with anxiety

problems?

H55. Has CHILD gotten his/her anxiety

No

No

No

No

No

No

problems under control? [IF NO, SKIP TO

Yes

Yes

Yes

Yes

Yes

Yes

NEXT CHILD OR END HEALTH MODULE]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

H56. If Yes, how old was CHILD when

his/her anxiety problems were under

control?

[IF NOT UNDER CONTROL, ENTER "97"]

24 of 53

CHILD'S EDUCATIONAL OUTCOMES MODULE

Interviewer: Complete for children ages 5 and older, starting with the oldest child

23469

Now I am going to ask you about your child(ren)'s experiences in school. As far as you know...

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

Name/Age of Child

ED1. Did any of your CHILDREN ever

No

No

No

No

No

No

attend an official, government-

Yes

Yes

Yes

Yes

Yes

Yes

sponsored Head Start program?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO ED5]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

ED2. If Yes, is/was this Head Start

No

No

No

No

No

No

program located in the neighborhood

Yes

Yes

Yes

Yes

Yes

Yes

where you live/lived?

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

ED3. If Yes, at what age did CHILD first

attend a Head Start program?

ED4. If Yes, how long did CHILD attend a

years

years

years

years

years

years

Head Start program?

months

months

months

months

months

months

ED5. Aside from Head Start, did any of

No

No

No

No

No

No

your CHILDREN ever go to a nursery

Yes

Yes

Yes

Yes

Yes

Yes

school, preschool, or child care center

NA

NA

NA

NA

NA

NA

before starting school?

DK

DK

DK

DK

DK

DK

[IF NO, SKIP TO ED8]

REF

REF

REF

REF

REF

REF

ED6. If Yes, at what age did CHILD first

attend a nursery school, preschool, or

child care center?

ED7. If Yes, how long did CHILD attend

years

years

years

years

years

years

all the nursery schools, preschools, and

child care centers that she/he went to?

months

months

months

months

months

months

ED8. At what age did your CHILDREN start

school? (Use Kindergarten; if did not

attend Kindergarten, use first grade)

ED9. How many schools have your

CHILDREN attended since they first

elementary

elementary

elementary

elementary

elementary

elementary

started school?

middle

middle

middle

middle

middle

middle

(Record the number of schools

attended for each level of school;

high

high

high

high

high

high

use "00" if not applicable)

ED10. Did any of your CHILDREN attend

Yes

Yes

Yes

Yes

Yes

Yes

elementary school in the neighborhood?

No

name & location

No

name & location

No

name & location

No

name & location

No

name & location

No

name & location

(IF NO, list name, city and state of

school)

25 of 53

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

Name/Age of Child

ED11. Did any of your CHILDREN attend

Yes

Yes

Yes

Yes

Yes

Yes

middle school in the neighborhood?

No

name & location

No

name & location

No

name & location

No

name & location

No

name & location

No

name & location

23469

(IF NO, list name, city and state of

school)

ED12. Did any of your CHILDREN attend

Yes

Yes

Yes

Yes

Yes

Yes

high school in the neighborhood?

No

name & location

No

name & location

No

name & location

No

name & location

No

name & location

No

name & location

(IF NO, list name, city and state of

school)

ED13. Are any of your CHILDREN

No

No

No

No

No

No

currently enrolled in school?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO ED15]

NA

NA

NA

NA

NA

NA

[IF CHILD NOT YET IN ELEMENTARY

DK

DK

DK

DK

DK

DK

SCHOOL, SKIP TO ED19]

REF

REF

REF

REF

REF

REF

ED14. If Yes, is this a regular public

Public school

Public school

Public school

Public school

Public school

Public school

school, a magnet school, a charter school,

Magnet school

Magnet school

Magnet school

Magnet school

Magnet school

Magnet school

a private nonreligious school, a religious

Charter school

Charter school

Charter school

Charter school

Charter school

Charter school

school, home schooling, or some other

Private-nonreligious

Private-nonreligious

Private-nonreligious

Private-nonreligious

Private-nonreligious

Private-nonreligious

type of special program?

Religious school

Religious school

Religious school

Religious school

Religious school

Religious school

Home schooling

Home schooling

Home schooling

Home schooling

Home schooling

Home schooling

[SKIP TO ED18]

Other, specify

Other, specify

Other, specify

Other, specify

Other, specify

Other, specify

ED15. If No, was the school most recently

Public school

Public school

Public school

Public school

Public school

Public school

attended a regular public school, a

Magnet school

Magnet school

Magnet school

Magnet school

Magnet school

Magnet school

magnet school, a charter school, a private

Charter school

Charter school

Charter school

Charter school

Charter school

Charter school

nonreligious school, a religious school,

Private-nonreligious

Private-nonreligious

Private-nonreligious

Private-nonreligious

Private-nonreligious

Private-nonreligious

home schooled or some other type of

Religious school

Religious school

Religious school

Religious school

Religious school

Religious school

special program?

Home schooled

Home schooled

Home schooled

Home schooled

Home schooled

Home schooled

Other, specify

Other, specify

Other, specify

Other, specify

Other, specify

Other, specify

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

ED16. If not in school, what is the

primary reason CHILD doesn't attend

1

1

1

1

1

1

school?

2

2

2

2

2

2

1. Health problems

3

3

3

3

3

3

2. Dropped out to help support family

4

4

4

4

4

4

3. Dropped out because didn't like school

5

5

5

5

5

5

4. Dropped out to take a job

6

6

6

6

6

6

5. Parental decision

7

7

7

7

7

7

6. Pregnancy/childbirth

8

8

8

8

8

8

7. Expelled

Other, specify

Other, specify

Other, specify

Other, specify

Other, specify

Other, specify

8. Graduated

96. Other, specify

97. NA

NA

NA

NA

NA

NA

NA

98. DK

DK

DK

DK

DK

DK

DK

99. REF

REF

REF

REF

REF

REF

REF

26 of 53

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

Name/Age of Child

ED17. If not in school, do you expect

No

No

No

No

No

No

23469

that CHILD will return to school?

Yes

Yes

Yes

Yes

Yes

Yes

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

ED18. What is the highest grade CHILD

has completed so far?

ED19. What is the highest grade that you

think CHILD will go in school?

1

1

1

1

1

1

1. 8th grade or less

2

2

2

2

2

2

2. Some high school

3

3

3

3

3

3

3. GED

4. High school diploma

4

4

4

4

4

4

5. Some college or technical school

5

5

5

5

5

5

6. College degree

6

6

6

6

6

6

7. Graduate or professional degree

7

7

7

7

7

7

98. DK

DK

DK

DK

DK

DK

DK

99. REF

REF

REF

REF

REF

REF

REF

ED20. Are/were any of your CHILDREN

No

No

No

No

No

No

on the honor roll or have other academic

Yes

Yes

Yes

Yes

Yes

Yes

achievements?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO ED22]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

ED21. If Yes, was this when CHILD was

elementary

elementary

elementary

elementary

elementary

elementary

in elementary school, middle school or

middle

middle

middle

middle

middle

middle

high school?

high

(Fill in all that apply)

high

high

high

high

high

ED22. What kind of grades did CHILD

All A's

All A's

All A's

All A's

All A's

All A's

generally earn while attending elementary

Mostly A's and B's

Mostly A's and B's

Mostly A's and B's

Mostly A's and B's

Mostly A's and B's

Mostly A's and B's

school?

All B's

All B's

All B's

All B's

All B's

All B's

All A's

Mostly A's and B's

Mostly B's and C's

Mostly B's and C's

Mostly B's and C's

Mostly B's and C's

Mostly B's and C's

Mostly B's and C's

All B's

All C's

All C's

All C's

All C's

All C's

All C's

Mostly B's and C's

Mostly C's and D's

Mostly C's and D's

Mostly C's and D's

Mostly C's and D's

Mostly C's and D's

Mostly C's and D's

All C's

Grades D or below

Grades D or below

Grades D or below

Grades D or below

Grades D or below

Grades D or below

Mostly C's and D's

Other, specify

Other, specify

Other, specify

Other, specify

Other, specify

Other, specify

Grades D or below

Other, specify

NA

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

ED23. What kind of grades did CHILD

All A's

All A's

All A's

All A's

All A's

All A's

generally earn while attending middle

Mostly A's and B's

Mostly A's and B's

Mostly A's and B's

Mostly A's and B's

Mostly A's and B's

Mostly A's and B's

school?

All B's

All B's

All B's

All B's

All B's

All B's

All A's

Mostly A's and B's

Mostly B's and C's

Mostly B's and C's

Mostly B's and C's

Mostly B's and C's

Mostly B's and C's

Mostly B's and C's

All B's

All C's

All C's

All C's

All C's

All C's

All C's

Mostly B's and C's

Mostly C's and D's

Mostly C's and D's

Mostly C's and D's

Mostly C's and D's

Mostly C's and D's

Mostly C's and D's

All C's

Grades D or below

Grades D or below

Grades D or below

Grades D or below

Grades D or below

Grades D or below

Mostly C's and D's

Other, specify

Other, specify

Other, specify

Other, specify

Other, specify

Other, specify

Grades D or below

Other, specify

NA

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

27 of 53

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

Name/Age of Child

ED24. What kind of grades did CHILD

All A's

All A's

All A's

All A's

All A's

All A's

23469

generally earn while attending high

Mostly A's and B's

Mostly A's and B's

Mostly A's and B's

Mostly A's and B's

Mostly A's and B's

Mostly A's and B's

school?

All B's

All B's

All B's

All B's

All B's

All B's

All A's

Mostly A's and B's

Mostly B's and C's

Mostly B's and C's

Mostly B's and C's

Mostly B's and C's

Mostly B's and C's

Mostly B's and C's

All B's

All C's

All C's

All C's

All C's

All C's

All C's

Mostly B's and C's

Mostly C's and D's

Mostly C's and D's

Mostly C's and D's

Mostly C's and D's

Mostly C's and D's

Mostly C's and D's

All C's

Grades D or below

Grades D or below

Grades D or below

Grades D or below

Grades D or below

Grades D or below

Mostly C's and D's

Other, specify

Other, specify

Other, specify

Other, specify

Other, specify

Other, specify

Grades D or below

Other, specify

NA

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

REF

ED25. Did any of your CHILDREN

No

No

No

No

No

No

participate in clubs or activities at school?

Yes

Yes

Yes

Yes

Yes

Yes

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO ED27]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

ED26. If Yes, was this when CHILD was

elementary

elementary

elementary

elementary

elementary

elementary

in elementary school, middle school or

middle

middle

middle

middle

middle

middle

high school?

(Fill in all that apply)

high

high

high

high

high

high

ED27. Did any of your CHILDREN

No

No

No

No

No

No

participate in sports teams at school?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO ED29]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

ED28. If Yes, was this when CHILD was

elementary

elementary

elementary

elementary

elementary

elementary

in elementary school, middle school or

middle

middle

middle

middle

middle

middle

high school?

(Fill in all that apply)

high

high

high

high

high

high

ED29. Did any of your CHILDREN ever

No

No

No

No

No

No

attend a special or advanced class or

Yes

Yes

Yes

Yes

Yes

Yes

school for gifted students?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO ED32]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

ED30. If Yes, how old was CHILD when

she/he first attended a special or advanced

class or school for gifted students?

ED31. If Yes, when did CHILD attend a

Starting grade

Starting grade

Starting grade

Starting grade

Starting grade

Starting grade

special or advanced class or school for

gifted students?

Ending grade

Ending grade

Ending grade

Ending grade

Ending grade

Ending grade

In what grade did this begin?

In what grade did this end?

Starting grade

Starting grade

Starting grade

Starting grade

Starting grade

Starting grade

Ending grade

Ending grade

Ending grade

Ending grade

Ending grade

Ending grade

ED32. Have any of your CHILDREN ever

No

No

No

No

No

No

been classified by school personnel as

Yes

Yes

Yes

Yes

Yes

Yes

needing special education?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO ED35]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

28 of 53

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

Name/Age of Child

ED33. If Yes, at what age was CHILD first

classified as needing special education?

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ED34. If Yes, when did CHILD attend

Starting grade

Starting grade

Starting grade

Starting grade

Starting grade

Starting grade

special education classes?

Ending grade

Ending grade

Ending grade

Ending grade

Ending grade

Ending grade

Starting grade

Starting grade

Starting grade

Starting grade

Starting grade

Starting grade

Ending grade

Ending grade

Ending grade

Ending grade

Ending grade

Ending grade

ED35. Do any of your CHILDREN have any

physical or mental condition that would limit

or prevent 'his/her' ability to:

a. do usual childhood activities?

a.

Yes

No

DK a.

Yes

No

DK a.

Yes

No

DK a.

Yes

No

DK a.

Yes

No

DK a.

Yes

No

DK

b. attend school regularly?

b.

Yes

No

DK b.

Yes

No

DK b.

Yes

No

DK b.

Yes

No

DK b.

Yes

No

DK b.

Yes

No

DK

c. do regular school work?

c.

Yes

No

DK c.

Yes

No

DK c.

Yes

No

DK c.

Yes

No

DK c.

Yes

No

DK c.

Yes

No

DK

[IF NO TO a, b, and c, SKIP TO ED38]

ED36. If Yes to ED35 a, b, or c, what

a.

a.

a.

a.

a.

a.

physical or mental condition is this?

b.

b.

b.

b.

b.

b.

c.

c.

c.

c.

c.

c.

ED37. If Yes, at what age were these

a.

a.

a.

a.

a.

a.

limitations first diagnosed?

b.

b.

b.

b.

b.

b.

[CODE "97" IF NOT APPLICABLE]

c.

c.

c.

c.

c.

c.

ED38. Have any of your CHILDREN ever

No

No

No

No

No

No

repeated a grade?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO ED40]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

ED39. If Yes, which grades did CHILD

grade

grade

grade

grade

grade

grade

repeat?

(Start with earliest grade first. Enter "0"

grade

grade

grade

grade

grade

grade

for Kindergarten. Enter 97 for no

additional grades repeated.)

grade

grade

grade

grade

grade

grade

ED40. Did any of your CHILDREN ever

No

No

No

No

No

No

have problems with skipping school?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO ED42]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

ED41. If Yes, was this when CHILD was

elementary

elementary

elementary

elementary

elementary

elementary

in elementary school, middle school or

middle

middle

middle

middle

middle

middle

high school?

(Circle all that apply)

high

high

high

high

high

high

ED42. Did any of your CHILDREN ever

No

No

No

No

No

No

receive detention in elementary school?

Yes

Yes

Yes

Yes

Yes

Yes

[ASK ONLY FOR CHILDREN WHO HAVE

NA

NA

NA

NA

NA

NA

ATTENDED GIVEN LEVEL OF SCHOOLING]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

ED43. Did any of your CHILDREN ever

No

No

No

No

No

No

receive detention in middle school?

Yes

Yes

Yes

Yes

Yes

Yes

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

29 of 53

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

Name/Age of Child

ED44. Did any of your CHILDREN ever

No

No

No

No

No

No

23469

receive detention in high school?

Yes

Yes

Yes

Yes

Yes

Yes

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

ED45. Were any of your CHILDREN ever

No

No

No

No

No

No

suspended from elementary school?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO ED48]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

ED46. If Yes, how many times was CHILD

suspended from elementary school?

ED47. If Yes, in which grades was CHILD

grade

grade

grade

grade

grade

grade

suspended from elementary school?

(Start with earliest grade)

grade

grade

grade

grade

grade

grade

(Grades should be between 0 and 5)

(Enter "97" if no additional grades suspended)

grade

grade

grade

grade

grade

grade

ED48. Were any of your CHILDREN ever

No

No

No

No

No

No

suspended from middle school?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO ED51]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

ED49. If Yes, how many times was CHILD

suspended from middle school?

ED50. If Yes, in which grades was CHILD

grade

grade

grade

grade

grade

grade

suspended from middle school?

(Start with earliest grade)

grade

grade

grade

grade

grade

grade

(Grades should be between 6 and 8)

(Enter "97" if no additional grades suspended)

grade

grade

grade

grade

grade

grade

ED51. Were any of your CHILDREN ever

No

No

No

No

No

No

suspended from high school?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO ED54]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

ED52. If Yes, how many times was CHILD

suspended from high school?

ED53. If Yes, in which grades was CHILD

grade

grade

grade

grade

grade

grade

suspended from high school?

(Start with earliest grade)

grade

grade

grade

grade

grade

grade

(Grades should be between 9 and 12)

(Enter "97" if no additional grades suspended)

grade

grade

grade

grade

grade

grade

ED54. Were any of your CHILDREN ever

No

No

No

No

No

No

expelled from elementary school?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO ED58]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

ED55. If Yes, how many times was CHILD

expelled from elementary school?

ED56. If Yes, in which grades was CHILD

grade

grade

grade

grade

grade

grade

expelled from elementary school?

(Start with earliest grade)

grade

grade

grade

grade

grade

grade

(Enter "97" if no additional

grade

grade

grade

grade

grade

grade

expulsions)

30 of 53

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

Name/Age of Child

ED57. If Yes, what was the primary reason

23469

for the most recent expulsion from

elementary school?

1. Possession of a firearm, knife or other

1

1

1

1

1

1

weapon

2

2

2

2

2

2

2. Use of a weapon/object as weapon

3

3

3

3

3

3

3. Physical assault of an employee/

volunteer

4

4

4

4

4

4

4. Destruction of property

5

5

5

5

5

5

5. Theft or receipt of stolen property

6

6

6

6

6

6

6. Arson

7

7

7

7

7

7

7. Sale/distribution of controlled substances

8

8

8

8

8

8

or illegal materials

8. Physical assault of another student

9

9

9

9

9

9

9. Criminal sexual conduct

10

10

10

10

10

10

10. Sexual misconduct

11

11

11

11

11

11

11. Forgery/fraud

Other

Other

Other

Other

Other

Other

96. Other, specify

NA

NA

NA

NA

NA

NA

97. NA

98. DK

DK

DK

DK

DK

DK

DK

99. REF

REF

REF

REF

REF

REF

REF

ED58. Were any of your CHILDREN ever

No

No

No

No

No

No

expelled from middle school?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO ED62]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

ED59. If Yes, how many times was CHILD

expelled from middle school?

ED60. If Yes, in which grades was CHILD

grade

grade

grade

grade

grade

grade

expelled from middle school?

(Start with earliest grade)

grade

grade

grade

grade

grade

grade

(Enter "97" if no additional expulsions)

grade

grade

grade

grade

grade

grade

ED61. If Yes, what was the primary reason

for the most recent expulsion from middle

school?

1. Possession of a firearm, knife or other

1

1

1

1

1

1

weapon

2

2

2

2

2

2

2. Use of a weapon/object as weapon

3

3

3

3

3

3

3. Physical assault of an employee/

volunteer

4

4

4

4

4

4

4. Destruction of property

5

5

5

5

5

5

5. Theft or receipt of stolen property

6

6

6

6

6

6

6. Arson

7

7

7

7

7

7

7. Sale/distribution of controlled substances

8

8

8

8

8

8

or illegal materials

8. Physical assault of another student

9

9

9

9

9

9

9. Criminal sexual conduct

10

10

10

10

10

10

10. Sexual misconduct

11

11

11

11

11

11

11. Forgery/fraud

Other

Other

Other

Other

Other

Other

96. Other, specify

NA

NA

NA

NA

NA

NA

97. NA

98. DK

DK

DK

DK

DK

DK

DK

99. REF

REF

REF

REF

REF

REF

REF

31 of 53

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

Name/Age of Child

23469

ED62. Were any of your CHILDREN ever

No

No

No

No

No

No

expelled from high school?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO END OF MODULE OR

NA

NA

NA

NA

NA

NA

CONTINUE WITH NEXT CHILD]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

ED63. If Yes, how many times was CHILD

expelled from high school?

ED64. If Yes, in which grades was CHILD

grade

grade

grade

grade

grade

grade

expelled from high school?

(Start with earliest grade)

grade

grade

grade

grade

grade

grade

(Enter "97" if no additional expulsions)

grade

grade

grade

grade

grade

grade

ED65. If Yes, what was the primary reason

for the most recent expulsion from high

school?

1. Possession of a firearm, knife or other

1

1

1

1

1

1

weapon

2

2

2

2

2

2

2. Use of a weapon/object as weapon

3

3

3

3

3

3

3. Physical assault of an employee/

volunteer

4

4

4

4

4

4

4. Destruction of property

5

5

5

5

5

5

5. Theft or receipt of stolen property

6

6

6

6

6

6

6. Arson

7

7

7

7

7

7

7. Sale/distribution of controlled substances

8

8

8

8

8

8

or illegal materials

8. Physical assault of another student

9

9

9

9

9

9

9. Criminal sexual conduct

10

10

10

10

10

10

10. Sexual misconduct

11

11

11

11

11

11

11. Forgery/fraud

Other

Other

Other

Other

Other

Other

96. Other, specify

NA

NA

NA

NA

NA

NA

97. NA

98. DK

DK

DK

DK

DK

DK

DK

99. REF

REF

REF

REF

REF

REF

REF

32 of 53

CHILD'S BEHAVIORAL OUTCOMES MODULE

Interviewers: Complete for children ages 8 and older, starting with the oldest child

Now I'd now like to ask you some questions about your child(ren)'s behavior. Again, some of these questions may not apply to your child(ren). Please let me know that and I will move on to the next question. Also, please remember to focus only on the children who lived with you in DHA housing for at least a year when they were under age 18.

23469

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

Name/Age of Child

ASK B1 if CHILD is age 8 or older.

No

No

No

No

No

No

B1. Have any of your CHILDREN run

Yes

Yes

Yes

Yes

Yes

Yes

away from home?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO B5]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

B2. If Yes, how many times has CHILD

run away from home?

B3. If Yes, how old was CHILD when

he/she first ran away from home?

B4. If Yes, how old was CHILD when

he/she ran away from home most

recently?

B5. Have any of your CHILDREN ever

No

No

No

No

No

No

smoked tobacco?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO B7]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

B6. If Yes, how old was CHILD when

he/she first began to smoke?

B7. Have any of your CHILDREN ever

No

No

No

No

No

No

drank alcohol?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO B9]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

B8. If Yes, how old was CHILD when

he/she first began to drink?

B9. Have any of your CHILDREN ever

No

No

No

No

No

No

smoked marijuana (pot)?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO B11]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

B10. If Yes, how old was CHILD when

he/she first smoked marijuana?

B11. Have any of your CHILDREN ever

No

No

No

No

No

No

used other drugs?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO B13]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

B12. If Yes, how old was CHILD when

he/she first began to use other drugs?

B13. Have any of your CHILDREN ever

No

No

No

No

No

No

belonged to a gang?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO B15]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

33 of 53

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

Name/Age of Child

B14. If Yes, how old was CHILD when

he/she first joined a gang?

23469

B15. Have any of your CHILDREN ever

No

No

No

No

No

No

used violent or aggressive behavior such

Yes

Yes

Yes

Yes

Yes

Yes

as fighting, slapping, shoving, hitting?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO B17]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

B16. If Yes, how old was CHILD when

he/she first used violent or aggressive

behavior?

B17. Have any of your CHILDREN ever

No

No

No

No

No

No

destroyed property?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO B19]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

B18. If Yes, how old was CHILD when

he/she first destroyed any property?

B19. Have any of your CHILDREN ever

No

No

No

No

No

No

engaged in activities that have resulted in

Yes

Yes

Yes

Yes

Yes

Yes

being stopped or arrested by the police?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO B21]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

B20. If Yes, how old was CHILD when

he/she first was stopped or arrested by

the police?

B21. Have any of your CHILDREN ever

No

No

No

No

No

No

spent time in a reform school, detention

Yes

Yes

Yes

Yes

Yes

Yes

center, jail, or prison?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO B25]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

B22. If Yes, how old was CHILD the first

time he/she spent time in a reform school,

detention center, jail, or prison?

B23. If Yes, how old was CHILD the most

recent time that he/she spent time in a

reform school, detention center, jail, or

prison?

B24. If Yes, what was the total time that

years

years

years

years

years

years

CHILD spent in a reform school, detention

center, jail, or prison for all times?

months

months

months

months

months

months

B25. In what grades did your CHILDREN

elementary

elementary

elementary

elementary

elementary

elementary

participate in clubs and sports teams

middle

middle

middle

middle

middle

middle

activities in the community?

[FILL IN ALL THAT APPLY]

high

high

high

high

high

high

B26. In what grades did your CHILDREN

elementary

elementary

elementary

elementary

elementary

elementary

attend church or religious services/

middle

middle

middle

middle

middle

middle

activities?

[FILL IN ALL THAT APPLY]

high

high

high

high

high

high

B27. In what grades did your CHILDREN

elementary

elementary

elementary

elementary

elementary

elementary

do volunteer work in the community?

middle

middle

middle

middle

middle

middle

[FILL IN ALL THAT APPLY]

high

high

high

high

high

high

34 of 53

CHILD'S EMPLOYMENT OUTCOMES

Now I'd like to ask you some questions about your children's employment since they turned age 14. Again, only focus on the children who lived with you in DHA housing for at least a year when they were under age 18.

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

23469

Name/Age of Child

[ONLY ask if age 14 or older]

CE1. Were any of your CHILDREN

No

No

No

No

No

No

employed before age 18?

Yes

Yes

Yes

Yes

Yes

Yes

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO CE4]

DK

DK

DK

DK

DK

DK

(Fill in NA for all children under 14)

REF

REF

REF

REF

REF

REF

CE2. If Yes, at what age did CHILD begin

working?

CE3. If Yes, on average, how many hours

per week did CHILD work before age 18?

[IF DK enter "98"; IF REF enter "99"]

CE4. [ONLY ask if age 18 or older]

Working FT

Working FT

Working FT

Working FT

Working FT

Working FT

Since turning 18, have any of your

Working PT

Working PT

Working PT

Working PT

Working PT

Working PT

CHILDREN been working full-time,

Not working but in school

Not working but in school

Not working but in school

Not working but in school

Not working but in school

Not working but in school

working part-time, not working but

attending school or neither working nor

Neither working nor in school

Neither working nor in school

Neither working nor in school

Neither working nor in school

Neither working nor in school

Neither working nor in school

attending school?

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

[IF NOT WORKING, SKIP TO CE6]

(Fill in NA for all children under 18)

REF

REF

REF

REF

REF

REF

CE5. If working, at what age since turning

18 did CHILD begin working at their first job?

CE6. Since turning 18, have any of your

No

No

No

No

No

No

CHILDREN been unemployed (looking for

Yes

Yes

Yes

Yes

Yes

Yes

work and unable to find work) for more

NA

NA

NA

NA

NA

NA

than one month?

DK

DK

DK

DK

DK

DK

[IF NO, GO TO CE10]

REF

REF

REF

REF

REF

REF

CE7. How many times has CHILD been

unemployed since turning 18?

CE8. If Yes, how old was CHILD when

he/she was unemployed the first time?

CE9. If Yes, how old was CHILD when

he/she was unemployed the last time?

CE10. Since turning 18, have any of your

No

No

No

No

No

No

CHILDREN received public assistance?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, GO TO NEXT CHILD OR END

NA

NA

NA

NA

NA

NA

MODULE]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

CE11. If Yes, how old was CHILD when

he/she first received public assistance?

CE12. If Yes, how old was CHILD when

he/she last received public assistance?

[GO TO NEXT CHILD AGE 14 OR OLDER, ELSE GO TO NEXT MODULE]

35 of 53

CHILD'S MARITAL AND CHILDBEARING HISTORY MODULE

Now I'd like to ask you some questions about your children who are aged 15 years or older. Again, some of these questions might not apply to your child(ren) so just let me know and I will move to the next question.

23469

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

Name/Age of Child

[Ask M1-M10 if age 15 or older]

M1. Have any of your CHILDREN ever

No

No

No

No

No

No

been married or lived with a partner?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO M6]

NA

NA

NA

NA

NA

NA

(Fill in NA for all children under 15)

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

M2. If Yes, how many times has CHILD

been married or lived with a partner?

M3. If Yes, at what age did CHILD first

get married or start living with a partner?

M4. If Yes, is CHILD still married or living

No

No

No

No

No

No

with partner?

Married

Married

Married

Married

Married

Married

Living w/ partner

Living w/ partner

Living w/ partner

Living w/ partner

Living w/ partner

Living w/ partner

[IF YES, SKIP TO M6]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

M5. [If no longer married or living with

partner] at what age did CHILD end the

marriage or stop living with their partner?

M6. Have any of your CHILDREN given

No

No

No

No

No

No

birth to or fathered children of their own?

Yes

Yes

Yes

Yes

Yes

Yes

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO NEXT CHILD 15+ OR

DK

DK

DK

DK

DK

DK

GO TO END OF MODULE]

REF

REF

REF

REF

REF

REF

M7. If Yes, how many children does

CHILD have?

M8. If Yes, how old was CHILD when

she/he had her/his first child?

M9. If Yes, how old was CHILD when

she/he had her/his last child?

M10. If Yes, was CHILD married when

No

No

No

No

No

No

she/he had her/his first child?

Yes

Yes

Yes

Yes

Yes

Yes

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

[CONTINUE WITH ALL CHILDREN 15 AND OLDER, ELSE END MODULE]

36 of 53

CHILD'S EXPOSURE TO VIOLENCE MODULE

Children today often witness violence in different places. I'd like to ask about times that your CHILDREN may have witnessed violence.

Again, some of these questions may not apply to your child(ren).

23469

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

Name/Age of Child

V1. Have any of your CHILDREN ever

No

No

No

No

No

No

witnessed violence in or around the

Yes

Yes

Yes

Yes

Yes

Yes

neighborhood?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO V3]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

V2. If Yes, how old was CHILD when

he/she first witnessed violence in or

around the neighborhood?

[IF DK enter "98"; IF REF enter "99"]

V3. Have any of your CHILDREN been

No

No

No

No

No

No

beaten up, chased, threatened, or robbed

Yes

Yes

Yes

Yes

Yes

Yes

in or around the neighborhood?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO V9]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

V4. If Yes, how many times was CHILD

beaten up, chased, threatened, or robbed

in or around the neighborhood?

[IF DK enter "98"; IF REF enter "99"]

V5. If Yes, how old was CHILD when this

occurred for the first time?

[IF DK enter "98"; IF REF enter "99"]

V6. If Yes, how old was CHILD when this

occurred most recently?

[IF DK enter "98"; IF REF enter "99"]

V7. If Yes, did CHILD ever have to go to

No

No

No

No

No

No

the emergency room because of injuries

Yes

Yes

Yes

Yes

Yes

Yes

sustained from being beaten or robbed in

NA

NA

NA

NA

NA

NA

the neighborhood?

DK

DK

DK

DK

DK

DK

[IF NO, SKIP TO V9]

REF

REF

REF

REF

REF

REF

V8. If Yes, how many times did CHILD go

to the emergency room for injuries

sustained because of being beaten or

robbed in the neighborhood?

[IF DK enter "98"; IF REF enter "99"]

V9. Have any of your CHILDREN ever

No

No

No

No

No

No

witnessed violence in or around school?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO V11]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

V10. If Yes, how old was CHILD when

he/she first witnessed violence in or

around school?

[IF DK enter "98"; IF REF enter "99"]

37 of 53

Child 1

Child 2

Child 3

Child 4

Child 5

Child 6

V11. Have any of your CHILDREN ever

No

No

No

No

No

No

gotten beaten up, chased, threatened, or

Yes

Yes

Yes

Yes

Yes

Yes

robbed in or around school?

NA

NA

NA

NA

NA

NA

23469

[IF NO, SKIP TO V17]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

V12. If Yes, how many times was CHILD

beaten up, chased, threatened, or robbed

in or around school?

[IF DK enter "98"; IF REF enter "99"]

V13. If Yes, how old was CHILD when

this occurred for the first time?

[IF DK enter "98"; IF REF enter "99"]

V14. If Yes, how old was CHILD when

this occurred most recently?

[IF DK enter "98"; IF REF enter "99"]

V15. If Yes, did CHILD ever have to go to

No

No

No

No

No

No

the emergency room because of being

Yes

Yes

Yes

Yes

Yes

Yes

beaten or robbed in or around school?

NA

NA

NA

NA

NA

NA

[IF NO, SKIP TO V17]

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

V16. If Yes, how many times did CHILD

go to the emergency room for injuries

because of being beaten or robbed in or

around school?

[IF DK enter "98"; IF REF enter "99"]

V17. Have any of your CHILDREN ever

No

No

No

No

No

No

witnessed violence in your home?

Yes

Yes

Yes

Yes

Yes

Yes

[IF NO, SKIP TO NEXT MODULE]

NA

NA

NA

NA

NA

NA

DK

DK

DK

DK

DK

DK

REF

REF

REF

REF

REF

REF

V18. If Yes, how old was CHILD when

he/she first witnessed violence in the

home?

[IF DK enter "98"; IF REF enter "99"]

38 of 53

NEIGHBORHOOD EFFECTS MODULE

NE1. Now I'd like to ask you about the people who might have or have had an influence on your child(ren). As I read the list of people, please tell me if each person's influence on your child(ren) has been very positive, somewhat positive, somewhat negative, very negative, no 23469

influence at all, or not applicable.

a. Child(ren)'s mother

Very Positive

Somewhat Positive

Neither Positive nor Negative; No Influence

Somewhat Negative

Very Negative

NA

DK

REF

b. Child(ren)'s father

Very Positive

Somewhat Positive

Neither Positive nor Negative; No Influence

Somewhat Negative

Very Negative

NA

DK

REF

c. Primary caregiver (if not

Very Positive

Somewhat Positive

Neither Positive nor Negative; No Influence

Somewhat Negative

Very Negative

NA

DK

REF

mother or father)

d. Child(ren)'s stepmother/

Very Positive

Somewhat Positive

Neither Positive nor Negative; No Influence

Somewhat Negative

Very Negative

NA

DK

REF

stepfather

e. Grandparents

Very Positive

Somewhat Positive

Neither Positive nor Negative; No Influence

Somewhat Negative

Very Negative

NA

DK

REF

f. Godparents

Very Positive

Somewhat Positive

Neither Positive nor Negative; No Influence

Somewhat Negative

Very Negative

NA

DK

REF

g. Child(ren)'s brothers and

Very Positive

Somewhat Positive

Neither Positive nor Negative; No Influence

Somewhat Negative

Very Negative

NA

DK

REF

sisters

h. Other relatives

Very Positive

Somewhat Positive

Neither Positive nor Negative; No Influence

Somewhat Negative

Very Negative

NA

DK

REF

i. Child(ren)'s friends

Very Positive

Somewhat Positive

Neither Positive nor Negative; No Influence

Somewhat Negative

Very Negative

NA

DK

REF

j. Child(ren)'s classmates

Very Positive

Somewhat Positive

Neither Positive nor Negative; No Influence

Somewhat Negative

Very Negative

NA

DK

REF

k. Adult neighbors

Very Positive

Somewhat Positive

Neither Positive nor Negative; No Influence

Somewhat Negative

Very Negative

NA

DK

REF

l. Teachers

Very Positive

Somewhat Positive

Neither Positive nor Negative; No Influence

Somewhat Negative

Very Negative

NA

DK

REF

m. School counselors/social

Very Positive

Somewhat Positive

Neither Positive nor Negative; No Influence

Somewhat Negative

Very Negative

NA

DK

REF

workers/administrators

n. Leaders of religious groups

Very Positive

Somewhat Positive

Neither Positive nor Negative; No Influence

Somewhat Negative

Very Negative

NA

DK

REF

o. Club/team leaders

Very Positive

Somewhat Positive

Neither Positive nor Negative; No Influence

Somewhat Negative

Very Negative

NA

DK

REF

p. Someone else not already

Very Positive

Somewhat Positive

Neither Positive nor Negative; No Influence

Somewhat Negative

Very Negative

NA

DK

REF

mentioned (specify):

39 of 53

INTERVIEWERS, ASK QUESTIONS NE2a through NE2d for families with appropriate age children. All families should have had

preschoolers, most will have had elementary school children, many but not all families will have currently or have had middle

and/or high school age children in their households. ONLY record the MOST IMPORTANT POSITIVE INFLUENCE during each

age group identified by the respondent.

NE2a. Who would you say has been the most important positive influence on your child(ren) when they were preschoolers?

23469

NE2b. Who would you say has been the most important positive influence on your child(ren) when they were in elementary school?

NE2c. Who would you say has been the most important positive influence on your child(ren) when they were in middle school?

NE2d. Who would you say has been the most important positive influence on your child(ren) when they were in high school?

[NE2a]

[NE2b]

[NE2c]

[NE2d]

Preschool

Elementary School

Middle School

High School

a. Child(ren)'s mother

a

a

a

a

b. Child(ren)'s father

b

b

b

b

c. Primary caregiver (if not mother or

c

c

c

c

father)

d. Child(ren)'s stepmother/stepfather

d

d

d

d

e. Grandparents

e

e

e

e

f. Godparents

f

f

f

f

g. Child(ren)'s brothers and sisters

g

g

g

g

h. Other relatives

h

h

h

h

i. Child(ren)'s friends

i

i

i

i

j. Child(ren)'s classmates

j

j

j

j

k. Adult neighbors

k

k

k

k

l. Teachers

l

l

l

l

m. School counselors/social workers/

m

m

m

m

administrators

n. Leaders of religious groups

n

n

n

n

o. Club/team leaders

o

o

o

o

p. Someone else not already mentioned

(specify):

q. Not applicable, not appropriate age

q

q

q

q

r. None

r

r

r

r

s. Don't Know

DK

DK

DK

DK

t. Refused

REF

REF

REF

REF

40 of 53

INTERVIEWERS, ASK QUESTIONS NE3a through NE3d for families with appropriate age children. All families should have had

preschoolers, most will have had elementary school children, many but not all families will have currently or have had middle

and/or high school age children in their households. ONLY record the MOST IMPORTANT NEGATIVE INFLUENCE during each

age group identified by the respondent.

NE3a. Who would you say has been the most important negative influence on your child(ren) when they were preschoolers?

23469

NE3b. Who would you say has been the most important negative influence on your child(ren) when they were in elementary school?

NE3c. Who would you say has been the most important negative influence on your child(ren) when they were in middle school?

NE3d. Who would you say has been the most important negative influence on your child(ren) when they were in high school?

[NE3a]

[NE3b]

[NE3c]

[NE3d]

Preschool

Elementary School

Middle School

High School

a. Child(ren)'s mother

a

a

a

a

b. Child(ren)'s father

b

b

b

b

c. Primary caregiver (if not mother or

c

c

c

c

father)

d. Child(ren)'s stepmother/stepfather

d

d

d

d

e. Grandparents

e

e

e

e

f. Godparents

f

f

f

f

g. Child(ren)'s brothers and sisters

g

g

g

g

h. Other relatives

h

h

h

h

i. Child(ren)'s friends

i

i

i

i

j. Child(ren)'s classmates

j

j

j

j

k. Adult neighbors

k

k

k

k

l. Teachers

l

l

l

l

m. School counselors/social workers/

m

m

m

m

administrators

n. Leaders of religious groups

n

n

n

n

o. Club/team leaders

o

o

o

o

p. Someone else not already mentioned

(specify):

q. Not applicable, not appropriate age

q

q

q

q

r. None

r

r

r

r

s. Don't Know

DK

DK

DK

DK

t. Refused

REF

REF

REF

REF

41 of 53

INTERVIEWERS, ASK QUESTIONS NE4a1 through NE4d2 for families with appropriate age children. All families should have had

preschoolers, most will have had elementary school children, many but not all families will have currently or have had middle

and/or high school age children in their households.

23469

Now I'd like to ask you to think about the ways in which living in particular neighborhoods influenced your child(ren) as they were growing up.

NE4a1. Thinking back to when your child(ren) were young - from birth until about age five - what kind of influence did the neighborhood you were living in have on your child(ren)? Was it very positive, somewhat positive, somewhat negative, very negative, or no influence at all?

Very positive

Somewhat positive

Neither positive nor negative influence, no influence at all

Somewhat negative

Very negative

NA

NE4a2. Please describe the ways in which the neighborhood influenced your child(ren) when they were young.

NE4b1. Thinking back to when your child(ren) were in elementary school, what kind of influence did the neighborhood you were living in have on your child(ren)? Was it very positive, somewhat positive, somewhat negative, very negative, or no influence at all?

Very positive

Somewhat positive

Neither positive nor negative influence, no influence at all

Somewhat negative

Very negative

NA

42 of 53

NE4b2. Please describe the ways in which the neighborhood influenced your child(ren) when they were in elementary school?

23469

NE4c1. Thinking back to when your child(ren) were in middle school, what kind of influence did the neighborhood you were living in have on your child(ren)? Was it very positive, somewhat positive, somewhat negative, very negative, or no influence at all?

Very positive

Somewhat positive

Neither positive nor negative influence, no influence at all

Somewhat negative

Very negative

NA

NE4c2. Please describe the ways in which the neighborhood influenced your child(ren) when they were in middle school?

NE4d1. Thinking back to when your child(ren) were in high school, what kind of influence did the neighborhood you were living in have on your child(ren)? Was it very positive, somewhat positive, somewhat negative, very negative, or no influence at all?

Very positive

Somewhat positive

Neither positive nor negative influence, no influence at all

Somewhat negative

Very negative

NA

43 of 53

NE4d2. Please describe the ways in which the neighborhood influenced your child(ren) when they were in high school?

23469

44 of 53

DEMOGRAPHIC MODULE FOR ADULT PARENT/CAREGIVER

23469

Finally, we would like to ask some questions about you. Again, because we are talking to many people with a lot of different life experiences some of these questions may not apply to you or your experiences. When that happens you can let me know and I will move on to the next question.

D1. Have you ever had a physical problem or disability that limited your ability to care for the needs of your children?

No

[IF NO,SKIP TO D4]

Yes

DK

REF

Illness 1

Illness 2

Illness 3

Illness 4

Illness 5

D2. If Yes, please describe

the problem/disability:

D3a. If Yes, how old were

you when this

problem/disability was first

age in years

age in years

age in years

age in years

age in years

diagnosed?

D3b. Is this

No

No

No

No

No

problem/disability under

Yes

Yes

Yes

Yes

Yes

control?

Dk

Dk

Dk

Dk

Dk

[IF NO, SKIP TO D4]

REF

REF

REF

REF

REF

D3c. How old were you

when the problem/disability

was controlled?

[LEAVE BLANK IF NOT

age in years

age in years

age in years

age in years

age in years

UNDER CONTROL]

45 of 53

D4. Now I am going to ask you about some of the ways you may have felt or behaved recently. For each statement I read, please tell me how often you have felt this way during the past week. Would you say rarely or none of the time, some of the time, occasionally, or most or all of the 23469

time.

Occasionally or a

Rarely or none of the

Some or a little of the

moderate amount of

Most or all of the

Don't

During the past week:

time (less than 1 day)

time (1-2 days)

time (3-4 days)

time (5-7 days)

know

a. I was bothered by things that usually don't

Rarely

Some of the time

Occasionally

Most of the time

bother me.

DK

b. I did not feel like eating; my appetite was

Rarely

Some of the time

Occasionally

Most of the time

poor.

DK

c. I felt that I could not shake off the blues

Rarely

Some of the time

Occasionally

Most of the time

DK

even with the help from family or friends.

d. I felt that I was just as good as other

people.

Rarely

Some of the time

Occasionally

Most of the time

DK

e. I had trouble keeping my mind on what I

Rarely

Some of the time

Occasionally

Most of the time

was doing.

DK

f. I felt depressed.

Rarely

Some of the time

Occasionally

Most of the time

DK

g. I felt that everything I did was an effort.

Rarely

Some of the time

Occasionally

Most of the time

DK

h. I felt hopeful about the future.

Rarely

Some of the time

Occasionally

Most of the time

DK

i. I thought my life had been a failure.

Rarely

Some of the time

Occasionally

Most of the time

DK

j. I felt fearful.

Rarely

Some of the time

Occasionally

Most of the time

DK

k. My sleep was restless.

Rarely

Some of the time

Occasionally

Most of the time

DK

l. I was happy.

Rarely

Some of the time

Occasionally

Most of the time

DK

m. I talked less than usual.

Rarely

Some of the time

Occasionally

Most of the time

DK

n. I felt lonely.

Rarely

Some of the time

Occasionally

Most of the time

DK

o. People were unfriendly.

Rarely

Some of the time

Occasionally

Most of the time

DK

p. I enjoyed life.

Rarely

Some of the time

Occasionally

Most of the time

DK

q. I had crying spells.

Rarely

Some of the time

Occasionally

Most of the time

DK

r. I felt sad.

Rarely

Some of the time

Occasionally

Most of the time

DK

s. I felt that people disliked me.

Rarely

Some of the time

Occasionally

Most of the time

DK

t. I could not get "going."

Rarely

Some of the time

Occasionally

Most of the time

DK

46 of 53

D5. Have you ever seen a psychiatrist, psychologist, or a counselor?

No

[IF NO,SKIP TO D7]

23469

Yes

Don't know

Missing/Refused

D6. If Yes, how old were you the first time; and the last time you saw a psychiatrist, psychologist, or a counselor?

First Time

Last Time

age in years

age in years

D7. In the past 30 days, have you:

a. Drunk alcohol to the point of intoxication?

Yes

No

REF

b. Smoked marijuana or "pot"?

Yes

No

REF

c. Used other illegal drugs (such as crystal meth, cocaine, heroin, or crack)?

Yes

No

REF

INTERVIEWERS: IF THE RESPONDENT ANSWERS NEVER TO ALL THREE QUESTIONS D8a - D8c, SKIP TO D11. IF THE RESPONDENT

ANSWERS 2 to 7 FOR ANY OF QUESTIONS D8a - D8c, CONTINUE WITH THE APPROPRIATE QUESTIONS D9a - D10c.

D8. Since becoming a parent, how many times have you...

c. Used other illegal drugs (such as crystal

a. Drunk alcohol to the point of intoxication?

b. Smoked marijuana or "pot"?

meth, cocaine, heroin, or crack)?

Never

Never

Never

Couple of times a year

Couple of times a year

Couple of times a year

Once a year

Once a year

Once a year

Once a month

Once a month

Once a month

Couple of times a month

Couple of times a month

Couple of times a month

Once a week

Once a week

Once a week

Couple of times a week

Couple of times a week

Couple of times a week

Daily

Daily

Daily

DK

DK

DK

REF

REF

REF

47 of 53

INTERVIEWERS: ASK QUESTIONS D9a - D9c and D10a - D10c ONLY OF RESPONDENTS WHO INDICATED THEY EVER HAVE

BEEN INTOXICATED, SMOKED MARIJUANA, OR USED ILLEGAL DRUGS SINCE BECOMING A PARENT. IF RESPONDENT

ANSWERED NEVER, DON'T KNOW, OR REFUSED TO ALL OF THE ABOVE, SKIP TO D11.

IF RESPONDENT HAS NOT HAD A PERIOD OF VOLUNTARY ABSTINENCE, CODE AS -1.

23469

D9. How long was your last period of voluntary abstinence from:

a. alcohol?

years

months

b. marijuana or "pot"?

years

months

c. other illegal drugs?

years

months

D10. How many months ago did this last period of voluntary abstinence end?

a. alcohol?

months

b. marijuana or "pot"?

months

c. other illegal drugs?

months

D11. Have you ever spent time in a reform school, detention center, jail, or prison?

No

[IF NO,SKIP TO D13]

Yes

Missing/Refused

If Yes, at what age did you first spend time in a reform school, detention center, jail, or prison?

Time 1

Time 2

Time 3

Time 4

Time 5

D12a. From age:

D12b. To age:

D12c. Total length of time

spent in facility.

years

months

years

months

years

months

years

months

years

months

D13. Let me ask about your current marital situation. [READ LIST] Are you...

Legally married (continue with D14a)

Living with a partner but not married (continue with D14a)

Divorced or separated (continue with D14a)

Widowed (continue with D14a)

Never married, and not now living with a partner (skip to D14b)

Single but previously living with a partner (skip to D14b)

Other (specify):

REF

48 of 53

D14a. How many times have you been legally married?

times

[IF NEVER MARRIED, SKIP TO D14b; OTHERWISE COMPLETE D14a1 THROUGH D14a3 FOR EACH MARRIAGE AND THEN ASK D14b] 23469

Marriage 1

Marriage 2

Marriage 3

Marriage 4

Marriage 5

D14a1. When did the

/

/

/

/

/

marriage begin?

month year

month year

month year

month year

month year

D14a2. When did the

marriage end?

/

/

/

/

/

[INTERVIEWER: IF THE

month year

month year

month year

month year

month year

MARRIAGE HAS NOT

ENDED, PLEASE LEAVE

THIS FIELD BLANK AND

SKIP TO D14b]

D14a3. How did the

marriage end?

1. Separation, no legal

Separation, no legal divorce

Separation, no legal divorce

Separation, no legal divorce

Separation, no legal divorce

Separation, no legal divorce

divorce

Legal divorce

Legal divorce

Legal divorce

Legal divorce

Legal divorce

2. Legal divorce

Death of spouse

Death of spouse

Death of spouse

Death of spouse

Death of spouse

3. Death of spouse

Annulment

Annulment

Annulment

Annulment

Annulment

4. Annulment

96. Other

Other

Other

Other

Other

Other

97. NA/Still married

NA/Still married

NA/Still married

NA/Still married

NA/Still married

NA/Still married

98. Don't know

DK

DK

DK

DK

DK

99. Refused/Missing

REF/Missing

REF/Missing

REF/Missing

REF/Missing

REF/Missing

D14b. How many times have you lived with a partner for at least one year without being legally married?

times

[IF YOU NEVER LIVED TOGETHER WITH A PARTNER, SKIP TO D15; OTHERWISE COMPLETE D14b1 AND D14b2 FOR EACH PARTNER]

Partner 1

Partner 2

Partner 3

Partner 4

Partner 5

D14b1. When was the first

/

/

/

/

/

time you lived with the

partner?

month year

month year

month year

month year

month year

D14b2. When did you stop

living with this partner?

/

/

/

/

/

[INTERVIEWER: IF THE

month year

month year

month year

month year

month year

RELATIONSHIP HAS

NOT ENDED, PLEASE

LEAVE THIS FIELD

BLANK AND SKIP TO

D15]

49 of 53

D15. When you were growing up, before you reached age 18, did you live in public housing at any time?

No

[IF NO,SKIP TO D17]

Yes

23469

Don't know

Missing/Refused

D16a. If Yes, how many years did you live in public housing before the age of 18?

Total years living in public housing

D16b1. From what age?

D16b2. To what age?

age in years

age in years

D17. When growing up (before age 18), did you ever live in a home that was owned by a parent?

No

[IF NO,SKIP TO D19]

Yes

Don't know

Missing/Refused

D18a. If Yes, how many years did you live in a home owned by your parent(s) before the age of 18?

Total years living in home owned by parent

D18b1. From what age?

D18b2. To what age?

age in years

age in years

D19. Are you currently in school?

No

Yes

Missing/Refused

D20a. What is the highest diploma, degree, or certificate you have earned, if any? (e.g. high school diploma, two-year college degree, technical certificate)

No degree or certification or diploma

Technical Certificate (no HS diploma)

G.E.D. (High School Equivalency)

High School Diploma

Technical Certificate (post High School)

Two-Year College Degree (AA, AS, AAS)

Four Year College Degree (BA, BS)

Graduate or Professional Degree (specify):

Other (specify):

DK

REF

D20b. How old were you when you received your HIGHEST diploma, degree, or certificate?

50 of 53

age in years

D21. Now I'd like to ask you a few questions about how you think children should be raised and your level of confidence in parenting your children. How confident are you about the following? Would you say very confident, somewhat confident or not confident at all?

23469

a. Your parenting skills?

Very Confident

Somewhat Confident

Not Confident at All

DK

Refused/Missing

b. Providing for your children's needs?

Very Confident

Somewhat Confident

Not Confident at All

DK

Refused/Missing

c. Raising healthy children?

Very Confident

Somewhat Confident

Not Confident at All

DK

Refused/Missing

d. Helping your child(ren) achieve their goals?

Very Confident

Somewhat Confident

Not Confident at All

DK

Refused/Missing

e. Setting a good example for your children?

Very Confident

Somewhat Confident

Not Confident at All

DK

Refused/Missing

f. Your ability to protect your child(ren) from negative

Very Confident

Somewhat Confident

Not Confident at All

DK

Refused/Missing

influences at school?

g. Your ability to protect your child(ren) from negative

Very Confident

Somewhat Confident

Not Confident at All

DK

Refused/Missing

influences in the neighborhood?

h. Your ability to keep your child(ren) out of trouble?

Very Confident

Somewhat Confident

Not Confident at All

DK

Refused/Missing

i. Your ability to maintain a balance between work,

Very Confident

Somewhat Confident

Not Confident at All

DK

Refused/Missing

school and parenting responsibilities?

j. Handling the stress related to raising children?

Very Confident

Somewhat Confident

Not Confident at All

DK

Refused/Missing

D22. For each of the following statements, please tell me how accurately it describes your beliefs about parenting. Would you say it perfectly describes me, closely describes me, fairly describes me, somewhat describes me, slightly describes me, or doesn't describe me at all?

Doesn't describe

Slightly

Somewhat

Fairly

Closely

Perfectly

me at all

describes me

describes me

describes me

describes me

describes me

DK

REF

a. I believe that a child should be seen

and not heard.

b. I believe that consistently picking

up a crying infant is wrong.

c. I do not allow my child to question

my decisions.

d. I agree with the statement, 'spare

the rod, spoil the child'.

e. When my child needs my help, I am

always there.

f. I feel that a child should be given

comfort and understanding when

he/she is scared or upset.

51 of 53

D23. Which of the following best describes your ethnic background?

African American/Black

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Latino/Hispanic

Other (Specify):

D24. What is your date of birth?

/

/

Month Day Year

D25. Were you born in the United States?

No

Yes

(If Yes, GO TO LOCATING PAGE)

D26. If No, how old were you when you first moved to the United States?

[IF LESS THAN ONE YEAR, ENTER 0]

age in years

These are all of the questions that we have. I want to thank you very much for you time today. We would like to send you a copy of the

study findings when the project is completed in 2007, as well as your $50 for participating in this study.

52 of 53

LOCATING INFORMATION

At this time I do need to verify your address information so that we can send your check or gift certificate.

A. INTERVIEWER:

CHECK CONTACT AND VERIFICATION PAGE. RECORD RESPONDENT'S FULL NAME. CHECK SPELLING FOR

23469

ALL NAMES AND ADDRESSES PLEASE PRINT CLEARLY. VERIFY SPELLING.

ON THE INFORMED CONSENT SHEET NOTE WHETHER R WANTS A CHECK OR GIFT CERTIFICATE. IF "CHECK", YOU MUST

ALSO GET THEIR SOCIAL SECURITY NUMBER (SSN) AND WRITE IT ON THE INFORMED CONSENT SHEET.

First Name

Middle Name

Last Name

B. What is your current street number and street name?

Address

City

State/Province

ZIP/Postal Code

C. What is your telephone number?

Home Phone

Work Phone

-

-

-

-

D. Would you please give me the names and phone numbers of two persons who would know how to contact you should you move?

Contact Person #1

Contact Person #2

First Name

First Name

Last Name

Last Name

Home Phone

Home Phone

53 of 53

REFERENCES

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Document Outline

- 508_Cover

- H-12612-CA front manuscript 080514_edit

- Appendix A Randomness Tests FINAL REVv2 080514_edit

- Appendix B Face Page FINAL 080514

- Appendix B Child Impacts Survey

- References FINAL 080514

Table of Contents

EXECUTIVE SUMMARY ..................................................................................

I.

Purpose and Contributions of Our Denver Child Study................................................

Structure of the Report.............................................................................

II.

Environmental Mechanisms ...........................................................................

Institutional Mechanisms............................................................................

Temporal Dimensions of Neighborhood Effects ........................................................

Gender and Ethnic Differences in Neighborhood Effects .............................................

Implications for the Denver Child Study ...........................................................

III.

Denver Child Study Household Survey ..............................................................

Characteristics of Children and Youth ..............................................................

Characteristics of Neighborhoods Experienced by Children and Youth................... 19

The Issue of Neighborhood Scale.....................................................................

Creation of Analytical Databases ...................................................................

Statistical Modeling Approaches ....................................................................

Hazard Models.......................................................................................

Temporal Aspects of Neighborhood Effects ...........................................................

Gender and Ethnic Differences in Neighborhood Effects ......................................... 27

IV.

Behavioral Health Outcomes Analysis ................................................................

Estimated Neighborhood Effects on Physical and Behavioral Health Outcomes ............ 31

Neurodevelopmental Disorders .......................................................................

Obesity.............................................................................................

Internalizing Behaviors.............................................................................

Behavioral Health Service Utilization ..............................................................

Gender and Ethnic Differences in Neighborhood Effects ..............................................

Nonlinear Neighborhood Effects .....................................................................

Discussion .........................................................................................

Neighborhood Ethnic and Nativity Composition ......................................................

Neighborhood Social Status .........................................................................

Neighborhood Institutional Resources ...............................................................

Neighborhood Physical Environment...................................................................

Conclusion .........................................................................................

V.

Exposure to School Violence Analysis................................................................

Model Estimation....................................................................................

Estimated Neighborhood Effects on Exposure to Violence Outcomes ............................ 62

Victim of Neighborhood Violence ....................................................................

Witnessing Violence at School ......................................................................

Victim of School Violence ..........................................................................

Witnessing Violence in the Home ....................................................................

Witnessing Neighborhood Violence....................................................................

Witnessing School Violence..........................................................................

Witnessing Violence at Home ........................................................................

Neighborhood Safety ................................................................................

Geographic Selection Bias Revisited.................................................................

VI.

Estimated Neighborhood Effects on Behavioral Outcomes .............................................