Disclaimer The contents of this report are the views of the authors and do not necessarily reflect the views or policies of the U.S. Department of Housing and Urban Development or the U.S. Government.

Assessment of Economic Opportunity Metrics

for HUD-Assisted Renters

Multidisciplinary Research Team

July 2019

Prepared by:

Hiren Nisar

Mallory Vachon

Charles Horseman

Jim Murdoch

ACKNOWLEDGMENTS

The study team would like to thank Gail Clark, Joshua Townley, MacKenzie Regier, and Cindy Romero for providing invaluable editing and formatting support throughout the study. The study team would also like to acknowledge our partners at the U.S. Department of Housing and Urban Development’s Office of Policy Development and Research, with specific assistance from Barry Steffen, Ransford Osafo-Danso, Carol Leming, Lydia Taghavi, and Lynn Rodgers.

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FOREWORD

We are pleased to provide this report on how the U.S. Department of Housing and Urban Development (HUD) can make better use of administrative data to follow the progress of work-able, rent-assisted tenants as they pursue self-sufficiency.

It is a major strategic priority of the Department that work-able tenants of public and assisted housing take full advantage of the economic opportunities available to them in their community. Most rigorous research has found that assisted housing by itself will not lead toward increased work and may have some modest negative impacts on work.

Assisted housing has been found to have extremely powerful effects of permanently ending homeless spells, improving health, reducing domestic violence victimization, reducing hunger, and improving school stability for children. It has also been found to have a positive economic impact for the children of assisted housing tenants when they become adults.

For the adults, however, housing assistance alone has not been shown to improve either employment rates or earned income. Some studies have shown housing assistance to reduce work effort.

How do we take advantage of the positive benefits of housing stability while also improving economic impacts for the adults? Research shows that when tenants take advantage of community resources, they can have improved economic outcomes. For example, HUD’s Jobs Plus program provides a set of work supports that have been shown to have strong positive impacts on employment outcomes.

Housing agencies, however, can play only a small role. Tenants need to be offered, and take advantage of, the variety of supports available in the larger community.

The purpose of this study was to develop four indicators that could be used to track and measure the extent to which work-able individuals and households in HUD programs are participating in work and making progress toward self-sufficiency. An important innovation used in two of these indicators is to contextualize them with census data by measuring employment outcomes of HUD populations as a proportion of similar adults in their metropolitan areas. This approach provides important context about local employment conditions that may affect HUD results.

The data show that assisted work-able adults, consistent with their disadvantaged backgrounds, are employed at about 59 percent of the rate for the work-able population in their regions, that they partially narrowed this gap in the wake of the recession, and that regional and programmatic variations in relative work participation can be substantial.

The work provides proof of concept that such indicators can characterize work participation of assisted households in multiple useful ways: at the national and program levels, for housing providers of specific regions, and over time. As a result, HUD can readily use these indicators or enhanced versions of them to guide further research on why tenants in some areas are exceeding expectations—for example, to identify if practices for connecting residents to job opportunities are responsible for those results, as well as why tenants in other areas are doing worse than expectations—to assess whether technical

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assistance to facilitate local adoption of proven best practices would be effective in producing better outcomes.

Seth D. Appleton

Assistant Secretary for Policy Development and Research

U.S. Department of Housing and Urban Development

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TABLE OF CONTENTS

Acknowledgments .................................................................................................................. i

Foreword .............................................................................................................................. ii

Table of Contents ................................................................................................................. iv

Executive Summary ............................................................................................................... 1

1 Introduction ................................................................................................................... 4

2 Data Sources and Methodology ...................................................................................... 6

2.1 Data Sources ......................................................................................................................................... 6

2.2 Methodology ........................................................................................................................................ 7

2.3 Specifics on the Estimation of Indicators of Economic Opportunity ...................................................... 8

2.4 Limitations of the Indicators ............................................................................................................... 10

3 Results ......................................................................................................................... 12

3.1 Indicator 1—Ratio of HUD-Assisted to Overal Population Employment Rates ................................... 14

3.2 Indicator 2—Percentage of HUD-Assisted Households with Stable Employment for 3 Years .............. 21

3.3 Indicator 3—Ratio Between HUD-Assisted and Overal Population Ful -Time Employment Rates ...... 28

3.4 Indicator 4—Percentage of HUD-Assisted Individuals Who Are Either Employed or in School ............ 35

3.5 MSAs and Nonmetropolitan Balance of State Trends Across Al Four Indicators ................................. 42

4 Conclusion .................................................................................................................... 46

Appendix A—Details of Data-Cleaning Process ..................................................................... 49

Appendix B—Quartile Means of MSA-Level Employment and Ful -Time Employment Rates for

HUD-Assisted and Overall Population .................................................................................. 58

Appendix C—Indicator Estimates for NonMetropolitan Balances of States, by Year .............. 62

Appendix D—List of Metropolitan Statistical Areas Served by Moving To Work (MTW) Public

Housing Authorities (PHAs) .................................................................................................. 70

References .......................................................................................................................... 71

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EXECUTIVE SUMMARY

In 2016, nearly 4.5 million households1 received housing assistance from the U.S. Department of Housing and Urban Development (HUD) across the three programs of interest in this study: public housing, housing choice voucher (HCV), and assisted multifamily. 2 These programs are intended to aid low-income families and individuals in finding safe, decent, and affordable housing and have the potential to assist work-able program participants in achieving the goals of economic opportunity, self-

sufficiency, and financial stability. Reliable metrics are a critical tool for evidence-based management and oversight. Such metrics must offer value in informing strategy and policy, managing programs to improve outcomes, and improving public accountability.

The purpose of this study is to estimate and assess four indicators of economic opportunity for assisted populations that offer a degree of balance and that integrate additional information to contextualize program performance:

 Indicator 1 is the ratio of the employment rate for the work-able, HUD-assisted population to the employment rate for the work-able general population. 3

 Indicator 2 is the percentage of work-able, HUD-assisted households with stable employment for 3 years.

 Indicator 3 is the ratio of the full-time employment rate for the work-able, HUD-assisted population to the full-time employment rate for the work-able general population.

 Indicator 4 is the percentage of work-able, HUD-assisted individuals who are either employed or in school.

Using HUD administrative data, combined with data from the American Community Survey (ACS), the study team provides estimates for each of the four indicators for the three programs of interest at the national level and for every metropolitan statistical area (MSA) (and the nonmetropolitan balance of each state) for 2012 through 2016. Exhibit ES-1 illustrates those estimates for 2016 (darker shades of green indicate higher values).

The national estimates for the four indicators of economic opportunity show increases in the financial stability and self-sufficiency of HUD-assisted, work-able households and individuals. Although we observe increases in all four metrics over time, 4 estimates for each indicator are considerably higher for individuals in public housing and HCV programs compared with those in assisted multifamily programs.

Furthermore, estimates of Indicators 1 and 3 suggest a convergence between the employment 1 Descriptive analysis from HUD administrative data indicates that nearly 4.5 mil ion households were in one of the three HUD-assisted programs of interest (public housing, HCV, and assisted multifamily programs) in al 50 states plus the District of Columbia in 2016, with approximately 3.1 mil ion work-able individuals residing in 2.1 mil ion work-able households. This total excludes (1) those households that participated in programs other than the three programs of interest; (2) those households that ended participation in one of the HUD programs of interest during the calendar year; and (3) those households without geographic identifiers in the HUD longitudinal data file.

2 For convenience in this report, the “assisted multifamily program” actual y refers to a number of subsidized and assisted housing programs involving private ownership.

3 Work-able individuals are nonelderly (ages 18 through 64) and nondisabled; work-able households are any households that have at least one work-able individual.

4 Because the indicators are estimated for the entire universe of households meeting the criteria, there is no sampling error, and al reported differences are statistical y significant. Other sources of error, however, may influence reported results.

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outcomes of HUD-assisted, work-able individuals and the general population over time. The estimates of Indicators 2 and 4 suggest that economic and educational opportunity, as wel as financial stability, are also increasing over time.

This study notes some of the results, differences, and trends that are apparent from examining the indicators. It does not, however, attempt to investigate or disentangle the numerous factors that could cause or influence the results seen.

These indicators are promising metrics of economic opportunity that may help policymakers, program managers, and stakeholders; however, we caution the reader that certain data limitations on these estimates should be considered when interpreting the results. Nonetheless, the findings from this study provide key insights into estimating indicators of financial stability, economic self-sufficiency, and economic opportunity for HUD-assisted populations. The indicators illustrate that the labor market and employment characteristics of the HUD-assisted population have improved since 2012. The indicators can be helpful in assessing the economic opportunities of the HUD-assisted population and can be easily adapted to monitoring and evaluating outcomes for individual housing providers’ tenants. The indicators also may have applications for other HUD programs geared toward providing employment opportunities, thereby supporting the goals of increasing self-sufficiency and financial stability among HUD-assisted populations.

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Exhibit ES-1 | Estimates of Economic Opportunity Indicators Across All HUD Programs, 2016

Sources: ACS 5-year estimates; HUD Inventory Management System (IMS)/Public and Indian Housing Information Center (PIC), and Tenant Rental Assistance Certification System (TRACS) data from the HUD longitudinal tenant data files

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1 INTRODUCTION

The U.S. Department of Housing and Urban Development (HUD) is interested in estimating metrics used to assess the status and progress in achieving economic opportunity, self-sufficiency, and financial stability for work-able individuals receiving various housing benefits. The need for such metrics is derived from HUD’s 2018–2022 Strategic Plan (HUD, 2018c), which outlines the Agency Priority Goal (APG) of “[promoting] economic opportunity for HUD-assisted residents by encouraging self-sufficiency and financial stability, as measured by increasing the proportion of households who exit HUD-assisted housing for positive reasons.” If available, those metrics can shed light on the extent to which HUD

programs provide program participants and their families a path to self-sufficiency and financial stability.

Through its programs, HUD actively seeks to improve the economic opportunities of those receiving assistance by supporting local services that improve education and training, workforce development, and financial literacy. The Strategic Plan outlines three tracking indicators to measure success toward meeting this APG of individuals and households exiting the program from a financially stable position.

These tracking indicators are—

1. Proportion of households exiting assisted housing for positive reasons.

2. Percentage of work-able households exiting assisted housing with low subsidy needs.

3. Median percentage change in income of individuals who complete an economic development program.

At present, HUD does not consistently collect data on the reasons that program participants exit the programs. Each of the three tracking indicators faces limitations due to data availability, scope, and timeliness. Because of these limitations, HUD has identified four alternative indicators of self-sufficiency, shown in Exhibit 1-1.

This report provides estimates for the four indicators for each of the following three types of programs: (1) public housing, (2) housing choice voucher (HCV), and (3) assisted multifamily programs5 from 2012

through 2016 and aggregated across the three programs for—

 Metropolitan statistical areas (MSAs) and the nonmetropolitan balance of each state. 6

5 Assisted multifamily programs include Project-Based Section 8, Rent Supplement, Rental Assistance Payments (RAP), HUD-Owned or Held, Section 202 Project Rental Assistance Contract (PRAC), Section 811 PRAC, and Section 202/162 PRAC. Section 236 and Below Market Interest Rate unassisted multifamily subsidy types are not included because their tenants do not receive income-based rent assistance as do other program participants. Moderate Rehabilitation program participants also are excluded from this analysis. See “Programs of HUD” (HUD, 2018b) for further information regarding HUD’s various housing assistance programs: https://www.huduser.gov/portal/sites/default/files/pdf/HUDPrograms2018.pdf.

6 According to the U.S Census Bureau (Census), MSAs are Core Based Statistical Areas (CBSAs) “associated with at least one urbanized area that has a population of at least 50,000. The [MSA] comprises the central county or counties or equivalent entities containing the core, plus adjacent outlying counties having a high degree of social and economic integration with the central county or counties as measured through commuting.” In contrast, micropolitan statistical areas are CBSAs with at least one urban cluster that have a population of at least 10,000 but less than 50,000. See Census’s “Geographic Terms and Concepts—Core Based Statistical Areas and Related Statistical Areas” (U.S. Census Bureau, n.d.) for further clarification:

https://www.census.gov/geo/reference/gtc/gtc_cbsa.html.

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 A summary national measure averaging the metropolitan and nonmetropolitan estimates weighted by the number of households served within that program in that area.

For the rest of the report, the term “MSAs” is used to refer to estimates for MSAs as well as estimates for the nonmetropolitan balance of each state.

Exhibit 1-1 | Alternative Indicators of Self-Sufficiency

Alternative

Indicator

Description

Measure

Numerator

Denominator

Ratio between HUD-assisted

Percentage of HUD-assisted,

Percentage of nonelderly,

Indicator 1 and overall population

nonelderly, nondisabled

nondisabled adults who are

employment rates for work-

adults who have earned

employed in the general

able adults

income

population

Number of HUD-assisted,

Number of HUD-assisted,

Percentage of work-able, HUD-

nonelderly, nondisabled

nonelderly, nondisabled

Indicator 2 assisted households with stable households that have stable households that have been

employment for 3 years

earned income over the past continuously assisted over

3 years

the past 3 years

Percentage of nonelderly,

Ratio between HUD-assisted

Percentage of HUD-assisted,

nondisabled adults in the

nonelderly, nondisabled

general population who have

Indicator 3 and overall population rates of

full-time employment for work- adults who have annual

worked at least 35 hours per

able adults

earnings of $12,500 or

week on average for at least

greater7

50 weeks during the past

year

Percentage of HUD-assisted,

Number of HUD-assisted,

Number of HUD-assisted,

Indicator 4 work-able individuals who are

nonelderly, nondisabled

nonelderly, nondisabled

either working or in school

individuals who are either

working or in school

individuals

7 For comparability between HUD-assisted and overal population rates of full-time employment, we calculate the annual earnings for adults who work at least 35 hours per week for at least 50 weeks per year at the minimum wage ($12,687.50), which is the definition of full-time employment in the American Community Survey. We use a lower bound of annual earnings of $12,500.00 for the HUD-assisted population ($12,500.00 represents the wage income of just under 35 hours of work for at least 50 weeks per year at the 2018 federal minimum wage of $7.25 per hour). In 2016, there were 9,957 (less than 0.5

percent) work-able individuals with annual wage income between $12,500.00 and $12,687.50 in the HUD-assisted population.

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2 DATA SOURCES AND METHODOLOGY

To produce the four indicators described in Chapter 1, the study team used data from three main sources: the HUD Inventory Management System/Public and Indian Housing Information Center (IMS/PIC), the HUD Tenant Rental Assistance Certification System (TRACS), and the American Community Survey (ACS). HUD provided the IMS/PIC and TRACS records as a longitudinal dataset (hereafter, “HUD longitudinal data”) comprising annual files formatted consistently across programs and structured for longitudinal analysis.

2.1 Data Sources

HUD Longitudinal Data File. The study team used the HUD longitudinal data to calculate the total counts of work-able, HUD-assisted individuals and households. The longitudinal data file contains household member-level data for HUD-assisted households participating in public housing programs, housing choice voucher (HCV) programs, and multifamily programs. Data on households participating in public housing and HCV programs were col ected from public housing authorities (PHAs) through forms HUD-50058 and HUD-50058 Moving to Work (MTW) using the IMS/PIC system. Data on households participating in multifamily programs were col ected from private owners of multifamily properties through form HUD-50059 using the TRACS system. The longitudinal files based on the IMS/PIC and TRACS data include characteristics of households and household members, including addresses, assets and income, and participation in the three HUD programs of interest. 8 The household addresses were geocoded to identify the metropolitan statistical area (MSA) locations of the households in the longitudinal data file. The data were cleaned to remove observations that lacked geographic information, data that were not associated with one of the three programs, and data that had an end-of-participation code. 9

Indicators 1, 3, and 4 use member-level data to determine the share of work-able, HUD-assisted individuals who are employed, employed full-time, and either employed or in school full-time, respectively. Indicator 2 uses household-level data to determine the share of work-able, HUD-assisted households that have had stable employment for 3 years. Because this indicator estimates the population with 3 years of stable employment, 2012 was the first year for which indicator 2 could be estimated using the 2010–2016 longitudinal data. 10

8 The data are separated into two files, a head-of-household file and a member file, which are merged to provide a member-level dataset of households for all three HUD programs.

9 See Appendix A for a more detailed accounting of the data-cleaning process.

10 Households associated with MTW PHAs are not excluded from this analysis. Because of different reporting requirements, the income data required for each of the indicators may be updated less frequently for participants in MTW than for other HUD

assistance programs. Our analysis of the MTW share of households in the MSAs with an MTW PHA did not show that reporting requirements were an issue because the percentage of MTW households in the MSAs remained constant from 2012

through 2016. See Section 2.4 and Appendix D for clarification on the treatment of MTW PHAs.

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ACS. ACS 5-year estimates provide data on local employment rates. 11 ACS is an annual survey of the U.S.

population, conducted by the U.S. Census Bureau (Census), that col ects social, economic, housing, and demographic characteristics for more than 2.1 million housing units across the country each year (U.S.

Census Bureau, 2018a). From the ACS responses, Census publishes pretabulated social, economic, housing, and demographic estimates of the general or specified subpopulation. 12 After consulting with HUD, the study team decided to use ACS 5-year estimates. 13 The team extracted ACS 5-year estimates for all 50 states plus the District of Columbia at the county level for a 5-year time span beginning with 2012 and used the data to calculate the employment and full-time employment rates of the work-able population. The ACS 5-year estimates provided data at more granular levels of geography and allowed for the aggregation of MSA and state-balance figures at the county level. Furthermore, the single 5-year estimate used for the indicator denominator for each MSA ensured that any observed changes during the period would be a reflection of changes in the program (numerator).

2.2 Methodology

In this section, we describe the key terms used in this report. Each of the four indicators is calculated for the work-able population of nondisabled and nonelderly adults in each MSA, along with a national-level weighted mean for each of the three programs. Appendix A outlines the variables and inclusion/exclusion criteria to restrict data in the longitudinal data file and to restrict ACS data to nondisabled and nonelderly adults. In Exhibit 2-1, we briefly summarize our definitions. Individuals are considered work-able if they are nondisabled and fall in the age range of 18 through 64. 14 Employment status is determined by an annual wage income of $500 or more, whereas full-time employment status is determined by an annual wage income of $12,500 or more. Ful -time student status is determined by an indicator variable in the longitudinal data that denotes whether a member of the household is a full-time student and 18 years or older. 15 Households are considered work-able if any member of the household is work-able. Household employment is determined by a total household annual income of $500 or more. In addition, important limitations exist in the variables selected or in their comparability across the HUD longitudinal data files and ACS estimates in this chapter. We describe those limitations in detail in Section 2.4.

11 Given that 5-year estimates include previous years of ACS responses, notable weaknesses include the age of data and a resulting lack of year-to-year variation (U.S. Census Bureau, 2018b). The 1-year estimates are deemed unreliable for geographic areas encompassing fewer than 65,000 persons and are not published by Census (U.S. Census Bureau, 2018b).

This threshold complicates the analysis of MSA and state-balance indicators due to the county-level delineation of MSAs and an MSA’s defining threshold of 50,000 persons (U.S. Census Bureau, 2016).

12 Fol owing discussion with HUD, the study team used ACS tables C18120—Employment Status by Disability Status and C18121—Work Experience by Disability Status to calculate the employment and full-time employment rates of the nondisabled population ages 18 through 64.

13 The ACS 5-year estimates are based on 60-month data col ections implemented via paper questionnaires, phone interviews, personal interviews, and Internet surveys. Over this 5-year timeframe, Census indicates that it “samples approximately 1-in-9

households nationwide” and notes that this sampling rate is higher in low-populated areas and intentional y targets areas with presumably low response rates (U.S. Census Bureau, 2018b).

14 HUD programs define elderly individuals as those older than 62 years, but this analysis includes individuals through the age of 64 to be consistent with ACS table categories (HUD, 2014; HUD PIH, 2004).

15 The variable mbr_rltn_cd is equal to “E” (for IMS/PIC) or “D” (for TRACS) if the household member is a full-time student.

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Exhibit 2-1 | Definitions of Terms Used for Economic Opportunity Metrics Indicator

Definition

Work-able Individual16

Nondisabled person ages 18 through 64

Work-able Household

Household with at least one individual who is work-able

Employment

For individuals, having earnings17 greater than $500 in a year18

Stable Employment

For households, having total earnings greater than $500 per year for the past 3

years

Full-Time Employment

For individuals, having earnings greater than $12,500 for the past year19

In School

For individuals, being reported as a ful -time student

2.3 Specifics on the Estimation of Indicators of Economic Opportunity

This section provides a detailed outline of how the study team calculated estimates of the alternative indicators proposed by HUD. For each indicator, we note the methods for providing MSA-specific, program-specific, and national estimates.

INDICATOR 1—RATIO BETWEEN HUD-ASSISTED AND OVERALL POPULATION EMPLOYMENT RATES

Indicator 1 is the ratio of the employment rate for the work-able, HUD-assisted population to the employment rate for the overall population. It is calculated as follows:

𝐸𝐸

⁄

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 1 = 𝐻𝐻𝐻𝐻𝐻𝐻 𝑁𝑁𝐻𝐻𝐻𝐻𝐻𝐻

𝐸𝐸𝐺𝐺𝐺𝐺𝐺𝐺⁄𝑁𝑁𝐺𝐺𝐺𝐺𝐺𝐺

The variable 𝐸𝐸𝐻𝐻𝐻𝐻𝐻𝐻 is the number of HUD-assisted, work-able, employed individuals, and 𝑁𝑁𝐻𝐻𝐻𝐻𝐻𝐻 is the number of HUD-assisted, work-able individuals (regardless of employment status). The variables 𝐸𝐸𝐻𝐻𝐻𝐻𝐻𝐻

and 𝑁𝑁𝐻𝐻𝐻𝐻𝐻𝐻 are calculated for each program and MSA using the HUD longitudinal data file. Similarly, the variable 𝐸𝐸𝐺𝐺𝐺𝐺𝐺𝐺 represents the number of work-able individuals who are employed in the general population (U.S. Census Bureau, 2017). 20 The variable 𝑁𝑁𝐺𝐺𝐺𝐺𝐺𝐺 represents the number of work-able 16 The study team used two variables (age and disability status) to determine work-ability. Household members who were live-in aides and foster children or adults were included in the counts of work-able individuals. Such members accounted for approximately 1.3 percent of work-able households in 2016. Further details of the data-cleaning process and variables used to determine work-ability are outlined in Appendix A.

17 Wage income is derived from business income, federal wages, PHA wages, military pay, and other wage income. Further clarification on HUD’s reported wage income can be found in the reporting form instructional guides (HUD PIH, 2004; HUD, 2014).

18 The value $500 represents just under 2 weeks of work at the 2018 federal minimum wage of $7.25 per hour.

19 The value $12,500 represents the lower bound of income for 1 year (at least 35 hours per week for at least 50 weeks, or 1,750 hours) of employment at the 2018 federal minimum wage of $7.25 per hour.

20 By ACS definition, the general population includes nondisabled, noninstitutionalized, civilian individuals ages 18 through 64.

Census classifies respondents as “employed” if they are either “at work” or “with a job but not at work” during the reference week. Further clarification on employment and disability status can be found at https://www2.census.gov/programs-

surveys/acs/tech_docs/subject_definitions/2017_ACSSubjectDefinitions.pdf.

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individuals in the general population. The variables 𝐸𝐸𝐺𝐺𝐺𝐺𝐺𝐺 and 𝑁𝑁𝐺𝐺𝐺𝐺𝐺𝐺 are calculated for each program and MSA using ACS data.

INDICATOR 2—PERCENTAGE OF HUD-ASSISTED HOUSEHOLDS WITH STABLE EMPLOYMENT FOR 3 YEARS

Indicator 2 is the percentage of work-able, HUD-assisted households who have stable employment for 3

years. It is calculated as follows:

𝑁𝑁𝑁𝑁𝐸𝐸

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 2 =

𝐻𝐻𝐻𝐻𝐻𝐻

𝑁𝑁𝑁𝑁𝐻𝐻𝐻𝐻𝐻𝐻

The variable 𝑁𝑁𝑁𝑁𝐸𝐸𝐻𝐻𝐻𝐻𝐻𝐻 is the number of HUD-assisted households with at least one work-able member and earnings exceeding $500 in each of 3 consecutive years, 21 and 𝑁𝑁𝑁𝑁𝐻𝐻𝐻𝐻𝐻𝐻 is the number of HUD-assisted households that have received assistance throughout the 3-year period and have at least one work-able member in the household. For example, for a household to be part of 𝑁𝑁𝑁𝑁𝐻𝐻𝐻𝐻𝐻𝐻 in 2012, the household should receive HUD assistance from 2010 through 2012 and have at least one member who is work-able in 2012.

INDICATOR 3—RATIO BETWEEN HUD-ASSISTED AND OVERALL POPULATION FULL-TIME EMPLOYMENT RATES

Indicator 3 is the ratio between the full-time employment rates of the work-able, HUD-assisted, and overall populations. It is calculated as follows:

𝐹𝐹𝑁𝑁𝐸𝐸

⁄

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 3 =

𝐻𝐻𝐻𝐻𝐻𝐻 𝑁𝑁𝐻𝐻𝐻𝐻𝐻𝐻

𝐹𝐹𝑁𝑁𝐸𝐸𝐺𝐺𝐺𝐺𝐺𝐺⁄𝑁𝑁𝐺𝐺𝐺𝐺𝐺𝐺

where 𝐹𝐹𝑁𝑁𝐸𝐸𝐻𝐻𝐻𝐻𝐻𝐻 and 𝐹𝐹𝑁𝑁𝐸𝐸𝐺𝐺𝐺𝐺𝐺𝐺 represent the number of work-able, full-time employed individuals for the HUD-assisted and overall populations, respectively. The study team used HUD longitudinal data files to estimate the total HUD-assisted population that is full-time employed (𝐹𝐹𝑁𝑁𝐸𝐸𝐻𝐻𝐻𝐻𝐻𝐻). The team then calculated ful -time employment for the general population (𝐹𝐹𝑁𝑁𝐸𝐸𝐺𝐺𝐺𝐺𝐺𝐺) using ACS estimates of the work-able, full-time employed population. The total HUD-assisted population, 𝑁𝑁𝐻𝐻𝐻𝐻𝐻𝐻, and the total general population, 𝑁𝑁𝐺𝐺𝐺𝐺𝐺𝐺, are both estimated as in Indicator 1.

INDICATOR 4—PERCENTAGE OF HUD-ASSISTED INDIVIDUALS WHO ARE EITHER EMPLOYED OR IN SCHOOL

Indicator 4 is the percentage of work-able, HUD-assisted individuals who are either employed or full-time students. It is calculated as follows:

𝐸𝐸

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 4 = 𝐻𝐻𝐻𝐻𝐻𝐻 ∪ 𝑁𝑁𝐻𝐻𝐻𝐻𝐻𝐻

𝑁𝑁𝐻𝐻𝐻𝐻𝐻𝐻

where the total employed (𝐸𝐸𝐻𝐻𝐻𝐻𝐻𝐻) and the total populations (𝑁𝑁𝐻𝐻𝐻𝐻𝐻𝐻) are calculated in Indicator 1. The numerator, 𝐸𝐸𝐻𝐻𝐻𝐻𝐻𝐻 ∪ 𝑁𝑁𝐻𝐻𝐻𝐻𝐻𝐻, is the number of individuals who are either employed, full-time students, or both. The study team estimated the total student population (𝑁𝑁𝐻𝐻𝐻𝐻𝐻𝐻) using HUD’s longitudinal data file, which includes an indicator for whether a household member is a full-time student. 22

21 Because indicator 2 is a household-level indicator, the earnings may be contributed by different members each year, including by non “work-able” members.

22 The variable mbr_rltn_cd is equal to “E” (for IMS/PIC) or “D” (for TRACS) if the household member is a full-time student.

| 9

NATIONAL-LEVEL ESTIMATES

National estimates for each indicator are calculated as a weighted average of the indicator values (𝑉𝑉) for each MSA weighted by the number of households (𝑃𝑃) served by a given HUD program in that MSA. 23 The national indicators for each program are calculated using the formula that follows for 5 years, from 2012

through 2016, for each of the three programs and overall across the three programs, where 𝐼𝐼

represents the number of MSAs.

2.4 Limitations of the Indicators

The preceding section outlines how the study team calculated the indicators using HUD longitudinal data and ACS 5-year estimates. Exhibit 2-2 summarizes several limitations of those two data sources, which affect the estimation of each of the indicators. Exhibit 2-2 also describes how the information from HUD longitudinal data and ACS were not fully comparable.

Exhibit 2-2 | HUD Longitudinal Data and ACS Data Limitations

Exclusion Restriction

or Indicator

Limitation

Disability is determined by a single indicator in the HUD longitudinal data versus a six-item composite index in ACS. For HUD programs, disability status is determined Nondisabled

by whether a person meets the Social Security Administration (SSA) definition of Population

disabled (Dawkins and Mil er, 2015). 24 In contrast, the ACS definition of disability relies on self-reported assessments of difficulties from four functional limitation categories (hearing, vision, cognition, ambulation) and is not tied to the SSA

definition (U.S. Census Bureau, 2017). 25

In the absence of an employment status variable in HUD longitudinal data, the study Indicator 1: Ratio of

team inferred employment status using an annual income threshold of $500 or

Employment

more per year. In contrast, ACS defines individuals as employed if they have done

“any work at al . . . as paid employees” (U.S. Census Bureau, 2017).

Indicator 2:

This indicator faces the same difficulties as Indicator 1: employment status of HUD-Percentage with

assisted households must be inferred from income sources and amounts. Similarly, Stable Employment

stable household employment is defined as any household with wage income of at

for 3 Years

least $500 per year for 3 years.

23 Weights used to calculate national and quartile estimates are the same for al four indicators.

24 In addition, HUD-assisted individuals may be classified as disabled if they have (1) physical, mental, or emotional impairment, which is expected to be of indefinite duration, substantial y impedes his or her ability to live independently, and is of such a nature that such ability could be improved by more suitable housing conditions; (2) a developmental disability, as defined in Section 102 of the Developmental Disabilities Assistance and Bil of Rights Act; or (3) acquired immune deficiency syndrome (AIDS) or any condition that arises from the etiologic agent for AIDS. Although those conditions are initial y self-reported, PHAs and project owners are supposed to verify (with a doctor or other professional) the disability status of HUD program participants (Dawkins and Miller, 2015).

25 In recent years, HUD has collaborated with the Census Bureau to attach a HUDADMIN flag to ACS records to identify HUD-assisted households. Such data could be used to compare overal prevalence of disability among assisted households, as determined by administrative variables and by ACS variables. Privacy considerations may make household-level comparisons more difficult.

| 10

Exclusion Restriction

or Indicator

Limitation

In addition, this indicator does not capture households with stable employment that may have been admitted recently or that exit the program before completing 3

years.

In the absence of a ful -time employment status variable in HUD longitudinal data, the study team inferred employment status using an income threshold of $12,500 or more per year, in which this value is the lower bound of the annual earnings of an individual who worked 35 hours per week for 50 weeks per year at the federal

Indicator 3: Ratio of

minimum wage of $7.25 per hour. This mirrors the ACS definition, in which

Full-Time

individuals are classified as employed ful -time, year-round if they worked a

Employment

minimum of 1,750 hours (at least 35 hours per week on average for at least 50

weeks) in the past year (U.S. Census Bureau, 2017). In addition, one major caveat regarding the ACS definition of ful -time employment is that the data do not contain income levels but rather counts of individuals meeting the requirements for hours and weeks worked.

The variable in the HUD longitudinal data file that indicates ful -time student status Indicator 4:

may lead to underestimates of the population of ful -time students receiving HUD

Percentage Working

assistance. First, ful -time student status is determined differently for participants in or in School

multifamily programs in the HUD-provided longitudinal data file. 26 Second, for individuals in public housing and HCV programs, ful -time student status is not

recorded if the member also has another position in the household. 27

The data required for each of the indicators are updated less frequently for

participants in MTW than for other HUD assistance programs due to different

reporting requirements. This may present a chal enge because estimates may not

reflect the entire HUD-assisted population. Such variation in reporting requirements may present special chal enges in estimating Indicator 2 because this measure

examines households that have 3 years of continuous HUD assistance. To the extent Moving to Work

that MTW reporting occurs less often, this indicator may underestimate the number (MTW)

of MTW households with 3 years of continuous assistance. Of the approximately

3,825 housing agencies across the United States, however, only 39 of them were

MTW PHAs in 2016. Our analysis of the MTW share of households in the MSAs with

an MTW PHA did not show that reporting requirements were an issue, as the

percentage of MTW households in the MSAs remained constant from 2012 through

2016. Although many MTW PHAs are in large MSAs, their lack of consistently

reported data does not seem to material y affect estimates. 28

26 Form HUD-50059 col ects data on dependent household members, including full-time students. To determine the number of full-time students in assisted multifamily programs, the study team used the code for dependent family members (HUD, 2014).

27 Ful -time student status is not recorded for household members who are also the head, spouse or cohead, foster child, or live-in aide (HUD PIH, 2004).

28 Observations for MTW PHAs are included in al estimates. For transparency, MSAs served by MTW PHAs are bolded and italicized in each exhibit. See Appendix D for a list of MSAs and nonmetropolitan areas served by MTW PHAs.

| 11

3 RESULTS

In 2016, nearly 4.5 million households were enrol ed in the three HUD housing assistance programs in the 50 states and the District of Columbia, with approximately 3.1 million work-able individuals residing in 2.3 million work-able households (see Exhibit 3-1, Panels A, B, and E). In addition, approximately 3.5

million households were receiving assistance for 3 years, including 1.6 million work-able households (Panels C and D). Exhibit 3-1 illustrates, from 2012 through 2016, the number of HUD-assisted households; HUD-assisted households with 3 years of assistance; HUD-assisted, work-able households and those receiving assistance for 3 years; and HUD-assisted, work-able individuals.

The number of HUD-assisted households increased slightly from 2012 to 2016 (Panel A), with growth in the HCV program offsetting decreases in public housing households. The overall number of work-able households and individuals across the three programs also had modest net decreases (Panels B and E), reflecting declines within the public housing and multifamily programs. In contrast, both household participation and the number of work-able households and individuals in the housing choice voucher (HCV) program increased from 2012 through 2016. The total number of households and work-able households with 3 years of HUD assistance increased from 2012 through 2016 across all three HUD

programs.

The remainder of this chapter focuses on the estimates of each indicator. Although the patterns observed may be of interest to HUD stakeholders and may suggest potential causal factors, the purposes of this study did not include detailed comparisons or causal analysis.

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Exhibit 3-1 | Total HUD-Assisted Households and Work-Able Individuals Panel A: Total HUD-Assisted Households

Across Al Three HUD Programs

Public Housing

HCV

Multifamily

2012

4,467,738

1,022,699

2,120,559

1,324,480

2013

4,425,166

1,014,143

2,084,735

1,326,288

2014

4,455,338

1,011,055

2,120,064

1,324,219

2015

4,494,405

989,785

2,182,709

1,321,911

2016

4,498,112

955,518

2,217,654

1,324,940

Panel B: Total HUD-Assisted, Work-Able Households

Across Al Three HUD Programs

Public Housing

HCV

Multifamily

2012

2,328,502

561,330

1,266,131

501,041

2013

2,296,688

560,067

1,237,842

498,779

2014

2,300,748

556,099

1,248,947

495,702

2015

2,306,481

540,446

1,273,792

492,243

2016

2,291,047

519,762

1,281,750

489,535

Panel C: Total HUD-Assisted Households with 3 Years of Assistance

Across Al Three HUD Programs

Public Housing

HCV

Multifamily

2012

3,281,498

708,684

1,630,430

942,384

2013

3,390,611

745,314

1,684,913

960,384

2014

3,468,610

756,263

1,737,277

975,070

2015

3,446,916

743,671

1,733,351

969,894

2016

3,459,670

726,696

1,760,343

972,631

Panel D: Total HUD-Assisted, Work-Able Households with 3 Years of Assistance

Across Al Three HUD Programs

Public Housing

HCV

Multifamily

2012

1,525,265

347,078

894,334

283,853

2013

1,578,522

366,438

920,355

291,729

2014

1,620,531

372,303

947,552

300,676

2015

1,600,465

365,237

938,899

296,329

2016

1,592,099

355,214

941,801

295,084

Panel E: Total HUD-Assisted, Work-Able Individuals

Across Al Three HUD Programs

Public Housing

HCV

Multifamily

2012

3,104,791

762,753

1,734,172

607,866

2013

3,072,398

761,650

1,705,926

604,822

2014

3,083,354

757,325

1,725,531

600,498

2015

3,081,682

733,018

1,753,156

595,508

2016

3,052,879

702,970

1,758,132

591,777

Notes: HUD-assisted households exclude (1) households that participated in programs other than public housing, HCV, and assisted multifamily; (2) households that ended participation in one of the HUD programs of interest during the calendar year; (3) households residing outside the 50 states and District of Columbia; and (4) households without geographic identifiers in the HUD longitudinal data file. Households are considered work-able if any member is nondisabled and ages 18

through 64. Individuals are considered work-able if they are nondisabled and ages 18 through 64. Total HUD-assisted households (Panel A) are the national totals of the variable used for weighting (total count of HUD-assisted households) to calculate the national and quartile estimates. Total HUD-assisted, work-able households with 3 years of assistance (Panel D) are the national totals of the variable used to calculate the denominator of Indicator 2. Total HUD-assisted, work-able individuals (Panel E) are the national totals of the variables used to calculate the HUD-assisted employment rate for Indicators 1, 3, and 4.

Sources: HUD Inventory Management System/Public and Indian Housing Information Center (IMS/PIC); Tenant Rental Assistance Certification System (TRACS) data from HUD longitudinal tenant data files

| 13

3.1 Indicator 1—Ratio of HUD-Assisted to Overal Population

Employment Rates

Indicator 1 is the ratio of the employment rate for the work-able, HUD-assisted population to the employment rate for the work-able general population. This indicator represents the relationship between the employment rates for the two populations, for which a value greater than 1 means that the employment rate of the HUD-assisted population is greater than that of the overall population, and a value between 0 and 1 means that the employment rate for the HUD-assisted population is lower than that of the overall population. 29

NATIONAL ESTIMATES

The national-level estimates for Indicator 1 are presented in Exhibit 3-2. These estimates increase over time for all programs, with the ratio across all three HUD programs increasing by approximately 6

percentage points, from 0.535 in 2012 to 0.590 in 2016. Similarly, from 2012 through 2016, the estimate for public housing increased from 0.596 to 0.655, the estimate for HCV increased from 0.537 to 0.595, and the estimate for multifamily programs increased from 0.460 to 0.506. Al programs displayed gains.

In addition, the estimates of Indicator 1 are higher for public housing and HCV compared with

multifamily programs. 30

Exhibit 3-2 | National-Level Estimates of Indicator 1: Ratio Between Work-Able, HUD-Assisted Individuals and Overal Population Employment Rates

National-Level Estimates of Indicator 1

Across Al Three HUD

Programs

Public Housing

HCV

Multifamily

2012

0.535

0.596

0.537

0.460

2013

0.553

0.620

0.553

0.479

2014

0.572

0.641

0.571

0.495

2015

0.585

0.656

0.586

0.504

2016

0.590

0.655

0.595

0.506

Note: National-level mean estimates are weighted by the number of households served by the HUD program in each MSA or nonmetropolitan balance of state.

Sources: American Community Survey (ACS) 5-year estimates; HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

Exhibit 3-3 presents quartile means of the metropolitan statistical area (MSA) estimates of Indicator 1.

These estimates provide insight similar to the trends illustrated in Exhibit 3-2. Across all programs and within each quartile, the estimates of Indicator 1 clearly increase from 2012 through 2016. Overall, these estimates suggest a partial convergence between the employment rates for HUD-assisted individuals and those for the overall population. Given that the national unemployment rate decreased from 8.3 percent to 4.9 percent from 2012 through 2016, 31 these trends suggest that employment outcomes of HUD-assisted individuals are improving at a faster rate than those of the general 29 Estimates of greater than 1 for Indicator 1 are not observed in the data.

30 Although the reasons for differences across programs are worthy of investigation, such analysis is beyond the scope of this research.

31 Estimates for unemployment were obtained from the Bureau of Labor Statistics (2018).

| 14

population. 32 In addition, comparing values for each program across quartiles suggests that assisted households in some MSAs are achieving markedly greater levels of success relative to their local labor markets. 33

Exhibit 3-3 | Quartile Means of MSA-Level Estimates of Indicator 1: Ratio Between Work-Able, HUD-Assisted Individuals and Overal Population Employment Rates

Quartiles of MSA-Level Estimates of Indicator 1

Across Al Three HUD Programs

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.456

0.517

0.564

0.627

2013

0.476

0.538

0.584

0.644

2014

0.493

0.556

0.602

0.658

2015

0.507

0.565

0.608

0.667

2016

0.508

0.564

0.609

0.668

Public Housing

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.464

0.571

0.645

0.741

2013

0.493

0.589

0.664

0.773

2014

0.515

0.619

0.685

0.795

2015

0.537

0.637

0.696

0.804

2016

0.537

0.636

0.693

0.795

Housing Choice Voucher

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.457

0.524

0.578

0.663

2013

0.476

0.548

0.599

0.675

2014

0.492

0.561

0.615

0.695

2015

0.512

0.576

0.624

0.697

2016

0.518

0.579

0.629

0.694

Multifamily

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.343

0.414

0.484

0.570

2013

0.363

0.436

0.504

0.588

2014

0.377

0.452

0.520

0.607

2015

0.382

0.450

0.526

0.611

2016

0.375

0.455

0.527

0.608

32 Exhibits B-1 and B-2 in Appendix B present the mean MSA-level employment rates for each quartile of Indicator 1 for the HUD-assisted and overal populations, respectively. These estimates show that the employment rate for the HUD-assisted, work-able population increased for each program and quartile from 2012 through 2016, whereas the employment rate for the overall work-able population remained steady or increased slightly within each quartile over time.

33 Differences between quartiles and between programs that appear in Exhibit 3-3 are not attributable entirely to changes in tenant outcomes because the mean denominator values presented in Exhibit B-2 reveal variations in MSA labor markets that necessarily affect these results. For example, differences in MSA denominator values account for almost 3 points of the 26-point difference between the first-quartile and fourth-quartile means shown for public housing in 2016.

| 15

Notes: Quartile mean estimates are weighted by the number of households served by the HUD program in each MSA or nonmetropolitan balance of state in that quartile.

Sources: ACS 5-year estimates; HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files MSA AND NONMETROPOLITAN BALANCE OF STATE ESTIMATES

To probe into differences among MSAs, the top 10 and bottom 10 MSA-level estimates of Indicator 1 are presented in Exhibit 3-4. This exhibit illustrates a large degree of variation in estimates of Indicator 1

across MSAs. Estimates of Indicator 1 in 2016 range from 0.383 in Enid, OK, to 0.843 in Champaign-

Urbana, IL. As seen in Exhibit 3-4, five MSAs (Lawrence, KS; Brownsville-Harlingen, TX; Hilton Head Island-Bluffton-Beaufort, SC; Flagstaff, AZ; and Santa Cruz-Watsonville, CA) consistently have among the highest estimates for Indicator 1 across the 5-year span, indicating that the employment rates for the HUD-assisted population in those areas are most similar to the employment rates for the overall population. Danville, IL; Tulsa, OK; Spokane-Spokane Valley, WA; and Johnstown, PA consistently have low estimates of Indicator 1, indicating that the employment rates for the HUD-assisted population in those areas is much lower than that for the overall population in each of those MSAs.

The estimates of Indicator 1 for the nonmetropolitan balance of each state presented in Exhibit 3-534

range from 0.435 in Maine to 0.753 in Louisiana. In addition, the Georgia, Kansas, Louisiana, Nebraska, and Texas nonmetropolitan balances of state had consistently high estimates for Indicator 1, and 7 of the 10 nonmetropolitan balances of state (Kentucky, Maine, Massachusetts, Nevada, Ohio, Virginia, and West Virginia) had consistently low estimates.

Such variations across the nation point to the potential value of such metrics for guiding further investigation of causal factors and for identifying opportunities for policy interventions. Areas with high indicator estimates might have useful lessons that could be shared to improve economic opportunity in areas with low indicator estimates. HUD also might be able to target monitoring resources or provide greater support for housing providers in areas with lower estimates.

34 Only 47 states have nonmetropolitan balance areas, and three states (Delaware, New Jersey, and Rhode Island) plus Washington, D.C. are covered entirely by MSAs.

| 16

Exhibit 3-4 | MSAs with the Highest and Lowest Estimates Across All Three HUD Programs for Indicator 1: Ratio Between Work-Able, HUD-Assisted Individuals and Overal Population Employment Rates

MSAs with the Highest Ratio for Indicator 1

2016

2015

2014

2013

2012

1

Champaign-Urbana, IL

0.843

Lawrence, KS

0.856

Lawrence, KS

0.891

Manhattan, KS

0.833 Hilton Head Island-Bluffton-Beaufort, SC 0.823

2

Lawrence, KS

0.804

Manhattan, KS

0.836

Manhattan, KS

0.835 Hilton Head Island-Bluffton-Beaufort, SC 0.822

Flagstaff, AZ

0.781

3

Ithaca, NY

0.800 Hilton Head Island-Bluffton-Beaufort, SC 0.813

Hilton Head Island-Bluffton-Beaufort, SC

0.802

Lawrence, KS

0.817

Midland, TX

0.774

4

Brownsville-Harlingen, TX

0.795

Brownsville-Harlingen, TX

0.806

Brownsville-Harlingen, TX

0.800

State Col ege, PA

0.794

Lawrence, KS

0.752

5 Hilton Head Island-Bluffton-Beaufort, SC 0.790

Santa Cruz-Watsonville, CA

0.785

Flagstaff, AZ

0.791

Bay City, MI

0.781

Manhattan, KS

0.745

6

Flagstaff, AZ

0.783

Houma-Thibodaux, LA

0.782

State Col ege, PA

0.786

Flagstaff, AZ

0.776

Santa Cruz-Watsonville, CA

0.737

7

Santa Cruz-Watsonville, CA

0.777

Flagstaff, AZ

0.782

Midland, TX

0.782

Midland, TX

0.765

Brownsville-Harlingen, TX

0.717

8

State Col ege, PA

0.770

Midland, TX

0.781

Houma-Thibodaux, LA

0.767

Brownsville-Harlingen, TX

0.761

Visalia-Porterville, CA

0.715

9

Florence, SC

0.765

Bay City, MI

0.771

Santa Cruz-Watsonville, CA

0.762

Charlottesville, VA

0.749

Houma-Thibodaux, LA

0.715

10

Laredo, TX

0.762

Crestview-Fort Walton Beach-Destin, FL

0.769 San Luis Obispo-Paso Robles-Arroyo Grande, CA 0.760

Santa Cruz-Watsonville, CA

0.738

State Col ege, PA

0.708

MSAs with the Lowest Ratio for Indicator 1

2016

2015

2014

2013

2012

10

Sebring, FL

0.436

Danville, IL

0.445

Homosassa Springs, FL

0.439

Johnson City, TN

0.423

Huntington-Ashland, WV-KY-OH

0.407

9

Dalton, GA

0.428

Lake Havasu City-Kingman, AZ

0.443

Portland-Vancouver-Hil sboro, OR-WA

0.431

Spokane-Spokane Valley, WA

0.421

Johnstown, PA

0.406

8

Weirton-Steubenvil e, WV-OH

0.424

Blacksburg-Christiansburg-Radford, VA

0.435

Johnson City, TN

0.430

Lake Havasu City-Kingman, AZ

0.418

Bend-Redmond, OR

0.402

7

Johnstown, PA

0.420

Carson City, NV

0.424

Spokane-Spokane Valley, WA

0.422

Portland-Vancouver-Hil sboro, OR-WA

0.415

Longview, WA

0.402

6

Lake Havasu City-Kingman, AZ

0.416

Tulsa, OK

0.423

Longview, WA

0.421

Homosassa Springs, FL

0.411

Albany, OR

0.401

5

Spokane-Spokane Valley, WA

0.414

Johnstown, PA

0.420

Tulsa, OK

0.418

Kingsport-Bristol-Bristol, TN-VA

0.410

Spokane-Spokane Valley, WA

0.394

4

Blacksburg-Christiansburg-Radford, VA

0.407

Spokane-Spokane Valley, WA

0.419

Danville, IL

0.408

Tulsa, OK

0.404

Kingsport-Bristol-Bristol, TN-VA

0.392

3

Tulsa, OK

0.405

Dalton, GA

0.415

Dalton, GA

0.399

Johnstown, PA

0.401

Danville, IL

0.391

2

Danville, IL

0.397

Enid, OK

0.407

Johnstown, PA

0.397

Longview, WA

0.383

Tulsa, OK

0.391

1

Enid, OK

0.383

Homosassa Springs, FL

0.403

Enid, OK

0.379

Danville, IL

0.375

Enid, OK

0.378

Notes: The general population employment rate is calculated by dividing the employed, nondisabled population by the total nondisabled population. This ratio reflects nondisabled, noninstitutionalized civilian populations ages 18 through 64. The U.S. Census Bureau (Census; n.d.) classifies respondents as “employed” if they are either “at work”

or “with a job but not at work” during the reference week. Further clarification on employment and disability status can be found at the fol owing link:

"https://www2.census.gov/programs-surveys/acs/tech_docs/subject_definitions/2017_ACSSubjectDefinitions.pdf"

The employment rate for individuals participating in HUD programs is calculated by dividing the employed, work-able population by the overal work-able population across al three HUD programs and within each program. Individuals are considered work-able if they are nondisabled and ages 18 through 64; individuals are considered employed if their wage income was at least $500 in the given year ($500 represents just under 70 hours of work at the 2018 federal minimum wage of $7.25 per hour). Wage income is derived from business income, federal wages, Public Housing Authority (PHA) wages, and other wage income. Further clarification on HUD’s reported wage income can be found in the reporting form instructional guides (HUD, 2014; HUD PIH, 2004). Values are bolded and italicized for areas served by Moving to Work (MTW) PHAs.

Sources: ACS 5-year estimates; HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

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Exhibit 3-5 | Estimates for Nonmetropolitan Balances of States of Indicator 1: Ratio Between Work-Able, HUD-Assisted Individuals and Overal Population Employment Rates

Indicator 1 for Nonmetropolitan Balances of State

State

2016

2015

2014

2013

2012

1

Louisiana

0.753

0.759

0.760

0.746

0.706

2

Texas

0.703

0.726

0.717

0.704

0.681

3

Georgia

0.697

0.683

0.669

0.645

0.621

4

Florida

0.689

0.672

0.657

0.624

0.596

5

Hawaii

0.672

0.674

0.655

0.626

0.610

6

Nebraska

0.663

0.683

0.709

0.686

0.669

7

Arizona

0.657

0.673

0.612

0.632

0.505

8

Alabama

0.650

0.650

0.629

0.611

0.587

9

Kansas

0.649

0.660

0.674

0.651

0.629

10

New Mexico

0.646

0.630

0.607

0.629

0.599

11

Maryland

0.641

0.634

0.648

0.641

0.572

12

South Carolina

0.632

0.639

0.620

0.597

0.557

13

Alaska

0.631

0.597

0.582

0.580

0.555

14

Michigan

0.627

0.622

0.632

0.613

0.609

15

South Dakota

0.618

0.624

0.608

0.643

0.585

16

Missouri

0.614

0.613

0.607

0.572

0.546

17

Il inois

0.612

0.629

0.631

0.625

0.606

18

Minnesota

0.611

0.619

0.623

0.616

0.586

19

Connecticut

0.609

0.578

0.635

0.570

0.609

20

Idaho

0.606

0.555

0.576

0.566

0.573

21

Arkansas

0.604

0.609

0.603

0.580

0.547

22

North Carolina

0.602

0.604

0.589

0.565

0.549

23

Wisconsin

0.597

0.604

0.602

0.585

0.566

24

Mississippi

0.596

0.593

0.566

0.560

0.548

25

Wyoming

0.596

0.646

0.636

0.639

0.612

26

North Dakota

0.586

0.632

0.601

0.644

0.652

27

New York

0.586

0.597

0.579

0.574

0.568

28

New Hampshire

0.583

0.599

0.585

0.564

0.556

29

California

0.582

0.563

0.568

0.556

0.511

30

Colorado

0.579

0.589

0.570

0.564

0.545

31

Oklahoma

0.576

0.587

0.604

0.599

0.587

32

Washington

0.569

0.569

0.564

0.541

0.528

33

Montana

0.559

0.570

0.569

0.584

0.554

34

Iowa

0.556

0.582

0.578

0.561

0.548

35

Tennessee

0.552

0.541

0.540

0.516

0.492

36

Vermont

0.551

0.527

0.499

0.484

0.454

37

Utah

0.548

0.567

0.563

0.611

0.620

38

Oregon

0.544

0.537

0.534

0.495

0.478

39

Massachusetts

0.527

0.498

0.461

0.477

0.458

40

Pennsylvania

0.526

0.520

0.526

0.539

0.516

41

Indiana

0.516

0.532

0.522

0.511

0.496

42

Virginia

0.506

0.513

0.505

0.476

0.466

43

Kentucky

0.501

0.509

0.513

0.503

0.489

| 18

Indicator 1 for Nonmetropolitan Balances of State

State

2016

2015

2014

2013

2012

44

West Virginia

0.471

0.494

0.482

0.479

0.464

45

Ohio

0.461

0.469

0.463

0.452

0.429

46

Nevada

0.443

0.447

0.449

0.495

0.476

47

Maine

0.435

0.441

0.432

0.410

0.390

Notes: The general population employment rate is calculated by dividing the employed, nondisabled population by the total nondisabled population. This ratio reflects nondisabled, noninstitutionalized civilian populations ages 18 through 64. The Census Bureau classifies respondents as “employed” if they are either “at work” or “with a job but not at work” during the reference week. Further clarification on employment and disability status can be found at the fol owing link:

https://www2.census.gov/programs-surveys/acs/tech_docs/subject_definitions/2017_ACSSubjectDefinitions.pdf.

The employment rate for individuals participating in HUD programs is calculated by dividing the employed, work-able population by the overal work-able population across al three HUD programs and within each program. Individuals are considered work-able if they are nondisabled and ages 18 through 64; individuals are considered employed if their wage income was at least $500 in the given year ($500 represents just under 70 hours of work at the 2018 federal minimum wage of $7.25 per hour). Wage income is derived from business income, federal wages, PHA wages, and other wage income.

Further clarification on HUD’s reported wage income can be found in the reporting form instructional guides (HUD, 2014; HUD PIH, 2004). Values are bolded and italicized for areas served by MTW PHAs.

Sources: ACS 5-year estimates; HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 19

Exhibit 3-6 maps the change in estimates of Indicator 1 for all three HUD programs from 2012 through 2016. This map shows that the estimates of Indicator 1 increased for most MSAs (85 percent) and nonmetropolitan balance areas across the country (as illustrated by the shades of green). Estimates of Indicator 1 increased for most of the MSAs and nonmetropolitan balance areas, with 64 areas experiencing reductions in estimates of Indicator 1. The nonmetropolitan balance of Arizona (increase of 0.153) and the Champaign-Urbana, IL, MSA (increase of 0.282) experienced the largest increases in Indicator 1 from 2012 through 2016. In contrast, the nonmetropolitan balance of Utah (decrease of 0.071) and the Harrisonburg, VA, MSA (decrease of 0.099) experienced the largest declines in Indicator 1.

Exhibit 3-6 | Change in Indicator 1 Estimates Across All Three HUD Programs, From 2012

Through 2016: Ratio Between Work-Able, HUD-Assisted Individuals and Overal Population Employment Rates

Sources: ACS 5-year estimates; HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 20

3.2 Indicator 2—Percentage of HUD-Assisted Households with Stable Employment for 3 Years

Indicator 2 is the share of work-able, HUD-assisted households with stable employment for 3 years. 35

NATIONAL ESTIMATES

The national-level estimates of Indicator 2 presented in Exhibit 3-7 show an increase over time for all programs, with the percentage of HUD-assisted households with stable employment across all three HUD programs increasing from 35.3 percent in 2012 to 39.4 percent in 2016. The stable employment rate for public housing and HCV increased by more than 4 percentage points, whereas the multifamily stable employment rate increased about 3 percentage points. Although the 3-year stable employment rate displayed gains across all programs, the 2012 estimates of Indicator 2 are greater than 12

percentage points higher for public housing and greater than 7 percentage points higher for HCV

compared with multifamily programs. In 2016, 45.4 percent of households in public housing and 40.1

percent of households with HCVs had stable employment, compared with 31.1 percent of households in multifamily programs.

Exhibit 3-7 | National-Level Estimates of Indicator 2: Percentage of HUD-Assisted, Work-Able Households Having Stable Employment Over 3 Years

National-Level Estimates of Indicator 2

Across Al Three HUD

Programs (%)

Public Housing (%)

HCV (%)

Multifamily (%)

2012

35.3

41.1

35.5

28.2

2013

35.8

41.5

36.2

28.3

2014

36.8

42.6

37.2

29.2

2015

38.1

44.1

38.4

30.1

2016

39.4

45.4

40.1

31.1

Notes: National-level mean estimates are weighted by the number of households served by the HUD program in each MSA or nonmetropolitan balance of state.

Sources: HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files Quartile means of the MSA estimates of Indicator 2 presented in Exhibit 3-8 provide similar insight into the trends illustrated in Exhibit 3-7. Across all programs and within each quartile, the estimates of Indicator 2 display a clear increase from 2012 through 2016. Overall, these estimates show increases in the stable employment rate of households that participate in the HUD programs of interest.

35 This indicator examines the population of work-able households that have received HUD assistance for each of the previous 3

years.

| 21

Exhibit 3-8 | Quartile Means of MSA-Level Estimates of Indicator 2: Percentage of HUD-Assisted, Work-Able Households Having Stable Employment Over 3 Years

Quartiles of MSA-Level Estimates of Indicator 2

Across Al Three HUD Programs

First Quartile

(Lowest) (%)

Second Quartile (%)

Third Quartile (%)

Fourth Quartile

(Highest) (%)

2012

25.0

30.2

34.4

45.7

2013

26.0

30.7

34.7

46.3

2014

26.8

31.6

36.1

47.1

2015

28.0

33.2

37.3

48.4

2016

28.7

34.2

38.5

49.8

Public Housing

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

26.0

34.8

42.5

53.2

2013

26.5

35.3

42.7

53.3

2014

27.5

36.8

44.4

54.3

2015

29.5

38.1

46.3

55.7

2016

30.4

39.1

46.9

57.1

Housing Choice Voucher

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

26.1

30.8

35.3

45.5

2013

26.8

31.6

36.0

45.8

2014

28.1

32.7

37.2

46.1

2015

29.0

33.8

38.2

47.8

2016

29.8

34.7

39.7

49.6

Multifamily

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

15.3

20.9

27.1

40.2

2013

15.4

20.9

27.2

40.5

2014

16.2

21.3

28.3

41.8

2015

17.1

22.3

29.7

43.0

2016

18.0

23.5

30.3

44.1

Note: Quartile mean estimates are weighted by the number of households served by the HUD program in each MSA or nonmetropolitan balance of state in that quartile.

Sources: HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 22

MSA AND NONMETROPOLITAN BALANCE OF STATE ESTIMATES

The top 10 and bottom 10 MSA-level estimates of Indicator 2 presented in Exhibit 3-9 illustrate a large degree of variation in the indicator estimates across areas. Estimates for 2016 range from 16.6 percent

in the Danvil e, IL, MSA to 65.4 percent in the Lawrence, KS, MSA. That is, 16.6 percent of work-able households in the Danville, IL MSA that received HUD assistance continuously from 2014 through 2016

had at least one member of the household who was employed in each of those 3 years, compared with 65.4 percent of work-able households in the Lawrence, KS MSA. Five of the MSAs36 with the highest estimates consistently appear in the top 10 during each year shown, with stable employment rates of 57

percent or greater in 2016. Conversely, four MSAs37 consistently appear in the bottom 10 in terms of stable household employment rates. These MSAs had stable household employment rates of less than 25 percent each year from 2012 through 2016.

The estimates of Indicator 2 for the nonmetropolitan balance of each state presented in Exhibit 3-10

illustrate stable household employment rates ranging from 26.6 percent in Maine to 53.9 percent in Hawaii, with seven of the top 10 and bottom 10 in 2016 consistently appearing in the top 10 and bottom 10, respectively, across the 5-year period.

36 Urban Honolulu, HI; Lawrence, KS; Lincoln, NE; Santa Cruz-Watsonvil e, CA; and Santa Maria-Santa Barbara, CA consistently appear in the top 10 MSA estimates of Indicator 2.

37 Blacksburg-Christiansburg-Radford, VA; Danvil e, IL; Enid, OK; and Tulsa, OK consistently appear in the bottom 10 MSA estimates of Indicator 2.

| 23

Exhibit 3-9 | MSAs with the Highest and Lowest Estimates Across All Three HUD Programs for Indicator 2: Percentage of HUD-Assisted, Work-Able Households Having Stable Employment Over 3 Years

MSAs with the Highest Share for Indicator 2

2016

2015

2014

2013

2012

1

Lawrence, KS

65.4%

Lawrence, KS

68.1%

Lawrence, KS

64.1%

Lawrence, KS

57.5%

Urban Honolulu, HI

56.8%

2

Lincoln, NE

63.8%

Lincoln, NE

64.6%

Lincoln, NE

58.9%

Urban Honolulu, HI

56.9%

Santa Maria-Santa Barbara, CA

54.8%

3

Urban Honolulu, HI

59.3%

Santa Maria-Santa Barbara, CA

58.2%

Midland, TX

58.1%

Santa Maria-Santa Barbara, CA

56.0%

Santa Cruz-Watsonville, CA

53.0%

4

Santa Maria-Santa Barbara, CA

57.9%

Urban Honolulu, HI

58.0%

Santa Maria-Santa Barbara, CA

57.3%

Lincoln, NE

55.6% New York-Newark-Jersey City, NY-NJ-PA 51.3%

5

Santa Cruz-Watsonville, CA

57.0%

Logan, UT-ID

57.9%

Visalia-Porterville, CA

57.2%

Midland, TX

55.6%

California-Lexington Park, MD

51.0%

6

Brownsville-Harlingen, TX

56.3%

Santa Cruz-Watsonville, CA

56.2%

Urban Honolulu, HI

57.1%

Santa Cruz-Watsonville, CA

53.3%

Midland, TX

50.9%

7

Bay City, MI

56.2%

Midland, TX

55.2%

Logan, UT-ID

55.6%

Visalia-Porterville, CA

53.2%

Lincoln, NE

50.7%

8

Oxnard-Thousand Oaks-Ventura, CA 55.7%

Salinas, CA

55.1%

Santa Cruz-Watsonville, CA

55.3%

Oxnard-Thousand Oaks-Ventura, CA 51.9%

Lawrence, KS

50.4%

9

Manhattan, KS

55.4%

Manhattan, KS

54.3%

State College, PA

54.5% New York-Newark-Jersey City, NY-NJ-PA 51.1%

Flagstaff, AZ

50.3%

10

Salinas, CA

55.2%

Bay City, MI

54.1%

Napa, CA

52.9%

La Crosse-Onalaska, WI-MN

50.5%

Visalia-Porterville, CA

50.1%

MSAs with the Lowest Share for Indicator 2

2016

2015

2014

2013

2012

10

Dalton, GA

24.2%

Kingsport-Bristol-Bristol, TN-VA

23.1%

Longview, WA

21.6%

Anniston-Oxford-Jacksonville, AL

22.2%

Jefferson City, MO

22.0%

9

Sebring, FL

23.7%

Johnstown, PA

22.8%

Tulsa, OK

21.6%

Sebring, FL

22.1%

Toledo, OH

21.7%

8

Lake Havasu City-Kingman, AZ

23.1%

Lake Havasu City-Kingman, AZ

22.6%

Johnstown, PA

21.4%

Enid, OK

21.7%

Kingsport-Bristol-Bristol, TN-VA

21.1%

7

Tulsa, OK

22.2%

Carson City, NV

22.3%

Muskegon, MI

20.8%

Tulsa, OK

21.2%

Danville, IL

20.3%

6

Johnstown, PA

20.5%

Tulsa, OK

21.9%

Dalton, GA

19.8%

Carson City, NV

21.1%

Tulsa, OK

20.2%

5

Prescott, AZ

20.5%

Prescott, AZ

21.4%

Prescott, AZ

19.6%

Longview, WA

20.7%

Muskegon, MI

20.2%

4

Cleveland, TN

20.3%

Enid, OK

21.2% Blacksburg-Christiansburg-Radford, VA 18.8%

Dalton, GA

19.4%

Cleveland, TN

19.8%

3 Blacksburg-Christiansburg-Radford, VA 19.8%

Cleveland, TN

21.2%

Carson City, NV

17.3%

Muskegon, MI

19.2%

Longview, WA

19.1%

2

Enid, OK

19.1% Blacksburg-Christiansburg-Radford, VA 20.5%

Danville, IL

16.8% Blacksburg-Christiansburg-Radford, VA 17.8%

Enid, OK

18.4%

1

Danville, IL

16.6%

Danville, IL

18.7%

Enid, OK

15.5%

Danville, IL

15.4% Blacksburg-Christiansburg-Radford, VA 16.9%

Notes: The employment rate for HUD program households is calculated by dividing the employed, work-able households by the overal work-able households across al three HUD programs and within each program. Households are considered work-able if any member is nondisabled and ages 18 through 64; households are considered employed if the total wage income in the work-able household was at least $500 in the given year ($500 represents just under 70 hours of work at the 2018 federal minimum wage of $7.25

per hour). Household total wage income is derived from business income, federal wages, PHA wages, military pay, and other wage income. Further clarification on HUD’s reported wage income can be found in the reporting form instructional guides (HUD, 2014; HUD PIH, 2004). Values are bolded and italicized for areas served by MTW PHAs.

Sources: HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 24

Exhibit 3-10 | Estimates for Nonmetropolitan Balances of States of Indicator 2: Percentage of HUD-Assisted, Work-Able Households Having Stable Employment Over 3 Years

Indicator 2 for Nonmetropolitan Balances of States

State

2016 (%)

2015 (%)

2014 (%)

2013 (%)

2012 (%)

1

Hawaii

53.9

50.5

49.6

46.9

44.9

2

Nebraska

52.6

52.9

51.9

50.4

47.4

3

Kansas

47.3

46.9

46.4

45.5

45.4

4

New Hampshire

46.4

46.4

44.9

42.5

40.7

5

Connecticut

45.2

39.2

42.2

42.2

38.0

6

Minnesota

44.8

44.8

42.7

40.7

40.7

7

Texas

44.6

44.6

43.6

43.4

42.8

8

Wyoming

44.6

43.4

38.8

39.8

38.7

9

Louisiana

43.7

45.4

43.4

42.0

40.6

10

Alaska

43.3

39.7

34.4

35.2

35.0

11

South Dakota

42.5

43.0

41.2

41.1

38.4

12

Maryland

42.0

43.3

41.8

40.0

39.8

13

North Dakota

41.9

44.5

46.1

43.9

44.8

14

Wisconsin

41.6

40.4

39.5

36.4

37.4

15

Florida

41.5

39.9

37.9

35.9

35.8

16

Il inois

40.1

38.9

38.5

38.5

38.2

17

New York

39.9

38.4

38.6

39.5

39.6

18

Iowa

39.4

37.6

37.6

36.6

35.1

19

Arizona

39.4

39.0

34.6

30.2

28.5

20

Georgia

38.4

36.7

35.9

34.8

34.0

21

Missouri

38.4

38.6

36.9

36.0

33.6

22

Michigan

38.3

37.3

37.1

36.2

35.4

23

Colorado

38.1

38.6

37.2

36.2

37.0

24

Massachusetts

38.0

36.3

32.7

31.4

31.4

25

New Mexico

37.1

36.5

35.4

35.1

34.3

26

Washington

35.9

33.7

32.9

32.3

32.2

27

Vermont

35.7

35.1

32.2

32.1

31.2

28

California

35.7

35.0

32.5

31.0

33.0

29

Alabama

35.5

34.6

34.0

33.4

33.0

30

Arkansas

35.4

34.0

32.6

32.1

30.7

31

Idaho

35.1

34.4

37.1

32.6

32.8

32

Utah

34.1

40.8

38.0

32.1

31.4

33

Montana

34.0

35.2

32.8

34.2

32.8

34

Oklahoma

33.5

35.4

35.1

36.3

34.5

35

Pennsylvania

33.3

32.9

33.9

34.7

33.5

36

Oregon

32.9

30.8

29.9

28.9

28.3

37

North Carolina

32.2

31.4

30.8

29.3

29.9

38

Mississippi

31.8

30.4

30.2

30.8

31.0

39

Indiana

31.7

31.1

29.7

28.7

27.6

40

South Carolina

31.6

30.8

29.4

29.0

27.8

41

Tennessee

31.6

30.8

30.2

27.6

25.6

42

Kentucky

29.7

29.4

28.3

27.9

28.3

43

Virginia

29.6

29.4

28.7

27.6

27.0

| 25

Indicator 2 for Nonmetropolitan Balances of States

State

2016 (%)

2015 (%)

2014 (%)

2013 (%)

2012 (%)

44

Nevada

29.5

29.4

29.7

30.6

33.3

45

Ohio

27.8

27.2

26.6

25.8

24.9

46

West Virginia

27.6

27.0

26.2

26.0

26.6

47

Maine

26.6

26.2

24.1

23.4

22.1

Notes: The employment rate for HUD program households is calculated by dividing the employed, work-able households by the overal work-able households across al three HUD programs and within each program. Households are considered work-able if any member is nondisabled and ages 18 through 64; households are considered employed if the total wage income in the work-able household was at least $500 in the given year ($500 represents just under 70 hours of work at the 2018

federal minimum wage of $7.25 per hour). Household total wage income is derived from business income, federal wages, PHA wages, military pay, and other wage income. Further clarification on HUD’s reported wage income can be found in the reporting form instructional guides (HUD, 2014; HUD PIH, 2004). Values are bolded and italicized for areas served by MTW

PHAs.

Sources: HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 26

Exhibit 3-11 maps the percentage point change in estimates of Indicator 2 for all three HUD programs from 2012 through 2016. This map shows that estimates of the stable employment rates of HUD-assisted households increased for many MSAs (89 percent) and nonmetropolitan balance areas (as illustrated by the shades of green). The nonmetropolitan balance of Arizona (increase of 10.9

percentage points) and Champaign-Urbana, IL (increase of 25.5 percentage points) experienced the largest increases in Indicator 2 from 2012 through 2016. In contrast, the nonmetropolitan balance of Nevada (decrease of 3.9 percentage points) and Homosassa Springs, FL (decrease of 10.5 percentage points) experienced the largest declines in estimates of Indicator 2.

Exhibit 3-11 | Change in Indicator 2 Estimate Across All Three HUD Programs, From 2012

Through 2016: Percentage of HUD-Assisted, Work-Able Households Having Stable

Employment Over 3 Years

Sources: HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 27

3.3 Indicator 3—Ratio Between HUD-Assisted and Overal Population Full-Time Employment Rates

Indicator 3 is the ratio of the full-time employment rate for the work-able, HUD-assisted population to the full-time employment rate for the work-able general population. This indicator represents the relationship between the ful -time employment rates for the two populations, in which a value greater than 1 means that the full-time employment rate for the HUD-assisted population is greater than that of the overall population, and a value between 0 and 1 means that the employment rate for the HUD-assisted population is lower than that of the overall population. 38

NATIONAL ESTIMATES

The national-level estimates for Indicator 3 presented in Exhibit 3-12 show relatively large increases over time for all programs, with the ratio across all three HUD programs increasing from 0.415 in 2012 to 0.513 in 2016. Although all programs displayed gains, the estimates of Indicator 3 are higher for public housing and HCV compared with multifamily programs. From 2012 through 2016, the estimate for public housing increased from 0.491 to 0.584, the estimate for HCV increased from 0.415 to 0.517, and the estimate for multifamily programs increased from 0.327 to 0.424. These national-level estimates

show that the ful -time employment rate for the HUD-assisted population was about one-half that of

the rate for the overal population in 2016, bearing in mind the differences in how full-time employment is determined .

Exhibit 3-12 | National-Level Estimates of Indicator 3: Ratio Between HUD-Assisted and Overal Population Rates of Full-Time Employment

National-Level Estimates of Indicator 3

Across Al Three HUD

Programs

Public Housing

HCV

Multifamily

2012

0.415

0.491

0.415

0.327

2013

0.437

0.515

0.436

0.349

2014

0.461

0.539

0.460

0.373

2015

0.491

0.569

0.491

0.403

2016

0.513

0.584

0.517

0.424

Notes: National-level mean estimates are weighted by the number of households served by the HUD program in each MSA or nonmetropolitan balance of state.

Sources: ACS 5-year estimates; HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files Quartile means of the MSA estimates of Indicator 3 presented in Exhibit 3-13 provide additional, but similar, insights into the trends illustrated in Exhibit 3-12. Across all programs and within each quartile, the estimates of Indicator 3 clearly increase from 2012 through 2016. Although the full-time employment rate for the HUD-assisted population is lower than that of the overall population, these trends suggest a slight convergence in these rates over time. Given that the full-time employment rate in the overall population remained fairly consistent, from 51.9 percent in 2012 to 53.8 percent in 2016, 38 Estimates greater than 1 for Indicator 3 are rarely observed in the data; see Exhibit 3-14 for additional details.

| 28

these positive trends suggest that full-time employment rates are improving faster for HUD-assisted individuals than for the general population. 39

Exhibit 3-13 | Quartile Means of MSA-Level Estimates of Indicator 3: Ratio Between HUD-Assisted and Overal Population Rates of Ful -Time Employment

Quartiles of MSA-Level Estimates of Indicator 3

Across Al Three HUD Programs

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.303

0.364

0.415

0.525

2013

0.320

0.386

0.441

0.550

2014

0.342

0.412

0.465

0.577

2015

0.366

0.441

0.493

0.607

2016

0.378

0.452

0.511

0.629

Public Housing

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.318

0.421

0.511

0.635

2013

0.343

0.448

0.537

0.652

2014

0.367

0.474

0.563

0.678

2015

0.385

0.504

0.603

0.704

2016

0.398

0.524

0.620

0.718

Housing Choice Voucher

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.305

0.373

0.425

0.511

2013

0.331

0.397

0.451

0.535

2014

0.348

0.416

0.485

0.588

2015

0.370

0.445

0.501

0.589

2016

0.382

0.469

0.528

0.621

Multifamily

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.177

0.238

0.309

0.448

2013

0.191

0.263

0.333

0.472

2014

0.212

0.285

0.374

0.512

2015

0.235

0.316

0.401

0.532

2016

0.246

0.332

0.431

0.565

Notes: Quartile mean estimates are weighted by the number of households served by the HUD program in each MSA or nonmetropolitan balance of state in that quartile.

Sources: ACS 5-year estimates; HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files 39 Exhibits B-3 and B-4 in Appendix B present the mean MSA-level, ful -time employment rates for each quartile of Indicator 3

for the HUD-assisted and overal populations, respectively. These estimates show that the ful -time employment rate for the HUD-assisted, work-able population increased for each program and quartile between 2012 and 2016, whereas the full-time employment rate for the overall, work-able population remained steady or increased slightly within each quartile over time.

| 29

MSA AND NONMETROPOLITAN BALANCE OF STATE ESTIMATES

The top 10 and bottom 10 MSA-level estimates of Indicator 3 presented in Exhibit 3-14 illustrate a very large degree of variation in the estimates across MSAs. Estimates for 2016 range from 0.249 in the Enid,

OK MSA to 0.959 in the Lawrence, KS MSA. 40 Six MSAs (Flagstaff, AZ; Lawrence, KS; San Luis Obispo-Paso Robles-Arroyo Grande, CA; Santa Cruz-Watsonvil e, CA; Santa Maria-Santa Barbara, CA; and Visalia-Porterville, CA) consistently have among the highest estimates for Indicator 3, suggesting that the full-time employment rates for the HUD-assisted populations in those areas are most similar to the full-time employment rates of the overall population. Those MSAs have estimates of Indicator 3 that are approximately 0.8 or higher from 2012 through 2016. Conversely, two MSAs (Johnstown, PA and Kingsport-Bristol-Bristol, TN-VA) consistently have among the lowest estimates for Indicator 3, below 0.26 each year from 2012 through 2016.

The estimates of Indicator 3 for the nonmetropolitan balance of each state presented in Exhibit 3-15

range from 0.311 in West Virginia to 0.746 in Alaska. The top six nonmetropolitan balances of states (Alaska, Connecticut, Hawaii, Maryland, Nebraska, and New Hampshire) are consistently in the top five across the 5 years, and 6 of the 10 balances of states (Indiana, Kentucky, Maine, Ohio, Virginia, and West Virginia) consistently have low estimates.

40 For 2014 and 2015, the value of Indicator 3 was greater than 1 in the Lawrence, KS MSA, meaning that the HUD-assisted population had a full-time employment rate that was greater than that of the overall population.

| 30

Exhibit 3-14 | MSAs with the Highest and Lowest Estimates Across All Three HUD Programs for Indicator 3: Ratio Between HUD-Assisted and Overal Population Rates of Ful -Time Employment

MSAs with the Highest Ratio for Indicator 3

2016

2015

2014

2013

2012

1

Lawrence, KS

0.959

Lawrence, KS

1.040

Lawrence, KS

1.050

Lawrence, KS

0.945

Lawrence, KS

0.834

2

Santa Cruz-Watsonville, CA

0.909

Santa Cruz-Watsonville, CA

0.908

Flagstaff, AZ

0.868

Flagstaff, AZ

0.838

Flagstaff, AZ

0.822

3

Visalia-Porterville, CA

0.867

Manhattan, KS

0.897

Santa Cruz-Watsonville, CA

0.854

Santa Cruz-Watsonville, CA

0.814

Santa Cruz-Watsonville, CA

0.781

4

Flagstaff, AZ

0.864

Flagstaff, AZ

0.853

Santa Maria-Santa Barbara, CA

0.814

Santa Maria-Santa Barbara, CA

0.774

Santa Maria-Santa Barbara, CA

0.735

5

Salinas, CA

0.843

Santa Maria-Santa Barbara, CA

0.832 San Luis Obispo-Paso Robles-Arroyo Grande, CA 0.774

Midland, TX

0.734

Visalia-Porterville, CA

0.721

6

Ithaca, NY

0.837

Visalia-Porterville, CA

0.820

Ames, IA

0.770

Visalia-Porterville, CA

0.730 San Luis Obispo-Paso Robles-Arroyo Grande, CA 0.712

7 San Luis Obispo-Paso Robles-Arroyo Grande, CA 0.816

Salinas, CA

0.817

Midland, TX

0.768 San Luis Obispo-Paso Robles-Arroyo Grande, CA 0.714

Midland, TX

0.705

8

Santa Maria-Santa Barbara, CA

0.810 San Luis Obispo-Paso Robles-Arroyo Grande, CA 0.776

Mount Vernon-Anacortes, WA

0.768

Ithaca, NY

0.713

Ithaca, NY

0.676

9

Champaign-Urbana, IL

0.793

Midland, TX

0.756

Visalia-Porterville, CA

0.762

Salinas, CA

0.698

Manhattan, KS

0.654

10

Corvallis, OR

0.761

Logan, UT-ID

0.753

Salinas, CA

0.748

Manhattan, KS

0.685

Hilton Head Island-Bluffton-Beaufort, SC

0.638

MSAs with the Lowest Ratio for Indicator 3

2016

2015

2014

2013

2012

10

Lawton, OK

0.306

Tulsa, OK

0.293

Johnson City, TN

0.268

Weirton-Steubenville, WV-OH

0.267

Lynchburg, VA

0.238

9

Goldsboro, NC

0.304

Johnson City, TN

0.292

Hickory-Lenoir-Morganton, NC

0.265

Dalton, GA

0.260

Hanford-Corcoran, CA

0.234

8

Johnson City, TN

0.295

Huntington-Ashland, WV-KY-OH

0.291

Huntington-Ashland, WV-KY-OH

0.258

Sebring, FL

0.257

Huntington-Ashland, WV-KY-OH

0.233

7

Sheboygan, WI

0.288

Hot Springs, AR

0.289

Danville, IL

0.253

Danville, IL

0.243

Enid, OK

0.230

6

Pocatello, ID

0.279

Hickory-Lenoir-Morganton, NC

0.286

Dalton, GA

0.238

Prescott, AZ

0.242

Dalton, GA

0.228

5

Tulsa, OK

0.272

Danville, IL

0.274

Morristown, TN

0.237

Hickory-Lenoir-Morganton, NC

0.239

Sheboygan, WI

0.225

4

Kingsport-Bristol-Bristol, TN-VA

0.257

Morristown, TN

0.256

Kingsport-Bristol-Bristol, TN-VA

0.232

Huntington-Ashland, WV-KY-OH

0.218

Jefferson City, MO

0.209

3

Danville, IL

0.249

Enid, OK

0.255

Prescott, AZ

0.209

Kingsport-Bristol-Bristol, TN-VA

0.209

Kingsport-Bristol-Bristol, TN-VA

0.206

2

Johnstown, PA

0.249

Kingsport-Bristol-Bristol, TN-VA

0.242

Johnstown, PA

0.174

Johnstown, PA

0.198

Johnstown, PA

0.191

1

Enid, OK

0.249

Johnstown, PA

0.223

Homosassa Springs, FL

0.156

Homosassa Springs, FL

0.123

Prescott, AZ

0.136

Notes: The general population ful -time employment rate is calculated by dividing the full-time, year-round-employed, nondisabled population by the total nondisabled population. This ratio reflects nondisabled, noninstitutionalized civilian populations ages 18 through 64. Census classifies respondents as “Employed” if they are either at work or with a job but not at work during the reference week. “Full-time, year-round” is defined as persons who usual y worked 35 hours or more per week for 50 to 52 weeks in the past 12 months. Further clarification on employment and disability status can be found at the fol owing link: https://www2.census.gov/programs-

surveys/acs/tech_docs/subject_definitions/2017_ACSSubjectDefinitions.pdf.

The employment rate for individuals participating in HUD programs is calculated by dividing the population with ful -time employment status by the overal work-able population across al three HUD programs and within each program. Individuals are considered work-able if they are nondisabled and ages 18 through 64; individuals are considered full time if their wage income was at least $12,500 in the given year ($12,500 represents the wage income of just under 35 hours of work for at least 50 weeks per year at the 2018

federal minimum wage of $7.25 per hour). Wage income is derived from business income, federal wages, PHA wages, and other wage income. Further clarification on HUD’s reported wage income can be found in the reporting form instructional guides (HUD, 2014; HUD PIH, 2004). Values are bolded and italicized for areas served by MTW PHAs.

Sources: ACS 5-year estimates; HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 31

Exhibit 3-15 | Estimates for Nonmetropolitan Balances of States of Indicator 3: Ratio Between HUD-Assisted and Overal Population Rates of Ful -Time Employment

Indicator 3 for Nonmetropolitan Balances of States

State

2016

2015

2014

2013

2012

1

Alaska

0.746

0.682

0.632

0.689

0.689

2

Hawaii

0.630

0.614

0.556

0.529

0.492

3

Maryland

0.615

0.588

0.572

0.545

0.461

4

Florida

0.581

0.535

0.501

0.456

0.424

5

New Hampshire

0.578

0.580

0.525

0.505

0.457

6

Connecticut

0.569

0.558

0.553

0.509

0.564

7

Washington

0.545

0.505

0.488

0.453

0.415

8

Nebraska

0.544

0.547

0.541

0.480

0.482

9

Minnesota

0.540

0.523

0.480

0.456

0.403

10

Massachusetts

0.531

0.478

0.419

0.400

0.391

11

Louisiana

0.515

0.523

0.528

0.518

0.487

12

Vermont

0.514

0.491

0.460

0.432

0.385

13

California

0.509

0.483

0.464

0.434

0.380

14

Arizona

0.506

0.473

0.408

0.410

0.274

15

Colorado

0.503

0.511

0.454

0.431

0.401

16

Alabama

0.499

0.490

0.456

0.449

0.417

17

Oregon

0.494

0.477

0.430

0.386

0.341

18

New York

0.490

0.485

0.450

0.418

0.412

19

South Dakota

0.487

0.499

0.443

0.447

0.384

20

Texas

0.485

0.507

0.506

0.476

0.444

21

Kansas

0.481

0.507

0.495

0.445

0.408

22

Georgia

0.475

0.446

0.408

0.383

0.366

23

North Dakota

0.473

0.510

0.482

0.504

0.467

24

Michigan

0.460

0.446

0.403

0.359

0.337

25

Montana

0.453

0.468

0.434

0.441

0.417

26

South Carolina

0.453

0.439

0.397

0.373

0.331

27

Wisconsin

0.447

0.439

0.422

0.399

0.372

28

Il inois

0.446

0.456

0.454

0.450

0.424

29

Wyoming

0.441

0.530

0.514

0.485

0.448

30

New Mexico

0.438

0.454

0.414

0.424

0.387

31

Arkansas

0.415

0.401

0.389

0.367

0.339

32

Missouri

0.414

0.419

0.408

0.370

0.345

33

Mississippi

0.407

0.412

0.367

0.357

0.346

34

Iowa

0.405

0.406

0.368

0.361

0.341

35

Idaho

0.405

0.373

0.312

0.307

0.312

36

North Carolina

0.401

0.400

0.379

0.349

0.335

37

Tennessee

0.401

0.377

0.363

0.341

0.303

38

Oklahoma

0.398

0.428

0.439

0.421

0.393

39

Nevada

0.390

0.345

0.387

0.412

0.410

40

Pennsylvania

0.369

0.358

0.344

0.348

0.332

41

Maine

0.364

0.321

0.313

0.297

0.275

42

Indiana

0.342

0.334

0.325

0.302

0.275

43

Kentucky

0.332

0.339

0.326

0.311

0.302

| 32

Indicator 3 for Nonmetropolitan Balances of States

State

2016

2015

2014

2013

2012

44

Ohio

0.330

0.328

0.300

0.282

0.256

45

Utah

0.323

0.303

0.372

0.340

0.347

46

Virginia

0.320

0.320

0.294

0.267

0.262

47

West Virginia

0.311

0.303

0.271

0.261

0.264

Notes: The general population ful -time employment rate is calculated by dividing the full-time, year-round-employed, nondisabled population by the total nondisabled population. This ratio reflects nondisabled, noninstitutionalized civilian populations ages 18

through 64. The Census Bureau classifies respondents as “Employed” if they are either at work or with a job but not at work during the reference week. “Ful -time, year-round” is defined as persons who usual y worked 35 hours or more per week for 50 to 52 weeks in the past 12 months. Further clarification on employment and disability status can be found at the fol owing link:

https://www2.census.gov/programs-surveys/acs/tech_docs/subject_definitions/2017_ACSSubjectDefinitions.pdf.

The employment rate for individuals participating in HUD programs is calculated by dividing the population with ful -time employment status by the overal work-able population across al three HUD programs and within each program. Individuals are considered work-able if they are nondisabled and ages 18 through 64; individuals are considered full-time if their wage income was at least $12,500 in the given year ($12,500 represents the wage income of just under 35 hours of work for at least 50 weeks per year at the 2018 federal minimum wage of $7.25 per hour). Wage income is derived from business income, federal wages, PHA wages, and other wage income.

Further clarification on HUD’s reported wage income can be found in the reporting form instructional guides (HUD, 2014; HUD PIH, 2004). Values are bolded and italicized for areas served by MTW PHAs.

Sources: ACS 5-year estimates; HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 33

Exhibit 3-16 presents a visual mapping of the change in estimates of Indicator 3 for all three HUD

programs from 2012 through 2016. This map shows that estimates of Indicator 3 increased for nearly all (409 out of 430) MSAs and nonmetropolitan balance areas (as illustrated by the shades of green). The nonmetropolitan balance of Arizona and the Champaign-Urbana, IL MSA experienced the largest increases in Indicator 3 (0.232 and 0.296, respectively). By contrast, the nonmetropolitan balances of Utah (decrease of 0.024) and the Casper, WY MSA (decrease of 0.077) experienced the largest decline in the estimates of Indicator 3.

Exhibit 3-16 | Change in Indicator 3 Estimate Across All Three HUD Programs, From 2012

Through 2016: Ratio Between HUD-Assisted and Overal Population Rates of Full-Time Employment

Sources: ACS 5-year estimates; HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 34

3.4 Indicator 4—Percentage of HUD-Assisted Individuals Who Are Either Employed or in School

Indicator 4 is the percentage of work-able, HUD-assisted individuals who are either employed or are full-time students.

NATIONAL ESTIMATES

The national-level estimates of Indicator 4 presented in Exhibit 3-17 show an increase over time for all programs, with the rate across all three HUD programs increasing from 44.9 percent in 2012 to 49

percent in 2016. From 2012 through 2016, the estimates of Indicator 4 for public housing, HCV, and multifamily programs each increased by approximately 4 percentage points. National estimates of

Indicator 4 il ustrate that nearly 50 percent of the HUD-assisted work-able population was either

employed or a ful -time student in 2016. Estimates of Indicator 4 are higher for public housing and HCV, compared with the multifamily program. In 2016, 53.5 percent of work-able individuals in public housing, 49.8 percent of work-able individuals in HCV, and 41.8 percent of work-able individuals in multifamily programs were either employed or full-time students.

Exhibit 3-17 | National-Level Estimates of Indicator 4: Percentage of HUD-Assisted, Work-Able Individuals Either Employed or in School

National -Level Estimates of Indicator 4

Across Al Three HUD

Programs (%)

Public Housing (%)

HCV (%)

Multifamily (%)

2012

44.9

49.2

45.7

38.0

2013

45.8

50.4

46.4

39.0

2014

47.1

51.9

47.6

40.3

2015

48.3

53.3

48.9

41.3

2016

49.0

53.5

49.8

41.8

Notes: National-level mean estimates are weighted by the number of households served by the HUD program in each MSA or nonmetropolitan balance of state.

Sources: HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files Quartile means of the MSA estimates of Indicator 4 presented in Exhibit 3-18 provide additional, but similar, insight into the trends illustrated in Exhibit 3-17. Across all programs and within each quartile, the estimates of Indicator 4 clearly increase from 2012 through 2016. Overall, these estimates of Indicator 4 suggest increases in the share of HUD-assisted individuals that are either employed or in school full time.

| 35

Exhibit 3-18 | Quartiles Means of MSA-Level Estimates of Indicator 4: Percentage of HUD-Assisted, Work-Able Individuals Either Employed or in School

Quartiles of MSA-Level Estimates of Indicator 4

Across Al Three HUD Programs

First Quartile

(Lowest) (%)

Second Quartile (%)

Third Quartile (%)

Fourth Quartile

(Highest) (%)

2012

37.3

41.9

45.9

52.3

2013

38.3

43.0

46.7

53.1

2014

39.2

44.4

48.1

54.3

2015

40.3

45.4

49.5

55.2

2016

40.8

46.1

50.1

55.3

Public Housing

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

38.1

46.3

52.9

59.6

2013

39.8

47.8

53.5

61.1

2014

41.1

49.5

54.7

62.4

2015

42.9

51.1

56.0

64.3

2016

44.2

51.5

55.8

63.8

Housing Choice Voucher

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

38.3

44.0

47.5

52.9

2013

39.2

44.5

47.9

53.6

2014

40.2

45.7

49.5

54.6

2015

41.0

46.3

50.6

55.4

2016

41.7

47.4

51.7

56.0

Multifamily

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

26.4%

32.2%

38.4%

47.5%

2013

28.0%

33.8%

39.9%

48.8%

2014

28.7%

34.8%

40.9%

50.2%

2015

29.3%

35.5%

41.9%

51.6%

2016

28.9%

35.9%

42.4%

52.2%

Notes: Quartile mean estimates are weighted by the number of households served by the HUD program in each MSA or nonmetropolitan balance of state in that quartile.

Sources: HUD IMS/PIC and TRACS data from the HUD longitudinal tenant data files

| 36

MSA AND NONMETROPOLITAN BALANCE OF STATE ESTIMATES

The top 10 and bottom 10 MSA-level estimates of Indicator 4 are presented in Exhibit 3-19. Estimates

for 2016 range from 29.9 percent in the Enid, OK, MSA to 63.7 percent in the Lawrence, KS, MSA—that is, 29.9 percent of HUD-assisted individuals in the Enid, OK, MSA were either employed or full-time students, compared with 63.7 percent of individuals in the Lawrence, KS, MSA. Four MSAs (Hilton Head Island-Bluffton-Beaufort, SC; Lawrence, KS; Lincoln, NE; and Santa Cruz-Watsonville, CA) consistently have among the highest estimates for Indicator 4, with rates of employed individuals and full-time students of nearly 60 percent or more in 2016. Conversely, two MSAs (Blacksburg-Christiansburg-Radford, VA and Danville, IL) have consistently low estimates of Indicator 4 (below 34 percent).

The estimates of Indicator 4 for the nonmetropolitan balance of each state presented in Exhibit 3-20

illustrate rates of employed individuals and full-time students ranging from 33.5 percent in Nevada to 57.5 percent in Nebraska, with the top 6 in 2016 consistently appearing in the top 10 and 8 of the bottom 10 consistently appearing in the bottom 10 across the 5-year period.

| 37

Exhibit 3-19 | MSAs with the Highest and Lowest Estimates Across All Three HUD Programs for Indicator 4: Percentage of HUD-Assisted, Work-Able Individuals Either Employed or in School

MSAs with the Highest Share for Indicator 4

2016

2015

2014

2013

2012

1

Lawrence, KS

63.7%

Lawrence, KS

67.1%

Lawrence, KS

71.8%

Lawrence, KS

68.4%

Midland, TX

65.9%

2 Hilton Head Island-Bluffton-Beaufort, SC 62.9%

Midland, TX

64.1%

Midland, TX

65.1% Hilton Head Island-Bluffton-Beaufort, SC 63.6% Hilton Head Island-Bluffton-Beaufort, SC 63.1%

3

Champaign-Urbana, IL

62.2% Hilton Head Island-Bluffton-Beaufort, SC 63.3% Hilton Head Island-Bluffton-Beaufort, SC 62.9%

Midland, TX

62.8%

Lawrence, KS

62.9%

4

La Crosse-Onalaska, WI-MN

61.9%

Houma-Thibodaux, LA

61.2%

Houma-Thibodaux, LA

60.2%

Lincoln, NE

62.2%

Flagstaff, AZ

59.7%

5

Logan, UT-ID

61.6% Crestview-Fort Walton Beach-Destin, FL 61.1%

Lincoln, NE

59.9%

Manhattan, KS

60.2%

Houma-Thibodaux, LA

58.5%

6

Santa Cruz-Watsonville, CA

61.5%

Santa Cruz-Watsonville, CA

61.1%

Manhattan, KS

59.9%

Flagstaff, AZ

58.9%

Lincoln, NE

58.4%

7

Boston-Cambridge-Newton, MA-NH

60.5%

Salinas, CA

60.7%

Flagstaff, AZ

59.3%

Houma-Thibodaux, LA

58.7%

Santa Cruz-Watsonville, CA

57.6%

8

Lincoln, NE

60.1%

Lincoln, NE

60.7%

Santa Cruz-Watsonville, CA

59.2%

Bay City, MI

58.6%

Fargo, ND-MN

56.6%

9

Oxnard-Thousand Oaks-Ventura, CA 60.0%

Manhattan, KS

60.7%

La Crosse-Onalaska, WI-MN

58.8%

Charlottesville, VA

58.0%

Lake Charles, LA

56.5%

10

Florence, SC

60.0%

Bloomington, IL

59.6%

Bloomington, IL

58.6%

Santa Cruz-Watsonville, CA

57.6%

Charlottesville, VA

56.3%

MSAs with the Lowest Share for Indicator 4

2016

2015

2014

2013

2012

10

Dalton, GA

34.2%

Tulsa, OK

34.6%

Spokane-Spokane Valley, WA

33.5%

Grants Pass, OR

32.4%

Lake Havasu City-Kingman, AZ

31.4%

9

Tulsa, OK

33.2%

Huntington-Ashland, WV-KY-OH

34.3%

Johnson City, TN

33.0%

Johnson City, TN

32.3%

Huntington-Ashland, WV-KY-OH

31.1%

8

Spokane-Spokane Valley, WA

33.0%

Dalton, GA

33.8%

Prescott, AZ

32.7%

Lake Havasu City-Kingman, AZ

32.0%

Spokane-Spokane Valley, WA

30.9%

7

Johnstown, PA

31.7%

Danville, IL

33.3%

Dalton, GA

32.6%

Sebring, FL

31.7%

Albany, OR

30.8%

6

Morgantown, WV

30.8%

Lake Havasu City-Kingman, AZ

33.2%

Longview, WA

32.4%

Kingsport-Bristol-Bristol, TN-VA

31.7%

Danville, IL

30.8%

5 Blacksburg-Christiansburg-Radford, VA 30.5%

Spokane-Spokane Valley, WA

32.9%

Carson City, NV

32.0%

Huntington-Ashland, WV-KY-OH

31.5%

Kingsport-Bristol-Bristol, TN-VA

30.6%

4

Lake Havasu City-Kingman, AZ

30.3%

Carson City, NV

32.9% Blacksburg-Christiansburg-Radford, VA 31.7%

Johnstown, PA

30.9%

Muskegon, MI

30.3%

3

Danville, IL

30.1%

Johnstown, PA

32.1%

Danville, IL

31.0%

Longview, WA

30.5% Blacksburg-Christiansburg-Radford, VA 30.0%

2

Sebring, FL

30.0% Blacksburg-Christiansburg-Radford, VA 31.9%

Johnstown, PA

30.4% Blacksburg-Christiansburg-Radford, VA 30.4%

Grants Pass, OR

29.9%

1

Enid, OK

29.9%

Enid, OK

31.5%

Enid, OK

30.0%

Danville, IL

28.6%

Sebring, FL

28.8%

Notes: The employment rate for individuals participating in HUD programs is calculated by dividing the total employed or full-time student, work-able population by the total overal work-able population across al three HUD programs and within each program. Individuals are considered work-able if they are nondisabled and ages 18 through 64; individuals are considered employed if their wage income was at least $500 in the given year ($500 represents just under 70 hours of work at the 2018 federal minimum wage of $7.25 per hour). Wage income is derived from business income, federal wages, PHA wages, military pay, and other wage income. Individuals are reported as full-time students if they are 18 years of age or older on the effective date of action and carry a subject load deemed ful time by the standards and practices of the educational institution attended.

Further clarification on HUD’s definitions of wage income or full-time student status can be found in the reporting form instructional guides (HUD, 2014; HUD PIH, 2004). Values are bolded and italicized for areas served by MTW PHAs.

Sources: HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 38

Exhibit 3-20 | Estimates for Nonmetropolitan Balances of States of Indicator 4: Percentage of HUD-Assisted, Work-Able Individuals Either Employed or in School Full Time

Indicator 4 for Nonmetropolitan Balances of States

State

2016 (%)

2015 (%)

2014 (%)

2013 (%)

2012 (%)

1

Nebraska

57.5

59.1

61.0

59.0

57.6

2

Louisiana

54.8

54.9

54.7

54.4

52.3

3

Connecticut

54.7

51.5

55.8

51.5

54.1

4

Kansas

54.2

54.9

55.7

53.8

52.5

5

Texas

53.7

55.1

54.4

53.5

52.6

6

Minnesota

52.2

52.7

52.5

51.3

49.4

7

Maryland

51.9

51.4

51.4

50.4

47.9

8

Georgia

51.8

50.6

49.1

47.4

46.5

9

Hawaii

51.4

50.7

49.8

47.4

46.9

10

Florida

51.1

49.2

47.5

45.7

44.1

11

South Dakota

50.9

51.2

49.9

52.8

48.9

12

New Hampshire

50.8

51.4

49.9

48.6

47.7

13

North Dakota

50.4

54.0

51.2

54.9

55.2

14

Wisconsin

49.8

49.9

49.2

47.6

46.5

15

Alabama

48.6

48.4

47.3

46.4

45.5

16

Il inois

48.4

49.5

49.3

48.8

48.0

17

Wyoming

48.0

52.1

51.5

52.0

49.7

18

Alaska

47.9

45.4

44.7

45.2

43.2

19

Missouri

47.8

47.4

46.5

44.0

42.4

20

Colorado

47.7

47.7

46.6

46.7

46.0

21

Iowa

47.4

49.3

49.0

47.7

46.9

22

Vermont

47.2

45.1

43.0

41.8

40.1

23

Michigan

47.1

46.2

46.3

44.6

44.5

24

South Carolina

47.1

46.4

44.9

43.5

40.9

25

Idaho

46.9

42.2

42.6

42.0

42.8

26

North Carolina

46.7

46.5

45.4

43.7

43.1

27

New York

46.7

47.3

46.2

45.9

46.1

28

Arkansas

46.0

45.9

45.6

44.2

42.1

29

New Mexico

45.6

44.7

43.4

45.1

43.3

30

Massachusetts

45.5

42.7

41.9

41.9

40.7

31

Mississippi

45.3

44.8

42.6

42.3

42.5

32

Utah

44.5

44.2

41.9

46.2

46.4

33

Arizona

44.2

44.4

40.3

42.3

36.3

34

Oklahoma

43.9

44.6

45.6

45.2

44.7

35

California

43.5

41.2

41.5

42.1

39.6

36

Montana

43.3

43.8

43.9

44.9

43.3

37

Washington

43.2

42.1

42.1

40.5

39.2

38

Indiana

42.7

43.2

41.8

40.6

39.8

39

Pennsylvania

42.1

41.4

41.8

42.5

41.1

40

Tennessee

41.6

40.6

39.9

38.0

37.1

41

Oregon

40.7

40.2

39.6

36.9

35.9

42

Virginia

40.4

40.7

39.6

38.0

37.7

43

Ohio

37.4

37.9

36.6

35.8

34.3

| 39

Indicator 4 for Nonmetropolitan Balances of States

State

2016 (%)

2015 (%)

2014 (%)

2013 (%)

2012 (%)

44

Kentucky

36.9

37.5

37.3

36.7

35.9

45

Maine

36.0

35.4

34.7

33.4

31.9

46

West Virginia

33.7

35.0

34.3

34.2

33.6

47

Nevada

33.5

32.8

33.8

36.6

35.5

Notes: The employment rate for individuals participating in HUD programs is calculated by dividing the total employed or full-time-student, work-able population by the total overal work-able population across al three HUD programs and within each program. Individuals are considered work-able if they are nondisabled and between the ages of 18 and 64; individuals are considered employed if their wage income was at least $500 in the given year ($500 represents just under 70 hours of work at the 2018 federal minimum wage of $7.25 per hour). Wage income is derived from business income, federal wages, PHA wages, military pay, and other wage income. Individuals are reported as full-time students if they are 18 years of age or older on the effective date of action and carry a subject load deemed full time by the standards and practices of the educational institution attended. Further clarification on HUD’s definitions of wage income or full-time student status can be found in the reporting form instructional guides (HUD, 2014; HUD PIH, 2004). Values are bolded and italicized for areas served by MTW PHAs.

Sources: HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 40

Exhibit 3-21 visually maps the percentage-point change in estimates of Indicator 4 for all three HUD

programs of interest, from 2012 through 2016. This map shows that estimates of the rates of HUD-assisted individuals who are either employed or full-time students increased for most MSAs (87 percent) and nonmetropolitan balance areas. The nonmetropolitan balance of Arizona and the Champaign-Urbana, IL MSA experienced large increases in their stable household employment rates (7.9 percentage points and 17.9 percentage points, respectively). In contrast, decreases in estimates of Indicator 4 in nonmetropolitan areas appear concentrated across the Midwest and the Western parts of the United States (Nevada, North Dakota, Utah, and Wyoming), as well as in several MSAs across the country. The nonmetropolitan balance of North Dakota experienced a large (4.8) percentage-point decline in the rates of employed individuals and full-time students, and the Midland, TX MSA experienced a 7.3-percentage-point decline.

Exhibit 3-21 | Change in Indicator 4 Estimate Across All Three HUD Programs, From 2012

Through 2016: Percentage of HUD-Assisted, Work-Able Individuals Either Employed or in School Full Time

Sources: HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 41

3.5 MSAs and Nonmetropolitan Balance of State Trends Across All Four Indicators

To estimate the top 10- and bottom 10-performing MSAs, each of the four indicators across all three HUD programs was added after scaling the indicators such that the highest value of each estimate is 1. 41

Exhibit 3-22 illustrates the MSAs that have consistently high and low estimates across all indicators. The top 10 MSAs may be of interest to policymakers in examining strategies employed by the PHAs that operate in the MSAs—strategies that may increase self-sufficiency and economic stability among HUD-assisted households and individuals and possibly could be applied in the bottom 10 MSAs (considering similar context). Five MSAs consistently appear in the top 10 across the 5-year period: Flagstaff, AZ; Hilton Head Island-Bluffton-Beaufort, SC; Lawrence, KS; Santa Cruz-Watsonville, CA; and Santa Maria-Santa Barbara, CA. Five MSAs appear in the bottom 10 across all 5 years: Blacksburg-Christiansburg-Radford, VA; Danville, IL; Johnstown, PA; Kingsport-Bristol-Bristol, TN-VA; and Tulsa, OK. Exhibit 3-23

illustrates the nonmetropolitan balance of each state, ranked by the sum of their estimates across all indicators. Six nonmetropolitan balances of state appear in the top 10 in each of the 5 years: Hawaii, Kansas, Louisiana, Maryland, Nebraska, and Texas. Similarly, seven states appear in the bottom 10 each year: Indiana, Kentucky, Maine, Ohio, Tennessee, Virginia, and West Virginia.

41 Other methods of scaling or weighting the indicators are possible; for example, the MSA rank values for the indicators could be averaged.

| 42

Exhibit 3-22 | MSAs with the Highest and Lowest Values for All Indicators Across All Three HUD Programs MSAs with the Highest Values Across the Four Indicators

2016

2015

2014

2013

2012

1

Lawrence, KS

Lawrence, KS

Lawrence, KS

Lawrence, KS

Lawrence, KS

2

Santa Cruz-Watsonville, CA

Manhattan, KS

Midland, TX

Midland, TX

Flagstaff, AZ

3

Champaign-Urbana, IL

Santa Cruz-Watsonville, CA

Santa Cruz-Watsonville, CA

Flagstaff, AZ

Midland, TX

4

Flagstaff, AZ

Santa Maria-Santa Barbara, CA

Santa Maria-Santa Barbara, CA

Santa Cruz-Watsonville, CA

Santa Cruz-Watsonville, CA

5

Salinas, CA

Midland, TX

Flagstaff, AZ

Santa Maria-Santa Barbara, CA

Santa Maria-Santa Barbara, CA

6

Santa Maria-Santa Barbara, CA

Hilton Head Island-Bluffton-Beaufort, SC

Manhattan, KS

Hilton Head Island-Bluffton-Beaufort, SC

Hilton Head Island-Bluffton-Beaufort, SC

7

Hilton Head Island-Bluffton-Beaufort, SC

Salinas, CA

Hilton Head Island-Bluffton-Beaufort, SC

Manhattan, KS

Visalia-Porterville, CA

8 San Luis Obispo-Paso Robles-Arroyo Grande, CA

Flagstaff, AZ

Salinas, CA

Lincoln, NE

San Luis Obispo-Paso Robles-Arroyo Grande, CA

9

Oxnard-Thousand Oaks-Ventura, CA

Logan, UT-ID

San Luis Obispo-Paso Robles-Arroyo Grande, CA

Salinas, CA

Manhattan, KS

10

Logan, UT-ID

Crestview-Fort Walton Beach-Destin, FL

Visalia-Porterville, CA

San Luis Obispo-Paso Robles-Arroyo Grande, CA

Oxnard-Thousand Oaks-Ventura, CA

MSAs with the Lowest Values Across the Four Indicators

2016

2015

2014

2013

2012

10

Cleveland, TN

Homosassa Springs, FL

Johnson City, TN

Sebring, FL

Huntington-Ashland, WV-KY-OH

9

Morgantown, WV

Huntington-Ashland, WV-KY-OH

Kingsport-Bristol-Bristol, TN-VA

Tulsa, OK

Prescott, AZ

8

Kingsport-Bristol-Bristol, TN-VA

Johnson City, TN

Tulsa, OK

Muskegon, MI

Danville, IL

7

Sebring, FL

Carson City, NV

Carson City, NV

Dalton, GA

Longview, WA

6

Lake Havasu City-Kingman, AZ

Kingsport-Bristol-Bristol, TN-VA

Prescott, AZ

Huntington-Ashland, WV-KY-OH

Tulsa, OK

5

Tulsa, OK

Tulsa, OK

Blacksburg-Christiansburg-Radford, VA

Kingsport-Bristol-Bristol, TN-VA

Johnstown, PA

4

Blacksburg-Christiansburg-Radford, VA

Blacksburg-Christiansburg-Radford, VA

Dalton, GA

Blacksburg-Christiansburg-Radford, VA

Muskegon, MI

3

Johnstown, PA

Danville, IL

Danville, IL

Longview, WA

Blacksburg-Christiansburg-Radford, VA

2

Enid, OK

Johnstown, PA

Enid, OK

Johnstown, PA

Kingsport-Bristol-Bristol, TN-VA

1

Danville, IL

Enid, OK

Johnstown, PA

Danville, IL

Enid, OK

Notes: The total value across the four indicators was estimated using a weighted total of the four indicators in each year. The weighted values of each indicator were calculated by normalizing the indicators on a scale of 0 to 1, using the maximum value for that indicator in each year, and then adding the weighted values for each indicator. Values are bolded and italicized for areas served by MTW PHAs. One MSA (The Vil ages, FL) is not included in these rankings due to lack of observations.

Sources: ACS 5-year estimates; HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 43

Exhibit 3-23 | Nonmetropolitan Balances of States, Ranked by Value Across All Indicators Nonmetropolitan Balances of States, Ranked by Value Across Al Indicators

2016

2015

2014

2013

2012

1

Hawaii

Nebraska

Nebraska

Nebraska

Nebraska

2

Nebraska

Hawaii

Louisiana

Louisiana

North Dakota

3

Louisiana

Louisiana

Hawaii

Texas

Louisiana

4

Alaska

Texas

Kansas

North Dakota

Texas

5

Connecticut

Kansas

Texas

Kansas

Connecticut

6

Texas

Maryland

Connecticut

Hawaii

Kansas

7

Florida

New Hampshire

Maryland

Maryland

Hawaii

8

Maryland

North Dakota

North Dakota

Wyoming

Alaska

9

Kansas

Wyoming

Minnesota

South Dakota

Wyoming

10

New Hampshire

Minnesota

New Hampshire

Connecticut

Maryland

11

Minnesota

Florida

Wyoming

Alaska

New Hampshire

12

Georgia

Alaska

Florida

Minnesota

Minnesota

13

South Dakota

South Dakota

South Dakota

New Hampshire

Il inois

14

North Dakota

Connecticut

Il inois

Il inois

South Dakota

15

Arizona

Georgia

Alaska

Florida

New York

16

Wyoming

Il inois

Georgia

New York

Florida

17

Alabama

Arizona

Wisconsin

Georgia

Wisconsin

18

Wisconsin

Wisconsin

Alabama

New Mexico

Georgia

19

Il inois

Alabama

New York

Alabama

Colorado

20

New York

Colorado

Michigan

Oklahoma

Alabama

21

Michigan

New York

Colorado

Wisconsin

Oklahoma

22

Colorado

Michigan

Missouri

Colorado

New Mexico

23

New Mexico

Missouri

Oklahoma

Montana

Michigan

24

Missouri

New Mexico

Iowa

Michigan

Utah

25

Vermont

Iowa

New Mexico

Iowa

Montana

26

Massachusetts

South Carolina

Washington

Missouri

Iowa

27

Washington

Montana

Arkansas

Arizona

Missouri

28

California

Oklahoma

Arizona

Utah

Washington

29

South Carolina

Arkansas

Montana

Arkansas

Idaho

30

Iowa

Vermont

California

Washington

Mississippi

31

Idaho

Washington

Utah

California

California

32

Arkansas

California

South Carolina

South Carolina

Arkansas

33

North Carolina

North Carolina

North Carolina

Pennsylvania

North Carolina

34

Montana

Utah

Idaho

Vermont

Pennsylvania

35

Mississippi

Massachusetts

Vermont

Mississippi

Massachusetts

36

Oregon

Mississippi

Mississippi

North Carolina

Nevada

37

Oklahoma

Idaho

Pennsylvania

Idaho

South Carolina

38

Tennessee

Oregon

Oregon

Massachusetts

Vermont

39

Utah

Pennsylvania

Massachusetts

Nevada

Oregon

40

Pennsylvania

Tennessee

Tennessee

Oregon

Indiana

41

Indiana

Indiana

Indiana

Indiana

Kentucky

42

Virginia

Virginia

Kentucky

Tennessee

Arizona

43

Kentucky

Kentucky

Virginia

Kentucky

Tennessee

44

Nevada

Ohio

Nevada

Virginia

Virginia

| 44

Nonmetropolitan Balances of States, Ranked by Value Across Al Indicators

2016

2015

2014

2013

2012

45

Ohio

West Virginia

Ohio

Ohio

West Virginia

46

Maine

Nevada

West Virginia

West Virginia

Ohio

47

West Virginia

Maine

Maine

Maine

Maine

Notes: The total value across the four indicators was estimated using a weighted total of the four indicators in each year.

The weighted values of each indicator were calculated by normalizing the indicators on a scale of 0 to 1, using the maximum value for that indicator in each year, and then adding the weighted values for each indicator. Values are bolded and italicized for areas served by MTW PHAs.

Sources: ACS 5-year estimates; HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 45

4 CONCLUSION

This study estimates four indicators of economic opportunity, self-sufficiency, and financial stability for work-able individuals receiving benefits from three housing assistance programs: public housing, housing choice voucher (HCV), and assisted multifamily. These indicators further our understanding of the economic wel -being of HUD-assisted households and individuals, how the economic characteristics of the HUD-assisted population compare with those of the overall population, and how to use HUD

administrative data and American Community Survey (ACS) data to estimate those metrics. Such measures of self-sufficiency and economic opportunity can help inform national and local housing policies.

OVERVIEW OF THE FINDINGS

Overall, estimates for all four indicators show increases, indicating financial stability and self-sufficiency of HUD-assisted households and individuals across each of the three HUD housing assistance programs.

Although the estimates for each program increase over time, the estimates are consistently higher for public housing and HCV programs compared with assisted multifamily programs, for reasons not examined in this study. Those gains were observed during a time of overall economic growth, so increases in the estimates of Indicators 2 and 4 over time may be expected; however, observed gains in Indicators 1 and 3 illustrate that the increases in the HUD-assisted population are greater than the increases in the overall population due to the economic growth. These indicators are promising metrics of economic opportunity that can help HUD monitor progress and improvement nationally and across metropolitan areas. Such indicators can help focus efforts and resources on public housing authorities (PHAs) and other housing providers where results are lagging relative to their metropolitan areas. These metrics can also be estimated for individual housing providers if that should prove worthwhile.

We would like to caution the reader, however, that certain data limitations on these estimates should be taken into consideration when interpreting the results. Those limitations largely stem from slight inconsistencies between HUD and ACS data definitions that may affect Indicators 1 and 3. Indicator 2

also has the limitation of relying on 3 years of data for a given household, and therefore excluding households that exit HUD programs within 3 years. Such households that exit more quickly may have relatively high levels of work stability. Furthermore, Indicator 4 potentially underestimates the population of full-time students receiving HUD assistance for public housing and vouchers due to data collection issues with that variable, and the use of “dependent” as a proxy for students in multifamily housing also has unproven reliability. Details of the limitations of each indicator are presented in Exhibit 2-2. In addition, we present one main caveat: the estimates of each indicator are descriptive and do not imply causality. For example, increases in indicators for a particular program should not be interpreted as causal relationships between that HUD program and the indicator.

Indicator 1 is the ratio of the employment rate for the work-able, HUD-assisted population to the employment rate for the work-able general population. The ratio across all three HUD programs increased by almost 6 percentage points, from 0.535 in 2012 to 0.590 in 2016. Overall, these estimates suggest a convergence between the employment rates for HUD-assisted individuals and those for the overall population.

| 46

Indicator 2 is the share of work-able, HUD-assisted households with stable employment for 3 years.

From 2012 through 2016, the stable household employment rates for Indicator 2 for public housing, HCV, and multifamily programs each increased by more than 3 percentage points.

Indicator 3 is the ratio of the full-time employment rate for the work-able, HUD-assisted population to the full-time employment rate for the work-able general population. The national-level estimates of Indicator 3 show that the ful -time employment rate for the HUD-assisted population is nearly one-half that of the overall population. Although the full-time employment rate for the HUD-assisted population is lower than that of the overall population, increases in estimates of Indicator 3 over time suggest a slight convergence in the ful -time employment rates of the two populations.

Indicator 4 is the percentage of work-able HUD-assisted individuals who are either employed or full-time students. Estimates of Indicator 4 illustrate that nearly 50 percent of the work-able, HUD-assisted population is either employed or attending school full time. From 2012 through 2016, the estimates of Indicator 4 for public housing, HCV, and multifamily programs each increased by approximately 4

percentage points, suggesting an increase in the share of HUD-assisted individuals who are either employed or in school full time.

USE FOR ACCOUNTABILITY AND POLICYMAKING

Estimates of these metrics may be used to monitor and support progress toward HUD’s agency priority goals, described in Chapter 1, and offer evidence to provide enhanced accountability for HUD’s stakeholders. For these indicators to be useful to policymakers, however, the indicators must provide useful and accurate estimates of the economic opportunities and financial stability of HUD-assisted populations in a way that complements other sources of qualitative and quantitative evidence.

These indicators may be used by federal, state, and local government agencies and other stakeholders to inform policy priorities that target areas and communities in need of assistance. Areas that have consistently demonstrated a high degree of financial stability and self-sufficiency among the program participants may potentially provide examples of policies that have been successful in making progress toward these goals (and understanding the context in which these policies may operate). For example, Lawrence, KS and Santa Cruz-Watsonville, CA have consistently high estimates across all indicators. The Champaign-Urbana, IL metropolitan statistical area (MSA), however, had the largest increase for all four indicators from 2012 to 2016. 42 Five MSAs in California (Salinas, San Luis Obispo-Paso Robles-Arroyo Grande, Santa Cruz-Watsonville, Santa Maria-Santa Barbara, and Visalia-Porterville) consistently appear in the top 10 estimates for Indicator 3. Summing all four indicators, Lawrence, KS; Santa Cruz-Watsonville, CA; Flagstaff, AZ; Santa Maria-Santa Barbara, CA; and Hilton Head Island-Bluffton-Beaufort, SC consistently appear in the top 10 MSAs across the 5 years. Local agencies, governments, or PHAs may have enacted strategies targeted at the HUD-assisted population in those MSAs that contributed to those improvements. Such strategies may be replicable for other MSAs and nonmetropolitan balances of each state to support areas in need of improvement.

These indicators may be helpful in the implementation, monitoring, and evaluation of other HUD

programs, including Section 3 and EnVision Centers, that were established to improve the labor market 42 Champaign-Urbana, IL increased from 0.561 in 2012 to 0.843 in 2016 for Indicator 1; had a 25.5 percentage point increase in Indicator 2; increased from 0.834 in 2012 to 0.959 in 2016 for Indicator 3; and experienced a 17.9 percentage point increase in Indicator 4.

| 47

outcomes of HUD-assisted populations. Section 3 of the HUD Act of 1968 was designed to promote local economic development and self-sufficiency among HUD program participants. Section 3 supplies the framework “for providing jobs for residents and awarding contracts to businesses in areas receiving certain types of HUD financial assistance” (HUD, n.d.). EnVision Centers were recently established to ensure that HUD-assisted individuals have access to support services in the areas of economic empowerment, educational advancement, health and wellness, and character and leadership (HUD, 2018a). If a relatively low share of the HUD-assisted population is employed, based on the indicators, then Section 3 and EnVision Centers could be used to target labor market opportunities for HUD-assisted populations in those areas. The success of other programs such as Family Self-Sufficiency and Jobs-Plus also can be assessed partly by using these indicators to characterize tenants’ self-sufficiency relative to the economic context of their metropolitan areas and relative to national program averages.

The findings from this study provide key insights into estimating four indicators related to self-sufficiency, financial stability, and economic opportunity for HUD-assisted populations at the national level for each MSA and nonmetropolitan balance of state. These indicators illustrate that the labor market and employment outcomes of the HUD-assisted population have improved since 2012. The indicators can be helpful to assess the economic opportunities of the HUD-assisted population and can be easily adapted in the monitoring and evaluations of other HUD programs geared toward providing employment opportunities, thereby supporting the goals of increasing self-sufficiency and financial stability among HUD-assisted populations.

| 48

APPENDIX A—DETAILS OF DATA-CLEANING

PROCESS

The study team conducted the data-cleaning process in three stages to address potential data duplication, usability, and adjustments for project-specific requirements. The U.S Department of Housing and Urban Development (HUD) provided longitudinal data files developed from household records contained in their Inventory Management System/Public and Indian Housing Information Center (IMS/PIC) system and Tenant Rental Assistance Certification System (TRACS). Programmatic data on participating households and individual household members were extracted from these sources, harmonized, and provided to the study team as annual datasets for years 2010 through 2016. Each data source was cleaned separately (Stage 1), merged (Stage 2), and then prepared for the estimation of each indicator (Stage 3). A few differences existed in the data-cleaning process for each indicator. For example, because households serve as the unit of analysis for Indicator 2, this indicator required slightly different assumptions for preprocessing, as work-ability was determined at the household level. A description of our data-cleaning process follows, and Exhibit A-1 provides a visual depiction of the data-cleaning process.

Stage 1: This stage involved cleaning the household and household member datasets from 2010 through 2016. The first step was to drop head-of-household observations (in the household dataset) that were exact duplicates in a given year, 43 followed by keeping only the most recent observations that were duplicates in terms of the head-of-household ID and the corresponding source of data44 so that only one observation remained for each household in each year. In the final step of cleaning the household dataset, we dropped any observations with missing geographic identifiers for counties, as these observations could not be aggregated to the metropolitan statistical area level. Exact figures on the number of dropped observations for each step are provided in Exhibit A-2.

For the household member dataset for Indicators 1, 3, and 4, our first step was to keep observations for populations ages 18 through 64 for each yearly dataset. Those members outside the age range of interest were not dropped for cleaning the household member file for Indicator 2. Next, for all four indicators, the study team checked for duplicate observations in terms of all variables (“exact duplicates”) and, later, for any head-of-household-member duplicates. Similar to the procedures conducted in cleaning the head-of-household file, household-member duplicate observations were dropped so that only one observation remained for each household member in each year. Exact figures on the number of dropped observations for each procedure are provided in Exhibit A-3 and Exhibit A-4.

Stage 2: The second stage of data cleaning centered around addressing observations outside the 50

states and the District of Columbia. After merging the 2017 Core Based Statistical Area (CBSA) delineation file with the head-of-household datasets, observations from U.S. territories were dropped using three geographic indicators. Using multiple geographic indicators allowed observations with inconsistent or missing geographical characteristics to be more certainly excluded. Because the 43 “Exact duplicates” are duplicate observations in terms of al variables.

44 The longitudinal dataset defines data sources by form types: in IMS/PIC data, “58” for form HUD-50058, “MTW” for form HUD-50058 MTW, and “TRACS” for HUD-50059 forms.

| 49

delineation variable would be present only for counties within a CBSA, additional HUD variables for state-level Federal Information Processing Standards (FIPS) codes and abbreviations were also used.

For the final step in Stage 2, the study team matched the current records with a list of Moving to Work (MTW) public housing authorities and merged together the current head-of-household datasets with their corresponding member files. Member records without matching head-of-household ID

observations were dropped45 because the head-of-household file contains geographic and other household information necessary for analysis. Exact figures for the number of dropped observations for each procedure and merge results are provided in Exhibit A-5.

Stage 3: The final stage of data cleaning adjusted the merged files to reflect nondisabled populations of HUD participants in the three programs of interest (public housing, housing choice voucher [HCV], and assisted multifamily). This meant that observations were dropped if either the member was disabled or the records did not align with the program categories outlined in Exhibit A-6. In addition, end-of-participation transactions were dropped at this stage because such households exited HUD programs during the year of interest. Counts of dropped observations for Indicators 1, 3, and 4 are provided in Exhibit A-7 and for Indicator 2 in Exhibit A-8.

In Exhibits A-9 and A-10, we present the variables used from the HUD longitudinal file for the key terms listed in Exhibit 2-1, namely work-ability, income, and full-time student status.

45Member records may not have an associated head-of-household during the merge process because associated head-of-household observations were dropped due to missing geographic identifiers or if those member records were from U.S.

territories.

| 50

Exhibit A-1 | Overview of the Data-Cleaning Process

| 51

Exhibit A-2 | Head-of-Household Data Cleaning in Stage 1

Observations

Procedure

Total

2010

2011

2012

2013

2014

2015

2016

Initial Head-of-Household Count

5,516,738

5,585,021 5,521,627 5,421,263 5,551,798 5,625,535

5,561,494

38,783,476

Drop exact duplicates

—

1

303

—

—

—

13,089

13,393

For observations with household and

form duplicates, keep observations

4

11

4

—

—

—

—

19

with most recent effective date

For observations with household and

form duplicates, keep one

observation in which effective dates

63

5

54

8

91

3

1,271

1,495

are the same

For observations with duplicate

households, drop HUD-50058 MTW

81,244

73,970

4,880

3,537

8,081

10,471

7,015

189,198

observationsa

For observations with duplicate

households, keep observations with

48,829

49,653

46,143

43,363

43,467

53,027

49,700

334,182

most recent effective date

For observations with duplicate

households, drop Form HUD-50058

observations for which effective dates

2,846

2,470

2,554

2,229

2,205

2,373

2,364

17,041

are the sameb

Drop observations of households with

missing geographic identifiers

288,874

287,780

290,837

246,508

318,400

332,604

279,297

2,044,300

Stage 1 Final Head-of-Household

Count

5,094,878

5,171,131 5,176,852 5,125,618 5,179,554 5,227,057

5,208,758

36,183,848

— = 0.

a Such cases occur when the household transaction is reported in more than one database or form (Form HUD-50058, HUD-50058 MTW, and TRACS), so household transactions are observed in HUD-50058 MTW and Form HUD-50058 and/or TRACS. In those cases, we dropped the observations associated with HUD-50058 MTW, given that the household characteristics were also associated with the Form HUD-50058 and TRACS observations.

b This situation can occur when the transaction is reported in more than one database (that is, PIC and TRACS). Since effective dates of the transactions do not differ, we retain the observation related to the multifamily program reported in the TRACS database.

| 52

Exhibit A-3 | Household Member Data Cleaning in Stage 1 for Indicators 1, 3, and 4

Observations

Procedure

Total

2012

2013

2014

2015

2016

Initial Member Count

11,519,661

11,437,290

11,642,024

11,714,692

11,505,261

57,818,928

Drop members younger than 18 years old or older than 64

years olda

6,124,588

6,060,770

6,130,848

6,163,799

6,076,727

30,556,732

Drop exact duplicates

—

—

—

—

—

0

For observations with duplicate household-member, drop

HUD-50058 MTW observations

5

8

35

51

11

110

For observations with duplicate household-member, drop

Form HUD-50058 observations

68

71

60

81

67

347

Stage 1 Final Member Count for Indicators 1, 3, and 4

5,395,010

5,376,441

5,511,081

5,550,761

5,428,456

27,261,749

— = 0.

a These members are dropped at this stage only for Indicators 1, 3, and 4 because those indicators rely on a work-able member-level dataset. In contrast, Indicator 2 relies on a household-level dataset, and dropping such members would potential y remove heads of household that fal outside the age range but have work-able members in their households. See Exhibit A-4 for the Stage 1 data-cleaning process for Indicator 2.

Exhibit A-4 | Household Member Data Cleaning in Stage 1 for Indicator 2

Observations

Procedure

Total

2010

2011

2012

2013

2014

2015

2016

Initial Member Count

11,585,860 11,703,896 11,519,661 11,437,290 11,642,024 11,714,692 11,505,261 81,108,684

Drop exact duplicates

—

—

—

—

—

—

—

0

For observations with duplicate household-

member, drop HUD-50058 MTW

2,349

1,607

100

106

255

326

188

4,931

observations

For observations with duplicate household-

member, drop Form HUD-50058

1,618

1,758

1,550

1,314

1,313

1,812

1,667

11,032

observations

Stage 1 Final Member Count for Indicator 2 11,581,893 11,700,531 11,518,011 11,435,870 11,640,456 11,712,554 11,503,406 81,092,721

— = 0.

| 53

Exhibit A-5 | Additional Head-of-Household Data Cleaning and Household-Member Merge in Stage 2

Observations

Procedure

Total

2010

2011

2012

2013

2014

2015

2016

Stage 1 Final Head-of-

Household

5,094,878

5,171,131

5,176,852

5,125,618

5,179,554

5,227,057

5,208,758

36,183,848

Drop observations from U.S.

territories (using delineation

104,378

106,759

106,978

106,448

106,001

106,635

106,565

743,764

file variable “state”)

Drop observations from U.S.

territories (unit_fips_state_cd)

5,713

5,829

5,904

6,070

9,282

9,553

9,536

51,887

Drop observations from U.S.

territories (unit_state_cd), and

5

8

5

14

17

1

5

55

drop missing observations

Head-of-Household

Observations Before Merging

4,984,782

5,058,535

5,063,965

5,013,086

5,064,254

5,110,868

5,092,652

35,388,142

Member Files

Members After Household-

Member Merge for Indicators

—

—

6,287,261

6,238,478

6,316,843

6,360,792

6,311,761

31,515,135

1, 3, and 4

Members with Matching Head-

of-Household for Indicators

—

—

4,988,989

4,954,895

5,011,275

5,025,836

4,966,545

34,668,507

1,3, and 4a

Members After Household-

Member Merge for Indicator 2 10,518,376 10,651,634 10,692,385 10,559,180 10,624,284 10,656,968

10,560,152

53,092,969

Members with Matching Head-

of-Household for Indicator 2a

10,430,858

10,570,595

10,646,012

10,537,126

10,604,359

10,632,930

10,539,646

73,961,524

— = 0.

a Member records may not have an associated head of household during the merge process as a result of heads of household being dropped through the data-cleaning process. For example, head-of-household observations were dropped due to observations with missing geographic identifiers or if the member records were from U.S.

territories.

| 54

Exhibit A-6 | List of HUD Program and Subsidy Categories Included Program or Subsidy Category

Specific Program or Subsidy Type

Public Housing

P = Public Housing

CE = Section 8 Certificates

H = MTW Homeownership Voucher

Housing Choice Voucher

PR = MTW Project-Based Voucher

T = MTW Tenant-Based Voucher

VO = Section 8 Vouchers

H1 = Project-Based Section 8

H2 = Rent Supplement

H3 = RAP

Multifamily

H6 = HUD-Owned/Held

H7 = Section 202 PRAC

H8 = Section 811 PRAC

H9 = Section 202/162 PRAC

Notes: Specific program type (in IMS/PIC) and subsidy type (in TRACS) are determined using the longitudinal file variable pgm_type_cd. Transactions related to the Moderate Rehabilitation program type (in IMS/PIC data) and the Section 236 (H4) and Below Market Interest Rate (H5) unassisted multifamily subsidy types (in TRACS data) are not included in the analysis.

| 55

Exhibit A-7 | Data Cleaning for Indicators 1, 3, and 4 in Stage 3

Observations

Procedure

Total

2012

2013

2014

2015

2016

Member Count (Row 6 of Exhibit A-5)

6,287,261

6,238,478

6,316,843

6,360,792

6,311,761

31,515,135

Drop observations not categorized as Public

Housing, HCV, or Assisted Multifamily

94,411

88,358

79,642

70,208

62,056

394,675

Drop observations with end-of-participation

codes

622,875

622,924

669,080

691,586

662,974

3,269,439

Drop heads of household younger than 18

years old or older than 64 years old

1,142,061

1,136,598

1,155,318

1,179,710

1,195,120

5,808,807

Drop observations of disabled members

(mbr_dsblty_indr=“Y” or missing)

1,323,123

1,318,200

1,329,452

1,337,606

1,338,732

6,647,113

Work-Able Members for Indicators 1, 3, and 4

3,104,791

3,072,398

3,083,351

3,081,682

3,052,879

15,395,101

Exhibit A-8 | Data Cleaning for Indicator 2 in Stage 3

Observations

Procedure

Total

2012

2013

2014

2015

2016

Member Count (Row 8 of Exhibit A-5)

10,692,385

10,559,180

10,624,284

10,656,968

10,560,152

53,092,969

Drop observations not categorized as Public

Housing, HCV, or Assisted Multifamily

136,758

126,579

113,782

99,650

87,296

564,065

Drop observations with end-of-participation

codes

1,064,891

1,066,689

1,129,464

1,154,723

1,115,550

5,531,317

Stage 3 Final Member Count

9,490,736

9,365,912

9,381,038

9,402,595

9,357,306

46,997,587

Stage 3 Final Household Count

4,467,738

4,425,166

4,455,338

4,494,405

4,498,112

22,340,759

Work-Able Households for Indicator 2

2,328,502

2,296,688

2,300,748

2,306,481

2,291,047

11,523,466

| 56

Exhibit A-9 | Variables Used to Determine Work-Ability

Variable

Description

Variable Name

Criteria

Variable Source

Age

mbr_age_yr_cnt

Ages 18 through 64

HUD

Disability

mbr_dsblty_indr

Not disabled

HUD

Exhibit A-10 | Variables for Each Indicator

Indicator

Variable Needed

Criteria

Variable

Unit of

Source

Analysis

1. Ratio between

HUD-assisted and

overall population mbr_wage_incm_amnt Greater than $500

HUD

Individual

employment rates

2. Percentage of

HUD-assisted,

work-able

households

total_wage_incm_amnt Greater than $500 per year over the

having stable

past 3 years

HUD

Household

employment over

3 years

3. Ratio between

HUD-assisted and

overall population mbr_wage_incm_amnt Greater than $12,500

HUD

Individual

rates of full-time

employment

4. Percentage of

HUD-assisted,

mbr_rltn_cd

E = ful -time student 18+ (IMS/PIC)

work-able

D = dependent or other child (TRACS)

individuals who

HUD

Individual

are either working mbr_wage_incm_amnt Greater than $500

or in school

| 57

APPENDIX B—QUARTILE MEANS OF MSA-LEVEL

EMPLOYMENT AND FULL-TIME EMPLOYMENT

RATES FOR HUD-ASSISTED AND OVERALL

POPULATION

Exhibit B-1 | Mean Employment Rate of the HUD-Assisted Population (Numerator of Indicator 1) by Quartile of MSA-Level Estimates of Indicator 1

Across Al Three HUD Programs

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.339

0.389

0.420

0.461

2013

0.353

0.398

0.433

0.469

2014

0.367

0.415

0.446

0.482

2015

0.381

0.426

0.454

0.491

2016

0.387

0.428

0.457

0.499

Public Housing

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.348

0.431

0.473

0.540

2013

0.367

0.439

0.486

0.555

2014

0.385

0.463

0.502

0.572

2015

0.407

0.474

0.516

0.584

2016

0.412

0.482

0.517

0.581

Housing Choice Voucher

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.338

0.394

0.429

0.487

2013

0.350

0.404

0.446

0.493

2014

0.364

0.414

0.459

0.505

2015

0.382

0.428

0.470

0.510

2016

0.394

0.434

0.474

0.520

Multifamily

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.257

0.306

0.362

0.428

2013

0.270

0.319

0.375

0.438

2014

0.281

0.334

0.387

0.454

2015

0.285

0.339

0.394

0.459

2016

0.285

0.346

0.400

0.458

Notes: This table presents the mean employment rates of the HUD-assisted, work-able population for MSAs and nonmetropolitan balances of each state in the given quartile of Indicator 1. The MSA-level employment rates for the HUD-assisted population are the numerators used to calculate Indicator 1. Quartile-level mean estimates of employment rates are weighted by the number of households served by the HUD program in each MSA or nonmetropolitan balance of state in that quartile.

So

urces: HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 58

Exhibit B-2 | Mean Employment Rate of the Overal MSA Population (Denominator of Indicator 1) by Quartile of MSA-Level Estimates of Indicator 1

Across Al Three HUD Programs

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.744

0.753

0.745

0.735

2013

0.742

0.740

0.742

0.729

2014

0.744

0.745

0.740

0.734

2015

0.752

0.754

0.747

0.736

2016

0.763

0.759

0.750

0.747

Public Housing

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.750

0.754

0.733

0.728

2013

0.744

0.746

0.731

0.718

2014

0.749

0.748

0.732

0.719

2015

0.758

0.745

0.741

0.726

2016

0.767

0.757

0.745

0.730

Housing Choice Voucher

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.739

0.753

0.742

0.736

2013

0.735

0.739

0.743

0.730

2014

0.740

0.737

0.747

0.728

2015

0.746

0.744

0.754

0.732

2016

0.760

0.750

0.754

0.752

Multifamily

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.749

0.739

0.749

0.751

2013

0.744

0.731

0.744

0.746

2014

0.745

0.739

0.744

0.749

2015

0.748

0.753

0.750

0.751

2016

0.757

0.760

0.760

0.753

Notes: This table presents the mean employment rates of the overal work-able population for MSAs and nonmetropolitan balances of each state in the given quartile of Indicator 1. The MSA-level employment rates for the overal population are the denominators used to calculate Indicator 1. Quartile-level mean estimates of employment rates are weighted by the number of households served by the HUD program in each MSA or nonmetropolitan balance of state in that quartile.

Sources: American Community Survey (ACS) 5-year estimates; HUD IMS/PIC and TRACS data from HUD longitudinal tenant da

ta files

| 59

Exhibit B-3 | Mean Full-Time Employment Rate of the HUD-Assisted Population (Numerator of Indicator 3) by Quartile of MSA-Level Estimates of Indicator 3

Across Al Three HUD Programs

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.157

0.194

0.221

0.284

2013

0.163

0.204

0.233

0.293

2014

0.178

0.217

0.248

0.305

2015

0.193

0.238

0.264

0.325

2016

0.203

0.247

0.278

0.340

Public Housing

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.170

0.225

0.270

0.344

2013

0.181

0.237

0.280

0.349

2014

0.195

0.251

0.293

0.363

2015

0.208

0.269

0.320

0.380

2016

0.217

0.285

0.334

0.391

Housing Choice Voucher

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.158

0.197

0.228

0.274

2013

0.170

0.208

0.240

0.280

2014

0.179

0.219

0.258

0.308

2015

0.192

0.237

0.271

0.313

2016

0.204

0.257

0.288

0.332

Multifamily

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.095

0.127

0.163

0.240

2013

0.101

0.139

0.174

0.250

2014

0.111

0.153

0.197

0.272

2015

0.126

0.171

0.213

0.285

2016

0.134

0.182

0.234

0.307

Notes: This table presents the mean full-time employment rates of the HUD-assisted, work-able population for MSAs and nonmetropolitan balances of each state in the given quartile of Indicator 3. The MSA-level employment rates for the HUD-assisted population are the numerators used to calculate Indicator 3. Quartile-level mean estimates of full-time employment rates are weighted by the number of households served by the HUD program in each MSA or nonmetropolitan balance of state in that quartile.

Sources: HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 60

Exhibit B-4 | Mean Full-Time Employment Rate of the Overal MSA Population (Denominator of Indicator 3) by Quartile of MSA-Level Estimates of Indicator 3

Across Al Three HUD Programs

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.519

0.533

0.533

0.542

2013

0.511

0.529

0.529

0.533

2014

0.521

0.526

0.533

0.530

2015

0.529

0.539

0.535

0.536

2016

0.538

0.547

0.545

0.541

Public Housing

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.532

0.535

0.528

0.541

2013

0.526

0.528

0.520

0.535

2014

0.532

0.530

0.519

0.536

2015

0.539

0.534

0.531

0.541

2016

0.546

0.545

0.538

0.545

Housing Choice Voucher

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.515

0.529

0.537

0.538

2013

0.513

0.524

0.532

0.526

2014

0.513

0.527

0.532

0.527

2015

0.520

0.531

0.542

0.532

2016

0.534

0.548

0.545

0.536

Multifamily

First Quartile

(Lowest)

Second Quartile

Third Quartile

Fourth Quartile

(Highest)

2012

0.534

0.535

0.527

0.536

2013

0.527

0.530

0.524

0.529

2014

0.525

0.538

0.527

0.530

2015

0.536

0.543

0.531

0.536

2016

0.544

0.549

0.544

0.543

Notes: This table presents the mean full-time employment rates of the overal , work-able population for MSAs and nonmetropolitan balances of each state in the given quartile of Indicator 3. The MSA-level employment rates for the overal population are the numerators used to calculate Indicator 3. Quartile-level mean estimates of full-time employment rates are weighted by the number of households served by the HUD program in each MSA in that quartile.

Sources: American Community Survey (ACS) 5-year estimates; HUD IMS/PIC and TRACS data from HUD longitudinal tenant da

ta files

| 61

APPENDIX C—INDICATOR ESTIMATES FOR NONMETROPOLITAN

BALANCES OF STATES, BY YEAR

Exhibit C-1 | Estimates of Indicator 1 for Nonmetropolitan Balances of States, 2012 Through 2016

Indicator 1 for Nonmetropolitan Balances of States

2016

2015

2014

2013

2012

1

Louisiana

0.753

Louisiana

0.759

Louisiana

0.760

Louisiana

0.746

Louisiana

0.706

2

Texas

0.703

Texas

0.726

Texas

0.717

Texas

0.704

Texas

0.681

3

Georgia

0.697

Georgia

0.683

Nebraska

0.709

Nebraska

0.686

Nebraska

0.669

4

Florida

0.689

Nebraska

0.683

Kansas

0.674

Kansas

0.651

North Dakota

0.652

5

Hawaii

0.672

Hawaii

0.674

Georgia

0.669

Georgia

0.645

Kansas

0.629

6

Nebraska

0.663

Arizona

0.673

Florida

0.657

North Dakota

0.644

Georgia

0.621

7

Arizona

0.657

Florida

0.672

Hawaii

0.655

South Dakota

0.643

Utah

0.620

8

Alabama

0.650

Kansas

0.660

Maryland

0.648

Maryland

0.641

Wyoming

0.612

9

Kansas

0.649

Alabama

0.650

Wyoming

0.636

Wyoming

0.639

Hawaii

0.610

10

New Mexico

0.646

Wyoming

0.646

Connecticut

0.635

Arizona

0.632

Connecticut

0.609

11

Maryland

0.641

South Carolina

0.639

Michigan

0.632

New Mexico

0.629

Michigan

0.609

12

South Carolina

0.632

Maryland

0.634

Il inois

0.631

Hawaii

0.626

Il inois

0.606

13

Alaska

0.631

North Dakota

0.632

Alabama

0.629

Il inois

0.625

New Mexico

0.599

14

Michigan

0.627

New Mexico

0.630

Minnesota

0.623

Florida

0.624

Florida

0.596

15

South Dakota

0.618

Il inois

0.629

South Carolina

0.620

Minnesota

0.616

Oklahoma

0.587

16

Missouri

0.614

South Dakota

0.624

Arizona

0.612

Michigan

0.613

Alabama

0.587

17

Il inois

0.612

Michigan

0.622

South Dakota

0.608

Utah

0.611

Minnesota

0.586

18

Minnesota

0.611

Minnesota

0.619

Missouri

0.607

Alabama

0.611

South Dakota

0.585

19

Connecticut

0.609

Missouri

0.613

New Mexico

0.607

Oklahoma

0.599

Idaho

0.573

20

Idaho

0.606

Arkansas

0.609

Oklahoma

0.604

South Carolina

0.597

Maryland

0.572

21

Arkansas

0.604

North Carolina

0.604

Arkansas

0.603

Wisconsin

0.585

New York

0.568

22

North Carolina

0.602

Wisconsin

0.604

Wisconsin

0.602

Montana

0.584

Wisconsin

0.566

23

Wisconsin

0.597

New Hampshire

0.599

North Dakota

0.601

Alaska

0.580

South Carolina

0.557

24

Mississippi

0.596

New York

0.597

North Carolina

0.589

Arkansas

0.580

New Hampshire

0.556

25

Wyoming

0.596

Alaska

0.597

New Hampshire

0.585

New York

0.574

Alaska

0.555

| 62

Indicator 1 for Nonmetropolitan Balances of States

2016

2015

2014

2013

2012

26

North Dakota

0.586

Mississippi

0.593

Alaska

0.582

Missouri

0.572

Montana

0.554

27

New York

0.586

Colorado

0.589

New York

0.579

Connecticut

0.570

North Carolina

0.549

28 New Hampshire 0.583

Oklahoma

0.587

Iowa

0.578

Idaho

0.566

Mississippi

0.548

29

California

0.582

Iowa

0.582

Idaho

0.576

North Carolina

0.565

Iowa

0.548

30

Colorado

0.579

Connecticut

0.578

Colorado

0.570

Colorado

0.564

Arkansas

0.547

31

Oklahoma

0.576

Montana

0.570

Montana

0.569

New Hampshire

0.564

Missouri

0.546

32

Washington

0.569

Washington

0.569

California

0.568

Iowa

0.561

Colorado

0.545

33

Montana

0.559

Utah

0.567

Mississippi

0.566

Mississippi

0.560

Washington

0.528

34

Iowa

0.556

California

0.563

Washington

0.564

California

0.556

Pennsylvania

0.516

35

Tennessee

0.552

Idaho

0.555

Utah

0.563

Washington

0.541

California

0.511

36

Vermont

0.551

Tennessee

0.541

Tennessee

0.540

Pennsylvania

0.539

Arizona

0.505

37

Utah

0.548

Oregon

0.537

Oregon

0.534

Tennessee

0.516

Indiana

0.496

38

Oregon

0.544

Indiana

0.532

Pennsylvania

0.526

Indiana

0.511

Tennessee

0.492

39 Massachusetts

0.527

Vermont

0.527

Indiana

0.522

Kentucky

0.503

Kentucky

0.489

40

Pennsylvania

0.526

Pennsylvania

0.520

Kentucky

0.513

Oregon

0.495

Oregon

0.478

41

Indiana

0.516

Virginia

0.513

Virginia

0.505

Nevada

0.495

Nevada

0.476

42

Virginia

0.506

Kentucky

0.509

Vermont

0.499

Vermont

0.484

Virginia

0.466

43

Kentucky

0.501

Massachusetts

0.498

West Virginia

0.482

West Virginia

0.479

West Virginia

0.464

44

West Virginia

0.471

West Virginia

0.494

Ohio

0.463

Massachusetts

0.477

Massachusetts

0.458

45

Ohio

0.461

Ohio

0.469

Massachusetts

0.461

Virginia

0.476

Vermont

0.454

46

Nevada

0.443

Nevada

0.447

Nevada

0.449

Ohio

0.452

Ohio

0.429

47

Maine

0.435

Maine

0.441

Maine

0.432

Maine

0.410

Maine

0.390

Notes: The general population employment rate is calculated by dividing the employed, nondisabled population by the total nondisabled population. This ratio reflects nondisabled, noninstitutionalized civilian populations ages 18 through 64. The Census Bureau classifies respondents as “Employed” if they are either “at work” or “with a job but not at work” during the reference week. Further clarification on employment and disability status can be found at the fol owing link: https://www2.census.gov/programs-

surveys/acs/tech_docs/subject_definitions/2017_ACSSubjectDefinitions.pdf.

The employment rate for individuals participating in HUD programs is calculated by dividing the employed, work-able population by the overal work-able population across al three HUD programs and within each program. Individuals are considered work-able if they are nondisabled and ages 18 through 64; individuals are considered employed if their wage income was at least $500 in the given year ($500 represents just under 70 hours of work at the 2018 federal minimum wage of $7.25 per hour). Wage income is derived from business income, federal wages, Public Housing Authority (PHA) wages, and other wage income. Further clarification on HUD’s reported wage income can be found in the reporting form instructional guides (HUD, 2014; HUD PIH, 2004). Values are bolded and italicized for areas served by Moving to Work (MTW) PHAs.

Sources: ACS 5-year estimates; HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 63

Exhibit C-2 | Estimates of Indicator 2 for Nonmetropolitan Balances of States, 2012 Through 2016

Indicator 2 for Nonmetropolitan Balances of States

2016 (%)

2015 (%)

2014 (%)

2013 (%)

2012 (%)

1

Hawaii

53.9

Nebraska

52.9

Nebraska

51.9

Nebraska

50.4

Nebraska

47.4

2

Nebraska

52.6

Hawaii

50.5

Hawaii

49.6

Hawaii

46.9

Kansas

45.4

3

Kansas

47.3

Kansas

46.9

Kansas

46.4

Kansas

45.5

Hawaii

44.9

4

New Hampshire

46.4

New Hampshire

46.4

North Dakota

46.1

North Dakota

43.9

North Dakota

44.8

5

Connecticut

45.2

Louisiana

45.4

New Hampshire

44.9

Texas

43.4

Texas

42.8

6

Minnesota

44.8

Minnesota

44.8

Texas

43.6

New Hampshire

42.5

Minnesota

40.7

7

Texas

44.6

Texas

44.6

Louisiana

43.4

Connecticut

42.2

New Hampshire

40.7

8

Wyoming

44.6

North Dakota

44.5

Minnesota

42.7

Louisiana

42.0

Louisiana

40.6

9

Louisiana

43.7

Wyoming

43.4

Connecticut

42.2

South Dakota

41.1

Maryland

39.8

10

Alaska

43.3

Maryland

43.3

Maryland

41.8

Minnesota

40.7

New York

39.6

11

South Dakota

42.5

South Dakota

43.0

South Dakota

41.2

Maryland

40.0

Wyoming

38.7

12

Maryland

42.0

Utah

40.8

Wisconsin

39.5

Wyoming

39.8

South Dakota

38.4

13

North Dakota

41.9

Wisconsin

40.4

Wyoming

38.8

New York

39.5

Il inois

38.2

14

Wisconsin

41.6

Florida

39.9

New York

38.6

Il inois

38.5

Connecticut

38.0

15

Florida

41.5

Alaska

39.7

Il inois

38.5

Iowa

36.6

Wisconsin

37.4

16

Il inois

40.1

Connecticut

39.2

Utah

38.0

Wisconsin

36.4

Colorado

37.0

17

New York

39.9

Arizona

39.0

Florida

37.9

Oklahoma

36.3

Florida

35.8

18

Iowa

39.4

Il inois

38.9

Iowa

37.6

Colorado

36.2

Michigan

35.4

19

Arizona

39.4

Colorado

38.6

Colorado

37.2

Michigan

36.2

Iowa

35.1

20

Georgia

38.4

Missouri

38.6

Idaho

37.1

Missouri

36.0

Alaska

35.0

21

Missouri

38.4

New York

38.4

Michigan

37.1

Florida

35.9

Oklahoma

34.5

22

Michigan

38.3

Iowa

37.6

Missouri

36.9

Alaska

35.2

New Mexico

34.3

23

Colorado

38.1

Michigan

37.3

Georgia

35.9

New Mexico

35.1

Georgia

34.0

24

Massachusetts

38.0

Georgia

36.7

New Mexico

35.4

Georgia

34.8

Missouri

33.6

25

New Mexico

37.1

New Mexico

36.5

Oklahoma

35.1

Pennsylvania

34.7

Pennsylvania

33.5

26

Washington

35.9

Massachusetts

36.3

Arizona

34.6

Montana

34.2

Nevada

33.3

27

Vermont

35.7

Oklahoma

35.4

Alaska

34.4

Alabama

33.4

California

33.0

28

California

35.7

Montana

35.2

Alabama

34.0

Idaho

32.6

Alabama

33.0

29

Alabama

35.5

Vermont

35.1

Pennsylvania

33.9

Washington

32.3

Montana

32.8

30

Arkansas

35.4

California

35.0

Washington

32.9

Utah

32.1

Idaho

32.8

| 64

Indicator 2 for Nonmetropolitan Balances of States

2016 (%)

2015 (%)

2014 (%)

2013 (%)

2012 (%)

31

Idaho

35.1

Alabama

34.6

Montana

32.8

Arkansas

32.1

Washington

32.2

32

Utah

34.1

Idaho

34.4

Massachusetts

32.7

Vermont

32.1

Massachusetts

31.4

33

Montana

34.0

Arkansas

34.0

Arkansas

32.6

Massachusetts

31.4

Utah

31.4

34

Oklahoma

33.5

Washington

33.7

California

32.5

California

31.0

Vermont

31.2

35

Pennsylvania

33.3

Pennsylvania

32.9

Vermont

32.2

Mississippi

30.8

Mississippi

31.0

36

Oregon

32.9

North Carolina

31.4

North Carolina

30.8

Nevada

30.6

Arkansas

30.7

37

North Carolina

32.2

Indiana

31.1

Tennessee

30.2

Arizona

30.2

North Carolina

29.9

38

Mississippi

31.8

Oregon

30.8

Mississippi

30.2

North Carolina

29.3

Arizona

28.5

39

Indiana

31.7

South Carolina

30.8

Oregon

29.9

South Carolina

29.0

Oregon

28.3

40

South Carolina

31.6

Tennessee

30.8

Nevada

29.7

Oregon

28.9

Kentucky

28.3

41

Tennessee

31.6

Mississippi

30.4

Indiana

29.7

Indiana

28.7

South Carolina

27.8

42

Kentucky

29.7

Nevada

29.4

South Carolina

29.4

Kentucky

27.9

Indiana

27.6

43

Virginia

29.6

Virginia

29.4

Virginia

28.7

Tennessee

27.6

Virginia

27.0

44

Nevada

29.5

Kentucky

29.4

Kentucky

28.3

Virginia

27.6

West Virginia

26.6

45

Ohio

27.8

Ohio

27.2

Ohio

26.6

West Virginia

26.0

Tennessee

25.6

46

West Virginia

27.6

West Virginia

27.0

West Virginia

26.2

Ohio

25.8

Ohio

24.9

47

Maine

26.6

Maine

26.2

Maine

24.1

Maine

23.4

Maine

22.1

Notes: The employment rate for HUD program households is calculated by dividing the employed, work-able households by the overal work-able households across al three HUD programs and within each program. Households are considered work-able if any member is nondisabled and ages 18 through 64; households are considered employed if the total wage income in the work-able household was at least $500 in the given year ($500 is just under 70 hours of work at the 2018 federal minimum wage of $7.25 per hour). Household total wage income is derived from business income, federal wages, PHA wages, military pay, and other wage income. Further clarification on HUD’s reported wage income can be found in the reporting form instructional guides (HUD, 2014; HUD PIH, 2004). Values are bolded and italicized for areas served by MTW PHAs.

Sources: HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 65

Exhibit C-3 | Estimates of Indicator 3 for Nonmetropolitan Balances of States, 2012 Through 2016

Indicator 3 for Nonmetropolitan Balances of States

2016

2015

2014

2013

2012

1

Alaska

0.746

Alaska

0.682

Alaska

0.632

Alaska

0.689

Alaska

0.689

2

Hawaii

0.630

Hawaii

0.614

Maryland

0.572

Maryland

0.545

Connecticut

0.564

3

Maryland

0.615

Maryland

0.588

Hawaii

0.556

Hawaii

0.529

Hawaii

0.492

4

Florida

0.581

New Hampshire

0.580

Connecticut

0.553

Louisiana

0.518

Louisiana

0.487

5

New Hampshire

0.578

Connecticut

0.558

Nebraska

0.541

Connecticut

0.509

Nebraska

0.482

6

Connecticut

0.569

Nebraska

0.547

Louisiana

0.528

New Hampshire

0.505

North Dakota

0.467

7

Washington

0.545

Florida

0.535

New Hampshire

0.525

North Dakota

0.504

Maryland

0.461

8

Nebraska

0.544

Wyoming

0.530

Wyoming

0.514

Wyoming

0.485

New Hampshire

0.457

9

Minnesota

0.540

Minnesota

0.523

Texas

0.506

Nebraska

0.480

Wyoming

0.448

10

Massachusetts

0.531

Louisiana

0.523

Florida

0.501

Texas

0.476

Texas

0.444

11

Louisiana

0.515

Colorado

0.511

Kansas

0.495

Florida

0.456

Il inois

0.424

12

Vermont

0.514

North Dakota

0.510

Washington

0.488

Minnesota

0.456

Florida

0.424

13

California

0.509

Texas

0.507

North Dakota

0.482

Washington

0.453

Alabama

0.417

14

Arizona

0.506

Kansas

0.507

Minnesota

0.480

Il inois

0.450

Montana

0.417

15

Colorado

0.503

Washington

0.505

California

0.464

Alabama

0.449

Washington

0.415

16

Alabama

0.499

South Dakota

0.499

Vermont

0.460

South Dakota

0.447

New York

0.412

17

Oregon

0.494

Vermont

0.491

Alabama

0.456

Kansas

0.445

Nevada

0.410

18

New York

0.490

Alabama

0.490

Colorado

0.454

Montana

0.441

Kansas

0.408

19

South Dakota

0.487

New York

0.485

Il inois

0.454

California

0.434

Minnesota

0.403

20

Texas

0.485

California

0.483

New York

0.450

Vermont

0.432

Colorado

0.401

21

Kansas

0.481

Massachusetts

0.478

South Dakota

0.443

Colorado

0.431

Oklahoma

0.393

22

Georgia

0.475

Oregon

0.477

Oklahoma

0.439

New Mexico

0.424

Massachusetts

0.391

23

North Dakota

0.473

Arizona

0.473

Montana

0.434

Oklahoma

0.421

New Mexico

0.387

24

Michigan

0.460

Montana

0.468

Oregon

0.430

New York

0.418

Vermont

0.385

25

Montana

0.453

Il inois

0.456

Wisconsin

0.422

Nevada

0.412

South Dakota

0.384

26

South Carolina

0.453

New Mexico

0.454

Massachusetts

0.419

Arizona

0.410

California

0.380

27

Wisconsin

0.447

Georgia

0.446

New Mexico

0.414

Massachusetts

0.400

Wisconsin

0.372

28

Il inois

0.446

Michigan

0.446

Georgia

0.408

Wisconsin

0.399

Georgia

0.366

29

Wyoming

0.441

South Carolina

0.439

Missouri

0.408

Oregon

0.386

Utah

0.347

30

New Mexico

0.438

Wisconsin

0.439

Arizona

0.408

Georgia

0.383

Mississippi

0.346

| 66

Indicator 3 for Nonmetropolitan Balances of States

2016

2015

2014

2013

2012

31

Arkansas

0.415

Oklahoma

0.428

Michigan

0.403

South Carolina

0.373

Missouri

0.345

32

Missouri

0.414

Missouri

0.419

South Carolina

0.397

Missouri

0.370

Oregon

0.341

33

Mississippi

0.407

Mississippi

0.412

Arkansas

0.389

Arkansas

0.367

Iowa

0.341

34

Iowa

0.405

Iowa

0.406

Nevada

0.387

Iowa

0.361

Arkansas

0.339

35

Idaho

0.405

Arkansas

0.401

North Carolina

0.379

Michigan

0.359

Michigan

0.337

36

North Carolina

0.401

North Carolina

0.400

Utah

0.372

Mississippi

0.357

North Carolina

0.335

37

Tennessee

0.401

Tennessee

0.377

Iowa

0.368

North Carolina

0.349

Pennsylvania

0.332

38

Oklahoma

0.398

Idaho

0.373

Mississippi

0.367

Pennsylvania

0.348

South Carolina

0.331

39

Nevada

0.390

Pennsylvania

0.358

Tennessee

0.363

Tennessee

0.341

Idaho

0.312

40

Pennsylvania

0.369

Nevada

0.345

Pennsylvania

0.344

Utah

0.340

Tennessee

0.303

41

Maine

0.364

Kentucky

0.339

Kentucky

0.326

Kentucky

0.311

Kentucky

0.302

42

Indiana

0.342

Indiana

0.334

Indiana

0.325

Idaho

0.307

Maine

0.275

43

Kentucky

0.332

Ohio

0.328

Maine

0.313

Indiana

0.302

Indiana

0.275

44

Ohio

0.330

Maine

0.321

Idaho

0.312

Maine

0.297

Arizona

0.274

45

Utah

0.323

Virginia

0.320

Ohio

0.300

Ohio

0.282

West Virginia

0.264

46

Virginia

0.320

West Virginia

0.303

Virginia

0.294

Virginia

0.267

Virginia

0.262

47

West Virginia

0.311

Utah

0.303

West Virginia

0.271

West Virginia

0.261

Ohio

0.256

Notes: The general population ful -time employment rate is calculated by dividing the full-time, year-round employed nondisabled population number by the total nondisabled population number. This ratio reflects nondisabled, noninstitutionalized civilian populations ages 18 through 64. The Census Bureau classifies respondents as

“Employed” if they are either at work or with a job but not at work during the reference week. “Full-time, year-round” is defined as persons who usual y worked 35 hours or more per week for 50 to 52 weeks in the past 12 months. Further clarification on employment and disability status can be found at the fol owing link:

https://www2.census.gov/programs-surveys/acs/tech_docs/subject_definitions/2017_ACSSubjectDefinitions.pdf.

The employment rate for individuals participating in HUD programs is calculated by dividing the population with ful -time employment status number by the overal work-able population number across al three HUD programs and within each program. Individuals are considered work-able if they are nondisabled and ages 18 through 64; individuals are considered ful -time if their wage income was at least $12,500 in the given year ($12,500 represents the wage income of just under 35 hours of work for at least 50 weeks per year at the 2018 federal minimum wage of $7.25 per hour). Wage income is derived from business income, federal wages, PHA wages, and other wage income. Further clarification on HUD’s reported wage income can be found in the reporting form instructional guides (HUD, 2014; HUD PIH, 2004). Values are bolded and italicized for areas served by MTW PHAs.

Sources: ACS 5-year estimates; HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 67

Exhibit C-4 | Estimates of Indicator 4 for Nonmetropolitan Balances of States, 2012 Through 2016

Indicator 4 for Nonmetropolitan Balances of States

2016 (%)

2015 (%)

2014 (%)

2013 (%)

2012 (%)

1

Nebraska

57.5

Nebraska

59.1

Nebraska

61.0

Nebraska

59.0

Nebraska

57.6

2

Louisiana

54.8

Texas

55.1

Connecticut

55.8

North Dakota

54.9

North Dakota

55.2

3

Connecticut

54.7

Louisiana

54.9

Kansas

55.7

Louisiana

54.4

Connecticut

54.1

4

Kansas

54.2

Kansas

54.9

Louisiana

54.7

Kansas

53.8

Texas

52.6

5

Texas

53.7

North Dakota

54.0

Texas

54.4

Texas

53.5

Kansas

52.5

6

Minnesota

52.2

Minnesota

52.7

Minnesota

52.5

South Dakota

52.8

Louisiana

52.3

7

Maryland

51.9

Wyoming

52.1

Wyoming

51.5

Wyoming

52.0

Wyoming

49.7

8

Georgia

51.8

Connecticut

51.5

Maryland

51.4

Connecticut

51.5

Minnesota

49.4

9

Hawaii

51.4

Maryland

51.4

North Dakota

51.2

Minnesota

51.3

South Dakota

48.9

10

Florida

51.1

New Hampshire

51.4

South Dakota

49.9

Maryland

50.4

Il inois

48.0

11

South Dakota

50.9

South Dakota

51.2

New Hampshire

49.9

Il inois

48.8

Maryland

47.9

12

New Hampshire

50.8

Hawaii

50.7

Hawaii

49.8

New Hampshire

48.6

New Hampshire

47.7

13

North Dakota

50.4

Georgia

50.6

Il inois

49.3

Iowa

47.7

Iowa

46.9

14

Wisconsin

49.8

Wisconsin

49.9

Wisconsin

49.2

Wisconsin

47.6

Hawaii

46.9

15

Alabama

48.6

Il inois

49.5

Georgia

49.1

Hawaii

47.4

Wisconsin

46.5

16

Il inois

48.4

Iowa

49.3

Iowa

49.0

Georgia

47.4

Georgia

46.5

17

Wyoming

48.0

Florida

49.2

Florida

47.5

Colorado

46.7

Utah

46.4

18

Alaska

47.9

Alabama

48.4

Alabama

47.3

Alabama

46.4

New York

46.1

19

Missouri

47.8

Colorado

47.7

Colorado

46.6

Utah

46.2

Colorado

46.0

20

Colorado

47.7

Missouri

47.4

Missouri

46.5

New York

45.9

Alabama

45.5

21

Iowa

47.4

New York

47.3

Michigan

46.3

Florida

45.7

Oklahoma

44.7

22

Vermont

47.2

North Carolina

46.5

New York

46.2

Oklahoma

45.2

Michigan

44.5

23

Michigan

47.1

South Carolina

46.4

Arkansas

45.6

Alaska

45.2

Florida

44.1

24

South Carolina

47.1

Michigan

46.2

Oklahoma

45.6

New Mexico

45.1

Montana

43.3

25

Idaho

46.9

Arkansas

45.9

North Carolina

45.4

Montana

44.9

New Mexico

43.3

26

North Carolina

46.7

Alaska

45.4

South Carolina

44.9

Michigan

44.6

Alaska

43.2

27

New York

46.7

Vermont

45.1

Alaska

44.7

Arkansas

44.2

North Carolina

43.1

28

Arkansas

46.0

Mississippi

44.8

Montana

43.9

Missouri

44.0

Idaho

42.8

29

New Mexico

45.6

New Mexico

44.7

New Mexico

43.4

North Carolina

43.7

Mississippi

42.5

30

Massachusetts

45.5

Oklahoma

44.6

Vermont

43.0

South Carolina

43.5

Missouri

42.4

| 68

Indicator 4 for Nonmetropolitan Balances of States

2016 (%)

2015 (%)

2014 (%)

2013 (%)

2012 (%)

31

Mississippi

45.3

Arizona

44.4

Idaho

42.6

Pennsylvania

42.5

Arkansas

42.1

32

Utah

44.5

Utah

44.2

Mississippi

42.6

Arizona

42.3

Pennsylvania

41.1

33

Arizona

44.2

Montana

43.8

Washington

42.1

Mississippi

42.3

South Carolina

40.9

34

Oklahoma

43.9

Indiana

43.2

Utah

41.9

California

42.1

Massachusetts

40.7

35

California

43.5

Massachusetts

42.7

Massachusetts

41.9

Idaho

42.0

Vermont

40.1

36

Montana

43.3

Idaho

42.2

Pennsylvania

41.8

Massachusetts

41.9

Indiana

39.8

37

Washington

43.2

Washington

42.1

Indiana

41.8

Vermont

41.8

California

39.6

38

Indiana

42.7

Pennsylvania

41.4

California

41.5

Indiana

40.6

Washington

39.2

39

Pennsylvania

42.1

California

41.2

Arizona

40.3

Washington

40.5

Virginia

37.7

40

Tennessee

41.6

Virginia

40.7

Tennessee

39.9

Tennessee

38.0

Tennessee

37.1

41

Oregon

40.7

Tennessee

40.6

Virginia

39.6

Virginia

38.0

Arizona

36.3

42

Virginia

40.4

Oregon

40.2

Oregon

39.6

Oregon

36.9

Oregon

35.9

43

Ohio

37.4

Ohio

37.9

Kentucky

37.3

Kentucky

36.7

Kentucky

35.9

44

Kentucky

36.9

Kentucky

37.5

Ohio

36.6

Nevada

36.6

Nevada

35.5

45

Maine

36.0

Maine

35.4

Maine

34.7

Ohio

35.8

Ohio

34.3

46

West Virginia

33.7

West Virginia

35.0

West Virginia

34.3

West Virginia

34.2

West Virginia

33.6

47

Nevada

33.5

Nevada

32.8

Nevada

33.8

Maine

33.4

Maine

31.9

Notes: The employment rate for individuals participating in HUD programs is calculated by dividing the employed or full-time student, work-able population number by the overal work-able population number across al three HUD programs and within each program. Individuals are considered work-able if they are nondisabled and ages of 18

through 64; individuals are considered employed if their wage income was at least $500 in the given year ($500 represents just under 70 hours of work at the 2018 federal minimum wage of $7.25 per hour). Wage income is derived from business income, federal wages, PHA wages, military pay, and other wage income. Individuals are reported as full-time students if they are 18 years of age or older on the effective date of action and carry a subject load deemed full-time by the standards and practices of the educational institution attended. Further clarification on HUD’s definitions of wage income or full-time student status can be found in the reporting form instructional guides (HUD, 2014; HUD PIH, 2004). Values are bolded and italicized for areas served by MTW PHAs.

Sources: HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 69

APPENDIX D—LIST OF METROPOLITAN STATISTICAL

AREAS SERVED BY MOVING TO WORK (MTW)

PUBLIC HOUSING AUTHORITIES (PHAs)

Exhibit D-1 | List of Areas That Are Served by MTW PHAs

Metropolitan Statistical Areas (and Nonmetropolitan Balance Areas) Served by MTW PHAs Akron, OH

Nonmetropolitan balance, NH

Anchorage, AK

Olympia-Tumwater, WA

Atlanta-Sandy Springs-Roswel , GA

Orlando-Kissimmee-Sanford, FL

Baltimore-Columbia-Towson, MD

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD

Barnstable Town, MA

Pittsburgh, PA

Boston-Cambridge-Newton, MA-NH

Pittsfield, MA

Boulder, CO

Portland-Vancouver-Hil sboro, OR-WA

Champaign-Urbana, IL

Providence-Warwick, RI-MA

Charlotte-Concord-Gastonia, NC-SC

Reno, NV

Chicago-Napervil e-Elgin, IL-IN-WI

Riverside-San Bernardino-Ontario, CA

Columbus, GA-AL

Salisbury, MD-DE

Dover, DE

San Antonio-New Braunfels, TX

Fairbanks, AK

San Diego-Carlsbad, CA

Lawrence, KS

San Francisco-Oakland-Hayward, CA

Lexington-Fayette, KY

San Jose-Sunnyvale-Santa Clara, CA

Lincoln, NE

Seattle-Tacoma-Bellevue, WA

Louisvil e/Jefferson County, KY-IN

Springfield, MA

Minneapolis-St. Paul-Bloomington, MN-WI

Visalia-Portervil e, CA

New Haven-Milford, CT

Washington-Arlington-Alexandria, DC-VA-MD-WV

Nonmetropolitan balance, AK

Worcester, MA-CT

Nonmetropolitan balance, MA

Exhibit D-2 | HUD-Assisted Households by Work-Able and MTW Status for Indicator 3

HUD-Assisted

HUD-Assisted,

HUD-Assisted, Work-Able

HUD-Assisted MTW Work-

Households

Work-Able

Householdsa

Able Households

Households

1 Year

2 Years

3 Years

1 Year

2 Years

3 Years

2012

4,467,738

2,328,502

398,395 404,842 1,525,265 38,260

75,262

99,324

2013

4,425,166

2,296,688

325,713 392,453 1,578,522 26,809

54,766

125,191

2014

4,455,338

2,300,748

354,778 325,439 1,620,531 28,295

41,774

151,125

2015

4,494,405

2,306,481

375,170 330,846 1,600,465 32,683

36,690

156,813

2016

4,498,112

2,291,047

359,561 339,387 1,592,099 29,040

31,090

164,921

a Households that appear in the year noted and have income information for 1, 2, or 3 previous years.

Approximately 66 percent of the households have data for al 3 years.

Sources: HUD IMS/PIC and TRACS data from HUD longitudinal tenant data files

| 70

REFERENCES

Dawkins, Casey, and Mark Miller. 2015 . A Picture of Disability and Designated Housing. U.S. Department of Housing and Urban Development, Office of Policy Development and Research. Washington, DC: HUD.

https://www.huduser.gov/portal/sites/default/files/pdf/mdrt_disability_designated_housing.pdf.

U.S. Census Bureau. n.d. “Geographic Terms and Concepts—Core Based Statistical Areas and Related Statistical Areas.” https://www.census.gov/geo/reference/gtc/gtc_cbsa.html.

———. 2016. “Metropolitan and Micropolitan, Glossary.” https://www.census.gov/programs-

surveys/metro-micro/about/glossary.html.

———. 2017. American Community Survey and Puerto Rico Community Survey: 2017 Subject Definitions.

https://www2.census.gov/programs-

surveys/acs/tech_docs/subject_definitions/2017_ACSSubjectDefinitions.pdf.

———. 2018a. “American Community Survey, Sample Size.”

https://www.census.gov/acs/www/methodology/sample-size-and-data-quality/sample-

size/index.php.

———. 2018b. Understanding and Using American Community Survey Data: What All Data Users Need to Know. Washington, DC: U.S. Government Publishing Office.

https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_hand

book_2018.pdf.

U.S. Department of Housing and Urban Development (HUD). n.d. “Section 3 Brochure.”

https://www.hud.gov/program_offices/fair_housing_equal_opp/section3/section3brochure.

———. 2014. Instructions for TRACS 202D form HUD-50059. Washington, DC: HUD.

———. 2018a. “EnVision Centers.” https://www.hud.gov/envisioncenters.

———. 2018b. Programs of HUD: Major Mortgage, Grant, Assistance, and Regulatory Programs.

https://www.huduser.gov/portal/sites/default/files/pdf/HUDPrograms2018.pdf.

———. 2018c. Strategic Plan 2018–2022.

https://www.hud.gov/sites/dfiles/SPM/documents/HUDSTRATEGICPLAN2018-2022.pdf.

U.S. Department of Housing and Urban Development (HUD), Office of Public and Indian Housing (PIH).

2004. Family Report: Form HUD-50058 Instruction Booklet.

https://www.hud.gov/sites/documents/50058I.PDF.

U.S. Department of Labor, Bureau of Labor Statistics. 2018. “Civilian Unemployment Rate [UNRATE].”

https://fred.stlouisfed.org/series/UNRATE.

| 71

| 72

Document Outline

- Acknowledgments

- Foreword

- Table of Contents

- Executive Summary

- 1 Introduction

- 2 Data Sources and Methodology

- 2.1 Data Sources

- 2.2 Methodology

- 2.3 Specifics on the Estimation of Indicators of Economic Opportunity

- Indicator 1—Ratio between HUD-Assisted and Overall Population Employment Rates

- Indicator 2—PerCENTAGE of HUD-Assisted Households with Stable Employment for 3 Years

- Indicator 3—Ratio between HUD-Assisted and Overall Population Full-Time Employment Rates

- Indicator 4—Percentage of HUD-Assisted Individuals who are either Employed or in School

- National-Level Estimates

- 2.4 Limitations of the Indicators

- 3 Results

- 3.1 Indicator 1—Ratio of HUD-Assisted to Overall Population Employment Rates

- National Estimates

- MSA and NonMetropolitan Balance of State Estimates

- 3.2 Indicator 2—Percentage of HUD-Assisted Households with Stable Employment for 3 Years

- National Estimates

- MSA and NonMetropolitan Balance of State Estimates

- 3.3 Indicator 3—Ratio Between HUD-Assisted and Overall Population Full-Time Employment Rates

- National Estimates

- MSA and NonMetropolitan Balance of State Estimates

- 3.4 Indicator 4—Percentage of HUD-Assisted Individuals Who Are Either Employed or in School

- National Estimates

- MSA and NonMetropolitan Balance of State Estimates

- 3.5 MSAs and Nonmetropolitan Balance of State Trends Across All Four Indicators

- 4 Conclusion

- Appendix A—Details of Data-Cleaning Process

- Appendix B—Quartile Means of MSA-Level Employment and Full-Time Employment Rates for HUD-Assisted and Overall Population

- Appendix C—Indicator Estimates for NonMetropolitan Balances of States, by Year

- Appendix D—List of Metropolitan Statistical Areas Served by Moving To Work (MTW) Public Housing Authorities (PHAs)

- References

Table of Contents

Acknowledgments

Foreword

Table of Contents

Executive Summary

1 Introduction

2 Data Sources and Methodology

2.1 Data Sources

2.2 Methodology

2.3 Specifics on the Estimation of Indicators of Economic Opportunity

Indicator 1—Ratio between HUD-Assisted and Overall Population Employment Rates

Indicator 2—PerCENTAGE of HUD-Assisted Households with Stable Employment for 3 Years

Indicator 3—Ratio between HUD-Assisted and Overall Population Full-Time Employment Rates

Indicator 4—Percentage of HUD-Assisted Individuals who are either Employed or in School

National-Level Estimates

2.4 Limitations of the Indicators

3 Results

3.1 Indicator 1—Ratio of HUD-Assisted to Overall Population Employment Rates

National Estimates

MSA and NonMetropolitan Balance of State Estimates

3.2 Indicator 2—Percentage of HUD-Assisted Households with Stable Employment for 3 Years

National Estimates

MSA and NonMetropolitan Balance of State Estimates

3.3 Indicator 3—Ratio Between HUD-Assisted and Overall Population Full-Time Employment Rates

National Estimates

MSA and NonMetropolitan Balance of State Estimates

3.4 Indicator 4—Percentage of HUD-Assisted Individuals Who Are Either Employed or in School

National Estimates

MSA and NonMetropolitan Balance of State Estimates

3.5 MSAs and Nonmetropolitan Balance of State Trends Across All Four Indicators

4 Conclusion

Appendix A—Details of Data-Cleaning Process

Appendix B—Quartile Means of MSA-Level Employment and Full-Time Employment Rates for HUD-Assisted and Overall Population

Appendix C—Indicator Estimates for NonMetropolitan Balances of States, by Year

Appendix D—List of Metropolitan Statistical Areas Served by Moving To Work (MTW) Public Housing Authorities (PHAs)

References