Moving to Opportunity for Fair Housing 

Demonstration Program

Final Impacts Evaluation

U.S. Department of Housing and Urban Development | Office of Policy Development and Research
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Moving to Opportunity for Fair 

Housing Demonstration Program

Final Impacts Evaluation

Prepared for:

U.S. Department of Housing and Urban Development

Office of Policy Development & Research

Prepared by:

Lisa Sanbonmatsu

Jens Ludwig, Project Director

Lawrence F. Katz, Principal Investigator

Lisa A. Gennetian

Greg J. Duncan

Ronald C. Kessler

Emma Adam

Thomas W. McDade

Stacy Tessler Lindau

with:

Matthew Sciandra

Fanghua Yang

Ijun Lai

William Congdon

Joe Amick

Ryan Gillette

Michael A. Zabek

Jordan Marvakov

Sabrina Yusuf

Nicholas A. Potter

National Bureau of Economic Research

October 14, 2011
Acknowledgments

This report was prepared under HUD Contract C–CHI–00808 to the National Bureau of Economic Research (NBER). For their 

support, advice, and open-mindedness about the study design, we are grateful to the Government Technical Representative 

for the study, Todd M. Richardson (Associate Deputy Assistant Secretary for Policy Development), as well as Mark D. Shroder 

(Associate Deputy Assistant Secretary for Research, Evaluation, and Monitoring), Kevin J. Neary (Deputy Assistant Secretary for 

Research, Evaluation, and Monitoring), Erika Poethig (Deputy Assistant Secretary for Policy Development), and Raphael Bostic 

(Assistant Secretary for Policy Development and Research). Michelle Sternthal, Jennifer Stoloff, and Ronald Wilson at HUD also 

provided valuable comments on an earlier draft of this report.

Additional support for the long-term (10–15 year) MTO follow-up study reported on here came from the National Science 

Foundation (SES–0527615), the National Institute for Child Health and Human Development (R01–HD040404, R01–HD040444), 

the Centers for Disease Control (R49–CE000906), the National Institute of Mental Health (R01–MH077026), the National Institute 

for Aging (P20–AG012180, R56–AG031259, P01–AG005842–22S1), the National Opinion Research Center’s Population Research 

Center (through R24–HD051152–04 from the National Institute of Child Health and Human Development), University of Chicago’s 

Center for Health Administration Studies, the U.S. Department of Education/Institute of Education Sciences (R305U070006), 

the John D. and Catherine T. MacArthur Foundation, the Smith Richardson Foundation, the Spencer Foundation, the Bill & 

Melinda Gates Foundation, and the Russell Sage Foundation. This study was also supported by a grant from the Annie E. Casey 

Foundation to the Urban Institute.

Outstanding advice about the design, execution, and interpretation of the MTO study was provided by our Technical Review 

Panel, which included: J. Lawrence Aber (New York University); Jeanne Brooks-Gunn (Columbia University); Charles Brown 

(University of Michigan); Thomas Cook (Northwestern University); Kenneth Dodge (Duke University); Felton Earls (Harvard 

Medical School); Ronald Ferguson (Harvard University); John Goering (City University of New York); Christopher Jencks (Harvard 

University); Kathleen Mullan Harris (University of North Carolina at Chapel Hill); Edgar Olsen (University of Virginia); Robert 

Sampson (Harvard University); Eldar Shafir (Princeton University); Laurence Steinberg (Temple University); and Jane Waldfogel 

(Columbia University). We also thank Larry Orr for feedback, and are particularly grateful to Jeffrey R. Kling for his tireless 

commitment and sage advice.

We also wish to thank numerous other collaborators who assisted in a variety of substantive and methodological components 

of the MTO study. A research team at the Urban Institute expertly developed the housing and neighborhood module for the 

long-term survey as well as a new methodology for using survey and administrative data to measure housing assistance 

receipt. Margery Turner, Susan Popkin, and Jennifer Comey provided feedback and guidance at various stages of design and 

analysis, and Megan Gallagher, David Price, and Kaitlin Franks supported measurement and analytic work. Numerous useful 

insights about the design of the long-term MTO study also came from discussions with the research team that has been 

carrying out qualitative data collection from MTO families, including Kathy Edin of Harvard University, Stefanie DeLuca of Johns 

Hopkins University, and Susan Clampet-Lundquist of St. Joseph’s University. We thank Debi McInnis and our colleagues at Abt 

Associates for their hard work tracking MTO families and for developing a method for constructing the participants’ address 

histories. Important insights about the analysis and interpretation of the medical findings in the long-term MTO data came from 

conversations with our collaborator Robert Whitaker of Temple University, as well as discussions with James Meigs of Harvard 

University. Don Rock assisted in developing a strategy for administering math and English achievement assessments to MTO 

youth, and Michelle Najarian at Educational Testing Service helped create the scaled achievement scores. Nancy Sampson and 

her research team at the Harvard Medical School constructed the mental health outcomes. William Congdon at the Brookings 

Institution was instrumental in designing many of the modules for the MTO survey.
Outstanding assistance with the development of the survey instruments, data preparation, and analysis was provided by 

Matthew Sciandra, Fanghua Yang, Ijun Lai, Joe Amick, Ryan Gillette, Michael A. Zabek, Jordan Marvakov, Sabrina Yusuf, and 

Nicholas A. Potter and former research assistants and analysts Alessandra Del Conte Dickovick, Santhi Hariprasad, Plamen 

Nikolov, Brandi Coates, and Magali Fassiotto. Janet Stein at NBER, Michael Weis at Northwestern University, and Kari Beardsley, 

Sabrina Fisher, and Aida Cavalic at the Brookings Institution provided exceptional assistance in managing the numerous grants 

that supported this study. We thank Dan Feenberg for his technical advice, Mohan Ramanujan and Len McCain for helping to 

support the technological needs of the project, and Korland Simmons for his administrative support of the project. Barbara Ray, 

owner of Hiredpen, Inc., helped with editing the report. 

The study would not have been possible without the incredible efforts of Nancy Gebler at the University of Michigan’s Survey 

Research Center (SRC) in leading the monumental task of directing, launching, and ensuring high-quality administration of 

the survey interview for thousands of MTO adults and youth. At SRC, we also thank senior project advisor Beth-Ellen Pennell; 

project manager Barbara Ward; production manager Margaret Hudson; data manager Lisa Wood; financial analyst Mary Kern; 

sampling statistician James Wagner; programmers James Hagerman and Pamela Swanson; project assistant Hongyu Johnson; 

the field team leaders Yvonne Ragland, Sima Ballinger, Ana Socarras, Beverly Hight, Susan Brown, Beth Babich, and Charlotte 

Morley; and the dedicated interviewers for their tremendous efforts.

We thank Jerry Yeo and Christine Lee at the University of Chicago for their assistance in carrying out separate analyses that 

helped validate our dried blood spot measures for health outcomes among MTO participants; John Rickford at Stanford 

University and Holly Craig at the University of Michigan for their assistance in developing measures of speech patterns; 

Michael Barr of the University of Michigan for his help designing measures of household savings behavior and access to credit 

and banking services; John List of the University of Chicago and Sendhil Mullainathan of Harvard University for assistance in 

developing measures of decision making processes; Heidi Guyer at the Health and Retirement Study for the dried blood spot 

protocols; Maryann Chandler at Flexsite Laboratories; and Eric Whitsel, leader of the biology core of the National Longitudinal 

Study of Adolescent Health, for guidance on dried blood spot measurement and assay.

We are also grateful to the large number of local, state, and federal government agencies that were willing to share the 

administrative data necessary for evaluating long-term MTO impacts.

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

We hope that the results of this study, one of the largest ever undertaken by HUD, will help guide government officials in their 

decisions about how best to improve the lives of some of our nation’s most disadvantaged residents.
Foreword

Begun in 1994, the Moving to Opportunity for Fair Housing (MTO) Demonstration Program is the most ambitious randomized 

social experiment ever conducted by HUD. The demonstration was designed to determine the benefits of offering mobility 

opportunities, with the goal of informing future voucher program policies. Over 4600 low-income families with children living in 

high-poverty central city public housing participated in this important demonstration. 

I am pleased to present the final report of the demonstration, which describes the impacts of the program ten to fifteen 

years after families enrolled in the program. This study confirms that mobility opportunities and, by extension, neighborhood 

characteristics matter for family outcomes. 

Three important themes emerge from the MTO demonstration: 

1. Lower poverty and safer neighborhoods. 

Mobility programs, which provide resources for families to move to a housing unit in a different location, result in 

families living in lower poverty neighborhoods. Both at the time of the move and at the final follow-up, families in the 

group that received rental housing vouchers without any restrictions (the “Section 8 group”) and in the group that 

received vouchers and mobility counseling but could only use those vouchers in neighborhoods with poverty rates 

below ten percent (the “experimental group”) lived in lower poverty neighborhoods than those in the group of families 

that enrolled in the demonstration but did not receive vouchers (the “control group”). The opportunity to move achieved 

an important goal of the participants: greater safety. Adults and female youths in both the Section 8 group and the 

experimental group felt safer in their neighborhoods than those in the control group. 

2. Better health outcomes.

The study found that the opportunity to live in lower poverty neighborhoods was associated with better health 

outcomes. Women in the experimental group were less likely to have extreme obesity and diabetes compared to 

women in the control group. The women and their female children in the experimental group also experienced less 

psychological distress and major depression. 

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

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

The children in the Section 8 and experimental groups did not have better educational achievements than those in the 

control group and were not significantly less likely to engage in most forms of risky or criminal behavior. This finding 

leads to two important lessons. 

•	Mobility programs designed to give families access to greater opportunity may need to define opportunity more

broadly than poverty rates or racial composition if improvements along these other dimensions are valued. Access 

to entry-level jobs, availability of high performing schools, and other neighborhood characteristics are additional 

factors that might need to be considered.
•	A more comprehensive approach is needed to reverse the negative consequences of living in neighborhoods

with heavily concentrated poverty. Housing is a platform for positive outcomes, but it is not sufficient alone for 

achieving these additional benefits.

HUD’s current programs support many of the MTO findings. For example, redevelopment programs like Choice Neighborhoods 

take a broader, more holistic approach to reforming a distressed, high poverty, high crime neighborhood into a safer one with 

lower poverty concentrations by linking housing improvements with appropriate services, schools, public assets, transportation, 

and access to jobs. 

The MTO demonstration has contributed significantly to our knowledge of how neighborhoods affect families. It continues to 

provide an exceptionally rich dataset for understanding the role environment plays in individual outcomes. Moreover, in addition 

to the work done under HUD’s guidance, other complementary quantitative and qualitative studies have been undertaken, 

funded by a large group of federal agencies and philanthropic organizations. Thus, the MTO demonstration has served as a 

foundation for the pursuit of a much larger research agenda. Despite the considerable work already done with these data, much 

remains to be explored as HUD continues to support using data to drive policy.

Raphael W. Bostic, Ph.D.

Assistant Secretary for Policy

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

Department of Housing and Urban Development, the U.S. Government, or any state or local agency that provided data. The use 

of South Carolina Department of Social Services and the Florida Department of Juvenile Justice records in the preparation of 

this material is acknowledged, but it is not to be construed as implying official approval of either department of the conclusions 

presented. New York State Division of Criminal Justice Services (DCJS) provided de-identified arrest data for the study. DCJS is 

not responsible for the methods of statistical analysis, as well as any conclusions derived therefrom.
vi TABLE OF CONTENTS

TABLE OF CONTENTS

EXECUTIVE SUMMARY

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

The Contributions of the MTO Demonstration XII

Interim Findings: Four to Seven Years After Baseline XIV

Long-Term Findings: 10 to 15 Years After Baseline XIV

CHAPTER 1 – The MTO Long-Term Study

1.1 How Housing Policies Might Affect Low-Income Families 3

1.2 Motivation for the Moving to Opportunity Experiment 8

1.3 Estimating the MTO Program Effects 14

1.4 MTO Mobility Outcomes 16

1.5 Previous MTO Findings 17

1.6 The Long-Term Study 24

1.7 Overview of the Report 30

CHAPTER 2 – Impacts on Mobility, Housing, 

Neighborhoods, and Social Networks

2.1 Baseline and Control Group Context 40

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

2.3 Data Sources and Measures 46

2.4 Long-Term Impacts on Mobility and Housing 51

2.5 Long-Term Impacts on Neighborhood Characteristics 57

2.6 Long-Term Impacts on Social Networks 58

2.7 Interpretation of Results 67

CHAPTER 3 – Impacts on Adult and Youth Physical Health

3.1 Hypotheses About Effects on Adult and Youth Physical Health 80

3.2 Earlier Research: Short-Term and Interim Impacts on Physical Health 84

3.3 Baseline and Control Group Context 84

3.4 Data Sources and Measures 84

3.5 Long-Term Impacts on Adult Physical Health 89

3.6 Long-Term Impacts on the Physical Health of Youth and Grown Children 95

3.7 Impacts on Hypothesized Mediators of Health 102

3.8 Interpretation of Physical Health Results 103

CHAPTER 4 – Impacts on Adult and Youth Mental Health

4.1 Hypotheses About Effects on Adult and Youth Mental Health 114

4.2 Earlier Research: Short-Term and Interim MTO Impacts on Mental Health 117

4.3 Data Sources and Measures 117

4.4 Context and Baseline Status of the Sample 121

4.5 Long-Term Impacts on Mental Health 121

4.6 Impacts on Hypothesized Mediators 133

4.7 Interpretation of Mental Health Results 134
vii

CHAPTER 5 – Impacts on Economic Self-Sufficiency

5.1 Baseline and Control Group Context 138

5.2 Hypotheses About Effects on Economic Self-Sufficiency 141

5.3 Data Sources for Measures of Employment and Earnings 145

5.4 Long-Term Impacts on Employment and Earnings of Adults 147

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

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

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

5.8 Data Sources for Measures of Savings and Assets 163

5.9 Long-Term Impacts on Savings and Assets 163

5.10 Interpretation of Economic Self-Sufficiency Results 163

CHAPTER 6 – Impacts on Risky and Criminal Behavior

6.1 Hypotheses About Effects on Risky and Criminal Behavior 186

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

6.3 Baseline and Control Group Context 194

6.4 Data Sources and Measures 195

6.5 Long-Term Impacts on Risky and Criminal Behavior 196

6.6 Interpretation of Results for Risky and Criminal Behavior 206

CHAPTER 7 – Impacts on Educational Outcomes

7.1 Baseline and Control Group Context 214

7.2 Hypotheses About Effects on Educational Outcomes 215

7.3 Earlier Research: On Neighborhoods and Educational Outcomes 

 Including Short-Term and Interim MTO Impacts 218

7.4 Data Sources and Measures 221

7.5 Long-Term Impacts on Hypothesized Mediators of Educational Effects 224

7.6 Long-Term Impacts on Achievement and Educational Outcomes 230

7.7 Interpretation of Education Results 238

CHAPTER 8 – Summary and Implications

8.1 Summary of Impact Estimates 254

8.2 Mobility, Housing, Neighborhoods, and Social Networks 254

8.3 Physical Health 256

8.4 Mental Health 257

8.5 Economic Self-Sufficiency 257

8.6 Risky and Criminal Behavior 258

8.7 Educational Outcomes 259

8.8 Can These MTO Results Be Trusted? 260

8.9 Implications for Social Science and Public Policy 261

References 267

List of Exhibits

EXECUTIVE SUMMARY

Exhibit ES–1. Allocation and Compliance of the Long-Term Evaluation Samples

by Site and Treatment Group XVII

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

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

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

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

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

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

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

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

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

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

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

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

CHAPTER 1

Exhibit 1.1. Hypothesized Pathways of MTO Impacts 5

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

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

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

Exhibit 1.5. Census Tract Characteristics for Key Addresses for 

MTO Adults Surveyed in the Long-Term Evaluation 20

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

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

Interim Evaluation (4–7 Years After Baseline) 22

Exhibit 1.8. Allocation and Compliance of the Long-Term Evaluation Samples, 

By Site and Treatment Group 27

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

Exhibit 1.10. MTO Adult and Youth Long-Term Survey Instrument Response Rates, 

by Random Assignment Group 32

Supplemental Exhibit 1.1. Baseline Characteristics of the Grown Child Sample 

by Random Assignment Group 32

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

by MTO Sample 33

CHAPTER 2

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

Exhibit 2.2. Adult Mobility and Homelessness 54

Exhibit 2.3. Adult Housing Tenure and Reasons for Moving 55

Exhibit 2.4. Adult Housing Assistance, Costs, and Stability 56

Exhibit 2.5. Adult Housing Quality 57

Exhibit 2.6. Average (Duration-Weighted) Neighborhood Census Tract Characteristics 

of All Adults’ Addresses From Random Assignment Through May 2008 60

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

Exhibit 2.8. Adult Neighborhood Characteristics and Satisfaction 62

Exhibit 2.9. Interviewer Observations of the Adult’s Current Neighborhood 63

Exhibit 2.10. Adult and Youth Neighborhood Safety and Crime Victimization 64

Exhibit 2.11. Neighborhood Social Process and Adult Social Networks 66

Supplemental Exhibit 2.1. Adult Current Housing Condition 68

Supplemental Exhibit 2.2. Youth Neighborhood Census Tract 

Characteristics for Current Address (May 2008) 69

Supplemental Exhibit 2.3. Adult Neighborhood Conditions 72

Supplemental Exhibit 2.4. Mediators: Adult Social Networks 73

Supplemental Exhibit 2.5. Mediators: Youth’s Social Network 73

Supplemental Exhibit 2.6. Mediators: Youth’s Peers 75

Supplemental Exhibit 2.7. Youth Self-Reflection 76
ix

CHAPTER 3

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

Exhibit 3.2. Adult Physical Health Outcomes 91

Exhibit 3.3. Adult Self-Rated Health and BMI Categories by Treatment Group 94

Exhibit 3.4. Youth Physical Health Outcomes 96

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

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

Exhibit 3.7. Parent-Reported Physical Health Outcomes of Grown Children Ages 21–30 101

Supplemental Exhibit 3.1. Physical Health Outcomes of Youth Ages 15–20 105

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

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

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

CHAPTER 4

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

Exhibit 4.2. Adult Psychological Well-Being 116

Exhibit 4.3. Adult Mental Disorders 122

Exhibit 4.4. Youth Psychological Well-Being, Ages 13–20 125

Exhibit 4.5. Youth Mental Disorders, Ages 13–20 127

Exhibit 4.6. Summary Measures of Youth Mental Health, Ages 13–20 131

Exhibit 4.7. Parent-Reported Mental Health of Grown Children Ages 21–30 133 

Supplemental Exhibit 4.1. Adult Mental Health Services in Past 12 Months 135

Supplemental Exhibit 4.2. Youth Psychological Well-Being, Ages 10–20 and Ages 15–20 135

CHAPTER 5

Exhibit 5.1. Selected Baseline Characteristics of Adults Surveyed in the Long-Term Evaluation: 

Work, Public Assistance, and Income 140

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

Exhibit 5.3. Adult Five-Site Quarterly Employment Rate 

by Random Assignment Group and Calendar Quarter 147

Exhibit 5.4. Adult Five-Site Quarterly Employment Rate 

by Random Assignment Group and Quarters Since Random Assignment 148

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

Exhibit 5.6. Adult Employment in 2007, Administrative Data by Site 152 

Exhibit 5.7. Adult Self-Reported Employment and Earnings 153

Exhibit 5.8. Employment and School Enrollment of Grown Children: 

Parent Reports and Administrative Data 155

Exhibit 5.9. Youth Employment and Earnings, Administrative Data 

by Site for Fourth Quarter 2007 to Third Quarter 2008 156

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

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

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

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

Exhibit 5.14. Adult Self-Reported Family Savings and Assets 164

Supplemental Exhibit 5.1. Adult Employment, Administrative Data 

by Site and Random Assignment (RA) Years 165

Supplemental Exhibit 5.2. Comparison of Self-Reported and Administrative Data, 

on Adult Current Employment, by Site 169

Supplemental Exhibit 5.3. Comparison of Self-Reported and Administrative Data on 

Adult Employment in Maryland, Illinois, and California (In-State Only) 170

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

Supplemental Exhibit 5.5. Adult Self-Reported Current Employment Benefits 172
x

Supplemental Exhibit 5.6. Four Mutually Exclusive Categories of Work and Schooling 

of Grown Children, Parent Reports 172

Supplemental Exhibit 5.7. Employment and Schooling Outcomes of Grown Children, 

Parent Reports and Administrative Data by Site 173

Supplemental Exhibit 5.8. Youth Employment and School Enrollment, 

Self-Reported and Administrative Data by Site 177

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

Supplemental Exhibit 5.10. Family Temporary Assistance for Needy Families (TANF) 

and Food Stamps Benefits in Massachusetts, Illinois, and Los Angeles County, Adult-Reported 

and Administrative Data, by Month Closest to Interview Month 182

Supplemental Exhibit 5.11. Family TANF and Food Stamps Benefits in Massachusetts, Illinois, 

and Los Angeles County, Adult-Reported and Administrative Data for the Same Month 183

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

CHAPTER 6

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

Exhibit 6.2. Developmental Pathways to Serious and Violent Offending 188 

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

Exhibit 6.4. National Trends in Substance Use and Teen Pregnancy 194 

Exhibit 6.5. Risky Behavior Outcomes for Youth Ages 13–20 197 

Exhibit 6.6. Behavior Problems and Delinquency for Youth Ages 13–20 199 

Exhibit 6.7. Number of Post-Random Assignment Arrests for Youth Ages 15–20 200 

Exhibit 6.8. Number of Post-Random Assignment Arrests for Grown Children Ages 21–30 202 

Exhibit 6.9. Incarceration of Grown Children Ages 21–30 204 

Exhibit 6.10. Number of Post-Random Assignment Arrests for Adults 205 

Supplemental Exhibit 6.1. Prevalence of Post-Random Assignment Arrests for Youth Ages 15–20 207 

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

Grown Children Ages 21–30 209 

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

CHAPTER 7

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

Exhibit 7.2. Hypothesized Pathways Through Which MTO May Impact Achievement 

and Educational Outcomes 220 

Exhibit 7.3. Characteristics of Schools Attended by Youth Ages 10–20 226 

Exhibit 7.4. School Mobility among Youth Ages 10–20 228 

Exhibit 7.5. Perceptions of School Climate among Youth Ages 10–17 229 

Exhibit 7.6. Achievement Assessment Results for Youth Ages 13–20 231 

Exhibit 7.7. Schooling Outcomes for Youth Ages 10–20 232 

Exhibit 7.8. Post-Secondary Outcomes from National Student Clearinghouse Data for Youth Ages 15–20 234 

Exhibit 7.9. Schooling Outcomes for Grown Children Ages 21–30 236 

Supplemental Exhibit 7.1. Absenteeism, Tardiness, or Problems at School for Youth Ages 10–20 239 

Supplemental Exhibit 7.2. Future Expectations of Youth Ages 10–20 241 

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

Supplemental Exhibit 7.4. Time Use and Access to Technology among Youth Ages 10–20 243 

Supplemental Exhibit 7.5. Achievement Assessment Results by Baseline Age among Youth Ages 13–20 246 

Supplemental Exhibit 7.6. Achievement Assessment Results by Site among Youth Ages 13–20 247 

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

Achievement Assessment 248 

Supplemental Exhibit 7.8. Post-Secondary Schooling Outcomes of Youth Ages 15–20, 

Self-Report versus Administrative Data 249 

Supplemental Exhibit 7.9. Post-Secondary Schooling Outcomes of Grown Children Ages 21–30, 

Self-Report versus Administrative Data 251
xi
xii

THE MOVING TO OPPORTUNITY FOR 

FAIR HOUSING DEMONSTRATION 

PROGRAM FINAL IMPACTS EVALUATION

This report presents the long-term impacts of a unique 

housing mobility demonstration, Moving to Opportunity 

(MTO), on housing and neighborhood conditions, 

physical and mental health, economic self-sufficiency, 

risky and criminal behavior, and educational outcomes. 

The MTO demonstration was authorized by the U.S. 

Congress in section 152 of the Housing and Community 

Development Act of 1992. In 1994, the U.S. Department 

of Housing and Urban Development (HUD) launched 

MTO to test whether offering housing vouchers to 

families living in public housing projects in high-poverty 

neighborhoods of large inner cities could improve their 

lives and the lives of their children by allowing them 

to move to lower-poverty neighborhoods. The original 

authorizing legislation for MTO charged HUD with 

describing “the long-term housing, employment, and 

educational achievements of the families assisted under 

the demonstration program.” This report discharges that 

responsibility.

Thanks to the generous support of other federal 

government agencies and private foundations, the scope 

of our long-term study of MTO families was expanded 

to include a number of outcome domains beyond 

those under HUD’s charge, most notably physical and 

mental health. The additional funders that enabled this 

important expansion to the study’s scope included the 

National Science Foundation, the National Institute for 

Child Health and Human Development, the Centers 

for Disease Control, the National Institute of Mental 

Health, the National Institute for Aging, the National 

Opinion Research Center’s Population Research Center 

(supported by the National Institute of Child Health and 

Human Development), University of Chicago’s Center 

for Health Administration Studies, the U.S. Department 

of Education’s Institute of Education Sciences, the John 

D. and Catherine T. MacArthur Foundation, the Smith 

Richardson Foundation, the Spencer Foundation, the 

Bill & Melinda Gates Foundation, the Russell Sage 

Foundation, and the Annie E. Casey Foundation.

The results show that MTO moves led to sustained 

improvements in housing quality and in many 

aspects of the neighborhood’s environment, including 

neighborhood socioeconomic composition and safety. 

These MTO-induced changes translate into a number 

of important improvements in mental and physical 

health for adults, including lower rates of extreme 

obesity, diabetes, psychological distress, and major 

depression. MTO had no detectable impacts on work, 

earnings, or other economic outcomes for adults. 

For youth, we see some signs of the same gender 

difference in responses to MTO as were found in the 

interim study, which reported on outcomes measured 

four to seven years after random assignment. One 

outcome for which we see some hints of beneficial 

MTO impacts on male youth is a reduction in illegal 

drug selling.

THE CONTRIBUTIONS OF THE MTO 

DEMONSTRATION

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

economically and socially distressed neighborhood 

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

life chances, owing to some combination of better 

housing conditions, lower crime, less stress, greater 

job opportunities, more supportive schools, and 

role models who are more affirming of pro-social 

behaviors. However, empirically isolating the effect 

of neighborhoods on people’s outcomes from other 

possible influences has been difficult.

MTO overcomes some of the empirical challenges 

of identifying neighborhood effects on people’s 

life outcomes because it was implemented as 

an experiment. Akin to drug trials in medicine, 

families at the beginning of the experiment were 

randomly assigned to a control group or one of two 

treatment groups. An MTO-type experiment enables 

EXECUTIVE SUMMARY
xiii

us to determine whether moving to a lower-poverty 

neighborhood itself, rather than some other characteristic 

of the individuals or families that might be related to 

both their propensity to move and their behavioral 

outcomes, directly caused improvements in health, 

economic security, or some other outcome of interest. 

Because of random assignment, the control group’s 

experience shows, on average, what would have happened 

to the families in the treatment groups had they not been 

offered a voucher through MTO. 

From 1994 to 1998, the MTO demonstration enrolled 

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

Chicago, Los Angeles, and New York. Eligibility for 

MTO was limited to households with children in public 

or other government-subsidized, project-based housing 

in selected high-poverty areas. Enrolled families were 

assigned at random to one of three groups: 

1. The experimental group received Section 8 rental 

assistance certificates or vouchers that they could 

use only in census tracts with 1990 poverty rates 

below 10 percent. The families received mobility 

counseling and help in leasing a new unit. Fortyeight percent of families assigned to this group 

moved to a lower-poverty neighborhood with 

an MTO voucher. One year after relocating, 

families could use their voucher to move again if 

they wished, without any special constraints on 

location. 

2. The Section 8 only group received regular 

Section 8 certificates or vouchers that they could 

use anywhere; these families received no special 

mobility counseling. Sixty-three percent moved 

using a voucher obtained through MTO. 

3. The control group received no certificates or 

vouchers through MTO, but continued to be 

eligible for project-based housing assistance and 

whatever other social programs and services to 

which they would otherwise be entitled.

The MTO program population was very economically 

disadvantaged at baseline. Almost all the households that 

signed up for MTO were headed by women. Nearly twothirds were African-American, and most of the remaining 

one-third were Hispanic. Three-quarters of household 

heads were on welfare, and fewer than 40 percent had 

graduated from high school. Most families had two or 

three children. In more than 40 percent of enrolled 

households, a household member had been victimized by 

a crime during the previous six months. 

Not all families offered a housing voucher through MTO 

actually used it to move to a different neighborhood. 

Those who did move with an MTO housing voucher 

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

treatment assignment) were on average relatively younger, 

more dissatisfied with their original neighborhoods, 

and had fewer children than those who did not use the 

voucher.

Accordingly, a useful starting point for our analysis is to 

compare the average outcomes of all families assigned 

to the experimental group, regardless of whether they 

relocated with an MTO voucher, with the average 

outcomes of those assigned to the control group. The 

same comparison was calculated for the Section 8 and 

control groups. These differences are called “intention to 

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

a family the chance to move with an MTO voucher as 

part of the experimental or Section 8 group. Our design 

also enables us to estimate treatment/control group 

differences for the subset of families who actually moved 

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

“treatment on the treated” (or TOT).

MTO is thus the first random-assignment social 

science experiment designed to identify the causal 

effects of moving from high-poverty to lower-poverty 

neighborhoods on the social, economic and educational 

prospects, risky and criminal behavior, health, and 

well-being of low-income families. Other unique 

characteristics of the demonstration project include:

•	 A large study sample.

•	 Multiple program sites drawn from different 

regions across the United States.

•	 Long-term survey and administrative data followup of program participants.

•	 Exhaustive tracking of program participants over 

time and several sophisticated survey follow-ups of 

participants that produced high response rates.
xiv

•	 Direct measurement of key physical health 

outcomes such as height, weight, blood pressure, 

and biomarkers based on blood samples.

•	 Multiple administrative data sources for 

independent measurements that complement 

information from the surveys.

•	 Examination of a large set of outcome domains.

INTERIM FINDINGS: FOUR TO SEVEN 

YEARS AFTER BASELINE

An interim multisite evaluation of MTO’s effects 

was conducted four to seven years after families 

entered the program. At that point, families who had 

moved to lower-poverty neighborhoods were doing 

significantly better than those in the control group in 

some important respects. On many other dimensions, 

however, the different groups had virtually identical 

outcomes, on average. Since previous theoretical work 

and observational studies had led to expectations of large 

neighborhood effects, this overall pattern of interim 

results was disappointing for many in the research and 

policy communities.

Adults assigned to either the experimental or Section 8 

groups were safer and more satisfied with their housing 

and neighborhoods compared with adults in the control 

group, and were also less likely to report a household 

member being a victim of a crime or seeing illicit drugs 

sold. Compared with the control group, adults in 

the experimental group had better mental health and 

some better physical health outcomes and behaviors, 

such as lower rates of obesity together with some signs 

of improved diet and exercise. However, the MTO 

“treatment” led neither to better labor market outcomes 

nor reductions in social program participation.

MTO’s impacts on children in participating families 

differed by gender, with generally beneficial effects for 

female youth and, on balance, some deleterious effects 

for male youth. Female youth in the experimental 

group had better mental health outcomes than their 

control group counterparts, whereas male youth in the 

experimental group were more likely to engage in risky 

behavior than control group males. Moving also reduced 

violent behavior for both male and female youth, as 

measured by arrests for violent crime. MTO had few 

detectable effects on child physical health or risky 

behaviors aside from an increase in nonsports injuries 

for male youth. MTO also had no detectable impacts on 

educational achievement as measured by standardized test 

scores.1 

LONG-TERM FINDINGS: 10 TO 15 

YEARS AFTER BASELINE

The final evaluation, which is the topic of the present 

report, is an opportunity to answer questions about the 

longer-term effects of housing mobility on poor families, 

measured 10 to 15 years after families enrolled in the 

program. The study’s populations of interest were all 

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

program. Subsets include: 

•	 4,604 adults who were heads of those households 

at the beginning of the program

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

31, 20072

 

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

2007). 

Executive Summary (ES) Exhibit 1 shows how these 

groups of adults and youth are distributed across 

demonstration sites and groups. It also shows the fraction 

of adults and youth assigned to the experimental and 

Section 8 groups who moved with an MTO housing 

voucher, that is, the “compliance rate.”

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

on survey interviews with 3,273 of the adult household 

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

end of 2007. The effective response rate for the longterm follow-up survey was 90 percent for the adult 

household heads and 89 percent for youth selected for 

the survey. These response rates were generally similar 

across randomly assigned MTO mobility groups, with 

the exception of a slightly lower response rate for adults 

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

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

some detected improvements in individual sites. Youth saw some gains 

in test scores in Baltimore and Chicago, for example.

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

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

relied on a variety of government administrative data 

sources. This report examines the impact of moving to 

lower-poverty neighborhoods on residential mobility, 

housing conditions, neighborhood conditions, and social 

networks of participating families. It also reports on 

physical health, mental health, economic self-sufficiency, 

risky and criminal behavior, and educational outcomes. 

These findings are outlined by topic below. 

MOBILITY, HOUSING, NEIGHBORHOOD 

CONDITIONS, AND SOCIAL NETWORKS

MTO had sizable positive impacts on housing and 

neighborhood conditions and the social networks with 

which participating families interacted. As Exhibit ES-2 

and 3 show, the experimental and Section 8 groups were 

more likely than the control group to: 

•	 Live in lower-poverty neighborhoods

•	 Live in higher-quality homes

•	 Reside in slightly less racially segregated 

neighborhoods (although most families even in 

the experimental and Section 8 groups were still in 

majority-minority neighborhoods)

•	 Have more social ties with relatively more affluent 

people

•	 Feel safer in their neighborhoods.

MTO enabled many families to move to lower-poverty 

neighborhoods. In the census tracts in which MTO 

participants lived at baseline, some 53 percent of tract 

residents had family incomes below the poverty line. 

Experimental and Section 8 families who moved with an 

MTO voucher (the “compliers”) lived in neighborhoods 

with mean tract poverty rates of 11 and 29 percent, 

respectively, immediately after their initial program 

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

though, the differences between the treatment and 

control groups narrowed, in large part because some 

control group families moved on their own to better 

neighborhoods and treatment group families moved 

on to neighborhoods with somewhat higher poverty 

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

study period, the control group’s neighborhood poverty 

rates were roughly 40 percent (see Executive Summary 

Exhibit 2). The average neighborhood poverty rates for 

all families assigned to the experimental and Section 

8 groups were 9 and 7 percentage points lower than 

this, respectively. (This intention to treat effect is 

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

experimental and Section 8 groups that moved through 

MTO, average neighborhood poverty rates were about 

18 and 11 percentage points lower, respectively.3

 (This 

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

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

higher in the census tracts where experimental group 

movers lived. MTO moves also made participants feel 

safer in their new neighborhoods, although this was less 

clearly evident for male youth in the program.

MTO moves only modestly reduced neighborhood racial 

segregation. For control group members, 88 percent 

of their neighbors were members of racial and ethnic 

minority groups. Families in the experimental group 

saw a decline of nearly 6 percentage points in the share 

minority, while the decline was smaller for those in the 

Section 8 group. Families in both treatment groups, even 

those who moved with an MTO voucher, were still living 

in census tracts that were three-quarters minority.

MTO also changed the social environments of the 

neighborhoods in which families were living. MTO 

helped families move into neighborhoods where 

neighbors were more willing to work together to support 

shared norms, a measure of informal social control that 

previous research suggests may be particularly important 

in improving the lives of neighborhood residents. MTO 

increased social connections of adults to other people 

who were employed full-time or had completed college. 

MTO moves did not appear to increase social isolation.

A majority of families in all three groups were still 

receiving some form of housing assistance, with slightly 

higher rates of assistance among the Section 8 group. 

However, fewer in the experimental and Section 8 groups 

than in the control group were living in public housing, 

and relatively more were instead using vouchers. Given 

that most families were still receiving some form of 

housing assistance, it is perhaps not surprising that MTO 

had only modest effects on out-of-pocket expenditures 

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

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

family lived in a particular census tract.
xvi

on housing. Compared with the control group adults, 

those in the experimental group were less likely to report 

being late with their rent or mortgage payments, but 

were more likely to have had trouble paying their utility 

bills on time. 

Finally, MTO improved the quality of housing in 

which families were living. Adults in the experimental 

group were about 5 percentage points more likely than 

control group adults to report that their current housing 

conditions were excellent or good. 

ADULT AND YOUTH PHYSICAL HEALTH

Living in disadvantaged neighborhoods has been 

hypothesized to worsen physical health. Previous research 

has found strong correlations between living in poor 

neighborhoods and prevalence rates of mortality, heart 

disease, obesity, depression, and substance abuse, even 

after controlling for observable individual-level health 

risk factors. These previous findings suggest that moving 

to a less disadvantaged neighborhood could conceivably 

improve health, and indeed there is some indication 

that this is the case for MTO participants on several 

important health outcomes.

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

the adults across all groups, including the control group, 

rated their health as good or excellent. Evidence from 

self-reports, physical biomarkers, and blood samples 

indicate that at the time of the long-term followup, compared with the control group, adults in the 

experimental and Section 8 group have:

•	 a lower prevalence of extreme obesity

•	 a lower prevalence of diabetes

•	 fewer self-reported physical limitations

•	 similar self-reported health status, as well as similar 

rates of hypertension and health-related risk 

behaviors.

Although there were no significant differences across 

groups in obesity (as measured by body mass index 

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

as weight in kilograms divided by height in meters 

squared (kg/m2

)), the share of adults with BMI of 35 

kg/m2 or greater was approximately 5 percentage points 

lower among those in the experimental group. The share 

of experimental group adults whose BMI was 40 kg/

m2

 or greater, a commonly used definition of extreme 

obesity, was about 3 percentage points lower than 

those in the control group. There were no significant 

differences between the Section 8 and control groups 

in the prevalence of extreme obesity. Diabetes was 3 to 

6 percentage points less prevalent for those in the two 

MTO treatment groups than in the control group as 

measured from blood samples. The effect is somewhat 

smaller for the experimental group if we use respondent 

self-reports about whether their doctors had told them 

they had diabetes. An advantage of the blood-based 

biomarkers over self-reports of diabetes is that they are 

free from the underreporting caused by respondents 

being unaware they have the condition.

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

asthma, hypertension, chronic pain, and substance use, 

were largely unaffected by moving to lower-poverty 

neighborhoods. Among youth, moving to a lower-poverty 

neighborhood had little to no measured effect on the 

health outcomes measured (see Exhibit ES-5). 

ADULT AND YOUTH MENTAL HEALTH

As with physical health, living in disadvantaged 

neighborhoods has long been thought to increase the risk 

for mental health problems. Research has documented 

large correlations between rates of neighborhood poverty 

or crime and depression, anxiety, and other mental health 

problems. Exposure to violence, for example, is associated 

with long-term behavioral and psychological harm 

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

exposed to violence than females, may be particularly 

vulnerable. 

Data from a battery of psychological measures show 

that, compared with the control group, adults in the 

experimental or Section 8 group have:

•	 lower levels of psychological distress

•	 lower prevalence of depression

•	 lower prevalence of anxiety

•	 similar rates of most other mental health problems.
xvii

As Exhibit ES-6 shows, MTO lowered the prevalence 

of depression among adults in both the experimental 

and Section 8 group by 3 and 5 percentage points, 

respectively (although the experimental group impact 

is just marginally significant). MTO did not have 

statistically significant effects on other mental health 

measures, such as levels of mental calm, normal sleep 

patterns, having any mood disorder, panic attacks, 

post-traumatic stress, or intermittent explosive disorder. 

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

were in the direction of improved mental health for 

movers relative to controls. One exception is that moves 

to lower-poverty neighborhoods seemed to slightly 

increase drug or alcohol abuse or dependence among 

adults in the experimental group compared with the 

control group.

Exhibit ES-7 shows selected mental health outcomes for 

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

moving to lower-poverty neighborhoods had an overall 

positive effect on their mental health. More specifically, 

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

youth in the control group, females in the experimental 

group have:

•	 a lower prevalence of any lifetime mood disorder

•	 fewer serious emotional or behavioral difficulties

•	 fewer panic attacks in the past year

•	 less psychological distress

•	 lower prevalence of oppositional defiant disorder in 

the past year

•	 similar rates of other mental health problems.

For male youth ages 10–20, however, moving to lowerpoverty neighborhoods may have increased lifetime 

post-traumatic stress disorder; prevalence of this disorder 

among male youth in the Section 8 group were about 

3 percentage points higher than in the control group. 

Although not reaching statistical significance, many of 

the other mental health indicators had worsened for male 

youth after moving.

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

TREATMENT GROUP

ALL 

GROUPS

CONTROL 

GROUP

EXPERIMENTAL GROUP SECTION 8 GROUP

N N N COMPLIANCE 

RATE (%)

N COMPLIANCE 

RATE (%)

ADULTS

Baltimore 572 197 252 53.5 123 79.8

Boston 868 326 366 43.6 176 51.1

Chicago 825 232 460 33.4 133 67.4

Los Angeles 929 389 340 60.5 200 71.6

New York City 948 295 401 46.4 252 45.2

All sites 4,142 1,439 1,819 47.4 884 61.6

YOUTH, AGES 10–20

Baltimore 762 240 268 59.1 254 79.2

Boston 1,267 440 475 38.2 352 54.6

Chicago 1,363 328 701 31.7 334 72.0

Los Angeles 1,539 592 502 62.2 445 78.2

New York City 1,377 418 471 49.6 488 49.2

All sites 6,308 2,018 2,417 47.6 1,873 66.4

Notes: Percentages are weighted to reflect the randomization ratios and sampling of Section 8 adults and up to three youth per family. "Compliance" is defined as 

leasing a unit using a housing voucher provided by the Moving To Opportunity (MTO) program. 

Data source and sample: MTO data system. The samples are N = 4,142 adults and N = 6,308 youth ages 10 to 20 as of December 2007 selected for the long-term 

survey. Excluded from the samples are the N = 462 Section 8 group adults and youth from households with greater than three youth ages 10–20 who were not 

randomly selected for the long-term survey.
xviii

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

CONTROL 

GROUP

EXPERIMENTAL GROUP SECTION 8 GROUP

ALL ALL COMPLIERS NONCOMPLIERSALL COMPLIERS NONCOMPLIERS

SHARE POOR IN

CENSUS TRACT OF

RESIDENCE FOR MTO

FAMILIES BY TIME SINCE 

RANDOM ASSIGNMENT

Baseline address 0.531 0.527 0.530 0.524 0.526 0.540 0.505

Initial MTO-assisted move 

address

0.107 0.287

Address at time of MTO 

interim evaluation (4–7 

years after baseline)

0.395 0.299 0.193 0.402 0.326 0.286 0.391

Address at time of MTO 

long-term evaluation (10–15 

years after baseline)

0.313 0.274 0.210 0.334 0.283 0.244 0.345

Average (durationweighted) of all addresses 

since Random Assignment

0.396 0.306 0.200 0.407 0.329 0.285 0.400

Notes: Characteristics for the census tracts of addresses at which Moving To Opportunity (MTO) families were living at different points in time were linearly 

interpolated from the 1990 and 2000 decennial Censuses as well as the 2005–09 American Community Survey. Addresses at the time of the interim and longterm evaluations are as of the beginning of the fielding period for each study (December 31, 2001, for the interim evaluation and May 31, 2008, for the long-term 

evaluation).

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

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

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

ADULT

HOUSING

Rates current housing as 

excellent or good [SR]

0.570 0.053* 0.109* 0.031 0.050 3,267

(0.021) (0.044) (0.029) (0.046)

Currently receiving any 

housing assistance [SR, HA]

0.620 0.026 0.054 0.045~ 0.072~ 3,273

(0.021) (0.043) (0.027) (0.044)

Total housing cost (2009 

dollars), monthly [SR]

$678.73 19.50 39.67 – 6.26 – 10.00 3,180

(23.30) (47.39) (30.73) (49.10)

NEIGHBORHOOD

Median household income 

(2009 dollars) [CEN]

$27,808.85 9,148.91* 18,848.48* 5,600.18* 9,027.10* 3,270

(544.97) (1,122.75) (660.90) (1,065.32)

Average (duration-weighted) 

census tract share persons 

who are poor [CEN] 

0.396 – 0.089* – 0.184* – 0.069* – 0.111* 3,270

(0.006) (0.012) (0.007) (0.011)

Average (duration-weighted) 

census tract share minority 

[CEN] 

0.880 – 0.061* – 0.125* – 0.018* – 0.029* 3,270

(0.007) (0.014) (0.009) (0.015)

Feels safe during the day [SR] 0.804 0.036* 0.074* 0.045* 0.072* 3,262

(0.016) (0.034) (0.021) (0.034)

Feels safe at night [SR] 0.596 0.043* 0.088* 0.073* 0.117* 3,246

(0.021) (0.043) (0.027) (0.043)

SOCIAL NETWORK

Has at least one close friend 

who graduated from college 

[SR]

0.532 0.071* 0.145* 0.007 0.010 3,203

(0.021) (0.044) (0.029) (0.046)

Has three or more close 

friends [SR]

0.432 0.006 0.013 – 0.026 – 0.042 3,265

(0.021) (0.044) (0.028) (0.045)
xx

EXHIBIT ES–3. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

YOUTH AGES 10–20

NEIGHBORHOOD

FEELS SAFE DURING THE

DAY [SR]

All 0.801 0.018 0.037 – 0.012 – 0.018 4,863

(0.016) (0.033) (0.018) (0.026)

Female 0.784 0.045* 0.090* 0.019 0.030 2,478

(0.022) (0.045) (0.025) (0.039)

Male 0.817 – 0.009 – 0.019 – 0.043~ – 0.062~ 2,385

(0.022) (0.047) (0.025) (0.035)

FEELS SAFE AT NIGHT [SR]

All 0.540 0.035~ 0.074~ 0.019 0.028 4,862

(0.021) (0.043) (0.022) (0.033)

Female 0.486 0.052~ 0.104~ 0.067* 0.105* 2,478

(0.028) (0.056) (0.030) (0.048)

Male 0.591 0.020 0.043 – 0.030 – 0.043 2,384

(0.028) (0.060) (0.031) (0.043)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered a Moving To 

Opportunity (MTO) housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" would 

indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an Ordinary Least Squares (OLS) regression model controlling for baseline covariates, weighted, 

and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with 

treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

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

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

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

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

SELF-RATED HEALTH

Currently good or better health [SR] 0.564 0.002 0.004 – 0.005 – 0.009 3,269

(0.020) (0.042) (0.027) (0.044)

ASTHMA

Asthma or wheezing attack during the past 

year [SR]

0.293 – 0.018 – 0.038 – 0.042 – 0.066 3,267

(0.020) (0.040) (0.026) (0.041)

OBESITY

Currently obese: Body Mass Index (BMI) ≥ 30 

[M, SR]

0.584 – 0.012 – 0.025 – 0.011 – 0.018 3,221

(0.022) (0.044) (0.029) (0.046)

BMI ≥ 35 [M, SR] 0.351 – 0.046* – 0.095* – 0.053* – 0.086* 3,221

(0.020) (0.042) (0.027) (0.043)

BMI ≥ 40 [M, SR] 0.175 – 0.034* – 0.071* – 0.029 – 0.048 3,221

(0.016) (0.032) (0.021) (0.034)

DIABETES

Had diabetes or treated for it during the past 

year [SR]

0.160 – 0.024 – 0.049 – 0.061* – 0.098* 3,251

(0.015) (0.030) (0.018) (0.029)

HbA1c test detected diabetes (HbA1c ≥

6.5%) [DBS]

0.204 – 0.052* – 0.108* – 0.011 – 0.017 2,737

(0.018) (0.038) (0.024) (0.038)

HbA1c test detected diabetes or had or 

treated for diabetes during the past year 

[DBS, SR]

0.234 – 0.034~ – 0.071~ – 0.008 – 0.013 2,732

(0.019) (0.040) (0.025) (0.039)

LIMITATIONS AND CHRONIC PAIN

Health limits respondent's ability to climb 

several flights of stairs/lifting or carrying 

groceries [SR]

0.510 – 0.048* – 0.100* – 0.023 – 0.038 3,270

(0.021) (0.043) (0.028) (0.045)

HYPERTENSION

Currently has high blood pressure (systolic ≥

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

0.315 0.007 0.015 – 0.026 – 0.041 3,102

(0.020) (0.042) (0.027) (0.043)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered a 

Moving To Opportunity (MTO) housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean 

is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 

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

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

Model: Experimental and Section 8 impacts were estimated jointly using an Ordinary Least Squares (OLS) regression model controlling for baseline covariates, 

weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an 

interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: For obesity inputs (height and weight), only a very small percent of the sample self-reported their height or weight. Body Mass Index (BMI) is measured 

as weight in kilograms divided by height in meters squared. For diastolic and systolic blood pressure, data are the average of two readings, if available; otherwise, 

data are from one reading. HbA1c = glycosylated hemoglobin.
xxii

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

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

GENERAL HEALTH

CURRENTLY GOOD OR BETTER HEALTH [SR]

All 0.883 0.005 0.009 0.000 – 0.001 5,100

(0.013) (0.027) (0.015) (0.022)

Female 0.862 0.003 0.007 0.006 0.010 2,600

(0.019) (0.038) (0.021) (0.034)

Male 0.903 0.006 0.012 – 0.007 – 0.010 2,500

(0.016) (0.035) (0.019) (0.027)

ASTHMA

ASTHMA OR WHEEZING ATTACK DURING THE

PAST YEAR [SR]

All 0.190 0.006 0.013 – 0.013 – 0.019 5,092

(0.015) (0.032) (0.016) (0.024)

Female 0.206 – 0.009 – 0.017 – 0.021 – 0.032 2,595

(0.021) (0.043) (0.023) (0.037)

Male 0.174 0.021 0.045 – 0.006 – 0.008 2,497

(0.020) (0.043) (0.022) (0.031)

OBESITY

CURRENTLY OBESE [M, SR]

All 0.229 – 0.010 – 0.022 – 0.010 – 0.014 5,034

(0.017) (0.035) (0.018) (0.028)

Female 0.274 – 0.028 – 0.057 – 0.020 – 0.031 2,560

(0.025) (0.051) (0.026) (0.041)

Male 0.187 0.007 0.016 0.000 0.000 2,474

(0.021) (0.045) (0.023) (0.033)

ACCIDENTS AND INJURIES

HAD ACCIDENTS OR INJURIES REQUIRING

MEDICAL ATTENTION IN THE PAST YEAR [SR]

All 0.178 0.006 0.013 0.008 0.012 5,097

(0.014) (0.030) (0.016) (0.024)

Female 0.164 – 0.023 – 0.046 – 0.024 – 0.037 2,597

(0.018) (0.037) (0.020) (0.031)

Male 0.192 0.035 0.076 0.039 0.056 2,500

(0.022) (0.047) (0.024) (0.034)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered a 

Moving To Opportunity (MTO) housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean 

is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 

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

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

Model: Experimental and Section 8 impacts were estimated jointly using an Ordinary Least Squares (OLS) regression model controlling for baseline covariates, 

weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as 

an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

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

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

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

Psychological Distress Index (K6) 

Z-score, past month (higher score 

indicates greater distress) [SR]

0.000 – 0.107* – 0.221* – 0.097~ – 0.156~ 3,273

(0.042) (0.087) (0.056) (0.091)

Major Depression with Hierarchy, 

Lifetime [SR]

0.203 – 0.032~ – 0.066~ – 0.048* – 0.077* 3,269

(0.017) (0.035) (0.021) (0.034)

Any Mood Disorder, Lifetime [SR] 0.255 – 0.028 – 0.058 – 0.036 – 0.058 3,270

(0.018) (0.038) (0.024) (0.039)

Generalized Anxiety Disorder with 

Hierarchy, Lifetime [SR]

0.065 – 0.003 – 0.005 – 0.020~ – 0.033~ 3,273

(0.010) (0.021) (0.011) (0.017)

Any Anxiety Disorder, Lifetime [SR] 0.308 – 0.020 – 0.042 – 0.005 – 0.008 3,269

(0.020) (0.041) (0.026) (0.042)

Panic Attacks, Lifetime [SR] 0.407 0.004 0.009 – 0.022 – 0.035 3,269

(0.021) (0.044) (0.028) (0.045)

Post-Traumatic Stress Disorder, Lifetime 

[SR]

0.219 – 0.012 – 0.024 0.004 0.006 3,269

(0.018) (0.037) (0.024) (0.038)

Calm and peaceful most of the time, 

past month [SR]

0.487 0.018 0.037 – 0.025 – 0.040 3,272

(0.022) (0.045) (0.029) (0.047)

Dependence on drugs or alcohol, past 

month [SR]

0.055 0.029* 0.060* 0.015 0.024 3,269

(0.011) (0.022) (0.015) (0.023)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered a 

Moving To Opportunity (MTO) housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean 

is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 

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

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

Model: Experimental and Section 8 impacts were estimated jointly using an Ordinary Least Squares (OLS) regression model controlling for baseline covariates, 

weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as 

an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: Psychological distress consists of 6 items (sadness, nervousness, restlessness, hopelessness, feeling that everything is an effort, worthlessness) scaled 

on a score from 0 (no distress) to 24 (highest distress) and then converted to z-scores using the mean and standard deviation of control group adults. Disorders 

with hierarchy take into account the comorbidity of other disorders: Depression with hierarchy takes into account Mania, and Hypomania; Generalized Anxiety 

Disorder (GAD) takes into account Depression and Mania; Intermittent Explosive Disorder (IED) takes into account Depression, Mania and Hypomania. Any Anxiety 

Disorder includes Panic Disorder, Generalized Anxiety Disorder (GAD) (without hierarchy), and Post-Traumatic Stress Disorder (PTSD). Any Mood Disorder includes 

Depression (without hierarchy), Bipolar I/II/Subthreshold, and Mania/Hypomania/Hypomania Subthreshold. Any Disorder includes all disorders from Any Anxiety 

Disorder and Any Mood Disorder as well as Intermittent Explosive Disorder (IED) (without hierarchy). Index of mental health problems is the average of depression 

(past year), anxiety (past year), mental calm, distress (K6) and normal sleep (7 to 8 hours last night) after standardizing by the control mean and standard deviation. 

Substance dependence consists of 5 items about drug and/or alcohol use (use out of control, anxiety or worry about missing fix or drink, worry about use, 

frequency of desire to end use, difficulty of going without use) scaled on a score from 0 (no dependence) to 15 (highest level of dependence). A score of 3 or higher 

indicates dependence. See Section 4.3 for additional details.
xxiv

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

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

PSYCHOLOGICAL DISTRESS INDEX (K6)

Z-SCORE, PAST MONTH (HIGHER SCORE

INDICATES GREATER DISTRESS) [SR]

All 0.000 – 0.040 – 0.084 0.036 0.054 4,644

(0.041) (0.085) (0.047) (0.070)

Female 0.115 – 0.119* – 0.241* – 0.013 – 0.021 2,371

(0.058) (0.116) (0.066) (0.104)

Male – 0.110 0.040 0.085 0.084 0.120 2,273

(0.054) (0.116) (0.060) (0.086)

MAJOR DEPRESSION WITH HIERARCHY,

LIFETIME [SR]

All 0.105 – 0.015 – 0.031 – 0.014 – 0.021 4,639

(0.012) (0.025) (0.013) (0.019)

Female 0.128 – 0.032~ – 0.065~ – 0.029 – 0.045 2,367

(0.017) (0.035) (0.019) (0.029)

Male 0.084 0.003 0.006 0.000 0.000 2,272

(0.016) (0.034) (0.017) (0.025)

ANY MOOD DISORDER, LIFETIME [SR]

All 0.178 – 0.013 – 0.027 0.003 0.004 4,644

(0.015) (0.031) (0.016) (0.024)

Female 0.218 – 0.048* – 0.096* – 0.032 – 0.050 2,371

(0.022) (0.044) (0.024) (0.038)

Male 0.140 0.022 0.047 0.038~ 0.054~ 2,273

(0.020) (0.043) (0.022) (0.032)

GENERALIZED ANXIETY DISORDER WITH

HIERARCHY, LIFETIME [SR]

All 0.019 – 0.002 – 0.004 0.010~ 0.015~ 4,644

(0.005) (0.010) (0.006) (0.009)

Female 0.022 – 0.002 – 0.003 0.010 0.016 2,371

(0.007) (0.014) (0.008) (0.013)

Male 0.016 – 0.002 – 0.005 0.010 0.015 2,273

(0.006) (0.013) (0.009) (0.013)

ANY ANXIETY DISORDER, LIFETIME [SR]

All 0.121 0.001 0.002 0.005 0.007 4,639

(0.013) (0.027) (0.014) (0.020)

Female 0.149 – 0.011 – 0.023 – 0.014 – 0.021 2,367

(0.019) (0.038) (0.020) (0.031)

Male 0.095 0.013 0.028 0.023 0.033 2,272

(0.017) (0.037) (0.019) (0.027)
xxv

EXHIBIT ES–7. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

SERIOUS BEHAVIORAL OR EMOTIONAL

PROBLEMS [SR]

All 0.103 – 0.022~ – 0.046~ 0.019 0.029 4,644

(0.011) (0.024) (0.014) (0.021)

Female 0.127 – 0.033* – 0.068* 0.030 0.047 2,371

(0.017) (0.034) (0.021) (0.032)

Male 0.081 – 0.010 – 0.021 0.007 0.010 2,273

(0.015) (0.032) (0.017) (0.024)

PANIC ATTACKS, LIFETIME [SR]

All 0.387 – 0.017 – 0.035 0.014 0.021 4,639

(0.019) (0.040) (0.021) (0.032)

Female 0.423 – 0.039 – 0.079 – 0.005 – 0.008 2,367

(0.027) (0.054) (0.029) (0.045)

Male 0.354 0.006 0.012 0.033 0.047 2,272

(0.027) (0.057) (0.030) (0.043)

POST-TRAUMATIC STRESS DISORDER,

LIFETIME [SR]

All 0.066 0.006 0.012 0.005 0.007 4,639

(0.010) (0.020) (0.011) (0.016)

Female 0.092 0.002 0.003 – 0.019 – 0.030 2,367

(0.016) (0.032) (0.016) (0.026)

Male 0.041 0.010 0.021 0.030* 0.043* 2,272

(0.012) (0.026) (0.015) (0.021)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered a 

Moving To Opportunity (MTO) housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean 

is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 

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

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

Model: Experimental and Section 8 impacts were estimated jointly using an Ordinary Least Squares (OLS) regression model controlling for baseline covariates, 

weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as 

an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: Psychological distress consists of 6 items (sadness, nervousness, restlessness, hopelessness, feeling that everything is an effort, worthlessness) scaled 

on a score from 0 (no distress) to 24 (highest distress) and then converted to z-scores using the mean and standard deviation for of control group youth. Serious 

mental illness is defined as a raw score of 13 or higher on the K6. Strengths and Difficulties consists of 5 behavioral and emotional items (obedience, worry/anxiety, 

unhappiness, getting along better with adults than peers, attention span) scaled on score from 0 (no behavioral/emotional problems) to 12 (severe behavioral or 

emotional problems). A score of 6 or higher indicates serious behavioral/emotional problems. See Section 4.3 for additional details.
xxvi

ECONOMIC SELF-SUFFICIENCY

A primary motivation of MTO was to determine whether 

moving to lower-poverty neighborhoods could boost 

earnings and labor force participation and ease reliance 

on welfare among vulnerable families. The study began 

shortly before the implementation of welfare reform, 

and only one-fourth of adults reported being employed. 

Three-fourths were receiving Aid to Families with 

Dependent Children (AFDC), and eight in ten were 

receiving food stamps. The average annual household 

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

below the official poverty line for a family of three. 

Ten to fifteen years later, many more families were 

employed, incomes were higher, and welfare receipt had 

plummeted, but these changes were roughly equal for 

treatment and control-group families. Overall, MTO 

generated few systematic, detectable long-term effects on 

economic self-sufficiency for adults, youth, and grown 

children. 

As Exhibit ES-8 shows, compared with control group 

members, experimental and Section 8 group adults have: 

•	 similar employment levels and earnings

•	 similar incomes

•	 less food insufficiency

•	 somewhat higher use of food stamps (experimental 

group only).

Earnings and employment trends fluctuated with 

macroeconomic and policy shifts, with rapidly rising 

employment rates during economic booms and a recent 

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

short-term decline in employment during the first two 

years after moving for those in the experimental and 

Section 8 groups compared to the control group, perhaps 

indicating difficulties finding work in a new location or 

in securing child care. Their employment rates recovered 

shortly thereafter and matched the ebbs and flows 

evident in the control group. Ten years after moving, 

employment outcomes were not statistically different 

across groups. The types of jobs the participants held also 

differed little across groups.

Those in the experimental group were slightly less likely 

to have incomes below the poverty line than control 

group members, but the differences were not statistically 

significant. In contrast, the Section 8 group saw a 

modest, but again not statistically significant, rise in 

poverty relative to the control group. The study finds 

higher rates of food stamp use among the experimental 

group—and less food insufficiency—but few differences 

in use of Temporary Assistance for Needy Families 

(TANF, the program that replaced AFDC after welfare 

reform). TANF receipt was on the decline nationally 

throughout this time period owing to welfare reform 

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

1990s. There are no significant differences in welfare 

receipt among the Section 8 and control groups.

For adults, it appears that training, education, and 

employment services that directly enhance marketable 

skills and changes in work incentives more directly 

affect labor market and economic outcomes of lowincome adults than do the indirect effects of changes 

in neighborhood environments, at least in the range 

observed in the MTO demonstration.

Likewise, unlike early childhood education programs 

or early school quality improvements, which have 

been shown to improve economic self-sufficiency later 

in life, moving to lower-poverty neighborhoods had 

little systematic detectable effects on the economic 

outcomes—including being idle, defined as neither 

working nor in school—for youth and grown children 

compared with those in the control group 

(see Exhibit ES-9).
xxvii

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

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

EMPLOYMENT

Currently employed [SR] 0.525 – 0.007 – 0.014 – 0.077* – 0.124* 3,264

(0.021) (0.042) (0.028) (0.045)

Calendar year 2007 [UI] 0.465 – 0.004 – 0.009 0.000 0.000 4,194

(0.017) (0.036) (0.019) (0.030)

EARNINGS AND INCOME

Annual individual earnings (previous 

calendar year, 2009 dollars) [SR]

$12,288.51 326.94 677.92 – 613.60 – 982.43 3,141

(583.44) (1,209.79) (807.20) (1,292.40)

Calendar year 2007 (2009 dollars) [UI] $11,325.14 – 347.83 – 731.73 112.93 180.50 4,194

(523.80) (1,101.92) (580.69) (928.11)

Total Household Income 

(2009 dollars) [SR]

$20,025.90 607.58 1,255.56 – 41.67 – 67.33 3,258

(727.58) (1,503.54) (1,009.18) (1,630.66)

Household income is at or below 100% of 

poverty line [SR]

0.590 – 0.032 – 0.067 0.036 0.059 3,258

(0.021) (0.043) (0.028) (0.045)

Adult reported that their household 

sometimes/often did not have enough to 

eat in the past 12 months [SR]

0.336 – 0.035~ – 0.072~ – 0.067* – 0.106* 3,266

(0.020) (0.042) (0.026) (0.042)

FOOD STAMPS

Currently receiving food stamps [SR] 0.470 0.016 0.032 0.029 0.046 3,253

(0.021) (0.043) (0.028) (0.045)

Total food stamps benefits received, July 

2007–June 2009 (2009 dollars) [FS]

$3,074.08 309.94* 664.54* 171.07 261.80 2,708

(156.50) (335.54) (184.98) (283.09)

TEMPORARY ASSISTANCE FOR NEEDY FAMILIES

Currently receiving TANF [SR] 0.158 0.010 0.021 0.026 0.041 3,262

(0.015) (0.031) (0.021) (0.033)

Total TANF benefits received, July 2007–

June 2009 (2009 dollars) [TANF]

$1,402.33 56.10 120.29 – 94.47 – 144.57 2,708

(114.48) (245.44) (123.82) (189.49)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered a 

Moving To Opportunity (MTO) housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean 

is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 

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

Square brackets indicate the source of the outcome information: SR = self-report, UI = Unemployment Insurance administrative records, TANF = Temporary 

Assistance for Needy Families records, FS = Food Stamps records.

Model: Experimental and Section 8 impacts were estimated jointly using an Ordinary Least Squares (OLS) regression model controlling for baseline covariates, 

weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an 

interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: For self-reports, data source is the adult long-term survey and the sample is all adults interviewed. Unemployment Insurance data uses 

individual level data from Maryland, Illinois, California, and Florida (representing individuals whose random assignment sites are Baltimore, Chicago, and Los 

Angeles) and aggregate data from New York and Massachusetts (representing individuals whose random assignment sites are New York City and Boston). FS and 

TANF analyses use individual data from Massachusetts, Illinois, Los Angeles County, and South Carolina and represent individuals whose random assignment sites 

are: Boston, Chicago and Los Angeles. We received TANF and food stamps data from Maryland, but the data quality is suspect. Sample for UI, TANF, and FS is all 

sample adults with baseline consent.
xxviii

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

EVALUATION

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

CURRENTLY EMPLOYED [SR]

All 0.395 – 0.046* – 0.095* – 0.033 – 0.050 3,604

(0.021) (0.043) (0.022) (0.034)

Female 0.410 – 0.051~ – 0.104~ – 0.042 – 0.066 1,838

(0.029) (0.059) (0.031) (0.049)

Male 0.381 – 0.041 – 0.087 – 0.025 – 0.036 1,766

(0.030) (0.064) (0.033) (0.049)

CURRENTLY IDLE (NEITHER EMPLOYED

NOR ENROLLED IN SCHOOL) [SR]

All 0.215 0.007 0.014 0.026 0.039 3,604

(0.018) (0.038) (0.020) (0.031)

Female 0.194 0.024 0.049 0.031 0.048 1,838

(0.024) (0.048) (0.027) (0.043)

Male 0.235 – 0.011 – 0.023 0.022 0.032 1,766

(0.027) (0.058) (0.031) (0.045)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered a 

Moving To Opportunity (MTO) housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean 

is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 

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

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

Model: Experimental and Section 8 impacts were estimated jointly using an Ordinary Least Squares (OLS) regression model controlling for baseline covariates, 

weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as 

an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

RISKY AND CRIMINAL BEHAVIOR

Previous nonexperimental studies suggest that risky 

and criminal behavior is the outcome domain that has 

among the strongest associations with neighborhood 

conditions (Sampson, Morenoff, and Gannon-Rowley, 

2002). Neighborhoods may affect risky and criminal 

behavior through peer influences, the ability of local 

adults to monitor and support pro-social behavior in 

the community, or other factors that make pro-social 

alternatives to crime (such as school and work) more 

or less attractive. The quality of policing services may 

also vary across areas. If less economically distressed 

areas have higher-quality policing, that could help deter 

criminal behavior but might also lead our analysis of 

administrative arrest records to understate any beneficial 

effects of MTO that reduce crime, and overstate any 

adverse effects towards increased criminality.

Ten to fifteen years after random assignment we found 

few statistically significant impacts of MTO on risky and 

criminal behavior, although we did find some signs of a 

similar sort of gender difference in how youth respond 

to MTO moves as was found at interim, and some 

indication of potentially beneficial impacts on arrests for 

drug selling.

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

we find:

•	 Male youth in the Section 8 group engage in 

more problem behavior, and those in both the 

experimental and Section 8 groups are more likely 

to smoke compared with those in the control 

group.

•	 Female youth in the experimental group are less 

likely to have tried alcohol than those in the 

control group.
xxix

•	 Few statistically significant differences in the 

number of arrests for violent crimes, property 

crimes, or other (non-violent, non-property, nondrug) crimes across treatment groups.

•	 Some indication that the number of arrests 

for drug-selling declined for male youth in the 

experimental group and for male grown children 

in the Section 8 group, and that the likelihood 

of ever having been arrested for drug-selling may 

have declined for adults in the experimental group 

compared with those in the control group.

The estimated effects of MTO on risky and criminal 

behavior of those who were teens at the long term follow 

up were more muted compared to MTO’s effects on those 

who were teens at the time of the interim study. The 

long term data do provide a few indications of the same 

type of gender difference in youth responses to MTO 

moves as were found in the interim data, with male youth 

who moved through MTO engaging in relatively more 

of some risky behaviors (smoking, behavior problems) 

compared with controls, and female youth who moved 

through MTO experiencing declines in some risky 

behaviors (drinking) compared to controls.

The fact that the effects of MTO on risky and criminal 

behavior are generally more muted in the long-term 

data compared to the interim data suggests that 

contemporaneous neighborhood environments may 

matter more for these outcomes than does accumulated 

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

of a gender difference in responses to residential mobility 

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

non-overlapping with the youth who were studied in the 

interim follow up suggests that the gender difference in 

MTO impacts documented at interim may not be just a 

statistical artifact unique to that particular sample.

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

of more pronounced impacts in the long-term data than 

in the interim data is with declining arrest rates for drug 

distribution among the MTO treatment groups compared 

to controls. These results are more pronounced for teens 

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

adults and are consistent with the widely-documented 

“age-crime curve” showing that aggregate arrest rates 

for most crimes peak during late adolescence or early 

adulthood. 

EDUCATIONAL OUTCOMES

We had anticipated that MTO might generate more 

pronounced positive impacts on educational outcomes in 

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

because more families may have been willing to send their 

children to schools in lower-poverty areas rather than 

remain in their original baseline schools. In addition, 

many social scientists believe that early childhood is a 

time in which cognitive, socio-emotional, and behavioral 

skills may be particularly “plastic” and susceptible to 

intervention, which led us to expect that MTO might 

have relatively larger effects on the long-term schooling 

outcomes of participants who were preschool age at 

baseline.

Ten to fifteen years after random assignment, we found 

few statistically significant impacts on educational 

outcomes, including for children who were very young 

at baseline, and mixed impacts of MTO on different 

measures of school quality.

More specifically, in comparing children assigned to the 

experimental and Section 8 groups with control group 

children, we found:

•	 Similar average scores on reading and math 

achievement tests across groups.

•	 Similar grades in school and rates of grade 

retention across groups.

•	 Indications of slightly worse outcomes in some 

respects for Section 8 males, who were less likely 

to be on track educationally and less likely to have 

attended college than control group males. 

•	 No evidence that impacts on educational outcomes 

varied systematically with the child’s age at the time 

of random assignment. 

•	 A mixed pattern of differences across randomly 

assigned groups with respect to school measures 

typically associated with “quality.” Youth in the 

experimental and Section 8 groups attended schools 

with lower shares of students who are low-income 

or members of racial and ethnic minority groups 

and for the experimental group schools that ranked 
xxx

slightly higher on state exams; but these youth also 

attended schools that were larger, in terms of the 

total size of the student body.

These findings do not mean that neighborhood 

environments never matter for educational outcomes. 

The MTO mobility intervention generated more 

pronounced impacts on neighborhood conditions than 

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

long term data show that MTO had modest and mixed 

impacts on school quality. A majority of MTO children 

were still attending majority-minority, overwhelmingly 

low-income public schools located in the districts serving 

the five original MTO cities.

THE CAUSES OF POVERTY ARE 

COMPLEX AND EXTEND BEYOND 

NEIGHBORHOOD EFFECTS

The findings of the long-term study of MTO suggest that 

housing mobility programs can improve the quality of 

the immediate environments that families experience -in 

particular living conditions related to housing quality, 

neighborhood poverty and other aspects of disadvantage, 

and safety. MTO-induced improvements in housing and 

neighborhood conditions appear to improve some aspects 

of physical and mental health. Most noteworthy here are 

reductions in extreme obesity and diabetes and better 

mental health for adults. MTO moves may also reduce 

involvement in some forms of risky or criminal behavior 

of youth, most notably drug selling. However, moving 

to lower-poverty neighborhoods as part of an MTOtype residential mobility program does not appear to 

improve educational outcomes, employment, or earnings. 

The cause of high rates of dropout and unemployment 

and low earnings found in many disadvantaged urban 

neighborhoods may rest with barriers that cannot be 

captured by neighborhood demographics, socio-economic 

composition, and social processes, at least within the 

range of neighborhood variation that mobility programs 

like MTO are able to modify. Put differently, MTO 

was more successful in improving mental and physical 

health in poor families that signed up to participate in 

the program than in bringing about the improvements 

in education and labor market outcomes needed to boost 

family incomes.

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

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

RISKY AND DELINQUENT

BEHAVIOR FOR YOUTH 

AGES 13–20

RISKY BEHAVIOR INDEX [SR]

All 0.467 – 0.001 – 0.002 0.007 0.010 4,623

(0.014) (0.029) (0.015) (0.022)

Female 0.442 – 0.027 – 0.054 – 0.017 – 0.026 2,358

(0.019) (0.037) (0.020) (0.031)

Male 0.491 0.025 0.053 0.029 0.042 2,265

(0.018) (0.039) (0.020) (0.028)

BEHAVIOR PROBLEMS INDEX [SR]

All 0.379 0.004 0.008 0.009 0.013 4,629

(0.010) (0.022) (0.011) (0.017)

Female 0.371 – 0.007 – 0.014 – 0.010 – 0.015 2,361

(0.014) (0.028) (0.015) (0.024)

Male 0.387 0.015 0.032 0.027~ 0.038~ 2,268

(0.015) (0.032) (0.016) (0.023)
xxxi

EXHIBIT ES–10. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

RISKY AND DELINQUENT

BEHAVIOR FOR YOUTH 

AGES 13–20 (CONTINUED)

DELINQUENCY INDEX [SR]

All 0.146 – 0.002 – 0.004 0.008 0.012 4,625

(0.008) (0.017) (0.009) (0.014)

Female 0.110 – 0.006 – 0.011 – 0.005 – 0.008 2,360

(0.009) (0.019) (0.010) (0.016)

Male 0.181 0.002 0.004 0.021 0.030 2,265

(0.013) (0.029) (0.016) (0.022)

EVER SMOKED [SR]

All 0.312 0.042* 0.088* 0.043* 0.064* 4,618

(0.019) (0.039) (0.020) (0.030)

Female 0.297 0.022 0.044 0.016 0.026 2,355

(0.025) (0.051) (0.028) (0.043)

Male 0.327 0.062* 0.134* 0.069* 0.098* 2,263

(0.025) (0.054) (0.028) (0.040)

EVER HAD ALCOHOLIC DRINK [SR]

All 0.534 – 0.032 – 0.067 – 0.017 – 0.026 4,618

(0.020) (0.041) (0.021) (0.032)

Female 0.541 – 0.061* – 0.124* – 0.032 – 0.050 2,355

(0.026) (0.053) (0.029) (0.045)

Male 0.528 – 0.003 – 0.006 – 0.004 – 0.005 2,263

(0.027) (0.058) (0.029) (0.041)

NUMBER OF ARRESTS BY

CRIME TYPE FOR YOUTH AGES

15–20

VIOLENT CRIME ARRESTS [CJR]

All 0.325 0.043 0.091 – 0.062 – 0.094 4,717

(0.037) (0.078) (0.039) (0.059)

Female 0.155 0.027 0.055 – 0.048 – 0.074 2,300

(0.033) (0.069) (0.033) (0.050)

Male 0.481 0.060 0.128 – 0.076 – 0.115 2,417

(0.064) (0.138) (0.068) (0.102)

PROPERTY CRIME ARRESTS [CJR]

All 0.239 0.065* 0.136* – 0.013 – 0.019 4,717

(0.031) (0.064) (0.034) (0.051)

Female 0.091 0.044~ 0.090~ – 0.010 – 0.015 2,300

(0.026) (0.053) (0.023) (0.035)

Male 0.375 0.086 0.183 – 0.016 – 0.025 2,417

(0.054) (0.117) (0.060) (0.090)
xxxii

EXHIBIT ES–10. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

NUMBER OF ARRESTS BY

CRIME TYPE FOR YOUTH AGES

15–20 (CONTINUED)

DRUG POSSESSION ARRESTS [CJR]

All 0.117 0.010 0.020 – 0.034~ – 0.052~ 4,717

(0.019) (0.039) (0.019) (0.028)

Female 0.013 0.005 0.009 – 0.011 – 0.017 2,300

(0.010) (0.021) (0.010) (0.016)

Male 0.213 0.015 0.032 – 0.057 – 0.085 2,417

(0.035) (0.075) (0.035) (0.052)

DRUG DISTRIBUTION ARRESTS [CJR]

All 0.091 – 0.025 – 0.052 – 0.014 – 0.021 4,717

(0.017) (0.036) (0.022) (0.033)

Female 0.006 0.011 0.023 – 0.006 – 0.009 2,300

(0.009) (0.020) (0.010) (0.015)

Male 0.169 – 0.059~ – 0.127~ – 0.020 – 0.031 2,417

(0.032) (0.069) (0.042) (0.063)

OTHER CRIME ARRESTS [CJR]

All 0.306 0.000 – 0.001 – 0.048 – 0.072 4,717

(0.034) (0.071) (0.037) (0.055)

Female 0.090 0.015 0.030 0.021 0.032 2,300

(0.023) (0.047) (0.026) (0.040)

Male 0.503 – 0.014 – 0.029 – 0.113~ – 0.170~ 2,417

(0.064) (0.136) (0.067) (0.100)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered a 

Moving To Opportunity (MTO) housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean 

is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 

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

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

Model: Experimental and Section 8 impacts were estimated jointly using an Ordinary Least Squares (OLS) regression model controlling for baseline covariates, 

weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as 

an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: For the self-reported measures, the data source is the youth long-term survey, and the sample is interviewed youth ages 13 to 20 as 

of December 31, 2007. For the arrest measures, the data source is individual criminal justice system arrest data (adult and juvenile data from California, Illinois, 

and Maryland; de-identified adult data from New York State; juvenile data from New York City; and adult or juvenile records from 8 additional states in which 

participants have lived), and the sample is all core household members ages 15 to 20 as of December 31, 2007.

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

The Behavior Problems Index is the fraction of 11 problem behaviors (for example, difficulty concentrating and having a strong temper) that the youth reported 

as true or sometimes true at present or in the past 6 months. The Delinquency Index is the fraction of 8 delinquent behaviors (for example, carrying a gun and 

destroying property) that the youth reported ever having exhibited. Violent crime arrests involve charges of force or threat of force including homicide, rape, 

robbery, assault, kidnapping, and weapons charges. Property crime arrests involve taking money or property and include burglary, motor vehicle theft, larceny, 

trespassing, and receiving stolen property. Drug crime arrests are classified as either possession or distribution arrests. "Other" arrest charges cover any charges 

not classified as violent, property, or drug crimes and include disorderly conduct and moving violations.
xxxiii

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

ADULTS, LONG-TERM EVALUATION

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

GROWN CHILDREN

NUMBER OF...

ANY CRIME ARRESTS [CJR]

All 2.862 – 0.090 – 0.212 – 0.116 – 0.202 4,641

(0.171) (0.404) (0.192) (0.332)

Female 0.967 – 0.054 – 0.126 – 0.037 – 0.063 2,277

(0.131) (0.306) (0.157) (0.269)

Male 4.673 – 0.124 – 0.296 – 0.192 – 0.335 2,364

(0.303) (0.726) (0.347) (0.607)

VIOLENT CRIME ARRESTS

[CJR]

All 0.626 – 0.055 – 0.129 – 0.042 – 0.072 4,641

(0.047) (0.112) (0.054) (0.093)

Female 0.279 – 0.038 – 0.089 – 0.047 – 0.080 2,277

(0.045) (0.105) (0.051) (0.087)

Male 0.958 – 0.071 – 0.169 – 0.038 – 0.066 2,364

(0.082) (0.196) (0.092) (0.161)

PROPERTY CRIME ARRESTS 

[CJR]

All 0.633 – 0.006 – 0.014 – 0.042 – 0.072 4,641

(0.054) (0.128) (0.061) (0.105)

Female 0.297 – 0.057 – 0.132 – 0.016 – 0.027 2,277

(0.046) (0.107) (0.065) (0.112)

Male 0.953 0.044 0.105 – 0.064 – 0.112 2,364

(0.093) (0.222) (0.100) (0.175)

DRUG POSSESSION ARRESTS 

[CJR]

All 0.461 – 0.042 – 0.100 – 0.011 – 0.020 4,641

(0.044) (0.104) (0.053) (0.093)

Female 0.069 0.001 0.002 0.015 0.026 2,277

(0.030) (0.070) (0.035) (0.060)

Male 0.835 – 0.084 – 0.202 – 0.037 – 0.065 2,364

(0.082) (0.196) (0.103) (0.179)

DRUG DISTRIBUTION ARRESTS 

[CJR]

All 0.338 – 0.047 – 0.111 – 0.063~ – 0.110~ 4,641

(0.035) (0.082) (0.037) (0.064)

Female 0.058 – 0.023 – 0.055 0.018 0.032 2,277

(0.024) (0.056) (0.028) (0.049)

Male 0.605 – 0.069 – 0.165 – 0.140* – 0.245* 2,364

(0.063) (0.151) (0.067) (0.116)
xxxiv

EXHIBIT ES–11. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

GROWN CHILDREN (CONTINUED)

OTHER CRIME ARRESTS

[CJR]

All 0.804 0.060 0.141 0.042 0.072 4,641

(0.066) (0.155) (0.074) (0.128)

Female 0.263 0.063 0.147 – 0.008 – 0.013 2,277

(0.052) (0.121) (0.054) (0.092)

Male 1.321 0.056 0.134 0.087 0.153 2,364

(0.116) (0.278) (0.137) (0.239)

ADULTS

NUMBER OF...

Any crime arrests [CJR] 0.567 0.052 0.110 – 0.016 – 0.026 4,376

(0.064) (0.135) (0.060) (0.096)

Violent crime arrests [CJR] 0.149 0.001 0.002 – 0.009 – 0.014 4,376

(0.020) (0.043) (0.022) (0.035)

Property crime arrests [CJR] 0.133 0.043~ 0.091~ – 0.002 – 0.002 4,376

(0.025) (0.052) (0.022) (0.036)

Drug possession arrests 

[CJR]

0.080 0.006 0.013 – 0.004 – 0.006 4,376

(0.018) (0.038) (0.019) (0.030)

Drug distribution arrests 

[CJR]

0.035 – 0.007 – 0.016 0.001 0.001 4,376

(0.008) (0.017) (0.009) (0.015)

Other crime arrests [CJR] 0.171 0.009 0.020 – 0.003 – 0.005 4,376

(0.027) (0.056) (0.025) (0.040)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered a 

Moving To Opportunity (MTO) housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean 

is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 

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

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS (Ordinary Least Squares) regression model controlling for baseline covariates, 

weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an 

interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details. 

Data source and sample: Individual criminal justice system arrest data. Adult and juvenile data from California, Illinois, Massachusetts, and Maryland; de-identified 

adult data from New York State; juvenile data from New York City; and adult or juvenile records from 8 additional states in which participants have lived. The grown 

children sample is all core household members who are now grown children (under age 18 at baseline and ages 21 to 30 as of December 31, 2007). The adult 

sample is all long-term survey sampling frame adults with a baseline consent form. 

Measures: Violent crime arrests involve charges of force or threat of force including homicide, rape, robbery, assault, kidnapping, and weapons charges. Property 

crime arrests involve taking money or property and include burglary, motor vehicle theft, larceny, trespassing, and receiving stolen property. Drug crime arrests 

are classified as either possession or distribution arrests. "Other" arrest charges cover any charges not classified as violent, property, or drug crimes and include 

disorderly conduct and moving violations.
xxxv

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

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

ACHIEVEMENT ASSESSMENT 

RESULTS FOR YOUTH AGES 

13–20

READING ASSESSMENT

SCORE [ECLS-K]

All 0.000 0.003 0.006 0.040 0.058 4,432

(0.041) (0.085) (0.044) (0.064)

Female 0.051 – 0.020 – 0.040 0.054 0.081 2,286

(0.055) (0.110) (0.061) (0.093)

Male – 0.050 0.027 0.058 0.025 0.035 2,146

(0.056) (0.118) (0.058) (0.081)

MATH ASSESSMENT

SCORE [ECLS-K]

All 0.000 – 0.025 – 0.052 0.000 0.000 4,420

(0.044) (0.090) (0.048) (0.069)

Female – 0.004 – 0.034 – 0.069 – 0.033 – 0.050 2,280

(0.055) (0.112) (0.061) (0.093)

Male 0.004 – 0.016 – 0.034 0.034 0.047 2,140

(0.060) (0.128) (0.067) (0.093)

CHARACTERISTICS OF

THE AVERAGE SCHOOL

ATTENDED BY YOUTH

AGES 10–20

Share minority [CCD, PSS] 0.904 – 0.037* – 0.077* – 0.016~ – 0.023~ 5,077

(0.008) (0.016) (0.008) (0.012)

Share eligible for free lunch 

[CCD]

0.701 – 0.048* – 0.101* – 0.026* – 0.039* 5,043

(0.007) (0.015) (0.008) (0.012)

Number of students [CCD, 

PSS]

927.1 25.4~ 53.0~ 41.4* 61.9* 5,077

(14.9) (31.1) (17.5) (26.1)

Pupil-teacher ratio [CCD, 

PSS]

17.834 – 0.103 – 0.214 – 0.061 – 0.091 5,076

(0.103) (0.215) (0.113) (0.170)

School-level percentile 

ranking on state exam [SLAD]

18.684 3.070* 6.430* 1.218~ 1.810~ 4,884

(0.651) (1.364) (0.661) (0.983)

SCHOOLING OUTCOMES

FOR OLDER YOUTH

EDUCATIONALLY ON-TRACK 

[SR]

All (ages 15–20) 0.814 – 0.014 – 0.028 – 0.029 – 0.044 3,614

(0.018) (0.037) (0.021) (0.031)
xxxvi

EXHIBIT ES–12. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

EDUCATIONALLY ON-TRACK 

[SR] (CONTINUED)

Female 0.827 – 0.007 – 0.015 0.008 0.012 1,842

(0.023) (0.046) (0.026) (0.040)

Male 0.801 – 0.019 – 0.041 – 0.066* – 0.096* 1,772

(0.026) (0.055) (0.029) (0.042)

HIGH SCHOOL DIPLOMA [SR]

All (ages 19–20) 0.622 – 0.073~ – 0.141~ – 0.056 – 0.092 1,125

(0.038) (0.073) (0.042) (0.068)

Female 0.708 – 0.087~ – 0.169~ – 0.054 – 0.089 576

(0.048) (0.094) (0.054) (0.088)

Male 0.536 – 0.058 – 0.110 – 0.058 – 0.094 549

(0.058) (0.109) (0.062) (0.102)

POST-SECONDARY

OUTCOMES FOR OLDER

YOUTH

SINCE 2007, ATTENDED 

ANY COLLEGE [NSC]

All (ages 15–20) 0.262 – 0.014 – 0.029 – 0.021 – 0.033 4,717

(0.016) (0.033) (0.017) (0.025)

Female 0.305 – 0.011 – 0.023 0.000 0.001 2,300

(0.023) (0.048) (0.025) (0.039)

Male 0.222 – 0.016 – 0.033 – 0.042* – 0.064* 2,417

(0.021) (0.044) (0.021) (0.032)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered a 

Moving To Opportunity (MTO) housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean 

is unadjusted. Unless otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 

0.250 for Working would indicate that 25 percent of the control group was working). Average school characteristics are weighted by the amount of time youth spent 

at each school.

Square brackets indicate the source of the outcome information: ECLS-K = achievement assessment from the Early Childhood Longitudinal Study-Kindergarten 

cohort study, CCD = Common Core of Data, PSS = Private School Universe Survey, SLAD = School-level assessment data from the National Longitudinal SchoolLevel State Assessment Score Database, SR = self-report, NSC = 

Model: Experimental and Section 8 impacts were estimated jointly using an Ordinary Least Squares (OLS) regression model controlling for baseline covariates, 

weighted, and clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as 

an interaction with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Executive Summary Exhibit 12. (continued)

Data source and sample: For ECLS-K scores and self-reported measures, the data source is the youth long-term survey. In some cases, the youth school histories 

on which the school characteristics measures are based combine self-reports from the long-term survey with parent reports from the interim survey. The sample is 

comprised of long-term survey interviewed youth of varying age ranges (as of December 31, 2007), with details included above. The sample for the NSC measure is 

all core household members ages 15 to 20 as of December 31, 2007.

Measures: The reading and math achievement assessment scores are theta scores transformed into z-scores via standardization on the mean and standard 

deviation for control group youth ages 13 to 20. The school-level percentile ranking on state exam measure includes schools through 8th grade only for New 

York and Massachusetts. On-track youth are those who were currently in school or received a high school diploma or GED (certificate of General Educational 

Development). 
xxxvii

MOVING TO OPPORTUNITY 

FOR FAIR HOUSING 

DEMONSTRATION PROGRAM

FINAL IMPACTS EVALUATION
xxxviii
1 Chapter 1: The MTO Long-Term Study

THE MTO LONG-TERM STUDY
2

CHAPTER 1

THE MTO LONG-TERM STUDY

The neighborhoods of almost every American 

city provide dramatic contrasts in the well-being 

of their residents. Children and adults in highpoverty neighborhoods fare worse on a wide range of 

socioeconomic and health outcomes than do residents 

of more affluent areas. These disparities have prompted 

concern about the geographic concentration of poverty 

in the United States and about the possibility that 

neighborhood environments themselves may directly 

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

1987).

Why might social problems be much more prevalent 

in some neighborhoods than in others? Some social 

scientists emphasize differences in the quality of the 

local housing stock and in the formal institutions that 

serve neighborhood residents, such as schools, police, 

or health care providers. Others point to differences 

in the capacity of residents to develop the informal 

social institutions that provide local public goods, 

as well as differences in the presence of positive peer 

influences and adult role models (Durlauf, 2004; 

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

a core group of middle-income residents with high 

school diplomas and stable employment may be crucial 

for serving as role models who reinforce (and signal) 

the value of education and work, and to support a 

neighborhood’s capacity to develop well-functioning 

informal local networks that may help monitor and 

support local children. Neighborhoods may also vary 

in the degree to which peer norms support prosocial 

rather than antisocial behaviors. Gauging the role 

that neighborhood environments play in determining 

the well-being of poor families is vital for housing 

and other public policies that affect the degree of 

geographic concentration of poverty in America.

Yet it is difficult in practice to determine the causal

effects of neighborhood contexts on the long-term 

life chances of poor families. The social and medical 

sciences have used data from population surveys to link 

individuals’ earnings, test scores, criminal involvement, 

health, and other outcomes to information about the 

characteristics of their residential neighborhoods, 

which are usually measured at the census tract or ZIP 

code level. Studies typically find that neighborhood 

attributes help predict the outcomes of adults and 

children, even after adjusting for a large number of 

family background and individual characteristics. (For 

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

1997; Kawachi and Berkman, 2003a; Leventhal and 

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

Gannon-Rowley, 2002.) A primary concern with 

these observational or “nonexperimental” studies is 

that families often have at least some degree of choice 

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

attributes directly determine both family well-being 

and selection of residential location, then what looks 

like a “neighborhood effect” might instead be caused 

by hard-to-observe individual attributes associated with 

both outcomes and residential decisions—what social 

scientists call “selection bias.”

To better understand the causal effects of neighborhood 

environments on the lives of low-income families, in 

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

Urban Development (HUD) launched a randomized 

housing mobility experiment known as Moving to 

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

demonstration program randomly assigned families the 

opportunity to receive housing vouchers and assistance 

in moving to low-poverty neighborhoods (census tracts 

with 1990 poverty rates below 10 percent). Eligibility 

for MTO was limited to families with children who 

were living in public housing or in project-based 

Section 8 assisted housing located in high-poverty areas 

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

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

experiment designed to identify the effects of moving 

from high-poverty to lower-poverty neighborhoods on 

the social and economic prospects, schooling, health, 

and well-being of low-income families.
3 Chapter 1: The MTO Long-Term Study

This report summarizes the results of the long-term 

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

surveys, physical health measures, and government 

administrative records to examine outcomes 10 to 15 

years after families entered the study. This introductory 

chapter provides background on the motivation and 

design of MTO and discusses how the randomized 

experimental design helps overcome concerns with 

selection bias. It also discusses the baseline characteristics 

and basic mobility outcomes of MTO families, the key 

results from previous waves of MTO research, and the 

design of the long-term MTO study. The remaining 

chapters present the main findings.

1.1 HOW HOUSING POLICIES MIGHT 

AFFECT LOW-INCOME FAMILIES

The MTO study contributes to debates on housing 

policy strategies as well as to the large body of scientific 

and policy research on neighborhood effects. The effects 

of moving families from one type of neighborhood 

environment to another need not be identical to 

what would happen if we changed the neighborhood 

environments in which people were living without 

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

moving itself could create some temporary disruption 

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

aspects of residential mobility that affect people’s wellbeing and life chances is still relevant to the research on 

neighborhood effects, and might help inform the design 

of “place-based” community-level interventions as well as 

residential mobility programs.

The federal government’s intervention in the housing 

market began in earnest during the New Deal in the 

1930s, motivated by concerns about the failure of the 

private housing market to provide decent shelter for 

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

quality—for example, units without windows or adequate 

plumbing or ventilation—was thought to contribute to 

infant mortality and other adverse health outcomes, as 

well as other social problems such as delinquency (Hunt, 

2009: 17; see also Newman, 1999; Patterson et al., 2004; 

Harkness and Newman, 2005). These concerns resulted 

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

of public housing that peaked in size in the mid-1990s at 

around 1.4 million units (Olsen, 2003).

Although public support for government housing 

assistance for the poor remains strong, policymakers 

have become increasingly interested in alternatives to 

public housing that may help deconcentrate poverty in 

America.1

 Of particular concern have been conditions 

inside the nation’s most distressed public housing 

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

West Side that Alex Kotlowitz chronicled in his book, 

There Are No Children Here (Kotlowitz, 1992; see also 

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

housing vouchers and related types of “tenant-based” 

subsidies have accounted for a growing share of all new 

federal commitments for low-income housing (Quigley, 

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

local public housing authorities (PHAs) to provide 

families in “distressed” public housing with housing 

vouchers, which provide subsidies to rent units in the 

private housing market. The 1998 Housing Act also 

makes it easier for PHAs to convert other projects to 

vouchers. 

Because tenant-based subsidies provide families with 

more choice over where they live, recipients tend to 

reside in lower-poverty census tracts than do families 

with project-based subsidies (Newman and Schnare, 

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

al., 2003; Olsen, 2003).3

 Other housing policy decisions 

also influence the geographic concentration of poverty, 

such as decisions about where to locate project-based 

units, how much funding to dedicate to developing 

mixed-income housing projects, and how to fund and 

operate tenant-based subsidy programs. If neighborhood 

context exerts an independent effect on family behavior, 

then housing programs could have cascading influences 

on different aspects of family life, such as earnings, 

schooling, health, and child development. This is 

important because the goal of federal housing policy 

1 A 2001 survey by National Public Radio, the Kaiser Family Foundation, and Harvard University found that 75 percent of respondents 

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

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

accessed May 5, 2010).

2 A third possibility is government subsidies to private providers of 

specific housing units. Such policies impose locational constraints on 

recipients as do other project-based subsidies.

3 A number of studies provide suggestive evidence that per-unit costs 

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

(GAO, 2001; HUD, 2000; Olsen, 2000; Shroder and Reiger, 2000).
4

since at least the Housing Act of 1949 has been to 

improve family well-being, broadly defined. 

A large theoretical literature in the social and medical 

sciences suggests several mechanisms through which 

neighborhood characteristics, above and beyond a 

family’s own economic and housing conditions, may 

affect family outcomes. Our own theoretical orientation 

leads us to believe that a model recognizing that people 

respond to incentives can be useful for understanding 

and predicting behavior in many circumstances. But 

unlike some “rational choice” approaches, we recognize 

that aspects of the social environment can also shape the 

way people respond to incentives, or even directly shape 

the incentives and opportunities that people face for 

engaging in different activities.

Exhibit 1.1 presents a general model depicting the 

MTO intervention’s hypothesized pathways that lead 

from community-level mediators, to individual- and 

family-level mediators, to outcomes (as originally 

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

influential typology of Jencks and Mayer (1990) defines 

four different types of models or theories about why 

neighborhood environments might affect people’s 

behavioral outcomes and well being—epidemic models, 

collective socialization, institutional models, and relative 

deprivation or competition models. Constructing strong 

non-experimental empirical tests of these models has 

been challenging for the research community, and new 

theoretical work has elaborated on many of the types of 

models mentioned by Jencks and Mayer (see for example, 

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

typology nevertheless remains useful for characterizing 

alternative hypotheses for how residential mobility 

through MTO, which alters neighborhood context, may 

affect the outcomes for MTO adults and children.
5

EXHIBIT 1.1. HYPOTHESIZED PATHWAYS OF MTO IMPACTS

Housing Market

Conditions

Economic

Opportunities

Social and Physical

Environment

Social Norms 

and Values

Schools

Housing Mobility 

and Assistance

Adult Education,

Employment, and Earnings

Household Income and

Public Assistance

Adult/Youth/Child

Mental and Physical Health

Youth/Child Social 

Well-Being

(including Delinquency and Risky Behavior)

Child/Youth

Educational Performance

Adult Social

Networks

Child/Youth

Peer Networks,

Peer Behavior

Home Environment

(including Parenting Behavior)

Parent Attitudes Towards

Own and Children’s

Education, Achievement

Child/Youth Attitudes

Toward Own Education,

Achievement

COMMUNITY-LEVEL

MEDIATORS

PERSON- AND FAMILY-LEVEL

MEDIATORS OUTCOMES

MTO INTERVENTION RELOCATION TO LOW-POVERTY NEIGHBORHOODS

MTO = Moving To Opportunity

Exhibit 1.1 is a reformatted version of Exhibit 1.1 in the MTO interim impacts evaluation (see Orr et al., 2003)
6

Epidemic models emphasize the roles played by peer 

processes, and predict that MTO moves into less 

disadvantaged and safer neighborhoods will improve 

outcomes by exposing people to different peer groups. 

Epidemic models suggest that peers can spread behaviors 

through learning, imitating their peers, stigma effects 

(that is, the negative stigma from antisocial behaviors 

declines when more people do them), and physical 

circumstances by which, for example, higher rates of 

crime reduce the chances of getting arrested (Cook and 

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

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

employed people can increase one’s chances of gaining 

employment through increased social support of work, or 

by receiving job information or references from neighbors 

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

health outcomes by changing exposure to different types 

of what Fitzpatrick and LaGory (2000) call “healthrelated subcultures,” which may influence the availability 

of information about healthy behaviors or social support 

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

affect the likelihood of feelings of anomie (Sampson and 

Bartusch, 1998; Browning and Cagney, 2003). 

Some epidemic models predict that peer influences on 

behavior will vary with the prevalence of the behavior 

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

in which the prevalence of the behavior can surge 

dramatically once the share of people engaging in that 

behavior crosses some threshold (Crane, 1991; Schelling, 

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

people respond to a given type of neighborhood or peer 

environment in the same way, then many epidemic 

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

where individuals respond to the average behavior of 

their neighborhood peers—predict that policies to reduce 

the concentration of disadvantaged families within 

highly distressed neighborhoods may simply redistribute 

the volume of anti-social behavior across different 

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

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

engages in delinquency is only influenced by their social 

environment if more than (say) a third of neighborhood 

youth engage in delinquency, then policies that help 

reduce the concentration of disadvantage can help reduce 

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

Moves to lower-poverty areas may also affect the extent 

to which neighborhood adults influence young people 

who are not their children, what Jencks and Mayer 

(1990) call collective socialization models. For example, 

children’s language development and other learning 

outcomes may benefit if children live in areas with more 

highly educated adults (Borjas, 1995). Neighborhood 

adults who have completed high school and work in the 

formal labor market might also help serve as role models 

who signal the value of prosocial activities (Wilson, 

1987). Neighborhoods may also vary in the degree to 

which local adults are willing or able to work together to 

enforce shared values, what Sampson, Raudenbush, and 

Earls (1997) call “collective efficacy” (see also Coleman, 

1988).

Moves might also enable families to access more 

productive institutional resources. Families may gain 

access to better schools or a higher quality (or quantity) 

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

and Kain, 2005). Adults and teenagers moving out 

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

opportunities, as in the “spatial mismatch” hypothesis 

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

places with higher quality health clinics, safe places 

for exercise, or grocery stores that sell fresh fruits and 

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

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

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

2003; Subramanian et al., 2006; Zenk et al., 2003). 

The move may also reduce exposure to pollution, noise, 

tobacco and alcohol ads, density of fast food restaurants 

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

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

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

Skogan, 1990; Thompson and Krause, 2000).

In addition, models from behavioral economics suggest 

that MTO could affect behavior by reducing people’s 

exposure to daily stressors that sap mental energy and 

willpower, and make it harder for people to resist 

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

local context of neighborhoods can also affect self-control 

and the salience of temptations to buy or use drugs or 

engage in crime.
7

Mobility could also improve children’s life chances 

through changes in their household environments by, 

for example, reducing parental stress in ways that lead to 

less harsh and more supportive or cognitively stimulating 

parenting. Neighborhoods could also vary in the 

availability of “marriageable men,” which could increase 

marriage rates and family income for the mostly female 

heads of household and reduce the likelihood that their 

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

2004; Wilson, 1987).

Not every social science theory predicts that moves will 

reduce involvement with risky or antisocial behaviors. 

Competition models emphasize the potential for 

increased competition for scarce resources like jobs or 

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

may face in more affluent neighborhoods. The failure 

to successfully compete for prosocial rewards could 

lead some people to instead reverse course and try 

competing for resources or social standing by engaging 

in antisocial behaviors. Comparisons with the status 

and accomplishments of new neighbors in more affluent 

areas could also have a negative psychological impact, 

as predicted in relative deprivation models (Luttmer, 

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

MTO adults and children choose social “micro-climates” 

similar to those in their original neighborhoods (Jencks 

and Mayer, 1990).4 Because these competing theories 

make different predictions about how moves could affect 

the behavior and well-being of parents and their children, 

MTO’s net effect on the well-being and life chances of 

families is ultimately an empirical question.

All of these theories of how neighborhood environments 

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

families are plausible. Yet determining the degree to 

which these theoretical mechanisms operate in real life 

is complicated by the fact that most people have at 

least some degree of choice over where they live. Those 

residential choices are influenced by an individual’s 

own characteristics, background, and other factors, not 

all of which can be adequately measured in any social 

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

distressed areas through MTO could be lost if youth simply sort themselves back into peer groups that engage in and support antisocial behaviors, even if these types of antisocial peer groups are somewhat less 

common in their new neighborhood than in their old neighborhood.

science study. This makes it difficult to disentangle the 

effects of neighborhoods on outcomes from those of 

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

that are associated with residential choices. Suppose 

a low-income child growing up in the racially and 

economically integrated Chicago neighborhood of Hyde 

Park (home to the University of Chicago and President 

Obama’s private residence) does better in school than 

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

very economically distressed, almost entirely AfricanAmerican neighborhood directly adjacent to Hyde Park). 

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

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

community per se or to whatever it was about her family 

that motivated or enabled them to live in Hyde Park.

This difficulty, called selection bias, is a challenge to 

observational (nonexperimental) studies that interview 

families at their current residence. MTO solves this 

concern by randomly assigning some families but not 

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

areas. By virtue of random assignment, the two groups 

of families should start out identical on average in 

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

group receives assistance to move to lower-poverty 

communities. Thus, we can attribute any differences in 

average outcomes across the groups to the causal effects 

of moving to low-poverty areas.

Although the design allows us to disentangle causal 

effects of neighborhoods from selection biases, the 

fact that MTO-induced moves change so many 

environmental characteristics at once makes it difficult 

to pinpoint the specific aspects of neighborhood 

environments that influence family outcomes. But as we 

discuss below, experimental estimates can help us rule 

out the importance of certain mediating mechanisms 

as explanations for the observed impacts. A review of 

the overall pattern of results can provide some support 

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

the theoretical relationship between mechanisms and 

outcomes. Analyses of the impact of MTO-induced 

moves on potential mediating variables can help us 

narrow the possible neighborhood aspects that are most 

important in accounting for MTO’s impacts on key 

socioeconomic and health outcomes. For example, if 
8

we observe an impact on obesity, and we also observe 

that MTO substantially improves the safety of the 

neighborhoods in which families reside but has few 

detectable effects on diet and exercise, this pattern 

would suggest that something about safety (such as 

psychological stress) might be more important in 

explaining the obesity results than explanations that 

revolve around changing diet and exercise, such as 

availability of exercise opportunities or food stores.

1.2 MOTIVATION FOR THE MOVING TO 

OPPORTUNITY EXPERIMENT

HUD sponsored MTO to identify the causal effects 

of neighborhood environments on the well-being and 

life outcomes of low-income families. It was motivated 

in part by an influential series of studies of the 

Chicago-based Gautreaux residential mobility program 

(Rubinowitz and Rosenbaum, 2000), which was named 

after the plaintiff (Dorothy Gautreaux) in a 1966 racial 

discrimination lawsuit filed against the Chicago Housing 

Authority (CHA) and HUD. The lawsuit charged 

discrimination on the basis of the heavy concentration 

of African-American families in public housing projects 

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

agreed in 1976 and ordered CHA to provide housing 

vouchers to African-American public housing residents 

that they could use only in neighborhoods in the city or 

suburbs that were less than 30 percent African-American. 

Units were assigned to families on a wait list who were 

either living in or eligible for public housing (Rubinowitz 

and Rosenbaum, 2000). Some of those apartments 

were in areas of Chicago that were poor and segregated, 

but improving, whereas others were in low-poverty, 

predominantly white or integrated suburban areas (Keels 

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

offered portable Section 8 vouchers. The nonprofit in 

charge of implementing the Gautreaux program helped 

identify rental units for families on the program wait list, 

and most families are believed to have taken the first or 

second unit offered to them.

A 1988 follow-up survey of 342 families who used 

Gautreaux vouchers found that moving to the Chicago 

suburbs instead of other parts of the city was associated 

with significant improvements in children’s eventual 

educational attainment. Compared with the surveyed 

students who remained in the city of Chicago, suburban 

movers were one-quarter as likely to have dropped out 

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

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

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

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

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

4 percent). The only educational attainment measure for 

which the suburban students did not appear to be doing 

significantly better than the city students was grade point 

average, which could reflect higher grading standards in 

suburban schools (Rubinowitz and Rosenbaum, 2000: 

134–136). However more recent studies of Gautreaux 

show fewer differences between city and suburban movers 

(Deluca et al., 2010).

Although the Gautreaux program has been extremely 

influential in housing policy circles, the study was 

nevertheless not a true randomized experiment. Families 

may have had some degree of choice about whether to 

accept the first apartment offered to them, and indeed 

there is some evidence that the baseline characteristics of 

families that ended up in the suburbs are systematically 

different from those who stayed in the city (Mendenhall, 

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

This has made researchers nervous that the Gautreaux 

city and suburban movers may have differed in 

preexisting unobserved characteristics as well, which 

could lead analysts to confound the causal effects of 

suburban moves with the influence of these unmeasured 

attributes that may affect both the likelihood of moving 

to the suburbs and the resulting outcomes.

THE MTO DEMONSTRATION

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

Housing and Community Development Act of 1992 (for 

more background on MTO, see Goering et al., 1999; 

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

enrolled in MTO between 1994 and 1998 in five cities: 

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

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

and live in public housing developments or project-based 

assisted housing in high-poverty areas (census tracts in 

which more than 40 percent of the population was living 

in poverty in 1990). The PHAs in each city conducted 
9

outreach to all eligible households through fliers, tenant 

associations, and other means, and all those interested 

received the opportunity to apply for this special 

program. Interested households joined waiting lists of 

the local PHAs. Families on the waiting list attended 

group orientation sessions where they learned about the 

demonstration program, its experimental design, and 

specifically the fact they would be randomly assigned to 

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

that remained interested after the briefing completed 

the baseline survey and signed an enrollment agreement. 

Before formal acceptance into the program, families were 

screened for Section 8 eligibility. 

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

the eligible families living within the designated eligible 

housing projects, enrolled in MTO from 1994 to 1998 

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

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

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

and most of the rest were Hispanic (see Exhibit 1.2 

for adult characteristics and Exhibit 1.3 for youth 

characteristics).5

 Three-quarters of household heads were 

on welfare and the average annual household income was 

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

Fewer than 40 percent had graduated from high school.

These households had an average of 2.5 children under 

age 18. Although we do not know their immigration 

status, most spoke English.

5 Supplemental Exhibit 1.1 shows characteristics of the MTO participants who were under 18 when enrolled but adults (aged 21 or older) at 

the time prior to the initiation of the long-term MTO study in December 2007, a group that we call “grown children” in this report.

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

GROUP

EXPERIMENTAL 

GROUP

SECTION 8 

GROUP

CONTROL 

GROUP

ALL GROUPS

SAMPLE ADULT 

CHARACTERISTICS (SR)

Female 98.2% 97.5% 97.4% 97.8%

AGE

As of December 2007 45.2 45.3 45.0 45.2

At baseline 33.6 33.7 33.3 33.5

RACE/ETHNICITY

African-American non-Hispanic 61.1% 60.7% 62.5% 61.4%

Hispanic 30.9% 31.9% 30.2% 31.0%

White non-Hispanic 2.9% 2.5% 2.4% 2.6%

American Indian non-Hispanic 0.5% 0.3% 0.2% 0.4%

Asian/Pacific Islander non-Hispanic 1.6% 2.1% 1.1% 1.6%

Other non-Hispanic 1.8% 1.9% 2.2% 1.9%

MARITAL STATUS

Never married 61.3% 62.0% 63.3% 62.1%

Married 11.5% 11.7% 10.4% 11.2%

Divorced 9.3% 8.8% 8.7% 9.0%

Widowed or separated 16.8% 16.5% 16.5% 16.6%

EDUCATION

High school diploma 38.9% 37.1% 36.1% 37.5%

GED 16.5% 17.8% 18.9% 17.6%

Currently in School 15.9% 16.7% 16.4% 16.3%
10

EXHIBIT 1.2. (CONTINUED)

EXPERIMENTAL 

GROUP

SECTION 8 

GROUP

CONTROL 

GROUP

ALL GROUPS

SAMPLE ADULT 

CHARACTERISTICS (SR)

EMPLOYMENT STATUS AND INCOME

Full-time 14.5% 14.0% 14.1% 14.2%

Part-time 11.5% 10.5% 9.8% 10.7%

Not working 70.4% 72.6% 72.5% 71.7%

Household Income $12,916.87 $12,703.70 $12,827.91 $12,826.81

GOVERNMENT BENEFITS

AFDC/TANF 75.0% 75.7% 74.6% 75.1%

Supplemental Security Income 17.7% 17.0% 17.0% 17.2%

Food Stamps 80.7% 81.1% 79.9% 80.6%

Women, Infants, and Children 35.4% 36.0% 35.1% 35.5%

TAKES OVER 30 MINUTES 

TO GET TO...

Grocery store 21.0% 22.5% 21.2% 21.5%

Doctor 43.2% 44.1% 44.7% 43.9%

HOUSEHOLD CHARACTERISTICS [SR]

Number of family members 3.7 3.8 3.7 3.7

Teens in household 39.8% 39.3% 37.4% 38.9%

Disabled household member 15.7% 14.4% 15.5% 15.2%

Female head of household 90.9% 90.8% 91.6% 91.1%

CENSUS TRACT CHARACTERISTICS [CEN]

Poor 52.3% 51.8% 52.7% 52.3%

Minority 91.0% 91.1% 91.1% 91.1%

Female-headed households 62.7% 62.2% 63.1% 62.7%

Adults with high school degree 90.9% 90.8% 91.6% 91.1%

Adults with college degree 10.3% 10.4% 10.3% 10.3%

Adults employed 74.4% 74.8% 74.4% 74.5%

Adults in professional job 30.1% 29.8% 29.9% 30.0%

MTO SITE DISTRIBUTION [SR]

Baltimore 13.8% 13.8% 13.8% 13.8%

Boston 20.8% 20.8% 20.8% 20.8%

Chicago 19.4% 19.4% 19.4% 19.4%

Los Angeles 22.5% 22.5% 22.5% 22.5%

New York City 23.5% 23.5% 23.5% 23.5%
11

EXHIBIT 1.2. (CONTINUED)

EXPERIMENTAL 

GROUP

SECTION 8 

GROUP

CONTROL 

GROUP

ALL GROUPS

MOTIVATIONS FOR SIGNING UP TO

MOVE THROUGH MTO [SR]

Primary or secondary reason for moving was...

To get away from drugs and gangs 76.9% 76.3% 78.2% 77.1%

Better schools for the children 48.7% 52.4% 47.3% 49.4%

To get a bigger or better apartment 44.9% 45.2% 46.3% 45.4%

To get a job 6.4% 5.2% 6.2% 6.0%

NEIGHBORHOOD CHARACTERISTICS

AND MOBILITY EXPERIENCES [SR]

Household member was crime victim in 

past 6 months

43.2% 42.6% 41.5% 42.5%

Streets very unsafe at night 49.4% 50.8% 50.4% 50.2%

Very dissatisfied with neighborhood 46.8% 47.0% 46.3% 46.7%

Very confident about finding new apartment 46.7% 49.5% 45.2% 47.1%

Lived in neighborhood 5 or more years 59.5% 62.0% 61.3% 60.8%

Moved more than three times in 5 years 

prior to baseline

9.0% 9.1% 10.5% 9.5%

Applied for Section 8 previously 40.7% 38.8% 43.0% 40.8%

No family in neighborhood 64.7% 63.2% 64.0% 64.1%

No friends in neighborhood 41.4% 38.8% 40.9% 40.5%

Chatted with neighbors at least once per 

week

51.7% 49.9% 54.0% 51.9%

Very likely to report neighbor’s child getting 

into trouble

55.0% 53.6% 56.6% 55.1%

SAMPLE SIZE 1,819 1,346 1,439 4,604

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

ratios.

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

Data source and sample: Moving To Opportunity (MTO) Participant Baseline Survey. “Sample adult” refers to the 4,604 adults included in the interview sampling 

frame for the long-term evaluation (see section 1.6 for details). Selection of one adult from each household gave priority to female adults who were more likely to 

be the children’s caretakers. The priority order for selecting both the interim and final evaluation samples was: female heads of the core household, female spouses 

of the core household head, female baseline heads, female spouses of the baseline head, and finally, non-female heads of the core households. The baseline head 

is often but not always the same person as the sample adult. The baseline head completed the baseline survey, providing information on both the household and 

its individual members.

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

decennial Censuses. 
12

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

EXPERIMENTAL 

GROUP

SECTION 8 

GROUP

CONTROL 

GROUP

ALL GROUPS

AGE [PR]

As of December 2007 16.1 16.2 16.1 16.1

At baseline 4.6 4.7 4.7 4.6

SEX OF YOUTH [PR]

Female 50.3% 49.3% 47.9% 49.2%

Male 49.7% 50.7% 52.1% 50.8%

OLDER YOUTH CHARACTERISTICS [PR]

Gifted student or did advanced coursework 13.0% 12.4% 12.4% 12.6%

Suspended or expelled from school in past 

two years

3.8% 4.3% 3.3% 3.8%

School called about behavior in past two 

years

19.9% 20.7% 19.6% 20.0%

Behavioral or emotional problems 5.5% 7.0% 5.1% 5.8%

Learning problem 11.8% 13.1% 12.3% 12.3%

YOUNGER YOUTH CHARACTERISTICS [PR]

In hospital before first birthday 18.1% 18.0% 19.4% 18.5%

Weight at birth (pounds) 7.1 7.1 7.0 7.1

Weighed less than 6 pounds at birth 13.3% 14.5% 14.8% 14.1%

Adult read to youth more than once per day 24.1% 21.5% 26.9% 24.3%

ALL YOUTH CHARACTERISTICS [PR]

Health problems that limited activity 6.1% 5.8% 5.4% 5.8%

Health problems that required special 

medicine or equipment

9.1% 9.3% 8.9% 9.1%

Sample Size 2,544 1,965 2,136 6,645

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

ages 0 to 5. 

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

Data source and sample: Moving To Opportunity (MTO) Participant Baseline Survey. N = 6,645 youth ages 10 to 20 as of December 2007 included in the interview 

sampling frame for the long-term evaluation (see section 1.6 for details) as reported by the baseline head of household.
13

Perhaps the most striking finding from the baseline 

surveys is that more than 40 percent of households that 

applied had a household member victimized by a crime 

during the previous six months. Prior qualitative work 

by several members of our present research team showed 

that the fear of violence among adults was “so deepseated that their entire daily routine was focused on 

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

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

experiences living in Boston public housing, reported 

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

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

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

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

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

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

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

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

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

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

shot in the neck”(p. 253).

It is probably not surprising, then, that fully threequarters of MTO families report that getting away from 

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

most important reason for enrolling in the program. 

About half of the households wanted to move so that 

their children could attend a better school.

Eligible applicant households were randomly assigned to 

one of three groups:

The experimental group received Section 8 rental 

assistance certificates or vouchers that they could use 

only in census tracts with 1990 poverty rates below 

10 percent. In each city, a nonprofit organization 

under contract to the PHA provided mobility 

counseling to help families locate and lease suitable 

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

families were able to use their voucher to relocate 

without any special MTO-imposed constraints 

on their moves. Families who stayed in their new 

low-poverty neighborhoods less than a year did not 

receive a new voucher. Aside from this requirement, 

experimental group families were required to abide 

by all of the regular rules and requirements of the 

Section 8 certificate and voucher programs, including 

having a limited amount of time to search for 

housing and sign a lease before they lost the rights to 

their subsidy, being required to contribute 30 percent 

of their adjusted income toward rent (the same rent 

requirement as in public housing), and prohibitions 

on rental assistance to households that engage in 

certain types of criminal activity.6

The Section 8 only group received regular Section 8 

certificates or vouchers that they could use anywhere; 

these families received no mobility counseling.

The control group received no certificates or 

vouchers, but continued to be eligible for projectbased assistance and whatever other social programs 

and services to which families would otherwise be 

entitled.

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

to which the experimental and Section 8 only groups 

are being compared, was not required to stay in public 

housing during the entire study period. In fact, during 

the course of the 1990s, HUD sponsored another largescale program called HOPE VI that ran in parallel to 

MTO and provided assistance to local PHAs to demolish 

tens of thousands of the nation’s most distressed public 

housing units, including many of the housing projects in 

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

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

result of HOPE VI sometimes received housing vouchers 

through that alternative program, or they received 

assistance in moving into other housing projects or 

mixed-income housing developments in other areas.

Exhibits 1.2 and 1.3 show that the observable baseline 

characteristics of families are on average very similar 

across the three randomly assigned MTO groups, 

suggesting that the randomization in MTO was carried 

out correctly. Because of random assignment, we 

6 HUD rules require PHAs to deny housing assistance to households 

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

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

evicted from federally assisted housing for drug-related criminal activity 

during the past three years. Local PHAs are also left with some discretion about the degree to which other types of criminal activity affect 

eligibility; see for example Kooklan (2008) at www.lahsa.org/docs/policyandplanning/section8.pdf.
14

expect even the unobserved attributes of families at 

baseline to be similar across MTO groups on average. 

Any differences in average outcomes across the three 

randomly assigned MTO groups can thus be attributed 

to the MTO intervention, as we discuss in greater detail 

in the next section.

1.3 ESTIMATING THE MTO PROGRAM 

EFFECTS

The great strength of MTO is the demonstration 

project’s randomized experimental design, which as 

we noted above helps overcome concerns that previous 

nonexperimental studies may confound the causal effects 

of neighborhoods with those of difficult-to-measure 

individual- or family-level attributes that affect both 

outcomes and residential selection. For this reason, we 

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

experimental design.7

 Estimates of MTO’s effects 

correspond to two different questions. The first is the 

effect of the intervention on the average outcomes of 

those assigned to one of the MTO mobility groups (the 

experimental group or Section 8 only group), regardless 

of whether they relocated through the MTO program. 

This is known as the intention to treat (ITT) effect. The 

ITT effect is estimated by applying ordinary least squares 

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

program participant (i); Expi

 and S8i are binary indicator 

variables equal to 1 if participant (i) was randomly 

assigned to the experimental or Section 8 groups. The 

control group is the omitted reference group. 

(1) Yi = α0 + α1 Expi + α2S8i + α3 Xi + ei

Our estimates for the ITT effects for the experimental 

and Section 8 treatments equal α1 and α2 in equation 

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

characteristics, Xi, which are basically the same as those 

used in the interim MTO study (Orr et al., 2003) and are 

described in Supplemental Exhibit 1.2. Under random 

assignment, the weighted distribution of the baseline 

variables should be similar across the three MTO groups; 

therefore, controlling for baseline characteristics should 

not have much effect on the estimated coefficients α1

7 For different perspectives about the value of the MTO experimental 

design versus its nonexperimental alternatives, see Clampet-Lundquist 

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

and α2. However, controlling for baseline characteristics 

accounts for additional variation in the outcomes in our 

data, and thus can improve the precision of our estimates 

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

residual or error term, and reflects the fact that outcomes 

for MTO program participants will also be affected by 

variables that are not included in our analysis. However 

any unmeasured background characteristics of families 

that affect outcomes should be uncorrelated with the 

indicators for treatment group assignment in equation 

(1) by virtue of random assignment.

We use ordinary least squares to estimate equation (1) 

for dichotomous (binary) outcome variables as well 

as continuous outcome variables. In practice, least 

squares estimates with dichotomous dependent variables 

yield results quite similar to the average marginal 

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

example, Angrist and Pischke, 2009). All estimates 

weight individuals by the inverse of their probability 

of assignment to their group to account for changes in 

the random assignment ratios over time, sampling of 

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

those interviewed in the phase 2 stage, by the inverse 

of the likelihood of selection for phase 2 subsampling. 

Standard errors were adjusted for family clustering and 

heteroskedasticity using the Huber-White robust method. 

Analyses were performed using Stata 11.0 Special 

Edition. 

If one is interested in the effectiveness of a program such 

as MTO in improving the situation of the entire class of 

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

appropriate results to examine. The overall effectiveness 

of such a policy depends on both the effects on families 

of using a voucher and the proportion of families in 

one of the MTO mobility groups who use a voucher. 

For continuous outcome variables, the ITT estimate is 

essentially equal to the difference in average outcomes 

between everyone assigned to the experimental or Section 

8 only group and everyone assigned to the control 

group. If the dependent variable is dichotomous, the 

ITT estimate is essentially the difference in prevalence 

rates for that outcome between those assigned to the 

experimental or Section 8 only group versus the control 

group. Note that this ITT estimate is not biased by the 
15

fact that only some families relocate through the MTO 

program because we derive the ITT by comparing the 

average outcomes of everyone assigned to one of the 

mobility groups, whether they relocate through the 

program or not, with the average outcomes of everyone 

assigned to the control group.

If instead one is interested in our second question about 

the effects on family outcomes from actually moving, 

one should consult the treatment on the treated (TOT) 

results, which are the effect on those who actually 

leased up and moved through MTO. Under some 

assumptions, we can estimate the TOT effect by using 

information about the proportion of families assigned to 

the experimental or Section 8 only group that actually 

moved through the MTO program. More specifically, the 

TOT estimate will be approximately equal to the ITT 

effect divided by the share of the experimental or Section 

8 only group that relocates using an MTO voucher 

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

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

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

more detailed discussions.

With both the TOT and ITT effects, we reiterate that 

we are comparing the outcomes of the experimental and 

Section 8 only groups with the average outcomes of a set 

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

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

control group families left public housing on their own, 

without any assistance from the MTO demonstration. 

Some control group families moved to the same kinds 

of neighborhoods as some of the treatment group 

families, and some even received vouchers through the 

regular Section 8 program. The estimates we present 

here represent the incremental effects of offering families 

MTO housing vouchers, relative to what was available to 

the control group, whose outcomes reflect what would 

have happened to the treatment group families in the 

absence of the MTO demonstration.

Although we can compare the ITT estimates for the 

experimental group with those of the Section 8 group, 

the TOT estimates for the two groups are not directly 

comparable. This is because families who signed leases 

in response to the experimental group voucher offer may 

differ from those who signed leases in response to the 

Section 8 group voucher. Differences in the TOT impacts 

between the two groups may reflect differences in the 

composition of families who relocate in the experimental 

versus Section 8 only group, rather than differences only 

in the types of changes in residential environments that 

families experience.

We must also bear in mind that even among the 

experimental group families who signed leases and 

initially moved to a low-poverty area with their MTO 

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

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

section, some experimental group families made a second 

or third move, sometimes to areas with higher poverty 

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

in the same neighborhood, the poverty level of that 

neighborhood increased over the course of the follow-up 

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

8 only participants spent significantly more time in lowpoverty areas than did the control group families.

Finally, we must recognize that MTO is most informative 

about small-scale or incremental voluntary mobility 

programs serving families in very distressed areas. A 

much larger-scale voluntary mobility intervention may 

generate much different effects. For example, if a large 

number of public housing families receive vouchers, then 

it may be more difficult for families to find affordable 

apartments under the housing voucher program payment 

standards, at least in the short run until housing market 

supply responds. A much larger scale voucher mobility 

program also could potentially increase the general 

dispersion of poor families, making it harder for any 

given voucher recipient to find and lease an apartment 

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

Furthermore, MTO may not be a good guide to the 

impacts of potentially involuntary moves with vouchers 

such as those following HOPE VI public housing 

demolitions.8

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

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

schooling outcomes.
16

EXHIBIT 1.4. MTO VOUCHER LEASE-UP RATES FOR MTO EXPERIMENTAL AND SECTION 8 GROUPS

Baltimore Boston Chicago Los

Angeles

New York

City

Experimental

Section 8

80

60

40

20

0

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

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

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

[MTO = Moving to Opportunity]

100

All Sites 

47

63

54

77

44

52

33

67

60

77

46

47

Percent

Percent

1.4 MTO MOBILITY OUTCOMES

Of those households assigned to the experimental 

group, 47 percent relocated using an MTO voucher 

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

assigned to the Section 8 group relocated through MTO. 

The compliance rate is higher for the Section 8 group 

presumably in part because their vouchers (unlike the 

vouchers offered to experimental group families) had no 

geographic restriction. 

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

differed substantially across the five cities, from a low 

of 33 percent in the Chicago experimental group to a 

high of 77 percent in the Los Angeles and Baltimore 

Section 8 groups. Compared with noncompliers, those 

families who moved with MTO vouchers tend to be 

younger, be relatively more dissatisfied with their original 

neighborhoods, and have fewer children (for details see 

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

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

economically distressed communities. As Exhibit 1.5 

shows, by this measure MTO was successful. For the 

baseline census tracts in which families were residing, 

53.1 percent of all tract residents had incomes below 

the federal poverty line.9

 The average census tract 

poverty rate for the experimental group families’ initial 

MTO-assisted move was 10.7 percent. These differences 

in census tract poverty rates narrow considerably by 

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

years after random assignment, the average control 

group family is living in a census tract with a poverty 

rate of 31.3 percent, while the average family in the 

experimental group is in a tract with a poverty rate of 

27.4 percent, and the average family who moved with an 

MTO experimental group voucher was in a tract with a 

21.0 percent poverty rate.10

9 For addresses at which MTO families were living during intercensal 

years (between 1990 and 2000 or between 2000 and 2010), we interpolate the characteristics of the census tract in which the MTO family was 

living using data from the 1990 and 2000 decennial censuses together 

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

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

10 Most of the convergence in census tract characteristics across the 

three randomly assigned MTO groups over time seems to occur because 

of residential mobility, rather than change over time in the neighborhoods due to gentrification or neighborhood decline. When we reanalyzed the data in Exhibit 1.5 holding the characteristics of the census 

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

to census tract characteristics measured in the 1990 or 2000 decennial 

census, we see qualitatively similar findings.
17

Despite this convergence, the difference is still sizable in the 

average tract poverty rate that families experience over the 

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

average duration-weighted tract poverty rate (which weights 

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

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

to 39.6 percent compared with 30.6 percent for the average 

experimental group family and 20.0 percent for those who 

moved with an MTO voucher as part of the experimental 

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

8 group on family neighborhood conditions is in the same 

general direction of the experimental treatment, but is 

generally more modest in magnitude.

Although MTO was explicitly designed to help public 

housing families move into lower-poverty census tracts, 

these MTO-induced moves changed other aspects of 

the socioeconomic composition of census tracts as 

well. For example, Exhibit 1.5 shows that the average 

duration-weighted tract share of adults with a college 

education was 16.1 percent for controls, 20.3 percent 

for all families assigned to the experimental group, and 

24.7 percent for those in the experimental group who 

moved through MTO. Previous research by Duncan et 

al. (1994) suggests that the presence of affluent, collegeeducated neighbors may have distinct effects above 

and beyond the presence or absence of low-income 

families. On the other hand, unlike the findings from 

the Gautreaux program, which was designed explicitly 

to reduce racial segregation rather than just economic 

segregation, MTO engendered relatively modest changes 

in neighborhood racial composition. The average tract 

11 The duration-weighted tract poverty rate is calculated from random assignment through May 31st, 2008. We chose to use the same endpoint for 

everyone rather than the actual date that a family was surveyed because differences in exactly when families were surveyed may be systematically related 

to other characteristics that influence outcomes (if, for example, more disadvantaged families were harder to find and therefore were interviewed later 

during the survey fieldwork period). We use linear interpolations between 

1990 and 2000 decennial censuses and from 2000 forward using Census 

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

percent poverty at a specific point in time. To simplify the calculation, we 

divide each family’s residential history by the time periods they lived at different addresses (or address “spells”), and we split any addresses that span April 

2000 into the period before and after 2000 to allow for different interpolation slopes. Let Ti

 be the total amount of time between when family i was 

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

that families reported during the study period, up to Ki total addresses; let 

Tki be the amount of time that family i spent at address k according to our 

address tracking data; and let Pki be our estimate for the census tract poverty 

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

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

MTO family is then equal to: Pi = ∑Ki (Tki/Ti)*Pki.

share during the study period was 88.0 percent minority 

for controls, 82.1 percent for all families assigned to the 

experimental group, and 75.0 percent for those in the 

experimental group who used an MTO voucher to move.

Chapter 2 will describe the mobility patterns of families 

over time in more detail, as well as the resulting 

characteristics of the neighborhoods in which they have 

resided. Naturally occurring mobility of families in the 

control group reduces somewhat the effect of the MTO 

mobility intervention on the differences in neighborhood 

environments across randomly assigned groups. But 

the moves out of the poorest neighborhoods by many 

in the control group may also make the MTO results 

more policy relevant: The neighborhood conditions that 

families in the experimental and Section 8 groups would 

have experienced had they not been assigned to the 

MTO experimental and Section 8 treatment groups are 

not restricted to just the highest poverty census tracts in 

which a relatively small share of public housing families 

reside.

1.5 PREVIOUS MTO FINDINGS

Initial studies of the MTO program examined its shortterm effects (two to three years after assignment), and 

individual research teams each focused on just one of 

the MTO cities. These initial short-term findings are 

generally consistent with the predictions of previous 

research suggesting that moving to a less distressed 

community generally improves adult and child 

outcomes.12

Several years later, HUD sponsored a multisite “interim 

MTO study” to examine outcomes for adults and youth 

four to seven years after random assignment, gathered 

uniformly across the five MTO sites (Fortson and 

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

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

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

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

Hirschfield (2001), Ludwig, Ladd, and Duncan (2001), and Ludwig, 

Duncan, and Pinkston (2005) for Baltimore; Hanratty, McClanahan, 

and Pettit (1998) for Los Angeles; Rosenbaum, Harris, and Denton 

(1999) and Rosenbaum and Harris (2003) for Chicago; and Leventhal and Brooks-Gunn (2003, 2004) for New York. Syntheses of these 

results appear in Goering and Feins (2003).
18

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

evaluation yielded a more complicated pattern than the 

“better neighborhoods, better outcomes” of previous 

research.

Exhibits 1.6 and 1.7 summarize selected results from the 

MTO interim evaluation. The subsequent exhibits in this 

report all use the same format. The first column shows 

the “Control Mean” or average outcomes of everyone 

assigned to the MTO control group, which (because of 

the randomized experimental design) tells us what would 

have happened to families in the experimental or Section 

8 group had they not been randomly assigned to receive 

mobility assistance. The next two columns present the 

ITT and TOT effects for the experimental treatment and 

then the following two columns present these estimates 

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

the interim evaluation, 45 percent of the control group 

adults were working and not on Temporary Assistance 

for Needy Families (TANF). The experimental group 

ITT estimate is 1.8 percentage points. This implies that 

46.8 percent of the experimental group was working. As 

we noted above, the TOT effect will essentially be equal 

to the ITT effect divided by the proportion of families 

in the experimental or Section 8 only group that moves 

as a result of MTO. Because approximately 47 percent 

of families assigned to the experimental group moved 

through MTO, the experimental TOT effect is about 

twice the size of the ITT effect.

The interim MTO data suggest that adults assigned 

to either of the mobility groups were safer and more 

satisfied with their housing and neighborhoods and 

were less likely to report a household member being a 

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

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

Exhibit 3.5). Compared with the control group, adults 

in the experimental group also had large beneficial 

13 The sample universe for the interim MTO study included the household head of the 4,248 families randomly assigned in the MTO demonstration through December 31, 1997, and up to two children (n= 

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

Surveys were conducted between January and September 2002 with a 

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

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

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

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

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

treatment groups.

impacts on mental health outcomes and some physical 

health outcomes, such as obesity, but had few detectable 

impacts on labor market outcomes or social program 

participation.14

Exhibit 1.7 shows that MTO’s impacts on youth differed 

substantially by gender, with generally beneficial effects 

on female youth and deleterious impacts for male 

youth. Female youth in the experimental group had 

better mental health outcomes than those in the control 

group and were less likely to engage in risky behaviors 

such as use of marijuana, whereas male youth in the 

experimental group had higher scores than control 

group males on an abbreviated index of 11 behavior 

problems including difficulty concentrating, cheating 

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

treatments also reduced violent behavior for male and 

female youth, as measured by arrests for violent crime. 

MTO had few detectable effects on child health aside 

from an increase in non-sports injuries for male youth 

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

detectable impacts on educational achievement test scores 

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

limitations to the cognitive assessments that the interim 

study used, as well as some differences in MTO impacts 

across cities, with some potential signs of test score gains 

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

Another goal of the interim study was to learn more 

about the mechanisms through which neighborhood 

environments influence youth behavior. The disruption 

of moving by itself does not appear to explain the gender 

differences in effects for youth. Mobility rates were 

actually slightly higher among households with female 

youth than among those with male youth, and the 

adverse effects of MTO on male risky behaviors begin to 

manifest themselves starting several years after the initial 

move, not right after as a simple mobility disruption 

model would predict (Kling, Liebman, and Katz, 

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

14 See Ludwig et al. (2008), Cove et al. (2008), and Clampet-Lundquist and Massey (2008) for additional discussions of MTO’s interim 

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

(2008) for a discussion of subsequent mobility among MTO families.
19

The observed pernicious effects of MTO moves on 

male youth are consistent with both relative deprivation 

and competition models that Jencks and Mayer (1990) 

discuss, which emphasize the potential adverse effects 

on behavior from experiencing a decline relative to one’s 

peers in socio-economic status or academic achievement. 

However, the existing theoretical literature does not 

explain why the effects of the relative deprivation and 

competition models should dominate for male but 

not female youth. MTO families with male youth did 

not move into different types of neighborhoods than 

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

standard administrative data for census tracts and police 

beats and from survey questions about neighborhoods 

developed by the Project on Human Development in 

Chicago Neighborhoods (PHDCN) (Kling, Ludwig, 

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

sisters exhibit different responses to similar neighborhood 

changes. The interim data suggest that MTO reduced 

violent-crime arrests for both male and female youth; the 

reduction in violence seems to be more closely associated 

with lower rates of racial segregation in the treatment 

neighborhoods than with changes in other neighborhood 

attributes such as poverty or overall levels of violent 

crime, or sociological process measures such as disorder 

(Ludwig and Kling, 2007).

To further illuminate the underlying processes through 

which MTO affects youth outcomes, and in particular 

the source of the gender difference in program impacts, 

MTO researchers later conducted qualitative interviews 

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

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

and Weismann, 2008).15 These interviews uncovered 

subtle gender differences in how MTO affects youth 

interactions with families, peers, and neighborhood 

institutions such as schools and police that were not 

readily apparent in the interim MTO survey data 

analysis.

For example, the nature of how boys and girls interact 

socially with their peers enables girls to more successfully 

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

to visit with friends on their porches or inside their 

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

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

homes in part because some parents placed girls on a 

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

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

hung out in public spaces, which put them at elevated 

risk for conflict with neighbors and police, and 

increased their exposure to delinquent peer groups as 

well as opportunities to engage in delinquent activities 

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

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

impacts on youth interactions with families beyond the 

stringency of parental monitoring. For example, boys 

who moved were less likely to report a nonbiological 

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

a difference across groups that did not appear for girls. 

And, despite strong preferences to move so that their 

children could attend better schools, MTO parents did 

not subsequently feel informed or empowered to make 

decisions about enrolling their children in academically 

promising schools. Thus, many MTO children ended up 

in schools that were very similar to those in their initial 

neighborhood (Ferryman et al., 2008).
20

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

SURVEYED IN THE LONG-TERM EVALUATION

CONTROL 

GROUP

EXPERIMENTAL GROUP SECTION 8 GROUP

OUTCOME ALL ALL COMPLIERS NONCOMPLIERSALL COMPLIERS NONCOMPLIERS

SHARE POOR [CEN]

Baseline address 0.531 0.527 0.530 0.524 0.526 0.540 0.505

Initial MTO-assisted move 

address

0.107 0.287

Address at time of MTO 

interim evaluation 

(4–7 years after baseline)

0.395 0.299 0.193 0.402 0.326 0.286 0.391

Address at time of MTO 

long-term evaluation 

(10–15 years after baseline)

0.313 0.274 0.210 0.334 0.283 0.244 0.345

Average (duration-weighted) 

of all addresses since 

Random Assignment

0.396 0.306 0.200 0.407 0.329 0.285 0.400

SHARE COLLEGE EDUCATED [CEN]

Baseline address 0.103 0.101 0.095 0.106 0.105 0.103 0.110

Initial MTO-assisted move 

address

0.284 0.173

Address at time of MTO 

interim evaluation 

(4–7 years after baseline)

0.154 0.197 0.243 0.152 0.175 0.186 0.157

Address at time of MTO 

long-term evaluation 

(10–15 years after baseline)

0.209 0.229 0.244 0.215 0.224 0.234 0.208

Average (duration-weighted) 

of all addresses since 

Random Assignment

0.161 0.203 0.247 0.162 0.182 0.194 0.163

SHARE MINORITY [CEN]

Baseline address 0.912 0.916 0.912 0.920 0.915 0.906 0.930

Initial MTO-assisted move 

address

0.624 0.835

Address at time of MTO 

interim evaluation 

(4–7 years after baseline)

0.883 0.836 0.768 0.901 0.875 0.860 0.900

Address at time of MTO 

long-term evaluation 

(10–15 years after baseline)

0.841 0.804 0.771 0.834 0.826 0.813 0.846

Average (duration-weighted) 

of all addresses since 

Random Assignment

0.880 0.821 0.750 0.888 0.860 0.841 0.892

Notes: Characteristics for the census tracts of addresses at which Moving To Opportunity (MTO) families were living at different points in time were linearly interpolated from the 1990 and 2000 decennial Censuses as well as the 2005–09 American Community Survey (ACS). Addresses at the time of the interim and long-term 

evaluations are as of the beginning of the fielding period for each study (December 31, 2001 for the interim evaluation and May 31, 2008 for the long-term evaluation). Square brackets indicate the source of the outcome information: CEN = 1990 and 2000 decennial census data as well as the 2005–09 ACS.

Data source and sample: Adult long-term survey. All adults interviewed (N = 3,273).
21

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

AFTER BASELINE)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

NEIGHBORHOOD OUTCOMES

Share persons in poverty [CEN] 0.385 – 0.078* – 0.167 – 0.062* – 0.102* 3,675

(0.008) (0.017) (0.008) (0.014)

Saw illicit drugs sold in past 30

days [SR]

0.445 – 0.117* – 0.248* – 0.103* – 0.171* 3,480

(0.022) (0.046) (0.024) (0.039)

Household member victim of crime 

in past six months [SR]

0.209 – 0.040* – 0.085* – 0.053* – 0.089* 3,499

(0.017) (0.036) (0.018) (0.030)

Felt safe during the day [SR] 0.750 0.093* 0.198* 0.096* 0.161* 3,514

(0.018) (0.039) (0.019) (0.032)

Satisfied or very satisfied with 

neighborhood [SR]

0.475 0.138* 0.293* 0.108* 0.180* 3,524

(0.022) (0.047) (0.024) (0.040)

ADULT OUTCOMES [SR]

Working and not on TANF 0.452 0.018 0.038 0.019 0.032 3,472

(0.020) (0.043) (0.023) (0.038)

Psychological distress index for 

past month

0.329 – 0.034* – 0.073* – 0.012 – 0.020 3,521

(0.015) (0.032) (0.016) (0.028)

Good or better health 0.669 – 0.016 – 0.033 – 0.007 – 0.013 3,523

(0.020) (0.042) (0.022) (0.036)

Obese, Body Mass Index ≥ 30 0.471 – 0.051* – 0.108* – 0.047 – 0.079 3,405

(0.022) (0.047) (0.025) (0.042)

Notes: * = p < 0.05 on two-tailed t-test. Robust standard errors are shown in parentheses. Square brackets indicate the source of the outcome information. The 

control mean is unadjusted. Intention-to-Treat estimate (ITT), or estimated impact of being offered an Moving To Opportunity (MTO) housing voucher; estimated 

effect of Treatment-on-the-Treated (TOT), or estimated impact of moving using an MTO housing voucher. 

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

Date source and sample: MTO Interim Impacts Evaluation (Orr et al., 2003). Tract poverty measure: Exhibit D2.1. Other neighborhood measures: Exhibit 3.5. Employment/TANF measure: Exhibit 8.8. Physical and mental health measures: Exhibit 4.2.
22

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

(4–7 YEARS AFTER BASELINE)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

WOODCOCK-JOHNSON REVISED

BROAD READING SCORE [M]

All youth (ages 5–19) 497.31 0.92 2.04 0.45 0.74 5,169

(0.93) (2.06) (1.06) (1.73)

Female 499.69 1.84 3.96 – 0.19 – 0.30

(1.14) (2.45) (1.24) (1.99)

Male 494.94 – 0.03 – 0.06 1.13 1.89

(1.38) (3.14) (1.59) (2.66)

WOODCOCK-JOHNSON REVISED

BROAD MATH SCORE [M]

All youth (ages 5–19) 501.23 0.22 0.49 – 1.07 – 1.74 5,187

(0.78) (1.75) (0.85) (1.39)

Female 502.30 0.81 1.73 – 1.21 – 1.92

(1.06) (2.26) (1.09) (1.73)

Male 500.18 – 0.36 – 0.82 – 0.91 – 1.52

(1.05) (2.42) (1.17) (1.95)

PSYCHOLOGICAL DISTRESS INDEX

[SR]

All youth (ages 12–19) 0.256 – 0.006 – 0.013 – 0.006 – 0.010 2,803

(0.015) (0.032) (0.016) (0.028)

Female 0.304 – 0.045* – 0.098* – 0.043 – 0.072

(0.020) (0.043) (0.023) (0.037)

Male 0.208 0.034 0.077 0.031 0.057

(0.020) (0.045) (0.022) (0.040)

VERY GOOD OR BETTER HEALTH 

[SR]

2,822

All youth (ages 12–19) 0.680 0.001 0.001 – 0.006 – 0.010

(0.024) (0.054) (0.027) (0.048)

Female 0.635 0.030 0.065 0.008 0.013

(0.034) (0.075) (0.038) (0.062)

Male 0.725 – 0.029 – 0.066 – 0.020 – 0.036

(0.034) (0.076) (0.036) (0.067)

EVER USED MARIJUANA [SR]

All youth (ages 15–19) 0.344 – 0.031 – 0.071 – 0.020 – 0.036 1,569

(0.034) (0.079) (0.038) (0.069)
23

EXHIBIT 1.7. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

EVER USED MARIJUANA [SR]

(CONTINUED)

Female 0.342 – 0.129* – 0.276* – 0.079 – 0.132

(0.044) (0.095) (0.050) (0.085)

Male 0.348 0.068 0.169 0.042 0.083

(0.049) (0.122) (0.052) (0.104)

ABBREVIATED BEHAVIOR

PROBLEMS INDEX [SR]

All youth (ages 12–19) 0.343 0.036* 0.080* 0.023 0.039 2,810

(0.014) (0.032) (0.016) (0.028)

Female 0.352 – 0.002 – 0.004 – 0.007 – 0.012

(0.019) (0.042) (0.021) (0.035)

Male 0.336 0.075* 0.169* 0.052* 0.095*

(0.020) (0.045) (0.022) (0.040)

FRACTION OF CALENDAR

QUARTERS WITH VIOLENT CRIME

ARREST [ADMIN]

All youth (ages 15–19) 0.0138 – 0.0038* – 0.0091* – 0.0010 – 0.0019 2,532

(0.0019) (0.0045) (0.0022) (0.0040)

Female 0.0088 – 0.0022 – 0.0053 – 0.0030 – 0.0054

(0.0020) (0.0049) (0.0026) (0.0047)

Male 0.0190 – 0.0054 – 0.0129 0.0006 0.0011

(0.0030) (0.0071) (0.0034) (0.0061)

EVER ARRESTED FOR PROPERTY

CRIME [ADMIN]

All youth (ages 15–19) 0.118 0.022 0.051 0.014 0.026 2,646

(0.015) (0.036) (0.017) (0.031)

Female 0.087 – 0.006 – 0.013 – 0.007 – 0.013

(0.018) (0.042) (0.021) (0.037)

Male 0.150 0.049* 0.115* 0.035 0.062

(0.024) (0.055) (0.027) (0.048)

Notes: * = p < 0.05 on two-tailed t-test. Robust standard errors are shown in parentheses. The control mean is unadjusted. Intention-to-Treat estimate (ITT), or 

estimated impact of being offered a Moving To Opportunity (MTO) housing voucher; estimated effect of Treatment-on-the-Treated (TOT), or estimated impact of 

moving using an MTO housing voucher. 

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

Date source and sample: MTO Interim Impacts Evaluation (Orr et al., 2003). Test score measures: Exhibits 6.5 (overall) and 6.6 (by gender). Mental health measure: 

Exhibit 4.5. Health measure: Exhibit 4.4. Behavior measures: Exhibits 5.2–5.5.

Measures: The fraction of calendar quarters with violent crime arrest is based on the first through fourth year after random assignment.
24

1.6 THE LONG-TERM STUDY

This report presents results from following up with 

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

time of random assignment. This evaluation is the first 

opportunity to answer questions about the longer term 

effects of being offered a housing voucher, and resulting 

changes in housing and neighborhood conditions, on 

subsequent well-being and life outcomes in the following 

domains: housing and neighborhood conditions; physical 

health; mental health; economic outcomes; educational 

outcomes; and risky and criminal behavior.

What can we conclude about the effects of MTO on 

low-income families so far? It generates persistent 

differences in housing and neighborhood satisfaction 

and characteristics across randomized groups, despite 

subsequent mobility among all three groups. Its impacts 

on other behaviors through four to seven years after 

assignment are more complex than previous research 

would predict.

But of primary importance for public policy are the 

long-term effects on participants. Predicting these longterm impacts on the basis of the interim MTO results 

is a perilous business, as highlighted by the surprising 

differences in MTO impacts between the short-term 

evaluations in each of the sites, which supported the 

“better neighborhoods, better outcomes” hypotheses, 

versus the more mixed pattern of results found with 

the interim evaluation. Social science is filled with 

examples of long-term impacts that would have been 

hard to predict based on short- or medium-term impacts. 

Consider that the Head Start early childhood program 

yields short-term test score impacts that seem to fade 

out after a few years, yet Head Start nonetheless seems 

to generate long-term lasting benefits for participants 

such as higher rates of high school graduation (Currie 

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

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

the operative mechanism or causal pathway behind these 

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

Head Start impacts on social-cognitive skills that are 

not captured by reading and math tests. But this pattern 

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

score impacts was certainly not anticipated prior to the 

collection and analysis of long-term outcome data.

The final impact evaluation was designed to exploit the 

MTO’s randomized experimental design to best address 

the following key questions: 

What are the long-term effects of a housing mobility 

program intervention on participating families and 

their children, and how did these effects evolve over 

time? 

If differences in average neighborhood characteristics 

across groups persist over time, MTO’s effects on wellbeing and behavior may increase over time. Ties to old 

social networks will diminish, whereas social ties to new 

communities and use of new neighborhood institutional 

resources will increase. For example, the benefits of 

greater exposure to more prosocial and affluent social 

networks may improve if families become more socially 

integrated into their new communities, more attuned 

to local social norms, and thus more responsive to the 

peer and adult social influences that are central to the 

epidemic and collective socialization models. Social 

integration itself might require families to learn new 

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

and families may also learn how to fully use better local 

services such as schools. Families may also learn how to 

better navigate the potential opportunities and pitfalls 

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

resources and opportunities makes MTO adults and 

youth more competitive over time for prosocial rewards, 

any deleterious effects from competition or declines in 

relative position may diminish over time. Any deleterious 

“moving effects” may also attenuate.

The long-term evaluation is also an opportunity to 

address a number of important open questions that 

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

the long-term MTO study enables us to investigate 

whether differences in the effects of MTO on male 

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

for the younger cohort of youth that the long-term 

study examines, and better understand why male and 

female youth may respond so differently to changing 

neighborhoods. Another example is determining whether 

MTO impacts on basic screening indicators of adult 

mental health found in the interim MTO study translate 

into clinically important changes in mental health, which 

impose the greatest costs to individuals and society at 
25

large, and so are of greatest relevance for public health 

and public policy. A final example comes from the 

possibility that the interim mental health improvements 

among mothers in MTO translate into improved labor 

market outcomes over time.

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

who had not yet entered school when the study began? 

MTO included many participants who were fairly young 

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

the time of the interim study to provide developmentally 

meaningful measures for a variety of key outcomes such 

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

long-term evaluation offers an important opportunity to 

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

of December 2007), including their math and reading 

achievement, school completion and employment, 

mental and physical health, and engagement in risky 

behavior. Those children who are very young at baseline 

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

year follow up) might exhibit even more pronounced 

responses to the MTO intervention than those who 

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

for several reasons. One reason is that for children 

relatively younger at baseline and assessed now, MTO 

has generated longer-term exposure to less economicallydistressed neighborhoods. Children who are relatively 

younger at baseline will also experience relatively 

more pronounced MTO-induced changes in exposure 

to violence, which has been shown to be potentially 

important for child development (Sharkey, 2010). In 

addition a growing body of research in developmental 

psychology, economics, neuroscience, and even animal 

studies suggests that younger children may be more 

responsive to social policy interventions than older 

people given the possibility of declining developmental 

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

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

Shonkoff and Phillips, 2000). 

Children relatively young at baseline are a particularly 

important group to study because they most closely 

parallel the groups to which study effects would most 

reasonably be extrapolated if an MTO-like intervention 

were implemented universally. Under a universal version 

of some MTO-like program, children in eligible families 

would have the benefits of MTO early in life instead 

of experiencing the stresses of relocation that the older 

MTO youth experienced.

What are the mechanisms through which MTO affects 

long-term outcomes?

Information about the mechanisms through which MTO 

affects the behavior and life chances of participants 

is important for the design of tenant-based subsidy 

programs as well as place-based interventions to modify 

specific elements of community environments. As 

noted, because MTO changes multiple neighborhood 

attributes simultaneously, isolating the effects of specific 

mechanisms is complicated. However, the study design 

nonetheless can help us at least rule out some possible 

mediating mechanisms. If, for example, MTO has longterm beneficial effects on the mental health of female 

adults, yet has no detectable effect on access to health 

care services including mental health care, the pattern 

of findings would provide some evidence against the 

importance of that mechanism.

SAMPLE DEFINITION AND DESCRIPTION

The sample we used in the long-term evaluation includes 

all 4,604 families randomly assigned in the MTO 

demonstration.16 This group includes the 356 families 

in the Los Angeles site who were randomized after 

December 31, 1997, and were excluded from the interim 

evaluation’s sample frame because the corresponding 

increase in sample size was deemed insufficient to justify 

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

include 19,932 baseline household members and 17,133 

core household members (family members whom the 

head of household believed would move with the family 

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

MTO study, our analysis focuses on the core household 

members.

The adult survey sampling frame for the long-term 

evaluation included one adult from each of the original 

MTO families. We prioritized female adults from the 

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

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

was discovered that four of the applicants in MTO were actually members of other MTO households.
26

core household when selecting our sample. The interview 

sample included all sample adults from the experimental 

and control groups, and (because of funding constraints) 

a random 66 percent subsample of adults from the 

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

The youth sampling frame consisted of all core 

household members from all three MTO groups who 

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

youth per household were eligible for interviews. In those 

families that contained more than three eligible youth, 

we randomly selected three youth for the interview 

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

survey sample was 6,308. We also collected parental 

reports on all core young adult household members who 

were not in the MTO long-term survey sample frame 

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

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

The allocation of this sample among the treatment 

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

The number of adults in each site ranges from 572 in 

Baltimore to 948 in New York City. Somewhat more 

were assigned to the experimental group than to the 

Section 8 group to ensure that enough families signed 

leases with an MTO voucher in the experimental group. 

The expectation was that the lease-up rate would be 

lower for experimental families whose vouchers initially 

required them to live in a low-poverty tract compared 

with families in the Section 8 only group, whose 

vouchers were not subject to any additional mobility 

constraints. Assignment rates within sites were further 

adjusted during implementation to compensate for the 

fact that the lease-up rate for the experimental group 

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

sample weights used in the quantitative analyses adjusted 

for differences among sites and over time in the random 

assignment ratio, and our regression models control for 

site indicators; see the Technical Appendices for more 

details).

17 Because of funding constraints, the original survey sample fielding design excluded the sample of Section 8 group adults. Additional 

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

of the eligible Section 8 adult sample came through in December 

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

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

subsample of experimental and control group adults was released to the 

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

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

technical appendices, forthcoming.

The adult surveys collected information about young 

adults who are not in the long-term survey sample 

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

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

adults in the survey sample frame (who are almost all 

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

member in the core MTO household who was not in 

the survey sample frame. We also asked MTO adults to 

provide proxy reports on other people who were currently 

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

regardless of whether they were in the baseline core 

household or not. Administrative data are also available 

to measure outcomes for those not in the survey sample 

frame.

CONTENT OF PARTICIPANT DATA 

COLLECTION

Like the interim evaluation, the final impact evaluation 

focuses on measuring and assessing MTO’s long-term 

effects on the following key outcome domains:

•	 Housing and mobility

•	 Neighborhoods and social networks

•	 Adult and youth physical and mental health

•	 Adult and youth economic self-sufficiency

•	 Adult education and youth schooling and 

educational achievement

•	 Adult and youth risky and criminal behavior.

These outcomes, as well as a variety of key possible 

mediating mechanisms, are measured through in-person 

surveys of MTO adults and youth, direct measurement 

of health biomarkers, achievement assessments, and 

administrative records. Exhibit 1.9 summarizes the 

topics about which data were collected, by method, and 

according to the age of sample members.
27

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

TREATMENT GROUP

ALL GROUPS CONTROL 

GROUP

EXPERIMENTAL GROUP SECTION 8 GROUP

N N N COMPLIANCE 

RATE (%)

N COMPLIANCE 

RATE (%)

ADULTS

Baltimore 572 197 252 53.5 123 79.8

Boston 868 326 366 43.6 176 51.1

Chicago 825 232 460 33.4 133 67.4

Los Angeles 929 389 340 60.5 200 71.6

New York City 948 295 401 46.4 252 45.2

All sites 4,142 1,439 1,819 47.4 884 61.6

YOUTH, AGES 

10–20

Baltimore 762 240 268 59.1 254 79.2

Boston 1,267 440 475 38.2 352 54.6

Chicago 1,363 328 701 31.7 334 72.0

Los Angeles 1,539 592 502 62.2 445 78.2

New York City 1,377 418 471 49.6 488 49.2

All sites 6,308 2,018 2,417 47.6 1,873 66.4

Notes: Percentages are weighted to reflect the randomization ratios and sampling of Section 8 adults and up to three youth per family. "Compliance" is defined as 

leasing a unit using a housing voucher provided by the MTO program. 

Data source and sample: MTO data system. The samples are N = 4,142 adults and N = 6,308 youth ages 10 to 20 as of December 2007 selected for the long-term 

survey. Excluded from the samples are the N = 462 Section 8 group adults and youth from households with greater than three youth ages 10 to 20 who were not 

randomly selected for the long-term survey.
28

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

ADULT YOUTH GROWN CHILDREN

Survey 

sampling plan

One adult per household (female 

caregivers prioritized)

Up to 4,142 adults total

Up to three youth ages 10–20 as 

of December 2007 per household 

Up to 6,308 youth total

Parent reports of household 

members under age 18 at 

baseline and up to age 30 as of 

December 2007

Survey content Household roster

Housing and mobility

Neighborhoods 

Social networks

Education

Employment and earnings

Income, public assistance

Savings and assets

Mental and physical health 

Decision making 

Relationships and parenting

Reports on household outcomes

Neighborhoods 

Social networks

Education and schooling

Employment and earnings

Risky behavior and behavior 

problems

Mental and physical health

Decision making

Youth reports on parenting

Adult respondent reports on 

eight items measuring education, 

employment, mental health, and 

risky behavior

Direct 

measurement

Height, weight, waist 

circumference, and blood 

pressure

Height and weight

Biomarkers Dried Blood Spots to measure 

total cholesterol, high-density 

lipoproteins, C-reactive protein 

(CRP), glycosylated hemoglobin 

(HbA1c), and Epstein-Barr virus 

(EBV)

Achievement 

assessments

Math and reading achievement 

tests based on ECLS-K 5th and 

8th grade assessments

Language 

assessments

Audio taping of open-ended 

questions and reading passage 

for pre-selected random 

subsample

Audio taping of open-ended 

question and reading passage for 

pre-selected random subsample

Administrative 

data

Addresses, housing program 

participation, earnings, social 

program participation, arrests, 

post-secondary school 

enrollment, and mortality

Elementary and secondary school 

records, post-secondary school 

enrollment, social program 

participation, arrests, earnings, 

and mortality

Earnings, social program 

participation, arrests, postsecondary school enrollment, and 

mortality

Neighborhood 

indicators

Census tract data

Interviewer observations from 

neighborhood walk-around

Census tract data

Interviewer observations from 

neighborhood walk-around
29

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

where appropriate) from the survey instruments used 

in the interim MTO study. This facilitates comparisons 

of impacts from interim to final evaluation. In the 

long-term evaluation, interviewers administered a 

different reading and math achievement test18 and 

different survey measurement of mental and physical 

health, criminal offending, and victimization. They also 

collected additional key mediating factors, including 

basic features of decision-making and audio recordings of 

speech patterns. Most of the survey items in the interim 

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

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

national surveys, which has the advantage of facilitating 

comparisons between MTO and national samples. The 

final MTO survey instruments are located at 

www.mtoresearch.org.

SURVEY DATA COLLECTION

Between June 2008 and April 2010, the Survey Research 

Center (SRC) at the University of Michigan collected the 

following information for the long-term study:

•	 An adult respondent survey to gather data on 

outcomes and mediating factors for the respondent 

and other members of the household;

•	 A youth survey to gather information on outcomes 

and mediating factors for youth who were ages 

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

the survey data collection period, and who resided 

with MTO families at time of enrollment;

•	 Educational achievement data from standardized 

reading and math tests administered to MTO 

youth;

•	 Biomarker data, including height and weight 

18 For the interim MTO study, children were administered standardized 

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

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

by age; for example, the average scores of 13-year-olds were not very different 

from those of 18-year-olds. This pattern made us nervous for the long-term 

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

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

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

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

Education’s Early Childhood Longitudinal Survey of Kindergarteners in 1998 

(ECLS-K) for the fifth and eighth grade waves. To avoid “ceiling effects” (cases 

where some children know everything that is covered on the test and get everything right, so that their actual achievement level cannot be pinned down), the 

ECLS assessments were supplemented with additional reading and math items 

from the 10th grade test that was used in the U.S. Department of Education’s 

National Education Longitudinal Study of 1988 (NELS).

measurement. For adults, SRC also measured waist 

circumference and blood pressure and collected 

blood spots from finger pricks and sent them to 

a lab for analysis of various long-term disease 

precursors; 

•	 Language assessments to measure speech patterns 

of adults and youth through audiotaping of openended survey questions and a reading passage;

•	 Observational data on the characteristics of the 

respondent’s immediate neighborhood block 

through neighborhood “walk-arounds” by trained 

interviewers.

Trained interviewers using Computer-Assisted Personal 

Interviewing (CAPI) on laptop computers administered 

the two surveys, household and youth, primarily in 

the respondents’ homes, with the session scheduled 

at the respondent’s convenience.19 Field interviewers 

also recorded their observations of the neighborhood 

environments. 

As in the interim study, the survey fielding design 

employed two-phase sampling to obtain responses from 

a representative subsample of hard-to-locate respondents 

(Groves et al., 2004). In the main sample phase, SRC 

sought to contact and interview all of the adults and 

youth who were in the survey sample frame. They 

offered $50 to everyone selected for the survey sample 

frame to complete the surveys. They offered adults an 

additional $25 to provide biomarkers (like blood spot 

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

youth an extra $25 to complete the reading and math 

achievement tests. Once SRC reached a response rate of 

approximately 75 percent for the adult or youth survey 

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

of the remaining cases for more intensive interviewing 

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

interviews they conducted as part of the second phase by 

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

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

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

and 89 percent for the youth sample.20 Exhibit 1.10 

19 A small number of adult and youth survey interviews were completed by telephone.

20 For example, if the phase one response rate was 74 percent, and a 

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

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

would be .74+.26*.5=.87, or 87 percent.
30

shows response rates for the adult and youth survey, by 

treatment status. Response rates are statistically similar 

between the experimental and control groups for adults 

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

The Section 8 group adult response rate is slightly lower, 

and statistically differs from the control group. 

ADMINISTRATIVE DATA COLLECTION

The long-term study also includes an extensive effort 

to collect administrative data from everyone who was 

a member of the core MTO household at baseline, 

regardless of whether they were included in the long-term 

survey sampling frame. At baseline, all household heads 

signed a waiver granting HUD-authorized researchers 

permission to access government administrative data on 

themselves and their families for purposes of evaluating 

MTO. 

The study collected administrative data on:21

•	 Housing assistance from HUD administrative data;

•	 Earnings and employment from state 

unemployment insurance records;

•	 Social program participation (welfare, food stamps) 

from state data records;

•	 Elementary and secondary school outcomes from 

student-level school records from local school 

districts and in some cases state education agencies;

•	 Post-secondary enrollment data from the National 

Student Clearinghouse;

•	 Adult and juvenile arrest histories from state 

criminal justice agencies (and in some cases 

county- or city-level juvenile justice agencies).

We collected administrative data for the five MTO sites 

and for a random sample of the other jurisdictions to 

which participants have moved. Sampling is necessary 

because participants have now resided in 40 states 

and attended schools in more than 300 public school 

districts. Sampling enables us to provide a representative 

picture of the outcomes of everyone in all three of the 

randomized MTO groups, but without having to collect 

administrative data from every agency that includes 

21 The study also collected mortality records from the National Center 

for Health Statistics’ National Death Index, although those outcomes 

are not included in this report.

MTO families. The details of the sampling scheme will 

appear in the forthcoming Technical Appendices. 

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

2000 decennial censuses and the 2005–09 waves of the 

American Community Survey (ACS) to measure MTO 

changes in the social and institutional environments 

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

continuous geographic area comprising between 1,500 

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

is delineated by observable physical objects (road, river, 

train tracks, state or county line).22 Census tracts are 

often used in the social science research literature as a 

geographic measure of “neighborhood.”

We are also currently in the process of collecting data on 

local-area crime rates from police departments serving 

the five main MTO demonstration cities.

1.7 OVERVIEW OF THE REPORT

MTO research provides direct evidence on the impact 

of housing mobility interventions on low-income 

families. The findings are critical to improving our 

understanding of the impacts of neighborhood context 

more generally on the contemporaneous well-being 

and longer-term outcomes for poor households. An 

experiment such as MTO that alters residential mobility 

and places families in different neighborhoods also can 

be useful for simulating the potential effects of wholesale 

neighborhood changes on individuals from disadvantaged 

backgrounds. It may be possible to identify specific 

mechanisms that will help target issues that can be 

directly addressed in today’s high-poverty communities.

MTO findings are also central to a number of ongoing 

housing-policy debates. The contrast in youth outcomes 

between the Section 8 and the control groups will be 

directly relevant to debates about whether to increase the 

number of Section 8 vouchers, and particularly about 

whether to offer vouchers to current public housing 

residents rather than renovate the aging public housing 

stock (Olsen, 2003; Quigley, 2000). 

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

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

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

statarea.pdf. (accessed May 5, 2010).
31

In extrapolating these results to other housing programs, 

it is important to keep in mind that different people may 

respond differently to the same policy intervention—a 

possibility that researchers refer to as “treatment 

heterogeneity.” We might be particularly concerned that 

people who volunteer for a housing mobility programs 

may be respond differently than would people who are 

involuntarily moved, for example because their public 

housing project is scheduled for demolition. MTO 

estimates will be most directly relevant to other voluntary 

mobility programs that involve similarly disadvantaged 

minority families living in very distressed public housing 

communities. In fact for MTO-type programs and 

populations, these MTO findings will be particularly 

valuable because most housing mobility programs are not 

implemented in a way that includes a valid control group, 

and so cannot be reliably evaluated directly themselves.

The treatment that the experimental group received does 

not correspond precisely to particular policies now under 

consideration, but this group may provide information 

about the value of more intensive counseling initiatives 

that could expand the number of neighborhoods into 

which voucher families move. Remember, though, MTO 

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

most directly applicable to strategies to incrementally 

expand or revise current programs, such as adding several 

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

there are too few families who moved to a given lowpoverty neighborhood in MTO to really change the 

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

as eliminating all public housing and issuing vouchers to 

former tenants, may have different effects than a smallerscale program.

This report is organized into eight chapters. Chapter 

2 provides more detailed information about the MTO 

experiment’s effect on mobility patterns for families, 

housing conditions and receipt of means-tested housing 

assistance from the government, and neighborhood 

characteristics and social networks. Chapters 3 and 

4 provide results on the physical and mental health 

outcomes, respectively, of MTO adults and youth. 

Chapter 5 provides results on economic outcomes, 

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

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

including employment, earnings, and income from 

government social programs and other sources. Chapter 

6 examines impacts of MTO on the risky and criminal 

behavior of participating adults and youth, while Chapter 

7 examines impacts on educational outcomes. Chapter 8 

concludes and discusses the implications of the long-term 

results of MTO for social science and public policy.
32

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

ASSIGNMENT GROUP

ADULT (%) YOUTH (%)

Experimental Group 90.8 90.1

Section 8 Group 86.6 86.9

Control Group 90.0 88.9

All Groups 89.6 88.7

Notes: The response rate calculations presented above account for the change over time in the MTO random assignment ratios as well as the two-phase survey 

sampling design of the long-term evaluation. The weights equal the product of the random assignment ratio weight (described previously) and the sampling weight 

(equal to 1 for families interviewed in Phase 1, equal to 1/0.35 for families who were randomly selected for the Phase 2 survey sample, and equal to 0 for families 

who were not randomly selected for the Phase 2 survey sample. The effective response rate is equal to the weighted number of interviews divided by the weighted 

survey sample frame total minus the weighted number of decedents.

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

and interviews were conducted with 3,273 adults and 5,101 youth.

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

ASSIGNMENT GROUP

EXPERIMENTAL 

GROUP

SECTION 8 

GROUP

CONTROL 

GROUP

ALL 

GROUPS

AGE [PR]

As of December 2007 24.6 24.4 24.4 24.5

At baseline 12.7 12.6 12.6 12.7

GENDER [PR]

Female 50.3% 49.3% 47.9% 49.2%

Male 49.7% 50.7% 52.1% 50.8%

OTHER CHARACTERISTICS [PR]

Gifted student or did advanced coursework 13.0% 12.4% 12.4% 12.6%

Suspended or expelled from school in past 2 years 3.8% 4.3% 3.3% 3.8%

School called about behavior in past 2 years 19.9% 20.7% 19.6% 20.0%

Behavioral or emotional problems 5.5% 7.0% 5.1% 5.8%

Learning problem 11.8% 13.1% 12.3% 12.3%

Health problems that limited activity 6.1% 5.8% 5.4% 5.8%

Health problems that required special medicine/

equipment

9.1% 9.3% 8.9% 9.1%

SAMPLE SIZE 1,887 1,336 1,420 4,643

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

ages 0 to 5. 

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

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

to 17 at baseline) from all 4,604 MTO families as reported by the baseline head of household.
33

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

BY MTO SAMPLE

ADULTS YOUTH AGES 10–20

DESCRIPTION MEAN STANDARD 

DEVIATION

SHARE 

MISSING

MEAN STANDARD 

DEVIATION

SHARE 

MISSING

CHARACTERISTICS OF

THE ADULT/YOUTH'S

PARENT

ADULT/PARENT'S AGE

AS OF 12/31/07 (OMITTED

CATEGORY: > 50)

≤ 35 0.141 0.348 0 0.184 0.387 0

36–40 0.224 0.417 0 0.305 0.460 0

41–45 0.231 0.422 0 0.247 0.431 0

46–50 0.187 0.390 0 0.149 0.356 0

RACE (OMITTED

CATEGORY: WHITE)

African-American 0.646 0.474 0.019 0.662 0.469 0.016

Other race 0.279 0.444 0.019 0.273 0.441 0.016

HISPANIC ETHNICITY 0.318 0.464 0.009 0.306 0.459 0.010

MALE ADULT/PARENT 0.018 0.133 0 0.018 0.132 0

EDUCATIONAL

ATTAINMENT AT BASELINE

Adult had a GED 0.191 0.393 0.065† 0.196 0.397 0.063†

Adult reported having 

completed high school

0.391 0.488 0.065† 0.373 0.484 0.063†

Adult is missing information on 

GED and high school diploma 

status

0.065 0.247 0.063 0.243

Adult was enrolled in school 0.167 0.366 0.036 0.182 0.380 0.034

NEVER MARRIED 0.628 0.476 0.031 0.663 0.465 0.032

UNDER AGE 18 AT BIRTH OF 

FIRST CHILD

0.256 0.426 0.044 0.296 0.447 0.043

WORKING 0.262 0.433 0.032 0.220 0.407 0.033

SITE (OMITTED

CATEGORY: NEW YORK

CITY)

Baltimore 0.136 0.343 0 0.128 0.334 0

Boston 0.204 0.403 0 0.183 0.387 0

Chicago 0.206 0.404 0 0.231 0.422 0

Los Angeles 0.225 0.418 0 0.238 0.426 0

FLAG INDICATING ADULT

INCLUDED IN FIRST

SURVEY RELEASE

0.358 0.479 0
34

SUPPLEMENTAL EXHIBIT 1.2. (CONTINUED)

ADULTS YOUTH AGES 10–20

DESCRIPTION MEAN STANDARD 

DEVIATION

SHARE 

MISSING

MEAN STANDARD 

DEVIATION

SHARE 

MISSING

CHARACTERISTICS

OF THE BASELINE

HOUSEHOLD

HOUSEHOLD MEMBER HAD 

A DISABILITY

0.152 0.358 0.008 0.143 0.348 0.009

HOUSEHOLD STRUCTURE

No teen (ages 13–17) children 

in core household at baseline

0.621 0.485 0 0.754 0.431 0

Core household size is two or 

smaller

0.210 0.408 0 0.086 0.280 0

Core household size equals 

three

0.308 0.462 0 0.247 0.432 0

Core household size equals 

four

0.231 0.421 0 0.259 0.438 0

HOUSEHOLD MEMBER 

WAS BEATEN/ASSAULTED; 

THREATENED WITH A 

GUN OR KNIFE; OR HAD 

THEIR PURSE, WALLET, 

OR JEWELRY SNATCHED 

DURING THE 6 MONTHS 

PRIOR TO BASELINE

0.422 0.492 0.007 0.426 0.493 0.008

RECEIVING AFDC 0.756 0.429 0.005 0.815 0.387 0.005

OWNED A CAR 0.183 0.387 0.004 0.188 0.390 0.005

HEAD OF HOUSEHOLD AT

BASELINE...

Had lived in their neighborhood 

for 5 or more years

0.606 0.483 0.025 0.557 0.490 0.028

Stopped to chat with neighbor 

in street or hallway at least 

once a week

0.521 0.497 0.009 0.527 0.497 0.009

Was very dissatisfied with his/

her neighborhood

0.474 0.497 0.009 0.486 0.497 0.01

Reported he/she would be very 

likely to tell neighbor if saw 

neighbor's child getting into 

trouble

0.546 0.497 0.005 0.550 0.496 0.005

Did not have any family living in 

their neighborhood

0.631 0.481 0.005 0.638 0.479 0.005

Did not have any friends in the 

their neighborhood

0.401 0.488 0.008 0.402 0.488 0.008
35

SUPPLEMENTAL EXHIBIT 1.2. (CONTINUED)

ADULTS YOUTH AGES 10–20

DESCRIPTION MEAN STANDARD 

DEVIATION

SHARE 

MISSING

MEAN STANDARD 

DEVIATION

SHARE 

MISSING

Felt streets near home were 

very unsafe at night

0.506 0.498 0.008 0.507 0.499 0.006

Was very sure he/she would 

find an apartment in a different 

area of the city

0.477 0.498 0.007 0.499 0.498 0.008

Had moved more than three 

times in the past 5 years

0.097 0.294 0.009 0.112 0.314 0.009

Had already previously applied 

for a Section 8 voucher or 

certificate at baseline

0.402 0.489 0.008 0.382 0.484 0.01

HEAD OF HOUSEHOLD'S

PRIMARY OR SECONDARY

REASON FOR WANTING

TO MOVE WAS...

To get away from gangs or 

drugs

0.773 0.414 0.023 0.766 0.418 0.024

To have access to better 

schools for children

0.505 0.494 0.027 0.527 0.492 0.03

YOUTH CHARACTERISTICS 

(AGES 10–20 IN 2007)

YOUTH'S AGE AS OF

12/31/07 (OMITTED

CATEGORY: 15)

10 0.009 0.094 0

11 0.030 0.170 0

12 0.048 0.214 0

13 0.093 0.291 0

14 0.102 0.303 0

16 0.128 0.334 0

17 0.128 0.335 0

18 0.125 0.330 0

19 0.110 0.314 0

20 0.112 0.315 0

MALE YOUTH 0.495 0.500 0

FLAG INDICATING AGE 6–17 

AT BASELINE (OMITTED 

CATEGORY: UNDER 6)

0.396 0.489 0

HEALTH PROBLEMS

Problems requiring special 

medicine and/or equipment

0.084 0.269 0.059
36

SUPPLEMENTAL EXHIBIT 1.2. (CONTINUED)

ADULTS YOUTH AGES 10–20

DESCRIPTION MEAN STANDARD 

DEVIATION

SHARE 

MISSING

MEAN STANDARD 

DEVIATION

SHARE 

MISSING

HEALTH PROBLEMS (CONTINUED)

Health problems limiting 

activity

0.056 0.230 0.068†

Flag indicating missing 

information on health problems 

(see preceding item)

0.068 0.252

CHARACTERISTICS

APPLYING ONLY TO

YOUTH AGES 0–5 AT

BASELINE

HOSPITALIZATION

Hospitalized before first 

birthday

0.191 0.393 0.057†

Flag indicating missing 

information on hospitalization 

(see preceding item)

0.057 0.232

BIRTHWEIGHT

Weighed less than 6 pounds 

at birth

0.150 0.357 0.093†

Flag indicating missing 

information on birthweight (see 

preceding item)

0.093 0.291

READ TO BY AN ADULT

Read to by an adult more than 

once a day

0.264 0.441 0.091†

Flag indicating missing 

information on being read to 

(see preceding item)

0.091 0.287

CHARACTERISTICS

APPLYING ONLY TO

YOUTH AGES 6–17 AT

BASELINE

BEHAVIORAL/EMOTIONAL

PROBLEMS

Behavioral/emotional problems 

in 2 years prior to baseline

0.067 0.250 0.157†

Flag indicating missing 

information on behavioral/

emotional problems (see 

preceding item)

0.157 0.364
37

SUPPLEMENTAL EXHIBIT 1.2. (CONTINUED)

ADULTS YOUTH AGES 10–20

DESCRIPTION MEAN STANDARD 

DEVIATION

SHARE 

MISSING

MEAN STANDARD 

DEVIATION

SHARE 

MISSING

SUSPENSIONS/

EXPULSIONS

Suspended or expelled from 

school in 2 years prior to 

baseline

0.036 0.187 0.061†

Flag indicating missing 

information on suspensions/

expulsions (see preceding item)

0.061 0.239

GIFTED STUDENT

Gifted student or did advanced 

course work

0.143 0.351 0.079†

Flag indicating missing 

information on gifted student 

(see preceding item)

0.079 0.269

LEARNING AND BEHAVIORAL PROBLEMS

Learning problem in 2 years 

prior to baseline

0.129 0.336 0.050†

Flag indicating missing 

information on learning 

problems (see preceding item)

0.050 0.217

School called about youth’s 

behavioral problems in 2 years 

prior to baseline

0.204 0.396 0.040

Notes: Weighted means, standard deviations, and share missing are shown for the interviewed adults and youth (ages 10–20). For baseline information that only 

applied to children age 0–5 or 6–17 at baseline, we exclude children to whom the item did not apply before calculating the means and other statistics. Missing 

values are replaced with mean imputed values for covariates that were missing for less than 5 percent of the full sampling frame. The imputed means for adult 

and household covariates are conditional on site and randomization in 1998 versus earlier years. Youth imputations are conditional on site, randomization in 1998 

versus earlier years, age at baseline, and gender. Missing values for covariates with at least 5 percent missing for the full sample are excluded from the statistics 

above and are flagged as having missing data and set to zero in the analysis. 

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

= 5,101).

† Indicates that baseline information (for the applicable sample) was missing on this covariate for at least 5 percent of the full sample. Due to the level of missing 

information, a missing flag was included in the analysis and the mean and standard deviation shown in this table are conditional on the baseline information being 

nonmissing (for example, the covariate is set to zero for individuals with missing information on this item however these zeros are not included in the means 

shown).
38
39 Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks

IMPACTS ON MOBILITY, 

HOUSING, NEIGHBORHOODS, 

AND SOCIAL NETWORKS
40 CHAPTER 2

IMPACTS ON MOBILITY, HOUSING, 

NEIGHBORHOODS, AND SOCIAL NETWORKS

This chapter describes the long-term impacts of 

MTO on the residential mobility patterns, housing 

conditions, neighborhood conditions, and social 

networks of participating families. These outcomes 

are measured 10 to 15 years after random assignment 

and are derived from survey self-reports by MTO 

participants, geocoded address histories of families 

linked to tract-level data from the Census Bureau, 

and interviewer observations about the respondents’ 

housing units and surrounding neighborhoods.

We find that, on average, control group families made 

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

study period; families in the experimental or Section 

8 groups who moved with a program voucher made, 

on average, approximately one extra move over the 

course of the study. MTO improved the quality of the 

housing units in which families were living but had 

no detectable impacts on housing costs for families, 

although the confidence intervals around these latter 

estimates do not allow us to rule out modest impacts. 

MTO had no detectable impacts on being “literally 

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

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

circumstances). However, being in the Section 8 group 

increased the chances of “doubling up” with friends or 

relatives. Families in the experimental and Section 8 

groups were less likely than controls to report having 

trouble paying their rent on time, but were more likely 

to have trouble paying their utility bills.

MTO allowed many families to move to lower-poverty 

neighborhoods. During the 10 to 15 years after families 

were randomly assigned, the poverty rate of the census 

tracts in which the control group lived averaged 

approximately 40 percent. For those families that 

moved with MTO vouchers in the experimental and 

Section 8 groups, average tract poverty rates were about 

18 and 11 percentage points lower, respectively. MTO 

had more modest effects on the degree of neighborhood 

racial segregation that families experienced, but the 

program did have sustained effects on a variety of other 

measures of neighborhood socioeconomic composition. 

Despite concerns about potential social isolation of 

MTO families who moved to more affluent areas, 

program moves increased the likelihood of having close 

friends who had a college degree. MTO also made adult 

and female youth participants, but not male youth, feel 

safer in their new neighborhoods.

2.1 BASELINE AND CONTROL 

GROUP CONTEXT

The MTO demonstration recruited families from 

public housing or assisted housing projects in some 

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

About 90 percent of families interviewed for the longterm evaluation lived in public housing at baseline. 

The other 10 percent lived in housing run by private 

operators with government assistance.1

Exhibit 2.1 shows baseline characteristics of the 

long-term survey respondents and the census tracts 

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

families expressed considerable dissatisfaction with 

their housing. Only one-quarter of adults rated the 

condition of their housing as excellent or good. At 

baseline, families lived in census tracts with poverty 

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

residents were members of racial or ethnic minority 

groups. The tracts had low labor force participation and 

adult education levels. They also had high proportions 

of families headed by a single parent or on welfare. 

1 Private developers built rental housing under a number of different federal programs from the 1960s to the 1980s. The developers 

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

rates on mortgages) in exchange for providing some units affordable 

to low-income renters. Such developments were built under a number 

of programs (rent supplement, 221(d)(3), BMIR, Section 202, Section 236, Section 8 new construction or substantial or moderate 

rehabilitation). Units in such developments are typically referred to as 

private, “assisted” housing.
41 Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks

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

EVALUATION: HOUSEHOLD AND NEIGHBORHOOD CHARACTERISTICS

OUTCOME CONTROL 

GROUP

EXPERIMENTAL 

GROUP

SECTION 8 

GROUP

ALL 

GROUPS

HOUSING CONDITION

Rated baseline housing as excellent or good [SR] 26.3% 29.3% 25.0% 27.1%

BASELINE TRACT CHARACTERISTICS INTERPOLATED

USING 1990 AND 2000 CENSUS DATA TO THE FAMILY'S

DATE OF ENROLLMENT BETWEEN 1994–1998

Persons who are poor [CEN] 53.1% 52.7% 52.6% 52.8%

Persons above 200% of the poverty line [CEN] 24.8% 24.9% 24.9% 24.9%

Minority [CEN] 91.2% 91.6% 91.5% 91.4%

Employed, civilian [CEN] 74.2% 73.9% 74.3% 74.1%

College graduates [CEN] 10.3% 10.1% 10.6% 10.3%

More than high school education [CEN] 22.1% 21.7% 22.3% 22.0%

Single female-headed households [CEN] 63.4% 63.1% 63.3% 63.3%

Households on public assistance [CEN] 33.4% 33.8% 33.1% 33.4%

BASELINE TRACT CHARACTERISTICS MEASURED USING

AMERICAN COMMUNITY SURVEY DATA (2005–09)

Persons who are poor [CEN] 41.5% 41.5% 42.1% 41.7%

Persons above 200% of the poverty line [CEN] 28.5% 28.7% 28.2% 28.5%

Minority [CEN] 90.4% 90.5% 91.0% 90.6%

Employed, civilian [CEN] 79.5% 79.9% 79.6% 79.7%

College graduates [CEN] 18.6% 18.2% 18.6% 18.4%

More than high school education [CEN] 34.2% 33.7% 34.2% 34.0%

Single female-headed households [CEN] 60.0% 59.4% 61.0% 60.1%

Households on public assistance [CEN] 14.0% 13.6% 13.5% 13.7%

NEIGHBORHOOD SAFETY

Any household member was a victim of crime in 

past 6 months [SR]

41.6% 43.4% 41.4% 42.2%

Streets are very unsafe at night [SR] 51.2% 49.3% 51.7% 50.6%

MOTIVATIONS FOR SIGNING UP TO MOVE THROUGH MTO

PRIMARY OR SECONDARY REASON FOR MOVING WAS...

To get away from drugs or gangs [SR] 77.9% 78.6% 74.9% 77.3%

For better schools for the children [SR] 48.1% 49.1% 55.3% 50.5%

To get a bigger or better apartment [SR] 45.7% 44.1% 43.8% 44.5%

HOPE VI PROJECT

Housing project has been demolished through 

HOPE VI or another housing initiative [HUD]

41.6% 39.5% 44.8% 41.6%

INTERVIEWED N 1,139 1,456 678 3,273

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

ratios and the selection of adults into the interview sample and the two-phase sampling design of the long-term evaluation.

Square brackets indicate the source of the outcome information: SR = self-report from the MTO Participant Baseline Survey, CEN = 1990 and 2000 decennial 

census data as well as the 2005–09 American Community Survey, HUD = Administrative data on demolished housing projects through 2009. 

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

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

2000 decennial census. 
42

The third panel of Exhibit 2.1 shows the characteristics 

of the same baseline census tracts but measured more 

recently using the 2005 to 2009 American Community 

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

demographic composition of these tracts at baseline 

(interpolated to the date families enrolled in the 

program, between 1994 and 1998, using 1990 and 2000 

census data) with the 2005 to 2009 demographics of 

these tracts suggests that, over time, these tracts have 

become slightly less poor and have higher proportions of 

college-educated residents. The share of residents who 

are members of a racial or ethnic minority group has 

remained about the same over time in these census tracts.

The baseline neighborhoods in which families were living 

were also quite dangerous. Exhibit 2.1 shows that 42 

percent of MTO household heads reported that someone 

in the home had been the victim of a crime during 

the six months prior to the survey. Around 50 percent 

reported that the streets in their neighborhood were 

unsafe at night. Perhaps not surprisingly, 77 percent said 

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

or second most important reason for wanting to move. 

The next most common reason for wanting to move was 

better schools (approximately 50 percent reported this as 

the first or second most important reason for enrolling), 

followed by wanting a bigger or better apartment 

(approximately 45 percent said this was the first or 

second reason).

As MTO was taking place, the urban landscape itself was 

shifting. The overall share of people who were poor in 

the United States declined between 1990 and 2000, but 

then increased again slightly during the 2000s (Anderson, 

2011; Bishaw and Macartney, 2010; Bishaw and 

Renwick, 2011). The geographic concentration of lowincome people (that is, residential segregation by income) 

was increasing from 1970 through 2000, the last year 

for which decennial census data are available (Watson, 

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

were unavailable, but there are reasons to suspect that 

residential segregation by income increased further. 

Throughout the MTO study period, rents rose faster 

than wages for low-skilled people in metropolitan areas 

nationwide. The stock of housing units with low to 

moderate rents declined, both because the overall rent 

distribution shifted and because units were removed 

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

or landlords opting out of the voucher programs) faster 

than affordable replacement units were built (U.S. 

Department of Housing and Urban Development, 

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

are particularly expensive. The majority of low-income 

renters in the five MTO sites—ranging from nearly 74 

percent of low-income renters in California to 64 percent 

in Massachusetts—spend more than 30 percent of their 

income on rent, which is a commonly-used definition 

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

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

renters in these same states spend more than 50 percent 

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

Housing markets in Boston, Chicago, Los Angeles, 

and New York were strongly affected by the recent 

housing market collapse. The resulting foreclosure of 

rental properties, tenant evictions, and the flood of new 

renters who were former homeowners appear to have 

increased competition in the rental market (Briggs, 

Popkin, and Goering, 2010). Increasing poverty rates 

during the 2000s presented further challenges to lowincome families. The net result of these two trends is 

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

renters who devoted more than 50 percent of their 

income to rent rose 20 percent, from 5.91 million to 

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

Urban Development, 2011).

Housing policies themselves changed in important ways 

as well. Cities demolished and redeveloped many of the 

most severely distressed public housing developments, 

mostly under the HOPE VI program operated by 

HUD. Some of the public housing projects in which 

MTO families were living at baseline were among 

those demolished as part of HOPE VI and other local 

and federal housing initiatives. The share of the MTO 

sample members from all three groups who at baseline 

were living in housing projects that were eventually 

demolished varied from 91.5 percent in Chicago to 6 

percent in New York City. Overall, approximately 42 

percent of the sample was living at baseline in a project 

that was eventually affected by a demolition program. 
43 Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks

Families living in projects targeted for demolition 

were typically either relocated to other public housing 

properties or offered housing vouchers similar to those 

that were offered to the Section 8 group. Most families 

who relocated through these programs seem to now be 

in better housing and safer neighborhoods (see Popkin 

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

returned to the redeveloped projects, but those who 

did now live in dramatically better housing and lowerpoverty environments (Turner and Kingsley, 2008).

Even housing projects untouched by HOPE VI have seen 

improvements in housing and neighborhood conditions 

over the last decade, owing to changes in HUD policy 

that encouraged income mixing and the exclusion of 

residents with criminal records. Crime rates in the 

United States also declined dramatically during the 

1990s with the largest proportional drops in crime often 

coming in the highest-crime places including places like 

the baseline housing projects from which MTO families 

were drawn (Harcourt and Ludwig, 2006; Cook and 

Laub, 2002; and Levitt, 2004).

These demographic, housing market, and policy 

changes may have influenced the impacts of the MTO 

demonstration on housing and neighborhood conditions 

by changing the housing and neighborhood options 

available to families in the two treatment groups, and 

by changing the conditions that control group families 

experienced. For example, HOPE VI may have led 

to more mobility among control group families than 

would otherwise have been expected of public housing 

residents. This does not bias our estimates, given that 

the experience of the control group is still an accurate 

picture of what would have happened to families assigned 

to the treatment groups in the absence of MTO. But 

it is important to bear in mind that we are comparing 

treatment families with a set of control families who 

were likely affected by substantial changes in housing 

policy. Although housing-market-induced changes in 

housing units available to voucher holders do not bias 

our comparisons, they are relevant in thinking about the 

conditions under which we would expect to see similar 

impacts from receiving housing vouchers.

2.2 HYPOTHESES ABOUT 

EFFECTS ON MOBILITY, HOUSING, 

NEIGHBORHOODS, AND SOCIAL 

NETWORKS

This section reviews the different pathways through 

which the MTO offer of a housing voucher might 

influence the housing and neighborhood conditions of 

families in the demonstration. Subsequent chapters of 

this report each include some discussion of the different 

theoretical pathways through which the housing and 

neighborhood changes described in this chapter might 

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

In thinking about the potential effects of housing 

vouchers on housing and neighborhood conditions, it 

is crucial to keep in mind that the MTO study sample 

consists of families who were all living in public housing 

at baseline. Setting aside for the moment the changes 

described above in other housing policies over the course 

of the study period, the MTO experiment essentially 

compares the effects of being offered a housing voucher 

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

very different intervention or “treatment” from what is 

examined by the HUD Welfare to Work (WtW) voucher 

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

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

effects of being offered a voucher versus living in the 

private-housing market without a subsidy. We return 

to the issue of how MTO compares with other voucher 

experiments at the end of this chapter.

HOUSING UNIT QUALITY

Whether the MTO comparison of housing vouchers 

versus public housing should have any impact at all 

on housing-unit quality is unclear, given that both the 

public housing and housing voucher programs share 

the same ostensible goal articulated by Title II of the 

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

suitable living environment for every American family.” 

If both public housing and housing vouchers provided 

families with “a decent home,” then MTO vouchers 

could in principle have no net effect on housing unit 

quality.
44

Any MTO impact on housing-unit quality will depend 

on the specifics of how public housing and housing 

voucher programs are implemented in practice. Some 

economists worry that the lack of financial incentives 

for government housing authorities to operate public 

housing projects efficiently could lead them to provide 

low-quality housing units relative to private-market 

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

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

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

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

The private housing market could itself also be operating 

inefficiently because of regulatory and other constraints 

on the available supply of low-income housing, or 

because the high costs of moving reduce the competitive 

pressure on landlords to provide high-quality housing. 

The degree to which housing vouchers actually improve 

the unit quality of housing for families will then depend 

in part on the degree of enforcement in the study sites 

of housing quality standards and inspections, which 

are required as part of the standard housing voucher 

program.

MOBILITY

If people respond to incentives, at least in the aggregate 

as standard rational choice theory predicts, then MTO 

should increase the number of moves that families make 

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

government-assisted housing projects receive large rental 

subsidies that help reduce their housing-cost burden, 

but only if they agree to live in the public housing 

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

housing would like to move to a different apartment or 

neighborhood, their only option would be to move into 

a private-market housing unit and give up their public 

housing subsidy.2

 We might therefore expect somewhat 

higher mobility rates among the MTO control families 

than among more representative samples of low-income 

families. MTO housing vouchers relaxed the location 

constraints that families faced in exchange for receiving a 

rental subsidy. Put differently, families who are offered an 

MTO housing voucher are able to move to different units 

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

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

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

program besides MTO. In practice in most cities there are long waitlists for both public housing and housing voucher subsidies.

and neighborhoods without having to give up a large 

housing subsidy. Therefore, an MTO housing voucher 

substantially reduces the costs to families of relocating. 

And economic models predict that a lower cost of 

moving should lead families to make more moves.

The net effect of MTO on residential mobility rates over 

the longer term could be different from the program’s 

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

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

made by families in the experimental and Section 8 

groups. Over time, control group families may enter into 

the private housing market on their own even without 

receiving a housing voucher subsidy. Control group 

families thereby could be placed at increased risk for 

housing instability if income fluctuations make them 

unable to meet monthly rent or utility expenses without 

a subsidy and if private-market landlords are quicker to 

evict families for nonpayment than are public housing 

authorities. The result could be additional involuntary, or 

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

higher initial mobility of the experimental and Section 8 

groups.3

HOMELESSNESS

The prospect of housing instability among control 

group families who move into the private housing 

market without a subsidy also raises the prospect that 

MTO vouchers could reduce homelessness. But even 

the net effect of MTO on homelessness is ambiguous 

from the perspective of social science theory. It could be 

the very fact of relying on the private housing market 

that puts a family at elevated risk for homelessness, 

regardless of whether the family has a voucher subsidy, 

because of the increased risk of landlord difficulties 

or sale or foreclosure of their rental property. Given 

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

Section 8 groups versus controls will wind up in the 

private housing market, MTO could increase rather than 

decrease homelessness.

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

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

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

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

poor families in particular are likely to move from one poor neighborhood to another (Crowder and South, 2005).
45 Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks

NEIGHBORHOOD ENVIRONMENTS

By relaxing the locational constraint that public housing 

families face to receive a housing subsidy, MTO vouchers 

should also increase the chances that families wind up 

living in lower-poverty neighborhoods. Public housing 

offers families limited choice over where they live. In 

exchange for a large housing subsidy, families must 

live in whatever public housing units are offered by 

the local housing authority. Given the politics of how 

local housing authorities first selected public housing 

locations, and given that the design and tenant selection 

policies often created high concentrations of poor 

families, many housing project residents wind up living 

in very poor, racially segregated neighborhoods (Olsen, 

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

vouchers enable public-housing families to move into 

lower-poverty neighborhoods without having to give 

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

changing their neighborhood conditions.

The relatively greater locational flexibility afforded 

to voucher families has its limits, however. Many 

housing units will be unaffordable to voucher holders.4

 

Affordable housing units will be even more difficult to 

find in low-poverty areas, given that standard economic 

theory predicts that any amenity associated with a 

housing unit—including the “quality” of the surrounding 

neighborhood—should be reflected in relatively higher 

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

housing unit quality for neighborhood quality.

We expect the constraint that MTO imposes on 

experimental group families, which limits them to 

using their housing voucher in a low-poverty area at 

least initially, to lead to more pronounced changes 

in tract poverty for families who move with an MTO 

experimental group voucher compared with those who 

move with a Section 8 group voucher. Even though 

experimental group families could move out of low4 The voucher subsidy amount is essentially equal to something like 

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

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

distribution, and 30 percent of the family’s adjusted income (total income minus some deductions allowed by the program rules; see Olsen, 

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

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

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

to voucher holders.

poverty census tracts after their initial one-year lease was 

up, MTO designers hoped that many experimental group 

families would end up liking their new areas and choose 

to stay. The costs of moving might also contribute to 

the “stickiness” of experimental group voucher holders 

in their initial low-poverty census tracts. Consistent 

with the hope of MTO program designers, previous 

research of families in the Gautreaux mobility program 

in Chicago found that many families who were initially 

assigned to relocate to low-poverty, mostly white Chicago 

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

We expect MTO moves to lead to changes in a 

wide range of other neighborhood attributes that 

families experience as well, given the correlation 

between neighborhood poverty and a variety of other 

neighborhood attributes such as prevalence of high 

school dropout and joblessness, crime, and quality of 

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

might be neighborhood racial composition, if the 

mostly minority program population in MTO has a 

preference for living in mostly minority neighborhoods, 

or encounters racial discrimination in the housing 

market. Another potential exception could be access 

to public transportation if higher-poverty inner-city 

neighborhoods are closer to train and bus lines than 

lower-poverty areas that may be concentrated more along 

the periphery of the demonstration cities.

SOCIAL ISOLATION

A large body of research in social psychology and 

sociology has documented a tendency of people to 

associate with other people who share the same social 

characteristics or preferences, known as “homophily” 

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

2001). One potential concern with MTO is that the 

families who moved with a housing voucher might wind 

up feeling socially isolated in their new low-poverty 

areas if the moves reduce their access to their baselineneighborhood social ties. At the same time, families 

can have difficulty establishing new connections with 

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

5 Keels (2008) found remarkable persistence in low rates of neighborhood poverty among Gautreaux children who are now adults, although 

many Gautreaux children have moved on to more racially balanced 

neighborhoods.
46

Briggs, 1998). The hope would be that, at the very 

least, experimental and Section 8 group movers would 

manage to make new social connections over time as they 

acculturate into their new neighborhood environments 

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

isolation for MTO movers could be declining over our 

study period as technological advances have reduced the 

costs of staying in close contact with the social networks 

from their origin neighborhoods.

2.3 DATA SOURCES AND MEASURES

From June 2008 through April 2010, we carried out 

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

youth from each family who were between 10 and 20 

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

capture outcomes on average 12 years after baseline 

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

address histories and linked each address to census tract 

characteristics, and also asked survey interviewers to 

record their observations of the neighborhoods in which 

families were living. In what follows we discuss each of 

these data sources and measures in detail.

PARTICIPANT ADDRESS HISTORY

Any effort to understand the neighborhood conditions 

requires that we reconstruct each family’s residential 

history from random assignment onward. Our strategy 

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

history from administrative records and previous 

canvasses and surveys of MTO families and then ask 

MTO adults to confirm or correct their full history, 

beginning with their current address and moving back in 

time to the point of random assignment. We also asked 

adults to indicate where they stayed during any gaps 

in their residential history in order to measure spells of 

homelessness, as described in more detail below.

In constructing the histories, we drew from 

administrative data from housing agency data used to 

track families receiving any form of housing assistance. 

These include several HUD data systems, including the 

Public and Indian Housing Information Center (PIC), 

the Tenant Rental Assistance Certification System 

(TRACS), and the Multifamily Tenant Characteristics 

System (MTCS). In addition, we accessed the U.S. Postal 

Service’s National Change of Address (NCOA) system. 

We supplemented these sources with other tracking 

activities that HUD supported over the course of the 

study period, including periodic canvasses of families by 

Abt Associates, and the interim survey that Abt, together 

with several members of our current National Bureau of 

Economic Research (NBER) research team, carried out 

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

more details).

We used the reconciled administrative and surveybased address histories to construct a “spell” file that 

incorporated information on the amount of time 

that MTO families spent at different addresses. Our 

information about the sequence of families’ addresses 

is probably more accurate than our data on the specific 

amounts of time that families spent at each address, 

given that it is difficult for people to accurately recall 

the timing of episodic events (Bound, Brown, and 

Mathiowetz, 2001). In cases of conflicting information 

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

we prioritized address information that came from actual 

contact with the family. We built on the algorithm that 

Abt Associates developed for the interim MTO study to 

determine which addresses were reliable and which dates 

should be associated with each address.

We created address histories for youth using previously 

collected administrative and survey data and asking 

adult survey respondents about whether the youth had 

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

adult reported that the youth had always lived with him 

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

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

adult supplied during the final survey to the youth (in 

addition to the other addresses we already had for the 

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

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

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

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

the estimated point when the youth was no longer living 

with the adult.

HOUSING TENURE

Tenure refers to whether the respondent occupies housing 

as a renter, as an owner, is living doubled-up with others, 

or is homeless. We asked adult heads of households 
47 Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks

whether they presently owned, rented, lived with family 

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

number of people did not fit into any of these categories 

and specified another living situation. We collected 

information on the top reason for moving out of the 

second most recent address as well as reasons for moving 

into the current neighborhood.

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

determined the exact type and duration of homelessness. 

Respondents who reported staying with a friend or a 

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

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

place to stay and were not living in a hotel or doubledup but were instead living at a shelter, on the street, in 

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

circumstance. Respondents who reported staying at 

a hotel or motel were not classified in either of those 

categories. This question also allowed for open-ended 

answers. Many of the open-ended answers were later 

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

are five final outcome variables reported in the analysis. 

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

homeless” on the basis of whether they fell into the 

respective category at least once after randomization. 

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

flagged as “doubled-up” or “literally homeless.” “Number 

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

spells” are the counts of respective spells that respondents 

reported.

HOUSING STABILITY AND UTILITY 

PAYMENTS

The adult survey also asked current renters if they had 

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

past 12 months and if they had been threatened with 

eviction in the past 12 months. Current homeowners 

were asked if they were more than 15 days late paying 

their mortgage in the past 12 months and if they had 

been threatened with foreclosure.6

 To measure the 

stability of households’ utility service, we asked renters 

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

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

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

we asked if they received a notice that their utilities 

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

shut off.

HOUSING CONDITIONS

Adult household heads reported the conditions of their 

current homes or apartments, including an overall 

assessment of their housing and whether a set of specific 

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

list of potential housing problems that we asked about 

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

Bureau, 2004) and included different types of pests, 

poorly maintained paint or plaster, and broken heating 

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

the number of people living in the household divided by 

the number of rooms in the home (excluding bathrooms 

and kitchens). We defined a situation as overcrowded if 

there was more than one person per room, and severely 

overcrowded if there were more than 1.5 people per 

room.8

HOUSING COSTS AND ASSISTANCE

Housing assistance refers to any financial subsidy 

for rental costs, such as whether the sample member 

received help in paying rent through one of the federal 

housing assistance programs. At the study’s outset, 

every participant was receiving housing assistance. At 

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

“triangulation” method developed by the Urban Institute 

that compares two types of administrative data against 

6 Our survey questions on evictions and foreclosures focused on the 

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

Thus, current renters were asked about evictions and current homeowners were asked about foreclosures. Under this survey design, we cannot 

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

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

questions can most easily be thought of as reflecting housing stability 

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

occurrences at other addresses with the same type of housing situation 

in the past 12 months.

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

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

ask homeowners if they were late on their utility payments.

8 For more information on measures of overcrowding, see Blake, Kellerson, and Simic (2007).
48

the MTO survey responses. The results slot families into 

the following categories: public housing; tenant-based 

federal rental assistance; project-based non-publichousing federal rental assistance; and no federal rental 

assistance (including owners, unassisted renters, the 

homeless, and those with other statuses).9

Housing cost burden is the ratio of monthly housing 

costs (of current rent or mortgage plus utilities) to 

household monthly income (primarily based on reports 

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

they devote more than 30 percent of their income to 

housing costs. They are severely cost-burdened if the 

ratio exceeds 50 percent. We assessed monthly housing 

costs from survey data on adults’ rent or mortgage 

payments and their most recent gas and electric bills. 

If a respondent did not know or refused to provide the 

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

that site and treatment group.10 Although the survey did 

not directly ascertain current monthly income, we were 

able to estimate it using survey data on the components 

of the previous year’s household income, adjusted for 

changes in employment status and receipt of government 

aid. For example, if a respondent had earnings the prior 

year but was currently not working, we excluded his or 

her earnings from our income estimate.

9 First, we use respondents’ self-reports to determine each of eight possible housing assistance categories: renter with tenant-based assistance 

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

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

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

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

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

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

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

the administrative file. If the status in the administrative data coincides 

with that in the survey data, it is assigned to the respondent. For respondents whose statuses are not identical, we match (based on ZIP+4 

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

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

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

we use the administrative data status when the administrative records 

match the respondent with certainty; otherwise, we use the survey data 

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

administrative analysis results and 14 percent disagree. Of those that 

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

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

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

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

78 percent agreement from the interim survey.

10 Reported utility costs in the MTO survey data are higher than documented average utility costs in comparable locations over a relatively 

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

do not qualitatively differ under a variety of sensitivity checks that 

impose caps or minimum values on utility costs.

NEIGHBORHOOD CENSUS TRACT 

CHARACTERISTICS

We measured the sociodemographic composition of 

the “neighborhoods” in which MTO families were 

living by linking their address to census tract-level data 

from the Census Bureau. As the Census Bureau notes, 

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

persons and, when first delineated, are designed to be 

homogeneous with respect to population characteristics, 

economic status, and living conditions.”11 The census 

tract characteristics we examined included median 

income, poverty and thresholds of poverty, household 

composition, homeownership, race, ethnicity, 

employment, occupation, and education.

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

decennial censuses, together with data from the 2005–09 

ACS. The ACS was designed to replace the census long 

form, which was discontinued after 2000, although the 

ACS has a smaller sample. The ACS contains many of 

the same questions as the census long form, although 

some variables are measured slightly differently between 

the two data sources.12 We can estimate census tract 

characteristics at a specific point in time assuming that 

tract characteristics changed in a constant way in between 

census data collection points, that is, by assuming a 

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

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

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

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

slightly lower on average than measures of income from the prior calendar year. (See, for example, Beaghan and Stern, 2009, available at www.

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

difference in reported income could contribute to differences in poverty 

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

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

living in a census block if he or she presently resides there. However, for 

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

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

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

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

changed its questions on race and ethnicity to allow for identification 

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

professions and careers, which changed with revisions to the Standard 

Occupational Classification and to the North American Industry Classification System. As a result, many of the major occupation groups 

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

people who work for travel agencies are classified as part of the transportation industry in the 1990 census but are classified as “administrative, support, and waste management” for the 2000 census. (See www.

census.gov/hhes/www/ioindex/pdfio/techpaper2000.pdf ).
49 Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks

linear trend for the evolution of tract characteristics.13

We present census tract characteristics for different 

points in time after random assignment for families, as 

well as the average duration-weighted tract characteristics 

of all the addresses that families lived at from the time of 

random assignment until just before the start of survey 

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

survey fielding in order to have a common endpoint for 

all families who were interviewed at different points in 

time from June 2008 through April 2010. We durationweighted the characteristics of each census tract in which 

a family lived during this time period by the number 

of days they lived at each address. This ensures that 

addresses where they lived longer count more heavily in 

the average.

NEIGHBORHOOD SATISFACTION AND 

CHARACTERISTICS

To gauge satisfaction with their neighborhoods of 

residence, we asked adults and youth to describe how 

much of a problem they have in their neighborhoods 

with things like litter or trash on the streets or sidewalks, 

graffiti or writing on walls, abandoned buildings, people 

drinking in public, and groups of people hanging 

out in their neighborhood. As a measure of access to 

neighborhood essentials (for example, shopping and 

parks) and employment, respondents also reported if 

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

minutes from public transportation.

NEIGHBORHOOD SOCIAL PROCESSES

Social science research has increasingly focused on the 

role of neighborhood social processes in individual 

outcomes, above and beyond the role of neighborhood 

structural characteristics such as social and demographic 

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

Gannon-Rowley, 2002). Our long-term survey asked 

adults to report on social processes such as “collective 

efficacy,” defined by Sampson, Raudenbush, and 

Earls (1997) as the willingness of local adults to work 

together to enforce shared norms. Specifically, we asked 

13 For this purpose, we assumed that the ACS characteristics represent the midpoint of fielding of the ACS, which is July 1, 2007. ACS 

fielding occurred continuously from 2005 to 2009. It is possible that 

characteristics in each census tract changed in a nonlinear way from the 

time of the ACS to the time of the interview, particularly because of the 

most recent recession.

respondents to describe how likely it was (very likely, 

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

neighbors would act if they saw a group of children or 

youth skipping school and “hanging out on the street,” 

or if they saw youth spray-painting graffiti on local 

buildings.

As a measure of the strength and breadth of available 

social support, we also asked respondents about their 

close friends using modified questions from the Social 

Capital Community Benchmark Survey (Saguaro 

Seminar, 2002). Interviewers asked adults to report how 

many close friends they had and how many of them 

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

or ethnicity than the respondent. Additional questions 

asked how many of their close friends had college degrees 

or were employed full-time.

FAMILY AND NEIGHBORHOOD SAFETY

As noted above, one of the key reasons MTO families 

signed up for the demonstration was their desire to 

live in safer neighborhoods. A greater sense of safety 

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

a variety of ways, including direct effects on mental 

health, physical health outcomes (for example, because 

of changes in the willingness to walk around in a 

neighborhood), and economic outcomes (for example, 

increased willingness to use public transportation to 

commute to jobs, or to leave children unattended or in 

the company of other caregivers after school).

We asked respondents how safe they felt in the streets 

around their neighborhood during the day and at night 

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

asked about how responsive the local police were to calls 

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

problem, or no problem) and whether the respondent 

saw people selling or using illegal drugs in the past 

30 days. As a measure of actual crime victimization 

experiences, the survey asked if any household members 

had been victims of crime (purse, wallet, or jewelry was 

snatched; threatened with a knife or gun; beaten up or 

assaulted; was stabbed or shot; and/or there was a breakin/attempted break-in to their house) in the past six 

months.
50

We asked youth about the prevalence of gangs in their 

neighborhood or by their school. This question is 

modified from the National Longitudinal Survey of 

Youth 1997 (Moore et al., 1999) and aims to measure the 

possibility of gang incidences in the neighborhood.

Previous studies have found that female youth in highpoverty urban areas may be affected (mentally and in 

overall well-being) by sexual harassment. In an effort to 

understand the level and type of such harassment that 

female participants in MTO might have experienced, we 

asked questions drawn from the Maryland Adolescent 

Development in Context Study (Goldstein, Davis-Kean, 

and Eccles, 2005) about how often (never, a couple of 

times each year, one or more times a month, once or 

twice a week, or every day) they faced unwanted or rude 

comments, unwanted sexual attention, or were afraid to 

go places because of unwanted attention or pressure.

INTERVIEWER OBSERVATIONS OF THE 

NEIGHBORHOOD BLOCK

In principle, different people might view conditions in 

the same neighborhood differently, and so ideally we 

would obtain reports about neighborhood conditions 

from other neighborhood residents as well, as was done 

in the Project on Human Development in Chicago 

Neighborhoods (PHDCN; Sampson, Raudenbush, and 

Earls, 1997; Raudenbush and Sampson, 1999). This 

was not feasible in the MTO study, so instead we asked 

interviewers to complete a checklist about different 

attributes of the respondent’s block (defined as both 

sides of the street that the respondent’s house is on or 

faces, from one street corner to the other). Most of the 

observations were gathered in a walking tour, although 

some were completed by driving around the block.14

Specifically, the interviewers reported on the condition 

of most of the buildings on the block, as well as the 

existence of window bars and any graffiti that was 

painted over. They also noted the types of institutions 

14 For PHDCN, interviewers walked all around the block, while for 

logistical reasons, MTO interviewers only walked up and down the 

street (block face) on which the MTO respondent was living. In some 

unpublished analyses that were carried out for us by Michael Bader, a 

graduate student at the time at the University of Michigan, we find the 

correlations for variables that come from observing a single block-face 

versus walking around the entire block typically ranged from .5 to .8, 

depending on the measure.

and buildings on the block (storefront church, 

abandoned/burned buildings, boarded up housing or 

building, green grocer/delicatessen, supermarket/grocery 

store, fast-food and take-out place, or none of the above). 

In addition to buildings, interviewers rated the condition 

of the streets on the block (very good: recent resurfacing 

and smooth; moderate: evidence of keeping in good 

repair; fair: minor repairs needed but not rough surface; 

and poor: potholes and other evidence of neglect). 

They reported on the amount of garbage (including 

litter or broken glass) and density of trees on the street. 

Finally, interviewers noted the types (if any) of signage 

(neighborhood or crime watch, security warnings, 

advertising for beer/whiskey/other alcohol, “for sale” 

signs, or none of the above).

ADULT SOCIAL NETWORKS AND CIVIC 

ENGAGEMENT

We asked respondents a series of questions to determine 

social connectedness, such as the number of friends the 

adults have in their neighborhood and how many times 

they visit their friends (whether or not they are living 

in the same neighborhood) at their homes (every day, 

several days a week, twice a week, once a week, two or 

three times in the past month, once in the past month, 

or not at all in the past month). To gain a sense of the 

respondent’s community involvement, we asked how 

often they chat with their neighbors (almost every day, 

once a week, once a month, a few times a year, or almost 

never) and go to church or religious services (never in 

the past 12 months, several times in the past 12 months, 

once a month, once a week, more than once a week, or 

no church/place of worship).

YOUTH SOCIAL NETWORKS AND 

SELF-ESTEEM

The MTO youth survey draws on questions from 

National Education Longitudinal Study of 1988 (NELS) 

(Ingels et al., 1994) and National Comorbidity Survey 

(Kessler et al., 2009) and asks youth about their friends’ 

involvement in school activities, use of illegal drugs, and 

whether they are school dropouts. The goal is to gain 

an understanding of their peers’ views on studying and 

continuing education beyond high school (whether it is 

very important, somewhat important, or not important 

at all). MTO youth were additionally asked about the 
51 Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks

number of close friends they have (none, one, two, 

three to five, six to ten, or more than ten), how many 

of their close friends live in their neighborhood, and 

whether these friendships are new or holdovers from 

their neighborhoods of origin. Another aspect of a 

youth’s social connections can come from church or other 

religious activities, so we asked them about how often 

they participated in activities such as youth groups, Bible 

classes, or choir (once a week or more, once a month or 

more, less than once a month, or never). Using items 

from the Ferguson Tripod Study (Ferguson, 2010) and 

NELS, we asked youth about speaking “proper” English 

(even when they are with their friends) and how often 

people would describe their behavior as “ghetto” (very 

often, somewhat often, or not at all), as well as a battery 

of items about their perception of themselves and their 

future.15

2.4 LONG-TERM IMPACTS ON 

MOBILITY AND HOUSING

Of those families randomly assigned to the experimental 

group, 48 percent “complied” or moved with an MTO 

voucher. Of those assigned to the Section 8 group, the 

compliance rate is 63 percent.

Exhibits 2.2 through 2.5 present MTO’s effects on the 

residential mobility patterns and housing assistance of 

program participants, as well as their housing conditions. 

These estimates are calculated using the methods 

described in Chapter 1 (Section 1.3). The intention to 

treat (ITT) estimates reflect the effects of being offered 

the chance to relocate with an MTO housing voucher. 

For example, the ITT estimate for the experimental 

treatment compares the average outcome for everyone 

assigned to the experimental group (regardless of whether 

they moved or not with an MTO voucher) with the 

average outcome for everyone assigned to the control 

group. The ITT estimate for the Section 8 treatment 

group is analogously defined. Mechanically, the ITT 

estimates are calculated by running an ordinary least 

15 Respondents were also asked how much they agree with the following statements: I feel good about myself, I am able to do things as well 

as most other people, and chance and luck are very important for what 

happens in my life. Possible answers are strongly agree, agree, disagree, 

or strongly disagree. Finally, youth were asked how they think other 

people see them in terms of popularity, athleticism, and academically 

(possible responses were: never, sometimes, or all the time).

squares regression of the outcome of interest against 

dichotomous indicators for random assignment to 

either the experimental group or the Section 8 group, 

controlling for a set of baseline control variables to 

improve statistical precision and are calculated using 

sample weights that account for changes over time in 

random-assignment ratios and for the random selection 

of some families but not others into the survey sample 

frame (see Section 1.3 for details). The estimated effects 

of treatment on the treated (TOT) capture the effects of 

MTO moves on those who initially moved with an MTO 

voucher. For each MTO treatment group the TOT is 

equal to the ITT effect for that treatment group divided 

by the share of that group that moved initially with an 

MTO voucher.

Because the remainder of the report uses tables with a 

similar structure, we spend a moment here to review 

how the table is laid out. Each row presents the results 

of running a regression for the variable listed on the lefthand side of the row, which is the outcome variable in 

the regression, on the two treatment-group assignment 

dummy variables as well as the baseline covariates listed 

in Supplemental Exhibit 1.2. The first number in each 

row is the control mean for that variable, that is, the 

average value of the outcome for the control group, 

calculated using the sample weights described in Chapter 

1. The coefficients on the treatment-group assignment 

variables together with their standard errors are listed 

under the ITT columns for the experimental and Section 

8 treatments respectively. Next to the ITT estimates are 

the estimated effects of the TOT and accompanying 

standard errors for that treatment group.

The ITT estimates capture the effect of what is most 

directly under the control of policymakers—offering 

families the opportunity to relocate with an MTO 

voucher. The TOT estimate is of additional interest 

because the share of families that would relocate if 

offered an MTO-like voucher could differ across settings. 

The ITT estimates require no statistical assumptions 

other than that random assignment was carried out 

correctly. The TOT estimates require some additional 

assumptions, the most important of which is that 

assignment to either of the treatment groups has no 

effect on those who do not move through MTO (the 
52

noncompliers). All of the tables that follow in the report 

show both the ITT and TOT effects, allowing readers to 

choose their preferred estimate. The text in most of the 

chapters focuses on the ITT effects. In this chapter, we 

devote additional discussion to the TOT effects as well, 

which are particularly relevant for thinking about the 

strength of MTO’s “treatment dose” on the housing and 

neighborhood conditions of participating families.

Over the course of the study period, which follows 

families on average for 12 years after baseline, adults in 

the control group moved just over two (2.17) times on 

average (as shown in Exhibit 2.2). For many of control 

group households, at least one of these moves was 

triggered by the demolition of their initial public housing 

through HOPE VI or a related program. Nonetheless, 

the total number of moves we observe for MTO control 

group families over the study period is, if anything, 

slightly lower than what we would predict from national 

data, which indicate that around 23 percent of lowincome African-American and Hispanic families move 

each year (Schachter, 2004). If 23 percent of control 

group families in MTO moved each year, then over the 

course of the 12-year follow-up period, we would expect 

2.76 moves. Mobility rates might be somewhat lower for 

the MTO control group than for national samples given 

that, as noted above, control families may be relatively 

more tied in place by their project-based housing 

subsidies.

Assignment to either the experimental or Section 8 

group (the ITT effect) increases the average number of 

moves for families by about .6 during the 10–15 year 

study period. Among those who actually moved through 

MTO, the TOT effect of the experimental treatment 

was 1.16 additional moves for the experimental group 

compliers and 1.01 additional moves for Section 8 

compliers (both statistically significant at the standard 5 

percent cut-off ). Learning more about the impacts of the 

MTO intervention on distances moved by families is an 

important question for future research.

Perhaps the most striking finding in Exhibit 2.2 is that 

approximately 20 percent of control group families 

report having been without a home of their own (that is, 

doubled-up or literally homeless) at some time during 

the course of the study period, underscoring the extreme 

level of economic disadvantage of the study sample. 

Typically, control group families would deal with the 

problem by doubling up with another family (19 percent 

of control families did this at least once during the study 

period), although around 5 percent of control group 

families were literally homeless at some point. Most 

families who were homeless were homeless just once, as 

revealed by the close correspondence between the share of 

control families who had been homeless and the average 

number of homelessness spells among the control group. 

There were no statistically significant differences between 

the control and treatment groups in the likelihood of 

being literally homeless; however, the Section 8 group 

was more likely to report having been doubled-up than 

the control group (ITT effect of 4.5 percentage points).

The lack of detectable MTO impacts on literal 

homelessness might initially seem contradictory with the 

findings from the Welfare to Work (WtW) voucher study, 

which found that housing vouchers reduced the risk of 

homelessness by around one-third of the control group 

mean (Mills et al., 2006). But it is important to keep 

in mind that the “treatment” studied in WtW—giving 

housing vouchers to families who were initially living in 

the private housing market without a subsidy—is quite 

different from that examined in MTO, which involved 

offering housing vouchers to families who already had a 

housing subsidy, in the form of a public housing or other 

project-based assisted housing unit. The importance 

of the different control group conditions can be seen 

in the much higher rate of homelessness among the 

(unsubsidized) control group in the WtW study. There, 

approximately one-quarter of respondents did not have a 

place to stay during the last year prior to the final survey, 

which is higher than the rate of homelessness that MTO 

control-group families experienced cumulatively over the 

entire 10–15-year follow-up period.

Exhibit 2.3 shows that at the time of the long-term 

survey, 85 percent of adults in the control group 

were living in rental housing (including both public 

housing or private market) and 12 percent had become 

homeowners, which is more than double the prevalence 

of homeownership reported at the interim study from 

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

Exhibit C3.2, p. C–16). Being in the Section 8 treatment 
53 Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks

group increased the likelihood of renting (the ITT effect 

was 4 percentage points, the TOT effect was 6 percentage 

points, compared with a control mean of 85 percent) and 

reduced the likelihood of owning a home compared with 

the control group (ITT and TOT effects of – 4 and – 6 

percentage points, respectively, compared with a control group 

homeownership rate of 12 percent). The effects of being 

assigned to the experimental group on renting and owning are 

qualitatively similar to those of the Section 8 group, but are not 

statistically significant at the 5 percent level.

Exhibit 2.3 also shows the reasons that survey respondents 

left their previous address and moved to their current 

neighborhood. These results are somewhat complicated to 

interpret because for most families their most recent move 

was a secondary move (see Exhibit 2.2). Therefore, for 

example, most experimental group families will have already 

made their initial MTO move into a safer neighborhood 

(discussed further below), and so their most recent move 

will use that safer address as the starting location. Among 

the control group, 16 percent of families reported that 

getting away from gangs or drugs was the most important 

reason they moved from their previous address. Those in the 

experimental group, in contrast, were nearly 3 percentage 

points less likely to say they left their most recent address 

for reasons of safety (the TOT effect is around 7 percentage 

points). Exhibit 2.3 also shows that when voucher-holders 

made their most recent move, they were more likely than 

control group members to have moved because of problems 

navigating the private housing market, such as landlord 

problems or their house or apartment was sold.

About 62 percent of the control group adults were receiving 

housing assistance at the time of the long-term follow-up. 

This estimate does not seem to be sensitive to whether we 

measure receipt of housing assistance from a combination 

of administrative and survey data, which is what we report 

in Exhibit 2.4 using the methods described above, or if 

we calculate it from only survey self-reports (not shown). 

Receipt of any sort of housing assistance did not differ 

statistically at the 5 percent level between the controls and 

the experimental group or between the controls and the 

Section 8 group. However, MTO did change the type of 

housing program in which families were participating. Of 

those in the control group who were receiving housing 

assistance, about one-half were currently in public housing 

(30 percent of the total control group) and 40 percent were 

using a voucher or certificate (25 percent of the total control 

group). Another 7.2 percent of the total control group was 

receiving other forms of housing assistance, such as the 

Rental Assistance Payment and Moderate Rehabilitation 

program. Families assigned to the experimental and Section 

8 groups were 16 and 19 percentage points more likely than 

controls to be receiving a housing voucher at the time of 

the long-term follow-up survey (the TOT effects for both 

groups are nearer 30 percentage points).

Exhibit 2.4 shows that monthly housing costs (rent or 

mortgage plus utilities) as reported by families in the longterm surveys averaged around $680 for controls. Exhibit 2.4 

also shows that nearly 68 percent of control group adults 

had a housing-cost burden greater than 30 percent, in line 

with the national statistics cited above. Given that MTO 

did not have statistically significant effects on the likelihood 

of receiving any sort of housing assistance (public housing 

plus housing vouchers), it is perhaps not surprising that 

MTO had no detectable impacts on the amount of money 

that families spent out-of-pocket on housing or on housing 

burdens. However, the confidence intervals around these 

estimates do not allow us to rule out effects of MTO moves 

on housing costs that may be as large as $100 per month. 

We do find that, compared with the control group adults, 

those in the experimental group were less likely to report 

being late with their rent or mortgage payments, but they 

were more likely to have had trouble paying their utility 

bills on time while renting.

Although MTO may not have had detectable effects on 

housing costs for families, the program did improve the 

quality of housing that families found. Exhibit 2.5 shows 

that adults assigned to the experimental group were about 

5 percentage points more likely than controls to report 

that their housing conditions were excellent or good (TOT 

effect of approximately 11 percentage points compared 

with 57 percent of control group adults). Adults in the two 

treatment groups also reported fewer specific problems with 

their housing units. For example, 52 percent of control 

group adults reported that their current housing units 

had problems with vermin. Adults in the experimental or 

Section 8 group were 6 and 9 percentage points less likely 

than controls to report having problems with vermin (TOT 

effects of 13 or 14 percentage points). Results for additional 

housing quality measures are shown in Supplemental 

Exhibit 2.1.
54

EXHIBIT 2.2. ADULT MOBILITY AND HOMELESSNESS

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

MOBILITY

Number of moves since 

Random Assignment [SR]

2.165 0.561* 1.157* 0.629* 1.014* 3,273

(0.073) (0.151) (0.096) (0.155)

Leased an apartment using 

an MTO voucher

0.000 0.484* 0.639* 3,273

(0.014) (0.022)

HOMELESSNESS 

AFTER RANDOM

ASSIGNMENT

Without a home of their own 

at least once ("doubled-up" 

or "literally homeless") [SR]

0.214 0.020 0.040 0.056* 0.090* 3,273

(0.017) (0.036) (0.024) (0.039)

"Doubled-up" at least 

once (stayed with family or 

friends) [SR]

0.191 0.008 0.017 0.045* 0.073* 3,273

(0.017) (0.034) (0.023) (0.037)

"Literally homeless" at least 

once [SR]

0.049 0.009 0.018 0.018 0.030 3,273

(0.009) (0.019) (0.014) (0.022)

Number of times "doubledup" [SR]

0.257 0.014 0.028 0.074* 0.120* 3,273

(0.026) (0.053) (0.034) (0.056)

Number of times "literally 

homeless" [SR]

0.061 0.003 0.006 0.024 0.038 3,273

(0.012) (0.025) (0.020) (0.032)

Notes: * = p < 0 .05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: "Doubled up" is defined as staying with friends or family when respondents were without a home of their own. "Literally homeless" is defined as staying 

at a shelter, on the street, abandoned building, car/van, movie theater/laundromat, and so on, when respondents were without a home of their own.
55 Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks

EXHIBIT 2.3. ADULT HOUSING TENURE AND REASONS FOR MOVING

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

CURRENT HOUSING TENURE

Currently rents house or apartment [SR] 0.850 0.024 0.049 0.040* 0.064* 3,265

(0.015) (0.032) (0.020) (0.032)

Currently owns house or apartment [SR] 0.120 – 0.024~ – 0.050~ – 0.039* – 0.063* 3,265

(0.014) (0.029) (0.018) (0.029)

Currently doubled-up and pays part of rent/

mortgage [SR]

0.015 – 0.003 – 0.006 – 0.001 – 0.002 3,266

(0.005) (0.010) (0.008) (0.012)

Currently doubled-up and does NOT pay part 

of rent/mortgage [SR]

0.009 0.005 0.010 – 0.003 – 0.005 3,267

(0.004) (0.009) (0.005) (0.009)

Currently lives in a group shelter [SR] 0.005 – 0.003 – 0.006 0.000 0.000 3,268

(0.002) (0.005) (0.004) (0.007)

REASONS FOR MOVING FROM

PREVIOUS ADDRESS

To get a bigger or better apartment [SR] 0.259 – 0.058* – 0.116* – 0.034 – 0.054 3,168

(0.018) (0.037) (0.024) (0.038)

To get away from drugs or gangs [SR] 0.159 – 0.033* – 0.067* – 0.051* – 0.080* 3,168

(0.015) (0.030) (0.020) (0.031)

Due to problems with landlord [SR] 0.073 0.022~ 0.045~ 0.022 0.035 3,168

(0.012) (0.025) (0.015) (0.024)

Because house or apartment was sold [SR] 0.051 0.036* 0.074* 0.043* 0.068* 3,168

(0.011) (0.022) (0.015) (0.024)

REASONS FOR MOVING TO CURRENT

NEIGHBORHOOD

For better or bigger living space [SR] 0.201 – 0.034* – 0.071* 0.005 0.008 3,261

(0.017) (0.034) (0.023) (0.036)

Because it is safer [SR] 0.156 0.010 0.020 0.036 0.057 3,261

(0.016) (0.033) (0.022) (0.035)

Because unit is affordable [SR] 0.134 – 0.037* – 0.076* – 0.048* – 0.076* 3,257

(0.015) (0.030) (0.018) (0.029)

Because it has fewer problems with gangs or 

drugs [SR]

0.085 – 0.010 – 0.020 – 0.013 – 0.021 3,261

(0.011) (0.023) (0.015) (0.025)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.
56

EXHIBIT 2.4. ADULT HOUSING ASSISTANCE, COSTS, AND STABILITY

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

CURRENT HOUSING ASSISTANCE

Any housing assistance [SR, HA] 0.620 0.026 0.054 0.045~ 0.072~ 3,273

(0.021) (0.043) (0.027) (0.044)

Currently in public housing [SR, HA] 0.296 – 0.107* – 0.220* – 0.110* – 0.177* 3273

(0.017) (0.035) (0.022) (0.036)

Receiving housing voucher or certificate 

[SR, HA]

0.252 0.159* 0.328* 0.194* 0.312* 3,273

(0.019) (0.040) (0.026) (0.042)

Using other types of housing assistance 

[SR, HA]

0.072 – 0.026* – 0.054* – 0.039* – 0.063* 3,273

(0.010) (0.020) (0.013) (0.020)

CURRENT HOUSING COSTS

Total housing cost (2009 dollars), monthly 

[SR]

$678.73 19.50 39.67 – 6.26 – 10.00 3,180

(23.30) (47.39) (30.73) (49.10)

Cost of rent or mortgage (2009 dollars), 

monthly [SR]

$493.04 3.16 6.43 – 24.44 – 39.05 3,180

(20.16) (41.01) (26.14) (41.76)

Household is housing burdened (monthly 

housing cost/income > 30%) [SR]

0.676 0.011 0.022 0.020 0.032 3,169

(0.020) (0.041) (0.027) (0.043)

Household is severely housing burdened 

(monthly housing cost/income > 50%) [SR]

0.426 – 0.004 – 0.007 0.017 0.027 3,169

(0.021) (0.043) (0.029) (0.046)

HOUSING STABILITY

More than 15 days late in paying rent/

mortgage at least once during past 12 

months [SR]

0.258 – 0.055* – 0.112* – 0.067* – 0.106* 3,242

(0.018) (0.037) (0.024) (0.038)

Received eviction/foreclosure threat due 

to nonpayment at least once during the 

past 12 months [SR]

0.124 – 0.017 – 0.035 – 0.033~ – 0.053~ 3,261

(0.014) (0.028) (0.018) (0.028)

LATE UTILITY PAYMENTS (RENTERS)

Rents and was more than 15 days late in 

paying utilities at least once during the 

past 12 months [SR]

0.333 0.052* 0.108* 0.033 0.053 3,236

(0.020) (0.041) (0.027) (0.043)

Rents and received shut-off notice due to 

nonpayment at least once in the past 12 

months [SR]

0.235 0.054* 0.112* 0.050* 0.081* 3,236

(0.018) (0.038) (0.025) (0.040)

Rents and utilities have been shut off 

for nonpayment at least once in past 12 

months [SR]

0.052 0.021* 0.043* 0.016 0.026 3,236

(0.010) (0.022) (0.014) (0.022)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: SR = self-report, HA = Housing Assistance data from HUD.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.
57 Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks

EXHIBIT 2.5. ADULT HOUSING QUALITY

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

OVERALL RATING OF CURRENT

HOUSING CONDITION

Rates current housing as excellent or 

good [SR]

0.570 0.053* 0.109* 0.031 0.050 3,267

(0.021) (0.044) (0.029) (0.046)

CURRENT HOUSING PROBLEMS

Vermin (mice, rats, and/or cockroaches) is 

a big or small problem [SR]

0.517 – 0.063* – 0.130* – 0.088* – 0.141* 3,266

(0.021) (0.043) (0.028) (0.045)

Heating or plumbing that does not work is 

a big or small problem [SR]

0.374 – 0.028 – 0.059 – 0.052~ – 0.083~ 3,258

(0.020) (0.042) (0.027) (0.043)

Peeling paint or plaster is a big or small 

problem [SR]

0.466 – 0.091* – 0.187* – 0.120* – 0.192* 3,265

(0.021) (0.044) (0.028) (0.044)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

2.5 LONG-TERM IMPACTS ON 

NEIGHBORHOOD CHARACTERISTICS

As Chapter 1 noted, the initially large differences 

between treatment and control groups in census tract 

poverty rates narrowed over time in large part because of 

declines in the control group’s average tract poverty rate. 

Exhibit 2.6 shows the effects of MTO on the average 

(duration-weighted) census tract characteristics that 

families experienced during the entire 10- to 15-year 

study period. As noted above, we calculate each family’s 

average tract characteristics by first estimating the 

characteristics of the census tract when the family was 

living there, using information from the 1990 and 2000 

decennial census together with data from the 2005–09 

ACS, and then average over all the family’s addresses 

during the study period, weighting each address by 

the share of the study period the family spends at that 

address. (Results for the youth sample are presented in 

Supplemental Exhibit 2.2.)

The average poverty rate of the census tracts in which the 

control group lived over the study period was about 40 

percent poor. For families assigned to the experimental 

and Section 8 groups, duration-weighted average tract 

poverty rates were 9 and 7 percentage points lower, 

respectively. The TOT effects of the experimental and 

Section 8 treatments on average tract poverty rates 

equaled 18 and 11 percentage points.

Exhibit 2.6 shows that MTO changed a variety of other 

measures of neighborhood advantage and disadvantage as 

well. For example, 54 percent of households in the census 

tracts where control group families lived were headed 

by females; the figure was 8 percentage points lower 

for those in the experimental group (and 16 percentage 

points lower for those in the experimental group who 

moved with an MTO voucher). Median household 

income was almost $19,000 higher in the census tracts 

where experimental group compliers lived, a sizable 

change compared with the average control group census 

tract median income of $27,800.

On the other hand, MTO moves had more modest 

impacts on neighborhood racial segregation. Among 

control group members, 88 percent of residents in their 

census tracts were members of racial and ethnic minority 

groups. The figure was slightly lower for those in the 

Section 8 group. Families in the experimental group 
58

experienced a statistically significant decline in census 

tract minority share equal to around 6 percentage points 

(TOT effect of nearly 13 percentage points). However, 

these families were still living in census tracts that were 

overwhelmingly populated by minority residents.

Exhibit 2.7 reports results for MTO’s impacts on the 

same set of census tract characteristics shown in Exhibit 

2.6, but now measured at the point in time just before 

we started the long-term survey data collection (May 

2008) rather than duration-weighted averages in Exhibit 

2.6. Most of these MTO effects on neighborhood 

sociodemographic characteristics are still statistically 

significant, particularly in the experimental group. 

However, given the gradual convergence over time 

between the two treatment groups and controls in their 

average neighborhood conditions, the treatment-control 

differences in tract characteristics for each family’s May 

2008 address tend to be smaller in magnitude than the 

duration-weighted averages.

The MTO families themselves report being more satisfied 

with their neighborhoods at the time of the long-term 

survey (as was also the case in the interim survey data). 

Exhibit 2.8 shows that approximately 52 percent of 

adults in the control group said they were satisfied or 

very satisfied with their current neighborhood; the 

figure was 9 percentage points higher for adults in 

the experimental group (TOT effect of 19 percentage 

points), and 8 percentage points higher for adults in the 

Section 8 group (TOT effect of 13 percentage points).

Adults in the experimental and Section 8 group also 

tended to report fewer signs of disorder than adults in the 

control group, particularly for problems like trash and 

graffiti (Exhibit 2.8). The interviewer observations for 

these neighborhoods tend to confirm the reports (Exhibit 

2.9). However, MTO did not seem to have any detectable 

impact on access to private or public transportation (last 

row of Exhibit 2.8). (See also Supplemental Exhibit 2.3 

for additional results.)

MTO also led to persistent improvements in 

neighborhood safety, as measured by self-reports in 

our long-term surveys (Exhibit 2.10). Adults in the 

experimental and Section 8 groups were around 4 

percentage points more likely than controls to feel safe 

in their neighborhoods during the day (TOT effects of 

around 7 percentage points), compared with a control 

mean of 80 percent. Adults in the experimental and 

Section 8 groups were 7 percentage points less likely 

than controls to say that police do not come when called 

compared with 42 percent of controls and were also less 

likely to see drugs sold or used in the neighborhood 

(TOT effects of 14 and 12 percentage points). Although 

MTO did not change feelings of safety among male 

youth, relocation did increase feelings of safety among 

female youth. MTO moves also reduced the exposure 

of female youth in the experimental group to unwanted 

sexual attention (Exhibit 2.10). The qualitative 

interviews with MTO families done at three of the 

MTO demonstration sites also found important impacts 

of MTO moves in reducing “female fear,” the concern 

that many female youth have about sexual harassment, 

coercion and rape (Popkin, Leventhal and Weismann, 

2010; see also Briggs, Popkin, and Goering, 2010).

Despite the improved feelings of safety, MTO had no 

statistically significant effects on self-reported crime 

victimization rates in the long-term surveys among 

adults, or among all youth pooled together. These results 

differ from what was found in the interim survey data, 

which showed declines in crime victimization (Orr et 

al., 2003). Somewhat surprisingly, the experimental 

group female youth—unlike adults or male youth—

reported lower levels of household crime victimization, 

which raises the possibility that female youth may not 

share information about their own crime victimization 

experiences with others in the home.

2.6 LONG-TERM IMPACTS ON SOCIAL 

NETWORKS

MTO also helped families move into neighborhoods 

where neighbors were more willing to work together 

to support shared norms, a measure of informal 

social control that previous research suggests may 

be particularly important in improving the lives of 

neighborhood residents (Sampson, Raudenbush, and 

Earls, 1997; Sampson, Morenoff, and Gannon-Rowley, 

2002). Exhibit 2.11 shows that 59 percent of control 

group adults reported that neighbors were likely or very 

likely to do something if neighborhood children were 

spray painting buildings with graffiti. The share is nearly 

8 points higher for those adults in the experimental 
59 Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks

group (and approximately 16 percentage points higher 

for the experimental group compliers, that is, the TOT 

effect). Adults in the Section 8 group were more likely 

than controls to say neighborhood adults would be 

likely or very likely to do something about local children 

skipping school.

The MTO experimental intervention increased the 

social connections to people who had completed 

college and may have increased connections to people 

employed full-time, with no net adverse impacts on 

social isolation. For example, 53 percent of controls said 

they have a friend who graduated from college, a figure 

that was 7 percentage points higher for those in the 

experimental group (see Supplemental Exhibit 2.4 for 

additional results). These findings are consistent with 

those from the MTO interim study and with qualitative 

investigations of housing mobility programs in Yonkers, 

New York (Orr et al., 2003; Briggs, 1998).

MTO effects on the social connections of youth 

differed somewhat for males and females, as shown in 

Supplemental Exhibits 2.5–2.7. More than 96 percent 

of control group youth reported having at least one close 

friend. Experimental and Section 8 youth (particularly 

male youth) were slightly less likely than controls to 

report having at least one close friend. Experimental 

and Section 8 youth were also less likely than 

controls to report visiting a friend from their original 

neighborhoods. Female youth in the experimental group 

were less likely to have friends who have ever used 

drugs than are girls in the control group, but otherwise, 

peers of MTO youth had similar characteristics across 

treatment groups (Supplemental Exhibit 2.6). Youth 

across treatment groups shared similar reports on how 

good they felt about themselves and whether they 

considered themselves as speaking proper English or 

if others would describe their behavior as “ghetto” 

(Supplemental Exhibit 2.7). We also found suggestive 

evidence (though somewhat inconsistent across treatment 

groups and gender) that youth in the two treatment 

groups may be less likely than controls to say that other 

people see them as popular or as good students.
60

EXHIBIT 2.6. AVERAGE (DURATION-WEIGHTED) NEIGHBORHOOD CENSUS TRACT CHARACTERISTICS OF ALL 

OF THE ADULTS’ ADDRESSES FROM RANDOM ASSIGNMENT THROUGH MAY 2008

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

Share of persons in tract who are 

poor [CEN]

0.396 – 0.089* – 0.184* – 0.069* – 0.111* 3,270

(0.006) (0.012) (0.007) (0.011)

Share of persons who are above 

200% of the poverty line [CEN]

0.361 0.108* 0.223* 0.068* 0.110* 3,270

(0.006) (0.013) (0.008) (0.012)

Share minority (which is, nonWhite or Hispanic) [CEN]

0.880 – 0.061* – 0.125* – 0.018* – 0.029* 3,270

(0.007) (0.014) (0.009) (0.015)

Share Hispanic [CEN] 0.315 – 0.038* – 0.078* – 0.003 – 0.005 3,270

(0.006) (0.011) (0.007) (0.012)

Share Black [CEN] 0.535 – 0.034* – 0.069* – 0.019~ – 0.031~ 3,270

(0.008) (0.016) (0.011) (0.017)

Share of households that are 

single female-headed families 

[CEN]

0.541 – 0.077* – 0.159* – 0.058* – 0.093* 3,270

(0.006) (0.012) (0.007) (0.011)

Share college graduates [CEN] 0.161 0.042* 0.087* 0.018* 0.029* 3,270

(0.004) (0.008) (0.005) (0.008)

Share with more than high school 

education [CEN]

0.318 0.063* 0.130* 0.034* 0.055* 3,270

(0.005) (0.010) (0.006) (0.009)

Share employed, civilian [CEN] 0.808 0.036* 0.075* 0.031* 0.050* 3,270

(0.003) (0.006) (0.003) (0.005)

Share workers in managerial or 

professional occupations [CEN]

0.236 0.035* 0.072* 0.012* 0.019* 3,270

(0.004) (0.008) (0.005) (0.008)

Median household income (2009 

dollars) [CEN]

$27,808.85 9,148.91* 1,8848.48* 5,600.18* 9,027.10* 3,270

(544.97) (1122.75) (660.90) (1065.32)

Per capita income (2009 dollars) 

[CEN]

$14,779.62 3,267.36* 6,731.38* 1,492.56* 2,405.91* 3,270

(266.65) (549.35) (340.51) (548.88)

Share owner-occupied housing 

units [CEN]

0.225 0.103* 0.213* 0.072* 0.115* 3,270

(0.007) (0.015) (0.009) (0.014)

Share households on public 

assistance [CEN]

0.176 – 0.047* – 0.096* – 0.039* – 0.064* 3,270

(0.003) (0.007) (0.004) (0.006)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: CEN = 1990 and 2000 decennial census data as well as the 2005–09 American Community Survey (

ACS).

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: Census tract characteristics are linearly interpolated from the 1990 decennial census, 2000 decennial census, and 2005–09 ACS. Each census outcome 

is also duration-weighted by the amount of time the respondent spent at each address.
61 Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks

EXHIBIT 2.7. CENSUS TRACT CHARACTERISTICS OF ADULT'S CURRENT NEIGHBORHOOD (MAY 2008)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

Share of persons in tract who 

are poor [CEN]

0.313 – 0.038* – 0.079* – 0.029* – 0.046* 3,206

(0.007) (0.014) (0.009) (0.014)

Share of persons who are above 

200% of the poverty line [CEN]

0.444 0.046* 0.095* 0.026* 0.043* 3,206

(0.008) (0.017) (0.011) (0.017)

Share minority (which is nonWhite or Hispanic) [CEN]

0.841 – 0.037* – 0.075* – 0.003 – 0.005 3,206

(0.010) (0.021) (0.014) (0.022)

Share Hispanic [CEN] 0.303 – 0.015~ – 0.030~ – 0.001 – 0.002 3,206

(0.008) (0.017) (0.011) (0.018)

Share Black [CEN] 0.498 – 0.024* – 0.049* – 0.005 – 0.007 3,206

(0.011) (0.022) (0.015) (0.024)

Share of households which are 

single female-headed families 

[CEN]

0.487 – 0.042* – 0.086* – 0.021* – 0.034* 3,206

(0.008) (0.017) (0.010) (0.017)

Share college graduates [CEN] 0.209 0.021* 0.043* 0.009 0.014 3,206

(0.006) (0.012) (0.007) (0.012)

Share with more than high 

school education [CEN]

0.388 0.028* 0.059* 0.017* 0.027* 3,206

(0.007) (0.013) (0.008) (0.013)

Share employed, civilian [CEN] 0.848 0.016* 0.033* 0.011* 0.018* 3,206

(0.003) (0.006) (0.004) (0.006)

Share workers in managerial or 

professional occupations [CEN]

0.241 0.016* 0.033* 0.001 0.002 3,206

(0.005) (0.011) (0.007) (0.011)

Median household income in 

2009 dollars [CEN]

$33,520.69 4,416.64* 9,099.20* 2,544.68* 4,121.36* 3,206

(754.05) (1553.50) (955.14) (1546.94)

Per capita income (2009 dollars) 

[CEN]

$17,134.84 1474.30* 3,037.36* 581.65 942.04 3,206

(389.69) (802.84) (499.28) (808.63)

Share owner-occupied housing 

units [CEN]

0.329 0.048* 0.099* 0.030* 0.049* 3,206

(0.010) (0.021) (0.013) (0.021)

Share households on public 

assistance [CEN]

0.100 – 0.019* – 0.039* – 0.014* – 0.023* 3,206

(0.003) (0.006) (0.004) (0.006)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: CEN = 1990 and 2000 decennial census data as well as the 2005–09 American Community Survey 

(ACS).

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: Census tract characteristics are linearly interpolated from the 1990 decennial census, 2000 decennial census, and 2005–09 ACS.
62

EXHIBIT 2.8. ADULT NEIGHBORHOOD CHARACTERISTICS AND SATISFACTION

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

NEIGHBORHOOD SATISFACTION

Very satisfied or satisfied with current 

neighborhood [SR]

0.515 0.094* 0.194* 0.082* 0.131* 3,265

(0.022) (0.044) (0.028) (0.045)

NEIGHBORHOOD

CHARACTERISTICS

Litter/trash/graffiti/abandoned 

buildings are a big or small problem in 

neighborhood [SR]

0.720 – 0.073* – 0.151* – 0.081* – 0.128* 3,266

(0.020) (0.041) (0.027) (0.043)

Public drinking/loitering are a big or 

small problem in neighborhood [SR]

0.625 – 0.051* – 0.105* – 0.020 – 0.031 3,258

(0.021) (0.044) (0.028) (0.045)

TRANSPORTATION

Has working car or lives less than 15 

minutes to public transportation [SR]

0.942 – 0.001 – 0.002 – 0.002 – 0.003 3,254

(0.010) (0.021) (0.014) (0.023)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.
63 Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks

EXHIBIT 2.9. INTERVIEWER OBSERVATIONS OF THE ADULT'S CURRENT NEIGHBORHOOD

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

BUILDINGS ON RESPONDENT'S BLOCK

Most buildings on this block are well kept 

[NWA]

0.348 0.024 0.049 0.007 0.011 3,199

(0.021) (0.044) (0.028) (0.045)

There is evidence of graffiti that has been 

painted over [NWA]

0.206 – 0.028~ – 0.057~ – 0.040~ – 0.063~ 3,100

(0.017) (0.035) (0.021) (0.033)

There are metal bars on windows above 

the basement level on at least one other 

building on this block [NWA]

0.436 – 0.022 – 0.045 0.015 0.025 3,203

(0.020) (0.042) (0.027) (0.044)

There are abandoned buildings on this 

block [NWA]

0.172 – 0.005 – 0.011 – 0.020 – 0.032 3,245

(0.016) (0.033) (0.021) (0.034)

There is a green grocer, delicatessen, 

supermarket, or grocery store on this 

block [NWA]

0.210 – 0.012 – 0.025 – 0.022 – 0.036 3,245

(0.017) (0.035) (0.021) (0.033)

There are no store-front churches, 

green grocers or delis, supermarkets or 

grocery stores, fast-food or take-out, or 

abandoned, burned out, or boarded up 

houses or buildings on this block [NWA]

0.592 0.013 0.028 0.035 0.056 3,245

(0.021) (0.044) (0.028) (0.044)

STREET CONDITION

Street is in very good/moderate condition 

[NWA]

0.654 0.012 0.026 0.015 0.024 3,207

(0.021) (0.043) (0.027) (0.044)

Street is in poor condition [NWA] 0.084 – 0.010 – 0.021 – 0.009 – 0.014 3,207

(0.011) (0.023) (0.016) (0.026)

Moderate-heavy amount of litter on the 

streets [NWA]

0.217 – 0.041* – 0.085* – 0.023 – 0.036 3,194

(0.017) (0.036) (0.023) (0.036)

Trees are on most/all of the block [NWA] 0.422 0.011 0.022 0.044 0.071 3,198

(0.021) (0.044) (0.028) (0.045)

No signs visible for neighborhood or 

crime watch, security warnings, alcohol 

advertisements, or houses “For Sale” on 

this block [NWA]

0.540 – 0.015 – 0.031 0.001 0.002 3,245

0.022 0.045 0.029 0.047

(0.022) (0.045) (0.029) (0.047)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: NWA = neighborhood walk-around conducted by interviewers.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.
64

EXHIBIT 2.10. ADULT AND YOUTH NEIGHBORHOOD SAFETY AND CRIME VICTIMIZATION

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

ADULT RESPONSES

Feels safe or very safe during the day 

[SR]

0.804 0.036* 0.074* 0.045* 0.072* 3,262

(0.016) (0.034) (0.021) (0.034)

Feels safe or very safe during the night 

[SR]

0.596 0.043* 0.088* 0.073* 0.117* 3,246

(0.021) (0.043) (0.027) (0.043)

Police don't respond when called [SR] 0.420 – 0.067* – 0.138* – 0.075* – 0.118* 3,146

(0.021) (0.044) (0.028) (0.045)

Saw drugs being sold or used in the 

neighborhood in the past 30 days [SR]

0.310 – 0.062* – 0.128* – 0.057* – 0.090* 3,249

(0.019) (0.039) (0.025) (0.040)

Any household member was a crime 

victim in the last 6 months [SR]

0.184 – 0.022 – 0.046 0.025 0.040 3,241

(0.016) (0.033) (0.022) (0.035)

YOUTH RESPONSES 

Feels safe or very safe during the day [SR]

All 0.801 0.018 0.037 – 0.012 – 0.018 4,863

(0.016) (0.033) (0.018) (0.026)

Female 0.784 0.045* 0.090* 0.019 0.030 2,478

(0.022) (0.045) (0.025) (0.039)

Male 0.817 – 0.009 – 0.019 – 0.043~ – 0.062~ 2,385

(0.022) (0.047) (0.025) (0.035)

Feels safe or very safe during the night [SR]

All 0.540 0.035~ 0.074~ 0.019 0.028 4,862

(0.021) (0.043) (0.022) (0.033)

Female 0.486 0.052~ 0.104~ 0.067* 0.105* 2,478

(0.028) (0.056) (0.030) (0.048)

Male 0.591 0.020 0.043 – 0.030 – 0.043 2,384

(0.028) (0.060) (0.031) (0.043)

Saw drugs being sold or used in the neighborhood in the past 30 days [SR]

All 0.388 – 0.056* – 0.116* – 0.041* – 0.062* 4,879

(0.020) (0.040) (0.021) (0.031)

Female 0.393 – 0.087* – 0.174* – 0.085* – 0.134* 2,486

(0.026) (0.053) (0.029) (0.046)

Male 0.384 – 0.026 – 0.056 0.002 0.003 2,393

(0.026) (0.057) (0.028) (0.040)

Any household member was a crime victim in the last 6 months (only asked of youth ages 13–20) [SR]

All 0.246 – 0.027 – 0.056 – 0.020 – 0.030 4,618

(0.018) (0.038) (0.020) (0.029)

Female 0.255 – 0.059* – 0.119* – 0.044~ – 0.068~ 2,358

(0.024) (0.047) (0.026) (0.040)

Male 0.238 0.005 0.011 0.002 0.003 2,260

(0.025) (0.053) (0.027) (0.038)
65 Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks

EXHIBIT 2.10. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

Gangs are present in neighborhood or school (only asked of youth ages 13–20) [SR]

All 0.672 – 0.022 – 0.045 0.000 0.000 4,597

(0.019) (0.040) (0.021) (0.031)

Female 0.673 – 0.025 – 0.051 – 0.051~ – 0.079~ 2,347

(0.026) (0.052) (0.029) (0.045)

Male 0.671 – 0.019 – 0.041 0.052~ 0.074~ 2,250

(0.028) (0.059) (0.030) (0.042)

FEMALE YOUTH ONLY

People have made unwanted or rude 

comments to youth at least a few times 

a year [SR]

0.749 0.008 0.017 0.026 0.041 2,584

(0.023) (0.046) (0.024) (0.038)

People have given unwanted sexual 

attention to youth at least a few times 

a year [SR]

0.596 – 0.052* – 0.104* – 0.045 – 0.070 2,577

(0.025) (0.051) (0.028) (0.044)

Is afraid to go places because of 

unwanted attention or pressure at least 

a few times a year [SR]

0.401 – 0.003 – 0.005 – 0.030 – 0.047 2,582

(0.026) (0.051) (0.028) (0.044)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: "Any household member was a crime victim in the past 6 months" refers to a series of questions in which the youth is asked if anyone in their 

household: had their purse, wallet, or jewelry snatched from them, threatened with a gun or knife, was beaten or assaulted, was stabbed or shot, or if anyone 

attempted to break into their home.
66

EXHIBIT 2.11. NEIGHBORHOOD SOCIAL PROCESS AND ADULT SOCIAL NETWORKS

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

COLLECTIVE EFFICACY

Likely or very likely neighbors would do 

something if neighborhood kids were 

skipping school and hanging out on a 

street corner [SR]

0.346 0.029 0.059 0.075* 0.119* 3,250

(0.021) (0.043) (0.028) (0.045)

Likely or very likely neighbors would do 

something if they saw kids spray-painting 

graffiti on a local building [SR]

0.589 0.076* 0.156* 0.042 0.067 3,255

(0.021) (0.043) (0.028) (0.045)

FRIENDS

Has three or more close friends [SR] 0.432 0.006 0.013 – 0.026 – 0.042 3,265

(0.021) (0.044) (0.028) (0.045)

Has at least one close friend who 

graduated from college [SR]

0.532 0.071* 0.145* 0.007 0.010 3,203

(0.021) (0.044) (0.029) (0.046)

Has at least one close friend who works 

full-time [SR]

0.742 0.033~ 0.068~ – 0.020 – 0.032 3,261

(0.019) (0.038) (0.025) (0.041)

Has at least one close friend who 

is a different race or ethnicity from 

respondent [SR]

0.413 0.022 0.046 – 0.017 – 0.027 3,266

(0.021) (0.043) (0.027) (0.043)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.
67 Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks

2.7 INTERPRETATION OF RESULTS

MTO offered housing vouchers to families living in 

public housing at baseline, with the primary goal of 

helping them move into lower-poverty areas. The results 

in Chapter 1 showed that in that regard MTO was clearly 

a success. Families in the experimental group used the 

MTO voucher to move to tracts with poverty rates that 

were on average 42 percentage points lower than their 

baseline tracts (Exhibit 1.5). Section 8 families used 

their vouchers to move to tracts with poverty rates 25 

percentage points lower. These findings are consistent 

with the hypothesis that many families living in public 

housing are basically tied to high-poverty areas if they 

wish to receive a housing subsidy, and (absent gaining 

access to a housing voucher or another housing program) 

must give up their rent subsidy to move into a lowerpoverty area. The MTO voucher reduces the cost to 

families of moving into a lower-poverty area.

These effects on neighborhood conditions attenuate over 

time for a combination of reasons, but primarily because 

some control families moved to lower-poverty areas over 

time even without benefit of an MTO housing voucher. 

In addition, many treatment group families did not stay 

in the very low-poverty neighborhoods into which they 

made their initial MTO moves, and the fact that poverty 

rates declined over time in the baseline neighborhoods. 

Nonetheless as we showed in this chapter, poverty rates 

averaged over the entire study period—as well as other 

indicators of neighborhood socioeconomic composition, 

and neighborhood safety as well—are still quite different 

between the MTO treatment groups and the control 

group.

MTO did not generate sizable changes in neighborhood 

racial composition, unlike the Gautreaux mobility 

program (Rubinowitz and Rosenbaum, 2000). In 

Gautreaux, moving families to less racially segregated 

neighborhoods was a key objective of the program. The 

non-profit organization that helped carry out Gautreaux 

was responsible for identifying rental apartments 

for families, which eliminated the need for program 

participants to search themselves for housing and at the 

same time also gave participants limited choice over 

where they would move. In MTO, program participants 

instead were involved in the housing search process with 

assistance from a local non-profit. What we cannot tell 

from the MTO data is the degree to which MTO families 

moved to low-poverty, mostly-minority neighborhoods 

because that was their preference or instead because of 

racial discrimination they encountered searching for 

housing in more racially integrated communities. Both of 

these explanations fit the MTO data equally well.

MTO did generate sizable and persistent differences in 

the quality of housing units as well, even though many 

families who moved with an MTO voucher were still 

living in units with problems such as vermin, peeling 

paint or plaster, or problems with their heating or 

plumbing. An even larger share of the control group 

families lived in units with these types of problems.

The findings reported in this chapter suggest that at least 

among the set of families who volunteered for MTO, 

changing the form of housing assistance they receive 

from public housing to vouchers leads to improved 

housing quality and changes in a range of neighborhood 

conditions as well. Our findings for MTO effects on 

neighborhood quality are quite different from studies 

such as HUD’s Welfare to Work voucher experiment 

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

lottery study of Jacob and Ludwig (2011), which 

compare outcomes of families that receive housing 

vouchers with those who are living unsubsidized in the 

private housing market. Those studies find that providing 

housing vouchers to families who are already living 

(unsubsidized) in private-market housing leads to very 

modest if any changes in neighborhood conditions. The 

limited effect of vouchers on neighborhood conditions 

for families already in private-market housing is not 

what we would predict from standard economic theory, 

which suggests that families receiving generous voucher 

subsidies would want to spend more on higher unit 

quality and higher neighborhood “quality.” Learning 

more about why vouchers change neighborhood 

conditions for public housing residents but not for 

families already in private-market housing is an 

important question for future research.

Whether these MTO-induced changes in housing and 

neighborhood conditions translate into changes in 
68

outcomes such as labor market earnings, schooling, 

health or delinquency will depend on which aspects of 

housing and neighborhoods are most relevant to these 

outcomes and the degree to which these outcomes are 

affected by immediate versus accumulated exposure to 

different housing and neighborhood conditions. But, 

at the very least, these moves have left the families 

better off, as judged by the growing share of families 

that report being satisfied with their housing units and 

neighborhoods.

SUPPLEMENTAL EXHIBIT 2.1. ADULT CURRENT HOUSING CONDITION

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

OVERCROWDING IN HOUSING

Overcrowded household (ratio of more 

than one person per room not including 

bathrooms and hallways) [SR]

0.229 – 0.008 – 0.017 – 0.021 – 0.034 3,260

(0.017) (0.034) (0.021) (0.033)

Severely overcrowded household (ratio 

of more than one and one-half people 

per room not including bathrooms and 

hallways) [SR]

0.080 0.012 0.024 0.019 0.030 3,260

(0.011) (0.024) (0.015) (0.023)

SPECIFIC HOUSING PROBLEMS

Walls with peeling paint or broken 

plaster are a big or small problem [SR]

0.466 – 0.091* – 0.187* – 0.120* – 0.192* 3,265

(0.021) (0.044) (0.028) (0.044)

Plumbing not working is a big or small 

problem [SR]

0.327 – 0.058* – 0.119* – 0.053* – 0.085* 3,265

(0.020) (0.040) (0.026) (0.041)

Rats or mice are a big or small problem 

[SR]

0.347 – 0.038~ – 0.079~ – 0.045~ – 0.072~ 3,265

(0.020) (0.041) (0.027) (0.043)

Cockroaches are a big or small 

problem [SR]

0.356 – 0.053* – 0.110* – 0.089* – 0.142* 3,267

(0.019) (0.040) (0.025) (0.039)

Broken or no locks on door to the unit 

are a big or small problem [SR]

0.144 – 0.029~ – 0.059~ – 0.019 – 0.031 3,267

(0.015) (0.030) (0.019) (0.030)

Broken windows or missing screens are 

a big or small problem [SR]

0.233 – 0.065* – 0.134* – 0.078* – 0.124* 3,267

(0.017) (0.036) (0.023) (0.036)

Heating system that does not work is a 

big or small problem [SR]

0.179 – 0.013 – 0.027 – 0.037~ – 0.059~ 3,252

(0.016) (0.033) (0.020) (0.033)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.
69 Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks

SUPPLEMENTAL EXHIBIT 2.2. YOUTH NEIGHBORHOOD CENSUS TRACT CHARACTERISTICS FOR CURRENT 

ADDRESS (MAY 2008)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

Share of persons in tract who are poor [CEN]

All 0.309 – 0.032* – 0.067* – 0.032* – 0.049* 4,974

(0.008) (0.017) (0.008) (0.012)

Female 0.311 – 0.033* – 0.065* – 0.032* – 0.050* 2,543

(0.009) (0.019) (0.010) (0.016)

Male 0.306 – 0.032* – 0.068* – 0.033* – 0.047* 2,431

(0.010) (0.022) (0.010) (0.014)

Share of persons who are above 200% of the poverty line [CEN]

All 0.450 0.034* 0.070* 0.030* 0.044* 4,974

(0.010) (0.020) (0.010) (0.015)

Female 0.446 0.034* 0.069* 0.031* 0.048* 2,543

(0.012) (0.023) (0.013) (0.020)

Male 0.454 0.033* 0.072* 0.029* 0.041* 2,431

(0.012) (0.026) (0.012) (0.018)

Share minority (non-White or Hispanic) [CEN]

All 0.832 – 0.016 – 0.034 – 0.019 – 0.028 4,974

(0.012) (0.026) (0.014) (0.021)

Female 0.831 – 0.018 – 0.035 – 0.023 – 0.036 2,543

(0.015) (0.029) (0.017) (0.027)

Male 0.834 – 0.015 – 0.033 – 0.014 – 0.020 2,431

(0.016) (0.033) (0.017) (0.025)

Share Hispanic [CEN]

All 0.299 – 0.003 – 0.007 0.015 0.023 4,974

(0.009) (0.019) (0.009) (0.014)

Female 0.306 – 0.004 – 0.008 0.012 0.019 2,543

(0.011) (0.022) (0.012) (0.019)

Male 0.292 – 0.003 – 0.007 0.019 0.027 2,431

(0.011) (0.024) (0.011) (0.016)

Share Black [CEN]

All 0.497 – 0.017 – 0.035 – 0.041* – 0.062* 4,974

(0.013) (0.027) (0.015) (0.022)

Female 0.488 – 0.019 – 0.039 – 0.045* – 0.071* 2,543

(0.015) (0.031) (0.019) (0.029)

Male 0.506 – 0.014 – 0.031 – 0.037* – 0.053* 2,431

(0.015) (0.033) (0.019) (0.026)

Share of households that are single female-headed families [CEN]

All 0.483 – 0.036* – 0.075* – 0.041* – 0.061* 4,974

(0.010) (0.020) (0.010) (0.015)
70

SUPPLEMENTAL EXHIBIT 2.2. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

Share of households that are single female-headed families [CEN] (CONTINUED)

Female 0.479 – 0.032* – 0.065* – 0.039* – 0.062* 2,543

(0.011) (0.022) (0.012) (0.019)

Male 0.486 – 0.040* – 0.085* – 0.043* – 0.061* 2,431

(0.012) (0.025) (0.012) (0.017)

Share college graduates [CEN]

All 0.209 0.015* 0.031* 0.015* 0.023* 4,974

(0.007) (0.014) (0.007) (0.011)

Female 0.208 0.015* 0.031* 0.021* 0.033* 2,543

(0.008) (0.016) (0.009) (0.014)

Male 0.209 0.015~ 0.033~ 0.009 0.013 2,431

(0.008) (0.018) (0.009) (0.013)

Share with more than high school education [CEN]

All 0.391 0.021* 0.044* 0.017* 0.026* 4,974

(0.007) (0.016) (0.008) (0.012)

Female 0.391 0.020* 0.041* 0.021* 0.033* 2,543

(0.009) (0.018) (0.010) (0.015)

Male 0.392 0.022* 0.048* 0.013 0.019 2,431

(0.010) (0.021) (0.010) (0.014)

Share employed, civilian [CEN]

All 0.848 0.012* 0.025* 0.014* 0.021* 4,974

(0.004) (0.008) (0.004) (0.006)

Female 0.849 0.010* 0.020* 0.011* 0.018* 2,543

(0.004) (0.008) (0.005) (0.008)

Male 0.846 0.014* 0.030* 0.017* 0.024* 2,431

(0.005) (0.010) (0.005) (0.007)

Share workers in managerial or professional occupations [CEN]

All 0.240 0.009 0.018 0.007 0.011 4,974

(0.006) (0.012) (0.006) (0.009)

Female 0.238 0.011~ 0.023~ 0.015* 0.024* 2,543

(0.007) (0.014) (0.007) (0.012)

Male 0.243 0.006 0.014 – 0.001 – 0.001 2,431

(0.008) (0.017) (0.008) (0.012)

Median household income (2009 dollars) [CEN]

All $34,189.96 3,627.81* 7,526.02* 3,035.12* 4,543.18* 4,974

(895.53) (1,857.81) (903.84) (1,352.93)

Female $34,130.44 3,440.25* 6,920.75* 2,792.55* 4,385.01* 2,543

(1,050.07) (2,112.42) (1,109.29) (1,741.86)

Male $34,246.61 3,815.73* 8,193.22* 3,277.41* 4,677.22* 2,431

(1,118.32) (2,401.28) (1,126.04) (1,606.98)
71 Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks

SUPPLEMENTAL EXHIBIT 2.2. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

Per capita income (2009 dollars) [CEN]

All $17,257.40 918.75* 1,905.98* 622.10 931.21 4,974

(451.78) (937.24) (461.82) (691.28)

Female $17,150.40 945.79~ 1,902.65~ 897.02 1,408.54 2,543

(543.00) (1,092.34) (585.07) (918.70)

Male $17,359.24 897.28 1,926.66 339.91 485.08 2,431

(563.61) (1,210.20) (545.94) (779.11)

Share owner-occupied housing units [CEN]

All 0.338 0.042* 0.088* 0.041* 0.062* 4,974

(0.012) (0.025) (0.013) (0.019)

Female 0.335 0.040* 0.081* 0.034* 0.054* 2,543

(0.014) (0.029) (0.015) (0.024)

Male 0.342 0.045* 0.096* 0.048* 0.069* 2,431

(0.015) (0.032) (0.015) (0.022)

Share of households on public assistance [CEN]

All 0.098 – 0.015* – 0.031* – 0.014* – 0.021* 4,974

(0.004) (0.008) (0.003) (0.005)

Female 0.100 – 0.016* – 0.031* – 0.015* – 0.024* 2,543

(0.004) (0.008) (0.004) (0.007)

Male 0.097 – 0.015* – 0.032* – 0.013* – 0.019* 2,431

(0.005) (0.010) (0.004) (0.006)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: CEN = 1990 and 2000 decennial census data as well as the 2005–09 American Community Survey 

(ACS).

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth impacts by gender were estimated as an interaction with treatment status. See Section 

1.3 and the technical appendices (forthcoming) for details.

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

Measures: Census tract characteristics are linearly interpolated from the 1990 decennial census, 2000 decennial census, and 2005–09 ACS.
72

SUPPLEMENTAL EXHIBIT 2.3. ADULT NEIGHBORHOOD CONDITIONS

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

DRUGS

Saw drugs being used or sold in 

neighborhood in the past 30 days [SR]

0.310 – 0.062* – 0.128* – 0.057* – 0.090* 3,249

(0.019) (0.039) (0.025) (0.040)

Saw someone using drugs in 

neighborhood in past 30 days [SR]

0.198 – 0.050* – 0.103* – 0.050* – 0.078* 3,247

(0.016) (0.033) (0.021) (0.034)

Saw someone selling drugs in 

neighborhood in past 30 days [SR]

0.264 – 0.047* – 0.096* – 0.051* – 0.081* 3,249

(0.018) (0.037) (0.024) (0.038)

NEIGHBORHOOD PROBLEMS

Litter or trash on the sidewalk is a big 

or small problem [SR]

0.646 – 0.075* – 0.155* – 0.069* – 0.111* 3,265

(0.021) (0.042) (0.028) (0.045)

Graffiti on the walls is a big or small 

problem [SR]

0.466 – 0.061* – 0.126* – 0.041 – 0.065 3,261

(0.021) (0.042) (0.028) (0.044)

People drinking in public is a big or 

small problem [SR]

0.513 – 0.056* – 0.114* – 0.020 – 0.031 3,246

(0.022) (0.045) (0.029) (0.046)

Abandoned buildings is a big or small 

problem [SR]

0.388 – 0.047* – 0.096* – 0.040 – 0.064 3,259

(0.020) (0.042) (0.027) (0.043)

Groups of people hanging out is a big 

or small problem [SR]

0.557 – 0.049* – 0.101* – 0.012 – 0.019 3,258

(0.021) (0.044) (0.029) (0.045)

HOUSEHOLD VICTIMIZATION (PAST 6 MONTHS)

Household member had their purse, 

wallet, or jewelry snatched [SR]

0.077 – 0.005 – 0.010 0.008 0.012 3,234

(0.011) (0.023) (0.014) (0.023)

Household member was threatened 

with a knife or gun [SR]

0.066 – 0.008 – 0.016 0.001 0.001 3,236

(0.010) (0.021) (0.013) (0.021)

Household member was beaten up or 

assaulted [SR]

0.074 – 0.014 – 0.030 – 0.005 – 0.008 3,238

(0.011) (0.022) (0.014) (0.023)

Household member was stabbed or 

shot [SR]

0.029 – 0.006 – 0.013 – 0.008 – 0.013 3,241

(0.007) (0.015) (0.008) (0.013)

Someone attempted to break into the 

home [SR]

0.043 – 0.003 – 0.007 0.015 0.025 3,242

(0.009) (0.019) (0.013) (0.021)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed
73 Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks

SUPPLEMENTAL EXHIBIT 2.4. MEDIATORS: ADULT SOCIAL NETWORKS

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

Has no friends in the neighborhood [SR] 0.571 0.009 0.019 0.009 0.014 3,267

(0.021) (0.044) (0.028) (0.045)

Visits friends in their home at least once 

a week [SR]

0.239 0.015 0.031 0.018 0.028 3,266

(0.018) (0.038) (0.024) (0.039)

Stops to chat with neighbor in street or 

hallway at least once a week [SR]

0.532 0.019 0.039 0.030 0.048 3,269

(0.021) (0.044) (0.028) (0.045)

Attended church or religious services at 

least once a month in the past year [SR]

0.460 – 0.028 – 0.057 0.013 0.020 3,264

(0.021) (0.044) (0.028) (0.045)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth impacts by gender were estimated as an interaction with treatment status. See Section 

1.3 and the technical appendices (forthcoming) for details.

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

SUPPLEMENTAL EXHIBIT 2.5. MEDIATORS: YOUTH SOCIAL NETWORK

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

Youth has at least one close friend [SR]

All 0.963 – 0.018* – 0.037* – 0.019* – 0.028* 5,094

(0.008) (0.016) (0.009) (0.014)

Female 0.967 -0.009 – 0.019 – 0.021~ – 0.034~ 2,595

(0.010) (0.021) (0.013) (0.020)

Male 0.959 – 0.027* – 0.057* – 0.016 – 0.022 2,499

(0.012) (0.026) (0.014) (0.020)

Youth has at least three close friends [SR]

All 0.640 – 0.023 – 0.048 – 0.032 – 0.047 5,094

(0.019) (0.039) (0.020) (0.030)

Female 0.616 – 0.040 – 0.080 – 0.054~ – 0.085~ 2,595

(0.026) (0.051) (0.028) (0.045)

Male 0.661 – 0.006 – 0.013 – 0.009 – 0.013 2,499

(0.026) (0.057) (0.029) (0.041)

Youth has at least one close friend in their neighborhood [SR]

All 0.624 – 0.014 – 0.030 – 0.028 – 0.041 5,089

(0.019) (0.040) (0.021) (0.031)

Female 0.608 – 0.009 – 0.018 – 0.052~ – 0.082~ 2,594

(0.026) (0.053) (0.029) (0.046)

Male 0.640 – 0.021 – 0.045 – 0.002 – 0.003 2,495

(0.027) (0.058) (0.029) (0.042)
74

SUPPLEMENTAL EXHIBIT 2.5. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

Youth has at least one friend that was also a friend at random assignment [SR]

All 0.487 – 0.027 – 0.055 – 0.039~ – 0.058~ 5,006

(0.020) (0.041) (0.022) (0.032)

Female 0.496 – 0.024 – 0.049 – 0.037 – 0.059 2548

(0.027) (0.055) (0.030) (0.047)

Male 0.479 – 0.029 – 0.062 – 0.040 – 0.057 2,458

(0.027) (0.058) (0.029) (0.042)

Youth visits baseline friends or they visit youth at least a few times a week [SR]

All 0.280 – 0.035* – 0.073* – 0.038* – 0.057* 5,002

(0.017) (0.036) (0.018) (0.027)

Female 0.257 – 0.030 – 0.060 – 0.016 – 0.025 2,546

(0.023) (0.047) (0.025) (0.040)

Male 0.302 – 0.040~ – 0.086~ – 0.060* – 0.086* 2,456

(0.024) (0.052) (0.026) (0.037)

Youth attended youth activities at church at least once a month during past year [SR]

All 0.360 – 0.023 – 0.049 0.001 0.002 5,086

(0.019) (0.039) (0.020) (0.030)

Female 0.356 – 0.031 – 0.062 – 0.003 – 0.005 2,589

(0.025) (0.051) (0.028) (0.044)

Male 0.363 – 0.016 – 0.035 0.005 0.007 2,497

(0.026) (0.056) (0.028) (0.040)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 10 to 20 as of December 31, 2007.
75 Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks

SUPPLEMENTAL EXHIBIT 2.6. MEDIATORS: YOUTH'S PEERS

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

Close friends think that studying is very important [SR]

All 0.428 – 0.022 – 0.046 – 0.016 – 0.024 5,083

(0.019) (0.040) (0.021) (0.032)

Female 0.474 – 0.009 – 0.019 – 0.029 – 0.046 2,589

(0.026) (0.053) (0.029) (0.045)

Male 0.385 – 0.035 – 0.076 – 0.002 – 0.003 2,494

(0.027) (0.058) (0.030) (0.042)

Close friends think that it is very important to continue their education past high school [SR]

All 0.732 0.006 0.013 0.003 0.005 5,082

(0.017) (0.034) (0.018) (0.028)

Female 0.787 0.024 0.048 0.010 0.016 2,589

(0.021) (0.042) (0.024) (0.037)

Male 0.680 – 0.012 – 0.026 – 0.003 – 0.005 2,493

(0.025) (0.054) (0.028) (0.041)

Close friends have ever been involved in school activities [SR]

All 0.810 – 0.014 – 0.029 – 0.018 – 0.027 5,041

(0.015) (0.030) (0.017) (0.025)

Female 0.820 – 0.016 – 0.033 – 0.027 – 0.042 2,568

(0.020) (0.041) (0.023) (0.036)

Male 0.800 – 0.012 – 0.026 – 0.009 – 0.012 2,473

(0.022) (0.047) (0.025) (0.035)

Close friends have ever used marijuana or other drugs [SR]

All 0.357 – 0.045* – 0.094* – 0.025 – 0.038 4,955

(0.019) (0.039) (0.021) (0.031)

Female 0.315 – 0.063* – 0.126* – 0.034 – 0.053 2,541

(0.024) (0.048) (0.027) (0.043)

Male 0.397 – 0.028 – 0.059 – 0.017 – 0.024 2,414

(0.026) (0.057) (0.028) (0.041)

Close friends have ever dropped out of school [SR]

All 0.214 – 0.005 – 0.010 – 0.005 – 0.008 5,082

(0.015) (0.031) (0.017) (0.026)

Female 0.200 – 0.018 – 0.037 – 0.040~ – 0.063~ 2,591

(0.020) (0.040) (0.022) (0.035)

Male 0.227 0.008 0.018 0.030 0.043 2,491

(0.022) (0.048) (0.025) (0.036)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250"" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 10 to 20 as of December 31, 2007.
76

SUPPLEMENTAL EXHIBIT 2.7. YOUTH SELF-REFLECTION

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

Youth speaks proper English very often or somewhat often (versus never) [SR]

All 0.894 0.001 0.001 0.004 0.005 5,092

(0.011) (0.024) (0.012) (0.018)

Female 0.896 0.011 0.022 0.006 0.009 2,595

(0.015) (0.031) (0.017) (0.027)

Male 0.891 – 0.010 – 0.021 0.002 0.002 2,497

(0.017) (0.036) (0.018) (0.026)

Youth thinks people would never describe his or her behavior as "ghetto" (versus very often or somewhat often) [SR]

All 0.613 0.000 0.000 – 0.012 – 0.018 5,077

(0.019) (0.039) (0.020) (0.030)

Female 0.638 – 0.016 – 0.031 – 0.007 – 0.011 2,586

(0.025) (0.050) (0.027) (0.043)

Male 0.589 0.016 0.034 – 0.018 – 0.026 2,491

(0.026) (0.056) (0.029) (0.041)

Youth strongly agrees that they feel good about themselves (versus agree, disagree, or strongly disagree) [SR]

All 0.675 0.013 0.027 – 0.011 – 0.016 5,095

(0.018) (0.037) (0.020) (0.029)

Female 0.667 0.028 0.057 – 0.024 – 0.037 2,596

(0.024) (0.049) (0.027) (0.043)

Male 0.683 – 0.003 – 0.007 0.003 0.004 2,499

(0.025) (0.054) (0.027) (0.039)

Youth strongly agrees that they can do things as well as other people (versus agree, disagree, or strongly disagree) [SR]

All 0.584 0.017 0.035 – 0.007 – 0.010 5,093

(0.019) (0.039) (0.020) (0.030)

Female 0.590 0.032 0.065 0.005 0.008 2,594

(0.026) (0.052) (0.028) (0.044)

Male 0.579 0.002 0.004 – 0.018 – 0.026 2,499

(0.026) (0.055) (0.029) (0.041)

Youth strongly disagrees or disagrees that chance and luck are very important in life (versus strongly agree or agree) [SR]

All 0.309 – 0.015 – 0.030 0.017 0.025 5,082

(0.018) (0.037) (0.019) (0.029)

Female 0.308 – 0.070* – 0.141* – 0.013 – 0.020 2,590

(0.024) (0.048) (0.026) (0.041)

Male 0.311 0.041 0.088 0.045 0.064 2,492

(0.025) (0.054) (0.028) (0.040)

Youth thinks other people always see them as popular (versus some of the time or never) [SR]

All 0.323 – 0.028 – 0.057 – 0.030 – 0.045 5,087

(0.017) (0.035) (0.018) (0.028)

Female 0.315 – 0.038~ – 0.077~ – 0.057* – 0.089* 2,593

(0.023) (0.047) (0.025) (0.040)
77 Chapter 2: Impacts on Mobility, Housing, Neighborhoods, and Social Networks

SUPPLEMENTAL EXHIBIT 2.7. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

Youth thinks other people always see them as popular (versus some of the time or never) [SR] (CONTINUED)

Male 0.330 – 0.017 – 0.037 – 0.003 – 0.004 2,494

(0.024) (0.052) (0.027) (0.038)

Youth thinks other people always see them as athletic (versus some of the time or never) [SR]

All 0.318 – 0.020 – 0.041 0.002 0.003 5,089

(0.017) (0.036) (0.019) (0.028)

Female 0.207 – 0.035~ – 0.071~ – 0.003 – 0.005 2,593

(0.021) (0.043) (0.024) (0.038)

Male 0.422 – 0.004 – 0.009 0.006 0.008 2,496

(0.027) (0.058) (0.030) (0.043)

Youth thinks other people always see them as a good student (versus some of the time or never) [SR]

All 0.404 0.002 0.004 – 0.016 – 0.024 5,092

(0.019) (0.039) (0.020) (0.031)

Female 0.455 0.027 0.055 0.014 0.022 2,595

(0.026) (0.052) (0.029) (0.045)

Male 0.356 – 0.023 – 0.050 – 0.046~ – 0.066~ 2,497

(0.026) (0.055) (0.027) (0.039)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 10 to 20 as of December 31, 2007.
78
79 Chapter 3: Impacts on Adult and Youth Physical Health

IMPACTS ON ADULT AND YOUTH 

PHYSICAL HEALTH
80 CHAPTER 3

IMPACTS ON ADULT AND 

YOUTH PHYSICAL HEALTH

In this chapter, we summarize MTO’s impacts on the 

physical health of adults and youth. At the end of 

2007, the adults were on average 45.2 years old, with 

29 percent over age 50, while the youth in our study 

sample ranged in age from 10 to 20. We relied in part 

on adult and youth self-reports of health status, health 

behaviors, and access to health care. Because differing 

access to health services across neighborhoods could 

affect self-reported health outcomes, we collected 

physical measurements of health outcomes as well. 

Specifically, we measured height and weight, and 

for adults we also measured waist circumference and 

blood pressure and collected blood samples to measure 

physiological indicators of long-term disease risk. We 

also asked adults to report on the health outcomes of 

their grown children (those who were under age 18 

at baseline but over age 20 at the end of 2007), who 

were too old to have been included in our youth survey 

sample frame.

Although MTO does not have detectable impacts 

on many of the physical health outcomes that we 

examined, particularly those focused on the health 

of the average adult in each randomized mobility 

group (central tendencies), we did find impacts on 

several measures of the prevalence of adverse health 

outcomes that are relevant for people with the most 

distressed health outcomes (those at the tails of the 

health outcome distribution). Specifically, we find 

that, compared with the control group, adults in the 

experimental group:

•	 Have a lower prevalence of extreme obesity 

(defined as a body mass index (BMI)1

 of greater 

than or equal to 35); 

•	 Have a lower prevalence of diabetes (measured by 

glycosylated hemoglobin [HbA1c] greater than or 

equal to 6.5 percent); 

1 Body mass index (BMI) is defined as weight in kilograms divided 

by height in meters squared.

•	 Have fewer self-reported physical limitations; and 

•	 Show no statistically significant differences on 

other health outcomes such as self-rated overall 

health, asthma, or blood pressure.

Results for adults in the Section 8 group tend to point 

in the same direction, but the effects in general were 

smaller and not statistically significant.

We detect no significant differences across treatment 

groups in the physical health of youth. Similarly, we 

see few effects on grown children, with the possible 

exception of an increased prevalence of physical 

limitations among male grown children in the Section 

8 group compared with those in the control group. 

We find few detectable differences across randomized 

groups on health-related behaviors, such as diet and 

moderate exercise, although we did find that a higher 

share of adults in the experimental group compared 

with the control group appear to engage in vigorous 

exercise. We also see signs of increased access to health 

care services for people in the Section 8 group versus 

those in the control group.

3.1 HYPOTHESES ABOUT EFFECTS 

ON ADULT AND YOUTH PHYSICAL 

HEALTH

Previous epidemiological research has found strong 

associations between living in disadvantaged 

neighborhoods and adverse health outcomes for adults, 

such as mortality, heart disease, obesity, depression, 

substance abuse, and poor overall health, even after 

controlling for individual health risk factors and 

protective factors. For example, Waitzman and Smith 

(1998) find that people living in federally designated 

poverty areas have higher rates of mortality even after 

controlling for individual characteristics. Ross and 

Mirowsky (2001) find that living in disadvantaged 

neighborhoods is associated with poorer self-reported 

health and physical functioning. Browning and Cagney 
81 Chapter 3: Impacts on Adult and Youth Physical Health Chapter 3: Impacts on Adult and Youth Physical Health

(2002) find that individuals residing in neighborhoods 

with greater collective efficacy report better overall 

health. Diez-Roux et al. (2001) find that adults living in 

disadvantaged neighborhoods are at significantly greater 

risk of developing coronary heart disease, even after 

controlling for income, occupation, and education (see 

Pickett and Pearl, 2001; Kawachi and Berkman, 2003b; 

Macintyre and Ellaway, 2003, for excellent reviews of 

this literature). More recently, Bird et al. (2010) find that 

“good cholesterol” (high-density lipoprotein or HDL) 

and lower blood pressure are associated with higher 

neighborhood socioeconomic status after controlling for 

other factors. For children, living in a high-poverty urban 

setting or unsafe neighborhood is also associated with 

adverse health outcomes (Curtis, Dooley, and Phipps, 

2004; Lumeng et al., 2006).

People living in high-poverty neighborhoods differ 

in many ways from those living in lower-poverty 

neighborhoods. Pickett and Pearl (2001) observe that 

estimates of neighborhood effects on health tend to 

be smaller in studies employing a richer set of control 

variables for the individual’s own (as opposed to 

neighborhood) socioeconomic condition. Because 

nonexperimental studies cannot adequately measure 

and control for all of the individual differences 

between people in different neighborhoods, the causal 

relationships between neighborhood and health remain 

unclear. The design of the MTO study overcomes these 

limitations and makes the estimation of causal effects 

possible.

MTO-assisted moves to lower-poverty neighborhoods 

could affect health by altering a participant’s physical 

and social environments and access to health-related 

community resources. Exhibit 3.1 shows how these 

changes might affect health, directly or indirectly, by 

changing health-related behaviors, including stress and 

health care access.

EXHIBIT 3.1. HYPOTHESIZED PATHWAYS THROUGH WHICH MTO MAY IMPACT PHYSICAL HEALTH

MTO INTERVENTION RELOCATION TO LOW-POVERTY NEIGHBORHOODS

COMMUNITY-LEVEL

MEDIATORS

PERSON- AND FAMILY-LEVEL

MEDIATORS

PHYSICAL HEALTH

OUTCOMES

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

Exhibit 3.1 is a modified version of Exhibit 4.1 in the MTO interim impacts evaluation (see Orr et al., 2003).

Community Resources

· Health care facilities

· Access to jobs with health

 benefits

· Access to fast food, grocery 

 stores, and liquor stores

· Exercise facilities

Physical Environment

· Cleaner air, fewer allergens

· More walkable

· Fewer hazards

Social Environment

· Reduced crime and violence

· Collective efficacy

· Peers who eat healthier and 

 exercise more

· Different neighbors

Physical Health

· General health status

· Difficulties with daily 

 activities

· Asthma

· Injuries

· Hypertension

· Obesity

· Diabetes

· Chronic pain

Unhealthy Behaviors

· Smoking

· Drinking

Access to Care

· Health insurance

· Access to medical care

Exercise and Nutrition

· Healthier eating 

· More exercise

Safety and Stress

· Less stress

Social Supports

· More/less isolation

· More/less support
82

PHYSICAL ENVIRONMENT

A drive through any major American city clearly shows 

that the physical environments of neighborhoods vary 

widely. The environment—both indoors and out—

may affect health by exposing individuals to pollution, 

contaminants, allergens, and physical hazards like broken 

glass. Poor housing quality, which can increase exposure 

to household dangers such as vermin, toxins, mold, 

and poor ventilation, may lead to a higher incidence 

of asthma (Bryant-Stephens, 2009). Carpeting can 

harbor dust mites, and its removal or cleaning is often 

recommended as an asthma remediation strategy (Crain 

et al., 2002; van der Heide et al., 1997). Low-income 

neighborhoods may also have compromised air quality, 

which has been linked to coronary heart disease (Kan 

et al., 2008) and poor health for infants (Currie and 

Walker, 2011). Poor children living in disadvantaged 

urban areas may be at higher risk of lead exposure 

as a result of lead dust from peeling paint and soil 

contamination. This can impair brain development 

and may be exacerbated by poor nutrition (Filippelli 

and Laidlaw, 2010). Exposure to secondhand smoke 

may also be greater in low-income areas because of a 

higher prevalence of smoking (Bombard et al., 2010). 

The prevalence of accidents and injuries may be higher 

among children living in distressed urban communities 

owing to unsafe playgrounds and other features of the 

environment (Scharfstein and Sandel, 1998; Quinlan, 

1996). Accidents may be a particularly important way in 

which neighborhoods affect child health, since accidents 

are the most common cause of death among children ages 

1 to 14 in the United States.

The “built environment” of a neighborhood—buildings, 

parks, and streets—may also influence health by affecting 

exercise patterns. Studies have found that neighborhood 

greenness is associated with lower BMI for youth (Bell, 

Wilson, and Liu, 2008), and the “walkability” of a 

neighborhood may be associated with more physical 

activity and lower rates of overweight and obesity 

(Mujahid et al., 2008; Lovasi et al., 2009; Sallis et al., 

2009).

COMMUNITY RESOURCES

Community resources such as stores, employers, and 

transportation are another part of the built environment 

that may affect health behaviors and outcomes. The 

presence of grocery stores could affect diet by reducing 

the purchase price or transportation costs associated 

with accessing fresh fruits and vegetables (Morland 

et al., 2002). The availability of liquor stores and fast 

food restaurants can also affect diet, by reducing the 

transportation costs associated with accessing inexpensive 

forms of alcohol or fast food (Zenk et al., 2005; Franco 

et al., 2008; Inagami et al., 2006; Dubowitz et al., 2008).

MTO’s potential effects on access to quality health care 

are more difficult to predict in advance. Low-income 

families may have difficulty accessing health care in part 

because they lack health insurance. MTO might increase 

access to health care if families move to areas with 

more opportunities for employment that offers health 

insurance benefits. MTO might also help families move 

to areas with better transportation or more local primary 

care physicians, making it easier to receive preventive 

care and other services. On the other hand, the MTO 

demonstration took place in cities with some of the topranked hospitals in the country, many of them academic 

medical centers that for historical reasons are located near 

economically disadvantaged inner-city neighborhoods.2

 

These academic medical centers may be more experienced 

in meeting the linguistic and cultural needs and daily 

realities of low-income patients than are the health care 

facilities located in more affluent areas.

SOCIAL ENVIRONMENT

Exposure to crime, social supports, and peer influences 

are all parts of the social environment that may affect 

health. Dangerous neighborhoods can have deleterious 

direct effects on health from injuries from assaults. 

Exposure to violence can be traumatic. Living in a 

dangerous neighborhood can produce chronic stress that 

in turn may be associated with asthma attacks (Wright, 

1998; Bloomberg and Chen, 2005), hypertension 

(Joint National Committee on Prevention, Detection, 

Evaluation, and Treatment of High Blood Pressure, 

1997; Kornitzer, Dramaix, and De Backer, 1999), and 

a lower ability of the immune system to respond to 

foreign substances, such as viruses and bacteria. Medical 

2 Four of the five MTO cities are home to at least one of the top 14 

honor-roll hospitals as ranked by U.S. News and World Report on the 

basis of specialty rankings (Comarow, 2010).
83 Chapter 3: Impacts on Adult and Youth Physical Health

researchers also speculate that exposure to trauma and 

other stressors may lead to dysregulation across multiple 

physiological systems (Merkin et al., 2009). Stress 

may affect eating patterns, for example, by leading to 

increased intake of dense-calorie foods (Torres and 

Nowson, 2007), and stress is associated with increased 

use of alcohol, tobacco, and other substances. In 

addition, living in a dangerous neighborhood may limit 

walking and other forms of outdoor exercise and could 

even influence access to health care facilities, especially in 

areas with inadequate or dangerous public transportation 

systems. Reduced outdoor time in more dangerous 

neighborhoods could also increase exposure to indoor 

pollutants.

MTO-assisted moves could help improve people’s 

health if they relocate to less distressed neighborhoods 

with higher levels of “collective efficacy,” that is, the 

level of trust among neighborhood residents and their 

willingness to work together to support shared values 

(Sampson, Raudenbush, and Earls, 1997). Living in a 

neighborhood with higher levels of collective efficacy 

could help families build social supports that buffer 

the effects of stress (Cohen, 2004; Berkman and Glass, 

2000). Of course, this assumes that families are willing 

and able to connect with their new neighbors. Moving 

to a lower-poverty area may not be helpful if families are 

unable to connect to their new neighbors, and could even 

potentially harm families’ mental or physical health if 

families feel deprived relative to the material wealth and 

social status of their new neighbors (Jencks and Mayer, 

1990; Luttmer, 2005; Lawler and Thye, 1999).

Peer influences are a final mechanism through which 

MTO-assisted moves may affect health behaviors and 

subsequent outcomes (Sallis, Prochaska, and Taylor, 

2000; Raudsepp and Viira, 2000). Epidemiological 

studies find a socioeconomic gradient in diets, with 

higher socioeconomic groups tending to eat a higherquality diet that includes more fresh fruits and 

vegetables, whole grains, fish, and lower-fat meats and 

dairy products and with lower socioeconomic groups 

eating on average higher-calorie, lower-nutrient diets 

(Darmon and Drewnoski, 2008). As a result, moves to 

lower-poverty neighborhoods may expose participants 

to neighbors with healthier lifestyles, including better 

diet and exercise habits and less smoking (Lee and 

Cubbin, 2002; Berkman and Breslow, 1983). On the 

other hand, alcohol consumption may be higher in the 

more affluent areas into which MTO families move, 

since alcohol consumption has a positive association 

with socioeconomic status (SES) over at least part of the 

SES distribution (Substance Abuse and Mental Health 

Services Administration, 2010). The net effect on health 

could be positive or negative given that previous research 

suggests that alcohol consumption may have a u-shaped 

relationship with mortality (Lee et al., 2008).

The degree to which exposure to people with different 

health habits translates into changes in health-related 

behavior by participants may depend on how socially 

connected participants are with their new neighbors 

(Christakis and Fowler, 2007). This exposure may be 

less helpful for Hispanic women in our study sample, 

given previous research indicating that greater levels 

of acculturation (often associated with higher income) 

may actually lead to less healthy lifestyles (see Lara et 

al., 2005 for a review of this literature). This previous 

research implies MTO moves for Hispanic women into 

neighborhoods with relatively larger shares of nonHispanic residents could lead to additional acculturation 

and potentially to increased prevalence of health-risk 

behaviors.

We hypothesize that moves to lower-poverty 

neighborhoods may improve physical health because the 

new neighborhoods may be safer and less stressful, have 

greater community resources, have residents who tend 

to smoke less, eat healthier, and exercise more, and have 

fewer environmental hazards. However, it is possible that 

health might not be affected at all or that it could be 

negatively affected if housing quality has deteriorated, 

participants feel socially isolated, new neighborhoods are 

less walkable, or access to health care—at least the sort 

that is responsive to the needs of low-income minority 

families—is more difficult. Most of these mechanisms 

suggest that the effects on health from MTO will be 

greater if families spend more time in low-poverty 

neighborhoods.
84

3.2 EARLIER RESEARCH: SHORTTERM AND INTERIM IMPACTS ON 

PHYSICAL HEALTH

Previous studies of the MTO demonstration’s impacts on 

various physical health outcomes for adults and youth 

have yielded a mixed pattern of results. The short-term 

study (on average two years after baseline) of MTO at 

the Boston site found that adults in the experimental 

group were more likely than control subjects to rate 

their health as good or better. Short-term data from 

Boston also showed a lower incidence of asthma and 

nonsports injuries (for example, physical injuries from 

falls, fights, needles, and broken glass) for children in the 

experimental group relative to the control group (Katz, 

Kling, and Liebman, 2001).

Findings from the interim evaluation (four to seven years 

after baseline, pooling data from all five sites) show a 

lower prevalence of obesity (BMI ≥ 30) for adults in 

the experimental group than those in the control group 

(42.0 percent vs. 47.1 percent), together with some signs 

of increased rates of exercise and improved diet (Orr et 

al., 2003, Exhibit 4.2, p. 77 and Exhibit E4.2, p. E–2). 

However, the interim study did not detect statistically 

significant effects for most other adult physical health 

outcomes, including self-rated health, hypertension, 

physical limitations, asthma, or a summary health index 

combining different health measures (Orr et al., 2003; 

Kling, Liebman, and Katz, 2007).

Among youth ages 15–20 at the time of the interim 

study, the overall health index revealed worse health for 

males in the experimental group relative to those in the 

control group, but beneficial effects on female youth 

(Kling, Liebman, and Katz, 2007). However, the interim 

study found no effects on any single health measure 

examined separately for children and youth ages 5 and 

up (Orr et al., 2003). More detailed analyses of impacts 

on children’s health by age confirmed the lack of overall 

health effects and revealed sporadic adverse effects for 

specific health measures and age groups, despite some 

beneficial effects on children’s diet, exercise, and safety 

(Fortson and Sanbonmatsu, 2010). The only health 

mediator with a significant adverse impact was the 

greater presence of wall-to-wall carpeting, which can 

make it more difficult to eliminate asthma triggers, in the 

homes of the experimental and Section 8 groups.

3.3 BASELINE AND CONTROL GROUP 

CONTEXT

The baseline survey that HUD administered to families 

when they applied to MTO included only a handful of 

questions about health status. From these questions, we 

know that approximately 15 percent of families had a 

disabled household member (Exhibit 1.2), 8.4 percent 

were receiving Social Security Disability Insurance 

(SSDI), and 16 percent were receiving Supplemental 

Security Income (SSI) (Exhibit 5.1). Roughly 6 percent 

of the youth sample had a health problem that limited 

their activities, and 9 percent had a health problem 

requiring special medicine or equipment. Among youth 

under age 6 at baseline, parents reported that 19 percent 

had been hospitalized before their first birthday (Exhibit 

1.3). At the time they entered the program, the majority 

of households were receiving Temporary Assistance for 

Needy Families (TANF), and about 25 percent of the 

adults indicated they were currently working. Families 

also expressed concerns about safety. Around 77 percent 

of adults said getting away from gangs or drugs was their 

primary or secondary reason for signing up for MTO, 

while 43 percent indicated someone in the household 

had been a victim of crime in the last six months (Exhibit 

1.2).

One of the main changes affecting adults’ health between 

the interim and final evaluations is that the adults are at 

least six years older at the final evaluation. A comparison 

of self-reports at interim data collection (2002) with 

the final collection (2008–2010) shows an apparent 

decline in the health of the control group. For example, 

the prevalence of obesity and self-reported physical 

limitations increased by 11 percentage points, the rate 

of asthma increased by 7 percentage points, and the 

likelihood that a control group adult rated his or her 

health as good or excellent declined by 10 percentage 

points.

3.4 DATA SOURCES AND MEASURES

We used self-reported data from the final adult and 

youth surveys, physical measurements of different health 

indicators, and dried blood spot samples (drops of whole 
85 Chapter 3: Impacts on Adult and Youth Physical Health

blood collected on filter paper following a simple finger 

stick; McDade et al., 2000) to measure impacts on 

physical health. Using objective physical and biological 

measures of health outcomes (called “biomarkers”) 

addresses potential concerns that neighborhood 

environments themselves may affect self-reporting errors. 

For example, people may be less willing to self-report 

some health problem when that problem is relatively 

less common in the local community, they may be more 

likely to find out about some health problem if they live 

in a community with better access to health care, or their 

assessment of whether their health is good or excellent 

may depend in part on the average health conditions 

of other people in their community.3 Another value 

of biomarkers as a complement to self-reported health 

outcomes is the ability to measure effects on hidden or 

undiagnosed health problems and precursors to longterm health problems that could be asymptomatic at the 

time of the final data collection. Previous research shows 

that nearly one-third of all diabetes cases are undiagnosed 

(Cowie et al., 2006); such cases, therefore, are unlikely to 

be detected through self-report but would be evident in 

biomarker data obtained from blood spot samples.

ADULT AND YOUTH SURVEY RESPONSES

Interviewers asked respondents a variety of questions 

about their health status, including physical limitations, 

health-related behaviors, morbidity, injuries, dental 

health, and access to health care. We selected questions 

from large national surveys (in particular, from the 

National Health Interview Survey) to measure, for 

example, general health status, dental health, injuries, 

asthma, height and weight, and smoking. The complete 

list of survey questions (and their sources) is available at 

mtoresearch.org.

SELF-REPORTED HEALTH OUTCOMES 

Self-Rated Health: Self-reported health is strongly 

related to life expectancy among adults (Idler and Kasl, 

1995; Idler and Benyamini, 1997). We measured selfreported health by asking respondents, “In general, 

how is your health: excellent, very good, good, fair, or 

poor?” Recent studies have noted that the strength of the 

relationship between self-rated health and other health 

3 See, for example, Mango and Park (forthcoming) for a discussion of 

local context and self-reported health status.

measures such as mortality and functional limitations 

may differ by the education level of the self-reporter. 

Some researchers have questioned whether self-rated 

health categories represent the same objective levels of 

health among respondents of different socioeconomic 

status (Delpierre et al., 2009; Dowd and Zajacova, 

2010). Thus, one potential challenge in comparing 

self-rated health across MTO groups is that the families 

living in more affluent neighborhoods may judge their 

own health by higher standards (for example, more 

negatively relative to others), and this might lead to 

impact estimates that understate any benefits on health 

status.4

Diabetes: We asked respondents if a doctor had ever 

told them that they have diabetes or high blood sugar, if 

they were currently taking medication to treat diabetes 

or to lower their blood sugar, and if in the past 12 

months they had diabetes or high blood sugar or received 

treatment for it. We present two self-reported measures 

of diabetes: (1) ever being told by a doctor they had 

diabetes/high blood sugar, and (2) having diabetes or 

having been treated for diabetes in the past 12 months. 

As noted above, underreporting of health problems could 

present a challenge in relying on self-reports. Moreover, 

the amount of the underreporting could vary across 

groups if, for example, experimental or Section 8 group 

families had more opportunities to interact with a doctor 

because of the program. This could lead to a larger share 

of experimental and Section 8 group than control group 

adults who have diabetes to know they have diabetes, 

thereby leading us to understate any beneficial impacts 

of MTO on these health outcomes. Partly to address 

this potential problem, we also collected blood spots 

from respondents and assayed them for diabetes and 

other health risks. (Similar issues arise with self-reported 

hypertension, which is why we also directly measure 

blood pressure as discussed below).

4 It is possible that Hispanics interpret questions about self-reported 

health differently from African-Americans on average, or choose differently from available response options. These sorts of self-reporting 

issues should balance out in our experimental analysis since, by virtue 

of random assignment, the share of MTO program participants who are 

Hispanic vs. African-American are similar across randomized mobility 

groups.
86

Asthma: We asked adults and youth if they have ever 

been told by a doctor or other health professional that 

they had asthma and if they had experienced wheezing or 

whistling sounds in their chest in the past 12 months and 

how many attacks they had. We asked youth additional 

follow-up questions about missed days of work and 

school due to wheezing or whistling in their chest.

Health limitations, chronic pain, and chronic health 

problems: Measures of activities of daily living (ADLs) 

are used in a number of national surveys to assess an 

individual’s functional status and quality of life (Wiener 

et al., 1990). We focused on ADLs that were likely to 

be relevant for our sample of largely middle-aged adults 

and asked them if their health limited them “in climbing 

several flights of stairs or lifting or carrying groceries” 

and considered them to have limitations if they indicated 

they were limited “a lot” or “a little” rather than “not 

limited at all.” We also asked adults if they ever had 

arthritis or rheumatism. To gain a better understanding 

of the severity of illness and injuries, we also asked 

adults “how many days did illness or injury keep you 

in bed more than half of the day?” We drew questions 

on chronic pain from the National Comorbidity Survey 

(Kessler et al., 1994, 2005) and asked adults about severe 

or frequent headaches and about back, neck, or other 

chronic pain in the past year or ever. We asked youth 

between ages 13 and 20 if they ever had chronic back or 

neck problems, frequent or very bad headaches, or other 

chronic pain. We also asked them if a doctor had ever 

told them they have diabetes or high blood sugar or a 

serious stomach or bowel problem, such as an ulcer or 

colitis. We also asked the parents if their grown children 

had a physical health problem that kept them from 

doing normal activities such as walking, getting dressed, 

housework, or working, and if their grown children had 

chronic physical health problems such as cancer, a heart 

problem, or any other serious health problem.

Accidents and injuries: We asked youth about both 

serious accidents or injuries requiring medical care in 

the past 12 months and, in the event they did not seek 

treatment for injuries, about any other serious accidents 

or injuries during the past 12 months that limited their 

usual activities but did not require medical attention. 

Youth categorized the causes of accidents or injuries, 

and we used this information to create a measure of 

nonsports injuries (relocation can affect opportunities 

for involvement with extracurricular or other athletic 

activities). Isolating the causal effect of neighborhood 

context on serious nonsports injuries is particularly 

important, given that injuries account for the two leading 

causes of death (accidents and homicide) in the United 

States among 15- to 19-year-olds (Anderson and Smith, 

2003).

Smoking and drinking (adults): We asked adults if they 

had ever smoked a cigarette and, if so, on how many 

of the past 30 days they had smoked and how many 

cigarettes they usually smoke each day. We also asked 

them if they ever had an alcoholic beverage (defined as 

beer, wine, a mixed drink, or a shot of liquor) and on 

how many of the past 30 days they had one or more. 

These questions were adapted from the 1997 National 

Longitudinal Survey of Youth (analyses of impacts on 

youth smoking and drinking are presented in Chapter 6).

Dental health (youth): Similar to self-reports of overall 

health, we asked youth to describe the condition of their 

teeth (excellent, very good, good, fair, or poor). This 

question was adapted from the National Health and 

Nutrition Examination Survey.

SELF-REPORTED MEASURES OF 

CANDIDATE MEDIATORS

Exercise and sedentary behavior: We asked adults how 

many times in a usual week they engaged in moderate 

activities for at least 10 minutes at a time (such as brisk 

walking, bicycling, vacuuming, gardening, or anything 

else that causes small increases in breathing or heart rate) 

and how many times they did vigorous activities for at 

least 10 minutes at a time (such as running, aerobics, 

heavy yard work, or anything else that causes large 

increases in breathing or heart rate). We asked youth how 

many days within the past seven days they engaged in 

aerobic exercise (physical activity for at least 20 minutes 

that made you sweat and breathe hard, such as basketball, 

soccer, running, swimming, fast bicycling, fast dancing, 

or similar aerobic activities) and how many days they 

engaged in moderate exercise (physical activity for at least 

30 minutes that did not make you sweat and breathe 

hard, such as fast walking, slow bicycling, skating, 
87 Chapter 3: Impacts on Adult and Youth Physical Health

pushing a lawn mower, or mopping floors).

We gauged the amount of television adults and youth 

watch in a typical week by having them select from the 

following categories: less than 2 hours per week, 3–10 

hours, 11–20 hours, 21–30 hours, 31–40 hours, or 40 

hours or more.

Diet: We asked adults how many times they eat fruits 

and vegetables in a typical week. The possible responses 

were: I do not typically eat fruit; one to three times per 

week; four to six times per week; one time per day; two 

times per day; three times per day; and four times or 

more per day. Adults also reported the number of days 

per week that they typically ate fast food. The youth 

answered the same questions about eating fruits and 

vegetables, as well as questions about the number of 

times each week they drank soda or other carbonated 

drinks, soft drinks, or juices that contained sugar (diet 

drinks are excluded); ate sweet snacks like cookies, 

chocolate bars, or candy; and ate fast food. The possible 

responses for these questions were: I do not typically eat/

drink [item]; one to three times per week; four to six 

times per week; one time per day; two times per day; 

three times per day; and four times or more per day.

Health care access: We asked adults to specify the types 

of health insurance or health care coverage they currently 

have for themselves (they could select multiple types of 

health insurance) and where they usually go (a clinic, 

doctor’s office, emergency room, or some other place) 

when they need routine or preventive care. We asked 

how long it had been since they last saw a doctor about 

their own health, and where their children usually go for 

health care. In addition, we asked how many times they 

had been to the emergency room in the past 12 months 

because of their own health, and whether in the past 12 

months they or their children did not get needed medical 

care for any of the following reasons: can’t afford it, 

didn’t have transportation, didn’t know whom to see, or 

had to take care of family or friends. We also asked youth 

if they had a routine physical exam or a dental exam in 

the past year.

PHYSICAL BIOMARKERS

We directly measured various health outcomes in part to 

address concerns that MTO moves may affect self-reports 

of health status (if respondents use their new, more 

affluent neighbors as their benchmark) or affect diagnosis 

of specific health problems if respondents are more likely 

to see a doctor. Physical health measures can also help 

detect the early stages of health problems that may not 

yet be symptomatic.

Height, weight, and obesity: We used protocols 

developed for the Health and Retirement Survey 

(Institute for Social Research, 2008) to measure adult 

and youth height and weight. For height, respondents 

removed their shoes and stood on a smooth surface with 

their heels and shoulders against a wall. Interviewers 

placed a rafter square on the respondent’s head, marked 

the height on a wall, and then measured it in inches 

(to the nearest quarter inch) using a tape measure. 

For weight, respondents removed their shoes and 

heavy objects from their pockets, as well as any heavy 

outer layers of clothing before stepping on the scale. 

Interviewers used a digital floor scale to measure weight 

to the nearest half pound. If weight or height could not 

be measured or if quality checks revealed an unusual 

value, we obtained self-reports from the respondents. 

Using their height and weight, we calculated respondents’ 

BMI equal to their weight in kilograms divided by height 

in meters-squared (BMI=kg/m2).5

In addition to examining impacts on mean BMI, 

overweight (defined as a BMI ≥25), and obesity 

(BMI ≥30), we also look at different parts of the BMI 

distribution, given previous evidence that having very 

high BMI values (≥35 or ≥40) may be strongly associated 

with subsequent adverse health outcomes (National 

Institutes of Health, National Heart, Lung and Blood 

Institute, and Obesity Education Initiative, 1998).

The standard BMI measure tends to work less well for 

children and adolescents because BMI tends to increase 

from birth through about age 20 as a natural byproduct 

of physical development. Instead, we define overweight 

and obesity for youth using criteria developed by the 

International Obesity Task Force (IOTF) (Cole et 

5 Additional details about the measurement protocols, as well as a 

description of the handling of outlier values, are in the technical appendices (forthcoming) of this report.
88

al., 2000).6 The IOTF uses growth curves from an 

international sample to create youth obesity measures 

by age and gender that align with the adult standards. 

Curves are further broken down by gender because while 

BMI tends to follow a linear trend for males, the trend 

line has more of a concave shape for females, and also 

because puberty tends to affect female bodies at different 

ages than males (Cole et al., 2000).

Waist circumference: Waist circumference is a measure 

of central obesity (abdominal or central fat) and has also 

been shown to be an important anthropometric correlate 

of cardiovascular risk, even after controlling for BMI. 

How weight is carried matters for health (Srinivasan et 

al., 2009; Wildman et al., 2008; Schneider et al., 2006; 

Depres and Lemieux, 2006; Lee et al., 2008). Central 

obesity has also been shown to be a major risk factor for 

type II diabetes (for more information, see discussion 

in National Institutes of Health, National Heart, Lung 

and Blood Institute, and Obesity Education Initiative, 

1998). Respondents removed bulky outer clothing, and 

interviewers took waist measurements over their regular 

clothing. Interviewers asked the respondents to wrap 

a flexible measuring tape around their waist at navel 

level, inhale and exhale normally, and, while holding the 

exhale, hold the tape measure where the two ends met 

and then hand it to the interviewer, who recorded the 

measurement in inches.

Blood pressure: Interviewers took respondents’ blood 

pressure using a large-sized automated blood pressure 

cuff (Omron automated sphygmomanometer model 

HEM-711DLX) designed to accommodate arm 

circumferences from 22 to 42 centimeters. Respondents 

sat at a table with both feet flat on the floor and their 

arm resting, palm up, on the table. The cuff was placed 

on the respondent’s upper left arm, about half an inch 

above the elbow. Interviewers tried to collect two separate 

readings from each adult respondent. If two valid 

diastolic and systolic readings were obtained, we used 

6 The Centers for Disease Control and Prevention has developed an 

alternative set of growth curves that are typically used to assess whether 

children are at risk of obesity. However, the growth curves apply only 

up to age 20 and thus would not apply to about 25 percent of our 

youth respondents who were over age 20 at the time of interview. As a 

result, we chose to use the IOTF criteria instead.

the average of each measure.7 If only one valid reading 

was obtained, we used the single-reading values. We use 

the definitions suggested by the National Institutes of 

Health, National Heart, Lung and Blood Institute, and 

Obesity Education Initiative (2004) for pre-hypertension 

(systolic blood pressure of 120 mmHg or higher or 

diastolic blood pressure of 80 mmHg or higher) and 

hypertension (systolic pressure of 140 mmHg or higher, 

or diastolic pressure of 90 mmHg or higher; see also 

Chobanian et al., 2003). Because our self-reported 

hypertension measure does not distinguish between 

having hypertension and being treated for hypertension, 

a limitation of our study is our inability to distinguish 

between people who are being effectively treated for 

hypertension versus those with no hypertension.

DRIED BLOOD SPOT ASSAYS

Blood assays can potentially capture otherwise undetected 

health problems, such as undiagnosed diabetes, as well 

as longer-term risks of disease, such as cardiovascular 

disease (CVD). Interviewers asked participants if they 

could collect a small sample of their blood by pricking 

their fingers with a small instrument and then collecting 

enough blood to fill six small circles on a collection card. 

Respondents were offered an additional $25 incentive 

(on top of the incentive provided for completing the 

interview) for this blood sample collection. To avoid 

risks to participants, anyone who indicated that they 

had hemophilia or were taking anticoagulants or blood 

thinners was excluded from the blood spot collection. 

Among those interviewed and eligible for blood spot 

collection, the consent rate was over 90 percent.

Interviewers used a sterile, single-use lancet to prick the 

finger of the participant and collected up to six drops 

of blood on specimen paper, with one spot pretreated 

for the analysis of glycosylated hemoglobin (HbA1c). 

Interviewers allowed the blood spots to dry for 15 

minutes and then placed them in a foiled envelope with 

a desiccant packet. Interviewers mailed the samples once 

a week to a central location for proper freezer storage 

at – 20°C to – 30°C. Because the interviews for Section 

8 adults started about eight months after experimental 

7 We considered a reading as valid if diastolic blood pressure was over 

40 mmHg, the systolic blood pressure was over 60 mmHg, and the 

systolic was at least 10 points higher than the diastolic.
89 Chapter 3: Impacts on Adult and Youth Physical Health

and control interviews, more of the Section 8 assays were 

done in the later batches by the laboratory.8

HbA1c and diabetes: In addition to our survey 

questions about diabetes that were discussed above, 

we measured the concentration of HbA1c from the 

dried blood spot specimens. This assessment provides 

an integrated measure of average blood glucose levels 

over several months prior to blood collection without 

requiring the respondent to fast. The American Diabetes 

Association (2010) recently recommended guidelines for 

use of HbA1c to diagnose and monitor diabetes, with a 

proposed HbA1c threshold of 6.5 percent for diagnosis. 

In addition to our two self-reported measures of diabetes, 

we created two additional measures using the HbA1c 

assays: diabetes as indicated by an elevated HbA1c 

level (≥ 6.5 percent) and a combined survey and blood 

measure of diabetes, defined as having either an elevated 

HbA1c level or self-reporting either having had diabetes 

or having been treated for diabetes during the past year. 

The combined measure is defined only for respondents 

with both survey and dried blood spot information. 

Because the self-reported diabetes measure does not 

distinguish between having diabetes and being treated for 

diabetes, a limitation of our study is the inability to fully 

differentiate between well-controlled diabetes (defined as 

a diagnosed case with normal HbA1c concentrations due 

to medication or other factors) and no diabetes.

Hs-CRP: The dried blood spot specimens were also 

assayed for high-sensitivity C-reactive protein (hs-CRP), 

a biomarker of inflammation that has recently emerged 

as an important predictor of cardiovascular disease 

(McDade, 2003; Ridker et al., 2002, 2003; Arima et al., 

2008).9

3.5 LONG-TERM IMPACTS ON ADULT 

PHYSICAL HEALTH

SELF-RATED HEALTH STATUS

Exhibit 3.2 shows estimates for long-term effects on adult 

8 More details about the dried blood spot data collection protocol and 

laboratory assays and calibration are in technical appendices, forthcoming.

9 Additional details on the assays and recalibration of hs-CRP using the 

results of a validation study will be described in technical appendices, 

forthcoming.

physical health. Fifty-six percent of adults in the control 

group rate their health as good, very good, or excellent; 

the others rate their health as only fair or poor. There 

is little difference in self-rated health across randomly 

assigned mobility groups. The estimated intention-totreat effect (ITT) for the experimental group treatment 

is just 0.2 percentage points, with a standard error of 

2 percentage points. These estimates enable us to rule 

out an experimental ITT effect of any more than 4 

percentage points in either direction around zero. The 

top panel of Exhibit 3.3 shows that the distribution of 

adults across each of the different possible self-reported 

health categories (poor, fair, good, very good, and 

excellent) is similar for the treatment and control groups. 

As noted above, this health measure has the potential 

limitation of confounding the effects of MTO moves on 

health status with effects on the benchmarks that families 

use to think about what counts as good or excellent 

health.

OBESITY AND WAIST CIRCUMFERENCE

Although we do not see statistically significant 

experimental treatment effects on waist circumference, we 

estimate an experimental group ITT effect on average BMI 

of – 0.64, (p value, .07), which is equal to about 2 percent 

of the control group’s mean BMI (32.8). The experimental 

treatment had no detectable impact at long-term followup on the likelihood of having a BMI ≥ 30, although we 

observed significant differences between the experimental 

and control groups at more severe levels of obesity. 

Estimates show that those in the experimental group were 

4.6 percentage points less likely than control subjects to 

have a BMI of ≥ 35 (an effect that is equal to about 13 

percent of the control group’s prevalence of 35.1 percent), 

and they were 3.4 percentage points less likely to have a 

BMI of ≥ 40 (an effect equal to around 19 percent of the 

control group’s prevalence of 17.5 percent).

For the Section 8 group, impacts on extreme obesity are 

of a similar magnitude, but only the effect on BMI ≥ 35 

is statistically significant. The bottom panel of Exhibit 3.3 

shows the implied distribution of each treatment group by 

obesity category. There are no statistically significant impacts 

of either group membership on waist circumference. In 

results not presented here, we find significantly fewer 
90

Section 8 adults than control group (27 percent versus 33 

percent) with a waist circumference above 43 inches (the 

90th percentile for women based on the third National 

Health and Nutrition Examination Survey; Ford, Mokdad, 

and Giles, 2003). However, we do not detect any significant 

effects for the experimental group.

DIABETES AND HbA1c

Compared with the control group, the experimental 

group had statistically significantly lower levels of 

diabetes as measured by blood assays for HbA1c ≥ 6.5 

percent (ITT effect of – 5.2 percentage points versus 

a control mean of 20.4 percent; see Exhibit 3.2). 

They were also lower when diabetes is measured by a 

combination of blood sample results and self-reported 

data (ITT of – 3.4 percentage points, versus a control 

mean prevalence of 23.4 percent; p value, .073). From 

self-reports of ever having been diagnosed with diabetes 

by a doctor, we find a nearly significant ITT effect of – 

2.8 percentage points for the experimental group (control 

mean of 19.3 percent). Our analyses of self-reports of 

having had diabetes, currently taking medication for 

it, or having been treated for it during the past year 

showed a nonsignificant experimental ITT effect of – 2.4 

percentage points versus a control mean of 16 percent.10 

Exhibit 3.2 provides evidence of under-reporting with 

the self-reported diabetes measure, given that 16 percent 

of controls report having diabetes or being treated for 

diabetes, while over 20 percent have HbA1c of 6.5 

percent or higher as measured from blood samples.11

Adults in the Section 8 group were less likely than those 

in the control group to report that a doctor told them 

10 Our combined measure using self-reported and HbA1c data is 

restricted to individuals with information from both sources. Alternatively, if we were to include everyone who had either self-reported or 

HbA1c data, the ITT estimates are -.036 for the experimental group (p 

< .05) and -.011 for the Section 8 group (not statistically significant) 

compared with a control mean of .259.

11 There is even more under-reporting of uncontrolled diabetes. 

Exhibit 3.2 shows that 16 percent of controls report having diabetes or 

being treated for diabetes, 20 percent of controls have HbA1c of 6.5 

percent or higher, and 23 percent have either self-reported diabetes or 

treatment for diabetes or have HbA1c of 6.5 percent or higher. These 

figures imply that around 3 percent of the control group say they have 

diabetes or are being treated for it, but do not have HbA1c of 6.5 

percent or higher– these are presumably control group adults who are 

being treated for diabetes. So 13 percent of the control group says they 

have diabetes and have HbA1c of 6.5 percent or higher as measured by 

blood samples, equal to about two-thirds of the total share of controls 

(20 percent) who have HbA1c of 6.5 percent or higher.

they had diabetes (ITT of – 5.7 percentage points, which 

represents a 30 percent relative decline compared with 

the control group prevalence). Although we observe 

no significant increase within the Section 8 group of 

elevated HbA1c levels, we cannot statistically rule out 

that the impacts using the survey measures are different 

(at a statistically significant level) from the impacts 

estimated using HbA1c.12 In addition, the timing of the 

dried blood spot assays differed between the Section 8 

group and the control group; interviews with adults in 

the Section 8 group occurred, on average, two months 

after the control group interviews.

HYPERTENSION AND hs-CRP

We detect no statistically significant differences between 

the treatment and control groups on systolic or diastolic 

blood pressure, prehypertension, or hypertension. 

However, we observe a nearly significant difference of – 

4.2 percentage points (p value, .08) for the experimental 

group on hs-CRP levels >3 mg/L, relative to a control 

mean of 58.6 percent. Previous research suggests that 

the relative risk of a major coronary event may be twice 

as high for people with hs-CRP level of greater than 

3 mg/L, relative to those with a level of <1.0 mg/L 

(Pearson et al., 2003). For the Section 8 group, we do 

not observe any statistically significant effects of MTO 

on hypertension or hs-CRP.

ASTHMA

We detect no statistically significant effects on selfreported asthma for either treatment group. Some of 

our estimates have fairly wide 95 percent confidence 

intervals. For example, the Section 8 group ITT interval 

for having an asthma or wheezing attack during the past 

year ranges from – 9 to +1 percentage points, relative to 

a control mean of 29 percent. Thus, we cannot rule out 

moderate effects.

HEALTH LIMITATIONS AND 

CHRONIC PAIN

We observe no statistically significant difference across 

treatment groups on reports of health limitations, arthritis 

12 Note that the impact estimates using our survey measure of diabetes 

are similar regardless of whether we use the full set of respondents 

interviewed or restrict the sample to only respondents with HbA1c data 

(results not shown).
91 Chapter 3: Impacts on Adult and Youth Physical Health

or rheumatism, headaches, chronic pain, or bed days, 

with two exceptions. The experimental group reported 

fewer health limitations than the control group, and the 

Section 8 group reported a higher prevalence of arthritis 

or rheumatism. Whether this estimated adverse Section 

8 effect on arthritis / rheumatism represents a real MTO 

impact, a statistical fluke, or is because Section 8 adults are 

more likely to see a doctor and be diagnosed with arthritis 

or rheumatism is not known, although below we report 

results indicating that the Section 8 treatment did seem to 

improve people’s access to medical care.

EXHIBIT 3.2. ADULT PHYSICAL HEALTH OUTCOMES

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

GOOD OR BETTER HEALTH 

CURRENTLY 

(VERSUS FAIR OR POOR) [SR]

0.564 0.002 0.004 – 0.005 – 0.009 3,269

(0.020) (0.042) (0.027) (0.044)

OBESITY AND WAIST

CIRCUMFERENCE

Height, in feet [M, SR] 5.315 0.009 0.019 – 0.001 – 0.002 3,242

(0.010) (0.020) (0.013) (0.021)

Weight, in pounds [M, SR] 189.985 – 3.163 – 6.521 – 2.852 – 4.615 3,222

(2.077) (4.281) (2.915) (4.717)

Body Mass Index (BMI) [M, SR] 32.803 – 0.636~ – 1.311~ – 0.489 – 0.792 3,221

(0.351) (0.724) (0.491) (0.796)

Currently obese (BMI ≥ 30) [M, SR] 0.584 – 0.012 – 0.025 – 0.011 – 0.018 3,221

(0.022) (0.044) (0.029) (0.046)

BMI ≥ 35 [M, SR] 0.351 – 0.046* – 0.095* – 0.053* – 0.086* 3,221

(0.020) (0.042) (0.027) (0.043)

BMI ≥ 40 [M, SR] 0.175 – 0.034* – 0.071* – 0.029 – 0.048 3,221

(0.016) (0.032) (0.021) (0.034)

Waist circumference, in inches [M, SR] 40.868 – 0.273 – 0.563 – 0.543 – 0.867 3,177

(0.302) (0.623) (0.401) (0.641)

DIABETES

Ever told by a doctor they had diabetes 

or high blood sugar [SR]

0.193 – 0.028~ – 0.058~ – 0.057* – 0.091* 3,251

(0.016) (0.033) (0.021) (0.033)

Had diabetes or treated for it during the 

past year [SR]

0.160 – 0.024 – 0.049 – 0.061* – 0.098* 3,251

(0.015) (0.030) (0.018) (0.029)

HbA1c test detected diabetes (HbA1c ≥

6.5%) [DBS]

0.204 – 0.052* – 0.108* – 0.011 – 0.017 2,737

(0.018) (0.038) (0.024) (0.038)

SMOKING AND DRINKING

Roughly one-third of adults in the control group 

reported that they currently smoked, and about 

44 percent reported having had an alcoholic drink 

during the past month. The estimate points toward 

greater smoking for the experimental group, but is not 

statistically significant. Respondents in the experimental 

group reported slightly more days in which they had an 

alcoholic drink. The frequency of alcohol consumption 

is low across all three groups: less than two days a 

month, on average. (In Chapter 4, we present analyses of 

dependence on alcohol or drugs for the adult sample, and 

in Chapter 6, we present results for youth smoking and 

alcohol use.)
92

EXHIBIT 3.2. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

DIABETES (CONTINUED)

HbA1c test detected diabetes or had or 

treated for diabetes during the past year 

[DBS, SR]

0.234 – 0.034~ – 0.071~ – 0.008 – 0.013 2,732

(0.019) (0.040) (0.025) (0.039)

HYPERTENSION AND RISK OF

CARDIOVASCULAR DISEASE

Systolic blood pressure, mm Hg [M] 123.482 0.629 1.292 0.456 0.723 3,102

(0.877) (1.802) (1.121) (1.778)

Diastolic blood pressure, mm Hg [M] 82.600 0.551 1.132 0.364 0.577 3,102

(0.577) (1.186) (0.744) (1.180)

Currently has high blood pressure 

(systolic ≥ 140 mm Hg or diastolic ≥ 90 

mm Hg) [M]

0.315 0.007 0.015 – 0.026 – 0.041 3,102

(0.020) (0.042) (0.027) (0.043)

Currently has prehypertension or higher 

(systolic ≥ 120 mm Hg or diastolic ≥ 80 

mm Hg) [M]

0.616 0.019 0.039 0.020 0.032 3,102

(0.021) (0.043) (0.027) (0.043)

Ever prescribed high blood pressure 

medicine [SR]

0.382 0.013 0.027 0.018 0.029 3,261

(0.019) (0.040) (0.026) (0.042)

Currently has high blood pressure or 

taking blood pressure medicine [M, SR]

0.479 0.009 0.018 – 0.012 – 0.019 3,158

(0.021) (0.042) (0.027) (0.043)

High sensitivity C-reaction Protein at 

high risk level (> 3 mg/L) [DBS]

0.586 – 0.042~ – 0.087~ 0.010 0.016 2,693

(0.024) (0.049) (0.030) (0.048)

ASTHMA

Has ever been told by a doctor that they 

had asthma [SR]

0.275 – 0.022 – 0.044 – 0.022 – 0.036 3,268

(0.019) (0.039) (0.025) (0.040)

Asthma or wheezing attack during the 

past year [SR]

0.293 – 0.018 – 0.038 – 0.042 – 0.066 3,267

(0.020) (0.040) (0.026) (0.041)

LIMITATIONS AND CHRONIC PAIN

Health limits respondent's ability to climb 

several flights of stairs or lift or carry 

groceries [SR]

0.510 – 0.048* – 0.100* – 0.023 – 0.038 3,270

(0.021) (0.043) (0.028) (0.045)

Ever had arthritis or rheumatism [SR] 0.304 0.017 0.036 0.051* 0.081* 3,259

(0.019) (0.039) (0.025) (0.040)

Ever had severe or frequent headaches 

[SR]

0.410 – 0.006 – 0.013 – 0.039 – 0.063 3,267

(0.021) (0.044) (0.028) (0.045)

Had severe or frequent headaches in 

past year [SR]

0.233 0.001 0.002 – 0.032 – 0.052 3,267

(0.019) (0.038) (0.025) (0.039)

Ever had back, neck, or other chronic 

pain [SR]

0.440 – 0.024 – 0.050 – 0.010 – 0.016 3,266

(0.021) (0.043) (0.028) (0.045)
93 Chapter 3: Impacts on Adult and Youth Physical Health

EXHIBIT 3.2. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

LIMITATIONS AND CHRONIC PAIN (CONTINUED)

Back, neck, or other chronic pain in the 

past year [SR]

0.295 – 0.024 – 0.050 – 0.003 – 0.004 3,265

(0.019) (0.040) (0.026) (0.042)

Number of days injury/illness kept adult 

in bed more than half the day [SR]

9.698 0.573 1.188 2.024 3.215 3,234

(1.575) (3.266) (2.251) (3.575)

SMOKING AND DRINKING

Currently a smoker [SR] 0.341 0.020 0.042 – 0.007 – 0.011 3,235

(0.020) (0.042) (0.027) (0.043)

Ever drank an alcoholic beverage [SR] 0.602 0.046* 0.095* – 0.037 – 0.058 3,239

(0.021) (0.043) (0.028) (0.044)

Had alcoholic beverage in past 30 days 

[SR]

0.435 0.024 0.049 – 0.013 – 0.021 3,238

(0.021) (0.044) (0.028) (0.045)

Fraction of days adult had alcoholic drink 

in past 30 days [SR]

0.053 0.011* 0.022* 0.004 0.006 3,238

(0.005) (0.010) (0.006) (0.010)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working).

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: For obesity inputs (height and weight), only a very small percent of the sample self-reported their height or weight. BMI is measured as weight in 

kilograms divided by height in meters squared. For diastolic and systolic blood pressure, data are the average of two readings, if available; otherwise, data are from 

one reading.
94

EXHIBIT 3.3. ADULT SELF-RATED HEALTH AND BMI CATEGORIES BY TREATMENT GROUP

Fair 

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

A. Self-Rated Health of Adults by Treatment Group

Excellent Very Good

Control Share

Adjusted Experimental

Adjusted Section 8

Good Poor

HEALTH CATEGORY

SHARE OF GROUP

0.40

0.35

0.30

0.20

0.25

0.10

0.15

0.00

0.05

B. BMI Categories for Adults by Treatment Group

Underweight/

normal

Overweight

but not obese

BMI ≥ 30 

and < 35

BMI ≥ 35 

and < 40

BMI ≥ 40

Control Share

Adjusted Experimental

Adjusted Section 8

BMI CATEGORY

0.30

0.20

0.25

0.10

0.15

0.00

0.05

SHARE OF GROUP

Notes: Share controls represents the unadjusted control mean. Adjusted experimental share represents the control mean plus the experimental ITT effect. 

Adjusted Section 8 share represents the control mean plus the Section 8 ITT effect. For obesity inputs (height and weight), only a very small percent of the sample 

self-reported their height or weight. BMI is measured as weight in kilograms divided by height in meters squared. ITT = Intention-to-Treat or estimated impact of 

being offered an MTO housing voucher. 

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates and field release, weighted, 

and clustering on family. Youth impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and technical appendices (forthcoming) 

for details. 

Data source and sample: Adult long-term survey. All interviewed adults. 
95 Chapter 3: Impacts on Adult and Youth Physical Health

3.6 LONG-TERM IMPACTS ON THE 

PHYSICAL HEALTH OF YOUTH AND 

GROWN CHILDREN

YOUTH AGES 10–20

Exhibit 3.4 shows the estimated effects of MTO on the 

physical health of youth.

Self-rated health status: Eighty-six percent of female 

youth and 90 percent of male youth in the control group 

rated their health as good, very good, or excellent. We 

detected no statistically significant differences between 

the treatment groups and control groups on this measure. 

As Exhibits 3.5 and 3.6 show, the distribution of 

categorical responses across treatment groups is similar 

for both females and males.

Obesity: Forty-five percent of youth in the control 

group met the IOTF criteria for overweight (designed 

to correspond to an adult BMI ≥25), and 23 percent 

met the criteria for obesity (designed to correspond to 

adult BMI ≥30). Control group females (27.4 percent) 

exhibited higher levels of obesity than do control 

group males (18.7 percent). There were no statistically 

significant effects of either treatment group overall or 

by gender on these outcomes, although we cannot rule 

out modest effects. For example, for female youth, we 

are only 95 percent confident that the experimental 

impact on being overweight is within the range of – 3.3 

to +7.7 percentage points, and we are only 95 percent 

confident that the impact of obesity is between – 7.7 to 

+2.1 percentage points (that is, we cannot rule out that 

the treatment reduces obesity by as much as 28 percent 

relative to the control prevalence nor that it increases 

obesity by as much as 8 percent of the control mean).

Asthma: Nineteen percent of youth in the control group 

reported an asthma or wheezing attack during the past 

year, and 7 percent reported that wheezing limited their 

activities or led them to miss school or work. We find 

no statistically significant effects of either treatment on 

asthma and wheezing attacks, overall or by gender.

Accidents, injuries, chronic pain, and serious illness:

We detect no significant differences between the 

treatment and control groups on accidents and injuries in 

the past year, or on the prevalence of chronic pain, bad 

headaches, or serious illnesses such as diabetes or stomach 

problems. More than one-third of youth ages 13–20 

in the control group reported chronic pain or frequent 

headaches. Although not statistically significant, MTO 

moves tend to reduce the prevalence of accidents and 

injuries for female youth relative to those in the control 

group. However, they tend to increase the prevalence for 

male youth, as we found in the interim study.

Dental health: We detect no differences across treatment 

groups in terms of whether youth report that their teeth 

are in “very good” or “excellent” condition rather than 

“good,” “fair,” or “poor” condition. We detect no impacts 

by gender on this measure. Across the full range of 

cut-points, we generally do not detect effects. However, 

a statistically significantly higher proportion of female 

youth in the experimental group reported that their teeth 

were in excellent condition than do controls.
96

EXHIBIT 3.4. YOUTH PHYSICAL HEALTH OUTCOMES

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

GENERAL HEALTH

Good or better health currently (versus fair or poor) [SR]

All 0.883 0.005 0.009 0.000 – 0.001 5,100

(0.013) (0.027) (0.015) (0.022)

Female 0.862 0.003 0.007 0.006 0.010 2,600

(0.019) (0.038) (0.021) (0.034)

Male 0.903 0.006 0.012 – 0.007 – 0.010 2,500

(0.016) (0.035) (0.019) (0.027)

ASTHMA

Asthma or wheezing attack during the past year [SR]

All 0.190 0.006 0.013 – 0.013 – 0.019 5,092

(0.015) (0.032) (0.016) (0.024)

Female 0.206 – 0.009 – 0.017 – 0.021 – 0.032 2,595

(0.021) (0.043) (0.023) (0.037)

Male 0.174 0.021 0.045 – 0.006 – 0.008 2,497

(0.020) (0.043) (0.022) (0.031)

Wheezing caused limited activities or missed school or work in the past year [SR]

All 0.071 0.003 0.007 – 0.012 – 0.018 5,091

(0.010) (0.021) (0.010) (0.015)

Female 0.079 – 0.001 – 0.002 – 0.008 – 0.012 2,594

(0.014) (0.028) (0.015) (0.023)

Male 0.064 0.008 0.017 – 0.017 – 0.025 2,497

(0.014) (0.029) (0.013) (0.018)

OBESITY

Currently overweight [M, SR]

All 0.449 0.015 0.031 0.009 0.013 5,034

(0.020) (0.042) (0.022) (0.032)

Female 0.482 0.022 0.045 0.039 0.061 2,560

(0.028) (0.056) (0.030) (0.048)

Male 0.418 0.008 0.016 – 0.022 – 0.031 2,474

(0.027) (0.059) (0.029) (0.042)

Currently obese [M, SR]

All 0.229 – 0.010 – 0.022 – 0.010 – 0.014 5,034

(0.017) (0.035) (0.018) (0.028)

Female 0.274 – 0.028 – 0.057 – 0.020 – 0.031 2,560

(0.025) (0.051) (0.026) (0.041)

Male 0.187 0.007 0.016 0.000 0.000 2,474

(0.021) (0.045) (0.023) (0.033)
97 Chapter 3: Impacts on Adult and Youth Physical Health

EXHIBIT 3.4. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL 

OUTCOME ITT TOT ITT TOT N

ACCIDENTS AND INJURIES

Had accidents or injuries requiring medical attention in the past year [SR]

All 0.178 0.006 0.013 0.008 0.012 5,097

(0.014) (0.030) (0.016) (0.024)

Female 0.164 – 0.023 – 0.046 – 0.024 – 0.037 2,597

(0.018) (0.037) (0.020) (0.031)

Male 0.192 0.035 0.076 0.039 0.056 2,500

(0.022) (0.047) (0.024) (0.034)

Had serious accidents or injuries that required medical attention or limited activities in the past year [SR]

All 0.273 0.001 0.002 0.006 0.009 5,092

(0.017) (0.035) (0.019) (0.028)

Female 0.233 – 0.024 – 0.049 – 0.011 – 0.017 2,595

(0.022) (0.044) (0.024) (0.038)

Male 0.309 0.026 0.056 0.022 0.031 2,497

(0.025) (0.054) (0.028) (0.040)

Had a nonsports accident or injury requiring medical attention in the past year [SR]

All 0.170 0.011 0.024 0.016 0.024 5,074

(0.014) (0.030) (0.016) (0.024)

Female 0.176 – 0.015 – 0.030 0.001 0.002 2,584

(0.020) (0.039) (0.022) (0.034)

Male 0.165 0.038~ 0.082~ 0.030 0.042 2,490

(0.020) (0.044) (0.023) (0.032)

CHRONIC PAIN AND SERIOUS ILLNESS 

AMONG YOUTH 13–20

Have ever had chronic back, neck, or other pain or frequent/very bad headaches [SR]

All 0.371 – 0.002 – 0.004 0.017 0.025 4,628

(0.019) (0.039) (0.021) (0.032)

Female 0.432 – 0.019 – 0.039 – 0.010 – 0.015 2,362

(0.027) (0.054) (0.031) (0.048)

Male 0.313 0.015 0.033 0.043 0.062 2,266

(0.027) (0.057) (0.030) (0.042)

Have ever had serious illness like diabetes, high blood sugar, and serious stomach problem [SR]

All 0.044 – 0.013 – 0.026 – 0.010 – 0.015 4,627

(0.008) (0.016) (0.009) (0.014)

Female 0.056 – 0.016 – 0.033 – 0.020 – 0.031 2,359

(0.012) (0.025) (0.014) (0.021)

Male 0.033 – 0.009 – 0.019 0.000 0.001 2,268

(0.009) (0.020) (0.012) (0.017)
98

EXHIBIT 3.4. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL 

OUTCOME ITT TOT ITT TOT N

DENTAL 

Teeth are in very good or better condition [SR]

All 0.400 – 0.004 – 0.008 0.000 0.000 5,095

(0.019) (0.039) (0.020) (0.030)

Female 0.379 0.021 0.042 0.026 0.040 2,596

(0.025) (0.051) (0.028) (0.043)

Male 0.419 – 0.028 – 0.060 – 0.025 – 0.036 2,499

(0.026) (0.057) (0.029) (0.041)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working).

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: Overweight and obesity are defined according to the International Obesity Task Force.

SUBSET OF YOUTH AGES 15–20

Supplemental Exhibit 3.1 presents impacts on youth ages 

15–20 at the end of 2007. This allows us to compare 

current results with interim results in 2001 reported 

by Kling, Liebman, and Katz (2007). This comparison 

holds constant the ages at which we measure the health 

outcomes, and examines whether the impact is different 

for children who were relatively younger at the time of 

MTO random assignment (than the sample analyzed 

by Kling, Liebman, and Katz) and thus had spent a 

relatively larger share of their lives in lower-poverty 

neighborhoods before we measured health outcomes. In 

principle, this comparison will confound differences in 

how MTO effects vary by someone’s age at baseline with 

differences in how effects vary by cohort and the calendar 

years in which the health outcomes are measured.

With that last caveat in mind, we detect no effects on 

overall health or self-reported asthma among this older 

age group, but the results indicate less serious or chronic 

illness among youth in the experimental group (p value, 

.03), more accidents and injuries requiring medical 

attention for Section 8 males (p value, .03), and hint at 

possibly lower obesity (ITT of – 4.9 percentage points, 

p value, .10) for female youth in the experimental group 

compared with female controls. Estimates for the smaller 

sample of youth ages 10–14 (not shown) are imprecise, 

with some estimates having the opposite sign from the 

older youth.

GROWN CHILDREN AGES 21–30

Exhibit 3.7 presents our results on the physical health 

of grown children. Parents in the control group reported 

that 4.5 percent of their grown children had physical 

health problems that kept them from normal activities, 

and 7.9 percent had a chronic health problem, such 

as cancer or a heart problem. We do not detect any 

significant effects for the experimental group or for 

females in either group. However, we do observe a 

significantly higher prevalence of health limitations 

among Section 8 males relative to males in the control 

group (ITT of 4.9 percent relative to a control mean of 

4.3 percent).
99 Chapter 3: Impacts on Adult and Youth Physical Health

EXHIBIT 3.5. FEMALE YOUTH SELF-RATED HEALTH AND BMI CATEGORIES BY TREATMENT GROUP

Excellent Very Good Good Fair/Poor

Control Share

Adjusted Experimental

Adjusted Section 8

HEALTH CATEGORY

0.40

0.35

0.30

0.20

0.25

0.10

0.15

0.00

0.05

A. Female Youth Self-Rated Health by Treatment Group

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

SHARE OF GROUP

Underweight/

normal

Overweight

but not obese

Obese

Control Share

Adjusted Experimental

Adjusted Section 8

BMI CATEGORY

SHARE OF GROUP

0.70

0.60

0.40

0.50

0.20

0.30

0.00

0.10

B. Female Youth BMI Categories by Treatment Group 

Notes: Share controls represents the unadjusted control mean. Adjusted experimental share represents the control mean plus the experimental ITT effect. Adjusted 

Section 8 share represents the control mean plus the Section 8 ITT effect. For obesity inputs (height and weight), only a very small percent of the sample selfreported their height or weight. BMI is measured as weight in kilograms divided by height in meters squared. ITT = Intention-to-Treat or estimated impact of being 

offered an MTO housing voucher. 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates and field release, weighted, 

and clustering on family. Youth impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and technical appendices (forthcoming) 

for details. 
100

EXHIBIT 3.6. MALE YOUTH SELF-RATED HEALTH AND BMI CATEGORIES BY TREATMENT GROUP

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

A. Male Youth Self-Rated Health by Treatment Group

Excellent Very Good Good Fair/Poor

Control Share

Adjusted Experimental

Adjusted Section 8

HEALTH CATEGORY

SHARE OF GROUP

0.40

0.35

0.30

0.20

0.25

0.10

0.15

0.00

0.05

B. Male Youth BMI Categories by Treatment Group

Underweight/

normal

Overweight

but not obese

Obese

Control Share

Adjusted Experimental

Adjusted Section 8

BMI CATEGORY

0.70

0.60

0.40

0.50

0.20

0.30

0.00

0.10

SHARE OF GROUP

Notes: Share controls represents the unadjusted control mean. Adjusted experimental share represents the control mean plus the experimental ITT effect. Adjusted 

Section 8 share represents the control mean plus the Section 8 ITT effect. For obesity inputs (height and weight), only a very small percent of the sample selfreported their height or weight. BMI is measured as weight in kilograms divided by height in meters squared. ITT = Intention-to-Treat or estimated impact of being 

offered an MTO housing voucher. 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates and field release, weighted, 

and clustering on family. Youth impacts by gender were estimated as an interaction with treatment status. See Section 1.3 and technical appendices (forthcoming) 

for details. 
101 Chapter 3: Impacts on Adult and Youth Physical Health

EXHIBIT 3.7. PARENT-REPORTED PHYSICAL HEALTH OUTCOMES OF GROWN CHILDREN AGES 21–30

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

PHYSICAL LIMITATIONS

Physical health problem keeps from normal activities like walking, housework, or working [PR]

All 0.045 0.014 0.031 0.026~ 0.047~ 3,096

(0.010) (0.023) (0.014) (0.025)

Female 0.048 0.013 0.029 0.001 0.001 1,525

(0.014) (0.032) (0.018) (0.031)

Male 0.043 0.014 0.034 0.049* 0.093* 1,571

(0.014) (0.034) (0.022) (0.041)

CHRONIC HEALTH PROBLEM

Has a chronic physical health problem such as cancer, a heart problem, or any other serious health problem [PR]

All 0.079 0.003 0.006 – 0.017 – 0.030 3,096

(0.013) (0.030) (0.015) (0.028)

Female 0.080 0.008 0.018 – 0.023 – 0.040 1,526

(0.018) (0.040) (0.021) (0.036)

Male 0.078 – 0.003 – 0.006 – 0.011 – 0.021 1,570

(0.017) (0.039) (0.020) (0.039)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working).

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. Grown children (who were under age 18 at baseline and over age 20 as of December 31, 2007) of interviewed 

adults.
102

3.7 IMPACTS ON HYPOTHESIZED 

MEDIATORS OF HEALTH

PHYSICAL ENVIRONMENT

Exhibits 2.2 through 2.9 in Chapter 2 showed 

statistically significant impacts on a range of housing 

and neighborhood conditions that could affect health. 

Adults in the experimental group rated the condition 

of their housing more favorably than did control 

subjects, and both treatment groups reported fewer 

problems with vermin (a problem that can aggravate 

asthma) and peeling paint or plaster, as well as greater 

satisfaction with their neighborhoods than their control 

group counterparts. Interviewers’ observations of the 

neighborhoods suggest persons in the experimental group 

lived in neighborhoods with less litter than did control 

subjects, which was also statistically significant.

EXERCISE AND NUTRITION

Supplemental Exhibit 3.2 presents estimated effects of 

the MTO treatments on adult health behaviors related to 

exercise, television viewing, sleeping, and consumption 

of fruits and vegetables, soda, and sweets. We detect no 

statistically significant impacts on these mediators, with 

the exceptions that adults in the experimental group were 

4.6 percentage points more likely to exercise vigorously at 

least once a week compared with controls, and adults in 

the Section 8 group were 4.8 percentage points less likely 

than control subjects to report long sleep (defined as 

>10 hours in the previous night). This is a large change 

compared with the control mean of 9.9 percent. There 

is no consistent pattern in the effects on healthy eating. 

In results not shown, we looked at effects for different 

cut points on the food consumption questions. In the 

rare instances that we detect differences across groups, 

the effects tend to be toward less healthy eating for the 

experimental group relative to the control group.

Supplemental Exhibit 3.3 shows little evidence of a clear 

effect of moving to different neighborhoods on youth 

exercise levels, eating habits, and sleep patterns. To the 

extent to which any of these impacts are statistically 

robust at 90 percent significance, they point in 

contradictory directions. For example, compared with 

those in the control group, youth in the experimental 

group may be somewhat less likely to eat fruits and 

vegetables four times per week (a nonsignificant effect 

suggesting less healthy eating), but male youth may 

be less likely to eat sweets four times per week (a 

nonsignificant effect suggesting more healthy eating).

ACCESS TO CARE

Supplemental Exhibit 3.4 shows how MTO affects access 

to health care for adults (top panel) and youth (bottom 

panel). Although there are no statistically significant 

effects of the experimental treatment on adult access to 

health care, we see some signs of improved access for 

Section 8 adults, relative to control adults. For example, 

the Section 8 ITT effect on whether the adult and his 

or her children have health insurance is 3.5 percentage 

points (p value, .096), relative to a control mean of 82.7 

percent. The Section 8 group did not differ significantly 

from the control group in terms of whether they got 

medical care when needed. However, they were less likely 

to report not receiving health care during the past 12 

months specifically because they could not afford it (ITT 

of – 2.6 percentage points, versus a control mean of 6.1 

percent). For female youth, being in the experimental 

group seems to have lessened the likelihood of receiving 

a routine physical exam in the past year. Even though 

most of the estimates on access to health care are not 

statistically significant for adults or youth, our statistical 

power is somewhat limited, and so the 95 percent 

confidence interval of some of the ITTs encompasses 

moderate-sized effects.

SOCIAL SUPPORTS AND SOCIAL 

ISOLATION

The results presented in Chapter 2 (and Supplemental 

Exhibits 2.4 and 2.5) suggest that the experimental group 

adults could have increased exposure to more affluent 

peers, but that youth may be more socially isolated as a 

result of their move. Specifically, for adults, assignment 

to the experimental group rather than control group 

did not change the likelihood of having three or more 

friends, but did increase the share of adults who reported 

that they had a friend who was a college graduate. For 

youth, those assigned to the experimental group were 

more likely than controls to report they had no close 

friends.
103 Chapter 3: Impacts on Adult and Youth Physical Health

SAFETY AND STRESS

Exhibit 2.10 suggests that adults in both treatment 

groups feel safer in their current neighborhoods than do 

control subjects. They are also less likely to have seen 

people selling or using drugs in their neighborhood 

in the past month. Similarly, female youth assigned to 

the treatment groups rather than to the control group 

reported improved neighborhood safety and less crime 

victimization, and those assigned to the experimental 

group reported less unwanted sexual attention. In 

contrast, there is no statistically significant effect on 

safety for male youth. In fact, the two effects with 

borderline significance (p values < .10) for males 

in the Section 8 group indicate that they are less 

safe and experience more gang activity in their new 

neighborhoods and schools compared with control group 

males.

3.8 INTERPRETATION OF PHYSICAL 

HEALTH RESULTS

We have examined the effects of MTO on the health 

outcomes of adults, youth, and grown children. For 

adults, our analyses of the long-term survey data and 

biomarkers suggest somewhat better health among the 

experimental group adults relative to control subjects in 

terms of fewer physical limitations and lower prevalence 

of extreme obesity and diabetes. We also observe higher 

levels of drinking for the experimental group adults 

compared with control subjects.13 We do not detect any 

statistically significant effects on other health outcomes, 

such as self-rated health, hypertension, asthma, or 

chronic pain. In interpreting these null effects, it is 

important to bear in mind the size of effects that can 

be ruled out, which varies depending on the measure. 

For some measures, our 95 percent confidence intervals 

do not allow us to rule out moderately sized impacts in 

either direction (toward better or worse health as a result 

of MTO moves).

Whereas at the interim evaluation, four to seven years 

after random assignment, results revealed lower obesity 

13 The control group’s reports of ever drinking alcohol (60 percent) 

and drinking in the past month (44 percent) are low but consistent 

with published rates for minorities and for people with lower levels 

of education (for example, see Substance Abuse and Mental Health 

Services Administration, 2010).

(BMI ≥ 30) for adults in the experimental group relative 

to the control group (Kling, Liebman, and Katz, 2007), 

results at 10–15 years show statistically significant 

experimental group impacts only at more severe levels 

of obesity (BMI ≥ 35 and BMI ≥ 40). This may be 

because obesity is now so common across the MTO adult 

sample—fully 58 percent of adults in the control group 

were obese at the time of our long-term data collection, 

whereas 47 percent of controls were obese at the time of 

the interim follow-up (Orr et al., 2003). We know that 

obesity is associated with other chronic health problems, 

including heart disease, cancer, and, in particular, 

diabetes. These comorbidities were not measured at 

interim, but the blood spots collected as part of our 

long-term study now enable us to confirm that reducing 

extreme obesity is accompanied by reductions in the 

prevalence of diabetes as well.

Our ability to physically measure a variety of health 

outcomes as part of the long-term study is an important 

enhancement over previous waves of MTO research. 

In general, we might prefer measured health outcomes 

to self-reported outcomes because of the possibility of 

self-report error. In the case of MTO, we might also 

be concerned that neighborhoods could affect selfreport error. One issue is that access to doctors might 

vary across neighborhoods and lead to differences in 

diagnosis. In part to address this problem, we collected 

biomarker data and for self-reported measures such as 

asthma and injuries. We also asked respondents about 

symptoms rather than simply medical diagnoses in an 

effort to minimize any potential bias. Another concern 

with self-reports is that neighborhoods could affect one’s 

frame of reference or expectations regarding one’s health. 

In theory, a person’s rating of her health status may 

reflect both her objective health status and the health 

level she could expect to achieve (Adams and White, 

2006). Although adults in the experimental group are 

more likely than control subjects to report having a close 

friend who is a college graduate, it seems unlikely that 

these types of changes in peer networks are large enough 

to explain the lack of differences observed between 

treatment and control groups on self-rated health.

The possibility of differing mortality or other healthrelated attrition across groups is another source of 
104

potential bias. We think it is unlikely that mortality 

affects our impact estimates because our survey response 

rates are high, our extensive tracking efforts reveal 

similar rates of mortality across the treatment groups, 

and interviewers traveled to participants rather than 

requiring participants to travel to them (making it easier 

for participants with health limitations to participate in 

the follow-up).

In our analyses of health outcomes for youth ages 

10–20, we do not detect statistically significant effects 

on self-rated health, asthma, obesity, injuries, chronic 

pain, serious illness, or very good dental health. Echoing 

the interim results, analyses restricted to only the older 

youth (ages 15–20) suggest some beneficial effects for the 

females in the experimental group and negative effects 

for the males in the Section 8 group, although these 

results should be interpreted with caution, as some of the 

results are in the opposite direction of those found for 

youth ages 10–14. In our analyses of parental reports on 

grown children who are now ages 21–30, we observe no 

detectable effects on chronic health problems of moving 

to a lower-poverty neighborhood, but we do observe a 

higher rate of health limitations for males in the Section 

8 group relative to control-group males.

We find little effect on most health mediators, except for 

safety and stress, which makes us think that safety and 

stress could potentially be the key mechanisms for the 

effects on obesity and diabetes that we observe for adults. 

At the interim report, we saw various effects on exercise 

and eating of fruits and vegetables of moving to lowerpoverty neighborhoods, whereas at the final evaluation 

we only see an impact on vigorous exercise for the 

experimental group. Of note, the wording and response 

categories on some of the exercise and diet questions 

differ between the interim and final surveys.14

As with the adult health mediators, we detect few 

differences on the eating habits, exercise, or sedentary 

14 At interim, we asked adults and youth, “In a typical week, how 

many days do you eat at least some green vegetables or fruit?” At final, 

we asked adults, “How many times do you eat fruit and vegetables other 

than french fries or potato chips” (from the National Longitudinal 

Survey of Youth 1997). The possible responses were “I do not typically 

eat fruit,” “1 to 3 times per week,” “4 to 6 times per week,” “1 time per 

day,” “2 times per day,” “3 times per day,” and “4 times or more per 

day.”

behaviors of youth across treatment groups. Female youth 

in the treatment groups felt greater safety relative to 

control subjects, and the experimental group experienced 

less psychological distress. For male youth in the Section 

8 group, some of our estimates point toward lower 

perceptions of safety. (See Chapter 2 for the safety results 

and Chapter 4 for the mental health results.)

In conclusion, the effects of MTO on adult or youth 

health outcomes are mixed, with relatively few 

statistically significant health impacts across a range 

of outcomes. However, the impacts that we do observe 

for adults involve some of the most important health 

outcomes—specifically, obesity and diabetes—and as 

such speak to the potential public health importance of 

these findings.
105 Chapter 3: Impacts on Adult and Youth Physical Health

SUPPLEMENTAL EXHIBIT 3.1. PHYSICAL HEALTH OUTCOMES OF YOUTH AGES 15–20

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

GENERAL HEALTH

Good or better health currently (versus fair or poor) [SR]

All 0.871 0.010 0.020 0.008 0.012 3,621

(0.016) (0.033) (0.017) (0.026)

Female 0.851 0.015 0.031 0.016 0.025 1,845

(0.022) (0.045) (0.025) (0.039)

Male 0.890 0.004 0.009 0.001 0.001 1,776

(0.020) (0.043) (0.023) (0.033)

ASTHMA

Asthma or wheezing attack during the past year [SR]

All 0.187 0.000 0.000 – 0.017 – 0.025 3,616

(0.018) (0.037) (0.019) (0.029)

Female 0.217 – 0.025 – 0.051 – 0.031 – 0.048 1,842

(0.025) (0.050) (0.027) (0.043)

Male 0.159 0.025 0.054 – 0.003 – 0.005 1,774

(0.024) (0.051) (0.026) (0.038)

Wheezing caused limited activities or missed school/work for youth in past year [SR]

All 0.067 – 0.003 – 0.006 – 0.018 – 0.027 3,616

(0.012) (0.024) (0.011) (0.017)

Female 0.077 – 0.009 – 0.019 – 0.013 – 0.020 1,842

(0.016) (0.032) (0.017) (0.026)

Male 0.057 0.004 0.009 – 0.023 – 0.033 1,774

(0.016) (0.034) (0.015) (0.021)

OBESITY

Currently overweight [M, SR]

All 0.463 0.012 0.026 – 0.001 – 0.002 3,580

(0.023) (0.049) (0.026) (0.039)

Female 0.495 0.025 0.050 0.031 0.047 1,819

(0.032) (0.065) (0.036) (0.056)

Male 0.432 0.000 0.001 – 0.032 – 0.047 1761

(0.032) (0.070) (0.035) (0.051)

Currently obese [M, SR]

All 0.246 – 0.016 – 0.034 – 0.013 – 0.020 3,580

(0.020) (0.041) (0.022) (0.033)

Female 0.301 – 0.049 – 0.098 – 0.032 – 0.050 1,819

(0.030) (0.060) (0.032) (0.049)

Male 0.193 0.017 0.036 0.005 0.007 1,761

(0.025) (0.054) (0.028) (0.040)
106

SUPPLEMENTAL EXHIBIT 3.1. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

ACCIDENTS AND INJURIES

Had accidents/injuries requiring medical attention in the past year [SR]

All 0.166 0.012 0.024 0.025 0.038 3,618

(0.017) (0.035) (0.019) (0.028)

Female 0.156 – 0.022 – 0.045 – 0.014 – 0.022 1,842

(0.022) (0.044) (0.024) (0.037)

Male 0.175 0.045~ 0.097~ 0.064* 0.094* 1,776

(0.025) (0.054) (0.028) (0.041)

Had serious accidents/injuries that required medical attention or limited activities in the past year [SR]

All 0.247 0.014 0.029 0.034 0.050 3,615

(0.019) (0.040) (0.022) (0.034)

Female 0.210 – 0.015 – 0.031 0.020 0.032 1,841

(0.025) (0.051) (0.029) (0.045)

Male 0.282 0.044 0.094 0.046 0.066 1,774

(0.029) (0.062) (0.032) (0.046)

Had a non-sports accident/injury requiring medical attention in the past year [SR]

All 0.159 0.015 0.032 0.035~ 0.053~ 3,603

(0.017) (0.034) (0.019) (0.029)

Female 0.164 – 0.006 – 0.012 0.020 0.031 1,832

(0.023) (0.046) (0.026) (0.040)

Male 0.153 0.036 0.078 0.050~ 0.072~ 1,771

(0.023) (0.050) (0.027) (0.039)

CHRONIC PAIN AND SERIOUS ILLNESS

Ever had chronic back, neck or other pain or frequent/very bad headaches [SR]

All 0.366 – 0.001 – 0.003 0.011 0.016 3,612

(0.021) (0.044) (0.024) (0.036)

Female 0.432 – 0.028 – 0.057 – 0.027 – 0.043 1,839

(0.031) (0.062) (0.035) (0.054)

Male 0.305 0.025 0.054 0.049 0.070 1,773

(0.029) (0.063) (0.033) (0.047)

Ever had serious illness like diabetes, high blood sugar, and serious stomach problem [SR]

All 0.051 – 0.021* – 0.043* – 0.018 – 0.026 3,610

(0.009) (0.019) (0.011) (0.016)

Female 0.066 – 0.027~ – 0.054~ – 0.030~ – 0.047~ 1,836

(0.015) (0.029) (0.016) (0.025)

Male 0.037 – 0.015 – 0.031 – 0.005 – 0.007 1,774

(0.011) (0.024) (0.014) (0.020)
107 Chapter 3: Impacts on Adult and Youth Physical Health

SUPPLEMENTAL EXHIBIT 3.1. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

DENTAL

Teeth are in very good or better condition [SR]

All 0.390 0.004 0.009 0.023 0.034 3,617

(0.022) (0.046) (0.024) (0.036)

Female 0.374 0.018 0.036 0.035 0.055 1,842

(0.030) (0.060) (0.033) (0.052)

Male 0.404 – 0.010 – 0.020 0.010 0.015 1,775

(0.032) (0.068) (0.034) (0.050)

Notes: * = p < 0.05, ~ = p <0 .10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working).

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: Overweight and obesity are defined according to the International Obesity Task Force.
108

SUPPLEMENTAL EXHIBIT 3.2. ADULT HEALTH BEHAVIORS: EXERCISE, NUTRITION, AND SLEEP

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

EXERCISE AND INACTIVITY

Number of times per week engages 

in moderate physical activity [SR]

3.877 – 0.144 – 0.298 – 0.292 – 0.465 3,253

(0.143) (0.294) (0.189) (0.302)

Engages in moderate physical 

activity ≥ four times a week [SR]

0.487 – 0.014 – 0.029 – 0.012 – 0.020 3,253

(0.022) (0.045) (0.029) (0.046)

Number of times per week engages 

in vigorous physical activity [SR]

1.483 0.010 0.021 0.006 0.010 3,263

(0.098) (0.203) (0.133) (0.212)

Engages in 10 or more minutes of 

vigorous physical activity at least 

one time per week [SR]

0.386 0.046* 0.094* 0.010 0.015 3,263

(0.021) (0.043) (0.028) (0.045)

Watches ≥ 11 hours of television per 

week [SR]

0.483 – 0.002 – 0.003 – 0.037 – 0.059 3,250

(0.022) (0.045) (0.029) (0.046)

NUTRITION

Eats fruit or vegetables ≥ four times 

per week [SR]

0.566 0.016 0.034 0.038 0.061 3,268

(0.021) (0.044) (0.028) (0.045)

Eats fast food ≥ one times per week 

[SR]

0.614 0.005 0.011 0.004 0.007 3,262

(0.020) (0.042) (0.027) (0.043)

SLEEP

Short sleep the previous night 

(< 6 hours) [SR]

0.194 – 0.017 – 0.035 – 0.004 – 0.006 3,241

(0.017) (0.034) (0.022) (0.036)

7–8 hours of sleep the previous 

night ("normal sleep") [SR]

0.291 0.015 0.031 0.020 0.033 3,241

(0.020) (0.042) (0.027) (0.044)

Long sleep the previous night 

(> 10 hours) [SR]

0.099 – 0.013 – 0.026 – 0.048* – 0.077* 3,241

(0.013) (0.026) (0.015) (0.024)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working).

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed.
109 Chapter 3: Impacts on Adult and Youth Physical Health

SUPPLEMENTAL EXHIBIT 3.3. YOUTH HEALTH BEHAVIORS: EXERCISE, NUTRITION, AND SLEEP

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

EXERCISE AND INACTIVITY

Fraction of past 7 days engaged in physical activity for ≥ 20 minutes that made youth sweat/breathe hard [SR]

All 0.440 0.006 0.012 0.013 0.019 5,082

(0.012) (0.025) (0.014) (0.020)

Female 0.350 0.010 0.021 0.019 0.030 2,589

(0.017) (0.034) (0.019) (0.030)

Male 0.525 0.001 0.003 0.006 0.009 2,493

(0.018) (0.038) (0.020) (0.028)

Fraction of past 7 days engaged in physical activity for ≥ 30 minutes that did not make youth sweat/breathe hard [SR]

All 0.393 – 0.005 – 0.010 0.006 0.010 5,076

(0.014) (0.029) (0.015) (0.023)

Female 0.397 – 0.009 – 0.018 – 0.006 – 0.009 2,587

(0.019) (0.038) (0.021) (0.032)

Male 0.390 0.000 – 0.001 0.019 0.027 2,489

(0.020) (0.042) (0.022) (0.031)

Watches ≥ 11 hours of television per week [SR]

All 0.317 0.003 0.006 – 0.009 – 0.014 5,052

(0.018) (0.038) (0.020) (0.030)

Female 0.302 0.014 0.029 – 0.012 – 0.018 2,577

(0.024) (0.049) (0.027) (0.042)

Male 0.330 – 0.009 – 0.019 – 0.007 – 0.009 2,475

(0.026) (0.057) (0.029) (0.041)

NUTRITION

Eats fruit or vegetables ≥ four times per week [SR]

All 0.536 – 0.036~ – 0.075~ – 0.017 – 0.026 5,091

(0.020) (0.041) (0.021) (0.032)

Female 0.528 – 0.039 – 0.078 0.018 0.028 2,595

(0.027) (0.055) (0.030) (0.047)

Male 0.544 – 0.032 – 0.069 – 0.054~ – 0.077~ 2,496

(0.028) (0.060) (0.030) (0.042)

Drinks soda or other sugary drinks like juice (diet soda excluded) ≥ four times per week [SR]

All 0.639 – 0.017 – 0.035 0.018 0.027 5,094

(0.019) (0.040) (0.020) (0.030)

Female 0.627 0.001 0.001 0.011 0.017 2,596

(0.026) (0.053) (0.028) (0.044)

Male 0.650 – 0.035 – 0.075 0.027 0.038 2,498

(0.026) (0.056) (0.028) (0.040)

Eats sweet snacks ≥ 4 times per week [SR]

All 0.598 – 0.018 – 0.038 – 0.010 – 0.015 5,093

(0.019) (0.039) (0.021) (0.031)
110

SUPPLEMENTAL EXHIBIT 3.3. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

Eats sweet snacks ≥ four times per week [SR] (CONTINUED)

Female 0.581 0.009 0.017 0.010 0.015 2,596

(0.026) (0.052) (0.028) (0.044)

Male 0.614 – 0.045~ – 0.097~ – 0.029 – 0.041 2,497

(0.026) (0.056) (0.029) (0.042)

Eats fast food ≥ four days per week [SR]

All 0.259 – 0.012 – 0.024 0.010 0.015 5,093

(0.018) (0.037) (0.019) (0.028)

Female 0.233 0.000 – 0.001 – 0.003 – 0.005 2,595

(0.023) (0.046) (0.025) (0.040)

Male 0.283 – 0.023 – 0.050 0.024 0.034 2,498

(0.025) (0.054) (0.027) (0.039)

SLEEP

Hours of sleep last night [SR]

All 8.479 – 0.011 – 0.023 0.053 0.080 5,073

(0.081) (0.169) (0.091) (0.135)

Female 8.597 0.065 0.131 0.072 0.113 2,587

(0.111) (0.223) (0.128) (0.200)

Male 8.368 – 0.087 – 0.189 0.038 0.054 2,486

(0.117) (0.253) (0.125) (0.177)

Short sleep (less than < seven hours or less than < six hours if over age 17) [SR]

All 0.129 – 0.015 – 0.031 – 0.011 – 0.016 5,073

(0.012) (0.026) (0.014) (0.020)

Female 0.121 – 0.027 – 0.055 – 0.001 – 0.002 2,587

(0.017) (0.034) (0.019) (0.030)

Male 0.136 – 0.002 – 0.003 – 0.021 – 0.029 2,486

(0.019) (0.041) (0.019) (0.027)

Long sleep (> 11 hours or > 10 hours if over age 17) [SR]

All 0.129 – 0.012 – 0.026 – 0.002 – 0.002 5,073

(0.012) (0.025) (0.014) (0.021)

Female 0.141 0.002 0.004 0.016 0.025 2,587

(0.018) (0.037) (0.020) (0.032)

Male 0.117 – 0.027~ – 0.058~ – 0.019 – 0.027 2,486

(0.016) (0.035) (0.018) (0.026)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working).

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 10 to 20 as of December 31, 2007.
111 Chapter 3: Impacts on Adult and Youth Physical Health

SUPPLEMENTAL EXHIBIT 3.4. SELF-REPORTED HEALTH CARE ACCESS FOR ADULTS AND YOUTH

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

ADULTS

Adult and children have health 

insurance [SR]

0.827 0.019 0.039 0.035~ 0.056~ 3,253

(0.017) (0.034) (0.021) (0.034)

Adult or children did not get medical 

care when needed during the past 12 

months [SR]

0.101 – 0.002 – 0.005 – 0.022 – 0.035 3,261

(0.013) (0.027) (0.016) (0.025)

Adult or children did not get needed 

medical care in the past 12 months 

because they could not afford it [SR]

0.061 – 0.002 – 0.005 – 0.026* – 0.041* 3,261

(0.010) (0.022) (0.012) (0.019)

Adult has been to emergency room 

for own health reasons at least once 

in the past 12 months [SR]

0.446 0.036~ 0.074~ – 0.002 – 0.003 3,262

(0.022) (0.045) (0.029) (0.046)

Adult usually goes to emergency 

room for routine care [SR]

0.051 0.015 0.031 – 0.011 – 0.018 3,264

(0.010) (0.020) (0.012) (0.019)

Adult usually goes to a clinic or 

health center for routine care [SR]

0.548 – 0.021 – 0.043 – 0.008 – 0.012 3,264

(0.021) (0.044) (0.028) (0.045)

Adult usually goes to a doctor's 

office or HMO for routine care [SR]

0.298 0.027 0.055 0.027 0.043 3,264

(0.020) (0.040) (0.026) (0.042)

Adult saw or talked to a health 

professional about their own health 

in the past 6 months [SR]

0.792 0.023 0.048 0.014 0.023 3,263

(0.017) (0.036) (0.022) (0.036)

Adult has children in the household 

and they usually go to emergency 

room for routine care [SR]

0.020 0.005 0.010 – 0.004 – 0.007 3,250

(0.006) (0.012) (0.007) (0.011)

Adult has children in the household 

and they usually go to a clinic or 

health center for routine care [SR]

0.412 – 0.010 – 0.020 0.009 0.014 3,250

(0.020) (0.042) (0.027) (0.044)

Adult has children in the household 

and they usually go to a doctor's 

office or HMO for routine care [SR]

0.210 0.014 0.029 0.014 0.023 3,250

(0.017) (0.035) (0.022) (0.035)
112

SUPPLEMENTAL EXHIBIT 3.4. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

YOUTH

Youth had routine physical exam in the past 12 months [SR]

All 0.719 – 0.036* – 0.075* 0.002 0.002 5,060

(0.017) (0.035) (0.019) (0.028)

Female 0.795 – 0.064* – 0.129* – 0.019 – 0.029 2,578

(0.022) (0.044) (0.024) (0.037)

Male 0.647 – 0.008 – 0.017 0.021 0.030 2,482

(0.026) (0.055) (0.029) (0.041)

Youth had dental exam in the past 12 months [SR]

All 0.610 – 0.012 – 0.026 0.000 0.000 5,086

(0.019) (0.040) (0.021) (0.032)

Female 0.640 – 0.023 – 0.046 – 0.003 – 0.005 2,592

(0.025) (0.050) (0.029) (0.045)

Male 0.582 – 0.002 – 0.003 0.003 0.004 2,494

(0.027) (0.059) (0.029) (0.042)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working).

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All adults interviewed. Youth long-term survey. Interviewed youth ages 10 to 20 as of December 31, 2007.
113 Chapter 4: Impacts on Adult and Youth Mental Health

IMPACTS ON ADULT AND YOUTH 

MENTAL HEALTH
114 CHAPTER 4

IMPACTS ON ADULT AND 

YOUTH MENTAL HEALTH

This chapter discusses MTO’s effects on the mental 

health of participating adults and youth. For adults, 

we find that MTO produces statistically significant 

improvements in important mental health outcomes, 

including general psychological distress and depression. 

We also find some evidence for an increase in substance 

dependence among adults in the MTO experimental 

group compared with controls.

We detect few significant effects on the mental health 

of youth ages 13–20. However, we observe statistically 

significant beneficial effects for female youth in the 

experimental group relative to controls on a subset of 

our measures of mental health: lower prevalence of any 

lifetime mood disorder, lower prevalence of lifetime 

oppositional defiant disorder, fewer serious emotional 

or behavioral difficulties, less psychological distress, 

and fewer panic attacks in the past year. For male 

youth in the Section 8 group, our analyses suggest that 

MTO may be associated with a higher prevalence of 

post-traumatic stress disorder. Parental reports on their 

grown children’s mental health show no significant 

differences across treatment groups.

4.1 HYPOTHESES ABOUT EFFECTS 

ON ADULT AND YOUTH MENTAL 

HEALTH

A substantial mental health literature documents that 

both adults and children who live in high-poverty, 

high-crime urban settings have elevated rates of a wide 

range of adverse mental health outcomes (for example, 

Bagley, Jacobson, and Palmer, 1973; Rezaeian et al., 

2005; Whitley et al., 1999). There is continuing 

debate, however, about the relative importance of 

“geographic drift,” that is, differential selection of 

people with a predisposition to mental illness into 

these environments rather than causal influences of 

the environment on mental illness (van Kamp et al., 

2004). To the extent that these associations reflect 

causal effects of the environment, we would expect 

the MTO demonstration to improve mental health 

outcomes. Exhibit 4.1 describes some of the suggested 

mechanisms by which relocating to a lower-poverty 

neighborhood might lead to improved mental health.

EXHIBIT 4.1. HYPOTHESIZED PATHWAYS THROUGH WHICH MTO MAY AFFECT MENTAL HEALTH

MTO INTERVENTION RELOCATION TO LOW-POVERTY NEIGHBORHOODS

COMMUNITY-LEVEL

MEDIATORS

PERSON- AND FAMILY-LEVEL

MEDIATORS

HEALTH

OUTCOMES

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

Exhibit 4.1 is a modified version of Exhibit 4.1 in the MTO interim impacts evaluation (see Orr et al., 2003).

Community Norms 

and Values

· More positive peer groups, 

 adult role models

Physical Environment

· Less noise, light pollution

· Safer environment

Community Resources

· Mental health care facilities

· Access to job with health

 benefits

Mental Health

· Psychological distress

· Mental calm 

· General happiness

· Major depression

· Bipolar Disorder

· Generalized Anxiety 

 Disorder

· Panic Attacks/Disorder

· Intermittent-Explosive 

 Disorder

· Oppositional-Defiant 

 Disorder

· Post-Traumatic Stress 

 Disorder

Substance Use

· Drinking

· Drug use

Exercise

· More exercise

Access to Care

· Health insurance

· Access to medical care

Safety and Stress

· Increased sense of 

 security, confidence

· Less stress, anxiety

Social Isolation

· Feelings of isolation

Social Environment

· Reduced crime and violence

· Different neighbors
115 Chapter 4: Impacts on Adult and Youth Mental Health Chapter 4: Impacts on Adult and Youth Mental Health

CHANGES IN PHYSICAL ENVIRONMENT 

A wide range of childhood adversities associated with the 

physical environment, such as poverty, poor nutrition, 

abuse, and neglect, have been linked to a wide range 

of mental disorders (Anda et al., 2006; Green et al., 

2010). Animal studies suggest that the effects of early 

environments on the body’s stress response system and 

brain development mediate these associations to some 

extent (Weaver, 2009). However, the social environments 

of neighborhoods also seem to be important by varying 

exposure to stressful experiences that can provoke mental 

disorders as well as in access to supportive resources for 

mental disorders (Kloos and Shah, 2009).

CHANGES IN THE SOCIAL ENVIRONMENT

Exposure to violence can cause long-term behavioral 

and psychological harm for both youth and adults 

(Famularo et al., 1996; Groves et al., 1993; Zapata et al., 

1992). This is especially true in predicting depression, 

generalized anxiety disorder, and post-traumatic stress 

disorder (PTSD). As a result, to the extent that MTO 

reduces exposure to crime and violence, we would expect 

it to improve overall well-being and reduce psychological 

distress, depression, and anxiety (Aneshensel and Sucoff, 

1996; Ross and Mirowsky, 2001; Silver, Mulvey, and 

Swanson, 2002).

It is also possible that moving to lower-poverty 

neighborhoods could influence externalizing disorders, 

including oppositional-defiant disorder, intermittentexplosive disorder, conduct disorder, and substance 

use disorder among youth, as these are strongly 

related to peer environments and norms regarding the 

appropriateness of violence and antisocial behaviors 

(Deater-Deckard, 2001; Gifford-Smith et al., 2005). 

The direction of these effects, however, is unknown, as 

there is no necessarily positive association between a 

decrease in neighborhood poverty levels and a decrease in 

exposure to maladaptive peer environments. It is possible 

that moves could have adverse effects by leaving MTO 

family members socially or culturally isolated in their 

new neighborhoods.

Previous research suggests that the local social 

environment could act differently on the mental health 

outcomes of male and female youth, in part because 

males may have higher exposure to neighborhood risk 

factors. Adolescent males tend to be subject to less 

parental supervision than females and also tend to 

be greater risk-takers (Block, 1983; Bottcher, 2001; 

LaGrange and Silverman, 1999). Male and female youth 

also appear to have different coping styles and capacities. 

Psychosocial stress sometimes can have more pronounced 

effects on males than females in part because males are 

more likely to use confrontational techniques to deal 

with stress, particularly stress involving interpersonal 

problems, whereas females are more likely to turn to 

supportive adults (Coleman and Hendry, 1999; Zaslow 

and Hayes, 1986). The gender difference in the use of 

adult supports could be magnified in situations like 

MTO where households are disproportionately headed by 

females.

CHANGES IN COMMUNITY RESOURCES

MTO-assisted moves to lower-poverty neighborhoods 

could improve access to mental health service providers, 

or improve access to job opportunities that include 

health benefits that would cover mental health services. 

On the other hand, free or very low-cost mental health 

services may be concentrated in high-poverty areas, and 

so access to low-cost mental health care services could 

decrease with moves to better neighborhoods.

EARLIER RESEARCH

Some nonexperimental studies suggest a possible 

relationship between neighborhood of residence and 

mental health status (for a recent review, see DiezRoux and Mair, 2010). Although almost entirely crosssectional, these studies have consistently found that 

important aspects of neighborhood environment are 

significantly related to the mental health of residents. 

Gidlow et al. (2010), for example, found that consensus 

perceptions of neighborhood quality were significantly 

related to the self-reported mental health of respondents 

in a sample of neighborhoods in the United Kingdom, 

with perceived social support the most important aspect 

of the neighborhood. Van den Berg et al. (2010) find 

evidence consistent with the idea that the presence of 

green space may buffer the effects of stressful experiences 

on mental health.
116

EXHIBIT 4.2. ADULT PSYCHOLOGICAL WELL-BEING

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

PSYCHOLOGICAL DISTRESS INDEX (K6), PAST MONTH [SR]

K6 Z-score (higher score 

indicates greater distress)

0.000 – 0.107* – 0.221* – 0.097~ – 0.156~ 3,273

(0.042) (0.087) (0.056) (0.091)

Serious mental illness (K6 raw 

score > =13)

0.133 0.001 0.001 – 0.015 – 0.024 3,273

(0.015) (0.030) (0.019) (0.031)

SUBSTANCE DEPENDENCE, PAST MONTH [SR]

Severity of dependence 

scale raw score (higher score 

indicates greater dependence)

0.329 0.117* 0.241* 0.048 0.076 3,269

(0.055) (0.114) (0.077) (0.122)

Dependence on drugs or 

alcohol (severity of dependence 

score of 3 or higher)

0.055 0.029* 0.060* 0.015 0.024 3,269

(0.011) (0.022) (0.015) (0.023)

MENTAL CALM [SR]

Calm and peaceful most of the 

time, past month

0.487 0.018 0.037 – 0.025 – 0.040 3,272

(0.022) (0.045) (0.029) (0.047)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details. 

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

Measures: Psychological distress consists of 6 items (sadness, nervousness, restlessness, hopelessness, feeling that everything is an effort, worthlessness) 

scaled on a score from 0 (no distress) to 24 (highest distress) and then converted to z-scores using the mean and standard deviation of control group adults. 

Serious mental illness is defined as a raw score of 13 or higher on the K6. Substance dependence consists of 5 items about drug and/or alcohol use (use out of 

control, anxiety or worry about missing fix or drink, worry about use, frequency of desire to end use, difficulty of going without use) scaled on a score from 0 (no 

dependence) to 15 (highest level of dependence). A score of 3 or higher indicates dependence. See Section 4.3 for additional details. 
117 Chapter 4: Impacts on Adult and Youth Mental Health

4.2 EARLIER RESEARCH: 

SHORT-TERM AND INTERIM MTO 

IMPACTS ON MENTAL HEALTH

Consistent with evidence from nonexperimental studies, 

short-term evidence from the MTO sites showed that 

adults in both treatment groups in the Boston site were 

more likely to feel calm and peaceful (Katz, Kling, and 

Liebman, 2001). Short-term data from both Boston 

and New York showed that experimental group children 

experienced less fearfulness than the control group (Katz, 

Kling, and Liebman, 2001; Leventhal and Brooks-Gunn, 

2003). Younger children in the Section 8 group also 

experienced fewer unhappy, sad, or depressed feelings 

(Leventhal and Brooks-Gunn, 2003).

The MTO interim study four to seven years after random 

assignment found that among adults, assignment to the 

experimental group rather than control group reduced 

psychological distress (as measured by the Kessler 6 [K6] 

measure of psychological distress) and increased reports 

of mental calm (Orr et al., 2003, Exhibit 4.2, p. 77).

The point estimate of the impact on the incidence of 

depression for the experimental group was substantial but 

only statistically significant when boundary cases were 

included, and the impact on anxiety (measured using 

two questions) was undistinguishable from zero. The 

estimates for the Section 8 group were not statistically 

distinguishable from zero for any mental health measure 

for adults.

For youth, the interim study found few statistically 

significant changes in mental health when examining 

male and female youth together. The main mental health 

measures on the interim surveys for respondents ages 

12–19 included the K6 and scales to measure lifetime 

depression and generalized anxiety disorder developed 

for the National Comorbidity Survey Replication: 

Adolescent Supplement (NCSR-AS). There was some 

sign of a decline in generalized anxiety disorder for the 

Section 8 group relative to controls. However, for all 

three mental health measures, the point estimates for 

females in both treatment groups suggest substantively 

large improvements in mental health, with four of the 

six estimates (three outcomes for each of two treatment 

groups) statistically significant. In contrast, for males, 

none of the estimates were statistically significant, and 

all estimates were in the direction of small declines in 

mental health. A qualitative follow-up to the interim 

survey suggests that these gender differences could be 

related to differences in how female and male youth 

in the experimental group navigated their new social 

environments, with distance from father figures and 

increased exposure to neighborhood public spaces being 

perhaps particularly important for male youth (ClampetLundquist et al., 2011).

4.3 DATA SOURCES AND MEASURES

All of the mental health outcomes we describe were 

measured with data from the long-term evaluation 

surveys. These measures have all been used before in large 

national surveys. The data on adult and youth health 

outcomes are self-reported, whereas the data on grown 

children (ages 21–30) are from parental or primary 

caregiver reports.

Adults responded to questions about their mental health 

that included the same set of questions from the MTO 

interim survey (K6 indicator of distress and a measure 

of calm). The K6 scale (Furukawa et al., 2003; Kessler 

et al., 2003) consists of six questions and is the most 

widely used scale of nonspecific psychological distress 

in the literature. It is a core part of all three of the 

major ongoing national health tracking surveys in the 

United States: the National Health Interview Survey 

(NHIS), the Behavioral Risk Factor Surveillance Survey 

(BRFSS), and the Substance Abuse and Mental Health 

Services Administration’s National Household Survey 

on Drug Use and Health. We also asked adults to 

complete disorder-specific mental health measures (for 

example, measures of depression, anxiety, intermittent 

explosive disorder) from the World Health Organization’s 

(WHO) Composite International Diagnostic Interview 

(CIDI) (Kessler and Üstün, 2004), the most widely used 

epidemiological interview for mental disorders in the 

world. The CIDI is used in the WHO World Mental 

Health Survey Initiative (Kessler and Üstün, 2008), the 

world’s largest series of coordinated epidemiological 

surveys of mental disorders, as well as in the U.S. 

National Comorbidity Surveys (Kessler et al., 1994, 

2005). MTO adults completed the CIDI sections 

designed to generate diagnoses of specific disorders 
118

defined by the Diagnostic and Statistical Manual of 

Mental Disorders, 4th edition (DSM-IV), such as major 

depression, generalized anxiety disorder, and posttraumatic stress disorder.

Interviews with youth ages 10–20 included questions 

about their mental calm, distress, and strengths and 

difficulties. The latter questions come from the Strengths 

and Difficulties Questionnaire (SDQ) (Goodman and 

Goodman, 2009), a widely used screening scale to detect 

clinically significant mental disorders among youth. The 

MTO screening version of the SDQ is identical to the 

screening scale developed for use in the Health Interview 

Survey to assess DSM-IV “serious mental illness” 

(Simpson et al., 2005). Interviews with youth 13 or older 

also included the CIDI diagnostic scales. We use parent 

reports on whether any of their grown children (ages 

21–30) suffer from serious mental health problems or an 

alcohol or drug problem. We focus our primary analyses 

on youth ages 13–20 because they were administered the 

CIDI diagnostic scales as well as the other mental health 

measures.1

MEASURES OF DISTRESS AND 

DIFFICULTIES

Kessler 6 (K6) measure of psychological distress: We 

asked adult and youth respondents how often in the past 

30 days they felt sad, nervous, restless, hopeless, that 

everything was an effort, or worthless.2 The raw score 

on the K6 can range from 0 (no distress) to 24 (highest 

distress). We report K6 scores transformed to z-scores, 

standardized using the control group’s mean and standard 

deviation. K6 scores of 13 or higher have been shown to 

be strongly predictive of serious mental illness (Kessler et 

al., 2003).

Strengths and Difficulties Questionnaire (SDQ): We 

administered youth ages 10–20 a brief version of the 

SDQ developed by Robert Goodman. It consists of 

five behavioral and emotional items: obedience, worry/

anxiety, unhappiness, getting along better with adults 

1 In Supplemental Exhibit 4.2, we report results expanding the sample 

to include 10- to 12-year-olds on the limited set of mental health measures that apply to youth of all ages (10–20 years old).

2 Response categories on the K6 items are: all of the time; most of the 

time; some of the time; a little of the time; and none of the time.

than with peers, and attention span. The raw score can 

range from 0 (no behavioral or emotional problems) to 

10 (severe emotional or behavioral problems). A score of 

6 or higher is considered indicative of serious behavioral 

or emotional problems.

MEASURES OF MENTAL CALM

Calm and peaceful: We replicated the question from the 

MTO interim evaluation that asked adults how much of 

the time in the past 30 days they felt calm and peaceful. 

Response categories match those for the K6 items. Our 

measure reflects being calm and peaceful most of the 

time during the past month.

MEASURES OF SUBSTANCE 

DEPENDENCE

Severity of Dependence Scale (SDS): The scale consists 

of five items about the use of drugs or alcohol: use out 

of control, anxiety/worry about missing a fix or drink, 

worry about use, frequency of desire to end use, and 

difficulty of going without use. The raw score on the 

SDS can range from 0 (no dependence) to 15 (highest 

level of dependence). A score of 3 or higher indicates that 

the respondent is likely to meet DSM-IV criteria for a 

diagnosis of substance dependence.

MEASURES OF DSM-IV DISORDERS

We used a fully structured diagnostic interview to 

assess the prevalence of disorders such as depression, 

bipolar disorder, anxiety, panic disorder, PTSD, and 

intermittent-explosive disorder (IED) among MTO 

participants. Our diagnostic instrument was the CIDI, 

which was revised to make diagnoses according to the 

definitions and criteria of the DSM-IV (American 

Psychiatric Association, 1994) and expanded and 

updated for the WHO World Mental Health Survey 

Initiative (2004). The CIDI was developed with 

careful attention to instruction, comprehension, and 

motivation to increase the accuracy of reports (Kessler 

and Üstün, 2004) and has been validated through clinical 

reappraisals (Haro et al., 2006). The CIDI forms the 

basis for the National Comorbidity Survey (NCS) for 

adults and the NCS-Adolescent Supplement for youth, 

which allows us to compare MTO participant responses 

to nationally representative samples. In addition to 

assessing lifetime occurrence of disorders, we also 
119 Chapter 4: Impacts on Adult and Youth Mental Health

assessed the 12-month prevalence and collected data on 

age of onset and recency. We administered the structured 

diagnostic interview only to older youth, ages 13–20.

Major depression: Major depression is diagnosed if the 

respondent has experienced a major depressive episode, 

defined as a two-week or longer period where at least 

one symptom is depressed mood or loss of interest or 

pleasure and where the respondent had at least five of 

the following nine symptoms: depressed mood, markedly 

diminished interest or pleasure, significant weight loss 

or gain (unrelated to dieting), insomnia, psychomotor 

agitation (for example, physical restlessness, pacing) or 

retardation (for example, being physically slowed down), 

fatigue or loss of energy, feelings of worthlessness or 

excessive or inappropriate guilt, diminished ability to 

think or concentrate or indecisiveness, and recurrent 

thoughts of death. In addition, the symptoms must 

cause clinically significant distress or impair social, 

occupational, or other functioning. Depression with 

hierarchy is diagnosed where mania or hypomania is not 

also diagnosed.

Bipolar disorders (BPD) I and II: These are diagnosed 

if the respondent has experienced a manic (BPD I) or 

hypomanic (BPD II) episode, a distinct period in which 

the respondent has an abnormally and persistently 

elevated, expansive, or irritable mood. During that 

period, the respondent must experience at least three 

(or four if the mood is irritable only) of the following 

seven symptoms: inflated self-esteem or grandiosity, 

decreased need for sleep, more talkative than usual or 

pressure to keep talking, flight of ideas or subjective 

sense of racing thoughts, distractibility, increase in goaloriented activity (often in the form of marathon writing 

sessions or other very intensive work-related activities) or 

psychomotor agitation (for example, inability to sit still 

without constant fidgeting), and excessive involvement 

in pleasurable but risky activities. In addition, a manic 

episode requires the mood disturbance to impair normal 

functioning, require hospitalization to prevent self-harm, 

or have psychotic features. The length and severity of the 

episode distinguish the manic episode from the less severe 

hypomania episode. Bipolar II also requires a history of 

at least one lifetime major depressive episode.

Generalized anxiety disorder (GAD): GAD includes 

excessive anxiety about multiple events or activities that 

the respondent finds difficult to control on more days 

than not over the course of at least six months. The 

anxiety must also be associated with at least three of 

the following six symptoms: restlessness, easy fatigue, 

difficulty concentrating, irritability, muscle tension, 

and sleep disturbance. Furthermore, the anxiety 

must cause clinically significant distress or impair 

social, occupational, or other functioning. Finally, the 

disturbance cannot occur exclusively during a mood 

disorder such as bipolar or depression. GAD with 

hierarchy is diagnosed when neither major depressive 

disorder nor mania is also diagnosed or where the onset 

of GAD is earlier than the onset of major depressive 

disorder and mania.

Panic attacks: These occur when a respondent 

experiences a discrete period of intense fear or discomfort 

where at least four of the following 13 symptoms 

developed abruptly and reached a peak within 10 

minutes: palpitations, pounding heart, or accelerated 

heart rate; sweating; trembling or shaking; sensation of 

shortness of breath or smothering; feeling of choking; 

chest pain, or discomfort; nausea or abdominal distress; 

feeling dizzy, unsteady, or lightheaded; feelings of 

unreality or depersonalization; fear of losing control 

or going crazy; fear of dying; numbing or tingling 

sensations; and chills or hot flushes.

Panic disorder: This requires recurrent, unexpected 

panic attacks where at least one of the attacks is followed 

by a month or more of persistent concern about having 

additional attacks, worry about the implications of the 

attack or its consequences, or a significant change in 

behavior related to the attacks. Finally, the panic attacks 

cannot be better accounted for by another mental 

disorder, such as PTSD, which we operationalize in the 

MTO study by focusing on “out of the blue” attacks.

Intermittent-explosive disorder: IED requires 

at least three discrete episodes of failure to resist 

aggressive impulses that result in serious assaultive 

acts or destruction of property in which the degree of 

aggressiveness expressed is grossly out of proportion to 

any precipitating psychosocial stressors. Additionally, 
120

it must not be the case that the aggressive episodes are 

better accounted for by another mental disorder (such 

as major depressive disorder or mania) or are due to the 

direct physiological effects of a substance or a general 

medical condition. The IED with hierarchy diagnosis 

takes into account major depressive disorder and mania/

hypomania, where the anger attacks cannot only have 

occurred when depressive or manic symptoms were also 

present and where the age of onset and recency for IED 

is compared with the age of onset and recency for major 

depressive disorder and mania/hypomania.

Oppositional defiant disorder (ODD): ODD, which 

is diagnosed only for youth, requires a pattern of 

negative, hostile, and defiant behavior lasting at least six 

months, during which at least four of the following eight 

symptoms are often present: loses temper, argues with 

adults, actively defies or refuses to comply with adult’s 

requests or rules, deliberately annoys people, blames 

others for his or her mistakes or misbehavior, touchy 

or easily annoyed by others, angry and resentful, and 

spiteful and vindictive. Furthermore, the disturbance in 

behavior must cause clinically significant impairment in 

social, academic, or occupational functioning, and the 

behaviors must not occur exclusively during the course of 

a mood disorder.

Post-traumatic stress disorder: PTSD occurs when 

a respondent has been exposed to and subsequently 

suffers because of a traumatic event in which the 

person experienced, witnessed, or was confronted with 

an event or events that involved actual or threatened 

death or serious injury to the respondent or to others.3 

Furthermore, the respondent must exhibit three of the 

following five behaviors: avoiding activities, places, or 

people that arouse recollections of the trauma; markedly 

diminished interest or participation in significant 

activities; feeling of detachment or estrangement from 

others; restricted range of affect (for example, unable 

to have loving feelings); and a sense of a foreshortened 

3 The MTO survey asked about the following events: beaten up as a 

child by a primary caregiver, a spouse/romantic partner, or someone 

else; raped or sexually assaulted; mugged or threatened with a weapon; 

unexpected death of a loved one; traumatic event experienced by a 

loved one; witnessed physical fights at home; witnessed death or saw a 

dead body or someone seriously hurt; and an unspecified other event 

(open-ended response option).

future (for example, does not expect to have a career, 

marriage, children, or a normal life span). Finally, the 

respondent must indicate difficulty falling or staying 

asleep and an exaggerated startle response, and the 

disturbance in behavior must cause clinically significant 

impairment in social, academic, or occupational 

functioning. The MTO version of the CIDI PTSD 

module contained fewer questions than the full CIDI 

battery, so we used responses to the included questions to 

generate a predicted probability of meeting the disorder 

among respondents who meet the disorder in a nationally 

representative sample.

Summary measures: In addition to the disorders 

listed above, we include four summary measures of 

disorders. Any mood disorder includes depression 

(without hierarchy), bipolar I/II/subthreshold, and 

mania/hypomania/hypomania subthreshold. Any 

anxiety disorder includes panic disorder, GAD (without 

hierarchy), and PTSD. Any disorder includes any mood 

disorder, any anxiety disorder, IED (without hierarchy), 

and for youth ODD (without hierarchy). Number of 

disorders is a count of the following disorders: depression 

(without hierarchy), bipolar I/II/subthreshold, panic 

disorder, GAD (without hierarchy), PTSD, IED (without 

hierarchy), and for youth ODD (without hierarchy).

Absence of mental health problems: This measure is 

based on an index we constructed for adults and youth 

as developed by Kling, Liebman, and Katz (2007).4

 The 

adult version includes the K6; CIDI diagnosis of major 

depressive disorder and GAD for the past year; mental 

calm; and a measure of normal sleep (between seven and 

eight hours per night). The youth version includes the 

K6, lifetime CIDI diagnosis of major depressive disorder, 

and CIDI lifetime diagnosis of GAD. We standardized 

each item in the index by the control group mean and 

standard deviation. We standardized items separately 

for male and female youth. We reversed measures of 

problems so that higher scores indicate an absence of 

4 We measure some of the mental health items included in the index 

differently than Kling, Liebman, and Katz (2007) did. As we described 

above, the adult long-term survey included the full CIDI modules for 

depression and generalized anxiety disorder, whereas the interim survey 

contained a CIDI short-form for depression and only two questions 

for anxiety. Also, the interim sleep question asked about how much the 

adult typically slept at night, whereas the long-term measure is based on 

how much the adult slept last night.
121 Chapter 4: Impacts on Adult and Youth Mental Health

problems, replaced missing values on individual measures 

with the mean for the treatment group, and then 

averaged standardized scores to create the index score.

MEASURES OF MENTAL HEALTH 

SERVICES

We asked adults a series of questions about any services 

that they have received in the past year for problems with 

mental health or substance abuse. We asked those who 

reported receiving any services how many sessions they 

received. We also asked all respondents whether they 

had taken any prescription medicine for these problems. 

Finally, we asked all respondents who neither received 

services nor took prescription medicine if there was a 

time in the past year when they felt they needed to see a 

medical professional for mental health or substance abuse 

issues.

4.4 CONTEXT AND BASELINE STATUS 

OF THE SAMPLE

We do not have baseline survey information on mental 

health status. However, the baseline surveys did indicate 

that half of all MTO heads of household reported feeling 

very unsafe in their neighborhoods at night. Fully 43 

percent reported that someone in the home had been the 

victim of a crime during the six months before enrolling. 

As noted in prior chapters, more than three-quarters of 

all household heads reported that safety (getting away 

from gangs and drugs) was the first or second most 

important reason for wanting to enroll in MTO and 

move out of their baseline neighborhoods.

4.5 LONG-TERM IMPACTS ON MENTAL 

HEALTH

ADULTS

We assessed the intention-to-treat (ITT) effects and 

treatment-on-the-treated (TOT) effects on adult mental 

health proxied by our measures of psychological wellbeing and mental disorders using the regression models 

described in Chapter 1. Exhibit 4.2 shows the estimated 

impacts of the MTO treatments on psychological distress 

(K6), substance dependence, and mental calm.

We observe a few statistically significant impacts on 

the mental health of adults. The experimental group 

adults have scores on the K6 that are, on average, a 

tenth of a standard deviation lower than the control 

group; however, the prevalence of serious mental illness, 

as measured by a K6 score of at least 13, is similar for 

both groups. The prevalence of dependence on drugs 

or alcohol for the experimental group is an estimated 

2.9 percentage points higher than among controls. 

On mental calm (Exhibit 4.2) and normal sleep 

(Supplemental Exhibit 3.2), we detect no statistically 

significant differences between adults in the experimental 

and control groups. We observe no statistically significant 

differences between the Section 8 group and the control 

group on the K6 measure of serious mental illness, 

substance dependence, mental calm, or normal sleep and 

only a marginally statistically significant decrease on the 

K6 z-score.

Exhibit 4.3 shows the estimated impacts on adult mental 

disorders that we assessed using the CIDI diagnostic 

interview. The lifetime prevalence of depression for the 

experimental and Section 8 adults is lower by 3.2 (p < 

.10) and 4.8 percentage points, respectively, than the 

control mean of 20.3 percent. We observe no statistically 

significant treatment impacts for either the experimental 

or Section 8 groups on panic attacks, IED, PTSD, our 

measure of “any mental health disorder,” the number 

of disorders, or the mental health index. However, the 

estimated MTO treatment effect on the mental health 

index for the experimental group is in the direction of 

improved mental health and on the margin of statistical 

significance with a p value of .095 (Exhibit 4.3). Some 

of the impact estimates have fairly wide confidence 

intervals. For example, the 95 percent confidence interval 

for “any lifetime mood disorder” for the Section 8 group 

ranges from – 8 to +1 percentage points compared with a 

control mean of 26 percent.
122

EXHIBIT 4.3. ADULT MENTAL DISORDERS

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

MAJOR DEPRESSION WITH HIERARCHY [SR]

Lifetime 0.203 – 0.032~ – 0.066~ – 0.048* – 0.077* 3,269

(0.017) (0.035) (0.021) (0.034)

Past year 0.119 – 0.021 – 0.043 – 0.013 – 0.021 3,269

(0.013) (0.028) (0.017) (0.027)

BIPOLAR I [SR]

Lifetime 0.013 – 0.003 – 0.007 – 0.001 – 0.002 3,269

(0.004) (0.009) (0.006) (0.010)

Past year 0.009 – 0.003 – 0.006 0.001 0.001 3,269

(0.004) (0.007) (0.006) (0.009)

BIPOLAR II [SR]

Lifetime 0.009 0.006 0.012 0.000 – 0.001 3,269

(0.005) (0.010) (0.007) (0.011)

Past Year 0.006 0.006 0.013 0.004 0.007 3,269

(0.004) (0.009) (0.006) (0.010)

GENERALIZED ANXIETY DISORDER WITH HIERARCHY [SR]

Lifetime 0.065 – 0.003 – 0.005 – 0.020~ – 0.033~ 3,273

(0.010) (0.021) (0.011) (0.017)

Past year 0.033 0.006 0.012 – 0.012 – 0.019 3,273

(0.008) (0.016) (0.008) (0.012)

PANIC DISORDER [SR]

Lifetime 0.081 0.003 0.007 0.002 0.003 3,269

(0.012) (0.024) (0.016) (0.025)

Past year 0.063 0.001 0.001 0.000 0.000 3,269

(0.010) (0.021) (0.014) (0.022)

PANIC ATTACKS [SR]

Lifetime 0.407 0.004 0.009 – 0.022 – 0.035 3,269

(0.021) (0.044) (0.028) (0.045)

Past year 0.087 0.004 0.009 0.004 0.006 3,269

(0.012) (0.026) (0.016) (0.026)

INTERMITTENT-EXPLOSIVE DISORDER WITH HIERARCHY [SR]

Lifetime 0.062 0.001 0.003 0.004 0.007 3,269

(0.010) (0.022) (0.015) (0.024)

Past year 0.051 – 0.008 – 0.015 – 0.008 – 0.013 3,269

(0.009) (0.019) (0.013) (0.021)

POST-TRAUMATIC STRESS DISORDER [SR]

Lifetime 0.219 – 0.012 – 0.024 0.004 0.006 3,269

(0.018) (0.037) (0.024) (0.038)

Past year 0.164 – 0.016 – 0.032 – 0.023 – 0.036 3,269

(0.015) (0.032) (0.020) (0.032)
123 Chapter 4: Impacts on Adult and Youth Mental Health

EXHIBIT 4.3 (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

ANY ANXIETY DISORDER [SR]

Lifetime 0.308 – 0.020 – 0.042 – 0.005 – 0.008 3,269

(0.020) (0.041) (0.026) (0.042)

Past year 0.220 – 0.012 – 0.024 – 0.021 – 0.034 3,269

(0.018) (0.036) (0.023) (0.037)

ANY MOOD DISORDER [SR]

Lifetime 0.255 – 0.028 – 0.058 – 0.036 – 0.058 3,270

(0.018) (0.038) (0.024) (0.039)

Past year 0.153 – 0.015 – 0.032 – 0.001 – 0.001 3,269

(0.015) (0.031) (0.020) (0.033)

ANY DISORDER [SR]

Lifetime 0.412 – 0.026 – 0.053 – 0.022 – 0.036 3,270

(0.021) (0.044) (0.028) (0.045)

Past year 0.286 – 0.021 – 0.043 – 0.019 – 0.031 3,269

(0.019) (0.040) (0.026) (0.041)

NUMBER OF DISORDERS [SR]

Lifetime 0.786 – 0.049 – 0.101 – 0.062 – 0.099 3,269

(0.049) (0.100) (0.065) (0.104)

Past year 0.535 – 0.031 – 0.064 – 0.041 – 0.066 3,269

(0.042) (0.087) (0.056) (0.090)

MENTAL HEALTH INDEX (Z-SCORE) [SR]

Absence of mental health problems: 

depression, anxiety, calm, distress 

and sleep (higher score indicates 

fewer mental health problems)

0.000 0.042~ 0.086~ 0.040 0.065 3,273

(0.025) (0.052) (0.034) (0.055)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: Disorders with hierarchy take into account the comorbidity of other disorders: Depression with hierarchy takes into account Mania and Hypomania; GAD 

takes into account Depression and Mania; IED takes into account Depression, Mania, and Hypomania. Any Anxiety Disorder includes Panic Disorder, GAD (without 

hierarchy), and PTSD. Any Mood Disorder includes Depression (without hierarchy), Bipolar I/II/Subthreshold, and Mania/Hypomania/Hypomania Subthreshold. Any 

Disorder includes all disorders from Any Anxiety Disorder and Any Mood Disorder as well as IED (without hierarchy). Index of mental health problems is the average 

of the z-scores for depression (past year), anxiety (past year), mental calm, distress (K6), and normal sleep (7 to 8 hours last night) after standardizing by the control 

mean and standard deviation. A higher mental health index score indicates better mental health. See Section 4.3 for additional details.
124

YOUTH

Exhibit 4.4 shows the estimated impacts on psychological 

well-being for youth ages 13–20. Supplemental Exhibit 

4.2 shows these same impacts after expanding the age 

range to youth ages 10–20. Impacts on mental disorders 

such as depression and generalized anxiety are shown in 

Exhibits 4.5 and 4.6. All the exhibits provide separate 

estimates for all, female, and male youth.

We detect very few significant treatment impacts for 

all youth taken together in either the experimental or 

Section 8 groups. However, we see some significant 

effects for experimental group females compared with 

control group females. Females in the experimental 

group have K6 scores that are over a tenth of a standard 

deviation lower than the control group, are 4.8 

percentage points less likely than controls to have had 

a mood disorder during their lifetime (control mean 

of 21.8 percent), have fewer serious behavioral and 

emotional problems as indicated by the SDQ, reported 

fewer panic attacks, and had a lower prevalence of ODD 

during the past year. We find no statistically significant 

differences between the experimental and control group 

females’ mental calm, bipolar, panic disorder, PTSD, 

or IED and only marginally significant effects (towards 

better mental health) on our absence of mental health 

problems index, depression, and a K6 score indicative 

of serious mental illness and a marginally significant 

effect towards greater past year GAD.5

 For females in 

the Section 8 group, we detect no statistically significant 

differences from controls on any mental health measure, 

and the only impact that is marginally statistically 

significant is a higher level of past year GAD.

For male youth, we detect no statistically significant 

effects on our mental health index, the K6, strengths 

and difficulties, mental calm, depression, bipolar, panic 

disorder, panic attacks, IED, or ODD. While males in 

the experimental group appear to have similar levels 

of lifetime and past-year PTSD as males in the control 

group, Section 8 group males have higher levels of 

5 However, the bottom of Supplemental Exhibit 4.2 shows that, 

when limited to youth ages 15–20, we find a positive and statistically 

significant effect on the mental health index for female youth in the experimental group relative to those in the control group, a result similar 

to findings for female youth aged 15–20 at the interim survey by Kling, 

Liebman, and Katz (2007).

lifetime PTSD. The estimated impact is a prevalence 

that is 3.0 percentage points higher than the controls, 

who have a prevalence of 4.1 percent. And, although 

not statistically significant, the estimated MTO impacts 

for males in the Section 8 groups (and to some extent 

the experimental group) were in the direction of worse 

mental health than controls, and the Section 8 males 

showed marginally significant impacts towards higher 

prevalences of bipolar, any mood disorder, and any 

mental health disorder.

GROWN CHILDREN

As reported by the sample adults, the mental health of 

MTO grown children (ages 21–30) appears to be similar 

across treatment groups (see Exhibit 4.7). Approximately 

11 percent of the grown children in the control group 

suffer from depression or another serious mental illness, 

and about 5 percent have an alcohol or drug problem. 

Adults report higher rates of alcohol and drug problems 

for male children (8.2 percent) than female children (2.6 

percent).
125 Chapter 4: Impacts on Adult and Youth Mental Health

EXHIBIT 4.4. YOUTH PSYCHOLOGICAL WELL-BEING, AGES 13–20

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

PSYCHOLOGICAL DISTRESS INDEX

(K6), PAST MONTH [SR]

K6 Z-SCORE (HIGHER SCORE

INDICATES GREATER DISTRESS)

All 0.000 – 0.040 – 0.084 0.036 0.054 4,644

(0.041) (0.085) (0.047) (0.070)

Female 0.115 – 0.119* – 0.241* – 0.013 – 0.021 2,371

(0.058) (0.116) (0.066) (0.104)

Male – 0.110 0.040 0.085 0.084 0.120 2,273

(0.054) (0.116) (0.060) (0.086)

SERIOUS MENTAL ILLNESS

(K6 RAW SCORE ≥ 13)

All 0.067 – 0.012 – 0.025 0.009 0.013 4,644

(0.010) (0.021) (0.011) (0.017)

Female 0.085 – 0.026~ – 0.053~ 0.001 0.002 2,371

(0.015) (0.030) (0.017) (0.026)

Male 0.050 0.002 0.005 0.016 0.023 2,273

(0.013) (0.028) (0.015) (0.021)

BRIEF STRENGTHS AND DIFFICULTIES

QUESTIONNAIRE (SDQ) [SR]

SDQ RAW SCORE (HIGHER SCORE

INDICATES MORE DIFFICULTIES)

All 3.149 – 0.102 – 0.211 0.071 0.106 4,644

(0.071) (0.147) (0.079) (0.119)

Female 3.245 – 0.174~ – 0.351~ 0.150 0.234 2,371

(0.100) (0.202) (0.114) (0.178)

Male 3.057 – 0.026 – 0.057 – 0.012 – 0.018 2,273

(0.093) (0.200) (0.101) (0.145)

SERIOUS BEHAVIORAL OR

EMOTIONAL PROBLEMS (SDQ RAW

SCORE ≥ 6)

All 0.103 – 0.022~ – 0.046~ 0.019 0.029 4,644

(0.011) (0.024) (0.014) (0.021)

Female 0.127 – 0.033* – 0.068* 0.030 0.047 2,371

(0.017) (0.034) (0.021) (0.032)

Male 0.081 – 0.010 – 0.021 0.007 0.010 2,273

(0.015) (0.032) (0.017) (0.024)
126

EXHIBIT 4.4. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

CALM AND PEACEFUL MOST OF THE

TIME OR MORE OFTEN IN THE PAST

MONTH [SR]

All 0.595 0.017 0.034 – 0.010 – 0.014 4,642

(0.019) (0.040) (0.021) (0.032)

Female 0.512 0.043 0.087 0.026 0.040 2,371

(0.028) (0.057) (0.031) (0.048)

Male 0.675 – 0.010 – 0.022 – 0.045 – 0.064 2,271

(0.026) (0.056) (0.030) (0.042)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: Psychological distress consists of 6 items (sadness, nervousness, restlessness, hopelessness, feeling that everything is an effort, worthlessness) scaled 

on a score from 0 (no distress) to 24 (highest distress) and then converted to z-scores using the mean and standard deviation for of control group youth (with 

male and female youth standardized separately). Serious mental illness is defined as a raw score of 13 or higher on the K6. Strengths and Difficulties consists 

of 5 behavioral and emotional items (obedience, worry/anxiety, unhappiness, getting along better with adults than peers, attention span) scaled on score from 0 

(no behavioral/emotional problems) to 10 (severe behavioral or emotional problems). A score of 6 or higher indicates serious behavioral/emotional problems. See 

Section 4.3 for additional details. 
127 Chapter 4: Impacts on Adult and Youth Mental Health

EXHIBIT 4.5. YOUTH MENTAL DISORDERS, AGES 13–20

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

MAJOR DEPRESSION WITH HIERARCHY [SR]

LIFETIME

All 0.105 – 0.015 – 0.031 – 0.014 – 0.021 4,639

(0.012) (0.025) (0.013) (0.019)

Female 0.128 – 0.032~ – 0.065~ – 0.029 – 0.045 2,367

(0.017) (0.035) (0.019) (0.029)

Male 0.084 0.003 0.006 0.000 0.000 2,272

(0.016) (0.034) (0.017) (0.025)

PAST YEAR

All 0.059 – 0.005 – 0.010 – 0.004 – 0.005 4,639

(0.010) (0.020) (0.011) (0.016)

Female 0.080 – 0.024~ – 0.049~ – 0.021 – 0.033 2,367

(0.014) (0.029) (0.015) (0.024)

Male 0.038 0.015 0.033 0.014 0.019 2,272

(0.012) (0.026) (0.014) (0.019)

BIPOLAR I [SR]

LIFETIME

All 0.014 0.004 0.009 0.004 0.006 4,639

(0.005) (0.011) (0.005) (0.008)

Female 0.010 0.010 0.020 – 0.001 – 0.001 2,367

(0.006) (0.013) (0.005) (0.008)

Male 0.017 – 0.002 – 0.004 0.008 0.012 2,272

(0.008) (0.017) (0.009) (0.013)

PAST YEAR

All 0.009 0.004 0.008 0.001 0.001 4,639

(0.004) (0.008) (0.004) (0.006)

Female 0.008 0.006 0.013 – 0.002 – 0.004 2,367

(0.005) (0.011) (0.004) (0.007)

Male 0.011 0.001 0.002 0.004 0.005 2,272

(0.006) (0.013) (0.007) (0.011)

BIPOLAR II [SR]

LIFETIME

All 0.006 0.001 0.002 0.008~ 0.011~ 4,639

(0.003) (0.006) (0.004) (0.006)

Female 0.010 – 0.001 – 0.003 0.008 0.012 2,367

(0.005) (0.011) (0.007) (0.012)

Male 0.001 0.004 0.008 0.007~ 0.010~ 2,272

(0.002) (0.005) (0.004) (0.005)
128

EXHIBIT 4.5. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

BIPOLAR II [SR] (CONTINUED)

PAST YEAR (CONTINUED)

All 0.006 0.000 0.000 0.007 0.010 4,639

(0.003) (0.006) (0.004) (0.006)

Female 0.010 – 0.003 – 0.006 0.007 0.011 2,367

(0.005) (0.010) (0.007) (0.012)

Male 0.001 0.003 0.006 0.006~ 0.009~ 2,272

(0.002) (0.005) (0.004) (0.005)

GENERALIZED ANXIETY

DISORDER WITH

HIERARCHY [SR]

LIFETIME

All 0.019 – 0.002 – 0.004 0.010~ 0.015~ 4,644

(0.005) (0.010) (0.006) (0.009)

Female 0.022 – 0.002 – 0.003 0.010 0.016 2,371

(0.007) (0.014) (0.008) (0.013)

Male 0.016 – 0.002 – 0.005 0.010 0.015 2,273

(0.006) (0.013) (0.009) (0.013)

PAST YEAR

All 0.007 0.005 0.010 0.008~ 0.012~ 4,644

(0.003) (0.007) (0.005) (0.007)

Female 0.007 0.009~ 0.019~ 0.010~ 0.015~ 2,371

(0.005) (0.010) (0.006) (0.009)

Male 0.007 0.001 0.002 0.006 0.008 2,273

(0.005) (0.010) (0.008) (0.011)

PANIC DISORDER [SR]

LIFETIME

All 0.047 0.000 0.000 0.001 0.002 4,639

(0.008) (0.017) (0.009) (0.013)

Female 0.052 – 0.013 – 0.027 – 0.004 – 0.006 2,367

(0.010) (0.021) (0.012) (0.018)

Male 0.042 0.014 0.030 0.006 0.008 2,272

(0.012) (0.027) (0.013) (0.018)

PAST YEAR

All 0.031 – 0.004 – 0.008 0.001 0.002 4,639

(0.007) (0.014) (0.007) (0.011)

Female 0.031 – 0.011 – 0.022 0.002 0.003 2,367

(0.008) (0.016) (0.009) (0.015)

Male 0.031 0.004 0.008 0.000 0.000 2,272

(0.011) (0.023) (0.011) (0.015)
129 Chapter 4: Impacts on Adult and Youth Mental Health

EXHIBIT 4.5. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

PANIC ATTACKS [SR]

LIFETIME

All 0.387 – 0.017 – 0.035 0.014 0.021 4,639

(0.019) (0.040) (0.021) (0.032)

Female 0.423 – 0.039 – 0.079 – 0.005 – 0.008 2,367

(0.027) (0.054) (0.029) (0.045)

Male 0.354 0.006 0.012 0.033 0.047 2,272

(0.027) (0.057) (0.030) (0.043)

PAST YEAR

All 0.058 – 0.020* – 0.042* – 0.007 – 0.010 4,639

(0.009) (0.019) (0.010) (0.015)

Female 0.060 – 0.029* – 0.059* – 0.003 – 0.005 2,367

(0.012) (0.025) (0.014) (0.021)

Male 0.056 – 0.010 – 0.022 – 0.011 – 0.016 2,272

(0.013) (0.029) (0.014) (0.020)

INTERMITTENTEXPLOSIVE DISORDER

WITH HIERARCHY [SR]

LIFETIME

All 0.164 – 0.001 – 0.003 0.006 0.009 4,639

(0.015) (0.032) (0.017) (0.025)

Female 0.162 – 0.008 – 0.016 – 0.006 – 0.009 2,367

(0.021) (0.042) (0.022) (0.034)

Male 0.166 0.005 0.011 0.017 0.025 2,272

(0.021) (0.045) (0.023) (0.033)

PAST YEAR

All 0.133 – 0.012 – 0.025 0.012 0.018 4,639

(0.014) (0.029) (0.016) (0.023)

Female 0.142 – 0.020 – 0.041 – 0.001 – 0.002 2,367

(0.019) (0.038) (0.021) (0.032)

Male 0.125 – 0.004 – 0.008 0.025 0.036 2,272

(0.018) (0.039) (0.022) (0.031)

OPPOSITIONAL-DEFIANT

DISORDER WITH

HIERARCHY [SR]

LIFETIME

All 0.120 – 0.001 – 0.002 0.009 0.013 4,639

(0.012) (0.025) (0.014) (0.021)

Female 0.118 – 0.014 – 0.028 0.001 0.001 2,367

(0.016) (0.032) (0.018) (0.028)
130

EXHIBIT 4.5. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

OPPOSITIONALDEFIANT DISORDER

WITH HIERARCHY [SR]

(CONTINUED)

LIFETIME (CONTINUED)

Male 0.122 0.012 0.026 0.016 0.023 2,272

(0.018) (0.038) (0.021) (0.030)

PAST YEAR

All 0.061 – 0.008 – 0.017 – 0.009 – 0.013 4,639

(0.009) (0.018) (0.010) (0.015)

Female 0.067 – 0.023* – 0.046* – 0.010 – 0.015 2,367

(0.012) (0.023) (0.013) (0.021)

Male 0.055 0.006 0.014 – 0.008 – 0.012 2,272

(0.014) (0.030) (0.015) (0.022)

POST-TRAUMATIC

STRESS DISORDER [SR]

LIFETIME

All 0.066 0.006 0.012 0.005 0.007 4,639

(0.010) (0.020) (0.011) (0.016)

Female 0.092 0.002 0.003 – 0.019 – 0.030 2,367

(0.016) (0.032) (0.016) (0.026)

Male 0.041 0.010 0.021 0.030* 0.043* 2,272

(0.012) (0.026) (0.015) (0.021)

PAST YEAR

All 0.045 0.010 0.021 0.000 0.000 4,639

(0.009) (0.018) (0.009) (0.013)

Female 0.063 0.007 0.013 – 0.019 – 0.030 2,367

(0.014) (0.027) (0.013) (0.020)

Male 0.028 0.014 0.029 0.020 0.028 2,272

(0.011) (0.024) (0.013) (0.018)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working).

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: Disorders with hierarchy take into account the comorbidity of other disorders: Depression with hierarchy takes into account Mania and Hypomania; GAD 

and ODD take into account Depression and Mania; and IED takes into account Depression, Mania, and Hypomania. See Section 4.3 for additional details. 
131 Chapter 4: Impacts on Adult and Youth Mental Health

EXHIBIT 4.6. SUMMARY MEASURES OF YOUTH MENTAL HEALTH, AGES 13–20

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

ANY ANXIETY DISORDER [SR]

LIFETIME

All 0.121 0.001 0.002 0.005 0.007 4,639

(0.013) (0.027) (0.014) (0.020)

Female 0.149 – 0.011 – 0.023 – 0.014 – 0.021 2,367

(0.019) (0.038) (0.020) (0.031)

Male 0.095 0.013 0.028 0.023 0.033 2,272

(0.017) (0.037) (0.019) (0.027)

PAST YEAR

All 0.083 0.007 0.014 0.003 0.004 4,639

(0.012) (0.024) (0.012) (0.018)

Female 0.098 0.002 0.003 – 0.006 – 0.010 2,367

(0.016) (0.032) (0.016) (0.026)

Male 0.069 0.011 0.024 0.011 0.016 2,272

(0.016) (0.034) (0.017) (0.024)

ANY MOOD DISORDER [SR]

LIFETIME

All 0.178 – 0.013 – 0.027 0.003 0.004 4,644

(0.015) (0.031) (0.016) (0.024)

Female 0.218 – 0.048* – 0.096* – 0.032 – 0.050 2,371

(0.022) (0.044) (0.024) (0.038)

Male 0.140 0.022 0.047 0.038~ 0.054~ 2,273

(0.020) (0.043) (0.022) (0.032)

PAST YEAR

All 0.104 0.003 0.005 0.006 0.008 4,643

(0.012) (0.026) (0.014) (0.020)

Female 0.141 – 0.025 – 0.051 – 0.020 – 0.030 2,370

(0.018) (0.037) (0.020) (0.031)

Male 0.070 0.030~ 0.065~ 0.030~ 0.043~ 2,273

(0.016) (0.034) (0.017) (0.025)

ANY MENTAL HEALTH

DISORDER [SR]

LIFETIME

All 0.370 – 0.001 – 0.002 0.011 0.017 4,640

(0.020) (0.041) (0.021) (0.032)

Female 0.402 – 0.039 – 0.080 – 0.025 – 0.038 2,368

(0.027) (0.055) (0.030) (0.046)

Male 0.339 0.037 0.080 0.047 0.066 2,272

(0.026) (0.057) (0.030) (0.042)
132

EXHIBIT 4.6. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

ANY MENTAL HEALTH 

DISORDER [SR] (CONTINUED)

PAST YEAR

All 0.259 – 0.001 – 0.003 0.019 0.028 4,639

(0.018) (0.037) (0.020) (0.030)

Female 0.298 – 0.032 – 0.065 – 0.012 – 0.019 2,367

(0.025) (0.050) (0.027) (0.042)

Male 0.222 0.029 0.062 0.050~ 0.070~ 2,272

(0.024) (0.051) (0.027) (0.039)

NUMBER OF MENTAL

HEALTH DISORDERS [SR]

LIFETIME

All 0.661 – 0.007 – 0.015 0.042 0.062 4,639

(0.044) (0.091) (0.048) (0.071)

Female 0.738 – 0.070 – 0.141 – 0.033 – 0.052 2,367

(0.063) (0.127) (0.069) (0.107)

Male 0.587 0.055 0.118 0.117~ 0.166~ 2,272

(0.057) (0.123) (0.063) (0.090)

PAST YEAR

All 0.425 0.000 0.001 0.020 0.030 4,639

(0.037) (0.076) (0.039) (0.057)

Female 0.504 – 0.061 – 0.122 – 0.026 – 0.041 2,367

(0.051) (0.104) (0.055) (0.086)

Male 0.351 0.062 0.133 0.065 0.092 2,272

(0.048) (0.103) (0.050) (0.070)

MENTAL HEALTH INDEX 

(Z-SCORE) [SR]

ABSENCE OF MENTAL HEALTH PROBLEMS: 

DEPRESSION, ANXIETY, AND DISTRESS 

(HIGHER SCORE INDICATES 

FEWER MENTAL HEALTH PROBLEMS)

All 0.000 0.031 0.065 – 0.016 – 0.024 4,644

(0.027) (0.056) (0.030) (0.045)

Female 0.000 0.069~ 0.139~ 0.016 0.025 2,371

(0.036) (0.073) (0.042) (0.065)

Male 0.000 – 0.006 – 0.013 – 0.048 – 0.068 2,273

(0.038) (0.081) (0.042) (0.060)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: Any Anxiety Disorder includes Panic Disorder, GAD (without hierarchy), and PTSD. Any Mood Disorder includes Depression (without hierarchy), Bipolar I/

II/Subthreshold, and Mania/Hypomania/Hypomania Subthreshold. Any Disorder includes all disorders from Any Anxiety Disorder and Any Mood Disorder as well as 

IED (without hierarchy) and ODD (without hierarchy). Index of mental health problems is the average of the z-scores for depression (lifetime), anxiety (lifetime), and 

distress (K6) after standardizing by the control mean and standard deviation (with male and female youth standardized separately). A higher mental health index 

score indicates better mental health. See Section 4.3 for additional details.
133 Chapter 4: Impacts on Adult and Youth Mental Health

EXHIBIT 4.7. PARENT-REPORTED MENTAL HEALTH OF GROWN CHILDREN, AGES 21–30

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

DEPRESSION OR OTHER SERIOUS 

MENTAL HEALTH PROBLEM [PR]

All 0.110 – 0.001 – 0.001 0.014 0.026 3,066

(0.014) (0.033) (0.020) (0.036)

Female 0.096 – 0.013 – 0.029 0.015 0.026 1,518

(0.019) (0.041) (0.027) (0.046)

Male 0.123 0.012 0.028 0.015 0.028 1,548

(0.021) (0.050) (0.028) (0.052)

ALCOHOL OR DRUG PROBLEM [PR]

All 0.054 – 0.012 – 0.027 0.017 0.031 3,056

(0.010) (0.023) (0.015) (0.028)

Female 0.026 – 0.002 – 0.005 0.010 0.017 1,514

(0.011) (0.024) (0.017) (0.030)

Male 0.082 – 0.022 – 0.050 0.023 0.043 1,542

(0.016) (0.038) (0.024) (0.046)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. Grown children (who were under age 18 at baseline and over age 20 as of December 31, 2007) of interviewed 

adults.

4.6 IMPACTS ON HYPOTHESIZED 

MEDIATORS

This section reviews effects on the mediating factors 

most directly relevant to the hypotheses we described in 

Section 4.1. The exhibits containing these results are in 

supplemental tables and Chapter 2.

SOCIAL ISOLATION

We analyzed a variety of measures of social networks, 

friendships, and social capital (Exhibit 2.11, 

Supplemental Exhibits 2.4–2.7). MTO has no 

detectable effect on adult reports of having friends 

in the neighborhood, visitations in their home, or 

casual chatting with neighbors, although experimental 

group adults are more likely to report having at least 

one friend who graduated from college. Effects on 

youth differ somewhat by gender. Female youth in the 

experimental group are less likely to have friends who 

have ever used drugs than controls, but otherwise, peers 

have similar characteristics for MTO youth across the 

randomly assigned treatment groups. Male youth in 

the experimental group are less likely to have at least 

one close friend and have less contact with friends from 

their baseline neighborhoods. Youth across treatment 

groups have similar reports on how good they feel about 

themselves or how others see them, except for some 

scattered, marginally significant effects toward others 

being less likely to see them as popular, athletic, or good 

students.

SAFETY AND EXPOSURE TO VIOLENCE

The MTO intervention appears to have produced 

improvements in safety and reductions in exposure 

to violence (see Exhibit 2.10). Compared with the 

control group, experimental and Section 8 adults report 

improved feelings of safety, a lower likelihood of seeing 

drugs sold or used, and a higher likelihood of police 

response to 911 emergency calls for service.
134

An apparent gender difference seems to emerge for youth 

in the extent to which MTO moves shape perceptions of 

safety in the local neighborhood, which could potentially 

help explain some of the gender differences in impacts 

on mental health. Assignment to the MTO experimental 

or Section 8 groups rather than to the control group 

improves feelings of safety and reduces the likelihood 

of seeing drugs in the neighborhood for female youth, 

but generally not for male youth. Female youth in the 

experimental group also report less unwanted sexual 

attention than those in the control group, a finding 

that is consistent with qualitative research collected 

during the interim study. These findings are mirrored by 

results suggesting that MTO moves are more likely to 

shift female youth than male youth into more prosocial 

peer groups. Whatever potential benefits MTO might 

generate from moving into areas with a lower prevalence 

of antisocial peers could be partially offset for male youth 

if they wind up in antisocial cliques in their new areas.

MENTAL HEALTH SERVICES

Supplemental Exhibit 4.1 shows MTO’s estimated 

impacts on mental health services. We observe no 

statistically significant effects on receiving services, 

the number of counseling sessions, being prescribed 

medicine, or feeling the need for services for mental 

health or substance use issues. Approximately 30 percent 

of control group adults report receiving or needing 

mental health services or prescription medicines; this 

number is 3.6 percentage points lower for adults in the 

experimental group. This difference is large as a share of 

the control mean but not quite statistically significant at 

the conventional 5 percent cutoff (p value of .063).

4.7 INTERPRETATION OF MENTAL 

HEALTH RESULTS

The results for adults show that MTO is associated 

with significant or nearly significant effects in reducing 

recent psychological distress and improving overall 

mental health, but largely by having effects on mild 

manifestations of mental disorder rather than on serious 

mental disorders. This is clearest in the K6 results, 

where the mean score in both intervention groups is 

significantly or close to significantly lower than the 

control group but the percentage of respondents with 

extremely high scores does not differ between groups. 

Results such as this are consistent with evidence from 

other community-level mental health interventions 

that low-intensity universal interventions are often 

successful in preventing relatively mild disorders that 

are comparatively easy to prevent but are ineffective in 

preventing more severe disorders (Offord, 2000). The 

standard approach to address this problem in community 

mental health intervention research is to use tiered 

interventions that add to the universal component 

a targeted component that provides more intensive 

intervention for people with high risk of disorders 

(Merry et al., 2004; Multisite Violence Prevention 

Project, 2004).

The results are somewhat different for youth, where 

the beneficial mental health effects of the intervention 

are limited to females and there is an adverse effect 

on Section 8 males with regard to PTSD. In contrast 

to the adults, female youth in the experimental group 

experienced reductions not only in mild distress but 

also for serious disorders. As a result, the same basic 

considerations apply in interpreting the results for 

female youth as for adults, with a special emphasis on 

the importance of finding individual-level indicators 

that can distinguish between females who are likely to 

profit from a universal MTO-like intervention and those 

who need more targeted interventions in conjunction 

with a universal intervention to prevent mental illness 

from occurring. Developing reliable algorithms to 

target indicated interventions is a major challenge in 

implementing effective multi-tiered community mental 

health interventions (Dodge, 2009).

Finally, the finding that MTO had no aggregate longterm effect on the mental health of grown children is 

unsurprising. We would expect a universal intervention 

such as this one to have no dramatic effect on the 

mental health of youth who had already spent a good 

part of their developmental years in more disadvantaged 

neighborhoods. Indeed, one might even expect long-term 

adverse mental health effects on these young adults, as 

they had established ties with their old neighborhoods 

before the MTO moves and were probably too old to 

benefit from the relocations.
135 Chapter 4: Impacts on Adult and Youth Mental Health

SUPPLEMENTAL EXHIBIT 4.1. ADULT MENTAL HEALTH SERVICES IN PAST 12 MONTHS

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

Received any professional mental health or 

substance use services [SR]

0.160 – 0.019 – 0.039 0.003 0.006 3,265

(0.016) (0.032) (0.021) (0.033)

Number of sessions of mental health or 

substance use counseling or therapy [SR]

2.478 – 0.128 – 0.263 – 0.680 – 1.078 3,256

(0.379) (0.780) (0.456) (0.723)

Prescribed medicine for mental health or 

substance use problems [SR]

0.162 – 0.021 – 0.043 – 0.011 – 0.018 3,264

(0.015) (0.031) (0.021) (0.033)

Felt the need for services for mental health or 

substance use problems but did not get it [SR]

0.088 – 0.003 – 0.007 – 0.022 – 0.035 3,265

(0.012) (0.025) (0.016) (0.026)

Received therapy, prescribed medicine, or 

felt the need for services for mental health or 

substance use problems [SR]

0.297 – 0.036~ – 0.075~ – 0.022 – 0.036 3,264

(0.019) (0.040) (0.026) (0.041)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category for example, a control mean of 0.250 for Working would 

indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

SUPPLEMENTAL EXHIBIT 4.2. YOUTH PSYCHOLOGICAL WELL-BEING, AGES 10–20 AND AGES 15–20

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

YOUTH AGES 10–20

PSYCHOLOGICAL DISTRESS INDEX (K6),

PAST MONTH [SR]

K6 Z-SCORE (HIGHER SCORE INDICATES

GREATER DISTRESS)

All 0.000 – 0.022 – 0.046 0.039 0.059 5,101

(0.040) (0.083) (0.045) (0.068)

Female 0.101 – 0.080 – 0.161 0.010 0.016 2,600

(0.056) (0.112) (0.064) (0.100)

Male – 0.096 0.036 0.077 0.067 0.096 2,501

(0.052) (0.113) (0.058) (0.082)

SERIOUS MENTAL ILLNESS (K6 RAW

SCORE ≥ 13)

All 0.064 – 0.008 – 0.016 0.009 0.013 5,101

(0.010) (0.020) (0.011) (0.016)

Female 0.081 – 0.021 – 0.042 0.004 0.006 2,600

(0.014) (0.028) (0.016) (0.025)

Male 0.048 0.005 0.012 0.013 0.019 2,501

(0.012) (0.027) (0.013) (0.019)
136

SUPPLEMENTAL EXHIBIT 4.2. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

BRIEF STRENGTHS AND DIFFICULTIES

QUESTIONNAIRE (SDQ) [SR]

SDQ RAW SCORE (HIGHER SCORE

INDICATES MORE DIFFICULTIES)

All 3.123 – 0.092 – 0.191 0.083 0.125 5,101

(0.070) (0.145) (0.077) (0.115)

Female 3.211 – 0.141 – 0.284 0.166 0.260 2,600

(0.097) (0.197) (0.111) (0.174)

Male 3.040 – 0.039 – 0.085 – 0.004 – 0.006 2,501

(0.090) (0.195) (0.096) (0.138)

SERIOUS BEHAVIORAL OR EMOTIONAL

PROBLEMS (SDQ RAW SCORE ≥ 6)

All 0.102 – 0.018 – 0.037 0.017 0.026 5,101

(0.011) (0.024) (0.013) (0.020)

Female 0.123 – 0.027 – 0.054 0.031 0.048 2,600

(0.016) (0.033) (0.020) (0.032)

Male 0.081 – 0.008 – 0.018 0.004 0.005 2,501

(0.015) (0.032) (0.016) (0.023)

CALM AND PEACEFUL MOST OF THE TIME 

OR MORE OFTEN IN THE PAST MONTH [SR]

All 0.599 0.020 0.042 – 0.007 – 0.010 5,099

(0.018) (0.038) (0.020) (0.030)

Female 0.527 0.035 0.070 0.021 0.032 2,600

(0.027) (0.053) (0.029) (0.046)

Male 0.667 0.006 0.013 – 0.035 – 0.050 2,499

(0.025) (0.054) (0.028) (0.041)

YOUTH AGES 15–20

MENTAL HEALTH INDEX (Z-SCORE) [SR]

ABSENCE OF MENTAL HEALTH PROBLEMS: 

DEPRESSION, ANXIETY, AND DISTRESS 

(HIGHER SCORE INDICATES FEWER MENTAL HEALTH PROBLEMS)

All 0.000 0.051~ 0.106~ – 0.003 – 0.005 3,621

(0.030) (0.062) (0.033) (0.050)

Female 0.000 0.096* 0.193* 0.048 0.075 1,845

(0.041) (0.082) (0.045) (0.071)

Male 0.000 0.007 0.014 – 0.054 – 0.078 1,776

(0.042) (0.090) (0.047) (0.069)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working).

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: Psychological distress consists of 6 items (sadness, nervousness, restlessness, hopelessness, feeling that everything is an effort, worthlessness) scaled 

on a score from 0 (no distress) to 24 (highest distress) and then converted to z-scores using the mean and standard deviation for control group youth (with male and 

female youth standardized separately). Serious mental illness is defined as a raw score of 13 or higher on the K6. Strengths & Difficulties consists of 5 behavioral and 

emotional items (obedience, worry/anxiety, unhappiness, getting along better with adults than peers, attention span) scaled on score from 0 (no behavioral/emotional 

problems) to 10 (severe behavioral or emotional problems). A score of 6 or higher indicates serious behavioral/emotional problems. Index of mental health problems is 

the average of the z-scores for depression (lifetime), anxiety (lifetime), and distress (K6) after standardizing by the control mean and standard deviation (with male and 

female youth standardized separately). A higher mental health index score indicates better mental health. See Section 4.3 for additional details.
137 Chapter 5: Impacts on Economic Self-Sufficiency

IMPACTS ON ECONOMIC 

SELF-SUFFICIENCY
138 CHAPTER 5

IMPACTS ON ECONOMIC SELF-SUFFICIENCY

This chapter summarizes the MTO demonstration’s 

impacts on employment, earnings, income and poverty, 

receipt of social assistance, and savings and assets. We 

examine economic self-sufficiency outcomes derived 

from the long-term survey based on adult and older 

youth self-reports and parental reports on their grown 

children. We also use administrative data from state 

unemployment insurance (UI) earnings records, 

Temporary Assistance for Needy Families (TANF) 

records, and food stamp records to capture quarterly 

earnings and employment levels, and receipt of public 

assistance.

Generally, we find no persistent systematic MTO 

impacts on employment, earnings, income, and 

other economic outcomes in our comparisons of the 

treatment groups. Our analysis of administrative UI 

data finds that experimental and Section 8 adults had 

significantly lower employment and earnings than 

the control adults in the first two years following 

random assignment, which suggests that moving may 

have had an initially disruptive effect. The effects on 

adult employment reversed and became positive—but 

not statistically significant—by five to six years after 

random assignment (seen in the interim follow-up).

The overall earnings and employment impacts 

measured using UI data remain small and positive (but 

still not statistically significant) for 10 years following 

random assignment. The experimental and Section 8 

groups experience few detectable effects on income, 

social assistance receipt, savings, and assets, although 

compared with controls, adults in both of the treatment 

groups report higher levels of food sufficiency in the 

long-term survey. There are no strong systematic 

impacts on the employment and earnings of older 

youth (15–20 years old as of the end of 2007) or grown 

children (under 18 years at baseline, but 21–30 years 

old at the end of 2007). 

5.1 BASELINE AND CONTROL 

GROUP CONTEXT

Exhibit 5.1 presents baseline characteristics for 

adults interviewed in the long-term survey, overall 

and separately by randomized treatment group. This 

exhibit (which is analogous to Exhibit 1.2) focuses 

on several baseline economic measures. At the time 

of random assignment, 25 percent (24 percent for the 

control group) of adults reported being employed. This 

low employment rate is not surprising given that the 

sample consisted largely of female household heads 

with limited education, most of whom were receiving 

public assistance (Aid to Families with Dependent 

Children [AFDC]; now called TANF) and living in 

public housing projects in high-poverty inner-city 

neighborhoods. Seventy-six percent of sample members 

were receiving AFDC at baseline and 81 percent 

were receiving food stamps (now called Supplemental 

Nutrition Assistance Program or SNAP). Further, 

nearly all adults (93 percent) had received AFDC at 

some point in the past. The average annual household 

income at baseline was $12,709 (in 2009 dollars),1

 

well below the official poverty line of $17,285 for a 

single parent with two children (Denavas-Walt, Proctor, 

and Smith, 2010). Moreover, median annual income at 

baseline was even lower—approximately $10,614.

The labor market conditions, tax-transfer system 

incentives, and constraints facing sample members 

have changed dramatically since they were enrolled 

in the demonstration in the mid-1990s. The national 

unemployment rate, which was 5.6 percent at the 

start of the demonstration in the last quarter of 1994, 

declined during the sustained 1990s boom, falling to 

3.9 percent in the last quarter of 2000. Labor market 

improvements during the late 1990s, which were 

strong in some large U.S. cities, including Boston, 

were particularly great for disadvantaged workers. But 

1 All dollar values reported in this chapter are in constant 2009 dollars, unless otherwise noted.
139 Chapter 5: Impacts on Economic Self-Sufficiency Chapter 5: Impacts on Economic Self-Sufficiency

the labor market deteriorated in the early 2000s, with 

the national unemployment rate rising to 6.0 percent in 

2003, recovering to 4.6 percent in 2007, skyrocketing 

during the recession of 2008–2009, reaching 10.0 

percent in the last quarter of 2009 and remaining at 9.4 

percent as of December 2010. (The median household 

income followed a generally similar pattern over this 

period.)2 The recent economic downturn, occurring 

at about the time of long-term survey data collection 

in 2008 and 2009, may have hit those at the lowest 

end of the income distribution the hardest. Data from 

the Census Bureau show that the official poverty rate 

increased from 12.5 percent in 2007 to 14.3 percent 

in 2009, with nearly 43.6 million people living below 

the federal poverty threshold in 2009 (Denavas-Walt, 

Proctor, and Smith, 2010).

State welfare systems also changed substantially over the 

course of the study period, starting with state welfare 

waivers in the early 1990s and then federal welfare 

reform in 1996. These changes, combined with the 

expansion of the Earned Income Tax Credit from 1993 

to 1996, increased the financial and social incentives for 

female heads of household to move off public assistance 

and into employment. National data on single female 

household heads indicate large declines in welfare receipt 

and large increases in employment rates and labor market 

earnings from the mid-1990s to 2000 (Blank, 2002). 

Much of the decline in TANF income support was the 

result of a decrease in the share of eligible families who 

were receiving assistance, rather than the result of any 

decrease in the share of families eligible for TANF. In 

2005, only 40 percent of eligible families received TANF 

cash assistance. By comparison, about 80 percent of 

eligible families received AFDC during the two decades 

prior to welfare reform (Schott, 2009). Food stamp 

caseloads increased dramatically during the recent 

economic downturn (by 37 percent through February 

2010). In contrast, TANF caseloads have increased 

only by 10 percent overall, and 23 states have caseloads 

that either increased by less than 5 percent or declined 

(Pavetti and Rosenbaum, 2010).

2 Median income increased in the mid- to late-1990s, peaked in 1999, 

declined in the early 2000s, recovered modestly from 2004 to 2007, 

and declined again through 2009, during the recent recession.

The changing economic and policy environment from 

the mid-1990s to the beginning of the 2000s improved 

labor market opportunities for single mothers and 

disadvantaged workers with concurrent large reductions 

in welfare caseloads. The recession of 2008–2009 and 

its aftermath (including a tightening of state and local 

government budgets) are expected to have created 

labor market difficulties and substantial fluctuations in 

labor market participation for adults during the longterm survey period. The weakening of labor market 

opportunities at the time of long-term follow-up in the 

late 2000s coincides with many of the MTO adults—

largely women from disadvantaged backgrounds—aging 

into their 40s and 50s and “naturally” cutting back on 

employment and hours worked, particularly in the face of 

mounting health problems.
140

EXHIBIT 5.1. SELECTED BASELINE CHARACTERISTICS OF ADULTS SURVEYED IN THE LONG-TERM 

EVALUATION: WORK, PUBLIC ASSISTANCE, AND INCOME

OUTCOME CONTROL 

GROUP

EXPERIMENTAL 

GROUP

SECTION 8 

GROUP 

ALL GROUPS

ADULT EMPLOYMENT STATUS

Working at baseline [SR] 23.8% 25.7% 26.5% 25.3%

Not working, but previously worked [SR] 56.4% 52.5% 55.1% 54.5%

Never worked for pay [SR] 19.8% 21.8% 18.4% 20.2%

HOUSEHOLD PUBLIC ASSISTANCE

RECEIPT

Receiving aid to families with dependent 

Children (AFDC) [SR]

76.3% 76.3% 73.6% 75.6%

Ever received AFDC [SR] 93.6% 94.3% 92.1% 93.4%

Working and not receiving AFDC [SR] 14.7% 15.9% 17.4% 15.9%

Receiving food stamps [SR] 81.8% 81.6% 80.7% 81.4%

Receiving Women, Infants and Children 

(WIC) Nutrition Program [SR]

36.7% 36.6% 37.7% 36.9%

Receiving Supplemental Security Income 

(SSI) [SR]

16.4% 16.1% 17.0% 16.4%

Receiving Social Security Disability 

Insurance (SSDI) [SR]

8.4% 9.0% 7.3% 8.4%

HOUSEHOLD INCOME (2009 DOLLARS)

Average income [SR] $12,438.64 $12,865.83 $12,788.32 $12,709.32

Median income [SR] $10,353.36 $10,629.45 $10,891.88 $10,613.94

MISCELLANEOUS

Owned a car at baseline [SR] 17.0% 19.0% 19.0% 18.3%

Very sure would like living in a neighborhood 

with neighbors who earn more than them 

[SR]

45.5% 41.5% 46.4% 44.2%

INTERVIEWED N 1,139 1,456 678 3,273

Notes: The numbers represent mean values on baseline characteristics for each treatment group and overall, weighted to reflect randomization ratios and the 

selection of adults into the interview sample and the two-phase sampling design of the long-term evaluation.

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

Data source and sample: MTO Participant Baseline Survey. Adult long-term survey. All adults interviewed.
141 Chapter 5: Impacts on Economic Self-Sufficiency

5.2 HYPOTHESES ABOUT EFFECTS 

ON ECONOMIC SELF-SUFFICIENCY

One primary motivation for the demonstration is to 

measure the impacts of neighborhood environments on 

the economic self-sufficiency outcomes of low-income 

families. These outcomes include employment and 

earnings, income and public assistance receipt, and 

savings and assets. Residential mobility might directly 

affect these outcomes in several ways (see Exhibit 5.2), 

described below.3

•	 Lower-poverty areas are likely to have lower 

unemployment rates and faster job growth than 

higher-poverty areas. Improved opportunities may 

generate higher employment and earnings, an 

increase in reservation wages (the lowest wage at 

which an individual will accept a job), and better 

jobs in terms of wages and benefits. Increased 

employment options for the experimental and 

Section 8 groups may also reduce welfare eligibility 

and participation and free up financial resources 

for savings.

•	 Access to housing vouchers through MTO increases 

the ability to relocate nearer potential jobs, rather 

than being tied to the location of a public housing 

project. This in turn reduces job-search costs and, 

once employed, commuting costs. This may lead 

to increased employment and earnings, lower 

reservation wages, and less dependence on public 

assistance.4

 If relocation leads to a broader range 

of employment opportunities, it may also lead to 

increased wages and fringe benefits (Weinberg, 

Reagan, and Yankow, 2004).

•	 Relocation to a safer neighborhood may reduce 

stress and anxiety and, more generally, may 

improve mental health and sense of control over 

one’s life. This in turn could result in more active 

job searching, lower absenteeism, improved 

workplace relations and productivity, and, 

3 See Orr et al. (2003) for the original conception of this logic model. 

Shroder (2002b) and Corcoran and Heflin (2003) provide a discussion 

and hypotheses on the more specific effects on labor supply of federal 

housing programs, compared with neighborhood environments more 

generally.

4 This is a formulation of the “spatial mismatch” hypothesis, for which 

an extensive literature exists (for example, see Ihlanfeldt and Sjoquist, 

1998).

therefore, increased employment and earnings 

and lower welfare dependence. Reduced stress and 

anxiety due to less crime and violence in a person’s 

neighborhood may also free up cognitive space for 

evaluating financial decisions and thinking about 

long-term financial planning.

•	 Community members in low-poverty areas may 

be more supportive of work and less accepting 

of welfare than those in public housing projects. 

If sample members are surrounded by social 

norms that encourage work over welfare, this 

might increase job-search effort, employment, 

and earnings, and may also reduce welfare 

participation.

•	 Social networks in less economically disadvantaged 

locations may provide access to new job 

opportunities. Although relocation may lead to an 

initial disruption of social networks, these effects 

may diminish as networks are established in new 

neighborhoods. Recent research (for example, 

Bayer, Ross, and Topa, 2008; Ioannides and Loury, 

2004; and Maurin and Moschion, 2009) points 

to potentially important effects of neighborhood 

social networks on labor market outcomes.

•	 Relocation may result in improved physical health 

potentially through fewer environmental hazards, 

more opportunities to walk or exercise in public 

spaces, or improved access to health care. Improved 

health can reduce rates of absenteeism and raise 

job productivity, leading to enhanced job prospects 

and earnings. 

•	 Relocation to new lower-poverty neighborhoods 

may lead to increased access to and reliance on 

formal banks, as opposed to high-cost alternatives 

more prevalent in high-poverty areas, such as check 

cashers and payday lenders. 

•	 The disruptions of social networks could make it 

more difficult for MTO movers to find work and 

arrange informal and affordable child care in the 

short run and thus lead to higher public assistance 

receipt. However, the impact of moves on access 

to job search assistance, affordable child care, and 

transportation is ambiguous over the long term.
142

EXHIBIT 5.2. HYPOTHESIZED PATHWAYS THROUGH WHICH MTO MAY IMPACT ECONOMIC SELF-SUFFICIENCY

MTO INTERVENTION RELOCATION TO LOW-POVERTY NEIGHBORHOODS

COMMUNITY-LEVEL

MEDIATORS

PERSON- AND FAMILY-LEVEL

MEDIATORS

ECONOMIC 

SELF-SUFFICIENCY

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

Exhibit 5.1 is a modified version of Exhibit 7.1 in the MTO interim impacts evaluation (see Orr et al., 2003)

Social and Physical 

Environment

· Reduced crime and violence

· Lower delinquency rate

· Welfare agency resources

Community Norms and 

Values

· Shared community value of 

 work

· Peer pressure to work/not 

 depend on welfare

· Welfare agency attitudes 

 toward work

· Shared community value of 

 saving

· Peer pressure to build assets

Economic Opportunities

· Lower unemployment rates

· Better access to the labor 

 market

· Better wages and fringe 

 benefits

· Greater proximity to jobs 

· Change in state welfare 

 system policies

· Access to formal banks

· Cheaper financial services

· Earnings

· Employment

· Decrease/stop monthly 

 receipt of TANF, AFDC, 

 Food Stamps/SNAP, 

 SSI benefits

· Bank account

· Car ownership

· Less debt

· Credit card ownership

Positive Effects

· Improved mental health

· Improved control over lives

· Reduced stress/anxiety

· Improved outlook

· Development of new 

 social networks capable 

 of reliable financial 

 support

· More active job search

· More use of networks for 

 labor market information

· Feelings of safety and 

 security

· Higher wages

 Negative Effects

· Short-run disruption of 

 existing social networks

· Less informal childcare 

 in the short run

· Social connections
143 Chapter 5: Impacts on Economic Self-Sufficiency

EMPLOYMENT

The existing evidence about the effects on adult 

employment from residential mobility programs is 

somewhat mixed. Rosenbaum and Popkin’s (1991) 

quasi-experimental analysis of female household heads 

in Chicago’s Gautreaux Assisted Housing Program found 

substantially higher employment rates (14 percentage 

points) for those who moved to the suburbs than for 

those who moved within the city. The longer-term and 

more comprehensive follow-up analysis of Gautreaux’s 

impacts on low-income, black female household heads by 

Mendenhall and colleagues (2006) finds little systematic 

impact on employment of moves to suburbs versus the 

city but persistent positive employment effects of being 

initially placed in less segregated neighborhoods (a lower 

percentage of black residents) with greater neighborhood 

resources. A recent study by Anil and colleagues (2010) 

also finds that moves through HOPE VI and similar 

programs had significant positive effects on employment 

for residents who moved, relative to other public housing 

tenants in Atlanta, Georgia.5

However, early analyses of MTO on adult labor market 

outcomes for Boston and Baltimore found no significant 

effects on adult household head employment in the first 

few years after randomization (Katz, Kling and Liebman, 

2001; Ludwig, Duncan and Pinkston, 2005). A survey of 

early enrollees in Los Angeles found no significant shortterm employment effects but did find modest evidence 

of increases in hours of work and weekly earnings 

for the experimental and Section 8 group household 

heads, relative to those in the control group (Hanratty, 

McLanahan and Pettit, 2003).

5 In related research, Jacob and Ludwig (2011) find negative effects of 

access to means-tested housing vouchers on adult labor supply (earnings and employment) using data from a randomized housing voucher 

wait-list lottery in Chicago. Their study sample consists of adults who 

were already living in private-market housing at the time they applied 

for a housing voucher. Those families (unlike MTO households) do 

not experience very much change in neighborhood “types,” and—also 

unlike MTO—do end up experiencing large increases in marginal tax 

rates on earnings and large increases in disposable income as a result of 

receiving a housing voucher. So the “treatment” in that study is quite 

different from the one examined in MTO. However, they also present 

results for the subset of Chicago housing voucher applicants who live in 

public housing at baseline, essentially a replication of our comparisons 

of the MTO Section 8 and control groups. For this subsample, Jacob 

and Ludwig find no significant impacts of access to means-tested housing vouchers on either employment or public assistance receipt in an 

analysis based on Illinois administrative UI and TANF data.

At the interim evaluation, analysts found no systematic, 

significant effects on adult employment, earnings, or 

welfare participation, even though experimental and 

Section 8 household heads moved to and remained in 

neighborhoods with higher employment rates than those 

of control families (Orr et al., 2003; Kling, Liebman 

and Katz, 2007; Ludwig et al., 2008). In a subsequent 

nonexperimental analysis of survey data from the interim 

evaluation, Clampet-Lundquist and Massey (2008) reestimate MTO impacts on the basis of the cumulative 

time families spent in low-poverty neighborhoods and 

find that families with longer stays in new neighborhoods 

saw positive impacts on employment and earnings. These 

results are likely subject to significant selection biases 

because within each treatment group families were not 

randomly assigned to different types of neighborhoods 

and since persistence in low-poverty neighborhoods 

also is likely to be correlated with unobserved family 

attributes that may lead to improved outcomes (as 

discussed by Ludwig et al., 2008). Moreover, the interim 

evaluation found no evidence of effects on average 

household income, poverty, or food insecurity.

It is possible that living in a less-disadvantaged 

neighborhood during childhood could have more 

pronounced effects on labor market outcomes. A large 

body of nonexperimental research finds that children 

who grow up in poor neighborhoods have worse 

economic outcomes during adolescence and adulthood 

than children who grow up around more affluent 

neighbors, although the effects are somewhat attenuated 

when controlling for other aspects of family background 

(see, for example, Corcoran et al., 1992; Page and 

Solon, 2003; Sharkey, 2008). Research using quasiexperimental designs (natural experiments) to evaluate 

the long-term impacts of housing mobility interventions 

on child economic outcomes yields mixed results. 

Oreopolous (2003) finds no systematic impacts of child 

neighborhood environments on adult labor market 

outcomes for children assigned to public housing projects 

in substantially different neighborhoods of Toronto. 

Rosenbaum’s (1995) analysis of a 1988 follow-up survey 

of families who moved with Gautreaux vouchers finds 

strong positive gains in educational and economic 

outcomes for the children of suburban movers relative to 

city movers, but longer-term follow-ups find less striking 
144

contrasts between suburban and city movers (DeLuca 

et al., 2010). Gould and colleagues (2009) find positive 

impacts on long-term adult economic outcomes for 

Yemenite refugees to Israel who as children were initially 

placed in more prosperous neighborhoods with better 

infrastructure.

At the interim evaluation, the MTO experimental 

treatment was associated with a substantial increase 

in female youth school enrollment and a substantial 

decrease in female youth idleness (defined as being 

neither employed nor enrolled in school) compared with 

the controls. The school enrollment rate increased by 16 

percentage points (p=.096) and the idleness rate declined 

by 16 percentage points (p=.074) for female youth in 

experimental group families who moved with an MTO 

voucher (Orr et al., 2003, p. 132, Exhibit 7.6, p. 133, 

and Exhibit D7.1b, p. D-11). MTO had no comparable 

treatment effects on males in the interim evaluation.

Other factors that vary across neighborhoods can also 

affect youth economic outcomes. School-to-work 

programs administered by local public schools may 

help youth secure internships while still enrolled in 

high school and help non-college-bound youth secure 

employment after high school. Local social norms about 

the stigma surrounding entry-level jobs or local criminal 

activity, the level of violence associated with the local 

illegal economy and the quality of local policing, and 

the level of difficulty in competing for jobs and related 

positive awards for behavior that supports schooling 

or employment can also affect youth decisions about 

whether to participate in the formal labor market or 

pursue underground or informal work.

PUBLIC ASSISTANCE AND INCOME

The MTO demonstration’s impacts on welfare receipt 

are likely to mirror the impacts on employment and 

earnings. Moves from public housing to private housing 

in lower-poverty areas may affect welfare participation 

(TANF, food stamps, and Supplemental Security 

Income). To the extent that the demonstration increases 

employment and income, it can be expected to reduce 

eligibility for and reliance on social assistance. Changes 

in norms and attitudes toward the use of public assistance 

in new neighborhoods may also play a role.

MTO impacts on household income depend primarily 

on earnings and public assistance outcomes, because 

these are the main components of income for most 

sample members. An increase in earnings can lead to a 

decrease in welfare benefits, depending on the thresholds 

set for earned income disregards; the overall effect on 

cash income is expected to be positive. However, the 

size of the effect on overall household income depends 

on the reduction in welfare benefits for a given increase 

in earnings and the proportion of sample members that 

receives cash assistance.

SAVINGS AND ASSETS

The MTO demonstration’s impacts on savings and assets 

are influenced by impacts on employment, earnings, and 

income, as well as access to community-based financial 

institutions, new social norms, and the ways in which 

these changes alter an individual’s financial and savings 

decision-making. The initial MTO moves may disrupt 

informal networks available for instrumental support and 

financial assistance in emergencies. If the demonstration 

improves economic conditions for families, this may lead 

to changes in their financial behavior or net worth simply 

by increasing their capacity to save or acquire assets.

The demonstration might also affect savings and 

asset outcomes conditional on earnings or income 

levels if moves to low-poverty neighborhoods provide 

increased access to formal banking opportunities, better 

information or knowledge about returns on investments 

through social networks and local institutions, or 

exposure to neighborhood residents who utilize 

mainstream financial services. Formal financial services 

might also be more readily available in communities 

where a larger proportion of residents are employed in 

the formal labor market (Vermilyea and Wilcox, 2002). 

High-poverty areas may be less likely to have local bank 

branches, although the evidence on this point is mixed 

(Caskey, 1997; Vermilyea and Wilcox, 2002). However, 

high-poverty areas are more likely to have check cashing 

services, payday or auto title lenders, pawnshops, and 

rent-to-own stores that charge very high interest rates and 

may, as Carr and Schuetz argue, “undermine the ability 

of individual households to accumulate assets and build 

wealth” (2001, p. 5; see also Temkin and Sawyer, 2004; 

Rhine et al., 2001). Moves to a low-poverty neighborhood 
145 Chapter 5: Impacts on Economic Self-Sufficiency

may affect financial decisions, because movers are exposed to 

information about the importance of saving. Social values in 

destination neighborhoods may encourage saving; moreover, 

individuals may realize they can save more using formal 

financial institutions, compared with informal institutions 

like check cashers and payday lenders, and use them more 

as a result. New surroundings may lead movers to see future 

consumption through a different psychological frame, and 

decreases in stress may free up some mental space that could 

be used for making financial decisions (Loewenstein and 

O’Donoghue, 2004; Gennetian, Mullainathan, and Shafir, 

forthcoming).

MOVES AND ECONOMIC 

SELF-SUFFICIENCY

The mobility experiences of the experimental and Section 

8 groups can help us understand how the demonstration 

might affect economic self-sufficiency. Treatment group 

differences in economic behavior over the long-term may 

not be detectable at a shorter-term follow-up. MTO moves 

may initially be disruptive, so any improvements in labor 

market behavior may not be observed until the effects of 

this initial disruption dissipate. The impact of an initial 

disruption will also be sensitive to the overall number 

of moves and whether the initial move precipitated high 

subsequent mobility, potentially including a move back 

to the participant’s neighborhood of origin. Labor market 

outcomes may also be responsive to threshold effects based 

on the cumulative environmental and social improvements 

that result from moves to lower-poverty neighborhoods and 

not be apparent until after a critical mass of exposure to 

improved neighborhood environments.

At the long-term follow-up, the experimental and Section 

8 groups on average had moved into neighborhoods that 

appear to have more favorable employment opportunities, 

as well as norms more supportive of work, than do the 

neighborhoods of the control group. The experimental 

and Section 8 group members reside in neighborhoods 

with substantially lower poverty rates, higher adult 

employment rates, and higher proportions of two-parent 

families (Exhibit 2.7). Adults in the experimental group 

are significantly more likely to have at least one close 

friend who graduated from college and, at a marginally 

significant level, more likely to have a close friend who 

works full-time (Exhibit 2.11). Experimental and Section 

8 adults report high satisfaction with their neighborhoods 

and improvements in neighborhood safety, compared with 

reports from control group adults. Observed improvements 

in safety (Chapter 2) and increases in potential adult role 

models with stable jobs in the new neighborhoods provide 

a potentially favorable context for improving labor market 

opportunities and the attitudes of youth. Gender differences 

for youth on some mental health outcomes (Chapter 4) 

and delinquency behaviors (Chapter 6) lead one to expect 

more beneficial impacts of the demonstration on the labor 

market experiences of female youth than male youth. The 

MTO demonstration did not significantly strengthen social 

networks or improve adult access to transportation.

5.3 DATA SOURCES FOR MEASURES 

OF EMPLOYMENT AND EARNINGS

Employment and earnings outcomes are derived from 

respondent self-reports from the long-term evaluation 

survey and quarterly earnings data of core household 

members come from state UI records. Administrative data 

is available for core adult household heads, children who are 

now grown adults, and older youth.

The long-term survey includes a series of questions to 

adults about their current employment status, hours of 

work, earnings from their current main job, total earnings 

for the most recent past calendar year, employee benefits, 

job-search behavior, informal work, and the duration 

and characteristics of their current main job.6 These 

employment questions are the same as the standard 

employment status questions from the Current Population 

Survey used by the Bureau of Labor Statistics to measure 

employment, unemployment, and labor force participation.

Respondent descriptions of their industry and occupation 

are coded using Census 2000 three-digit industry and 

occupation codes. Older youth are similarly asked a battery 

of standard employment status questions, as well as a further 

series of questions on earnings, informal employment 

arrangements, and enrollment status and participation in 

job training programs. A host of items on the long-term 

survey cover potential mediators such as social networks, 

6 All survey and administrative records outcomes are adjusted for extremely high and unreasonably low outliers and any logical inconsistencies. Outliers on survey outcomes are adjusted based on a case-by-case 

review and then any value above the 99th percentile is set to missing. 

Zero values of total income are assigned the fifth percentile value. 

Sensitivity analyses show that these adjustments to outliers do not 

qualitatively alter the results. We’ve also conducted sensitivity analyses 

limiting analyses of survey data outcomes to the available sites with UI 

data, and, separately for the sites with available TANF/food stamp data 

(not shown).
146

neighborhood satisfaction, and household composition. 

Census data on the characteristics of current neighborhoods 

provide information on mediating factors for employment, 

which can be used to test alternative hypotheses about 

MTO impacts on labor market outcomes.

Administrative data from state UI records collected 

and processed from each of the states of the five main 

demonstration sites—California (for the Los Angeles site), 

Illinois (for the Chicago site), Maryland (for the Baltimore 

site), Massachusetts (for the Boston site), and New York (for 

the New York City site)—and the state of Florida provide 

quarterly earnings information for core household members 

(adults, youth, and grown children). Agencies matched 

these administrative records to core members by social 

security numbers. Adult data are collapsed into years since 

random assignment and grouped into two-year periods. The 

results are annualized to illustrate the impact over time from 

1 to 10 years since random assignment.

Three of the five main site states provided individual-level 

earnings information on each sample member who matched 

to the UI records. Data from Massachusetts and New York 

are not available at the individual level but rather collapsed 

at a group level.7

 For Massachusetts, cell sizes were 

restricted to 10 or more individuals; for New York, each 

7 To maximize use of the individual and aggregate cell data, UI analyses 

use the weighted share of participants in each site to create the combined 

impact estimates. To create the combined estimates, we use the weighted 

average of the estimate from each site. To calculate the combined standard 

error, we used the formula SE = sqrt(se1^2*share1^2 + se2^2*share2 ^2+...+ 

se4^2*share4^2) where “se1” is the standard error of the ITT estimate of the 

first state, “share1” is the weighted share of the sample for the first state, etc. 

In our adult analyses for sites with individual-level data (i.e., Baltimore, Chicago, and Los Angeles) we control for our standard set of covariates (except 

for the survey release flag) and also include dummies for quarter of random 

assignment, and earnings in the year prior to random assignment and 

weighted to account for changes in randomization ratios. The analyses with 

Boston aggregate adult data control for the weighted cell means for baseline 

work status, baseline high school completion status, and dummies for quarter 

of random assignment. The New York City aggregate analyses do not control 

for any baseline covariates. Youth analyses for sites with individual-level data 

include youth ages 15 to 20 as of December 31, 2007 and control for our 

standard set of covariates. The Boston aggregate youth analyses include youth 

ages 12 to 20 as of December 2007 (because our request file to Massachusetts did not split out ages 15-20) and include only the following covariates: 

gender, age as of December 2007, and the following baseline characteristics 

of the youth: required special medicine and/or equipment and had health 

problems that limited activity. The New York site youth analyses focus on 

youth ages 15 to 20 and do not control for any baseline covariates other than 

gender. In the aggregate youth analyses we are unable to cluster on family. All 

adult and youth UI analyses use only participants for whom we had a social 

security number from prior to random assignment that met a minimum 

level of verification by the Social Security Administration (a “best SSN” determined based on an algorithm developed by Jeffrey Liebman for the MTO 

interim evaluation). Participants without best SSNs were omitted from the 

main results, but included in sensitivity tests.

cell was required to have 10 or more matches, resulting 

in larger cell sizes to ensure a reportable match rate. 

The most recent quarter of data varies by state: quarter 

4 of 2008 for Maryland, quarter 3 of 2008 for Illinois, 

quarter 1 of 2009 for California, and quarter 4 of 2009 

for both Massachusetts and New York.

We focus on two primary labor market outcomes using 

the UI administrative data: annual earnings and the 

proportion of quarters that individuals were employed. 

Annual earnings were calculated by adding quarterly 

earnings data. For adults, annual earnings were then 

averaged across two-year periods.8 The share of quarters 

employed indicates the portion of the period for which 

a person had recorded earnings more than zero. The 

number of quarters with positive earnings is divided by 

eight (the number of quarters in the two-year period).

More generally, the administrative data serve an 

important complement to the survey data on 

employment by allowing us to analyze the evolution 

of employment and earnings impacts over time, 

since random assignment for the full sample of core 

participants (whether or not they were eligible for or 

responded to the long-term survey). However, UI data 

does have some disadvantages. Specifically, informal and 

uncovered employment are not measured, errors in social 

security numbers may lead to incorrect matches, earnings 

are not captured for out-of-state movers, information is 

not provided about job quality and employee benefits, 

and it is not possible to disentangle the contributions of 

weekly hours, quarterly weeks worked, and hourly wages 

to quarterly earnings.

Traditional measures of employment, labor force 

participation, and earnings gauge the labor market 

success of sample adults. But the interpretation of 

employment outcomes for youth also needs to take into 

account the importance of schooling as an alternative 

use of time that can improve eventual adult labor market 

outcomes. We use a summary measure of “idleness”—

being out of work and not enrolled in school—to provide 

a more accurate picture of youth labor market outcomes.

8 Any quarterly wages over $45,000 are considered outliers and first 

checked by examining the two surrounding quarters to try to rectify any 

outliers caused by single-quarter data entry error, and otherwise top coded 

at $45,000.
147 Chapter 5: Impacts on Economic Self-Sufficiency

5.4 LONG-TERM IMPACTS ON 

EMPLOYMENT AND EARNINGS 

OF ADULTS

Exhibit 5.3 plots the employment rate of sample adults 

by random assignment group from 1995 to 2008 using 

data from state UI records covering all five demonstration 

sites. For adults in the control group, the employment 

rate (the percentage with positive quarterly earnings) 

more than doubled, from 23.7 in the first quarter of 

1995 to 51.5 in 2000, in the face of welfare reform and the 

rapid economic expansion of the second half of the 1990s. 

However, the employment gains for adults in the control 

group somewhat eroded in the weaker labor market of the 

2000s, with some employment decline in the 2001 recession 

and at the start of the recession of 2008–2009. Part of the 

employment gains in the late 1990s may have reflected lifecycle work patterns that ceased as the typical MTO adult 

entered her 40s in the 2000s.

The experimental and Section 8 adults show broad 

patterns quite similar to the control group, with rapidly 

rising employment rates from 1995 to 2000 and modest 

employment declines in the 2000s. By 2000, higher 

employment rates are apparent for experimental and 

especially Section 8 adults, relative to control group adults. 

However, this pattern of treatment group employment 

advantage eroded by 2003. Adults in all three random 

assignment groups had similar employment trends and 

ended up with much higher employment rates by the final 

survey in 2008 (around 45 percent) than at the time of 

random assignment in 1995 (around 25 percent).

EXHIBIT 5.3. ADULT FIVE-SITE QUARTERLY EMPLOYMENT RATE BY RANDOM ASSIGNMENT GROUP AND 

CALENDAR QUARTER PERCENT EMPLOYED

40

50

60

30

20

10

CALENDAR QUARTER

1995 Q11996 Q11997 Q11998 Q11999 Q12001 Q12002 Q12003 Q12004 Q12005 Q12006 Q12007 Q12008 Q1

Experimental group mean

Section 8 group mean

Control group mean

Exhibit 5.3. Adult Five-Site Quarterly Employment Rate by Random Assignment Group and Calendar Quarter

Data source and sample: These analyses use individual data from Unemployment Insurance records from Maryland, Illinois, California, and Florida for those 

whose random assignment sites are Baltimore, Chicago, or Los Angeles. It also incorporates aggregated Unemployment Insurance data from Massachusetts and 

New York, which represent individuals whose random assignment sites are Boston or New York City. All sample adults with baseline consent (N = 4,194).
148

Exhibit 5.4 plots the group employment rates by time 

since random assignment using quarterly data for 

all five sites. The patterns suggest a small disruption 

effect of moves on employment during the first two 

years of follow-up, as evidenced by the slightly higher 

employment rate for the control group (shown in the 

solid gray line) than for either the experimental or 

Section 8 groups. The adult employment rates for the 

experimental and Section 8 groups increased relative 

to the control group in the second year after random 

assignment and were slightly higher than the control 

group roughly four years after random assignment. The 

employment rates for all three groups followed the same 

trend and remain generally similar over the remaining 

follow-up period through 43 quarters, or roughly 10.5 to 

11 years after random assignment.

EXHIBIT 5.4. ADULT FIVE-SITE EMPLOYMENT RATE BY RANDOM ASSIGNMENT GROUP AND THE NUMBER OF 

QUARTERS SINCE RANDOM ASSIGNMENT

PERCENT 

EMPLOYED

40

50

60

30

20

10

NUMBER OF QUARTERS SINCE RANDOM ASSIGNMENT

13579

11

1315

17

19

21232527293133353739

4143

Experimental group mean

Section 8 group mean

Control group mean

Exhibit 5.4. Adult Five-Site Employment Rate by Random Assignment Group and Quarters Since Random Assignment PERCENT EMPLOYED

Data source and sample: These analyses use individual data from Unemployment Insurance records from Maryland, Illinois, California, and Florida for those 

whose random assignment sites are Baltimore, Chicago, or Los Angeles. It also incorporates aggregated Unemployment Insurance data from Massachusetts and 

New York, which represent individuals whose random assignment sites are Boston or New York City. All sample adults with baseline consent (N = 4,194).
149 Chapter 5: Impacts on Economic Self-Sufficiency

Exhibit 5.5 more formally presents estimates of the 

demonstration impacts on employment and earnings of 

sample adults using the state UI records from a prerandom assignment period to the ninth and tenth year 

of follow-up for the four sites—Baltimore, Boston, Los 

Angeles, and Chicago—with complete data. The UI 

administrative data indicate modest negative impacts 

on employment and earnings for the experimental 

and Section 8 groups (marginally significant for the 

experimental group) in the first two years following 

random assignment.9 This pattern may reflect initial 

disruption effects of MTO moves on employment 

relations and child care arrangements.

The experimental and Section 8 groups gain on the 

control group in employment over the next several years 

with slightly beneficial (especially for Section 8 adults), 

but not statistically significant, employment impact 

estimates by nine to ten years after random assignment. 

The impacts on average annual earnings over the first 

ten years from random assignment are modestly negative 

but insignificant for both the experimental and Section 

8 groups, relative to the control group. Supplemental 

Exhibit 5.1 presents these same estimates separately 

by site, and suggests beneficial (but generally not 

significant) impacts on employment for the Los Angeles 

and Chicago sites, and more negative impacts for the 

Baltimore site and especially for the experimental group 

from the Boston site. The bottom line is one of little 

systematic MTO impact on employment and earnings 

averaged across sites during the first ten years following 

random assignment.

Exhibit 5.6 incorporates UI data on employment and 

earnings from all five demonstration sites for 2007, 

the most recent calendar year for which comparable 

information is available for all demonstrations sites. There 

9 This initial disruption effect is also described in Orr et al. (2003, 

p. 128): “The employment rate (fraction of quarters employed) of 

the experimental group moves from being modestly (but statistically 

significantly) lower than the control group (by 2.5 percentage points) 

in the first two years after random assignment to being only slightly 

lower (by 0.9 percentage points) and statistically indistinguishable from 

the control group on average in the third and fourth years after random 

assignment.” The estimates in Exhibit 5.5 slightly differ from Orr et 

al. in that data are available for only four sites—New York is excluded. 

Exhibit 5.6 suggests that adding data from New York does not qualitatively alter the main conclusions.

are no sizable or significant differences in employment 

and earnings in 2007 by random assignment group.10

Exhibit 5.7 shows estimates of impacts on employment 

outcomes based on adult self-reports from the long-term 

survey. The survey data mirror the administrative data in 

that there are no significant differences in employment 

or earnings outcomes for the experimental and control 

groups. But the long-term survey-based estimates do show 

a significant negative impact on current employment 

and a sizable negative (but not significant) impact on 

annual earnings for the Section 8 adults—findings not 

corroborated by the patterns observed in the UI data.

The observed negative effects on work and earnings in 

the self-reported survey data among those in the Section 

8 group is a likely artifact that results from the slightly 

later timing of when those adults were interviewed, 

compared with the experimental and control groups. 

The supplemental exhibits examine, in more detail, 

the differences in employment impact estimates for the 

Section 8 adults between the long-term survey and UI 

administrative data. By the time the Section 8 adults 

were interviewed (starting February 2009), they were 

facing higher national unemployment compared with the 

experimental and control group adults.

A number of additional analyses examine possible 

explanations for the varying results by data source related 

to calendar timing of data collection and differences 

in the type and location of employment captured by 

source. Supplemental Exhibit 5.2 compares intentionto-treat (ITT) and treatment-on-the-treated (TOT) 

effects for survey data and UI administrative data for 

current employment around the time of the long-term 

survey. The estimated impacts are quite similar for the 

two data sources for the experimental group relative to 

the control group, but the Section 8 group impacts are 

substantially more negative for current employment in 

10 UI data from Boston and New York were delivered as aggregate cell 

data. Data from Boston included means and standard deviations for 

each quarter, as well as annualized. The New York aggregated cell data 

did not include annualized standard deviations. We estimated an upper 

bound to employment levels using the individual level data from Baltimore, Chicago, and Los Angeles to calculate the annualized New York 

share employed outcome, and took the average correlations of earnings 

from these same individual UI sources to calculate the standard deviation for the New York annual earnings measure.
150

the survey than in the UI data. Supplemental Exhibit 

5.3 explores this discrepancy by examining employment 

in the previous calendar year and including only the 

sample of respondents who were living in the state of 

the initial demonstration site at the time of the survey 

interview (that is, out-of-state movers were excluded). 

The gap between the survey and UI data results is 

modestly reduced overall, and specifically in California 

and Illinois. In fact, for Illinois, the Section 8 results 

for survey and UI employment are quite similar in 

Supplemental Exhibit 5.3, excluding out-of-state 

movers, as opposed to Supplemental Exhibit 5.2 where 

the direction of the effect on employment measured 

via survey is opposite the direction of the effect on 

employment measured via UI data. As it turns out, 

Chicago has the highest number of out-of-state movers 

compared with the other sites. Thus, this closer aligning 

of survey and administrative records points to a similar 

negative effect on current employment among Section 8 

adults.

Supplemental Exhibit 5.4 further shows the industry and 

occupation of employed adults, measured at the time of 

the survey interview.11 Adults in the experimental and 

control groups have similar distributions of employment 

across the retail, health care, education, and social service 

sectors and have similar broad occupational distributions. 

Section 8 adults, however, generally show patterns of 

lower employment rates in the long-term survey than 

the control group, across all sectors including retail and 

social services, and occupation levels. And, as shown in 

Supplemental Exhibit 5.5, experimental group adults 

were slightly (but not significantly) more likely than 

control group adults to be employed at the time of 

the long-term survey in jobs offering health insurance, 

vacation, and paid sick leave, while the Section 8 adults 

were less likely to be employed in jobs with such benefits.

11 Industry and occupation are coded using 1990 census codes.

EXHIBIT 5.5. ADULT EMPLOYMENT AND EARNINGS, ADMINISTRATIVE DATA BY YEARS SINCE RANDOM 

ASSIGNMENT (RA)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

EMPLOYED [UI]

THREE-SITE WEIGHTED AVERAGE

(MD, IL, CA ONLY)

Pre-RA year 1 0.276 – 0.001 – 0.003 0.005 0.006 2,365

(0.015) (0.031) (0.017) (0.024)

FOUR-SITE WEIGHTED AVERAGE

(MD, IL, CA, MA)

RA years 1 and 2 0.408 – 0.023~ – 0.049~ – 0.017 – 0.025 3,250

(0.012) (0.026) (0.014) (0.020)

RA years 3 and 4 0.499 – 0.003 – 0.006 0.005 0.008 3,250

(0.016) (0.033) (0.017) (0.025)

RA years 5 and 6 0.519 0.005 0.010 0.015 0.022 3,250

(0.016) (0.034) (0.018) (0.027)

RA years 7 and 8 0.485 0.002 0.005 0.029 0.043 3,250

(0.017) (0.036) (0.020) (0.029)

RA years 9 and 10 0.465 0.007 0.014 0.026 0.038 3,250

(0.017) (0.036) (0.019) (0.028)

RA years 1–10 0.475 – 0.003 – 0.005 0.012 0.017 3,250

(0.012) (0.026) (0.013) (0.020)
151 Chapter 5: Impacts on Economic Self-Sufficiency

EXHIBIT 5.5. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

ANNUAL WAGES (2009 DOLLARS) [UI]

THREE-SITE WEIGHTED AVERAGE

(MD, IL, AND CA ONLY)

Pre-RA Year 1 $3,163.83 94.20 191.41 56.61 76.72 2,365

(240.38) (488.46) (274.91) (372.61)

FOUR-SITE WEIGHTED AVERAGE

(MD, IL, CA, MA)

RA years 1 and 2 $5,781.17 – 377.39~ – 786.86~ – 214.97 – 317.03 3,250

(221.62) (462.10) (255.44) (376.71)

RA years 3 and 4 $8,398.43 – 422.45 – 880.83 – 541.97 – 799.28 3,250

(348.14) (725.88) (368.47) (543.40)

RA years 5 and 6 $10,111.07 – 274.30 – 571.93 – 24.84 – 36.63 3,250

(436.74) (910.63) (459.28) (677.33)

RA years 7 and 8 $10,526.24 – 358.94 – 748.41 – 131.96 – 194.60 3,250

(518.30) (1,080.68) (570.49) (841.34)

RA years 9 and 10 $10,643.49 -179.70 – 374.68 90.45 133.39 3,250

(550.89) (1,148.63) (617.70) (910.96)

RA years 1–10 $9,092.08 – 322.56 – 672.54 – 164.66 – 242.83 3,250

(343.66) (716.54) (369.25) (544.56)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: UI = Unemployment Insurance administrative records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Individual level data from Maryland, Illinois, California, and Florida (representing individuals whose random assignment sites are 

Baltimore, Chicago, and Los Angeles) and aggregate data from Massachusetts (representing individuals whose random assignment site is Boston). All sample 

adults with baseline consent.
152

EXHIBIT 5.6. ADULT EMPLOYMENT IN 2007, ADMINISTRATIVE DATA BY SITE

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

EMPLOYED [UI]

Maryland 0.593 – 0.097* – 0.181* – 0.090~ – 0.115~ 629

(0.044) (0.082) (0.048) (0.061)

Illinois 0.471 0.021 0.064 0.020 0.030 883

(0.038) (0.112) (0.045) (0.067)

California 0.424 0.059 0.095 0.064 0.082 853

(0.037) (0.060) (0.039) (0.050)

Massachusetts 0.444 – 0.033 – 0.074 – 0.003 – 0.006 885

(0.036) (0.081) (0.044) (0.085)

New York 0.431 0.004 0.010 – 0.015 – 0.033 944

(0.040) (0.087) (0.040) (0.088)

Five-site weighted average 0.465 – 0.004 – 0.009 0.000 0.000 4,194

(0.017) (0.036) (0.019) (0.030)

ANNUAL WAGES 

(2009 DOLLARS) [UI]

Maryland $14,420.03 – 3,090.98* – 5,770.22* – 1472.88 – 1,884.70 629

(1,316.30) (2,457.27) (1,607.95) (2,057.55)

Illinois $9,733.91 696.73 2,070.28 180.33 267.88 883

(1,121.28) (3,331.79) (1,297.35) (1,927.19)

California $9,649.18 709.15 1,141.41 971.73 1,256.06 853

(1,127.95) (1,815.48) (1,125.39) (1,454.68)

Massachusetts $12,245.56 – 755.34 – 1692.58 745.68 1429.99 885

(1,397.82) (3,132.25) (1,599.76) (3,067.87)

New York $11,448.08 – 73.08 – 158.73 – 260.76 – 580.57 944

(887.41) (1,927.62) (880.92) (1,961.30)

Five-site weighted average $11,325.14 – 347.83 – 731.73 112.93 180.50 4,194

(523.80) (1101.92) (580.69) (928.11)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: UI = Unemployment Insurance administrative records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Individual level data from Maryland, Illinois, California, and Florida (representing individuals whose random assignment sites are 

Baltimore, Chicago, and Los Angeles) and aggregate data from New York and Massachusetts (representing individuals whose random assignment sites are New 

York City and Boston). All sample adults with baseline consent.
153 Chapter 5: Impacts on Economic Self-Sufficiency

EXHIBIT 5.7. ADULT SELF-REPORTED EMPLOYMENT AND EARNINGS

CONTROL 

MEAN

 EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

Currently employed [SR] 0.525 – 0.007 – 0.014 – 0.077* – 0.124* 3,264

(0.021) (0.042) (0.028) (0.045)

Currently employed full time [SR] 0.367 – 0.016 – 0.033 – 0.047~ – 0.076~ 3,232

(0.020) (0.042) (0.027) (0.043)

Currently employed at a job with 

weekly earnings above the poverty line 

[SR]

0.267 0.014 0.029 0.009 0.015 3,068

(0.019) (0.038) (0.026) (0.042)

Currently employed at a job with 

weekly earnings above the federal 

minimum wage [SR]

0.345 0.013 0.026 – 0.025 – 0.040 3,068

(0.020) (0.042) (0.028) (0.044)

Currently employed at a job with 

weekly earnings above $10/hr [SR]

0.243 0.005 0.010 – 0.010 – 0.016 3,068

(0.018) (0.037) (0.024) (0.038)

Annual individual earnings (previous 

calendar year, 2009 dollars) [SR]

$12,288.51 326.94 677.92 – 613.60 – 982.43 3,141

(583.44) (1,209.79) (807.20) (1,292.40)

Current weekly earnings at main job 

(2009 dollars) [SR]

$222.98 0.56 1.15 – 21.03 – 33.54 3,068

(11.69) (24.03) (15.82) (25.23)

Employed over 1 year at current main 

job [SR]

0.469 – 0.007 – 0.014 – 0.063* – 0.101* 3,225

(0.021) (0.043) (0.028) (0.045)

In past 2 years, worked for pay [SR] 0.677 0.011 0.023 – 0.048~ – 0.077~ 3,270

(0.019) (0.038) (0.026) (0.041)

Currently working and household is 

not receiving Temporary Assistance for 

Needy Families [SR]

0.499 – 0.020 – 0.041 – 0.071* – 0.115* 3,260

(0.021) (0.043) (0.028) (0.045)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: Weekly earnings are first adjusted to 2009 dollars and then applied to the appropriate statistic. Poverty thresholds use the 2009 values and adjusts for 

head of household's age, household size, and number of kids. For the federal minimum wage, the 2009 value of $7.25 was used to measure whether respondents 

earned more than the minimum wage. All comparisons with hourly wage rates assumed a 40 hour work week at the hourly wage rate. Job tenure looks at the year 

in which respondent started their current job. If the interview year is a different year than the year they started their job, we assume that they have been at their job 

for more than 1 year.
154

5.5 LONG-TERM IMPACTS ON 

EMPLOYMENT AND EARNINGS OF 

YOUTH AND GROWN CHILDREN

Exhibit 5.8 shows the employment and school 

enrollment rates of grown children in the control group 

and summarizes the estimated impacts on the labor 

force status and earnings of these grown children. The 

top panel of the exhibit displays parental reports in the 

long-term survey on the current situation of their grown 

children. The bottom panel presents demonstration 

impact estimates based on administrative UI data 

covering grown children for quarter 4, 2007, to quarter 

3, 2008, for four sites.

Compared with the control group, adult respondents 

in the experimental group report that their male grown 

children are more likely to be currently employed and 

less likely to be idle at the time of the long-term survey 

than those in the control group. Female grown children 

in the Section 8 group are less likely to be employed and 

more likely to be idle than those in the control group. 

Supplemental Exhibit 5.6 provides further detail on these 

patterns by presenting treatment group impact estimates 

for four mutually exclusive categories of work and 

schooling for grown children.

In contrast to the parental reports in the long-term 

survey, the UI data show that male grown children in 

the experimental group have negative employment and 

earnings outcomes, compared with the control group. 

Supplemental Exhibit 5.7 shows that the positive 

employment outcomes in the survey data are driven 

by the New York site, which unfortunately had to be 

excluded from the UI analyses because of the aggregate 

nature of the data and our inability to specifically 

examine the grown children subgroup. Los Angeles and 

Chicago have consistent estimated effects on male grown 

children from UI and parental reports. However, for the 

Boston site, the UI and survey data yield substantially 

different experimental employment impacts of opposite 

sign. Unfortunately, the Massachusetts UI agency did not 

provide individual level data for the Boston site limiting 

further analysis of this discrepancy.

MTO impacts on the labor market activities of youth 

are summarized in Exhibits 5.9 and 5.10. Exhibit 5.9 

shows employment and annual earnings impacts for 

youth based on state UI administrative data covering the 

time from quarter 4, 2007, to quarter 3, 2008, for the 

four sites with complete administrative data: Baltimore, 

Boston, Chicago, and Los Angeles. The UI data indicate 

that youth in the experimental group have lower 

employment rates and earnings than youth in the control 

group (this finding is statistically significant overall 

and for males). There were no significant differences 

between the Section 8 and control group youth. Exhibit 

5.10 presents estimates based on youth self-reports of 

employment, earnings, and enrollment outcomes. The 

survey data indicate that experimental group youth are 

significantly less likely to be employed than control 

group youth. The impact estimates on employment for 

female and male youth are similarly negative, although 

gender-specific estimates are not statistically significant. 

Site-by-site analyses presented in Supplemental Exhibit 

5.8 suggest that experimental group youth, especially 

in Baltimore, are more likely to be currently enrolled in 

school, compared with the control group. There are no 

statistically significant differences by random assignment 

group in the long-term survey in idleness, as seen in 

Exhibit 5.10. A further decomposition of impacts on four 

mutually exclusive youth labor market and enrollment 

categories is given in Supplemental Exhibit 5.9.
155 Chapter 5: Impacts on Economic Self-Sufficiency

EXHIBIT 5.8. EMPLOYMENT AND SCHOOL ENROLLMENT OF GROWN CHILDREN: PARENT REPORTS AND 

ADMINISTRATIVE DATA

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

SURVEY DATA

CURRENTLY EMPLOYED [PR]

All 0.562 0.017 0.039 – 0.049 – 0.089 2,953

(0.023) (0.051) (0.031) (0.056)

Female 0.615 – 0.030 – 0.066 – 0.086* – 0.149* 1,480

(0.031) (0.067) (0.040) (0.070)

Male 0.505 0.066* 0.156* – 0.009 – 0.018 1,473

(0.032) (0.076) (0.041) (0.078)

CURRENTLY ENROLLED IN

SCHOOL [PR]

All 0.158 – 0.007 – 0.016 – 0.001 – 0.002 3,089

(0.016) (0.036) (0.023) (0.042)

Female 0.205 – 0.015 – 0.033 0.006 0.010 1,527

(0.025) (0.054) (0.036) (0.062)

Male 0.110 0.000 0.000 – 0.007 – 0.013 1,562

(0.020) (0.047) (0.025) (0.047)

CURRENTLY IDLE (NEITHER

ENROLLED IN SCHOOL NOR WORKING) [PR]

All 0.372 – 0.012 – 0.028 0.026 0.047 2,958

(0.022) (0.049) (0.029) (0.054)

Female 0.299 0.036 0.079 0.063 0.109 1,483

(0.029) (0.063) (0.039) (0.067)

Male 0.451 – 0.062~ – 0.147~ – 0.015 – 0.028 1,475

(0.032) (0.075) (0.040) (0.076)

UNEMPLOYMENT INSURANCE 

RECORDS (FROM THE FOURTH 

QUARTER OF 2007 THROUGH 

THE THIRD QUARTER OF 2008 

FROM MD, MA, IL, AND CA)

EMPLOYED [UI]

All 0.484 – 0.027 – 0.063 – 0.010 – 0.016 3,052

(0.020) (0.047) (0.024) (0.037)

Female 0.551 – 0.004 – 0.008 – 0.002 – 0.002 1,507

(0.017) (0.038) (0.027) (0.042)

Male 0.418 – 0.040* – 0.095* – 0.018 – 0.029 1,545

(0.016) (0.039) (0.023) (0.037)

ANNUAL WAGES (2009 

DOLLARS) [UI]

All $9,732.33 – 967.89 – 2,256.02 – 381.43 – 595.62 3,052

(634.09) (1,477.98) (718.02) (1,121.24)
156

EXHIBIT 5.8. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

UNEMPLOYMENT INSURANCE 

RECORDS (FROM THE FOURTH 

QUARTER OF 2007 THROUGH 

THE THIRD QUARTER OF 2008 

FROM MD, MA, IL, AND CA) (CONTINUED)

ANNUAL WAGES (2009 

DOLLARS) [UI] (CONTINUED)

Female $10,661.72 335.84 760.18 285.00 441.40 1,507

(682.66) (1,545.23) (922.52) (1,428.76)

Male $8,781.12 – 1,839.40* – 4,434.65* – 872.72 – 1,375.28 1,545

(533.73) (1,286.78) (735.46) (1,158.97)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: PR = parent report, UI = Unemployment Insurance administrative records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: For parent reports, data source is the adult long-term survey. Grown children (who were under age 18 at baseline and over age 20 as of 

December 31, 2007) of interviewed adults. Unemployment Insurance data use individual level data from Maryland, Illinois, California, and Florida (representing 

individuals whose random assignment sites are Baltimore, Chicago, and Los Angeles) and aggregate data from Massachusetts (representing individuals whose 

random assignment site is Boston). All core grown children whose parent has a baseline consent.

EXHIBIT 5.9. YOUTH EMPLOYMENT AND EARNINGS, ADMINISTRATIVE DATA BY SITE FOR THE FOURTH 

QUARTER OF 2007 THROUGH THE THIRD QUARTER 0F 2008 

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

EMPLOYED [UI]

All 0.256 – 0.036* – 0.076* 0.004 0.006 3,490

(0.015) (0.032) (0.018) (0.025)

Female 0.272 – 0.030 – 0.063 0.012 0.017 1,691

(0.024) (0.051) (0.026) (0.037)

Male 0.241 – 0.045* – 0.093* – 0.002 – 0.003 1,799

(0.021) (0.044) (0.023) (0.032)

ANNUAL WAGES (2009 DOLLARS) [UI]

All $2,371.26 – 487.12* – 1,025.87* 30.26 42.34 3,490

(225.21) (474.29) (247.74) (346.68)

Female $2,174.63 – 291.02 – 616.18 370.47 528.51 1,691

(297.80) (630.55) (326.36) (465.58)

Male $2,552.76 – 681.06* – 1,426.76* – 293.28 – 403.26 1,799

(323.22) (677.12) (358.88) (493.46)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: UI = Unemployment Insurance administrative records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Individual level data from Maryland, Illinois, California, and Florida (representing individuals whose random assignment sites are 

Baltimore, Chicago, and Los Angeles) and aggregate data from Massachusetts (representing individuals whose random assignment site is Boston). All core youth 

ages 15 to 20 as of December 31, 2007, whose parent has a baseline consent. Massachusetts includes all youth ages 12 to 20. Since our request file did not split 

by age, we cannot look separately at those who were 15 to 20 in Massachusetts. 
157 Chapter 5: Impacts on Economic Self-Sufficiency

EXHIBIT 5.10. YOUTH SELF-REPORTED EMPLOYMENT, EARNINGS, AND SCHOOL ENROLLMENT

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

CURRENTLY EMPLOYED [SR]

All 0.395 – 0.046* – 0.095* – 0.033 – 0.050 3,604

(0.021) (0.043) (0.022) (0.034)

Female 0.410 – 0.051~ – 0.104~ – 0.042 – 0.066 1,838

(0.029) (0.059) (0.031) (0.049)

Male 0.381 – 0.041 – 0.087 – 0.025 – 0.036 1,766

(0.030) (0.064) (0.033) (0.049)

CURRENTLY ENROLLED IN SCHOOL [SR]

All 0.615 0.018 0.037 – 0.019 – 0.029 3,621

(0.020) (0.041) (0.022) (0.034)

Female 0.637 0.001 0.002 0.001 0.001 1,845

(0.027) (0.054) (0.030) (0.047)

Male 0.594 0.036 0.077 – 0.039 – 0.057 1,776

(0.028) (0.061) (0.030) (0.044)

CURRENTLY IDLE (NEITHER EMPLOYED 

NOR ENROLLED IN SCHOOL) [SR]

All 0.215 0.007 0.014 0.026 0.039 3,604

(0.018) (0.038) (0.020) (0.031)

Female 0.194 0.024 0.049 0.031 0.048 1,838

(0.024) (0.048) (0.027) (0.043)

Male 0.235 – 0.011 – 0.023 0.022 0.032 1,766

(0.027) (0.058) (0.031) (0.045)

CURRENT WEEKLY EARNINGS (2009 

DOLLARS) [SR]

All $88.98 – 11.81~ – 24.59~ – 8.55 – 12.74 3,463

(6.51) (13.55) (7.17) (10.68)

Female $88.76 – 14.31~ – 28.89~ – 12.16 – 18.74 1,767

(8.44) (17.05) (9.59) (14.78)

Male $89.19 – 9.35 – 20.16 – 5.00 – 7.21 1,696

(9.72) (20.95) (10.84) (15.63)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: "Currently enrolled in school" also includes youth who are on summer vacation.
158

5.6 DATA SOURCES FOR AND 

MEASURES OF INCOME, POVERTY, 

FOOD CONSUMPTION, AND SOCIAL 

ASSISTANCE

Information about public assistance receipt and income 

is available from respondent self-reports on the longterm survey and administrative records from state TANF 

agencies. In the long-term survey, we asked adults about 

current receipt of TANF, food stamps, Supplemental 

Security Income, and Medicaid; the Earned Income Tax 

Credit (EITC),12 and to recall their total household 

income for the most recent calendar year preceding 

the survey—2007, 2008, or 2009 depending on their 

interview date. The long-term survey used an innovative 

technique for collecting retrospective reports of total 

household income by embedding an income calculator.

Respondents are first asked about specific sources of 

income in the most recent calendar year for earnings, 

government sources, and then income from other 

sources. If a respondent does not know the total value 

of these income sources, the interviewer asks a series 

of yes-no questions about different income amounts to 

determine the income category.13 These income sources 

are then added up to calculate for the respondent a 

total household income level. The respondent then has 

the opportunity to agree or adjust the total household 

income. Moreover, survey data is available on food 

sufficiency,14 constructed measures of self-sufficiency 

(the proportion of adults who were working and not 

receiving TANF at the time of the survey), and food 

expenditure. Individual-level administrative data on 

monthly TANF and food stamp receipt are used to 

12 EITC refunds were ascertained using the following wording: Workers sometimes receive a tax refund check- early in the year- from the 

Earned Income Tax Credit or because they overpaid taxes in the previous year. Did you receive a tax refund check from the federal government early in [year of survey]? How much was your tax refund?

13 The “unfolding scale” (see Duncan and Petersen, 2001) for income 

consisted of the following questions: Would it amount to $10,000 

or more? Would it amount to $20,000 or more? Would it amount to 

$25,000 or more? Would it amount to $15,000 or more? Would it 

amount to $5,000 or more?

14 Food sufficiency is measured using a modified version of a single 

item used by the U.S. Department of Agriculture (see Bickel et al., 

2000).We asked respondents: Which of these statements best describes 

the foot eaten in your household in the last 12 months: I/we always had 

enough to eat, sometimes I/we did not have enough to eat, or often I/

we did not have enough to eat.

construct the proportion of months receiving benefits 

and the amount received from both AFDC/TANF and 

food stamps.

Administrative records were collected and analyzed 

from seven state agencies and one county agency:15 

Massachusetts, Illinois, and Los Angeles County, plus 

five additional states (Indiana, Georgia, South Carolina, 

North Carolina, and Florida) where participants 

have moved since enrolling in the demonstration. 

Participants were matched to administrative records using 

different personal information, depending on the state. 

Massachusetts residents were matched strictly on social 

security number, Los Angeles County residents on social 

security number and date of birth, and Illinois residents 

on social security number, date of birth, and name. Once 

matched, government agencies returned both benefit 

amounts and a status variable indicating whether a case 

was active for TANF and food stamps.

We present results for MTO participants from the 

Boston, Chicago and Los Angeles site for the 24-month 

interval from July 2007 through June 2009. We construct 

variables for two units of analysis: the entire household 

and grown children. The household unit variables are 

designed to capture all TANF and food stamps received 

by all core members in each family by summing all 

distinct monthly benefit amounts received by family 

members, whether that person was active or not. This 

creates an upper bound on welfare receipt. This measure 

includes members who have started their own households 

and captures benefit amounts transferred to children 

born after randomization under any core member’s care.

The subgroup of grown children captures those who 

enter adulthood and receive TANF and food stamps as 

household heads. Food stamp and TANF receipt are 

examined at the individual level. These results present 

two measures for grown children: the share of months 

actively on food stamps and TANF, and the total amount 

of food stamps and TANF benefits received while active 

on their public assistance case. We limit this analysis to 

only those grown children who are actively on TANF 

to prevent attributing benefits that are received in their 

15 The analyses currently exclude New York and Maryland.
159 Chapter 5: Impacts on Economic Self-Sufficiency

parent’s household to their newly formed household. 

This also implies that information might be missing 

about child-only cases for young adults with children.

5.7 LONG-TERM IMPACTS ON 

INCOME, POVERTY, FOOD 

CONSUMPTION, AND SOCIAL 

ASSISTANCE

Exhibits 5.11, 5.12, and 5.13 present estimates of MTO 

effects on income, TANF, and food stamp receipt. 

Exhibit 5.11 shows household self-reported responses on 

income, food security, and poverty status. Exhibit 5.12 

shows welfare receipt information for grown children 

culled from administrative data from July 2007 to 

June 2009. Exhibit 5.13 shows self-reported data on 

receipt of public assistance at the time of interview and 

administrative data indicating the share of months that 

households received public assistance during the same 

two-year time period.

We find no systematic, detectable MTO treatment effects 

on total reported income or social assistance receipt. 

This is further shown in an analysis that more closely 

aligns the timing of social assistance receipt as reported 

in the survey and documented in the social assistance 

administrative data in Supplemental Exhibits 5.10 and 

5.11. Experimental households are less likely to be living 

in poverty—defined as having an income at or below 

the poverty line16—than those in the control group in 

the calendar year before the long-term survey, but this 

finding is not statistically significant. The opposite 

pattern of a modest (also statistically insignificant) rise in 

poverty is found for the Section 8 group, relative to the 

control group (Exhibit 5.11). However, we worry here 

about the later timing of the survey for the Section 8 

group. Supplemental Exhibit 5.12 shows similar rates of 

earned income tax credit receipt in the previous calendar 

year among the experimental, Section 8, and control 

group adults, although Section 8 adults claimed higher 

total refund amounts (above $2,500) compared with the 

control group.

16 Poverty status was determined by comparing household income in 

the previous year to the U.S. Census Bureau’s poverty thresholds for the 

adult respondent’s current household size, number of children under 

18, and age of the head of household (Denavas-Walt, Proctor, and 

Smith, 2010).

Adults in the experimental and Section 8 groups report 

decreased food insufficiency (roughly 3.5 percentage 

points lower for experimental adults and 7 percentage 

points lower for Section 8 adults) compared with the 

control group rate of 34 percent. Experimental and 

Section 8 adults also report lower food expenditures—

roughly $7 per week less than control group adults. These 

effects are difficult to explain or have confidence in 

without further investigation. The results may be chance 

findings or may be in part the result of a reduction in 

grocery expenditure as measured in the long-term survey 

or lower food prices in the new neighborhoods among 

experimental and Section 8 families.

The administrative data show that experimental group 

adults have significantly higher rates of food stamp 

receipt but similar TANF receipt, compared with control 

group adults (see Exhibit 5.13). Rates of TANF receipt 

generally follow national patterns of declines over time, 

with nearly 75 percent of the sample reporting receipt of 

welfare at baseline (measured during the earlier 1990s), 

28.6 percent on average as reported at the interim 

survey interview (roughly during the early 2000s), and 

16 percent reporting TANF receipt at the time of the 

long-term survey interview (in the later 2000s). The 

long-term survey data reveals a qualitatively similar but 

statistically insignificant pattern of slightly higher TANF 

and food stamp receipt for the experimental group. There 

are no significant differences between Section 8 and 

control group adults in terms of social assistance receipt 

in either the administrative or survey data. In addition, 

the administrative data indicate that grown children 

in the experimental group receive more total TANF 

benefits than the controls (this is marginally statistically 

significant). There is a similar (but statistically 

insignificant) pattern for the Section 8 grown children 

(Exhibit 5.12).
160

EXHIBIT 5.11. ADULT SELF-REPORTED HOUSEHOLD INCOME, POVERTY STATUS, AND FOOD SUFFICIENCY

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL 

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

HOUSEHOLD INCOME, 

LAST CALENDAR YEAR

Total earnings of sample adult and 

spouse/partner (2009 dollars) [SR]

$14,518.70 – 483.91 – 998.61 – 1,752.38~ – 2,826.29~ 3,266

(744.03) (1,535.41) (1,015.66) (1,638.08)

Earnings from other household members 

(2009 dollars) [SR]

$2,672.24 – 17.56 – 36.16 427.21 675.34 3,079

(302.68) (623.35) (465.34) (735.61)

Government income (2009 dollars) [SR] $3,542.62 244.44 505.49 323.53 511.54 3,139

(217.77) (450.33) (303.01) (479.08)

Other sources of income (2009 dollars) 

[SR]

$464.94 57.05 117.67 212.05* 339.02* 3,176

(72.58) (149.71) (103.04) (164.74)

Total household income (2009 dollars) 

[SR]

$20,025.90 607.58 1,255.56 – 41.67 – 67.33 3,258

(727.58) (1,503.54) (1,009.18) (1,630.66)

POVERTY STATUS, 

LAST CALENDAR YEAR

Household income is at or below 50% of 

poverty line [SR]

0.298 – 0.012 – 0.024 0.036 0.058 3,258

(0.019) (0.040) (0.026) (0.043)

Household income is at or below 100% of 

poverty line [SR]

0.590 – 0.032 – 0.067 0.036 0.059 3,258

(0.021) (0.043) (0.028) (0.045)

Household income is at or below 150% of 

poverty line [SR]

0.756 – 0.007 – 0.014 0.008 0.013 3,258

(0.018) (0.038) (0.024) (0.039)

FOOD SUFFICIENCY

Adult reported that their household 

sometimes/often did not have enough to 

eat in the past 12 months [SR]

0.336 – 0.035~ – 0.072~ – 0.067* – 0.106* 3,266

(0.020) (0.042) (0.026) (0.042)

Current total weekly food expenditure 

(2009 dollars) [SR]

$113.05 – 7.01* – 14.39* – 7.27~ – 11.59~ 3,165

(2.88) (5.92) (3.82) (6.09)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: Poverty levels are determined by household size and age of household head. Government income includes welfare, SSI, unemployment benefits, and 

social security. "Other sources of income" includes money from any businesses, help from friends or relatives, child support, and any other money income. "Food 

Insufficiency" reflects the adult's response that in the past 12 months, there are some times or always not enough to eat.
161 Chapter 5: Impacts on Economic Self-Sufficiency

EXHIBIT 5.12. WELFARE AND FOOD STAMP RECEIPT OF GROWN CHILDREN, ADMINISTRATIVE DATA

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

TEMPORARY ASSISTANCE FOR NEEDY

FAMILIES (TANF) BENEFITS, 

JULY 2007–JUNE 2009 [TANF]

FRACTION OF MONTHS ACTIVELY

RECEIVING TANF

All 0.046 0.012 0.029 0.010 0.017 2,833

(0.008) (0.019) (0.009) (0.015)

Female 0.083 0.021 0.046 0.016 0.026 1,398

(0.016) (0.034) (0.017) (0.027)

Male 0.010 0.004 0.009 0.004 0.007 1,435

(0.006) (0.014) (0.007) (0.012)

TOTAL TANF BENEFITS WHILE ACTIVE ON 

CASE (2009 DOLLARS)

All $270.13 98.32~ 230.41~ 88.60 145.10 2,833

(55.54) (130.15) (59.30) (97.12)

Female $499.05 139.69 308.15 127.25 204.59 1,398

(103.06) (227.36) (109.64) (176.28)

Male $43.11 57.62 143.69 50.16 83.62 1,435

(38.61) (96.28) (41.26) (68.78)

FOOD STAMPS BENEFITS, 

JULY 2007–JUNE 2009 [FS]

FRACTION OF MONTHS ACTIVELY

RECEIVING FOOD STAMPS

All 0.228 0.004 0.010 0.009 0.014 2,833

(0.016) (0.038) (0.018) (0.029)

Female 0.322 0.013 0.029 0.006 0.010 1,398

(0.026) (0.057) (0.028) (0.045)

Male 0.134 – 0.004 – 0.010 0.011 0.018 1,435

(0.019) (0.047) (0.021) (0.034)

TOTAL FOOD STAMPS BENEFITS WHILE

ACTIVE ON CASE (2009 DOLLARS)

All $879.05 13.20 30.93 0.21 0.35 2,833

(76.64) (179.59) (82.65) (135.36)

Female $1,388.81 – 20.51 – 45.24 – 82.65 – 132.89 1,398

(134.74) (297.23) (140.46) (225.83)

Male $373.56 46.19 115.18 79.65 132.78 1,435

(74.85) (186.66) (78.66) (131.13)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: TANF = Temporary Assistance for Needy Families, FS = Food Stamps.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: FS and TANF analyses use individual data from Massachusetts, Illinois, Los Angeles County, and South Carolina and represent individuals 

whose random assignment sites are: Boston, Chicago and Los Angeles. We received TANF and food stamps data from Maryland, but the data quality is suspect. 

Sample for UI, TANF, and FS are all sample adults with baseline consent. All core grown children.

Measures: "Active" means the family is receiving welfare benefits directly through the grown child. The measures presented in this table include only payments 

made to the grown child while they were active on the case. We restrict this measure to "active" status to avoid picking up payments made to a youth's parent 

even though the youth is no longer part of the case (due to their age). This measure would not capture "child-only" payments made to children of the grown child. 
162

EXHIBIT 5.13. ADULT GOVERNMENT ASSISTANCE, SELF REPORTS AND ADMINISTRATIVE DATA

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

SURVEY DATA [SR]

Currently receiving Temporary Assistance for 

Needy Families (TANF)

0.158 0.010 0.021 0.026 0.041 3,262

(0.015) (0.031) (0.021) (0.033)

Currently receiving food stamps 0.470 0.016 0.032 0.029 0.046 3,253

(0.021) (0.043) (0.028) (0.045)

Currently receiving Supplemental Security 

Income

0.293 0.004 0.008 0.019 0.030 3,252

(0.019) (0.039) (0.026) (0.041)

Currently receiving Medicaid 0.367 – 0.003 – 0.005 0.037 0.060 3,273

(0.019) (0.040) (0.026) (0.043)

ADMINISTRATIVE DATA

FAMILY TANF BENEFITS [TANF]

Fraction of months received TANF, July 2007–

June 2009

0.238 0.003 0.005 – 0.027 – 0.042 2,708

(0.017) (0.037) (0.018) (0.028)

Total TANF benefits received, July 2007–June 

2009 (2009 dollars)

$1,402.33 56.10 120.29 – 94.47 – 144.57 2,708

(114.48) (245.44) (123.82) (189.49)

FAMILY FOOD STAMPS BENEFITS [FS]

Fraction of months received food stamps (July 

2007–June 2009)

0.594 0.046* 0.098* – 0.007 – 0.011 2,708

(0.019) (0.040) (0.020) (0.031)

Total food stamps benefits received, July 2007–

June 2009 (2009 dollars)

$3,074.08 309.94* 664.54* 171.07 261.80 2,708

(156.50) (335.54) (184.98) (283.09)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: FS and TANF analyses use individual data from Massachusetts, Illinois, Los Angeles County, and South Carolina and represent individuals 

whose random assignment sites are: Boston, Chicago and Los Angeles. We received TANF and food stamps data from Maryland, but the data quality is suspect. 

Sample for UI, TANF, and FS are all sample adults with baseline consent. All sample adults with a baseline consent.

Measures: The measures represented in this table are at the family level. The outcome reflects the fraction of months during the 12 month period that any family 

members' case received TANF or Food Stamps benefits. The benefit amount represents the total amount of benefits received during those 12 months for all family 

members' cases. This family level measure does not restrict to "active" months in order to capture all benefits received by the family and dependents. For example, 

a payment could have been made to a child even though the adult on the case was sanctioned.
163 Chapter 5: Impacts on Economic Self-Sufficiency

5.8 DATA SOURCES FOR MEASURES 

OF SAVINGS AND ASSETS

The inclusion of savings and assets measures in the 

long-term survey is a new addition, compared with 

measures available from the interim survey. The longterm survey items are drawn from common sources 

of savings and asset questions, such as the Survey of 

Consumer Finances, the Health and Retirement Survey, 

and the Panel Study of Income Dynamics. Information 

is collected on savings or checking accounts, home or 

vehicle ownership, credit cards, and debt on any of these 

assets and also unpaid medical bills. Detailed questions 

about matters related to portfolio composition or use 

of more sophisticated savings vehicles are not included, 

given the low incomes of the demonstration participants 

and the first-order hypothesized effect of reducing the 

share of adults who are unbanked.

5.9 LONG-TERM IMPACTS ON 

SAVINGS AND ASSETS

Exhibit 5.14 shows that the demonstration had few 

detectable effects on measures of savings, assets, and 

debt—including owning a car or having a credit card or 

bank account—for both the experimental and Section 8 

groups, relative to the control group. The experimental 

group adults are more likely to report having any 

household debt, and, at marginal statistical significance, 

the Section 8 adults are somewhat more likely to report 

having any household debt and to report owing more 

than $5,000 in credit card debt, compared with control 

group adults. As shown in Chapter 2, experimental and 

Section 8 participants are less likely to be homeowners 

than control group adults.

5.10 INTERPRETATION OF ECONOMIC 

SELF-SUFFICIENCY RESULTS

MTO has few systematic, detectable long-term effects 

on economic self-sufficiency outcomes for adults, youth, 

and grown children, as measured through survey and 

administrative records data up to the long-term follow-up 

survey period. Experimental and Section 8 group adults 

have similar employment, earnings, and income levels 

as control group adults. Experimental group adults are 

more likely to receive TANF benefits. Both experimental 

and Section 8 adults are less likely to experience food 

insufficiency than control group adults. Ownership of 

assets, such as cars and homes, are the same or lower 

among experimental and Section 8 families, compared 

with those in the control group.

Yet debt levels are slightly higher for the experimental 

and Section 8 groups. Youth idleness rates are similar 

among random assignment groups at the long-term 

survey. Parental reports are suggestive of beneficial 

impacts on employment and reduction in idleness for 

male grown children, but the limited administrative UI 

data do not provide support for this finding.

On average, experimental and Section 8 households 

moved to neighborhoods with a greater share of “middleclass” (higher income, more educated, and steadily 

employed) neighbors and greater safety than control 

households. The advantages of these neighborhood 

attributes felt by the treatment groups eroded a bit from 

the time of initial moves to interim to the long-term 

survey. Still, these changes in environment are associated 

with persistent improvements in some mental health and 

physical health outcomes for adults in the experimental 

and Section 8 groups, relative to the control group adults 

(Chapters 3 and 4).But such neighborhood changes 

initially are associated with (if anything) an adverse 

initial disruption effect on labor market outcomes and 

do not appear to generate any sustained improvement 

in labor market outcomes or other measures of adult 

economic self-sufficiency.

The lack of impacts of a housing mobility intervention 

on economic outcomes for adult residents of housing 

developments contrasts with recent evaluations showing 

sustained positive impacts on earnings of disadvantaged 

adults from the direct provision of high-quality 

training and employment services. Examples of such 

successful employment programs include the Jobs-Plus 

demonstration, which provides training, outreach, and 

improved work incentives for public housing residents 

(Riccio, 2010), and sectoral employment programs, 

which provide training with strong links to local 

employer demands (Maguire et al., 2010). Training, 

education, and employment services that directly enhance 

marketable skills and changes in work incentives appear 
164

to more directly impact labor market and economic 

outcomes of low-income adults than do the indirect 

effects of changes in neighborhood environments, at least 

in the range observed in the MTO demonstration.

Furthermore, the demonstrations have not generated 

systematic detectable gains in economic outcomes for 

youth and grown children in the experimental and 

Section 8 groups, compared with those in the control 

group. These results contrast with the long-term positive 

impacts on youth economic outcomes of some early 

childhood education programs (Almond and Currie, 

2010) and improvements in school quality in early grades 

(Chetty et al., 2010). Housing mobility interventions 

that affect neighborhood environments by a similar 

magnitude as MTO may not be powerful enough to 

greatly affect youth long-term economic outcomes 

without being combined with further direct services for 

the children and training and education programs to 

improve parental earnings opportunities.

EXHIBIT 5.14. ADULT SELF-REPORTED FAMILY SAVINGS AND ASSETS

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

Household currently owns a car 

[SR]

0.436 – 0.023 – 0.047 – 0.033 – 0.053 3,239

(0.020) (0.041) (0.026) (0.041)

Adult currently has a bank account 

[SR]

0.559 0.014 0.029 – 0.039 – 0.062 3,191

(0.021) (0.043) (0.028) (0.044)

Any household debt [SR] 0.410 0.055* 0.113* 0.056~ 0.089~ 3,209

(0.021) (0.044) (0.029) (0.045)

Household owes ≥ $5,000 on 

medical bills (2009 dollars) [SR]

0.058 0.001 0.003 0.014 0.023 3,218

(0.010) (0.020) (0.016) (0.025)

Adult has a credit card [SR] 0.259 0.025 0.051 0.002 0.004 3,238

(0.019) (0.038) (0.024) (0.038)

Household owes ≥ $5,000 in credit 

card debt (2009 dollars) [SR]

0.065 0.016 0.033 0.028~ 0.044~ 3,211

(0.011) (0.022) (0.015) (0.023)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All interviewed adults.
165 Chapter 5: Impacts on Economic Self-Sufficiency

SUPPLEMENTAL EXHIBIT 5.1. ADULT EMPLOYMENT, ADMINISTRATIVE DATA, BY SITE AND RANDOM 

ASSIGNMENT (RA) YEARS

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

SHARE EMPLOYED [UI]

PRE-RA YEAR 1

Maryland 0.286 – 0.037 – 0.070 – 0.036 – 0.046 629

(0.031) (0.057) (0.033) (0.042)

Illinois 0.270 0.009 0.026 0.018 0.027 883

(0.026) (0.076) (0.030) (0.045)

California 0.274 0.017 0.027 0.005 0.007 853

(0.025) (0.041) (0.027) (0.035)

Three-site weighted average 0.276 – 0.001 – 0.003 0.005 0.006 2,365

(0.015) (0.031) (0.017) (0.024)

RA YEARS 1 AND 2

Maryland 0.414 – 0.013 – 0.024 – 0.050 – 0.064 629

(0.028) (0.053) (0.032) (0.041)

Illinois 0.408 – 0.058* – 0.173* – 0.012 – 0.017 883

(0.025) (0.073) (0.029) (0.043)

California 0.401 – 0.021 – 0.034 – 0.042 – 0.054 853

(0.025) (0.041) (0.028) (0.036)

Massachusetts 0.410 0.001 0.003 0.024 0.047 885

(0.021) (0.047) (0.022) (0.042)

Four-site weighted average 0.408 – 0.023~ – 0.049~ – 0.017 – 0.025 3,250

(0.012) (0.026) (0.014) (0.020)

RA YEARS 3 AND 4

Maryland 0.549 – 0.011 – 0.021 – 0.025 – 0.032 629

(0.035) (0.065) (0.038) (0.049)

Illinois 0.476 0.021 0.062 0.051 0.076 883

(0.030) (0.090) (0.036) (0.054)

California 0.455 0.035 0.056 0.000 0.000 853

(0.031) (0.050) (0.033) (0.042)

Massachusetts 0.530 – 0.058~ – 0.131~ – 0.013 – 0.025 885

(0.030) (0.067) (0.029) (0.055)

Four-site weighted average 0.499 – 0.003 – 0.006 0.005 0.008 3,250

(0.016) (0.033) (0.017) (0.025)

RA YEARS 5 AND 6

Maryland 0.615 0.012 0.022 – 0.055 – 0.070 629

(0.035) (0.065) (0.039) (0.050)

Illinois 0.501 0.025 0.075 0.040 0.060 883

(0.033) (0.099) (0.039) (0.058)

California 0.454 0.052 0.083 0.039 0.050 853

(0.033) (0.053) (0.034) (0.044)
166

SUPPLEMENTAL EXHIBIT 5.1. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

SHARE EMPLOYED [UI] (CONTINUED)

RA YEARS 5 AND 6 (CONTINUED)

Massachusetts 0.534 – 0.066* – 0.149* 0.017 0.034 885

(0.030) (0.067) (0.032) (0.062)

Four-site weighted average 0.519 0.005 0.010 0.015 0.022 3,250

(0.016) (0.034) (0.018) (0.027)

RA YEARS 7 AND 8

Maryland 0.610 – 0.039 – 0.072 – 0.037 – 0.047 629

(0.038) (0.070) (0.042) (0.054)

Illinois 0.462 0.050 0.149 0.041 0.061 883

(0.034) (0.101) (0.041) (0.060)

California 0.436 0.027 0.044 0.062~ 0.080~ 853

(0.035) (0.056) (0.035) (0.046)

Massachusetts 0.468 – 0.041 – 0.092 0.032 0.062 885

(0.032) (0.071) (0.039) (0.075)

Four-site weighted average 0.485 0.002 0.005 0.029 0.043 3,250

(0.017) (0.036) (0.020) (0.029)

RA YEARS 9 AND 10

Maryland 0.579 – 0.058 – 0.108 – 0.040 – 0.051 629

(0.041) (0.076) (0.044) (0.057)

Illinois 0.445 0.047 0.140 0.059 0.088 883

(0.034) (0.100) (0.040) (0.060)

California 0.425 0.042 0.067 0.060~ 0.077~ 853

(0.035) (0.056) (0.036) (0.046)

Massachusetts 0.443 – 0.021 – 0.048 0.006 0.011 885

(0.032) (0.073) (0.033) (0.063)

Four-site weighted average 0.465 0.007 0.014 0.026 0.038 3,250

(0.017) (0.036) (0.019) (0.028)

RA YEARS 1–10

Maryland 0.553 – 0.022 – 0.041 – 0.041 – 0.053 629

(0.028) (0.053) (0.031) (0.040)

Illinois 0.458 0.017 0.051 0.036 0.053 883

(0.025) (0.075) (0.029) (0.044)

California 0.434 0.027 0.043 0.024 0.031 853

(0.025) (0.040) (0.026) (0.034)

Massachusetts 0.477 – 0.037~ – 0.083~ 0.013 0.026 885

(0.022) (0.049) (0.021) (0.041)

Four-site weighted average 0.475 – 0.003 – 0.005 0.012 0.017 3,250

(0.012) (0.026) (0.013) (0.020)
167 Chapter 5: Impacts on Economic Self-Sufficiency

SUPPLEMENTAL EXHIBIT 5.1. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

ANNUAL WAGES (2009 DOLLARS) [UI]

PRE-RA YEAR 1

Maryland $2,885.21 – 180.81 – 337.54 – 335.68 – 429.54 629

(402.44) (751.27) (423.82) (542.32)

Illinois $3,095.51 371.97 1,105.29 404.78 601.29 883

(400.14) (1,188.98) (480.48) (713.75)

California $3,432.36 293.02 471.62 – 211.87 – 273.86 853

(425.58) (684.98) (453.71) (586.47)

Three-site weighted average $3,163.83 94.20 191.41 56.61 76.72 2,365

(240.38) (488.46) (274.91) (372.61)

RA YEARS 1 AND 2

Maryland $4,786.21 96.59 180.31 – 346.50 – 443.38 629

(402.81) (751.96) (449.83) (575.60)

Illinois $5,232.02 – 603.87 – 1,794.35 – 268.39 – 398.68 883

(417.76) (1,241.33) (490.36) (728.42)

California $5,670.21 – 924.22* – 1,487.57* – 1,032.71~ – 1,334.88~ 853

(435.28) (700.61) (527.91) (682.38)

Massachusetts $7,161.93 44.67 100.10 723.97 1,388.36 885

(480.79) (1,077.37) (528.41) (1,013.34)

Four-site weighted average $5,781.17 – 377.39~ – 786.86~ – 214.97 – 317.03 3,250

(221.62) (462.10) (255.44) (376.71)

RA YEARS 3 AND 4

Maryland $8,125.47 – 166.07 – 310.02 – 353.63 – 452.51 629

(765.92) (1,429.82) (792.12) (1,013.60)

Illinois $7,504.97 – 124.31 – 369.38 – 481.40 – 715.11 883

(660.57) (1,962.83) (762.10) (1,132.08)

California $7,477.82 – 88.18 – 141.93 – 997.51 – 1,289.38 853

(639.05) (1,028.57) (718.58) (928.83)

Massachusetts $10,418.11 – 1,230.45~ – 2,757.22~ – 295.14 – 565.99 885

(725.29) (1,625.24) (675.26) (1,294.95)

Four-site weighted average $8,398.43 – 422.45 – 880.83 – 541.97 – 799.28 3,250

(348.14) (725.88) (368.47) (543.40)

RA YEARS 5 AND 6

Maryland $10,509.31 332.50 620.72 – 284.52 – 364.07 629

(874.49) (1,632.50) (1,019.74) (1,304.87)

Illinois $9,011.70 492.27 1,462.74 27.38 40.67 883

(804.95) (2,391.85) (902.33) (1,340.39)

California $8,824.30 – 85.27 – 137.24 – 652.90 – 843.94 853

(834.91) (1,343.82) (855.17) (1,105.39)

Massachusetts $12,222.01 – 1,658.96~ – 3,717.42~ 715.98 1,373.03 885

(946.40) (2,120.72) (911.08) (1,747.18)

Four-site weighted average $10,111.07 – 274.30 – 571.93 – 24.84 – 36.63 3,250

(436.74) (910.63) (459.28) (677.33)
168

SUPPLEMENTAL EXHIBIT 5.1. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

ANNUAL WAGES (2009 DOLLARS) [UI]

(CONTINUED)

RA YEARS 7 AND 8

Maryland $11,947.92 – 586.02 – 1,093.97 – 358.35 – 458.55 629

(1,033.78) (1,929.86) (1,193.35) (1,527.02)

Illinois $9,264.16 699.30 2,077.93 – 181.69 – 269.89 883

(911.22) (2,707.61) (1,070.05) (1,589.53)

California $9,489.59 – 282.26 – 454.30 – 670.89 – 867.20 853

(997.39) (1,605.34) (980.19) (1,266.99)

Massachusetts $11,812.85 – 1,329.14 – 2,978.36 600.70 1,151.96 885

(1,155.54) (2,589.36) (1,280.68) (2,455.95)

Four-site weighted average $10,526.24 – 358.94 – 748.41 – 131.96 – 194.60 3,250

(518.30) (1,080.68) (570.49) (841.34)

RA YEARS 9 AND 10

Maryland $13,334.45 – 2,569.26* – 4,796.29* – 931.10 – 1,191.45 629

(1,170.38) (2,184.85) (1,402.79) (1,795.02)

Illinois $9,048.23 1,227.64 3,647.83 395.85 588.03 883

(1,010.41) (3,002.36) (1,205.51) (1,790.75)

California $9,415.62 203.63 327.76 93.96 121.45 853

(1,036.58) (1,668.42) (1,059.42) (1,369.40)

Massachusetts $11,543.13 – 252.62 – 566.08 508.26 974.69 885

(1,178.90) (2,641.70) (1,290.40) (2,474.59)

Four-site weighted average $10,643.49 – 179.70 – 374.68 90.45 133.39 3,250

(550.89) (1,148.63) (617.70) (910.96)

RA YEARS 1–10

Maryland $9,740.67 – 578.45 – 1,079.85 – 454.82 – 581.99 629

(704.70) (1,315.52) (806.73) (1,032.30)

Illinois $8,012.22 338.21 1,004.95 – 101.65 – 151.00 883

(633.52) (1,882.45) (720.37) (1,070.10)

California $8,175.51 – 235.26 – 378.66 – 652.01 – 842.79 853

(644.21) (1,036.88) (684.88) (885.27)

Massachusetts $10,631.61 – 885.30 – 1,983.79 450.75 864.41 885

(747.31) (1,674.60) (747.08) (1,432.67)

Four-site weighted average $9,092.08 – 322.556 – 672.542 – 164.660 – 242.834 3,250

(343.663) (716.550) (369.255) (544.564)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered an MTO 

housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless otherwise 

indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" would indicate that 25 

percent of the control group was working). 

Square brackets indicate the source of the outcome information: UI = Unemployment Insurance administrative records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on family. 

Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment status. See 

Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Individual level data from Maryland, Illinois, California, and Florida (representing individuals whose random assignment sites are Baltimore, Chicago, 

and Los Angeles) and aggregate data from Massachusetts (representing individuals whose random assignment site is Boston). All sample adults with baseline consent.
169 Chapter 5: Impacts on Economic Self-Sufficiency

SUPPLEMENTAL EXHIBIT 5.2. COMPARISON OF SELF-REPORTED AND ADMINISTRATIVE DATA ON ADULT 

CURRENT EMPLOYMENT BY SITE

CONTROL 

MEAN

EXPERIMENTAL

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

"CURRENTLY EMPLOYED," BY SITE AND SOURCE

MASSACHUSETTS

Self-report 0.502 – 0.046 – 0.107 – 0.086 – 0.153 678

(0.045) (0.105) (0.059) (0.106)

Unemployment Insurance records 0.413 – 0.068~ – 0.152~ – 0.020 – 0.039 885

(0.039) (0.087) (0.049) (0.094)

THREE SITE (MARYLAND, ILLINOIS, CALIFORNIA)

Self-report 0.533 – 0.005 – 0.011 – 0.078* – 0.109* 1,856

(0.027) (0.054) (0.037) (0.052)

Unemployment Insurance records 0.467 – 0.013 – 0.026 0.008 0.010 2,365

(0.024) (0.049) (0.027) (0.037)

MARYLAND

Self-report 0.610 – 0.115* – 0.211* – 0.138* – 0.180* 450

(0.055) (0.100) (0.070) (0.091)

Unemployment Insurance records 0.582 – 0.121* – 0.226* – 0.082 – 0.105 629

(0.046) (0.087) (0.052) (0.066)

ILLINOIS

Self-report 0.545 0.011 0.033 – 0.074 – 0.109 670

(0.046) (0.138) (0.068) (0.101)

Unemployment Insurance records 0.436 0.033 0.099 0.060 0.088 883

(0.041) (0.122) (0.049) (0.072)

CALIFORNIA

Self-report 0.478 0.061 0.097 – 0.028 – 0.039 736

(0.043) (0.068) (0.054) (0.075)

Unemployment Insurance records 0.415 0.046 0.074 0.033 0.043 853

(0.041) (0.066) (0.042) (0.054)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: SR = self-report, UI = Unemployment Insurance administrative records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: For self-reports, data source is the adult long-term survey and the sample is all adults interviewed. Unemployment Insurance data 

analyses use individual level data from Maryland, Illinois, California, and Florida (representing individuals whose random assignment sites are Baltimore, Chicago, 

and Los Angeles) and aggregate data from Massachusetts (representing individuals whose random assignment site is Boston). All sample adults with baseline 

consent.

Measures: Time period for administrative data is June 2008–December 2009.
170

SUPPLEMENTAL EXHIBIT 5.3. COMPARISON OF SELF-REPORTED AND ADMINISTRATIVE DATA ON ADULT 

EMPLOYMENT IN MARYLAND, ILLINOIS, AND CALIFORNIA (IN-STATE ONLY)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

EMPLOYMENT IN THE PREVIOUS CALENDAR 

YEAR, BY SITE AND SOURCE

ALL THREE SITES

Self-report 0.664 0.021 0.040 – 0.077* – 0.106* 1,546

(0.028) (0.054) (0.039) (0.054)

Unemployment Insurance records 0.607 0.004 0.009 – 0.047 – 0.064 1,546

(0.029) (0.057) (0.041) (0.056)

MARYLAND

Self-report 0.731 – 0.100~ – 0.187~ – 0.190* – 0.243* 408

(0.055) (0.102) (0.079) (0.101)

Unemployment Insurance records 0.711 – 0.119* – 0.220* – 0.116 – 0.149 408

(0.055) (0.103) (0.074) (0.094)

ILLINOIS

Self-report 0.667 0.060 0.172 – 0.075 – 0.109 576

(0.045) (0.130) (0.065) (0.094)

Unemployment Insurance records 0.610 0.036 0.102 – 0.082 – 0.120 576

(0.049) (0.140) (0.071) (0.104)

CALIFORNIA

Self-report 0.613 0.118* 0.182* 0.067 0.091 562

(0.047) (0.072) (0.057) (0.078)

Unemployment Insurance records 0.529 0.122* 0.188* 0.105~ 0.144~ 562

(0.049) (0.075) (0.061) (0.083)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: SR = self-report, UI = Unemployment Insurance administrative records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Sample is restricted to adults with annual earnings available in both survey and administrative data for the year prior to the interview 

and who are currently living in their random assignment state. For self-reports, data source is the adult long-term survey and the sample is all adults interviewed. 

Unemployment Insurance data use individual level data from Maryland, Illinois, California, and Florida (representing individuals whose random assignment sites 

are Baltimore, Chicago, and Los Angeles) and aggregate data from Massachusetts (representing individuals whose random assignment site is Boston). All sample 

adults with baseline consent.

Measures: Time period for administrative data is from June 2008-December 2009.
171 Chapter 5: Impacts on Economic Self-Sufficiency

SUPPLEMENTAL EXHIBIT 5.4. ADULT SELF-REPORTED CURRENT INDUSTRY AND OCCUPATION

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

INDUSTRY

Retail [SR] 0.060 – 0.003 – 0.006 – 0.022~ – 0.036~ 3,250

(0.010) (0.021) (0.013) (0.021)

Medical/health care [SR] 0.122 0.010 0.020 – 0.005 – 0.008 3,250

(0.014) (0.029) (0.018) (0.029)

Education [SR] 0.059 0.002 0.004 – 0.012 – 0.019 3,250

(0.011) (0.023) (0.013) (0.021)

Social services [SR] 0.075 – 0.002 – 0.005 – 0.017 – 0.027 3,250

(0.011) (0.023) (0.014) (0.023)

Industry other than retail, 

medical, education, or social 

services [SR]

0.207 – 0.013 – 0.026 – 0.025 – 0.040 3,250

(0.017) (0.036) (0.024) (0.038)

OCCUPATION

Blue collar [SR] 0.062 – 0.009 – 0.018 – 0.015 – 0.024 3,253

(0.010) (0.021) (0.013) (0.020)

White collar [SR] 0.202 0.001 0.003 – 0.032 – 0.050 3,253

(0.016) (0.033) (0.021) (0.034)

Professional or managerial [SR] 0.061 0.002 0.005 – 0.001 – 0.001 3,253

(0.010) (0.020) (0.013) (0.021)

Service [SR] 0.260 – 0.001 – 0.003 – 0.034 – 0.055 3,253

(0.019) (0.040) (0.025) (0.040)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

"Measures: Industry and occupation outcomes indicate that the adult respondent was currently working and was working in that industry or occupation. 

Occupation categories are not mutually exclusive. Industry coding is based on CPS codes: Retail trade 580–691; Medical trade 812–840, Education trade 

842–860, and Social services 861–871. Occupation is coded as follows: Blue collar 473–889 (production, craft, repair, machine operators, assemblers, inspectors, 

transportation, handlers, cleaners, helpers, and laborers); White collar 3–389 (executive/managerial, professional specialty, technical/administrative support, or 

sales), Managerial/professional 3–199 (executive, administrative, and managerial or professional specialty), and Service occupation 403–469 (private household 

service, protective service, and all other services).
172

SUPPLEMENTAL EXHIBIT 5.5. ADULT SELF-REPORTED CURRENT EMPLOYMENT BENEFITS

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

EMPLOYMENT BENEFITS

Health insurance [SR] 0.342 0.013 0.027 – 0.044~ – 0.070~ 3,229

(0.020) (0.041) (0.026) (0.042)

Paid sick leave [SR] 0.307 0.016 0.034 – 0.035 – 0.056 3,220

(0.019) (0.040) (0.025) (0.040)

Paid vacation [SR] 0.350 0.004 0.007 – 0.039 – 0.062 3,227

(0.020) (0.041) (0.027) (0.043)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

SUPPLEMENTAL EXHIBIT 5.6. FOUR MUTUALLY EXCLUSIVE CATEGORIES OF WORK AND SCHOOLING OF 

GROWN CHILDREN, PARENT REPORTS 

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

SURVEY DATA

CURRENTLY EMPLOYED ONLY [PR]

All 0.456 0.021 0.047 – 0.020 – 0.037 2,958

(0.023) (0.051) (0.031) (0.056)

Female 0.486 – 0.024 – 0.052 – 0.062 – 0.108 1,485

(0.032) (0.069) (0.041) (0.071)

Male 0.424 0.067* 0.158* 0.024 0.046 1,473

(0.032) (0.076) (0.041) (0.078)

CURRENTLY ENROLLED IN SCHOOL 

ONLY [PR] 

All 0.059 – 0.006 – 0.014 0.024 0.043 3,084

(0.010) (0.022) (0.016) (0.029)

Female 0.080 – 0.008 – 0.018 0.024 0.042 1,522

(0.016) (0.036) (0.025) (0.044)

Male 0.037 – 0.004 – 0.009 0.024 0.045 1,562

(0.011) (0.026) (0.018) (0.034)

CURRENTLY EMPLOYED AND

ENROLLED IN SCHOOL [PR]

All 0.097 – 0.004 – 0.009 – 0.028 – 0.050 3,084

(0.013) (0.031) (0.018) (0.032)

Female 0.122 – 0.008 – 0.018 – 0.020 – 0.034 1,524

(0.020) (0.046) (0.028) (0.048)

Male 0.071 0.000 0.001 – 0.035~ – 0.066~ 1,560

(0.017) (0.040) (0.019) (0.035)
173 Chapter 5: Impacts on Economic Self-Sufficiency

SUPPLEMENTAL EXHIBIT 5.6. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

SURVEY DATA

CURRENTLY IDLE (NEITHER

EMPLOYED NOR ENROLLED IN

SCHOOL) [PR]

All 0.372 – 0.012 – 0.028 0.026 0.047 2,958

(0.022) (0.049) (0.029) (0.054)

Female 0.299 0.036 0.079 0.063 0.109 1,483

(0.029) (0.063) (0.039) (0.067)

Male 0.451 – 0.062~ – 0.147~ – 0.015 – 0.028 1,475

(0.032) (0.075) (0.040) (0.076)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. Grown children (who were under age 18 at baseline and over age 20 as of December 31, 2007) of interviewed adults. 

SUPPLEMENTAL EXHIBIT 5.7. EMPLOYMENT AND SCHOOLING OUTCOMES OF GROWN CHILDREN, PARENT 

REPORTS AND ADMINISTRATIVE DATA BY SITE

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

CURRENTLY EMPLOYED [PR]

ALL

Maryland 0.594 0.010 0.021 – 0.063 – 0.089 430

(0.059) (0.130) (0.076) (0.108)

Massachusetts 0.662 – 0.050 – 0.133 – 0.111 – 0.223 553

(0.056) (0.148) (0.071) (0.144)

Illinois 0.511 – 0.072 – 0.255 – 0.055 – 0.095 631

(0.048) (0.170) (0.059) (0.102)

California 0.554 0.026 0.042 0.007 0.009 662

(0.050) (0.078) (0.061) (0.085)

New York 0.513 0.099~ 0.233~ – 0.013 – 0.039 677

(0.052) (0.121) (0.063) (0.194)

FEMALE

Maryland 0.610 0.089 0.189 – 0.032 – 0.041 215

(0.076) (0.162) (0.102) (0.129)

Massachusetts 0.739 – 0.152* – 0.378* – 0.159~ – 0.280~ 297

(0.072) (0.180) (0.085) (0.150)

Illinois 0.556 – 0.093 – 0.311 – 0.098 – 0.182 307

(0.070) (0.233) (0.091) (0.169)

California 0.636 – 0.066 – 0.091 – 0.072 – 0.093 326

(0.067) (0.092) (0.076) (0.098)

New York 0.537 0.031 0.077 – 0.045 – 0.154 335

(0.069) (0.171) (0.087) (0.298)
174

SUPPLEMENTAL EXHIBIT 5.7. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

CURRENTLY EMPLOYED [PR] (CONTINUED)

MALE

Maryland 0.580 – 0.075 – 0.172 – 0.089 – 0.143 215

(0.089) (0.204) (0.117) (0.186)

Massachusetts 0.560 0.076 0.215 – 0.035 – 0.083 256

(0.079) (0.225) (0.094) (0.225)

Illinois 0.469 – 0.051 – 0.196 – 0.017 – 0.028 324

(0.076) (0.292) (0.079) (0.127)

California 0.465 0.120~ 0.212~ 0.094 0.147 336

(0.069) (0.122) (0.084) (0.130)

New York 0.487 0.173* 0.384* 0.025 0.069 342

(0.069) (0.155) (0.084) (0.236)

CURRENTLY ENROLLED IN

SCHOOL [PR]

ALL

Maryland 0.106 – 0.010 – 0.023 – 0.032 – 0.044 457

(0.037) (0.086) (0.051) (0.071)

Massachusetts 0.182 0.003 0.007 – 0.038 – 0.076 577

(0.039) (0.106) (0.057) (0.113)

Illinois 0.118 0.011 0.037 0.058 0.100 659

(0.032) (0.114) (0.044) (0.076)

California 0.196 – 0.012 – 0.020 – 0.058 – 0.082 688

(0.038) (0.060) (0.051) (0.073)

New York 0.172 0.017 0.039 0.053 0.160 708

(0.035) (0.082) (0.049) (0.147)

FEMALE

Maryland 0.158 – 0.005 – 0.011 – 0.061 – 0.077 228

(0.055) (0.124) (0.065) (0.081)

Massachusetts 0.227 – 0.069 – 0.177 – 0.071 – 0.125 305

(0.052) (0.135) (0.077) (0.135)

Illinois 0.188 0.006 0.021 0.129~ 0.242~ 311

(0.049) (0.164) (0.072) (0.136)

California 0.203 0.025 0.034 – 0.011 – 0.014 336

(0.055) (0.077) (0.073) (0.094)

New York 0.232 0.012 0.031 0.006 0.021 347

(0.059) (0.146) (0.069) (0.232)

MALE

Maryland 0.059 – 0.017 – 0.040 – 0.003 – 0.005 229

(0.053) (0.122) (0.069) (0.106)

Massachusetts 0.124 0.089 0.250 0.010 0.023 272

(0.055) (0.155) (0.067) (0.154)

Illinois 0.057 0.014 0.053 0.003 0.005 348

(0.039) (0.147) (0.046) (0.074)
175 Chapter 5: Impacts on Economic Self-Sufficiency

SUPPLEMENTAL EXHIBIT 5.7. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

CURRENTLY ENROLLED IN

SCHOOL [PR] (CONTINUED)

FEMALE (CONTINUED)

California 0.189 – 0.049 – 0.087 – 0.109~ – 0.173~ 352

(0.050) (0.090) (0.060) (0.096)

New York 0.109 0.023 0.051 0.097~ 0.267~ 361

(0.044) (0.096) (0.056) (0.155)

CURRENTLY IDLE (NEITHER

EMPLOYED NOR ENROLLED 

IN SCHOOL) [PR]

ALL

Maryland 0.367 0.006 0.014 0.021 0.030 431

(0.056) (0.125) (0.071) (0.101)

Massachusetts 0.284 0.071 0.189 0.101 0.202 556

(0.052) (0.139) (0.069) (0.137)

Illinois 0.435 0.055 0.197 0.012 0.021 630

(0.050) (0.177) (0.061) (0.106)

California 0.351 – 0.014 – 0.021 0.035 0.049 665

(0.046) (0.073) (0.057) (0.081)

New York 0.416 – 0.113* – 0.264* – 0.063 – 0.191 676

(0.050) (0.116) (0.061) (0.185)

FEMALE

Maryland 0.306 – 0.054 – 0.115 0.047 0.059 215

(0.068) (0.144) (0.103) (0.130)

Massachusetts 0.204 0.185* 0.467* 0.176* 0.310* 299

(0.067) (0.168) (0.083) (0.147)

Illinois 0.360 0.075 0.253 – 0.002 – 0.003 305

(0.072) (0.240) (0.092) (0.172)

California 0.268 0.044 0.061 0.091 0.117 329

(0.065) (0.088) (0.071) (0.092)

New York 0.359 – 0.041 – 0.102 – 0.016 – 0.054 335

(0.064) (0.158) (0.081) (0.273)

MALE

Maryland 0.420 0.072 0.169 – 0.005 – 0.009 216

(0.087) (0.205) (0.112) (0.179)

Massachusetts 0.389 – 0.072 – 0.204 – 0.006 – 0.013 257

(0.076) (0.218) (0.091) (0.210)

Illinois 0.506 0.037 0.140 0.021 0.033 325

(0.077) (0.291) (0.080) (0.129)

California 0.441 – 0.072 – 0.127 – 0.027 – 0.043 336

(0.065) (0.116) (0.080) (0.127)

New York 0.480 – 0.190* – 0.421* – 0.115 – 0.322 341

(0.070) (0.154) (0.083) (0.233)
176

SUPPLEMENTAL EXHIBIT 5.7. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

UNEMPLOYMENT INSURANCE 

DATA, FOURTH QUARTER OF 2007 

THROUGH THIRD QUARTER OF 2008 [UI]

EMPLOYED

ALL

Maryland 0.540 – 0.055 – 0.120 0.018 0.024 650

(0.043) (0.093) (0.052) (0.070)

Illinois 0.405 – 0.021 – 0.074 – 0.052 – 0.088 826

(0.038) (0.135) (0.048) (0.081)

California 0.486 – 0.009 – 0.015 – 0.010 – 0.013 829

(0.039) (0.066) (0.045) (0.062)

Massachusetts 0.513 – 0.028 – 0.071 0.010 0.019 747

(0.039) (0.099) (0.043) (0.085)

All four sites 0.484 – 0.027 – 0.063 – 0.010 – 0.016 3,052

(0.020) (0.047) (0.024) (0.037)

FEMALE

Maryland 0.606 – 0.071 – 0.162 – 0.004 – 0.005 316

(0.059) (0.134) (0.072) (0.093)

Illinois 0.445 0.032 0.108 0.005 0.009 404

(0.056) (0.188) (0.067) (0.121)

California 0.594 – 0.090 – 0.140 – 0.073 – 0.097 401

(0.058) (0.089) (0.062) (0.081)

Massachusetts 0.564 0.090 0.183 0.060 0.079 386

(0.084) (0.172) (0.159) (0.211)

All four sites 0.551 – 0.004 – 0.008 – 0.002 – 0.002 1,191

(0.017) (0.038) (0.027) (0.042)

MALE

Maryland 0.478 – 0.039 – 0.081 0.039 0.054 334

(0.063) (0.129) (0.075) (0.102)

Illinois 0.368 – 0.072 – 0.277 – 0.109~ – 0.174~ 422

(0.055) (0.210) (0.063) (0.101)

California 0.388 0.066 0.123 0.057 0.081 428

(0.051) (0.094) (0.060) (0.086)

Massachusetts 0.458 – 0.152 – 0.537 – 0.056 – 0.209 361

(0.108) (0.384) (0.177) (0.652)

All four sites 0.418 – 0.040* – 0.095* – 0.018 – 0.029 1,545

(0.016) (0.039) (0.023) (0.037)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: PR = parent report, UI = Unemployment Insurance administrative records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: For parent reports, data source is the adult long-term survey. Grown children (who were under age 18 at baseline and over age 20 as of 

December 31, 2007) of interviewed adults. Unemployment Insurance data use individual level data from Maryland, Illinois, California, and Florida (representing 

individuals whose random assignment sites are Baltimore, Chicago, and Los Angeles) and aggregate data from Massachusetts (representing individuals whose 

random assignment site is Boston). All core grown children whose parent has a baseline consent. 

Measures: The administrative data only look at the last four common calendar quarters (fourth quarter of 2007 through the third quarter of 2008).
177 Chapter 5: Impacts on Economic Self-Sufficiency

SUPPLEMENTAL EXHIBIT 5.8. YOUTH EMPLOYMENT AND SCHOOL ENROLLMENT, SELF-REPORTED AND 

ADMINISTRATIVE DATA BY SITE

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

CURRENTLY EMPLOYED [SR]

ALL

Maryland 0.450 – 0.158* -0.271* – 0.116~ – 0.155~ 486

(0.062) (0.107) (0.060) (0.080)

Massachusetts 0.456 – 0.032 – 0.081 – 0.032 – 0.057 716

(0.046) (0.117) (0.047) (0.084)

Illinois 0.339 0.002 0.006 – 0.006 – 0.009 757

(0.046) (0.159) (0.050) (0.068)

California 0.321 – 0.035 – 0.054 – 0.069~ – 0.088~ 874

(0.039) (0.059) (0.041) (0.052)

New York 0.439 – 0.070 – 0.142 – 0.041 – 0.080 771

(0.046) (0.094) (0.050) (0.098)

FEMALE

Maryland 0.430 – 0.148~ – 0.247~ – 0.109 – 0.146 244

(0.087) (0.145) (0.086) (0.115)

Massachusetts 0.453 – 0.004 – 0.008 0.004 0.008 376

(0.063) (0.136) (0.065) (0.127)

Illinois 0.368 – 0.051 – 0.180 – 0.018 – 0.025 392

(0.063) (0.225) (0.072) (0.100)

California 0.371 – 0.057 – 0.086 – 0.151* – 0.187* 428

(0.055) (0.082) (0.060) (0.074)

New York 0.439 – 0.029 – 0.058 – 0.023 – 0.049 398

(0.067) (0.135) (0.069) (0.148)

MALE

Maryland 0.468 – 0.168* – 0.297* – 0.122 – 0.163 242

(0.085) (0.151) (0.085) (0.113)

Massachusetts 0.459 – 0.061 – 0.189 – 0.073 – 0.117 340

(0.069) (0.213) (0.072) (0.115)

Illinois 0.313 0.056 0.187 0.003 0.004 365

(0.067) (0.224) (0.075) (0.103)

California 0.274 – 0.011 – 0.017 0.010 0.013 446

(0.050) (0.079) (0.060) (0.079)

New York 0.439 – 0.112 – 0.230 – 0.058 – 0.102 373

(0.070) (0.142) (0.071) (0.126)

CURRENTLY ENROLLED IN SCHOOL [SR]

ALL

Maryland 0.423 0.143* 0.245* 0.040 0.053 486

(0.057) (0.098) (0.062) (0.082)
178

SUPPLEMENTAL EXHIBIT 5.8. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

CURRENTLY ENROLLED IN SCHOOL 

[SR] (CONTINUED)

ALL (CONTINUED)

Massachusetts 0.685 – 0.023 – 0.059 – 0.007 – 0.012 722

(0.046) (0.117) (0.046) (0.083)

Illinois 0.666 – 0.031 – 0.108 0.043 0.060 760

(0.041) (0.143) (0.048) (0.065)

California 0.624 0.051 0.078 – 0.046 – 0.059 877

(0.037) (0.057) (0.042) (0.054)

New York 0.609 0.022 0.044 – 0.038 – 0.074 776

(0.043) (0.086) (0.045) (0.088)

FEMALE

Maryland 0.501 0.107 0.179 – 0.081 – 0.108 244

(0.073) (0.121) (0.077) (0.103)

Massachusetts 0.730 – 0.056 – 0.123 – 0.030 – 0.059 380

(0.061) (0.134) (0.064) (0.125)

Illinois 0.673 – 0.023 – 0.082 0.068 0.093 394

(0.051) (0.183) (0.063) (0.086)

California 0.636 0.043 0.065 0.032 0.039 428

(0.051) (0.076) (0.054) (0.067)

New York 0.598 – 0.005 – 0.011 0.032 0.069 399

(0.062) (0.124) (0.066) (0.141)

MALE

Maryland 0.355 0.172* 0.303* 0.156~ 0.207~ 242

(0.081) (0.144) (0.083) (0.111)

Massachusetts 0.636 0.013 0.040 0.020 0.032 342

(0.063) (0.194) (0.063) (0.101)

Illinois 0.659 – 0.039 – 0.129 0.020 0.028 366

(0.064) (0.214) (0.067) (0.092)

California 0.613 0.056 0.088 – 0.118~ – 0.155~ 449

(0.053) (0.083) (0.061) (0.081)

New York 0.619 0.055 0.111 – 0.117* – 0.209* 377

(0.056) (0.113) (0.056) (0.100)

CURRENTLY IDLE (NEITHER EMPLOYED

NOR ENROLLED IN SCHOOL) [SR]

ALL

Maryland 0.297 – 0.039 – 0.066 0.022 0.029 486

(0.059) (0.101) (0.059) (0.078)

Massachusetts 0.167 0.030 0.076 0.009 0.017 716

(0.038) (0.097) (0.038) (0.068)
179 Chapter 5: Impacts on Economic Self-Sufficiency

SUPPLEMENTAL EXHIBIT 5.8. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

CURRENTLY IDLE (NEITHER EMPLOYED

NOR ENROLLED IN SCHOOL) [SR]

(CONTINUED)

ALL (CONTINUED)

Illinois 0.206 0.028 0.097 – 0.020 – 0.028 757

(0.037) (0.128) (0.043) (0.059)

California 0.201 0.018 0.027 0.071~ 0.091~ 874

(0.036) (0.055) (0.040) (0.051)

New York 0.230 – 0.039 – 0.079 0.002 0.004 771

(0.038) (0.077) (0.042) (0.082)

FEMALE

Maryland 0.278 0.002 0.003 0.102 0.136 244

(0.072) (0.121) (0.078) (0.104)

Massachusetts 0.133 0.053 0.114 0.045 0.088 376

(0.052) (0.112) (0.052) (0.100)

Illinois 0.191 0.030 0.108 – 0.024 – 0.033 392

(0.050) (0.178) (0.061) (0.084)

California 0.158 0.043 0.065 0.040 0.050 428

(0.047) (0.071) (0.050) (0.062)

New York 0.239 – 0.031 – 0.063 – 0.013 – 0.029 398

(0.052) (0.106) (0.060) (0.129)

MALE

Maryland 0.315 – 0.076 – 0.133 – 0.054 – 0.072 242

(0.088) (0.154) (0.086) (0.115)

Massachusetts 0.202 0.007 0.021 – 0.031 – 0.050 340

(0.055) (0.170) (0.054) (0.087)

Illinois 0.220 0.026 0.086 – 0.017 – 0.024 365

(0.061) (0.203) (0.065) (0.089)

California 0.241 – 0.006 – 0.010 0.099~ 0.131~ 446

(0.049) (0.077) (0.059) (0.079)

New York 0.222 – 0.048 – 0.099 0.020 0.035 373

(0.055) (0.113) (0.061) (0.108)
180

SUPPLEMENTAL EXHIBIT 5.8. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

UNEMPLOYMENT INSURANCE DATA, FOURTH QUARTER OF 2007 THROUGH THIRD QUARTER OF 2008 [UI]

EMPLOYED

All

Maryland 0.338 – 0.042 – 0.069 – 0.031 – 0.039 588

(0.043) (0.071) (0.047) (0.060)

Illinois 0.134 0.016 0.047 0.040 0.055 896

(0.027) (0.083) (0.036) (0.049)

California 0.293 – 0.036 – 0.056 0.001 0.002 923

(0.031) (0.048) (0.034) (0.043)

Massachusetts 0.278 – 0.075* – 0.198* – 0.004 – 0.006 1,083

(0.027) (0.070) (0.030) (0.053)

Four-site average 0.256 – 0.036* – 0.076* 0.004 0.006 3,490

(0.015) (0.032) (0.018) (0.025)

FEMALE

Maryland 0.394 – 0.019 – 0.034 – 0.038 – 0.050 284

(0.058) (0.102) (0.062) (0.081)

Illinois 0.183 0.020 0.063 0.032 0.042 440

(0.042) (0.132) (0.051) (0.067)

California 0.282 – 0.009 – 0.015 0.031 0.039 423

(0.042) (0.066) (0.047) (0.059)

Massachusetts 0.276 – 0.091~ – 0.219~ 0.009 0.017 544

(0.049) (0.117) (0.047) (0.091)

Four-site average 0.272 – 0.030 – 0.063 0.012 0.017 1,691

(0.024) (0.051) (0.026) (0.037)

MALE

Maryland 0.250 – 0.043 – 0.066 – 0.022 – 0.027 353

(0.047) (0.071) (0.053) (0.064)

Illinois 0.073 0.008 0.022 0.046 0.064 545

(0.027) (0.079) (0.037) (0.051)

California 0.232 – 0.047 – 0.072 – 0.012 – 0.015 646

(0.033) (0.051) (0.034) (0.044)

Massachusetts 0.280 – 0.064~ – 0.184~ – 0.015 – 0.024 539

(0.038) (0.111) (0.044) (0.070)

Four-site average 0.207 – 0.037* – 0.075* 0.001 0.001 2,083

(0.018) (0.037) (0.020) (0.027)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: SR = self-report, UI = Unemployment Insurance administrative records.

Model: Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and 

clustering on family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction 

with treatment status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: For self-reports, data source is youth long-term survey. Interviewed youth ages 15 to 20 as of December 31, 2007. Unemployment 

Insurance data use individual level data from Maryland, Illinois, California, and Florida (representing individuals whose random assignment sites are Baltimore, 

Chicago, and Los Angeles) and aggregate data from Massachusetts (representing individuals whose random assignment site is Boston). All core youth ages 15 to 

20 as of December 31, 2007 whose parent has a baseline consent. Massachusetts includes all youth ages 12 to 20. Since our request file did not split by age, we 

cannot look separately at those who were 15 to 20 in Massachusetts. 

Measures: The administrative data only looks at the last four common calendar quarters (fourth quarter of 2007 through the third quarter of 2008).
181 Chapter 5: Impacts on Economic Self-Sufficiency

SUPPLEMENTAL EXHIBIT 5.9. SELF-REPORTED YOUTH EMPLOYMENT AND SCHOOL ENROLLMENT

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

CURRENTLY EMPLOYED ONLY [SR]

All 0.169 – 0.023 – 0.048 – 0.005 – 0.008 3,604

(0.015) (0.031) (0.018) (0.027)

Female 0.165 – 0.023 – 0.046 – 0.028 – 0.043 1,838

(0.021) (0.043) (0.023) (0.037)

Male 0.173 – 0.024 – 0.051 0.017 0.025 1,766

(0.022) (0.047) (0.026) (0.038)

CURRENTLY ENROLLED IN SCHOOL

ONLY [SR]

All 0.390 0.039* 0.081* 0.007 0.011 3,604

(0.019) (0.040) (0.021) (0.031)

Female 0.396 0.027 0.055 0.011 0.017 1,838

(0.027) (0.054) (0.028) (0.044)

Male 0.384 0.052~ 0.111~ 0.003 0.004 1,766

(0.028) (0.061) (0.031) (0.045)

CURRENTLY ENROLLED IN SCHOOL

AND EMPLOYED [SR]

All 0.226 – 0.023 – 0.048 – 0.028 – 0.042 3,604

(0.019) (0.040) (0.021) (0.031)

Female 0.245 – 0.029 – 0.058 – 0.014 – 0.022 1,838

(0.027) (0.054) (0.029) (0.046)

Male 0.208 – 0.017 – 0.036 – 0.042 – 0.061 1,766

(0.026) (0.057) (0.028) (0.040)

CURRENTLY IDLE (NEITHER EMPLOYED

NOR ENROLLED IN SCHOOL) [SR]

All 0.215 0.007 0.014 0.026 0.039 3,604

(0.018) (0.038) (0.020) (0.031)

Female 0.194 0.024 0.049 0.031 0.048 1,838

(0.024) (0.048) (0.027) (0.043)

Male 0.235 – 0.011 – 0.023 0.022 0.032 1,766

(0.027) (0.058) (0.031) (0.045)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: "Currently enrolled in school" also includes youth who are on summer vacation.
182

SUPPLEMENTAL EXHIBIT 5.10. FAMILY TANF AND FOOD STAMPS BENEFITS IN MASSACHUSETTS, ILLINOIS, 

AND LOS ANGELES COUNTY, ADULT REPORTED AND ADMINISTRATIVE DATA BY MONTH CLOSEST TO 

INTERVIEW MONTH

CONTROL 

MEAN

EXPERIMENTAL VERSUS 

CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

SURVEY DATA

Currently receiving TANF 

[SR]

0.174 0.020 0.042 0.020 0.032 1,985

(0.020) (0.043) (0.028) (0.044)

Currently receiving food 

stamps [SR]

0.498 0.008 0.017 0.010 0.016 1,977

(0.027) (0.056) (0.036) (0.055)

ADMINISTRATIVE DATA

Received TANF in the month 

closest to the survey month 

[TANF]

0.244 – 0.009 – 0.019 – 0.004 – 0.006 1,985

(0.023) (0.048) (0.030) (0.047)

Active and received TANF 

in the month closest to the 

survey month [TANF]

0.187 0.011 0.024 0.006 0.009 1,985

(0.020) (0.043) (0.027) (0.041)

Received food stamps in 

the month closest to the 

survey month [FS]

0.632 0.029 0.060 0.040 0.061 1,977

(0.025) (0.052) (0.033) (0.051)

Active and received food 

stamps in the month closest 

to the survey month [FS]

0.557 0.022 0.045 0.033 0.051 1,977

(0.027) (0.056) (0.037) (0.056)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

records.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: For self-reports, data source is the adult long-term survey and the sample is all adults interviewed. FS and TANF analyses use individual 

data from Massachusetts, Illinois, Los Angeles County, and South Carolina and represent individuals whose random assignment sites are: Boston, Chicago and 

Los Angeles. We received TANF and food stamps data from Maryland, but the data quality is suspect. Samples for TANF, and FS are all sample adults with baseline 

consent.

Measures: The time period is the month closest to the month of interview. "Active" means the family is receiving welfare benefits directly through the adult. 

Otherwise, general receipt of the welfare benefits means that the family is receiving welfare benefits either through the adult or another family member. 
183 Chapter 5: Impacts on Economic Self-Sufficiency

SUPPLEMENTAL EXHIBIT 5.11. FAMILY TANF AND FOOD STAMPS BENEFITS IN MASSACHUSETTS, ILLINOIS, 

AND LOS ANGELES COUNTY, ADULT REPORTED AND ADMINISTRATIVE DATA FOR THE SAME MONTH

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

SURVEY DATA

Currently receiving TANF [SR] 0.178 0.034~ 0.071~ – 0.007 – 0.011 1,875

(0.021) (0.043) (0.027) (0.042)

Currently receiving food stamps [SR] 0.501 0.029 0.061 0.003 0.005 1,871

(0.027) (0.056) (0.037) (0.057)

ADMINISTRATIVE DATA

Received TANF in the month of survey 

[TANF]

0.253 – 0.006 – 0.013 – 0.011 – 0.016 1,875

(0.023) (0.048) (0.031) (0.047)

Active and received TANF in the month 

of survey [TANF]

0.194 0.011 0.023 0.013 0.020 1,875

(0.021) (0.043) (0.028) (0.043)

Received food stamps in the month of 

survey [FS]

0.650 0.034 0.070 0.028 0.044 1,871

(0.025) (0.051) (0.034) (0.051)

Active and received food stamps in the 

month of survey [FS]

0.575 0.023 0.049 0.021 0.032 1,871

(0.027) (0.055) (0.038) (0.058)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: For self-reports, data source is the adult long-term survey and the sample is all adults interviewed. FS and TANF analyses use individual 

data from Massachusetts, Illinois, Los Angeles County, and South Carolina and represent individuals whose random assignment sites are: Boston, Chicago and 

Los Angeles. We received TANF and food stamps data from Maryland, but the data quality is suspect. Samples for TANF and FS are all sample adults with baseline 

consent.

Measures: The time period is the month of survey. "Active" means the family is receiving welfare benefits directly through the adult. Otherwise, general receipt of 

welfare benefits means that the family is receiving welfare benefits either through the adult or another family member. 

SUPPLEMENTAL EXHIBIT 5.12. ADULT SELF-REPORTED EITC

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

Received EITC for the previous calendar 

year [SR]

0.421 0.011 0.023 0.020 0.033 3,242

(0.021) (0.043) (0.028) (0.045)

Received EITC and amount was more than 

$2,500 (2009 dollars) [SR]

0.194 0.007 0.014 0.042~ 0.067~ 3,119

(0.017) (0.035) (0.024) (0.038)

Received EITC and used it to pay bills 

(credit card, housing costs, tuition) [SR]

0.303 – 0.007 – 0.015 – 0.029 – 0.047 3,231

(0.020) (0.040) (0.026) (0.041)

Received EITC and put money into 

savings account [SR]

0.019 – 0.001 – 0.002 0.014 0.023 3,231

(0.006) (0.012) (0.009) (0.015)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of "0.250" for "Working" 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long-term survey. All interviewed adults.
184
185 Chapter 6: Impacts on Risky and Criminal Behavior

IMPACTS ON RISKY AND 

CRIMINAL BEHAVIOR
186 CHAPTER 6

IMPACTS ON RISKY AND 

CRIMINAL BEHAVIOR

This chapter discusses the reasons why moving to 

better neighborhoods might affect the risky or criminal 

behavior of youth and adults. We present data on risky 

and criminal behavior from combinations of survey 

self‐reports, proxy reports, and administrative arrest 

records for those who were youth at the time of our 

long‐term follow‐up (ages 13 to 20 or 15 to 20), grown 

children (ages 21 to 30), or adults (mostly household 

heads).

The long‐term follow‐up data show slightly more 

favorable treatment impacts for females than for 

males. These gender differences in long‐term MTO 

impacts are more muted than the interim evaluation 

results, but are qualitatively similar. Assignment to 

the experimental and Section 8 groups increased the 

prevalence of smoking for male youth in the long‐term

MTO survey. Section 8 males also exhibited increased 

scores on a behavior problems index by a marginally 

significant amount equal to about 7 percent of the 

control group’s mean. For female youth, assignment to 

the experimental group seemed to reduce the prevalence 

of drinking by around 11 percent of the control mean. 

We found few statistically significant impacts for most 

other measures of risky or criminal behavior among 

the different age groups we examined. One potentially 

important exception is arrests for drug distribution, 

for which we find mixed but suggestive indications of 

lower arrest rates among youth, grown children, and 

adults assigned to the MTO treatment groups relative 

to those assigned to the control group.

6.1 HYPOTHESES ABOUT EFFECTS 

ON RISKY AND CRIMINAL BEHAVIOR 

This section begins with a brief review of age and 

developmental patterns of risky and criminal behavior. 

These age patterns are relevant for thinking about 

what sort of specific anti‐social behaviors may be

most susceptible to influence from MTO and for 

which particular age sub‐groups within the study 

sample. We then discuss what previous theories in the 

“neighborhood effects” literature predict regarding 

MTO’s impacts on risky and delinquent behaviors.

DEVELOPMENTAL PATTERNS OF RISKY 

AND CRIMINAL BEHAVIOR1

Criminal behavior is disproportionately concentrated 

during adolescence and early adulthood. Exhibit 

6.1 provides an example of the “age‐crime curve,” 

which shows the fraction of all arrests for the Federal 

Bureau of Investigation’s Uniform Crime Report “part 

1” violent crime offenses (murder, rape, robbery, 

aggravated assault) and property offenses (burglary, 

motor vehicle theft, larceny) committed by different 

age groups using national data from 2009. Rates of 

criminal involvement increase during adolescence, 

starting around middle‐school age, and peak during

late adolescence and early adulthood (depending on 

the exact type of crime being examined). This age 

pattern is observed for both males and females, and has 

been documented across different countries and times 

(Hirschi and Gottfredson, 1983).

1 This section draws heavily on material from Ander et al. (2010).
187 Chapter 6: Impacts on Risky and Criminal Behavior Chapter 6: Impacts on Risky and Criminal Behavior

EXHIBIT 6.1. THE “AGE‐CRIME CURVE” FOR CRIMINAL BEHAVIOR: NATIONAL DATA ON PERCENT OF CRIME 

COMMITTED BY AGE GROUP

20

25

30

35

15

10

05

00

 AGE GROUP

Under 10

10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-64

65 +

Violent Crime

Murder and Non-Negligent Manslaughter

Property Crime

Exhibit 6.1. The "Age-Crime Curve" for Criminal Behavior: National Data on Percent of Crime Committed by Age Group PERCENT OF ARRESTS

Notes: Percentages represent the fraction of arrests involving different age groups based on 2009 data from the Federal Bureau of Investigation’s Uniform Crime 

Report. Violent offenses include murder, rape robbery, and aggravated assault. Property crimes include burglary, motor vehicle theft, and larceny.

Data source: Bureau of Justice Statistics (2011).

More generally, there is a substantial concentration of 

anti‐social behavior within the population—particularly 

the most serious forms of anti‐social behavior. In a 

landmark longitudinal study of 10,000 boys born 

in Philadelphia in 1945, University of Pennsylvania 

criminologist Marvin Wolfgang found that 6 percent of 

all boys accounted for one‐half of all arrests and fully 

two‐thirds of all arrests for violent crimes (Wolfgang, 

Figlio, and Sellin, 1972; Wolfgang, 1983). This pattern 

has also been widely replicated in other places and time 

periods (see, for example, Tracy, Wolfgang, and Figlio, 

1985; Shannon, 1982; Tillman, 1987; Farrington, 2002).

A growing body of research building on Wolfgang’s 

finding suggests that the age‐crime curve shown in 

Exhibit 6.1 reflects the behavior of two largely distinct 

“types” of people. For a small share of all youth, 

anti‐social and aggressive behavior begins during early 

childhood, and persists through adolescence into 

adulthood, although the manifestations of this behavior 

change over time. Psychologist Terrie Moffitt (1993) 

calls this group the life‐course‐persistent offenders, and 

argues that their behavior stems from ill health, difficult 

temperament, and subtle neuropsychological and 

cognitive deficits, which are likely to be compounded 

by challenging family backgrounds (in part because 

many of these traits are heritable). More common are 

adolescent‐limited offenders, who engage in delinquency 

because of “social mimicry.” For some youth in this latter 

group, adolescent offending can be “ensnaring”—as when 

a youthful offense contributes to school dropout and 

harms future employment prospects, thereby making 

persistent offending more likely.

Gender is one important predictor of involvement in risky 

or criminal behavior, whether of the adolescent‐limited or 

life‐course persistent varieties. Male youth are much more 

likely than females to engage in most anti‐social behaviors, 

particularly delinquency and violence. For example, 

national data from 1994 show that for youth under age 18, 

males are arrested more than four times as often as females 

for aggravated assault, and seven or eight times as often for 

robbery (Cook and Laub, 2002).
188

A variety of individual‐ and family‐level characteristics 

help distinguish life‐course persistent and 

adolescent‐limited offenders from people who never 

engage in crime. These characteristics include poor 

academic performance, particularly on verbal tasks 

(Maguin and Loeber, 1996), and levels of warmth and 

consistent discipline within the home as opposed to 

hostility and conflict (Widom, 1989; Gonzalez and 

Dodge, 2009; Steinberg, Blatt‐Eisengart, and Cauffman, 

2006). Low levels of parental monitoring are also 

predictive of the risk of involvement in drug selling 

(Little and Steinberg, 2006). Since so many of these 

family‐level risk factors are correlated with economic 

disadvantage, rates of anti‐social behavior in the 

low‐income MTO study sample are likely to exceed what 

we see in more nationally representative populations.

EXHIBIT 6.2. DEVELOPMENTAL PATHWAYS TO SERIOUS AND VIOLENT OFFENDING

MINOR AGGRESSION

annoying others,

bullying

MINOR COVERT BEHAVIOR

shoplifting, frequent lying

MODERATELY SERIOUS 

DELINQUENCY

fraud, pick pocketing

PROPERTY DAMAGE

vandalism, fire setting

SERIOUS

DELINQUENCY

auto theft,

burglary

PHYSICAL FIGHTING

physical fighting, gang fighting

VIOLENCE

rape, attack,

strong-arm, 

homicide

AUTHORITY 

AVOIDANCE

truancy, running away, 

staying out late

DEFIANCE/DISOBEDIENCE

STUBBORN BEHAVIOR

Overt Pathway Covert Pathway

(before age 15)

Authority Conflict Pathway

(before age 12)

PERCENTAGE OF BOYS:

Few

Many

AGE OF ONSET:

Late

Early

Exhibit 6.2. Developmental Pathways to Serious and Violent Offending

Exhibit 6.2 is reproduced from Thornberry, Huizinga, and Loeber (2004), Figure 1.

Criminal behavior and particularly violence are 

perhaps of greatest public policy concern, given their 

disproportionately large social costs (Anderson, 1999; 

Cook and Ludwig, 2000; Cohen, 2005; Ludwig, 2006). 

But serious forms of anti‐social behavior are almost

always preceded by less‐serious risky or anti‐social 

behaviors. Exhibit 6.2 (reproduced from Thornberry, 

Huizinga, and Loeber, 2004) shows that most youth 

who wind up engaging in violent behavior have a 

history of engaging in less‐serious problem behavior

previously—suggesting a “developmental ordering to 

seriousness” of anti‐social behavior. Exhibit 6.2 also 

makes clear that more youth engage in less‐serious risky 

or anti‐social behaviors than in more‐serious forms of 

delinquency such as violence.2 One important additional 

behavioral marker for youth‐violence risk not reflected

2 Criminal offenders in general “tend to engage in a diversity of crime 

types [but] with a somewhat greater tendency to repeat the same crime 

or to repeat within the group of property crimes or the group of violent 

crimes” (Piquero, Farrington, and Blumstein, 2003).
189 Chapter 6: Impacts on Risky and Criminal Behavior Chapter 6: Impacts on Risky and Criminal Behavior

in Exhibit 6.2 is substance use, including levels of use for 

intoxicating substances that fall below clinical thresholds 

for a substance‐abuse disorder.

One implication of these developmental patterns is 

that less‐serious forms of anti‐social behavior by youth 

(such as disobeying parents and teachers or hanging out 

with other youth who tend to get into trouble), even 

if not of great social policy concern for their own sake, 

are important outcomes to examine because they are 

indicative of developmental trajectories that may lead to 

more serious and socially costly behaviors in the future.

These patterns also help guide our decisions about how 

to analyze the data from the MTO long‐term study. 

The large differences in base rates for risky and criminal 

behavior by gender help motivate our decision to present 

results separately for male and female youth. Given the 

age pattern to anti‐social behavior, we will tend to focus 

our analysis of more serious forms of criminal behavior 

on youth who are in their peak offending ages at the time 

of our survey (15 to 20 at the end of 2007) and also look 

separately at grown children in MTO who were under 18 

at baseline but have now reached young adulthood (ages 

21 to 30 at the end of 2007). Our outcomes of interest 

include measures that are primarily related to onset (for 

example, ever arrested or lifetime prevalence of drug use) 

as well as those that capture duration and desistance of 

the behavior of interest (for example, total number of 

arrests).

HYPOTHESES ABOUT POTENTIAL 

NEIGHBORHOOD INFLUENCES

Neighborhood context (and by extension neighborhood 

mobility) may influence youth involvement with 

risky and criminal behavior through the physical 

and institutional environments, as well as through 

the social environment (Exhibit 6.3). Most of the 

same mechanisms may be relevant for the behavior of 

MTO grown children and adults as well, although for 

convenience in this subsection we just refer to “youth.”
190

EXHIBIT 6.3. HYPOTHESIZED PATHWAYS THROUGH WHICH MTO MAY IMPACT DELINQUENCY AND RISKY 

BEHAVIOR

MTO INTERVENTION RELOCATION TO LOW-POVERTY NEIGHBORHOODS

COMMUNITY-LEVEL

MEDIATORS

PERSON- AND FAMILY-LEVEL

MEDIATORS

DELINQUENCY AND RISKY

BEHAVIOR OUTCOMES

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

Social and Physical 

Environment

· Reduced crime, violent 

 activity

· Better school resources

· Increased recreational 

 activities for youth

· More or higher quality 

 policing

Community Norms 

and Values

· More pro-social peer group

· Better adult role models

· Better community 

 monitoring of youth 

 (collective efficacy)

· Possible adverse effects 

 from increased competition 

 with higher socio-economic 

 status peers

Economic Opportunities

· Better labor market 

 opportunities

· Tobacco/alcohol use

· Use of marijuana and 

other drugs

· Self-reports of delinquent 

and illegal behaviors

· Gang involvement

· Fighting/violence

· Arrests/contact with the 

criminal justice system

· Sexual activity/contraceptive use/pregnancies

Exhibit 6.3 is a modified version of Exhibit 5.1 in the MTO interim impacts evaluation (Orr et al., 2003).

Adults

Positive Effects

· Increased sense of 

safety

· Less stress, anxiety

Negative Effects

· Feelings of isolation

· Depression caused by 

change

Youth

Positive Effects

· Increased sense of 

 safety

· Increased perceived 

 returns to "legitimate" 

 activities (work, education)

· Increased orientation 

 towards the future

Negative Effects

· Feelings of inadequacy, 

 inability to compete
191 Chapter 6: Impacts on Risky and Criminal Behavior Chapter 6: Impacts on Risky and Criminal Behavior

The physical features of a neighborhood might influence 

youth involvement with risky behavior or crime in a 

number of ways (Macintyre and Ellaway, 2003). For 

decades urban planners and criminologists have been 

concerned with the possibility that some building 

designs contribute to crime, for example, through the 

construction of enclosed stairwells in public housing 

buildings that are difficult for local residents to monitor. 

Other physical features of neighborhoods, such as access 

to public transportation and job opportunities, may 

influence the legal labor market opportunities that are 

available to people and that represent a key alternative 

to criminal behavior in the standard economic model of 

crime (Becker, 1968).

The quality or availability of local institutions may also 

be relevant for youth involvement in risky or criminal 

behavior. For example, previous research suggests that 

the quality of local public schools varies dramatically 

across areas (Rivkin, Hanushek, and Kain, 2005), which 

influences student success in school and consequently the 

relative attractiveness of pro‐ versus anti‐social behavior. 

It is also possible that high‐crime neighborhoods might

have policing of lower quality or quantity compared to 

more affluent areas.3 If youth move to neighborhoods 

with greater policing, MTO could increase the likelihood 

of a youth being arrested for any given level of actual 

criminal activity. Previous research raises the possibility 

that minorities may be at particularly elevated risk for 

being arrested, even after controlling for frequency of 

involvement with criminal behavior and other risk factors 

such as neighborhood type, family socio‐economic status,

and educational history (Huizinga et al., 2007).

Most of the theoretical work in the public health 

and social science literatures has been devoted to 

understanding the possible psychosocial mechanisms 

through which the neighborhood social context may 

influence behavior, including risky or criminal behavior. 

One influential typology of models in this literature 

comes from Jencks and Mayer (1990). Epidemic models

are those that emphasize the power of peers to spread 

behaviors. Such contagion effects can arise from 

3 Whether or not such variation in policing exists depends in part on 

the degree to which policymakers choose to allocate additional police 

resources to the highest‐crime neighborhoods (Sherman, 2002).

learning criminal behavior from peers, pure preference 

externalities (individuals enjoy imitating their peers), 

stigma effects and social norms (the negative signal 

from criminal behavior declines when more people do 

them), and physical externalities (for example, higher 

rates of crime reduce the chances of getting arrested 

because of congestion effects in law enforcement); see 

Kleiman (1993), Cook and Goss (1996), Glaeser and 

Scheinkman (1999), Brock and Durlauf (2001), Manski 

(2000), and Moffitt (2001). Some epidemic models 

predict peer influences on youth criminal behavior that 

varies with the prevalence of peer criminal behavior 

within a community, which can lead to nonlinearities in 

peer effects or “tipping points.” Collective socialization

models concentrate on the way adults in a neighborhood 

influence young people who are not their children, 

for example, through human capital externalities that 

increase the opportunity costs of involvement with 

crime (Borjas, 1995), by acting as role models (Wilson, 

1987), or the willingness of local adults to enforce shared 

values as in the “collective efficacy” model of Sampson, 

Raudenbush, and Earls (1997) (see also Coleman, 1988).

More recently new models from behavioral economics raise 

the possibility that moving to a less distressed area could 

reduce youth involvement with risky or criminal behavior 

by influencing future orientation, attitudes towards risk, 

and other aspects of decision making. For example, work 

by Loewenstein and O’Donoghue (2004) suggests that 

moves to less dangerous, less distressed areas could lead 

to more future‐oriented, less risky, or violent behavior 

by reducing youth exposure to neighborhood stressors, 

which sap mental energy and reduce people’s ability to 

control the more impulsive parts of their brains.

MTO could have additional effects on risky or criminal 

behavior that are mediated through MTO’s impacts 

on household environments. For example, parental 

unemployment, substance use, poor mental health, 

exposure to community violence, and inadequate housing 

may all be risk factors for child maltreatment, and each 

of these risk factors could be affected by MTO. This 

possibility highlights the importance of measuring youth 

violence victimization that occurs within as well as 

outside of the home.
192

Although most behavioral models predict that MTO 

moves should reduce youth involvement with risky or 

criminal behavior, Jencks and Mayer’s discussion implies 

that other outcomes are possible. Competition models

emphasize the competition between neighbors for scarce 

resources like grades or jobs. Failure in the competition 

for pro‐social rewards may lead youth to compete instead 

for anti‐social rewards, which could elevate risk for 

criminal involvement. Relative deprivation models focus 

on negative psychological impacts from experiencing a 

decline in one’s relative material or social standing in a 

community (Luttmer, 2005).

Additional uncertainty about MTO’s net effects on youth 

risky or criminal behavior comes from the possibility that 

youth may choose their own social “micro‐climates” from 

within their new neighborhood social “macro‐climates” 

(Jencks and Mayer, 1990; Sharkey, 2006; Cicala, Fryer, 

and Spenkuch, 2011). For example, it is possible that 

any benefits from moving to less distressed areas through 

MTO could be lost if youth simply sort themselves back 

into peer groups that engage in and support anti‐social

behaviors that put youth at elevated risk for violent 

victimization or offending. However “fitting in” to new 

lower‐poverty areas for MTO youth may entail learning 

new modes of language, dress, expected emotional 

responses (Lawler and Thye, 1999), and other aspects 

of social interactions, which could vary by gender, given 

evidence that boys and girls value different attributes and 

personal characteristics (Ferguson, 2001). Boys may have 

more difficulty integrating into their new neighborhoods, 

given evidence that boys are more likely than girls to “use 

aggressive or confrontational techniques to deal with 

interpersonal difficulties” (Coleman and Hendry, 1999, 

p. 218). Compared to girls, boys might also have more 

reputational or social capital to lose from moving to a 

new neighborhood, as suggested by Anderson’s (1999) 

findings that many youth growing up in dangerous 

neighborhoods may develop violent social networks or 

strategic involvement in violence to help deter violence 

against themselves in the future.

Chapters 1 and 2 of this report show that the difference 

in average neighborhood environments between the 

MTO mobility groups (experimental and Section 8) and 

the MTO control group declines somewhat over time, 

which raises the possibility that the behavioral impact 

of assignment to an MTO mobility group could decline 

over time.

On the other hand most of the psychosocial behavioral 

models discussed here suggest that the benefits of a 

given change in neighborhood environment may become 

more beneficial (or less detrimental) over time, as MTO 

families spend more time in lower‐poverty areas. The 

benefits of exposure to higher‐achieving peers, more pro‐

social adults, and higher‐quality neighborhood schools,

labor market options and policing environments may 

accumulate over time and lead to more pronounced 

positive effects on youth behavior through the epidemic, 

collective socialization, and institutional models, as 

well as more pronounced changes in youth decision‐

making processes. To the extent to which exposure to 

the opportunities available in lower‐poverty areas makes 

MTO youth more competitive over time for getting good 

grades or jobs, any deleterious effects from competition 

or declines in relative position may also decline over 

time. We may also expect that any deleterious “moving 

effects” on families attenuate over time.

Moreover theories about developmentally “sensitive” 

or “critical” periods (Shonkoff and Phillips, 2000; 

Knudsen et al., 2006) predict that MTO impacts on 

risky or criminal behavior should be more pronounced 

for program participants who are adolescents at the 

long‐term follow‐up (and so were ages 10 or younger 

early in the study period, when MTO’s impacts on 

neighborhood conditions were particularly large) 

compared to those who were adolescents at the interim 

follow‐up (most of whom would already have been 10 

or older at baseline). Sensitive periods are hypothesized 

to be ages in which development of certain skills or 

developmental processes are particularly susceptible to 

the social environment, but are not necessarily the only 

times in which those skills or processes can be modified. 

Critical periods are thought to be ages at which some 

skills or developmental processes are shaped, after which 

they are largely fixed.

The non‐experimental empirical literature reveals 

mixed results on the importance of these theoretical 

neighborhood mechanisms in affecting risky and 
193 Chapter 6: Impacts on Risky and Criminal Behavior Chapter 6: Impacts on Risky and Criminal Behavior

criminal behaviors. Case and Katz (1991) found strong 

relationships between one’s own risky and delinquent 

behaviors and that of one’s peers for illegal drug use, 

alcohol use, and criminal offending in the Boston Youth 

Survey. However, Esbensen and Huizinga (1990) found 

that the level of disorganization of the neighborhood did 

not affect neighborhood‐level prevalence or frequency of 

drug use. Studies of a sample of young black women in 

Chicago found some relationship between pregnancy risk 

and low neighborhood socioeconomic status (Hogan and 

Kitagawa, 1985) and evidence that this risk was related to 

lower contraceptive use (Hogan, Astone, and Kitagawa, 

1985). The proportion of managerial workers in a census 

tract has been shown to be related to teen childbearing 

(Crane, 1991; Brooks‐Gunn et al., 1993), but Case and 

Katz did not find direct evidence of peer influences on 

out‐of‐wedlock childbearing.

The non‐experimental research seems to provide stronger 

support for an association between neighborhood attributes 

and involvement with crime or violence specifically. 

One of the best‐known non‐experimental studies on this

question is Sampson, Raudenbush, and Earls’ (1997) 

analysis of data from the Project on Human Development 

in Chicago Neighborhoods (PHDCN). Their analysis finds 

that one of the best predictors for variation in violence 

was the neighborhood’s degree of informal social control 

together with social cohesion and trust, what they term 

“collective efficacy.” Collective efficacy is found to have 

a strong association with violence even after controlling 

for a rich set of individual‐level characteristics, and seems 

to mediate the effects of other neighborhood attributes 

such as socioeconomic composition (see also Sampson 

and Raudenbush, 1999, and Morenoff, Sampson, and 

Raudenbush, 2001). Qualitatively similar findings for 

violence offending rather than victimization that also draw 

on data from the PHDCN have been reported by Sampson, 

Morenoff, and Raudenbush (2005). Similar patterns of 

results for exposure to violence and violent behavior have 

been reported in a number of other studies (for example, 

Hirschfield and Bowers, 1997; Warner and Rountree, 1997; 

Rountree and Warner, 1999; Bellair, 2000; Peterson, Krivo, 

and Harris, 2000; Wikström and Loeber, 2000; Beyers et 

al., 2001; Simons et al., 2004; for an excellent summary, see 

Sampson, Morenoff, and Gannon‐Rowley, 2002).4

6.2 EARLIER RESEARCH: 

SHORT‐TERM AND INTERIM IMPACTS 

ON RISKY AND CRIMINAL BEHAVIOR

Initial short‐term findings from MTO drawing on data 

collected for individual demonstration sites showed 

that during the first few years after random assignment, 

males in both the experimental and Section 8 groups 

exhibited fewer behavior problems (disobedience, 

bullying, depression) than those in the control group in 

the Boston MTO site (Katz, Kling, and Liebman, 2001) 

and in the New York MTO site as well (Leventhal and 

Brooks‐Gunn, 2003). Evidence from the Baltimore MTO 

site suggested that the experimental group males had 

fewer arrests for violent crimes relative to the control 

group (Ludwig, Duncan, and Hirschfield, 2001).

Results from the MTO interim evaluation, which 

measured outcomes for youth in a consistent way across all 

five MTO demonstration sites 4 to 7 years after random 

assignment, suggested that the impacts of moving to a 

less dangerous and distressed neighborhood on youth 

behavior might be more complex than the previous 

empirical literature would suggest. Although the results 

indicated that neighborhood influence on risky and 

criminal behavior by female youth follows a pattern 

predicted by most prior theoretical and non‐experimental

empirical studies, the pattern of program impacts is more 

complicated for male youth (Kling, Ludwig, and Katz, 

2005). In the first few years after random assignment, 

male youth in the experimental group had fewer violent‐

crime arrests and behavior problems than those in the 

control group.5 However by three to four years after 

randomization males in the experimental group were 

arrested more often than those in the control group, 

primarily for property crimes, and were also more likely to 

engage in a variety of other delinquent and risky behaviors.

4 A more recent observational study drawing on PHDCN data finds 

some evidence of a non‐linear relationship between youth violence 

involvement and exposure to violent peers, where the effect of exposure 

to additional violent peers declines at higher levels of peer violence 

(Zimmerman and Messner, 2011).

5 These short‐term findings are consistent with those reported for the 

Boston and Baltimore sites by Katz, Kling, and Liebman (2001) and 

Ludwig, Duncan, and Hirschfield (2001).
194

Evidence of a similar gender difference in how youth 

outcomes change in response to moving out of public 

housing into less distressed areas comes from a recent 

analysis of mortality records matched to an entirely 

separate sample of public housing families who applied 

for housing vouchers in Chicago in the late 1990s (Jacob, 

Ludwig, and Miller, 2011). That study found housing 

voucher offers produced declines in mortality rates for 

female youth compared to those who were not offered a 

voucher, but did not have the same protective effect on 

male youth.

Kling, Ludwig, and Katz (2005) argue that the most 

likely explanation for the unexpected gender difference 

in how youth risky and criminal behavior responds 

to residential mobility rests with a variant of the 

competition model discussed by Jencks and Mayer 

(1990). Over time some MTO males may have realized 

that they had a comparative advantage in the competition 

for anti‐social rather than pro‐social rewards within their 

new, lower‐poverty neighborhoods.

6.3 BASELINE AND CONTROL 

GROUP CONTEXT

At baseline, three‐quarters of control group families 

reported that their primary or secondary reason for 

moving was to get away from drugs and gangs and over 

40 percent indicated that a member of their household 

had been victimized during the past 6 months (see 

Exhibit 1.2). Exhibit 6.4 shows that most forms of risky 

behaviors in the United States (other than marijuana use) 

have held steady or declined in prevalence since the MTO 

demonstration was planned in the early 1990s. The top 

panels report data from the Monitoring the Future survey 

on self‐reported use rates over the past 30 days, and show 

that smoking and drinking have declined over time, and 

use of illicit drugs other than marijuana has been fairly 

constant. Marijuana use, in contrast, has increased over 

time. The bottom panel of Exhibit 6.4 shows that rates of 

teen pregnancy, births, and abortions have also declined 

over this period for 15‐ to 19‐year‐olds; this pattern holds 

for minority as well as white teens (not shown).

EXHIBIT 6.4. NATIONAL TRENDS IN SUBSTANCE USE AND TEEN PREGNANCY

YEAR

SUBSTANCE USE IN THE LAST 30 DAYS 1991 2006

ANY ILLICIT DRUG (%)

8th graders 5.7 9.5

12th graders 16.4 23.8

ANY ILLICIT DRUG OTHER THAN MARIJUANA (%)

8th graders 3.8 3.5

12th graders 7.1 8.6

MARIJUANA/HASHISH (%)

8th graders 3.2 8.0

12th graders 13.8 21.4

ALCOHOL (%)

8th graders 25.1 13.8

12th graders 54.0 41.2

SMOKING (%)

8th graders 14.3 7.1

12th graders 28.3 19.2

TEEN PREGNANCY (RATES PER 1,000 FEMALES AGES 15–19) 1991 2010

PREGNANCY RATE 115.3 71.5

BIRTHRATE 61.8 41.9

ABORTION RATE 37.4 19.3

Data source: Johnston et al. (2011) and Guttmacher Institute (2010).

Crime rates, particularly for violent crime, were also declining dramatically over the MTO study period—although 
195 Chapter 6: Impacts on Risky and Criminal Behavior Chapter 6: Impacts on Risky and Criminal Behavior

rates of violence had been surging in the MTO 

demonstration cities in the years during which MTO 

was being planned. While the overall homicide rate in 

the U.S. increased only modestly from 1985 to 1991 

(from 8.2 to 10.4 per 100,000), the rate at which teen 

ages 13 to 17 were arrested for murder more than tripled 

over this time period (Cook and Laub, 2002). Most of 

this increase was driven by gun homicides, committed 

disproportionately by and against minority youth 

(Cook and Laub, 1998, 2002; Blumstein, 2000). Many 

criminologists believe that this surge in youth crime was 

driven by violence associated with the growth of crack 

cocaine, which may have contributed to growing gun use 

by teens involved in crack distribution and eventually 

other youth as well (Blumstein, 1995). The rate at which 

teens age 13 to 17 committed murder proceeded to drop 

by more than half over the next several years (Cook and 

Laub, 2002). The most likely explanations seem to be 

the ebbing of the crack epidemic, as well as increased 

spending on police and prisons (Levitt, 2004).

The declines in crime observed over the 1990s were 

most pronounced in those places that had the highest 

crime rates at the peak of the crack epidemic (Blumstein, 

2000; Raphael and Ludwig, 2003; Levitt, 2004; 

Harcourt and Ludwig, 2006). The largest cities in 

particular experienced unusually pronounced declines 

in crime during the 1990s, and even within cities the 

highest‐crime areas experienced above‐average declines. 

This pattern will serve to narrow the differential in 

neighborhood crime rates between families assigned 

to the MTO experimental or Section 8 groups and the 

control group, because control group families were more 

likely to stay behind in the highest‐crime neighborhoods,

which subsequently experienced the largest drops in their 

crime rates.

Although crime dropped almost everywhere in the 

U.S. over the 1990s, the size of the drop was somewhat 

larger in some of our MTO demonstration sites than 

others, leading to some difference in crime levels across 

cities during our study period. In particular, Baltimore 

and Chicago have greatly elevated levels of crime and 

violence compared with the other three MTO cities. For 

example, 1998 homicide rates per 100,000 equaled 47.1 

in Baltimore and 25.6 in Chicago, compared with 6.1 in 

Boston, 11.8 in Los Angeles, and 8.6 in New York City.

6.4 DATA SOURCES AND MEASURES

A variety of survey‐based and administrative data sources 

were used to measure MTO participants’ involvement in 

risky or criminal behavior.

DATA SOURCES

One source of information about risky and criminal 

behavior is the long‐term follow‐up surveys we collected. 

Youth who were ages 13 to 20 at the end of 2007 were 

asked to self‐report on their own risky behavior (youth 

ages 10 to 12 in our survey sample frame were asked a 

smaller subset of questions that were developmentally 

appropriate). For MTO participants who were children 

(under 18) at baseline but ages 21 to 30 at the time of 

our survey, and so not eligible for our survey sample 

frame, we asked MTO adult household heads to answer 

a few survey questions that provide proxy information 

about whether grown children in the study have been in 

prison or jail.

Our second major source of information about 

involvement in risky or criminal behavior is from 

official administrative arrest histories maintained by 

government agencies. We have obtained individual 

adult arrest histories from state‐level criminal justice 

agencies in the five states that contain the original MTO 

demonstration sites (California, Illinois, Maryland, 

Massachusetts, and individual‐level de‐identified data

from New York) as well as from eight other states in 

which MTO families have lived (Connecticut, Florida, 

Georgia, Iowa, Pennsylvania, South Carolina, Tennessee, 

and Virginia). These adult arrest histories capture 98 

percent of all person‐years for adults in the MTO study 

sample. We also obtained juvenile arrest histories with 

state‐wide coverage for four of the five original site states 

(California, Illinois, Maryland, and Massachusetts), 

city‐wide coverage for New York City, and state‐wide 

coverage for two other states in which MTO families 

have lived (Florida and Iowa). Combined with the data 

from the adult arrest agencies, the juvenile arrest histories 

capture 97 percent and 95 percent of all person‐years for 

grown children and youth, respectively, in the sample.
196

MEASURES

Most of the relevant survey questions about youth risky 

and criminal behavior are taken from the National 

Longitudinal Survey of Youth and replicate items used in 

the interim survey. These measures have been linked in 

the literature to many neighborhood characteristics, and 

they have been shown to be highly correlated with other 

measures of behavior problems (see Moore et al., 1999). 

Following standard practice in the research literature, we 

aggregate information on specific outcomes into several 

key indices:

•	 Abbreviated behavior problems index—Fraction 

of 11 behavior problems that youth respondent 

self‐reported as “often or sometimes true” of 

themselves: has difficulty concentrating, cheats 

or lies, bullies or is cruel or mean to others, is 

disobedient at home, has trouble getting along with 

other children, is restless or overactive, has a very 

strong temper, is withdrawn/does not get involved 

with others, hangs around with kids who get into 

trouble, is disobedient at school, and has trouble 

getting along with teachers.

•	 Delinquency index—Fraction of 8 delinquent 

behaviors that the youth self‐reported ever having 

engaged in: carrying a hand gun, belonging to a 

gang, purposely damaging or destroying property, 

stealing something worth less than $50, stealing 

something worth more than $50, engaging in other 

property crimes, attacking someone with idea of 

hurting them, having a situation end in serious 

fight or assault, and selling drugs. 

•	 Risky behavior index—Fraction of 4 risky 

behaviors that a youth self‐reported ever having 

engaged in: alcohol use, cigarette smoking, 

marijuana use, and sexual intercourse.

Both the delinquency and risky behavior indices measure 

having ever engaged in certain behaviors. This reference 

period has the advantage of encompassing events 

throughout the entire period since random assignment. 

Most youth were randomly assigned prior to the ages 

at which the behaviors tend to occur frequently. Some 

of these behaviors may have occurred before random 

assignment, but these should be both small in number 

(since these youth were relatively young prior to random 

assignment) and approximately the same in prevalence in 

all random assignment groups.

We used our administrative arrest records to construct 

measures of whether the person was ever arrested (and 

the number of times arrested) between the time of MTO 

random assignment and December 31, 2007, which was 

the last date for which we could obtain arrest records 

from the major states containing most of the MTO 

families.6

 We also construct measures of ever arrested 

and number of arrests for specific types of crimes, namely 

violence (most commonly for aggravated or simple 

assault, and, less frequently, for more serious crimes 

like murder, rape, robbery, and kidnapping), property 

offenses (motor vehicle theft, larceny, burglary), drug 

offenses (drug possession and drug distribution), and all 

other offenses (a category that is dominated by relatively 

less serious crimes like disorderly conduct). If more than 

one charge was tied to that arrest date, each arrest was 

categorized using the most serious charge from that arrest 

event.7

 Arrests that occurred prior to randomization 

were controlled for in the statistical analysis (that is, used 

as explanatory variables), but were not included in the 

outcome measures.

6.5 LONG‐TERM IMPACTS ON RISKY 

AND CRIMINAL BEHAVIOR

This section discusses the main results for the impacts 

of the MTO mobility experiment on risky and criminal 

behavior by MTO youth, grown children, and adult 

household heads.

6 Accessing administrative arrest records was more straightforward in 

some jurisdictions than in others, and so the point in calendar time at 

which we received data varied across jurisdictions—which means the 

last calendar year for which we have arrest data varies across areas. We 

chose to use administrative arrest records through December 31, 2007 

as a common endpoint, which we have available for all jurisdictions 

mentioned in the text and is consistent with the original study design 

we planned before beginning data collection.

7 We select the most serious charge based on how New York State 

criminal law classifies offenses. We use New York state law because the 

criminal justice agency in that state only provides us with the most 

serious criminal charge per arrest, so using their selection criteria in the 

other states (where we have all charges associated with each arrest) helps 

improve consistency in the data across states. We have explored other 

systems for selecting the most serious charge per arrest and obtain quite 

similar results, in part because in the large majority of cases an arrestee 

is charged with a single criminal offense.
197 Chapter 6: Impacts on Risky and Criminal Behavior Chapter 6: Impacts on Risky and Criminal Behavior

YOUTH OUTCOMES

The first panel of Exhibit 6.5 shows that the estimated 

effects of the MTO experimental and Section 8 

treatments on the overall risky behavior index for all 

youth (boys and girls together) are not statistically 

significant. Sometimes statistically insignificant results 

are hard to interpret because they are not very precisely 

estimated—that is, the statistical uncertainty (confidence 

interval) around an estimate is so large that they cannot 

rule out even moderately large impacts. As a way to 

think about the precision of the insignificant estimates 

presented in the top panel of Exhibit 6.5, the variation 

in the MTO experimental and Section 8 intent‐to‐treat 

(ITT) effects on the behavior problems index for all 

youth does not permit us to detect an impact if the 

difference from the control group is smaller than around 

.03, or roughly 6 percent of the control group’s mean 

(.47). Looking at the individual components of this index 

reveals some signs of the same gender difference in youth 

responses to MTO mobility as were found in the interim 

study.

EXHIBIT 6.5. RISKY BEHAVIOR OUTCOMES FOR YOUTH AGES 13–20

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

RISKY BEHAVIOR INDEX [SR]

All 0.467 – 0.001 – 0.002 0.007 0.010 4,623

(0.014) (0.029) (0.015) (0.022)

Female 0.442 – 0.027 – 0.054 – 0.017 – 0.026 2,358

(0.019) (0.037) (0.020) (0.031)

Male 0.491 0.025 0.053 0.029 0.042 2,265

(0.018) (0.039) (0.020) (0.028)

COMPONENTS OF THE

RISKY BEHAVIOR INDEX

EVER SMOKED [SR]

All 0.312 0.042* 0.088* 0.043* 0.064* 4,618

(0.019) (0.039) (0.020) (0.030)

Female 0.297 0.022 0.044 0.016 0.026 2,355

(0.025) (0.051) (0.028) (0.043)

Male 0.327 0.062* 0.134* 0.069* 0.098* 2,263

(0.025) (0.054) (0.028) (0.040)

EVER HAD ALCOHOLIC 

DRINK [SR]

All 0.534 – 0.032 – 0.067 – 0.017 – 0.026 4,618

(0.020) (0.041) (0.021) (0.032)

Female 0.541 – 0.061* – 0.124* – 0.032 – 0.050 2,355

(0.026) (0.053) (0.029) (0.045)

Male 0.528 – 0.003 – 0.006 – 0.004 – 0.005 2,263

(0.027) (0.058) (0.029) (0.041)

EVER USED MARIJUANA [SR]

All 0.363 – 0.008 – 0.017 0.003 0.005 4,609

(0.020) (0.042) (0.022) (0.032)

Female 0.327 – 0.042 – 0.084 – 0.026 – 0.040 2,350

(0.027) (0.054) (0.029) (0.045)
198

EXHIBIT 6.5. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

EVER USED MARIJUANA [SR] (CONTINUED)

Male 0.397 0.025 0.054 0.031 0.044 2,259

(0.028) (0.061) (0.030) (0.043)

EVER HAD SEX [SR]

All 0.662 – 0.003 – 0.005 – 0.001 – 0.001 4,580

(0.017) (0.036) (0.019) (0.029)

Female 0.605 – 0.022 – 0.044 – 0.022 – 0.035 2,334

(0.025) (0.050) (0.027) (0.042)

Male 0.716 0.017 0.036 0.020 0.029 2,246

(0.023) (0.050) (0.026) (0.037)

Notes: * = p < 0.05, ~ = p < 0.10 on two‐tailed t‐test. Robust standard errors shown in parentheses. ITT= Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: The Risky Behavior Index is the fraction of the 4 risky behaviors listed above that the youth reports ever having exhibited. 

The second panel of Exhibit 6.5 shows that compared to 

male youth assigned to the control group, those assigned 

to either the MTO experimental or Section 8 groups 

are about 13 percent more likely to smoke. In contrast, 

female youth assigned to the MTO experimental group 

are 6 percentage points less likely than those assigned 

to the control group to drink (about 11 percent of the 

control prevalence). The estimated impact for female 

youth assigned to the MTO Section 8 group is in the 

same direction but slightly smaller in magnitude and not 

statistically significant. And consistent with those gender 

differences, although not statistically significant, are the 

results for sexual activity and marijuana use, where the 

sign of the estimate for males in both treatment groups 

is positive (more sex and marijuana use) and the sign for 

females in both treatment groups is negative (less sex and 

marijuana use).These hints in the long‐term MTO data 

of the same gender difference in youth response to MTO 

moves that were found in the interim MTO data are 

interesting in part because overlap between the two study 

samples is limited. The interim MTO study focused on 

delinquency and risky behavior outcomes among youth 

ages 12 to 19 as of the end of 2001, while the long‐term

youth survey examines youth ages 13 to 20 as of the end 

of 2007 (who would have been ages 7 to 14 at the end 

of 2001). The fact that a similar finding arises for two 

largely different samples of MTO youth would seem 

to increase our confidence that this gender difference 

in how youth respond to MTO‐induced moves is not a 

chance finding.

Exhibit 6.6 shows that there are no statistically 

significant impacts of assignment to either the MTO 

experimental or Section 8 groups on more serious forms 

of anti‐social or criminal behavior, including a measure 

of 11 problem behaviors the youth self‐reports ever 

having engaged in (the behavior problems index) or 8 

delinquent behaviors the youth self‐reports ever having 

engaged in (the delinquency index) when we pool male 

and female youth together. However the estimated MTO 

impacts are all negative for female youth and positive 

for male youth, even if most of these estimates are not 

statistically significant. (The one exception is the positive 

Section 8 impact on the behavioral problems index for 

male youth, which is marginally significant.)
199 Chapter 6: Impacts on Risky and Criminal Behavior Chapter 6: Impacts on Risky and Criminal Behavior

EXHIBIT 6.6. BEHAVIOR PROBLEMS AND DELINQUENCY FOR YOUTH AGES 13–20

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

BEHAVIOR PROBLEMS INDEX [SR]

All 0.379 0.004 0.008 0.009 0.013 4,629

(0.010) (0.022) (0.011) (0.017)

Female 0.371 – 0.007 – 0.014 – 0.010 – 0.015 2,361

(0.014) (0.028) (0.015) (0.024)

Male 0.387 0.015 0.032 0.027~ 0.038~ 2,268

(0.015) (0.032) (0.016) (0.023)

DELINQUENCY INDEX [SR]

All 0.146 – 0.002 – 0.004 0.008 0.012 4,625

(0.008) (0.017) (0.009) (0.014)

Female 0.110 – 0.006 – 0.011 – 0.005 – 0.008 2,360

(0.009) (0.019) (0.010) (0.016)

Male 0.181 0.002 0.004 0.021 0.030 2,265

(0.013) (0.029) (0.016) (0.022)

Notes: * = p < 0.05, ~ = p < 0.10 on two‐tailed t‐test. Robust standard errors shown in parentheses. ITT= Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: The Behavior Problems Index is the fraction of 11 problem behaviors (for example, difficulty concentrating and having a strong temper) that the youth 

reported as true or sometimes true at present or in the past 6 months. The Delinquency Index is the fraction of 8 delinquent behaviors (for example, carrying a gun 

and destroying property) that the youth reported ever having exhibited.

Assignment to either of the MTO treatment groups did 

not have any statistically significant impacts on overall 

arrest rates of male or female youth ages 15 to 20, but 

impacts on these outcomes are somewhat imprecisely 

estimated (Exhibit 6.7). For example, the top panel of 

Exhibit 6.7 shows that for the MTO experimental ITT 

effect, for female youth we cannot rule out impacts on 

number of arrests that range from – .03 to +.24, that is, 

– 10 percent to +66 percent of the control group’s mean 

number of arrests (0.36). For male youth, we cannot 

rule out impacts that range from around – 16 percent 

to +26 percent of the control group mean. Exhibit 6.7 

suggests that the MTO experimental treatment may 

have increased propertycrime arrest rates, with positive 

point estimates for both male and female youth that are 

not quite statistically significant when disaggregated by 

gender. The MTO experimental treatment may have 

reduced arrest rates for drug selling among male youth 

(marginally significant), with an ITT effect that is equal 

to over one‐third of the control group mean.
200

EXHIBIT 6.7. NUMBER OF POST‐RANDOM ASSIGNMENT ARRESTS FOR YOUTH AGES 15–20

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

ARRESTS BY CRIME TYPE

NUMBER OF...

ANY CRIME ARRESTS [CJR]

All 1.078 0.093 0.195 – 0.170 – 0.259 4,717

(0.101) (0.212) (0.112) (0.170)

Female 0.356 0.101 0.207 – 0.054 – 0.082 2,300

(0.069) (0.142) (0.071) (0.109)

Male 1.741 0.087 0.187 – 0.283 – 0.425 2,417

(0.185) (0.397) (0.204) (0.306)

VIOLENT CRIME ARRESTS [CJR]

All 0.325 0.043 0.091 – 0.062 – 0.094 4,717

(0.037) (0.078) (0.039) (0.059)

Female 0.155 0.027 0.055 – 0.048 – 0.074 2,300

(0.033) (0.069) (0.033) (0.050)

Male 0.481 0.060 0.128 – 0.076 – 0.115 2,417

(0.064) (0.138) (0.068) (0.102)

PROPERTY CRIME ARRESTS [CJR]

All 0.239 0.065* 0.136* – 0.013 – 0.019 4,717

(0.031) (0.064) (0.034) (0.051)

Female 0.091 0.044~ 0.090~ – 0.010 – 0.015 2,300

(0.026) (0.053) (0.023) (0.035)

Male 0.375 0.086 0.183 – 0.016 – 0.025 2,417

(0.054) (0.117) (0.060) (0.090)

DRUG CRIME ARRESTS [CJR]

All 0.208 – 0.015 – 0.032 – 0.048 – 0.073 4,717

(0.030) (0.063) (0.034) (0.051)

Female 0.019 0.016 0.032 – 0.017 – 0.026 2,300

(0.016) (0.033) (0.016) (0.025)

Male 0.381 – 0.044 – 0.094 – 0.077 – 0.116 2,417

(0.057) (0.121) (0.064) (0.096)

OTHER CRIME ARRESTS [CJR]

All 0.306 0.000 – 0.001 – 0.048 – 0.072 4,717

(0.034) (0.071) (0.037) (0.055)

Female 0.090 0.015 0.030 0.021 0.032 2,300

(0.023) (0.047) (0.026) (0.040)

Male 0.503 – 0.014 – 0.029 – 0.113~ – 0.170~ 2,417

(0.064) (0.136) (0.067) (0.100)
201 Chapter 6: Impacts on Risky and Criminal Behavior Chapter 6: Impacts on Risky and Criminal Behavior

EXHIBIT 6.7. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL 

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

DRUG CRIME ARRESTS 

BY TYPE

NUMBER OF...

DRUG POSSESSION ARRESTS 

[CJR]

All 0.117 0.010 0.020 – 0.034~ – 0.052~ 4,717

(0.019) (0.039) (0.019) (0.028)

Female 0.013 0.005 0.009 – 0.011 – 0.017 2,300

(0.010) (0.021) (0.010) (0.016)

Male 0.213 0.015 0.032 – 0.057 – 0.085 2,417

(0.035) (0.075) (0.035) (0.052)

DRUG DISTRIBUTION ARRESTS 

[CJR]

All 0.091 – 0.025 – 0.052 – 0.014 – 0.021 4,717

(0.017) (0.036) (0.022) (0.033)

Female 0.006 0.011 0.023 – 0.006 – 0.009 2,300

(0.009) (0.020) (0.010) (0.015)

Male 0.169 – 0.059~ – 0.127~ – 0.020 – 0.031 2,417

(0.032) (0.069) (0.042) (0.063)

Notes: * = p < 0.05, ~ = p < 0.10 on two‐tailed t‐test. Robust standard errors shown in parentheses. ITT= Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Individual criminal justice system arrest data. Adult and juvenile data from California, Illinois, Maryland, and Massachusetts; de‐identified

adult data from New York State; juvenile data from New York City; and adult or juvenile records from 8 additional states in which participants have lived. All core 

household members ages 15 to 20 as of December 31, 2007.

Measures: Violent crime arrests involve charges of force or threat of force including homicide, rape, robbery, assault, kidnapping, and weapons charges. Property 

crime arrests involve taking money or property and include burglary, motor vehicle theft, larceny, trespassing, and receiving stolen property. Drug crime arrests 

are classified as either possession or distribution arrests. "Other" arrest charges cover any charges not classified as violent, property, or drug crimes and include 

disorderly conduct and moving violations.

GROWN CHILDREN OUTCOMES

Exhibit 6.8 presents results for administratively‐recorded 

arrest rates of grown children—MTO participants who 

were ages 21 to 30 at the end of 2007 and would have 

been roughly ages 15 to 25 at the time of the interim 

study. Data from the interim study showed that MTO 

reduced violent‐crime arrests for male and female 

youth through 4 to 7 years after random assignment, 

but increased property‐crime arrests for male youth 

(Kling, Ludwig, and Katz, 2005). The administrative 

arrest records that we have collected for the long‐term

follow‐up show no statistically significant impacts on 

either violent or property crimes, although we do see 

some suggestive evidence for a decline in arrests for 

drug selling that echoes what we find for MTO youth 

in the long‐term follow‐up. The last row of Exhibit 6.8 

shows that the Section 8 ITT effect for males on number 

of arrests since random assignment for drug selling is 

negative and equal to around 23 percent of the control 

group mean. The experimental ITT effect is in the same 

direction but half as large (in absolute value) and not 

statistically significant.
202

EXHIBIT 6.8. NUMBER OF POST‐RANDOM ASSIGNMENT ARRESTS FOR GROWN CHILDREN AGES 21–30

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL 

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

ARRESTS BY CRIME TYPE

NUMBER OF...

ANY CRIME ARRESTS [CJR]

All 2.862 – 0.090 – 0.212 – 0.116 – 0.202 4,641

(0.171) (0.404) (0.192) (0.332)

Female 0.967 – 0.054 – 0.126 – 0.037 – 0.063 2,277

(0.131) (0.306) (0.157) (0.269)

Male 4.673 – 0.124 – 0.296 – 0.192 – 0.335 2,364

(0.303) (0.726) (0.347) (0.607)

VIOLENT CRIME ARRESTS [CJR]

All 0.626 – 0.055 – 0.129 – 0.042 – 0.072 4,641

(0.047) (0.112) (0.054) (0.093)

Female 0.279 – 0.038 – 0.089 – 0.047 – 0.080 2,277

(0.045) (0.105) (0.051) (0.087)

Male 0.958 – 0.071 – 0.169 – 0.038 – 0.066 2,364

(0.082) (0.196) (0.092) (0.161)

PROPERTY CRIME ARRESTS [CJR]

All 0.633 – 0.006 – 0.014 – 0.042 – 0.072 4,641

(0.054) (0.128) (0.061) (0.105)

Female 0.297 – 0.057 – 0.132 – 0.016 – 0.027 2,277

(0.046) (0.107) (0.065) (0.112)

Male 0.953 0.044 0.105 – 0.064 – 0.112 2,364

(0.093) (0.222) (0.100) (0.175)

DRUG CRIME ARRESTS [CJR]

All 0.799 – 0.089 – 0.211 – 0.075 – 0.129 4,641

(0.065) (0.154) (0.076) (0.131)

Female 0.127 – 0.022 – 0.052 0.033 0.057 2,277

(0.044) (0.103) (0.051) (0.087)

Male 1.440 – 0.153 – 0.366 – 0.177 – 0.310 2,364

(0.119) (0.285) (0.144) (0.251)

OTHER CRIME ARRESTS [CJR]

All 0.804 0.060 0.141 0.042 0.072 4,641

(0.066) (0.155) (0.074) (0.128)

Female 0.263 0.063 0.147 – 0.008 – 0.013 2,277

(0.052) (0.121) (0.054) (0.092)

Male 1.321 0.056 0.134 0.087 0.153 2,364

(0.116) (0.278) (0.137) (0.239)
203 Chapter 6: Impacts on Risky and Criminal Behavior Chapter 6: Impacts on Risky and Criminal Behavior

EXHIBIT 6.8. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

DRUG CRIME ARRESTS BY TYPE

NUMBER OF...

DRUG POSSESSION ARRESTS [CJR]

All 0.461 – 0.042 – 0.100 – 0.011 – 0.020 4,641

(0.044) (0.104) (0.053) (0.093)

Female 0.069 0.001 0.002 0.015 0.026 2,277

(0.030) (0.070) (0.035) (0.060)

Male 0.835 – 0.084 – 0.202 – 0.037 – 0.065 2,364

(0.082) (0.196) (0.103) (0.179)

DRUG DISTRIBUTION ARRESTS [CJR]

All 0.338 – 0.047 – 0.111 – 0.063 – 0.110~ 4,641

(0.035) (0.082) (0.037) (0.064)

Female 0.058 – 0.023 – 0.055 0.018 0.032 2,277

(0.024) (0.056) (0.028) (0.049)

Male 0.605 – 0.069 – 0.165 – 0.140* – 0.245* 2,364

(0.063) (0.151) (0.067) (0.116)

Notes: * = p < 0.05, ~ = p < 0.10 on two‐tailed t‐test. Robust standard errors shown in parentheses. ITT= Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Individual criminal justice system arrest data. Adult and juvenile data from California, Illinois, Maryland, and Massachusetts; de‐identified 

adult data from New York State; juvenile data from New York City; and adult or juvenile records from 8 additional states in which participants have lived. All core 

household members who are now grown children (under age 18 at baseline and ages 21 to 30 as of December 31, 2007).

Measures: Violent crime arrests involve charges of force or threat of force including homicide, rape, robbery, assault, kidnapping, and weapons charges. Property 

crime arrests involve taking money or property and include burglary, motor vehicle theft, larceny, trespassing, and receiving stolen property. Drug crime arrests 

are classified as either possession or distribution arrests. "Other" arrest charges cover any charges not classified as violent, property, or drug crimes and include 

disorderly conduct and moving violations.
204

Exhibit 6.9 shows that there are no statistically significant effects of the experiment based on household head proxy 

reports about whether grown children were ever in jail or prison.

EXHIBIT 6.9. INCARCERATION OF GROWN CHILDREN AGES 21–30

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

EVER IN JAIL/PRISON [PR]

All 0.205 0.007 0.016 – 0.008 – 0.014 3,087

(0.018) (0.041) (0.023) (0.041)

Female 0.057 0.014 0.031 – 0.003 – 0.006 1,526

(0.017) (0.038) (0.022) (0.038)

Male 0.357 – 0.001 – 0.001 – 0.013 – 0.024 1,561

(0.030) (0.072) (0.037) (0.070)

Notes: * = p < 0.05, ~ = p < 0.10 on two‐tailed t‐test. Robust standard errors shown in parentheses. ITT= Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult long‐term survey. Grown children (who were under age 18 at baseline and ages 21 to 30 as of December 31, 2007) of interviewed 

adults.
205 Chapter 6: Impacts on Risky and Criminal Behavior Chapter 6: Impacts on Risky and Criminal Behavior

ADULT OUTCOMES

Exhibit 6.10 presents results for number of arrests 

following random assignment for the adults in our long‐

‐term follow‐up survey sample. The vast majority of adults 

in MTO are female heads of household, so the exhibit 

does not present results separately by gender. The exhibit 

shows that adults assigned to the experimental group are 

arrested more often than controls for property crimes, 

with an ITT effect that is marginally significant (p<.10) 

and equal to around one‐third of the control group mean. 

The experimental ITT effect for drug selling is negative 

and not significant when we look at total number of 

post‐randomization arrests (Exhibit 6.10), but marginally 

significant (p<.10) when we look at a measure of “ever 

arrested” for drug selling (Supplemental Exhibit 6.3).

EXHIBIT 6.10. NUMBER OF POST‐RANDOM ASSIGNMENT ARRESTS FOR ADULTS

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

ARRESTS BY CRIME TYPE

NUMBER OF...

Any crime arrests [CJR] 0.567 0.052 0.110 – 0.016 – 0.026 4,376

(0.064) (0.135) (0.060) (0.096)

Violent crime arrests [CJR] 0.149 0.001 0.002 – 0.009 – 0.014 4,376

(0.020) (0.043) (0.022) (0.035)

Property crime arrests [CJR] 0.133 0.043~ 0.091~ – 0.002 – 0.002 4,376

(0.025) (0.052) (0.022) (0.036)

Drug crime arrests [CJR] 0.114 – 0.001 – 0.002 – 0.003 – 0.005 4,376

(0.022) (0.047) (0.023) (0.037)

Other crime arrests [CJR] 0.171 0.009 0.020 – 0.003 – 0.005 4,376

(0.027) (0.056) (0.025) (0.040)

DRUG CRIME ARRESTS BY TYPE

NUMBER OF...

Drug possession arrests [CJR] 0.080 0.006 0.013 – 0.004 – 0.006 4,376

(0.018) (0.038) (0.019) (0.030)

Drug distribution arrests [CJR] 0.035 – 0.007 – 0.016 0.001 0.001 4,376

(0.008) (0.017) (0.009) (0.015)

Notes: * = p < 0.05, ~ = p < 0.10 on two‐tailed t‐test. Robust standard errors shown in parentheses. ITT= Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult and juvenile data from California, Illinois, Maryland, and Massachusetts; de‐identified adult data from New York State; and adult or 

juvenile records from 8 additional states in which participants have lived. All long‐term survey sampling frame adults with a baseline consent form.

Measures: Violent crime arrests involve charges of force or threat of force including homicide, rape, robbery, assault, kidnapping, and weapons charges. Property 

crime arrests involve taking money or property and include burglary, motor vehicle theft, larceny, trespassing, and receiving stolen property. Drug crime arrests 

are classified as either possession or distribution arrests. “Other” arrest charges cover any charges not classified as violent, property, or drug crimes and include 

disorderly conduct and moving violations.
206

6.6 INTERPRETATION OF RESULTS 

FOR RISKY AND CRIMINAL BEHAVIOR

Overall the estimated effects of the MTO residential‐

mobility intervention on risky and criminal behavior 

that we found in the long‐term MTO data (measuring 

outcomes 10 to 15 years after baseline) were more muted 

than what was found in the interim MTO study (4 to 7 

years after baseline). Because MTO treatment impacts 

on neighborhood environments have been declining 

over the course of the study period (Chapter 2), the 

pattern of impacts on risky and criminal behavior over 

time is consistent with the idea that contemporaneous 

neighborhood conditions may be more important than 

accumulated exposure for outcomes in this domain.

We do not see signs of any sustained decline in violent 

criminal behavior of the sort found in the interim data, 

although the confidence intervals around our long‐

term estimates are fairly sizable and so we cannot rule 

out either moderately‐sized declines (or increases) in 

that outcome. Our long‐term follow‐up data also yield

suggestive evidence that arrests for drug selling may 

have declined for male youth, male grown children, 

and perhaps adults as well as a result of the MTO 

intervention. It is not clear why a residential mobility 

intervention would reduce this type of criminal behavior, 

but perhaps drug selling is a crime that requires 

cooperation among numerous people.

As with the interim MTO data, we find some signs 

that assignment to the MTO experimental rather than 

the control group might increase risky behaviors for 

male youth (smoking) while at the same time decreasing 

the prevalence of at least some of these behaviors 

(drinking) for female youth. A concern in interpreting 

the administrative arrest data is that the likelihood of 

arrest could vary across neighborhoods. In particular, 

if the quality of policing is relatively higher in lower‐

poverty areas, then any evidence of an increase in arrests 

in administratively‐recorded arrest records may overstate 

any adverse effects of MTO in the direction of increasing 

criminal behavior. However the fact that we see signs that 

MTO moves increased self‐reported smoking among male 

youth as well as administratively‐recorded property‐crime

arrests suggests the (muted) gender difference in how 

youth respond to MTO in the long‐term follow‐up is not

simply an artifact of varying policing quality across areas.

We now have at least suggestive evidence of gender 

differences in how youth respond to residential mobility 

from three different samples. This pattern was found very 

strongly in the interim MTO youth sample, and now in 

a more muted way for those program participants who 

were youth at the time of our long‐term follow‐up—a

largely different cohort of youth compared to those 

examined in the interim study. In addition a qualitatively 

similar pattern has been found in an independent 

sample of public housing families in Chicago who 

applied for housing vouchers, using information from 

mortality records (Jacob, Ludwig, and Miller, 2011). 

Understanding the mechanisms behind this pattern and 

possible policy responses remain important priorities for 

social science research.
207 Chapter 6: Impacts on Risky and Criminal Behavior Chapter 6: Impacts on Risky and Criminal Behavior

SUPPLEMENTAL EXHIBIT 6.1. PREVALENCE OF POST‐RANDOM ASSIGNMENT ARRESTS 

FOR YOUTH AGES 15–20

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

ARRESTS BY CRIME TYPE

EVER ARRESTED AFTER RANDOM 

ASSIGNMENT FOR...

ANY CRIME [CJR]

All 0.297 0.013 0.027 – 0.016 – 0.024 4,717

(0.016) (0.034) (0.018) (0.028)

Female 0.189 0.012 0.025 – 0.012 – 0.019 2,300

(0.021) (0.044) (0.023) (0.035)

Male 0.397 0.014 0.029 – 0.019 – 0.029 2,417

(0.024) (0.052) (0.028) (0.041)

VIOLENT CRIME [CJR]

All 0.167 0.015 0.032 – 0.023 – 0.035 4,717

(0.014) (0.029) (0.015) (0.023)

Female 0.103 0.006 0.012 – 0.030~ – 0.046~ 2,300

(0.017) (0.034) (0.017) (0.026)

Male 0.226 0.025 0.053 – 0.016 – 0.025 2,417

(0.022) (0.047) (0.024) (0.035)

PROPERTY CRIME [CJR]

All 0.132 0.019 0.040 – 0.001 – 0.001 4,717

(0.012) (0.026) (0.014) (0.021)

Female 0.070 0.011 0.023 – 0.003 – 0.005 2,300

(0.014) (0.029) (0.015) (0.023)

Male 0.189 0.027 0.057 0.001 0.002 2,417

(0.020) (0.043) (0.023) (0.034)

DRUG CRIME [CJR]

All 0.091 0.004 0.008 – 0.014 – 0.021 4,717

(0.010) (0.022) (0.011) (0.017)

Female 0.016 0.009 0.018 – 0.008 – 0.012 2,300

(0.008) (0.016) (0.007) (0.011)

Male 0.161 – 0.001 – 0.002 – 0.020 – 0.030 2,417

(0.019) (0.040) (0.020) (0.030)

OTHER CRIME [CJR]

All 0.159 – 0.010 – 0.020 – 0.016 – 0.025 4,717

(0.013) (0.027) (0.014) (0.021)

Female 0.078 – 0.001 – 0.002 0.009 0.014 2,300

(0.015) (0.030) (0.016) (0.025)

Male 0.234 – 0.017 – 0.037 – 0.041~ – 0.061~ 2417

(0.021) (0.045) (0.023) (0.034)
208

SUPPLEMENTAL EXHIBIT 6.1. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

DRUG CRIME ARRESTS BY TYPE

EVER ARRESTED AFTER RANDOM 

ASSIGNMENT FOR...

DRUG POSSESSION [CJR]

All 0.065 0.008 0.017 – 0.008 – 0.012 4,717

(0.009) (0.019) (0.009) (0.014)

Female 0.012 0.004 0.008 – 0.007 – 0.010 2,300

(0.007) (0.014) (0.006) (0.010)

Male 0.113 0.012 0.026 – 0.010 – 0.015 2,417

(0.016) (0.034) (0.017) (0.026)

DRUG DISTRIBUTION [CJR]

All 0.048 – 0.004 – 0.009 – 0.005 – 0.008 4,717

(0.007) (0.016) (0.008) (0.012)

Female 0.005 0.009 0.018 – 0.003 – 0.004 2,300

(0.005) (0.011) (0.005) (0.007)

Male 0.087 – 0.016 – 0.035 – 0.007 – 0.011 2,417

(0.014) (0.029) (0.015) (0.023)

Notes: * = p < 0.05, ~ = p < 0.10 on two‐tailed t‐test. Robust standard errors shown in parentheses. ITT= Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Individual criminal justice system arrest data. Adult and juvenile data from California, Illinois, Maryland, and Massachusetts; de‐identified 

adult data from New York State; juvenile data from New York City; and adult or juvenile records from 8 additional states in which participants have lived. All core 

household members ages 15 to 20 as of December 31, 2007.

Measures: Violent crime arrests involve charges of force or threat of force including homicide, rape, robbery, assault, kidnapping, and weapons charges. Property 

crime arrests involve taking money or property and include burglary, motor vehicle theft, larceny, trespassing, and receiving stolen property. Drug crime arrests 

are classified as either possession or distribution arrests. "Other" arrest charges cover any charges not classified as violent, property, or drug crimes and include 

disorderly conduct and moving violations.
209 Chapter 6: Impacts on Risky and Criminal Behavior Chapter 6: Impacts on Risky and Criminal Behavior

SUPPLEMENTAL EXHIBIT 6.2. PREVALENCE OF POST‐RANDOM ASSIGNMENT ARRESTS FOR GROWN 

CHILDREN AGES 21–30

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

ARRESTS BY CRIME TYPE

EVER ARRESTED AFTER RANDOM 

ASSIGNMENT FOR...

ANY CRIME [CJR]

All 0.511 – 0.003 – 0.006 – 0.017 – 0.030 4,641

(0.018) (0.043) (0.019) (0.034)

Female 0.348 0.009 0.022 – 0.020 – 0.034 2,277

(0.025) (0.059) (0.027) (0.047)

Male 0.666 – 0.014 – 0.034 – 0.015 – 0.026 2,364

(0.025) (0.059) (0.027) (0.046)

VIOLENT CRIME [CJR]

All 0.279 – 0.007 – 0.017 – 0.017 – 0.030 4,641

(0.016) (0.038) (0.018) (0.031)

Female 0.152 0.004 0.010 – 0.026 – 0.045 2,277

(0.019) (0.045) (0.021) (0.036)

Male 0.400 – 0.018 – 0.044 – 0.009 – 0.016 2,364

(0.025) (0.060) (0.028) (0.049)

PROPERTY CRIME [CJR]

All 0.274 0.008 0.019 – 0.016 – 0.027 4,641

(0.017) (0.039) (0.018) (0.031)

Female 0.164 – 0.006 – 0.013 – 0.021 – 0.036 2,277

(0.020) (0.047) (0.022) (0.037)

Male 0.379 0.022 0.052 – 0.010 – 0.018 2,364

(0.025) (0.060) (0.027) (0.048)

DRUG CRIME [CJR]

All 0.255 – 0.012 – 0.029 – 0.034* – 0.059* 4,641

(0.015) (0.035) (0.016) (0.028)

Female 0.077 – 0.005 – 0.011 – 0.005 – 0.009 2,277

(0.015) (0.035) (0.016) (0.028)

Male 0.425 – 0.020 – 0.047 – 0.061* – 0.107* 2,364

(0.025) (0.059) (0.028) (0.048)

OTHER CRIME [CJR]

All 0.311 0.024 0.056 0.008 0.014 4,641

(0.017) (0.039) (0.018) (0.031)

Female 0.169 0.019 0.044 – 0.014 – 0.025 2,277

(0.020) (0.047) (0.022) (0.037)

Male 0.447 0.029 0.068 0.029 0.050 2,364

(0.026) (0.062) (0.028) (0.049)
210

SUPPLEMENTAL EXHIBIT 6.2. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

DRUG CRIME ARRESTS BY TYPE

EVER ARRESTED AFTER RANDOM 

ASSIGNMENT FOR...

DRUG POSSESSION [CJR]

All 0.198 – 0.011 – 0.026 – 0.015 – 0.026 4,641

(0.014) (0.032) (0.015) (0.026)

Female 0.053 – 0.003 – 0.008 – 0.007 – 0.012 2,277

(0.013) (0.030) (0.014) (0.024)

Male 0.336 – 0.019 – 0.044 – 0.023 – 0.040 2,364

(0.024) (0.056) (0.027) (0.047)

DRUG DISTRIBUTION [CJR]

All 0.155 – 0.018 – 0.042 – 0.032* – 0.055* 4,641

(0.013) (0.030) (0.013) (0.023)

Female 0.037 – 0.009 – 0.021 0.001 0.002 2,277

(0.011) (0.026) (0.012) (0.021)

Male 0.268 – 0.026 – 0.062 – 0.062* – 0.108* 2,364

(0.022) (0.053) (0.024) (0.041)

Notes: * = p < 0.05, ~ = p < 0.10 on two‐tailed t‐test. Robust standard errors shown in parentheses. ITT= Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Individual criminal justice system arrest data. Adult and juvenile data from California, Illinois, Maryland, and Massachusetts; de‐identified 

adult data from New York State; juvenile data from New York City; and adult or juvenile records from 8 additional states in which participants have lived. All core 

household members who are now grown children (under age 18 at baseline and ages 21 to 30 as of December 31, 2007).

Measures: Violent crime arrests involve charges of force or threat of force including homicide, rape, robbery, assault, kidnapping, and weapons charges. Property 

crime arrests involve taking money or property and include burglary, motor vehicle theft, larceny, trespassing, and receiving stolen property. Drug crime arrests 

are classified as either possession or distribution arrests. "Other" arrest charges cover any charges not classified as violent, property, or drug crimes and include 

disorderly conduct and moving violations.
211 Chapter 6: Impacts on Risky and Criminal Behavior Chapter 6: Impacts on Risky and Criminal Behavior

SUPPLEMENTAL EXHIBIT 6.3. PREVALENCE OF POST‐RANDOM ASSIGNMENT ARRESTS FOR ADULTS

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

ARRESTS BY CRIME TYPE

EVER ARRESTED AFTER RANDOM 

ASSIGNMENT FOR...

Any crime [CJR] 0.235 0.004 0.009 – 0.016 – 0.026 4,376

(0.015) (0.031) (0.016) (0.025)

Violent crime [CJR] 0.095 0.007 0.014 0.000 0.000 4,376

(0.010) (0.022) (0.012) (0.018)

Property crime [CJR] 0.080 0.020~ 0.041~ 0.004 0.007 4,376

(0.010) (0.022) (0.011) (0.017)

Drug crime [CJR] 0.061 – 0.008 – 0.017 – 0.002 – 0.003 4,376

(0.008) (0.018) (0.010) (0.015)

Other crime [CJR] 0.109 – 0.005 – 0.010 – 0.008 – 0.012 4,376

(0.011) (0.023) (0.012) (0.019)

DRUG CRIME ARRESTS BY TYPE

EVER ARRESTED AFTER RANDOM 

ASSIGNMENT FOR...

Drug possession [CJR] 0.041 0.004 0.007 0.000 0.000 4,376

(0.007) (0.015) (0.008) (0.013)

Drug distribution [CJR] 0.029 – 0.010~ – 0.020~ – 0.003 – 0.004 4,376

(0.006) (0.012) (0.006) (0.010)

Notes: * = p < 0.05, ~ = p < 0.10 on two‐tailed t‐test. Robust standard errors shown in parentheses. ITT= Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Adult and juvenile data from California, Illinois, Maryland, and Massachusetts; de‐identified adult data from New York State; and adult or 

juvenile records from 8 additional states in which participants have lived. All long‐term survey sampling frame adults with a baseline consent form.

Measures: Violent crime arrests involve charges of force or threat of force including homicide, rape, robbery, assault, kidnapping, and weapons charges. Property 

crime arrests involve taking money or property and include burglary, motor vehicle theft, larceny, trespassing, and receiving stolen property. Drug crime arrests 

are classified as either possession or distribution arrests. "Other" arrest charges cover any charges not classified as violent, property, or drug crimes and include 

disorderly conduct and moving violations.
212
213 Chapter 7: Impacts on Educational Outcomes

IMPACTS ON 

EDUCATIONAL 

OUTCOMES
214 CHAPTER 7

IMPACTS ON EDUCATIONAL OUTCOMES

By providing opportunities for children and their 

families to move to less-distressed neighborhoods, 

MTO was expected to provide access to superior 

educational opportunities in the form of better schools 

and increased exposure to peers, neighbors, and 

communities that value academic achievement. This 

in turn could lead to improved educational outcomes 

for these youth, including increased achievement test 

scores, completion rates of high school, and advanced 

educational degrees, as well as higher earnings.

This chapter describes the educational experiences and 

behaviors of the MTO youth and the long-term impacts 

of the program on their educational outcomes. The results 

include effects on post-secondary school participation 

among children who were already in school when the 

program started, as well as the first estimates of MTO’s 

effects on the youngest children at baseline—those 

between infancy and age 5—all of whom were too young 

to provide reliable measures of educational success at the 

MTO interim evaluation. These children are of particular 

interest because their opportunity to move through MTO 

to better neighborhoods and potentially better schools 

occurred at the earliest, and most malleable, stage of their 

cognitive and socio-emotional development.

We find that relative to youth in the control group, youth 

in the experimental and Section 8 groups experienced 

mixed impacts on school quality—they attended schools 

with lower proportions of low-income and minority 

students and with slightly higher average test scores, but 

the schools serving treatment-group youth also had larger 

overall student bodies and the experimental group’s most 

recent school had higher student-teacher ratios. School 

mobility is similar across treatment and controls groups, 

although youth in the experimental group may have 

been distributed across a slightly larger number of school 

districts compared with the control group. Youth in the 

experimental group, especially females, reported more 

hospitable school climates than the control group. 

In keeping with the results from the interim study (Orr 

et al., 2003; Sanbonmatsu et al., 2006), MTO has few 

detectable effects on the school performance of youth. 

There are no statistically significant differences across 

randomized groups in achievement test scores. We see 

signs of reduced rates of grade retention for Section 

8 female youth compared with controls, but on a few 

other measures such as high school graduation we see 

statistically significant or marginally significant declines 

in educational outcomes for youth in one or both of 

the treatment groups compared with controls. Given 

the large number of comparisons made, these impacts 

could potentially be chance findings. Disappointingly, 

the generally null results also extended to the younger 

children who had not yet entered school at baseline. 

Results for grown children are mixed.

7.1 BASELINE AND CONTROL 

GROUP CONTEXT 

The plight of children in urban schools is widely 

documented. Children in urban communities perform 

well below their counterparts in suburban schools 

on standardized tests, are more likely to drop out 

of school, and are much less likely to attend college 

(Casserly, 2002; Swanson, 2009). Drop-out rates 

among low-income students are almost three times 

the rate of students in the middle three income 

quintiles (Chapman, Laird, and Kewal-Ramani, 

2010). Graduation rates for whites and Asians are 

approximately 50 percent greater than those for blacks, 

Hispanics, and American Indians (Swanson, 2004). 

Ninth graders in the richest 20 percent of American 

households are almost seven times more likely than 

those in the poorest 20 percent to place in the top fifth 

of math percentile scores (Ingels et al., 2011). These 

poor educational outcomes have been linked to both 

low-quality schools and home environments that do 

not support educational achievement. 
215 Chapter 7: Impacts on Educational Outcomes Chapter 7: Impacts on Educational Outcomes

The experiences of MTO children are not unlike the 

experiences of low-income children in urban communities 

nationwide. Although the measures used in MTO do not 

correspond exactly to those used in studies of nationally 

representative samples of children, available data indicate 

elevated rates of problem behaviors among MTO children 

compared with the average child in America. Exhibit 7.1 

presents summary statistics for the education-related 

variables collected at baseline for MTO children.1 

Approximately 6 percent of the older youth (ages 6 to 

11 at baseline) had behavioral problems and 12 percent 

had learning problems at baseline, and 3 percent had been 

suspended or expelled from school in the two years prior 

to baseline. Over 20 percent of parents indicated that 

during the two years prior to baseline someone from their 

child’s school had asked them to come in to talk about 

behavior or other problems the child was having at school. 

MTO children attended schools at baseline that were 

characterized by both low achievement and high poverty. 

Exhibit 7.1 shows that 86 percent of their classmates 

were eligible for free or reduced lunch and 94 percent 

were members of minority groups. On average, students 

at these schools ranked at the 15th percentile on state 

assessments of academic skills. As discussed in Chapter 1, 

over half of MTO families indicated that the primary or 

secondary reason they wanted to move was to send their 

children to better schools.

The parents of MTO children also had relatively low 

levels of school attainment themselves at baseline 

compared with national samples, which is relevant given 

the strong documented association between parental 

schooling and children’s learning outcomes (Jencks and 

Phillips, 1998). Only 35 percent of the sample adults had 

completed a high school degree at the time of random 

assignment (another 18 percent had earned a GED).

The MTO demonstration was implemented during 

the mid- to late 1990s, a time of increased emphasis 

on urban school reform. Since then, all five of the 

MTO cities have initiated high-profile efforts aimed 

at improving the educational outcomes for students 

in city schools. As a result, although experimental and 

1 No information on standardized achievement test scores was collected 

at baseline.

Section 8 children might have moved to lower-poverty 

neighborhoods, the differences between the quality of the 

public schools in their new and original neighborhoods 

may have been attenuated by urban school reform and 

any new public and private investments that came as a 

result.

One example is the small high schools initiative that 

occurred in one of the five MTO demonstration sites—

New York City—which together with other reforms 

boosted achievement test scores and graduation rates 

(Bloom, Thompson, and Unterman, 2010). Another 

example comes from the accountability efforts in the 

Chicago Public Schools, which has increased substantially 

the number of children performing at grade level in 

reading and mathematics over the last ten years, with the 

most dramatic increases occurring in some of the city’s 

lowest performing schools (Roderick et al., 1999). The 

performance of students in the Chicago Public Schools 

—where a majority of MTO youth in the Chicago site 

attended schools throughout their school careers—has 

increased relative to other schools in the state (Jacob, 

2003). Boston saw similar improvements across all grades 

and subject areas, including some narrowing of racial 

test-score gaps as well (Office of Research Assessment and 

Evaluation, 2010a). In Baltimore, from 2006 to 2010 

the percentage of elementary school students achieving 

a level of proficient increased from 82 percent to 89 

percent in math and from 78 to 88 percent in reading 

(Baltimore City Public Schools, 2011).

7.2 HYPOTHESES ABOUT EFFECTS 

ON EDUCATIONAL OUTCOMES

The explicit goal of MTO was to help families residing 

in public housing move to lower-poverty areas. And, 

as demonstrated in Chapter 2, following random 

assignment MTO experimental and Section 8 families 

were substantially more likely than controls to live 

in low-poverty neighborhoods—even measured 10 to 

15 years after baseline. A large literature in the social 

sciences suggests multiple pathways through which 

neighborhood socioeconomic composition could directly 

or indirectly influence the amount that students learn 

in school, or their persistence in school and ultimate 

schooling attainment.
216

Exhibit 7.2 presents a conceptualization of these 

pathways. Four important community characteristics 

are associated with the level of affluence of the 

neighborhood: the quality of the schools, community 

values and the community as a socializing agent for these 

values, the safety of the community, and the economic 

opportunities available in the community. As the model 

shows, one of these community characteristics—higher 

quality schools—may directly influence students’ 

educational outcomes. In addition, in response to living 

in more affluent communities that may (a) have better 

educational institutions, (b) be safer and more cohesive 

communities, and (c) provide employment opportunities 

for adults, the attitudes and behavior of the children and 

their families are expected to change in ways that better 

support learning and achievement.

The first, and most direct, mediator of educational 

outcomes is school quality. Previous research typically 

finds that higher-income neighborhoods have better 

schools (Connell and Halpern-Felsher, 1997). Typically, 

“better” schools are defined as having higher teaching 

quality, greater educational resources, more rigorous 

course offerings, smaller class sizes, and a school climate 

that values learning and achievement and holds high 

expectations for students (Darling-Hammond, 1996). 

These school characteristics are, in turn, hypothesized to 

increase students’ commitment to academic achievement 

and promote behaviors that produce achievement—

studying, attendance, and engagement in school.

However, changing schools could have unintended 

negative consequences on school performance as well. 

One reason stems from whatever initial disruption 

may arise as a result of residential mobility induced by 

MTO. In addition, there may be increased competition 

for grades and academic success in more affluent 

schools, which could lead children who move through 

MTO to develop low self-confidence in the midst of a 

more affluent, higher-achieving peer group. A recent 

survey found a consistent negative association between 

school mobility and school achievement or high school 

graduation rates, especially for minority groups such as 

Hispanics and African-Americans (Beatty, 2010).

Children growing up in relatively more affluent 

communities may also be exposed to adults and peers 

who place relatively greater importance on educational 

achievement, learning, and pro-social behavior. More 

affluent neighborhoods also tend to have relatively 

higher rates of professional and managerial employment; 

people in these jobs may act as positive role models 

who help signal the returns to educational completion 

and academic success. In addition, peers could directly 

influence learning outcomes by providing children with 

opportunities to participate in more developmentally 

productive study groups, or by influencing the content or 

pace of teaching within the classroom environment.

A third community characteristic that may be relevant 

for children’s educational outcomes is the neighborhood’s 

physical environment—including safety. Parents who 

believe that their children are safe may feel less stress 

and anxiety, resulting in improved mental health and 

improved parenting (McLoyd and Flanagan, 1990). 

Children who feel safe in their physical surroundings 

may be more likely to flourish academically and 

personally. Previous observational research has shown a 

negative association between exposure to serious violence 

and children’s short-term academic outcomes (Sharkey, 

2010).

Finally, the economic opportunities within a 

neighborhood could influence children’s schooling 

outcomes for several reasons. Communities with 

improved economic opportunities may increase the 

resources of parents and the local parent-teacher 

association enabling them to supplement public school 

resources or offering enrichment activities (for example, 

by raising money to hire teacher’s aides or by offering 

after school programs). Living in a community with 

improved job opportunities could also improve the 

economic conditions of MTO families themselves, 

which may enable parents to increase their own private 

investments in their children’s learning. And the presence 

of formal labor market opportunities in an area could 

influence perceptions of the economic returns to 

schooling attainment and achievement.
217 Chapter 7: Impacts on Educational Outcomes Chapter 7: Impacts on Educational Outcomes

EXHIBIT 7.1. BASELINE CHARACTERISTICS OF THE YOUTH SAMPLE BY RANDOM ASSIGNMENT GROUP

CONTROL 

GROUP

EXPERIMENTAL 

GROUP

SECTION 8 

GROUP

ALL 

GROUPS

N

YOUTH CHARACTERISTICS 

AT BASELINE

Age (years) [PR] 4.7 4.6 4.7 4.7 5,101

Gender (%) [PR]

Female 48.7 51.7 51.2 50.5 5,101

Male 51.3 48.3 48.8 49.5 5,101

YOUNGER YOUTH 

CHARACTERISTICS–AGES 0-5 

AT BASELINE (%)

Someone read to youth more than once 

per day [PR]

26.9 23.7 21.0 24.0 3,085

Enrolled in Head Start [PR] 17.3 22.8 21.7 20.7 2,674

OLDER YOUTH 

CHARACTERISTICS–AGES 6-11 

AT BASELINE (%)

Gifted student/did advanced 

coursework [PR]

14.5 12.3 12.9 13.2 2,016

Suspended or expelled from school in 

past two years [PR]

3.2 3.1 4.1 3.4 2,016

School called about behavior or 

schoolwork in past two years [PR]

19.6 20.0 21.8 20.4 2,016

Behavioral or emotional problems [PR] 6.1 5.1 5.9 5.7 2,016

Learning problem [PR] 13.4 10.1 13.7 12.3 2,016

OLDER YOUTHS' SCHOOL 

CHARACTERISTICS AT BASELINE

Baseline grade [PR] 1.50 1.37 1.49 1.45 2,006

Percent free/reduced-price lunch [CCD] 85.4 85.9 86.5 85.9 1,534

Percent minority [CCD] 94.4 92.9 93.5 93.6 1,621

Number of students per teacher [CCD] 19.4 19.1 18.8 19.1 1,625

School's percentile rank on state exam 

[SLAD]

14.2 14.9 15.3 14.8 1,530

SAMPLE ADULT/PARENT 

CHARACTERISTICS AT BASELINE (%)

High school diploma [PR] 34.0 35.7 35.1 35.0 5,101

GED [PR] 21.9 15.1 18.7 18.4 5,101

Enrolled in school [PR] 18.6 17.0 19.2 18.2 5,101

PARENT SCHOOL INVOLVEMENT 

AT BASELINE (%)

IN THE PAST 12 MONTHS, A 

HOUSEHOLD ADULT HAD...

Attended a school meeting [PR] 80.2 79.2 78.9 79.4 5,078

Attended a school event [PR] 69.2 67.7 69.2 68.6 5,080

Volunteered at school [PR] 47.8 47.9 46.2 47.4 5,081
218

EXHIBIT 7.1. (CONTINUED)

CONTROL 

GROUP

EXPERIMENTAL 

GROUP

SECTION 8 

GROUP

ALL 

GROUPS

N

FAMILY'S PRIMARY OR SECONDARY 

REASON FOR SIGNING UP FOR MTO 

WAS BETTER SCHOOLS (%) [PR]

49.4 52.0 57.6 52.7 5,101

Notes: The numbers represent mean values on baseline characteristics for each treatment group and overall, weighted to reflect randomization ratios and the 

selection of youth into the interview sample and the two-phase sampling design of the long-term evaluation.

Square brackets indicate the source of the outcome information: PR = parent report. CCD = Common Core of Data. SLAD = School-level assessment data from the 

National Longitudinal School-Level State Assessment Score Database. 

Data source and sample: Parent reported items were reported by the baseline head of household on the Moving to Opportunity Participant Baseline survey (age 

and gender were verified by the youth as part of the long-term survey). School characteristics come from the above-referenced data sources and are based on 

parent reports from the baseline survey or from the interim survey (if baseline school is missing in the baseline data).

Measures: The older youth (N = 2,016) and younger youth (N = 3,085) sample sizes are reflected in measures presented above that are also standard covariates in 

our regression analysis. Missing values for the covariates were imputed by site, treatment group, age, and gender. See Section 1.3 and the technical appendices 

(forthcoming) for details. Other measures, such as the Head Start measure and the school characteristics, have lower sample sizes because missing values were 

not imputed. 

7.3 EARLIER RESEARCH 

ON NEIGHBORHOODS AND 

EDUCATIONAL OUTCOMES 

INCLUDING SHORT-TERM AND 

INTERIM MTO IMPACTS

A number of observational (nonexperimental) studies 

have shown that neighborhood socio-economic status is 

correlated with the educational outcomes of children, even 

after statistically adjusting for observable characteristics of 

the children and their families. Previous research studies 

have found that the presence of affluent neighborhood 

residents in particular is positively related to young 

children’s IQ and verbal ability scores (e.g., Brooks-Gunn 

et al., 1993; Duncan, Brooks-Gunn, and Klebanov, 1994; 

Klebanov et al., 1997); higher reading recognition (ChaseLansdale et al., 1997), math achievement (Entwisle, 

Alexander, and Olson, 1994); and adolescent school 

completion and self-reported grades (Brooks-Gunn et al., 

1993; Connell and Halpern-Felsher, 1997; Dornbusch, 

Ritter, and Steinberg, 1991; Duncan, 1994).

The most influential “natural experiment” study of the 

relationship between neighborhood environments and 

schooling outcomes is the Gautreaux mobility program 

in Chicago. Starting in the 1970s, under Gautreaux 

thousands of African-American public housing residents 

were offered the opportunity to use housing vouchers 

to relocate to more racially and economically mixed 

neighborhoods in either the suburbs of Chicago or in 

other parts of the city itself. Students whose families 

moved to the suburbs initially had difficulties adapting 

to the higher academic expectations in suburban schools, 

and their grades suffered as a result (Rosenbaum, 1995). 

However, later findings from Gautreaux showed that 

children who moved to suburban neighborhoods were 

more likely to graduate from high school and attend 

four-year colleges compared with those whose families 

moved to other parts of the city of Chicago (Rubinowitz 

and Rosenbaum, 2000; Popkin et al., 2000; DeLuca and 

Rosenbaum, 2003).

MTO was inspired by Gautreaux and designed as a 

true randomized experiment to overcome lingering 

concerns about selection bias that arise with studies of 

Gautreaux. As discussed in previous chapters, through 

the time of the interim follow-up (4 to 7 years after 

baseline), MTO was successful in helping experimental 

and Section 8 group families move to neighborhoods that 

are hypothesized to be more educationally “productive” 

than those serving controls with respect to some of the 

community characteristics that the previous section 

hypothesizes may be relevant for educational outcomes 

(Orr et al., 2003; Kling, Ludwig, and Katz, 2005; 

Sanbonmatsu et al., 2006; Kling, Liebman, and Katz, 

2007). MTO helped families move into neighborhoods 

that had lower poverty rates and greater presence of 

college graduates, employed people, and people employed 

specifically in managerial or professional occupations. 

MTO adults in the experimental and Section 8 group 

were also more likely than controls to report feeling 

safe, and that their neighbors would work together to 

support shared pro-social norms—what sociologists call 

“collective efficacy” (Sampson, Raudenbush and Earls, 

1997).
219 Chapter 7: Impacts on Educational Outcomes Chapter 7: Impacts on Educational Outcomes

However MTO had much more muted impacts on the 

characteristics of the schools that children attended 

(Sanbonmatsu et al., 2006; see also Ferryman et al., 2008 

and Deluca and Rosenblatt, 2010). Even though children 

in the experimental group were living in considerably 

safer and more affluent neighborhoods, some 80 

percent were attending schools in the same struggling 

urban school districts as they had been at baseline. 

MTO was found to have had some small but positive 

effects on children’s educational experiences based on 

the characteristics of children’s schools, but few were 

attending schools that could even be considered above 

average for the states in which they resided.

Taken as a whole, the pattern of impacts on educational 

outcomes for MTO children found at interim was 

disappointing (Orr et al., 2003; Sanbonmatsu et al., 

2006). Across the five MTO sites, the changes in 

neighborhood conditions associated with being assigned 

to the MTO experimental group were not sufficient to 

generate noteworthy impacts on the education-related 

behaviors, attitudes, or achievement test scores of MTO 

children. However, Burdick-Will and coauthors (2011) 

find a positive impact on reading achievement scores for 

the combined Baltimore and Chicago demonstration 

sites.2

If the underlying MTO process model is correct, the 

program’s lack of impacts on educational outcomes might 

be attributed to the relatively small changes observed 

in the characteristics of the schools attended by the 

MTO children. MTO’s qualitative report (Popkin et 

al., 2002) also suggested that increased opportunities 

for school choice, the children’s desires to remain with 

friends, negative experiences in more affluent schools, 

and student special needs led some experimental group 

children to remain in the same school or to attend 

schools in their old neighborhoods. More recent 

qualitative interviews with Baltimore families found that 

parents’ decisions about schooling often had little to do 

with academic quality. For many poor families, moving 

priorities began with proximity to transportation, family 

2 These results are consistent with the positive impact on reading scores 

for African-Americans in the MTO sample reported by Sanbonmatsu et 

al. (2006) and driven by results for the Chicago and Baltimore sites.

members, and mothers’ jobs—with schools sometimes 

coming after that, if at all (Deluca and Rosenblatt, 

2010).

The interim report also observed that it might have been 

too soon to observe impacts on educational outcomes 

for these children, given the relatively small changes 

observed in the mediators. At the time of the interim 

evaluation some MTO children had been exposed to 

their new environment for as little as four years and 

there was some evidence that they were having difficulty 

adjusting to these new environments. It was hoped that 

more beneficial impacts would be observed in the longterm follow-up.
220

EXHIBIT 7.2. HYPOTHESIZED PATHWAYS THROUGH WHICH MTO MAY IMPACT ACHIEVEMENT AND 

EDUCATIONAL OUTCOMES

MTO INTERVENTION RELOCATION TO LOW-POVERTY NEIGHBORHOODS

COMMUNITY-LEVEL

MEDIATORS

STUDENT- AND FAMILY-LEVEL

MEDIATORS

EDUCATIONAL 

OUTCOMES

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

Social and Physical 

Environment

· Lower crime and violence

· Increased recreational 

 activities for youth

Community Norms 

and Values

· Better adult/peer role 

 models

· Better community monitor- 

 ing of youth (collective 

 efficacy)

· Shared community value of 

 education

Economic Opportunities

· More job openings

· Better wages

Exhibit 7.2 is a modified version of Exhibit 6.1 in the MTO interim impacts evaluation (Orr et al., 2003).

Student Attitudes 

and Behavior

· Increased value on learning

· More positive feelings 

 toward school

· Greater sense of safety and 

 security in and out of 

 school

· Better attendance

· More time spent studying

· More participation in 

 extracurricular activities

· Fewer disciplinary 

 sanctions from school

Parent Attitudes and 

Behavior

· Greater sense of 

 confidence, safety for self 

 and family

· Less stress, anxiety

· Greater parent support for 

 education/achievement

· Increased wages, higher 

 employment

· Changed parenting 

 practices (less restrictive,

 authoritarian)

School Performance

· Standardized achievement/ 

 proficiency test scores

· Academic track (honors/ 

AP, regular, remedial)

· Special education

· Academic honors, awards

Educational Progress

· Retention in grade

· On-time high school 

 graduation

· Post-graduation 

educational plans, 

enrollment

Quality of Schools

· Higher student achievement

· More resources in schools

· Lower student/teacher ratio

· Lower counselor/student 

 ratio

· School environments that 

 support learning
221 Chapter 7: Impacts on Educational Outcomes Chapter 7: Impacts on Educational Outcomes

7.4 DATA SOURCES AND MEASURES

A variety of survey-based and administrative data sources 

were used to measure MTO youth’s school environments, 

experiences in school, educational expectations and 

educational and achievement outcomes.

SCHOOL MOBILITY AND SCHOOL 

CHARACTERISTICS

As part of our in-person survey data collection we 

obtained measures of school-level characteristics and 

mobility from an in-depth retrospective history of 

schools that children attended. We asked youth to 

report all of the schools they attended for each grade 

from their current or most recent school back through 

to their grade at baseline (or back through kindergarten 

for youth who were not yet of elementary school age at 

baseline). For approximately 60 percent of the youth 

sample, the school history is constructed by combining 

youth reports from their most recent school attended to 

the last school reported by their parents at the interim 

survey point.3

 One challenge in constructing the school 

history was to identify all of the specific schools that 

were mentioned by survey respondents. Most schools 

were accurately identified by using a school-lookup to 

identify the school during the interview. For schools 

that could not be identified during the interview, the 

respondent was asked for the name and address of the 

school. The information provided was sometimes, but 

not always, sufficiently detailed to identify the school. In 

some cases the respondent did not report the name of the 

school. Across our sample, we were able to identify the 

schools for approximately 92 percent of all student-years 

of elementary and secondary education attended from 

baseline to interview date.

We use the school history to construct measures of school 

mobility such as counts of the total number of different 

schools and school districts attended. Additionally, we 

used the school history information to match the schools 

attended by the MTO youth to data from the National 

Center for Education Statistics (NCES) Common Core 

of Data (1993-94 to 2009-10 academic years; U.S. 

Department of Education, 2011a) and Private School 

3 For the long-term survey, these youth were asked to report on their 

schools and grades going back only to the highest grade reported by 

their parent at interim.

Surveys (available years between 1993-94 and 2007-08; 

U.S. Department of Education, 2011b). This allows us to 

construct a host of school-level characteristics depicting 

sociodemographic composition of the student body 

(racial/ethnic composition, free and reduced price lunch 

eligibility), school size (the number of students as of 

October), pupil-teacher ratio, Title I status, and the type 

of school (magnet or charter).4

In addition, we matched the schools to the National 

Longitudinal School-Level State Assessment Score 

Database (NLSLSASD) spanning 1999 to 2007 to 

obtain school-level test score data that we then used 

to rank the schools within MTO’s five main states 

(NLSLSASD, 2010).5 We use school-level statistics on 

the share of students at each school performing at or 

above proficiency in math and in reading (or language 

arts scores when reading is not available) to rank schools 

for a specific grade, academic year, and subject. We 

then average the reading and math rankings to obtain a 

combined measure.

We present measures of school characteristics for both the 

most recent school (with data available) that the youth 

attended, and for the average across all of the schools 

attended by the youth from baseline to the interview 

weighted by the number of years they attended a school. 

If a youth repeated a grade at a school, we count that 

school-grade twice. If a youth attended more than one 

school during the same year, we divide that year equally 

among the schools. To the extent possible, we link each 

year of a youth’s school history to the closest year of data 

available for that school and that measure.

ACHIEVEMENT ASSESSMENTS

The youth in-person interviews included a 45-minute 

achievement assessment in math and reading as 

designed for the 5th and 8th grade follow-up waves of 

4 A Title I eligible school is a school designated as being high poverty 

and eligible to participate in federal Title I programs. Schoolwide Title 

I programs apply to Title I schools with student bodies that consist of 

at least 40 percent low-income students. Schools designated by state 

legislatures as “charter schools” provide free public education under a 

special charter granted by the legislature. Magnet schools and programs 

are designed to either attract students of different racial and ethnic 

backgrounds to reduce racial isolation or to focus on a particular academic or social theme such as science or performing arts.

5 We limit our analyses of test score rankings to schools within the five 

original MTO demonstration states.
222

the U.S. Department of Education’s Early Childhood 

Longitudinal Study-Kindergarten Cohort (ECLS-K).6 

Youth ages 10 to 12 were administered the 5th grade 

test and youth ages 13 to 20 were administered the 8th 

grade test. The ECLS-K assessments are adaptive—

respondents first take a short “routing test” that then 

directs the interviewer to provide them with test forms of 

different difficultly levels—to reduce the time required 

to accurately measure a subject’s academic achievement 

level.7 Because not all youth were asked every test 

item, our assessment results were scored for us by the 

Educational Testing Service (ETS) to provide us with 

estimates of each youth’s underlying academic ability 

(known as a “theta score” in the testing literature) as 

estimated from a statistical model based on item response 

theory.

One concern was the possibility that some of the older 

youth in our survey sample would find the items on the 

tests too easy. If these youth correctly answered every 

item on the test, the assessment would lose its ability 

to provide information about which youth in the study 

know more than others, known as a “ceiling effect” in 

the testing literature. To guard against ceiling effects, we 

supplemented the ECLS-K 8th grade test with a small 

set of math and reading items from the U.S. Department 

of Education’s National Educational Longitudinal 

Survey-1988 (NELS) assessment for high school 

students. Only about 8 percent of the MTO sample of 

13- to 20-year-olds performed well enough to take these 

additional NELS items in math or reading. Another 

concern is the possibility of a “floor effect,” in which the 

assessment is too difficult for some children and so loses 

its ability to distinguish the achievement of students 

at the bottom of the distribution. About 14 percent of 

6 The ECLS-K 5th grade test is administered in the home via paper 

and pencil and an interviewer-controlled easel (both for MTO and for 

ECLS-K itself ). The ECLS-K 8th grade national test is administered 

in a group format in school whereas the MTO 8th grade assessment is 

individually administered to youth in their homes.

7 The ECLS-K 5th test is administered via an easel. Both the 5th and 

8th grade tests are two-stage tests. Nationally, the first stage is a short 

routing test so that the combination of age, grade and performance help 

determine the difficulty of the second stage of the test (as compared 

with age or grade alone). This routing test is scored on-site by an assessor and is then used to direct children to the appropriate level for the 

second stage. The second stage 5th grade test is available in three levels 

and the second stage 8th grade test is available in two levels, where 

items in the highest level of the 5th grade test, for example, overlap 

with some items in the lowest level of the 8th grade test.

youth ages 13-20 performed below the level of chance on 

the reading test and about 7 percent on the math test.

The ECLS-K achievement assessments address many of 

the limitations documented from the administration 

of the Woodcock Johnson-Revised (WJ-R) assessment 

at the MTO interim study, which included evidence 

of “interviewer effects” (where results systematically 

differed across children according to which interviewer 

administered the tests) and average scores that did not vary 

much by age among adolescents. Equally important, the 

ECLS-K assessments are designed to more closely measure 

what children learn in school (as opposed to measuring 

aptitude only), and to be more sensitive to capturing 

whether MTO moved children into improved schooling 

and learning environments. The ECLS-K assessments 

have several other desirable features including appropriate 

coverage of material that is relevant for the wide dispersion 

of ages of MTO youth in the long-term MTO study, good 

discriminating power across a wide range of ability levels. 

They also have been extensively pre-tested and piloted 

to ensure that, for example, the test items work equally 

well for subgroups of youth such as those from racial and 

ethnic minority groups.

INTERVIEW OBSERVATIONS OF YOUTH 

DURING ACHIEVEMENT ASSESSMENTS

During the administration of the math and reading 

achievement assessments, the interviewers documented 

their perceived observations of youth attentiveness, 

cooperation and motivation using five predetermined 

response categories. We constructed a series of binary 

outcomes contrasting the two most positive responses 

with the three most negative responses. “Complete and 

full attention” and “very attentive” were contrasted 

with “attentive”, “difficulty attending,” and “unable to 

attend”; high and very high motivation with average, 

low, and very low motivation; and “very cooperative” 

and “cooperative” with “matter of fact”, “uncooperative”, 

and “very uncooperative.” (We report these interviewer 

assessments in Supplemental Exhibit 7.7; they do not 

appear to be affected by the MTO intervention.)

SELF-REPORTED YOUTH OUTCOMES

The MTO youth survey additionally asked youth a 

series of questions about their school performance and, 
223 Chapter 7: Impacts on Educational Outcomes Chapter 7: Impacts on Educational Outcomes

as discussed below in the section on mediators, their 

perceptions and experiences in school and where and how 

they spent time outside of school.

Schooling Outcomes: Youth reported their overall grades 

in their last year of school and, as measures of their 

educational track, whether they had ever been enrolled in 

an advanced math or Advanced Placement (AP) course or 

a program for the gifted and talented; whether they had 

ever been enrolled in any special class or received special 

help for any emotional, physical or mental condition; 

and whether they had ever repeated a grade. For older 

youth (ages 15 to 20), we constructed a measure of being 

educationally “on track”—currently in school or had 

received a high school degree or certificate of General 

Educational Development (GED)—and as a measure of 

college preparation, we also asked whether youth had 

taken the SAT or ACT.8

PARENT-REPORTED OUTCOMES FOR 

GROWN CHILDREN

In the MTO adult or household head survey, parents 

reported on their MTO grown children, who were ages 

21 to 30 as of December 31, 2007, and so too old for 

our long-term follow-up youth survey sample, but who 

were under age 18 at baseline. Specifically, parents were 

asked to report whether each of their grown children was 

currently enrolled in school, had attained a GED or high 

school diploma, and had ever been enrolled in college.

SELF-REPORTED MEASURES OF 

CANDIDATE MEDIATORS

School Climate: Youth reported whether they strongly 

agreed, agreed, disagreed, or strongly disagreed with five 

statements about the climate of their most recent school:9

 

(1) teachers are interested in students; (2) students who 

study hard are teased; (3) discipline is fair; (4) the youth 

feels safe at the school; and (5) the youth often feels 

“put down” by teachers. We calculated a school climate 

index from these measures as the fraction of normatively 

8 Most of these items were also asked at the interim study and are 

borrowed from (or are original items similar to items from) NLSY97. 

Exceptions include the repeated grade item, asked at interim and 

borrowed from the Los Angeles Family and Neighborhood Survey (LAFANS). The gifted/talented program and special education items are 

new to the long-term survey and come from NELS.

9 Youth who were enrolled in post-secondary school or who were no 

longer in school were asked to report on their most recent year in high 

school.

positive responses on the five items: strongly agree/

agree responses to the positive items (teachers interested 

in students, fair discipline, and safety) and disagree/

strongly disagree responses to the negative items (teasing 

and feeling “put down”). These items were drawn from 

a variety of sources. The teacher interest in students, 

discipline, and safety items replicate items asked in the 

interim MTO study and are drawn from the National 

Longitudinal Survey of Youth-1997 (NLSY97). The 

teasing item comes from Harvard University professor 

Ronald Ferguson’s Tripod Study, and the “put down” item 

comes from NELS.

Problem behavior: We constructed tardiness and 

absenteeism outcomes to indicate whether the youth was 

late for school once or more per month, whether the 

youth was absent more than five percent of the school 

year, and the fraction of the school year that the youth 

was absent. Youth also reported whether they had been 

suspended or expelled from school in the past two years. 

These items all replicate items used at interim.10

Educational expectations: Youth are asked to report the 

highest degree they expect to complete ranging from less 

than high school graduation to graduate or professional 

school. We also asked youth a similar question about how 

far in school they believed their mother wanted them to 

go (same response options). These items were borrowed 

from NELS.

School engagement: The school engagement measures 

focus on homework and reading. We asked youth how 

much of their assigned homework that they usually 

complete, with response options ranging from “All plus 

some extra” to “None” and including a “Never assigned” 

option. For youth who indicated that homework was 

assigned, we also asked separate questions about how 

many hours per week they spent on homework in 

school and out of school. Additionally, youth reported 

the number of hours per week they spent doing 

additional reading on their own outside of school (not in 

connection with schoolwork).11

10 Original source surveys for these adapted items are as follows: tardiness—Survey of Program Dynamics-1998; absenteeism—NLSY97; 

suspension/expulsion—LAFANS.

11 Original source surveys for these adapted items are as follows: homework completion—Ronald Ferguson’s Tripod Study; homework hours—

NELS; and additional reading—the 1979 wave of NLSY.
224

Time use and access to technology: To measure where 

and how youth spend their time outside of school, 

youth report on their participation in school sports or 

school clubs or groups and, if so, whether any leadership 

positions were held in those school-related activities. 

We also asked how often youth participate in organized 

group activity, hang out in their neighborhood or a 

nearby basketball court, shop at a mall or a store, and 

hang out at their own house and at someone else’s house. 

We also determined access to technology by asking 

whether they use a computer in their home and how 

often they use the internet.

PARENT-REPORTED MEASURES OF 

CANDIDATE MEDIATORS 

Parents were asked to report on the schooling, health, 

and behavior of one randomly selected youth ages 10 

to 17 as of December 2007 in their household. Parents 

reported whether in the past two years someone from the 

youth’s school had asked someone from the household 

to “come in and talk about problems [the youth] was 

having with school work or behavior.” To measure their 

engagement with their child’s schooling, we asked parents 

whether in the past 12 months they or another household 

adult had attended a school meeting or event (such as 

a back-to-school night or parent/teacher organization 

meeting). We also asked whether they had volunteered 

or worked at the youth’s school or been on a school 

committee or club outside of school. Finally, parents 

reported whether they were very, somewhat, or not all 

satisfied with the education their child had received up to 

that point.12

POST-SECONDARY ENROLLMENT 

FROM THE NATIONAL STUDENT 

CLEARINGHOUSE

In addition to obtaining self-reported data on college 

enrollment from youth, we obtained post-secondary 

enrollment data on MTO youth and grown children 

from a match through the StudentTracker service of 

the National Student Clearinghouse (NSC). The NSC 

is a non-profit organization that works with colleges 

and universities to provide degree and enrollment 

12 The satisfaction item was new to the long-term survey and came 

from NELS, while the remaining items were originally asked on the 

MTO baseline survey and were also asked as part of the interim study.

verification. The NSC enrollment database covers over 

3,000 colleges and over 90 percent of students in the 

United States (National Student Clearinghouse, 2011). 

We focus our analyses on enrollment data for the threeyear period from January 2007 to January 2010.13 The 

NSC data allowed us to construct measures of whether 

MTO participants had been enrolled in any postsecondary institution in their database as well as the 

types of institutions (two- and four-year colleges; private, 

public, and for-profit colleges) in which they were 

enrolled and the length of their enrollment. NSC data 

coverage is generally even across the treatment groups 

and there is little variation in coverage by randomization 

site. For interviewed youth and grown children whose 

parents were interviewed, we also have survey reports of 

any college attendance and attendance at two- vs. fouryear colleges. Comparisons of the NSC data measures 

and the survey outcome measures appear in Supplemental 

Exhibits 7.8 and 7.9. The results from the NSC do not 

differ substantially from the results from self-reporting.

7.5 LONG-TERM IMPACTS ON 

HYPOTHESIZED MEDIATORS OF 

EDUCATIONAL EFFECTS

Exhibit 7.3 presents MTO’s impacts on the 

characteristics of the schools attended by MTO youth 

over the course of the long-term follow-up period. 

Compared with schools attended by control group 

youth, MTO experimental and Section 8 youth attended 

schools that differed across a variety of socioeconomic 

and demographic characteristics. Youth in the 

experimental and Section 8 groups attended schools with 

lower proportions of minority students and students 

receiving free or reduced-price lunch, and youth in the 

experimental group attended schools that performed 

better on state exams. Experimental and Section 8 youth 

attended schools that were 8 to 11 percent larger on 

average than those attended by control group students, 

and for youth in the experimental group, the most recent 

schools had more pupils per teacher as well. The ratio of 

pupils to teachers differs from the number of students 

per classroom because it includes reading specialists and 

13 NSC data were available back through 2001, but it took until the 

end of 2006 for the NSC to be near complete (96% of schools had 

joined the NSC by then), so we have limited our analysis to data from 

2007 forward.
225 Chapter 7: Impacts on Educational Outcomes Chapter 7: Impacts on Educational Outcomes

other types of teachers. MTO did not affect the type of 

school youth attended in terms of whether the school was 

a charter or a magnet.14

Youth in the experimental group attended schools 

that ranked slightly higher on statewide reading and 

math exams compared with controls. Youth in all three 

treatment groups disproportionately attended low 

performing schools, with average school rankings ranging 

from the 19th to 22nd percentiles across the groups. 

Only about one in six youth from either the treatment or 

control groups recently attended a school in the top half 

of their state’s schoolwide test score distribution.

Exhibit 7.4 presents MTO treatment effects on school 

mobility. At a marginally significant level, experimental 

group youth may have attended school in a slightly larger 

number of total different school districts compared with 

the control group. The total number of schools attended 

was similar across the MTO treatment groups.

Broad objective measures of school-level student 

demographics and standardized test score performance 

provide one picture of school quality. This picture can 

be complemented by the youth’s own perceptions of 

school quality (Exhibit 7.5). Experimental group youth 

reported marginally more favorable and hospitable 

interactions and perceptions of the school environment 

than the control group, as measured by our index of 

school climate. More specifically, compared with their 

control group peers, experimental group youth were less 

likely to report feeling put down by teachers, and female 

experimental group youth in particular were more likely 

to report that teachers took interest in students.

Supplemental Exhibits 7.1 through 7.4 present various 

other aspects of school participation and related activities 

that could mediate the effects of MTO on educational 

outcomes. Youth in the experimental and Section 8 group 

have similar levels of tardiness, absenteeism, and behavior 

problems in school as the control group (Supplemental 

Exhibit 7.1). However, MTO male youth in both the 

14 Only a small percentage of MTO youth attended private elementary 

or secondary school; youth in all three random assignment groups spent 

an average of about 4% of their school years since baseline attending 

private schools. 

experimental and Section 8 groups are more likely to 

be suspended or expelled from school in the past two 

years compared with controls. MTO experimental group 

youth—especially males—also are less likely to report 

that they expect to complete college. These experimental 

group youth are no more likely to report their mothers 

want them to complete college than are the control group 

youth (Supplemental Exhibit 7.2). The MTO treatments 

had few detectable effects on time spent on homework or 

reading, or on parent involvement in the youth’s school 

(Supplemental Exhibit 7.3). MTO had little effect on 

parental satisfaction of their children’s education for the 

experimental group, however parents in the Section 8 

group were less satisfied with their children’s education.

Youth reports of where their out-of-school time is spent, 

computer use and access to the internet are generally 

similar for MTO experimental, Section 8 and control 

groups, except for some indication of higher rates of 

home computer use for male youth in the Section 8 

group compared with controls (Supplemental Exhibit 

7.4). There is a small reduction in MTO experimental 

and Section 8 youth spending time at someone else’s 

house compared with the control youth.
226

EXHIBIT 7.3. CHARACTERISTICS OF SCHOOLS ATTENDED BY YOUTH AGES 10–20

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

RACE/ETHNICITY OF STUDENTS 

AT SCHOOL

MINORITY [CCD, PSS]

Most recent school 0.879 – 0.023* – 0.047* – 0.014 – 0.021 5,045

(0.010) (0.021) (0.011) (0.016)

Average school 0.904 – 0.037* – 0.077* – 0.016~ – 0.023~ 5,077

(0.008) (0.016) (0.008) (0.012)

BLACK [CCD, PSS]

Most recent school 0.495 – 0.009 – 0.020 – 0.021~ – 0.031~ 4,857

(0.010) (0.022) (0.012) (0.019)

Average school 0.532 – 0.022* – 0.046* – 0.025* – 0.037* 5,054

(0.007) (0.016) (0.009) (0.013)

HISPANIC [CCD, PSS]

Most recent school 0.342 – 0.015~ – 0.032~ 0.003 0.005 5,058

(0.009) (0.019) (0.010) (0.014)

Average school 0.339 – 0.022* – 0.046* 0.006 0.009 5,077

(0.007) (0.015) (0.007) (0.011)

SOCIOECONOMIC STATUS OF 

STUDENTS AT SCHOOL

SHARE ELIGIBLE FOR FREE 

LUNCH [CCD]

Most recent school 0.598 – 0.025* – 0.053* – 0.021~ – 0.031~ 4,766

(0.010) (0.021) (0.011) (0.017)

Average school 0.701 – 0.048* – 0.101* – 0.026* – 0.039* 5,043

(0.007) (0.015) (0.008) (0.012)

SHARE ELIGIBLE FOR FREE OR 

REDUCED-PRICE LUNCH [CCD]

Most recent school 0.668 – 0.026* – 0.054* – 0.021~ – 0.031~ 4,749

(0.010) (0.022) (0.011) (0.017)

Average school 0.752 – 0.040* – 0.083* – 0.019* – 0.029* 5,043

(0.007) (0.014) (0.008) (0.012)

SHARE SCHOOLS ELIGIBLE 

FOR TITLE I [CCD]

Most recent school 0.731 – 0.069* – 0.145* – 0.052* – 0.078* 5,036

(0.017) (0.036) (0.018) (0.027)

Average school 0.774 – 0.047* – 0.099* – 0.020~ – 0.029~ 5,077

(0.010) (0.022) (0.011) (0.017)
227 Chapter 7: Impacts on Educational Outcomes Chapter 7: Impacts on Educational Outcomes

EXHIBIT 7.3. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

SCHOOL SIZE

NUMBER OF STUDENTS 

[CCD, PSS]

Most recent school 1235.1 139.4* 290.6* 104.5* 156.4* 5,058

(38.4) (80.0) (41.2) (61.7)

Average school 927.1 25.6~ 53.4~ 41.8* 62.5* 5,077

(14.9) (31.1) (17.5) (26.1)

PUPIL–TEACHER RATIO 

[CCD, PSS]

Most recent school 16.463 0.377* 0.784* 0.230 0.344 4,997

(0.170) (0.354) (0.191) (0.286)

Average school 17.834 – 0.103 – 0.214 – 0.061 – 0.091 5,076

(0.103) (0.215) (0.113) (0.170)

SCHOOL TYPE

CHARTER SCHOOL [CCD]

Most recent school 0.061 – 0.009 – 0.019 – 0.013 – 0.019 5,038

(0.010) (0.020) (0.010) (0.015)

Average school 0.025 – 0.006~ – 0.013~ – 0.005 – 0.008 5,077

(0.004) (0.008) (0.004) (0.006)

MAGNET SCHOOL [CCD]

Most recent school 0.163 0.011 0.022 – 0.004 – 0.005 5,020

(0.015) (0.031) (0.016) (0.024)

Average school 0.161 – 0.013 – 0.028 – 0.016 – 0.025 5,077

(0.010) (0.020) (0.010) (0.015)

SCHOOL-LEVEL RANKING ON 

STATE EXAM

PERCENTILE RANKING [SLAD]

(ONLY THROUGH 8TH GRADE 

FOR NEW YORK AND MARYLAND)

Most recent school 24.848 1.204 2.523 0.178 0.265 4,884

(1.064) (2.229) (1.089) (1.619)

Average school 18.684 3.070* 6.430* 1.218~ 1.810~ 4,884

(0.651) (1.364) (0.661) (0.983)

SHARE SCHOOLS RANKING 

ABOVE 50TH PERCENTILE [SLAD]

Most recent school 0.169 0.012 0.025 – 0.014 – 0.020 4,884

(0.017) (0.035) (0.017) (0.025)

Average school 0.104 0.019* 0.040* – 0.003 – 0.004 4,884

(0.009) (0.019) (0.009) (0.014)
228

EXHIBIT 7.3. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

SHARE SCHOOLS RANKING 

ABOVE 75TH PERCENTILE [SLAD]

Most recent school 0.065 0.016 0.033 0.006 0.009 4,884

(0.011) (0.023) (0.011) (0.017)

Average school 0.029 0.014* 0.030* 0.007 0.010 4,884

(0.005) (0.011) (0.005) (0.007)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). Average school characteristics are weighted by the amount of time youth spent at each school.

Square brackets indicate the source of the outcome information: CCD = Common Core of Data, PSS = Private School Universe Survey, SLAD = School-level 

assessment data from the National Longitudinal School-Level State Assessment Score Database.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: In some cases, youth school histories combine self-reports from the long-term survey with parent reports from the interim survey. Longterm survey interviewed youth ages 10 to 20 as of December 31, 2007.

Measures: The number of students measure is based on enrollment as of October. A Title I eligible school is a school designated as being high poverty and eligible 

to participate in federal Title I programs. Schoolwide Title I programs apply to Title I schools with student bodies that consist of at least 40 percent low-income 

students. Schools designated by state legislatures as “charter schools” provide free public education under a special charter granted by the legislature. Magnet 

schools and programs are designed to either attract students of different racial and ethnic backgrounds to reduce racial isolation or to focus on a particular 

academic or social theme such as science or performing arts.

EXHIBIT 7.4. SCHOOL MOBILITY AMONG YOUTH AGES 10–20

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

Number of schools attended [SR, PR] 4.022 0.072 0.150 0.070 0.105 5,078

(0.062) (0.128) (0.070) (0.104)

Number of public school districts attended 

[SR, PR]

1.540 0.071~ 0.147~ 0.055 0.082 5,078

(0.039) (0.082) (0.042) (0.062)

Most recent school in 5 main districts 

[SR, PR]

0.640 – 0.031 – 0.064 – 0.018 – 0.027 5,078

(0.021) (0.045) (0.024) (0.036)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: In some cases, youth school histories combine self-reports from the long-term survey with parent reports from the interim survey. Longterm survey interviewed youth ages 10 to 20 as of December 31, 2007.

Measures: The 5 main districts are Baltimore City Public Schools, Boston Public Schools, Chicago Public Schools, Los Angeles Unified School District, and the 

New York City Department of Education.
229 Chapter 7: Impacts on Educational Outcomes Chapter 7: Impacts on Educational Outcomes

EXHIBIT 7.5. PERCEPTIONS OF SCHOOL CLIMATE AMONG YOUTH AGES 10–17

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

SCHOOL CLIMATE INDEX [SR]

All 0.797 0.020~ 0.043~ – 0.002 – 0.003 3,328

(0.011) (0.023) (0.012) (0.017)

Female 0.786 0.025~ 0.052~ 0.006 0.010 1,694

(0.015) (0.032) (0.016) (0.025)

Male 0.807 0.016 0.034 – 0.011 – 0.015 1,634

(0.014) (0.031) (0.016) (0.022)

STUDENTS GET TEASED IF THEY 

STUDY HARD [SR]

All 0.294 – 0.016 – 0.034 – 0.001 – 0.002 3,323

(0.021) (0.045) (0.023) (0.033)

Female 0.296 – 0.011 – 0.023 – 0.048 – 0.072 1,692

(0.030) (0.063) (0.031) (0.047)

Male 0.292 – 0.022 – 0.047 0.047 0.065 1,631

(0.030) (0.064) (0.034) (0.047)

DISCIPLINE IN SCHOOL IS FAIR [SR]

All 0.758 0.017 0.037 – 0.032 – 0.046 3,316

(0.020) (0.043) (0.023) (0.034)

Female 0.740 0.027 0.055 – 0.019 – 0.028 1,685

(0.028) (0.058) (0.032) (0.049)

Male 0.775 0.008 0.018 – 0.046 – 0.063 1,631

(0.028) (0.061) (0.032) (0.044)

OFTEN FEELS PUT DOWN BY 

TEACHERS [SR]

All 0.129 – 0.031* – 0.065* – 0.005 – 0.007 3,327

(0.015) (0.031) (0.017) (0.025)

Female 0.124 – 0.033 – 0.067 0.005 0.008 1,693

(0.021) (0.044) (0.024) (0.036)

Male 0.133 – 0.029 – 0.062 – 0.015 – 0.021 1,634

(0.020) (0.043) (0.024) (0.033)

FEELS SAFE IN SCHOOL [SR]

All 0.831 – 0.001 – 0.003 – 0.001 – 0.001 3,325

(0.017) (0.037) (0.020) (0.028)

Female 0.808 0.007 0.015 0.013 0.020 1,694

(0.026) (0.053) (0.028) (0.043)

Male 0.853 – 0.010 – 0.021 – 0.015 – 0.020 1,631

(0.023) (0.049) (0.025) (0.035)

TEACHERS INTERESTED IN 

STUDENTS [SR]

All 0.816 0.040* 0.084* 0.015 0.022 3,325

(0.017) (0.036) (0.019) (0.027)
230

EXHIBIT 7.5. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

TEACHERS INTERESTED IN 

STUDENTS [SR] (CONTINUED)

Female 0.801 0.051* 0.105* – 0.004 – 0.006 1,692

(0.025) (0.051) (0.028) (0.043)

Male 0.831 0.029 0.062 0.036 0.049 1,633

(0.023) (0.050) (0.025) (0.034)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. Interviewed youth ages 10 to 17 as of December 31, 2007.

Measures: The school climate index is the fraction of positive responses on the five individual school climate items presented in this exhibit. If the youth agreed or 

strongly agreed with a positive school climate item (for example fair discipline) or if the youth disagreed or strongly disagreed with a negative school climate item 

(for example students teased if they study hard), then the response was counted as positive. 

7.6 LONG-TERM IMPACTS ON 

ACHIEVEMENT AND EDUCATION 

OUTCOMES

Exhibit 7.6 shows that MTO had no detectable effects 

on math or reading achievement test scores among youth 

aged 13 to 20 at the end of 2007.15 Recall that we 

have converted our achievement test score results into 

“Z-scores,” which come from subtracting the control 

group’s average test score from each child’s individual 

test score result, and then dividing by the standard 

deviation of the control group’s test score distribution. 

By construction the control group’s average test score in 

this Z-score metric will equal 0.

The null result on achievement test scores holds even 

for the subset of children who were less than 6 years old 

at the time of study enrollment—those who had been 

hypothesized to benefit the most from moves to lower 

poverty neighborhoods (see Supplemental Exhibit 7.5 for 

subgroup analyses by baseline age).16 In Supplemental 

Exhibit 7.6, we see some signs of a positive effect on test 

15 Similarly, we do not detect any significant effects on reading or 

math for the smaller sample of youth ages 10-12 who took the ECLS 

5th grade test.

16 The standard errors on these estimates are quite large. A 95 percent 

confidence interval allows us to rule out a beneficial (or detrimental) 

impact any larger than about .08 to .09 standard deviations for the 

either the experimental or Section 8 groups. Analyses of reading and 

math achievement measured as proficiency probabilities at varying cutpoints also show no statistically detectable effect.

scores (at least for reading) for the Section 8 group in 

the Chicago site and a negative effect in New York City 

but overall few signs of any consistent site differences 

in program effects—unlike with the interim follow-up 

data, where both Baltimore and Chicago seemed to show 

consistent large, positive effects on achievement test 

scores (Burdick-Will et al., 2011).

MTO also has few detectable effects on youth self-reports 

of their own schooling performance, as shown in Exhibit 

7.7. Experimental and control group youth were similar 

in terms of reporting they were educationally on track; 

however, male youth in the Section 8 group appear less 

likely to be on track than their male counterparts in the 

control group. Experimental youth appear less likely to 

have a diploma or to get Bs or higher in school compared 

with control group youth (both of these differences are 

only marginally significant with p<.10), and experimental 

youth were also less likely to take advanced coursework 

as compared with the control group. And Section 8 

youth, especially males, are less likely to take advanced 

coursework and the SAT or ACT. However, female youth 

in the Section 8 group are substantially less likely to have 

repeated a grade than females in the control group.

Approximately one-quarter of youth ages 15 to 20 in 

the control group were enrolled in some type of postsecondary institution since 2007; and, this is similar 
231 Chapter 7: Impacts on Educational Outcomes Chapter 7: Impacts on Educational Outcomes

across the MTO treatment groups. MTO generally 

had few detectable effects on post-secondary school 

enrollment and attendance for youth in our long-term 

follow up (Exhibit 7.8). Male youth in the Section 8 

group appear to be less likely to attend college than those 

in control group.

Exhibit 7.9 shows that female grown children in the 

section 8 group were somewhat more likely to graduate 

from high school than the control group. But female 

grown children in both the experimental and Section 8 

groups were less likely than controls to enroll in college.

EXHIBIT 7.6. ACHIEVEMENT ASSESSMENT RESULTS FOR YOUTH AGES 13–20

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

READING ASSESSMENT SCORE [ECLS-K]

All 0.000 0.003 0.006 0.040 0.058 4,432

(0.041) (0.085) (0.044) (0.064)

Female 0.051 – 0.020 – 0.040 0.054 0.081 2,286

(0.055) (0.110) (0.061) (0.093)

Male – 0.050 0.027 0.058 0.025 0.035 2,146

(0.056) (0.118) (0.058) (0.081)

MATH ASSESSMENT SCORE [ECLS-K]

All 0.000 – 0.025 – 0.052 0.000 0.000 4,420

(0.044) (0.090) (0.048) (0.069)

Female – 0.004 – 0.034 – 0.069 – 0.033 – 0.050 2,280

(0.055) (0.112) (0.061) (0.093)

Male 0.004 – 0.016 – 0.034 0.034 0.047 2,140

(0.060) (0.128) (0.067) (0.093)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: ECLS-K = achievement assessment from the Early Childhood Longitudinal Study-Kindergarten 

cohort study. 

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details. 

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

Measures: The reading and math achievement assessment scores are theta scores transformed into z-scores via standardization on the mean and standard 

deviation for control group youth ages 13 to 20.
232

EXHIBIT 7.7. SCHOOLING OUTCOMES FOR YOUTH AGES 10–20

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

EDUCATIONALLY ON-TRACK [SR]

All (ages 15–20) 0.814 – 0.014 – 0.028 – 0.029 – 0.044 3,614

(0.018) (0.037) (0.021) (0.031)

Female 0.827 – 0.007 – 0.015 0.008 0.012 1,842

(0.023) (0.046) (0.026) (0.040)

Male 0.801 – 0.019 – 0.041 – 0.066* – 0.096* 1,772

(0.026) (0.055) (0.029) (0.042)

HIGHEST K-12 GRADE ENROLLED [SR]

All (ages 10–20) 10.482 – 0.053 – 0.109 – 0.006 – 0.009 5,087

(0.039) (0.081) (0.042) (0.064)

Female 10.533 – 0.091 – 0.184 0.018 0.028 2,592

(0.059) (0.118) (0.064) (0.101)

Male 10.435 – 0.012 – 0.027 – 0.033 – 0.047 2,495

(0.048) (0.103) (0.053) (0.076)

HIGH SCHOOL DIPLOMA [SR]

All (ages 19–20) 0.622 – 0.073~ – 0.141~ – 0.056 – 0.092 1,125

(0.038) (0.073) (0.042) (0.068)

Female 0.708 – 0.087~ – 0.169~ – 0.054 – 0.089 5,76

(0.048) (0.094) (0.054) (0.088)

Male 0.536 – 0.058 – 0.110 – 0.058 – 0.094 549

(0.058) (0.109) (0.062) (0.102)

MOSTLY BS OR HIGHER [SR]

All (ages 10–20) 0.438 – 0.035~ – 0.073~ 0.002 0.002 5,066

(0.019) (0.039) (0.021) (0.031)

Female 0.502 – 0.041 – 0.082 – 0.010 – 0.015 2,582

(0.027) (0.054) (0.029) (0.045)

Male 0.379 – 0.030 – 0.064 0.013 0.018 2,484

(0.026) (0.055) (0.029) (0.042)

ADVANCED COURSEWORK [SR]

All (ages 10–20) 0.889 – 0.027* – 0.056* – 0.041* – 0.062* 5,092

(0.012) (0.025) (0.014) (0.021)

Female 0.887 – 0.028~ – 0.056~ – 0.034~ – 0.053 2,596

(0.016) (0.033) (0.019) (0.029)

Male 0.891 – 0.026 – 0.055 – 0.049* – 0.070* 2,496

(0.017) (0.036) (0.019) (0.028)

EVER RECEIVED SPECIAL EDUCATION 

SERVICES [SR]

All (ages 10–20) 0.153 0.007 0.015 – 0.006 – 0.009 5,088

(0.014) (0.028) (0.015) (0.023)

Female 0.115 0.028~ 0.057~ 0.013 0.020 2,592

(0.017) (0.034) (0.019) (0.030)
233 Chapter 7: Impacts on Educational Outcomes Chapter 7: Impacts on Educational Outcomes

EXHIBIT 7.7. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

EVER RECEIVED SPECIAL EDUCATION 

SERVICES [SR] (CONTINUED)

Male 0.189 – 0.014 – 0.031 – 0.025 – 0.036 2,496

(0.020) (0.043) (0.022) (0.031)

EVER REPEATED A GRADE [SR]

All (ages 10–20) 0.377 0.000 0.000 – 0.048* – 0.072* 5,096

(0.018) (0.038) (0.020) (0.030)

Female 0.349 – 0.029 – 0.059 – 0.085* – 0.133* 2,598

(0.025) (0.050) (0.026) (0.041)

Male 0.405 0.029 0.063 – 0.011 – 0.016 2,498

(0.026) (0.056) (0.028) (0.041)

TOOK SAT/ACT [SR]

All (ages 15–20) 0.518 – 0.031 – 0.065 – 0.040~ – 0.060~ 3,599

(0.022) (0.046) (0.024) (0.036)

Female 0.569 – 0.049 – 0.098 – 0.024 – 0.037 1,833

(0.030) (0.061) (0.034) (0.052)

Male 0.470 – 0.013 – 0.028 – 0.056~ – 0.082~ 1,766

(0.032) (0.069) (0.033) (0.048)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Youth long-term survey. All interviewed youth.

Measures: On-track youth are those who were currently in school or received a high school diploma or GED. The highest grade enrolled measure was top-coded at 

12th grade. The Mostly Bs or Higher measure reflects the overall grades that the youth received in their most recent school year (youth in post-secondary school 

reported on their most recent year of high school). Advanced coursework that the youth reported ever having taken an advanced math or AP class or having been 

enrolled in a program for the gifted and talented. The special education services measure is based on a report of whether the youth had ever been enrolled in any 

special class or received special help for any emotional, physical or mental condition. Grade repetition is the proportion of youth who reported that they had ever 

repeated a grade, including kindergarten.
234

EXHIBIT 7.8. POST-SECONDARY OUTCOMES FROM NATIONAL STUDENT CLEARINGHOUSE DATA FOR YOUTH 

AGES 15–20

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL 

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

SINCE 2007, ATTENDED...

ANY COLLEGE [NSC]

All 0.262 – 0.014 – 0.029 – 0.021 – 0.033 4,717

(0.016) (0.033) (0.017) (0.025)

Female 0.305 – 0.011 – 0.023 0.000 0.001 2,300

(0.023) (0.048) (0.025) (0.039)

Male 0.222 – 0.016 – 0.033 – 0.042* – 0.064* 2,417

(0.021) (0.044) (0.021) (0.032)

TWO-YEAR COLLEGE [NSC]

All 0.184 – 0.015 – 0.032 – 0.015 – 0.023 4,717

(0.014) (0.029) (0.015) (0.022)

Female 0.205 – 0.017 – 0.036 0.011 0.017 2,300

(0.021) (0.042) (0.022) (0.034)

Male 0.165 – 0.013 – 0.027 – 0.041* – 0.061* 2,417

(0.018) (0.039) (0.019) (0.028)

FOUR-YEAR COLLEGE [NSC]

All 0.105 0.004 0.009 – 0.004 – 0.007 4,717

(0.011) (0.024) (0.012) (0.018)

Female 0.130 0.015 0.031 0.005 0.008 2,300

(0.018) (0.037) (0.019) (0.029)

Male 0.083 – 0.006 – 0.013 – 0.013 – 0.020 2,417

(0.013) (0.029) (0.014) (0.021)

PRIVATE COLLEGE [NSC]

All 0.048 0.000 – 0.001 0.003 0.005 4,717

(0.008) (0.016) (0.008) (0.013)

Female 0.060 0.000 – 0.001 0.012 0.018 2,300

(0.012) (0.025) (0.014) (0.021)

Male 0.036 0.000 0.000 – 0.005 – 0.008 2,417

(0.009) (0.019) (0.009) (0.014)

PUBLIC COLLEGE [NSC]

All 0.225 – 0.013 – 0.028 – 0.022 – 0.033 4,717

(0.015) (0.032) (0.016) (0.024)

Female 0.261 – 0.013 – 0.026 – 0.004 – 0.006 2,300

(0.022) (0.046) (0.024) (0.037)

Male 0.192 – 0.014 – 0.029 – 0.040* – 0.059* 2,417

(0.020) (0.042) (0.020) (0.030)

FOR-PROFIT COLLEGE [IES]

All 0.011 0.004 0.008 0.006 0.009 4,717

(0.004) (0.009) (0.005) (0.007)
235 Chapter 7: Impacts on Educational Outcomes Chapter 7: Impacts on Educational Outcomes

EXHIBIT 7.8. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL 

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

SINCE 2007, ATTENDED... (CONTINUED)

FOR-PROFIT COLLEGE [IES] (CONTINUED)

Female 0.014 0.003 0.006 0.003 0.004 2,300

(0.007) (0.014) (0.007) (0.011)

Male 0.008 0.005 0.010 0.009 0.014 2,417

(0.005) (0.010) (0.006) (0.009)

SINCE 2007, NUMBER OF MONTHS

ENROLLED IN....

TWO-YEAR COLLEGES [NSC]

All 1.267 0.103 0.216 – 0.066 – 0.100 4,717

(0.142) (0.299) (0.137) (0.208)

Female 1.374 0.246 0.509 0.185 0.284 2,300

(0.218) (0.451) (0.213) (0.326)

Male 1.169 – 0.031 – 0.066 – 0.304~ – 0.457~ 2,417

(0.183) (0.392) (0.173) (0.260)

FOUR-YEAR COLLEGES [NSC]

All 1.182 0.007 0.014 – 0.235 – 0.357 4,717

(0.158) (0.333) (0.152) (0.230)

Female 1.564 – 0.019 – 0.039 – 0.268 – 0.411 2,300

(0.258) (0.532) (0.252) (0.385)

Male 0.833 0.031 0.066 – 0.204 – 0.306 2,417

(0.176) (0.378) (0.170) (0.255)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: NSC = National Student Clearinghouse. IES = Institute for Education Sciences.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: The sample for the NSC and IES measures is all core household members ages 15 to 20 as of December 31, 2007.

Measures: Number of days enrolled in college was calculated according to enrollment begin date and enrollment end date reported by NSC, and the number of 

days was then divided by 30 to create number of months enrolled in school.
236

EXHIBIT 7.9. SCHOOLING OUTCOMES FOR GROWN CHILDREN AGES 21–30

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

CURRENT ENROLLMENT

AND SECONDARY

SCHOOLING OUTCOMES

CURRENTLY ENROLLED IN

SCHOOL [PR]

All 0.158 – 0.007 – 0.016 – 0.001 – 0.002 3,089

(0.016) (0.036) (0.023) (0.042)

Female 0.205 – 0.015 – 0.033 0.006 0.010 1,527

(0.025) (0.054) (0.036) (0.062)

Male 0.110 0.000 0.000 – 0.007 – 0.013 1,562

(0.020) (0.047) (0.025) (0.047)

HAS HIGH SCHOOL DIPLOMA 

[PR]

All 0.554 – 0.013 – 0.029 0.007 0.013 3,088

(0.024) (0.054) (0.031) (0.055)

Female 0.624 – 0.004 – 0.008 0.002 0.003 1,530

(0.031) (0.068) (0.042) (0.072)

Male 0.482 – 0.022 – 0.052 0.011 0.022 1,558

(0.032) (0.076) (0.039) (0.074)

HAS HIGH SCHOOL DIPLOMA 

OR GED [PR]

All 0.707 – 0.016 – 0.036 0.055* 0.099* 3,088

(0.022) (0.050) (0.027) (0.049)

Female 0.751 – 0.001 – 0.003 0.069~ 0.118~ 1,530

(0.028) (0.061) (0.036) (0.061)

Male 0.662 – 0.030 – 0.071 0.042 0.078 1,558

(0.031) (0.073) (0.038) (0.071)

POST-SECONDARY

SCHOOLING OUTCOMES

SINCE 2007, ATTENDED...

ANY COLLEGE [NSC]

All 0.196 – 0.015 – 0.035 – 0.011 – 0.019 4,639

(0.014) (0.034) (0.016) (0.027)

Female 0.282 – 0.046* – 0.106* – 0.052* – 0.089* 2,275

(0.023) (0.054) (0.025) (0.042)

Male 0.115 0.014 0.034 0.028 0.048 2,364

(0.017) (0.040) (0.019) (0.033)

TWO-YEAR COLLEGE [NSC]

All 0.135 – 0.018 – 0.042 – 0.013 – 0.022 4,639

(0.012) (0.029) (0.014) (0.024)
237 Chapter 7: Impacts on Educational Outcomes Chapter 7: Impacts on Educational Outcomes

EXHIBIT 7.9. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

POST-SECONDARY

SCHOOLING OUTCOMES

(CONTINUED)

SINCE 2007, ATTENDED...

(CONTINUED)

TWO-YEAR COLLEGE [NSC]

(CONTINUED)

Female 0.198 – 0.040* – 0.094* – 0.044* – 0.076* 2,275

(0.020) (0.047) (0.022) (0.037)

Male 0.076 0.004 0.008 0.017 0.030 2,364

(0.014) (0.033) (0.015) (0.027)

FOUR-YEAR COLLEGE [NSC]

All 0.083 – 0.003 – 0.007 0.006 0.010 4,639

(0.010) (0.023) (0.011) (0.019)

Female 0.121 – 0.020 – 0.047 – 0.010 – 0.017 2,275

(0.016) (0.037) (0.018) (0.031)

Male 0.047 0.013 0.032 0.020 0.035 2,364

(0.011) (0.027) (0.013) (0.023)

PRIVATE COLLEGE [NSC]

All 0.053 0.001 0.002 – 0.006 – 0.010 4,639

(0.008) (0.019) (0.009) (0.015)

Female 0.074 – 0.008 – 0.018 – 0.021 – 0.035 2,275

(0.013) (0.030) (0.014) (0.024)

Male 0.033 0.009 0.022 0.008 0.014 2,364

(0.010) (0.023) (0.011) (0.019)

PUBLIC COLLEGE [NSC]

All 0.153 – 0.010 – 0.025 – 0.004 – 0.006 4,639

(0.013) (0.031) (0.014) (0.025)

Female 0.226 – 0.035~ – 0.082~ – 0.033 – 0.057 2,275

(0.021) (0.049) (0.023) (0.039)

Male 0.085 0.013 0.032 0.024 0.043 2,364

(0.015) (0.036) (0.016) (0.029)

FOR-PROFIT COLLEGE [IES]

All 0.031 – 0.002 – 0.005 – 0.001 – 0.002 4,639

(0.006) (0.015) (0.007) (0.013)

Female 0.039 – 0.005 – 0.012 – 0.005 – 0.008 2,275

(0.010) (0.023) (0.011) (0.019)

Male 0.023 0.001 0.003 0.002 0.004 2,364

(0.008) (0.019) (0.009) (0.016)
238

EXHIBIT 7.9. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

POST-SECONDARY

SCHOOLING OUTCOMES

(CONTINUED)

SINCE 2007, NUMBER OF

MONTHS ENROLLED IN...

TWO-YEAR COLLEGES [NSC]

All 1.052 – 0.196 – 0.462 – 0.198 – 0.344 4,639

(0.128) (0.302) (0.129) (0.224)

Female 1.535 – 0.275 – 0.639 – 0.396~ – 0.679~ 2,275

(0.221) (0.515) (0.219) (0.375)

Male 0.592 – 0.122 – 0.293 – 0.012 – 0.022 2,364

(0.128) (0.306) (0.137) (0.240)

FOUR-YEAR COLLEGES [NSC]

All 0.875 0.043 0.100 0.079 0.137 4,639

(0.133) (0.314) (0.146) (0.253)

Female 1.275 – 0.108 – 0.252 – 0.023 – 0.039 2,275

(0.226) (0.525) (0.244) (0.419)

Male 0.495 0.188 0.450 0.177 0.310 2,364

(0.148) (0.354) (0.166) (0.290)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: PR = parent report. NSC = National Student Clearinghouse. IES = Institute for Education Sciences.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Parent report measures come from the adult long-term adult survey and are presented for grown children (under age 18 at baseline and 

ages 21 to 30 as of December 31, 2007) whose parents were interviewed as part of the long-term survey. The sample for the NSC and IES measures is all core 

household members who are grown children, regardless of whether their parent was interviewed.

Measures: Number of days enrolled in college was calculated according to enrollment begin date and enrollment end date reported by NSC, and the number of 

days was then divided by 30 to create number of months enrolled in school.

7.7 INTERPRETATION OF 

EDUCATIONAL RESULTS

The long-term follow-up data examined here found no 

detectable impacts on achievement test scores in reading 

or math measured 10 to 15 years after baseline. We 

also found few statistically significant impacts on other 

educational outcomes for youth. There is some indication 

that females in the Section 8 group might be less likely 

than those in the control group to repeat a grade, but on 

several other measures such as high school graduation, 

grades, advanced course taking, and taking the SAT or 

ACT, there were statistically significant or marginally 

significant declines in outcomes for youth in one or both 

of the treatment groups compared with controls. Given 

the large number of impact estimates examined in this 

chapter, and the fact that the sign of the impacts are 

somewhat mixed, the few statistically significant effects 

presented in this chapter may simply arise from chance.

We also found few statistically significant effects on 

educational outcomes even for those children who 

were very young (preschool age) at baseline. This is 

perhaps surprising because Chapter 2 showed that MTO 

generated particularly large differences across randomized 

groups in average neighborhood conditions during the 

first few years after random assignment—exactly when 

children who were of preschool age at baseline would 

have been in what researchers believe to be a particularly 

“plastic” stage of human development.
239 Chapter 7: Impacts on Educational Outcomes Chapter 7: Impacts on Educational Outcomes

These results should not be interpreted as implying that 

neighborhoods never matter for academic achievement, 

since school quality may be one especially important 

mediator through which neighborhoods influence 

learning and MTO wound up generating very modest 

differences across randomized groups in the average 

characteristics of the schools children attended. For 

example all children across all three MTO groups 

attended schools that had overwhelmingly majorityminority student bodies, in which about two-thirds 

were eligible for free or reduced-price lunch and that 

ranked on average near the 20th to 25th percentile 

relative to other schools based on the achievement 

test performance of their students. Part of the issue 

could be that most children in our study sample stayed 

within the large urban school districts that served the 

five MTO demonstration cities. While many of these 

cities were engaging in urban school reform during our 

study period, which might have narrowed some of the 

difference in school quality found between very poor and 

less poor neighborhoods, the school systems as a whole 

were still not performing terribly well compared with 

national norms. It is certainly possible that a mobility 

intervention that achieved larger impacts on school 

quality—for example, a program that helped more 

families move to affluent suburbs—could have more 

pronounced impacts on educational outcomes. MTO 

is informative instead about the impact on educational 

outcomes that neighborhood change can have without 

much change in school quality.

SUPPLEMENTAL EXHIBIT 7.1. ABSENTEEISM, TARDINESS, OR PROBLEMS AT SCHOOL FOR YOUTH AGES 10–20

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

LATE FOR SCHOOL ONCE OR

MORE PER MONTH [SR] 

All (ages 10–20) 0.626 0.000 0.001 – 0.010 – 0.015 5,085

(0.020) (0.041) (0.022) (0.032)

Female 0.637 – 0.021 – 0.042 0.000 0.000 2,592

(0.027) (0.054) (0.028) (0.044)

Male 0.616 0.022 0.048 – 0.022 – 0.031 2,493

(0.026) (0.057) (0.029) (0.042)

ABSENT MORE THAN 5% OF THE 

SCHOOL YEAR [SR] 

All (ages 10–20) 0.392 – 0.020 – 0.041 0.019 0.028 4,951

(0.019) (0.039) (0.021) (0.032)

Female 0.382 – 0.007 – 0.015 0.026 0.041 2,522

(0.026) (0.051) (0.030) (0.046)

Male 0.402 – 0.032 – 0.069 0.012 0.017 2,429

(0.026) (0.056) (0.030) (0.043)

FRACTION OF SCHOOL YEAR

ABSENT FROM SCHOOL [SR] 

All (ages 10–20) 0.060 0.000 0.001 0.001 0.002 4,951

(0.003) (0.007) (0.003) (0.005)

Female 0.059 – 0.003 – 0.005 0.001 0.001 2,522

(0.004) (0.008) (0.005) (0.007)

Male 0.060 0.003 0.007 0.002 0.002 2,429

(0.005) (0.012) (0.005) (0.007)
240

SUPPLEMENTAL EXHIBIT 7.1. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

SCHOOL ASKED SOMEONE TO COME 

IN TO DISCUSS YOUTH'S SCHOOLWORK 

OR BEHAVIOR PROBLEMS IN PAST 2 YEARS [PR]

All (ages 10–17) 0.367 – 0.034 – 0.072 – 0.037 – 0.056 1,963

(0.030) (0.063) (0.038) (0.057)

Female 0.309 – 0.036 – 0.078 – 0.014 – 0.022 998

(0.039) (0.084) (0.050) (0.078)

Male 0.419 – 0.030 – 0.063 – 0.058 – 0.083 965

(0.044) (0.092) (0.053) (0.076)

SUSPENDED OR EXPELLED

FROM SCHOOL IN PAST TWO

YEARS [SR]

All (ages 10–17) 0.308 0.021 0.045 0.029 0.042 3,322

(0.022) (0.046) (0.024) (0.034)

Female 0.288 – 0.035 – 0.073 – 0.012 – 0.018 1,692

(0.028) (0.058) (0.032) (0.048)

Male 0.327 0.078* 0.170* 0.069* 0.095* 1,630

(0.031) (0.068) (0.034) (0.047)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Self-reported items come from the youth long-term survey, while the parent report measure comes from the adult long-term survey. The 

sample for the parent report measure is all core household members ages 10 to 17 as of December 31, 2007. The sample for the suspension/expulsion measure is 

limited to interviewed youth ages 10 to 17 as of December 31, 2007. All other measures use the full interviewed youth sample (ages 10 to 20 as of December 31, 

2007).
241 Chapter 7: Impacts on Educational Outcomes Chapter 7: Impacts on Educational Outcomes

SUPPLEMENTAL EXHIBIT 7.2. FUTURE EXPECTATIONS OF YOUTH AGES 10–20

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

YOUTH EXPECTS TO AT LEAST...

COMPLETE TWO YEARS OF

COLLEGE [SR]

All 0.696 – 0.048* – 0.100* – 0.025 – 0.037 5,078

(0.018) (0.038) (0.020) (0.030)

Female 0.746 – 0.031 – 0.062 – 0.020 – 0.032 2,591

(0.024) (0.048) (0.026) (0.041)

Male 0.647 – 0.066* – 0.143* – 0.028 – 0.040 2,487

(0.027) (0.058) (0.029) (0.041)

FINISH COLLEGE [SR]

All 0.559 – 0.035~ – 0.074~ – 0.029 – 0.043 5,078

(0.019) (0.040) (0.021) (0.031)

Female 0.632 – 0.033 – 0.066 – 0.015 – 0.023 2,591

(0.026) (0.052) (0.028) (0.045)

Male 0.490 – 0.038 – 0.082 – 0.043 – 0.061 2,487

(0.027) (0.059) (0.030) (0.042)

YOUTH THINKS MOTHER WANTS

HIM/HER TO AT LEAST...

COMPLETE TWO YEARS OF

COLLEGE [SR]

All 0.870 – 0.009 – 0.018 – 0.002 – 0.003 5,039

(0.013) (0.026) (0.014) (0.021)

Female 0.897 – 0.008 – 0.016 – 0.014 – 0.022 2,569

(0.016) (0.032) (0.018) (0.028)

Male 0.845 – 0.010 – 0.021 0.010 0.014 2,470

(0.019) (0.041) (0.020) (0.029)

FINISH COLLEGE [SR]

All 0.820 – 0.006 – 0.012 0.006 0.009 5,039

(0.015) (0.032) (0.016) (0.024)

Female 0.854 – 0.003 – 0.006 – 0.016 – 0.025 2,569

(0.018) (0.037) (0.020) (0.031)

Male 0.788 – 0.008 – 0.018 0.029 0.041 2,470

(0.023) (0.050) (0.024) (0.034)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: Completing two years of college includes a two-year degree. Finishing college indicates the completion of a four- or five-year degree. Youth who 

reported thinking that they will or that their mother wants them to attain education beyond completing two years of college or finishing college are included with 

the "complete two years of college" and "finish college" groups, respectively.
242

SUPPLEMENTAL EXHIBIT 7.3. SCHOOL ENGAGEMENT AND PARENTAL INVOLVEMENT IN SCHOOL

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

SCHOOL ENGAGEMENT

ALWAYS COMPLETES HOMEWORK [SR]

All (ages 10–20) 0.478 – 0.005 – 0.010 – 0.014 – 0.020 5,095

(0.019) (0.040) (0.021) (0.031)

Female 0.536 0.018 0.036 0.010 0.015 2,596

(0.026) (0.052) (0.028) (0.044)

Male 0.424 – 0.027 – 0.058 – 0.036 – 0.052 2,499

(0.027) (0.058) (0.029) (0.041)

HOURS PER WEEK SPENT ON

HOMEWORK [SR]

All (ages 10–20) 5.294 0.067 0.140 – 0.166 – 0.248 4,762

(0.204) (0.424) (0.220) (0.329)

Female 5.800 – 0.158 – 0.317 – 0.032 – 0.050 2,377

(0.306) (0.612) (0.333) (0.528)

Male 4.834 0.291 0.629 – 0.307 – 0.434 2,385

(0.275) (0.595) (0.290) (0.410)

HOURS PER WEEK READING [SR]

All (ages 10–20) 3.234 – 0.027 – 0.056 0.034 0.051 5,093

(0.199) (0.415) (0.233) (0.348)

Female 3.915 0.190 0.384 0.209 0.328 2,595

(0.310) (0.625) (0.346) (0.542)

Male 2.595 – 0.244 – 0.526 – 0.136 – 0.193 2,498

(0.243) (0.524) (0.298) (0.426)

PARENT INVOLVEMENT IN SCHOOL

ADULT ATTENDED AN EVENT OR A

MEETING AT YOUTH’S SCHOOL IN PAST

YEAR [PR]

All (ages 10–17) 0.706 0.026 0.056 0.008 0.012 1,969

(0.028) (0.058) (0.037) (0.055)

Female 0.696 0.017 0.036 0.002 0.004 1,003

(0.039) (0.082) (0.051) (0.079)

Male 0.715 0.036 0.076 0.013 0.019 9,66

(0.038) (0.081) (0.050) (0.072)

ADULT VOLUNTEERED AT YOUTH’S

SCHOOL IN PAST YEAR [PR]

All (ages 10–17) 0.166 – 0.001 – 0.002 0.002 0.003 1,970

(0.023) (0.049) (0.027) (0.041)

Female 0.178 – 0.013 – 0.028 – 0.001 – 0.002 1,004

(0.034) (0.073) (0.040) (0.062)

Male 0.155 0.012 0.025 0.005 0.008 966

(0.031) (0.064) (0.036) (0.052)
243 Chapter 7: Impacts on Educational Outcomes Chapter 7: Impacts on Educational Outcomes

SUPPLEMENTAL EXHIBIT 7.3. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

PARENT INVOLVEMENT IN SCHOOL 

(CONTINUED)

ADULT IS SOMEWHAT OR VERY

SATISFIED (VERSUS NOT AT ALL

SATISFIED) WITH YOUTH'S EDUCATION [PR]

All (ages 10–17) 0.920 – 0.015 – 0.031 – 0.054* – 0.080* 1,966

(0.018) (0.038) (0.025) (0.038)

Female 0.926 0.008 0.018 – 0.044 – 0.068 1,001

(0.022) (0.047) (0.032) (0.050)

Male 0.915 – 0.038 – 0.080 – 0.063~ – 0.091~ 965

(0.029) (0.061) (0.038) (0.054)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Self-reported items come from the youth long-term survey, while the parent report measures comes from the long-term adult survey. The 

sample for the self-reported measures is interviewed youth ages 10 to 20 as of December 31, 2007. The sample for the parent report measures is all core household 

members ages 10 to 17 as of December 31, 2007.

Measures: The sample size for the weekly homework hours measure is lower than other self-reported measures in this exhibit due to a survey instrument skip error 

that was corrected in the midst of the interview fielding period. Imputing missing values by randomization site and treatment group and by site, group, and using 

responses from youth who answered other homework questions similarly to those affected by the skip error yields qualitatively similar results. The weekly reading 

hours measure refers to reading outside of school that is not in connection with schoolwork.

SUPPLEMENTAL EXHIBIT 7.4. TIME USE AND ACCESS TO TECHNOLOGY AMONG YOUTH AGES 10–20

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

TIME USE: EXTRACURRICULAR ACTIVITY

PARTICIPATES IN SCHOOL SPORTS [SR]

All 0.445 – 0.016 – 0.033 – 0.001 – 0.002 5,088

(0.019) (0.039) (0.021) (0.031)

Female 0.374 – 0.034 – 0.068 – 0.015 – 0.024 2,592

(0.025) (0.051) (0.028) (0.043)

Male 0.511 0.002 0.004 0.012 0.018 2,496

(0.028) (0.059) (0.030) (0.043)

PARTICIPATES IN SCHOOL CLUBS/

GROUPS [SR]

All 0.278 0.004 0.008 – 0.004 – 0.005 5,048

(0.017) (0.035) (0.019) (0.028)

Female 0.321 0.015 0.030 – 0.008 – 0.012 2,573

(0.024) (0.049) (0.026) (0.041)

Male 0.239 – 0.007 – 0.015 0.001 0.002 2,475

(0.023) (0.049) (0.025) (0.036)
244

SUPPLEMENTAL EXHIBIT 7.4. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

TIME USE: EXTRACURRICULAR ACTIVITY 

(CONTINUED)

LEADERSHIP POSITION IN SCHOOL

SPORTS OR CLUBS/GROUPS [SR]

All 0.361 – 0.006 – 0.013 0.015 0.022 5,092

(0.018) (0.038) (0.020) (0.030)

Female 0.351 – 0.014 – 0.028 – 0.024 – 0.038 2,596

(0.025) (0.051) (0.028) (0.044)

Male 0.370 0.000 0.001 0.054~ 0.077~ 2,496

(0.026) (0.055) (0.029) (0.042)

PARTICIPATES IN ORGANIZED

ACTIVITY ONCE OR MORE PER

WEEK [SR]

All 0.506 – 0.024 – 0.050 – 0.006 – 0.009 5,093

(0.019) (0.039) (0.020) (0.030)

Female 0.390 – 0.038 – 0.077 – 0.014 – 0.022 2,596

(0.025) (0.050) (0.027) (0.042)

Male 0.614 – 0.010 – 0.021 0.001 0.002 2,497

(0.026) (0.057) (0.029) (0.041)

TIME USE: INFORMAL ACTIVITY

HANGS OUT IN NEIGHBORHOOD

OR AT BASKETBALL COURT ONCE

OR MORE PER WEEK [SR]

All 0.450 – 0.008 – 0.017 – 0.018 – 0.026 5,095

(0.019) (0.039) (0.020) (0.030)

Female 0.327 – 0.017 – 0.034 – 0.038 – 0.060 2,596

(0.023) (0.047) (0.025) (0.039)

Male 0.566 0.000 0.001 0.003 0.004 2,499

(0.027) (0.059) (0.030) (0.042)

SHOPS AT MALL OR STORE ONCE

OR MORE PER WEEK [SR]

All 0.476 – 0.001 – 0.003 0.002 0.003 5,092

(0.019) (0.040) (0.021) (0.031)

Female 0.515 – 0.009 – 0.018 0.014 0.022 2,596

(0.027) (0.054) (0.029) (0.046)

Male 0.439 0.007 0.015 – 0.011 – 0.016 2,496

(0.027) (0.057) (0.029) (0.042)

HANGS OUT AT SOMEONE ELSE'S

HOUSE ONCE OR MORE PER WEEK [SR]

All 0.561 – 0.037~ – 0.077~ – 0.042* – 0.062* 5,071

(0.019) (0.039) (0.020) (0.030)
245 Chapter 7: Impacts on Educational Outcomes Chapter 7: Impacts on Educational Outcomes

SUPPLEMENTAL EXHIBIT 7.4. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

HANGS OUT AT SOMEONE ELSE'S

HOUSE ONCE OR MORE PER WEEK [SR]

(CONTINUED)

Female 0.512 – 0.031 – 0.062 – 0.030 – 0.047 2,586

(0.027) (0.054) (0.028) (0.044)

Male 0.606 – 0.042 – 0.092 – 0.053~ – 0.076~ 2,485

(0.026) (0.056) (0.029) (0.041)

NEVER HANGS OUT AT HOME [SR]

All 0.077 – 0.004 – 0.008 – 0.007 – 0.010 5,095

(0.010) (0.020) (0.010) (0.015)

Female 0.075 – 0.008 – 0.016 0.000 0.000 2,596

(0.014) (0.028) (0.015) (0.024)

Male 0.079 0.000 0.001 – 0.014 – 0.020 2,499

(0.014) (0.031) (0.015) (0.021)

ACCESS TO TECHNOLOGY

USES COMPUTER AT HOME [SR]

All 0.704 – 0.009 – 0.019 0.049* 0.073* 5,095

(0.020) (0.041) (0.020) (0.030)

Female 0.720 – 0.027 – 0.055 0.029 0.045 2,596

(0.024) (0.048) (0.026) (0.041)

Male 0.688 0.009 0.019 0.068* 0.097* 2,499

(0.026) (0.056) (0.027) (0.039)

USES INTERNET AT LEAST 4 HOURS 

PER WEEK [SR]

All 0.555 0.001 0.001 – 0.014 – 0.021 3,949

(0.021) (0.043) (0.024) (0.035)

Female 0.571 – 0.020 – 0.039 – 0.017 – 0.027 2,028

(0.029) (0.055) (0.031) (0.049)

Male 0.540 0.022 0.047 – 0.012 – 0.017 1,921

(0.030) (0.064) (0.033) (0.047)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

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

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: The sample size for the weekly hours of internet use measure is lower than other self-reported measures in this exhibit due to a survey instrument skip 

error that was corrected in the midst of the interview fielding period. Imputing missing values by randomization site and treatment group yields qualitatively similar 

results.
246

SUPPLEMENTAL EXHIBIT 7.5. ACHIEVEMENT ASSESSMENT RESULTS BY BASELINE AGE AMONG YOUTH AGES 

13–20

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

BASELINE AGES 0–5

READING ASSESSMENT SCORE

[ECLS-K]

All – 0.092 0.026 0.057 0.080 0.108 2,542

(0.054) (0.115) (0.056) (0.076)

Female – 0.031 0.021 0.045 0.087 0.122 1,309

(0.074) (0.156) (0.081) (0.113)

Male – 0.152 0.032 0.069 0.072 0.094 1,233

(0.072) (0.158) (0.073) (0.095)

MATH ASSESSMENT SCORE [ECLS-K]

All 0.001 – 0.057 – 0.121 – 0.060 – 0.080 2,535

(0.058) (0.123) (0.060) (0.081)

Female 0.019 – 0.067 – 0.141 – 0.066 – 0.092 1,306

(0.077) (0.162) (0.079) (0.111)

Male – 0.017 – 0.046 – 0.101 – 0.054 – 0.069 1,229

(0.078) (0.171) (0.086) (0.112)

BASELINE AGES 6–11

READING ASSESSMENT SCORE [ECLS-K]

All 0.119 – 0.026 – 0.050 – 0.008 – 0.013 1,890

(0.058) (0.113) (0.065) (0.105)

Female 0.160 – 0.061 – 0.118 0.017 0.029 977

(0.076) (0.147) (0.092) (0.155)

Male 0.081 0.014 0.028 – 0.036 – 0.055 913

(0.083) (0.167) (0.090) (0.138)

MATH ASSESSMENT SCORE [ECLS-K]

All – 0.001 0.017 0.034 0.081 0.129 1,885

(0.060) (0.119) (0.070) (0.112)

Female – 0.035 0.014 0.026 0.024 0.042 974

(0.072) (0.140) (0.089) (0.152)

Male 0.032 0.019 0.038 0.138 0.209 911

(0.090) (0.182) (0.103) (0.155)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: ECLS-K = achievement assessment from the Early Childhood Longitudinal Study-Kindergarten 

cohort study.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: The reading and math achievement assessment scores are theta scores transformed into z-scores via standardization on the mean and standard 

deviation for control group youth ages 13 to 20. 
247 Chapter 7: Impacts on Educational Outcomes Chapter 7: Impacts on Educational Outcomes

SUPPLEMENTAL EXHIBIT 7.6. ACHIEVEMENT ASSESSMENT RESULTS BY SITE AMONG YOUTH AGES 13–20

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

READING ASSESSMENT SCORE [ECLS-K]

All 0.000 0.003 0.006 0.040 0.058 4,432

(0.041) (0.085) (0.044) (0.064)

Baltimore – 0.020 – 0.025 – 0.043 – 0.071 – 0.093 589

(0.108) (0.183) (0.112) (0.147)

Boston 0.107 – 0.081 – 0.206 0.128 0.225 878

(0.099) (0.253) (0.095) (0.167)

Chicago – 0.281 0.074 0.256 0.237* 0.330* 974

(0.088) (0.302) (0.099) (0.138)

Los Angeles – 0.057 0.090 0.136 0.050 0.061 1,069

(0.078) (0.119) (0.088) (0.107)

New York City 0.266 – 0.074 – 0.147 – 0.188* – 0.335* 922

(0.085) (0.170) (0.096) (0.170)

MATH ASSESSMENT SCORE [ECLS-K]

All 0.000 – 0.025 – 0.052 0.000 0.000 4,420

(0.044) (0.090) (0.048) (0.069)

Baltimore – 0.084 – 0.164 – 0.276 – 0.111 – 0.144 591

(0.105) (0.177) (0.116) (0.151)

Boston 0.204 – 0.126 – 0.321 0.040 0.070 875

(0.114) (0.291) (0.108) (0.187)

Chicago – 0.314 0.028 0.095 0.137 0.191 973

(0.101) (0.349) (0.125) (0.174)

Los Angeles – 0.061 0.080 0.122 0.044 0.053 1,063

(0.076) (0.116) (0.088) (0.107)

New York City 0.262 – 0.021 – 0.042 – 0.166~ – 0.296~ 918

(0.084) (0.169) (0.088) (0.157)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: ECLS-K = achievement assessment from the Early Childhood Longitudinal Study-Kindergarten 

cohort study.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: The reading and math achievement assessment scores are theta scores transformed into z-scores via standardization on the mean and standard 

deviation for control group youth ages 13 to 20. 
248

SUPPLEMENTAL EXHIBIT 7.7. INTERVIEWER OBSERVATIONS OF YOUTH AGES 13–20 DURING ACHIEVEMENT 

ASSESSMENT

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

HIGHLY OR VERY HIGHLY MOTIVATED 

DURING ASSESSMENT [OBS]

All 0.840 – 0.019 – 0.039 0.019 0.028 4,582

(0.015) (0.032) (0.015) (0.022)

Female 0.851 – 0.008 – 0.017 0.019 0.030 2,335

(0.019) (0.039) (0.020) (0.031)

Male 0.830 – 0.029 – 0.063 0.019 0.027 2,247

(0.022) (0.047) (0.022) (0.031)

COOPERATIVE OR VERY COOPERATIVE 

DURING ASSESSMENT [OBS]

All 0.914 – 0.001 – 0.003 0.008 0.012 4,580

(0.011) (0.022) (0.012) (0.018)

Female 0.921 0.005 0.010 0.021 0.033 2,335

(0.014) (0.028) (0.014) (0.022)

Male 0.908 – 0.008 – 0.017 – 0.006 – 0.008 2,245

(0.016) (0.035) (0.019) (0.026)

VERY OR FULLY ATTENTIVE DURING

ASSESSMENT [OBS]

All 0.811 0.019 0.039 0.041* 0.060* 4,578

(0.016) (0.033) (0.016) (0.024)

Female 0.826 0.024 0.048 0.031 0.047 2,334

(0.020) (0.040) (0.022) (0.033)

Male 0.797 0.014 0.030 0.051* 0.072* 2,244

(0.022) (0.048) (0.024) (0.033)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: OBS = interviewer observations.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

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

Measures: For interviewer observations of motivation, high and very high motivation were two of five choices (the other three were average, low, and very low). For 

cooperation, cooperative and very cooperative were two of five choices (the other three were matter of fact, uncooperative, and very uncooperative). For attention, 

very attentive and complete and full attention were two of five choices (the other three were attentive, difficulty attending, and unable to attend).
249 Chapter 7: Impacts on Educational Outcomes Chapter 7: Impacts on Educational Outcomes

SUPPLEMENTAL EXHIBIT 7.8. POST-SECONDARY SCHOOLING OUTCOMES OF YOUTH AGES 15–20, SELFREPORT VERSUS ADMINISTRATIVE DATA

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

ANY COLLEGE

EVER ATTENDED ANY COLLEGE [SR]

All (ages 15–20) 0.243 0.011 0.022 – 0.009 – 0.014 3,610

(0.019) (0.040) (0.021) (0.032)

Female 0.271 0.023 0.047 0.011 0.017 1,838

(0.026) (0.053) (0.031) (0.048)

Male 0.218 – 0.002 – 0.004 – 0.029 – 0.042 1,772

(0.026) (0.056) (0.027) (0.039)

ATTENDED ANY COLLEGE SINCE

2007 [NSC]

All 0.262 – 0.014 – 0.029 – 0.021 – 0.033 4,717

(0.016) (0.033) (0.017) (0.025)

Female 0.305 – 0.011 – 0.023 0.000 0.001 2,300

(0.023) (0.048) (0.025) (0.039)

Male 0.222 – 0.016 – 0.033 – 0.042* – 0.064* 2,417

(0.021) (0.044) (0.021) (0.032)

EVER ATTENDED ANY COLLEGE [SR]

OR ATTENDED ANY COLLEGE SINCE

2007 [NSC], OVERLAPPING SAMPLE

All 0.355 – 0.022 – 0.044 – 0.019 – 0.028 3,611

(0.019) (0.038) (0.021) (0.031)

Female 0.408 – 0.025 – 0.051 – 0.001 – 0.002 1,841

(0.027) (0.054) (0.031) (0.047)

Male 0.302 – 0.018 – 0.037 – 0.037 – 0.053 1,770

(0.026) (0.053) (0.027) (0.039)

FOUR-YEAR COLLEGE

EVER ATTENDED FOUR-YEAR

COLLEGE [SR]

All (ages 15–20) 0.111 0.003 0.006 – 0.023 – 0.035 3,610

(0.014) (0.030) (0.016) (0.023)

Female 0.130 0.012 0.024 – 0.017 – 0.026 1,838

(0.021) (0.042) (0.024) (0.037)

Male 0.094 – 0.006 – 0.013 – 0.029 – 0.042 1,772

(0.019) (0.040) (0.020) (0.029)

ATTENDED FOUR-YEAR COLLEGE

SINCE 2007 [NSC]

All 0.105 0.004 0.009 – 0.004 – 0.007 4,717

(0.011) (0.024) (0.012) (0.018)

Female 0.130 0.015 0.031 0.005 0.008 2,300

(0.018) (0.037) (0.019) (0.029)
250

SUPPLEMENTAL EXHIBIT 7.8. (CONTINUED)

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

FOUR-YEAR COLLEGE (CONTINUED)

ATTENDED FOUR-YEAR COLLEGE

SINCE 2007 [NSC] (CONTINUED)

Male 0.083 – 0.006 – 0.013 – 0.013 – 0.020 2,417

(0.013) (0.029) (0.014) (0.021)

EVER ATTENDED FOUR-YEAR

COLLEGE [SR] OR ATTENDED FOURYEAR COLLEGE SINCE 2007 [NSC],

OVERLAPPING SAMPLE

All 0.148 – 0.001 – 0.002 – 0.013 – 0.020 3,611

(0.015) (0.031) (0.017) (0.025)

Female 0.181 0.002 0.003 – 0.012 – 0.018 1,841

(0.022) (0.045) (0.025) (0.039)

Male 0.115 – 0.004 – 0.008 – 0.015 – 0.021 1,770

(0.019) (0.040) (0.020) (0.029)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working 

would indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: SR= self-report, NSC = National Student Clearinghouse.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Self-reported measures come from the youth long-term survey and are presented for interviewed youth ages 15 to 20 as of December 

31, 2007. The sample for the NSC and IES measures is all core household members ages 15 to 20 as of December 31, 2007, regardless of whether they were 

interviewed.

Measures: Number of days enrolled in college was calculated according to enrollment begin date and enrollment end date reported by NSC, and the number of 

days was then divided by 30 to create number of months enrolled in school.
251 Chapter 7: Impacts on Educational Outcomes Chapter 7: Impacts on Educational Outcomes

SUPPLEMENTAL EXHIBIT 7.9. POST-SECONDARY SCHOOLING OUTCOMES OF GROWN CHILDREN AGES 21–30, 

SELF-REPORT VERSUS ADMINISTRATIVE DATA

CONTROL 

MEAN

EXPERIMENTAL 

VERSUS CONTROL

SECTION 8 

VERSUS CONTROL

OUTCOME ITT TOT ITT TOT N

EVER ENROLLED IN COLLEGE [PR]

All 0.399 – 0.039~ – 0.089~ – 0.020 – 0.035 3,081

(0.022) (0.051) (0.029) (0.052)

Female 0.484 – 0.034 – 0.075 – 0.016 – 0.027 1,524

(0.031) (0.068) (0.041) (0.071)

Male 0.311 – 0.044 – 0.104 – 0.024 – 0.045 1,557

(0.030) (0.072) (0.036) (0.067)

ATTENDED ANY COLLEGE SINCE

2007 [NSC]

All 0.196 – 0.015 – 0.035 – 0.011 – 0.019 4,639

(0.014) (0.034) (0.016) (0.027)

Female 0.282 – 0.046* – 0.106* – 0.052* – 0.089* 2,275

(0.023) (0.054) (0.025) (0.042)

Male 0.115 0.014 0.034 0.028 0.048 2,364

(0.017) (0.040) (0.019) (0.033)

EVER ENROLLED IN COLLEGE [PR] 

OR ATTENDED ANY COLLEGE SINCE 

2007 [NSC], OVERLAPPING SAMPLE

All 0.415 – 0.033 – 0.076 – 0.006 -0.010 3,216

(0.021) (0.047) (0.028) (0.049)

Female 0.522 – 0.043 -0.095 – 0.028 -0.048 1,570

(0.029) (0.065) (0.039) (0.066)

Male 0.312 -0.024 – 0.056 0.015 0.028 1,646

(0.028) (0.065) (0.035) (0.065)

Notes: * = p < 0.05, ~ = p < 0.10 on two-tailed t-test. Robust standard errors shown in parentheses. ITT = Intention-to-Treat or estimated impact of being offered 

an MTO housing voucher; TOT = Treatment-on-the-Treated or estimated impact of moving using an MTO housing voucher. The control mean is unadjusted. Unless 

otherwise indicated, the control mean and impacts are expressed as "shares" of the sample in the category (for example, a control mean of 0.250 for Working would 

indicate that 25 percent of the control group was working). 

Square brackets indicate the source of the outcome information: PR = parent report, NSC = National Student Clearinghouse.

Model: Experimental and Section 8 impacts were estimated jointly using an OLS regression model controlling for baseline covariates, weighted, and clustering on 

family. Outcomes from the adult survey also control for field release. Youth and grown children impacts by gender were estimated as an interaction with treatment 

status. See Section 1.3 and the technical appendices (forthcoming) for details.

Data source and sample: Parent report measures come from the adult long-term survey and are presented for grown children (under age 18 at baseline and ages 

21 to 30 as of December 31, 2007) whose parents were interviewed as part of the long-term survey. The sample for the NSC and IES measures is all core household 

members who are grown children, regardless of whether their parent was interviewed.

Measures: Number of days enrolled in college was calculated according to enrollment begin date and enrollment end date reported by NSC, and the number of days 

was then divided by 30 to create number of months enrolled in school.
252
253 Chapter 8: Summary and Implications

SUMMARY AND IMPLICATIONS
254 CHAPTER 8

SUMMARY AND IMPLICATIONS

In this chapter, we summarize the pattern of MTO 

impacts 10 to 15 years after random assignment and 

discuss their implications both for social science and 

for public policies designed to improve the well-being 

of low-income families.

MTO provided very disadvantaged families living 

in public housing developments in high-poverty 

neighborhoods a chance to move to a better life. 

We find that the offer of an MTO housing voucher, 

which enables families to move to lower-poverty 

neighborhoods, generated sustained reductions over 

the next 10 to 15 years in neighborhood (census tract) 

poverty rates for families in both treatment groups 

(Section 8 and the experimental group) relative to 

families in the control group. The differences in 

neighborhood conditions between the MTO treatment 

(experimental and Section 8) groups and the control 

group narrowed over time, in large part because of 

residential mobility and ongoing declines in the average 

poverty rates in the census tracts of families in the 

control group. MTO moves also generated sustained 

improvements in housing conditions and in other 

neighborhood attributes beyond economic segregation, 

such as safety; the latter is particularly important given 

that concern for safety was the most important reason 

families signed up for MTO.

Neighborhood improvements induced by MTO moves 

boosted mental and physical health much more than 

labor market outcomes, educational outcomes, and for 

the most part more than for risky and criminal behavior 

as well. Moreover, the health effects in MTO seem 

more pronounced for adult women than for youth. The 

MTO study sample was very disadvantaged at baseline, 

and so our results may not be fully applicable to more 

representative samples of families. But our results are 

certainly consistent with the idea that some of the 

variation in health outcomes across neighborhoods 

in current-day America is caused by neighborhood 

conditions themselves. On the other hand, the cause 

of low earnings and high rates of unemployment, 

criminal involvement, and high school dropout found 

in many disadvantaged urban neighborhoods may rest 

more with individual barriers to success in school, 

the labor market and other pro-social settings than to 

purely neighborhood-level sources of disadvantage, 

at least within the range of neighborhood variation 

that mobility programs like MTO are able to modify. 

Accordingly, interventions that directly and effectively 

address individual-level barriers remain essential 

components in strategies to improve the long-term 

life chances of low-income families in addition to any 

housing mobility and neighborhood-based policies.

8.1 SUMMARY OF IMPACT 

ESTIMATES

Chapters 2 through 7 of this report presented impact 

estimates on the following outcomes for adults and 

youth:

•	 Residential mobility, housing conditions, 

neighborhood environments, and social networks;

•	 Physical health;

•	 Mental health; 

•	 Employment, earnings, income, receipt of social 

assistance, and savings and assets;

•	 Risky and criminal behavior; and

•	 Educational outcomes.

A Technical Appendix will provide additional technical 

details about our data collection and estimation 

procedures, as well as the results of sensitivity analyses.

8.2 MOBILITY, HOUSING, 

NEIGHBORHOODS, AND SOCIAL 

NETWORKS

MTO provided help to families in the experimental 

group to move out of public housing into privatemarket housing located in low-poverty census tracts 
255 Chapter 8: Summary and Implications Chapter 8: Summary and Implications

(those with 1990 poverty rates below 10 percent). To 

understand the consequences of such moves for the 

subsequent socioeconomic and health outcomes of these 

families, we compared their experiences with those of the 

families in the control group. The MTO demonstration 

also allowed us to analyze the impacts of moving 

using a standard Section 8 voucher through analogous 

comparisons of the Section 8 group and the control 

group.

Among those assigned to the experimental group in 

the long-term survey sample, 47 percent moved with 

an MTO housing voucher (the “compliers”), while 63 

percent of families in the Section 8 group moved with an 

MTO housing voucher. Readers might be surprised that 

so few families took advantage of what would initially 

seem like such an appealing opportunity to move out 

of public housing, and conclude that there must be 

something wrong with how MTO was implemented. 

But the voucher use rate in MTO is in line with what 

other studies have found. The voucher “take-up” 

or “compliance” rate in the Experimental Housing 

Allowance Program was approximately 65 percent (Leger 

and Kennedy, 1990), and it was approximately 20 

percent in the Gautreaux mobility program in Chicago 

(Rubinowitz and Rosenbaum, 2000, p. 67).

Assignment to either the experimental or Section 8 

groups increased the number of moves families made 

compared with controls, but a perhaps surprising finding 

is that mobility rates for the control group were lower 

than might be expected on the basis of previous studies 

of low-income households who were not receiving 

government housing assistance. Over the 10 to 15-year 

study period, the average control group family moved 

about twice compared with about three total moves for 

families who relocated as part of the MTO experimental 

or Section 8 groups.

MTO moves clearly improved the quality of the housing 

units in which families were living. Experimental group 

adults were about 5 percentage points more likely than 

controls to rate their housing at the time of the longterm survey as good or excellent (the effects of treatment 

on the treated [TOT] was approximately 11 percentage 

points), a sizable change compared with the control 

group prevalence rate of 57 percent. MTO movers in 

both the experimental and Section 8 groups were also less 

likely than controls to report specific housing problems, 

such as vermin, broken windows or missing screens, 

plumbing that is not working, or peeling paint and 

plaster.

MTO moves did not seem to affect overall housing costs 

for families, although, as discussed below, the confidence 

interval for this estimate was fairly wide. Compared 

with control group families, those in the experimental 

group were less likely to have problems paying their 

rent on time, but they were more likely to have trouble 

paying utilities. Being in the Section 8 treatment group 

increased the likelihood of renting and reduced the 

likelihood of homeownership; the experimental treatment 

effects were qualitatively similar but not statistically 

significant.

Among families who moved as part of the experimental 

group, MTO was clearly successful in helping them 

experience substantially different neighborhood 

environments compared with controls, at least in the 

short term. The results presented in Exhibit 1.5 of 

Chapter 1 showed that at baseline, the average MTO 

family was living in a census tract in which 53 percent of 

all residents were poor. Among those in the experimental 

and Section 8 groups who moved with an MTO voucher 

(the “compliers”), the average tract poverty rates 

decreased to 11 and 29 percent respectively after the 

initial move through the MTO program.

Over time, these poverty differences narrow substantially. 

At the time of the long-term follow-up surveys, the 

difference in average census-tract poverty rates between 

experimental group compliers and control group families 

had declined from around 35 or 40 percentage points 

to about 10 percentage points. Overall the durationweighted average census-tract poverty rate for the control 

group over the entire 10- to 15-year study period was 

about 40 percent. This compares with 20 percent for 

experimental group compliers and 29 percent for Section 

8 group compliers.

The convergence in neighborhood poverty rates over 

time occurred in part because some experimental group 
256

compliers moved after their first year in a low-poverty 

area to neighborhoods with somewhat higher poverty 

rates. A majority of the convergence since interim, 

however, occurred because of declines over time in the 

control group’s average census-tract poverty rate, which 

is itself due mostly to the relocation of control group 

families over time into lower-poverty areas (rather than 

from control families living in neighborhoods that were 

gentrifying around them).

A different approach to thinking about the size of the 

MTO impact on neighborhood poverty is to ask how far 

MTO shifted families within the national distribution of 

neighborhood poverty rates. For example, Quigley and 

Raphael (2008) note that at the time of the interim MTO 

study, the impact for all experimental group families 

(regardless of whether they moved in conjunction with the 

program) amounted to a shift from the 96th percentile 

of the nationwide census tract poverty distribution to the 

88th percentile. Of course, the effect of MTO on those 

who actually move via the program will be larger. But 

perhaps more important, and less obvious, is the fact that 

there are just not that many census tracts in the United 

States with extremely high poverty rates of the sort in 

which MTO families are living (as Figure 4 in Quigley 

and Raphael, 2008, p. 22, makes clear). This means very 

large changes in the absolute tract poverty level can lead to 

modest changes in a family’s rank in the nationwide tract 

poverty distribution.

An alternative way to think about the relative magnitudes 

of MTO’s impacts on tract poverty rates within the 

nationwide tract-poverty distribution, which is less 

sensitive to the fact that there are relatively few areas 

with extremely high poverty rates, is in terms of standard 

deviations within the national Census 2000 distribution 

for tract poverty rates. Experimental group moves 

generate decreases in tract poverty rates equal to nearly 

3 standard deviations measured one year after random 

assignment, 0.8 standard deviations measured 10 to 

12 years after random assignment, and 1.5 standard 

deviations when averaged over the entire post-study 

period (results from analyses not shown in tables). Not 

many policy interventions induce changes of this size in 

the social environments of families.

The presumption in MTO was that the census tract poverty 

rate is in part an indicator of the constellation of living 

conditions that may be important to the well-being of 

low-income families. Consistent with this presumption, we 

found that MTO also generated large, persistent changes 

in a range of other neighborhood attributes, including 

exposure to more affluent neighbors, less physical and social 

disorder, and perceptions of greater safety. Despite concerns 

about the risk of social isolation for MTO families in lowerpoverty areas, moves increased the likelihood that families 

were friends with someone who had a college degree.

Although MTO moves also led to statistically significant 

declines in neighborhood racial composition, the 

experimental group compliers were still living in census 

tracts in which the large majority of all residents were 

members of racial and ethnic minority groups. This 

is one important way in which the MTO “treatment” 

is different from that of the widely cited Gautreaux 

mobility program. The latter program was more explicitly 

focused on moves to reduce residential racial segregation 

(Rubinowitz and Rosenbaum, 2000). Another difference 

is that Gautreaux was explicitly designed to help families 

move into affluent suburban areas, which MTO achieved 

at a much lower rate.

8.3 PHYSICAL HEALTH

A large theoretical and empirical literature dating back 

to the 1700s has hypothesized that neighborhood 

environments may influence physical health outcomes 

through exposure to environmental risk factors, social 

norms about health-related behaviors, and access to 

health care providers. The interim MTO study found 

that, four to seven years after random assignment, MTO 

moves reduced the likelihood that adults were obese 

(as defined by a body mass index [BMI] of 30 kg/m2 

or higher). MTO had no detectable impacts on most 

other measures of physical health collected as part of the 

interim study. Given the potential importance of physical 

health impacts for both MTO participants themselves 

and society as a whole, the long-term study included a 

detailed battery of health outcomes, including directly 

measured height and weight (rather than self-reported, as 

in the interim study), blood pressure, and blood samples 

that were assayed for “biomeasures” that can be used to 

diagnose disease or predict future risk.
257 Chapter 8: Summary and Implications Chapter 8: Summary and Implications

The results showed that 10 to 15 years after random 

assignment, there was no longer a statistically significant 

impact on obesity (BMI ≥ 30 kg/m2), perhaps because 

such a large share of the sample became obese during 

that interval (58 percent of control group families). 

However, assignment to either the experimental or 

Section 8 groups reduced the chances of more extreme 

obesity (BMI ≥ 35 kg/m2) by around 5 percentage points 

compared with a control group prevalence of around 35 

percent, and reduced the chances of BMI ≥ 40 kg/m2 

by around 3 percentage points (although not statistically 

significant for the Section 8 group) compared with a 

control group prevalence of about 18 percent.

Alongside these sizable differences in extreme obesity, 

MTO also generates declines in diabetes prevalence of up 

to 6 percentage points for the experimental group using 

our preferred measures from blood assays of glycosylated 

hemoglobin (HbA1c), effects that were somewhat smaller 

if we rely instead on self-reports of diabetes status or 

treatment. These impacts are sizable compared with 

the control group prevalence rate of 16 to 20 percent. 

We also found that experimental group moves may 

have reduced the likelihood of having high-risk levels 

(>4 mg/L) of high-sensitivity C-reactive protein (a 

measure of inflammation), which have been shown to be 

predictive of cardiovascular disease risk.

There were few impacts on most other physical health 

measures for adults, with the exception of self-reported 

functional limitations (e.g., difficulty climbing stairs or 

carrying groceries). MTO had few significant impacts 

on the physical health outcomes of youth in the 

participating families. We discuss below possible ways in 

which these health impacts may have arisen.

8.4 MENTAL HEALTH

Given the importance of safety in motivating families to 

sign up for the demonstration as well as MTO’s impacts 

on neighborhood safety, mental health might be expected 

to be one of the outcome domains most improved by 

MTO.

The long-term follow-up found sustained MTO impacts 

on a widely used index of psychological distress (Kessler’s 

“K6” index). The long-term data showed that K6 scores 

were about 0.1 standard deviations lower for adults 

assigned to the experimental group than to the control 

group. This is about the same size as found in the interim 

MTO study (Kling, Liebman, Katz, 2007, Table 3, p. 

92). Given the importance of mental health for the 

well-being of MTO program participants, the long-term 

surveys included more detailed mental health measures. 

We found that those in the experimental group had a 

3 percentage point reduction and those in the Section 

8 group a 5 percentage point reduction (marginally 

significant) in lifetime prevalence of major depression 

among adults compared with a control group prevalence 

of 20 percent. Although MTO had no statistically 

significant effects on other specific disorders, almost all 

of the impact estimates are in the direction of improved 

mental health. One exception is that MTO moves may 

have increased rates of substance abuse or dependence.

Female youth (ages 15–20 at the time of the longterm surveys) in the experimental group had improved 

mental health (as measured by the mental health index) 

compared to controls. The changes were as large in 

magnitude as adult improvements. For female youth ages 

13–20 in the experimental group, MTO also reduced 

serious behavioral or emotional problems, as measured 

by the commonly used “strengths and difficulties 

questionnaire” (SDQ); reduced psychological distress; 

and appears to have reduced the prevalence of panic 

attacks (past year). Male youth in the Section 8 group 

may have experienced an increased prevalence of posttraumatic stress disorder relative to controls. This general 

pattern of generally beneficial impacts on female youth 

and some detrimental impacts on male youth echoes the 

pattern found in the interim report (Kling, Ludwig and 

Katz 2005, Kling, Liebman and Katz, 2007).

8.5 ECONOMIC SELF-SUFFICIENCY

The MTO program began in the mid-1990s, a time 

when the economy was booming and welfare reform 

was increasing the number of single mothers who were 

working in the formal labor market. Employment rates 

of mothers in both treatment groups and the control 

group increased sharply as well from the mid-1990s to 

2000. The increases, however, were no higher for either 

the experimental or Section 8 groups than for the control 

group. In fact, previous studies of MTO found that the 
258

first few years after random assignment, the experimental 

and Section 8 group adults had somewhat lower average 

earnings than controls, presumably from the disruptions 

and transitions associated with moving (Orr et al., 2003).

For the long-term follow-up, we had hypothesized that 

MTO moves might increase employment rates and 

earnings more for experimental than control mothers 

for a variety of reasons. The disruptions associated with 

moving would presumably have subsided. Additional 

time in lower-poverty neighborhoods might have 

increased the chances that movers had made new social 

connections that could serve as sources of job referrals, 

or would have internalized or responded to social norms 

in low-poverty areas that were more supportive of work. 

The children in these households were mostly adolescents 

by the long-term survey, thus limiting child-care 

constraints. We also speculated that the improved mental 

and physical health outcomes documented in the interim 

report and in the long-term survey could eventually 

translate into improved labor market outcomes.

None of these hypotheses were evident in the long-term 

data. The initial negative effect of MTO moves on labor 

market outcomes attenuated over time, but there were no 

statistically significant gains in longer-term employment 

rates, earnings, or total family income as measured by 

either survey self-reports or administrative records. We 

found some signs of negative effects of the Section 8 

treatment on self-reported employment rates in our longterm survey data, but we suspect this is likely an artifact 

of the slight differences in timing between when the 

surveys were carried out for the Section 8 versus control 

groups during the Great Recession in 2008–2009.

For the youth in MTO households, moves had no 

detectable effects on idleness rates but seem to have had, 

if anything, adverse effects on employment rates and 

earnings. These youth were age 15–20 at the time of the 

long-term survey. For youth who were teenagers at the 

time of the interim study and were grown children at the 

long-term study, males in the experimental group were 

more likely than controls to be employed, according 

to the proxy reports by their parents. However, state 

unemployment insurance (UI) data showed they were 

less likely to be employed. MTO may have increased 

employment rates in jobs that are not covered by the UI 

system, but it is also possible these are chance findings.

8.6 RISKY AND CRIMINAL BEHAVIOR

We had anticipated that MTO might have even more 

pronounced impacts on the risky and criminal behavior 

of those who were adolescents (age 15–20) at the time 

of the long-term follow-up (10–15 years after random 

assignment) compared to those who were age 15–20 

at the interim study (4–7 years after baseline). The 

interim study found declines in violent-crime arrests 

for both male and female youth who moved through 

MTO relative to controls, but for most other forms 

of risky behavior found that male youth did worse as 

a result of moving through MTO while female youth 

did better. The effects of MTO might have been even 

stronger for those who were teenagers at the time of the 

long-term study (compared to those who were teens at 

interim) because they would have been exposed for a 

longer period of time to less dangerous and economically 

distressed neighborhood environments. Moreover those 

who were teens at the time of the long-term follow-up 

would have lived in less disadvantaged communities early 

in childhood – a life stage in which cognitive, socioemotional and behavioral skills have been hypothesized 

by many social scientists to be particularly amenable to 

social policy intervention.

However the estimated effects of MTO on risky and 

criminal behavior of those who were teens at the longterm follow-up were more muted compared to what the 

effects on teens were at the time of the interim study. 

We find no statistically significant effects of MTO on 

violent crime arrests in the long term data. The long term 

data provide a few indications of the same type of gender 

difference in youth responses to MTO moves as were 

found in the interim data, with male youth who moved 

through MTO engaging in relatively more of some risky 

behaviors (smoking) compared to controls and female 

youth who moved through MTO experiencing declines 

in some risky behaviors (drinking) compared to controls.

The fact that the effects of MTO on risky and 

criminal behavior are generally more muted in the 

long-term data compared to the interim data suggests 

that contemporaneous neighborhood environments 
259 Chapter 8: Summary and Implications Chapter 8: Summary and Implications

may matter more for this outcome domain than does 

accumulated exposure to less distressed areas. The fact 

that we see signs of a gender difference in responses to 

residential mobility in a sample of teens in the long term 

data that is mostly non-overlapping with the youth who 

were studied in the interim follow-up, and also in an 

entirely different set of public housing families examined 

in a Chicago study by Jacob, Ludwig and Miller (2011), 

suggests that the gender difference in MTO impacts 

documented at interim may not be just a statistical 

artifact unique to that particular study and sample.

The one outcome for which we do see at least some 

hints of more pronounced impacts in the long-term 

data than in the interim data is with declining arrest 

rates for drug distribution among the MTO treatment 

groups compared to controls. The results are large and 

statistically significant for male youth in the experimental 

group, somewhat sizable for both the experimental and 

Section 8 effects for male grown children (but only 

significant for the Section 8 group), and for adults is 

sensitive to whether we focus on the extensive versus 

intensive margin of involvement in drug selling. The 

stronger results for adolescents than for older people is 

consistent with the widely-documented “age-crime curve” 

showing that aggregate arrest rates for most crimes peak 

during late adolescence or early adulthood. Evidence of 

impacts on all age groups for drug distribution, even if 

the results are somewhat mixed for the older cohorts, 

would itself seem to provide some support for the idea 

that this might be a real effect and not a chance finding.

8.7 EDUCATIONAL OUTCOMES

As with the interim study, MTO had few statistically 

significant effects on educational outcomes in the longterm follow up. Disappointingly, the null results also 

extended to children who had not yet enrolled in school 

at baseline, who would have experienced particularly 

large MTO-induced changes in neighborhood 

environments during a life stage in which cognitive, 

social-emotional and behavioral skills are hypothesized 

by many social scientists to be unusually “plastic” and 

susceptible to intervention.

Compared to the control group, children assigned to the 

experimental and Section 8 groups had similar scores on 

reading and math achievement tests, grades in school, 

and rates of grade retention. However, Section 8 males 

were less likely to be educationally on track and to 

have attended college than control group males. There 

was no evidence that impacts on educational outcomes 

varied systematically with a child’s age at time of random 

assignment.

These findings do not mean that neighborhood 

environments never matter for educational outcomes. 

The MTO mobility intervention generated more 

pronounced impacts on neighborhood conditions than 

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

the long term data show that MTO had modest and 

mixed impacts on school quality. Children assigned to 

the treatment groups rather than to the control group 

were in schools that had slightly lower proportions of 

low-income and minority students, and those assigned 

to the experimental group were in schools that had 

slightly higher test scores, but were also larger in terms 

of the total size of the student body. A majority of 

MTO children were still attending majority-minority, 

overwhelmingly low-income public schools located in the 

districts serving the five original MTO cities.

The potential contingent nature of neighborhood effects 

on educational outcomes also comes from considering 

the pattern of MTO impacts across cities and over time 

from the interim to the long-term follow-up. Data from 

the interim study showed some signs of MTO impacts 

on achievement test scores for children in the Baltimore 

and Chicago sites, the cities where baseline levels of 

both concentrated neighborhood disadvantage and 

neighborhood crime and violence were more extreme 

than in the other three MTO cities (Boston, Los Angeles, 

and New York City). That this might be a real effect 

rather than a spurious finding is suggested by evidence 

of neighborhood effects on test scores drawn from two 

entirely different Chicago study samples (see BurdickWill et al., 2011). Over time from the interim to the 

long-term study in MTO, the neighborhood conditions 

in which Baltimore and Chicago families lived became 

more similar to what we see in the other three cities, 

and any indication of cross-site differences in MTO 

impacts on test scores attenuates. This pattern seems 

consistent with our results for the risky and criminal 
260

behavior chapter in suggesting that contemporaneous 

neighborhood conditions may be more important than 

accumulated exposure in affecting outcomes in these two 

domains. 

8.8 CAN THESE MTO RESULTS 

BE TRUSTED?

Although we have sought to maximize the scientific 

quality of our long-term MTO follow-up, it remains 

possible that we have missed some important MTO 

impacts.

One potential problem with any study such as ours is 

sample attrition. If, for example, the most successful 

MTO families could not be tracked or refused to be 

interviewed, then our estimates likely understate MTO’s 

benefits. We do not believe systematic differences across 

randomized MTO groups in attrition from the study 

sample are a major concern. Thanks to the efforts of the 

University of Michigan’s Survey Research Center (SRC), 

the effective response rates for the long-term MTO 

follow-up were 90 percent for adults and 89 percent for 

youth. Moreover, the response rates were quite similar 

across randomized groups. High response rates such as 

these limit the degree to which any differences between 

respondents and nonrespondents can affect our estimates. 

Moreover, the characteristics (measured when the study 

began) of those who responded to the long-term survey 

appear to be quite similar across randomized groups.

Another potential threat comes from survey misreporting. 

Of particular concern is the possibility that living in a 

more versus less disadvantaged neighborhood could affect 

survey misreporting patterns, if, for example, people were 

more embarrassed to admit being unemployed in more 

affluent areas where relatively more people are working. 

We have tried to limit this problem by using multiple 

sources of data. For example, we directly measured health 

outcomes whenever possible (such as height, weight, 

blood pressure, and even using blood spots to measure 

health outcomes like diabetes) rather than relying only 

on self-reports. We also used administrative government 

records along with survey measures of labor market 

outcomes. The impacts as measured across these various 

sources of data are generally consistent.

A third threat to the MTO study is the possibility of 

“interference” between program participants, called 

the “stable unit treatment value assumption” (SUTVA) 

(Rubin, 1980; see also Sobel [2006] for a discussion 

within the context of MTO). In MTO, this problem 

could arise if the neighbors of MTO participants 

were offered MTO housing vouchers, or if there were 

other MTO participants living in their destination 

neighborhoods. The basic concern is that MTO 

participation by other people in the community could 

modify the impacts of MTO participation on a given 

family, so that the effects of MTO moves might depend 

on the share of other families in the community that also 

participate in the program. If this were the case, then 

our estimates would be relevant only for other mobility 

programs with similar types of interactions among 

residents and compliance rates.

Although this assumption cannot be tested directly, 

some suggestive evidence argues against its practical 

importance in the MTO context. First, most families 

that signed up for MTO were fairly socially isolated at 

baseline: fully 41 percent of household heads reported 

that they had no friends in the neighborhood, and 

65 percent reported that they had no family in the 

neighborhood. Given that around one-quarter of eligible 

families signed up for MTO, and some public housing 

residents would not have been eligible (for example, 

because they did not have children), social interactions 

among MTO families may have been limited. Second, 

the MTO intervention deliberately aimed to avoid 

concentrating MTO families in new neighborhoods (as 

can be seen in exhibits C2.2–C2.6 in Orr et al., 2003). 

In future work, we plan to explore this issue more by 

taking advantage of exact address information that 

included apartment numbers for families at baseline. 

These addresses will allow us to examine whether MTO’s 

impacts vary according to how many other families in 

nearby units were also offered MTO vouchers.

Perhaps the most serious scientific challenge with this 

study concerns its statistical power, which, of course, is 

an issue with almost any feasible social experiment. All 

of the statistical tests presented in this report examine 

whether the 95 percent confidence intervals for our 

estimate of MTO’s impact on some outcome include zero 
261 Chapter 8: Summary and Implications Chapter 8: Summary and Implications

or not. When our confidence interval includes zero, we 

cannot reject the null hypothesis that there is no effect, 

but that does not mean the effect is actually zero. There 

are many estimated impacts that are not statistically 

significant and where we can be confident that MTO 

does not have large effects. However, we sometimes 

cannot rule out moderately-sized impacts. The size of 

the effect that we can rule out varies across outcomes 

depending on the statistical properties of the outcome 

variable itself. For example, for employment rates as 

measured by administrative unemployment insurance 

(UI) records (Exhibit 5.5), our 95 percent confidence 

interval ranges from –10 percent of the control mean 

employment rate to +10 percent of the control mean. 

The 95 percent confidence interval of our estimated 

effect of MTO on housing costs ranged from around 

–10 percent up to +20 percent of the control mean. It is 

important for readers to keep in mind that an effect that 

is “not statistically significant” does not mean its effect is 

necessarily zero.

8.9 IMPLICATIONS FOR SOCIAL 

SCIENCE AND PUBLIC POLICY

In this section, we address the following social science 

and policy questions:

•	 What are the mechanisms through which MTO 

improves long-run health outcomes, and perhaps 

some measures of risky and criminal behavior as 

well? 

•	 Why does MTO not have more pronounced effects 

on labor market or educational outcomes?

•	 What do the MTO results imply for the scientific 

study of “neighborhood effects”?

•	 What do the MTO results imply for housing policy 

in particular and social policy in general?

WHAT ARE THE MECHANISMS THROUGH 

WHICH MTO IMPROVES HEALTH 

OUTCOMES AND PERHAPS RISKY AND 

CRIMINAL BEHAVIOR AS WELL?

MTO’s evaluation design provides a strong platform for 

estimating the causal effects of moving as part of either 

the experimental or Section 8 groups. Determining why 

MTO affects outcomes is more challenging because 

assignment to one of the treatment groups changed 

such a wide range of housing and neighborhood 

attributes for families. One useful step for assessing 

the potential mechanisms of action behind MTO’s 

impacts is to examine those outcomes where MTO did 

generate statistically significant impacts, and consider 

which potentially relevant measures of candidate 

mediating mechanisms were or were not also changed by 

MTO. Unchanged measures of a candidate mediating 

mechanism would indicate that this causal pathway 

was unlikely to be the active ingredient in changing 

outcomes.

For example, one striking feature of the results is that 

MTO seemed to have improved many aspects of health 

without generating detectable changes in many of the 

most obvious mediating pathways. For adults the MTO 

experimental treatment reduced diabetes, extreme 

obesity prevalence, and the risk of having elevated rates 

of C-reactive protein (a predictor of cardiovascular 

disease risk). Yet the experimental treatment did not 

have detectable impacts on diet (recognizing that our 

measures are limited for those health behaviors), or on 

the likelihood of having health insurance, or on other 

measures of access to medical care, and had at best mixed 

impacts on our measures of physical exercise.

It is plausible that MTO improved health by reducing 

psychological and social stress. The baseline MTO 

surveys make clear that concerns about safety were the 

most common motivation for signing up for MTO 

and moving to a new neighborhood. Our follow-up 

surveys show that MTO moves succeeded in improving 

perceptions of neighborhood safety. Medical research 

has shown that stress may affect metabolism and other 

physiological systems (Kuo et al., 2008; Merkin et al., 

2009), even independent of the effects of stress on diet, 

such as the consumption of “comfort foods” that are 

high in fat and sugar. This stress pathway would also 

be consistent with our findings of beneficial MTO 

impacts on a measure of general psychological distress (as 

measured by the “K6” index) and on the prevalence of 

major depression.

Learning more about the mediating mechanisms of 

impacts on health is an important goal for future 
262

research. One way to push the experimental data is to 

examine whether the patterns of MTO health impacts 

are relatively larger in those sites where assignment to 

the treatment group generates relatively larger impacts 

on health or related outcomes, which is the quasiexperimental approach applied by Kling, Liebman and 

Katz (2007) using the interim data.

We also see some hints in the long-term follow-up data 

that the MTO intervention may have reduced drug 

selling among male youth, male grown children, and 

perhaps adults (household heads) as well. Drug selling—

at least outdoor drug selling of the sort that is most 

vulnerable to law enforcement discovery—may be even 

more geographically concentrated across neighborhoods 

than other types of crime in part because of differences 

in the capacity of community residents to control public 

spaces through informal social control or by marshaling 

public sector resources. In addition, selling regularly 

from the same location—usually in economically 

disadvantaged areas—is a way to help solve the market 

coordination problem of buyers and sellers finding one 

another, given that formal advertising is not possible in 

this market (see for example Cook et al., 2007). Why 

we should see more pronounced signs of MTO effects 

on drug selling in the long-term data compared to the 

interim data is not entirely clear, but could be related to 

the large increase in marijuana use among young people 

that occurred over the MTO study period.

The long-term follow-up of MTO participants 

also produced some muted echoes of the same 

gender difference in how youth respond to mobility 

interventions as was found in the interim study (see 

Kling, Ludwig, and Katz, 2005; Kling, Liebman, 

and Katz, 2007). The survey and administrative data 

collected for the interim study, together with subsequent 

qualitative work, raised several hypotheses for this 

gender difference. For example, previous work raised 

the possibility that male youth might spend more of 

their time hanging out in public spaces than do female 

youth, which could put them at elevated risk for getting 

into trouble or into conflict with community residents. 

Male youth who move through MTO might also have 

more difficulty than do female youth in navigating their 

new neighborhood environments, making friends, or 

staying connected to father figures (Clampet-Lundquist 

et al., 2011). Compared to females, male youth may 

also experience a larger decline in their relative academic 

standing as a result of moving through MTO, and have 

more of a comparative advantage in anti-social behavior 

as a means of securing status or resources (Kling, Ludwig, 

and Katz, 2005). While the long-term MTO surveys were 

designed to help test most of these candidate hypotheses, 

we have not yet found the “smoking gun” to definitively 

explain why male and female youth seem to respond 

differently to residential mobility interventions like 

MTO.

WHY DID MTO FAIL TO INFLUENCE 

LABOR MARKET AND EDUCATIONAL 

OUTCOMES?

Given the large body of previous (nonexperimental) 

research suggesting that neighborhood environments 

might play a role in affecting labor market and 

educational outcomes, it is puzzling that our long-term 

follow-up found few detectable beneficial impacts. One 

possible reason is that MTO seems to have had only 

modest impacts on some of the mechanisms through 

which neighborhood environments are thought to affect 

these outcome domains.

For example, much of the previous research on labor 

market outcomes has focused on a “mismatch” in 

geographic proximity to jobs among low-income 

individuals. This theory argues that one reason 

employment rates are so low for many urban minority 

workers is because racial discrimination prevents them 

from relocating to suburbs and other locations where job 

opportunities are becoming more plentiful. Yet MTO 

moves did not seem to have very large impacts on these 

mediating mechanisms. Even at the interim study, there 

was little impact of moves on local job availability as 

measured by employment growth by residential ZIP code 

(Kling, Liebman and Katz, 2007, p. 99). From a social 

science perspective, one implication is that the MTO 

experiment provides a weak test of the spatial mismatch 

hypothesis (Kain, 1968). Yet the fact that MTO 

provides a weak test of the spatial mismatch idea is itself 

informative. Because MTO is at least as intensive as any 

residential mobility program that could be implemented 

at large scale, it suggests that mobility programs might 
263 Chapter 8: Summary and Implications Chapter 8: Summary and Implications

not be the most promising way to overcome any spatial 

mismatch problems and improve access to jobs for 

disadvantaged urban workers.

MTO did seem to have some effect on social connections 

to people who could be good sources of job referrals, 

but the effect of those connections on labor market 

outcomes is unclear. Chapter 2 showed that moving 

via the experimental group increased the likelihood of 

having at least one close friend who graduated from 

college by nearly 15 percentage points (nearly one-third 

of the control group mean of 53 percent), and increased 

the likelihood of having at least one close friend who 

worked full-time by 7 percentage points (around 10 

percent of the control mean of 74 percent; p < .10). Yet 

in the interim survey data, only a very modest share of 

MTO participants reported that they found their jobs 

“through someone living in their neighborhood such as 

a friend, relative or acquaintance” (Kling, Liebman, and 

Katz, 2007). Some of the qualitative work with MTO 

families also suggests that neighbors may not have had 

information about the types of jobs that would be most 

relevant for MTO program participants (Turney et al., 

2006).

With respect to educational outcomes, many parents 

at least seem to believe that the quality of local public 

schools is an important mechanism through which 

neighborhood location may influence children’s academic 

achievement and attainment. Yet MTO had more modest 

impacts on school quality compared to the intervention’s 

impacts on other neighborhood social conditions, 

consistent with the trend of many urban school systems 

to provide families with more “public school choice” 

that enables them to attend schools outside their local 

neighborhoods (see for example Cullen, Jacob, and 

Levitt, 2006). Children assigned to the MTO treatment 

groups wound up attending schools that served students 

who were slightly less likely to have very low test scores, 

be poor, or be members of racial and ethnic minority 

groups compared to the student bodies of the control 

group’s schools. But children in the MTO treatment 

groups still wound up attending schools that were very 

low performing and served overwhelmingly poor and 

majority-minority student populations.

Another question is why the mental and physical health 

impacts in both the interim and long-term follow-ups 

did not translate into improved labor market outcomes, 

given the connection between health and employment, 

or in children’s schooling outcomes, given that improved 

mental health for parents should presumably translate 

into more developmentally productive parenting 

practices. The answer may be that while MTO created 

changes in mental and physical health that are large 

enough to be important for policy, they are not large 

enough by themselves to lead to impacts on educational 

or labor market outcomes that are sizable enough to be 

detected in the MTO data.1

In sum, the challenges that MTO participants face in 

schools and in the labor market do not appear to have 

been caused by the kinds of neighborhood-level risk 

factors that a typical MTO move could alleviate. Perhaps 

these problems arise from individual- or family-level 

risk factors, or perhaps they are caused by neighborhood 

conditions that MTO did not address (for example, 

concentrations of minority neighbors or school quality). 

At any rate, these findings suggest that housing mobility 

programs should be supplemented by more direct 

individual- and family-level interventions to generate 

substantial improvements in schooling, work and 

earnings.

WHAT DO THE RESULTS IMPLY ABOUT 

THE EXISTENCE OF NEIGHBORHOOD 

EFFECTS?

Because MTO is the first true randomized experiment 

that has been carried out to test the effects of residential 

mobility on low-income families, the study is often 

looked to as a key source of information about the 

effects of neighborhood environments on families 

(“neighborhood effects”). It is important to keep in 

1 For example, Ettner, Frank, and Kessler (1997) estimated that mental 

health disorders reduced labor force participation rates by between 14 

and 34 percentage points for adult women. The effect of moving via 

the experimental group on the lifetime prevalence of any mental health 

disorder is around 5 percentage points, which is not quite statistically 

significant (p value of .22). But even if we were confident that this were 

a “real” 5 percentage point impact, not a statistical fluke, a 5 percentage 

point change in prevalence of mental health disorders would imply an 

MTO impact on labor force participation rates of no more than about 

2 percentage points (5 percent times 34 percent). Our estimates for 

MTO’s impacts on labor force participation would not enable us to rule 

out an impact of about this size (see Exhibit 5.7).
264

mind that the MTO “treatment” combines the effects 

of moving per se, changes in housing quality, and 

neighborhood conditions, which in principle make it 

difficult to disentangle the effects of these different 

changes. In practice, we suspect that the effects of 

moving per se may not be such an important determinant 

of differences in outcomes across MTO groups since the 

effects of the extra moves that treatment-group families 

make are likely to dissipate over time. Moreover, the 

control group itself on average moved twice over this 

time period as well, so most families in MTO in all 

three groups have experienced the disruptions of moving 

several times during the study period.

If we do not believe that differences in move rates 

across MTO groups are a key determinant of differences 

in outcomes, to what degree are the impacts that we 

find owing to changes in housing conditions versus 

neighborhood conditions? Some suggestive evidence that 

our results might be driven by changes in neighborhoods 

rather than housing quality comes from the analysis by 

Kling, Liebman and Katz (2007) of the interim data. 

They find that the pattern across MTO demonstration 

sites and randomized treatment groups in effects on 

adult mental health is strongly related to the pattern 

across sites and groups in changes in tract poverty rates 

specifically, much more so than to the site-group pattern 

in MTO impacts on the likelihood of changing housing 

units with a housing voucher.

Also relevant in thinking about what we can learn about 

neighborhood effects from MTO is that the study 

enrolled a particularly disadvantaged group of public 

housing families who volunteered to participate in this 

program. If the effects of MTO-like moves are different 

for different types of families, then MTO’s impacts 

may not tell us what would happen if we changed the 

neighborhoods of other, more representative samples 

of American families. The possibility that MTO-like 

moves might have distinctive effects on different types 

of families also implies that MTO does not necessarily 

inform us about the effects of involuntary moves across 

neighborhoods, as occurs when local housing authorities 

demolish housing projects for example. If families sign 

up for MTO because they believe they have relatively 

more to gain from moving, then MTO findings would 

overstate the effects of an involuntary mobility program.

Finally, some social scientists have reacted with 

surprise to the fact that MTO has not had more 

pronounced impacts on outcomes like employment 

rates or earnings. They have argued that MTO is 

not a fair test of the neighborhood effects hypothesis 

because the demonstration did not generate changes 

in neighborhood conditions that are “large enough” 

(Clampet-Lundquist and Massey, 2008). Of course, 

with any social experiment it is always the case that a 

more intensive intervention can be imagined. MTO 

does not have anything to say about, for example, the 

effects of moving a family from the distressed, crimeridden South Side Chicago neighborhood of Washington 

Park into a condominium in Chicago’s Gold Coast or a 

house in the upscale Chicago suburb of Oak Park. But 

the range of neighborhood variation induced by MTO 

is about as large as what we could possibly imagine any 

feasible housing policy achieving. In fact, it is hard to 

imagine an elected official in contemporary America 

ever pushing to implement anything on a large scale 

that was even remotely like the experimental treatment, 

which required families to move at least initially into a 

low-poverty census tract. Therefore, we think the proper 

interpretation of the neighborhood effects question that 

can be answered by MTO is: What are the effects on the 

outcomes of this very disadvantaged inner-city sample 

of public housing families from helping them make 

moves that change neighborhood conditions by about as 

much as what we could imagine any actual policy ever 

achieving in the real world?2

WHAT DO THE RESULTS IMPLY FOR 

HOUSING POLICY IN PARTICULAR AND 

SOCIAL POLICY GENERALLY?

MTO tells us most about the effects of a modestly 

scaled, voluntary housing-mobility intervention on the 

outcomes of low-income families. MTO offered housing 

vouchers to no more than a few thousand families in 

five of the nation’s largest cities, and so was unlikely to 

2 It is true that the Gautreaux mobility program in Chicago generated even more dramatic changes in neighborhood conditions than the 

MTO results (see Rubinowitz and Rosenbaum, 2000). Similarly large 

changes in neighborhood conditions can be found in a recent Baltimore 

mobility program as well (see DeLuca and Rosenblatt, undated). 

But, crucially, both of these mobility programs were initiated at the 

instigation of judges, rather than elected policymakers. Gautreaux and 

the Baltimore mobility program also differ from MTO in many ways 

with respect to their target populations, which is a separate point from 

the political feasibility for elected officials to push for Gautreaux-like 

mobility programs.
265 Chapter 8: Summary and Implications Chapter 8: Summary and Implications

have had any important effect on the way the overall 

private housing market operated in these cities. A larger 

mobility program might have different impacts on 

families from those estimated in MTO if efforts to move 

relatively more low-income families cause changes in the 

overall private housing market and the options available 

to families, what sociologists often call “system-level 

effects” and economists call “general-equilibrium effects.” 

A larger mobility program could, for example, increase 

rents in the private housing market, at least in the short 

term, or lead to the re-concentration of former public 

housing families in new neighborhoods.

Some additional calculations show that an MTO-type, 

small-scale mobility intervention for disadvantaged 

families may well generate more benefits than costs. 

Our rough estimate is that the costs of the extra 

housing assistance and counseling provided to the 

MTO experimental treatment group might add up to 

around $800 per treatment-group “complier” per year, as 

averaged over the 10 to 15 year follow-up study period.3

 

On the benefit side of the ledger, it seems possible that 

MTO’s impacts on diabetes and depression alone could 

be enough to approximately equal the tangible costs 

of the intervention.4 Whether MTO passes a benefitcost test then would depend on whether any intangible 

costs of the program, in the form of negative spillovers 

of MTO moves on residents of origin or destination 

3 The cost of the housing vouchers themselves in the short term are 

negligible, or even negative, since the alternative is public housing initially for families. Many housing economists believe that public housing 

might be even more expensive than housing vouchers (Olsen, 2003). 

Exhibit 2.4 shows that the effect of experimental moves on the likelihood of receiving any housing assistance 10–15 years after baseline was 

+.05 (not statistically significant). If the average housing subsidy cost 

for families is around $8,000 per year, then the increased persistence on 

housing subsidies might cost $400 per year. The other tangible financial 

cost would be the additional counseling services provided to families, 

which averaged approximately $4,500 per family (Goering et al., 1999, 

Table 4, which we assume is 1995 dollars). If we amortize this over 12 

years of post-study data, on average, this would be approximately $400 

per year per experimental group mover.

4 In Chapter 3, we showed that moving via the experimental group 

reduced the prevalence of diabetes by nearly 11 percentage points. 

Previous research suggests that diabetes increases annual medical 

expenditures by around 240 percent per person, equal to at least $4,500 

per year in 2011 dollars (Trogdon and Hyland, 2008). Therefore, an 11 

percentage point decline in diabetes prevalence would generate benefits 

of approximately $500 per year per MTO adult assigned to the experimental group. In Chapter 4, we found that the effect on depression 

prevalence during the past year is 4.3 percentage points, which is not 

quite statistically significant. A review by Berto et al. (2000) suggests 

the total costs of depression in the United States could be on the order 

of $110 billion per year. If 7 percent of American adults have clinical 

depression in a given year, this implies $7,500 per case in social costs, 

so that the estimated experimental TOT effect on depression implies 

benefits of around $340.

neighborhoods, are outweighed by the intangible benefits 

to MTO families themselves of improved quality of life 

from better housing and neighborhood conditions.

The MTO findings suggest that housing mobility 

programs alone are unlikely to be a panacea for the 

schooling problems and labor market difficulties faced by 

disadvantaged families living in public housing projects 

and other high-poverty, inner-city neighborhoods. 

Policies to increase skills and directly address other 

individual barriers to work remain essential if we are 

to improve the long-term life chances and economic 

self-sufficiency of disadvantaged families living in 

high-poverty areas. Several recent random-assignment 

evaluations indicate that high-quality education, 

training, and employment services programs can improve 

schooling outcomes and produce sustained earnings 

increases for inner-city, disadvantaged youth and adults. 

For example, Success for All, a comprehensive reading 

intervention that involves extra time for reading, 

ability grouping, frequent assessment, and remediation 

(including tutoring), has been found to improve 

reading scores for elementary school children and 

perhaps middle schoolers as well (Borman et al., 2007, 

Chamberlain et al., 2007). The Jobs-Plus demonstration 

produced sustained (seven-year) earnings gains for 

adult, nondisabled public housing residents through 

employment and training services, changes in rent rules 

to increase work incentives, and neighbor-to-neighbor 

outreach centering on work (Riccio, 2010). Sectoral 

employment programs—industry-specific training 

programs to prepare underskilled workers for skilled 

positions and connect them with employers—have 

produced substantial earnings gains for disadvantaged 

adults in large U.S. cities (Maguire et al., 2010). And 

high schools organized as Career Academies integrating 

academic and technical curricula and work-based 

learning opportunities with local employers produce 

sizable long-term (11-year) earnings improvements for 

youth in low-income urban settings (Kemple, 2008).

The MTO results point to the possible value in paying 

increased attention to community-level interventions 

for improving health outcomes of disadvantaged 

families, such as those in MTO, who live in some of 

our most distressed inner-city areas. In principle, the 
266

effects of a community-level intervention that changed 

neighborhood environments without moving families 

could be different from the effects of an MTO-like 

mobility program that moves families into different 

types of neighborhoods. This type of difference in the 

effects of a mobility intervention versus community-level 

intervention could arise if, for example, moving disrupts 

important social ties and social support that would 

otherwise help families take full advantage of changes 

in neighborhood environments. But as a general rule, 

poor families in the United States tend to move often.5 

In practice, the effects of an intervention that changed 

all severely distressed urban neighborhoods in some way 

might wind up being little different from interventions 

that helped very disadvantaged families, such as those in 

MTO, move out of such neighborhoods.

Perhaps the more difficult challenge for drawing 

inferences about community-level interventions from 

MTO is the difficulty of determining which aspects 

of neighborhood environments should be the highest 

priority targets for such programs. The MTO findings do 

not suggest that access to health care or opportunities, 

or neighborhood racial segregation, are the key ways 

in which community environments influence health 

outcomes like diabetes, extreme obesity, psychological 

distress and depression. More likely intervention 

priorities are neighborhood socioeconomic composition, 

informal social control as suggested by the “collective 

efficacy” theory of Sampson, Raudenbush, and Earls 

(1997), and, perhaps most important, the neighborhood 

attribute that the MTO families themselves have cared 

the most about since the beginning—safety.

5 For example, data from the Current Population Survey show that 

between 2002 and 2003, 14 percent of people changed residences; 

the figure was 18 percent for African-Americans and Hispanics, and 

23 to 24 percent for minorities with incomes below the poverty line 

(Schachter, 2004).
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