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Moving to Opportunity 

Interim Impacts Evaluation 

Prepared for: 

U.S. Department of Housing and Urban Development 

Office of Policy Development & Research 

Prepared by 

Larry Orr 

Judith D. Feins 

Robin Jacob 

Erik Beecroft 

Abt Associates Inc. 

Lisa Sanbonmatsu 

Lawrence F. Katz 

Jeffrey B. Liebman 

Jeffrey R. Kling 

National Bureau of Economic Research 

September 2003 
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 or the U.S. Government. 
Table of Contents 

Acknowledgments

Executive Summary .............................................................................................................................. i

Introduction to Moving to Opportunity ........................................................................................ i

Context of MTO.......................................................................................................................... iv

The Interim Evaluation ............................................................................................................... vi

Study Findings on MTO Mobility .............................................................................................vii

Evidence about Short to Mid-Term Effects of MTO .................................................................. ix

Evidence about Longer Term Effects of MTO ........................................................................... xi

Major Conclusions ..................................................................................................................... xv

Policy Implications of the Interim Evaluation Results .............................................................. xv

Chapter One – The Moving to Opportunity Interim Evaluation..................................................... 1

1.1 The Moving to Opportunity Demonstration ...................................................................... 1

1.2 Previous Studies of Mobility Programs and the Effects of Neighborhood........................ 3

1.3 Previous Research on the MTO Demonstration ................................................................ 5

1.4 Research Questions Addressed by the Interim Impact Evaluation.................................... 7

1.5 Using the Experimental Design to Estimate Impacts ...................................................... 10

1.6 Sample and Data Collection for the Interim Evaluation.................................................. 12

1.7 Overview of this Report .................................................................................................. 19

Chapter Two – Geographic Mobility in the MTO Interim Evaluation Sample ........................... 21

2.1 Hypotheses about Mobility in MTO................................................................................ 21

2.2 Mobility Data Sources and Measures .............................................................................. 23

2.3 Baseline Conditions and Initial Leaseups........................................................................ 24

2.4 Sample Mobility in the Followup Period ........................................................................ 27

2.5 Geographic Mobility Impacts .......................................................................................... 40

2.6 Interpretation of Results .................................................................................................. 46

Chapter Three – Impacts on Housing, Neighborhoods, and Safety............................................... 49

3.1 Hypotheses about Housing, Neighborhood, and Safety in MTO .................................... 49

3.2 Data Sources and Measures ............................................................................................. 54

3.3 Baseline Housing and Neighborhood Status of MTO Participants and Control Group 

Context ............................................................................................................................ 55

3.4 Impacts on Housing, Neighborhoods, and Safety ........................................................... 60

3.5 Interpretation of Results .................................................................................................. 68

Chapter Four – Impacts on Adults’ and Children’s Health........................................................... 69

4.1 Hypotheses about Adult and Child Health in MTO ........................................................ 69

4.2 Data Sources and Measures ............................................................................................. 71

4.3 Context and Baseline Status of the Sample ..................................................................... 73

4.4 Mediators for Health Impacts in MTO ............................................................................ 74

4.5 Interim Impacts on Health ............................................................................................... 75

4.6 Interpretation of Results .................................................................................................. 84

Table of Contents 
Chapter Five – Impacts on Delinquency and Risky Behavior Among Youth............................... 85

5.1 Hypotheses about Youth Delinquency and Risky Behavior in MTO.............................. 85

5.2 Data Sources and Measures ............................................................................................. 88

5.3 Context and Baseline Status of the Sample ..................................................................... 89

5.4 Effects on Mediators for Youth Delinquency and Risky Behavior in MTO ................... 90

5.5 Interim Impacts on Youth Delinquency and Risky Behavior.......................................... 91

5.6 Interpretation of Results ................................................................................................ 100

Chapter Six – Impacts on Children’s Education ........................................................................... 101

6.1 Hypotheses about Education in MTO ........................................................................... 101

6.2 Data Sources and Measures ........................................................................................... 105

6.3 Baseline Education Experiences and Control Group Context ....................................... 107

6.4 Impacts on Hypothesized Mediators of Educational Effects in MTO........................... 109

6.5 Impacts on Hypothesized Outcomes ............................................................................. 116

Chapter Seven – Impacts on Employment and Earnings ............................................................. 123

7.1 Hypotheses about Employment and Earnings in MTO ................................................. 123

7.2 Data Sources and Measures ........................................................................................... 125

7.3 Context and Baseline Employment Status of the Sample ............................................. 126

7.4 Impacts on Hypothesized Mediators ............................................................................. 127

7.5 Interim Employment and Earnings Impacts on Adults.................................................. 128

7.6 Interim Employment and Earnings Impacts on Youth .................................................. 132

7.7 Interpretation of Results ................................................................................................ 134

Chapter Eight – Impacts on Income and Receipt of Public Assistance....................................... 135

8.1 Hypotheses about MTO’s Impacts on Public Assistance Receipt and Income ............. 135

8.2 Data Sources and Measures ........................................................................................... 136

8.3 Baseline Income and Public Assistance Status and Control Group Context ................. 137

8.4 Impacts On Hypothesized Mediators ............................................................................ 140

8.5 Interim Impacts on Public Assistance Receipt and Income........................................... 140

8.6 Interpretation of Results ................................................................................................ 147

Chapter Nine – Summary and Implications of the Estimated Impacts of MTO ........................ 149

Summary of Impact Estimates ................................................................................................. 149

Assessing the Impact Estimates ............................................................................................... 155

Implications of the Interim Evaluation Results for Policy ....................................................... 160

References ......................................................................................................................................... 165

Appendix A – Data Collection Sources and Methods 

Appendix B – Samples and Analysis Methods 

Appendix C – Descriptive Tables and Maps 

Appendix D – Detailed Estimation Results – Outcomes by Domain 

Appendix E – Detailed Estimation Results – Mediating Factors 

Appendix F – Tests for Nonresponse Bias 

Appendix G – Assessment of the Size and Significance of the Impact Estimates 

Table of Contents 
Acknowledgments 

This report was prepared under HUD Contract C-OPC-21484 by the MTO Interim Evaluation team of 

Abt Associates Inc., the National Bureau of Economic Research (NBER), and The Urban Institute. 

The authors wish to acknowledge first the unstinting involvement, guidance, and support provided by 

Todd M. Richardson, the Government Technical Representative for the study. We also wish to thank 

Mark Shroder, the Government Technical Monitor, for his substantial input to the study’s design and 

analysis. From the Office of Policy Development and Research, our thanks are also due to Kevin J. 

Neary and to Jeffrey Lubell for ongoing guidance and policy insights. 

Special thanks are due to all the families in the MTO demonstration who have continued to open their 

homes and their lives to us. We particularly value the willingness of teens and younger children to 

become direct contributors to this study. 

The evaluation team had the benefit of technical review and guidance from a number of eminent 

social scientists. We wish to thank Jeanne Brooks-Gunn (Princeton University), Thomas Cook 

(Northwestern University), Greg Duncan (Northwestern University), John M. Goering (CUNY), 

Kristin Moore (Child Trends), Lynn Olson (American Academy of Pediatrics), and Robert Sampson 

(Harvard University) for their insightful comments and suggestions. Any errors contained here are 

our responsibility rather than theirs. 

Susan J. Popkin of The Urban Institute played a vital role in the interim evaluation, leading the 

qualitative study that was the first step in this research. We are grateful for her input to this report. We 

also thank Margery A. Turner of The Urban Institute for her valuable insights and thoughtful 

comments on the shape of the study and the presentation of its results. 

Another of our collaborators, Jens Ludwig of Georgetown University skillfully conducted the analysis 

of arrest record histories in this report. The records were compiled through the steadfast efforts of 

Ludwig and Eric Younger, supported by the National Consortium on Violence Research and by a 

fellowship from the Brookings Institution funded by the Andrew W. Mellon Foundation. 

The MTO Interim Evaluation benefited from the assistance and contributions of many others who 

deserve recognition. At NBER, we wish to thank Alessandra Del Conte Dickovick for her assistance 

throughout this research effort. At The Urban Institute, Laura Harris played a significant role. At Abt 

Associates, we wish to thank Stephen Kennedy for his incisive technical review, Jill Khadduri for her 

policy input, and Barbara Goodson for her design and analysis input. Others who made significant 

contributions to the study and this report are Carissa Climaco, Donna DeMarco, Debi McInnis, Emily 

Finnin, Rhiannon Patterson, and Robert Teitel. Missy Robinson provided coordination and production 

assistance. We also thank Diane Stoner, who managed the data collection, and her survey team. 

In addition to the Department of Housing and Urban Development, a number of other federal 

agencies and several foundations made the wide scope of this study possible by their generosity. This 

research was partially supported with grants to the National Bureau of Economic Research by the 

MacArthur Foundation, the National Institute of Child Health and Human Development and National 

Institute of Mental Health (R01-HD40404 and R01-HD40444), the National Science Foundation 

(SBE-9876337 and BCS-0091854), the Russell Sage Foundation, the Spencer Foundation, the Smith 

Acknowledgments 
Richardson Foundation, the William T. Grant Foundation, and the U.S. Department of Housing and 

Urban Development. 

Additional support was provided by grants to Princeton University from the Robert Wood Johnson 

Foundation and from NICHD (5P30-HD32030 for the Office of Population Research) and the 

Princeton Industrial Relations Section, the Bendheim-Thoman Center for Research on Child 

Wellbeing, the Princeton Center for Health and Wellbeing, and the National Bureau of Economic 

Research. 

Acknowledgments 
Executive Summary 

In 2000, 3.5 million poor people across the United States lived in neighborhoods with poverty 

concentrations in excess of 40 percent. A growing social science literature suggests that such 

concentration has a variety of detrimental effects on the residents of these areas in terms of both their 

current well-being and their future opportunities. The harmful effects of high-poverty areas are 

thought to be especially severe for children whose behavior and prospects may be particularly 

susceptible to a number of neighborhood characteristics, such as peer group influences, school 

quality, and the availability of supervised after school activities. 

Less has been written about whether and how other neighborhood environments exert positive 

influences on behavior and life changes. Ellen and Turner (1997) summarize the literature in this area, 

citing various theories about the mechanisms by which middle-class (often predominantly white) 

neighborhoods shape or reshape the lives of their residents. 

This study reports interim results from a major federal initiative to explore whether living in better 

neighborhoods can improve the lives of low-income parents and children. That initiative is the 

Moving to Opportunity for Fair Housing Demonstration, originally mandated by Congress and 

carried out by the U.S. Department of Housing and Urban Development (HUD). 

Introduction to Moving to Opportunity 

Moving to Opportunity (MTO) was designed to answer questions about what happens when very 

poor families have the chance to move out of subsidized housing in the poorest neighborhoods of five 

very large American cities. MTO was a demonstration program: its unique approach combined 

tenant-based housing vouchers (from the Section 8 program1

) with location restrictions and housing 

counseling. MTO was also a randomized social experiment, carefully designed and rigorously 

implemented to test the effects of this approach on participating families. 

Between 1994 and 1998, the housing authorities in five demonstration sites—Baltimore, Boston, 

Chicago, Los Angeles, and New York—worked in partnership with local nonprofit counseling 

organizations to recruit about 4,600 very low-income families for MTO. The families, all of whom 

lived in public housing or private assisted housing projects in the poorest parts of these cities, 

responded to outreach that offered them a chance to move with housing vouchers from their current 

homes and neighborhoods. Exhibit ES.1 summarizes key facts about demonstration implementation. 

The demonstration sites shared some characteristics, including the presence of large, distressed public 

housing developments in concentrated poverty neighborhoods (where more than 40 percent of the 

population lived below the poverty line). The cities differed in other ways: in the racial and ethnic 

In 1999 the Section 8 program was renamed the Housing Choice Voucher Program. In this report we will 

continue to refer to the program as Section 8, because the rules of the demonstration were set under that 

program. 

Executive Summary i 

1 
composition of their eligible populations and in the nature of their housing markets. Despite these 

differences, the demonstration was implemented with considerable uniformity, particularly with 

respect to recruitment, informed consent of participants, issuance of vouchers, and the rules 

governing their use. Through joint training, central oversight, and regular monitoring and data 

collection, HUD made sure that the procedures developed for MTO were carefully followed. 

EXHIBIT ES.1 

Moving to Opportunity Implementation—Basic Facts 

• Origin—The MTO demonstration was funded by Congress, with $70 million in Section 8 rental 

assistance for fiscal year 1992 (carried over to FY93), with additional vouchers allocated by 

participating housing authorities and with additional funds from the local housing authorities 

and nonprofit counseling agencies. 

• Sites—Baltimore, Boston, Chicago, Los Angeles, and New York City. 

• Family eligibility—Families had to live in public housing or private assisted housing in areas 

of the central cities with very high poverty rates (40 percent or more), have very low incomes, 

and have children under 18 years old. 

• Program size—Among those who applied for the program between June 1994 and July 1998, 

4,608 families were found to be eligible. Of those, 3,169 families were offered vouchers and 

1,676 were able to find a unit and successfully move. 

• Continuous tracking—HUD has been working to keep in touch with the MTO families since 

they joined. In 2002 researchers contacted almost 8,900 adults and children for this study. 

Taking into account a subsample of hard-to-find families, the effective response rate for the 

interim evaluation is 89 percent. 

A key reason for developing special procedures and making sure they were uniformly implemented 

was that MTO was a randomized social experiment as well as a demonstration program. The critical 

feature of MTO’s research design was random assignment of the families who joined the 

demonstration (with their informed consent). Each family was randomly assigned to one of three 

groups: 

• The experimental group was offered housing vouchers that could only be used in low-poverty 

neighborhoods (where less than 10 percent of the population was poor). Local counseling 

agencies helped the experimental group members to find and lease units in qualifying 

neighborhoods. 

• The Section 8 group was offered vouchers according to the regular rules and services of the 

Section 8 program at that time, with no geographical restriction and no special assistance. 

• Finally, control group members were not offered vouchers but continued to live in public 

housing or receive other project-based housing assistance. 

ii Executive Summary 
To use their vouchers, families assigned to the experimental group had to move to low-poverty areas. 

Those in the Section 8 group could use their vouchers to move to neighborhoods of their own 

choosing. Both groups were required to make these moves within a limited amount of time. In order 

to retain their vouchers, experimental families were required to stay in low-poverty areas for one year, 

after which they could move without locational constraints. 

Exhibit ES.2 summarizes the key features of MTO’s research design. Random assignment makes the 

three groups of participating families statistically the same, so that any later significant differences 

(differences greater than chance would produce) in the neighborhoods, housing, employment, or other 

aspects of the experimental group’s lives in comparison with the control group can be attributed to the 

MTO intervention. Of course, such differences should only be attributed to MTO if there are social 

scientific hypotheses suggesting that changing location can influence these outcomes. And in fact, a 

considerable theoretical foundation does exist for the MTO experiment (as described below). 

EXHIBIT ES.2 

MTO Experimental Design—Basic Facts 

• Research objective—to test the long-term effects on adult and child well-being when families 

move from public or project-based assisted housing in very poor areas to private-market rental 

housing in areas with much lower poverty rates. 

• Experimental design—random assignment of the families who joined the program to one of 

three groups: 

− an experimental group, which received Section 8 vouchers useable only in low-poverty 

areas (census tracts with less than 10 percent of the population below the poverty line in 

1990), along with counseling and assistance in finding a private rental unit. 

− a Section 8 group, which received regular vouchers (geographically unrestricted) and 

whatever briefing and assistance the local Section 8 program regularly provided. 

− a control group, which received no vouchers but continued receiving project-based 

assistance. 

• Longitudinal study—By following the families over a period of about 10 years, collecting data 

on various aspects of the adults’ and children’s lives, and comparing the experiences of each 

treatment group to that of the control group, the experiment would permit answers to these vital 

questions: 

− What are the impacts of joining the MTO demonstration on household location and on the 

housing and neighborhood conditions of the participants? 

− What are the impacts of moving to a low-poverty neighborhood on the employment, 

income, education, health, and social well-being of family members? 

MTO eligibility was targeted to residents of project-based subsidized housing in neighborhoods with 

poverty rates of 40 percent or more. The mean poverty rate of baseline locations was, in fact, much 

higher at 56 percent. And a substantial proportion of MTO families were living in severely distressed 

Executive Summary iii 
public housing when they joined, including a number of the earliest developments to be demolished 

under the HOPE VI program. 

After random assignment, members of the experimental group received their geographically restricted 

vouchers and worked with the local nonprofit counseling agencies to prepare for and conduct their 

housing searches in low-poverty areas. Just under half of the experimental group families moved to 

low-poverty areas with MTO vouchers. Families in the Section 8 group received their regular 

vouchers and housing authority briefings and assistance and then searched for housing on their own. 

Just over 60 percent of this group was able to use the MTO vouchers, which required moving to other 

housing but without the restriction to low-poverty areas. After random assignment, members of the 

control group continued to live in their project-based subsidized housing in these areas of great 

poverty. The nonmovers in both the experimental and Section 8 groups also initially remained in their 

baseline public or assisted housing units. 

Despite its unique aspects, the MTO experiment can tell us a great deal about HUD’s main current 

housing programs. While not representative of public housing nationwide, the conditions of distress 

and concentrated poverty where the families were living when they joined MTO were not uncommon 

in big city public housing across the country. By offering tenant-based subsidies (vouchers) to such 

families, MTO provides a test of what difference it might make to switch very low-income families 

from place-based to mobile subsidies. At the present time, these are the major forms of low-income 

rental assistance with about 1.1 million families and individuals living in public housing, 1.5 million 

households in privately owned assisted projects, and 1.8 million households using vouchers. By 

constraining the experimental group to move to low-poverty communities, MTO was testing whether 

vouchers can be a vehicle for substantial changes in neighborhood environment. If the long-term 

results of MTO research show significant improvements in the well-being and life chances of 

experimental group members, we will have learned that housing vouchers can provide access to 

meaningful opportunities for poor families. 

Of course, policies designed to move low-income families from public housing in high-poverty areas 

to private housing in low-poverty areas can take forms other than the location-restricted vouchers 

used in MTO. Mobility counseling or other supports for moving to low-poverty areas could be 

incorporated into the regular voucher program. HUD could create goals and performance incentives 

for program administrators to encourage moves to opportunity areas, and both assisted and affordable 

housing in low-poverty areas can be created or preserved through decisions with respect to state 

agency refinancing policies, allocations of low-income housing tax credits, use of HOME funds, 

public housing authority (PHA) project basing of vouchers, and other existing housing programs and 

policies. 

Context of MTO 

Policy and social science background 

Recent interest in geographic location and mobility as important factors shaping the futures of lowincome families began with the results of the Gautreaux Program, a federal court-ordered racial 

desegregation program in Chicago. Under the name of tenant activist Dorothy Gautreaux, applicants 

iv Executive Summary 
and residents of Chicago public housing brought a class-action housing segregation lawsuit against 

HUD and the Chicago Housing Authority (CHA) in 1966. The courts ordered HUD and CHA to 

remedy the extreme racial segregation they had imposed on public housing applicants and residents 

by providing (among other remedies) a housing mobility option throughout the Chicago region for 

about 7,100 black families. 

This option became known as the Gautreaux Program, which took shape in the late 1970s. 

Participating families were helped to move out of racially isolated areas through the (then new) 

tenant-based Section 8 program. Families chosen for the Gautreaux program received Section 8 

certificates2

 that required them to move either to predominantly white or racially mixed 

neighborhoods. They also received assistance from housing counselors to make these moves. 

Beginning in the late 1980s, research on the Gautreaux Program suggested that, over time, the moves 

to less segregated suburban locations were associated with measurable improvements in the lives of 

participating adults and children. Researchers found that suburban movers were more likely to have 

been employed than city movers. Positive changes were also reported for small samples of children 

who had been living in less segregated neighborhoods. Although they had initially experienced 

declines in school performance, in the long run (7 to 10 years) such children were less likely to drop 

out of school and were more likely to take college-track classes than their peers in a comparison 

group who moved to city neighborhoods, which were both poorer and more racially segregated than 

the suburban locations. After graduating from high school, the Gautreaux children were also more 

likely than their city peers to attend a 4-year college or become employed full-time. 

At roughly the same time, several influential studies were drawing attention to the increasing 

concentration of poverty and the harm done to residents of high-poverty areas, in terms of both their 

current well-being and their future opportunities. The Gautreaux research excited great interest in 

both social scientific and policy circles because it seemed to suggest that there were remedies to the 

damaging effects of life in concentrated poverty neighborhoods. Yet the Gautreaux findings were 

limited by the fact that the causal link between the new residential locations and the improvements 

was not certain: The observed differences might reflect differences between the kinds of people who 

moved to the suburbs through Gautreaux and those who moved within the city rather than reflecting 

the effects of the different residential locations. Because this was a nonexperimental comparison of 

families who moved to different types of neighborhoods, there was a serious risk of selection bias in 

drawing conclusions from such a comparison. 

MTO was designed to be the experiment that directly and rigorously tests whether moves to lowpoverty areas can bring about positive changes in the lives of poor families. Because families in MTO 

were randomly assigned, the three groups started out comparable by definition. And as long as 

comparisons made thereafter are based on the three groups as a whole (all their members, not just 

movers), the risk of selection bias is eliminated. 

The form of the voucher program current at that time. 

Executive Summary v 

2 
Prior studies of MTO 

Research on MTO began while the operational phase of the demonstration was still under way. HUD 

issued a first report to Congress once all the sites had begun enrolling and counseling families. 

Observations and analyses of the counseling delivered to experimental group families through MTO 

were documented about midway through the operations period. When enrollment and lease-up ended 

in 1999, HUD reported initial findings about the participating families and the program moves made 

by experimental and Section 8 group families. 

In 1997 HUD’s Office of Policy Development and Research conducted an MTO grant competition 

and ultimately made eight small awards to teams of researchers with varied topics and approaches. 

Each team was given access to the MTO participants in one of the five sites for purposes of assessing 

different aspects of the families’ early experiences there. The small grant research results suggested 

that the demonstration might well be having—at least in the short term—impacts on such dimensions 

as health, safety, delinquency patterns, and educational outcomes. The early studies did not find any 

employment or other economic effects. 

An important contribution of this research was to suggest the appropriate breadth of a full-scale 

evaluation. But because the timing of program entry extended from 1994 to 1998 and because each 

study was done in a single site, the small grant research needed to be followed by more 

comprehensive and uniform research when more time had elapsed for the families in the program. It 

was clear that the MTO design and sample could be used to learn about a wide range of topics. It was 

equally clear that many questions remained to be answered. 

The Interim Evaluation 

The present study—the MTO interim evaluation—was designed to examine MTO’s impacts at about 

the midpoint of the 10-year research period originally mandated by Congress. A final impact 

evaluation will be conducted approximately a decade after the end of program operations. This 

interim research does not utilize the entire MTO program population because the families that joined 

MTO in 1998 (and in some cases did not move until early 1999) had less than 4 years exposure to the 

program after random assignment. The final evaluation will include the entire set of families in MTO. 

The interim evaluation has two major components, one using qualitative methods and the other using 

quantitative methods, to assess MTO’s effects in six study domains: 

1. Mobility, housing, and neighborhood 

2. Adult and child physical and mental health. 

3. Child educational achievement. 

4. Youth delinquency and risky behavior. 

5. Adult and youth employment and earnings. 

6. Household income and public assistance receipt. 

vi Executive Summary 
The main goals of the qualitative research were to help enrich our understanding of how 

neighborhood affects families, to help illuminate the mechanisms that underlie such effects, and to 

assist in the interpretation of the quantitative findings from the analysis of the survey and 

administrative data. 

The central quantitative objective is to estimate the impacts of the housing vouchers received by the 

experimental and Section 8 groups—after 4 to 7 years—on a wide range of outcomes across the six 

domains. MTO’s random assignment design ensures that the measured differences can be attributed 

to the demonstration intervention and not to differences in the families’ characteristics or motivation. 

However, it is certainly too soon to conclude that the absence of significant differences in one or 

more domains means MTO had no impact. In its timing, this study is directed at relatively short-term 

or midterm effects, those most immediately associated with changes in residential location. The final 

evaluation (after 10 years) may show that the midterm effects have (or have not) endured. And it may 

detect additional effects that took longer to appear. 

The questions addressed in this interim evaluation are of great importance. To what extent are the 

adverse outcomes associated with living in very poor neighborhoods the products of the 

neighborhoods rather than of the characteristics of those living there? If the adverse outcomes are 

products of the neighborhoods, to what extent do opportunity moves to areas with minimal poverty 

offer a means of ameliorating them? If public housing residents are given unrestricted tenant-based 

housing assistance, do they make locational choices that afford them access to some or all of the same 

life improvements as opportunity moves? 

But MTO can teach us even more. They also offer a perspective on the importance of creating or 

preserving assisted housing in low-poverty locations. This latter point is relevant to quite a number of 

current housing policy issues and initiatives affecting new and existing private project-based assisted 

housing: mark to market, mark-up-to-market, state agency refinancing policies, allocation of Low 

Income Housing Tax Credits, use of HOME funds, and PHA project basing of vouchers. The MTO 

research results addressing these important questions could help inform social policy in the United 

States for years to come. 

Study Findings on MTO Mobility 

The move out of public housing into a low-poverty neighborhood is intended to expose the 

experimental group to an environment that might improve life chances. The move to private market 

housing—whatever the neighborhood—is intended to expose the Section 8 group to an environment 

that might also alter future paths, as compared to the lives of those who remain, at least initially, in 

project-based public or assisted housing in high-poverty areas. 

Families in all three groups may have moved since random assignment. These moves could result 

from changes in peoples' lives related to MTO—such as increased employment and earnings—and 

they could in turn affect the outcomes in other areas such as education or housing assistance. Thus, it 

was important to examine both initial and subsequent moves as they relate to the outcomes of interest 

to this study. 

Executive Summary vii 
In this section, we first present estimated impacts for the entire experimental group or Section 8 group 

randomly assigned, including those who did not lease up, and then show the corresponding findings 

for the families who did move with program vouchers. The former estimates show the effect of the 

demonstration on the entire group offered vouchers, the latter on those who actually experienced a 

program-induced change in residential location. 

We found that MTO had substantial, positive effects on the mobility of families in the experimental 

and Section 8 groups and on the characteristics of the neighborhoods in which they lived. Almost half 

of the families assigned to the experimental group leased up with program vouchers, as did more than 

three-fifths of the families in the Section 8 group (Exhibit 2.1). To use the voucher, experimental 

group families were required to move to census tracts with poverty rates below 10 percent in the 1990 

Census. Because many moved to neighborhoods where the poverty rate was increasing between 1990 

and 2000, we estimate that only about half of their destinations had poverty rates below 10 percent at 

the time of the move, although virtually all had rates below 20 percent (Exhibit 2.3). Among the 

Section 8 group, who could use the voucher anywhere they could find housing that met Section 8 

quality standards (with a rent they could afford and a willing landlord), fewer than 30 percent of those 

who moved with program vouchers moved to census tracts with poverty rates below 20 percent, 

although the overwhelming majority moved to neighborhoods with lower poverty rates than the areas 

where they had lived in public housing. 

As noted earlier, the experimental families were only constrained to live in low-poverty areas for one 

year. By the time of the interim evaluation, these differentials in poverty rates had narrowed 

somewhat, in part because of subsequent moves by the experimental families and in part because of 

changes over time in neighborhood poverty rates, but they had by no means disappeared. Among 

those who moved with program vouchers, 60 percent of the experimental group families were still in 

census tracts with poverty rates below 20 percent, while 30 percent of the Section 8 families were in 

such tracts (Exhibit 2.5). The treatment-control differentials had narrowed as well, in part as a result 

of changes in the poverty rates of the neighborhoods where treatment group families resided but also 

because over two-thirds of the control group families had moved (either on their own or in connection 

with public housing demolition or redevelopment—e.g., as part of the HOPE VI program). By the 

time of the interim evaluation, about 17 percent of the control group families lived in census tracts 

with poverty rates below 20 percent, and just over half lived in tracts with rates below 40 percent. 

It is noteworthy that even those families who moved to low-poverty areas did not necessarily move to 

predominantly white or racially integrated areas. Among families in the Section 8 group, at the time 

of the interim evaluation over three quarters of both those who moved with program vouchers and 

those who did not were living in census tracts that were over 80 percent minority, about the same 

proportion as among control group families (Exhibit 2.6). Among experimental families, 60 percent 

of those who moved with program vouchers were in heavily minority areas. For minority families in 

the experimental group who moved with program vouchers, the experiment reduced the average 

percent minority in their neighborhood by less than 10 percentage points. There was no significant 

effect on this measure for Section 8 families (Exhibit 2.8). 

These mobility patterns resulted in a number of significant improvements in the environment in 

which experimental group families lived and lesser improvements for Section 8 group families. At the 

time of the interim evaluation, experimental group families who moved with program vouchers lived 

in neighborhoods with higher proportions of adults employed, substantially higher proportions of 

viii Executive Summary 
two-parent families and high school graduates, and nearly twice the rate of homeownership as in the 

neighborhoods where they would have lived absent the demonstration, as evidenced by where the 

controls lived (Exhibit 2.10). Section 8 group families who moved with program vouchers also saw 

significant gains in these neighborhood attributes, but those gains were generally only about half as 

large as those experienced by experimental group families. 

These changes in the neighborhood environment substantially increased the chances that adults in 

experimental group families would have college educated friends or friends earning $30,000 or more 

(Exhibit 2.10). There was no significant effect on these outcomes for adults in Section 8 families, who 

lived in somewhat higher poverty areas than the families in the experimental group. 

Evidence about Short to Mid-Term Effects of MTO 

Among the expected impacts of the MTO demonstration, some might occur in the short term (1 to 3 

years), others in the middle term (perhaps 5 to 6 years), while still others would not be expected to 

occur until more time had passed for the people in the program. We expected short- to midterm 

effects on housing, neighborhood, safety, health, and delinquency (based on the earlier MTO 

research). 

Improved housing, neighborhood conditions, and safety 

The families who moved with program vouchers markedly improved their neighborhood conditions, 

reporting large reductions in the presence of litter, trash, graffiti, abandoned buildings, people 

“hanging around,” and public drinking, relative to the control group (Exhibit 3.5). They also reported 

that they had less difficulty getting police to respond to their calls. The proportion of families who 

expressed satisfaction with their current neighborhoods was much higher in both treatment groups 

than in the control group. On every one of these measures, the proportion of the experimental group 

reporting improved conditions was about 10 percentage points larger among the Section 8 group. 

Perhaps most notable from the perspective of the families themselves is the fact that they were 

successful in achieving the goal that loomed largest in their motivation to move out of their old 

neighborhoods: improvements in safety. The adults reported substantial increases in their perception 

of safety in and around their homes and large reductions in the likelihood of observing or being 

victims of crime (Exhibit 3.5). These gains were greater for the experimental group families, but they 

were still substantial for those in the Section 8 group who moved with program vouchers. 

MTO substantially improved the quality of housing occupied by the families who moved with 

program vouchers. A markedly higher proportion of adults voiced satisfaction with their housing at 

the time of the interim evaluation, compared to the control group—21 percent more for the 

experimental group adults and 12 percent more for the Section 8 group adults (Exhibit 3.5). MTO 

also increased somewhat the proportion of families receiving housing subsidies, while substantially 

reducing the fraction living in public housing (Exhibit 3.4). However, some of this effect was 

probably due to the impacts of HOPE VI and Vacancy Consolidation on a number of the 

developments where the control group lived during the period since random assignment. 

Executive Summary ix 
In sum, the MTO demonstration succeeded in substantially improving the housing and residential 

environments of the families who moved with program vouchers on a wide range of measures. While 

these improvements were greater for the experimental group, who were constrained to move to lowpoverty areas at least initially, the Section 8 group also experienced sizeable improvements in housing 

and neighborhood environment relative to the control group. 

Improvements in adult and child health 

Urban residents of high-poverty neighborhoods are likely to have high incidences of health problems. 

The high rates of activity limitations, asthma, high blood pressure, obesity, psychological distress, 

depression, and anxiety observed in the control group at the time of the interim evaluation bear out 

this expectation (Exhibit 4.2). 

Estimation of MTO’s impacts on these outcomes and on measures of smoking, drinking, and general 

physical health revealed one significant impact on adults’ physical health: a large reduction in the 

incidence of obesity among both experimental and Section 8 families (Exhibit 4.2). There were also 

improvements in mental health among adults in the experimental group families: a reduction in 

psychological distress, a reduction in depression (statistically significant on one measure of 

depression though not on the other), and an increase in feelings of calm and peacefulness. There were 

no significant mental health improvements among those in the Section 8 group and there were no 

significant effects on the other adult health measures in the interim evaluation among those in either 

the experimental or Section 8 group. 

Among children, the significant effects of MTO on health were confined to mental health measures— 

a moderately large reduction in psychological distress for girls in the experimental group; a 

substantial decrease in the incidence of depression among girls in the Section 8 group; and very large 

reductions in the incidence of generalized anxiety disorder among girls in both treatment groups 

(Exhibit 4.5). These findings of significant impacts on measures of mental health, for both adults and 

children, are consistent with the improvements in the families’ perceptions of personal safety 

discussed above. 

Mixed effects on youth delinquency and risky behavior 

At baseline, when the children who were ages 15 to 19 at the time of the interim evaluation were ages 

8 to 15, significant proportions had already exhibited problem behavior or been suspended from 

school. By the time of the interim evaluation, among youth in this age range, 24 percent of the girls 

and 39 percent of the boys in the control group had been arrested—half of them for violent crimes 

(Exhibit 5.3). 

In the interim evaluation, survey data from parents and from the youth themselves were used to 

measure a number of delinquent, risky, and problem behaviors. The youth were also asked whether 

they had ever been arrested. In addition, administrative data from the criminal justice system were 

used to measure the number of arrests for specific crimes. 

For both boys and girls in the experimental and Section 8 groups, there were no significant effects on 

either an index of 15 problem behaviors reported by parents or on a narrower index of self-reported 

x Executive Summary 
delinquent behaviors related to criminal behavior (Exhibit 5.2). However, there were significant 

increases in self-reported behavior problems among boys ages 12 to 19, in both treatment groups. 

Participation in MTO resulted in a large reduction in the proportion of girls ages 15 to 19 in the 

Section 8 group who had ever been arrested for violent crimes (Exhibit 5.3). This effect contributed to 

a significant reduction in the frequency of arrests for violent crimes for all youth (Exhibit 5.4). There 

were no effects on the incidence of arrests for other crimes for girls. The only effects on arrests for 

boys were very substantial increases in the proportion ever arrested and the frequency of arrests for 

property crimes in the experimental group (Exhibits 5.3 and 5.4). This increase in arrests might reflect 

more stringent policing in new locations, rather than (or in addition to) more criminal behavior. 

For girls ages 15 to 19 in the experimental group, but not for those in the Section 8 group, there were 

reductions in risky behavior, concentrated in marijuana use and smoking. Among boys in this age 

range in both treatment groups there were significant increases in smoking, but not in other types of 

risky behavior (Exhibit 5.5). 

This pattern of gender differences in effects—positive for girls and negative for boys—suggests that 

boys and girls react differently to the disruption of moving and the challenge of integrating into a new 

environment. However, the available results do not allow us to say specifically why this is the case. 

To the extent that this difference reflects a response to the transition from a high-poverty environment 

to a lower poverty environment, one might expect this pattern to be different in the longer term for 

youths who have completed that transition or who have grown up in the new environment. 

Evidence about Longer Term Effects of MTO 

In the hypotheses generated about MTO effects, it was expected that impacts in several important 

domains would take longer than 4 to 7 years to become evident. These domains were education, 

employment, and economic self-sufficiency (an end to public assistance receipt). The Gautreaux 

research suggested that children moving to schools with very different characteristics might show 

achievement losses in the short run even though in the longer run they would catch up with their new 

schoolmates. Evidence is lacking about the time path of neighborhood effects on changes in economic 

self-sufficiency due to the absence of prior long-term research. 

It is important to recognize that the control group—the benchmark against which we measure the 

effects of MTO—has not been static in the period since random assignment. For example, between 

1995 and 2001 the employment rate among sample adults more than doubled, from 24 to 51 percent, 

and welfare receipt rates declined by more than half. Many control families moved out of public 

housing; as a result, at the time of the interim evaluation the average poverty rate in the 

neighborhoods where controls lived was 15 percentage points lower than it had been at baseline. In 

part, these improvements in the lives of controls represent natural turnover in welfare caseloads and 

the labor market. In addition, powerful external forces were at work during this period. The welfare 

system changes implemented in the mid-1990s (the shift from Aids to Families with Dependent 

Children (AFDC) to Temporary Assistances for Needy Families (TANF) and the advent of time 

limits) had substantial effects on nearly all low-income families. And the sustained economic boom of 

the 1990s offered increased opportunities to MTO families regardless of their group assignment. By 

Executive Summary xi 
improving conditions for control group members, these powerful external forces could make it less 

likely that MTO would show significant impacts on employment and earnings relative to the control 

group. 

Small impacts on children’s education 

For the interim evaluation, education research focused on children ages 5 to 19 at the time the data 

were collected. We interviewed parents about the school-related attitudes, behaviors, and 

performance of all children in the sample. We interviewed children ages 8 to 19 about their own 

views and experiences in school. We also administered four different achievement tests from the 

Woodcock-Johnson Battery-Revised to sample children and collected data from published sources 

about the schools the children attended over the period since random assignment. 

MTO had significant but small effects on the characteristics of the schools sample children attended 

(Exhibit 6.3). Experimental group children attended schools with somewhat lower percentages of 

poor and minority children and of students with limited English proficiency than they would have in 

the absence of the demonstration. The schools attended by experimental group children were ranked 

marginally higher on state exams than the schools attended by control students, but were less likely to 

be magnet schools. All of these differences were relatively small. For example, the schools attended 

by those who moved with program vouchers were only at about the 25th percentile on state exams, as 

compared with the 17th percentile for the schools attended by controls at the time of the interim 

evaluation. MTO had no significant effect on the teacher-pupil ratio. 

Among the children in the Section 8 group, participation in MTO reduced the schools’ percentages of 

minority and poor (exhibit 6.3). There were no other significant effects on the schools attended by 

children in the Section 8 group at the time of the interim evaluation, although the average ranking of 

schools attended by children in that group over the course of the followup period was slightly higher 

than that of the schools attended by control children. All of these effects were smaller than those on 

the schools of experimental group children. 

These relatively modest impacts on school characteristics reflect the fact that, at the time of the 

interim evaluation, nearly three quarters of the children in families in the experimental group who 

leased up with program vouchers were attending schools in the same school district they were in at 

baseline. This may be because, as suggested in the MTO qualitative analysis, some children did not 

change schools when their families moved or because the families did not move very far. In 

particular, many families remained within the same big city school districts where they lived at 

baseline. 

Not surprisingly, given the small impact on school characteristics, the demonstration had virtually no 

significant effects on any of the measures of educational performance analyzed, for either the 

experimental group or the Section 8 group (Exhibits 6.5–6.7). Of the 58 outcomes analyzed, there 

were significant impacts on only two: the Woodcock-Johnson calculation score for all children in the 

Section 8 group and the broad math score for children ages 8 to 11in the Section 8 group. 

xii Executive Summary 
Impacts on economic well-being 

Data on employment, earnings, household income, and public assistance were obtained from both 

administrative records and the interim survey. Administrative data provided a continuous history of 

employment, earnings, and AFDC/TANF and food stamp benefits from random assignment through 

the time of the interim evaluation. Survey data provided measures of employment, earnings, unearned 

income, receipt of SSI and Medicaid, and food security in 2001. 

No effects on employment or earnings 

At baseline, only about a quarter of all MTO adults were working. This proportion more than doubled 

over the followup period for both treatment and control group members. But the only statistically 

significant treatment-control difference in any of the measures of adult employment or earnings 

analyzed was a slight reduction in the employment rate in the first two years after random assignment 

among adults in the experimental group (Exhibits 7.3–7.4). 

Although there were no statistically significant impacts on the employment or earnings of youth, 

either overall or by gender (Exhibit 7.4), there was a large reduction in the proportion of female youth 

working and not in school, with a concomitant (though not statistically significant) increase in the 

proportion attending school (Exhibit D7.1). Consistent with these findings, girls in the treatment 

groups perceived their chances of going to college and getting a well paying, stable job as much 

higher than their control counterparts (Exhibit E6.4). These findings are also consistent with the 

positive effects on girls’ mental health and criminal behavior reported above. 

No impacts on receipt of public assistance 

At the time they were randomly assigned, the MTO adult sample members had very high rates of 

public assistance receipt and average incomes well below the poverty line. About three-fourths of the 

sample members were receiving AFDC at baseline, and four out of five were receiving food stamps 

(Exhibit 8.2). Further, nearly all sample adults (94 percent) had received AFDC at some point. 

Average income was about $9,300 at baseline, well below the poverty line for a family of three. 

Median income was still lower, approximately $7,800. These results show that sample members were 

quite disadvantaged when they entered the MTO demonstration. 

Four to seven years later, the AFDC/TANF receipt rates had fallen by half across the entire sample. 

Less than 30 percent were receiving welfare benefits, although 46 percent were still receiving food 

stamps. Forty-five percent of the sample adults were working and off TANF in 2001. These figures 

did not differ among the randomly assigned groups. The only significant impacts of MTO on receipt 

of transfer payments were small increases in the receipt and amount of AFDC/TANF and/or food 

stamp benefits during portions of the followup period for each group (exhibits 8.4-8.7). 

At the time of the interim evaluation survey, average household income was about $15,500. Twothirds of the sample had incomes below the poverty level, and half of these households had incomes 

below 50 percent of the poverty level. Some 11 percent of the sample households had experienced 

food insecurity with hunger in the previous 6 months. Participation in MTO did not affect incomes or 

Executive Summary xiii 
food security, as there were no significant differences in these outcome measures between either of 

the treatment groups and the control group (Exhibit 8.8). 

Hypotheses about long-term effects 

There are a number of reasons to expect that observing the MTO population over a longer period of 

time may reveal significant program impacts in domains with no midterm effects. For example, the 

Gautreaux Program research suggested that children would need a prolonged period in better schools 

before making up prior deficits and moving ahead. Rosenbaum (1991) found that 1 to 6 years after 

their families moved to the suburbs many children “were still struggling to catch up, and it was not 

clear if they would succeed.” But 7 years later, he found substantial, statistically significant impacts 

on eight of nine education- and employment-related outcomes for the same children. 

There are strong theoretical reasons why it may take many years for the full effects of neighborhood 

to manifest themselves. Developmental outcomes such as educational performance almost certainly 

reflect the cumulative experience of the child from an early age. Children who spend their first ten 

years in an environment that does not facilitate educational achievement may never fully overcome 

that disadvantage, even if they then move to an environment that supports educational achievement. 

On the other hand, if a safer neighborhood and exposure to more educated adults affects long-term 

educational outcomes, we may yet see some educational effects. 

In the interim evaluation, the youth sample is composed of children who moved out of public housing 

at ages 5 to 15. In the final evaluation, the youth sample will have left public housing at ages birth to 

10. Those youth will have spent a much larger proportion of their formative years outside the 

concentrated poverty of public housing. Therefore, they may show much greater gains in educational 

achievement and other developmental outcomes. 

It is also true that the move from high-poverty areas to lower poverty neighborhoods is likely to be 

disruptive and require some adjustment period, during which positive behavioral effects may not 

appear and, in fact, negative effects may be observed. If these effects indicate that the first 4 to 7 

years after random assignment has been an adjustment period for these youth, we may observe 

different impacts in the longer term, once that transition is complete. 

We cannot, of course, predict the impacts that will be observed 5 years after our data were collected. 

We can, however, examine the interim findings for evidence that impacts are related to time since 

random assignment. The most direct evidence on this question is provided by the time path of impacts 

on those outcomes for which we have longitudinal data over the entire followup period—the 

employment, earnings, and public assistance outcomes measured with administrative data. Examining 

the impacts in years 1 to 2 and years 3 to 4 after random assignment for each of the main outcomes 

measured with these data (Exhibit G.6), we found at least modest evidence of increasingly favorable 

effects over time. 

xiv Executive Summary 
Major Conclusions 

Assessment of results 

A summary assessment of the findings presented in this report and the impact estimates described 

above suggests that: 

• The findings do provide convincing evidence that MTO had real effects on the lives of 

participating families in the domain of housing conditions and assistance and on the 

characteristics of the schools attended by their children; 

• There is no convincing evidence of effects on educational performance; employment and 

earnings; or household income, food security, and self-sufficiency. 

• The statistically significant impact estimates are uniformly large enough to be relevant for policy. 

Many are, in fact, quite large. 

• Given the size of the interim evaluation sample and the leaseup rates in the two treatment groups, 

the impact estimates are sufficiently imprecise that some true impacts that are large enough to be 

relevant for policy may not have been detected as statistically significant. 

• Although MTO induced substantial differences in the proportion of time spent in low-poverty 

areas by the three assignment groups, it was not a pure test of the effects of living in low-poverty 

areas compared to living in public housing in high-poverty areas, even for the families in the 

experimental group who moved with program vouchers. Extrapolating the effect of living 

continuously in low-poverty areas might show them to be more substantial than those observed in 

the demonstration. However, our ability to measure those effects quantitatively is limited. 

• There is at least modest evidence that the impacts of the demonstration are becoming more 

favorable over time, at least for public assistance, which was the only outcome for which we were 

able to estimates effects over time. If this holds for other outcomes, we might expect more and 

larger impacts in the final evaluation, 10 years after random assignment. 

Policy Implications of the Interim Evaluation Results 

The interim findings allow us to address three fundamental questions related to policy with respect to 

low-income families in public housing: 

• What social benefits and costs accrue as a result of moving low-income families out of public 

housing projects in high-poverty areas into private housing, and how do those benefits differ 

between policies that restrict such moves to low-poverty areas and those that do not? 

• How effective is policy likely to be in changing the environment of low-income families? 

Executive Summary xv 
• What do the interim results have to say about alternative approaches to improving the lives of 

low-income families? 

The social benefits and costs of moving low-income families out of public housing in distressed 

neighborhoods into private housing 

Although we have not attempted to conduct a formal cost-benefit analysis, the interim evaluation 

results provide relatively clear evidence of the main social benefits and costs of MTO. From the 

families’ perspectives, the principal benefit of the move was a substantial improvement in housing 

and neighborhood conditions. Families who moved with program vouchers largely achieved the 

single objective that loomed largest for them at baseline: living in a home and neighborhood where 

they and their children could feel and be safe from crime and violence. On a list of observable 

characteristics, their homes and neighborhoods were substantially more desirable than those where 

control group members lived. These benefits accrued to families in both the experimental group and 

the Section 8 group, although the improvements tended to be roughly twice as large for experimental 

group families, who were required to move to low-poverty areas, at least initially. 

Perhaps not surprisingly, these improvements in living environment led to significant gains in mental 

health among adults in the experimental group. The levels of psychological distress and depression 

were substantially reduced in this group. In addition, adults in both the experimental and Section 8 

groups experienced substantial reductions in obesity for reasons we do not yet understand. 

Among the children in these families, girls appear to have benefited from the move in several ways. 

They experienced improved psychological well-being, reporting lower rates of psychological distress, 

depression, and generalized anxiety disorder, and improved perceptions of their likelihood of going to 

college and getting a well paid, stable job as an adult. These girls’ behaviors changed as well, with a 

smaller proportion working instead of attending school. They were less likely to engage in risky 

behavior or to use marijuana. Finally, both these girls and society as a whole benefited from a reduced 

number of arrests for violent crimes. 

The principal social costs that must be offset against these benefits are the costs of the MTO mobility 

counseling, any increased costs due to the greater likelihood of receiving housing assistance among 

those who leased up with program vouchers, and an increase in the rate of behavior problems, 

smoking, and arrests for property crimes among boys ages 15 to 19. 

We cannot place values on these social costs and benefits. Policymakers will have to decide whether 

the gains of this kind of policy outweigh the costs. But the interim evaluation has demonstrated that 

there are substantial social benefits as well as some costs associated with facilitating the movement of 

public housing residents who desire to move to low-poverty areas. 

How effective is policy likely to be in changing the environment of low-income families? 

One of the clearest messages of the interim evaluation results is that policy can influence, but it 

cannot dictate, the residential location of low-income families. As noted above, the demonstration 

reduced the proportion of the followup period that families who moved with program vouchers spent 

in areas of concentrated poverty by 47 percentage points in the experimental group and 35 percentage 

xvi Executive Summary 
points in the Section 8 group (exhibit 2.9). It increased the proportion of time spent in areas with 

poverty rates below 20 percent by 53 percentage points among families in the experimental group. 

Another lesson of the MTO demonstration is that the poverty rate, while important, may be an overly 

simplistic way to characterize neighborhoods. Residential environments are multidimensional, and no 

single measure will capture all the attributes that are important to the life chances of low-income 

families. Thus, for example, the fact that a majority of the program movers in the experimental group 

moved to areas with low, but rising, poverty rates may have had an important effect on their 

subsequent outcomes. Similarly, even in the experimental group, a large proportion of those who 

moved with program vouchers stayed within the city rather than moving to suburban areas. This 

meant that their children attended schools in the same school systems as control group children, 

which almost certainly limited the improvement in school quality they experienced as compared with 

(for example) a move to the suburbs. Moreover, the low-income areas to which families in the 

experimental group moved were still heavily minority. To the extent that racial integration or 

diversity has a positive influence on any of the outcomes analyzed here, that influence was largely 

absent in this demonstration. These considerations suggest that policy makers seeking to improve the 

environment of poor families may want to consider other characterizations of neighborhood than that 

provided by the poverty rate alone. 

When thinking about the implications of these results for policy, it is also important to recognize that 

all of the impacts presented here are measured relative to a control group receiving some mix of 

existing housing subsidies. Some control families eventually received regular Section 8 vouchers, 

some continued to benefit from public housing subsidies, and some left housing assistance altogether. 

Indeed, some control group members were unable to remain in public housing because their units 

were demolished under HOPE VI or other revitalization efforts. We did not attempt to eliminate the 

influence of these changes in control circumstances, including the receipt of Section 8 vouchers, from 

the estimates. Rather, we view the results as measures of the incremental effects of offering vouchers, 

with or without locational restrictions, to residents of public housing in areas of concentrated poverty 

during the particular period encompassed by the study. These findings answer this question: How 

much better off are the recipients of the demonstration vouchers than families who started out in the 

same situation and who received no help from the demonstration? This means that the estimates from 

this study are not applicable to all types of policy. For example, for a policy that replaces public 

housing with vouchers, the appropriate control benchmark would probably be continued residence in 

public housing. That is not what was tested here—indeed, it probably cannot be tested—and the 

results of the present test probably understate the effects that would be expected from such a policy. 

What do the interim results have to say about alternative approaches to improving the lives of 

low-income families? 

The most fundamental question addressed by MTO is this: To what extent are the problems 

encountered by public housing residents the result of the high concentration of poor families in those 

developments and the surrounding neighborhoods, and to what extent are they caused by attributes of 

the families themselves? To the extent that these problems are environmental, the appropriate policy 

response is to foster dispersion of these families to more positive environments. To the extent that 

these problems reflect family characteristics—e.g., lack of education, limited work experience, or 

membership in a group that faces discrimination—the appropriate policy response is to address these 

characteristics directly. 

Executive Summary xvii 
By the final evaluation, the effects of environment will have had more time to manifest themselves. 

At this point, however, we can say that some of the problems of public housing residents do appear to 

be environmental. These include the housing and neighborhood quality deficiencies and 

psychological and behavioral problems on which MTO had significant effects. It remains to be seen 

whether the problems of physical health, educational performance and attainment, employment, 

earnings, and welfare dependence that characterize public housing residents are amenable to housing 

policies designed to change their residential environment. At this point, there is no evidence that they 

are. If that finding is confirmed in the final evaluation, that would suggest that policies designed to 

deal directly with these specific problems—educational improvements, employment and training, or 

welfare-to-work policies—will be more effective solutions. 

xviii Executive Summary 
Chapter One 

The Moving to Opportunity Interim Evaluation 

In 2000, 3.5 million poor people across the United States lived in high-poverty neighborhoods 

(census tracts).3

 A growing literature suggests that such concentration has a variety of detrimental 

effects on the residents of these areas in terms of both their current well-being and future 

opportunities.4

 The deleterious effects of high-poverty areas are thought to be especially severe for 

children whose behavior and prospects may be particularly susceptible to a number of neighborhood 

characteristics such as peer group influences, school quality, and the availability of supervised 

afterschool activities. Less has been written about whether and how other neighborhood environments 

exert positive influences on behavior and life changes. Ellen and Turner (1997) summarize the 

literature in this area, citing various theories about the mechanisms by which middle-class (often 

predominantly white) neighborhoods shape or reshape the lives of their residents. 

Until recently such effects could only be studied by comparing the behavior and life outcomes of lowincome residents of high-poverty areas with those of poor families in low-poverty neighborhoods. 

Such comparisons potentially confused the effects of neighborhood with the effects of the 

characteristics of families who lived in those two types of residential areas. The Moving to 

Opportunity (MTO) demonstration was designed to support direct analysis of neighborhood impacts 

by employing an experimental design (random assignment) to provide the first opportunity to 

measure the effects of neighborhood without these confounding factors. 

1.1 The Moving to Opportunity Demonstration 

The MTO demonstration was conducted in five cities—Baltimore, Boston, Chicago, Los Angeles, 

and New York—between 1994 and 1998. To be eligible for MTO families had to have children under 

18 and be living in public housing developments or other project-based assisted housing in highpoverty areas (census tracts in which more than 40 percent of the population was living in poverty in 

1990). The public housing authorities (PHAs) in each city conducted outreach to all eligible 

households and all those interested were given the opportunity to apply for this special program. 

Interested households were placed on the MTO waiting lists of the local PHAs in the five 

demonstration sites. 

Applicants were drawn from the waiting lists for intake and given an explanation of the MTO 

demonstration. If they were still interested, they signed the Enrollment Agreement and completed the 

Participant Baseline Survey. Then they went through the process of eligibility determination for 

3 See Jargowsky (2003). In total, there were 7.8 million persons living in high-poverty census tracts in 2000. 

These figures represent a reduction in poverty concentration, in contrast to the increases from 1970 to 1990 

(see Jargowsky 1997). 

4 See, for example, Wilson (1987, 1996); Jencks and Mayer (1990); and Brooks-Gunn, et al. (1993). 

Chapter 1 – The Moving to Opportunity Interim Evaluation 1 
Section 8.5

 Eligible applicant families—4,608 in all—were randomly assigned to one of three groups. 

The experimental group received rental assistance vouchers that could be used 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 families in the experimental group to help them locate and lease 

suitable housing in a low-poverty area. This counseling was intended to help the experimental group 

families meet the locational constraint within the time limit for leasing up.6

 The Section 8 group 

received regular Section 8 vouchers, which could be used anywhere. These families also had a time 

limit and they did not receive any mobility counseling. The control group received no vouchers, but 

continued to be eligible for project-based assistance. Families in the experimental group and the 

Section 8 group who failed to lease up with demonstration vouchers were also eligible to continue to 

receive project-based assistance.7 

The Department of Housing and Urban Development (HUD) sponsored the MTO demonstration to 

ascertain the effects of improved neighborhood environments—low-poverty areas—on various 

aspects of the lives of low-income families. We discuss below how the design of the MTO 

demonstration and its evaluation will help us measure these effects. This interim evaluation measures 

demonstration effects 4 to 7 years after program entry. A final impact evaluation will be conducted 

approximately 10 years after the end of program operations. 

HUD specified six key subject domains of possible social and economic impact for investigation in 

this interim evaluation: mobility and housing; adults’ and children’s health; delinquent or criminal 

behavior of juveniles and adults; children’s educational achievement; employment history, earnings, 

and benefits; and income and public assistance. Each of these domains is covered in this report. 

HUD has used this interim evaluation to establish a framework for the final evaluation of MTO’s 

impacts by: 

• Combining quantitative and qualitative methods, drawing on the strengths of each to tell the full 

story. 

• Defining a set of measures for each impact area that are appropriate for investigating impacts at 

the interim point (4 to 7 years after random assignment) and are also appropriate to the final 

evaluation after 10 years. 

5 It is estimated that just over one-fourth of the eligible families in targeted public housing developments in 

Baltimore, Boston, Los Angeles, and New York City enrolled in MTO. See Goering et al. (1999), Table 5, 

p. 32. 

6 Program rules set the maximum search period at 90 days. While the experimental group generally had more 

time to lease up than the Section 8 group, the time was still limited. Also, the counseling agencies could 

require that some of the extra time be spent on preparation for housing search. 

7 At the time of MTO demonstration operations, the Section 8 program was still issuing both certificates and 

vouchers. Subsequently HUD converted all certificates to vouchers. In 1999 Section 8 was renamed the 

Housing Choice Voucher Program and changed in a number of ways. In this report, to simplify the text, the 

term voucher will be used to refer to all the tenant-based resources issued through MTO. However, we will 

continue to refer to the program as Section 8, because the rules of the demonstration were set under the 

tenant-based version of that program. 

2 Chapter 1 – The Moving to Opportunity Interim Evaluation 
• Contributing to our state of knowledge about the mechanisms by which the neighborhood 

environment affects the future of resident adults and children. 

The questions addressed in this interim evaluation are of great importance. To what extent are the 

adverse outcomes associated with living in very poor neighborhoods the products of the 

neighborhoods rather than of the characteristics of those living there? If the adverse outcomes are 

products of the neighborhoods, to what extent do opportunity moves to areas with minimal poverty 

offer a means of ameliorating them? If public housing residents are given tenant-based housing 

assistance, do they make locational choices that afford them access to some or all of the same life 

improvements as opportunity moves? The research results addressing these questions could shape 

social policy in the United States for years to come. 

1.2 Previous Studies of Mobility Programs and the Effects of 

Neighborhood 

Recent interest in geographic location and mobility as important factors shaping the futures of lowincome families began with the results of the Gautreaux Program, a federal court-ordered racial 

desegregation program in Chicago. Under the name of tenant activist Dorothy Gautreaux, applicants 

and residents of Chicago public housing brought a class-action housing segregation lawsuit against 

HUD and the Chicago Housing Authority (CHA) in 1966 (Davis 1993; Rubinowitz and Rosenbaum 

2000). After years of litigation the courts ordered HUD and the CHA to remedy the extreme racial 

segregation that they had imposed on public housing applicants and residents. Starting in the late 

1970s these agencies had to provide, among other remedies, a housing mobility option throughout the 

Chicago region for approximately 7,100 Black families. 

The Gautreaux Program took shape as a result of the court’s ruling. Participating families were helped 

to move out of racially isolated areas through the (then new) tenant-based Section 8 program. 

Families chosen for the Gautreaux program received Section 8 certificates that required them to move 

to either predominantly white or racially mixed neighborhoods. They also received assistance from 

housing counselors to make these moves. 

Beginning in the late 1980s research on the Gautreaux Program suggested that the moves to less 

segregated suburban locations were associated with measurable improvements in the lives of 

participating adults and children. Popkin, Rosenbaum, and Meaden (1993) found that participants 

who had moved to white suburban areas were significantly more likely to report having had a job 

since they moved than participants who moved to neighborhoods in the city.8 

Positive changes were also reported for small samples of children who had been living in less 

segregated neighborhoods for periods of 7 to 10 years. Such children were less likely to drop out of 

8 These findings were based on a survey only of participants who remained in their suburban communities, 

excluding others who moved back to the city (or elsewhere) and still others in the program who never 

moved to the suburbs at all. 

Chapter 1 – The Moving to Opportunity Interim Evaluation 3 
school and were more likely to take college-track classes than their peers in a comparison group who 

moved within the city of Chicago rather than to suburban areas. The city neighborhoods were both 

poorer and more racially segregated than the suburban locations. After graduating from high school, 

the Gautreaux children were also more likely than their city peers to attend a 4-year college or 

become employed full-time (Rubinowitz and Rosenbaum 2000). 

Although the results from the research on Gautreaux Program participants were encouraging, they did 

show that such moves could create initial setbacks. Children who moved to white suburban 

communities initially experienced declines in school performance, and children of suburban movers 

appeared to be somewhat more likely to be placed in special education (at an average of 2 years 

postmove for those so placed). However, after 9 years these children did better on a number of 

measures than those whose families moved within the city (Kaufman and Rosenbaum 1992, 

Rubinowitz and Rosenbaum 2000). 

In assessing both the positive and negative findings of the Gautreaux study, it is important to bear in 

mind that this was a nonexperimental comparison of different families who moved to different types 

of neighborhoods. There is a serious risk of selection bias in such a comparison—that is, the observed 

differences may reflect differences between the kinds of people who moved to the suburbs and those 

who moved within the city rather than the effects of these different residential locations. It was 

precisely this danger that led HUD to sponsor the current experimental study to investigate these 

effects. The Gautreaux findings for children were also based on a very small sample (69 households). 

Other recent research has focused on possible theoretical causes for both positive and negative effects 

of neighborhoods (Manski 1993, 2000; Galster and Killen 1995; Galster, Quercia, and Cortes 2000; 

Leventhal and Brooks-Gunn 2001a). The core question is whether there are clear, independent effects 

of neighborhood and, if so, whether they are favorable or unfavorable from the standpoint of the 

family. Only recently has there been evidence and discussion about how neighborhood environments 

may exert positive influences on behavior and life chances (Brooks-Gunn, Duncan, and Aber 1997; 

Sampson, Morenoff, and Gannon-Rowley 2002). 

Galster and Killen (1995) noted the complexity of the causal influences linking metropolitan and 

neighborhood-based opportunities and pointed out the dynamic nature of opportunities and the critical 

issue of residents’ willingness and ability to take advantage of contextually positioned resources. 

Ellen and Turner’s (1997) summary of literature in this area suggests various mechanisms by which 

middle-class (often predominantly white) neighborhoods shape or reshape the lives of their residents. 

The effects of neighborhood appear to be more pronounced for children rather than for adults, with 

Leventhal and Brooks-Gunn (2001a) offering evidence that neighborhood influences on achievement 

measures such as IQ are most important at ages below 5 years and less important at later ages. 

Other research has looked qualitatively at the issue of how neighborhood environments shape 

residents’ lives. Patillo-McCoy (1999) explores the influences on teens growing up in a moderateincome African American neighborhood in Chicago, looking at both the positive aspects of the 

community and the ways in which proximity to poorer neighborhoods poses risks for youth. Bourgois 

(1995) uses his portrait of drug dealers in New York to show how in many troubled neighborhoods a 

different set of social rules lead youth to become involved in deviant behavior. Two qualitative 

studies of Chicago’s public housing (Popkin et al. 2000; Venkatesh 2000) describe how residents in 

public housing in Chicago cope with the extreme dangers of their environment and the key role that 

4 Chapter 1 – The Moving to Opportunity Interim Evaluation 
gangs play in the community. Other ethnographic researchers have documented the importance of 

social networks for low-income families, focusing on systems of mutual help that allow families to 

cope with extreme poverty and manage to support their families (Stack 1974; Edin and Lein 1997). 

However, these studies have also documented the ways in which these relationships may undermine 

an individual’s attempts to get ahead. 

It is important to bear in mind that the empirical basis for all of these studies was naturally occurring 

correlations between outcomes and neighborhood characteristics. These correlations may reflect 

differences in the individual characteristics (e.g., motivation, ability, etc.) of the low-income families 

who live in different areas, rather than the effects of those areas. The only way to ensure that this is 

not the case is to offer a randomly selected group of low-income families the opportunity to move 

from high-poverty areas to lower poverty areas, as was done in MTO. 

1.3 Previous Research on the MTO Demonstration 

Research on MTO began while the operational phase of the demonstration was still underway. HUD 

issued a first report to Congress once all the sites had begun enrolling and counseling families (U.S. 

Department of Housing and Urban Development, 1996). Observations and analyses of the counseling 

delivered to experimental group families through MTO were documented about midway through the 

operations period (Feins, McInnis, and Popkin 1997). At the end of enrollment and leaseup in 1999, 

HUD reported initial findings about the participating families and the program moves made by 

experimental and Section 8 group families (Goering et al. 1999). 

In 1997 HUD’s Office of Policy Development and Research conducted a grant competition and 

ultimately made eight small grant awards to teams of researchers with varied topics and approaches. 

Each team was given access to the MTO participants in one of the five sites9

 for purposes of assessing 

different aspects of the families’ experiences. A substantial number of analyses and articles resulted 

from these grants.10 Researchers taking advantage of the experimental design of MTO to eliminate 

selection bias found distinct improvements in neighborhood and school environments: 

• Significantly lower crime rates in the neighborhoods of the experimental and Section 8 groups in 

Los Angeles compared with the control group’s neighborhoods (Hanratty, McLanahan, and Pettit 

2001). 

• Major gains in safety for program movers in both the experimental and Section 8 groups in New 

York, with significantly greater increases in neighborhood quality and satisfaction for the 

experimental group than for the Section 8 group (Leventhal and Brooks-Gunn 2001b). 

9 Access was also provided to data collected at baseline about these participants. 

10 For a volume containing the major pieces of work from the HUD-sponsored grants research, see Goering 

and Feins (2003). 

Chapter 1 – The Moving to Opportunity Interim Evaluation 5 
• Significant and positive differences in average test scores for schools attended by the Boston 

experimental group children in 1997 compared with the schools of the control group children 

(Katz, Kling, and Liebman 2001). 

• Positive differences in the resources and characteristics of schools attended by Baltimore children 

in the experimental and Section 8 groups compared with the schools of children in the control 

group (Ludwig, Ladd, and Duncan 2001). 

• A lower rate of decline in test scores for younger children in the Baltimore MTO experimental 

and Section 8 comparison groups as they grew older compared with children in the control group, 

suggesting that the move might have helped to prevent the kinds of dramatic decline in test scores 

often found in inner-city schools (Ludwig, Ladd, and Duncan 2001b). 

• A slightly greater likelihood that Baltimore teens in the experimental and Section 8 groups would 

experience grade retention or be suspended or expelled compared with teens in the control group 

(Ludwig, Ladd, and Duncan 2001b). 

• Significantly fewer behavior problems among boys in the Boston experimental and Section 8 

groups compared with boys in the control group (Katz, Kling, and Liebman 2001). 

• Significant improvements in child health (reductions in the percentage experiencing asthma 

attacks or accidents requiring medical attention) for the Boston experimental and Section 8 group 

children compared with the control group children (Katz, Kling, and Liebman 2001). 

• Reduced numbers of arrests per 100 juveniles ages 11 to 16 in the Baltimore experimental and 

Section 8 groups compared with control group juveniles (Ludwig, Duncan, and Hirschfield 

2001). 

At the same time, there were areas of research in which little or no effect was found: 

• Adults in the experimental and Section 8 groups in Boston showed no differences from control 

group adults in key economic outcomes—the percentage with employment earnings and the 

percentage receiving public assistance (Katz, Kling, and Liebman 2001). 

• Adults in the experimental and Section 8 groups in Los Angeles worked significantly more hours 

per week than the control group adults but did not have higher earnings or income (Hanratty, 

McLanahan, and Pettit 2001). 

• There were no significant differences in any of the other sites on welfare receipt, employment, or 

hours worked. 

These early MTO research results suggested that the demonstration might well be having some 

notable effects on participants. But because the timing of program entry extended from 1994 to 1998 

and because each study was done in a single site, with relatively small sample sizes, the small grant 

research needed to be followed by more comprehensive and uniform research when more time had 

elapsed for the families in the program. It was clear that the MTO design and sample could be used to 

6 Chapter 1 – The Moving to Opportunity Interim Evaluation 
learn about a wide range of topics.11 It was equally clear that many questions remained to be 

answered. 

1.4 Research Questions Addressed by the Interim Impact 

Evaluation 

The MTO demonstration was intended both to provide information about the effects of neighborhood 

on families and to test possible programs to induce changes in where low-income families live. The 

programmatic interventions tested were vouchers restricted to low-income areas (the experimental 

group) and regular housing vouchers (the Section 8 group). In each of the six outcome domains of 

this study (housing, health, delinquency and risky behavior, education, employment and earnings, and 

income and public assistance), we analyzed the impacts of the special (location-restricted) vouchers 

and regular Section 8 vouchers on the members of the households that joined MTO, with a particular 

emphasis on the household heads and their school-age children. 

For each of these two treatment groups, we produced estimates corresponding to two different 

questions: the effect of the intervention on the average level of the outcome on the entire treatment 

group, including those who failed to lease up with the MTO voucher,12 and the effect on only those 

who leased up.13 The former estimate, known in statistical terms as the “intent to treat” (ITT) effect, 

measures the degree to which, on average, the intervention affected all individuals who were eligible 

to receive it (assigned to the experimental or Section 8 group) whether they leased up or not. 

Obviously, the size of this estimate will vary with the proportion of families who received the 

intervention (in this case, with the proportion who leased up). The effect on only those who lease up 

is known in statistics as the effect of the “treatment on the treated” (TOT). 

All estimates are measured relative to the control group members, who received no vouchers but were 

eligible to remain in public housing. To improve the precision of the estimates, we used regression 

analysis to control for any chance differences between the treatment and control groups on a number 

of characteristics measured at baseline (when families joined MTO). For a complete description of the 

estimation procedures, see appendix B. 

If one is interested in the effectiveness of a program like MTO in improving the situation of the entire 

class of families to whom it is offered, the ITT estimates are the appropriate set of results to examine. 

The overall effectiveness of such a policy depends both on the proportion of families who use the 

11 Later sections of this chapter and appendix A contain further information on the sample and on the data 

collected for the study. 

12 Leasing up means that the family finds a housing unit that passes the program’s quality standards, has a 

willing landlord, and has rents affordable to the family under program rules. A lease is then signed that 

obligates the administering agency to pay the voucher amount toward the rent and obligates the tenant to 

pay the remainder. Someone who rents a housing unit with the help of the voucher assistance is said to 

lease up. If these conditions are not met, the voucher expires and the family has failed to lease up. 

13 See appendix B for a detailed description of how these estimates were generated. 

Chapter 1 – The Moving to Opportunity Interim Evaluation 7 
voucher and its effects on those who do. This point is particularly salient in comparing the ITT 

impacts on the experimental group with the ITT impacts on the Section 8 group. Because of the 

restriction of the experimental group’s vouchers to low-poverty areas, a smaller proportion of them 

leased up than in the Section 8 group. This lower success rate will offset to some extent the 

presumably greater effects of a lower poverty environment on the members of the experimental group 

who did lease up. 

If, instead, one is interested in the effects of neighborhood on family outcomes, one should consult 

the TOT results. They reflect the difference in outcomes between similar families in different 

residential environments.14 However, the TOT estimates are non-experimental, while the ITT 

estimates reflect the full power of the experimental design. For a complete description of the 

estimation procedures, see appendix B. 

In both cases, it is important to note that effects are not measured relative to living in public housing. 

They are measured relative to the outcomes of a set of families that started out living in public 

housing. As we will see in the next chapter, many of the control group families subsequently left 

public housing and moved to the same kind of neighborhoods as some of the treatment group 

families. Some even received vouchers through the regular Section 8 program.15 The estimates 

presented here represent the incremental effects of demonstration vouchers relative to what happened 

to the controls—and, therefore, what would have happened to the treatment group families in the 

absence of the MTO demonstration. 

It is also important to bear in mind that even the experimental group families that leased up did not 

spend the entire followup period in low-poverty areas. As we will see in the next chapter, some made 

a second or third move to relatively higher poverty areas than those they originally chose to meet the 

voucher’s location constraint. And in some cases, even if the family stayed in the same neighborhood, 

its poverty level increased over the course of the followup period due to other changes in population 

and incomes.16 It is true, however, as we will show in the next chapter, that both the experimental 

group and the Section 8 group spent significantly more time in low-poverty areas than the control 

families. Thus the experimental contrasts on which the impact estimates are based do reflect the effect 

of a lower poverty residential environment. They also reflect lower rates of residence in public 

housing. 

14 These differences in residential environment may include differences in the dwelling unit as well as 

differences in the neighborhood. For example, dwelling units in lower poverty areas may have fewer health 

hazards. Because we believe that neighborhood effects will generally predominate, however, we use 

neighborhood as a shorthand term for the entire set of environmental factors. 

15 The implications of this fact for interpreting program impacts are addressed in chapter 9. 

16 The MTO families constituted extremely small proportions of the population of the destination census 

tracts, so they had no noticeable effect on these changes. Experimental group movers constituted at most 

2.3 per 1,000 households in their new neighborhoods. See Goering et al. (1999) p. 42. 

8 Chapter 1 – The Moving to Opportunity Interim Evaluation 
Understanding the Impact Estimates 

low-poverty area). 

this measure at this point in time. 

experimental

vouchers. 

ically 

l

iffer from nonprogram movers in various ways. 

If an estimated impact is statisti

Throughout this report the exhibits that present the results of statistical tests for 

program impacts take a standard format. Each exhibit lists the outcome measures 

being analyzed in the left-most column. Various hypotheses predicted that these 

outcomes would be affected by the MTO experimental treatment (moving to a 

The next column, Control Mean, gives the average value for the control group on 

each outcome measure. If the averages for the experimental and Section 8 

groups are not different from the control mean, then there is no MTO impact on 

The remaining four columns give the estimated impacts. The first pair shows the 

estimated ITT (intent-to-treat) and TOT (treatment-on-treated) effects for the 

 group; the second pair shows the estimates for the Section 8 group. 

The ITT columns show the estimated impacts on the assigned group as a whole, 

including both families who leased up and families who never rented with a 

voucher obtained through MTO. The TOT columns show the estimated impacts 

on the program movers—the sample members who actually moved with program 

The TOT estimates are non-experimental, in the sense that they are not directly 

observed for whole randomly assigned groups. They are based on the 

assumption that the program had no impact on the nonprogram mover families. 

The TOT estimates are calculated by dividing the ITT estimates for each group by 

the group’s leaseup rate. Because only 47 percent of the experimental group and 

62 percent of the Section 8 group leased up, this makes the TOT estimates 

substantially larger than the ITT estimates. However, since the standard errors for 

the TOT estimates are adjusted in the same way, TOT impacts are statist

significant only if the corresponding experimenta ITT estimates are significant. 

The control means indicate what the outcomes for the experimental and Section 8 

groups as a whole would have been without MTO. However, they may not 

represent the no-treatment outcomes for program movers (those who leased up), 

because these families d

The error of estimate for each estimated impact is shown below it, in parentheses. 

cally unlikely to have occurred by chance (is more 

than 1.96 times its standard error), it is marked with an *. 

Chapter 1 – The Moving to Opportunity Interim Evaluation 9 
Finally, it should be noted that although the ITT estimates for the experimental and Section 8 groups 

are directly comparable because these two groups were randomly assigned from the same pool, the 

TOT estimates for the two groups are not directly comparable. That is because different proportions 

of the two groups leased up. As expected, the leaseup rate was higher for families assigned to the 

Section 8 group than for families assigned to the experimental group. Differences between the 

average effects on participants for the two groups may arise because of differences in effects for the 

families that leased up under both groups or because effects were different for the additional families 

that leased up in the Section 8 group. 

1.5 Using the Experimental Design to Estimate Impacts 

This study was based on a set of hypotheses from the social science literature about the effects of a 

change in neighborhood environment on adult and child wellbeing. Distilling the literature allowed us 

to identify a set of hypothetical pathways by which relocation to low-poverty neighborhoods could 

affect a variety of outcomes. Exhibit 1.1 is a generalized model showing these pathways, with the 

experimental intervention at the top and the six outcome domains at the right. The hypothesized 

influence of neighborhood or community is mediated by a series of factors that bear a logical 

relationship to each other. Relocation brings about changes in community-level factors, which have 

consequences for person- and family-level mediators. In turn, these affect the outcomes of interest in 

this study. 

Analytic framework 

This study’s impact analysis has been structured to shed light not only on the ultimate impacts of 

moving out of public housing but also on the causal mechanisms through which those effects occur. 

In each domain we have not only specified the outcomes of interest but also described alternative 

pathways through which impacts on those outcomes might occur and the mediating factors along 

those pathways. The qualitative research carried out early in this project, along with the prior research 

cited above, played a key role in helping to define the mediating factors. For example, the extended 

interviews with adults and youth suggested new hypotheses about how changing neighborhoods 

affected families. 

Estimation of impacts on the mediating factors (as well as on final outcomes) helps to distinguish the 

causal mechanisms responsible for the estimated impacts. For example, it might be hypothesized that 

families who moved to low-poverty neighborhoods would experience higher employment rates, 

because the move to a lower stress environment improved their mental health (e.g., reduces the 

incidence of depression) and so increased their employability. Direct estimation of impacts on 

measures of mental health would help to determine whether this potential mediating factor could have 

contributed to any observed impacts on employment and earnings. 

10 Chapter 1 – The Moving to Opportunity Interim Evaluation 
MTO Intervention→ Relocation to Low-Poverty Neighborhoods

Housing Market

Conditions

Economic

Opportunities

Social and Physical

Environment

Social

Norms and Values

Schools

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

towards Own Education, 

Achievement

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

Housing Market

Conditions

Economic

Opportunities

Social and Physical

Environment

Social

Norms and Values

Schools

EXHIBIT 1.1 

HYPOTHESIZED PATHWAYS OF MTO IMPACTS 

Community-Level 

Mediators Outcomes Person- and Family-Level Mediators 

MTO Intervention→ Relocation to Low-Poverty Neighborhoods 

Housing Market 

Conditions 

Economic 

Opportunities 

Social and Physical 

Environment 

Social 

Norms and Values 

Schools 

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 

towards Own Education, 

Achievement 

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 

Community-Level

Mediators Outcomes Person- and Family-LevelMediators

Note that both qualitative exploration and quantitative examination of the mediating factors can 

suggest causal mechanisms and can rule out certain theories if, for example, there are no impacts on 

the mediating factors involved in those theories. But they cannot conclusively prove causality. 

Continuing the example, a finding that the experimental vouchers both reduced the incidence of 

depression and increased employment rates would be consistent with the theory that moving out of 

the unsafe, high-stress public housing environment improved family members’ mental health, making 

them more employable and/or more willing to search for work. But that finding would also be 

consistent with the interpretation that family members’ mental health improved because they worked 

more. Thus when there are significant effects on the hypothesized mediating factors, inferences about 

causality will necessarily be judgmental. However, when there are no significant impacts on the 

mediating factors, the interpretation will be clearer. If, for example, we find that employment 

increased but mental health did not improve we can rule out the theory that employment increased 

because of improvements in mental health.17 

We cannot, of course, definitively rule out an impact on any mediator or outcome. We can only say that if 

there was an impact it must be less than the minimum effect detectable with this sample. 

Chapter 1 – The Moving to Opportunity Interim Evaluation 11 

17 
The most fundamental mediating factor we are studying is, of course, the poverty level of the 

neighborhood. We will examine the degree to which MTO affected several alternative measures of 

this basic environmental factor in chapter 2. Other mediating factors examined vary across the 

outcome domains. At the community level, however, they can be grouped into three broad categories: 

community norms and values, the social and physical environment, and economic opportunities. 

Individuals who move to a new community are likely to be affected by the norms and values of that 

community through peer pressure and community expectations. We would expect these effects to be 

stronger the more the individual interacts with members of the new community. We would also 

expect such effects to be stronger if the norms and values of the new community are substantially 

different from those of the individual’s old community. 

The social and physical environment in the community may affect a number of outcomes. For 

example, the incidence of crime and violence in the community may be a potentially important 

mediating factor, affecting not only the families’ sense of security and well-being but also the 

likelihood that they themselves would become involved in illegal activities. The social resources of 

the community, including school quality, recreational facilities, public and private social services, and 

healthcare facilities, will facilitate or limit certain behaviors and outcomes. The physical environment, 

including safety hazards, air quality, and presence of allergens, may have important effects on family 

health. 

Economic opportunities in the local community may influence family members’ employment and 

earnings directly and a number of other outcomes indirectly. For example, if family members obtain 

jobs with better health insurance coverage, they may have better access to medical care and, as a 

result, improved health. Better economic opportunities may also provide constructive alternatives to 

crime and delinquency. 

1.6 Sample and Data Collection for the Interim Evaluation 

Sample definition and description 

The sample used in the interim evaluation includes all 4,248 families randomly assigned in the MTO 

demonstration through December 31, 1997.18 This is not the entire MTO population: family intake 

continued in one site (Los Angeles) through July 1998, and leaseups occurred there until March 1999. 

However, the sample for the present study was restricted in order to assure that at least 4 years had 

passed since random assignment for all its members. The allocation of this sample among the 

18 The full MTO population consists of 4,608 families. The 4,248 families in the interim evaluation sample 

represent 92.2 percent of the full population. This study’s sample includes all of the families in four of the 

five sites (Baltimore, Boston, Chicago, and New York). Exhibit C1.1 in appendix C compares the sample to 

the entire MTO population.

 An additional 356 families were randomly assigned after January 1, 1998. These families were not included 

in the sample for this analysis because the increase in sample size they would have provided was deemed 

insufficient to justify shortening the followup to include them. 

12 Chapter 1 – The Moving to Opportunity Interim Evaluation 
treatment groups, by site and overall, is shown in exhibit 1.2. The number of families in each site 

range from 636 families in Baltimore to 1,081 in New York City.19 

EXHIBIT 1.2 

ALLOCATION OF THE INTERIM EVALUATION SAMPLE FAMILIES BY SITE AND TREATMENT GROUP 

Experimental 

Group 

Section 8 

Group Control Group Total 

Baltimore 252 187 197 636 

Boston 366 267 326 959 

Chicago 460 202 232 894 

Los Angeles 250 168 260 678 

New York City 401 385 295 1,081 

All Sites 1,729 1,209 1,310 4,248 

Source: MTO data system 

Sample: All families randomly assigned through December 31, 1997. 

Although MTO enrollment took place by family, the interim evaluation focuses on individual 

members of these families and their experiences. It was designed to answer questions midway 

through the 10-year followup period about one adult and up to two children in each of the families in 

the sample. The children were sampled randomly from among all age-eligible children (ages 5 to 19) 

in each family.20 Exhibit 1.3 shows the sample allocation by treatment group and site for the sampled 

adults and children. On average, the sample included 2.6 members per family, including 1.6 children. 

19 A somewhat larger number of families were assigned to the experimental group than to the Section 8 group 

to achieve the desired sample sizes despite a likely lower leaseup rate in the experimental group. 

Assignment rates within sites were further adjusted to compensate for differences between expected and 

actual leaseup rates. (The sample weights used in the quantitative analyses adjusted for differences among 

sites and over time in the rate of random assignment. See the discussion in appendix B.) 

20 See appendix B for details of sample selection. 

Chapter 1 – The Moving to Opportunity Interim Evaluation 13 
EXHIBIT 1.3 

ALLOCATION OF THE INTERIM EVALUATION SAMPLE MEMBERS 

BY SITE AND TREATMENT GROUP 

Experimental Section 8 

Group Group Control Group Total 

Baltimore 

Adults 

Children 

Boston 

Adults 

Children 

Chicago 

Adults 

Children 

Los Angeles 

Adults 

Children 

New York City

Adults 

Children 

All Sites 

All 

Adults 

Children 

252 187 197 636 

361 289 303 953 

366 267 326 959 

555 408 509 1,472 

460 202 232 894 

764 332 373 1,469 

250 168 260 678 

420 272 429 1,121 

401 385 295 1,081 

591 606 471 1,668 

4,420 3,116 3,395 10,931 

1,729 1,209 1,310 4,248 

2,691 1,907 2,085 6,683 

Source: MTO data system 

Sample: All families randomly assigned through December 31, 1997. 

Exhibit 1.4 shows the allocation of the child sample by age among the treatment groups. A child’s age 

for data collection purposes was uniformly measured as of May 31, 2001.21 Different information was 

collected about different age groups. For this analysis, the key age groups for the sampled children 

were ages 5 to 7, 8 to 11, and 12 to 19. These age groups were set to differentiate among children by 

developmental stage and by hypothesized differences in neighborhood influence. However, other age 

breaks were used in some analyses as the text and tables will indicate.22 

21 Since the field data collection continued through September 2002, this means that—at the moment they 

were interviewed or tested— some children were more than a year older than their age as defined for 

sampling. 

22 Although their ages were similar at the time of the interim evaluation, the children varied considerably in 

the length of their exposure to the MTO treatment. Children 5 to 7 at the time of the study were from birth 

14 Chapter 1 – The Moving to Opportunity Interim Evaluation 
EXHIBIT 1.4 

ALLOCATION OF THE INTERIM EVALUATION CHILD SAMPLE BY AGE AND TREATMENT GROUP 

Experimental Section 8 

Group Group Control Group Total 

Ages 5 to 7 371 264 309 944 

Ages 8 to 11 885 638 679 2,202 

Ages 12 to 19 1,435 1,005 1,097 3,537 

All Children 2,691 1,907 2,085 6,683 

Source: MTO data system. 

Sample: All families randomly assigned through December 31, 1997. 

Socioeconomic and Demographic Characteristics of the Sample at Baseline 

As with the MTO program population as a whole, the racial composition of the interim evaluation 

sample (exhibit 1.5) is heavily African American. Two-thirds of the overall sample is African 

American, and one-third is Hispanic. By site (exhibit C1.2), Baltimore and Chicago have almost 

entirely African American samples while the other three sites have substantial proportions of 

Hispanic families (40 to 50 percent). Only the Boston program enrolled a significant number of 

nonHispanic white families. Women headed most MTO households at baseline, although in Los 

Angeles a substantial minority of households had male heads and two parents present. The median 

number of children was three. 

Recruited from public housing residents, the MTO program families had average cash incomes of 

about $9,300 when they entered the program and about 60 percent depended on public assistance, Aid 

to Families with Dependent Children (AFDC) or Temporary Assistance for Needy Families (TANF) 

as their primary income source. While some were employed, most were not. Approximately 70 

percent were not working when they joined MTO. About 40 percent of the household heads were not 

high school graduates and did not have a GED, although some (about 16 percent) were then in school. 

to age 4 at baseline. The 8 to 11-year-olds were ages 1 to 8 at baseline. And the youth (ages 12 to 19) 

ranged in age from 5 to 16 at baseline. 

Chapter 1 – The Moving to Opportunity Interim Evaluation 15 
EXHIBIT 1.5 

DEMOGRAPHIC AND SOCIOECONOMIC CHARACTERISTICS OF 

MTO FAMILIES AT BASELINE BY RANDOM ASSIGNMENT GROUP 

Experimental Section 8 Control 

Group Group Group All Groups 

Race/Ethnicity of Head of Household a 

African American non-Hispanic 62.4% 61.9% 63.5% 62.6% 

Hispanic 30.3% 30.9% 29.9% 30.4% 

White non-Hispanic 3.1% 2.7% 2.6% 2.9% 

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

Asian/Pacific Islander non-Hispanic 1.9% 2.3% 1.3% 1.8% 

Other non-Hispanic 1.8% 2.0% 2.5% 2.1% 

Sex of Head of Household 

Male 

Female 

8.5% 

91.5% 

8.7% 

91.3% 

8.0% 

92.0% 

8.4% 

91.6% 

Head of Household’s Marital Status 

Never married 61.7% 61.8% 63.2% 62.2% 

Married 11.7% 11.5% 10.5% 11.3% 

Divorced 9.7% 9.4% 9.3% 9.5% 

Widowed or separated 16.9% 17.3% 17.0% 17.1% 

Median Number of Children 3 3 3 3 

Average Total Household Income $9,385 $9,189 $9,337 $9,314 

Percent with AFDC as Primary 

Income Source 61.1% 62.2% 61.5% 61.6% 

Head of Household Currently in School? 

Yes 16.0% 16.8% 15.8% 16.2% 

No 84.0% 83.2% 84.2% 83.8% 

Head of Household a Graduate? 

High school 42.2% 40.6% 38.5% 40.6% 

GED 17.8% 20.0% 22.0% 19.7% 

Neither 40.0% 39.4% 39.5% 39.7% 

Head of Household Currently Working? 

Full-time 16.1% 16.0% 16.3% 16.1% 

Part-time 12.8% 11.1% 10.3% 11.6% 

Not working 71.1% 72.6% 73.4% 72.2% 

Working for benefits 0.0% 0.2% 0.1% 0.1% 

Source: MTO Participant Baseline Survey 

Sample: Adults from families randomly assigned through December 31, 1997. 

Notes: The respondent to the Baseline Survey was usually the same person as the sample adult for the interim 

evaluation. Household income was defined following the rules for Section 8 eligibility. Percentages may not add 

to 100 because of rounding. Data are weighted as described in appendix B. 

a) Respondent self-reports. A number of African American respondents skipped the ethnicity question and are 

not included in the distribution reported. Many Hispanics used the Other category for the race question. 

16 Chapter 1 – The Moving to Opportunity Interim Evaluation 
In exhibit 1.5, the figures for each of the randomly assigned groups are very similar on all these 

characteristics. In fact, statistical tests show that the MTO random assignment process worked. The 

experimental, Section 8, and control groups are the same (any differences being no greater than 

chance would produce). 

Data on these families' background conditions and experiences (exhibit C1.3) offer insights into their 

motivations for joining a mobility program. At least three-fourths of the respondents in every group 

indicated that getting away from drugs and gangs was the first or second most important reason for 

wanting to move. A high proportion of respondents were dissatisfied with their current 

neighborhoods, and high rates of victimization were reported on a range of crimes.23 About half 

indicated they wanted to relocate to areas with better schools for their children. But not all the 

motivation to move at baseline was neighborhood related. Nearly half the sample mentioned getting 

better housing as the first or second reason for wanting to move. 

Data collection for the interim evaluation 

Extensive qualitative and quantitative data were collected in 2001 and 2002 for the sample. There 

were three main components of the data collection: 

• Individual data on sample members were collected through in-person interviews with adults and 

children ages 8 to 19 from the families in MTO and through educational achievement tests for 

children ages 5 to 19. 

• Administrative and published data were collected about the employment and public assistance 

outcomes for the sample and about the families’ residential locations and the schools the children 

have attended. 

• A qualitative study involved extended interviews with parents and teenagers in MTO families (a 

small subset of the sample), focusing on their views about the effects of neighborhood on the 

behavior and experiences of family members. The data collection for this component was 

described fully in Popkin, Harris, and Cunningham, 2001. 

Participant data collection for this study 

Data about the MTO sample members in the interim evaluation sample were collected between 

January and September 2002 through interviews with the sample members and through direct 

measurement and educational testing. Exhibit 1.6 summarizes the topics about which data were 

collected, by method, according to the age of sample members. 

The three surveys—household, youth, and child—were administered largely in person by trained 

interviewers, using Computer-Assisted Personal Interviewing (CAPI) on laptop computers. The 

surveys for all three samples were administered primarily in the respondents’ homes, with the session 

The victimization rates reported in the Participant Baseline Survey were about four times higher than those 

reported in a 1994 national survey of residents of public housing family developments. 

Chapter 1 – The Moving to Opportunity Interim Evaluation 17 

23 
scheduled at the respondents’ convenience.24 Field interviewers also recorded their observations of 

the home and neighborhood environments. 

EXHIBIT 1.6 

CONTENT OF PARTICIPANT DATA COLLECTED FOR THE MTO INTERIM EVALUATION 

Adult1 Youth 12-19 Children 5-113 

Housing and neighborhood Education Education 

Survey 

Contents 

Education and training 

Employment and earnings 

Income and public assistance 

Outlook and social networks 

Health 

Household composition 

Child education2 

Employment and earnings 

Risky behavior 

Health 

Neighborhood and social 

networks 

Emotions 

Time use 

Neighborhood, 

danger, and risk 

Health 

Behavior and family 

dynamics 

Child health2 Future plans 

Child behavior2 

Child time use2 

MTO experience 

Direct 

Measurement Blood pressure None Height and weight 

Educational 

Testing None Woodcock-Johnson 

Revised—selected tests 

Woodcock-Johnson 

Revised—selected 

tests 

Interior conditions 

Interviewer 

Observation Exterior conditions 

Neighborhood conditions 

Interaction between adult 

and youth 

Interaction between 

adult and child 

Notes: 

1 Adults were selected for interviewing in the following order of precedence: female head of family intending to 

move through MTO; female spouse of family intending to move through MTO; wife of baseline head, if a 

member of the family intending to move through MTO; non-female (male or unknown gender) head of family 

intending to move through MTO. 2 The adult respondent was asked questions about each sampled child in the household, up to two. 3 Surveys were administered only to sampled children ages 8 to 11. Direct measurement and educational 

testing were carried out for sampled children ages 5 to 11. 

Data were collected from sample members in two phases: the full sample phase (in which all 10,931 

sample cases were worked) and the subsample phase (in which additional efforts were made to 

complete data collection with a subsample of full phase nonrespondents). An intensive data collection 

effort involving more than 100 interviewers achieved high response rates for both adults and children. 

When the responses for the full sample are combined with the weighted responses for the subsample 

24 A small number of surveys with adult and youth respondents were administered by telephone. 

18 Chapter 1 – The Moving to Opportunity Interim Evaluation 
of hard-to-find households, the effective response rate for the interim evaluation was 90 percent for 

the adults and 88 percent for the children. Appendix A of this report provides further details about the 

data sources, methods, sample sizes, and other features of the study’s participant data collection. 

Collection of administrative and published data for this study 

The MTO Interim Evaluation drew upon several administrative databases for measuring both 

outcomes and mediating factors. A number of sources of published data were also used. There were 

five main categories of administrative and published data collected for this study: 

1. State administrative data on earnings from covered employment in unemployment insurance 

programs and on benefits provided through TANF and food stamps programs. 

2. Arrest and disposition data from local police agencies and courts. 

3. Data from HUD administrative systems on participants in the public housing and Section 8 

programs, on Section 8 Fair Market Rents, and on PHA expenditures for public housing 

operations. 

4. Data on the schools attended by sample children (and their school districts) from state and 

local sources and the National Center for Educational Statistics Common Core of Data. 

5. Published data from the U.S. Census of 1990 and 2000 at the census tract and block group 

levels. 

The specific data sets in each of these categories are described in appendix A, which provides 

information on their time coverage and sources. 

1.7 Overview of this Report 

The balance of this report is organized into eight chapters with a number of appendices supporting 

them. Chapter 2 provides essential background information to the impact analyses across all the 

study’s domains. It describes the sample’s leaseup and mobility patterns after random assignment and 

estimates the program’s impacts on mobility. Understanding these patterns is essential to interpreting 

the analytic results presented in chapters 3 through 8. 

The quantitative analyses of MTO’s impacts in each of the six study domains are found in chapters 3 

through 8. Each of these chapters provides a review of the hypotheses about the potential effects of 

moves to low-poverty neighborhoods on outcomes and mediating factors in the domain analyzed, and 

then shows the results of testing those hypotheses against the quantitative data collected for this 

interim evaluation. 

Chapter 9 synthesizes the results of the analyses across all the domains, in combination with the 

earlier qualitative study results. It addresses the policy implications of these interim evaluation 

findings as a whole. What have we learned about the impact of moves to low-poverty areas, at the 5-

Chapter 1 – The Moving to Opportunity Interim Evaluation 19 
year mark? What still remains to be investigated? Why might results differ at the end of the 10-year 

study period? 

The report’s appendices (referenced throughout the text) provide important details on the interim 

evaluation’s data sources and data collection methods (appendix A) and estimation methods 

(appendix B). Appendices C through E contain supplementary tables for chapters 2 through 8. 

Appendix F provides additional analytic results for variants on the sample used in this study. 

Appendix G contains supporting materials for chapter 9. 

20 Chapter 1 – The Moving to Opportunity Interim Evaluation 
Chapter Two 

Geographic Mobility in the MTO Interim Evaluation 

Sample 

This chapter presents an analysis of the interim evaluation sample’s geographic mobility. It gives a 

picture of the sample members’ old and current neighborhoods and assesses MTO’s impacts on 

residential location. 

Summary 

The MTO intervention had statistically significant and moderately sizable effects on the sample 

members’ choice of neighborhoods in 2002 and on the amount of time since random assignment spent 

in census tracts with lower poverty levels. While experimental group members did not (on average) 

spend very much time in low-poverty areas—and while many of the areas they first moved to were 

marked by increasing poverty over the decade between 1990 and 2000—there were still substantial 

and positive differences in neighborhood characteristics for the experimental group at the time of this 

study relative to the control group. There were also significant but smaller positive differences for the 

Section 8 group relative to the control group. These analyses provide critical context for 

understanding and interpreting the quantitative findings on program effects in housing, health, 

delinquency, education, employment and earnings, and income and receipt of public assistance. 

Those findings are presented in chapters 3 through 8. 

2.1 Hypotheses about Mobility in MTO 

All the hypothesized impacts of participation in MTO across the six study domains depend on 

residential mobility, the characteristics of neighborhoods to which sample members move, and the 

length of time they remain there. For the experimental group, the move out of public housing to a 

low-poverty neighborhood is meant to set the stage for exposure to the influences of an environment 

that might improve life chances. For the Section 8 group, the move out of public housing to privatemarket housing, whatever the neighborhood, sets the stage for exposure to differences in environment 

that might also alter future paths as compared with those remaining in project-based public or assisted 

housing. 

Hypotheses about mobility shaped the MTO demonstration as well as the design of this interim 

evaluation. The initial hypotheses concerned what the families would do after random assignment: 

• It was expected that the experimental group families would have difficulty using their vouchers 

due to the challenges of finding a unit in a low-poverty location. Mobility counseling was 

provided to help meet those challenges. 

• Section 8 group families were expected to succeed in using their vouchers at about the same rate 

as local voucher holders generally and at a higher rate than the experimental group families. 

Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 21 
• Control group families were expected largely to remain in their project-based subsidized housing. 

Other hypotheses concerned what the sample members might do subsequent to those immediate 

results of the random assignment and voucher offers. Among the hypotheses about later mobility 

were these: 

• After the initial period in their low-poverty locations (a year was required), experimental group 

families might choose to move again. Now unconstrained as to location, their choices would be 

broader. The experience of safer neighborhoods with better schools and more opportunities could 

lead these families to stay in lower poverty neighborhoods even if they moved from their original 

units. On the other hand, those experiencing isolation from friends, families, church, or other 

support networks might decide to move to higher poverty neighborhoods to be closer to their 

networks. 

• Sample members in the Section 8 group would be expected to remain primarily in mediumpoverty areas, based on earlier analyses.25 

• Factors such as rent increases, unit conversions, building sales, or other features of the private 

market could lead to greater mobility for both treatment groups offered vouchers than for the 

control group remaining in project-based housing. 

For families in the experimental and Section 8 groups, subsequent moves could result from changes 

in peoples' lives related to MTO—such as increased employment and earnings—and they could in 

turn affect the outcomes in other areas such as education or housing assistance. Thus it is important to 

examine both initial and subsequent moves as they relate to the outcomes of interest to this study. 

Moves by the control group are also of keen interest to this study. Changes in income or family 

composition might have led sample members to leave public housing on their own. Control group 

families could have applied on their own for Section 8. Since the mid-1990s changes to public 

housing—such as the expansion of the HOPE VI program for severely distressed developments— 

have probably brought more mobility to public housing residents than would have been observed at 

an earlier period. Some private project-based developments have also undergone change, notably 

conversion to tenant-based assistance (vouchers).26 We are interested here in all kinds of mobility so 

that we can detect and understand any impacts of MTO on movement patterns. The impacts we 

measure are those beyond what was happening to control group members. 

The importance of mobility to MTO hypotheses overall 

If the hypotheses about initial and followup mobility in each randomly assigned group proved true, 

positive effects on the treatment group sample members would be anticipated both in the short and 

the long run. Shorter term positive effects would include improved safety, better housing, and better 

25 See Feins (2003) and Katz, Kling, and Liebman (1999a), both analyzing MTO data from 1997. 

26 As noted in chapter 1, the estimates presented in this report represent the incremental effects of MTO 

demonstration vouchers relative to what happened to the controls. Chapter 9 provides some additional 

information on this issue. 

22 Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 
neighborhood conditions. Longer term positive effects could include greater educational attainment, 

higher rates of employment, and higher earnings and incomes. If the mobility outcome is that the 

experimental group tends to stay in private-market housing in lower poverty neighborhoods, while the 

control group tends to stay in public housing in higher poverty areas, then we can definitively test 

both the shorter term and longer term hypothesized impacts. The contrast between Section 8 group 

mobility outcomes and a fairly stationary control group would reflect the impact of moving away 

from project-based housing. 

However, if the mobility effect is that both treatment groups—or even all three, including the control 

group families—move away from initial locations to neighborhoods with the same or similar average 

poverty levels (and other characteristics) over time, the hypotheses about MTO’s impacts might 

predict instead that shorter term effects (e.g., on safety) would diminish over time and that longer 

term impacts might not ever be observed from the MTO demonstration. 

2.2 Mobility Data Sources and Measures 

Data sources 

Using a combination of existing information from the 1997 and 2000 canvasses of the MTO sample, 

other tracking efforts, and interim household survey data, we assembled basic longitudinal 

information about the mobility history of each adult or child in the sample: 

• The sequence (chain) of all residential moves for each sample member from the point of random 

assignment to the time of the interim data collection, 

• The location of each confirmed dwelling in the chain of moves, 

• The duration of stay in each confirmed dwelling in the chain of moves. 

We used Census data from 1990 and 2000 to explore the nature of the surrounding neighborhood for 

each dwelling in the chain of moves. 

The qualitative research identified factors leading to subsequent moves and explored in depth why 

some individuals and families who had initially moved to low-poverty areas later moved to higher 

poverty neighborhoods. The portions of the household and youth survey instruments that dealt with 

subsequent moves were developed using the evidence gathered through the qualitative field work on 

the most salient aspects of neighborhood and the ways that neighborhood differences were 

experienced by sample members. 

Discussions of mobility during the qualitative interviews also offered the opportunity to ask sample 

members about their adjustment to the private housing market, the challenges of switching 

neighborhoods and schools, and how the respondents assessed each home and living environment 

encountered along the chain of moves. Obstacles to adjustment were identified and the adult survey 

instrument contained questions directed at determining how common these obstacles were across the 

interim evaluation sample. 

Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 23 
Mobility measures 

The key measures of residential mobility outcomes—also used as mediating factors in the impact 

analysis for other domains—included: 

• The length of time at each location, 

• The neighborhood characteristics of each location (via geocoding addresses to link with census 

variables), 

• The respondent’s interaction with the community, 

• The respondent’s assessment of the neighborhood, 

• The respondent’s reasons for leaving/staying in the community, including landlord willingness to 

continue leasing the unit to the respondent, 

• The proportion of families moving from low-poverty to higher poverty locations. 

One important use of these measures was for characterizing the areas where sample members lived 

when they enrolled in MTO and where they were living at the time of the interim evaluation. For all 

known addresses, links to 1990 and 2000 Census data at the tract and block group levels allowed 

construction of indicators about the local areas. The indicators were used to examine how the current 

locations of the experimental and Section 8 groups compared with those of the control group. 

A second important use of these measures was to construct variables representing the length of 

exposure (in months) to specific kinds of environments—e.g., to low-poverty areas. Such exposure 

measures are a way to sum up a series of locations as they might be relevant to MTO outcomes. 

Which of these is more relevant depends to some extent on the outcome being considered. 

Neighborhood effects on the incidence of recent asthma attacks, for example, would be expected 

more in relation to current location than to neighborhood history. But neighborhood effects on school 

quality and educational achievement might be expected to result from the entire sequence of locations 

and schools after random assignment. 

2.3 Baseline Conditions and Initial Leaseups 

Neighborhood conditions at baseline 

The MTO demonstration recruited families from public or assisted housing from among the poorest 

census tracts of five central cities. Using 1990 and 2000 Census data, we can characterize the 

families' locations at the time they joined MTO. These areas exhibited the effects of concentrated 

poverty: 

• More than half their populations (on average) were living in poverty. 

24 Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 
• Nearly three-fourths of the families in these neighborhoods were headed by a single, female 

parent. 

• More than 30 percent of all residents were high school dropouts. 

• Unemployment was over 25 percent and labor force participation was low for both men (55 

percent) and women (38 percent). 

• More than 40 percent of the families had no member working. 

As described briefly in chapter 1 (and shown in exhibit C1.3), those who joined MTO reported very 

high levels of dissatisfaction with their neighborhoods. The threatening presence of drug and gang 

activity and high rates of crime victimization were powerful motivators for these adults who longed to 

raise their children in safer environments. To some of them, moving to safety might have been just as 

appropriate a name as moving to opportunity for this demonstration. They hoped to obtain vouchers 

through MTO that would enable them to move out and away from the threats and hazards of their 

baseline developments. 

Leaseup rates and the determinants of leaseup success 

All of the families randomly assigned in MTO to the experimental group and the Section 8 group 

were offered housing vouchers. Not every family randomly assigned to receive a voucher was able to 

find a unit to which they wanted to move that met the Section 8 Housing Quality Standards, with a 

landlord who would accept the family and a voucher and would sign a lease.27 As shown in exhibit 

2.1, some 47 percent of the families assigned to the experimental group in the interim evaluation 

sample moved under the program, while 62 percent of the families assigned to the Section 8 group 

participated.28 The sample thus contains 1,566 program movers out of a total of 4,248 families.29 

Lease-up rates ranged from 47 to 79 percent for the Section 8 group across the five sites, due to 

housing market differences and other factors. Lease-up rates for the experimental group (ranging 

from 33 to 67 percent) were also affected by differences in the counseling provided through MTO. 

The nonprofit counseling organizations varied in the breadth and intensity of their services, in the 

extent to which they included non-housing as well as housing-related assistance, and in the degree to 

27 This process is known in program terms as leasing up. That not all voucher holders lease up is true of the 

national Housing Choice Voucher program as well. Only a portion of those offered vouchers are able to use 

them. The most recent study of participation rates in the program showed that 69 percent of families and 

individuals receiving vouchers in 2000 from large metropolitan housing authorities succeeded in using 

them (Finkel and Buron 2001, p. i.). That rate was higher in 1993 (just prior to the start of MTO), 

measuring 81 percent at that time. 

28 MTO did not offer or give those assigned to the control group tenant-based subsidies with which to make 

such moves. 

29 These leaseup rates mirror closely the leaseup rates for the MTO population overall (47 percent for the 

experimental group and 60 percent for the Section 8 group). That population includes additional families 

enrolled in 1998 in Los Angeles. 

Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 25 
which they acted as advocates for their clients.30 Changes in counseling personnel during MTO 

operations—and changes of the agencies filling this role in two sites (Chicago and Los Angeles)— 

may also have had some effect on client success. 

EXHIBIT 2.1 

MTO PROGRAM LEASEUP RATES BY GROUP 

0% 

47.4% 52.6% 

38.3% 

20% 40% 60% 80% 100% 

Experimental 

Section 8 

Leased Up 

Did Not Lease Up 

61.7% 

Source: MTO tracking logs. 

Note: Weighted data.

Two prior research efforts examined the factors affecting leaseups in the MTO program. Feins, 

McInnis, and Popkin (1997) tested whether counseling affected the leaseup rate for families in the 

experimental group. They showed that client characteristics, market factors, and counseling 

utilization (the degree to which participants used services offered by the nonprofit agencies) were 

associated with the chance that experimental group families could lease up under the low-poverty 

location constraint. Families with one child (rather than more than one) were more likely to move as 

were families with lower incomes. In contrast, Hispanic families and those with higher incomes were 

less likely to move. Families with the head of the household in school and those dissatisfied with their 

neighborhoods were more likely to move. 

A more recent analysis (Shroder 2002) looked at the program as a whole, examining the factors 

behind variations in leaseup rates across both the experimental and comparison groups. The author 

tested the effects of three sets of factors: 

• Indicators of the probability of being accepted by a landlord including market factors, 

demographic characteristics, and personal factors.31 

The MTO demonstration design allowed for a considerable range of counseling practices. See Feins, 

McInnis, and Popkin (1997). 

26 Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 

30 
• Indicators of the net benefit of changing units including preferences and attitudes expressed at 

baseline in survey data. 

• Indicators of the costs of housing search, including wage and income characteristics of the sample 

member, access to transportation, and features of the counseling treatment offered participants in 

the experimental group at the sample member’s site. 

Factors from each of the three sets were associated with leasing up. For example, looser metropolitan 

housing markets increased the probability of leasing up while larger family size reduced it. The 

adult’s positive attitude (as expressed in answers to the baseline survey) was a significant predictor of 

success, as was greater dissatisfaction with the sample member’s baseline location. For the 

experimental group—which faced a locational constraint (the requirement to move to a low-poverty 

area) but which could take advantage of counseling services from a local nonprofit organization to 

assist in the process—on average the effect of the constraint outweighed the effect of the counseling. 

The locational constraint reduced the leaseup rate for that group (compared with the Section 8 group), 

although counseling utilization was positively associated with leaseup rates (partially helped 

overcome the constraint). 

The leaseups that occurred through the MTO program were by no means the only moves sample 

members made after random assignment, as will be seen in the next section. Control group families 

could move on their own. Families that did not succeed in leasing up could move on their own. 

Experimental group families that leased up could move again after a year, without the location 

constraint. But the experimental treatment was intended to alter the pattern of moves that might 

ordinarily occur, and the quantitative analyses conducted for this study address whether that purpose 

was achieved. 

2.4 Sample Mobility in the Followup Period 

In the 4 to 7 years after random assignment, the interim evaluation sample was quite mobile.32 

Exhibit 2.2 divides the adult sample members into those who leased up (moved from their baseline 

locations with the vouchers MTO provided) and those who did not lease up. The latter group is 

divided between those who moved subsequently (left their baseline homes without the help of MTO) 

and those who stayed (were still in their baseline locations in 2002). 

Exhibit 2.2 shows that 30 percent of the families assigned to the control group were still at their 

baseline addresses in 2002. The high percentages of movers in the control group (69.6 percent) and 

31 Factors influencing whether the owner of the unit is likely to agree to lease to a family holding a housing 

voucher might include the local vacancy rate (i.e., whether there are many other potential tenants for the 

unit), the size and composition of the household, their race or ethnicity, and the household head’s selfpresentation in seeking to rent (reflecting her confidence in her ability to find a unit but also some 

otherwise unmeasured attributes). See Shroder (2002) for details. 

32 See exhibit C2.1 in appendix C for a map of the sample adults’ locations across the United States in 2002. 

Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 27 
among the experimental and Section 8 group families who did not move with the MTO vouchers (35 

percent and 22.7 percent, respectively) result in part from major changes in their public housing 

environments due to federal and local efforts to deal with distressed public housing. These efforts 

included the HOPE VI program (which provided federal funds to demolish many units and to rebuild 

some), vacancy consolidation (demolition) efforts, and local comprehensive modernization projects. 

At baseline, 22 percent of the control group members in the sample lived in developments that were 

(or were scheduled to be) affected by these programs during the period in which the MTO program 

was recruiting and enrolling families. 

EXHIBIT 2.2 

GEOGRAPHIC MOBILITY IN THE INTERIM EVALUATION ADULT SAMPLE BY TREATMENT GROUP 

Experimental Section 8 

Group Group Control Group Total 

Leased Up 47.4% 61.7% N/A 36.9% 

Did Not Lease Up 

Moved 35.0% 22.7% 69.6% 42.2% 

Stayed 17.5% 15.7% 30.4% 21.0% 

All Adults 100% 100% 100% 100% 

Sources: MTO data system, adult survey 

Sample: Adults from families randomly assigned through December 31, 1997. 

Notes: Data are weighted as described in appendix B. Numbers may not sum to 100 due to rounding. Mobility 

patterns in this exhibit are for the full period since random assignment. 

Thus some of the families in all three groups who did not lease up through MTO but did move 

subsequently may have been required to move around by the housing authorities. Those in the 

treatment groups who did not move through MTO might well have been affected by the same public 

housing changes as the control group. In fact, another 3 percent of the experimental group members 

lived in the same block group in 2002 as at random assignment, as did another 2 percent of the 

Section 8 group. These adults could be considered to have stayed, too. 33 

Some 16 to 17 percent of the experimental and Section 8 groups were still in their baseline locations 

by 2002 when this study’s data were collected. Total mobility rates for these two groups were thus 

33 According to the Census Bureau a block group is a cluster of census blocks generally containing 600 to 

3,000 people, with an optimum size of 1,500 people. By contrast, a census tract generally has 1,500 to 

8,000 people, with an optimum size of 4,000 people. In urban areas block groups might typically combine 3 

to 4 city blocks. Although the block group is useful for establishing tightly clustered locations, in this report 

we primarily use census tract-level data since (a) that was the basis for the MTO experiment, (b) the 

boundaries of tracts generally change less over time than those of block groups, and (c) the standard errors 

for block groups tend to be quite large because of their relatively small sample sizes and the Census 

Bureau’s data masking methodology. 

28 Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 
roughly equal. The initial leaseup rate for the Section 8 group was larger than the initial leaseup rate 

for the experimental group, but a larger proportion of the experimental group subsequently moved on 

their own. 

Program moves—experimental group 

For the members of the experimental group who leased up through MTO, the initial moves to lowpoverty areas were both satisfying and challenging. Respondents in the qualitative study sample 

frequently commented on the improvement in safety as the most important aspect of their moves. 

However, some households in low-poverty neighborhoods experienced a loss of access to convenient 

transportation, free recreational activities, health care, shopping, and church that those in more central 

locations enjoy. MTO families moved from large public housing developments to a variety of housing 

types including single-family homes, duplexes, townhouses, and apartment complexes. Experimental 

group movers were more likely than Section 8 movers to live in single-family homes or townhouses.34 

Most of the experimental group respondents in the qualitative sample rented from small landlords 

rather than large management companies. To a great extent, their perceptions of their housing 

depended on the quality of their relationships with the buildings’ owners. 

To use their MTO vouchers the families in the experimental group were required to move to census 

tracts with less than 10 percent poverty according to the 1990 Census. Exhibit 2.3 shows the poverty 

rate of the neighborhoods to which experimental group families moved with their MTO vouchers. In 

the first panel we see the poverty rates measured in 1990 Census data—the same data used to identify 

low-poverty areas when the demonstration was operating. The first panel shows that there was 

substantial compliance with the locational constraint, with 89 percent of the experimental group 

making program moves to areas with less than 10 percent poverty, and 94 percent moving to areas 

with less than 11 percent poverty. 35 

Now that Census 2000 data are available, what do they tell us about the initial destinations of 

experimental group movers? The second panel of exhibit 2.3 shows the poverty rates for the same 

locations as the first panel, but with poverty measured in April 2000 rather than April 1990. These 

figures are quite different from the previous ones. They show that only about 40 percent of the 

experimental group’s program move locations were still low-poverty areas in 2000, although 90 

percent were still in areas of less than 20 percent poverty. 

We can also use the 1990 and 2000 poverty rates to estimate what the actual poverty rates may have 

been in the census tracts to which experimental group families moved at the time they leased up. 

These estimates are shown in the third panel of exhibit 2.3. These estimates are based on the change 

in poverty rates in the destination census tracts over the decade from 1990 to 2000. As the 

comparison of the exhibit’s first two panels indicated, a considerable proportion of the experimental 

group’s destination tracts increased in poverty population during the decade. As a result, just half of 

the moves were to areas estimated to really have poverty rates below 10 percent at the time of the 

34 This information comes from the Neighborhood Assessments that the qualitative interviewers completed. 

35 A considerable number of leaseups occurred in census tracts with poverty rates up to 10.9 percent. HUD 

also granted a small number of waivers in special circumstances for leaseups in higher poverty locations. 

Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 29 
move and another third were to areas of 10 to 15 percent poverty at the time. All told, 97 percent were 

to areas with less than 20 percent poverty. The remaining 3 percent of experimental group program 

moves were to census tracts with still higher poverty rates. 

EXHIBIT 2.3 

NEIGHBORHOOD POVERTY RATE AT TIME OF FIRST LEASEUP 

LT 10% 10–15% 15–20% 20–30% 30–40% 40%+ Mean 

Poverty Rate in 1990 Census 

Experimental Group 

Program movers 7.6 1.4 1.4 0.5 0.2 7.5% (n=813) 89.0 

Section 8 Group 

Program movers 10.7 12.9 14.1 24.1 21.1 17.2 26.9% (n=735) 

Poverty Rate in 2000 Census 

Experimental Group 

Program movers 38.9 33.2 17.9 8.8 0.5 0.6 12.4% (n=815) 

Section 8 Group 

Program movers 5.9 7.6 15.0 28.8 24.0 18.7 28.4% (n=737) 

Estimated Poverty Rate at Time of Movea 

Experimental Group 

Program movers 33.8 12.2 2.3 0.6 0.4 10.8% (n=813) 50.7 

Section 8 Group 

Program movers 6.9 7.9 14.9 29.3 21.2 19.9 27.8% (n=735) 

Source: MTO data system, 1990 and 2000 census tract-level data. 

Sample: Adults from families randomly assigned through December 31, 1997. 

Notes: Data are weighted as described in appendix B. 

(a) Estimates were made using a simple linear interpolation over the decade between the 1990 census and 2000 

census. For example, if the 1990 poverty rate in the destination census tract was 8 percent but in the 2000 

census it was 12 percent, over the decade the rate was assumed to change by .4 percent per year. For a 

leaseup in 1995 in this tract, the estimated poverty rate at that time would be about 10 percent. (The formula 

used the actual date of the program move and estimated the poverty rate based on days elapsed from April 1, 

1990.) 

The dynamic underlying this finding is the demographic and socioeconomic change in these local 

areas during the 1990s. Students of the geography of poverty have noted that the 1990s saw a 

reduction in poverty concentration in many U.S. cities (Jargowsky 2003; Kingsley and Pettit 2003). 

The sharp decline in the proportion of poor people living in high-poverty census tracts has garnered 

the most attention. But at the same time, poverty spread into the middle ranges. Balancing the 5 

percent reduction in the concentrated poverty tracts were increases of 5 percent in the tracts with 10 to 

30 percent poverty rates (Kingsley and Pettit 2003, p. 3). Most salient to MTO, the share of the 

metropolitan poor living in census tracts with poverty rates of 10 to 20 percent increased by 2 percent 

over the decade. 

30 Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 
1 

To explore the dynamic further, we can categorize the neighborhoods to which experimental group 

families actually moved by the changes in poverty rates during the decade from 1990 to 2000. We 

have broken the distribution into three categories—decreasing poverty rates, stable poverty rates, and 

increasing poverty rates. Stable areas are defined as census tracts with changes of no more than 5 

percent in either direction during the decade. 

Exhibit 2.4 shows that just over half of the locations chosen by experimental group families were 

characterized by decreasing or stable poverty rates, but the other half had increasing poverty rates 

through the 1990s. We can speculate on the reasons for this pattern, which is quite distinct from the 

pattern for the Section 8 group (described below). But the implication for the MTO experiment is to 

raise questions about some of the neighborhoods to which the experimental group families moved. It 

seems likely that neighborhoods with increasing poverty rates are neighborhoods beginning to 

decline. Moves to low-poverty neighborhoods in decline may not provide the opportunity-rich 

environments hypothesized to improve the lives and well-being of the movers. 

The likely reason for this pattern is that experimental group families found it easier to rent units in 

neighborhoods experiencing some decline in prosperity. These might be areas seeing reduced demand 

for their rental stock, with softening rents. Landlords might therefore be more willing to rent to 

families on Section 8 or families from public housing than in the past. However, the arrival of 

experimental group families alone (or of the few Section 8 group families making low-poverty 

program moves) did not play a role in the decline of these areas, because the numbers of MTO 

families moving to any single neighborhood were too small to be influencing tract-level changes. At 

the most, experimental group families accounted for 2.3 per 1,000 households in the low-poverty 

destination tracts (Goering et al., 1999, p.42). 

EXHIBIT 2.4 

CHANGING NEIGHBORHOOD AT TIME OF FIRST LEASEUP?1 

Decreasing Stable Poverty Increasing 

Poverty Rate, Rate, Poverty Rate, 

1990–2000 1990–2000 1990–2000 Total 

Experimental Group 

Program movers (n=813) 1.2% 54.3% 44.6% 100% 

Section 8 Group 

Program movers (n=735) 18.0% 46.3% 35.7% 100% 

Source: MTO data system, 1990 and 2000 census tract-level data. 

Sample: Adults from families randomly assigned through December 31, 1997. 

Notes: Data are weighted as described in appendix B. 

Stable poverty rates are defined as tract-level poverty rates changing less than 5 percent in either direction 

between 1990 and 2000. Decreasing poverty rates are tract-level rates that fell 5 or more percentage points in 

the decade, while increasing poverty rates are tract-level rates that rose 5 or more percentage points in the 

decade. 

The dynamics of the neighborhood changes differed among the five MTO sites (see the maps in 

appendix exhibits C2.2 to C2.6 for reference). In Boston and Chicago, about 30 percent of the 

experimental group program movers chose locations in census tracts that increased in poverty from 

1990 to 2000. In Baltimore, 41 percent of the experimental group program movers did the same. Just 

Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 31 
over half the Los Angeles experimental group lease-ups were in destination tracts marked by rising 

poverty, but LA was the only MTO site where the MSA-wide poverty concentration increased, 

cutting against the national pattern (Jargowsky 2003, pp. 11, 14-17). The site with the highest 

proportion of experimental group movers (60 percent) leasing up in tracts with rising poverty was 

New York. By the time of the 2000 Census, only one-third of the leaseup locations in that site had 

poverty rates under 10 percent.36 

Examining the locations of the low-poverty tracts where MTO families lived in 2002, it is also 

notable that more than half of these tracts were outside the central cities of the five metropolitan 

areas. About 58 percent of the low-poverty tracts were in suburban areas, compared to 27 percent of 

the tracts with poverty rates in the 10 to 15 percent range, 18 percent of the tracts with poverty rates 

in the 15 to 20 percent range, and less than 10 percent of the tracts with poverty rates of 20 percent or 

more. 

Program moves—Section 8 group 

When they were issued their vouchers through MTO the families assigned to the Section 8 group 

encountered the same Section 8 rules and procedures and faced the same range of choices as any 

other person seeking to lease up for the first time with tenant-based housing assistance. Because most 

of them were public housing residents, they could not lease in place—that is, they could not use their 

vouchers for their current apartments. This often makes it easier to become a Section 8 participant.37 

But neither was there a constraint related to the characteristics of the neighborhoods they could 

consider, as there was for the experimental group. 

Most of the 60 percent of Section 8 group adults who did move with the MTO voucher leased up in 

areas with poverty rates of 20 percent or more (exhibit 2.3). Indeed, almost one-fifth of them leased 

up in concentrated poverty areas (40 percent plus). Only a small share of the Section 8 group (about 

10 percent) leased in census tracts with poverty rates below 10 percent. Comparing the experimental 

and Section 8 groups in exhibit 2.3 makes it clear that there was a substantial difference in the initial 

locations chosen by the two groups. 

From the vantage point offered by 2000 Census tract-level data, almost half of the neighborhoods 

chosen by the experimental group program movers were increasing in poverty during the decade. The 

picture is very different regarding the neighborhoods to which the Section 8 group families made 

program moves (exhibit 2.4). Almost 20 percent of the families moved to neighborhoods 

characterized by decreases in poverty after 1990 and about 45 percent moved to areas with stable 

poverty rates. Because the areas chosen had much higher poverty rates to begin with, this does not 

36 Most of the remainder (58 percent) were in areas with poverty rates of 10 to 19 percent. About 9 percent 

were in areas with poverty rates of 20 percent or more. 

37 Some families joined MTO from project-based assisted housing and under some circumstances it was 

possible for them to lease in place. At least one family did so. Over the long term leasing in place has been 

a major source of units for Section 8 participants nationwide. But Finkel and Buron (2001) report in their 

recent Section 8 success study that only 21 percent of successful Section 8 enrollees leased in place in 2000 

compared with 37 percent in 1993. 

32 Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 
mean that the Section 8 group chose more promising communities, only that these areas started from 

a different level and were on a different trend line. 

Control group mobility 

The mobility of the control group tells us what mobility patterns the experimental and Section 8 

group families would have followed without MTO. The control group families started out in highpoverty census tracts (40 percent or more persons living in poverty according to the 1990 census).38 

Exhibit 2.5 shows that by 2002, only 48 percent of the control group continued to live in concentrated 

poverty areas. This decline is largely due to control group members moving; two-thirds moved 

between random assignment and 2002. Of the control movers, in 2002 66 percent lived in areas with 

somewhat lower poverty rates than at baseline, and 24 percent lived in areas with less than 20 percent 

poverty. Note in exhibit 2.5 the similarity in patterns for the stayers in all three groups and for the 

nonprogram movers in all three groups. 

The stayers in the control group were still concentrated in high-poverty neighborhoods in 2002, but 

40 percent of them were in census tracts that had decreased in poverty over the previous decade. The 

same was true for a smaller share of the control group movers (26 percent). Overall, 44 percent of the 

control group adults were living in areas with stable poverty rates from 1990 to 2000. 

Experimental group mobility—subsequent moves 

MTO demonstration rules required that experimental group families sign 1-year leases for the units 

they rented with the program’s vouchers. After that year, there was no further locational constraint 

and the families could stay or move as they wished within the rules of the Section 8 program. By the 

time of the interim evaluation, one-third of the experimental group program movers remained in the 

same neighborhood (census tract) as their initial move, although perhaps not at the same address. A 

full two-thirds of these movers had moved outside these tracts. 

After leasing up, 66 percent of the adults in experimental group program mover families made one or 

more additional moves. The survey data show that when sample members began looking for housing 

to make a subsequent move, over two-thirds reported searching in the same or similar neighborhoods 

to that of their leaseup address. However, we know from the mobility histories that by 2002 many had 

ended up changing locations. And for the experimental group, this meant moving to areas more like 

the ones where Section 8 families and control group movers lived.39 

38 There was an exception made to this 40 percent requirement in Boston. HUD agreed to include in MTO 

three public housing developments in areas below the 40 percent threshold because of high local crime 

rates and other conditions consistent with MTO targeting. 

39 Exhibits C2.2 through C2.6 in appendix C are maps showing the 2002 locations of program movers in the 

experimental and Section 8 groups. 

Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 33 
EXHIBIT 2.5 

CENSUS 2000 POVERTY RATE OF CURRENT RESIDENTIAL LOCATION1 

(PERCENT DISTRIBUTIONS–WEIGHTED DATA) 

Under 

10% 10–15% 15–20% 20–30% 30–40% 40%+ Mean 

Control Group 

Stayed in place (n=343) 0 0 0 3.4 17.2 79.4 51.1% 

Moved (n=793) 5.4 8.1 10.0 20.0 22.7 33.8 33.6% 

Total control group 3.8 5.7 7.0 15.0 21.1 47.6 38.9% (n=1136) 

Experimental Group 

Did not lease up 2.7 4.3 6.5 13.1 26.4 47.0 39.6% 

Stayed (n=267) 0 0 0 3.1 24.7 72.1 49.1% 

Moved (n=518) 4.1 6.5 9.8 18.3 27.3 34.1 34.6% 

Leased up 25.3 19.1 15.7 18.5 11.7 9.7 20.0% 

Did not move again (n=245) 38.4 33.1 14.4 12.9 0.6 0.6 12.6% 

Moved again (n=456) 18.2 11.6 16.4 21.5 17.7 14.6 24.0% 

Total experimental 13.3 11.3 10.8 15.7 19.5 29.4 30.4% group (n=1,485) 

Section 8 Group 

Did not lease up 2.8 7.2 6.3 11.6 23.7 48.4 38.3% 

Stayed (n=166) 0 0 0 3.1 26.3 70.6 46.8% 

Moved (n=242) 4.7 12.1 10.6 17.5 21.9 33.2 32.5% 

Leased up 6.4 9.2 15.0 26.3 23.2 20.0 28.6% 

Did not move again (n=215) 5.1 6.5 15.6 27.5 22.6 22.8 29.1% 

Moved again (n=426) 7.0 10.6 14.6 25.7 23.5 18.6 28.4% 

Total Section 8 group 

(n=1,050) 5.0 8.4 11.6 20.6 23.4 31.1 32.4% 

Source: MTO data system 

Sample: Adults from families randomly assigned through December 31, 1997. 

Note: Data are weighted as described in appendix B. 

1

 Measured at the census tract level. 

These second movers tended to move to higher poverty neighborhoods than those that stayed but 

lower poverty areas than those who did not lease up (exhibit 2.5). It is also worth noting that twothirds of the families that did not lease up under the program did eventually move on their own. These 

families, too, tended to move to neighborhoods with moderate to high poverty rates. 

The main reasons experimental group movers moved again after their initial leaseup were because of 

leasing problems (22 percent) and conflicts with their landlords (20 percent). Getting a bigger or 

better apartment was the second most common reason for moving (18 percent). Safety was a small 

but not insignificant reason (9 percent) for second moves by experimental group program mover 

34 Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 
families. Although costs may have been a factor in disputes with landlords, higher rents (1 percent) 

and utilities (1.3 percent) were not generally reasons the experimental group program movers gave for 

leaving the homes to which they moved through MTO. 

A number of experimental group respondents to the qualitative interviews reported that the MTO 

move was the first time they had ever rented from an individual landlord. Some had very good 

relationships with their landlords and were pleased with the way they maintained their units. Others 

had landlords whom they felt were hostile, unresponsive about maintenance problems, and raised 

their rents. Some owners sold their buildings with little warning. 

The qualitative interviews suggested that those who had good relationships with landlords were more 

likely to stay in their new communities, while those who were less satisfied were more likely to make 

subsequent moves.40 An experimental group mover in New York talked at length about how happy 

she was with her apartment and her landlord. He owned a dry cleaning business in the first floor of 

the building and was friendly with both her and her daughter. The mover raved about how well he 

maintained the building: 

My landlord, anything that breaks, he up here the next day. He’s very helpful. ... 

He keeps up with everything. The exterminator comes in once a month. He’s 

fabulous. He’s very helpful. 

Other respondents complained about problems with their landlords. For example, one woman in the 

experimental group in New York reported a range of maintenance issues with her private market unit, 

including rats and rodents, problems getting exterior lights repaired, and paper-thin doors. She said 

her landlord was unresponsive about making repairs: 

Before the tenant upstairs moved up there, we had rats and rodents a couple of 

months. Three or four months ‘fore he send the exterminator. ... The landlord is the 

pits! ... He’s one step from being a slumlord. Nothing gets fixed. Nothing. He does 

absolutely nothing. 

Indeed, landlord problems frequently were the factor that prompted a subsequent move. In the survey 

data, landlord problems were the second largest category (20 percent) of reasons given for moves 

away from the initial housing in low-poverty neighborhoods. 

Section 8 group mobility—subsequent moves 

Like the sample members assigned to the experimental group, many families in the Section 8 group 

who had moved through MTO moved again in the period before the interim evaluation. In fact, twothirds of the families that leased up in the Section 8 group were living in different locations by the 

time of the interim evaluation. 

The survey data indicating problems with landlords were not collected in a way that allowed us to test these 

relationships more generally. 

Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 35 

40 
Families moved again for a variety of reasons including: problems with their current housing unit or 

landlord; housing costs; desire to be closer to (or further from) friends and family; lack of adequate 

public transportation; and distance from shopping, school, or employment. The survey data indicate 

that program movers’ motivations for leaving their initial homes differed somewhat between the 

experimental and Section 8 groups. For the Section 8 group, safety issues (23 percent) and building 

issues (13 percent) figured prominently while landlord problems did not.41 Both groups put getting a 

bigger or better apartment high on the list of reasons they moved again. 

A considerable number of the Section 8 families who failed to lease up during MTO were also able to 

move from their baseline locations. Exhibit 2.5 showed the poverty characteristics of this group’s 

current locations according to the move patterns of the members. About 5 percent were living in areas 

with poverty rates below 10 percent when this study’s data were collected and a quarter of them were 

living in areas with poverty rates under 20 percent. 

Geographic mobility and neighborhood racial composition 

Up to this point, we have examined sample mobility in terms of a single characteristic of residential 

locations—the poverty rate. Broadening this perspective, exhibit 2.6 provides parallel figures on the 

racial and ethnic makeup of the census tracts where the sample lived at the time of this study’s data 

collection. (The measure combines racial minorities with Hispanic ethnicity into total percent 

minority population.) MTO was not designed to address issues of racial or ethnic concentration 

directly. But its roots in the Gautreaux Program and its parallels with several remedial mobility 

programs ordered by courts during the 1990s make the racial composition of destination 

neighborhoods a question of real interest. Also, early MTO research showed some significant 

treatment effects on the racial composition of the sample’s locations in 1997 (Feins 2003). 

In 2002 the vast majority of sample members (87 percent) lived in areas of extremely high minority 

concentration (80 percent or more). Only a handful of the adults (2 percent) lived in areas with less 

than 20 percent minority population and just 8 percent lived in areas with less than 40 percent 

minority population. The pattern appeared to differ only slightly by random assignment group. About 

10 percent of the experimental group and 6 percent for the Section 8 group lived in areas with less 

than 40 percent minority population. Thus sample members from all groups were living in highly 

segregated neighborhoods, although the areas might differ in their poverty rates. 

The low-poverty areas initially chosen by experimental group movers in the interim evaluation 

sample were considerably less segregated, with an average minority population of 51 percent 

according to the 1990 Census. However, by 2000 these same areas had an average percent minority 

population of 67 percent. 

The neighborhoods of the MTO families who lived in low-poverty census tracts in 2002 were still 

much less racially concentrated than tracts with higher poverty rates where other MTO families were 

living. For example, experimental group members’ low-poverty tracts averaged 46 percent minority 

Experimental group members cited safety much less frequently but leasing problems more frequently. 

Some of these problems may have forced the families to move. For example, some respondents indicated 

that landlords would not renew leases or that Section 8 would not approve the unit on HQS again. 

36 Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 

41 
population, and Section 8 group members’ low-poverty tracts averaged 53 percent minority 

population. But as Exhibit 2.5 made clear, only a small proportion of the sample in either group did 

live in low-poverty areas. The 2002 neighborhoods of the families living in areas with poverty rates 

of 15 percent or more averaged 75 percent or more minority population.42 

EXHIBIT 2.6 

CENSUS 2000 PERCENT MINORITY OF CURRENT RESIDENTIAL LOCATION1 

(PERCENT DISTRIBUTIONS) 

Under Over 

20% 20–40% 40–60% 60–80% 80% Mean 

Control Group 

Stayed in place (n=343) 

Moved (n=793) 

Total (n=1136) 

Experimental Group 

0 

2.6 

1.8 

2.4 

5.3 

4.4 

3.1 

6.5 

5.5 

5.2 

10.1 

8.7 

89.3 

75.4 

79.6 

93.8% 

85.3% 

87.8% 

Did not lease up 1.5 3.0 4.7 6.0 84.9 90.2% 

Stayed (n=267) 0 1.1 4.2 2.5 92.2 94.2% 

Moved (n=518) 2.3 3.9 5.0 7.8 81.1 88.1% 

Leased up 5.7 10.5 10.9 13.5 59.4 75.4% 

Did not move again (n=245) 7.5 17.0 11.0 19.3 45.2 68.0% 

Moved again (n=456) 4.7 7.1 10.8 10.3 67.1 79.4% 

Total (n=1,486) 3.5 6.5 7.6 9.5 72.9 83.2% 

Section 8 Group 

Did not lease up 

Stayed (n=166) 

Moved (n=242) 

2.4 

0 

4.0 

5.4 

2.7 

7.6 

6.1 

8.6 

4.0 

7.4 

2.7 

10.6 

78.7 

85.9 

73.8 

86.7% 

91.4% 

83.5% 

Leased up 2.2 3.2 9.6 9.0 76.0 85.5% 

Did not move again (n=215) 1.4 1.4 8.5 9.9 78.8 87.6% 

Moved again (n=426) 2.5 4.2 10.2 8.6 74.6 84.4% 

Total (n=1,049) 2.2 4.2 8.2 8.4 77.0 86.0% 

Source: MTO data system 

Sample: Adults from families randomly assigned through December 31, 1997. 

Notes: Data are weighted as described in appendix B. 

1

 Measured at the census tract level. 

This pattern varied little by site. The MTO families in Boston lived in areas averaging 66 percent minority 

for the experimental group and 72 percent minority for the Section 8 and control groups. In all the other 

sites, the average percent minority in the neighborhoods of all three groups was over 80 percent. 

Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 37 

42 
Geographic mobility and other neighborhood characteristics 

Exhibit 2.7 provides a summary view of other area characteristics for the baseline locations of all the 

adults in the interim evaluation sample and for the control group’s locations at the time of this study. 

The exhibit’s upper panel presents a number of concentrated poverty indicators while the lower panel 

presents several opportunity indicators—the types of neighborhood characteristics hypothesized to 

benefit participants who moved to low-poverty areas. The exhibit first shows how the sample’s origin 

(baseline) locations looked on these indicators measured in 1990 Census data. Then it shows the same 

characteristics of the same addresses measured in 2000 Census data. Finally, the exhibit shows the 

characteristics of the control group’s current residential locations (also measured in 2000 Census 

data). 

The origin locations for all sample members were clearly in neighborhoods with multiple problems— 

high poverty and dependence on public assistance, many high school dropouts, high unemployment. 

The 1990 Census data show that three-fourths of all households in these areas consisted of femaleheaded families with children and that 42 percent of the families had no working members. Only half 

of the families had wage or salary income and less than one-fifth of the adult population had any 

education beyond high school. 

The 1990s saw two major interventions to change these neighborhoods and the lives of their 

residents: the major public housing initiatives described earlier in this chapter and the major changes 

in the federal welfare laws with the end of Aid to Families with Dependent Children (AFDC) and the 

advent of the Temporary Assistance for Needy Families (TANF) program. The decade was also a 

period of sustained economic growth. By the mid-1990s when MTO was recruiting volunteer 

families, some of the indicators shown in exhibit 2.7 had probably begun to change, but we have no 

small-area data to examine from that period. 

By April 2000 when the data for the next census were gathered, a number of the indicators for the 

baseline neighborhoods showed improvement (column 2). The rate of public assistance receipt had 

fallen to fewer than a quarter of the population, less than one-third of the families had no working 

members, 60 percent of the families had wage or salary income, and one-fourth of the adult 

population reported education beyond high school. On the other hand, half the populations of these 

areas were still living below the poverty line and the local unemployment rate averaged 25 percent. 

The differences between the first two columns of exhibit 2.7 are all due to changes in the decade from 

1990 to 2000, with the locations held constant. But the last column—showing the same indicators for 

the 2002 locations of control group adults—reveals that the contrasts are much greater when the 

control group’s mobility is taken into account. These differences (between columns 1 and 3) are the 

joint result of movement by control group members to different neighborhoods and changes in the 

neighborhoods themselves. 

38 Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 
EXHIBIT 2.7 

MEAN CHARACTERISTICS OF ORIGIN LOCATIONS 

AND CONTROL GROUP 2002 LOCATIONS 

(PERCENT DISTRIBUTIONS) 

All Groups All Groups 

Group 

Origin Origin 2002 

56.0 49.1 38.9 

45.9 23.0 17.6 

72.8 64.5 56.8 

32.8 30.2 25.8 

26.7 24.5 19.0 

Males 54.9 49.9 54.6 

38.2 43.5 47.2 

42.0 29.9 24.2 

poverty level 23.7 27.2 37.3 

51.8 60.1 66.4 

HS 19.3 25.3 30.7 

10.1 13.0 15.7 

9.1 12.3 15.0 

67.2 72.7 74.8 

i 7.6 9.8 22.9 

Black 65.4 57.2 55.6 

26.6 32.1 28.9 

Minority 90.7 91.3 87.8 

Tract Characteristic Combined Combined 

Control 

Locations 

1990 Census 

Locations 

2000 Census 

Locations 

2000 Census 

Concentrated Poverty Indicators for the Sample’s Residential Locations 

Persons in poverty 

Households receiving public 

assistance income 

Female-headed families with 

own Children 

High school dropouts 

Unemployment rate 

Labor force participation 

Females 

Families with no workers 

Opportunity Indicators for the Sample’s Residential Locations 

Persons with incomes twice the 

Households with wage or salary 

income 

Persons with education beyond 

Some college 

College graduate 

16 to 19-year-olds in school 

Owner-occup ed housing 

Racial and Ethnic Composition of Population 

Hispanic 

Sources: MTO data system, U.S. Census 

Sample: Adults from families randomly assigned through December 31, 1997. 

Notes: Data are weighted as described in appendix B. Results for origin addresses are measured first in 1990 

Census tract-level data and then in 2000 Census tract-level data. Results for control group’s 2002 locations are 

measured in 2000 Census tract-level data. 

Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 39 
The neighborhoods where control group members lived at the time of this study had average poverty 

rates just under 40 percent (the widely used threshold for high-poverty areas and MTO’s targeting 

criterion for recruiting families into the demonstration). Compared with the baseline neighborhoods, 

the areas were characterized by lower unemployment, higher labor force participation, and fewer 

families with no workers. Two-thirds of all households in these areas had wage or salary income and 

30 percent of the adults had more than a high school education. Perhaps most striking, the areas were 

marked by much higher rates of owner-occupied housing (23 percent compared to 10 percent), an 

indicator often taken as a sign of residents having a greater stake in their areas and being more likely 

to maintain or improve conditions there. 

2.5 Geographic Mobility Impacts 

Thus it appears that even without the MTO intervention the residential locations of the participants, as 

measured by where the control group families were living in 2002, would have been considerably 

better than they were when these families joined MTO. Even so, the mobility patterns described 

above for the two treatment groups differ in a statistically significant way from the mobility patterns 

of the control group. By measuring the impacts of the two treatments (experimental and Section 8) on 

locations and exposure periods, we can ascertain to what extent the premise of MTO—moving 

families from concentrated poverty areas to low-poverty areas—was actually met.43 

Early in this chapter, we discussed a number of hypotheses about the mobility of the MTO sample 

members. There were reasons to expect the treatment groups to show greater mobility, after their 

initial moves than the control group. However, changes in public housing during and after the MTO 

program were an important stimulus to nonMTO mobility. More than two-thirds of the control group 

adults have moved since they joined MTO, and so have two-thirds of the Section 8 group members 

and 60 percent of the experimental group members who did not lease up with the MTO vouchers. 

We also described hypotheses about why experimental group families might stay in (or leave) lowpoverty neighborhoods. As earlier tables have shown, the high mobility since random assignment has 

led experimental group movers to move toward higher poverty locations while control group families 

moved toward lower poverty areas. However, exhibit 2.8 shows that even with these moves, families 

that moved to low-poverty areas are still in considerably lower poverty neighborhoods than control 

families, with Section 8 group families falling in between.44 

43 Exhibits C2.7 through C2.11 show the 2002 locations of sample members from all three groups (for the 

original MTO metropolitan areas). 

44 Section 1.4 and appendix B provide assistance in interpreting exhibit 2.8 and the other exhibits with tests of 

outcomes in the remainder of the report. 

40 Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 
Exhibit 2.8 shows the effects of the experimental and Section 8 treatments on the sample adults’ 

current choices of locations, in terms of two characteristics: the poverty rate and the percent minority. 

The first panel shows the effects on poverty rates for the whole sample and for specific subgroups 

within it.45 The average poverty rate (percent poverty population) in current neighborhoods for the 

control group—combining all racial and ethnic groups—was 38.7 percent. There are statistically 

significant effects for both the experimental and Section 8 groups. Being assigned to the experimental 

group alters the families’ neighborhood choices and (as a result) lowers the average poverty rate by 8 

points, while for the Section 8 group the intent-to-treat effect is 6.5 points. The treatment-on-treated 

effects, which estimate effects only for those in the treatment groups who actually moved through 

MTO, are larger.46 They indicate that the low-poverty location constraint led the experimental group 

families who leased up to live in neighborhoods with poverty rates an estimated 17 points lower than 

they would have without their involvement in MTO. The effect for those leasing up in the Section 8 

group was an estimated 11 point reduction in the poverty rates of their chosen neighborhoods. The 

participants’ race and ethnicity make some difference in the effects concerning the poverty rate of the 

current location, with slightly smaller effects for Hispanic sample members. 

Thus with respect to neighborhood poverty rates, the MTO treatments do affect locational choices, 

resulting in significantly reduced poverty rates of areas chosen by both experimental and Section 8 

group members compared to those occupied by control group families. The experimental group 

counterfactual mean of 37.2 percent poverty is reduced to 20 percent. The Section 8 group 

counterfactual mean of 38.9 percent is reduced to 28.6 percent. The magnitude of these reductions is 

notable, particularly the fact that experimental group movers on average face local poverty rates just 

half that of the concentrated poverty threshold level of 40 percent. The reduction is also notable 

because it is measured relative to control group locations, many of which underwent major changes 

through HOPE VI and other programs to remove or redevelop severely distressed public housing. 

The lower panel of exhibit 2.8 tests for differences relative to the control group in the racial and 

ethnic composition (percent minority) of the sample members’ current residential locations. Only the 

experimental group showed a significant impact on this outcome, with reductions in percent minority 

population 4 percent (ITT) and 10 percent (TOT) relative to the neighborhoods where control group 

adults were living. However, the control mean for percent minority population is very high. On 

average the control group adults were living in areas of about 88 percent minority population at the 

time of the interim evaluation. Reductions of 4 or even 10 percentage points make little difference to 

these figures. 

45 All subgroups in the impact analyses are defined by baseline characteristics, so that they are exogenous to 

the treatment and can be tested within the experimental design. 

46 They are calculated by dividing through the coefficients by the (fractional) share of leaseups in each group. 

Thus the TOT effects will always be larger than the ITT effects and so will their standard errors (by the 

same proportion). The adjustment does not, therefore, affect statistical significance. See appendix B for 

further information on this “Bloom adjustment.” 

Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 41 
EXHIBIT 2.8 

MTO MOBILITY OUTCOMES, PART 1 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

Average 2000 Census Poverty Rate of Current Location (n=3670) 

All racial/ethnic groups 0.386 -0.080* -0.172* -0.062* -0.103* 

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

African Americans 0.401 -0.084* 

(0.010) 

-0.178* 

(0.021) 

-0.069* 

(0.011) 

-0.108* 

(0.017) 

Hispanics 0.365 -0.067* -0.141* -0.050* -0.089* 

(0.013) (0.027) (0.013) (0.023) 

0.389 -0.079* 

(0.008) 

-0.172* 

(0.017) 

-0.063* 

(0.008) 

-0.103* 

(0.014) 

All minorities 

Lived in early HOPE VI 0.379 -0.054* -0.127* -0.049* -0.076* 

development at baseline (0.013) (0.031) (0.013) (0.021) 

Average 2000 Census Percent Minority of Current Location (n=3670) 

All racial/ethnic groups 0.876 -0.045* -0.096* -0.013 -0.022 

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

African Americans 0.912 -0.048* 

(0.011) (0.023) 

-0.025 

(0.013) 

-0.039 

(0.020) 

-0.103 * 

Hispanics 0.845 -0.019 -0.040 0.009 0.017 

(0.022) (0.045) (0.021) (0.037) 

0.883 -0.036* 

(0.010) 

-0.078* 

(0.021) 

-0.013 

(0.011) 

-0.021 

(0.017) 

All minorities 

Lived in early HOPE VI 0.875 -0.029 -0.068 -0.012 -0.018 

development at baseline (0.015) (0.035) (0.016) (0.024) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Household survey, other locating data, 2000 Census tract-level data. See appendix A for details. 

Sample: Adults from families randomly assigned through December 31, 1997. 

Notes: Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

MTO was created to test more rigorously the impacts attributed to the Gautreaux program in Chicago. 

As discussed in chapter 1, the Gautreaux Program was a racial desegregation initiative designed to 

give families in minority-concentrated public housing an opportunity to live in less segregated 

neighborhoods. When MTO was created it was theorized that requiring families to move to lowpoverty neighborhoods would also result in desegregation (hence the full program name of MTO was 

the Moving to Opportunity for Fair Housing Demonstration Program). At the time there were many 

more low-poverty white neighborhoods than low-poverty minority neighborhoods in the five 

metropolitan areas. Exhibit 2.8 shows to what extent that expectation proved accurate. The table 

shows that the Section 8 group families, without the low-poverty requirement, have not moved to 

42 Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 
neighborhoods with lower concentrations of minorities. Experimental group families have moved to 

neighborhoods with lower minority concentrations, but the effect is small relative to the level of racial 

isolation characterizing the locations where the control group families live. 

Exhibit 2.9 shows the results of testing for MTO’s impacts on length of exposure to neighborhoods 

with different poverty levels. By separating the locations according to ranges of poverty rates (10-

point intervals between zero and 40 percent), we can examine in what ranges each treatment had 

significant impacts and whether the effects of assignment to the experimental group were different 

from the effects of assignment to the Section 8 group. 

The estimates in exhibit 2.9 show that assignment to the experimental group materially reduces the 

amount of time sample members spent in concentrated poverty areas and increases the time spent in 

areas with poverty rates of less than 20 percent. The TOT effect is an average reduction relative to the 

control group in the time spent in concentrated poverty neighborhoods of almost 3 years (32 months) 

or 47 percent of the time between random assignment and followup, with a corresponding increase in 

the time spent in areas with poverty rates of less than 20 percent. Assignment to the Section 8 group 

also produces material reductions in the time spent in concentrated-poverty neighborhoods compared 

to the control group. This reduction is smaller than the experimental reduction (2 years or 35 percent 

of the time since random assignment for those leasing up in the Section 8 group). In addition, 

assignment to the Section 8 group tends to shift people to somewhat poorer neighborhoods than 

assignment to the experimental group. It primarily increases time in neighborhoods with poverty rates 

of 10 to 30 percent, whereas the experimental treatment increases are concentrated in neighborhoods 

with poverty rates below 20 percent. 

We saw in exhibit 2.7 that the places where control group members were living at the time of this 

evaluation appeared more favorable (relative to baseline locations) on a set of concentrated poverty 

indicators and opportunity indicators related to employment, income, family composition, education, 

and the like. Exhibit 2.10 is designed to illustrate the context of the MTO treatment—to show the 

differences mobility made in these types of indicators for the experimental and Section 8 group 

families relative to the control group. There are often substantial and statistically significant impacts 

on associated neighborhood characteristics for those leasing up in the experimental group, with 

particularly large impacts on the percent of their neighbors who are homeowners and the percent with 

incomes greater than twice the poverty line. While noticeably smaller, the impacts for those leasing 

up in the Section 8 group are still statistically significant. Having higher-income and more educated 

neighbors does appear to translate into having more friends with higher incomes and more education. 

Experimental group families making program moves showed significant increases in the proportion 

reporting having college-educated friends or friends who earn more than $30,000 a year. The 

estimated effects for Section 8 group families making program moves were smaller and not 

statistically significant. Neither group showed any effect on access to transportation, compared to the 

control group. 

Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 43 
EXHIBIT 2.9 

MTO MOBILITY OUTCOMES, PART 2 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

Rates (n=4248):

Number of Months Since Random Assignment Living in Areas with Poverty 

Below 10 percent 2.8 9.3* 19.6* 1.2* 2.0* 

(0.6) (1.3) (0.5) (0.8) 

6.4 8.5* 

(0.7) 

17.9* 

(1.5) 

5.8* 

(0.8) 

9.4* 

(1.3) 

At least 10 percent but 

less than 20 percent 

At least 20 percent but 7.1 0.4 0.9 6.6* 10.7* 

(0.6) (1.2) (0.8) (1.3) 

14.7 -2.8* 

(0.8) 

-6.0* 

(1.7) 

1.6 

(1.0) 

2.6 

(1.6) 

less than 30 percent 

At least 30 percent but 

less than 40 percent 

40 percent or above 41.0 -15.4* -32.4* -15.2* -24.6* 

(1.0) (2.1) (1.1) (1.8) 

Rates (n=4248):

Proportion of Months Since Random Assignment Living in Areas with Poverty 

Below 10 percent 0.039 0.128* 0.270* 0.020* 0.032* 

(0.011) (0.023) (0.009) (0.014) 

0.085 0.123* 

(0.010) 

0.259* 

(0.021) 

0.084* 

(0.011) 

0.136* 

(0.017) 

At least 10 percent but 

less than 20 percent 

At least 20 percent but 0.094 0.008 0.017 0.089* 0.144* 

(0.008) (0.017) (0.011) (0.017) 

0.195 -0.038* 

(0.014) 

-0.081* 

(0.029) 

0.033* 

(0.015) 

0.053* 

(0.025) 

less than 30 percent 

At least 30 percent but 

less than 40 percent 

40 percent or above 0.583 -0.224* -0.472* -0.216* -0.351* 

(0.014) (0.030) (0.015) (0.025) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Household survey, other locating data, 2000 Census tract-level data. See appendix A for details. 

Sample: Adults from families randomly assigned through December 31, 1997. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Poverty rates have been interpolated to reflect the part of the decade 1990 to 2000 when the sample lived in

these locations.

44 Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 
EXHIBIT 2.10 

CONTEXT OF THE MTO TREATMENT 

Experimental Section 8 

Control vs. Control vs. Control 

Context Mean ITTa TOTa ITTa TOTa 

Characteristics of the Current Neighborhood (2000 Census) 

Share of adults employed 0.810 0.035* 0.075* 0.032* 0.052* 

(n=3669) (0.004) (0.008) (0.004) (0.006) 

Share of two-parent 0.385 0.067* 

(0.007) 

0.142* 

(0.014) 

0.047* 

(0.007) 

0.076* 

families (n=3670) (0.012)

Share of owner-occupied 0.230 0.095* 0.201* 0.062* 0.101* 

(0.009) (0.019) (0.009) (0.015) 

Share of persons with 

incomes twice the 

0.374 0.103* 

(0.008) 

0.218* 

(0.016) 

0.061* 

(0.008) 

0.100* 

(0.013) 

housing units (n=3670) 

poverty level (n=3670) 

Share of persons with 0.307 0.060* 0.128* 0.039* 0.064* 

education beyond HS (0.007) (0.014) (0.007) (0.011) 

(n=3670) 

Share of persons with 0.151 0.038* 

(0.005) 

0.080* 

(0.010) 

0.020* 

(0.005) 

0.032* 

(0.008) 

[SR] (n=3515) 

Share of adults with 0.948 0.005 0.011 0.008 0.014 

college degree (n=3670) 

Accessibility of Transportation 

working car or less than (0.010) (0.022) (0.011) (0.018) 

15 minutes to public 

transport 

Adult Friendship [SR] 

Share with 3+ close 0.349 0.016 0.034 0.008 0.014 

friends (n=3517) (0.021) (0.044) (0.023) (0.039) 

Share with collegeeducated friends 

(n=3416) 

0.410 0.066* 

(0.022) 

0.140* 

(0.046) 

0.044 

(0.024) 

0.073 

(0.041) 

Share with friends 0.424 0.052* 0.112* 0.003 0.005 

earning more than (0.023) (0.048) (0.026) (0.042) 

$30,000 (n=3036) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult survey, 2000 Census 

Samples: Adults from families randomly assigned through December 31, 1997. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 45 
2.6 Interpretation of Results 

The results presented in this chapter show that MTO had substantial, favorable impacts on the 

geographic mobility of families in the experimental and Section 8 groups and on the characteristics of 

the neighborhoods they chose, relative to the areas chosen by the control group families. These 

impacts began with families’ program moves. Almost half of the families assigned to the 

experimental group leased up with program vouchers, as did three-fifths of the families in the Section 

8 group. Because many experimental group adults moved to neighborhoods where the poverty rate 

was increasing between 1990 and 2000, we estimate that only about half of their destinations had 

poverty rates below 10 percent at the time of the move, although virtually all had rates below 20 

percent. Among the Section 8 group, less than 30 percent of those who moved with program vouchers 

moved to census tracts with poverty rates below 20 percent, even though the overwhelming majority 

moved to neighborhoods with lower poverty rates than the areas where they had lived in public 

housing. While 70 percent of the control group families had moved away from their baseline 

locations, half were still living in concentrated poverty areas. 

Thus the MTO treatment led to significant differences in where families moved with the program 

vouchers. By the time of the interim evaluation these differences had narrowed somewhat because of 

subsequent moves and changes over the period in neighborhood poverty rates, but they had not 

disappeared. In 2002 the TOT reduction measured 17 points in the poverty rates of current residential 

locations for those who leased up in the experimental group. The TOT estimate was a 10- point 

reduction for those who leased up from the Section 8 group. Looking at the entire period after random 

assignment, there were very substantial reductions in the months and proportion of time that program 

movers spent living in concentrated-poverty neighborhoods relative to controls: reductions of 32 

months (47 percent) for those leasing up in the experimental group and 24 months (35 percent) in the 

time program movers from the Section 8 group spent in high-poverty areas. 

Even families who moved to low-poverty areas did not necessarily move to predominantly white or 

racially integrated areas. At the time of the interim evaluation, more than three quarters of the Section 

8 group families—both those who moved with program vouchers and those who did not—were living 

in census tracts that were over 80 percent minority, about the same proportion as among control group 

families. Among experimental families, 60 percent of those who moved with program vouchers were 

also living in heavily minority areas (see exhibit 2.6). At the time of this study, families in the 

experimental group who moved with program vouchers lived in areas where the average percent 

minority was an estimated 10 percentage points lower; there was no significant effect on this measure 

for Section 8 families. 

These mobility patterns of the experimental group families (as shaped by the MTO intervention) 

placed them in significantly better environments, compared to the living environments of the control 

group. There were some lesser improvements for Section 8 group families. At the time of the interim 

evaluation, experimental group families who moved with program vouchers lived in neighborhoods 

with higher proportions of employed adults, substantially higher proportions of two-parent families 

and high school graduates, and nearly twice the rate of homeownership as in the neighborhoods 

where the controls lived (and where they would have lived absent the demonstration). Living in these 

better neighborhood environments substantially increased the chances that adults in experimental 

group families would have college-educated friends or friends earning $30,000 or more. 

46 Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 
Section 8 group families who moved with program vouchers also saw significant gains in the same 

neighborhood attributes relative to the control group, but those gains were generally only about half 

as large as those experienced by experimental group families. There was no significant effect on 

having college-educated or high-earnings friends for adults in Section 8 families. 

In sum, the period from random assignment to the interim evaluation data collection saw substantial 

MTO impacts on mobility and on the locations chosen by experimental group members. Significant 

neighborhood differences remained at the time of the interim evaluation relative to the control 

group’s locations, although they were smaller than the initial differences caused by the low-poverty 

leaseup constraint. Across the whole sample, this interval of 4 to 7 years saw other location changes 

as some control group members and nonprogram movers exited public housing and moved into the 

private market. Thus even with these significant impacts, the complex mobility patterns of the MTO 

sample make it difficult to predict what the program’s impacts might be in other areas, such as 

housing, health, education, or employment. The next chapters address these topics. 

Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 47 
48 Chapter 2 – Geographic Mobility in the MTO Interim Evaluation Sample 
Chapter Three 

Impacts on Housing, Neighborhoods, and Safety 

This chapter discusses the reasons why joining MTO would be expected to affect the housing and 

neighborhood conditions of the sample members assigned to the experimental or Section 8 groups. It 

describes the initial housing and neighborhood conditions and the safety issues that played a major 

role in the decision to join MTO. Then it presents the interim findings on housing and neighborhood 

impacts using respondent self-reports from the interim evaluation survey and administrative data from 

HUD. 

Summary 

We found substantial program effects on a wide variety of measures related to housing, neighborhood 

conditions, and safety. These effects were significant for the experimental group and the Section 8 

group each taken as a whole (intent-to-treat effects). The estimated effects for program movers were 

often much larger for the experimental group than for the Section 8 group. Families assigned to the 

experimental and Section 8 groups were more likely to be receiving housing assistance 4 to 7 years 

after random assignment than members of the control group. On specific measures of housing quality 

(problems with vermin and with paint or wallpaper within apartments), neighborhood quality (litter 

and trash in the area, public drinking), and neighborhood safety (residents witnessing drug 

transactions, feelings of safety at night), the positive effects for experimental group program movers 

were particularly large relative to the control mean. On one measure—the percent of survey 

respondents reporting problems with the police not responding to calls in the area—the estimated 

effect for experimental group families that leased up reduced the control mean nearly to zero. Taken 

together, MTO’s effects in this domain showed clear housing, neighborhood, and safety 

improvements relative to controls, and they were of great importance to participants whose primary 

motivation for joining MTO (in many cases) was improved safety. 

3.1 Hypotheses about Housing, Neighborhood, and Safety in 

MTO 

There are several hypotheses about MTO’s potential impacts on housing and neighborhood. The 

hypotheses about housing impacts focus on housing assistance, housing status (tenure and security), 

and housing conditions. Those about neighborhood impacts focus on improvements in the physical 

conditions and safety of the local areas to which the experimental group families moved. 

Hypothesized housing impacts 

Housing status. Housing status outcomes consist of housing tenure, housing costs, and housing 

insecurity. Tenure refers to whether the sample member occupies housing as a renter, as an owner, is 

living doubled up with others, or is homeless. All MTO sample members were renters when they 

Chapter 3 – Impacts on Housing, Neighborhoods, and Safety 49 
joined the demonstration.47 Because tenure and income are closely related (lower income families are 

more likely to be renters) sample members would be expected to remain renters for some time after 

random assignment. But some may have become homeowners. And it is also possible that some 

sample members will be found to be living with friends or relatives due to the loss of their own 

housing. Some may even be living in a homeless shelter or on the street. 

It is difficult to say whether these outcomes are expected for members of one assignment group more 

than another. It is possible that the experimental group members—because they received counseling 

for moves out of public housing and into low-poverty areas—would be better prepared to assume the 

responsibilities of private tenancy and so be less likely to lose their Section 8 housing. On the other 

hand, if rents in low-poverty areas rise more than in other areas, experimental group families in such 

areas might encounter increasing rent burdens and be at greater risk of losing their housing than the 

families in the Section 8 group that moved to other kinds of neighborhoods or the controls who at 

least initially were insulated from market pressures. Thus there were no clear ex-ante hypotheses 

about shorter run MTO outcomes on housing tenure, costs, or insecurity. 

However, the qualitative data collected in the first phase of this study did suggest that program 

movers in both treatment groups might experience difficulty meeting utility costs and that rising rent 

and utility costs could make it difficult for families to continue to afford housing in the private 

market, particularly in better neighborhoods where housing costs were often higher and might 

increase faster. 

In the longer run, it was hypothesized that members of the experimental group—taking advantage of 

the opportunities provided by low-poverty neighborhoods—would be more likely to move toward 

economic self-sufficiency than control group members. One aspect of self-sufficiency is 

homeownership. If living in low-poverty areas brings about increased employment and income, 

buying a home may become attainable for experimental group families especially with the variety of 

special programs to help low- and moderate-income families achieve homeownership. It is expected 

that some sample members will become homeowners by the time of the final impact evaluation. This 

may even be observed for a few at the point of the interim evaluation. 

Housing assistance. Housing assistance refers to the receipt of rental subsidies: whether or not the 

sample member is getting help paying rent through one of the federal housing programs. All those 

who joined the MTO demonstration were already receiving housing assistance because they lived in 

subsidized housing—either in public housing or in private, assisted housing developments. These 

types of housing have supply-side (project-based) subsidies, which are reflected in low rents for the 

units. 

The MTO demonstration offered those assigned to the experimental and Section 8 groups an 

opportunity to change the form of their housing subsidy from project-based to tenant-based so they 

could take the subsidy with them and use it wherever they chose to live. Leasing up in either group 

changed the form of assistance being received, reducing the proportion living in public or assisted 

housing. 

Some were lease holders, while others were living (with their children) as part of extended or

multigenerational families. 

50 Chapter 3 – Impacts on Housing, Neighborhoods, and Safety 

47 
But in the short run, and even in the range of 4 to 7 years after random assignment, it was not 

hypothesized that MTO participants would leave housing assistance altogether. There is little prior 

research on the subject of transitions off housing assistance either for public housing residents or for 

recipients of tenant-based rental subsidies. HUD can now measure the average length of tenure in the 

public housing and Section 8 programs, but there are no followup data on where people go when they 

leave or why they do so. We are not aware of any recent analyses that shed light on expectations for 

this demonstration about the duration of housing assistance or the timing of exits. 

While the expected effect is unclear, the major factors with potential to change housing assistance 

status are worth mentioning. First, leasing up in the Section 8 or the experimental group requires 

families to rent in the private market. If families that lease up are later unable to renew their leases or 

cannot find other units to rent with their vouchers, they can lose assistance. And without the subsidy 

to help pay rent they may lose their housing and be forced to double up with friends or become 

homeless. 

Second, having a housing voucher gives experimental and Section 8 group families that leased up 

much more choice about their housing and neighborhoods. This may make families whose earnings 

improve over time more likely to continue using the voucher than to stay in public housing, especially 

distressed public housing in concentrated-poverty areas. Third, if the changes in neighborhood 

engendered by the program moves of experimental group families lead to substantial increases in 

income, some families may have their assistance payments reduced to minimal levels or become 

ineligible for continued assistance. Finally, the counseling associated with the experimental 

treatment—and the possibility of different rent increases in different neighborhoods—could lead to 

differences between experimental group and Section 8 group sample members in their ability to 

remain in the program. 

Housing conditions. Because the families that moved as a result of joining MTO were using the 

vouchers MTO provided, the rental housing they leased had to meet Section 8’s Housing Quality 

Standards (HQS), a set of requirements about the safety and habitability of the dwellings. These 

standards are enforced through inspections by the local housing authorities, which can require that a 

landlord make repairs or improvements to meet the standards before a voucher holder can occupy the 

unit.48 Although most housing authorities also inspect the public housing units in their stock, MTO 

participants were recruited from some of the worst big-city public housing sites in the country (as 

chapter 2 noted). Therefore, the housing conditions of the control group were unlikely to measure up 

to the Section 8 housing quality standards. 

While HQS establishes the minimum unit quality, these standards are not the only reason why 

improvements in housing conditions were hypothesized for the experimental group. Low-poverty 

areas tend to offer newer housing with more amenities, which could exceed both the program 

standards and the quality of rental housing available in other areas. Movers to low-poverty areas 

would be expected to rent housing units in better condition, with fewer maintenance problems, than 

Of course, the landlord must be willing to do so. In most jurisdictions owners of rental property may 

choose whether or not to accept voucher holders as tenants. Some landlords do not want to deal with the 

paperwork and inspection requirements of the program, although the standard lease has been changed in 

recent years to make the voucher program more landlord-friendly. 

Chapter 3 – Impacts on Housing, Neighborhoods, and Safety 51 

48 
the rental housing available in poorer neighborhoods. Experimental group program movers to such 

areas could be expected to obtain better housing than the control group—and perhaps better than the 

Section 8 group’s housing—as long as such housing was available within the Section 8 rent limits 

(called Fair Market Rents). On the other hand, experimental group program movers could be faced 

with tradeoffs between housing quality and neighborhood quality. If rents were generally higher or 

increased more rapidly in low-poverty areas, they might have to accept a unit of lesser quality at an 

affordable rent in order to remain in a better neighborhood. 

Hypothesized neighborhood and safety impacts 

Changing experimental group families’ residential neighborhoods from high-poverty to low-poverty 

is the primary MTO intervention. Chapter 2 showed that assignment to the experimental group in 

MTO did, in fact, affect the locations where sample members were living at the time of the interim 

evaluation. Experimental group sample members were living in areas with poverty rates significantly 

lower than the places control group members were living. Further, the reductions in neighborhood 

poverty for experimental group program movers (and the improvements in other neighborhood 

indicators) were substantially and significantly greater than those for the leased-up families in the 

Section 8 group. 

But moves to low-poverty areas would be expected to change many other aspects of the neighborhood 

environment, not just the proportion of the residents living in poverty. Movers to low-poverty areas 

would be expected to find better maintained and safer neighborhoods. The adverse environment of 

concentrated poverty areas—the physical decay, social disorder, and danger characteristics of these 

areas—would be left behind. 

Most of all, moves to low-poverty areas were hypothesized to improve safety. It was expected that 

those living in low-poverty areas would not need to be alert for gunfire, would not need to be 

concerned about the hazards posed by abandoned buildings or empty lots, would not need to worry 

about getting children safely home from school, and would not need to protect their children by 

keeping them from playing outdoors. Indeed, these kinds of changes in neighborhood (and their 

expected effects on the physical and mental well-being of the sample members) were hypothesized to 

be the source of immediate improvements in the lives of the experimental group families. The 

qualitative data collected early in this evaluation supported these hypotheses about neighborhood 

impacts. 

The main outcomes of interest in the areas of housing and neighborhoods are shown in exhibit 3.1, in 

the right-most box. The outcomes are grouped into three sets—housing assistance, housing status, and 

housing and neighborhood conditions. The MTO intervention, which brought about the program 

moves by experimental group families into low-poverty neighborhoods, is shown at the top. Below 

and to the left, the diagram suggests that the effects of low-poverty moves may be mediated by 

various community level and person- or family-level factors. 

As chapter 1 explained, this study’s impact analysis was structured not only to assess MTO’s impacts 

across the six domains but also to allow some investigation of causal mechanisms. The general model 

(see exhibit 1.1) showed the wide range of mediating factors that were hypothesized to play a role in 

shaping the outcomes for families moving to low-poverty areas. These included community-level 

52 Chapter 3 – Impacts on Housing, Neighborhoods, and Safety 
Short-Term Effects

• Increased rents if payment 

standards are higher in lowpoverty areas.

Long-Term Effects

• Increased interest in 

ownership.

• Increased taste for better

quality housing.

• Increased employment at

higher wages.

Economic 

Opportunities

• More jobs available, with

higher wages.

Housing and 

Neighborhood Quality

• Better housing.

• More neighborhood 

amenities.

• Higher rents.

Housing Assistance (HA)

• Currently receiving HA.

• Current type of HA.

Housing Status

• Current tenure (rent/own/

homeless/doubled up).

• Current total housing cost.

• Housing insecurity.

Housing and Neighborhood

Conditions

• Current housing unit

problems.

• Current housing unit

satisfaction.

• Current neighborhood 

problems.

• Current neighborhood safety.

• Current neighborhood 

satisfaction.

MTO Intervention→ Relocation to Low-Poverty Neighborhoods

mediators (such as economic opportunities and schools) as well as person- and family-level factors 

(such as adult social networks and parental attitudes). 

Exhibit 3.1 shows the mediators expected to affect housing and neighborhood outcomes. The key 

mediators for the housing and neighborhood impacts explored in this chapter are community-level 

factors, which would be expected to differ due to the moves made by the experimental and Section 8 

groups with vouchers received through MTO. The sample’s moves were analyzed in chapter 2 and 

shown there to have significantly affected where sample members had lived after random assignment 

and where they were living at the time of the interim evaluation. Thus it is already clear that 

neighborhood conditions and economic opportunities (as measured by the characteristics shown in 

exhibit 2.10) did change for the families as a result of their moves to different neighborhoods due to 

MTO. 

The other mediators that could play a role in MTO impacts on housing and neighborhood conditions 

are person- and family-level factors such as the employment and earnings of participants and tastes 

for better quality housing or homeownership. Effects on various indicators of the sample’s well-being 

will be tested in later chapters.49 

EXHIBIT 3.1 

HOUSING AND NEIGHBORHOOD OUTCOMES AND MEDIATING FACTORS 

Community-Level 

Mediators 

Person- and Family-Level 

Mediators 

Housing Assistance 

Outcomes 

Community-Level

Mediators

Person- and Family-Level

Mediators

Housing Assistance

Outcomes

Short-Term Effects 

• Increased rents if payment 

standards are higher in lowpoverty areas. 

Long-Term Effects 

• Increased interest in 

ownership. 

• Increased taste for better 

quality housing. 

• Increased employment at 

higher wages. 

Economic 

Opportunities 

• More jobs available, with 

higher wages. 

Housing and 

Neighborhood Quality 

• Better housing. 

• More neighborhood 

amenities. 

• Higher rents. 

Housing Assistance (HA) 

• Currently receiving HA. 

• Current type of HA. 

Housing Status 

• Current tenure (rent/own/ 

homeless/doubled up). 

• Current total housing cost. 

• Housing insecurity. 

Housing and Neighborhood 

Conditions 

• Current housing unit 

problems. 

• Current housing unit 

satisfaction. 

• Current neighborhood 

problems. 

• Current neighborhood safety. 

• Current neighborhood 

satisfaction. 

MTO Intervention→ Relocation to Low-Poverty Neighborhoods 

See chapter 7 (on employment and earnings) and chapter 8 (on household income and receipt of public 

assistance). 

Chapter 3 – Impacts on Housing, Neighborhoods, and Safety 53 

49 
3.2 Data Sources and Measures 

Data sources 

For this study there are two main sources of data on housing and neighborhood conditions and safety. 

For adult sample members who completed the interim evaluation survey there are a variety of survey 

responses about current conditions including some items directly comparable to baseline measures. 

There are questions about the respondent’s sense of safety and any recent experience with crime 

victimization. There are also observational data on the housing units and neighborhoods reported by 

the field interviewers who visited the sample members during 2002. 

In addition, administrative data from HUD systems have been matched to identifiers of the sample to 

determine whether and when sample members were receiving HUD housing assistance and collect 

information on assistance type and amounts. We used data from the Multifamily Tenant 

Characteristics System (MTCS), the HUD system that collects information from the public housing 

authorities (PHAs) about public housing residents and about participants in the tenant-based voucher 

program. We also got data from the Tenant Rental Assistance Certification System (TRACS), a 

similar system for households assisted in project-based Section 8 developments and other assisted 

projects. 

The data from the two HUD systems span the period 1995 through 2001. Over this period, there was 

a substantial increase in reporting to MTCS, with most housing authorities submitting data by 2001. 

However, it still cannot be assumed that these administrative data are complete. 

Measures 

Three sets of outcome measures were used in the analysis of housing, neighborhood, and safety 

impacts. The housing assistance measures addressed whether the sample member was still receiving a 

housing subsidy and, if so, in what form—whether living in public housing, living in a private 

assisted development,50 or receiving tenant-based assistance through the voucher program. 

For housing status, we tested MTO’s effects on current housing tenure, monthly housing costs paid 

by sample members, and housing cost burden. In addition, we explored whether experimental group 

or Section 8 group members were experiencing more housing insecurity than those in the control 

group. By insecurity we mean various difficulties sample members could be having in regard to their 

housing. Such difficulties could be financial (problems paying rent or utilities) or nonfinancial (being 

homeless or doubled up in someone else’s housing unit, encountering discrimination in housing 

search, or having landlord problems). We built two indexes (as shown in exhibit 3.2) reflecting the 

severity of problems with ability to pay for housing and then tested them for program impacts. 

50 This is another form of project-based assistance, different from public housing. 

54 Chapter 3 – Impacts on Housing, Neighborhoods, and Safety 
Third, we analyzed impact measures concerning housing and neighborhood conditions and safety. We 

tested for differences in the incidence of housing problems reported by the survey respondents. We 

examined respondent reports on several types of neighborhood problems as well as their answers to 

questions on the safety of their current locations and any recent experience of victimization. Taken 

together, this set of housing and neighborhood measures gave a broad picture of the sample members’ 

current living situations. 

EXHIBIT 3.2 

HOUSING INSECURITY MEASURES 

Measure Values Components Weights 

Index of rent or Minimum=0 

Respondent reports being 15 or more 

days late in paying rent or mortgage at 

least once in the past 12 months. 

1 

mortgage problems Maximum=2 Respondent reports receiving an 

eviction or foreclosure threat due to 

nonpayment. 

1 

Respondent reports being 15 or more 

Index of utility 

payment problems 

Minimum=0 

Maximum=2 

days late in paying utilities at least once 

in the past 12 months and/or being 

charged a late fee and/or receiving a 

shutoff. 

1 

Respondent reports either having 

services shut off or moving out (even for 

a little while) because utilities were shut 

off. 

1 

Index of combined 

problems in ability to 

pay for housing 

Minimum=0 

Maximum=4 

Index of rent or mortgage problems 2 

Index of utility payment problems 2 

Homeless, doubled 

up, or evicted 

Indicator 

(0,1) 

Respondent reports being homeless, 

doubled up, or evicted during last 12 

months. 

Any/all of 

these 

conditions=1 

Faced discrimination 

or bias in housing 

search 

Indicator 

(0,1) 

Respondent reports being turned down 

for housing based on race, ethnicity, 

gender, family status, disability, source 

of income, or bias against public 

housing residents. 

Any/all of 

these 

conditions=1 

Had problems with 

recent landlord 

Indicator 

(0,1) 

Respondent reports leaving housing unit 

because of issues with landlord. 

Any/all of 

these 

conditions=1 

3.3 Baseline Housing and Neighborhood Status of MTO 

Participants and Control Group Context 

Baseline conditions for the MTO sample 

The MTO demonstration recruited families from each participating city’s poorest neighborhoods. By 

targeting program eligibility to census tracts with poverty rates of 40 percent or more (as required by 

program rules), the local housing authorities also focused MTO recruitment on some of their worst 

public housing developments—worst in physical condition, worst in the incidence of crime and 

Chapter 3 – Impacts on Housing, Neighborhoods, and Safety 55 
violence, and worst as places to live. These developments were located in very troubled 

neighborhoods with the multiple problems typical of concentrated poverty areas. 

As chapter 2 noted, during the same time period as MTO major efforts were made to improve or 

demolish the most distressed public housing developments around the country. The HOPE VI 

program (along with vacancy consolidation and comprehensive modernization) affected 

developments from which MTO families were recruited in four of the five MTO sites (all sites but 

New York). About one-fifth of the interim evaluation sample (21 percent) lived at baseline in public 

housing developments affected by early HOPE VI and related programs.51 The proportion of MTO 

sample members in developments affected by these initiatives varied from 16 percent of the Los 

Angeles families in this study to 44 percent of the Baltimore families in this study. 

That one-fifth of the interim evaluation sample came from the earliest public housing sites to receive 

major funding for distressed conditions indicates how bad the living conditions at baseline were for 

many sample members. The prospect of disruption and demolition, when residents learned about the 

plans for their developments, may have been a factor encouraging residents to join MTO and it may 

have affected the effort those in the experimental and Section 8 groups made to lease up with their 

MTO vouchers. In addition, HOPE VI and vacancy consolidation provided some residents in affected 

developments with an opportunity to obtain Section 8 vouchers. In Baltimore this occurred almost 

simultaneously with the start of MTO, while in Boston, Chicago, and Los Angeles it occurred 1 to 2 

years afterward (but when MTO was still recruiting families). We estimate that about 14 percent of 

the control group obtained vouchers outside of MTO. In addition, about seven percent of the 

experimental group and two percent of the Section 8 group obtained vouchers outside MTO.52 

Baseline neighborhood conditions. In the context of the mobility analysis, chapter 2 described the 

neighborhoods where the sample members were living at baseline. These areas of concentrated 

poverty were marked by low labor force participation, high proportions of single-parent families, low 

educational attainment, and high rates of welfare receipt (see exhibit 2.7). For the families who joined 

MTO, the dangers of their housing developments were a primary motivation for trying to move (see 

exhibit C1.3). More than 80 percent put getting away from drugs and gangs as key reasons for 

wanting to move. Fewer than 10 percent wanted to move within the same neighborhood. 

When asked about their neighborhoods in the baseline survey, a high proportion of the respondents 

reported big problems on several indicators: presence of abandoned buildings (38 percent), presence 

of litter or trash in the streets (53 percent), presence of graffiti (63 percent), and presence of drug 

dealers (87 percent). Only one-third felt safe or very safe on the street during the day and just 12 

percent felt safe or very safe on the street at night. Most striking, only four percent felt very safe when 

home alone at night. 

51 By early HOPE VI sites we mean sites designated for HOPE VI implementation during MTO operations, 

which ended intake in mid-1998. HOPE VI implementation grants have continued to be made by HUD 

through Federal fiscal year 2002. 

52 These were nonprogram movers from the experimental and Section 8 groups. 

56 Chapter 3 – Impacts on Housing, Neighborhoods, and Safety 
Baseline housing situation. A factor common to all the families joining the MTO demonstration was 

the receipt of housing assistance—either as residents of public housing or as residents of projectbased assisted housing. About 90 percent of the interim evaluation sample lived in public housing at 

baseline; the remaining 10 percent (some 444 of these 4,248 families) came from project-based, 

assisted developments in the private market.53 

Of the large share from public housing, nearly all came from distressed public housing, much of 

which would meet the formal definition of substandard housing (Fitzpatrick and LaGory 2000). 

Residents of distressed public housing were exposed to a range of hazards, including lead paint, 

asbestos, cockroach and rodent infestations, exposed electrical wiring and pipes, broken plumbing, 

unscreened windows, unlit halls and stairwells, and broken elevators (Scharfstein and Sandel 1998; 

National Commission on Severely Distressed and Troubled Public Housing 1992). Conditions in 

high-rise developments were particularly bad; in some developments, it was common for young 

children to play in front of unprotected windows or for asthmatic mothers and children to have to 

climb many flights of stairs every day. 

The families enrolling in MTO also expressed marked dissatisfaction with their housing (see exhibit 

C1.3). Only one-fourth of the study sample rated the condition of their current units at the time as 

excellent or good and the same proportion rated their current housing as poor. More than one-third of 

the baseline respondents reported big problems with rats or mice in their apartments and with peeling 

paint or wallpaper. About 20 percent of the sample members reported big problems with plumbing or 

heat that did not work, broken or missing locks, and broken windows or windows without screens. 

Nearly half the sample identified getting a bigger or better apartment as the first or second most 

important reason for wanting to move (the next most frequent response after getting away from drugs 

and gangs). 

At baseline it was quite common for MTO applicants to come from multigenerational families living 

together in public housing. For the younger generations of these families—the daughters raising their 

own children while living with their mothers (and sometimes their sisters and sisters’ children, too)— 

MTO offered the chance to obtain their own apartments and their own housing subsidies. 

Because all the MTO families were already living in subsidized housing at baseline, they were paying 

rent in proportion to their incomes. Families recruited from public housing were paying rents limited 

to 30 percent of adjusted income. The federal housing subsidy was provided through funds paid to the 

housing authorities to make up the difference between the family’s contribution and the cost of 

maintaining and operating the housing.54 Families living in private, assisted developments at baseline 

53 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.) Here and elsewhere they are called 

private, assisted housing. 

54 Although PHAs have some other income sources (e.g., from commercial space in their properties) and also 

receive funds for modernization, the Federal operating subsidy is by far the largest supplement to rental 

income. 

Chapter 3 – Impacts on Housing, Neighborhoods, and Safety 57 
also paid reduced rents, with rent levels calculated in a manner similar to that for public housing 

residents. The private owners had received subsidies to build or rehabilitate the housing in exchange 

for renting to low- and moderate-income tenants for a certain period of time. 

Current housing situation and neighborhood conditions for the control group 

In the 4 to 7 years after random assignment, there were changes in the housing and neighborhood 

conditions of the control group members. By the time of the interim evaluation there had been 

substantial mobility (as chapter 2 described). Approximately two-thirds of the control group adults 

were living in a different location than their baseline address. And some of those in the same 

locations had changed apartments as a result of public housing renovation or demolition. 

Neighborhood conditions. Although a substantial proportion had moved from their baseline 

locations, many adults in the control group still reported dissatisfaction with their current 

neighborhoods (see exhibit C3.1 in appendix C). While 75 percent said they felt safe during the day 

only 55 percent reported feeling safe at night. Two-thirds of the control group respondents indicated 

there were problems with trash or litter in their neighborhoods and over half said there were problems 

with public drinking. Two-thirds reported a problem with people hanging out in the area, and about 

one-third said police did not come when called. Twenty-one percent reported that they or someone 

else in their household had been victimized in the past 6 months.55 Overall, one-third said they were 

very or somewhat dissatisfied with the area where they currently lived. Still, this proportion was 

considerably reduced from baseline (see for comparison exhibit C1.3). 

The field staff collecting the survey data for this study usually interviewed sample members in their 

homes. While there the interviewers recorded observations on the condition of the building exteriors 

and the surrounding block.56 Twenty-nine percent of the control group families’ buildings had metal 

bars on windows above the basement level. The majority of the buildings in which the control group 

members were living were observed to be in fair condition (52 percent), but 16 percent were rated as 

in poor condition or badly deteriorated. Other residential structures on the same block received the 

same mix of ratings. One-third of the streets had major or minor accumulations of trash. 

The sociodemographics of the neighborhoods where control group members were living at the time of 

this study were described in chapter 2, in terms of 2000 Census indicators (see exhibit 2.7). The 

neighborhoods were quite poor—39 percent of the population lived in poverty on average—but less 

than one-fifth of the families living there were receiving public assistance, and three-fourths of the 

families had at least one member in the labor force. The mobility of the control group members in the 

period after random assignment, combined with other, large-scale changes (HOPE VI and public 

55 Specific questions were asked about: having a purse, wallet, or jewelry stolen; being threatened with a 

knife or gun; being beaten or assaulted; being shot or stabbed; or having an attempted or actual housebreaking. Responses on each item are shown in exhibit C3.1. Their joint occurrence for the control group 

was 21 percent. 

56 The field staff who conducted interviews with sample members were trained to record observations of the 

housing and neighborhood where the respondents lived. However, these were not full housing or 

neighborhood inspections. See appendix A for details. 

58 Chapter 3 – Impacts on Housing, Neighborhoods, and Safety 
housing modernization, welfare system changes, and the flourishing economy of the 1990s) had 

apparently put them in areas with more favorable characteristics than their baseline neighborhoods. 

Current housing situation of control group members. At the time of this study’s data collection, 

most of the adults in the control group were still living in rental housing (89 percent), either 

unsubsidized or subsidized (including public housing). About four percent were doubled up (living 

with family or friends, some paying rent and others not). There were a few adults who were living in 

shelters or group quarters, and a few were incarcerated. About five percent had become homeowners 

(see exhibit C3.2). 

Current housing assistance receipt in the control group. According to both survey responses and 

HUD administrative data, about one-third of the control group adults were not receiving housing 

assistance at the time of the interim evaluation data collection (see exhibit C3.3).57 Of those with 

assistance, administrative data indicate that the largest share was living in public housing (44 percent 

of the whole group). Another 10 percent were living in private, assisted housing. And 12 percent of 

the control group had vouchers, according to the administrative data.58 These tenant-based vouchers 

did not come from MTO, of course. Some control group members may have received vouchers by 

being on the regular Section 8 waiting list, while others probably received vouchers through the 

relocation efforts of the local housing authorities when HOPE VI or vacancy consolidation efforts 

began in the origin public housing developments. 

Housing quality and condition. At the time of the interim evaluation, according to field 

observations, 36 percent of the control group adults were living in detached or attached single-family 

housing, about 30 percent in lowrise multifamily housing, and about 24 percent in highrise buildings 

(see exhibit C3.4). Five percent of the control group’s housing was rated by the observers as badly 

deteriorated and another 11 percent was considered to be in poor condition. About 14 percent of the 

control group families’ dwellings were observed to have open cracks or holes in the walls. 

The adults in the control group expressed general dissatisfaction with the condition of their housing. 

More than 50 percent reported problems with rats, mice, or roaches in their units, the same proportion 

reported problems with peeling paint or wallpaper, and 40 percent indicated there were problems with 

57 Some control group adults were homeowners, homeless, or doubled up when the data were collected for 

this study. Others were simply unassisted renters no longer in subsidized housing. Although this might 

seem surprising, it has been noted elsewhere that a substantial number of public housing residents have left 

or been “lost” to housing assistance during the process of HOPE VI implementation in housing authorities 

across the country. Kingsley, Johnson, and Pettit give an estimate of 20 percent for those who moved to 

other types of HUD assisted projects or no longer receive HUD assistance (2001, p. 3). Others suggest that 

the “lost” alone may be as many as 20 percent of the original residents (National Housing Law Project 

2002). 

58 These figures are from administrative data. The corresponding survey results for the whole group are: 

public housing 45 percent; private, assisted housing 2 percent; vouchers 21 percent; unknown assistance 

type 2 percent. It appears that the wording of a survey question (about project-based Section 8 housing) led 

to respondents or interviewers confusing vouchers and project-based assistance. The number with vouchers 

was overreported as a result, and the number in private, assisted housing was underreported. We think the 

administrative data are more accurate on this subsidy program item. 

Chapter 3 – Impacts on Housing, Neighborhoods, and Safety 59 
heating or plumbing.59 Nearly half of the control group respondents reported their housing to only be 

in fair or poor condition. Although these self-reports indicate a higher level of problems than the field 

observations (and a greater incidence of problems than at baseline), the sample members were 

certainly more familiar with the condition of their entire housing units and with the functioning of 

their basic systems. 

Overall, then, at the time of the interim evaluation the control group adults viewed their housing and 

neighborhoods with considerable dissatisfaction, noting a variety of problems that signified neglect 

and that in some cases made them feel unsafe. Whether the housing situations or neighborhood 

conditions were significantly better for the experimental group or the Section 8 group is the question 

addressed in the next section. 

3.4 Impacts on Housing, Neighborhoods, and Safety 

This section presents the results of testing for experimental effects on the key housing, neighborhood, 

and safety outcomes identified above. (Section 2.5 provided a boxed explanation of how to interpret 

the impact estimate tables.) 

Current housing status 

All of the sample members from the experimental and Section 8 groups who made program moves 

were shifting from project-based to tenant-based housing assistance. The simplicity of paying 30 

percent of income for rent (adjusted for utilities) was replaced by the complexity of rent payments 

that not only took income into account but also reflected the landlord’s asking price and the local 

housing authority’s payment standard60 and utility allowances.61 

Exhibit 3.3 shows MTO’s effects on various housing status outcomes. Sample members in the control 

group were paying an average of $412 per month in total housing costs (rent or mortgage payment 

plus utilities). The amount was no different for the experimental and Section 8 groups, nor did a 

sample member’s employment or welfare receipt at baseline make a significant difference in program 

effects on total housing cost. 

59 Exhibit C3.3 shows the responses to each item separately, while responses on combinations of items are 

discussed here. 

60 Housing vouchers are designed to offer a shopping incentive to apartment seekers by letting them keep the 

difference if they find a unit with rent below the payment standard. Voucher holders are also allowed to 

lease units with rents above the payment standard, but they pay the difference. These features must be 

understood enough to be part of the prospective renter’s calculus for determining what is affordable with 

the voucher. And they are considerably more complex than the way the rent and utility calculations for 

public housing are done. 

61 Utility allowances are the amounts the participant is assumed to incur for the utilities paid out of pocket, 

based on the size of the unit. They are set by the agency administering the voucher. The subsidy payment is 

calculated to cover the difference between 30 percent of income and the sum of contract rent plus utility 

allowance. 

60 Chapter 3 – Impacts on Housing, Neighborhoods, and Safety 
EXHIBIT 3.3 

KEY MTO HOUSING STATUS OUTCOMES 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

1

 (n=3386)

All $412 $25 

(20) 

$53 

(42) 

$6 

(18) 

$10 

(31) 

$562 -$47 

(52) 

-$98 

(109) 

-$60 

(51) 

-$97 

(81) 

Current Total Monthly Housing Cost

Employed at baseline 

 Receiving cash $372 $22 $45 $7 $11 

assistance at baseline (19) (39) (16) (26) 

Current Total Housing Cost Burden (n=3113) 

All (mean burden) 0.312 0.016 0.035 0.011 .018 

(0.010) (0.021) (0.009) (.015) 

0.307 0.016 

(0.021) 

0.034 

(0.044) 

0.004 

(0.020) 

.006 

(.032) 

Employed at baseline 

 Receiving cash 0.319 0.003 0.006 0.004 .007 

assistance at baseline (0.011) (0.023) (0.011) (.017) 

Housing Insecurity Indexes (Ability To Pay during Last 12 Months) 

Rent or mortgage 0.293 -0.042 -0.090 -0.046 -0.077 

Utility payment problems3

(n=3509) 

0.270 0.105* 

(0.024) 

0.223* 

(0.052) 

0.072* 

(0.025) 

0.120* 

(0.041) 

problems2

 (n=3502) (0.026) (0.055) (0.029) (0.048) 

Combined problems with 0.565 0.061 0.129 0.026 0.043 

ability to pay for housing4 (0.038) (0.080) (0.041) (0.069) 

(n=3497) 

Other Housing Insecurity Indicators during Last 12 Months 

Homeless, doubled up, 0.080 0.014 0.030 0.019 0.032 

or evicted (n=3521) (0.013) (0.028) (0.016) (0.026) 

) 

0.030 0.037* 

(0.009) 

0.080* 

(0.019) 

0.064* 

(0.012) 

0.107* 

(0.020) 

 Reported facing 

discrimination or bias in 

housing search (n=3520

Reported problems with 0.036 0.006 0.012 0.013 0.022 

recent landlord (n=3526) (0.008) (0.016) (0.009) (0.014) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult survey data. See appendix A for details. 

Sample: Adults from families randomly assigned through December 31, 1997. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. See appendix B 

for detailed explanation of estimation procedures. 

d) Due to the statistical software used to generate the impact estimates the numbers of cases used are slightly smaller 

than the numbers in the corresponding appendix C tables. This accounts for the small differences in control means. 1

 Excludes cases with missing rent or mortgage payment information. 2

 Items included are: Respondent reports being 15 or more days late in paying rent or mortgage at least once in the 

past 12 months, Respondent reports receiving an eviction or foreclosure threat due to nonpayment. Maximum value of 

index is 2. 

3

 Items included are: Respondent reports being 15 or more days late in paying utilities at least once in the past 12 

months and/or being charged a late fee and/or receiving a shutoff, Respondent reports either having services shut off or 

moving out (even for a little while) because utilities were shut off. Maximum value of index is 2. 4

 Items included are utility problems, rent or mortgage problems. Maximum value of index is 4. 

Chapter 3 – Impacts on Housing, Neighborhoods, and Safety 61 
The second panel of exhibit 3.3 shows the results of testing for MTO’s impacts on housing cost 

burden (the ratio of total housing costs to income). Burden for the treatment groups did not differ 

significantly from burden for the control group, nor were there significant subgroup differences on 

this measure. 

The third panel of exhibit 3.3 reports the results of testing for differences in three housing insecurity 

indexes (as defined in exhibit 3.2). The control group mean for the index of rent or mortgage payment 

problems in the past 12 months was .29, and this value did not differ significantly for the 

experimental or Section 8 groups. For the index of utility payment problems, the control mean was 

.27, but it was .34 for the Section 8 group as a whole and .38 for the experimental group as a whole. 

Both were significantly higher than the control group. Among those who leased up in the 

experimental group, the estimated impact on the incidence of utility payment problems was 22 points 

(nearly double the control value), while for the Section 8 group the estimated TOT effect was 12 

points. 

These data are consistent with the qualitative report findings (Popkin, Harris, and Cunningham 2001) 

of difficulty adapting to paying utilities separate from rent.62 Rising rent and utility costs made it 

difficult for families to continue to afford housing in the private market, according to the qualitative 

interviews. Heating bills can be particularly high especially for those who moved to single-family 

homes, duplexes, or townhouses. In the qualitative interviews respondents described problems with 

regard to utility payments. A Section 8 comparison group mover from New York described how she 

and her family conserved electricity during the day and heat during the night, in an effort to lower her 

bills. 

When I took the papers and everything for the apartment, they was like, you have to 

pay for your heat. How are you going to do this? And my attitude was, the Lord 

will provide. They didn’t want to hear that. They wanted to know how you’re going 

to pay these bills. I’m like, the Lord will provide. But then, like I said, I just had to 

come to terms with it being either/or. You can’t have both. During the day, I really 

don’t use no lights because of the daylight, but during the night, I may like turn 

them on, like if they’re taking a shower or what have you, just to warm it up 

enough to get in and out of the shower, and turn it off and all go to bed. 

Conserving electricity was not enough to make the bills affordable. Several respondents described 

how they juggled bills to make ends meet. Deborah went on to say: 

It was like taking from Peter to pay Paul. You know, it was like – eenie, meenie, 

miney, moe. I’ll pay ConEd this week, rent this one. I had to work it out like that. 

Returning to the impact estimates, the final panel of exhibit 3.3 reports survey responses indicating 

nonfinancial aspects of housing insecurity over the past 12 months. Some eight percent of the sample 

had been homeless, had lived doubled up with another family, or had been evicted during the last year 

In older public housing many or all utilities are provided by the housing authority and are not paid 

separately from the monthly rent amount. 

62 Chapter 3 – Impacts on Housing, Neighborhoods, and Safety 

62 
(evictions for reasons other than non-payment of rent). This did not differ by assigned group nor did 

the incidence of reported problems with landlords. 

Adult survey respondents were asked about their experiences since random assignment searching for 

a house or apartment to rent. Three percent of the control group reported encountering bias or 

discrimination in housing search. But the incidence of such reports was significantly higher for the 

experimental group and higher still for the Section 8 group. Experimental group program movers 

reportedly experienced about double the incidence of bias or discrimination as control group 

members. For the Section 8 program movers, the estimated effect was even greater, more than three 

times the control mean.63 

We turn now to the second group of housing outcome measures, which concern receipt of housing 

subsidy. Data from two different sources (the interim survey and HUD administrative records) were 

used to test for MTO’s impact on the receipt of housing assistance. The first panel of exhibit 3.4 

shows that, based on self-reports in the survey, the rates of housing subsidy receipt by experimental 

and Section 8 group members were significantly higher than the rate for the control group (71 

percent). For the treatment groups as a whole, the assistance rates were 7.7 and 7.9 percentage points 

higher, respectively. The estimates for program movers in these groups show the impacts to be 16 and 

13 percentage points higher, respectively. Those employed at baseline had a lower mean rate of 

assistance but similar effects for both treatment groups. Sample members receiving cash assistance at 

baseline had a slightly higher control mean rate of assistance, but the pattern of significant impacts for 

both groups was the same. 

The second panel of exhibit 3.4 shows the impact estimates on housing subsidy receipt measured 

using administrative data.64 The control mean from these data is somewhat lower than for the selfreported measure, at 66 percent. There is a significant effect only for the Section 8 group. Rates of 

housing assistance receipt were somewhat lower for those employed at baseline and somewhat higher 

for recipients of welfare at baseline, again only for the Section 8 group. 

As exhibit 3.4 shows, very few movers in either experimental group of the Section 8 group had 

returned to public housing at the time of the interim evaluation.65 One panel shows results using 

survey responses on this topic; the other shows results with administrative data from HUD. In both 

sets of data about 44 percent of the control group families were public housing residents at the time of 

63 Survey respondents were asked about the most recent time since random assignment that they had searched 

for housing and been told a unit was not available to them. We examined the pattern of response by group 

and by type of housing assistance. In all groups tenant-based voucher holders were the most likely to report 

encountering bias or discrimination while public housing residents were the least likely. However, we do 

not have the necessary data to adjust these figures for differences in search activity. 

64 The rate of agreement between the survey and administrative data on receipt of housing assistance for the 

adult sample was 78 percent. 

65 This outcome measure is not a simple reflection of the fact that leasing up in the experimental or Section 8 

groups meant shifting to tenant-based assistance. Additional variation was introduced by the high level of 

control group mobility and the sample members’ access to vouchers outside of MTO in conjunction with 

HOPE VI and other public housing development changes. 

Chapter 3 – Impacts on Housing, Neighborhoods, and Safety 63 
EXHIBIT 3.4 

KEY MTO HOUSING ASSISTANCE OUTCOMES 

Experimental Section 8 

Control Vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

[ (n=3525) 

0.631 0.111*

(0.042) 

0.227*

(0.086) 

0.117*

(0.047) 

0.187*

(0.074) 

Living in Household Currently Receiving Housing Assistance Survey]

Employed at baseline 

All 0.706 0.077* 0.164* 0.079* 0.133* 

(0.019) (0.041) (0.021) (0.035) 

 Receiving cash 0.716 0.083* 0.171* 0.089* 0.142* 

assistance at baseline (0.022) (0.045) (0.024) (0.038) 

Living in Household Currently Receiving Housing Assistance [Admin] (n=4248)

All 0.657 0.032 0.067 0.100* 0.162* 

(0.018) (0.037) (0.019) (0.030) 

0.615 0.059 

(0.035) 

0.124 

(0.074) 

0.111* 

(0.038) 

0.175* 

(0.060) 

Employed at baseline 

 Receiving cash 

assistance at baseline 

0.678 0.039 

(0.020) 

0.078 

(0.040) 

0.098* 

(0.021) 

0.153* 

(0.033) 

Currently Living in Public Housing [Survey] (n=3525) 

All 0.454 -0.197* -0.419* -0.232* -0.388* 

(0.020) (0.044) (0.021) (0.035) 

0.378 -0.166* 

(0.039) 

-0.338* 

(0.080) 

-0.184* 

(0.040) 

-0.293* 

(0.064) 

Employed at baseline 

 Receiving cash 0.468 -0.216* -0.445* -0.251* -0.403* 

assistance at baseline (0.023) (0.048) (0.024) (0.039) 

Currently Living in Public Housing [Admin] (n=4248)

All 0.435 -0.184* -0.388* -0.226* -0.366* 

(0.018) (0.037) (0.018) (0.030) 

0.397 -0.161* 

(0.034) 

-0.338* 

(0.072) 

-0.234* 

(0.035) 

-0.367* 

(0.055) 

Employed at baseline 

 Receiving cash 0.445 -0.200* -0.402* -0.241* -0.375* 

assistance at baseline (0.020) (0.041) (0.021) (0.033) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult survey, HUD administrative data. See appendix A for details. 

Sample: Adults from families randomly assigned through December 31, 1997. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. See 

appendix B for detailed explanation of estimation procedures. 

d) Due to the statistical software used to generate the impact estimates the numbers of cases used are slightly

smaller than the numbers in the corresponding appendix C tables. This accounts for the small differences in 

control means.

64 Chapter 3 – Impacts on Housing, Neighborhoods, and Safety 
this study, and there were strong and significant reductions to this proportion for those assigned to a 

group receiving vouchers through MTO. According to the administrative data, members of the 

experimental group were 18 percentage points less likely to live in public housing, and those in the 

Section 8 group were 23 percentage points less likely. Estimated effects on experimental group 

families that leased up suggest a rate of residence in public housing below 5 percent. For Section 8 

program movers, the estimated effect reduced public housing residency to under 8 percent. It is clear 

that few of those who were enabled to move through the MTO demonstration returned to public 

housing during the 4 to 7 years since moving. 

Housing and neighborhood quality and satisfaction 

The third set of housing outcomes addresses the areas of housing and neighborhood quality and 

satisfaction. The impact estimates for these measures, which use self-reports from the adult survey 

data, are shown in exhibit 3.5. There were significant, positive effects for both the experimental and 

Section 8 groups on all of the neighborhood safety measures, on most of the housing quality 

measures, and on every outcome related to neighborhood conditions and quality, but the effects were 

often substantially greater for the experimental group. For example, about half the control group 

adults reported problems with peeling paint or plaster, but these kinds of problems only affected 

about 21 percent of those who leased up in the experimental group and about 38 percent of the 

Section 8 program movers. The experimental group families had significant reductions (relative to 

controls) in the incidence of problems with rats, mice, and roaches in their dwellings; Section 8 

families did not differ from control group families on this measure. Consistent with these findings, 

while 52 percent of the control group adults rated their current housing as excellent or good, this 

proportion was 7 to 10 points higher for those assigned to the treatment groups, 12 points higher for 

Section 8 group program movers, and 21 points higher for experimental group program movers. 

A similar pattern is shown for the neighborhood safety and condition measures in exhibit 3.5. Some 

21 percent of control group adults reported that they or a family member had been a crime victim in 

the past 6 months. Significantly smaller proportions of the experimental and Section 8 groups 

reported crime victimization (4 percentage points less for all the experimental group adults, 5 

percentage points less for all the Section 8 group adults). Three-fourths of the control group adults 

reported feeling safe in their current neighborhoods during the day. At night, however, only about 

half of them (55 percent) reported feeling safe where they were living at the time of this study. The 

estimated effect of a Section 8 group program move was to raise this by 16 points; the estimated 

effect of an experimental group program move was to raise it by 30 points. While nearly half the 

adults in the control group (45 percent) reported seeing drug transactions in the neighborhood in the 

past month, this was true for one-third of the experimental and Section 8 group members. For those in 

the experimental group who leased up, the estimated effect brought this down to only 20 percent 

seeing such transactions. 

Chapter 3 – Impacts on Housing, Neighborhoods, and Safety 65 
EXHIBIT 3.5 

MTO HOUSING AND NEIGHBORHOOD CONDITIONS AND SAFETY OUTCOMES 

Control 

Experimental 

vs. Control 

Section 8 

vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

0.528 -0.047* 

(0.022) 

-0.100* 

(0.046) 

-0.007 

(0.024) 

-0.012 

(0.039) 

0.393 -0.038 

(0.021) 

-0.082 

(0.046) 

-0.006 

(0.023) 

-0.010 

(0.039) 

Measures of Current Housing Quality 

Share reporting problem 

with vermin (n=3524) 

Share reporting problem 

with heating/ plumbing 

(n=3514) 

Share reporting problem 0.492 -0.087* -0.186* -0.064* -0.108* 

with peeling paint/plaster (0.022) (0.047) (0.024) (0.040) 

good (

0.520 0.099* 

(0.022) 

0.210* 

(0.046) 

0.071* 

(0.024) 

0.119* 

(0.040) 

(n=3525) 

 Share rating current 

housing as excellent or 

n=3525) 

Measures of Current Neighborhood Safety 

Share feeling safe during 0.750 0.093* 0.198* 0.096* 0.161* 

the day (n=3514) (0.018) (0.039) (0.019) (0.032) 

) 

0.549 0.142* 

(0.022) 

0.303* 

(0.046) 

0.093* 

(0.024) 

0.156* 

(0.040) 

Share feeling safe at night 

(n= 3482

Share saw drugs past 30 0.445 -0.117* -0.248* -0.103* -0.171* 

days (n=3480) (0.022) (0.046) (0.024) (0.039) 

0.209 -0.040* 

(0.017) 

-0.085* 

(0.036) 

-0.053* 

(0.018) 

-0.089* 

(0.030) 

Share any household 

member crime victim in last 

6 months (n= 3499) 

Measures of Current Neighborhood Quality

 Share reporting 0.704 -0.111* -0.236* -0.076* -0.127* 

litter/trash/graffiti/ (0.021) (0.046) (0.024) (0.040) abandoned buildings 

) 

0.695 -0.170* 

(0.022) 

-0.360* 

(0.046) 

-0.099* 

(0.024) 

-0.166* 

(.040) 

(n=3502) 

Share reporting public 

drinking/groups of people 

hanging out (n=3489

Share reporting police not 0.337 -0.128* -0.266* -0.092* -0.157* 

responding (n=3286) (0.020) (0.042) (0.022) (0.038) 

Share very satisfied or 

sati

) 

0.475 0.138* 

(0.022) 

0.293* 

(0.047) 

0.108* 

(0.024) 

0.180* 

sfied with current (0.040) neighborhood (n=3524

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult Survey

Sample: Adults from families randomly assigned through December 31, 1997. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. See 

appendix B for detailed explanation of estimation procedures. 

d) Due to the statistical software used to generate the impact estimates the numbers of cases used are slightly

smaller than the numbers in the corresponding appendix C tables. This accounts for the small differences in 

control means.

66 Chapter 3 – Impacts on Housing, Neighborhoods, and Safety 
These findings are consistent with those of the qualitative report (Popkin, Harris, and Cunningham 

2001), which highlighted the dramatic improvements in neighborhood safety respondents described, 

in contrast to their previous neighborhoods in public housing. In addition to noting reductions in 

graffiti and trash, respondents in the experimental group described their new neighborhoods as quiet 

and peaceful. Respondents in the comparison group did not speak as much about quiet but still 

acknowledged the reduced visibility of drugs and violence. This point is particularly salient 

considering the ways that some of the respondents described their subsequent moves from their initial 

low-poverty areas to neighborhoods with higher poverty levels: Both adults and children described 

feeling vividly the loss of their sense of safety. 

Measures of neighborhood quality and satisfaction (found in the last panel of exhibit 3.5) also showed 

effects that were significant for the experimental and Section 8 groups, but the impact estimates were 

about double in size for the experimental group program movers relative to the Section 8 group 

program movers. Overall, the control group had about a 50-50 chance of saying they were very or 

somewhat satisfied with their neighborhoods. But this chance was increased by over 10 points for the 

treatment groups taken as a whole, and the treatment-on-treated impact for the experimental group 

raised this chance by nearly 30 points. Thus the members of the experimental group, even after 

subsequent moves, continued to reap the benefits of moves to better neighborhoods. 

In the qualitative interviews, some teenagers recalled and talked clearly about the differences between 

their former developments and their current neighborhoods. For example, Jordan, a 16-year-old boy 

from Baltimore living in a low-poverty neighborhood, said: 

Yes, it’s definitely better here [low-poverty neighborhood] than at Murphy Homes. 

Some examples, not too much drug activity over here, cops patrol here every, they 

patrol here mostly all day. There’s not too many people out vandalizing things. 

We’ve got good places here that’s not touched with any graffiti. 

The Section 8 comparison group program movers in the qualitative sample also mentioned safety as 

the most valuable aspect of their current neighborhoods. Some still complained about problems with 

drugs and crime, describing their neighborhoods as safe during the day but unsafe at night. Others 

talked about problems like graffiti or a few bad teenagers. But because the public housing 

developments they came from were so dangerous the reductions in violent crimes felt like a 

substantial improvement to most movers. 

Nicolasa, a Section 8 group respondent from Boston, was living in a neighborhood with a 32 percent 

poverty rate (a relatively high rate). Yet she talked about how she enjoyed her neighbors and the 

comparative quiet and safety of her neighborhood. 

I’m happy here. I’ve been here for 3 years. I’m not thinking of moving. If I do 

move, it’ll be because I found something even better. But for now, I’m not thinking 

of moving. I get along with the people in the house, and, more importantly, the 

street is quiet, especially for the kids. If I want to go downstairs, walk around with 

them or sit outside, I can do that and feel safe. 

Chapter 3 – Impacts on Housing, Neighborhoods, and Safety 67 
3.5 Interpretation of Results 

We found substantial, positive program effects on a wide variety of measures related to housing, 

neighborhood, and safety. These effects were significant for the experimental and the Section 8 

groups taken as a whole (intent-to-treat effects), and the estimated effects for program movers were 

often much larger for the experimental group than for the Section 8 group. Examples include the 

effects on crime victimization, on measures of housing quality (problems with vermin and with paint 

or plaster within apartments), on measures of neighborhood quality (litter and trash in the area, public 

drinking) and on measures of neighborhood safety (residents witnessing drug transactions, feelings of 

safety at night). 

MTO program movers did experience some adverse effects compared to the control group in two 

areas. The proportion encountering problems paying utility bills was significantly higher, particularly 

for the experimental group. And more of the experimental and Section 8 group members reported 

facing bias or discrimination in housing search. Experimental group program movers reportedly 

experienced about double the incidence of bias or discrimination as control group members, while the 

estimated effect for Section 8 program movers was over three times the control mean. We speculate 

that this significant impact is due to the greater likelihood that the treatment group members rent in 

the private market, while a far greater share of the control group adults are occupying public housing. 

Yet the positives come through clearly in the survey responses of experimental group and Section 8 

group adults. They rate their current housing and neighborhoods far more highly than the controls. 

Taken together, these findings suggest that at the mid-term of the 10-year research period the MTO 

program had produced notable housing and neighborhood improvements for the participants, 

compared with the housing, neighborhood and safety conditions faced by members of the control 

group. Despite substantial control group mobility and despite some convergence of neighborhood 

characteristics for the experimental and Section 8 groups, estimated effects for experimental group 

program movers were consistently larger (even twice as large) as the effects for the Section 8 group 

movers. 

68 Chapter 3 – Impacts on Housing, Neighborhoods, and Safety 
Chapter Four 

Impacts on Adults’ and Children’s Health 

This chapter reviews the reasons why MTO participation might be expected to affect the health of the 

adults and children in the sample and then presents the interim findings on health impacts. Most of 

the results for adults and youth ages 12 to 19 are based on self-reported health indicators from the 

interim surveys. The results for children under age 12 are based on reports from their parents or 

primary caregivers. The findings on adult hypertension come from direct measurements obtained at 

the time of the survey. 

Summary 

MTO reduced the incidence of obesity and psychological distress, and increased feelings of calm and 

peacefulness among sample adults in the experimental group but not in the Section 8 group. MTO 

also appears to have reduced the incidence of depression among the experimental group, although this 

reduction was statistically significant for only one of the two measures of depression. MTO did not 

have a statistically significant impact on general health, activities of daily living, asthma, or blood 

pressure. Nor were there statistically significant results on smoking or alcohol consumption. 

For children, there were no statistically significant overall effects on general health, asthma, obesity, 

or injuries, although there was a statistically significant increase in injuries among male youth ages 12 

to 19. In terms of mental health youth (ages 12 to 19) in the Section 8 group showed a significant 

decrease in generalized anxiety. For psychological distress and depression there were no statistically 

significant overall impacts for either the experimental or Section 8 groups. There was, however, 

evidence of mental health improvements among girls in both the experimental and Section 8 groups 

that deserves further study. 

4.1 Hypotheses about Adult and Child Health in MTO 

Conceptual framework 

A substantial literature in epidemiology suggests that living in a high-poverty urban setting is 

associated with a wide range of adverse health outcomes for both adults and children. If these 

associations reflect causal relationships then the MTO demonstration would be expected to improve 

health outcomes. In particular, relocation to a low-poverty neighborhood could affect health status 

through various mechanisms described below (and in exhibit 4.1). 

Changes in the physical environment. Children in urban areas are more likely to suffer from asthma 

(Weiss et al., 1992), possibly due to poor air quality (Thurston, 1997; Mortimer et al. 2002, Ostro et 

al. 2001) and exposure to allergens from cockroaches, mites, and cats (Gelber et al. 1993). Accidents 

are the leading cause of death among children ages 1 to 14 and urban children have higher rates of 

injuries and accidents possibly due to unsafe playgrounds and other features of the environment 

Chapter 4 – Impacts on Adults’ and Children’s Health 69 
Physical Health

• General health status.

• Difficulties with daily

activities.

• Asthma.

• Injuries.

• Hypertension.

• Obesity.

Unhealthy Behaviors

• Smoking.

• Drinking.

Mental Health

• Psychological distress.

• Depression.

• Anxiety.

MTO Intervention→ Relocation to low-poverty neighborhoods

Physical Environment

• Cleaner air, fewer 

allergens.

• Safer environment.

Exercise and Nutrition

• Healthier eating habits.

• 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.

Community Resources

• Healthcare facilities.

• Access to jobs with 

health benefits.

Community Norms

and Values

• More positive peer 

groups, adult role

models.

Social Environment

• Reduced crime and 

violence.

• Different neighbors.

MTO Intervention→ Relocation to low-poverty neighborhoods

(Scharfstein and Sandal, 1998; Quinlan, 1996). This evidence suggests that encouraging people to 

move to lower poverty neighborhoods might lead to reductions in asthma and injuries. 

EXHIBIT 4.1 

HEALTH OUTCOMES AND MEDIATING FACTORS 

Community-Level 

Mediators 

Person- and Family-Level 

Mediators Health Outcomes Community-LevelMediators

Person- and Family-Level

Mediators

Physical Health 

• General health status. 

• Difficulties with daily 

activities. 

• Asthma. 

• Injuries. 

• Hypertension. 

• Obesity. 

Unhealthy Behaviors 

• Smoking. 

• Drinking. 

Mental Health 

• Psychological distress. 

• Depression. 

• Anxiety. 

Health Outcomes

MTO Intervention→ Relocation to low-poverty neighborhoods 

Physical Environment 

• Cleaner air, fewer 

allergens. 

• Safer environment. 

Exercise and Nutrition 

• Healthier eating habits. 

• 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. 

Community Resources 

• Healthcare facilities. 

• Access to jobs with 

health benefits. 

Community Norms 

and Values 

• More positive peer 

groups, adult role 

models. 

Social Environment 

• Reduced crime and 

violence. 

• Different neighbors. 

Changes in the social environment. Preliminary short-term evidence from the MTO sites 

demonstrated that MTO reduced criminal victimization and exposure to violence among members of 

both treatment groups (Katz, Kling, and Liebman 2001), thereby reducing stress and increasing 

participants’ feelings of tranquility (Katz, Kling, Liebman 2003). 

Exposure to violence can have long-term behavioral and psychological consequences for both 

children and adults (Groves et al. 1993; Famularo et al. 1996; Zapata et al. 1992). Stress is also 

hypothesized to be a trigger for asthma (Wright 1998) and possibly a risk factor for hypertension 

(Black et al. 1997; Kornitzer et al. 1999). If the MTO treatment reduces exposure to crime and 

violence, it could improve overall well-being and reduce psychological distress, depression, anxiety, 

and hypertension (Ross and Mirowsky, 2001, Silver et al. 2002, Aneshensel and Sucoff, 1996). 

Reduced exposure to violence could also directly reduce injuries from assaults. It is also possible that 

moves to lower poverty neighborhoods could have adverse effects on mental health by leaving MTO 

family members socially or culturally isolated. 

70 Chapter 4 – Impacts on Adults’ and Children’s Health 
Changes in community norms and values. Moves to lower poverty neighborhoods could change the 

characteristics of sample members’ peers. Peers in the low-poverty neighborhoods may have healthier 

eating and exercise habits (Lee and Cubbin, 2002) and may be less likely to engage in unhealthy 

habits such as smoking (Berkman and Breslow 1983). Peer influences have been documented for 

these behaviors (Sallis et al. 2000; Raudsepp and Viira 2000). Therefore, MTO could lead to reduced 

obesity through healthier diet and exercise and to reductions in unhealthy behaviors. 

Changes in community resources. Higher income neighborhoods could have medical professionals 

who are accustomed to providing more resource-intensive healthcare than is provided in lower 

income neighborhoods. If this is the case, then members of the MTO treatment groups might have 

higher quality care available to them after their moves. It is not clear whether low-income residents of 

high-income neighborhoods would be able to access this higher quality care, especially if language or 

cultural barriers are a factor. Moreover, since many academic medical centers are located in urban 

areas it is possible that moves to low-poverty neighborhoods would reduce access to high-quality 

care. It is also possible, that higher income communities offer more job opportunities that include 

health benefits. In this case treatment group members could be more likely to have health insurance 

and to receive preventive care. 

Earlier research 

There is a moderate-sized nonexperimental literature that suggests the possibility of a relationship 

between neighborhood of residence and health status. Recent contributions include Waitzman and 

Smith (1998) who find that people living in federally designated poverty areas have higher rates of 

mortality even after controlling for individual characteristics; Silver et al. (2002) who find that 

neighborhood disadvantage is associated with higher rates of major depression and substance abuse 

disorder; Ross and Mirowsky (2001) who find that living in a disadvantaged neighborhood is 

associated with lower levels of self-reported health and physical functioning; and Browning and 

Cagney (2002) who find that individuals residing in neighborhoods with greater collective efficacy 

report better overall health. Diez Roux (2001), Kawachi and Berkman (2003), and Macintyre and 

Ellaway (2003) provide useful reviews of this literature. 

Preliminary short-term evidence from the MTO sites showed that adults in both treatment groups had 

improvements in general health status and were more likely to feel calm and peaceful (Katz, Kling, 

and Liebman 2001). Children in the experimental group experienced reductions in asthma, injuries, 

and fearfulness relative to the control group (Katz, Kling, and Liebman 2001; Leventhal & BrooksGunn 2003). Younger children in the Section 8 group also experienced reductions in feeling unhappy, 

sad, or depressed (Leventhal & Brooks-Gunn 2003). 

4.2 Data Sources and Measures 

Data sources 

All of the health outcomes described here except high blood pressure were measured with data from 

the interim surveys. The data on adult and youth (ages 12 to 19) health outcomes are self-reported. 

The data on child (ages 5 to 11) health outcomes are based on parental or primary care-giver reports. 

Chapter 4 – Impacts on Adults’ and Children’s Health 71 
Adult blood pressure was measured directly at the time of survey administration using readings from 

an automated sphygmomanometer (blood pressure monitor). 

Measures 

All of the survey questions are ones that have been used before in large national surveys. Many are 

those used by the National Health Interview Survey, including: general health, asthma, height and 

weight, smoking, and psychological distress. Several of the measures require additional explanation. 

Limited activities. Reports whether the sample adult had difficulty with two activities of daily living 

(ADLs): lifting and carrying groceries and climbing stairs. We estimate the fraction of the sample 

who reported being limited a little or a lot on at least one of the two activities. These were chosen 

from the larger universe of ADLs as the ones most likely to be relevant in a sample in which most 

adults were fairly young. Measures of ADLs are used by a number of national surveys and are 

important for measuring an individual’s functional status and quality of life (Wiener et al., 1990). 

High blood pressure currently. This is defined as an adult with a systolic blood pressure of 140 or 

higher or a diastolic blood pressure of 90 or higher. This definition is standard (Black et al. 1997). 

Obese currently. Defined as the percentage of the sample with a body mass index (weight in 

kilograms divided by height in meters squared) of 30 or higher. This criterion is consistent with the 

National Health Interview Study definition of obese (Pleis & Cole, 2002).66 

Moderate or heavy drinker during past year. Defined as an adult who drank an average of at least 

three drinks per week during the past 12 months. 

Psychological distress index. Reports the fraction of six mental health outcomes that the adult 

sample member reported feeling at least some of the time during the past 30 days. For adults the six 

items are feeling: so sad nothing could cheer you up, nervous, restless or fidgety, hopeless, everything 

was an effort, or worthless. For youth, the question so sad nothing could cheer you up is replaced by 

so depressed nothing could cheer you up to allow us to compare results with nationally representative 

surveys administered to youth. 

Depressed during the past year (adult). Estimates the fraction of the sample that experienced an 

episode of major depression at some point during the past year using the (CIDI-SF) Major Depressive 

Episode scale. The CIDI-SF, the development of which is described by Kessler et al. (1998), yields a 

symptom count for depression. A count of 3 or more means that it is likely that a respondent with this 

profile would meet full diagnostic criteria for major depressive episode if given the complete 

Composite International Diagnostic Interview (CIDI), which is the instrument used to generate 

population estimates of psychiatric conditions in the U.S. (Kessler et al. 1994). We used version 1.0 

of the CIDI-SF. This version contained an error in the skip pattern for major depression which makes 

it impossible to classify certain boundary cases as “depressed” or “not depressed.” We therefore 

The obesity measures reported in this chapter are based upon self-reported height and weight. For young 

children we also collected direct measurements. However, we are still assessing the quality of those data. 

72 Chapter 4 – Impacts on Adults’ and Children’s Health 

66 
present two sets of results, one set in which the boundary cases are classified as “depressed” and a 

second set in which the boundary cases are classified as “not depressed.” 67 

Anxiety during the past year (adult). Constraints on interview length prevented us from 

administering a full screen for Generalized Anxiety Disorder (GAD). However, we did include a 

short two-question sequence asking whether the sample adult had experienced either “a period lasting 

one month or longer when most of the time he or she felt worried, tense or anxious,” or “a time when 

he or she worried a lot more than most people would in his or her situation.” 

Calm and peaceful most of the time in the past month (adult). We included one question on a 

positive aspect of mental health: feeling calm and peaceful. We report whether the sample adult felt 

calm and peaceful at least some of the time during the past 30 days. 

Depression and generalized anxiety disorder during lifetime (youth). For both of these outcomes, 

we used scales developed for use by the National Comorbidity Survey Replication: Adolescent 

Supplement (NCSR-AS).68 

4.3 Context and Baseline Status of the Sample 

No data on current health status were collected in the baseline survey administered when families 

joined MTO. It was not until qualitative research with MTO families conducted as part of HUD’s 

small grant program suggested that the evaluation might have large impacts on the health of 

participating families that health became a focus of the evaluation (Kling, Liebman, and Katz, 2001). 

This qualitative research suggested that fear of random violence was the prime motivation for trying 

to move, that MTO moves led to reductions in stress levels among both children and adults, and that 

children were less likely to be injured in their new neighborhoods. 

There is some evidence in the baseline data of environmental conditions that might produce health 

problems. For example, 35 percent of MTO families reported that their apartments had big problems 

with rats or mice suggesting that environmental triggers of asthma may have been quite prevalent. 

Similarly, 24 percent reported a family member having been beaten or assaulted in the past 6 months, 

a rate that was high even when compared to other samples of public housing residents (Katz, Kling, 

and Liebman 2001). More generally, as urban residents of high-poverty neighborhoods MTO sample 

members were likely at baseline to have had high rates of obesity, hypertension, substance abuse, 

asthma, depression, and exposure to violence. Data collected in the first few years after random 

assignment from members of the control group confirmed that, absent the treatment, the overall 

health of MTO sample members was generally worse than demographically similar members of the 

U.S. population (Katz, Kling, and Liebman 2001). 

67 For a discussion of methods for handling the CIDI-SF v1.0 skip pattern errors, see the “CIDI-SF Memo: 

Edits” (December 2002) which is available through the CIDI-SF website 

(http://www.who.int/msa/cidi/CIDI-SFeditsmemo.pdf). 

68 For a description of the NCSR-AS see www.hcp.med.harvard.edu/ncs/. 

Chapter 4 – Impacts on Adults’ and Children’s Health 73 
4.4 Mediators for Health Impacts in MTO 

This section reviews effects on the mediating factors most directly relevant to the hypotheses 

described in section 4.1. The tables containing these results are found in appendix E and in chapters 3 

and 7. 

Home physical environment 

Chapter 3 shows that experimental group adults were less likely to report problems with vermin than 

were control group adults and that members of both treatment groups reported improvements in 

housing quality (exhibit 3.4). Thus exposure to some environmental triggers of asthma may have been 

reduced by MTO. However, exhibit E4.1 shows that both treatment groups had higher percentages of 

housing with wall-to-wall carpets than the control group did. Carpeting can harbor dust mites and its 

removal or treatment is often recommended as an asthma remediation strategy (Crain et al. 2002, van 

der Heide et al., 1997). 

Exercise and nutrition 

We analyzed several measures of exercise and nutrition (exhibit E4.2). Among young children ages 8 

to 11 there were no overall statistically significant improvements in aerobic exercise. However, boys 

in the Section 8 group experienced a statistically significant decline in aerobic exercise. For youth 

(ages 12 to 19) there was an increase in aerobic exercise in the experimental group, but no statistically 

significant change in the amount of light physical activity. Nor was there a change in the fraction of 

days during the past week in which children (ages 8 to 19) ate fruits or vegetables. Among adults 

there was an increase in moderate physical activity among members of the Section 8 group, but no 

statistically significant change for the experimental group. There was an increase in the frequency of 

adult fruit and vegetable consumption in the experimental group, but no statistically significant 

change in the Section 8 group. 

Access to healthcare 

We analyzed several measures of access to healthcare, including whether family members have health 

insurance, did not get medical care when they needed it, have a usual place to receive medical care, or 

have spoken to a health professional in the past 6 months (exhibit E4.3). There were no statistically 

significant impacts of the MTO demonstration on access to health care for either treatment group. In 

addition, exhibit 7.4 shows that there was no impact on the probability that an adult sample member is 

currently employed at a job offering health insurance. 

Social isolation 

We analyzed a variety of measures of social networks, friendships, and social capital (exhibits E5.2 

and E5.3). There were no statistically significant impacts on adult friendships or social networks. 

There were increases in both treatment groups in the fraction of adults who said that their neighbors 

would do something about kids spray-painting graffiti or skipping school and hanging out on a street 

corner. Among children there was little impact of the intervention on the number of friends. The only 

74 Chapter 4 – Impacts on Adults’ and Children’s Health 
statistically significant result was a 2.4 percentage point increase in the fraction of experimental group 

children with at least one close friend above a control mean of 91.4 percent. There were, however, 

large reductions in contacts with friends from the old neighborhood for children in both treatment 

groups. 

Safety and exposure to violence 

The MTO intervention appears to have produced large improvements in safety and exposure to 

violence. Exhibit 3.4 shows that adults in both treatment groups were more likely to feel safe both 

during the day and at night. They were also less likely to see someone dealing drugs or to report any 

household member being victimized by crime in the past 6 months. Children in the experimental 

group were less likely to say that there were gangs in their neighborhood, that they had seen people 

selling or using illegal drugs in their neighborhood, or that they had heard gunshots in their 

neighborhood (exhibit E5.5). Reductions for the Section 8 group were of a similar magnitude for 

seeing people with drugs and hearing gunshots, but only the result for gunshots was statistically 

significant. Among children there were no statistically significant effects for seeing a person shot or 

stabbed, having a gun or knife pulled on them, or having been cut, shot, or stabbed. 

4.5 Interim Impacts on Health 

Effects on adults’ health 

Preliminary research on MTO in the Boston site (Katz, Kling, and Liebman, 2001) suggested that 

MTO caused large improvements in general health as measured by the standard question “In general 

is your health excellent, very good, good, fair, or poor?” Preliminary studies of Boston and New York 

(Katz, Kling, and Liebman, 2001, Leventhal and Brooks-Gunn 2000) showed improvements in adult 

mental health. An important question for the interim evaluation was therefore whether the 

improvements in general health were attributable solely to improvements in mental health or whether 

MTO also caused improvements in physical health. 

General health. The first set of results in exhibit 4.2 shows effects on adult physical health. General 

health was measured by the question “In general is your health excellent, very good, good, fair, or 

poor?” The percentage of treatment group adults who reported good or better health was not 

statistically different than the percentage for the control group. 

Physical health. To isolate the effects on physical aspects of health, the interim evaluation collected 

data on whether sample adults had difficulty with two activities of daily living (ADLs): lifting and 

carrying groceries and climbing stairs. These were chosen from the larger universe of ADLs as the 

ones most likely to be relevant in a sample in which most adults were fairly young. Exhibit 4.2 shows 

that there was no discernable effect of MTO on the percentage of the sample who reported being 

limited a little or a lot in at least one of the two activities. Forty-four percent of control group 

members reported being limited, as did 42 percent of experimental and 41 percent of Section 8 group 

members. 

Chapter 4 – Impacts on Adults’ and Children’s Health 75 
The interim evaluation also collected data for three health conditions that are particularly prevalent in 

inner-city neighborhoods: asthma, high blood pressure, and obesity. Twenty-one percent of control 

group sample adults reported having had an asthma or wheezing attack during the past year. Roughly 

20 percent of experimental and Section 8 sample adults had experienced such attacks. Thus there was 

no statistically significant impact of the program on asthma. 

This result is interesting because, as noted above in the section on mediators, MTO does appear to 

have reduced exposure to two triggers of asthma: stress-producing violence and rats, mice, and 

cockroaches. However, MTO also caused the percentage of sample adults living in housing with wallto-wall carpeting to increase. It is possible that the detrimental effects of the increase in carpeting 

offset the benefits of reduced exposure to the other two asthma triggers. 

While blood pressure has a significant genetic component, it is also a function of stress, weight, and 

activity patterns such as exercise and diet, all of which could be affected by living in a new 

neighborhood (Kornitzer et al. 1999). Increasing evidence suggests that variations in the magnitude 

and timing of blood pressure response to stress are associated with heightened risk of developing 

hypertension and accelerated arteriosclerosis (Kamarck et al. 1997; Everson et al. 1997; Lynch et al. 

1998). There is also considerable evidence of links between distressed psychosocial states and 

heightened blood pressure (Everson, Kaplan & Salonen, 1997; Everson et al. 1998). These are all 

possible pathways through which MTO could, in theory, affect blood pressure. In addition, by 

affecting access to healthcare, MTO could influence whether people take their blood pressure 

medicine. Exhibit 4.2 shows, however, that MTO did not have a statistically significant impact on the 

share of sample adults with high blood pressure (defined as an adult with a systolic blood pressure of 

140 or higher or a diastolic blood pressure of 90 or higher). In the control group 30 percent of sample 

adults had high blood pressure, whereas 32 percent of adults in the experimental and Section 8 groups 

had high blood pressure. 

76 Chapter 4 – Impacts on Adults’ and Children’s Health 
EXHIBIT 4.2 

ADULT PHYSICAL AND MENTAL HEALTH OUTCOMES 

Experimental Section 8 

Control Vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

[SR] (n=3523) 

Good or better health 0.669 -0.016 -0.033 -0.007 -0.013 

General Health 

currently (0.020) (0.042) (0.022) (0.036) 

Physical Health 

Activities limited a little or 0.436 -0.018 -0.037 -0.023 -0.039 

a lot currently [SR] (0.021) (0.045) (0.023) (0.038) 

Asthma or wheezing 0.214 -0.014 

(0.018) 

-0.031 

(0.037) 

-0.010 

(0.019) 

-0.016 

(0.032) 

(n=3517) 

attack during past year 

[SR] (n=3522) 

 High blood pressure 0.301 0.022 0.049 0.019 0.032 

currently (0.021) (0.045) (0.023) (0.039) 

[SR] 0.471 -0.051* 

(0.022) 

-0.108* 

(0.047) 

-0.047 

(0.025) 

-0.079 

(0.042) 

[SR]

[M] (n=3230) 

 Obese currently 

(n=3405) 

Unhealthy Behaviors 

 Smoker currently (n=3499) 0.290 0.010 0.021 0.003 0.006 

(0.020) (0.042) (0.022) (0.036) 

Moderate or heavy 0.094 -0.008 

(0.013) 

-0.017 

(0.027) 

0.003 

(0.014) 

0.004 

(0.023) 

[SR]

drinker during past year 

(n=3477) 

Mental Health 

 Psychological distress 0.329 -0.034* -0.073* -0.012 -0.020 

index for past month (0.015) (0.032) (0.016) (0.028) 

year, including boundary 

cases 

0.219 -0.036* 

(0.018) 

-0.078* 

(0.039) 

-0.019 

(0.020) 

-0.032 

(0.034) 

(n=3521) 

 Depressed during past 

(n=3520) 

 Depressed during past 0.180 -0.026 -0.056 -0.010 -0.016 

year, excluding boundary (0.017) (0.036) (0.019) (0.032) 

cases 

0.393 -0.028 

(0.022) 

-0.060 

(0.047) 

-0.011 

(0.024) 

-0.019 

(0.040) 

(n=3520) 

Anxiety during past year 

(n=3473) 

Calm and peaceful most 0.466 0.061* 0.129* 0.016 0.027 

of the time or more often (0.022) (0.047) (0.024) (0.041) 

in the past month (n=3520) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Source: Adult survey

Sample: Adults from families randomly assigned by December 31, 1997. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b)Control means and impact estimates are regression-adjusted, with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. See 

appendix B for more details. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

Chapter 4 – Impacts on Adults’ and Children’s Health 77 
MTO does appear to have significantly reduced the incidence of obesity (measured as the percentage 

of the sample with a body mass index of 30 or more) in the experimental group.69 Measured as the 

impact on those who leased up, obesity was reduced by 11 percentage points in the experimental 

group. 

Given that obesity was the only one of six adult physical health items that showed any program 

impact, it is worth asking whether this is likely to be a true impact of the program or simply the result 

of sampling variation. Some corroborating evidence comes from questions about adult diet. As 

discussed above, exhibit E4.2 provides some evidence that eating habits changed in response to the 

MTO treatment. There was a 3 percentage point increase for adults in the experimental group in the 

fraction of days in the last week that they ate some fruits or vegetables. Regarding adult exercise, the 

Section 8 group experienced a statistically significant increase in moderate physical activity. The 

increase in exercise for the experimental group was smaller and not statistically significant. 

Mental health. The interim evaluation collected data on two detailed measures of adult mental 

health: a psychological distress index and a measure of depression. The psychological distress index 

gives the fraction of six mental health outcomes that the adult sample member reported feeling at 

least some of the time during the past 30 days. The depression measure estimates the fraction of the 

sample that experienced an episode of major depression at some point during the past year using the 

CIDI-SF Major Depressive Episode scale. 

As exhibit 4.2 shows, the control group had a mean level of about 33 percent on the psychological 

distress index and between 18 and 22 percent, depending on which measure is used, had been 

depressed in the last year. MTO reduced psychological distress among experimental group sample 

adults by about 3.5 percentage points. The effect on those who leased up was a 7 percentage point 

reduction in psychological distress. This experimental group impact is of substantial magnitude, 

reducing the incidence of psychological distress by over one-fifth. The point estimates of the impact 

on depression for the experimental group are of a similar magnitude to that for psychological distress, 

though only the depression measure that includes the boundary cases is statistically significant (the pvalue on the more restrictive measure is .12). The estimates for the Section 8 group were not 

statistically distinguishable from zero for either psychological distress or depression.70 

In addition to the detailed measures, we also included two mental health outcomes measured using 

only one or two questions: generalized anxiety disorder and feeling calm and peaceful. Generalized 

anxiety disorder (GAD) is the second most prevalent psychological disorder. Although constraints on 

interview length prevented us from administering a full screen for GAD, we did include a short twoquestion sequence asking whether the sample adult had experienced either “a period lasting one 

69 The p-value of the experimental effect on obesity is .021. The p-value of the Section 8 effect on obesity is 

.057. 

70 We explored the sensitivity of our results to excluding data from one interviewer whose data appear to have 

been of questionable quality. In the 112 adult interviews conducted by this interviewer, 36 percent of the 

adults were recorded as having volunteered that they were on medication for depression, and scored as 

depressed. Among the 3408 other interviews, one percent were recorded as on medication for depression. 

Excluding the interviews conducted by this interviewer reduces the estimated prevalence of depression, but 

has little impact on the between-group differences. 

78 Chapter 4 – Impacts on Adults’ and Children’s Health 
month or longer when most of the time he or she felt worried, tense or anxious” or “a time when he or 

she worried a lot more than most people would in his or her situation.” The results for anxiety could 

not be statistically distinguished from zero. On the second measure, feeling calm and peaceful, the 

experimental group showed a statistically significant increase. The results for the Section 8 group 

were not statistically significant. 

Unhealthy behaviors. The interim evaluation collected data on two unhealthy behaviors: smoking 

and drinking. There are several mechanisms through which moves to low-poverty neighborhoods 

could influence these behaviors. Depression and stress are correlated with both tobacco and alcohol 

use and exposure to tobacco and alcohol advertising, particularly advertising targeted at minority 

groups, could decline. 

In our analysis, a person is considered a smoker if he or she smoked at least one cigarette in the past 

30 days. Exhibit 4.2 shows that MTO had no impact on smoking behavior. Sample adults in all three 

program groups had smoking rates of between 29 and 30 percent. Exhibit 4.2 also reports the 

percentage of sample adults who were moderate or heavy drinkers during the past year. There was no 

effect of MTO on this drinking behavior. Between 9 and 10 percent of sample adults in all three 

groups were moderate or heavy drinkers. 

Effects on children’s health 

Physical health. The physical health measures and the mechanisms by which MTO might affect 

children’s health generally parallel those for adults. Exhibits 4.3 and 4.4 show the physical health 

outcomes for children ages 5 to 11 and ages 12 to 19, respectively. We collected data on general 

health using the same question as that used for adults. For children and youth we estimate the impact 

of having very good or better health. There were no statistically significant program effects on 

general health for either treatment group. We also collected data on whether the children or youth had 

had an asthma or wheezing attack during the past year. Again there were no statistically significant 

impacts. We also collected data on obesity (measured by whether the youth’s body mass index placed 

them in the 95th percentile or greater for his or her age and sex). In contrast with the adults, there was 

no effect of MTO on youth obesity. 

In addition, the interim evaluation collected data on one health outcome that was not collected for 

adults: whether the child or youth had had an accident or injury requiring medical attention in the 

prior year. Accidents and injuries are an important factor in overall health for children and youth. 

Low-poverty neighborhoods may be safer in some respects (e.g., better housing, less exposure to 

violence, safer playgrounds), but they may encourage more exercise and outdoor play. Thus, the 

causes of accidents and injuries may change as a result of the MTO treatment. Exhibits 4.3 and 4.4 

show that there was no discernable impact of the MTO treatment on accidents and injuries for either 

treatment group overall for either young children or for youth.71 However, boys ages 12 to 19 in the 

Section 8 group experienced a statistically significant increase in injuries (exhibit 4.4). 

In results not shown, we have examined a measure of injuries that excludes sports-related injuries. We 

similarly find no overall impact of MTO on injuries using this alternative measure. 

Chapter 4 – Impacts on Adults’ and Children’s Health 79 

71 
The lack of positive impacts on asthma and child injuries is inconsistent with the earlier evidence 

from the Boston site. We have confirmed that this is not simply the result of cross-site variation. 

Using the interim evaluation data and restricting the sample to the same households studied in the 

earlier work similarly results in no impact of MTO on asthma or child injuries. Therefore, the initial 

effects appear to have been short-term effects that did not persist to the time of the interim evaluation. 

Mental health. Three mental health measures were obtained for youth ages 12 to 19. First, we used a 

six-item scale of psychological distress during the past month. Second, we used a lifetime depression 

scale that is meant to produce estimates of major depressive episodes. This scale was developed for 

use by the National Comorbidity Survey Replication: Adolescent Supplement (NCSR-AS). Finally, 

we use the NCSR-AS lifetime generalized anxiety disorder scale. 

Exhibit 4.5 shows that the results across the three mental health outcomes are similar. There is little 

overall effect on youth as a whole except for a decline in generalized anxiety for the Section 8 group. 

None of the other aggregate impact measures were statistically significant. However, the zero overall 

impact estimates on these measures appear to mask substantial differences between boys and girls. 

For all three measures, the point estimates for girls in both treatment groups suggest substantively 

large improvements in mental health and four of the six estimates are statistically significant. In 

contrast, the point estimates for boys generally indicate small declines in mental health, though none 

of them are statistically significant.72 

The one exception is the result for generalized anxiety disorder where both boys and girls in the Section 8 

group experienced improvements. 

80 Chapter 4 – Impacts on Adults’ and Children’s Health 

72 
EXHIBIT 4.3 

CHILD PHYSICAL HEALTH OUTCOMES, AGES 5 TO 11 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

[PR]

) 0.707 -0.005 

(0.026) 

-0.010 

(0.055) 

-0.011 

(0.029) 

-0.017 

(0.044) 

Girls 0.752 -0.001 

(0.033) 

-0.002 

(0.067) 

0.008 

(0.038) 

0.012 

(0.058) 

Child’s Health is Very Good or Better Currently (n=2525) 

All children (ages 5 to 11

Boys 0.660 -0.009 -0.020 -0.031 -0.047 

(0.038) (0.086) (0.042) (0.063) 

Had Asthma or Wheezing Attack During Past Year [PR] (n=2516) 

All children (ages 5 to 11) 0.150 -0.009 -0.018 0.015 0.023 

(0.018) (0.038) (0.021) (0.032) 

Girls 0.119 -0.008 

(0.022) 

-0.017 

(0.045) 

-0.004 

(0.026) 

-0.006 

(0.039) 

Boys 0.182 -0.009 -0.020 0.035 0.053 

(0.027) (0.061) (0.034) (0.051) 

Had Injury Requiring Medical Attention During Past Year [PR] (n=2521)

All children (ages 5 to 11) 0.074 -0.006 -0.013 0.003 0.005 

(0.014) (0.029) (0.015) (0.024) 

Girls 0.052 0.001 

(0.016) 

0.003 

(0.033) 

0.003 

(0.019) 

0.005 

(0.029) 

Boys 0.097 -0.013 

(0.022) 

-0.030 

(0.048) 

0.004 

(0.025) 

0.006 

(0.037) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Source: Adult survey

Sample: Children. All child ages as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted, with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. See 

appendix B for more details. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

Chapter 4 – Impacts on Adults’ and Children’s Health 81 
EXHIBIT 4.4 

CHILD PHYSICAL HEALTH OUTCOMES, AGES 12 TO 19 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

[SR]

19) 

0.680 0.001 

(0.024) 

0.001 

(0.054) 

-0.006 

(0.027) 

-0.010 

(0.048) 

Girls 0.635 0.030 

(0.034) 

0.065 

(0.075) 

0.008 

(0.038) 

0.013 

(0.062) 

Child’s Health is Very Good or Better Currently (n=2822) 

All children (ages 12 to 

Boys 0.725 -0.029 -0.066 -0.020 -0.036 

(0.034) (0.076) (0.036) (0.067) 

Had Asthma or Wheezing Attack During Past Year [SR] (n=2812) 

All children (ages 12 to 0.163 0.029 0.065 0.012 0.022 

19) (0.019) (0.042) (0.021) (0.036) 

Girls 0.192 0.034 

(0.029) 

0.073 

(0.063) 

-0.011 

(0.031) 

-0.018 

(0.050) 

Boys 0.134 0.025 0.055 0.035 0.065 

(0.025) (0.055) (0.028) (0.052) 

Had Injury Requiring Medical Attention During Past Year [SR] (n=2817)

All children (ages 12 to 0.122 0.004 0.010 0.030 0.053 

19) (0.016) (0.035) (0.019) (0.034) 

Girls 0.108 -0.021 

(0.020) 

-0.047 

(0.043) 

-0.010 

(0.023) 

-0.016 

(0.037) 

Boys 0.136 0.030 0.068 0.070* 0.130* 

(0.025) (0.056) (0.031) (0.058) 

[SR]

19) 

0.165 0.018 

(0.021) 

0.041 

(0.048) 

0.001 

(0.023) 

0.002 

(0.040) 

Girls 0.164 0.013 

(0.027) 

0.029 

(0.060) 

-0.002 

(0.031) 

-0.004 

(0.050) 

Boys 0.166 0.024 

(0.032) 

0.053 

(0.071) 

0.005 

(0.034) 

0.009 

(0.063) 

Body Mass Index in the 95th Percentile or Greater Currently (n=2676) 

All children (ages 12 to 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Source: Youth survey

Sample: Youth. All ages as of May 31, 2001. 

Notes: a) a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b)Control means and impact estimates are regression-adjusted, with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. See 

appendix B for more details. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

82 Chapter 4 – Impacts on Adults’ and Children’s Health 
EXHIBIT 4.5 

YOUTH MENTAL HEALTH OUTCOMES, AGES 12 TO 19 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

[SR] ) 

All ( ) 0.256 -0.006 

(0.015) 

-0.013 

(0.032) 

-0.006 

(0.016) 

-0.010 

(0.028) 

Girls 0.304 -0.045* 

(0.020) 

-0.098* 

(0.043) 

-0.043 

(0.023) 

-0.072 

(0.037) 

Psychological Distress Index for Past Month (n=2803

ages 12 to 19

Boys 0.208 0.034 0.077 0.031 0.057 

(0.020) (0.045) (0.022) (0.040) 

Depression During Lifetime [SR] (n=2709) 

All (ages 12 to 19) 0.065 -0.007 -0.015 -0.014 -0.024 

(0.013) (0.028) (0.013) (0.022) 

Girls 0.102 -0.025 

(0.020) 

-0.054 

(0.044) 

-0.041* 

(0.021) 

-0.067* 

(0.033) 

Boys 0.028 0.011 

(0.015) 

0.025 

(0.034) 

0.013 

(0.016) 

0.024 

(0.029) 

Generalized Anxiety Disorder During Lifetime [SR] (n=2652) 

All (ages 12 to 19) 0.067 -0.016 -0.035 -0.035* -0.061* 

(0.013) (0.028) (0.013) (0.022) 

Girls 0.091 -0.042* 

(0.018) 

-0.092* 

(0.040) 

-0.047* 

(0.019) 

-0.078* 

(0.031) 

Boys 0.042 0.012 0.026 -0.023 -0.042 

(0.018) (0.040) (0.016) (0.029) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Source: Youth survey

Sample: Youth. Ages as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b)Control means and impact estimates are regression-adjusted, with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. See 

appendix B for more details. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

Chapter 4 – Impacts on Adults’ and Children’s Health 83 
4.6 Interpretation of Results 

The results of the interim evaluation confirm one of the important findings of the earlier preliminary 

MTO research. Moves from high-poverty public housing to low-poverty neighborhoods appear to 

have had important mental health benefits for sample adults, most likely due to reduced exposure to 

crime and violence and therefore an improved sense of safety. The interim evaluation suggests a 

second important health benefit from the MTO demonstration that had not been identified before: 

lower rates of obesity. Given that obesity is associated with several serious health conditions 

including diabetes, gallstones, hypertension, heart disease, stroke, osteoarthritis, and certain types of 

cancers (Fields et al. 2001; Must et al. 1999; Wellman and Friedberg 2002), this is a potentially 

important finding. 

An interesting question is whether the mental health and obesity results are related. For example, 

could the mental health improvements be the mediating factor that led to the lower rates of obesity? 

While several studies have documented an association between obesity and depression, the pathways 

and mechanisms behind this association have not been determined (Carpenter et al. 2000). Some 

theories suggest obesity as a cause of depression, others posit depression as a cause of obesity, and 

still others suggest that a third factor could be responsible for both. 

There are two puzzles in the child health results. The first is that the positive impacts of MTO on 

asthma and child injuries that were documented for the Boston site in early research were not 

confirmed in the interim evaluation. We currently have no good explanation for why the initial 

findings on asthma and injuries did not persist.73 The second puzzle is the difference in mental health 

results between girls and boys. Some possible explanations for differential impacts on boys and girls 

are discussed at the end of chapter 5. 

The failure to confirm the early Boston results on asthma and injuries is not simply the result of differences 

across sites. Even when the sample is restricted to the same 540 families studied in the Boston pilot study, 

we find no effects in the interim evaluation. 

84 Chapter 4 – Impacts on Adults’ and Children’s Health 

73 
Chapter Five 

Impacts on Delinquency and Risky Behavior Among 

Youth 

This chapter discusses reasons why MTO participation might be expected to affect the behavior of 

youth (ages 12 to 19) in the sample and then presents the interim findings on their delinquency and 

risk-taking behavior. To study delinquency we examined behavior problems at home and school, gun 

and gang involvement, property crimes and violent behavior, assaults, and arrests. To study risky 

behavior we looked at substance use and sexual activity. Such behavior may impose costs on society 

in several ways and by means of different mechanisms—through its implications for the youth 

involved, through its possible damaging effects on the social environment, and (in the case of crimes 

and violent behavior) through its direct effects on victims. 

Summary 

The pattern of results differs by treatment group and gender. For experimental group girls, there were 

significant effects of reduced risky behavior (of which marijuana use and smoking were significant 

components) compared to the control group. For Section 8 group girls, there were significant effects 

of a smaller fraction arrested (concentrated in violent crime arrests). For both experimental and 

Section 8 group boys, there were significant effects of more self-reported behavior problems and 

more smoking than in the control group. For experimental group boys, there were also significant 

effects of more property crime arrests than in the control group. Pooling boys and girls together, there 

was a significant effect of fewer violent crime arrests in the experimental group than in the control 

group during the first four years after random assignment. For girls and boys in both the experimental 

and Section 8 groups, there were no significant effects on other measures of behavior problems, 

delinquency, arrest, alcohol use, or sexual activity. 

5.1 Hypotheses about Youth Delinquency and Risky Behavior in 

MTO 

Conceptual framework 

MTO may have important effects on behavior problems for youth and, to a lesser extent, adults. An 

extensive literature (summarized in Brock and Durlauf, 1999) posits theories that neighborhoods may 

affect social pathologies such as delinquency, substance use, and early childbearing. Jencks and 

Mayer (1990) highlight five types of models that have been proposed to describe the mechanisms 

through which neighborhoods may exert influence (see exhibit 5.1). We discuss each in turn. 

Chapter 5 – Impacts on Delinquency and Risky Behavior Among Youth 85 
• Tobacco/alcohol use.

• Use of marijuana/other drugs.

• Self-reports of delinquent and 

illegal behaviors.

• Involvement in gangs.

• Fighting/violence.

• Arrests/contact with criminal

justice system.

• Sexual activity/contraceptive

use/pregnancies.

• Other behavioral problems.

Adults

Positive Effects

• Increased sense of safety.

• Less stress, anxiety.

Negative Effects

• Feelings of isolation.

• Depression caused by

change.

Economic Opportunities

• Better labor market 

opportunities.

• Higher perceived returns to 

education.

Community Norms

and Values

• More positive peer group.

• Negative peer influence due to

competition with higher 

socioeconomic status (SES) kids.

• Better adult role-models.

• Better community monitoring of 

youth.

Social and Physical

Environment

• Reduced crime, violent activity.

• Better school resources.

• Increased recreational activities 

for youth.

Youth

Positive Effects

• Increased sense of safety.

• Increased perceived returns 

to “legitimate” activities 

(work, education).

Negative Effects

• Feelings of inadequacy, 

inability to compete.

MTO Intervention→ Relocation to low-poverty neighborhoods

EXHIBIT 5.1 

DELINQUENCY AND RISKY BEHAVIOR OUTCOMES AND MEDIATING FACTORS 

Community-Level 

Mediators 

Person and Family-Level 

Mediators 

Delinquency and 

Risky Behavior 

Outcomes 

Community-Level

Mediators

Person and Family-Level

Mediators

• Tobacco/alcohol use. 

• Use of marijuana/other drugs. 

• Self-reports of delinquent and 

illegal behaviors. 

• Involvement in gangs. 

• Fighting/violence. 

• Arrests/contact with criminal 

justice system. 

• Sexual activity/contraceptive 

use/pregnancies. 

• Other behavioral problems. 

Delinquency and

Risky Behavior

Outcomes

Adults 

Positive Effects 

• Increased sense of safety. 

• Less stress, anxiety. 

Negative Effects 

• Feelings of isolation. 

• Depression caused by 

change. 

Economic Opportunities 

• Better labor market 

opportunities. 

• Higher perceived returns to 

education. 

Community Norms 

and Values 

• More positive peer group. 

• Negative peer influence due to 

competition with higher 

socioeconomic status (SES) kids. 

• Better adult role-models. 

• Better community monitoring of 

youth. 

Social and Physical 

Environment 

• Reduced crime, violent activity. 

• Better school resources. 

• Increased recreational activities 

for youth. 

Youth 

Positive Effects 

• Increased sense of safety. 

• Increased perceived returns 

to “legitimate” activities 

(work, education). 

Negative Effects 

• Feelings of inadequacy, 

inability to compete. 

MTO Intervention→ Relocation to low-poverty neighborhoods 

Peer influences through contagion effects. This model would predict that youth in the MTO 

treatment groups will display lower levels of those delinquent and risky behaviors for which high 

socioeconomic status (SES) youth have a lower prevalence than low-SES youth. This prediction 

follows from epidemic or contagion models which emphasize the power of peers to influence one 

another’s behavior and assume “like begets like.” These models imply that if children grow up in a 

neighborhood where their peers are more likely to commit crimes or drink too much, they are more 

likely to do these things themselves. 

Relative deprivation or competition effects. Models of relative deprivation suggest that well-off 

neighbors may provoke resentment among those from poorer backgrounds so that poor youth are 

more likely to develop or fall into a deviant subculture when living in low-poverty neighborhoods. 

Academic competition with more affluent and better-prepared peers may cause some youth to work 

harder, but it may lead others to become frustrated and more likely to drop out of the competition and 

to engage in deviant behaviors. These models suggest youth in the experimental group may show 

higher levels of delinquent behaviors than youth in the control group. 

Neighborhood adult influences. Collective socialization models posit that adults in a neighborhood 

may influence young people who are not their children. More affluent adults may act as role models 

who demonstrate that success is possible if you work hard and play by the rules. High-SES adults 

may act as enforcers who help maintain public order. In this model, youth in the MTO treatment 

groups may have lower social pathologies than control group members since treatment group 

86 Chapter 5 – Impacts on Delinquency and Risky Behavior Among Youth 
members who lease up through MTO end up in neighborhoods containing a larger proportion of highSES adults. Among the treatment groups, those who lease up in the experimental group, who must 

move to low-poverty areas, may show lower rates of antisocial behavior than those in the Section 8 

group, who may move into higher poverty areas. 

Community resources. More affluent neighborhoods are likely to offer better labor market 

opportunities for youth, greater school resources, and possibly a larger range of positive recreational 

and extracurricular activities. Enhanced community resources may increase the perceived returns to 

legitimate work, educational investments, and clean recreational activities relative to illegal activities 

and other delinquent behaviors. On the other hand, more affluent communities may also present more 

lucrative opportunities for theft. As a result this theory predicts that compared to the controls, the 

experimental group (and possibly the Section 8 group) may commit property crimes at a higher or 

lower rate but should be expected to have lower delinquency rates and higher rates of involvement in 

positive activities (such as work and schooling). 

Neighborhood safety influence. Greater neighborhood safety (lower crime and violence rates) 

reduces the need to join gangs for protection and may thereby reduce delinquent behavior and 

increase positive activities for the MTO treatment groups. 

Earlier research 

The nonexperimental empirical literature reveals mixed results on the importance of these theoretical 

neighborhood mechanisms in affecting delinquency and risky behaviors. Case and Katz (1991) found 

a strong relationship between one’s own illegal drug use and that of one’s peers, and also some 

relationship between own and peer 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 of 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. Sampson, 

Raudenbush, and Earls (1997) argue that collective efficacy (social cohesion among neighbors 

combined with willingness to intervene on behalf of the common good) is linked to lower levels of 

violence within neighborhoods. 

Preliminary short-term evidence from the MTO sites showed that boys in both the experimental and 

Section 8 groups exhibited fewer behavior problems (disobedience, bullying, depression) than those 

in the control group in Boston (Katz, Kling, and Liebman, 2001) and in New York (Leventhal and 

Brooks-Gunn 2003). Evidence from MTO in Baltimore suggested that the experimental group males 

had fewer arrests for violent crimes but a short-term increase in arrests for property crime (Ludwig, 

Duncan, and Hirschfield 2001) relative to the control group. 

Chapter 5 – Impacts on Delinquency and Risky Behavior Among Youth 87 
5.2 Data Sources and Measures 

Data sources 

All of the delinquency and risky behavior outcomes described here, except arrests and contacts with 

the criminal justice system, were measured with data from the interim surveys with youth or their 

parents. For data on arrests and contacts with the criminal justice system, we obtained administrative 

data from California, Illinois, Maryland, Massachusetts, and New York.74 Mediating factors of time 

use, social interactions, church attendance, and the behaviors and attitudes of neighborhood and 

school peers were measured with interim survey data. Neighborhood characteristics were derived 

from census data. 

Measures 

Most of the relevant survey questions came directly from the National Longitudinal Survey of Youth 

(NLSY97). 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 delinquency and behavior 

problems (see Moore et al. 1999). To aggregate information on specific outcomes in order to examine 

broader patterns, we used several indices that represent the fraction of the behaviors engaged in by the 

youth. 

• Abbreviated behavior problems index: fraction of 11 behavior problems reported by sample adult 

and youth, to be often or sometimes true of a youth: 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 9 delinquent behaviors that the youth reported 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, selling drugs, and being arrested. 

• 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. 

The main analyses of arrests also focused on having ever been arrested. This reference period has the 

advantage of encompassing events throughout the entire period since random assignment. Most youth 

74 Arrest histories for adults and juveniles came from state criminal justice agencies in California, Illinois, 

Maryland, and Massachusetts. For New York, state-level criminal justice data were used to capture arrests 

anywhere in the state for ages 16 and up (as well as any arrests before age 16 that were prosecuted in adult 

rather than juvenile courts). For additional information about arrests before age 16, we obtained juvenile 

records from the New York City Department of Probation. 

88 Chapter 5 – Impacts on Delinquency and Risky Behavior Among 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. The use of “ever” as the reference period was 

also used as an approximation to the concept of “in the (4 to 7) years since random assignment” for 

asking questions of youth during the survey data collection, where it had the advantage of simplicity 

and of eliminating error due to erroneous recall of whether the event occurred before or after random 

assignment. For consistency, in analyses of administrative data, the same concept of “ever arrested” 

was used, and pre-random assignment arrests were explicitly controlled for in the analysis. Other 

reference periods were also explored in the analysis. 

5.3 Context and Baseline Status of the Sample 

The MTO program was initiated in 1994 near the end of a period in which youth violence had 

increased dramatically. For example, from 1985 to 1993 the rate at which teenagers were arrested for 

murder more than doubled. Most of this increase was driven by gun homicides, committed 

disproportionately by and against minority youth (Cook and Laub, 1998, 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 rates at which teens were arrested for both violent and property offenses crested nationwide 

during the mid-1990’s and have since declined by approximately one-third (Cook and Laub, 2002). 

The source of this decline in youth crime remains unclear, although candidate explanations include 

changes in the nature of crack use or distribution, increases in the nation’s prison population, 

changing demographics, and a booming economy (Blumstein and Wallman, 2000, Cook and Laub, 

2002, Donohue and Levitt, 2001). Whatever the cause, the arrest rates for MTO teens generally 

mirror the pattern found in national data. For example, the number of arrests of 16-year-olds declined 

by around one-third from 1997 to 2001.75 The level of arrests is substantially higher in the MTO 

population than in a national sample, consistent with the disadvantaged family and community 

circumstances in which most MTO youth grew up.76 

This chapter focuses mainly on youth ages 12 to 19 on May 31, 2001, who entered the MTO 

demonstration 4 to 7 years earlier. Thus, the ages of this group at baseline ranged from 5 to 16. At 

that time, parents of all children ages 6 to 17 were asked a series of questions about the children’s 

behavior. These measures show large baseline differences between girls and boys. Two are most 

75 For this calculation the sample is defined as being youth in any of the three MTO groups who were age 16 

for at least one-quarter of each calendar year. 

76 Since categories used in national data differ somewhat from the juvenile arrest data available for MTO 

participants, we have compared arrests for robbery where the definition is the most consistent. Robbery 

arrest rates for MTO youth ages 15 to 19 are roughly five times higher than the national average reported 

by Zimring (1998). 

Chapter 5 – Impacts on Delinquency and Risky Behavior Among Youth 89 
relevant for this chapter: problem behavior (going to a special class or school or having received 

special help in school during the past 2 years for behavioral or emotional problems) and suspension 

(suspended or expelled from school during the past 2 years). At baseline, 6 percent of the girls in the 

sample had exhibited problem behavior and 7 percent had been suspended. Fourteen percent of the 

boys had exhibited problem behavior and 15 percent had been suspended. 

5.4 Effects on Mediators for Youth Delinquency and Risky 

Behavior in MTO 

This section reviews effects on the mediating factors most directly relevant to the hypotheses 

described in Section 5.2. The tables containing these results are found in appendix E. 

Peer influences 

We analyzed whether the program changed the characteristics of sample members’ peers. In the 

qualitative research that preceded the interim evaluation survey, youth respondents spoke about a 

difference in peer influences. A 15-year-old boy from a Los Angeles Section 8 family that had leased 

up said (referring to the public housing development where he had lived): 

At Nickerson, it was cool. But now when I go to visit over there, I probably would have ended 

up in a gang or smoking or something. So I'm glad that I moved from over there. 

There were no significant effects for either the experimental or Section 8 group on having friends 

involved in school activities or on friends carrying weapons to school (exhibit E5.1). There were 

significant effects for both the experimental and Section 8 groups relative to the control group of 

having more friends who use drugs, largely concentrated among boys (exhibit E5.1), and for the 

experimental group of having a higher fraction with at least one close friend (exhibit E5.2). Note that 

while there was some visiting of baseline neighborhoods by those whose families leased up through 

MTO and moved out of the origin neighborhood, the fraction either living in or making visits to the 

baseline neighborhood was much higher in the control group than in the experimental or Section 8 

groups. The exposure of the control group youth to the baseline neighborhoods was much greater than 

the experimental or Section 8 groups (exhibit E5.2). 

Characteristics of families in the neighborhood 

We also analyzed census data about nearby residents (discussed in depth in chapter 2), which is 

relevant for both the relative deprivation and neighborhood adult influence hypotheses. The youth 

interviewed for the qualitative study were very aware of the differences between the families in their 

new neighborhoods and the neighbors in their old ones. Speaking about the public housing 

development from which his experimental group family had moved, a 16-year-old Baltimore youth 

described the contrast: 

I’d say it’s overall better than back in Murphy Homes. People at Murphy Homes would 

rather steal a car than to buy one. Out here, everyone’s just working, has a job. 

90 Chapter 5 – Impacts on Delinquency and Risky Behavior Among Youth 
Briefly, assignment to the experimental and Section 8 groups had positive effects on the fraction of 

families in the neighborhood with two parents, with an employed adult, and with incomes above the 

poverty line. The interim survey also collected reports from sample adults about neighborhood 

collective efficacy, which is among the measures of adult characteristics most specifically related to 

delinquency and risky behavior. There were significant effects for both the experimental and Section 

8 groups of greater perception that neighbors would likely do something about kids doing graffiti and 

about kids skipping school in the neighborhood (exhibit E5.3). 

Community resources 

We analyzed employment possibilities and participation in institutions outside school. There were 

positive effects for the experimental and Section 8 groups on local area unemployment rates (exhibit 

D2.1). Contrary to a hypothesis that participation in community activities would decrease for 

experimental and Section 8 youth, there was no impact on attendance at church youth activities. For 

the most part, there were no effects on participation in structured or supervised activities after 

school—with the exception of experimental group girls, who were more likely to be in a structured 

activity at 5:30 p.m. in places such as school, church, or a community center (exhibit E5.4). 

Neighborhood safety 

We analyzed a variety of measures of exposure to violence (exhibit E5.5). Youth interviewed for the 

qualitative research early in this study spoke at length about safety. A 14-year-old girl from a Chicago 

experimental group family that had leased up was reflective about the trade-off: 

Like, OK, you can wake up every day and we’re not worried about seeing anybody getting 

shot and no gang members, nothing like that and it’s quiet and it’s cool and calm up here. In 

the city there’s a lot of activities that’s going on that’s negative. Here there’s a lot of positive. 

Yeah, the only thing is, it’s like too quiet out here. Um, it's boring but it's good that I'm safe, 

rather be bored than unsafe. 

For measures that specifically asked about the neighborhood, such as hearing gunshots in the 

neighborhood at least once in the past week, there were significant improvements for both the 

experimental and Section 8 groups relative to the control group. There was also a significantly lower 

fraction in the experimental group in the proportion of youth seeing people using or selling drugs in 

the neighborhood than in the control group. For measures of overall exposure to violence not specific 

to the neighborhood—including prevalence of gangs, witnessing of stabbings or shootings, or having 

a knife or gun pulled on the youth—there were no significant effects for either group (with the 

exception of a significant decrease in gang prevalence for the experimental group). 

5.5 Interim Impacts on Youth Delinquency and Risky Behavior 

This section discusses the main results for delinquency and risky behavior outcomes. It begins by 

examining behavior problems and delinquent acts that are prevalent among all youths in the sample 

ages 12 to 19. The most serious of these acts may result in arrests. For this outcome, we focused on 

the age range in which arrests are most prevalent, 15 to 19, since including ages for which arrests are 

Chapter 5 – Impacts on Delinquency and Risky Behavior Among Youth 91 
uncommon dampens the power of our analyses. Finally, we examined program effects on risky 

behavior (substance use and sexual activity), which are also most prevalent among ages 15 to 19. 

Behavior problems and delinquency 

As described above in the section on measures, sample adults reported the fraction of 11 behavior 

problems that their child might have. There were no significant effects on behavior problems as 

reported by parents for boys or girls in either the experimental or the Section 8 group (exhibit 5.2). 

These results contrast with the earlier findings at the Boston and New York sites after 2 years, 

discussed above, that behavior problems for boys were significantly less prevalent in the experimental 

and Section 8 groups than the control group.77 In fact, in the present analysis, there were significantly 

more self-reported behavior problems for boys ages 12 to 19 in the experimental and Section 8 groups 

than in the control group.78 

We also examined impacts on the outcomes shown in exhibit 5.2, with separate estimates by both 

gender and baseline problem behavior status (not shown in the exhibits). For the experimental group, 

effects of more behavior problems relative to the control group do appear to be concentrated among 

those who had problem behaviors at baseline, particularly among boys.79 

While the behavior problems index covers a broad array of troubles that many children have, the 

delinquency index focuses more narrowly on issues that tend to be related to illegal activity. For this 

outcome, there were no significant effects for either group or gender. 

77 Analysis of results specifically for Boston and for New York, using the same age groups and measures 

(when available) as in previous research, did not show statistically significant effects on behavior problems 

4 to 7 years after random assignment. Analysis based on the same children in Boston having data from 

both 1997 and 2002 showed a significant effect of more behavior problems in the experimental group 

relative to the control group in 2002. 

78 Although not the age group focused on in this chapter, boys ages 5 to 11 also had a significantly higher 

fraction of behavior problems in the experimental group than in the control group (exhibit D5.1) according 

to parental reports. 

79 In addition to baseline behavior problems and gender, we examined interactions with mother’s education 

and child gender, with family size and child gender, with race and child gender, and with younger age at 

time of random assignment and child gender—but found no consistent pattern of results for behavior 

problems or for other delinquency outcomes. 

92 Chapter 5 – Impacts on Delinquency and Risky Behavior Among Youth 
EXHIBIT 5.2 

BEHAVIOR PROBLEMS & DELINQUENCY OUTCOMES, YOUTH AGES 12 TO 19 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

[PR] (n=2770)

All youth (ages 12 to 19) 0.256 0.019 

(0.014) 

0.042 

(0.032) 

0.004 

(0.015) 

0.006 

(0.026) 

Female 0.230 0.005 

(0.019) 

0.011 

(0.041) 

-0.002 

(0.021) 

-0.003 

(0.034) 

Male 0.282 0.032 0.074 0.009 0.016 

Abbreviated Behavior Problems Index 

(0.020) (0.046) (0.021) (0.038) 

Abbreviated Behavior Problems Index [SR] (n=2810)

All youth (ages 12 to 19) 0.343 0.036* 0.080* 0.023 0.039 

(0.014) (0.032) (0.016) (0.028) 

Female 0.352 -0.002 

(0.019) 

-0.004 

(0.042) 

-0.007 

(0.021) 

-0.012 

(0.035) 

Male 0.336 0.075* 0.169* 0.052* 0.095* 

(0.020) (0.045) (0.022) (0.040) 

Delinquency Index [SR] (n=2819)

All youth (ages 12 to 19) 0.089 0.004 0.008 0.007 0.012 

(0.008) (0.017) (0.009) (0.016) 

Female 0.061 0.001 

(0.008) 

0.001 

(0.018) 

0.001 

(0.010) 

0.001 

(0.016) 

Male 0.118 0.006 0.015 0.013 0.024 

(0.013) (0.029) (0.015) (0.027) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult and Youth surveys. 

Sample: Children ages 12 to 19 as of May 31, 2001, from families randomly assigned through December 31, 

1997. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted, with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. See 

appendix B for detailed explanation of estimation procedures

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations).

Chapter 5 – Impacts on Delinquency and Risky Behavior Among Youth 93 
Arrests 

The peak age of arrest in the MTO population is 18, and the prevalence of arrest at age 18 is more 

than three times higher than at age 13. To focus on the prime offending ages where the statistical 

power of our analysis is greatest, our analysis focused on ages 15 to 19.80 Exhibit 5.3 shows results 

from both the interim survey and administrative records on whether youth ages 15 to 19 in 2001 had 

ever been arrested. For all types of crimes taken together, there was no significant effect on the 

fraction of either boys or girls ever arrested in the self-reported survey data. In the administrative data 

there was no significant effect for boys, but there was a significant effect of a smaller proportion 

arrested among girls in the Section 8 group than the control group. 

Regarding other results in exhibit 5.3, there was a large and significant effect of a smaller proportion 

of girls ever arrested for violent crimes (which were largely assaults) in the Section 8 group. There 

was also a significantly greater proportion of boys who had ever been arrested for property crimes in 

the experimental group than in the control group. Other effects for violent and property crime arrests 

were insignificant, and there were no significant effects for other (non-violent non-property) crimes in 

any group. 

Although not the focus of this chapter, we also analyzed arrest outcomes of sample adults. As seen in 

exhibit D5.1, for adults there are no statistically significant differences across groups in the likelihood of 

ever having been arrested for any offense, for property crimes, or for violent crimes (p-value .054). There 

was a significant effect of a smaller proportion arrested for other offenses (nonviolent nonproperty, 

including drugs) in the experimental group relative to the control group. The sample adults are mainly 

women, and these results parallel the evidence of beneficial program effects for older female youth. 

94 Chapter 5 – Impacts on Delinquency and Risky Behavior Among Youth 

80 
EXHIBIT 5.3 

ARREST OUTCOMES 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

Ever Arrested [SR] (n=1574)

All youth (ages 15 to 19) 0.224 -0.004 

(0.028) 

-0.008 

(0.064) 

0.001 

(0.032) 

0.002 

(0.057) 

Female 0.140 -0.022 

(0.032) 

-0.048 

(0.070) 

-0.023 

(0.035) 

-0.039 

(0.059) 

Male 0.311 0.016 0.039 0.027 0.054 

(0.044) (0.109) (0.052) (0.103) 

Ever Arrested [ADMIN] (n=2646)

All youth (Ages 15-19) 0.313 0.018 0.043 -0.020 -0.036 

(0.022) (0.050) (0.024) (0.042) 

Female 0.238 -0.022 

(0.028) 

-0.051 

(0.065) 

-0.063* 

(0.029) 

-0.112* 

(0.052) 

Male 0.388 0.058 0.135 0.021 0.038 

(0.031) (0.072) (0.035) (0.061) 

Ever Arrested for Violent Crime [ADMIN] (n=2646)

All youth (Ages 15-19) 0.174 -0.010 -0.024 -0.035 -0.063 

(0.017) (0.040) (0.020) (0.035) 

Female 0.144 -0.040 

(0.022) 

-0.093 

(0.051) 

-0.088* 

(0.022) 

-0.155* 

(0.039) 

Male 0.204 0.019 0.045 0.015 0.026 

(0.026) (0.060) (0.030) (0.053) 

Ever Arrested for Property Crime [ADMIN] (n=2646)

All youth (Ages 15-19) 0.118 0.022 0.051 0.014 0.026 

(0.015) 

-0.013 

(0.042) 

(0.036) (0.017) 0.031 

Female 0.087 -0.006 -0.007 -0.013 

(0.018) (0.021) (0.037) 

Male 0.150 0.049* 0.115* 0.035 0.062 

(0.024) (0.055) (0.027) (0.048) 

Ever Arrested for Non-Violent, Non-Property Crime [ADMIN] (n=2646)

All youth (Ages 15-19) 0.168 0.003 0.007 0.0004 0.001 

(0.017) 

-0.080 

(0.041) 

-0.019 

(0.020) 

-0.033 

(0.035) 

(0.039) (0.019) (0.033) 

Female 0.097 -0.034 

(0.018) 

Male 0.240 0.040 0.094 0.019 0.034 

(0.027) (0.064) (0.030) (0.053) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Youth survey data and individual criminal justice system arrest data. 

Sample: Children ages 15 to 19 as of May 31, 2001, from families randomly assigned through December 31, 

1997. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted, with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. See 

appendix B for more details. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

Chapter 5 – Impacts on Delinquency and Risky Behavior Among Youth 95 
Exhibit 5.4 shows results for the proportion of calendar quarters with at least one arrest during the 

first four years after random assignment, for the cohort of youth who were ages 15 to 19 four years 

after random assignment. (That is, they were ages 11 to 15 at baseline.) This measure complements 

the measure of ever arrested in exhibit 5.3 by capturing the frequency of arrest. This is also essentially 

the same measure analyzed in previous research on MTO’s Baltimore site by Ludwig et al. (2001). 

For all types of crimes pooled together, there were no significant effects on the proportion of calendar 

quarters with an arrest during the first four years after random assignment. 

We found an effect of significantly less frequent violent crime arrests in the experimental group 

relative to the control group when pooling boys and girls together, although results separately for 

boys (p-value .07) and for girls were not significant. Further analysis (not shown in the exhibits) of 

the smaller groups of these youth for whom we have data at least 6 years after random assignment 

showed similar effects in years 1-4 but no significant effects for either gender on violent crime in year 

6; although imprecise, these results may suggest that the relatively lower rate of violent crime arrests 

for the experimental group did not persist. The effect on Section 8 girls using the measure of 

proportion of quarters arrests goes in the same direction as the results for fraction ever arrested, but it 

was not significant.81 

The results showed significant effects of more frequent property crime arrests for boys in the 

experimental group than in the control group during the first four years after random assignment. 

Further analysis (also not shown in the exhibits) of the smaller groups of these youth for which we 

have data at least 6 years after random assignment showed similar effects in years 1-4 but no 

significant effects for either gender or group on property crime in year 6; again although imprecise, 

these results may suggest that the relatively higher rate of property crime arrests for experimental 

group boys did not persist. 

Previous research on MTO’s Baltimore site (Ludwig et al. 2001) found that the most significant effect 

was a smaller proportion arrested for violent crime (concentrated among boys) in the experimental 

group, and also found some effects of increased property crime arrests for experimental group boys 

relative to the control group during the period shortly after random assignment. The analysis of data 

for all five sites generally echoes this previous research, with less frequent violent crime arrests and 

more frequent property crime arrests in the experimental group than in the control group. However, in 

the five-site analysis, the higher property crime arrest rate for the experimental group relative to the 

control group was not particularly concentrated 1 to 2 years after random assignment (not shown in 

the exhibits). 

When the analyses in exhibits 5.3 and 5.4 use the same sample (restricted to youth ages 15 to 19 on May 

31, 2001 and ages 15 to 19 four years after random assignment) then there is a significant effect of fewer 

violent crime arrests for Section 8 group girls than for the control group using both measures. 

96 Chapter 5 – Impacts on Delinquency and Risky Behavior Among Youth 

81 
EXHIBIT 5.4 

Fraction of Calendar Quarters Youth Had Any Arrest 

1-4 years Since Random Assignment for Ages 15 to 19 Four Years 

After Random Assignment 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

] (n=2532)

All youth 0.0364 0.0006 

(0.0036) 

0.0013 

(0.0087) 

0.0007 

(0.0040) 

0.0012 

(0.0072) 

Female 0.0191 -0.0036 

(0.0035) 

-0.0085 

(0.0085) 

-0.0003 

(0.0042) 

-0.0005 

(0.0076) 

Male 0.0533 0.0048 0.0115 0.0019 0.0034 

All Crime 1st Through 4th Years After Random Assignment [ADMIN

(0.0061) (0.0146) (0.0063) (0.0115) 

Violent Crime 1st Through 4th Years After Random Assignment [ADMIN] (n=2532)

All youth 0.0138 -0.0038* -0.0091* -0.0010 -0.0019 

(0.0019) (0.0045) (0.0022) 0.0040 

Female 0.0088 -0.0022 

(0.0020) 

-0.0053 

(0.0049) 

-0.0030 

(0.0026) 

-0.0054 

(0.0047) 

Male 0.0190 -0.0054 -0.0129 0.0006 0.0011 

(0.0030) (0.0071) (0.0034) (0.0061) 

Property Crime 1st Through 4th Years After Random Assignment [ADMIN] (n=2532)

All youth 0.0098 0.0021 0.0050 0.0007 0.0012 

(0.0016) (0.0039) (0.0017) (0.0031) 

Female 0.0055 -0.0011 

(0.0015) 

-0.0027 

(0.0036) 

0.0016 

(0.0021) 

0.0028 

(0.0038) 

Male 0.0139 0.0054* 

(0.0027) 

0.0130* 

(0.0064) 

0.0001 

(0.0025) 

0.0002 

(0.0045) 

Assignment ] (n=2532)

All youth 0.0140 0.0017 

(0.0022) 

0.0042 

(0.0052) 

0.0015 

(0.0022) 

0.0027 

(0.0041) 

Female 0.0051 -0.0011 

(0.0020) 

-0.0025 

(0.0048) 

0.0007 

(0.0019) 

0.0013 

(0.0031) 

Non-Violent, Non-Property Crime 1st Through 4th Years After Random 

[ADMIN

Male 0.0227 0.0046 0.0111 0.0025 0.0045 

(0.0038) (0.0090) (0.0039) (0.0072) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Individual criminal justice system arrest data. 

Sample: Children ages 15 to 19 four years after random assignment. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted, with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. See 

appendix B for more details. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

Chapter 5 – Impacts on Delinquency and Risky Behavior Among Youth 97 
The overall pattern of results by crime type described above was also generally shown in analysis on 

the outcome of number of arrests (not shown in the exhibits)—complementing that shown on the 

prevalence of arrest. There were no significant effects on ever being arrested for other (nonviolent, 

nonproperty) crimes. Focusing on arrests in year 2001 for youth ages 15 to 19 as of May 31, 2001 

(not shown in the exhibits), there were no significant effects on self-reported arrests or in 

administrative records of violent, property, or other arrests for either group, except for a lower 

fraction of violent crime arrests for girls in the Section 8 group than in the control group. 

Risky behavior 

Results in exhibit 5.5 show that a significantly smaller fraction of the experimental group girls than 

control group girls engaged in four risky behaviors. The control group mean was about .43, indicating 

that the typical youth had engaged in almost one-half of the four risky behaviors. The estimated 

magnitude of the effect for girls in families who leased up in the experimental group was -.16. 

Among the components of the risky behavior index, also shown in exhibit 5.5, there were significant 

effects of less smoking and marijuana use for girls in the experimental group than in the control 

group. There were significant effects of more smoking for boys in both the experimental and Section 

8 groups than in the control group. The effects on smoking for boys were very large in magnitude, 

with an estimated effect for boys who leased up (in either the experimental or Section 8 group) of 

over .30 in the proportion who ever smoked, with a control mean of .26. The effects on use of 

marijuana, on smoking, and on alcohol use in the past month (not shown in the exhibits) had a general 

pattern of less use for girls and more use for boys in the experimental and Section 8 groups relative to 

the control group, with most of these estimates being statistically significant for both genders. The 

gender pattern of effects on sexual activity is more mixed, and the estimates were not statistically 

significant. 

98 Chapter 5 – Impacts on Delinquency and Risky Behavior Among Youth 
EXHIBIT 5.5 

RISKY BEHAVIOR AND DRUG OUTCOMES 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

[SR] (n=1588)

All youth (ages 15 to 19) 0.439 -0.008 

(0.023) 

-0.019 

(0.053) 

0.016 

(0.025) 

0.030 

(0.047) 

Female 0.431 -0.072* 

(0.031) 

-0.156* 

(0.067) 

-0.016 

(0.034) 

-0.027 

(0.057) 

Male 0.447 0.057 0.142 0.050 0.100 

Risky Behavior Index 

(0.030) (0.075) (0.034) (0.070) 

Ever Used Marijuana [SR] (n=1569)

All youth (ages 15 to 19) 0.344 -0.031 -0.071 -0.020 -0.036 

(0.034) (0.079) (0.038) (0.069) 

Female 0.342 -0.129* 

(0.044) 

-0.276* 

(0.095) 

-0.079 

(0.050) 

-0.132 

(0.085) 

Male 0.348 0.068 0.169 0.042 0.083 

(0.049) (0.122) (0.052) (0.104) 

Ever Had Alcoholic Drink [SR] (n=1582)

All youth (ages 15 to 19) 0.421 -0.024 -0.054 -0.005 -0.008 

(0.035) (0.081) (0.038) (0.070) 

Female 0.410 -0.072 

(0.049) 

-0.155 

(0.107) 

-0.003 

(0.052) 

-0.005 

(0.088) 

Male 0.432 0.025 0.062 -0.007 -0.013 

(0.049) (0.121) (0.054) (0.109) 

Ever Smoked [SR] (n=1583)

All youth (ages 15 to 19) 0.290 0.019 0.044 0.067 0.122 

(0.032) 

-0.184* 

(0.093) 

-0.015 

(0.046) 

-0.024 

(0.077) 

(0.074) (0.035) (0.065) 

Female 0.314 -0.085* 

(0.043) 

Male 0.264 0.125* 0.314* 0.152* 0.305* 

(0.045) (0.113) (0.051) (0.102) 

Ever Had Sex [SR] (n=1548)

All youth (ages 15 to 19) 0.717 -0.009 -0.021 0.014 0.025 

(0.029) 

-0.013 

(0.090) 

0.033 

(0.043) 

0.055 

(0.073) 

(0.068) (0.032) (0.058) 

Female 0.667 -0.006 

(0.041) 

Male 0.769 -0.013 -0.032 -0.007 -0.014 

(0.040) (0.099) (0.046) (0.091) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Youth survey. 

Sample: Children ages 15 to 19 as of May 31, 2001, from families randomly assigned through December 31, 

1997. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. See 

appendix B for more details. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations).

Chapter 5 – Impacts on Delinquency and Risky Behavior Among Youth 99 
5.6 Interpretation of Results 

Overall, for most measures of environmental context, the experimental and Section 8 groups were at 

least as well off and often more well off than the control group—with similar effects for boys and 

girls. The magnitudes of many effects were quite large. For gunshots in the past week, as an example, 

the control group mean was .12, and the estimated reduction for those who leased up in the Section 8 

group was .06. For neighbors doing something about graffiti, the control group mean was .54 and the 

estimated effect for those who leased up in the experimental group was .24. For all the hypotheses 

except relative deprivation, improvement in environmental context predicted improvements in 

individual outcomes. 

The deleterious effects on outcomes such as behavior problems and smoking for boys were 

unexpected. A hypothesis to be investigated further is the relative deprivation hypothesis discussed in 

section 5.2. However, most aspects of the experimental and Section 8 group effects on social 

environment—such as the reduction in exposure to violence and greater neighborhood collective 

efficacy discussed in section 5.3—were predicted to have the same effect on boys and girls. Other 

mediators that could have had different effects by gender, such as social adjustment, number of 

friends, and contact with fathers or other adults, did not generally have program effects significantly 

different from zero for either gender or that differed significantly between boys and girls. 

Experimental group girls were significantly more likely to be in a structured activity after school, but 

the effect on boys was not significant. Understanding the differential effects by gender will require 

further research. 

At the beginning of this chapter we noted that delinquency and risky behavior may impose costs on 

society—through their implications for the youth involved, through possible damaging effects on the 

social environment, and in the case of crimes and violent behavior, through direct effects on victims. 

The direct effects on youth themselves—of increases or decreases in substance use or smoking—are 

relatively straightforward. The net effect on the social environment of increases in smoking for boys 

and decreases for girls are partially offsetting. The increases in behavior problems and property crime 

arrests for boys are social costs in that they spill over to others, while reductions in drug use for 

experimental group girls and in violent crime arrests for some Section 8 group girls are social benefits 

as well as direct benefits for these youth. 

100 Chapter 5 – Impacts on Delinquency and Risky Behavior Among Youth 
Chapter Six 

Impacts on Children’s Education 

One expectation for the MTO demonstration was that moving children to better neighborhoods would 

provide access to better educational opportunities in the form of better schools and increased 

exposure to peers and communities that value academic achievement and would, in turn, lead to 

improved educational outcomes. This chapter summarizes the reason why MTO participation might 

be expected to affect the educational outcomes of the children in the interim evaluation sample, 

describes the educational experiences of the MTO children, and presents the interim findings on 

educational impacts. 

Summary 

At the time of the interim evaluation, approximately 80 percent of the children in the experimental 

group were attending schools in the same school district as they had been at baseline. Even so, the 

evidence suggests that MTO had a small but positive effect on the characteristics of children’s 

schools. There were also significant impacts on some of the other community-level variables 

hypothesized to mediate educational outcomes. At this point in the demonstration, however, these 

positive effects on the mediators of educational achievement have not had significant impacts on 

education-related behaviors or attitudes of MTO children or on their school achievement and 

educational progress. 

6.1 Hypotheses about Education in MTO 

There are a number of pathways by which the MTO demonstration might influence educational 

outcomes for the children of MTO families who move to more affluent neighborhoods. A great deal 

of research has demonstrated the positive relationship between neighborhood socioeconomic status 

and educational outcomes. In a series of studies, Brooks-Gunn and colleagues have shown that— 

controlling for family background characteristics—affluent neighbors are positively related to young 

children’s IQ and verbal ability scores (e.g., Brooks-Gunn et al., 1993; Duncan et al., 1994; Klebanov 

et al., 1997), higher reading recognition (Chase-Lansdale et al., 1997) and math achievement 

(Entwistle, Alexander, and Olson, 1994), adolescent school completion, educational attainment, and 

self-reported grades (Brooks-Gunn et al., 1993; Connell and Halpern-Felsher, 1997; Dornbusch, 

Ritter, and Steinberg, 1991; Duncan, 1994). Similarly, studies of the Gautreaux housing mobility 

program in Chicago found lower dropout rates and an increased likelihood of attending college 

among children whose families relocated from public housing to suburban neighborhoods 

(Rosenbaum, 1995). 

This research has pointed to a number of potential mediators of the relationship between 

neighborhood and educational outcomes. Exhibit 6.1 presents a conceptualization of these pathways. 

As shown in the exhibit 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 

Chapter 6 – Impacts on Children’s Education 101 
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.

Student Attitudes and Behavior

• Increased value on learning and

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 and anxiety.

• Greater parent support for

education/achievement.

• Increased/higher wage

employment.

• Changed parenting practices:

less restrictive, authoritarian.

Quality of Schools

• Higher student achievement.

• More resources in schools.

• Better teaching.

• Lower student-teacher ratio.

• Lower counselor-student ratio.

• School environment that

supports learning.

MTO Intervention→ Relocation to Low-Poverty Neighborhoods

Community Norms

and Values

• Better adult-/peer-role models.

• Better community monitoring

of youth.

• Shared community value of

Education.

Social and Physical

Environment

• Lower crime and violence.

• Increased recreational

activities for youth.

Economic Opportunities

• More job openings.

• Better wages.

available in the community. As the model shows, one of these community characteristics—higher 

quality schools—may directly influence students’ educational outcomes. In addition, all four of these 

community characteristics are hypothesized to affect educational outcomes through changes in the 

behavior and attitudes of both students and their families. 

EXHIBIT 6.1 

EDUCATIONAL OUTCOMES AND MEDITATING FACTORS 

Community-Level 

Mediators 

Student and Family-Level 

Mediators 

Educational 

Outcomes 

Community-Level

Mediators

Student and Family-Level

Mediators

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. 

Educational

Outcomes

Student Attitudes and Behavior 

• Increased value on learning and 

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 and anxiety. 

• Greater parent support for 

education/achievement. 

• Increased/higher wage 

employment. 

• Changed parenting practices: 

less restrictive, authoritarian. 

Quality of Schools 

• Higher student achievement. 

• More resources in schools. 

• Better teaching. 

• Lower student-teacher ratio. 

• Lower counselor-student ratio. 

• School environment that 

supports learning. 

MTO Intervention→ Relocation to Low-Poverty Neighborhoods 

Community Norms 

and Values 

• Better adult-/peer-role models. 

• Better community monitoring 

of youth. 

• Shared community value of 

Education. 

Social and Physical 

Environment 

• Lower crime and violence. 

• Increased recreational 

activities for youth. 

Economic Opportunities 

• More job openings. 

• Better wages. 

Community characteristics linked to student educational outcomes 

The first and most direct mediator of educational outcomes is school quality. Higher income 

neighborhoods have been shown to 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 hypothesized to increase students’ commitment to academic achievement and to 

promote behaviors that produce achievement—studying, attendance, and engagement in school. All 

of the children in the sample for the interim evaluation were of school age during the period leading 

102 Chapter 6 – Impacts on Children’s Education 
up to the evaluation, and therefore it is reasonable to expect that differences in school quality could be 

an important influence on educational outcomes of these students.82 

Conversely, it is possible that relocating families in more affluent neighborhoods and sending 

children to new schools could have a negative effect on school achievement for MTO children. This 

is due to increased competition for grades and academic success in more affluent schools and because 

MTO students might develop low self-confidence in the midst of more affluent, higher achieving peer 

group. Indeed, studies of the Gautreaux housing mobility program in Chicago found that students 

whose families moved to the suburbs initially had difficulties adapting to the higher expectations in 

the suburban schools and their grades suffered as a result (Rosenbaum, 1995). 

A second characteristic associated with the affluence of the community is the extent to which the 

community socializes its youth towards achievement, learning, and productive social behavior (i.e., 

whether and how neighborhoods provide individual and communitywide support and motivation for 

achievement). Both adults and peers in the community play a role in this socialization. The presence 

or absence of adult role models who provide examples of the advantages of being well-educated and 

employed is one way in which a community supports the values of educational achievement. More 

affluent neighborhoods tend to have higher rates of professional and managerial employment, and 

adults with these jobs act as positive role models for educational completion, academic success, and 

career-mindedness. In support of this hypothesis, Crane (1991) found that after controlling for the 

individual background characteristics of youth, a higher percentage of professional/ managerial 

workers in a neighborhood was associated with lower dropout rates for youth. As shown in exhibit 

6.1, it is hypothesized that youth growing up in communities that have adult role models for, and 

consistent valuing of, education and achievement will themselves have values and expectations more 

consistent with educational achievement and learning, which will ultimately lead to improved 

educational outcomes. 

Peer groups can also be a factor in either promoting or devaluing academic achievement. Youth in 

more affluent communities may be more likely to value education and achievement, and they may be 

more supportive of staying in school, doing well in school, and getting involved in school. If children 

of MTO families who relocate to more affluent neighborhoods develop new peer groups with values 

and behaviors more conducive to high educational achievement, then they may adopt more positive 

attitudes towards school and academic achievement and their own educational performance may be 

positively affected. 

A third community characteristic in the model is the physical environment itself. More affluent 

communities are likely to be safer, which may have consequences for the attitudes and mental health 

of both parents and youths. Parents who believe that their children are safe may feel less stress and 

anxiety, resulting in improved mental health and sense of control over their lives. This improved 

mental health status may then lead to different parenting behaviors. McLoyd (1990) has posited that 

restrictive and authoritarian patterns of parenting, which are more often exhibited by low-income 

parents and which have been shown to be associated with poorer educational outcomes for children, 

It is possible that some children, even if their families moved, did not change schools. Certainly the 

qualitative data point to this. Other children will have changed schools more than once. School 

continuity—or lack of continuity—may well have its own effects. 

Chapter 6 – Impacts on Children’s Education 103 

82 
are themselves linked to the mental health stresses associated with poverty. In addition to the effect of 

safe communities on parents and parenting behavior, children who feel safe in their physical 

surroundings may be more likely to flourish academically and personally. 

A fourth characteristic of more affluent communities is increased economic opportunities for the 

adults in that community. This not only includes more job opportunities, but also better paying jobs. 

If these opportunities result in improved economic circumstances for MTO families who move out of 

low-poverty neighborhoods, there will be more resources in the family. Greater family resources can 

be linked to improved educational outcomes for children in the family, if the resources translate into 

more materials in the home that support educational achievement (books, creative materials, 

educational media). Increased economic self-sufficiency can also affect educational outcomes for 

children if parental employment leads to increased familial support for achievement in general. 

Student and parent attitudes and behavior linked to educational outcomes 

Exhibit 6.1 shows the links between community characteristics and the behavior and attitudes of 

students and their parents. These behaviors are in turn linked to student educational outcomes. For 

students, attitudes in four areas are thought to be related to levels of educational performance. First 

are students’ beliefs about themselves: expectations for their own educational achievement, how 

much they value education and achievement in life, and their belief in their own abilities and chances 

for educational success. Second are attitudes about school: how strongly they feel connected to 

school, whether or not they feel teachers in the school care about them, and how they do in school. 

Third are their attitudes about their own family: whether and how much they think their parents care 

about school. Fourth are attitudes about their peer group: how they think their friends feel about 

school and about doing well in school (Ryan, 2001; Cairns et al., 1989; French et al., 2001; Murdock 

et al., 2000) 

Student behavior is also an important link between the community and educational outcomes. Schoolrelated behavior includes the amount of time spent studying, rigor of course work taken, and 

engagement in school—both participation in school activities such as sports and clubs and evidence 

of attendance or disciplinary problems. 

Parental attitudes that have been thought to mediate student achievement include educational 

expectations, emphasis on educational achievement, and support for the school. Parental behavior 

includes active involvement in the school (teacher conferences, parent-teacher organizations, etc.), 

support for homework completion, and parenting practices that encourage students to think about and 

evaluate their own actions and consequences. 

Student educational outcomes 

Student educational outcomes are the final outcomes of interest. As shown in exhibit 6.1, the two 

major components of educational outcomes are academic achievement, as measured by standardized 

test scores; and overall progress through school—promotion or retention in grade, ontime high school 

graduation, and post-high school graduation educational plans. 

104 Chapter 6 – Impacts on Children’s Education 
6.2 Data Sources and Measures 

To understand the mechanisms through which residential location affected educational performance, 

we estimated impacts on a variety of community and school characteristics as well as student and 

parent attitudes and behaviors. Community-level data on crime and economic health were drawn from 

published statistics at the state level and from Census 2000 data. Data on the socialization patterns in 

the community (adult and peer) were obtained through interviews with the adults in the MTO 

families. Data on school characteristics were obtained from multiple sources, including parent and 

youth reports and administrative data from both state and national records. The administrative sources 

included (a) school-level administrative data from each of the five MTO states (Illinois, New York, 

California, Massachusetts, and Maryland) collected from state Web sites and state departments of 

education; (b) the National School-Level State Assessment Score Database for student test scores; (c) 

the National Center for Education Statistics (NCES) Public School Elementary and Secondary School 

Universe Survey Data Annual Files (the Common Core of Data (CCD))83; and (d) the 1999-2000 

Private School Survey conducted by NCES.84 The schools attended by the MTO children from 

baseline until the time of the interim data collection were identified from detailed school histories 

collected from parents in the adult survey. 

Finally, parent and child attitudes and behaviors were measured through interviews with parents and 

all children age 8 to 19 in the sample. In addition, for all children ages 5 to 7 we collected data on 

student behavior and attitudes through interviews with parents (see appendix A). 

The data used to measure the impact of MTO on educational outcomes came from two primary 

sources. Survey data were collected from children, youth, and adults (appendix A). From surveys we 

obtained information about grades, coursework taken, grade retention, high school completion, and 

college attendance. All children ages 5 to 19 were administered four achievement subtests from the 

Woodcock-Johnson Battery-Revised (WJ-R). The WJ-R is a set of individually administered tests for 

measuring cognitive ability and achievement. The wide age range and breadth of coverage of the tests 

make it possible to use the same set of tests with children of all age levels. The WJ-R tests were 

standardized on a nationally representative group of 6,359 subjects, ages 24 months to 95 years. The 

tests are recognized as the premier battery for measuring both the cognitive abilities and school 

achievement of school-aged children and young adults. 

The WJ-R subtests administered included letter-word identification, passage comprehension, math 

calculation, and applied problems. Each subtest produces a total score. Two composites scores can 

also be computed: broad reading (which is an average of the child’s letter-word identification and 

passage comprehension scores) and broad math (which is an average of the math calculation and 

applied problems scores). In addition, one child under the age of 11 from each family was 

administered the concept formation subtest, which measures a child’s reasoning ability. 

83 Eight years of CCD data were collected starting with 1993. 

84 This file includes names, addresses, enrollment, and other descriptive data for 29,845 private schools in the 

50 states and the District of Columbia. 

Chapter 6 – Impacts on Children’s Education 105 
To provide a context for understanding the impact estimates obtained from the Woodcock- Johnson, 

exhibit 6.2 shows some descriptive statistics for the MTO children in the control group. In this 

analysis the WJ-R test scores are presented as scale scores centered on 500. The exhibit shows the 

average scale score by age in the nationally representative norming sample and the corresponding 

average scale score for children in the MTO control group. The exhibit also shows the average age 

equivalent score for each age group in the MTO control group (in terms of years above and below the 

national average).85 Surprisingly, the youngest children in the MTO sample were performing better 

than average at the time of the interim evaluation.86 However, as the age of the children increased 

their relative performance decreased substantially. 

85 An age equivalent scores indicates the age level at which the average score is equal to the subject’s score. 

For example, an age equivalent score of eight indicates that the child scored as well as the average 8-yearold on the test. 

86 The scores on the Woodcock-Johnson for the youngest MTO children (5 to 11 years) are higher than was 

expected based on the demographic characteristics of the children. On most other measures at-risk children 

consistently scores substantially below the national average. For example, in the national evaluation of the 

Comprehensive Child Development Program, on the PPVT and the K-ABC, 5-year-old children had an 

average score more than a standard deviation below the national mean. In the Head Start FACES study, on 

the PPVT, preschool children (who had not yet experienced Head Start) scored nearly a standard deviation 

below the mean. 

To investigate the reason for the high WJ-R scores in the MTO sample, we examined WJ-R scores from 

earlier research on similar samples of at-risk children. First, we looked at scores from previous studies of 

the New York City MTO site. In the New York sample, scores on the WJ-R subtests were also high relative 

to scores on the PPVT. In other studies of low income children, results on the Woodcock-Johnson were 

variable. In the Head Start FACES study, children scored two-thirds of a standard deviation below the 

mean on the WJ-R at pre-test. On the other hand, the low-income child sample from the PSID scored at or 

above the norm for their age group on the same WJ-R subtests used in the MTO Interim Evaluation. 

Careful examination of the test administration and scoring procedures revealed neither evidence of 

consistent tester bias nor evidence of consistent errors in scoring the WJ-R tests or computing standard 

scores in the MTO sample. Therefore, we have to conclude either that (a) children do better on tests, like 

the WJ-R, that test a relatively narrow set of skills than they do on general aptitude tests, or (b) the norming 

of the WJ-R subtests for the youngest children consistently inflates their scores. 

Findings are the same as those shown here if the youngest children (ages 5 to 7) are removed from the 

sample. If all children under the age of 11 are eliminated from the sample the negative effect on the 

calculation tests for Section 8 children is no longer statistically significant but there are no other substantive 

changes in the results. 

106 Chapter 6 – Impacts on Children’s Education 
EXHIBIT 6.2 

CONTROL GROUP CHILDREN’S WOODCOCK JOHNSON BROAD READING SCORES 

Age of Child 

Nationally 

Representative 

Sample 

Scale Score 

MTO Control 

Children 

Scale Score 

MTO Children Age 

Equivalent Score 

(Years Above/Below 

National Average) 

Five (n=35) 418 433 0.35 

Six (n=76) 452 449 0.05 

Seven (n=123) 475 472 0.15 

Eight (n=114) 490 484 -0.11 

Nine (n=126) 498 491 -0.30 

Ten (n=134) 506 494 -0.89 

Eleven (n=144) 511 505 -0.58 

Twelve (n=128) 514 507 -1.25 

Thirteen (n=115) 520 507 -2.31 

Fourteen (n=103) 523 509 -3.11 

Fifteen (n=99) 526 511 -3.86 

Sixteen (n=92) 529 512 -4.57 

Seventeen (n=76) 532 513 -5.90 

Eighteen (n=63) 532 516 -6.31 

Nineteen (n=71) 534 517 -7.79 

Source: Woodcock Johnson Battery-Revised tests. 

Sample: Control group children ages 5 to 19 as of May 31, 2001. 

6.3 Baseline Education Experiences and Control Group Context 

Before turning to a discussion of the results it is important to understand both where our sample of 

children began and how the context of urban education has changed over the demonstration period. 

Baseline education experiences of the sample 

There has been growing concern about the plight of children in urban schools in recent years. 

Previous research has shown that 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 (NCES, 1999; Casserly, 2002; Council of the Great City Schools, 

1999). These poor educational outcomes have been linked to both low-quality schools and home 

environments that do not support educational achievement. 

While we do not know baseline test scores of the MTO children, we do know a number of things 

about their educational experiences before participation in the MTO demonstration began. 

Approximately twenty-seven percent of the original sample who were ages 6 to 17 years old at 

baseline had learning or behavioral problems at baseline, including having been expelled from school. 

The families of 26 percent of these children indicated that during the 2 years prior to random 

Chapter 6 – Impacts on Children’s Education 107 
assignment, someone from their child’s school had asked them to discuss behavior or other problems 

the child was having at school. Approximately 16 percent of the children who were ages 6 to 17 at 

baseline had been enrolled in a gifted class or school at the time of random assignment. These 

numbers are somewhat higher than national averages both in terms of special education and gifted 

classes. Nationwide, a little more than 13 percent of the school-age population receives special 

education services (NCES, 2001), 10 percent of children ages 12 to 17 have ever been suspended 

from school, and 10 percent are enrolled in gifted classes (Fields et al., 2001). 

However, the MTO children attended schools characterized by both low achievement and high 

poverty. At baseline MTO children attended schools in which 78 percent of the students were eligible 

for free lunch and which ranked, on average, at the 15th percentile on state assessments. Fifty-three 

percent of the MTO families indicated that the primary or secondary reason they wanted to move was 

to send their children to better schools. 

We also know something about the educational environment in the homes of the children at baseline. 

Only 60 percent of the sample adults had completed a high school degree or GED at the time of 

random assignment. Fewer than 27 percent of the sample adults who had a child under the age of 5 at 

the time of random assignment reported that the child was read to more than once a day. 

Thus when the MTO demonstration began, the opportunity was ripe to improve the educational 

experiences of these children and, as a result, their educational performance. 

Control group context 

To understand the impact of MTO on experimental and Section 8 group members, it is important to 

understand whether and how the characteristics of schools attended by the children in the control 

group changed over the time. Although we hypothesized that the educational experience of children 

in the experimental and Section 8 groups would change as a result of MTO, the educational 

experiences of the control group children may also have changed over this period of time. There are 

two reasons for this. 

First, the MTO demonstration was implemented at a time of increased emphasis on urban school 

reform. Over the past decade, in every one of the MTO sites, there have been high-profile initiatives 

aimed at improving the educational outcomes for students in city schools. As a result, while 

experimental and Section 8 children might, because they were able to move, attend schools in lower 

poverty neighborhoods, the differences between the schools in the more affluent communities and 

those in the original, higher poverty neighborhoods, might be attenuated by urban school reform. For 

example, in most Chicago public schools, the number of children performing at grade level in reading 

and mathematics on the Iowa Test of Basic Skills has increased substantially over the past 10 years 

and these increases have been most dramatic in the lowest performing schools in the city (Roderick, 

et al., 1999). The performance of Chicago public school students has increased relative to other 

schools in the state (Jacob, 2002). Similarly, over the past several years, Boston's state assessment 

results have improved across all grades and subject areas. Boston's gains exceeded statewide gains in 

every grade and subject, except grade 8 in reading and science where Boston and state gains were 

equal. Dropout rates have also fallen considerably in Boston over the past 10 years. The Los Angeles, 

New York, and Baltimore public schools have shown similar improvements with the number of 

students scoring at the proficient level on state assessments improving substantially in recent years. 

108 Chapter 6 – Impacts on Children’s Education 
Second, control group families were not precluded from moving, and as earlier chapters have shown, 

many of the control group families had left public housing and experienced a substantial increase in 

income in the years between random assignment and the interim evaluation. So control group children 

may have moved to schools with characteristics similar to those attended by students whose family 

moved to a more affluent neighborhood as part of MTO. 

For the children in the control group, the average performance level of the schools attended improved 

from baseline to the current time. At baseline the control group children attended schools that ranked 

around the 13th percentile on state assessments. They are currently attending schools that rank around 

the 17th percentile. Control group children are also more likely to attend a magnet school now than 

they were at baseline. The schools attended by the control group now have fewer students eligible for 

free lunch (66 percent currently versus 80 percent at baseline) and fewer limited English proficient 

students. All this suggests that urban schools may have been changing or control group families may 

have been seeking new schools for their children during the time since random assignment. As a 

consequence there are likely to be fewer differences in the school experiences of the MTO and the 

control children and less reason to expect differential student achievement. 

6.4 Impacts on Hypothesized Mediators of Educational Effects in 

MTO 

As outlined above we hypothesized that a variety of factors would have an influence on the 

educational outcomes of MTO children, including the quality of the schools they attended and the 

characteristics of the community in which they live. In turn we hypothesized that these factors would 

have an effect on the education-related behaviors and attitudes of both parents and children. We 

explore the evidence for each of these mediators of educational outcomes below. 

School characteristics 

The most direct mediator of educational outcomes is the quality of the schools attended by MTO 

children. If children in the experimental and Section 8 groups attended higher quality schools, as 

defined by higher student achievement, better resources, lower student-teacher ratios, and more stable 

academic environments, then we hypothesized that it would lead to improved educational outcomes 

for MTO children. Improvements in student achievement could result directly from improved 

instruction in these schools or because better schools could promote more positive attitudes among 

parents and children about school and educational achievement, which would lead in turn to more 

productive school-related behavior (e.g., better attendance, more time studying). 

The evidence suggests that MTO has had a small but positive impact on children’s school experiences 

based on the characteristics of the schools the children attended. At the time of the interim survey the 

experimental group children were attending schools with fewer students eligible for free lunch, fewer 

minority students, and fewer limited English proficient students than were control group children 

(exhibit 6.3). Experimental group children were also attending schools that had higher performance 

overall. The effect on the entire experimental group was to increase the percentile rank of the school 

Chapter 6 – Impacts on Children’s Education 109 
EXHIBIT 6.3 

CHARACTERISTICS OF THE SCHOOLS ATTENDED BY MTO CHILDREN 

Experimental Section 8 

Control vs. Control vs. Control 

Mean ITTa TOTa ITTa TOTa 

Percent Free Lunch [ADMIN] (n=3562)

Current school 0.657 -0.066* -0.130* -0.026* -0.044* 

(0.010) (0.020) (0.011) (0.018) 

Average school 0.721 -0.067* 

(0.009) 

-0.133* 

(0.018) 

-0.034* 

(0.010) 

-0.059* 

(0.017) 

Percent White [ADMIN] (n=4875)

Current school 0.106 0.040* 0.089* 0.029* 0.046* 

(0.009) (0.021) (0.014) (0.022) 

Average school 0.086 0.047* 

(0.007) 

0.101* 

(0.016) 

0.029* 

(0.010) 

0.046* 

(0.016) 

Pupil-Teacher Ratio [ADMIN] (n=4876)

Current school 15.038 0.181 0.395 -0.176 -0.285 

(0.225) (0.498) (0.220) (0.355) 

Average school 14.609 -0.038 

(0.188) 

-0.083 

(0.409) 

-0.269 

(0.208) 

-0.435 

(0.337) 

Percent Limited English Proficient [ADMIN] (n=4019)

Current school 0.168 -0.027* -0.058* -0.005 -0.009 

(0.006) (0.013) (0.007) (0.011) 

Average school 0.181 -0.030* 

(0.006) 

-0.063* 

(0.013) 

-0.008 

(0.007) 

-0.013 

(0.011) 

Magnet School [ADMIN] (n=3945)

Current school 0.249 -0.051* -0.113* -0.029 -0.043 

(0.019) (0.041) (0.023) (0.034) 

0.182 -0.035* 

(0.015) 

-0.077* 

(0.034) 

-0.026 

(0.018) 

-0.039 

(0.027) 

Percentile Rank on State Exam ] (n=3935) 

 Average school 

[ADMIN

Current school 0.167 0.038* 0.085* 0.017 0.026 

(0.008) (0.018) (0.008) (0.013) 

Average school 0.144 0.041* 

(0.006) 

0.091* 

(0.014) 

0.017* 

(0.007) 

0.027* 

(0.010) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: U.S. Department of Education, Common Core of Data 1993 to 2001, National School-Level State 

Assessment Score Database, 2000 to 2001 

Sample: All children ages 5 to 19 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations).

110 Chapter 6 – Impacts on Children’s Education 
currently attended by four percentage points (from the 17th percentile to the 21st percentile), while 

the effect on those who leased up was to increase it by eight percentage points. Experimental group 

children were also less likely than control group families to be attending a magnet school. Because 

they were given the opportunity to move, experimental group families may not have felt the need to 

seek out alternative schooling options like magnet schools for their children. The trends for the 

Section 8 group were similar to the experimental group, although generally the impacts were smaller 

and in many cases not statistically significant. 

Some of the families who moved to low-poverty neighborhoods subsequently moved back to 

neighborhoods with higher levels of poverty (see chapter 2) and there is also some evidence from the 

qualitative evaluation that students may have switched schools frequently (Popkin et al., 2001). To 

account for these factors, the exhibit also displays a weighted average of the characteristics of the 

schools attended by the children since the time of random assignment. The weighted averages may 

provide a better estimate of children’s school environments over the course of the entire 

demonstration than a simple description of the child’s current school. These exposure measures cover 

anywhere from 1 to 7 years of schooling, depending on the age of the child at the time of random 

assignment. On average they represent approximately 5.5 years of schooling. 

As seen in exhibit 6.3, there was little difference between the estimates obtained when the weighted 

average of school characteristics was used in the impact analyses although the estimated effects for 

the average school were slightly larger. 

In summary, MTO had a modest impact on children’s educational experiences based on the 

characteristics of the schools attended by the experimental group children. The children in the 

experimental group were attending somewhat higher performing schools with fewer poor and 

minority students than the control group children. However the differences may not be educationally 

significant. Moving from a school ranked at the 17th percentile in the state to one ranked at the 21st 

or even 25th percentile is not a substantial improvement. To determine how many children were 

attending substantially higher performing schools at the time of the interim evaluation, we explored 

the percentage of children in the experimental group who attended schools that ranked above the 50th 

percentile in their state and those who attended schools that ranked above the 75th percentile. Only 

two percent of the experimental group children attended schools that performed at or above the 75th 

percentile. Less than 10 percent of the experimental group children attended schools ranked at even 

the 50th percentile or higher. This suggests that most experimental group children were not attending 

substantially higher performing schools as a result of MTO. These findings differ greatly from those 

of the Gautreaux Program in Chicago where 88 percent of the sample who moved to suburban 

schools attended schools with average ACT scores at the national average or above (Rosenbaum, 

1995). 

This suggests that a further exploration of the pattern of school moves among the MTO children is 

warranted. The qualitative report on the MTO demonstration (Popkin et al. 2001), suggests that one 

reason MTO may have had a limited impact on children’s school environments is that a number of 

experimental group children did not attend their local neighborhood school, even if their families had 

relocated to and remained in low-poverty areas. The report states, “...MTO children’s educational 

experiences since program assignment are more complex than anticipated, because many children 

attend schools outside their immediate communities...Families cited many different reasons for 

placing children in out-of-area schools, including ties to friends or previous communities, school 

Chapter 6 – Impacts on Children’s Education 111 
quality, and children’s special needs.” In Chicago, several families who had moved chose to place or 

keep their children in schools near their former public housing developments. This was possible, the 

report suggested, because “Many urban school systems now allow children to apply citywide for 

special programs or schools and even offer charter schools as alternatives. School choice at the high 

school level appears to be particularly common, with families in Chicago, Boston, and New York all 

reporting that their children could apply to attend different high schools throughout the city.” 

Using detailed school history information collected about MTO children from their parents, we can 

examine whether many children switched schools when their families moved or returned to schools 

near public housing shortly after moving to a lower poverty area. While the proportion of children 

currently attending the same school as they were at the time of random assignment could be explored, 

because children naturally change schools as they get older, and move from elementary school to 

middle school and from middle school to high school, these numbers would significantly 

underestimate the number of children who were attending the same school they would have been 

attending had their families not been give the opportunity to move. 

As an alternative we explored the proportion of experimental group children who are attending school 

in the same ZIP Code area as the school they were attending at baseline. Because ZIP Codes cover a 

relatively small geographic area (generally not more than a few miles), this may provide a good 

estimate of the number of children who were attending schools near their old public housing 

developments at the time of the interim evaluation. Approximately 20 percent of the experimental 

group children were attending schools in the same ZIP Code area as at baseline compared with 27 

percent in the control group. Among those families who leased up, the number was 16 percent. These 

numbers probably undercount the number of experimental group children attending schools near their 

original public housing developments because many elementary schools in urban areas feed into high 

schools with different ZIP Codes. A child may have remained in the elementary school near their 

development and may have attended the same high school as they would have had they remained in 

public housing, but those schools may have had different ZIP Codes. While such an analysis is only 

suggestive it seems likely that at least some experimental group children remained in or returned to 

schools in their old neighborhoods after random assignment. 87 

The qualitative report suggests a number of reasons why families chose to place their children in outof-area schools. Some parents cited negative experiences with the schools in their new community. 

Many did not feel their children were safe in the new schools and others were concerned with the 

educational quality of the schools, noting that their children’s grades had suffered when they moved 

to the new school. The qualitative report also suggests that some children were attending out-of-area 

Obtaining a more exact estimate of the number of children still attending schools close to their public 

housing developments would require obtaining additional data or more in-depth analysis. One avenue that 

might be pursued would be to obtain information from school districts about the neighborhood school 

associated with a residential location, although increased opportunities for school choice might complicate 

such analysis. Alternatively, using geocoded data, the distance between the residential location and the 

school location of both experimental and control group children could be compared, although such an 

analysis would still not identify the actual number of experimental or Section 8 children attending schools 

near their original public housing developments. 

112 Chapter 6 – Impacts on Children’s Education 

87 
schools because they were attending schools for children with special needs. Finally, the qualitative 

findings noted parental reluctance to make their children change schools multiple times. 

Quantitative analyses support some of these findings. For example, it seems possible that some of the 

negative experiences children had in the schools in low-poverty areas were due to the difficulties 

children faced when transitioning to schools in which few students were of their same racial or ethnic 

background. Of the experimental group children attending schools in a different ZIP Code area than 

they were at baseline, 36 percent were attending a school where the dominant race or ethnicity was 

different than their own.88 Children in the both the experimental and Section 8 groups also changed 

schools more frequently than did the control group. At the time of the interim evaluation MTO 

control group children had attended an average of 2.3 schools and experimental and Section 8 group 

children had attended an average of 2.4 schools, a statistically significant but small difference. 

However, it does not appear that a large proportion of children were attending schools for students 

with special needs. The data indicate that less than 3 percent of the total MTO sample was attending a 

special needs school at the time of the interim evaluation. 

Another possibility is simply that the MTO children did not move far enough from their baseline 

neighborhood to make a substantial difference in the type of school they attended. To determine 

whether MTO children were actually attending schools in substantially different neighborhoods than 

at baseline, we explored the proportion of children in the experimental group who were attending 

schools in the same district as they were at baseline. In every site with the exception of Los Angeles, 

this is essentially an urban-suburban comparison. At the time of the interim evaluation, approximately 

80 percent of the experimental group members were attending schools in the same district as they had 

attended at baseline. Among those whose families leased up, almost 70 percent of the children were 

attending schools in the same district as at baseline. In general, the schools in the new districts appear 

to be of higher quality than the schools in the urban districts. Of the experimental group children 

attending schools in a different district than at baseline, 20 percent were in schools that were 

performing above the 50th percentile on state assessments compared with 8 percent of the children 

who remained in the urban districts. 

Exhibit 6.4 shows the percentage of children attending schools in a different district than at baseline 

for each of the five MTO sites by random assignment group. There is some variation by site, with 

more experimental children in Baltimore, Boston, and Los Angeles attending schools in a different 

district than in either Chicago or New York.89 

88 Dominant is defined as greater than 50 percent. A Hispanic student attending a school that was more than 

50 percent black would be considered to be attending a school with a different racial/ethnic composition 

than his or her own. 

89 The Los Angeles public schools are located in several different districts, which explains the higher number 

of experimental children attending schools in different districts than baseline in this site. 

Chapter 6 – Impacts on Children’s Education 113 
Exhibit 6.4 

Percentage of Children Attending Schools in a Different District than at Baseline 

40 

35 

30 

25 

20 

15 

10 

5 

0

Baltimore Boston Chicago Los Angeles New York

i

Secti

l 

32.9 

37.8 

16.03 

18.3 

23.6 

13.8 

10.7 

15.19 

24.6 25.6 

20.63 

9.7 

14.2 

12.2 

25.0 

Exper mental 

on 8 

Contro

Other community-level mediators 

Although school characteristics were thought to be extremely important, other community-level 

factors were also hypothesized to affect educational outcomes, including community norms and 

values, the social and physical environment, and economic opportunities. The evidence on these 

mediating factors was mixed but generally positive. Impact estimates for the mediators discussed 

below that are not found in earlier chapters of the report are provided in appendix E (exhibits E6.1 to 

E6.7). 

The strongest impact of MTO was on the social and physical environment in which the children lived. 

It increased the sense of safety reported by adults and (as detailed in chapter 3) significantly improved 

families housing and neighborhood quality. MTO also reduced youths’ reported frequency of seeing 

people selling and using drugs and of hearing gunshots in the neighborhood. This suggests that as a 

result of MTO children were living in safer, more secure environments. 

There was also some evidence that MTO had a positive impact on the community norms and values 

to which children were exposed by increasing the number of positive adult role models with whom 

the children in the experimental group had contact. Based on parent report, MTO increased the 

likelihood that sample adults in the experimental group had at least one friend who was a college 

graduate or earned more than $30,000 a year. MTO also increased the likelihood of adults reporting 

that their neighbors would intervene if they saw children doing graffiti, skipping school, or hanging 

out, suggesting that children experienced increased monitoring from adults in the community. 

However, as already shown in chapter 5, MTO’s impact on peer role models was less clear. MTO had 

no effect on the likelihood that a child had friends who were involved in school activities. It also had 

114 Chapter 6 – Impacts on Children’s Education 
no effect on the likelihood that a child had a friend who carried a weapon, and it increased the 

likelihood that a child had friends who took drugs. Although the demonstration may have been 

successful in moving families to neighborhoods with norms and values that encouraged and supported 

educational achievement, it does not appear to have substantially altered the behavior or values of the 

children’s peer networks, the individuals with whom children have the most contact. 

Finally, there is some evidence that MTO had a positive impact on the economic opportunities for 

adults. Both the experimental and Section 8 groups lived in neighborhoods with a lower poverty rate, 

and where there was a higher percentage of adults in the neighborhood who were currently employed 

(see chapter 2). However, although families moved to neighborhoods with greater economic 

opportunities, MTO had no effect on the annual earnings or employment levels of the experimental or 

Section 8 adults relative to the control group, so the circumstances of the adults with whom the 

children live and with whom they have the most contact were not impacted. 

Student- and family-level mediators 

Despite the changes observed in the community-level mediators, few impacts of MTO were found on 

the student- or family-level mediators hypothesized to affect educational outcomes. 

Although there was evidence that MTO did impact parent attitudes, these changes did not translate 

into changes in parenting behavior. Experimental and Section 8 adults reported a greater sense of 

well-being, stronger feelings of safety, and lower levels of stress and anxiety relative to the control 

group (see chapters 3 and 4). However, there was no effect on the level of parental monitoring of 

children as reported by the parents (e.g., whether the adult knew the child’s friends or teachers, or 

who the child was with when he or she was not at home) or on the degree of parental warmth 

exhibited to the child by the sample adult at the time of the home visit. MTO also did not increase the 

frequency with which the sample adult reported attending school events, general school meetings, or 

volunteering at the child’s school. 

Similarly, although MTO children were attending schools with different characteristics, MTO had no 

impact on children’s reports of the overall school climate. MTO had no effect on how safe children 

felt at school, how interested they felt teachers at their schools were in their school performance, the 

extent to which behavior problems interfered with learning, and the amount of cheating at their 

school. Results also suggest that MTO did not impact the educational resources available to students 

in school (e.g., there was no effect on the number of children who indicated they had their own math 

book to bring home from school). 

Finally, MTO did not have any impact on student school-related behaviors. There was no effect of 

MTO on the number of students who reported having been tardy for school more than once a month, 

on the number of students whose parents had been called by the school about problems with the 

child’s behavior or schoolwork, or on the number of children who had been suspended or expelled 

from school in the past 2 years. MTO also had no impact on the number of hours children spent doing 

homework, watching television or reading for pleasure, or on the degree to which children reported 

working hard in school or paying attention in class. 

Chapter 6 – Impacts on Children’s Education 115 
6.5 Impacts on Hypothesized Outcomes 

To assess the degree to which MTO affected children’s school performance and educational progress, 

we estimated the impacts of MTO on academic achievement, as measured by standardized test scores 

and on overall progress through school—including promotion or retention in grade, coursework 

taken, high school graduation, and post-high school graduation educational plans.90 

Because there were few impacts on the hypothesized mediators of educational outcomes, it is not 

surprising that few impacts were found on the outcomes themselves. Exhibit 6.5 shows impact 

estimates for the Woodcock-Johnson Achievement tests scores. MTO had no significantly positive 

impacts on scores from any of the five subtests or on the composite reading or mathematics scores. 

There was a negative effect on the calculation subtest for children in the Section 8 group. Exhibit 6.6 

shows these same results broken down by gender, age, mother’s education, and whether or not the 

child had any behavior problems at baseline. There were no statistically significant positive effects on 

the Woodcock-Johnson for any of these subgroups. The 8- to 11-year-olds in the Section 8 group 

scored significantly lower on the broad math test. The sample size and precision of the WoodcockJohnson estimates were such that we would have been very likely to detected an impact as small as 6 

or 7 points on any one of these tests, which is equivalent to answering approximately two or three 

more question correctly. The standard deviations on the WJ-R subtests were around twenty-five 

points, so we would have been able to detect an increase of a little over a quarter of a standard 

deviation on most subtests. 

Exhibit 6.7 shows impact estimates for the survey measures. MTO also had no positive significant 

impacts on any of the measures of educational progress obtained from the surveys. However, there 

are some findings worth noting here. Although it was hoped that the MTO demonstration would lead 

to positive educational outcomes for children, it was also hypothesized that MTO could have a 

detrimental effect on children’s self-esteem and self-confidence if children who moved to more 

affluent communities felt isolated because there were few student of the same background or if they 

felt inadequate compared to higher performing peers. A negative self-image might lead to negative 

educational outcomes such as lower grades. It was also possible that poor and minority children who 

moved to more affluent schools might be more likely to be placed in special education or retained in 

grade. 

When data were available, impacts on educational outcomes were estimated for all children age 5 to 19 in 

the interim evaluation sample. However, in many instances survey questions were only relevant to a 

smaller age range and were only asked of a subsample of the population. In these instances the exhibits 

indicate the subsample used to obtain the estimates. 

116 Chapter 6 – Impacts on Children’s Education 

90 
EXHIBIT 6.5 

EDUCATION OUTCOMES: WOODCOCK-JOHNSON ACHIEVEMENT TESTS 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

Woodcock-Johnson Scores [ADMIN]

Broad reading (ages 5 to 497.31 0.92 2.04 0.45 0.74 

19) (0.93) (2.06) (1.06) (1.73) 

19) 

501.23 0.22 

(0.78) 

0.49 

(1.75) 

-1.07 

(0.85) 

-1.74 

(1.39) 

(n= 5169) 

Broad math (ages 5 to 

(n= 5187) 

 Letter/word identification 498.86 0.58 1.27 0.23 0.38 

) (1.20) (2.66) (1.33) (2.17) 

19) 

495.71 0.89 

(0.84) 

1.96 

(1.85) 

0.54 

(0.95) 

0.89 

(1.56) 

(ages 5 to 19 (n= 5229) 

Passage comp (ages 5 to 

(n= 5192) 

Applied problems (ages 5 499.11 1.25 2.79 -0.39 -0.63 

to 19) (0.90) (2.00) (1.03) (1.69) 

19) 

503.35 -0.91 

(0.82) 

-2.01 

(1.81) 

-1.77* 

(0.86) 

-2.89* 

(1.40) 

(n= 5202) 

Calculation (ages 5 to 

(n= 5239) 

Concept form (ages 5 to 485.55 0.42 0.93 -0.80 -1.22 

11) (n= 1764) (1.42) (3.10) (1.70) (2.59) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Source: Woodcock Johnson-Revised tests. 

Sample: All children ages 5 to 19 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Analyses take into account correlation within families. 

e) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

f) Standard deviations for each of the subtest for the MTO sample were as follows: Broad Reading: 29, Broad 

Math: 25, Letter/word Identification: 37, Passage Comprehension: 25, Applied Problems: 26, Calculation: 28, 

Concept Formation: 23. 

Chapter 6 – Impacts on Children’s Education 117 
EXHIBIT 6.6 

EDUCATION OUTCOMES: WJ ACHIEVEMENT TESTS FOR SUBGROUPS, AGES 5-19 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

Broad Reading [ADMIN] (n= 5164)

Female (ages 5 to 19) 499.69 1.84 3.96 -0.19 -0.30 

(1.14) (2.45) (1.24) (1.99) 

Male (ages 5 to 19) 494.94 -0.03 

(1.38) 

-0.06 

(3.14) 

1.13 

(1.59) 

1.89 

(2.66) 

Ages 5 to 7 456.48 2.57 5.08 3.72 5.72 

(2.51) (4.96) (2.88) (4.43) 

492.32 -.719 

(1.47) 

-1.61 

(3.28) 

-1.49 

(1.61) 

-2.24 

(2.41) 

Ages 8 to 11 

Ages 12 to 19 511.02 1.50 3.38 0.88 1.56 

(1.26) (2.84) (1.48) (2.62) 

problems1 

503.64 2.17 

(2.07) 

4.95 

(4.73) 

0.86 

(2.21) 

1.71 

(4.39) 

 Baseline behavior 

Mother HS grad/GED 500.07 -1.13 -2.84 1.04 1.69 

(1.55) (3.60) (1.87) (3.03) 

Broad Math [ADMIN] (n= 5182)

Female (ages 5 to 19) 502.30 0.81 1.73 -1.21 -1.92 

(1.06) (2.26) (1.09) (1.73) 

Male (ages 5 to 19) 500.18 -0.36 

(1.05) 

-0.82 

(2.42) 

-0.91 

(1.17) 

-1.52 

(1.95) 

Age 5 to 7 460.44 2.01 4.08 1.72 2.62 

(1.97) (3.99) (1.95) (2.96) 

Age 8 to 11 498.33 -1.74 

(1.11) 

-3.90 

(2.48) 

-4.59* 

(1.24) 

-6.82* 

(1.84) 

Age 12 to 19 513.94 0.99 2.24 0.50 0.88 

(1.14) (2.57) (1.21) (2.15) 

problems 

508.93 0.06 

(1.78) 

0.15 

(4.10) 

-1.77 

(1.97) 

-3.51 

(3.90) 

 Baseline behavior 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Source: Woodcock Johnson-Revised tests. 

Sample: All children ages 5 to 19 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Analyses take into account correlation within families. 

e) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

1

 Combines special education for behavior problems, suspended or expelled and someone asked to talk about 

child’s problems at school or behavior in last 2 years. 

118 Chapter 6 – Impacts on Children’s Education 
EXHIBIT 6.7 

EDUCATION OUTCOMES: SCHOOLING 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

School Outcomes [SR, PR] 

Mostly Bs or higher1 0.393 -0.043 -0.096 -0.050 -0.087 

2 0.200 -0.002 

(0.016) 

-0.004 

(0.036) 

0.010 

(0.017) 

0.016 

(0.028) 

(ages 12 to 19) (n=2753)

 Advanced coursework

(ages 5 to 19) (n=5457) 

(0.026) (0.058) (0.028) (0.048) 

Special ed. recipient3 0.245 0.024 0.051 0.013 0.020 

Educationally on track4 0.741 0.029 

(0.028) 

0.064 

(0.062) 

0.036 

(0.031) 

0.068 

(ages 15 to 19) (n=1550) (0.058)

(ages 5 to 17) (n=4731) (0.017) (0.036) (0.018) (0.029) 

Ever repeated a grade5 0.221 0.026 0.058 -0.020 -0.032 

) (0.016) (0.035) (0.017) (0.028) 

Took SAT/ACT 0.342 -0.032 

(0.032) 

-0.073 

(0.074) 

0.022 

(0.035) 

0.041 

(0.063) 

Attended College [SR] (n=2819)

Some (ages 12 to 19)6 

(ages 5 to 19 (n= 5354) 

(ages 15 to 19) (n=1562) 

0.043 -0.003 -0.007 0.005 0.009 

(0.010) (0.021) (0.010) (0.018)

7 0.017 -0.005 

(0.006) 

-0.011 

(0.013) 

-0.001 

(0.007) 

-0.001 

(0.012) 

 4 years

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Child survey, Youth survey, POCY survey. See appendix A for details. 

Sample: All children ages 5 to 19 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Analyses take into account correlation within families. 

e) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

1

 Overall grades child [12-19] received last year were high (mostly Bs or higher) as self reported. 2

 Youth self-reported taking an advanced math class, AP class, or being enrolled in a gifted class or school in the 

past 2 years. 

3 The proportion of children whose parent reported they had received special education services for learning

and/or behavior/emotional problems in the last two years. Results were the same for “ever” received special 

education services. 

4

 Youth is currently in school or received a HS diploma or GED. 

5 The proportion of children whose parent reported that they had ever repeated a grade, including kindergarten. 

Results were the same for repeated excluding kindergarten. 

6

7

 Currently enrolled in a two or four year college. Results were the same for “ever” attended. 

 Currently enrolled in a four year college. 

Chapter 6 – Impacts on Children’s Education 119 
Although not statistically significant, the coefficients on some of the survey measures were negative, 

suggesting that the MTO children may have had some difficulty adjusting to new educational 

environments. This may be another reason why few achievement effects were found. 

Analyses were conducted to determine if there were MTO impacts on subgroups. Each of the 

educational outcomes was estimated separately for girls versus boys, for children of different ages, 

for children whose mothers had different levels of education, for families who indicated that a 

primary reason for moving was to attend better schools, and for children with and without baseline 

school behavior problems (see appendix D, exhibits D6.1 to D6.3 for the results of these analyses). 

MTO had a negative effect on the self-reported grades of the boys in the sample. The effect of MTO 

was to reduce the proportion of boys in the experimental group who reported they received mostly Bs 

or higher by 10 percentage points; among those who leased-up, the reduction was 21 percentage 

points. This finding is consistent with the results presented in chapter 5, which suggested that MTO 

may have had some detrimental effects for boys. In addition, the subgroup estimates showed that 

experimental group children age 8 to11 were more likely to have repeated a grade than control group 

children, while children 12-19 in the Section 8 group were less likely to have repeated a grade. 

Children in the experimental group whose families indicated that a primary reason for moving was so 

their children could attend better schools, had lower self-reported grades than similar families in the 

control group. These findings may reflect differences in the schools attended by children in these two 

groups. As can be seen in exhibit D6.3 children whose families were motivated to move because they 

wanted better schools for their children were attending schools with fewer students eligible for free or 

reduced price lunch. There were no other statistically significant differences for any of the subgroups. 

While there were few statistically significant impacts among children of different ages on any of the 

outcomes explored, it is possible that age at random assignment is more important in predicting 

outcomes than the child’s age at the time of the interim evaluation. It seems plausible that children 

who moved when they were younger might have more positive outcomes than those who were older 

and had already spent substantial time in low poverty schools and neighborhoods. Due to variations in 

the time of random assignment, the ages of the children at baseline varied widely even in the 

relatively small age ranges we used to conduct the subgroup analyses. Children who were between 5 

and 7 years old when they were interviewed were between –1 and 4 years old at baseline. Those who 

were between 8 and 11 years old at the time of the evaluation were 1 to 8 years old at baseline, and 

those aged 12 to 19 where between 5 and 16 years old at baseline. To explore whether age at random 

assignment had an effect on outcomes for the MTO children we conducted subgroup analyses for 

children who were 5 years old or younger at random assignment, for children between 6 and 11 years 

old at random assignment and for those who were 12 or older (see appendix D, exhibit D6.4). 

Experimental group children who were 12 or older at the time of random assignment were more 

likely to have repeated a grade than children of the same age in the control group. There were no 

other statistically significant differences among children randomly assigned at different ages. It 

should be noted that these results are not experimental, since the child’s age at random assignment is 

related to the time the family was randomly assignment. As discussed in chapter 9, there were large 

differences between families randomly assigned early and those randomly assigned later in the 

demonstration. 

Finally, previous research conducted in the Boston and Baltimore MTO sites found that the quality of 

schools improved significantly in both sites. In Baltimore findings suggested that MTO had had a 

positive impact on the test scores of the experimental group children (Katz, Kling, and Liebman, 

120 Chapter 6 – Impacts on Children’s Education 
2001; Ludwig, Ladd, and Duncan, 2001). In addition, there was some variation by site in the number 

of children attending schools in different districts than at baseline at the time of the interim 

evaluation. Given these findings, we thought it was important to do an impact analysis by site on the 

achievement score data. The findings are generally consistent with previous research (see Appendix 

D, exhibit D6.5). There is a strong and significant experimental group impact in Baltimore on the WJR reading exam. There is also a strong and significant impact in reading in Chicago. There were no 

statistically significant results in the other three sites and no significant impacts in math. These 

findings suggest that further analyses by site, which explore differences in school quality, might be 

useful. 

Interpretation of results 

We hypothesized that there were four important community characteristics associated with the level 

of affluence of a neighborhood that could impact educational outcomes: 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. These factors were 

hypothesized to influence the education-related attitudes of both parents and children, which would in 

turn influence educational achievement and educational progress. 

The findings suggest that MTO had a small but positive effect on children’s educational experiences 

based on the characteristics of children’s schools, although at the time of the interim evaluation, 

approximately 80 percent of the children in the experimental group were attending schools in the 

same district as they had been at baseline. There were also significant impacts on some of the other 

community-level variables hypothesized to mediate educational outcomes. At this point in the 

demonstration, however, these positive effects on the mediators of educational achievement have not 

had significant impacts on education-related behaviors or attitudes of MTO children or positive and 

significant impacts on any of the measures of school achievement or educational progress. 

If the underlying model is correct, the lack of impacts might be attributed to the relatively small 

changes observed in the characteristics of the schools attended by the MTO children. At the time of 

the interim survey, experimental children were attending only slightly higher performing schools than 

the control group and were relatively similar in composition to the schools they were attending at 

baseline. Only 20 percent of the children in the experimental group were attending schools in a 

different district than at baseline. The qualitative report (Popkin et al., 2001) 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. 

It is also possible that it is simply too soon to observe impacts on educational outcomes for these 

children, especially 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 4 

years and there is some evidence that they were having difficulty adjusting to these new 

environments. The Gautreaux Program in Chicago found that it took time for children to feel 

comfortable in their new environments and positive results were not observed for some children until 

6 years after they had moved (Rosenbaum, 1995). 

Chapter 6 – Impacts on Children’s Education 121 
122 Chapter 6 – Impacts on Children’s Education 
Chapter Seven 

Impacts on Employment and Earnings 

This chapter summarizes the reasons why MTO participation might be expected to affect the 

employment and earnings of adults and older youth in the interim evaluation sample. It then presents 

the interim findings on employment and earnings impacts, using respondent self-reports from the 

interim evaluation survey and administrative data from state unemployment insurance (UI) wage 

records. 

Summary 

Although the experimental and Section 8 group families tended to move to neighborhoods with more 

favorable employment opportunities and norms more supportive of work than those where controls 

lived, we found virtually no significant effects on the employment or earnings of either adults or 

youth in either of these groups. The only significant effect on earnings or employment was a shortterm negative effect on employment for the experimental group relative to the control group, an effect 

that dissipates over time. The most encouraging finding in this analysis was a relatively large (22 

percentage point) increase in the proportion of female youth attending school as a major activity in 

the experimental group among families who leased up, and a concomitant 16 percentage point 

reduction in idleness (neither employed nor enrolled in school) among female youth in this same 

group. No corresponding effects were found for boys. 

7.1 Hypotheses about Employment and Earnings in MTO 

A primary motivation for the MTO demonstration is to measure the impacts of neighborhood on the 

employment and earnings of low-income families. Residential mobility might affect employment and 

earnings through any or all of the following causal mechanisms (see exhibit 7.1): 

• Low-poverty areas are likely to have lower unemployment rates and faster job growth than higher 

poverty areas. This may result in higher employment and earnings for MTO movers and may lead 

to better jobs in terms of wages and fringe benefits. 

• The ability to locate near potential sources of employment rather than being tied to the location of 

a public housing project may reduce job search costs and (once employed) commuting costs. This 

may lead to increased employment and earnings and reduced reservation wages. If relocation 

leads to a broader range of employment opportunities, it may also tend to increase wages and 

fringe benefits. 

Chapter 7 – Impacts on Employment and Earnings 123 
• Earnings.

• Employment rate.

• Hours worked per week.

• Average hourly earnings.

• Fringe benefits.

• Job search method.

• Job duration/intensity.

• Reservation wage.

Positive Effects

• Improved mental health.

• Improved control over lives.

• Reduced stress/anxiety.

• Improved outlook.

• Development of new social 

networks.

Negative Effects

• Disruption of existing social

networks.

• Decrease in informal 

childcare.

• Social connections. Economic Opportunities

• Lower unemployment

rate.

• More job openings.

• Better wages and fringe

benefits.

• Greater proximity to jobs.

Community Norms

and Values

• Shared community value 

of work.

• Peer pressure to work/not

depend on welfare.

Social and Physical

Environment

• Lower crime and 

violence.

MTO Intervention→ Relocation to Low-Poverty Neighborhoods

EXHIBIT 7.1 

EMPLOYMENT AND EARNINGS OUTCOMES AND MEDIATING FACTORS 

Community-Level 

Mediators 

Person- and Family-Level 

Mediators 

Employment and 

Earnings Outcomes 

Community-Level

Mediators

Person- and Family-Level

Mediators

• Earnings. 

• Employment rate. 

• Hours worked per week. 

• Average hourly earnings. 

• Fringe benefits. 

• Job search method. 

• Job duration/intensity. 

• Reservation wage. 

Employment and 

Earnings Outcomes

Positive Effects 

• Improved mental health. 

• Improved control over lives. 

• Reduced stress/anxiety. 

• Improved outlook. 

• Development of new social 

networks. 

Negative Effects 

• Disruption of existing social 

networks. 

• Decrease in informal 

childcare. 

• Social connections. Economic Opportunities 

• Lower unemployment 

rate. 

• More job openings. 

• Better wages and fringe 

benefits. 

• Greater proximity to jobs. 

Community Norms 

and Values 

• Shared community value 

of work. 

• Peer pressure to work/not 

depend on welfare. 

Social and Physical 

Environment 

• Lower crime and 

violence. 

MTO Intervention→ Relocation to Low-Poverty Neighborhoods 

• Relocation to a safer neighborhood may lead to reduced stress and anxiety, and more generally, 

improved mental health and sense of control over their lives. This may result in more active job 

search and, therefore, increased employment and earnings. It may also reduce reservation wages. 

If so, it may lead to lower wage rates and fringe benefits. 

• Community norms in low-poverty areas are likely to be more supportive of work and less 

accepting of welfare than those in public housing projects. To the extent that sample members 

feel pressure to work rather than collect welfare, this might be expected to increase job search, 

employment, and earnings, and may reduce reservation wages. 

• Relocation may disrupt pre-existing social support networks that are important sources of 

informal childcare and labor market information and connections. Relocation may also disrupt 

pre-existing employment relationships. In the short run, this could lead to reduced employment 

and earnings and increased reservation wages, although we would expect these effects to be 

reduced over time as new social networks are established. 

• Relocation may result in improved physical health, either through a reduction in environmental 

hazards or through better healthcare. Improved health represents an increase in human capital 

through lower rates of absenteeism and other channels that could result in improved job 

prospects. 

124 Chapter 7 – Impacts on Employment and Earnings 
The existing evidence concerning the effects of residential mobility programs on the employment of 

adults is somewhat mixed. Rosenbaum and Popkin’s (1991) analysis of a survey of female household 

heads in the Gautreaux Program in Chicago found substantially higher employment rates (14 

percentage points) for those who moved to the suburbs than for those who moved to other parts of the 

central city. But analyses of the early labor market impacts of MTO using administrative data found 

no significant effects in the first few years after randomization in either the Boston (Katz, Kling, and 

Liebman, 2001) or Baltimore sites (Ludwig, Duncan, and Pinkston, 2000). Earlier household survey 

data for the Boston site also indicated little evidence of early employment effects. A household 

survey of early enrollees in the Los Angeles site found no significant 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, 

1998). 

7.2 Data Sources and Measures 

Respondent self-reports from the interim evaluation survey and administrative data from state 

unemployment insurance (UI) records were used to assess the impact of MTO participation on 

employment and earnings of sample adults and of older youth (those 14 to 19 years old as of May 31, 

2001). Sample adults in the interim evaluation survey were asked a series of questions on their current 

employment status, hours of work, earnings from their current main job, earnings from all jobs for 

calendar year 2001, employee benefits, job search behavior, informal work, and the duration and 

characteristics of their current main job. Employment for sample adults at the survey date was 

measured using the standard employment status questions from the current population survey that are 

the basis for official Bureau of Labor Statistics estimates of employment, unemployment, and labor 

force participation. Older youth were also asked a battery of standard employment status questions 

and a further series of questions on earnings, informal employment arrangements, and enrollment 

status. Further questions from the interim evaluation survey and census data on the characteristics of 

current neighborhoods provide information to examine mediating factors for employment and to test 

alternative hypotheses about the impacts of MTO on labor market outcomes. 

Administrative data on UI records of the quarterly earnings of sample adults and youth were collected 

and processed from the five MTO states (California, Illinois, Maryland, Massachusetts and New 

York). The available administrative data for each state provided earnings information for at least 

calendar years 1995 to 2001. Administrative earnings information is typically complete for the first 

16 quarters after random assignment. States used social security numbers (SSNs) to match MTO 

sample members to the UI records. 

Four of the states provided individual-level earnings information on each MTO sample member who 

matched to the UI records. However, the fifth state (Massachusetts) could only provide the data 

aggregated across groups consisting of at least 10 MTO individuals. For methodological consistency, 

the UI data from all of the states were aggregated into cells of at least 10 individuals and then 

analyzed at the cell level. The same algorithm was used to construct the cells for all five states. The 

algorithm maintained the distinctions between sites, randomly assigned groups, and randomization 

periods. The cells were constructed to be as small as possible (but with at least 10) and to be as 

Chapter 7 – Impacts on Employment and Earnings 125 
homogeneous as possible on selected characteristics. For the adults, the selected characteristics were 

randomization quarter, baseline education, and baseline work status. For the youth, the selected 

characteristics were age group and gender. The cell-level analyses of mean outcomes control for these 

mean characteristics. We have compared cell-level analyses of treatment effects for the four states 

with individual-level data; in all cases, the results are very similar using the two approaches. 

The administrative data analyses allow us to examine the evolution of the employment and earnings 

impacts of MTO with time in the program (time since random assignment). The disadvantages of the 

administrative data are: the failure to include informal and uncovered employment, and potential 

errors in SSNs and in the process of matching respondents to the state UI records. 

Traditional measures of employment, labor force participation, and earnings can be used to 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 

adult labor market outcomes in the long run. Idleness (being out of work and not enrolled in school) 

was also examined as possibly a more accurate indicator of poor youth labor market outcomes. 

7.3 Context and Baseline Employment Status of the Sample 

According to the baseline survey the employment rate of the sample adults was only around 26 

percent (25 percent for the control group) at the time of random assignment. This low employment 

rate is not surprising because the sample consisted largely of female household heads with limited 

education who at baseline were Aid to Families with Dependent Children (AFDC)/ Temporary 

Assistance for Needy Families (TANF) recipients living in public housing projects in high-poverty, 

inner-city neighborhoods. 

However, the labor market conditions and tax-transfer system incentives and constraints facing the 

sample members have changed dramatically since the mid-1990s. The national unemployment rate 

declined from 5.6 percent at the start of the MTO demonstration in the last quarter of 1994 to 4.0 

percent in 2000, before rising back to 5.8 percent in 2002. The employment and earnings 

improvements of the late 1990s were particularly great for disadvantaged workers and particularly 

strong in some large U.S. cities, including Boston. Changes in the welfare system began with state 

welfare waivers in the early 1990s, followed by federal legislation for welfare reform (implemented 

by states starting in late 1996). These changes, combined with the expansion of the earned income tax 

credit from 1993 to 1996, served to increase greatly the financial and social incentives for female 

household heads to move off public assistance and into employment. National data indicate large 

declines in the welfare rolls and large increases in the employment rates and labor market earnings of 

single female household heads from the mid-1990s to 2000 (Blank 2002). Thus the changing 

economic and policy environment would lead us to expect substantial increases in the labor market 

attachment of the sample adults at least through 2000, even in the absence of any MTO impacts. 

In fact, the employment rate of the sample adults has increased greatly over the past 7 years. The 

sharply rising employment rate of sample adults in all three treatment groups from 1995 to 2000 is 

illustrated in exhibit 7.2, using data from state UI records for sample adults from all five MTO sites. 

For sample adults in the control group, the employment rate (share with positive quarterly earnings) 

126 Chapter 7 – Impacts on Employment and Earnings 
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measured from administrative data more than doubled from 23.6 percent in the first quarter of 1995 to 

50.9 percent in the first quarter of 2001. The employment gains slowed with the onset of the 

economic downturn in 2001 and employment rates for sample adults declined slightly from late 2000 

to late 2001. Nevertheless, although aggregate labor market conditions significantly weakened in 

2001 and 2002, the (self-reported) employment rate of sample adults in the control group still more 

than doubled from 25 percent at baseline to 52 percent at the time of the interim evaluation survey in 

2002. 

EXHIBIT 7.2 

ADULT EMPLOYMENT BY CALENDAR QUARTER AND TREATMENT GROUP 

(ADMINISTRATIVE DATA) 

��������

������������������

������� ��������

����������������������� ����������������������������� �������������

1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 

1995 1996 1997 1998 1999 2000 2001 

Calendar Quarter 

Employment Rate

l 

0.0 

0.2 

0.4 

0.6 

Exp 

Sec 8 

Contro

Sources: State administrative unemployment insurance (UI) records from California, Illinois, Maryland, 

Massachusetts, and New York. 

Sample: Adults from families randomly assigned through December 31, 1997.

7.4 Impacts on Hypothesized Mediators 

The mobility experiences of the experimental and Section 8 groups provide further context for 

assessing the alternative mechanisms through which one might observe impacts of MTO on the labor 

market outcomes of sample adults. The experimental and Section 8 groups on average have moved 

into neighborhoods that appear to have more favorable employment opportunities and norms more 

supportive of work than the neighborhoods of the control group. The experimental and Section 8 

groups reside in neighborhoods with substantially lower poverty rates, higher adult employment rates, 

and higher shares of two-parent families than the control group (exhibits 2.8 and 2.10). The MTO 

treatment does not appear to have increased social isolation. And sample adults in the experimental 

group are significantly more likely to have friends who are college educated and who earn more than 

$30,000 (exhibit 2.14). Improvements in neighborhood safety and modest evidence of improvements 

Chapter 7 – Impacts on Employment and Earnings 127 
in some indicators of mental health for sample adults in the experimental group were also 

hypothesized to facilitate labor market participation and success (chapters 3 and 4). But moves to new 

neighborhoods do not seem to have improved the perceived access to transportation of those in the 

experimental and Section 8 groups (exhibit 2.14). 

The qualitative research that preceded the interim evaluation survey identified two main barriers to 

adult employment. Health problems posed the greatest challenge to a number of qualitative sample 

respondents; again, the improvements on mental health indicators for adults in the experimental group 

might suggest a reduction in this impediment to working. The other barrier was lack of available or 

affordable childcare. For example, a Section 8 group respondent noted: 

...There isn’t anyone to take care of the little one because he has asthma. I have my mother, 

but she’s very busy and takes care of other children. (3A349) 

Reported improvements in safety (chapter 3) and increases in potential adult role models with stable 

jobs in the experimental and Section 8 groups’ new neighborhoods provide a potentially favorable 

context for improving the labor market opportunities and attitudes of youth. Gender differences for 

youth in some mental health outcomes (chapter 4) and delinquency behaviors (chapter 5) imply likely 

more positive impacts of MTO on the labor market experiences of female youth than male youth. 

7.5 Interim Employment and Earnings Impacts on Adults 

Exhibit 7.3 shows estimates of the employment and earnings impacts of MTO on sample adults using 

respondent self-reports from the interim evaluation survey. There were no significant effects on the 

employment rates of adults in either the experimental or Section 8 groups at the time of the survey, or 

on the likelihood of being employed full-time (for 35 or more hours) at that time. There are also no 

statistically significant impacts of MTO on self-reported earnings (as measured by either annual 

earnings across all jobs in 2001 or current weekly earnings at the main job) or on the likelihood of 

employment in a job with weekly earnings above the poverty line. MTO does appear to have a 

marginally statistically significant positive impact on the likelihood of labor force participation (being 

employed or actively searching for work) of 3.8 percentage points (p=.058) for the experimental 

group and of 4.1 percentage points (p=.066) for the Section 8 group (appendix exhibit D7.1a). 

The estimated impacts of MTO on adult employment and earnings in the first 4 years after random 

assignment using state administrative UI records are displayed in the top two panels of exhibit 7.4. 

The time patterns of employment outcomes with time from random assignment by treatment group 

are illustrated in more detail in exhibit 7.5. The overall message from the state UI data is similar to 

the basic results from the interim evaluation survey data. There are no statistically significant impacts 

of the experimental or Section 8 treatments on overall cumulative adult employment or on cumulative 

earnings for the first 4 years after random assignment. But the results from the UI records do hint at 

some differences in the impacts of MTO on employment with exposure to the treatment (time since 

random assignment). 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 2 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. 

128 Chapter 7 – Impacts on Employment and Earnings 
EXHIBIT 7.3 

IMPACTS ON ADULT EMPLOYMENT AND EARNINGS, SURVEY DATA 

Experimental Section 8 

Control Vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

Adult Employment [SR]

Currently employed 0.522 0.014 0.030 0.026 0.044 

(n=3517) (0.021) (0.044) (0.023) (0.039) 

insurance 

0.296 0.023 

(0.019) 

0.050 

(0.041) 

0.004 

(0.021) 

0.007 

(0.035) 

Currently employed at a 

job offering health 

(n=3483) 

Currently employed full- 0.394 -0.001 -0.002 0.001 0.001 

time (35 or more hours at (0.021) (0.044) (0.023) (0.038) 

all jobs)

job with weekly earnings 

above poverty 

0.329 -0.008 

(0.020) 

-0.017 

(0.043) 

0.016 

(0.022) 

0.026 

(0.037) 

[SR]

 (n=3488) 

Currently employed at a 

(n=3311) 

Adult Earnings 

 Annual individual $8,899 137 292 47 79 

earnings in 2001 (449) (957) (495) (829) 

at main job

$182 -1 

(9) 

-3 

(20) 

-3 

(10) 

-4 

(17) 

(n=3313) 

Current weekly earnings 

 (n=3311) 

Employed over one year 0.362 0.029 0.062 0.030 0.049 

at current main job (0.021) (0.045) (0.022) (0.037) 

 (n=3475) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult survey. 

Sample: Adults from families randomly assigned through December 31, 1997. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

Chapter 7 – Impacts on Employment and Earnings 129 
EXHIBIT 7.4 

IMPACTS ON EMPLOYMENT AND EARNINGS, ADMINISTRATIVE DATA 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

Adult Employment [ADMIN] (n=4070)

Fraction of quarters 0.386 -0.025* -0.053* -0.011 -0.017 

employed, 1st and 2nd (0.012) (0.026) (0.013) (0.022) 

years after RA 

employed, 3rd and 4th 

years after RA 

0.473 -0.009 

(0.014) 

-0.019 

(0.030) 

0.008 

(0.015) 

0.013 

(0.025) 

0.430 -0.017 -0.036 -0.001 -0.002 

Fraction of quarters 

Annualized fraction of 

quarters employed, 1st (0.012) (0.025) (0.013) (0.021) 

through 4th years after 

RA 

Adult Earnings [ADMIN] (n=4070)

 Earnings, 1st and 2nd $4835 -260 -552 -154 -251 

rd and 4th

years after RA 

$6859 -170 

(310) 

-360 

(658) 

45 

(337) 

73 

(549) 

 Earnings, 3

years after RA (245) (520) (282) (459) 

 Annualized earnings, 1st $5847 -215 -456 -55 -89 

through 4th years after (254) (539) (288) (470) 

RA 

Youth Employment: Fractions of Quarters Employed in 2001 [ADMIN] (n=2619)

All youth (ages 14-19) 0.222 0.006 0.014 0.018 0.029 

(0.014) (0.031) (0.015) (0.025) 

Girls 0.246 0.013 

(0.020) 

0.029 

(0.044) 

0.030 

(0.025) 

0.048 

(0.039) 

Boys 0.200 0.001 0.001 0.006 0.011 

(0.019) (0.044) (0.021) (0.036) 

Youth Earnings During 2001 [ADMIN] (n=2619)

All youth (ages 14-19) $1366 77 173 170 277 

(140) (315) (133) (216) 

Girls $1369 172 

(205) 

385 

(460) 

260 

(205) 

410 

(322) 

Boys $1361 -10 -23 87 146 

(165) (372) (173) (290) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Data sources: State administrative Unemployment Insurance (UI) records from Maryland, Massachusetts, Illinois, 

California and New York. 

Samples: Adults and youth in families randomly assigned through December 31, 1997. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Mean cell outcomes were regressed controlling for mean covariates. For adults, the covariates included site, quarter 

of randomization, baseline work status and baseline education. For youth, the covariates included site, age and gender. 

Standard errors were adjusted to account for the actual number of individuals. This method produces cell mean 

outcome estimates that correspond exactly to those using microdata outcomes and mean covariates. 

e) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR – 

parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

130 Chapter 7 – Impacts on Employment and Earnings 
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EXHIBIT 7.5 

ADULT EMPLOYMENT BY QUARTER SINCE RANDOM ASSIGNMENT 

AND TREATMENT GROUP 

(ADMINISTRATIVE DATA) 

������������ ����������� ������������������������������

������������

0 

0.2 

0.4 

0.6 

1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 

2nd Yr 3rd Yr 

Employment Rate

l 

1st Yr 4th Yr 

Time After Random Assignment 

Exp 

Sec 8 

Contro

Sources: State administrative unemployment insurance (UI) records from California, Illinois, Maryland, 

Massachusetts, and New York. 

Sample: Adults from families randomly assigned through December 31, 1997.

A similar pattern of improvement over time is observed in the comparison of the Section 8 group and 

the control group. The time pattern of the findings from the UI data suggests some short-run 

disruption effects of moving on employment outcomes in the first 2 years after random assignment, 

effects that dissipate over time. Modest improvements in labor market opportunities offset these 

disruptions for both the Experimental and Section 8 groups by 4 to 5 years after random assignment.91 

Again, however, the estimated employment impacts by the end of the observed period in the 

administrative data are not statistically different from zero. 

As shown in exhibit 7.3, there were no significant impacts on the proportion of either the 

experimental group or the Section 8 group employed at the time of the survey, employed full-time or 

in jobs offering health insurance, or working at a job with weekly earnings above the poverty line. 

Unreported results for the sub-sample of cases with administrative data available for the full first five years 

after random assignment indicate an improvement in the employment of the Experimental group relative to 

the controls of .026 (p=.074) from -.019 (se=.013) in the first two years after random assignment to .007 

(se=.016) for the fourth and fifth years after random assignment. The analogous improvement for the 

Section 8 group is a statistically significant .037 (p=.016) from -.017 (se=.014) to .020 (se=.018). 

Chapter 7 – Impacts on Employment and Earnings 131 

91 
MTO does appear to have increased the likelihood that members of the experimental group were 

employed in a white collar job (managerial, professional, or technical occupation) by 3.3 percentage 

points (p=.063, appendix exhibit D7.3). But there were no significant effects on annual earnings in 

2001 or weekly earnings at the time of the survey. 

7.6 Interim Employment and Earnings Impacts on Youth 

Exhibit 7.6 summarizes the estimates of the effects of MTO on the labor force status and earnings of 

older youth aged 15 to 19 (as of May 31, 2001), based on interim evaluation survey data.92 Some 27.5 

percent of the youth in the control group were employed at the survey date and almost the exact same 

share were idle (neither in school nor working). MTO had no statistically significant effects on the 

youth employment rate, the idleness rate, or weekly earnings at the time of the survey. The state UI 

data similarly show no statistically significant overall effects of MTO on the employment or earnings 

of youth in calendar year 2001 (exhibit 7.4). There was, however, a statistically significant increase 

for experimental group youth in schooling as their major activity (being enrolled in school but not 

employed) of 6.2 percentage points (appendix exhibit D7.1b). 

The differences in MTO effects on the mental health and risky behavior outcomes of boys and girls, 

documented in earlier chapters, motivated us to examine differences in MTO impacts on their labor 

force behavior. MTO does not have statistically significant effects on the employment rates of boys or 

girls in either survey or administrative UI data. But MTO does appear to have possible differential 

effects on the time allocated toward schooling and work of boys and girls. MTO substantially reduced 

the idleness rate of girls by 7.3 percentage points for the entire experimental group (p=.074) and by 

15.7 percentage points for those in families who leased up (Exhibit 7.6). The idleness rate for male 

youth appears to be unaffected. 

A more detailed decomposition of employment and enrollment status for youth (shown in appendix 

exhibit D7.1b) indicates that the decline in idleness for female youth is driven by a large and 

statistically significant increase in time allocation to schooling as the major activity, with a 10.2 

percentage point increase(p=.020) in the share enrolled in school and not employed among all female 

youth in the experimental group (and 22.0 percentage point increase for those who leased up). The 

overall school enrollment rate for female youth in the experimental group increased by 7.2 percentage 

points (p=.096). The reductions in idleness and increases in time allocation to schooling of female 

youth in the experimental group are consistent with positive MTO impacts for this group on 

perceptions of their likelihood of going to college and getting a well-paid, stable job as an adult 

(appendix exhibit E6.4). Male youth in the experimental group did not experience similar effects on 

future expectations and if anything have lower (but statistically insignificant) perceptions of their 

likelihood of future educational and employment success. 

92 Since few 14-year-olds participate in the formal labor market, we focus the analysis of youth employment 

outcomes using the interim evaluation survey data on those aged 15 to 19. The analysis of the UI 

administrative data includes youth aged 14 to 19 because the cell data from Massachusetts do not allow us 

to separate out the 14-year-olds. 

132 Chapter 7 – Impacts on Employment and Earnings 
For youth in Section 8 families, the only statistically significant effect was a 6.4 percent (p = .042) 

reduction in the share of girls employed and not in school. This decline in employment as the major 

activity for Section 8 girls was not associated with a significant increase in idleness because it was 

offset by a rise in schooling as a major activity. 

EXHIBIT 7.6 

IMPACTS ON YOUTH EMPLOYMENT AND EARNINGS, SURVEY DATA 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

Youth Employed [SR] (n=1581) 

All youth (Ages 15-19) 0.275 -0.028 -0.065 -0.021 -0.038 

(0.030) (0.070) (0.033) (0.060) 

Girls 0.320 -0.026 

(0.046) 

-0.057 

(0.098) 

-0.060 

(0.047) 

-0.101 

(0.078) 

Boys 0.229 -0.029 -0.073 0.021 0.042 

(0.039) (0.098) (0.044) (0.090) 

Youth Current Weekly Earnings at Main Job [SR] (n=1531) 

All youth (Ages 15-19) $55 -5 -12 -4 -7 

(8) (18) (9) (17) 

Girls $59 1 

(12) 

3 

(26) 

-14 

(12) 

-23 

(20) 

Boys $50 -12 -29 6 13 

(10) (24) (13) (27) 

Youth Idle (neither employed nor in school)[SR] (n=1587) 

All youth (Ages 15-19) 0.277 -0.033 -0.077 0.003 0.006 

(0.029) (0.067) (0.032) (0.058) 

Girls 0.266 -0.073 

(0.041) 

-0.157 

(0.088) 

0.012 

(0.043) 

0.021 

(0.071) 

Boys 0.288 0.006 0.015 -0.006 -0.012 

(0.040) (0.100) (0.044) (0.090) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Youth survey. 

Sample: Youth from families randomly assigned through December 31, 1997. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

Chapter 7 – Impacts on Employment and Earnings 133 
7.7 Interpretation of Results 

This chapter examined the effects of MTO on the labor market outcomes of sample adults and older 

youth using respondent self-reports from the interim evaluation survey and administrative data from 

state UI records. Although MTO increased the likelihood of families living in neighborhoods that 

appear to have better employment opportunities and norms towards work, both data sources indicate 

no significant overall impacts of MTO on the employment rates or earnings of adults and of older 

youth. 

The most encouraging finding here is the reduction in the share of female youth who were idle 

(neither currently employed nor enrolled in school), raising the share of full-time students (enrolled in 

school and not employed) by a very substantial 22 percentage points among girls in families who 

leased up. This impact has the potential for longer term positive effects for these girls. 

134 Chapter 7 – Impacts on Employment and Earnings 
Chapter Eight 

Impacts on Income and Receipt of Public 

Assistance 

This chapter presents interim findings on MTO’s impacts on household income, public assistance 

receipt, and poverty status. We begin by discussing the relationship between MTO participation and 

household income and receipt of public assistance. We then describe the data sources and measures 

used. The next section provides context, by discussing the baseline status of sample members with 

respect to income and public assistance receipt, and by presenting impacts on mediators—that is, 

intermediate outcomes that might help explain MTO’s impacts on income and public assistance. Then 

we present impacts on the outcomes of interest. The final section discusses the extent to which the 

results are consistent with expectations, and the implications of the findings. 

Summary 

Consistent with the findings of no effects on employment and earnings in the previous chapter, we 

find no evidence that MTO reduced public assistance receipt or increased average household income, 

income relative to poverty, or food security. There is also no evidence that any of the subgroups 

examined experienced reductions in welfare benefits relative to controls. Most of the estimated 

impacts were small in size (much less than 10 percent of the control group average), so that even if 

the estimated impacts had been statistically significant they might not be important in a policy sense. 

The few statistically significant estimated impacts indicated increases in welfare receipt. 

8.1 Hypotheses about MTO’s Impacts on Public Assistance 

Receipt and Income 

The impacts of MTO on welfare receipt are likely to be the mirror image of the impacts on 

employment and earnings—to the extent that MTO increases employment, it can be expected to 

reduce participation in such income-tested programs as Aid to Families with Dependent Children 

(AFDC), Temporary Assistance to Needy Families (TANF), food stamps, Supplemental Security 

Income (SSI), and Medicaid (see Exhibit 8.1). As we saw in Chapter 7, however, there had been no 

significant impacts on employment and earnings by the time of the interim evaluation. Therefore, it 

seems unlikely that we will see effects on receipt of public assistance. 

The likely effects of MTO on household income depend, in turn, primarily on the effects on earnings 

and public assistance, because these are the main components of income for most sample members. If 

the demonstration interventions did not affect receipt of public assistance, then, given the lack of 

effect on employment and earnings we would expect little or no effect on household income. 

Chapter 8 – Impacts on Income and Receipt of Public Assistance 135 
• Monthly TANF, AFDC, food

stamp, SSI participation and 

benefits.

• Medicaid participation.

• Welfare receipt in the recent 

past.

• Reasons for leaving welfare.

• Welfare sanctions.

• Participation in welfare-towork programs.

Positive Effects

• More active job search.

• More use of networks for

labor market information.

• Improved mental health.

• Feelings of safety and 

security.

• Formation of new networks 

(in the long run).

Negative Effects

• Short-run disruption of social

networks.

• Less informal childcare in the 

short run.

Economic

Opportunities

• Higher rates of 

employment.

• Better earnings.

• Better access to the labor

market.

• Change in state welfare 

system policies.

Community Norms

and Values

• Shared community value of 

work.

• Negative value of welfare.

• Welfare agency attitudes 

toward work.

MTO Intervention→ Relocation to Low-Poverty Neighborhoods

Social and Physical

Environment

• Reduced crime and 

violence.

• Lower delinquency rate.

• Welfare agency resources.

MTO Intervention→ Relocation to Low-Poverty Neighborhoods

EXHIBIT 8.1 

WELFARE AND OTHER TRANSFER PROGRAMS OUTCOMES AND MEDIATING FACTORS 

Community-Level 

Mediators 

Person- and Family-Level 

Mediators Welfare Outcomes Community-LevelMediators

Person- and Family-Level

Mediators

• Monthly TANF, AFDC, food 

stamp, SSI participation and 

benefits. 

• Medicaid participation. 

• Welfare receipt in the recent 

past. 

• Reasons for leaving welfare. 

• Welfare sanctions. 

• Participation in welfare-towork programs. 

Welfare Outcomes

Positive Effects 

• More active job search. 

• More use of networks for 

labor market information. 

• Improved mental health. 

• Feelings of safety and 

security. 

• Formation of new networks 

(in the long run). 

Negative Effects 

• Short-run disruption of social 

networks. 

• Less informal childcare in the 

short run. 

Economic 

Opportunities 

• Higher rates of 

employment. 

• Better earnings. 

• Better access to the labor 

market. 

• Change in state welfare 

system policies. 

Community Norms 

and Values 

• Shared community value of 

work. 

• Negative value of welfare. 

• Welfare agency attitudes 

toward work. 

MTO Intervention→ Relocation to Low-Poverty Neighborhoods 

Social and Physical 

Environment 

• Reduced crime and 

violence. 

• Lower delinquency rate. 

• Welfare agency resources. 

Previous analyses of data for MTO enrollees in Boston and Baltimore provided mixed evidence about 

MTO’s impacts on welfare receipt. In Baltimore Ludwig et al. (2000) found that MTO reduced 

welfare receipt for the experimental group by an average of 6 percentage points (equal to about 15 

percent of the average receipt rate for the control group) over a 3-year followup period, but there was 

no evidence of an impact on welfare receipt for the Section 8 group. In Boston Katz, Kling, and 

Liebman (2001) found no evidence of reductions in welfare receipt for either the experimental or 

Section 8 group over a 2-year followup period. 

8.2 Data Sources and Measures 

This chapter assesses the impact of MTO on public assistance receipt and income based on 

respondent self-reports from the interim survey and administrative records from state TANF agencies. 

Sample adults in the interim survey were asked about current receipt of TANF, food stamps, SSI, and 

Medicaid, and also about their total household income in 2001 (the calendar year preceding the 

survey). The survey provided two measures of income: one based on a single question about the total 

combined income of all members of the household in 2001, and the other constructed from a series of 

questions about specific sources of income for each member of the household (i.e., earnings, self136 Chapter 8 – Impacts on Income and Receipt of Public Assistance 
employment income, government benefits, and other sources). This chapter presents results for the 

first measure of income; results for the second measure are presented in an appendix. Data from the 

interim survey were also used to estimate impacts on food security and a measure of self-sufficiency 

(the proportion of sample adults who were working and off TANF at the time of the survey). 

Using administrative records, this chapter presents estimated impacts on the proportion of sample 

members receiving benefits and the amount of benefits received from both AFDC/TANF and food 

stamps. State welfare agencies in all five sites provided these data, matched to sample members by 

social security numbers. Because the data received from several states did not cover the entire 

followup period, we present results for sample members from all five sites only in the fifth year after 

random assignment.93 In addition, we present results on AFDC/TANF receipt and benefit amounts for 

5 years after random assignment for sample members in three sites, and results on food stamp receipt 

and benefit amount for sample members in two sites.94 In both cases, the results are based on the 

subsample for whom data were available for the entire 5-year period. All benefit amounts are 

measured in 2001 dollars. 

Additional questions from the interim survey and census data on the characteristics of current 

neighborhoods were examined as mediating factors to help explain the impact results for public 

assistance receipt and income and to assess the hypotheses discussed in Section 8.1. Many of the 

same mediating factors that are likely to affect labor market outcomes may also affect public 

assistance receipt and income. These include labor market opportunities and access to jobs, 

community norms with respect to employment and receipt of welfare, sense of physical safety in 

neighborhood, and any effects on physical and mental health. In addition, income and public 

assistance outcomes are likely to be mediated by the families’ social networks. Impacts on these 

mediating variables were presented in previous chapters.95 

8.3 Baseline Income and Public Assistance Status and Control 

Group Context 

This section provides context for the impact results in the next section by presenting baseline 

characteristics for the full sample, showing the time path of welfare receipt for the control group, and 

summarizing impacts on hypothesized mediating outcomes. 

93 Several sites provided little or no data in the years immediately following random assignment. By followup 

year 5, we had data on a large portion of our sample from all five sites. Even the year 5 data, however, is 

available for only a subset of our observations: We have TANF data for 2,984 of the 4,248 sample adults, 

and we had food stamp data for 2,710. 

94 Five years of AFDC/TANF data are available for New York, Chicago, and Boston. Five years of food 

stamp data are available for Chicago and Boston only. Los Angeles and Illinois did not provide 5 years of 

administrative data for any sample members for either program. 

95 Chapter 7 discussed impacts on employment-related mediators, chapter 2 presented effects on community 

norms, chapter 3 covered impacts on sample members’ sense of safety and neighborhood conditions, and 

chapter 4 presented effects on physical and mental health. 

Chapter 8 – Impacts on Income and Receipt of Public Assistance 137 
Baseline characteristics 

A snapshot of the sample at the time of random assignment provides a useful context for 

understanding the impact results presented later in this chapter. This section addresses the baseline 

characteristics most relevant to the impacts shown in this chapter. Previous chapters provide results 

for a range of other baseline measures. Exhibit 8.2 shows characteristics separately for the 

experimental, Section 8, and control groups, although random assignment guarantees that only chance 

differences exist at baseline. 

At the time they were randomly assigned, the MTO adult sample members had very high rates of 

public assistance receipt, low rates of employment, and average incomes well below the poverty line. 

Approximately three out of four sample members were receiving AFDC at baseline, and four out of 

five were receiving food stamps. Further, nearly all sample adults (93 percent) had received AFDC at 

some point. Consistent with these high rates of welfare receipt, only one of four sample members was 

working at baseline. Average income was about $9,300, well below the poverty line for a family of 

three. Median income was still lower, approximately $7,700. These results show that sample 

members were quite disadvantaged when they entered the MTO demonstration. 

EXHIBIT 8.2 

SELECTED BASELINE CHARACTERISTICS: WORK, PUBLIC ASSISTANCE, AND INCOME 

Characteristic Experimental Section 8 Control 

Employment Status at Baseline 

 Working 25.7 25.6 24.2 

Not working, but previous y worked l 54.1 57.3 57.2

Never worked for pay 20.2 17.1 18.6 

Public Assistance Receipt at Baseline 

 Receiving AFDC 74.4 75.3 74.5 

80.3 81.0 80.1 

93.5 92.6 92.1 

16.6 15.9 15.9 

34.5 35.6 34.8 

18.3 17.3 17.6 

Receiving food stamps 

Ever received AFDC 

Working and not receiving AFDC 

 Receiving WIC 

 Receiving SSI 

Receiving SSDI 9.6 8.0 8.4 

Household Income at Baseline 

Average income $9,385 $9,189 $9,337 

 Median income $8,064 $7,536 $7,824

Source: Participant baseline survey. 

Sample: Adults 

Notes: Data are weighted as described in appendix B. 

138 Chapter 8 – Impacts on Income and Receipt of Public Assistance 
Receipt of public assistance over time for the control group 

Impacts are measured as the average outcomes for the treatment groups (experimental or Section 8) Fraction of Months Received Assistance 

relative to the outcomes for the control group. The control group’s experience over time therefore 

represents the standard against which outcomes for the treatment groups are evaluated. In the three 

sites for which we have AFDC/TANF data for the entire followup period, the administrative data 

show high initial rates of receipt (consistent with the baseline survey), and a steady decline in receipt 

over time. Over the followup period, AFDC/TANF receipt rates fell by more than half. This pattern of 

declining receipt is typical for a cohort of individuals who were all initially receiving benefits. Food 

stamp receipt exhibits a similar pattern (shown for two sites), except that initial receipt rates were 

higher and the decline was more gradual (exhibit 8.3). In order for MTO to reduce welfare receipt, it 

would be necessary for the receipt rates of the treatment groups not only to fall over time but to fall 

by more than the rate for the control group. 

Exhibit 8.3 

Receipt of Public Assistance Over Time for the Control Group 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

1 2 3 4 5 

Years since Random Assignment 

TANF Receipt 

Food Stamp Receipt 

Note: TANF receipt rates based on data from New York, Chicago, and Boston.

Food stamp receipt rates based on data from Chicago and Boston.

Chapter 8 – Impacts on Income and Receipt of Public Assistance 139 
8.4 Impacts On Hypothesized Mediators 

The hypotheses discussed earlier in this chapter suggest that MTO might affect welfare receipt and 

income by inducing experimental and Section 8 group members to move to neighborhoods that have 

better employment opportunities, are safer, and have norms more supportive of work and less 

accepting of welfare. In fact, MTO has produced such effects. On average over the followup period, 

the experimental and Section 8 groups moved into neighborhoods with substantially lower poverty 

rates, higher adult employment rates, and a higher proportion of two-parent families than the control 

group (exhibit 2.10). MTO-induced mobility also led to improvements in neighborhood safety 

(exhibit 3.3) and in some indicators of mental health (exhibit 4.1). The reduction in neighborhood 

poverty, however, did not produce significant impacts on employment and earnings, which are the 

most direct mediators of public assistance receipt and income. Further, the demonstration did not 

significantly strengthen social networks or improve adult access to transportation (which was 

reportedly very high for all). 

8.5 Interim Impacts on Public Assistance Receipt and Income 

This section summarizes impact results for public assistance receipt, household income, food security, 

and a measure of self-sufficiency. 

Impacts on public assistance receipt 

We find no evidence of MTO impacts on public assistance receipt over the followup period through 

the interim data collection. AFDC/TANF receipt rates at the time of the survey were two to four 

percentage points lower for the experimental and Section 8 groups compared to the control group, but 

this difference was not statistically significant (exhibit 8.4, top panel). Estimated impacts on current 

receipt of food stamps, SSI, and Medicaid were also not significant, and they varied in sign. Five 

years after random assignment, estimated impacts on welfare benefits were similarly not significant 

(exhibit 8.4, bottom panel). Estimated impacts on TANF and food stamp receipt and payments were 

of mixed sign and small in size for both the experimental and Section 8 groups. 

Impacts on AFDC/TANF receipt over time, for the three states where we have 5 years of followup 

data, do not demonstrate a clear trend, although there is some evidence MTO increased AFDC/TANF 

receipt and the amount of benefits received in the experimental group during the first 4 years after 

random assignment (exhibit 8.5). A similar pattern is found for receipt of food stamp benefits (exhibit 

8.6). For the same subsample of persons with 5 years of followup data, we see a different trend in 

impacts for the Section 8 group. For this group there were insignificant impacts in years 1 and 2; 

however, in later years, receipt rates for AFDC/TANF and food stamps were significantly higher for 

the Section 8 group than for the control group (exhibits 8.5 and 8.6).96 Note, however, that the 

96 Specifically, for the Section 8 group compared to the control group, AFDC/TANF receipt rates were higher 

in years 3 and 4 combined and in year 5. Food stamp receipt rates were higher in years 3 and 4, and total 

food stamp benefits received were higher in year 5. 

140 Chapter 8 – Impacts on Income and Receipt of Public Assistance 
significant impacts on public assistance in the Section 8 group were not found in the five-site sample 

in year 5. 

Subgroup impacts on total AFDC/TANF plus food stamp payments were also not significant (exhibit 

8.7). Impacts were estimated for subgroups defined by race and ethnicity and by several measures of 

barriers to employment—welfare history, education, employment status at baseline, and access to a 

car. The results provide no evidence that MTO had different welfare receipt impacts on sample adults 

who varied according to these barriers or by race and ethnicity. 

Impacts on household income 

Consistent with the general lack of impacts on earnings and public assistance, MTO produced no 

significant impacts on household income. Total household income for the control group in 2001 was 

approximately $15,500 per household, and estimated impacts for the experimental and Section 8 

groups was $500 or less.97 Results for income of the sample adult alone, the sample adult and spouse 

combined, and the distribution of income relative to the poverty line also revealed no evidence of 

impact (exhibit 8.8). 

Results for the other measure of household income presented in appendix exhibit D8.1 also show no 

impacts on income. We also examined impacts on different locations on the distribution of both the selfreported and constructed total household income measures, using quantile regressions to estimate impacts 

at the 25th percentile, the median, and the 75th percentile of each distribution. These results, shown in 

appendix exhibit D8.2, also showed no evidence of impacts. 

Chapter 8 – Impacts on Income and Receipt of Public Assistance 141 

97 
EXHIBIT 8.4 

HOUSEHOLD RECEIPT OF PUBLIC ASSISTANCE – ALL SITES 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

[SR]

0.286 -0.023 

(0.019) 

-0.049 

(0.040) 

-0.035 

(0.021) 

-0.058 

(0.034) 

Food stamps (n=3514) 0.460 -0.011 

(0.021) 

-0.023 

(0.044) 

0.002 

(0.023) 

0.003 

(0.038) 

Proportion Receiving Benefits at Time of Survey 

 AFDC/TANF (n=3509) 

 SSI (n=3511) 0.228 0.020 0.043 0.025 0.041 

(0.016) (0.035) (0.019) (0.031) 

0.561 -0.030 

(0.021) 

-0.064 

(0.045) 

-0.042 

(0.022) 

-0.071 

(0.038) 

] 

0.255 -0.006 -0.013 0.028 0.044 

 Medicaid (n=3468) 

AFDC/TANF [ADMIN

Fraction of months Sample 

adult received AFDC/TANF, (0.016) (0.035) (0.019) (0.030)

year 5 (n=2984) 

sample adult, year 5 

(n=2984) 

$1,264 -$33 

(93) 

-$71 

(205) 

$49 

(102) 

$77 

(161) 

sample adult received food 

stamps, year 5 (n=2710) 

0.435 0.012 

(0.019) 

0.026 

(0.041) 

0.034 

(0.021) 

0.052 

(0.032) 

received by sample adult, 

year 5 (n=2710) 

$1,249 -$22 

(65) 

-$48 

(139) 

$117 

(76) 

$178 

(116) 

 Total AFDC/TANF 

payments received by 

Fraction of months any 

Total food stamp payments 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult survey, administrative data from state welfare agencies. See appendix A for details. 

Sample: Adults from families randomly assigned by December 31, 1997. Administrative outcomes: sample 

adults (and their households), data from the 49th to the 60th month after random assignment, all sites. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b)Control means and impact estimates are regression-adjusted, with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. See 

appendix B for more details. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

142 Chapter 8 – Impacts on Income and Receipt of Public Assistance 
EXHIBIT 8.5 

RECEIPT OF AFDC/TANF: TRENDS OVER TIME – THREE SITES WITH LONGITUDINAL DATA 

Experimental Section 8 

Control Vs. Control Vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

AFDC/TANF [ADMIN]

Fraction of months sample 0.216 0.009 0.023 0.048* 0.084* 

adult received AFDC/TANF, (0.019) (0.050) (0.024) (0.042)

year 5 (n=1962) 

year 5 (n=1962) 

$1,037 $2 

(107) 

$6 

(280) 

$37 

(121) 

$64 

(210) 

 Total AFDC/TANF 

payments to sample adult, 

Fraction of months sample 0.313 0.035* 0.084* 0.044* 0.081* 

adult received AFDC/TANF, (0.016) (0.039) (0.018) (0.033)

years 3 and 4 (n=2934) 

years 3 and 4 (n=2934) 

$3,144 $184 

(191) 

$443 

(460) 

$74 

(206) 

$137 

(378) 

 Total AFDC/TANF 

payments to sample adult, 

Fraction of months sample 0.469 0.036* 0.086* 0.027 0.048 

adult received AFDC/TANF, (0.017) (0.041) (0.019) (0.034)

years 1 and 2 (n=2632) 

years 1 and 2 (n=2632) 

$4,522 $485* 

(201) 

$1,160* 

(481) 

$249 

(217) 

$440 

(384) 

 Total AFDC/TANF 

payments to sample adult, 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Source: Administrative data from state welfare agencies. See appendix A for details. 

Sample: Sample adults from three sites: New York, Chicago, and Boston. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b)Control means and impact estimates are regression-adjusted, with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. See 

appendix B for more details. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

Chapter 8 – Impacts on Income and Receipt of Public Assistance 143 
EXHIBIT 8.6 

RECEIPT OF FOOD STAMPS: TRENDS OVER TIME – TWO SITES WITH LONGITUDINAL DATA 

Experimental Section 8 

Control Vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

Food Stamps [ADMIN]

Fraction of months sample 0.390 0.034 0.090 0.052 0.082 

adult received food stamps, (0.025) (0.066) (0.029) (0.046)

year 5 (n=1423) 

to sample adult, year 5 

(n=1423) 

$1,056 $95 

(81) 

$253 

(214) 

$213* 

(101) 

$334* 

(158) 

Total food stamps payments 

Fraction of months sample 0.451 0.032 0.083 0.054* 0.092* 

adult received food stamps, (0.020) (0.052) (0.022) (0.037)

years 3 and 4 (n=1853) 

to sample adult, years 3 and 

4 (n=1853) 

$2,491 $206 

(140) 

$532 

(362) 

$271 

(158) 

$460 

(268) 

Total food stamps payments 

Fraction of months sample 0.577 0.037* 0.096* 0.030 0.051 

adult received food stamps, (0.017) (0.044) (0.020) (0.034)

years 1 and 2 (n=1853) 

to sample adult, years 1 and 

2 (n=1853) 

$3,043 $297* 

(118) 

$768* 

(306) 

$194 

(139) 

$329 

(235) 

Total food stamps payments 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Administrative data from state welfare agencies. See appendix A for details. 

Sample: Sample adults. Data from two sites: Chicago and Boston. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted, with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. See 

appendix B for more details. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

144 Chapter 8 – Impacts on Income and Receipt of Public Assistance 
EXHIBIT 8.7 

SUBGROUP IMPACTS ON TOTAL AFDC/TANF PLUS FOOD STAMP PAYMENTS, 

YEAR 5

Experimental Section 8 

Control vs. Control vs. Control 

Mean ITTa TOTa ITTa TOTa Subgroup 

Race/Ethnicity [ADMIN]

 Black (n=2925) $1,590 -$26 -$56 $20 $29 

(109) (237) (124) (182) 

$1,513 -$37 

(201) 

-$80 

(437) 

-$161 

(210) 

-$301 

(393) 

 Non-Black (n=2925) 

 Hispanic (n=2952) $1,648 -$4 -$9 -$353 -$649 

(228) (484) (231) (426) 

$1,534 -$33 

(105) 

-$73 

(230) 

$50 

(117) 

$75 

(177) 

] (n= 2984)

 Non-Hispanic (n=2952) 

Barriers to Employment at Baseline [ADMIN

 Receiving AFDC/TANF $1,944 -$96 -$199 -$30 -$46 

at random assignment (120) (249) (131) (199) 

Had no high school 

diploma 

$1,781 -$67 

(128) 

-$148 

(282) 

-$130 

(139) 

-$213 

(229) 

Not working at random $1,816 -$73 -$159 -$51 -$80 

assignment (117) (256) (129) (204) 

at random assignment 

$1,634 -$41 

(103) 

-$92 

(233) 

-$100 

(115) 

-$159 

(182) 

Did not have car that ran 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Household survey, other locating data, 2000 Census tract-level data. See appendix A for details. 

Sample: Adults from families randomly assigned by December 31, 1997. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted, with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. See 

appendix B for more details. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

Chapter 8 – Impacts on Income and Receipt of Public Assistance 145 
EXHIBIT 8.8 

IMPACTS ON INCOME, FOOD SECURITY, AND SELF-SUFFICIENCY 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

Total Income in 2001 [SR] 

Total income of sample $11,890 $442 $938 $101 $169 

adult (n=3365) (427) (906) (482) (811) 

Total income of sample 

(n=3261) 

$13,166 $322 

(600) 

$683 

(1,273) 

-$436 

(665) 

-$729 

(1,111) 

$15,536 $239 $505 -$162 -$271 

adult and current spouse 

Household total income 

(n=3211) (571) (1,205) (636) (1,062) 

Poverty Status [SR] (n= 3526)

Percent of households 0.347 -0.016 -0.035 0.017 0.028 

<50% of poverty line in 

2001 

(0.020) (0.043) (0.023) (0.038) 

2001 

0.326 0.018 

(0.021) 

0.039 

(0.044) 

0.006 

(0.023) 

0.010 

(0.038) 

Percent of households 0.170 -0.005 -0.011 -0.026 -0.044 

Percent of households 50 

to 99% of poverty line in 

100 to 149% of poverty (0.017) (0.036) (0.017) (0.028)

line in 2001 

Percent of households 

>150% of poverty line in 

2001 

0.157 0.003 

(0.015) 

0.007 

(0.032) 

0.004 

(0.017) 

0.006 

(0.028) 

Food Security [SR]

Percent of households 0.111 -0.022 -0.046 -0.003 -0.004

 (n=3519) 

food insecure with (0.013) (0.028) (0.015) (0.025)

hunger 

Self-Sufficiency [SR] (n= 3472)

Percent of sample adults 0.452 0.018 0.038 0.019 0.032 

working and off TANF in (0.020) (0.043) (0.023) (0.038)

2001 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Source: Adult survey. See appendix A for details. 

Sample: Adults from families randomly assigned by December 31, 1997. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted, with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. See 

appendix B for more details. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

146 Chapter 8 – Impacts on Income and Receipt of Public Assistance 
Impacts on other outcomes 

MTO also had no effects on measures of food security and self-sufficiency (exhibit 8.8, bottom 

panel). The proportion of households who experienced hunger98 in the year prior to the survey was 20 

percent lower in the experimental group than the control group (8.8 compared to 11.0 percent), but 

the difference was not significant. For the Section 8 group the point estimate was very close to zero. 

Almost one-half of the control group was working and not receiving TANF at the time of the survey, 

about two percentage points less than the estimates for the experimental and Section 8 groups. 

8.6 Interpretation of Results 

MTO has so far not caused any significant reductions in public assistance receipt or increases in 

household income for either the experimental group or the Section 8 group. If anything, it may have 

increased welfare receipt in the first 4 years after random assignment. The absence of impacts is 

consistent across outcome measures and subgroups, and it is also consistent with the lack of impacts 

on employment and earnings shown in chapter 7. 

These results may seem somewhat surprising, given the differences that MTO produced in the 

neighborhoods of the experimental and Section 8 groups. Neighborhoods with lower poverty rates 

and higher adult employment rates (neighborhoods that were perceived as safer) apparently had little 

influence on employment or welfare receipt for sample members and therefore not on income either. 

One interpretation of these findings is that place alone is not enough to produce substantial changes in 

employment and welfare receipt, or that the sample’s exposure to different places was not sufficient 

to affect these changes. It is possible that combining better neighborhoods with other policies (such as 

employment assistance or training, financial work incentives, affordable high-quality child care) 

would have larger effects. It is also possible that larger changes in family environment (through a 

greater proportion of moves to low-poverty neighborhoods) would produce increases in employment 

and reductions in welfare receipt.99 Or participants may need more time to adjust to new 

neighborhoods and take advantage of greater opportunities. Additional analysis and longer followup 

may shed further light on the results. 

98 The interim survey measured hunger using a six-item scale developed by Abt Associates for the U.S. 

Department of Agriculture. For a detailed explanation see Bickel et al. (2000). 

99 Higher leaseup rates would not be expected to produce larger TOT impacts but could improve the precision 

of the impact estimates and thereby increase the likelihood of detecting small to moderate effects. 

Chapter 8 – Impacts on Income and Receipt of Public Assistance 147 
148 Chapter 8 – Impacts on Income and Receipt of Public Assistance 
Chapter Nine 

Summary and Implications of the Estimated Impacts 

of MTO 

This chapter summarizes the impact estimates presented in the previous chapters, assesses the size 

and statistical significance of those estimates, as well as the likelihood that further impacts will 

emerge before the final evaluation 10 years after random assignment, and discusses the implications 

of the findings for policy. 

Summary of Impact Estimates 

In chapters 2 through 8, we presented estimates of the impact of MTO on the mobility of participating 

families and the characteristics of the neighborhoods in which they lived, as well as on outcomes in 

six aspects of their lives: 

• Housing conditions and receipt of housing assistance. 

• Adults’ and children’s mental and physical health. 

• Delinquency and risky behavior among youth. 

• The schools attended by children in the sample and their educational achievement. 

• Employment and earnings of adults and youth. 

• Household income and receipt of public assistance. 

In this section, we review the impacts in each of these areas. 

Impacts on residential location, housing quality, and receipt of housing subsidies 

MTO had substantial, positive effects on the mobility of families in the treatment groups and on the 

characteristics of the neighborhoods in which they lived. Almost half of the families assigned to the 

experimental group were able to use their program vouchers, as were over three-fifths of the families 

in the Section 8 group (exhibit 2.1). In order to use the voucher, experimental group families were 

required to move to census tracts with poverty rates below 10 percent in the 1990 Census. Because 

many moved to neighborhoods where the poverty rate was increasing between 1990 and 2000, we 

estimate that only about half of their destinations had poverty rates below 10 percent at the time of the 

move, although virtually all had rates below 20 percent (exhibit 2.3). Among the Section 8 group, 

who could use the voucher anywhere they could find housing that met Section 8 quality standards, 

less than 30 percent of those who leased housing units with program vouchers moved to census tracts 

with poverty rates below 20 percent, although the overwhelming majority moved to neighborhoods 

with lower poverty rates than those where they had lived in public housing. 

Chapter 9 – Summary and Implications of the Estimated Impacts of MTO 149 
As noted earlier, the experimental families were only constrained to live in low-poverty areas for one 

year. By the time of the interim evaluation, these differentials in poverty rates had narrowed 

somewhat, in part because of subsequent moves by the experimental families and in part because of 

changes over time in neighborhood poverty rates, but they had not disappeared entirely. Among those 

who moved with program vouchers, 60 percent of the experimental group families were still in census 

tracts with poverty rates below 20 percent, while 30 percent of the Section 8 families were in such 

tracts (exhibit 2.5). The treatment-control differentials had narrowed as well, partly as a result of 

changes in the poverty rates of the neighborhoods where treatment group families resided, but also 

because over two-thirds of the control families had moved to housing in different locations—some to 

private housing, with or without Section 8 subsidies, and some to other public housing units.100 By the 

time of the interim evaluation, about 17 percent of the control group families lived in census tracts 

with poverty rates below 20 percent and just over half lived in tracts with rates below 40 percent. 

It is noteworthy, however, that even those treatment group families who moved to low-poverty areas 

did not necessarily move to predominantly white or racially integrated areas. Among families in the 

Section 8 group, at the time of the interim evaluation over three quarters of both those who moved 

with program vouchers and those who did not were living in census tracts that were over 80 percent 

minority, about the same proportion as among control families (exhibit 2.6). Among experimental 

group families, 60 percent of those who moved with program vouchers were in heavily minority 

areas. For minority families in the experimental group who moved with program vouchers, the 

experiment reduced the average percent minority in their neighborhood by less than 10 percentage 

points. There was no significant effect on this measure for the Section 8 group (exhibit 2.8). 

These mobility patterns resulted in a number of significant improvements in the environment in 

which experimental group families lived and lesser improvements for Section 8 group families. 

Relative to the control group, MTO reduced the proportion of the followup period that such families 

spent in areas of concentrated poverty by 47 percentage points in the experimental group and 35 

percentage points in the Section 8 group (exhibit 2.9). It increased the proportion of time spent in 

areas with poverty rates below 20 percent by 53 percentage points among families in the experimental 

group. Section 8 families were more likely to locate in moderate-poverty areas; the demonstration 

increased the proportion of time these families spent in areas with poverty rates below 30 percent by 

31 percentage points. 

At the time of the interim evaluation, experimental group families who moved with program vouchers 

lived in neighborhoods with higher adult employment rates, a substantially higher proportion of twoparent families and high school graduates, and nearly twice as many homeowners as in the 

neighborhoods they would have lived in absent the demonstration (exhibit 2.10). Section 8 group 

families who moved with program vouchers also saw significant improvements in these 

neighborhood attributes, relative to the control group, although those gains were generally only about 

half as large as those experienced by the experimental group families. 

These environmental changes substantially increased the chances that adults in experimental group 

families would have college-educated friends or friends earning $30,000 or more (exhibit 2.10). 

It should be noted that some of these moves may have been associated with the redevelopment of public 

housing projects as part of the HOPE VI program. 

150 Chapter 9 – Summary and Implications of the Estimated Impacts of MTO 

100 
There was no significant effect on these outcomes for adults in the Section 8 group who lived in 

somewhat higher poverty areas than the families in the experimental group. 

The families who moved with program vouchers also markedly improved the quality of their physical 

and social environments, reporting large reductions in the presence of litter, trash, graffiti, abandoned 

buildings, people hanging around, and public drinking, relative to the control group (exhibit 3.5). 

They also reported that they had less difficulty getting police to respond to their calls. The proportion 

of families who expressed satisfaction with their current neighborhoods was much higher in both 

treatment groups than in the control group. On every one of these measures, the proportion of the 

experimental group reporting improved conditions was about 10 percentage points larger among the 

Section 8 group. 

Perhaps most notable from the perspective of the families themselves is the fact that they were 

successful in achieving the goal that loomed largest in their motivation to move out of their old 

neighborhoods—substantial increases in their perceived safety in and around their homes and large 

reductions in the likelihood of observing or being victims of crime (exhibit 3.5). These gains were 

greater for the experimental group families, but they were still substantial for those in the Section 8 

group who moved with program vouchers. 

As with the effects on neighborhood, MTO substantially improved the quality of housing occupied by 

the families who moved with program vouchers. A markedly higher proportion of families in both 

treatment groups voiced satisfaction with their housing at the time of the interim evaluation than in 

the control group (exhibit 3.5). It also substantially reduced the fraction living in public housing, 

while increasing somewhat the proportion of families receiving housing subsidies. It should be noted 

that, even without an MTO voucher, over two-thirds of the families in the control group left their 

original public housing project during the followup period, either through their own efforts or because 

their units were renovated or demolished by Federal and local programs. Even so, at the time of the 

interim evaluation, controls lived in substantially less desirable housing treatment group members, 

both in terms of measurable characteristics of the housing and in terms of the families’ satisfaction 

with their housing. 

In sum, the demonstration succeeded in substantially improving the housing and residential 

environment of the families who moved with program vouchers on a wide range of measures. While 

these improvements were greater for the experimental group, who were constrained to move to lowpoverty areas, at least initially, the Section 8 group also experienced sizeable environmental 

improvements, relative to the control group. 

Impacts on adults’ and children’s health 

While health data were not collected at baseline, it seems reasonable to suppose that participating 

families were subject to a wide range of health conditions and risks. At baseline, over one-third of the 

families reported serious problems with rats or mice and a quarter said that a family member had been 

beaten or assaulted in the prior 6 months. More generally, urban residents of high-poverty 

neighborhoods are likely to have high rates of obesity, hypertension, substance abuse, asthma, 

depression, and exposure to violence. The high rates of activity limitations, asthma, high blood 

pressure, obesity, psychological distress, depression, and anxiety observed in the control group at the 

time of the interim evaluation bear out these expectations (exhibit 4.2). 

Chapter 9 – Summary and Implications of the Estimated Impacts of MTO 151 
Estimation of impacts on these outcomes and on measures of smoking, drinking, and general physical 

health revealed one significant impact on adults’ physical health: a large reduction in the incidence of 

obesity, among adults in the experimental group (exhibit 4.2). There was also a substantial reduction 

in psychological distress among adults in the experimental group families, but not among those in the 

Section 8 group. There were no significant effects on the other measures of adult health measured in 

the interim evaluation. 

Among children, the significant effects of MTO on health were confined to mental health measures— 

a moderately large reduction in psychological distress for girls in the experimental group; a 

substantial decrease in the incidence of depression among girls in the Section 8 group; and very large 

reductions in the incidence of generalized anxiety disorder among girls in both treatment groups 

(Exhibit 4.5). 

These findings of significant impacts on measures of mental health, especially for the experimental 

group, are consistent with the improvements in the families’ perceptions of personal safety discussed 

above. 

Impacts on delinquency and risky behavior among youth 

Children who were ages 15 to 19 at the time of the interim evaluation were between the ages of 8 and 

15 at baseline. Even so, significant proportions had already exhibited problem behavior or been 

suspended from school. By the time of the interim evaluation, among youth this age, 24 percent of the 

girls and 39 percent of the boys in the control group had been arrested—over half of them for violent 

crimes (exhibit 5.3). 

In the interim evaluation, survey data from parents and from the youth themselves were used to 

measure a number of delinquent, risky, and problem behaviors. The youth were asked whether they 

had ever been arrested. In addition, administrative data from the criminal justice system were used to 

measure the number of arrests for specific crimes. 

For boys and girls ages 12 to 19 in both treatment groups, there were no significant effects on either 

an index of 15 problem behaviors reported by parents or on a narrower index of self-reported 

delinquent behaviors related to criminal behavior (exhibit 5.2). However, there were significant 

increases in self-reported behavior problems among boys ages 12 to 19, in both treatment groups. 

Participation in MTO resulted in a large reduction in the proportion of girls ages 15 to 19 in the 

Section 8 group who had ever been arrested for violent crimes (Exhibit 5.3). This effect contributed to 

a significant reduction in the frequency of arrests for violent crimes for all youth (Exhibit 5.4). There 

were no effects on the incidence of arrests for other crimes for girls. The only effects on arrests for 

boys were very substantial increases in the proportion ever arrested and the frequency of arrests for 

property crimes in the experimental group (Exhibits 5.3 and 5.4). This increase in arrests might reflect 

more stringent policing in new locations, rather than (or in addition to) more criminal behavior. 

For girls ages 15 to 19 in the experimental group, but not for those in the Section 8 group, there were 

reductions in risky behavior, concentrated in marijuana use and smoking. Among boys in this age 

range in both treatment groups there were significant increases in smoking, but not in other types of 

risky behavior (exhibit 5.5). 

152 Chapter 9 – Summary and Implications of the Estimated Impacts of MTO 
This pattern of gender differences in effects—positive for girls and negative for boys—suggests that 

boys and girls react differently to the disruption of moving and the challenge of integrating into a new 

environment. However, the available results do not allow us to say specifically why this is the case. 

To the extent that this difference reflects a response to the transition from a high-poverty environment 

to a lower poverty environment, one might expect this pattern to be different in the longer term for 

youths who have completed that transition or who have grown up in the new environment. 

Impacts on children’s education 

For the interim evaluation, education research focused on children ages 5 to 19 at the time the data 

were collected. We interviewed parents about the school-related attitudes, behaviors, and 

performance of all children in the sample. We interviewed children ages 8 to 19 about their own 

views and experiences in school. We also administered four achievement different tests from the 

Woodcock -Johnson Battery-Revised to all sample children ages 5 to 19 and collected data from 

published sources about the schools the children attended. 

MTO had significant but small effects on the characteristics of the schools sample children attended 

(exhibit 6.3). Experimental group children attended schools with somewhat lower percentages of poor 

and minority children and of students with limited English proficiency than they would have in the 

absence of the demonstration. The schools attended by experimental group children were ranked 

marginally higher on state exams than the schools attended by control students but were less likely to 

be magnet schools. All of these differences were relatively small. For example, the schools attended 

by those who moved with program vouchers were only at about the 25th percentile on state exams, as 

compared with the 17th percentile for the schools attended by controls at the time of the interim 

evaluation. MTO had no significant effect on the student-teacher ratio. 

Among the children in the Section 8 group, participation in MTO reduced the schools’ percentages of 

minority and poor (exhibit 6.3). There were no other significant effects on the schools attended by 

children in the Section 8 group at the time of the interim evaluation, although the average ranking of 

schools attended by children in that group over the course of the followup period was slightly higher 

than that of the school attended by control children. All of these effects were smaller than those on the 

schools of experimental group children. 

These relatively modest impacts on school characteristics reflect the fact that, at the time of the 

interim evaluation, nearly three quarters of the children in families in the experimental group who 

leased up with program vouchers were attending schools in the same school district as at baseline. 

This may be because, as suggested in the MTO qualitative analysis, some children did not change 

schools when their families moved or because the families did not move out of the city. Since most of 

the cities in the study have a single citywide school district, families who moved within the city 

remained in the same school district. 

Not surprisingly, given the small impact on school characteristics, the demonstration had virtually no 

significant effects on any of the measures of educational performance analyzed for either the 

experimental group or the Section 8 group (exhibits 6.5 to 6.7). Of the 58 outcomes analyzed, there 

were significant impacts on only two: small reductions in scores on the Woodcock-Johnson 

calculation score for all children in the Section 8 group and the broad math score for children ages 8 

to 11in the Section 8 group. 

Chapter 9 – Summary and Implications of the Estimated Impacts of MTO 153 
Impacts on employment, earnings, household income, and receipt of public assistance 

Data on employment, earnings, household income, and public assistance were obtained from a 

combination of administrative records and the interim survey. Administrative data provided a 

continuous history of employment, earnings, and Aid to Families with Dependent Children 

(AFDC)/Temporary Assistance for Needy Families (TANF) and food stamp benefits from random 

assignment through the time of the interim evaluation. Survey data provided measures of 

employment, earnings, and unearned income in 2001, and food security and receipt of SSI and 

Medicaid at the time of the interview. 

At baseline, approximately a quarter of the sample adults were working. This proportion more than 

doubled over the followup period for both treatment and control group members. But the only 

statistically significant treatment-control difference in any of the measures of adult employment or 

earnings analyzed was a slight reduction in the employment rate in the first two years after random 

assignment among adults in the experimental group (Exhibits 7.3–7.4). 

Although there were no statistically significant impacts on the employment or earnings of youth, 

either overall or by gender (exhibit 7.4), there was a large reduction in the proportion of female youth 

working and not in school , with a concomitant (though not statistically significant) increase in the 

proportion attending school (exhibit D7.1). Consistent with these findings, girls in the treatment 

groups perceived their chances of going to college and getting a well-paying, stable job as much 

higher than their control counterparts (exhibit E6.4). These findings are also consistent with the 

positive effects on girls’ mental health and criminal behavior reported above. 

At the time they were randomly assigned, the MTO adult sample members had very high rates of 

public assistance receipt and average incomes well below the poverty line. Approximately threefourths of the sample members were receiving AFDC at baseline, and four out of five were receiving 

food stamps (exhibit 8.2). ). Further, nearly all sample adults (94 percent) had received AFDC at 

some point. 

Average income was about $9,300 at baseline, well below the poverty line for a family of three. 

Median income was still lower, approximately $7,800. These results show that sample members were 

quite disadvantaged when they entered the MTO demonstration. 

Four to seven years later, the AFDC/TANF receipt rates had fallen by half across the entire sample. 

Less than 30 percent were receiving welfare benefits, although 46 percent were still receiving food 

stamps. Forty-five percent of the sample adults were working and off TANF in 2001. These figures 

did not differ among the randomly assigned groups. The only significant impacts of MTO on receipt 

of transfer payments were small increases in the receipt and amount of AFDC/TANF and/or food 

stamps benefits during portions of the followup period for each group (exhibits 8.4 to 8.7). 

At the time of the interim evaluation survey, average household income was about $15,500. Twothirds of the sample had incomes below the poverty level and one-third of these households had 

incomes below 50 percent of the poverty level. Eleven percent of the sample households had 

experienced food insecurity with hunger in the prior 6 months. Participation in MTO did not affect 

incomes or food security in either of the treatment groups (exhibit 8.8). 

154 Chapter 9 – Summary and Implications of the Estimated Impacts of MTO 
Assessing the Impact Estimates 

In Appendix G we examine several questions related to the size and statistical significance of the 

estimated impacts of MTO, and the likelihood that more and larger impacts will be observed in the 

future: 

• Do the findings provide evidence of real effects on family outcomes? 

• Were the estimated effects of MTO large enough to be relevant for policy? 

• Might the study have missed some effects that were large enough to be relevant for policy? 

• How different would the results have been, if the families who moved with program vouchers had 

stayed in low-poverty areas longer? 

• Can larger effects be expected in the longer term? 

In this section, we summarize the conclusions of those analyses. 

Do the findings provide evidence of real effects on family outcomes? 

As indicated in the previous section, a number of the estimated effects of MTO were statistically 

significant. For an individual estimate, statistical significance at the .05 level means that the chance of 

obtaining an estimate that large or larger by chance alone is less than 5 in 100. This is generally 

regarded as a low enough risk of a false positive to be disregarded and to treat the estimate as 

convincing evidence of a real effect. But when large numbers of estimates are derived, the likelihood 

that some of them will exceed the .05 significance threshold by chance alone is substantially higher. 

If, for example, we derived 100 estimates, we would expect 5 of them to be significant by chance 

alone. The question therefore arises: given the number of estimates presented here, how much 

credence should be placed in those that were statistically significant? 

Unfortunately, there is no simple answer to this question. For any given number of estimates, the 

number that would be expected to be significant by chance alone is easily calculated. But, as with any 

expected value, the actual number of false positives in any given sample can be greater or less than 

the expected value. If the impacts are correlated across outcomes, the actual number of false positives 

can be substantially greater or less than the expected number. Further, the number of statistically 

significant estimates presented in the report is affected by the apparent significance of the summary 

measure for a domain, such as when we present the components of the risky behavior index and not 

the delinquency index in Chapter 5, because the former index has a significant treatment effect and 

we explore its components in greater detail. Nevertheless, we believe that there is some information 

to be gained by examining the numbers and patterns of statistically significant estimates across the 

domains and subdomains analysed. 

In several domains, for example, the number of statistically significant estimates is actually less than 

would be expected by chance alone. These include the employment and earnings domain (one 

Chapter 9 – Summary and Implications of the Estimated Impacts of MTO 155 
significant estimate out of 46), the subdomain of educational performance (two significant estimates 

out of 58), and the subdomain of household income, food security, and self-sufficiency (zero 

significant estimates out of 18).101 In these cases, it is clear that the interim evaluation provides little 

or no evidence of real effects of MTO on the outcomes of interest. Conversely, there are some 

domains and subdomains where the number of significant estimates substantially exceeds the number 

that would be expected by chance alone. These include the housing conditions and housing assistance 

domain (17 significant outcomes out of 24) and the subdomain of school characteristics (16 

significant estimates out of 24). It seems unlikely that this many estimates would be significant by 

chance alone.102 

The situation is less clear in the remaining domains—health; delinquency, crime, and risky behavior; 

and public assistance. In these domains, about 15 percent of the estimates were statistically 

significant, as compared with the 5 percent that would be expected by chance alone. This could 

simply reflect sampling variability within this particular sample. We leave it to the reader to assess 

the validity of these estimates. 

Were the estimated effects of MTO large enough to be relevant for policy? 

As detailed in the previous section, MTO had statistically significant effects on a number of 

outcomes. Even if we accept these estimates as evidence of real effects of MTO, that does not 

necessarily mean that they are large enough to be of practical significance. To assess the importance 

of these effects for policy, in appendix G we compare these estimated impacts with the mean 

outcomes that would have been experienced in the absence of the demonstration by families who 

leased up with program vouchers, which we term the counterfactual (exhibit G.1).103 This comparison 

provides a measure of the relative size of the change in these families’ lives caused by MTO. 

Although the answer to this question is necessarily judgmental, we conclude that virtually all of the 

statistically significant impacts of MTO were substantial enough to be important for policy. Some are, 

in fact, quite large, especially in the experimental group. For example, among adults in the 

experimental group who moved with program vouchers, MTO increased the proportion who felt safe 

at night by two-thirds and the fraction who rated their housing good or excellent by 40 percent, while 

reducing the proportion who saw drugs being sold in their neighborhood by nearly 60 percent. The 

impacts on these measures in the Section 8 group were only about half as large, but still substantial. 

101 These counts, and those given below, are for the ITT estimates presented in the text. 

102 It can be shown, for example, that the probability of obtaining 16 significant estimates out of 24, as we did 

in the school characteristics subdomain, when there are no true impacts on these outcomes, is less than 

.075, regardless of the correlation among the impacts. 

103 Note that the counterfactual is different from the control group mean. It is our best estimate of the mean 

outcome that would have been experienced in the absence of the intervention by the subgroup of treatment 

group members who leased up with program vouchers. It is computed by subtracting the estimated impact 

on this group (i.e., the TOT impact) from their actual mean outcome. 

156 Chapter 9 – Summary and Implications of the Estimated Impacts of MTO 
The 11 percentage point reduction in obesity among adults in the experimental group represents more 

than a 20 percent reduction in the obesity rate for this group. Similarly, MTO reduced the 

psychological distress index by 20 percent among adults. 

The effects on youth mental health were similarly large. MTO reduced the rate of generalized anxiety 

disorder by over two thirds—relative to what it would have been in the absence of the 

demonstration—for girls who moved with program vouchers in both the experimental group and the 

Section 8 group and for youth overall in the Section 8 group. 

In the delinquency and risky behavior domain, both the favorable effects for girls and the unfavorable 

effects for boys were also quite substantial, relative to the counterfactual.104 MTO reduced the rate of 

arrests for violent crimes by two-thirds among girls in the Section 8 group who moved with program 

vouchers, and reduced marijuana use and smoking by about half in the experimental group. Among 

boys who moved with program vouchers in the experimental group, MTO increased the behavior 

problem index by two-thirds, tripled the rate of arrests for property crimes, and quadrupled the 

incidence of smoking. Among boys in the Section 8 group, the demonstration raised the behavior 

problem index by a third, nearly doubled the arrest rate for property crimes, and tripled the proportion 

who smoked. 

Might the study have missed some effects that were large enough to be relevant for policy? 

The ability of this evaluation to detect the effects of MTO was limited by the size of the 

demonstration sample and the proportions of the experimental and Section 8 groups who leased up 

with program vouchers. In comparing the minimum effects detectable with this sample to the 

counterfactual for families who leased up with program vouchers (exhibit G.2), we found that the 

MTO impact estimates are sufficiently imprecise that we may have missed some impacts that are 

large enough to be relevant for policy, but not large enough to pass the test of statistical significance. 

To further investigate this possibility, we examined the 95 percent confidence intervals around the 

estimated impacts—i.e., the range within which the estimated impact can be expected to fall 95 

percent of the time, in repeated sampling (exhibit G.4). The more imprecise the estimate, the wider 

the confidence interval will be and the greater the chance that an impact that is relevant for policy will 

not be detected as statistically significant. Again we found that for about two-thirds of the outcomes 

for which insignificant impact estimates were obtained, the estimates were sufficiently imprecise that 

there is a non-negligible chance that the true impact was large enough to be relevant for policy. 

How different would the results have been if the families who moved with program vouchers 

had stayed in low-poverty areas longer? 

The vouchers issued to families in the experimental group were only valid in census tracts with 

poverty rates below 10 percent in 1990, but these families were only required to stay in such areas for 

As explained earlier, as used here, the counterfactual is our best estimate of the outcome that would have 

been experienced in the absence of the intervention by the subgroup of treatment group members who 

leased up with program vouchers. It is computed by subtracting the estimated impact on this group (the 

TOT impact) from their actual mean outcome. 

Chapter 9 – Summary and Implications of the Estimated Impacts of MTO 157 

104 
one year to keep the voucher, and many of them moved elsewhere after the first year. Many moved 

initially to areas where the poverty rates rose between 1990 and 2000, so that even if they stayed in 

their initial locations, they were not necessarily in a low-poverty area at the time of the interim 

evaluation. And, of course, families in the Section 8 group were not constrained in where they could 

use the voucher. 

As a result, even those families in the experimental group who moved with program vouchers spent, 

on average, only about 20 percent of the followup period in areas with poverty levels below 10 

percent, and only about 60 percent of the followup period in areas with poverty rates below 20 

percent (exhibit 2.5). At the same time, many families in the control group left public housing and 

moved to areas with lower poverty rates. On average, control families spent about 11 percent of the 

followup period in areas with poverty rates below 20 percent (exhibit 2.5). 

These are substantial differences in the proportion of time spent in low-poverty areas, and one might 

reasonably expect them to result in a number of positive effects for the families who moved with 

program vouchers. But the MTO demonstration was not a pure test of the effects of living in lowpoverty areas versus living in public housing in high-poverty areas, even for families in the 

experimental group. Therefore, we cannot infer those effects from these results with any confidence. 

However, we can obtain some suggestive evidence on this question by comparing the results for the 

experimental group and the Section 8 group. In appendix G, we used the estimated impacts for these 

two groups, and the proportions of the followup period that they lived in low-poverty areas, to 

extrapolate the effects of MTO to a hypothetical group living continuously in low-poverty areas 

(exhibit G.5). Using several different extrapolation methods, we found that the impacts of living 

continuously in low-poverty areas might be much more substantial than those observed in the 

demonstration. However, the results were quite sensitive to the specific form of the extrapolation and 

for a few outcomes, this method predicted smaller effects for a group that spent the entire followup 

period in low-poverty areas. 

Can larger effects be expected in the longer term? 

This is an interim evaluation. A final evaluation of the MTO demonstration is planned in roughly 5 

years—9 to 12 years after random assignment. One potential reason why impacts were not observed 

for some outcomes is that those impacts have not yet had time to develop. If that is the case, we might 

expect the final evaluation to find more and larger impacts. 

The existing literature provides little guidance on this question because few studies follow their 

samples for more than 5years. The most relevant precedent is Rosenbaum (1991), who compared 

families in the Gautreaux program who moved to the Chicago suburbs with those who moved to other 

housing within the city of Chicago. That study found that, 1 to 6 years after the move, many children 

in the families who moved to the suburbs “were still struggling to catch up, and it was not clear if 

they would succeed.” Seven years later Rosenbaum found substantial, statistically significant impacts 

on eight of nine education- and employment-related outcomes for the same children. Largely on the 

basis of these findings, MTO was designed to have a 10-year followup. 

There are strong theoretical reasons why it may take many years for the full effects of neighborhood 

to manifest themselves. Developmental outcomes like educational performance almost certainly 

reflect the cumulative experience of the child from an early age. Children who spend their first 10 

158 Chapter 9 – Summary and Implications of the Estimated Impacts of MTO 
years in an environment that does not facilitate educational achievement may never fully overcome 

that disadvantage, even if they then move to an environment that supports educational 

achievement.105 In the interim evaluation, the youth sample is composed of children who moved out 

of public housing at ages 5 to 15. In the final evaluation the youth sample will have left public 

housing at birth to age 10. These youth will have spent a much larger proportion of their formative 

years outside the concentrated poverty of public housing. Therefore, they may show much greater 

gains in educational achievement and other developmental outcomes. 

It is also true that the move from high-poverty areas to lower poverty neighborhoods is likely to be 

disruptive and require some adjustment period during which positive behavioral effects may not 

appear and, in fact, negative effects may be observed. There is some evidence of such transitional 

effects in the negative behavioral effects observed for male youth in the interim evaluation. If these 

effects indicate that the first 4 to 7 years after random assignment has been an adjustment period for 

these youth, we may observe different impacts in the longer term once that transition is complete. 

We cannot, of course, predict the impacts that will be observed 5 years after our data were collected. 

We can, however, examine the interim findings for evidence that impacts are related to time since 

random assignment. We can do that in several ways. 

The most direct evidence on this question is provided by the time path of impacts on those outcomes 

for which we have longitudinal data over the entire followup period—the employment, earnings, and 

public assistance outcomes measured with administrative data. Examining the impacts in years 1 and 

2 and years 3 and 4 after random assignment for each of the main outcomes measured with these data 

(exhibit G.6), we found at least modest support for the hypothesis of more favorable effects over time. 

For outcomes measured in the survey, we have no way to estimate impacts at different points in time 

after random assignment for a given sample. We considered comparing the impacts for those who 

were randomly assigned early, and therefore have been exposed to the treatment longer, with those 

who were randomly assigned later. If there were no systematic differences between these two 

assignment cohorts unrelated to duration of exposure to the intervention, this comparison would 

measure the difference in impacts resulting from a difference in exposure to the treatment of roughly 

19 months. As a check for such differences between the early and late random assignment cohorts, we 

examined the estimated impacts within each of the two cohorts on outcomes measured with 

administrative data in the fifth year after random assignment (Exhibit G.7). If the two cohorts are 

similar, they should show similar impacts in a given year after random assignment. In fact, there were 

large differences in the impact estimates between the two cohorts. We take these results to indicate 

that we cannot interpret differences in impacts between the two cohorts as indicative of the effect of 

length of exposure to the treatment.106 

105 In “The Culture of Poverty”, Oscar Lewis argued that, “By the time slum children are six or seven, they 

have usually absorbed the basic values and attitudes of their subculture and are not psychologically geared 

to take full advantage of changing conditions or increased opportunities that may occur in their lifetime” 

(Lewis, 1968). 

106 In appendix G, we discuss some reasons why families assigned early in the demonstration might differ 

from those assigned later. 

Chapter 9 – Summary and Implications of the Estimated Impacts of MTO 159 
Summary assessment of impact estimates 

Overall, the assessment of the impact estimates presented in this section suggests that: 

• The findings do provide convincing evidence that MTO had real effects on the lives of 

participating families in the domain of housing conditions and assistance and on the 

characteristics of the schools attended by their children; 

• There is no convincing evidence of effects on educational performance; employment and 

earnings; or household income, food security, and self-sufficiency. 

• The statistically significant impact estimates are uniformly large enough to be relevant for policy. 

In fact, many are quite large. 

• Given the size of the interim evaluation sample and the leaseup rates in the two treatment groups, 

the impact estimates are sufficiently imprecise that some true impacts that are large enough to be 

relevant for policy may not have been detected as statistically significant. 

• Although MTO induced substantial differences in the proportion of time spent in low-poverty 

areas by the three assignment groups, it was not a pure test of the effects of living in low-poverty 

areas versus living in public housing in high-poverty areas, even for the families in the 

experimental group who moved with program vouchers. Extrapolating the effects of living 

continuously in low-poverty areas might be more substantial than those observed in the 

demonstration. However, our ability to measure those effects quantitatively is limited. 

• There is at least modest evidence that the impacts of the demonstration are becoming more 

favorable over time, at least for public assistance, which was the only outcome for which we were 

able to estimates effects over time. If this holds for other outcomes, we might expect more and 

larger impacts in the final evaluation 10 years after random assignment. 

Implications of the Interim Evaluation Results for Policy 

The interim findings allow us to address three fundamental questions related to policy with respect to 

low-income families in public housing: 

• What social benefits and costs accrue as a result of moving low-income families out of public 

housing projects in high-poverty areas into private housing, and how do those benefits differ 

between policies that restrict such moves to low-poverty areas and those that do not? 

• How effective is policy likely to be in changing the environment of low-income families? 

• What do the interim results have to say about alternative approaches to improving the lives of 

low-income families? 

160 Chapter 9 – Summary and Implications of the Estimated Impacts of MTO 
The social benefits and costs of moving low-income families out of public housing in distressed 

neighborhoods into private housing 

While we have not attempted to conduct a formal benefit-cost analysis, the interim evaluation results 

provide relatively clear evidence of the main social benefits and costs of MTO. From the families’ 

perspectives, the principal benefit of the move was a substantial improvement in housing and 

neighborhood conditions. Families who moved with program vouchers largely achieved the single 

objective that loomed largest for them at baseline: living in a home and neighborhood where they and 

their children could feel and be safe from crime and violence. They not only reported feeling safer but 

were also less likely to be victims of crimes. On a whole list of observable characteristics —from 

plumbing problems and peeling paint or wallpaper to litter, graffiti, and public drinking in the 

streets—their homes and neighborhoods were substantially more desirable than those where controls 

lived. These benefits accrued to families in both the experimental group and the Section 8 group, 

although the improvements tended to be roughly twice as large for experimental group families, who 

were required to move to low-poverty areas, at least initially. 

Perhaps not surprisingly, these improvements in living environment led to significant gains in mental 

health among adults in the experimental group. The level of psychological distress was substantially 

reduced in this group. In addition, adults in both the experimental and Section 8 groups experienced 

substantial reductions in obesity for reasons we do not yet understand. 

Among the children in these families, girls appear to have benefited from the move in several ways. 

They experienced improved psychological well-being, reporting lower rates of psychological distress, 

depression, and generalized anxiety disorder, and improved perceptions of their likelihood of going to 

college and getting a well paid, stable job as an adult. These girls’ behaviors changed as well, with a 

smaller proportion working instead of attending school. They were less likely to engage in risky 

behavior or to use marijuana. Finally, both these girls and society as a whole benefited from a reduced 

number of arrests for violent crimes. 

The principal social costs that must be offset against these benefits are the costs of the MTO mobility 

counseling, any increased costs due to the greater likelihood of receiving housing assistance among 

those who leased up with program vouchers, and an increase in the rate of behavior problems, 

smoking, and arrests for property crimes among boys ages 15 to 19. 

We cannot place values on these social costs and benefits. Policymakers will have to decide whether 

the gains of this kind of policy outweigh the costs. But the interim evaluation has demonstrated that 

there are substantial social benefits as well as some costs associated with facilitating the movement of 

public housing residents who desire to move to low-poverty areas. 

How effective is policy likely to be in changing the environment of low-income families? 

One of the clearest messages of the interim evaluation results is that housing-related policy can 

influence, but it cannot dictate, the residential location of low-income families. As noted above, the 

demonstration reduced by half the proportion of the followup period spent in areas of concentrated 

poverty by families in the experimental group who moved with program vouchers. Among Section 8 

families who moved with programs vouchers, the proportion of time spent in areas of concentrated 

poverty was cut by about a third (Exhibit 2.9). MTO increased the proportion of time spent in areas 

Chapter 9 – Summary and Implications of the Estimated Impacts of MTO 161 
with poverty rates below 20 percent by 53 percentage points among families in the experimental 

group. 

Of course, policies designed to move low-income families from public housing in high-poverty areas 

to private housing in low-poverty areas need not take the form of location-restricted vouchers like 

those used in MTO. One might, for example, incorporate mobility counseling or other supports for 

moving to low-poverty areas into the regular voucher program. One could also create goals and 

performance incentives for program administrators to encourage moves to opportunity areas, and one 

can create or preserve both assisted and affordable housing in low-poverty area through decisions 

with respect to state agency refinancing policies, allocations of Low Income Housing Tax Credit 

(LIHTC), use of HOME funds, public housing authority (PHA) project-basing of vouchers, and other 

existing housing programs and policies. These policies seem likely to have effects on the residential 

location of low-income families that would fall somewhere between those of the MTO experimental 

and Section 8 groups. Somewhat stronger effects on residential location might be achieved by 

coupling locationally restricted vouchers with ongoing supports and/or counseling for families that 

move to low-poverty areas. 

One of the lessons of the MTO demonstration is that the poverty rate, while important, may be an 

overly simplistic way to characterize neighborhoods. Residential environments are multidimensional, 

and no one measure will capture all the attributes that are important to the life chances of low-income 

families. Thus the fact that a majority of the program movers in the experimental group moved to 

areas with low, but rising, poverty rates may have had an important effect on their subsequent 

outcomes. Similarly, even in the experimental group, a large proportion of those who moved with 

program vouchers stayed within the city; this meant that their children attended schools in the same 

school system as control children. This almost certainly limited the improvement in school quality 

they experienced as compared with a move to the suburbs. Moreover, the low-income areas to which 

families in the experimental group moved were still heavily minority. To the extent that racial 

integration has a positive influence on any of the outcomes analyzed here, that influence was largely 

absent in this demonstration. These considerations suggest that policymakers seeking to improve the 

environment of poor families may want to consider other characterizations of neighborhood than that 

provided by the poverty rate alone. 

When thinking about the implications of these results for policy, it is important to recognize that all of 

the impacts presented here are measured relative to a control group receiving some mix of existing 

housing subsidies. Some control families eventually received regular Section 8 vouchers, some 

continued to live in public housing, and some left housing assistance altogether. Indeed, some control 

group members were unable to remain in public housing because their units were demolished under 

HOPE VI or other revitalization programs. We did not attempt to eliminate the influence of these 

changes in control circumstances—including the receipt of Section 8 vouchers—from the estimates. 

Rather we view the results as measures of the incremental effects of offering vouchers, with or 

without locational restrictions, to residents of public housing in areas of concentrated poverty, during 

the particular period encompassed by the study. These findings answer this question: How much 

better off are the recipients of the demonstration vouchers than families who started out in the same 

situation and who received no help from the demonstration? This means that the estimates from this 

study are not applicable to all types of policy. For example, for a policy that replaces public housing 

with vouchers, the appropriate control benchmark would probably be continued residence in public 

162 Chapter 9 – Summary and Implications of the Estimated Impacts of MTO 
housing. That is not what was tested here—indeed it probably cannot be tested—and the results of the 

present test probably understate the effects that would be expected from such a policy. 

What do the interim results have to say about alternative approaches to improving the lives of 

low-income families? 

The most fundamental question addressed by MTO is, to what extent are the problems encountered 

by public housing residents the result of the high concentration of poor families in those 

developments and the surrounding neighborhoods and to what extent are they caused by attributes of 

the families themselves? To the extent that these problems are environmental, the appropriate policy 

response is to foster dispersion of these families to more positive environments. To the extent that 

these problems reflect family characteristics—such as lack of education, limited work experience, or 

membership in a group that faces discrimination—the appropriate policy response is to address these 

characteristics directly. 

By the final evaluation, the effects of environment will have had more time to manifest themselves. 

At this point we can say that some of the problems of public housing residents do appear to be 

environmental. These include the housing and neighborhood quality deficiencies and psychological 

and behavioral problems on which MTO had significant effects. 

It remains to be seen whether the problems of physical health, educational performance and 

attainment, employment, earnings, and welfare dependence that characterize public housing residents 

are amenable to housing policies designed to change their residential environment. At this point, there 

is no evidence that they are. If that finding is confirmed in the final evaluation, that would suggest 

that policies designed to deal directly with these specific problems—educational improvements, 

employment and training, or welfare-to-work policies—will be more effective solutions. 

Issues for Further Research 

The interim evaluation has addressed a wide range of questions about the impacts of the MTO 

demonstration. In the course of this analysis, other questions have been raised, and some questions 

were deemed outside the scope of this analysis. Some of these outstanding questions will be 

addressed in the long-term analysis, to be conducted approximately 9-12 years after random 

assignment. In the meantime, HUD plans to make the data used in the interim evaluation available to 

other researchers, through an application process that will allow controlled access to the data, to begin 

to address these questions. Among the issues that might be fruitfully addressed in such analyses are: 

• What are the mechanisms underlying the significant impacts found in the interim analysis? 

• Were impacts larger for families who stayed longer in low-poverty areas? For families who 

moved to non-minority areas or areas where the poverty rate was decreasing? 

• Why were impacts on delinquency, criminal activity, and risky behavior consistently beneficial 

for girls and adverse for boys? 

Chapter 9 – Summary and Implications of the Estimated Impacts of MTO 163 
• What are the linkages among residential location, schools, school performance, and behavior for 

girls and boys? 

• Are child outcomes (health, delinquency, school performance) better for children where adults 

experienced improved physical or mental health? 

• Why are the impacts on public assistance outcomes substantially different for families who were 

randomly assigned early in each site than for those assigned later? 

Answers to many of these questions will require nonexperimental methods. It will be important that 

the researchers conducting those analyses be sensitive to the risk that the results will be distorted by 

selection bias, which was, of course, the problem that initially prompted the MTO demonstration. 

164 Chapter 9 – Summary and Implications of the Estimated Impacts of MTO 
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Appendix A 

Data Collection Sources and Methods 

Appendix A 

Data Collection Sources and Methods 

The MTO Interim Evaluation collected data from a myriad of sources to cover all of the study’s 

domains and to produce a rich data set on the status of the sample in 2001–2002. The study employed 

two modes of participant data collection—quantitative and qualitative. Data about sample members 

were also collected from administrative agencies. In addition, information was drawn from published 

data sets, such as the U.S. Census Bureau, the National Center for Education Statistics Common Core 

of Data on schools, and HUD data on Section 8/Housing Choice Voucher Fair Market Rents. Also 

data were drawn from the history of address data stored in the MTO data system since the 

demonstration started in 1994. This appendix presents a summary of the interim evaluation data and a 

description of the process of implementing each aspect of the data collection. 

The data collected for this evaluation came from three source types: primary data collection by Abt 

Associates, administrative data collected from HUD, and administrative data collected from other 

sources. The specific types of data discussed in this appendix include the following: 

3⁄4 Data collected on participants using primary data collection methods 

• Qualitative data. 

− Indepth interviews with adults. 

− Indepth interviews with youth. 

− Neighborhood observations. 

• Quantitative survey data. 

− Interim survey of households. 

− Interim survey of youth.

− Interim survey of children. 

− Achievement testing of youth. 

− Achievement testing of children. 

− Direct measurement of height and weight for children. 

− Household and neighborhood observations. 

• Location data (address histories). 

3⁄4 Administrative data collected from HUD. 

• Multifamily Tenant Characteristics System (MTCS) data. 

• Tenant Rental Assistance Certification System (TRACS) data. 

• Fair Market Rent (FMR) data. 

• Public housing authority (PHA) Expenditure data. 

3⁄4 Administrative data collected from other secondary data sources. 

• Social Security Number Verification through the Social Security Administration. 

• State Temporary Assistance to Needy Families (TANF) and food stamp (FS) data. 

Appendix A – Data Collection Sources and Methods A-1 
• State Unemployment Insurance (UI) data. 

• School data from the National Center for Education Statistics (NCES). 

• School data from State, county and city boards of education. 

• Census data. 

• Adult and juvenile arrest and criminal disposition data. 

Qualitative Participant Data Collection 

The main purpose of the qualitative interviews was to explore the question of how neighborhood 

mechanisms may influence outcomes for participants. Qualitative interviews were later integrated 

with survey data to help enrich our understanding of the experiences of MTO families since their 

initial moves from public housing. 

The qualitative data collection had three components: 

• Indepth interviews with adults. 

• Indepth interviews with youth. 

• Neighborhood observations. 

Qualitative interviews were completed with a total of 58 adults and 39 youth. The indepth interviews 

with adults took 1 to 2 hours, and those with youth lasted approximately 45 minutes. To provide a 

complete picture of the families’ experiences, we sampled both movers and nonmovers. Movers were 

also sampled based on neighborhood poverty rates. The qualitative data collection took place between 

February and March 2001. 

For more information on the qualitative study data collection, refer to Families in Transition: A 

Qualitative Analysis of the MTO Experience (Popkin et al., December 2001). 

Quantitative Participant Data Collection 

For this evaluation, Abt Associates interviewed 8,870 respondents. Of these, 3,526 were adult heads 

of household and the remainder, children. Quantitative data were collected for up to three sample 

members per household, the adult respondent and up to two sampled children ages 5 to 19. A child’s 

age for data collection purposes was uniformly measured as of May 31, 2001.1

 The child sampling is 

described in appendix B. 

Since the field data collection continued through September 2002, this means that at the moment they were 

interviewed or tested some children were more than a year older than their age as defined for sampling. 

A-2 Appendix A – Data Collection Sources and Methods 

1 
Participant data collection involved seven main components: 

• Interim survey of households. 

• Interim survey of youth. 

• Interim survey of children. 

• Achievement testing of youth. 

• Achievement testing of children. 

• Direct measurement of height and weight for children. 

• Interviewer observations of the household and neighborhood. 

Exhibit A.1 shows the contents of the quantitative participant data collection components. Exhibit A.2 

shows the sample definitions and sizes and summarizes the survey strategy for each sample. Further 

details are provided in the text below. 

EXHIBIT A.1 

CONTENT OF PARTICIPANT DATA COLLECTED FOR THE MTO INTERIM EVALUATION 

Adulta Youth 12-19 Children 5-11c 

Survey Topics 

Housing and neighborhood 

Education and training 

Employment and earnings 

Income and public assistance 

Outlook and social networks 

Health 

Household composition 

Child educationb 

Child healthb 

Child behaviorb 

Child time useb 

MTO experience 

Education 

Employment and earnings 

Risky behavior 

Health 

Neighborhood and social 

networks 

Emotions 

Time use 

Future plans 

Education 

Neighborhood, danger, and 

risk 

Health 

Behavior and family dynamics 

Direct Measurement Blood pressure None Height and weight 

Educational Testing None Woodcock-Johnson Revised 

selected tests 

Woodcock-Johnson Revised— 

selected tests 

Notes: 

a) Adults were selected for interviewing in the following order of precedence: female head of family intending to 

move through MTO; wife of core head intending to move through MTO; wife of baseline head, if a member of the 

family intended to move through MTO; and nonfemale (male or unknown gender) head of family intending to 

move through MTO. 

b) The adult respondent was asked questions about each sampled child in the household, up to two. 

c) Surveys were administered only to sampled children ages 8 to 11. Direct measurement and educational 

testing were carried out for sampled children ages 5 to 11. 

Appendix A – Data Collection Sources and Methods A-3 
EXHIBIT A.2 

PARTICIPANT DATA COLLECTION FEATURES 

Household Head Youth Children 5-11 

Sample Size: 

One adult person per 

family enrolled in MTO 

through 12/31/1997. 

N=4,248 adults 

Sampled children ages 

12 to 19 in households 

enrolled in MTO 

through 12/31/1997 

N=3,537 youth 

Sampled children 

ages 5 to 11 in 

households enrolled 

in MTO through 

12/31/1997 

N=3,146a

children 

Weighted Response 

Rate:b 

89.6% 89.0% 87.4% 

December 2001–June December 2001–June December 2001–June 

2002 for the main 2002 for the main 2002 for the main 

Field Period: sample; July– 

September 2002 for 

subsample 

sample; July– 

September 2002 for 

subsample. 

sample; July– 

September 2002 for 

subsample. 

Mode of Data Inperson with some Inperson with some Inperson with some 

Collection: telephone telephone telephonec 

Notes: 

a) Of the 3,146 children ages 5 to 11, a total of 2,202 were between 8 and 11 and the remainder were between 5 

and 7. 

b) To increase the response from hard-to-find cases and reduce nonresponse bias, data collection continued on

a subsample of cases. These cases were then weighted when calculating the final response rates. 

c) Only children ages 10 and 11 were allowed to complete an interview by telephone. 

Field interviewers from Abt Associates interviewed one adult per household for the interim survey of 

households. In most instances, the adult interviewed was the head of the household that joined MTO 

and also the primary caregiver for the children of interest to the study.2

 The interim survey of youth 

was carried out with sampled children ages 12 to 19 in each household. The interim survey of 

children was administered to sampled children aged 8 to 11. The next two components involved 

direct administration of standardized achievement tests to all sampled children ages 5 to 11 and 

sampled youth aged 12 to 19. The final component required interviewers to weigh and measure 

children ages 5 to 11 following specific measurement procedures. 

Data collection components 

The interim survey of households. The interim survey of households consisted of a 65-minute 

interview with one adult per core MTO household. This adult was usually the female head of the 

MTO core family, as defined by the applicant during the Section 8 eligibility determination process. 

Of the 4,248 households in the interim evaluation sample, two parents were present at the time of MTO 

enrollment in 108 households. In these instances, we interviewed the mother (unless she was deceased). 

Typically, the mother had completed the baseline survey and was more likely to be the primary caregiver 

for the children. 

A-4 Appendix A – Data Collection Sources and Methods 

2 
The respondent was asked questions about a wide range of topics across the study domains: housing 

and mobility, employment and earnings, income and public assistance, health, and risky behavior. 

When the adult respondent was the primary caretaker of children in the household, she or he was 

asked a series of questions about the health, education, and social behavior of the sampled children. 

Additional survey modules covered household composition (similar to the annual MTO canvass) and 

reactions to the MTO experience. 

The interim survey of youth. The interim survey of youth was administered to sample children 

between the ages of 12 and 19. The youth survey averaged about 30 minutes in length. It covered 

topics including attitudes toward school, ties to the neighborhood, involvement in afterschool and 

community activities, health, and risky behavior. Because the surveys were administered using laptop 

computers, youth were offered the opportunity to self-administer sections of the survey that involved 

sensitive topics. A small number of youth took advantage of this option. 

The interim survey of children. The interim survey of children was administered to sampled 

children ages 8 to 11. The child survey took 15 to 20 minutes to complete and focused on school, 

health, friends, the neighborhood, and family support. 

Educational achievement testing for children and youth. Sampled children ages 5 to 19 were 

asked to complete a series of educational achievement tests, part of the Woodcock-Johnson Revised 

(WJ-R) battery. The testing was expected to average 30 minutes in length for those under 8 and 45 

minutes in length for those 8 to 19. Late in the data collection period, interviewers were asked to 

record the start and end times for each of the individual tests. The average time spent on the WJ-R 

testing for youth and for children ages 5 to 11 was about 90 minutes. In both groups, each of the math 

tests took at least three minutes longer than the language tests. 

Direct measurement of blood pressure, height, and weight. In conjunction with the survey data 

collection, field interviewers took direct measurements of blood pressure for adult respondents and of 

height and weight for child respondents (ages 5 to 11). These measurements were taken in the home, 

using appropriate and up-to-date equipment, by interviewers trained in these procedures. 

Housing and neighborhood observations. The final components of the household-level data 

collection were interviewer observations of the household and neighborhood. The household 

observation form had several questions about the interaction between the head of household and the 

selected sample children. It is important to note that this aspect of the data collection could only be 

done if the sample child was present at the time of the household respondent’s interview. There were 

also a few questions about the interviewers perception of how well the respondent understood the 

questions. 

Upon leaving the household, the interviewer was instructed to complete the neighborhood observation 

form. This form collected information about the neighborhood condition—physical appearance, land 

use, and the overall climate. If interviewers felt that a neighborhood was unsafe they were not 

required to visit the neighborhood just to complete the observation form. 

Appendix A – Data Collection Sources and Methods A-5 
Participant data collection procedures 

Abt Associates designed data collection procedures to coordinate the various parts of this effort. The 

three surveys—household, youth, and child—were administered in person by trained interviewers 

using the Bellview Computer-Assisted Personal Interviewing (CAPI) system on a laptop computer. 

The surveys for all three samples were administered primarily in the respondent’s homes, with the 

session scheduled at the respondent’s convenience. Conducting most of the youth interviews in the 

home was a change from the original data collection protocol, which had called for the majority of the 

youth interviews to be done in teen centers. These teen center interviews were planned to take place 

in community facilities where small groups of teens would be invited to come and complete the 

interviews and testing sessions one-on-one with an interviewer. It was thought that the youth would 

be more likely to attend interview sessions with peers and would feel more comfortable responding to 

questions of a sensitive nature outside their homes. In addition, this approach would be more costeffective than inhome interviews, because many could be done simultaneously. 

A pilot test of the teen center approach was conducted in Boston at the end of November 2001 to 

assess whether to collect the youth data in this manner. Field management reported that the teen 

centers for the pilot took hours to arrange and staff. Also, after the first three pilot sessions the 

numbers of broken appointments increased, so the sessions produced almost no completed interviews. 

Therefore, the decision was made to switch to inhome interviewing. 

Data collection implementation. To conduct the extensive participant data collection effort, Abt 

Associates held a 2-week training in Golden, Colorado, for 5 field managers and more than 100 

interviewers between October 22 and November 2, 2001. The five field managers (one for each of the 

original MTO sites) reported to the national field manager, who supervised this project closely. The 

training covered all aspects of the data collection, including: 

• Administration of all survey instruments. 

• Completion of the interviewer observation forms. 

• Conducting educational testing with the WJ-R battery. 

• Taking direct measurements of height, weight, and blood pressure. 

• Use of the CAPI software and the laptop computers. 

• Transmission of electronic data to Abt Associates. 

CAPI testing and revisions delayed the field period start until the end of December 2001. Data were 

collected from sample members in two phases: the main sample phase, in which all 10,932 sample 

cases were worked, and the subsample phase, in which a subset of the remaining cases was worked. 

The full sample was worked from late December 2001 through June 2002. At that time, it was 

decided to concentrate the remaining data collection resources on a 3-in-10 subsample to maximize 

the final response rates and reduce nonresponse bias. Subsample data collection began in early July 

and continued through mid-September 2002.3

 Exhibit A.3 shows the field period by site for the main 

and subsamples. 

For more information about the subsample, refer to appendix B. 

A-6 Appendix A – Data Collection Sources and Methods 

3 
EXHIBIT A.3 

FIELD PERIOD BY MTO SITE 

Full Sample Subsample (and Remote Cases) 

Start End Start End 

Baltimore Late December 2002 June 22, 2002 June 29, 2002 September 15, 2002 

Boston Late December 2002 July 6, 2002 July 13, 2002 September 15, 2002 

Chicago Late December 2002 July 6, 2002 July 13, 2002 September 15, 2002 

Los Angeles Late December 2002 June 22, 2002 June 29, 2002 September 15, 2002 

New York Late December 2002 July 6, 2002 July 13, 2002 September 15, 2002 

Maximizing Response Rates. To maximize response rates, Abt Associates used several different 

approaches. First, in sites with lower response rates, Abt sent a team of expert interviewers for a week 

to conduct a blitz. This allowed the field manager to review carefully the outstanding sample, target 

particular areas that had clusters of sample, and then send a team out to interview them. Sample 

shuffling was also very helpful, particularly in converting refusals. Often, field managers found that 

assigning cases to a different interviewer resulted in a completed interview. 

Remote cases (those not within 50 miles of the 5 MTO cities) were only worked during the subsample 

period. But beginning in mid-April 2002 (about 6 weeks before the subsample work began) 

interviewers were authorized to conduct adult and youth interviews over the phone, especially for 

cases that would require travel. This allowed them to obtain at least a partial complete, although the 

educational testing, direct measurement, and interviewer observations could not be carried out. 

During the subsample period, interviewers traveled to all out-of-state cases that were in the 

subsample. Cases were completed in 29 states, Puerto Rico, and the Dominican Republic. 

To show our appreciation for the time they spent to complete the data collection, all respondents were 

given an incentive payment. Adults who completed the household interview were given a $50 money 

order. Younger children (ages 5 to 11) were given a small gift, and the parent/guardian was given a 

$25 money order for each child between the ages of 5 and 11 who completed the survey and 

achievement testing. The older children who completed the youth survey (those between the ages of 

12 and 19) were given a $50 money order for completing their surveys and testing. During the 

subsample period, interviewers were authorized to offer double incentives for those respondents who 

had refused to participate. This increased incentive was very helpful in converting refusals into 

completed interviews. 

The overall response rate for the household survey (after weighting the subsample data to represent 

all the cases eligible for subsampling) was 89.6 percent. The response rates for each aspect of the 

participant data collection are shown in exhibit A.4. 

Appendix A – Data Collection Sources and Methods A-7 
Appendix A – Data Collection Sources and Methods 

EXHIBIT A.4 

WEIGHTED COMBINED RESPONSE RATES FOR KEY INSTRUMENTS 

Total Sample 

Number of Completed Cases By Sample: 

Effective 

Response Rate 

(RR)a 

RR A

Dea

MAIN SUB SUM 

Survey 4248 3398 128 3526 89.6% 9Household Observations 4248 3344 119 3463 87.6% 8Neighborhood Observations 4248 3281 121 3402 86.4% 8Child Survey 2202 1701 79 1780 88.8% 8Child WJ-R 2202 1683 87 1770 88.5% 8Youth Survey 3537 2693 136 2829 89.0% 8Youth WJ-R 3537 2624 135 2759 87.4% 8Young Child H/W 944 665 30 695 80.1% 8Young Child WJR 944 704 31 735 84.4% 8All WJ-R 6683 5011 253 5264 87.3% 8All Child and Youth Surveys 5739 4394 215 4609 88.9% 8

Notes: a) Response rates are measured as the number of completes divided by the total sample count. The effective response rate (RR) is cal

sample response rate (MRR) and the subsample response rate (SRR). The formula is: 

RR = MRR + SRR*(1-MRR) 

b) Deceased sample members are removed from the total sample count in this adjustment. 

c) Deceased and incapacitated sample members are removed from the total sample count in this adjustment. 

A-8 
Data processing and delivery. The data collection ended on September 15, 2002 in all sites. The 

remainder of September was spent reconciling samples, ensuring that all supplemental forms were 

entered, all respondent information booklets were returned, and all addresses were updated. The 

months of October and most of November were spent cleaning the survey data. The child survey and 

height/weight data were the first of the data sets delivered to analysts in mid- to late October. The 

final data set, containing household data, was delivered to analysts on December 5, 2002. 

Administrative Data Collection 

The MTO Interim Evaluation drew upon several administrative databases both for measuring 

outcomes and mediating factors. Exhibit A.5 summarizes the full set of administrative data sources. 

Each of the sources used is described below, after a discussion of matching methods for these data. 

Social Security Number verification 

Social Security Numbers (SSNs) have been collected from MTO sample members since the time they 

joined MTO. From time to time since random assignment, in conjunction with passive tracking of 

sample location, new numbers and other new identifiers have been gathered from various sources. As 

a result, some of the MTO sample members have multiple SSNs, shared or reversed SSNs, and/or 

alias (alternative) names in the MTO data system. 

Such situations can make determining matches to administrative data more difficult. Both to facilitate 

the matching process and ensure the reliability of the matches, Abt Associates Inc. and HUD worked 

out an agreement with the Social Security Administration to verify social security numbers of MTO 

sample members through their Employment Verification Service (EVS). The EVS results and 

information in the MTO data system were used together to select the best SSN for each sample 

member from among those collected prior to random assignment. These identifiers were then used to 

determine correct administrative data matches. 

State administrative data collection 

TANF and UI data. Abt staff, with assistance from HUD and colleagues at NBER worked with state 

agencies on data agreements for participant-level data from three programs: TANF, food stamps, and 

UI data. Progress was very slow. In some sites the process began in spring 2001 but data were not 

received until 2002. However, by March 2003 staff had received data from all sites. 

In Massachusetts (where confidentiality protections are so strict that no nongovernmental 

organization can gain data access) Abt could not obtain direct access to the Massachusetts UI data. 

We were able to coordinate this request through HUD, since the Massachusetts Department of 

Revenue agency could provide data to other governmental organizations. 

Appendix A – Data Collection Sources and Methods A-9 
EXHIBIT A.5 

ADMINISTRATIVE DATA SOURCES FOR OUTCOME AND MEDIATING FACTOR MEASURES 

Domain Outcomes Data Sources 

Employment and earnings Quarterly employment and State UI wage records 

earnings 

Delinquency and risky Arrests and court dispositions State agencies that maintain data on 

behavior criminal records 

Welfare and other transfer 

programs 

Monthly TANF, food stamp, and 

SSI benefits and exits from cash 

assistance, date of TANF time 

limit, TANF sanctions, and 

participation in welfare-to-work 

activities 

State welfare agency records 

Housing assistance Amount of housing assistance 

Receipt of housing assistance 

Multifamily Tenant Characteristics 

System (HUD) 

Tenant Rental Assistance Certification 

System (HUD) 

Domain Mediating Factors Administrative Data Sources 

Education School quality 

School resources 

U.S. Department of Education 

Common Core of Data on schools 

Crime rates for local area 

Unemployment rate 

School attendance 

Grade completion 

FBI, local police departments, Census 

2000, Bureau of Labor Statistics 

(BLS) 

Local school district Web sites and 

published data 

Employment and earnings Crime rates for local area FBI, local police departments 

Unemployment rate BLS 

Delinquency and risky School resources U.S. Department of Education 

behavior School quality Common Core of Data on schools 

SES level 

Welfare and other transfer Unemployment rate BLS 

programs Receipt of public assistance in Census 2000 

the local area 

Crime rate in the local area FBI, local police departments 

Housing assistance FMRs for local area by housing HUD 

unit size 

The original plan was to collect TANF, food stamp and UI data as far back as January 1994 (before 

random assignments in MTO began). However, two agencies could only go as far back as 2000. The 

California TANF agency could go back only to October 2000. In New York, staff could get FS data 

only as far back as October 1999 and TANF data back only to December 1996. All other sites 

provided data as far back as the beginning of 1995 (and most as far back as January 1994). 

Exhibit A.6 summarizes the periods covered by the TANF and UI data used in the analysis. 

A-10 Appendix A – Data Collection Sources and Methods 
EXHIBIT A.6 

STATUS OF STATE ADMINISTRATIVE DATA COLLECTION 

Agency Period Covered 

California UI January 1994–December 2001 

California TANF/FS October 2000–December 2001 

Illinois UI January 1994–December 2001 

Illinois TANF/FS January 1994–December 2001 

Maryland UI January 1994–December 2001 

Maryland TANF/FS January 1994–December 2001 

Massachusetts UI 4th Quarter 1994–4th Quarter 2001 

Massachusetts TANF/FS July 1994–December 2001 

New York UI January 1995–December 2001 

New York TANF/FS December 1996 (TANF), October 1999 (FS)– 

December 2001 

HUD administrative data collection 

There were four types of administrative data collected from HUD: MTCS, TRACS, FMR, and 

operating costs. 

MTCS and TRACS data. Two extracts from HUD’s MTCS system were collected, one containing 

data through May 2001 and the other containing data through December 2001. MTCS data served 

two purposes. They provided updated address data and also yielded data on certain housing outcomes. 

The address portion of these data is discussed under the Locational Analysis section of this appendix. 

In October 2002 staff received additional HUD administrative data. The MTCS files covered 1995 to 

2002, with the exception of 1999. The TRACS data covered 1995 to 2001. All of the MTCS and 

TRACS data were processed using fuzzy matching on SSN, name, and date of birth. 

Fair Market Rent data. FMR data was collected from HUD’s Web site for use in measuring the 

rental conditions of the MTO sites and how the rents paid by MTO respondents compare to FMRs 

published by HUD. 

HUD data on PHA expenditures. Staff from Abt Associates Inc. and NBER worked together to 

collect the PHA operating expenditures data. Requests were made to the PHAs in all five MTO sites 

to submit their HUD form 52599s. As the forms were received, they were sent to Abt for entry and 

then readied for analysis. 

Appendix A – Data Collection Sources and Methods A-11 
Other administrative data collection 

School data. The administrative database designed to contain measures of school-level and districtlevel characteristics for use in the educational achievement analysis was created in April 2002. In 

November 2002 the school data from NCES and from the States and localities were turned into 

uniform data sets and fully documented as to source. 

Achievement test data. The College Board has agreed to provide test score data directly to the 

National Bureau of Economic Research (NBER) because only NBER analysts are authorized to use 

these data. Achievement test scores will be aggregated to the school level. 

Arrest and disposition data. Adult and juvenile arrest and criminal disposition data were collected 

for this study under a cooperative agreement between NCOVR and HUD. This effort was carried out 

by Professor Jens Ludwig, of the Georgetown University Center for Public Policy. The individual 

offense data could be used only by Georgetown University analysts. Therefore, their analysts 

coordinated with our team members to test and report outcome measures in the same manner as the 

rest of the analyses. 

Location Data (Address Histories) for the MTO Sample 

Data on the current and past residential locations of MTO sample members came from several 

sources: the MTO tracking database, MTCS and TRACS data, and the interim evaluation field data 

collection. All address data were processed in a systematic way. Addresses were first standardized 

into U.S. Post Office standard format and each piece of the address was parsed into its own field. 

Next all addresses were geocoded by vendor Tele-Atlas, using both 1990 and 2000 census 

geography.4 Careful analysis of the geocoding of more than 46,000 MTO addresses was done to 

ensure that the addresses were geocoding correctly and that differences between 1990 and 2000 

geocodes appeared reasonable according to table of relationships maintained by the census bureau. 

Then the addresses were posted to the MTO tracking database. (For a description of the data system 

and the tracking database within it, see Feins et al., 1999.) 

Once all new addresses were loaded to the data system, address histories were constructed for each 

member of the interim evaluation sample. The series of addresses for each sample member was run 

through a spell generator program, which linked consecutive addresses in the same location into 

spells with start and end dates and all necessary geocodes. This program compared the dwellings at 

varying points in time, determined whether an address was the baseline (prerandom assignment) 

location, the MTO move location, the location at the time of the 1997 or 2000 canvasses, or the 

residential location at the time of this study’s data collection in 2002. Spells containing these 

The latitude and longitude of a specific address may differ slightly between 1990 and 2000, due to the 

advent of digital mapping technology during that decade. In addition, some addresses that had changed (for 

example, due to renaming of streets) or were no longer valid (due to demolition) were able to be geocoded 

for 1990 but not directly for 2000. 

A-12 Appendix A – Data Collection Sources and Methods 

4 
confirmed addresses were tagged with that information. The address spell data were used for all 

mobility analyses (see chapter 2) and for any other analysis involving the sample members’ baseline 

or current locations. 

Collection of Published Data 

Census data 

This study used data from the SF3 (census tract- and block-group-level files) from the 1990 and 2000 

census. After defining an extensive set of variables to characterize the neighborhood settings of the 

sample, tract-level variables for the 1990 Census for the whole country were calculated and made 

ready for linking to MTO locations. In addition, 1990 Census block-group-level variables were 

prepared for all parts of the five states containing the original MTO sites. 

The census bureau released the small-area files for Census 2000 for the five MTO states in August 

2002. By the end of September, tract- and block-group-level data were available for the whole 

country. In August staff began converting the 1990 code to work with the Census 2000 files. This 

task—and the creation and documentation of the 2000 variables for tracts and block groups—was 

completed at the end of September with files provided to analysts shortly thereafter. 

HUD provided 2000 Census data at the tract level for the remainder of the country in October with 

block group data for the other States coming shortly after. All the remaining tract- and block-grouplevel variables were created and delivered to the analysts in November. 

Appendix A – Data Collection Sources and Methods A-13 

Appendix B 

Samples and Analysis Methods 

Appendix B 

Samples and Analysis Methods 

This appendix describes the basic analysis strategy for the quantitative experimental component of the 

Moving to Opportunity (MTO) Interim Evaluation. It is divided into nine sections: 

1. Sample Selection 

2. Data Sources 

3. Estimation Methods 

4. Outcomes and Mediators 

5. Covariates 

6. Weights 

7. Missing Data 

8. Interpretation and Reporting of Results 

9. References 

B1. Sample Selection 

Two samples were used for the analyses: an adult sample and a child sample. Both samples draw on 

members of the “core” household: members of the family who planned to move together if awarded a 

voucher or certificate through the MTO program. For the purposes of the interim evaluation, we 

considered core household members to be those individuals who a) were identified as part of the core 

family on the HUD 50058 form or b) were members of fifteen families without core status 

information AND were identified as core members on the Baseline Survey. Only families who were 

randomly assigned by December 31, 1997 were included in the samples, to allow for a minimum of 

approximately four years to have passed since random assignment. In addition to these two analysis 

samples, we describe here a subsample of hard-to-interview respondents that was used to reduce 

survey non-response bias. 

B1.1 Adult sample 

The adult sample consists of one adult from each of 4,248 MTO households.1

 The order of 

precedence for selecting the sample adult is female core head, wife of core head, female baseline head 

if she is a core member, wife of baseline head if she is a core member, and male or unknown gender 

core head. (Deceased sample adults were not replaced.) For over 90% of the cases, the sample adult is 

the female core head. 

A total of 4252 MTO households were randomly assigned by December 31, 1997. The interim evaluation 

sample excludes four households whose Sample Adult was a core member of another MTO household 

randomized earlier. Thus, we included 4248 households in the interim evaluation. 

Appendix B – Samples and Analysis Methods B-1 

1 
B1.2 Child sample 

Core children ages 5 to 19 as of May 31, 2001 were eligible to be part of the Child Sample. For 

survey purposes, we included up to two children from each core household in the sample. For core 

households with more than two children ages 5-19, we randomly selected two children. Children 

remained in the sample even if they no longer lived with the sample adult. The final sample consisted 

of 6,683 children: 3,146 ages 5 to 11 and 3,537 ages 12 to 19.2 

We selected up to one sample child ages 5 to 11 from each core household for the Concept Formation 

(CF) test. For families with two sample children ages 5 to 11, we randomly selected one child to take 

the CF test. 

B1.3 The 3-in-10 subsample 

After completion of the main field period for the followup survey, we implemented a subsampling 

procedure to reduce non-response bias. Our strategy was to continue to work 3 in 10 of the cases that 

had not been completed during the main field period. By continuing to work a random subsample of 

cases, we were able to achieve a higher effective response rate than if we had used the same resources 

to continue to work the full sample. The higher effective response rate helps reduce the bias due to 

non-response. The effective response rate is equal to the main sample response rate (MRR) plus the 

subsample response rate (SRR) multiplied by one minus the main sample response rate: MRR+ 

SRR*(1 – MRR). 

The subsample period began after June 22, 2002 for Los Angeles and Baltimore and after July 6, 

2002 for Boston, Chicago and New York. Subsample field work continued until September 15, 2002 

across all five sites. We identified subsample cases as those cases that had any major component 

missing at the end of the main field period and that had a family ID number ending with one of three 

digits: 2, 5 or 8 for Baltimore and LA and 3, 6, or 9 for Boston, Chicago, and New York. We 

determined these last digits after the main field period had ended. 

The subsample consisted of cases that had not been completed for any reason, including unlocated 

cases, initial refusals, and out-of-area cases. (Interviewers only traveled to out-of-area cases during 

the subsample period.) Within the same family and even for the same individual, some components 

may have been completed during the main field period while other components were either never 

completed or were not completed until the subsample period. This is particularly true for out-of-area 

cases. During the main field period, if a family was located out-of-area, the interviewer conducted a 

telephone interview with the adult and any sample children ages 10 to 19 (as of May 31, 2001).3 If a 

family’s ID number did not end in one of the digits selected for the subsample, no additional 

fieldwork was done beyond the telephone interviews. However, if the number ended in a selected 

digit, an interviewer traveled to the family to administer the WJ-R tests, conduct the Child Survey for 

2 A total of 6,694 children were originally selected for the sample. After the resolution of cases involving 

individuals who were core members of two different families and after updating the children’s date of 

births using the best available information, the final child sample consisted of 6,683. 

3 The management team decided that children under age 10 were too young to be interviewed by phone. 

B-2 Appendix B – Samples and Analysis Methods 
any children ages 5 to 9, weigh children ages 5 to 11, and take the blood pressure of the adult 

respondent. 

B2. Data Sources and Analyses 

B2.1 Data sources 

Our analyses utilize data from direct survey, proxy report, measurement, testing, interviewer 

observation, administrative data, and published data sources. Below we briefly describe each of these 

sources and in exhibit B.1 we summarize the data sources for the adult and child samples. 

A) Direct Survey –Our direct survey sources include interviews with the sample adult using the 

Adult Survey and with the sample children using the Child Survey (ages 8 to 11) or the Youth 

Survey (ages 12-19). Most of the interim surveys were conducted in face-to-face interviews; 

however, some telephone interviews were conducted for out-of-area cases. 

B) Proxy Report –We asked the sample adult about the education, health, behavior and time use 

of the sample children using the Parent-on-Child/Youth (POCY) module of the Adult Survey. 

C) Measurement – The interviewers measured the height and weight of children ages 5 to 11 and 

took the blood pressure of the sample adults. 

D) Testing –Interviewers administered the following Woodcock Johnson Revised (WJ-R) 

achievement tests to the sample children: Letter-Word Identification, Passage 

Comprehension, Calculation, and Applied Problems. In addition, they administered the WJ-R 

Concept Formation test to children ages 5 to 11 (up to one child per family). 

E) Interviewer Observation – The interviewers recorded their observations of the adult 

respondent’s interaction with a sample child ages 5 to 11, the adult respondent, the interior of 

the home, and the exterior of the home. 

F) Administrative Data – We collected administrative data from federal, state, and local 

agencies on an individual’s employment and earnings, housing assistance, AFDC/TANF and 

food stamps assistance, and criminal justice involvement. In addition, we collected 

information on the characteristics of the schools that MTO children attended. 

G) Published Data – We constructed aggregate neighborhood characteristics such as 

unemployment rates using data from the Census Bureau, Department of Housing and Urban 

Development (HUD), the Federal Bureau of Investigation (FBI), local law enforcement 

agencies, and other sources. 

Appendix B – Samples and Analysis Methods B-3 
EXHIBIT B.1 

SAMPLES AND DATA SOURCES 

Adult Child 

Direct Survey Adult Survey Child Survey (ages 8 to 11) or 

Youth Survey (ages 12 to 19) 

Measurement Blood pressure height and weight (ages 5 to 11) 

Proxy Report POCY Module of the Adult Survey 

Testing Woodcock-Johnson Revised 

Interviewer Adult-child interaction, adult 

Observation respondent, interior of home, 

and exterior of home. 

Administrative Unemployment insurance, School and school district 

Data on Individuals AFDC/TANF and food stamps, characteristics and juvenile 

and Schools housing assistance, and criminal records. 

criminal justice records. 

Published Data on Area statistics from HUD, the Area statistics from HUD, the 

Neighborhood Census Bureau, FBI, and from Census Bureau, FBI, and from 

Characteristics local police departments. local police departments. 

B2.2 Child-centered and adult-centered analyses 

The adult-centered or household-centered analyses use outcomes and mediators constructed from the 

Adult Survey, interviewer observations, and administrative data. We constructed core household 

aggregate outcomes using administrative data and constructed current household aggregate outcomes 

using Section G (Household Composition) of the Adult Survey; these are the only sources of 

information on all household members. 

The child-centered analyses included child outcomes and mediators from the Child Survey, Youth 

Survey, the Parent on Child/Youth (POCY) module of the adult interview, Woodcock-Johnson tests, 

and administrative data. Because the POCY module was completed as part of the adult interview, if a 

sample child no longer lived with the sample adult, the POCY information was therefore reported by 

someone who did not live with the child. Similarly, for these “child leavers” the mediators contained 

in the main sections of the Adult Survey, such as community monitoring of youth and neighborhood 

quality, may not reflect the child’s neighborhood. Thus, for these Adult Survey mediators, we only 

conducted adult-centered analyses. 

B2.3 Household and neighborhood measures 

We used three types of household measures in our analyses: current household characteristics; 

current household aggregates; and core household aggregates. Each is described below. 

B-4 Appendix B – Samples and Analysis Methods 
Current household characteristics. We created household and neighborhood characteristics for an 

individual’s current household using data from the Adult Survey, Youth Survey, Child Survey, 

interviewer observation, and published statistics such as crime rates. We created Current Household 

Characteristics for each individual from his or her responses to the survey. For children, we have 

additional household and neighborhood information reported by the sample adult. 

Current household aggregates. We constructed household aggregates such as income for an 

individual’s current household using the Adult Survey and administrative data. For the sample adult, 

we constructed Current Household Aggregates such as income. For the child sample, we did not have 

current household income for child leavers and thus we could not construct current household 

income. 

Core household aggregates. We created household aggregates such as earnings for all members of 

the original core household using the Adult Survey and administrative data. After creating the core 

household aggregates, using the administrative data and the Adult Survey, this information was then 

merged by household onto the adult sample and the child sample. 

B3. Estimation Methods 

B3.1 Comparisons 

The MTO experiment was designed to measure the effects of providing housing vouchers or 

certificates to families in public housing projects. Specifically, the demonstration is designed to 

estimate the effects of (a) living in private housing of the family’s own choosing relative to living in 

public housing and (b) living in private housing in a low-poverty area relative to living in public 

housing. To address these questions, families were randomly assigned to the control group, the 

Section 8 group, or the experimental group.4

 As agreed upon previously, we define treatment 

compliance as leasing up with a voucher or certificate provided through the MTO program. On 

average, a family in the treatment group had 4 to 6 months after random assignment to leaseup using 

a voucher or certificate (for more details see Feins, McInnis, Popkin, 1997). 

We performed two basic comparisons: 

• Section 8 versus Controls, and 

• Experimental group versus Controls. 

A third possible comparison would have been to compare the Section 8 and Experimental groups with 

each other; however, we did not make such a comparison. Such a comparison would not have added 

any information to the two comparisons we did make and, in fact, would have been less informative 

than those comparisons, because it would not provide a measure of the net effect of either treatment. 

In some previous HUD reports, these groups have been labeled the "MTO Experimental," "Section 8 

Comparison," and "In-place Control" groups. We used simpler labels (e.g., “Experimental,” “Section 8”, 

and “Control”) for the Interim Evaluation for ease of exposition. 

Appendix B – Samples and Analysis Methods B-5 

4 
It should be noted, however, that the difference in impacts estimates between the Section 8 and 

experimental groups reflect not only effects on “compliers” but also the differential take-up rates for 

the two treatment groups (i.e., the composition of compliers may differ between the two groups). 

B3.2 Estimation of Intent-To-Treat (ITT) effects 

In a randomized experiment, the difference in mean outcomes for the treatment and control groups 

provide an estimate of the impact of being offered the treatment. This estimate captures the average 

Intent-to-Treat (ITT) effect across all of the individuals included in the study, regardless of whether 

or not an individual assigned to the treatment actually complied with the treatment. Using a linear 

regression model, we can estimate the effect of Intent-to-Treat on outcome (Y) using whether an 

individual (indexed by i) was randomly assigned to the group offered the treatment (Z=1) or to the 

group not offered the treatment (Z=0): 

(1) Yi = α + ZiπITT + εi 

where Zi indicates assignment status and πITT (the coefficient on Z i) captures the ITT effect. 

To reduce the residual variation and thereby increase the precision of our estimate, we included in our 

regression models individual and household characteristics observed prior to random assignment (i.e., 

baseline characteristics): 

(2) Yi = α + ZiπITT + Xiβ + εi 

where X represents a vector of characteristics for each individual (indexed by i), β represents the 

vector of coefficients for X, and α represents a constant. For all analyses using data pooled across 

sites, X included fixed-effects or dummy variables for each of the sites (with NY serving as the 

omitted or reference category). For simplicity, we used a linear regression model for the dichotomous 

as well as the continuous outcomes. Robust standard errors were necessary due to heteroscedasticity. 

In the MTO demonstration, individuals were randomized into two different treatment groups. We 

could have estimated the effect of these treatments using separate regressions for the Section 8 group 

(denoted by superscript s): 

(3) Yi = α + Zs 

i πs ITT + Xiβ + εi 

and for the Experimental group (denoted by superscript e): 

(4) Yi = α + Ze 

i πe ITT + Xiβ + εi.. 

For greater efficiency, however, we used a single regression that included separate dummy variables 

for assignment to the Section 8 treatment group (Zs

) and to the Experimental treatment group (Ze) to 

estimate the two treatment effects: 

(5) Yi = α + Zs 

i πs ITT + Ze i πe ITT + Xiβ + εi. 

The coefficients πs 

ITT and πe ITT are the estimated impacts of the Section 8 and Experimental 

treatments, respectively. 

B-6 Appendix B – Samples and Analysis Methods 
B3.3 Estimation of Treatment-on-Treated (TOT) effects 

Another estimate of interest is the Treatment-on-Treated (TOT) effect – the effect of actually 

receiving the treatment. Under the weak assumption that the effect of the treatment occurs entirely 

through moving using an MTO program voucher or certificate, the TOT is the difference in outcomes 

between those individuals in the treatment group who complied with treatment by leasing up and 

those in the control group who would have complied or leased up if offered the treatment.5

 More 

formally, we can define the “compliers” (C=1) and “never takers” (C=0) as in exhibit B.2 below. 

Note that, for simplicity, this table describes the case of a single treatment group. 

EXHIBIT B.2 

DEFINITIONS OF COMPLIERS AND NEVER TAKERS 

All Complier (C=1) Never Taker (C=0) 

Offered the 

Treatment (Z=1) 

“Treatment Group” – 

offered a voucher. 

“Treatment Compliers” 

– offered a voucher 

and used it to move. 

“Treatment Never 

Takers” – offered a 

voucher but did not 

use it to move. 

Sample mean or 

E[Y|Z=1] observed. 

Sample mean or 

E[Y|C=1, Z=1] 

observed. 

Sample mean or 

E[Y|C=0, Z=1] 

observed. 

Not Offered the 

Treatment (Z=0) 

“Control Group” –not 

offered a voucher. 

“Control Compliers” – 

not offered a voucher 

but would have used a 

“Control Never 

Takers” –not offered a 

voucher and would 

voucher to move if not have used a 

offered one. voucher to move even 

if offered one. 

Sample mean or 

E[Y|Z=0] observed. 

Sample mean or 

E[Y|C=1, Z=0] not 

observed. 

Sample mean or 

E[Y|C=0, Z=0] not 

observed. 

Using these definitions the effect of the Treatment-on-Treated can be expressed as the difference in 

the expected value of outcome Y for “treatment compliers” and “control compliers”: 

(6) TOT = E[Y|C=1, Z=1] – E[Y|C=1, Z=0]. 

While we observed who in the treatment group was a complier based on whether they leased up 

through the program, we did not observe who in the control group would have leased up if they had 

been offered a voucher. In this analysis, we used an adjustment attributed to Bloom (1984) to infer the 

TOT impact from the ITT impact. 

The housing search process may affect the non-compliers; however, we would expect these effects to be 

small in comparison to the main effects on compliers. 

Appendix B – Samples and Analysis Methods B-7 

5 
Under the assumption that the treatment occurs entirely through using an MTO program voucher to 

move, the entire ITT effect is due to differences between the compliers in the treatment group and the 

would-be compliers in the control group since the outcomes for the non-compliers in both groups 

should be the same.6 

Algebraically, we know that the effect of the intent-to-treat is the difference in the expected values of 

the outcomes for the treatment and control group: 

(7) ITT = E[Y|Z=1] – E[Y|Z=0]. 

We can express the expected values for each group as the expected value for the compliers and noncompliers weighted by the proportion who comply with treatment or leaseup (ρ) and the proportion 

who do not (1 - ρ): 

(8) E[Y|Z=1] = (ρ) E[Y|C=1, Z=1] + (1 - ρ) E[Y|C=0, Z=1] 

(9) E[Y|Z=0] = (ρ) E[Y|C=1, Z=0] + (1 - ρ) E[Y|C=0, Z=0]. 

Substituting back into Equation 7, the ITT effect can be rewritten as: 

(10) ITT = (ρ) E[Y|C=1, Z=1] + (1 - ρ) E[Y|C=0, Z=1] 

- (ρ) E[Y|C=1, Z=0] - (1 - ρ) E[Y|C=0, Z=0]. 

Under our assumption that the treatment has no effect on the non-compliers, the expected value of the 

outcome for treatment non-compliers and control non-compliers will be the same and these two terms 

will offset each other leaving: 

(11) ITT = (ρ) E[Y|C=1, Z=1] - (ρ) E[Y|C=1, Z=0]. 

Dividing both sides by ρ: 

(12) ITT/ρ = E[Y|C=1, Z=1] - E[Y|C=1, Z=0], 

we see that the ITT effect divided by the proportion of compliers equals the difference in expected 

outcomes for the treatment compliers and the control compliers. By definition this difference is the 

effect of the TOT. Thus, we can estimate the TOT by dividing the ITT by the proportion of leaseups: 

(13) TOT = ITT/ ρ. 

If we can regard ρ as fixed (i.e., if it does not vary from sample to sample, so that we can assume that 

the lease-up rate would have been the same in the control group), the standard error of the TOT 

estimate is:7 

6 This derivation of the TOT estimate is due to Bloom (1984). 

7 While not strictly true, this assumption should be very close to correct in large samples. 

B-8 Appendix B – Samples and Analysis Methods 
8 

(14). SEETOT = SEEITT /ρ, 

i.e., in going from the ITT estimate to the TOT estimate, the standard error of estimate is inflated by 

exactly the same factor as the impact estimate itself. This means that the t-statistic of the estimate is 

unchanged by this transformation. The same procedure for calculating the TOT standard error can be 

used for both simple mean differences and regression adjusted ITT estimates. 

Some members of all three groups may obtain Section 8 vouchers on their own initiative rather than 

through the MTO program. An alternative definition of the treatment for the Section 8 group could 

have been any move through a Section 8 voucher (i.e., not limited to moving through an MTO 

program voucher). However, we did not report such TOT estimates for several reasons. First, they 

would have required us to make stronger assumptions than we made above to estimate the TOT effect 

of leasing up only through the program and we felt these stronger assumptions were implausible.8 

The three assumptions that would be necessary for estimating the TOT of any Section 8 voucher are as 

follows. 

i) The MTO program has no effect on families who do not leaseup through any form of Section 8 (this is 

similar to the assumption in the more standard TOT analysis above). 

ii) The MTO program has no effect on families who would obtain Section 8 whether or not they were 

assigned to the treatment group. 

iii) All Control group families who receive Section 8 on their own initiative would have received Section 8 

(either from MTO or on their own) if they had been assigned to a treatment group. 

The second assumption, that the MTO program has no effect on those who always leaseup using a Section 

8 voucher, is presumably only plausible for the Section 8 group since the Experimental group was offered 

counseling services and offered a voucher restricted to certain neighborhoods. Even for the Section 8 

group, the assumption is problematic since families assigned to the treatment group would presumably have 

leased up over a shorter time period than if they had obtained a Section 8 voucher on their own. Thus, to 

make Section 8 participation on one’s own more comparable to Section 8 participation through the 

program, we would want to restrict Section 8 participation to a similar time period since randomization. 

Moreover, once we make the necessary restriction on timing, the number of non-MTO program Section 8 

families may be so small that the estimated TOT will not be very different from the program TOT 

discussed above. 

 More formally, let λ be the fraction of the treatment group that obtains Section 8 from any source, let υ be 

the fraction of the control group that obtains Section 8 (on their own). 

If we define, 

• Compliers - people who receive Section 8 if in the treatment group, but not if in the Control group 

(C=1); 

• Always takers - people who receive Section 8 if in any group (C=2), and 

• Never takers - people who do not receive Section 8 in either group (C=0) 

 then: 

 (15) ITT = (λ - υ)E[Y|Z=1, C=1] + υE[Y| Z=1, C=2] + (1- λ - υ) E[Y| Z=1, C=0] 

- (λ - υ)E[Y| Z=0, C=1] - υE[Y| Z=0, C=2] - (1- λ - υ)E[Y| Z=0, C=0]. 

Appendix B – Samples and Analysis Methods B-9 
Second, defining the treatment as use of any voucher would require us to adjust away the effects of 

nondemonstration vouchers in the control group; we believe that the appropriate counterfactual for 

the two treatment groups is a control group that receives nondemonstration vouchers. Estimates based 

on this counterfactual measure the effects of replacing the current mix of housing subsidies for public 

housing residents with a program of locationally restricted vouchers (in the experimental group) or 

unrestricted vouchers (in the Section 8 group). Alternatively, if one views the MTO impacts as the 

effects of changing the neighborhoods in which the families live, treatment is more appropriately 

defined as the difference in neighborhood characteristics between the treatment and control group, 

however that difference is achieved. 

B3.4 Variation in random assignment ratios 

To produce unbiased estimates of treatment effects, we needed to take into account the variation in 

random assignment (RA) ratios. At the start of the experiment, RA ratios were set to produce equal 

numbers of leased-up families in the Section 8 and Experimental groups (based on expected leaseup 

rates). In the midst of the experiment, these RA ratios were adjusted to accommodate higher than 

anticipated leaseup rates for MTO families in comparison to the rates for the Section 8 group. On 

average, RA ratios were adjusted twice at each site (see appendix A of Goering et al., 1999). Within 

each of the 15 resulting site-ratio periods applicants were randomly assigned to the control and 

treatment groups. However, across the entire experiment, the change in random assignment ratios 

produces systematic differences in timing and site location between the control group and each of the 

treatment groups. Thus, a simple comparison of means across the pooled sample that did not take into 

account the changes in the ratios could have produced biased estimates by confounding the treatment 

effect with site and period. 

The approach we used to remove the systematic relationship between site-time period and assignment 

status was to assign weights to each observation. Using weights to adjust for the ratio changes offered 

the advantage of producing a single estimate of the treatment effect and allowing for comparisons of 

means, as well as simplifying the estimation procedure. 

The weights selected had to eliminate the relationship of assignment to both site and time period. A 

number of different weights could have been used to accomplish this. We created weights that 

preserved the overall proportions of Control, Section 8, and Experimental families in the sample. 

Section 6.1 of this memo describes the construction of these weights. 

B3.5 Effects for subgroups determined prior to randomization 

Estimating impacts for different subgroups of participants helps us better understand who may benefit 

the most from MTO and on which types of outcomes. For example, in the early impact evaluations 

youth seemed to be particularly impacted by the treatment. In dividing the sample into subgroups, it 

was important to rely upon characteristics determined prior to randomization. Below are some of the 

categories for which we estimated subgroup impacts: 

The three assumptions above allow us to drop the second, third, fifth, and sixth terms. We can therefore 

identify the impact of Section 8 for the compliers as ITT/( λ - υ). Note that this simply scales the ITT 

number by a bigger multiple than in the standard TOT estimate. 

B-10 Appendix B – Samples and Analysis Methods 
• race/ethnicity, 

• welfare status, 

• work status, 

• gender of child, 

• age group of child, 

• child behavior problems, 

• age of sample adult, and 

• family size. 

Subgroup estimates for exhaustive, mutually exclusive subgroups were derived by interacting the 

treatment dummy with dummies for each subgroup (e.g., Worked at Baseline, Did Not Work at 

Baseline). We adopt this approach, rather than running separate regressions for each subgroup, 

because of its computational simplicity and the fact that it allows straightforward joint tests for the 

statistical significance of the differences in impacts among subgroups. 

In most cases, the impact coefficients for the different subgroups were of interest in their own right. 

For example, it was of interest to see whether moving from public housing to a low-poverty area had 

a larger effect for whites or non-whites. In the case of the subgroups formed by the sites, however, 

our interest was simply in the variation of impacts across sites, since HUD’s interest was not in these 

specific sites, but in how neighborhoods affect these outcomes more broadly. Of course, if it had been 

possible to attribute differences in impacts across sites to differences in site characteristics, that would 

have been very valuable information. Unfortunately, that was not possible. With only five sites, 

which differ in innumerable potentially relevant ways, it was simply not possible to disentangle the 

underlying factors that cause impacts to vary across sites. (This is true for both the quantitative 

analysis and for any qualitative analysis of the impacts that might be undertaken.) Therefore, we did 

not report identifiable results by site. 

B3.6 Effects for different periods since random assignment 

On all survey measures there was considerable variation in the time since random assignment. 

However, this variation captures both time since randomization and cohort. This makes it impossible 

to interpret the variation in impacts over time. Therefore, for survey outcomes for which we have 

only a single point-in-time measure, we simply estimated impacts on that measure, conditional on the 

distribution of time since random assignment in the analysis sample. 

For some outcomes, such as quarterly earnings and public assistance, we had longitudinal 

administrative data outcomes. For these outcomes, we estimated treatment effects for different 

periods since random assignment by performing separate regressions for specific periods since 

random assignment. For example, we treated earnings in the first two years after randomization as the 

outcome measure in one regression and earnings in the third and fourth years after randomization as 

the outcome in a separate regression. One advantage of using separate regressions to estimate 

treatment effects for each period is that it allows the effect of baseline characteristics to differ 

depending on the period. 

Longitudinal measures expressed in monetary terms, such as earnings and income, were converted to 

constant 2001 dollars using the Bureau of Labor Statistics’ Consumer Price Index for All Urban 

Consumers (CPI-U). 

Appendix B – Samples and Analysis Methods B-11 
B3.7 Clustering on household 

In our child analyses we included up to two individuals from each household. As a result, we could 

not assume that the residuals were independent across all observations. To address this issue, we 

adjusted the standard errors by clustering on household. 

B3.8 Cell-level analyses 

For four of the five states, we were able to obtain individual-level administrative data on earnings and 

employment. However, for the fifth state (Massachusetts), we were only able to obtain data 

aggregated across groups of at least 10 MTO participants. For methodological consistency, we 

constructed cells for all five sites using the same algorithm. The cell-level analyses used the mean 

outcome for the cell and mean covariates for the cell for a limited set of covariates, and were 

weighted randomization ratio weight for individuals within that cell multiplied by the number of 

individuals in the cell. We used only a reduced set of covariates for the cell analyses, because the 

cells only have substantial variation on certain covariates that were used in the algorithm to split 

individuals into cell that were as homogeneous as possible. For the adults, the reduced set of 

covariates consisted of site dummies, baseline work status and education, and dummies for quarter of 

randomization. For the youth, the reduced set of covariates consisted of site dummies, single-year age 

dummies, and gender. 

B4. Outcomes and Mediators 

B4.1 Outcome domains 

A large number of different variables in each of the domains were available for analysis. The analysis 

team for each domain selected the most important outcomes and mediating factors to include in the 

evaluation. The six domains are: 

• Housing conditions and assistance, 

• Health, 

• Delinquency, crime, and risky behavior, 

• Education, 

• Employment and earnings, 

• Household income and public assistance. 

In the MTO Research Design Report (Orr, Feins, and Popkin, 2001), we laid out a number of theories 

about the pathways through which neighborhood might affect the outcomes of interest in each of the 

six domains. These theories predicted impacts on a number of “mediating factors.” To test these 

theories, we estimated impacts on each of these mediating factors, using the same models as we used 

for the outcomes. It must be recognized that these are relatively weak tests, because impacts on a 

given mediating factor may be consistent with multiple theories of causation. Where the theory 

predicts an impact on a mediating factor and none is found, however, we can reject the theory. 

B-12 Appendix B – Samples and Analysis Methods 
Because they were measured after random assignment, mediating factors were never used as righthand-side variables in the impact regressions. We were not trying to “model” the path of effects; we 

were subjecting the implications of the causal theories to statistical tests. 

B4.2 Non-experimental analyses for endogenous subgroups 

We observed outcomes such as wages or housing search discrimination only for the subgroups of 

respondents who worked or moved. Since these subgroups are endogenous to the treatment, we could 

not identify experimental impacts. For example, although we could have observed differences in 

wages between the treatment and control groups, these differences would not be experimental 

estimates of the impact on wages because we could be observing wages for different mixes of 

respondents from each group. We did, of course, create some descriptive tabulations to help 

understand the experience of families in these subgroups. Because of the risk of selection bias, 

however, we did not attempt to estimate impacts for these groups using nonexperimental methods. 

It is important to note that for many outcomes we were able to define the outcome so that we did not 

create endogenous subgroups. For example, we defined earnings to include zero earnings. 

B4.3 Categorical measures 

For these analyses, we converted categorical outcomes to dichotomous ones. The cut-point for 

dividing the categories depended on the type of categories, the distribution of the data, and on 

expository or theoretical considerations as well. In general, 

a) we divided non-polarized categorical data using the central cut-point or the point that divided the 

data as close to 50:50 as possible, and 

b) we divided Likert-type scales in a way that maintained the distinction between agree and disagree 

(except in those instances in which the data were extremely skewed). 

In some cases, we selected a cut-point that differed from the above guidelines in order to be 

consistent with the existing literature or to be internally consistent with the coding of closely related 

variables. 

B4.4 Addresses of participants 

We have participant address information from a variety of sources. The quality of this address 

information depends on the source of the information and the frequency with which it was collected. 

Address information based on actual contacts with respondents is highly reliable for the point in time 

at which it was collected; unfortunately, we have had at most four relatively widely spaced contacts 

with the families – at baseline, at the 1997 and 2000 canvasses, and at the interim followup survey. 

These data may therefore miss a number of moves that occurred between these contacts. Moreover, 

even for the addresses they capture, they cannot tell us with any precision the date on which the 

family moved to that address. In addition, we have information that was gathered more continuously 

through the tracking process, from change of address forms, credit bureaus, and the Public Housing 

Authority/Multifamily Tenant Characteristic System Data (PHA/MTCS). The addresses provided by 

these data are probably of lower reliability than those collected through direct contact with the 

Appendix B – Samples and Analysis Methods B-13 
families, but they are more likely to include a complete set of addresses and, where the addresses 

were accurate, they allowed us to date the families’ moves more precisely. 

Therefore, in constructing address histories, we used all available addresses except those that could 

not be geocoded using standard commercial software for standardizing, parsing, and geocoding 

addresses. 

B4.5 Exposure measures 

The MTO intervention is expected to affect sample members’ outcomes through exposure to a 

different environment than the one they would have experienced had they remained (at least initially) 

in public housing – i.e., attributes of the local community are important intervening factors. But the 

degree to which the families’ environment was actually changed is not self-evident – even for those 

who moved through the demonstration. Section 8 families tended to stay in high-poverty areas, some 

MTO families moved back into high-poverty areas after their initial move to a low-poverty area, and 

some controls moved out of public housing. We needed to measure the treatment-control difference in 

exposure to important community influences (e.g., poverty level, school quality, community values 

and mores, safety, etc.) in order to understand the strength of the intervention. Unfortunately, the 

same factors that made this important made it difficult. We could not simply measure the attributes of 

the community in which the family lived at followup and assume that that was the environment that 

had shaped their behavior over the entire followup interval. We had to somehow aggregate the 

different environments the family had experienced since the initial intervention. 

We did so by taking the time-weighted average of the different communities in which the family had 

lived between random assignment and the followup interview. If, for example, a family lived in 

Community A for the first two years after random assignment and Community B for the next three 

years, up to the time of the followup interview, we would measure the local crime rate to which it was 

exposed as a weighted average of the crime rates in Communities A and B, with weights two-fifths 

and three-fifths, respectively. We would treat the resulting measure as an intervening factor, 

estimating the impact of the intervention on it to obtain a measure of how much the demonstration 

changed the crime rate to which the family was exposed, on average, over time. 

This measure is admittedly arbitrary. For example, it gives the same weight to community influences 

experienced early in the followup period as it does to those experienced closer to the followup survey. 

To the extent that family behavior and outcomes primarily reflect recent experience, this presumably 

places too much weight on the early experiences. But one could equally well argue that the outcomes 

reflect the families’ cumulative experience, or that they reflect environmental influences with a lag. In 

the absence of confident knowledge of the functional form of this relationship, we used a simple 

weighted average. 

For all community-level mediators (i.e., small area statistics), we constructed both a current measure 

and a measure that reflects exposure since randomization. 

B-14 Appendix B – Samples and Analysis Methods 
B5. Covariates 

B5.1 Baseline covariates 

As mentioned above, we included covariates in our regression models to improve the precision of our 

estimates. Since individuals were randomly assigned to control and treatment groups, the addition of 

these covariates does not affect the expected value of the estimate itself. All covariates had to be 

characteristics that were known (or determined) prior to randomization. In selecting covariates, we 

considered (a) the importance of the variable in predicting the outcomes of interest, (b) the extent of 

variation on the variable for the sample, and (c) the completeness of the data. 

For relatively small numbers of missing values (less than 5 percent of all observations), we imputed 

values using the mean for non-missing observations weighted by the weights constructed to equalize 

RA ratios. We conditioned the imputed means for missing adult or household covariates on the site. 

For missing child covariates that were applicable to children of that baseline age we imputed the 

mean conditional on age at baseline and gender (if known) as well as site. To impute the child means 

we used all non-missing observations of core children who were under age 18 at baseline (including 

those who were 20 and older as of May 31, 2001). If a child covariate was not applicable to a 

particular child because of the age of the child at baseline, we assigned the covariate a value of zero. 

(For families randomized in 1998, means were imputed separately.) 

We used the following baseline covariates in all impact analyses: 

Baseline characteristics for the sample adult, household or baseline respondent 

• Site (dummies; omitted category: NY) 

• Current age of sample adult (dummies for ages 19-29, 30-39, 40-49; omitted category: 50+) 

• Ethnicity of sample adult (dummy for Hispanic; omitted category: non-Hispanic) 

• Race of sample adult (dummies for African-American and Other non-white race; omitted 

category: white) 

• Marital status of sample adult (dummy for never married; omitted category: married, separated, 

or divorced) 

• Sample adult is male (dummy) 

• Sample adult was working (dummy) 

• Sample adult was under age 18 at birth of first child (dummy) 

• Sample adult was enrolled in school (dummy) 

• Sample adult was a high school graduate (dummy) 

• Sample adult had a GED (dummy) 

• Size of core family (dummies for 1-2, 3, 4; omitted category: 5+) 

• Core household did not contain any teen children (ages 13 to 17) at baseline (dummy) 

• Any householder had been robbed, assaulted, or threatened with a weapon within the six months 

prior to the survey (dummy) 

• Any householder was disabled (dummy) 

• Baseline Respondent was receiving AFDC/TANF (dummy) 

• Baseline Respondent had a car that runs (dummy) 

• Baseline Respondent had moved more than 3 times in past 5 years (dummy) 

Appendix B – Samples and Analysis Methods B-15 
• Baseline Respondent had no friends in neighborhood (dummy) 

• Baseline Respondent had no family in neighborhood (dummy) 

• Baseline Respondent had lived in neighborhood for 5 or more years (dummy) 

• Baseline Respondent had previously applied for Section 8 voucher or certificate (dummy) 

• Baseline Respondent’s primary or secondary reason for moving was to get away from drugs and 

gangs (dummy) 

• Baseline Respondent’s primary or secondary reason for moving was for better schools for 

children (dummy) 

• Baseline Respondent was very dissatisfied with neighborhood (dummy) 

• Baseline Respondent considered streets near home very unsafe at night (dummy) 

• Baseline Respondent stopped to chat with a neighbor in the street or hallway at least once a week 

(dummy) 

• Baseline Respondent was very likely to tell neighbor if saw neighbor’s child getting into trouble 

(dummy) 

• Baseline Respondent was very sure would be able to find an apartment in a different area of the 

city) (dummy) 

For the analyses using the child data, we also included child characteristics from the baseline survey. 

The baseline survey had two separate forms for children – one for children ages 0 to 5 and one for 

ages 6 to 17. Some of the same questions were asked on both forms; however, other questions were 

specific to one age group or the other. 

In the analyses, we included covariates from both child forms along with a dummy variable for age 

group at baseline (i.e., 6 to 17) to indicate which form was relevant to the child. This strategy allowed 

us to use covariates from both forms, avoided creating separate impacts for different subsets of 

children, and was consistent with our general strategy for handling covariate non-response. 

We controlled for the following child characteristics, listed below by form. 

Baseline child characteristics from both forms and current age 

• Child is male (dummy; omitted category: female) 

• Child’s age (single-year age dummies; age as of May 31, 2001) 

• Child’s age at Baseline was 6 to 17 (i.e., “6 to 17” form should have been filled out for child) 

• Child has problems that require special medicine or equipment (dummy) 

• Child has problems that make it hard to get to school or to play active sports or games (dummy) 

Baseline child characteristics from the “0 to 5” form 

• Child weighed less than 6 pounds at birth (dummy) 

• Child was in the hospital before his/her first birthday because the child was sick or injured 

(dummy) 

• Someone in home reads a book or story to the child more than once a day (dummy) 

B-16 Appendix B – Samples and Analysis Methods 
Baseline child characteristics from the “6 to 17” form 

• Special class or help for learning problems during the 2 years prior to baseline (dummy) 

• Special class or help for behavioral or emotional problems during the 2 years prior to baseline 

(dummy) 

• Suspended or expelled from school during the 2 years prior to baseline (dummy) 

• School asked someone to come in and talk about problems child was having with school work or 

behavior during the 2 years prior to baseline (dummy) 

• Went to special class for gifted students or did advanced work in any subjects (dummy) 

B5.2 Pre-randomization values of outcomes 

For outcomes for which we had both pre and post-randomization information, we controlled for the 

pre-randomization values of these outcomes in our analyses. Administrative data outcomes such as 

employment, earnings, welfare receipt, food stamps, and arrests often offer both pre- and postrandomization observations. For these outcomes, we generally included annualized outcomes for one 

or two years prior to randomization if available. 

B5.3 Variation in timing 

To produce more precise estimates we also included controls for the timing of random assignment 

and data collection. For outcomes collected at a single point in time, as in surveys, the time since 

random assignment will capture both cohort and timing effects. For longitudinal administrative data 

such as quarterly earnings, we generally controlled for the calendar quarter, as well as the number of 

quarters since random assignment. 

B6. Weights 

We weighted the MTO data for all of the interim analyses. We weighted both the administrative and 

survey data to adjust for the changes in randomization ratios during the study. In addition, we 

weighted the survey data to account for sampling. We sampled up to two children from each family 

for the survey component, up to one of the sampled children from each family for the Concept 

Formation test, and three of every ten non-completes for the subsample of hard-to-interview families. 

In the sections below, we describe the weights that were needed for each of these adjustments. In the 

last section, we summarize the weights and describe how we combined them for different analyses. 

B6.1 Randomization ratio weights 

As we discussed in Section 3.4, we used weights to adjust for randomization ratio changes. We used 

weights that preserved the overall proportions of Control, Section 8, and Experimental families in the 

sample. 

For each group, site and ratio period, the weight applied was equal to: 

(16) wjkh = (nj / n) / (njkh / nkh) 

Appendix B – Samples and Analysis Methods B-17 
where: 

• j indexes the treatment and control groups, 

• k indexes the site, and 

• h indexes the RA ratio period 

such that: 

• nj = total # of observations for a specific group (Control, Section 8, or Experimental), 

• n = total # of observations in the entire sample, 

• njkh = # of observations for a specific group, site, and period, and 

• nkh = # of observations for a site and period combining all groups. 

Exhibit B.3 shows the actual weights for each site-ratio period using this formula. When using these 

RA ratio weights in conjunction with the child sampling weights, the combined weight is the product 

of the RA ratio weight and the sampling weight. 

B-18 Appendix B – Samples and Analysis Methods 
EXHIBIT B.3 

RANDOM ASSIGNMENT RATIO WEIGHTS 

RA Ratios 

(Exper’l to 

Section 8 

to Control) 

RAR 

Dummy 

Variable 

# Dates in Effect 

Weighta 

for 

Exper’l 

Group 

Weighta 

for 

Section 8 

Group 

Weighta 

for 

Control 

Group 

Baltimore 8:3:5 1 < 02/01/96 0.82 1.49 1.00 

3:8:5 2 >= 02/01/96 2.11 0.57 1.00 

Boston 8:3:5 1 < 03/01/96 0.81 1.52 0.99 

3:6:7 3 >= 03/01/96 & 

< 07/24/97 2.15 0.76 0.71 

8:5:3 7 >= 07/24/97 0.82 0.90 1.66 

Chicago 8:3:5 1 < 11/09/96 0.82 1.50 0.99 

10:3:3 6 >= 11/09/96 & 

< 11/26/97 0.66 1.49 1.62 

6:7:3 8 >= 11/26/97 1.10 0.65 1.63 

Los 8:3:5 1 < 03/20/96 0.82 1.49 0.99 

Angeles 4:4:6 4 >= 03/20/96 & 

< 05/01/98 1.43 1.00 0.72 

3:7:4 11 >= 05/01/98 NA NA NA 

New York 8:3:5 1 < 07/24/96 0.81 1.49 1.00 

5:7:4 5 >= 07/24/96 & 

<10/24/97b 1.30 0.65 1.22 

3:7:6 9 >=10/23/97c 

& < 12/03/97 1.91 0.64 0.91 

8:4:4 10 >= 12/03/97 0.79 1.17 1.27 

a. Weights shown are rounded to two decimal places and apply to cases randomized through 1997. 

b. For New York assignments on 10/23/97 the 5:7:4 ratios only applied to sequence numbers in the 2,000 range. 

c. For New York assignments on 10/23/97 the 3:7:6 ratios only applied to sequence numbers in the 3,000 range. 

Source: Goering et al., 1999. 

B6.2 Survey sample selection weights 

We had survey data for two samples: the sample adults and sample children. No adjustment for 

selection of adults into the sample was necessary since we selected one sample adult from each core 

household. For the children, however, we needed weights to adjust for the fact that we interviewed a 

random sample of children (up to two per family) rather than all eligible children. 

The likelihood of including a child in the sample depended on the number of children ages 5-19 in the 

core household.9

 Since a maximum of two children were sampled from each family, children in 

We did not have the exact date of birth (DOB) for all children at the time of sample selection. Sample 

selection was based on an imputed age for children without a DOB. Our final weights reflect any new 

information on the DOBs of core members (i.e., the number of children ages 5 to 19 in a family sometimes 

differs from our initial calculation). 

Appendix B – Samples and Analysis Methods B-19 

9 
families with more than two children had a lower probability of selection. We weighted the data in 

order to produce average treatment effects across all children. We weighted each observation by the 

inverse probability of the child being selected into the sample. The probability of a child being 

selected into the sample was: 

# selected (17) Sampling Probability = # eligible 

where: 

# selected = the number of children ages 5-19 in the family who were selected for the interim 

evaluation, 

# eligible = the total number of core children ages 5-19 in the family who were eligible to be selected 

for the interim evaluation. 

The weight is equal to the inverse of the sampling probability such that: 

# eligible (18) Child Survey Sample Weight = # selected 

We calculated age as of May 31, 2001 using updated information on date of birth. Note that since “# 

selected” and “# eligible” rely on updated date of birth information it is possible to have only one 

selected child in a family with two or more eligible children (i.e., if it turned out that a selected child 

was not age 5-19). 

No special weighting was necessary to pool children of different ages since selection was not 

stratified by age. Separate survey instruments were, however, used to interview children ages 8 to 11 

and those ages 12 to 19. 

B6.3 Concept formation child weights 

We needed a special set of weights to analyze the results of the WJ-R Concept Formation (CF) test 

because we administered the test only to sampled children between the ages of 5 and 11 and we 

administered the test to no more than one child per family. For families with two sampled children 

ages 5 to 11, we randomly selected one child for the CF test. 

To account for this additional selection step, we multiplied the child survey weights by the inverse 

probability of selecting a sample child between the ages of 5 and 11 for the CF test. (The selection of 

a child into the child sample was discussed in the previous section on “Survey Sample Selection 

Weights.”) The probability of a sample child age 5 to 11 being selected for the Concept Formation 

test depends on whether there was a second sample child in the family and if so, on the age of the 

other sampled child. If there was only one sample child in the family, the probability of selecting the 

child (ages 5 to 11) for the CF test is one: 

(19) Pr(Selection for the CF Test | Sample Age 5 to 11 

and No Other Sample Children) = 1.0 

B-20 Appendix B – Samples and Analysis Methods 
If there was another sampled child, the probability of selection depended on the age of the other child. 

If the other child was between the ages of 12 and 19, the probability is 1.0. If the other child was also 

between the ages of 5 and 11, the child had only a 50% chance of being selected for the CF test. Since 

there were sometimes more than two eligible children in the family, the age of the other child had its 

own probability distribution. For families with two or more eligible children, the probability of 

selection for the Concept Formation test can be expressed as follows: 

(20) Pr(Selection for the CF Test | Sample Age 5 to 11 

and Another Sample Child Present) 

= 1.0 * Pr(Age of Other Sampled Child is 12 to 19) 

+ 0.5 * Pr(Age of Other Sampled Child is 5 to 11). 

The probability of the other child being age 12 or older is the ratio of children ages 12 to 19 in the 

household (y) divided by the number of children (other than the sampled child) in the household (c – 

1): y/(c – 1). The probability that the other child was age 5 to 11 is one minus the probability of being 

12 to 19: 1 – [y/(c – 1)]. We can substitute these two probabilities back into equation 20: 

(21) Pr(Selection for the CF Test | Sample Child Age 5 to 11 

and Another Sample Children Present) 

= 1.0 * [y/(c – 1)] + 0.5 * [1 – (y/(c – 1))] 

= 0.5 * [1 + (y/(c – 1))] 

where: 

c = the number of children age 5 to 19 in the core household, and 

y = the number of youth age 12 to 19 in the core household. 

In summary, the probability of selecting a sample child ages 5 to 11 for the CF test is: 

(22) Sampling Probability for the CF Test for sample child ages 5 to 11 

= 







1 if c = 1 

0.5 * [1 + (y/(c - 1))] if c >1 

where c = number of children ages 5-19 and y = the number of youth. The weight adjustment for the 

Concept Formation test is equal to the inverse of this sampling probability such that: 

(23) CF Weight for Sampled Child Ages 5 to 11 = 





1 if c = 1 

2 / [1 + (y/(c - 1))] if c >1 

B6.4 The 3-in-10 subsample weights 

We also adjusted the survey data to account for selection into the 3-in-10 subsample. After 

completion of the main field period, we randomly selected 3 in 10 uncompleted cases for further 

fieldwork. The probability of selecting one of these cases was 3/10. Thus, subsample components 

were weighted up by the inverse of this probability or by 10/3. 

Appendix B – Samples and Analysis Methods B-21 
The main sample field period ended on June 22, 2002, for the LA and Baltimore sites and on July 6, 

2002, for the Boston, Chicago, and New York sites. We considered a case as “uncompleted” at the 

end of this period if any of the instruments (2-3 depending on the sample type) had not yet been 

completed. Exhibit B.4 lists the specific instruments whose subsample eligibility was tracked for each 

sample type. Uncompleted cases with family id numbers ending in a selected digit (see Section 1.3) 

were eligible for the subsample. 

EXHIBIT B.4 

INSTRUMENTS TRACKED FOR EACH SAMPLE TYPE 

Sample Survey/Instrument 1 Instrument 2 Instrument 3 

Adult Adult Survey Household Observation Neighborhood 

Observation 

Child 5-7 (not applicable) Height & Weight WJ-R 

Child 8-11 Child Survey (not applicable) WJ-R 

Child 12-19 Youth Survey WJ-R (not applicable) 

We assigned the subsample adjustment weights using two nested criteria: 

i) Was the instrument completed during the main field period? 

• If yes, no adjustment was necessary and the subsample weight equals one. 

• If not, ii) Was the instrument eligible for the subsample? 

− If yes, the weight of this observation was adjusted by a factor of 10/3. 

− If not eligible, this observation was not used in the weighted survey results and the 

subsample weight was set to zero. 

Exhibit B.5 summarizes the relationship between the two criteria and the subsample weight 

adjustments. 

EXHIBIT B.5 

SUBSAMPLE WEIGHT ADJUSTMENT DEPENDING ON I) COMPLETION STATUS 

AT THE CLOSE OF THE MAIN FIELD PERIOD AND II) SUBSAMPLE ELIGIBILITY 

Eligible for the Not Eligible for the 

Subsample Subsample 

Complete During Main Period 1 1 

Not Complete During Main Period 10/3 0 

For simplicity, we assigned subsample weights to groups of survey items or measures rather than to 

each item. In addition to the instruments identified in exhibit B.4, we also needed subsample weights 

for two additional measures: a) adult blood pressure and b) height and weight of children ages 8-11. 

Separate weights were needed because the subsample status of these measures did not necessarily 

align with other instruments. (For example, although the Adult Survey and adult blood pressure 

B-22 Appendix B – Samples and Analysis Methods 
measures were usually done at the same time, there were some adults with a telephone survey done 

during the main field period and a blood pressure taken during the subsample period.) Since 

subsample status for the two measures was not specifically tracked, we used final disposition 

information on telephone interviews to help determine subsample eligibility. We also created a 

separate weight for the POCY module. 

In all, we created six subsample weights for the different instruments: survey (Adult, Child, and 

Youth), POCY, measurement (height and weight or blood pressure), Household Observation, 

Neighborhood Observation and WJ-R. Exhibit B.6 summarizes the different subsample weights and 

the criteria used to assign them: 

• Sample (column 1) - indicates whether the weight applies to the adult or child sample, 

• Instrument (column 2) - indicates the specific instrument, 

• Weight Variable (column 3) - indicates the name of the subsample weight variable, 

• Complete at end of Study (column 4) - shows the criteria for determining that the instrument was 

“complete” by the end of the field period, 

• Complete at End of Main Sample Period? (column 5) - shows the criteria for determining that the 

instrument was completed during the main field period, and 

• Eligible for the Subsample? (column 6) - shows the criteria for determining that the instrument 

was eligible to be pursued during the subsample field period. (Note that this is essentially a check 

of whether the last digit of the FAMID was eligible for the subsample and a double check that the 

criteria in column 5 are complete.) 

Columns 5 and 6 determined the subsample weight: 

• If Column 5 (Complete at End of Main Sample Period?) was true, the subsample weight was set 

to one (i.e., component was part of the main sample). 

• If Column 5 was false AND Column 6 (Eligible for the Subsample?) was true, the subsample 

weight was set to 10/3 (i.e., component was part of the subsample). 

• If neither Column 5 nor Column 6 was true, the subsample weight was set to zero (component 

was not completed during the main period and was not pursued during the subsample because the 

last digit of the family ID was not eligible). 

In theory, we could have identified subsample components by interview dates after June 22, 2002, for 

LA and Baltimore and after July 6, 2002, for Boston, Chicago, and New York. However, due to data 

retrievals and postponed appointments, components with later dates are not always part of the 

subsample. 

Appendix B – Samples and Analysis Methods B-23 
EXHIBIT B.6 

INFORMATION USED TO DETERMINE COMPLETION STATUS AND SUBSAMPLE 

ELIGIBILITY FOR EACH INSTRUMENT 

Sample Instrument Weight 

Variable 

“Complete” at end of 

Study 

i) Complete at End 

of Main Field 

Period? 

ii) Eligible for the 

Subsample? 

Yes if "Complete" at 

end of Study 

AND 

Yes if last digit of family 

ID was eligible for 

subsample 

AND 

Adult Adult 

Survey 

wt_310svy FINAL_DISP is 

Telephone Complete, InPerson Complete, or 

Breakoff 

a) SURVEY_MAIN = 

1, or 

b) SUB_ELIG ~= 1 

a) SURVEY_MAIN = 0, or 

b) FINAL_DISP not 

telephone, in-person, or 

breakoff. 

Adult Blood wt_310msr a) BP measurement, a) SURVEY_MAIN = a) FINAL DISP = 

Pressure b) Adult Survey 1 and either: Deceased, 

(BP) completed but unable to FINAL_DISP not b) SURVEY_MAIN = 0, 

measure BP for reason 

other than “Telephone 

equal to “Telephone 

Complete” 

or 

c) FINAL_DISP = 

Interview,” or 

c) "Telephone Complete" 

but no sample children 10 

or No Sample 

Children 11 

, or 

b) SUB_ELIG ~= 1 

telephone complete and 

sample children in the 

household. 

Adult Household 

Observati 

on 

wt_310hobs a)HHOBS_COMPLETED 

= 1, or 

b) HH_COMPLETED12 

FINAL_DISP is not 

deceased13

 nor 

Cannot Locate (and 

inst2_sub ~= 1) 

AND either: 

a) INS2_MAIN = 0 and 

SURVEY_MAIN = 0, or 

b) FINAL DISP = 

Deceased or Cannot 

Locate 

a) INS2_MAIN = 1, 

b) SURVEY_MAIN = 

1, or 

c) SUB_ELIG ~= 1 

10 If a telephone interview was conducted, subsample attempts to measure blood pressure were only made if 

there were also sample children in the household (e.g., who needed to be administered the WJ-R). 

11 “No sample children” refers to no sample children in the original sample selection and does not include 

families in which the children were later determined to be ineligible. 

12 In calculating the subsample weights, we make the simplifying assumption that HHOBS and NHOBS were 

not pursued during the subsample period unless the Adult Survey had not yet been completed. Although, 

this is different than the process described by the field, it is consistent with the data and resolves a number 

of discrepancies. 

13 In some instances, a household and neighborhood observation was completed for a sample child’s 

household even though the Sample Adult was deceased. 

B-24 Appendix B – Samples and Analysis Methods 
Sample Instrument 

Weight 

Variable 

“Complete” at end of 

Study 

i) Complete at End 

of Main Field 

Period? 

ii) Eligible for the 

Subsample? 

Yes if "Complete" at 

end of Study 

AND 

Yes if last digit of family 

ID was eligible for 

subsample 

AND 

Adult Neighborh 

ood 

wt_310nobs a)NHOBS_COMPLETED 

= 1, or 

FINAL DISP is not 

Deceased nor Cannot 

a) INS3_MAIN = 0 and 

SURVEY_MAIN = 0, or 

Observati 

on 

b) HH_COMPLETED Locate (and 

inst3_sub ~= 1) 

AND either: 

b) FINAL DISP = 

Deceased or Cannot 

Locate 

a) INS3_MAIN = 1, 

b) SURVEY_MAIN = 

1, or 

c) SUB_ELIG ~= 1. 

Child 5-7 Height 

&Weight 

(HW) 

wt_310msr a) height or weight 

measured, or b) HW 

attempted but unable to 

measure for a reason 

a) INS2_MAIN = 1, or 

b) SUB_ELIG ~= 1. 

a) INS2_Main = 0, or 

b) FINAL DISP = 

Deceased 

other than “Telephone 

Complete” 

Child 8- Height wt_310msr a) height or weight a) SURVEY_MAIN = a) SURVEY_MAIN = 0, b) 

11 &Weight measured, or b) HW 1 and FINAL_DISP is FINAL DISP = Deceased, 

(HW) attempted but unable to 

measure for a reason 

NOT telephone 

complete, or 

or 

c) INS3_Main = 0 

other than “Telephone b) SUB_ELIG ~= 1. and FINAL_DISP is 

Complete” telephone complete 

Child 8- Child wt_310svy FINAL_DISP is a) SURVEY_MAIN = a) SURVEY_MAIN = 0, or 

11 Survey Telephone or In-Person 1, or b) FINAL DISP = 

Complete b) SUB_ELIG ~= 1. Deceased or Breakoff 

Child 12- Youth wt_310svy FINAL_DISP is a) SURVEY_MAIN = a) SURVEY_MAIN = 0, or 

19 Survey Telephone or In-Person 1, or b) FINAL DISP = 

Complete b) SUB_ELIG ~= 1. Deceased or Breakoff 

Child 5- WJ-R wt_310wjr any WJ-R subtest a) INS3_MAIN = 1, or a) INS3_MAIN = 0, 

11 completed b) SUB_ELIG ~= 1. b) FINAL DISP = 

(WJR_COMPLETED = Deceased 

1) 

Child 12- WJ-R wt_310wjr any WJ-R subtest a) INS2_MAIN = 1, or a) INS2_MAIN = 0, 

19 completed b) SUB_ELIG ~= 1. b) FINAL DISP = 

(WJR_COMPLETED = Deceased 

1) 

Child 5- POCY wt_310pocy (same as Adult Survey if (same as Adult (same as Adult Survey if 

19 sample child present) Survey if sample child sample child present) 

present) 

Appendix B – Samples and Analysis Methods B-25 
B6.5 Combined weights 

In all, we created ten component weights: 

RA Ratio Weight (wt_Raratio) - a family-level weight to adjust for changes in the randomization ratio 

for families assigned by the end of 1997. This weight is: 

= 0 for families randomized after 1997 and for families with no sample adult after resolution 

of individuals assigned to two core families, and 

= wjkh (see equation 16) for all other families randomized by December 31, 1997. 

RA Ratio Weight Including Families Randomized in 1998 (wt_Raratio98) - a family-level weight to 

adjust for changes in the randomization ratio for all families (including those randomized in 1998). 

This weight is: 

= 0 for families with no sample adult after resolution of individuals assigned to two core 

families, and 

= wjkh (see equation 16) for all other families. 

Survey Sample Weight (wt_sampsvy) - a person-level weight equal to the inverse probability of 

selection for the survey or testing. This weight is: 

= 0 for individuals who are neither sample adults nor sample children; 

= 1 for sample adults; and 

= the inverse probability of being selected for sample children. 

Concept Formation Weight (wt_Cftest) - a person-level weight to adjust for selection of children for 

the Concept Formation test. This weight is: 

= 0 for all adults and for all children not selected for the Concept Formation test; and 

= for children selected for the Concept Formation test, the inverse probability of being 

selected for the Concept Formation test having already been selected for the child 

sample. 

3-in-10 Survey Subsample Weight (wt_310svy) - a person-level survey weight to adjust for selection 

into the subsample. This weight is: 

= 0 for a) all individuals who are neither sample adults nor sample children ages 8-19, and b) 

individuals without a completed survey by the end of the main field period and who 

were not eligible for the subsample; 

= 1 for all sample adults and sample children with surveys completed during the main field 

period; 

= 10/3 for sample adults and sample children ages 8-19 without a completed survey by the 

end of the main field period and who were eligible for the subsample. 

3-in-10 POCY Subsample Weight (wt_310pocy) - a person level POCY weight to adjust for selection 

into the subsample. This weight is equal to: 

= 0 for all adults, for children not selected for the sample, and for children who did not have a 

completed POCY by the end of the main field period and who were not eligible for 

the subsample; 

= 1 for all sample children with a POCY completed during the main field period; and 

B-26 Appendix B – Samples and Analysis Methods 
= 10/3 for all sample children who did not have a POCY completed during the subsample 

period and whose parent was eligible for the subsample. 

3-in-10 Measurement Subsample Weight (wt_310msr) - a person- level measurement weight to adjust 

for selection into the subsample. This weight is: 

= 0 for a) all individuals who are neither sample adults nor sample children ages 5-11, and b) 

sample adults and sample children ages 5-11 without a completed measure (blood 

pressure or height and weight, respectively) at the end of the main field period and 

who were not eligible for the subsample; 

= 1 for sample adults and sample children ages 5-11 with a completed measure (BP or height 

and weight, respectively) from the main field period; 

= 10/3 for sample adults and sample children ages 5-11 without a completed measure (blood 

pressure or height and weight) at the end of the main field period and who were 

eligible for the subsample. 

3-in-10 WJ-R Subsample Weight (wt_310wjr) - a person-component level weight to adjust for 

selection into the subsample. This weight is: 

= 0 for a) all adults, b) all children not selected for the sample, and c) sample children who 

did not have any WJ-R subtests completed by the end of the regular field period and 

who were not eligible for the subsample; 

= 1 for sample children with at least one WJ-R subtest completed during the main field 

period; 

= 10/3 for sample children who did not have any WJ-R subtests completed by the end of the 

main field period and who were eligible for the subsample. 

3-in-10 Household Observation Subsample Weight (wt_310hobs) - a person-level household 

observation weight to adjust for selection into the subsample. This weight is: 

= 0 for a) all individuals who are not sample adults, and b) sample adults without completed 

Household Observation data by the end of the main field period and who were not 

eligible for the subsample; 

= 1 for sample adults with Household Observation data completed during the main field 

period; 

= 10/3 for sample adults without Household Observation data by the end of the main field 

period and who were eligible for the subsample. 

3-in-10 Neighborhood Observation Subsample Weight (wt_310nobs) - a person-level neighborhood 

observation weight to adjust for selection into the subsample. This weight is: 

= 0 for a) all individuals who are not sample adults, and b) sample adults without completed 

Neighborhood Observation data by the end of the main field period and who were not 

eligible for the subsample; 

= 1 for sample adults with Neighborhood Observation data completed during the main field 

period; 

= 10/3 for sample adults without Neighborhood Observation data by the end of the main field 

period and who were eligible for the subsample. 

Appendix B – Samples and Analysis Methods B-27 
More than one weight is applicable to each survey item. The final weights used in the analyses of the 

survey data were the product of these multiple weights. Below are the nine total weights used to 

analyze the data. 

Total Core Weight (wt_Totcore) – a person-level weight that is equal to the randomization ratio 

weight for all core members randomized through 1997 and is equal to zero for non-core members and 

members randomized after 1997 (i.e., wt_Raratio*core status through 1997). 

Total Core Weight Through 1998 (wt_Totcore98) – a person-level weight that is equal to the 

randomization ratio weight through 1998 for all core members and equal to zero for non-core 

members (i.e., wt_Raratio98*core status through 1998). 

Total Survey Weight (wt_Totsvy) – a person-level weight for self-reported surveys that is equal to the 

product of wt_Raratio * wt_Sampsvy * wt_310svy. 

Total POCY Weight (wt_Totpocy) - a child-level weight for the parental report portion of the survey 

that is equal to the product of wt_Raratio * wt_Sampsvy * wt_310pocy. 

Total Measurement Weight (wt_Totmsr) – a person-level weight for measurement data (Blood 

Pressure and Child Height and Weight for ages 5-11) that is equal to the product of wt_Raratio * 

wt_Sampsvy * wt_310msr. 

Total WJ-R Weight (wt_Totwjr) – a child-level weight for WJ-R test data that is equal to the product 

of wt_Raratio * wt_Sampsvy * wt_310wjr. 

Total Household Observation Weight (wt_Tothobs) – an adult-level weight for the adult’s Household 

Observation data that is equal to the product of wt_Raratio * wt_Sampsvy * wt_310hobs. 

Total Neighborhood Observation Weight (wt_Totnobs) – an adult-level weight for the adult’s 

Neighborhood Observation data that is equal to the product of wt_Raratio * wt_sampsvy * 

wt_310nobs. 

Total Concept Formation Weight (wt_Totcf) – a child-level weight for the Concept Formation tests 

that is equal to the product of wt_Raratio* wt_sampsvy * wt_310wjr * wt_cftest. 

Exhibit B.7 summarizes which combined weight is appropriate for each level of analysis and data 

source. 

B-28 Appendix B – Samples and Analysis Methods 
EXHIBIT B.7 

COMBINED WEIGHTS FOR DIFFERENT ANALYSIS LEVELS 

Analysis Level Data Source for Outcome or Mediator Weight 

Adult, Child, or All Core 

Members Randomized By 

the End of 1997 

Administrative Data (TANF, Food Stamps, 

Earnings, Criminal Justice) and Published Data 

(Census data linked to an individual’s address) 

wt_Totcore 

Adult, Child, or All Core 

Members Randomized 

Through 1998 

Administrative Data wt_Totcore98 

Adult (or Current 

Household of Adult) 

Adult Survey Sections A-G* and N (excluding 

blood pressure) 

wt_Totsvy 

Adult Adult blood pressure (F13-F16), wt_Totmsr 

Adult Household Observation (observation of the 

adult respondent and the parent-child 

interaction) 

wt_Tothobs 

Adult Neighborhood Observation (observations 

about the interior and exterior of the home) 

wt_Totnobs 

Child Child Survey (except height and weight), 

Youth Survey (including self-reported height 

and weight) 

wt_Totsvy 

Child POCY (Sections J-M of the Adult Survey), 

School Characteristics 

wt_Totpocy 

Child Child (ages 5-11) height and weight measures wt_Totmsr 

Child WJ-R test scores (except Concept Formation 

subtest) 

wt_Totwjr 

Child WJ-R Concept Formation Subtest wt_Totcf 

*This includes questions F7 through F9 which refer to one randomly selected child. 

B7. Missing Data 

We addressed two different types of missing data issues: unit nonresponse and item nonresponse. 

Below we describe the options we had for handling missing data and the options that were most 

considering the actual magnitude and nature of the problem. 

B7.1 Unit nonresponse 

We had no direct survey data on individuals who are deceased, could not be located, or refused to be 

interviewed. Observed and unobserved differences between these nonrespondents and the respondents 

could have potentially biased our estimates. 

To address unit nonresponse, we used two approaches: 

1) For outcomes measured with administrative data (e.g., earnings, welfare benefits), we 

estimated impacts on both the entire sample and on survey respondents only. The 

Appendix B – Samples and Analysis Methods B-29 
difference in these two estimates provided a measure of the bias attributable to 

nonresponse. 

2) By comparing the effects estimated using only the main sample with the effects estimated 

using the main sample plus the subsample, we explored the reduction in nonresponse bias 

achieved using the subsample. 

The results of these analyses are described in appendix F. 

B7.2 Item nonresponse 

The second type of problem concerned item nonresponse due to a subject not knowing or refusing to 

answer a particular question. In most cases, where the outcome itself was missing, the observation 

had to be excluded from the analysis; individuals with unknown values of the outcome can contribute 

nothing to the impact estimate. Such individuals were treated the same way we treat unit 

nonrespondents. The only exceptions to this rule were cases where data were missing for a 

component of family income; such components were imputed using the procedure described below. 

The treatment of missing covariates depended in part on their prevalence. If only a small percentage 

of the sample failed to respond on an item, it is unlikely that the treatment of these missing data 

would materially affect the impact estimates. Moreover, given the large number of outcomes 

analyzed, the use of sophisticated imputation techniques would have been extremely costly and timeconsuming. Therefore, for covariates with less than 5 percent nonresponse, we replaced missing 

values with conditional means (e.g., missing child covariates were replaced with mean values 

conditional on a child’s site, single-year age at baseline, and gender). Such an approach yields 

unbiased estimates if data are missing at random. An alternative, still relatively simple, approach is to 

include a dummy variable for “missing” along with each covariate. This approach yields unbiased 

impact estimates even if the data are not missing at random. Adopting this approach for all covariates, 

however, would have doubled the number of right-hand-side variables. Therefore, we used this 

strategy only for those covariates where more than 5 percent of the data were missing. 

In computing total household income, missing values of income components were imputed as 

follows. Missing values of TANF benefits, business income, and “other income” were replaced with 

sample means, by site and treatment group. Missing values of earned income for a sample member 

were imputed by regressing the known values of earned income on age, site, TANF benefits, business 

income, “other” income, and the difference between the value of total household income reported by 

the respondent in question G5 and the sum of other household members’ earnings.14 

Details of the imputation procedures are available from the authors on request. 

B-30 Appendix B – Samples and Analysis Methods 

14 
B8. Interpretation and Reporting of Results 

B8.1 Reporting unadjusted versus regression-adjusted means and impacts 

We presented only regression-adjusted means and impacts. While unadjusted means have some 

intuitive appeal as representing what “really” happened, they are clearly less reliable indicators of 

impact than the adjusted estimates. To the extent that two estimates differ, we wanted the reader to 

focus on the more reliable estimate. We reported only the control mean and the impact. These two 

numbers give the reader a sense of what difference the intervention made (the impact estimate) and 

how large that difference was relative to what would have happened in the absence of the intervention 

(the control mean). The treatment group mean adds no independent information. 

B8.2 Reporting of results from multiple data sources 

Many of the child survey outcomes could have been constructed using either the child’s self-report or 

using the adult’s report about the child. The Analysis Team agreed that it was only necessary to report 

one of the effects in the text (and footnote the other) if the reporting sources were in agreement 

regarding the treatment effect. 

B8.3 Interpretation of multiple tests 

In interpreting the results and assessing their joint significance, we considered the number of 

estimates produced with this unusually rich data set. In estimating effects for different subgroups, we 

also needed to keep in mind the overall impacts and impacts for all of the subgroups rather than just 

focusing on subgroups with positive findings. To minimize the risk of Type 1 errors in these analyses, 

we adopted a significance threshold of 5 percent, rather than the conventional 10 percent. Even with 

this safeguard, it was important to be alert to the danger of estimates that may have been statistically 

significant by chance alone. When estimating impacts on a large number of outcomes, for example, 

we considered whether the number of statistically significant impacts materially exceeded the 5 

percent level that would be expected under the null hypothesis of no true effect. See appendix I for a 

detailed discussion of this issue. 

Appendix B – Samples and Analysis Methods B-31 

Appendix C 

Descriptive Tables and Maps 

EXHIBIT C1.1 

COMPARISON OF INTERIM EVALUATION SAMPLE WITH FULL MTO POPULATION 

ON DEMOGRAPHIC AND SOCIOECONOMIC CHARACTERISTICS 

Interim Evaluation Full MTO 

Sample1 Population2 

(All Groups) (All Groups) 

Race/Ethnicity of Head of Household 

African-American non-Hispanic 

Hispanic 

White non-Hispanic 

American Indian non-Hispanic 

Asian/Pacific Islander non-Hispanic 

Other non-Hispanic

Sex of Head of Household 

Male 

Female

 62.6% 

30.4% 

2.9% 

0.4% 

1.8% 

2.1% 

8.4% 

91.6% 

62.1% 

31.3% 

2.6% 

0.4% 

1.7% 

2.0% 

8.8% 

91.2% 

Head of Household’s Marital Status 

Never married 

Married 

Widowed 

Divorced

 62.2% 

11.3% 

9.5% 

17.1% 

61.9% 

11.5% 

9.3% 

17.3% 

Median Number of Children 3 3 

Average Total Household Income $9,314 $9,310 

Percent with AFDC as Primary Income 

Source 61.6% 59.1% 

Head of Household Currently in School? 

Yes 

No

 16.1% 

83.9% 

16.2% 

83.8% 

Head of Household a Graduate? 

High School 

GED 

Neither

 40.6% 

19.7% 

39.7% 

40.7% 

18.6% 

40.7% 

Head of Household Currently Working? 

Full-time 

Part-time 

Not working 

Working for benefits

 16.1% 

11.5% 

72.2% 

0.1% 

16.5% 

11.4% 

72.1% 

0.1% 

Source: MTO Participant Baseline Survey, initial HUD Form 50058. 

Sample: Adults. 

Notes: The respondent to the baseline survey was usually the same person as the sample adult for the interim 

evaluation. Household income was defined following the rules for Section 8 eligibility. Percentages may not add 

to 100 because of rounding. Data are weighted as described in appendix B. 

1

 MTO population randomly assigned through December 1997. N=4248 

2

 Full MTO program population (all cases ever randomly assigned). N=4608 

Appendix C – Descriptive Tables and Maps C-1 
Exhibit C1.2 

Demographic and Socioeconomic Characteristics of 

MTO Families By Site Group 

Baltimor Los New All 

e Boston Chicago Angeles York Sites 

Race/Ethnicity of Head of Household a 

African-American non-Hispanic 95.9% 32.8% 98.2% 50.0% 47.8% 62.6% 

Hispanic 1.6% 45.2% 0.9% 45.3% 49.2% 30.4% 

White non-Hispanic 0.2% 11.0% 0.1% 0.8% 0.8% 2.9% 

American Indian non-Hispanic 0.7% 0.7% 0.1% 0.2% 0.1% 0.4% 

Asian/Pacific Islander non-Hispanic 0.0% 5.1% 0.0% 3.0% 0.6% 1.8% 

Other non-Hispanic 1.6% 5.2% 0.8% 0.7% 1.5% 2.1% 

Sex of Head of Household 

Male 

Female 

2.4% 

97.7% 

8.5% 

91.5% 

3.9% 

96.1% 

20.8% 

79.2% 

8.1% 

91.9% 

8.4% 

91.6% 

Head of Household’s Marital Status 

Never married 74.2% 57.9% 73.9% 56.0% 53.6% 62.2% 

Married 3.5% 12.4% 6.5% 22.6% 11.4% 11.3% 

Divorced 8.8% 11.9% 6.3% 6.0% 12.4% 9.5% 

Widowed or separated 13.5% 17.7% 13.4% 15.3% 22.6% 17.1% 

Median Number of Children 3 3 3 3 3 3 

Average Total Household Income $6,838 $10,701 $7,980 $9,949 $10,3 

16 $9,314 

Percent with AFDC as Primary 

Income Source 63.2% 50.9% 58.7% 74.1% 64.7% 61.6% 

Head of Household Currently in School? 

Yes 16.2% 17.7% 14.6% 14.6% 16.9% 16.1% 

No 83.8% 82.3% 85.4% 85.4% 83.1% 83.9% 

Head of Household a Graduate? 

High School 42.8% 45.4% 43.7% 37.5% 34.4% 40.6% 

GED 14.8% 22.7% 18.4% 7.4% 28.2% 19.7% 

Neither 42.4% 31.9% 37.9% 55.1% 37.4% 39.7% 

Head of Household Currently Working? 

Full-time 13.9% 20.8% 15.2% 16.5% 13.8% 16.1% 

Part-time 9.9% 13.9% 10.7% 11.7% 11.0% 11.5% 

Not working 76.3% 65.3% 74.1% 71.8% 74.8% 72.2% 

Working for benefits 0.0% 0.0% 0.0% 0.0% 0.4% 0.1% 

Source: MTO Participant Baseline Survey, initial HUD Form 50058. 

Sample: Adults from families randomly assigned through December 1997.

Notes: The respondent to the baseline survey was usually the same person as the sample adult for the interim 

evaluation. Household income was defined following the rules for Section 8 eligibility. Percentages may not add 

to 100 because of rounding. Data are weighted as described in appendix B. 

(a) Respondent self-reports. A number of African-American respondents skipped the ethnicity question and are 

not included in the distributions reported. Many Hispanics used the Other category for the race question. 

C-2 Appendix C – Descriptive Tables and Maps 
EXHIBIT C1.3 

BACKGROUND CONDITIONS AND EXPERIENCES OF 

MTO FAMILIES BY RANDOM ASSIGNMENT GROUP 

(PERCENT DISTRIBUTIONS—WEIGHTED DATA) 

Experimental Section 8 Control 

Group Group Group All Groups 

Moved More Than 3 Times in 5 Years? 

Yes 8.6 9.2 10.6 9.4 

No 91.4 90.8 89.4 90.6 

Ever Lived Outside the [City] Area? 

Yes 30.5 29.2 27.2 29.1% 

No 69.5 70.8 72.8 70.9 

Most Important Reason for Wanting to Move? 

Get away from drugs, gangs 54.7 50.7 52.5 52.9 

Get a bigger/better apartment 22.6 23.3 23.7 23.1 

Better schools for my children 16.0 19.5 16.8 17.3 

Get a job 1.4 1.0 1.2 1.2 

Be near my job 0.4 0.4 0.3 0.3 

Be near my family 0.8 0.8 1.0 0.9 

Have better transportation 0.2 0.3 0.1 0.2 

Other reasons 3.9 4.0 4.4 4.1 

Second Most Important Reason for Wanting to Move? 

Get away from drugs, gangs 28.1 30.2 30.3 29.4 

Get a bigger/better apartment 26.1 25.5 25.5 25.7 

Better schools for my children 32.2 33.0 30.9 32.0 

Get a job 4.4 4.6 4.7 4.6 

Be near my job 0.7 0.7 1.0 0.8 

Be near my family 2.6 1.7 2.2 2.2 

Have better transportation 1.1 0.3 1.3 0.9 

Other reasons 4.9 4.1 4.1 4.4 

Where Want to Move? 

Elsewhere in my neighborhood 6.5 6.6 6.8 6.6 

Different neighborhood in city 56.3 59.8 58.8 58.1 

Different neighborhood in suburbs 17.3 16.0 15.4 16.3 

Different city outside the area 17.5 16.0 16.6 16.8 

Other 2.5 1.6 2.5 2.2 

Condition of Current House or Apartment? 

Excellent 

Good 

Fair 

Poor 

5.4 

22.8 

47.0 

24.8 

4.2 

19.9 

49.6 

26.3 

5.3 

22.8 

46.1 

25.8 

5.0 

22.0 

47.5 

25.5 

Satisfaction With Current Neighborhood? 

Very/somewhat satisfied 

Ambivalent 

Very/somewhat dissatisfied 

11.1 

18.6 

70.3 

Experienced in the Past 6 Months? 

Purse/wallet/jewelry snatched 

Threatened with knife or gun 

Beaten or assaulted 

Stabbed or shot 

Break-in (attempted or actual) 

25.3 

24.5 

23.5 

10.9 

26.4 

19.8 

12.3 

67.9 

19.7 

12.1 

68.2 

19.3 

11.8 

69.0 

24.6 

24.3 

24.7 

10.5 

28.6 

23.5 

24.6 

23.3 

11.7 

25.5 

24.6 

24.4 

23.8 

11.0 

26.7 

Source: MTO Participant Baseline Survey 

Sample: Adults from families randomly assigned through December 1997. 

Notes: The respondent to the baseline survey was usually the same person as the sample adult for the interim 

evaluation. Household income was defined following the rules for Section 8 eligibility. Percentages may not add 

to 100 because of rounding. Data are weighted as described in appendix B. 

Appendix C – Descriptive Tables and Maps C-3 
Exhibit C2.1

Location of MTO Interim Evaluation Sample in 2002

Original MTO State 

States with MTO Sample 

States with No MTO Sample 

Source: Adult Survey. 

Sample: Adults. 

Note: Sample adults also live in Puerto Rico. 

Abt Associates Inc. 05/08/2003 
EXHIBIT C3.1 

CURRENT NEIGHBORHOOD CONDITIONS AND QUALITY FOR 

THE INTERIM EVALUATION SAMPLE BY RANDOM ASSIGNMENT GROUP 

(PERCENT DISTRIBUTIONS) 

 Experimental Section 8 Control 

Group Group Group All Groups 

Length of Time in Current Neighborhood (Self-Report) 

Less than 6 months 30.9 13.0 8.5 18.9 

6–12 months 3.1 26.4 15.7 13.0 

1–2 years 18.9 28.8 33.3 26.3 

3–4 years 3.1 11.3 19.0 10.6 

5–7 years 6.9 0.0 6.8 5.3 

Over 7 years 37.1 20.6 16.7 26.1 

Seen People Selling Drugs in Your Neighborhood 

During the Past 30 Days? 

No 67.4 65.5 54.8 63.0 

Yes, a couple times a month 3.0 4.4 4.9 3.9 

Yes, about once a week 3.9 4.6 5.7 4.7 

Yes, almost every day 25.8 25.5 34.7 28.4 

Experienced This in The Past Six Months? 

Purse/wallet/jewelry snatched 5.9 4.2 7.2 5.8 

Threatened with knife or gun 4.8 4.8 7.4 5.6 

Beaten or assaulted 6.5 6.8 9.5 7.5 

Stabbed or shot 3.7 2.7 4.4 3.6 

Break-in (attempted or actual) 7.5 6.5 7.8 7.3 

Feel Safe in Your Neighborhood during the Day? 

Yes 84.6 84.5 74.8 81.6 

No 15.4 15.5 25.2 18.4 

Feel Safe in Your Neighborhood at Night? 

Yes 69.3 64.2 54.7 63.4 

No 30.8 35.9 45.3 36.7 

Percent With Specific Neighborhood Problem (Self-Report) a 

Litter or trash on the streets or 

sidewalks 53.1 56.6 63.4 57.2 

Graffiti on the walls 34.7 38.5 47.9 39.8 

People drinking in public 39.7 45.0 56.3 46.3 

Abandoned buildings 27.2 27.5 33.8 29.3 

Groups of people hanging out 47.6 51.3 64.5 53.8 

Police not coming when called 20.9 24.6 33.7 25.9 

Mean Index of Neighborhood 

Problems b 2.2 2.4 3.0 2.5 

Current Neighborhood Satisfaction 

Very satisfied 32.4 30.0 19.5 27.8 

Somewhat satisfied 29.7 29.1 28.1 29.0 

Ambivalent 15.4 18.1 20.4 17.7 

Somewhat dissatisfied 9.8 10.0 13.6 11.0 

Very dissatisfied 12.7 12.8 18.5 14.5 

Source: 

Sample: Adults from families randomly assigned through December 1997.. 

Notes: Data are weighted as described in appendix B. 

(a) Percent reporting big problem or small problem with item. 

(b) Index is sum of items reported to be big or small problem by the adult respondent. Maximum value 6. 

Appendix C – Descriptive Tables and Maps C-15 
EXHIBIT C3.2 

CURRENT HOUSING STATUS OF THE INTERIM EVALUATION SAMPLE 

BY RANDOM ASSIGNMENT GROUP

Current Housing Tenure 

Rent 

 Experimental 

Group 

90.4% 

Section 8 

Group 

91.5% 

Control 

Group 

89.3% 

All Groups 

90.4% 

Own 4.7% 4.6% 5.3% 4.8% 

Live with others and pay rent 

Live with others and pay no rent 

Other living situationsa 

0.7% 

3.9% 

0.3% 

0.3% 

2.9% 

0.7% 

0.9% 

3.9% 

0.7% 

0.7% 

3.6% 

0.5% 

Length of Time in Current Housing Unit 

Under 1 year 

1–2 years 

3–4 years 

5–10 years 

Over 10 years 

17.9% 

20.4% 

24.7% 

26.9% 

10.2% 

16.1% 

20.0% 

25.3% 

27.8% 

10.8% 

15.3% 

21.0% 

22.0% 

26.7% 

15.0% 

16.6% 

20.5% 

24.1% 

27.0% 

11.8% 

Number of Moves Since Random Assignment (Self-report) 

None 28.6% 31.1% 34.0% 30.9% 

1–3 66.9% 62.5% 61.1% 63.95 

4 or more 4.6% 6.4% 4.9% 5.2% 

Average Monthly Payment For: 

Rent $286 $277 $298 $287 

Mortgage 

Utilities 

$1,491 

$101 

$996 

$105 

$898 

$80 

$1,153 

$96 

Total Monthly Housing Cost b 

Mean $440 $417 $409 $424 

Median $355 $339 $320 $339 

Total Monthly Housing Cost Burden c 

Mean 0.33 0.32 0.31 0.32 

Median 0.30 0.30 0.30 0.30 

Those Reporting Encountering 

Bias or Discrimination In Housing 

Search 

6.7% 9.5% 3.0% 6.4% 

Those Reporting Being Homeless 

or Evicted In The Past Year 9.4% 9.8% 8.1% 9.1% 

Those Reporting Recent Landlord 

Problems 4.2% 4.8% 3.6% 4.2% 

Those Experiencing Utility Payment Problems 

Moderate d 25.6% 25.5% 20.2% 23.9% 

Severe e 6.0% 4.3% 3.5% 4.7% 

Those Experiencing Payment Problems 

Moderatef 12.3% 12.3% 12.2% 12.3% 

Severeg 6.5% 6.1% 8.6% 7.0% 

Source: Adult survey

Sample: Adults from families randomly assigned through December 1997.

Notes: Data are weighted as described in appendix B. 

(a) Other living situations include homeless and living in a group shelter; homeless and living on the street; 

incarcerated; living in a group home, dorm, or barracks; and living in a hospital, nursing home, or special school. 

(b) Includes all utility payments, rent (for renters), and payments to principal, interest, taxes, and insurance (for owners). 

(c) Ratio of total monthly housing cost to total monthly household income. 

(d) Respondent reports up to 3 of the following: 15 or more days late in paying utilities at least once in the past 12 

months, charged a late fee, or received a shut-off notice. 

(e) Respondent reports all 3 of problems in (d) as well as either having services shut off or moving out (even for a little 

while) because utilities were shut off. 

(f) Respondent reports being 15 or more days late in paying rent or mortgage at least once in the past 12 months. 

(g) Respondent reports problem in (f) as well as receiving an eviction or foreclosure threat due to nonpayment. 

C-16 Appendix C – Descriptive Tables and Maps 
EXHIBIT C3.3 

CURRENT RECEIPT OF HOUSING ASSISTANCE IN THE INTERIM EVALUATION SAMPLE 

BY RANDOM ASSIGNMENT GROUPa 

(PERCENT DISTRIBUTIONS) 

Experimental Section 8 Control 

Group Group Group All Groups 

Living in a Household Receiving Housing 

Assistance (self-report) (n=3525) 

Yes 78.1 78.4 70.8 76.0 

No 21.9 21.6 29.2 24.0 

Living in a Household Receiving Housing 

Assistance (Administrative data) (n=4248) 

Yes 68.9 75.7 65.8 69.9 

No 31.1 24.3 34.2 30.1 

Type of Housing Assistance (self-report) (n=3525) 

Public housing 25.4 22.4 45.4 30.7 

Tenant-based vouchers 47.8 51.9 20.6 40.7 

Project-based vouchers 2.6 2.7 2.4 2.6 

Assisted, type unknown 2.2 1.4 2.3 2.0 

No Housing Assistance 21.9 21.6 29.2 24.0 

Type of Housing Assistance (Administrative data) 

(n=4248) 

Public housing 24.9 21.1 43.5 29.6 

Tenant-based vouchers 37.8 48.4 12.4 33.0 

Project-based vouchers 6.1 6.2 9.9 7.3 

No housing assistance 31.1 24.3 34.2 30.1 

Main Reason Left Housing Assistance 

Wanted/bought house 19.6 22. 1 17.1 19.5 

Problem with PHA 16.4 15.0 7.9 13.3 

Income over limits 14.1 14.3 6.0 11.5 

Other 4.7 11.2 13.5 9.4 

Dangerous neighborhood 9.5 3.1 13.9 9.1 

Wanted better neighborhood/unit 5.3 7.7 12.4 8.3 

Evicted 6.4 4.1 10.4 7.0 

Rent/utilities too high 7.2 4.4 3.7 5.3 

Relocated 5.8 4.6 5.3 5.3 

Landlord would not take Section 8 5.5 8.8 1.5 5.1 

Building no longer inhabitable 2.9 1.5 5.7 3.4 

Family reasons 2.5 3.1 2.8 2.8 

Sources: Adult survey, HUD MTCS and TRACS 

Sample: Adults from families randomly assigned through December 1997.

Notes: Data are weighted as described in appendix B. 

(a) Receiving housing assistance includes living in public housing, living in private assisted housing, or having a 

tenant-based housing voucher. 

Appendix C – Descriptive Tables and Maps C-17 
EXHIBIT C3.4 

CURRENT HOUSING CONDITION AND QUALITY OF 

THE INTERIM EVALUATION SAMPLE BY RANDOM ASSIGNMENT GROUP 

(PERCENT DISTRIBUTIONS) 

Experimental Section 8 Control 

Group Group Group All Groups 

Respondent’s Rating of Current Housing Condition 

Excellent 20.4 19.4 14.7 18.3 

Good 42.5 40.1 38.1 40.5 

Fair 27.2 28.5 34.8 29.9 

Poor 9.9 12.0 12.5 11.3 

Number of Rooms in Current Housing Unit 

1–3 rooms 18.1 21.1 22.9 20.4 

4 rooms 26.2 25.1 26.2 25.9 

5 rooms 29.9 27.3 28.4 28.7 

6 or more rooms 25.8 26.5 22.5 25.0 

Type of Structure a 

Single-family detached 14.5 14.8 11.2 13.6 

Single-family attached 23.8 21.4 24.0 23.2 

1–3 Story multifamily 34.8 33.1 30.1 32.9 

4–6 Story multifamily 9.0 12.0 8.5 9.7 

7+ Story multifamily 17.4 18.5 25.7 20.2 

Mobile home 0.1 0.1 0.2 0.1 

Other 0.4 0.2 0.2 0.3 

General Condition of Unit/Building a 

Well kept, good repair 38.0 34.8 31.9 35.2 

Fair condition 48.4 50.7 52.4 50.2 

Poor condition 11.2 12.5 10.5 11.4 

Badly deteriorated 2.4 2.1 5.2 3.2 

Observed Problems in Unit a 

Open cracks or holes in walls 13.1 13.0 13.6 13.2 

Peeling paint/plaster 12.2 10.5 12.1 11.7 

 Moderate clutter 30.7 29.1 30.2 30.1 

Severe clutter 10.4 14.1 12.7 12.1 

Percent with Specific Housing Problem (Self-Report) b 

Walls with peeling paint or broken 

plaster 40.5 42.7 49.2 43.8 

Plumbing that does not work 28.6 31.2 32.4 30.5 

Rats or mice 30.3 35.9 35.2 33.4 

Cockroaches 32.3 34.0 37.7 34.4 

Broken locks or no locks on doors 12.9 15.0 16.6 14.6 

Heating system that does not work 16.7 18.3 19.6 18.0 

Mean Index of Housing 1.8 2.0 2.1 2.0 Problems c 

Sources: Adult Survey, Interviewer Household and Neighborhood Observations 

Sample: Adults from families randomly assigned through December 1997. 

Notes: Data are weighted as described in appendix B. 

(a) Interviewer observation items. Other items are self-reports by adult sample member. 

(b) Percent reporting big problem or small problem with item. 

(c) Index is sum of items reported to be big or small problem by the adult respondent. Maximum value is 7. 

C-18 Appendix C – Descriptive Tables and Maps 
Appendix D 

Detailed Estimation Results – Outcomes by Domain 

EXHIBIT D2.1 

CONTEXT OF THE MTO TREATMENT 

Control 

Experimental 

vs. Control 

Section 8 

vs. Control 

Context Mean ITTa TOTa ITTa TOTa 

Characteristics of the Current Neighborhood (Census 2000) 

Percent persons in 38.5 -7.8* -16.7* -6.2* -10.2* 

poverty (n=3675) (0.8) (1.7) (0.8) (1.4) 

receiving public 

(n=3675) 

17.4 -4.0* 

(0.4) 

-8.6* 

(0.9) 

-3.4* 

(0.4) 

-5.5* 

(0.7) 

56.8 -6.7* -14.3* -5.2* -8.5* 

 Percent households 

assistance income 

 Percent female-headed 

households with their (0.8) (1.6) (0.8) (1.3) 

25.7 -3.2* 

(0.5) 

-6.9* 

(1.0) 

-2.5* 

(0.5) 

-4.1* 

(0.8) 

own children (n=3674) 

Percent high school 

dropouts (n=3675) 

 Unemployment rate 18.9 -3.4* -7.3* -3.1* -5.1* 

(n=3674) (0.4) (0.8) (0.4) (0.6) 

Labor Force Participation (n=3675) 

Males 54.7 4.0* 

(0.5) 

8.5* 

(1.0) 

2.9* 

(0.5) 

4.7* 

(0.8) 

Females 47.3 3.9* 8.4* 2.6* 4.3* 

(0.4) (0.8) (0.4) (0.6) 

Percent families with no 

workers (n=3675) 

24.1 -4.3* 

(0.4) 

-9.1* 

(0.9) 

-3.6* 

(0.4) 

-5.8* 

(0.7) 

Percent households with 66.6 3.8* 8.2* 3.5* 5.7* 

wage or salary income (0.5) (1.1) (0.6) (0.9) 

(n=3675) 

Percent of persons with 

(n=3675) 

15.8 2.0* 

(0.2) 

4.3* 

(0.5) 

1.7* 

(0.2) 

2.7* 

some college completed (0.4)

Percent 16- to 19-year- 74.7 1.3* 2.8* 0.9 1.4 

(0.5) (1.0) (0.5) (0.8) 

23.3 9.3* 

(0.9) 

19.7* 

(1.9) 

6.4* 

(1.0) 

10.4* 

(1.6) 

olds in school (n=3669) 

 Percent owner-occupied 

housing (n=3675) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Source: Census 2000 

Sample: Adults from families randomly assigned through December 31, 1997. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

Appendix D – Detailed Estimation Results – Outcomes by Domain D-1 
EXHIBIT D5.1 

CHILD AND ADULT OUTCOMES 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

[PR] (n=2514)

) 0.287 0.008 

(0.015) 

0.017 

(0.031) 

0.011 

(0.016) 

0.017 

(0.024) 

Female 0.249 -0.026 

(0.020) 

-0.051 

(0.039) 

-0.017 

(0.022) 

-0.026 

(0.033) 

Male 0.327 0.042* 0.095* 0.041 0.061 

Abbreviated Behavior Problems Index 

All children (ages 5 to 11

(0.020) (0.045) (0.023) (0.034) 

Adult Arrests [ADMIN] (n=4211)

Ever arrested for any 0.317 -0.005 -0.010 -0.011 -0.019 

(0.011) (0.024) (0.012) (0.020) 

Ever arrested for 

property crime 

0.141 (0.008) 

(0.007) 

0.016 

(0.015) 

0.003 

(0.007) 

0.004 

(0.012) 

Ever arrested for violent 0.134 0.014 

(0.007) 

0.029 

(0.015) 

0.005 

(0.008) 

0.009 

(0.013) 

Ever arrested other crime 0.168 -0.015* 

(0.008) 

-0.032* 

(0.016) 

-0.010 

(0.008) 

-0.016 

(0.013) 

crime 

crime 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult survey for all five sites and individual criminal justice system arrest data from Baltimore, Boston, 

Chicago, Los Angeles, and New York. 

Sample: Children ages 5 to 11 on May 31, 2001. All sample adults. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – arrest data and SR – selfreport). 

D-2 Appendix D – Detailed Estimation Results – Outcomes by Domain 
EXHIBIT D6.1 

SCHOOLING OUTCOMES BY SUBGROUP 

Experimental Section 8 

Control vs. Control vs. Control 

Mean ITTa TOTa ITTa TOTa 

Mostly Bs or Higher [SR] (n=2752)

Female 0.440 0.006 0.013 0.006 0.010 

(0.037) (0.080) (0.040) (0.066)

Male 0.342 -0.093* 

(0.036) 

-0.213* 

(0.082) 

-0.106* 

(0.038) 

-0.194* 

(0.070) 

Advanced Coursework [SR] (n=5505)

Female 0.212 0.004 0.008 0.011 0.018 

(0.022) (0.047) (0.025) (0.039) 

Male 0.188 -0.009 

(0.023) 

-0.015 

(0.053) 

0.009 

(0.024) 

0.014 

(0.039) 

Special Ed Recipient [PR] (n=4724)

Female 0.185 0.012 0.024 -0.011 -0.018 

(0.022) (0.045) (0.023) (0.037) 

Male 0.304 0.038 

(0.026) 

0.086 

(0.058) 

0.038 

(0.029) 

0.062 

(0.046) 

Ages 5 to 7 0.191 0.032 0.064 0.027 0.042 

(0.041) (0.082) (0.045 (0.070) 

0.259 0.043 

(0.031) 

0.095 

(0.068) 

0.035 

(0.033) 

0.057 

(0.049) 

Ages 8 to 11 

Ages 12 to 19 0.253 0.015 0.033 -0.011 -0.018 

Educationally on Track [SR] (n=1549)

Female 0.761 

(0.023) 

0.068 

(0.051) 

0.136 

(0.027) 

0.057 

(0.045) 

0.098 

(0.038) (0.076) (0.042) (0.073) 

Male 0.721 -0.008 

(0.039) 

-0.019 

(0.099) 

0.017 

(0.044) 

0.034 

(0.090) 

[PR] (n=5375)

 Female 0.199 0.019 0.039 -0.047 -0.076 

Ever Repeated a Grade 

(0.023) (0.048) (0.024) (0.038) 

Male 0.245 0.033 

(0.023) 

0.077 

(0.048) 

0.006 

(0.024) 

0.011 

(0.038) 

Ages 5 to 7 0.121 -0.022 -0.043 -0.029 -0.044 

(0.032) (0.064) (0.036) (0.056) 

0.185 0.072* 

(0.030) 

0.159* 

(0.065) 

0.033 

(0.030) 

0.050 

(0.046) 

Ages 8 to 11 

Ages 12 to 19 0.272 0.016 0.036 -0.053* -0.092* 

(0.023) (0.052) (0.024) (0.042) 

Took SAT/ACT [SR]

 Female 0.386 -0.017 -0.037 -0.021 -0.035 

 (n=1561)

(0.048) (0.102) (0.050) (0.083) 

Male 0.297 -0.046 

(0.042) 

-0.114 

(0.106) 

0.068 

(0.049) 

0.136 

(0.098) 

Appendix D – Detailed Estimation Results – Outcomes by Domain D-3 
EXHIBIT D6.1 (CONT.)

SCHOOLING OUTCOMES BY SUBGROUP

Experimental Section 8 

Control vs. Control vs. Control 

Mean ITTa TOTa ITTa TOTa 

[SR] (n=2818)

 Female 0.056 -0.005 -0.010 0.010 0.017 

Attends College 

(0.016) (0.034) (0.017) (0.028) 

Male 0.030 -0.002 

(0.011) 

-0.004 

(0.024) 

0.001 

(0.012) 

0.001 

(0.022) 

Attends 4-Year College [SR] (n=2818)

 Female 0.028 -0.011 -0.024 -0.003 -0.005 

(0.010) (0.022) (0.011) (0.019) 

Male 0.007 0.001 

(0.005) 

0.002 

(0.012) 

0.002 

(0.006) 

0.003 

(0.011) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Youth survey, POCY 

Sample: All children ages 5-19 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – arrest data, PR – parental

report, SR – self-report, OBS—interviewer observations). 

D-4 Appendix D – Detailed Estimation Results – Outcomes by Domain 
EXHIBIT D6.2 

EDUCATIONAL OUTCOMES BY MOTIVATION TO MOVE 

Experimental Section 8 

Control vs. Control vs. Control 

Mean ITTa TOTa ITTa TOTa 

Mostly B’s or Higher (ages 12 to 19) [SR] (n=2752)

Schools primary reason 0.405 -0.081* -0.166* -0.072 -0.127 

for wanting to move (0.038) (0.077) (0.039) (0.069) 

reason for wanting to 

move 

0.374 -0.004 

(0.036) 

-0.009 

(0.086) 

-0.025 

(0.039) 

-0.042 

(0.066) 

[SR] (n=5450) 

Schools not a primary 

Advanced Coursework (ages 5 to 19) 

Schools primary reason 0.205 -0.008 -0.016 -0.002 -0.003 

for wanting to move (0.024) (0.049) (0.025) (0.040) 

reason for wanting to 

move 

0.195 0.005 

(0.022) 

0.012 

(0.052) 

0.022 

(0.024) 

0.037 

(0.040) 

Special Ed Recipient (ages 5 to 17) [PR] (n=4724) 

Schools not a primary 

Schools primary reason 0.250 0.021 0.043 0.007 0.012 

for wanting to move (0.024) (0.049) (0.026) (0.041) 

reason for wanting to 

move 

0.240 0.028 

(0.023) 

0.065 

(0.052) 

0.020 

(0.025) 

0.033 

(0.041) 

[SR] (n=1549) 

Schools not a primary 

Educationally on Track (ages 15 to 19) 

Schools primary reason 0.712 0.045 0.097 0.054 0.109 

for wanting to move (0.042) (0.090) (0.046) (0.092) 

reason for wanting to 

move 

0.774 0.012 

(0.037) 

0.027 

(0.087) 

0.016 

(0.040) 

0.028 

(0.069) 

[PR] (n=5347) 

Schools not a primary 

Ever Repeated a Grade (ages 5 to 19) 

Schools primary reason 0.207 0.030 0.062 0.003 0.005 

for wanting to move (0.023) (0.048) (0.025) (0.039) 

reason for wanting to 

move 

0.237 0.023 

(0.022) 

0.023 

(0.051) 

-0.046 

(0.024) 

-0.078 

(0.040) 

[SR] (n=1561) 

Schools not a primary 

Took SAT/ACT (ages 15 to 19) 

Schools primary reason 0.353 -0.031 -0.065 0.051 0.098 

for wanting to move (0.050) (0.099) (0.046) (0.096) 

reason for wanting to 

move 

0.332 -0.034 

(0.044) 

-0.084 

(0.109) 

-0.011 

(0.047) 

-0.018 

(0.082) 

Schools not a primary 

Appendix D – Detailed Estimation Results – Outcomes by Domain D-5 
EXHIBIT D6.2 (CONT.) 

EDUCATIONAL OUTCOMES BY MOTIVATION TO MOVE

Experimental Section 8 

Control vs. Control vs. Control 

Mean ITTa TOTa ITTa TOTa 

Attends College (ages 12 to 19) [SR] (n=2818)

Schools primary reason 0.045 -0.018 -0.037 -0.003 -0.006 

for wanting to move (0.013) (0.027) (0.014) (0.025) 

reason for wanting to 

move 

0.042 0.012 

(0.015) 

0.028 

(0.035) 

0.014 

(0.016) 

0.024 

(0.027) 

Attends 4-Year College (ages 12 to 19) [SR] (n=2818) 

Schools not a primary 

Schools primary reason 0.019 -0.010 -0.021 -0.001 -0.002 

for wanting to move 0.009 0.019 0.010 0.019 

reason for wanting to 

move 

0.016 0.000 

0.007 

0.000 

0.017 

-0.001 

0.008 

-0.001 

0.014 

] (n=5164) 

Schools not a primary 

WJ-R Broad Reading (ages 5 to 19) [ADMIN

Schools primary reason 498.78 1.026 2.122 -0.517 -0.831 

for wanting to move 1.261 2.609 1.530 2.460 

reason for wanting to 

move 

495.83 0.846 

1.356 

1.997 

3.203 

1.565 

1.450 

2.626 

2.433 

[

Schools not a primary 

WJ-R Broad Reading (ages 5 to 19) ADMIN] (n=5182) 

Schools primary reason 502.947 0.304 0.628 -2.086 -3.336 

for wanting to move 1.109 2.290 1.254 2.005 

reason for wanting to 

move 

499.386 0.223 

1.069 

0.533 

2.551 

0.123 

1.129 

0.205 

1.882 

Schools not a primary 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Child Survey data, POCY, Woodcock Johnson-Revised tests. 

Sample: All children ages 5-19 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – arrest data, PR – parental

report, SR – self-report, OBS—interviewer observations). 

D-6 Appendix D – Detailed Estimation Results – Outcomes by Domain 
EXHIBIT D6.3 

SCHOOL CHARACTERISTICS BY MOTIVATION TO MOVE 

Control 

Experimental 

vs. Control 

Section 8 

vs. Control 

Mean ITTa TOTa ITTa TOTa 

Percent Free Lunch [SR] (n=3557)

Schools primary reason for 0.653 -0.073* -0.134* -0.027* -0.046* 

wanting to move (0.015) (0.026) (0.016) (0.027) 

reason for wanting to move 

0.658 -0.060* 

(0.014) 

-0.126* 

(0.029) 

-0.249* 

(0.015) 

-0.042* 

(0.025) 

Percent White [SR] (n=4868) 

Schools not a primary 

Schools primary reason for 0.010 0.043* 0.089* 0.041 0.064 

wanting to move (0.014) (0.029) (0.023) (0.036) 

reason for wanting to move 

0.114 0.040* 

(0.012) 

0.092* 

(0.029) 

0.017 

(0.015) 

0.028 

(0.025) 

Pupil-Teacher Ratio [PR] (n=4870) 

Schools not a primary 

Schools primary reason for 16.638 0.496 1.016 -0.091 -0.142 

wanting to move (0.303) (0.697) (0.341) (0.476) 

reason for wanting to move 

13.354 -0.116 

(0.297) 

-0.268 

(0.688) 

-0.237 

(0.317) 

-0.396 

(0.529) 

Percent Limited English Proficient [SR] (n= 4014) 

Schools not a primary 

Schools primary reason for 0.140 -0.024* -0.048* -0.008 -0.013 

wanting to move (0.009) (0.018) (0.009) (0.015) 

reason for wanting to move 

0.195 -0.030* 

(0.009) 

-0.068* 

(0.020) 

-0.002 

(0.010) 

-0.004 

(0.018) 

[PR] (n=3941) 

Schools not a primary 

Magnet School 

Schools primary reason for 0.280 -0.032 -0.067 -0.002 -0.003 

wanting to move (0.029) 0.061 0.035 0.051 

reason for wanting to move 

0.217 -0.070* 

(0.023) 

-0.165* 

0.054 

-0.058* 

0.028 

-0.092* 

0.044 

Schools not a primary 

Percentile Rank on State Exam [ADMIN] (N =3929) 

Schools primary reason for 0.163 0.044* 0.094* 0.023 0.033 

wanting to move (0.011) (0.024) (0.012) (0.017) 

reason for wanting to move 

0.170 0.032* 

(0.011) 

0.077* 

(0.027) 

0.010 

(0.012) 

0.017 

(0.021) 

Schools not a primary 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: U.S. Department of Education, Common Core of Data 1993 to 2001, National School-Level State 

Assessment Score Database, 2000 to 2001.

Sample: All children ages 5-19 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – arrest data, PR – parental

report, SR – self-report, OBS—interviewer observations). 

Appendix D – Detailed Estimation Results – Outcomes by Domain D-7 
EXHIBIT D6.4 

EDUCATIONAL OUTCOMES BY AGE AT RANDOM ASSIGNMENT 

Control 

Experimental 

vs. Control 

Section 8 

vs. Control 

Mean ITTa TOTa ITTa TOTa 

Advanced Coursework [SR] (n=5450) 

Less than 5 years old at 0.129 -0.013 -0.028 -0.007 -0.010 

RA (0.025) (0.052) (0.027) (0.039) 

6-11 years old at RA 0.181 0.023 

(0.022) 

0.049 

(0.046) 

0.016 

(0.023) 

0.026 

(0.039) 

12 or older at RA 0.370 -0.051 -0.132 0.019 0.037

(0.049) (0.126) (0.050) (0.096) 

Special Ed Recipient [PR] (n=4724) 

Less than 5 years old at 0.227 0.045 0.094 0.031 0.046 

RA (0.026) (0.055) (0.029) (0.043) 

6-11 years old at RA 0.257 0.019 

(0.024) 

0.041 

(0.053) 

0.012 

(0.027) 

0.020 

(0.046) 

12 or older at RA 0.275 -0.081 -0.227 -0.096 -0.167

(0.066) (0.183) (0.065) (0.114) 

Ever repeated a grade [PR] (n=5347) 

Less than 5 years old at 0.170 0.021 0.043 -0.016 -0.025 

RA (0.024) (0.049) (0.026) (0.039) 

6-11 years old at RA 0.266 0.012 

(0.026) 

0.025 

(0.057) 

-0.038 

(0.028) 

-0.064 

(0.048) 

12 or older at RA 0.219 0.084* 0.213* 0.025 0.049

(0.040) (0.103) (0.041) (0.080) 

WJ-R Broad Reading [ADMIN] (n=5164) 

Less than 5 years old at 478.804 0.902 1.926 0.650 0.950 

RA (1.476) (3.151) (1.631) (2.384) 

6-11 years old at RA 507.828 1.118 

(1.231) 

2.406 

(2.650) 

0.891 

(1.484) 

1.528 

(2.544) 

12 or older at RA 514.463 0.199 0.522 -1.574 -3.115

(2.651) (6.940) (2.523) (4.992) 

WJ-R Broad Reading [ADMIN] (n=5182) 

Less than 5 years old at 483.846 -0.323 -0.692 -2.170 -3.152 

RA (1.127) (2.413) (1.222) (1.774) 

6-11 years old at RA 511.583 0.869 

(1.125) 

1.884 

(2.437) 

0.164 

(1.254) 

0.280 

(2.136) 

12 or older at RA 516.667 -0.183 -0.479 -1.869 -3.740 

(2.199) (5.745) (2.111) (4.223) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Child Survey data, POCY, Woodcock Johnson-Revised tests. 

Sample: All children ages 5-19 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – arrest data, PR – parental report, 

SR – self-report, OBS—interviewer observations). 

e) Results are shown only for those outcomes available for all children age 5-19 at the time of the interim evaluation. 

D-8 Appendix D – Detailed Estimation Results – Outcomes by Domain 
EXHIBIT D6.5 

WOODCOCK-JOHNSON SCORES BY SITE 

Control 

Experimental vs. 

Control 

Section 8 

vs. Control 

Mean ITTa TOTa ITTa TOTa 

] (n=5169) 

501.665 -2.525 -5.357 -6.345* -15.248* 

WJ-R Broad Reading [ADMIN

 New York 

(2.092) (4.437) (2.030) (4.878) 

Baltimore 494.661 5.421* 

(2.254) 

10.899* 

(4.532) 

3.821 

(2.290) 

5.017 

(3.006) 

Boston 499.724 -0.703 -1.797 1.372 2.648 

(1.896) (4.850) (2.193) (4.233) 

Chicago 492.288 4.572* 

(2.108) 

14.395* 

(6.635) 

3.961 

(2.668) 

5.854 

(3.943) 

 Los Angeles 497.746 -1.347 - 2.068 1.055 1.395 

(1.884) (2.892) (1.990) (2.629) 

503.105 -1.217 -2.577 -4.072* -9.758* 

WJ-R Broad Reading [ADMIN] (n=5187) 

 New York 

(1.846) (3.910) (1.828) (4.380) 

Baltimore 499.729 2.316 

(1.521) 

4.689 

(3.080) 

0.139 

(1.672) 

0.182 

(2.178) 

Boston 499.437 0.238 0.603 1.804 3.481 

(1.503) (3.814) (1.668) (3.218) 

Chicago 501.417 0.878 

(1.951) 

2.786 

(6.192) 

-1.231 

(2.130) 

-1.799 

(3.113) 

 Los Angeles 501.686 -0.616 -0.953 -1.127 -1.487 

(1.389) (2.148) (1.614) (2.130) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Child Survey data, POCY, Woodcock Johnson-Revised tests. 

Sample: All children ages 5-19 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – arrest data, PR – parental

report, SR – self-report, OBS—interviewer observations). 

Appendix D – Detailed Estimation Results – Outcomes by Domain D-9 
EXHIBIT D7.1a 

IMPACTS ON EMPLOYMENT STATUS, ADULTS & YOUTH 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

Adult Employment Status [SR]

Employed (n=3517) 0.522 0.014 0.030 0.026 0.044 

(0.021) (0.044) (0.023) (0.039) 

0.096 0.023 

(0.013) 

0.049 

(0.028) 

0.015 

(0.015) 

0.025 

(0.025) 

 Unemployed (n=3508) 

Not in labor force (n=3508) 0.381 -0.038 -0.080 -0.041 -0.069 

(0.020) (0.042) (0.022) (0.037) 

Youth Employed [SR] (n=1581) 

All youth (Ages 15-19) 0.275 -0.028 -0.065 -0.021 -0.038 

(0.030) (0.070) (0.033) (0.060) 

Girls 0.320 -0.026 

(0.046) 

-0.057 

(0.098) 

-0.060 

(0.047) 

-0.101 

(0.078) 

Boys 0.229 -0.029 -0.073 0.021 0.042 

(0.039) (0.098) (0.044) (0.090) 

Youth Employed [SR] (Ages 17-19 Only) (n=912) 

All youth (Ages 17-19) 0.349 -0.025 -0.061 -0.030 -0.054 

(0.043) (0.103) (0.047) (0.087) 

Girls 0.387 -0.018 

(0.063) 

-0.043 

(0.150) 

-0.085 

(0.067) 

-0.143 

(0.112) 

Boys 0.306 -0.033 -0.079 0.027 0.055 

(0.058) (0.141) (0.065) (0.131) 

Youth Unemployed [SR] (Ages 17-19 Only) (n=901)

All youth (Ages 17-19) 0.267 -0.017 -0.042 -0.001 -0.002 

(0.040) (0.096) (0.044) (0.081) 

Girls 0.216 -0.041 

(0.050) 

-0.099 

(0.120) 

0.046 

(0.058) 

0.077 

(0.097) 

Boys 0.324 0.008 0.021 -0.050 -0.101 

(0.061) (0.148) (0.066) (0.134) 

Youth Not in Labor Force [SR] (Ages 17-19 Only) (n=901) 

All youth (Ages 17-19) 0.383 0.040 0.098 0.027 0.049 

(0.045) (0.109) (0.050) (0.091) 

Girls 0.398 0.055 

(0.066) 

0.131 

(0.159) 

0.039 

(0.074) 

0.065 

(0.124) 

Boys 0.367 0.024 0.060 0.013 0.027 

(0.060) (0.147) (0.069) (0.138) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult and Youth surveys 

Sample: Adults and children ages 15 to 19 as of May 31, 2001 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

D-10 Appendix D – Detailed Estimation Results – Outcomes by Domain 
EXHIBIT D7.1b 

IMPACTS ON EMPLOYMENT STATUS, YOUTH AGES15-19 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

Youth Employment Only: Not Enrolled in School [SR] (n=1587)

All youth (Ages 15-19) 0.133 -0.013 -0.030 -0.038 -0.069 

(0.023) (0.053) (0.024) (0.043) 

Girls 0.139 0.004 

(0.034) 

0.009 

(0.074) 

-0.064* 

(0.032) 

-0.107* 

(0.053) 

Boys 0.127 -0.030 -0.074 -0.011 -0.021 

(0.031) (0.077) (0.033) (0.067) 

Youth School Enrollment Only: Not Employed [SR] (n=1584)

All youth (Ages 15-19) 0.446 0.062* 0.143* 0.017 0.032 

(0.031) (0.073) (0.033) (0.060) 

Girls 0.410 0.102* 

(0.044) 

0.220* 

(0.095) 

0.049 

(0.046) 

0.083 

(0.077) 

Boys 0.483 0.021 0.052 -0.016 -0.032 

(0.043) (0.107) (0.047) (0.096) 

Youth Concurrent Employment and School Enrollment [SR] (n=1584)

All youth (Ages 15-19) 0.141 -0.015 -0.034 0.018 0.032 

(0.024) (0.055) (0.026) (0.047) 

Girls 0.180 -0.029 

(0.037) 

-0.063 

(0.079) 

0.005 

(0.040) 

0.008 

(0.067) 

Boys 0.101 -0.000 -0.000 0.031 0.063 

(0.029) (0.074) (0.031) (0.064) 

Youth Full-Time School Enrollment [SR] (n=1590)

All youth (Ages 15-19) 0.558 0.017 0.039 -0.004 -0.008 

(0.030) (0.070) (0.031) (0.057) 

Girls 0.563 0.045 

(0.043) 

0.098 

(0.093) 

-0.012 

(0.043) 

-0.021 

(0.072) 

Boys 0.552 -0.012 -0.029 0.004 0.009 

(0.041) (0.103) (0.043) (0.088) 

Youth School Enrollment [SR] (n=1590)

All youth (Ages 15-19) 0.589 0.047 0.109 0.034 0.062 

(0.030) (0.071) (0.031) (0.058) 

Girls 0.593 0.072 

(0.043) 

0.155 

(0.093) 

0.052 

(0.044) 

0.086 

(0.073) 

Boys 0.585 0.022 0.056 0.015 0.030 

(0.042) (0.104) (0.043) (0.088) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Youth survey

Sample: Children ages 15 to 19 as of May 31, 2001 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

Appendix D – Detailed Estimation Results – Outcomes by Domain D-11 
EXHIBIT D7.2 

IMPACTS ON BENEFITS & JOB TENURE , ADULTS & YOUTH AGES15-19 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

Adult Employment Benefits [SR]

Employed at job offering 0.296 0.023 0.050 0.004 0.007 

) (0.019) (0.041) (0.021) (0.035) 

0.300 0.018 

(0.019) 

0.038 

(0.041) 

0.007 

(0.021) 

0.013 

(0.035) 

health insurance (n=3483

Employed at job offering 

paid sick leave (n=3480) 

Employed at job offering 0.346 0.025 0.052 0.007 0.011 

(0.020) (0.043) (0.022) (0.037) 

Adult Job Tenure [SR] (n=3475) 

0.362 0.029 0.062 0.030 0.049 

paid vacation (n=3483) 

Job tenure of more than 

one year (0.021) (0.045) (0.022) (0.037) 

Youth Has Job Tenure of More Than One Year [SR] (n=1572)

All youth (Ages 15-19) 0.073 0.006 0.015 0.005 0.009 

(0.020) (0.046) (0.021) (0.038) 

Girls 0.081 0.003 

(0.030) 

0.006 

(0.064) 

-0.025 

(0.028) 

-0.041 

(0.048) 

Boys 0.064 0.010 0.026 0.036 0.073 

(0.025) (0.064) (0.030) (0.060) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult and Youth surveys 

Sample: Adults and children ages 15 to 19 as of May 31, 2001 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

D-12 Appendix D – Detailed Estimation Results – Outcomes by Domain 
EXHIBIT D7.3 

IMPACTS ON OCCUPATION, ADULTS 

Control 

Experimental 

vs. Control 

Section 8 

vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

Adult Employed in a White-Collar Occupation [SR] (n=3456)

Employed in a white- 0.216 0.033 0.071 0.030 0.050 

collar occupation (0.018) (0.038) (0.019) (0.032) 

(managerial, 

professional, technical, 

sales or administrative 

support) 

managerial or 

professional specialty 

occupation 

0.043 0.012 

(0.009) 

0.026 

(0.020) 

0.014 

(0.010) 

0.023 

(0.017) 

Employed in a 

Employed in a technical, 0.173 0.021 0.045 0.016 0.027 

sales, or administrative (0.017) (0.036) (0.018) (0.030) 

support occupation 

Adult Employed in a Service Occupation [SR] (n=3456)

Employed in a service 0.238 -0.006 -0.013 -0.012 -0.020 

occupation (0.019) (0.040) (0.020) (0.034) 

Adult Employed in a Blue-Collar Occupation [SR] (n=3456)

Employed in blue-collar 0.059 -0.014 -0.031 0.001 0.001 

occupation (0.009) (0.020) (0.012) (0.020) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult survey

Sample: Adults from families randomly assigned through December 31, 1997. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

e) Respondent descriptions of their occupations and duties were assigned 3-digit occupation codes, consistent 

with the Current Population Survey, by the Census Bureau’s National Processing Center. 

Appendix D – Detailed Estimation Results – Outcomes by Domain D-13 
EXHIBIT D8.1 

ADDITIONAL SURVEY OUTCOMES 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

Earnings, Income, Poverty [SR] 

Total earnings of head $8,969 $136 $289 $108 $182 

(n=3365) $443 $941 $491 $826 

income (n=3261) 

$15,165 $729 

$636 

$1,546 

$1,348 

-$389 

$692 

-$651 

$1,156 

0.182 0.001 0.002 0.019 0.032 

Calculated total household 

Household is self-sufficient 

(n=3499) 0.016 0.035 0.018 0.030 

Income of head + spouse > 

poverty line (n=3526) 

0.301 0.002 

0.020 

0.005 

0.042 

-0.019 

0.021 

-0.032 

0.035 

Household income to poverty 0.941 0.029 0.061 0.003 0.004 

ratio (n=3211) 0.032 0.068 0.036 0.060 

Supplemental Security Income [SR]

Adult is currently receiving SSI 0.134 0.003 0.006 -0.010 -0.016 

(n=3511) 0.013 0.028 0.016 0.027 

currently receiving SSI 

(n=3511) 

0.129 0.009 

0.014 

0.020 

0.030 

0.024 

0.016 

0.040 

0.027 

Children in household are 

Adult or children currently get 0.110 0.013 0.027 0.005 0.008 

SSI and began after random 0.014 0.030 0.016 0.026 

assignment (n=3416) 

Earned Income Tax Credit [SR] 

Did adult receive a tax 0.431 0.012 0.026 0.022 0.037

refund/EITC return in 2001? 0.021 0.045 0.024 0.040 

(n=3482) 

$951 $56 

$68 

$118 

$144 

$114 

$73 

$192 

$122 

Amount of tax refund/EITC 

return in 2001 (n=3218) 

Was adult’s tax refund/EITC 0.175 0.013 0.026 0.032 0.054 

return > $2,500 in 2001? 0.018 0.037 0.019 0.032 

(n=3218) 

Families (TANF) [SR] 

Adult or children received 0.382 0.002 0.004 -0.033 -0.055 

Aid to Families With Dependent Children (AFDC)/Temporary Assistance for Needy 

AFDC/TANF in past 2 years 0.020 0.042 0.022 0.037 

(n=3512) 

Adult or children have received 

past 2 years (n=3513) 

0.208 -0.009 

0.017 

-0.020 

0.036 

-0.017 

0.019 

-0.028 

AFDC/TANF continuously over 0.031

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult survey. See appendix A for details. 

Sample: Adults from families randomly assigned through December 31, 1997. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

D-14 Appendix D – Detailed Estimation Results – Outcomes by Domain 
EXHIBIT D8.2 

IMPACTS ON DISTRIBUTION OF THE TWO HOUSEHOLD INCOME MEASURES 

COMPARISON OF MEAN AND QUANTILE REGRESSION IMPACTS 

MTO Control Group

Model Impact S8 Impact Distribution 

Calculated Total Household Income [SR] (n=3261)

 OLS Regression 729 -389 Mean: 15,165 

636 691 

375 

423 

56 

466 

25th 25th percentile regression percentile: 6,200

 Median regression 104 -459 Median: 12,012 

532 582 

376 

720 

-1,434 

781 

75th 21,000 

Self-Reported Total Household Income [SR] (n=3211)

75th percentile regression percentile: 

 OLS regression 239 -162 Mean: 15,536 

571 636 

-60 

285 

-214 

312 

25th 25th percentile regression percentile: 7,000

 Median regression -201 -766 Median: 12,000 

424 464 

-345 

724 

-1,077 

793 

75th 75th percentile regression percentile: 20,000

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult survey. See appendix A for details. 

Sample: Sample adults 

Notes: Control means and impact estimates are regression-adjusted, with robust standard errors in the OLS 

regressions, as described in appendix B. 

Appendix D – Detailed Estimation Results – Outcomes by Domain D-15 
EXHIBIT D8.3 

DETAILED YEARLY IMPACTS: AID TO FAMILIES WITH DEPENDENT CHILDREN (AFDC)/

TEMPORARY ASSISTANCE FOR NEEDY FAMILIES (TANF)

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

AFDC/TANF for Sample Adult 

Fraction of months sample .216 0.009 0.023 0.048* 0.084* 

adult received AFDC/TANF, (0.019) (0.050) (0.024) (0.042) 

Year 5 (n=1962) 

Year 4 (n=2934) 

.275 0.029 

(0.017) 

0.070 

(0.041) 

0.039* 

(0.019) 

0.072* 

(0.035) 

Fraction of months sample 

adult received AFDC/TANF, 

Fraction of months sample .350 0.041* 0.099* 0.049* 0.090* 

adult received AFDC/TANF, (0.018) (0.043) (0.020) (0.037) 

Year 3 (n=2934) 

Year 2 (n=2632) 

.422 0.044* 

(0.019) 

0.105* 

(0.045) 

0.036 

(0.020) 

0.064 

(0.035) 

Fraction of months sample 

adult received AFDC/TANF, 

Fraction of months sample .516 0.033 0.078 0.016 0.028 

adult received AFDC/TANF, (0.018) (0.043) (0.020) (0.035) 

Year 1 (n=2448) 

Total AFDC/TANF benefit 

received by sample adult, 

Year 5 (n=1962) 

$1,037 $2 

($107) 

$6 

($280) 

$37 

($121) 

$64 

($210) 

Total AFDC/TANF benefit $1,372 $62 $149 $1 $3 

received by sample adult, ($99) ($238) ($105) ($193) 

Year 4 (n=2934) 

Total AFDC/TANF benefit 

received by sample adult, 

Year 3 (n=2934) 

$1,772 $122 

($103) 

$294 

($247) 

$73 

($114) 

$134 

($209) 

Total AFDC/TANF benefit $2,104 $280* $670* 106 $188 

received by sample adult, ($110) ($264) (119) ($211) 

Year 2 (n=2632) 

Total AFDC/TANF benefit 

received by sample adult, 

Year 1 (n=2448) 

$2,588 $238* 

($108) 

$564* 

($255) 

$177 

($117) 

$308 

($204) 

D-16 Appendix D – Detailed Estimation Results – Outcomes by Domain 
EXHIBIT D8.3 (CONT.)

DETAILED YEARLY IMPACTS: AID TO FAMILIES WITH DEPENDENT CHILDREN (AFDC)/

TEMPORARY ASSISTANCE FOR NEEDY FAMILIES (TANF)

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

AFDC/TANF for Household

 Fraction of months 0.273 0.011 0.029 0.020 0.035

household received (0.021) (0.055) (0.025) (0.044) 

AFDC/TANF, Year 5 

(n=1962) 

AFDC/TANF, Year 4 

(n=2934) 

0.320 0.031 

(0.018) 

0.075 

(0.043) 

0.021 

(0.020) 

0.039 

(0.037) 

0.394 0.045* 0.108* 0.031 0.057 

 Fraction of months 

household received 

 Fraction of months 

household received (0.018) (0.043) (0.020) (0.037) 

AFDC/TANF, Year 3 

(n=2934) 

AFDC/TANF, Year 2 

(n=2632) 

0.469 0.047* 

(0.018) 

0.112* 

(0.043) 

0.018 

(0.020) 

0.032 

(0.035) 

0.566 0.033* 0.078* 0.002 0.003 

 Fraction of months 

household received 

 Fraction of months 

household received (0.016) (0.038) (0.019) (0.033) 

AFDC/TANF, Year 1 

(n=2448) 

Total AFDC/TANF benefit 

received by household, 

Year 5 (n=1962) 

$1,230 $22 

($110) 

$57 

($288) 

-$73 

($123) 

-$127 

($215) 

Total AFDC/TANF benefit $1,538 $65 $156 -$88 -$162 

received by household, ($101) ($244) ($107) ($197) 

Year 4 (n=2934) 

Total AFDC/TANF benefit 

received by household, 

Year 3 (n=2934) 

$1,934 $124 

($104) 

$299 

($251) 

$3 

($115) 

$5 

($211) 

Total AFDC/TANF benefit $2,286 $273* $652* $49 $87 

received by household, ($111) ($266) ($120) ($212) 

Year 2 (n=2632) 

Total AFDC/TANF benefit 

received by household, 

Year 1 (n=2448) 

$2,817 $190 

($109) 

$450 

($258) 

$76 

($119) 

$132 

($206) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult survey. See appendix A for details. 

Sample: Sample adults. TANF data is from three sites: New York, Chicago, and Boston. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

Appendix D – Detailed Estimation Results – Outcomes by Domain D-17 
EXHIBIT D8.4a 

DETAILED YEARLY IMPACTS: FOOD STAMPS 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

Food Stamps for Sample Adult

Fraction of months sample 0.390 0.034 0.090 0.052 0.082 

adult received food stamps, (0.025) (0.066) (0.029) (0.045) 

Year 5 (n=1423) 

Year 4 (n=1853) 

0.435 0.018 

(0.021) 

0.047 

(0.054) 

0.043 

(0.024) 

0.073 

(0.041) 

Fraction of months sample 

adult received food stamps, 

Fraction of months sample 0.467 0.046* 0.119* 0.064* 0.109* 

adult received food stamps, (0.021) (0.054) (0.024) (0.041) 

Year 3 (n=1853) 

Year 2 (n=1853) 

0.527 0.049* 

(0.020) 

0.127* 

(0.052) 

0.051* 

(0.023) 

0.087* 

(0.039) 

Fraction of months sample 

adult received food stamps, 

Fraction of months sample 0.627 0.024 0.062 0.010 0.017 

adult received food stamps, (0.017) (0.044) (0.020) (0.034) 

Year 1 (n=1853) 

received by sample adult, 

Year 5 (n=1423) 

$1,056 $95 

($81) 

$253 

($214) 

$213* 

($101) 

$334* 

($158) 

Total food stamps benefit 

Total food stamps benefit $1,202 $51 $131 $104 $176 

received by sample adult, ($75) ($195) ($84) ($143) 

Year 4 (n=1853) 

received by sample adult, 

Year 3 (n=1853) 

$1,289 $155* 

($74) 

$401* 

($192) 

$167* 

($85) 

$284* 

($145) 

Total food stamps benefit 

Total food stamps benefit $1,394 $193* $500* $154* $262* 

received by sample adult, ($66) ($170) ($77) ($131) 

Year 2 (n=1853) 

received by sample adult, 

Year 1 (n=1853) 

$1,650 $104 

($60) 

$268 

($156) 

$39 

($70) 

$67 

($119) 

Total food stamps benefit 

D-18 Appendix D – Detailed Estimation Results – Outcomes by Domain 
EXHIBIT D8.4b 

DETAILED YEARLY IMPACTS: AFDC/TANF 

Control 

Experimental

vs. Control 

Section 8 

vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

AFDC/TANF for Household

 Fraction of months 0.429 0.035 0.093 0.043 0.067

household received food (0.025) (0.066) (0.028) (0.044) 

stamps, Year 5 (n=1423) 

stamps, Year 4 (n=1853) 

0.465 0.018 

(0.021) 

0.047 

(0.054) 

0.036 

(0.024) 

0.061 

(0.041) 

0.498 0.043* 0.111* 0.054* 0.092* 

 Fraction of months 

household received food 

 Fraction of months 

household received food (0.021) (0.054) (0.024) (0.041) 

stamps, Year 3 (n=1853) 

stamps, Year 2 (n=1853) 

0.561 0.039 

(0.020) 

0.101 

(0.052) 

0.039 

(0.022) 

0.066 

(0.037) 

0.660 0.013 0.034 -0.002 -0.003 

 Fraction of months 

household received food 

 Fraction of months 

household received food (0.016) (0.041) (0.019) (0.032) 

stamps, Year 1 (n=1853) 

received by household, 

Year 5 (n=1423) 

$1,185 $89 

($82) 

$238 

($219) 

$201 

($103) 

$315 

($161) 

Total food stamps benefit 

Total food stamps benefit $1,283 $59 $152 $107 $182 

received by household, ($76) ($196) ($85) ($144) 

Year 4 (n=1853) 

received by household, 

Year 3 (n=1853) 

$1,372 $158* 

($74) 

$408* 

($191) 

$150 

($84) 

$255 

($142) 

Total food stamps benefit 

Total food stamps benefit $1,483 $172* $445* $137 $233 

received by household, ($65) ($167) ($75) ($127) 

Year 2 (n=1853) 

received by household, 

Year 1 (n=1853) 

$1,736 $77 

($58) 

$200 

($151) 

$19 

($67) 

$32 

($113) 

Total food stamps benefit 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Administrative data from state welfare agencies. See appendix A for details. 

Sample: Sample adults. Food stamp data is from two sites: Chicago and Boston. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

Appendix D – Detailed Estimation Results – Outcomes by Domain D-19 

Appendix E 

Detailed Estimation Results – Mediating Factors 

EXHIBIT E4.1 

MEDIATORS: HOUSING AND NEIGHBORHOOD PHYSICAL CONDITIONS 

Control 

Experimental 

vs. Control 

Section 8 

vs. Control 

Mediator Mean ITTa TOTa ITTa TOTa 

Housing Conditions 

Housing has cat, dog, or 0.180 -0.011 -0.023 -0.022 -0.037 

other pet with fur [OBS] (0.018) (0.037) (0.019) (0.033) 

(n=3269) 

Housing has wall-to-wall 

carpets 

0.277 0.088* 

(0.020) 

0.187* 

(0.042) 

0.085* 

(0.022) 

0.143* 

(0.037) 

Housing has problem with 

broken windows or 

0.235 -0.051* 

(0.018) 

-0.108* 

(0.038) 

-0.025 

(0.020) 

-0.041 

(0.033) 

negative items (noise, 

) 

0.189 -0.013 

(0.010) 

-0.028 

(0.020) 

-0.014 

(0.011) 

-0.024 

(0.018) 

Exterior-of-Home index: 7 

negative items (poor 

conditions, signs of 

0.189 -0.038* 

(0.010) 

-0.080* 

(0.021) 

-0.030* 

(0.011) 

-0.051* 

(0.018) 

[OBS] (n=3314) 

windows without screens 

[SR] (n=3501) 

 Interior-of-Home index: 7 

clutter, deterioration, etc.

[OBS] (n=3345) 

Neighborhood Conditions 

deterioration and danger) 

[OBS] (n=3385) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult survey and Interviewer observations. 

Sample: Adults from families randomly assigned through December 31, 1997. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations).

Appendix E – Detailed Estimation Results – Mediating Factors E-1 
EXHIBIT E4.2 

MEDIATORS: EXERCISE AND NUTRITION 

Experimental Section 8 

Control vs. Control vs. Control 

Mediator Mean ITTa TOTa ITTa TOTa 

Fraction of Past Week Child Did Aerobic Exercise [SR] (n=1759) 

All children (ages 8 to 11) 0.570 0.010 0.022 -0.035 -0.054 

(0.024) (0.052) (0.027) (0.041) 

Girls 0.488 0.045 

(0.034) 

0.091 

(0.068) 

0.022 

(0.037) 

0.034 

(0.058) 

Boys 0.647 -0.021 -0.050 -0.090* -0.133* 

(0.034) (0.081) (0.038) (0.056) 

Fraction of Past Week Youth Did Aerobic Exercise for 20+ Minutes [SR] (n=2803)

All youth (ages 12 to 19) 0.483 0.041* 0.091* 0.026 0.045 

(0.018) (0.040) (0.021) (0.037) 

Girls 0.394 0.061* 

(0.025) 

0.133* 

(0.055) 

0.011 

(0.030) 

0.018 

(0.049) 

Boys 0.571 0.020 0.045 0.040 0.074 

(0.026) (0.058) (0.029) (0.054) 

Fraction of Past Week Youth Did Light Physical Activity (No Sweat) For 30+ 

Minutes [SR] (n=2790) 

All youth (ages 12 to 19) 0.464 0.025 0.057 -0.008 -0.014 

(0.020) (0.044) (0.022) (0.039) 

Girls 0.431 0.030 

(0.026) 

0.066 

(0.058) 

-0.039 

(0.031) 

-0.064 

(0.051) 

Boys 0.497 0.020 0.046 0.023 0.042 

(0.029) (0.064) (0.030) (0.056) 

Fraction of Past Week Child Had Some Fruits or Vegetables [SR] (n=4565) 

) 0.613 0.006 

(0.015) 

0.014 

(0.033) 

-0.010 

(0.016) 

-0.016 

(0.027) 

Girls 0.607 0.022 

(0.020) 

0.046 

(0.044) 

-0.013 

(0.022) 

-0.021 

(0.035) 

Boys 0.620 -0.010 

(0.021) 

-0.023 

(0.049) 

-0.006 

(0.023) 

-0.010 

(0.038) 

[SR] 

moderate physical activity 

for 10+ min 

0.471 0.026 

(0.018) 

0.055 

(0.039) 

0.045* 

(0.020) 

0.075* 

(0.034) 

vegetables 

0.671 0.029* 

(0.014) 

0.062* 

(0.030) 

0.021 

(0.015) 

0.035 

(0.026) 

All children (ages 8 to 19

Adult Exercise and Nutrition 

Fraction of past week did 

(n=3507) 

Fraction of past week 

adult had some fruits or 

(n=3498) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult, Child and Youth surveys 

Sample: All children ages 8 to 19 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR – 

parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

E-2 Appendix E – Detailed Estimation Results – Mediating Factors 
EXHIBIT E4.3 

MEDIATOR: ACCESS TO HEALTH CARE 

Control 

Experimental 

vs. Control 

Section 8 

vs. Control 

Mediator Mean ITTa TOTa ITTa TOTa 

Access to Health Care [SR] 

Adult and child have 0.798 0.012 0.026 -0.005 -0.008

access to health (0.018) (0.039) (0.020) (0.033) 

In past year, adult or 0.062 -0.005 

(0.011) 

-0.010 

(0.023) 

0.007 

(0.013) 

0.011 

(0.021) 

- l 0.935 -0.009 -0.019 0.011 0.019 

insurance (n=3340) 

children did not get 

medical care when they 

needed it (n=3272) 

Adult and child have usua

place to go when they are (0.012) (0.025) (0.012) (0.019) 

sick or need health advice 

(n=3461) 

In past 6 months, 

(n=3225) 

0.760 -0.013 

(0.020) 

-0.028 

(0.042) 

-0.003 

(0.021) 

-0.005 

someone has seen/talked (0.035)

to health professional 

about child’s health 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Source: Adult survey

Sample: Adult from families randomly assigned through December 31, 1997. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations).

Appendix E – Detailed Estimation Results – Mediating Factors E-3 
EXHIBIT E5.1 

MEDIATORS: YOUTH’S PEERS, AGES 12 TO 19 

Experimental Section 8 

Control vs. Control vs. Control 

Mediator Mean ITTa TOTa ITTa TOTa 

Has Friends Who Get Involved in School Activities [SR] (n=2717) 

All youth (ages12 to 19) 0.712 0.015 0.034 0.022 0.038 

(0.023) (0.053) (0.025) (0.045) 

Girls 0.684 0.055 

(0.035) 

0.121 

(0.077) 

0.036 

(0.037) 

0.060 

(0.062) 

Boys 0.740 -0.026 -0.060 0.007 0.013 

(0.032) (0.074) (0.035) (0.064) 

Has Friends Who Use Drugs [SR] (n=2659) 

All youth (ages 12 to 19) 0.251 0.046* 0.101* 0.062* 0.108* 

(0.023) (0.051) (0.026) (0.046) 

Girls 0.247 0.011 

(0.030) 

0.024 

(0.066) 

0.024 

(0.035) 

0.040 

(0.058) 

Boys 0.256 0.083* 0.187* 0.100* 0.184* 

(0.034) (0.076) (0.038) (0.070) 

Has Friends Who Carry Weapons [SR] (n=2711)

All youth (ages 12 to 19) 0.106 0.021 0.046 0.013 0.023 

(0.017) (0.038) (0.019) (0.033) 

Girls 0.091 0.006 

(0.020) 

0.012 

(0.043) 

0.024 

(0.024) 

0.040 

(0.040) 

Boys 0.122 0.037 0.086 0.002 0.004 

(0.027) (0.063) (0.029) (0.054) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Source: Youth survey

Sample: All children ages 12 to 19 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

E-4 Appendix E – Detailed Estimation Results – Mediating Factors 
EXHIBIT E5.2 

MEDIATORS: CHILD’S SOCIAL NETWORKS 

Experimental Section 8 

Control vs. Control vs. Control 

Mediator Mean ITTa TOTa ITTa TOTa 

Child Has at Least One Close Friend [PR] (n=4732) 

All children (ages 5 to 19) 0.914 0.024* 0.052* 0.009 0.015 

(0.011) (0.022) (0.013) (0.020) 

Girls 0.918 0.026 

(0.014) 

0.054 

(0.029) 

0.007 

(0.017) 

0.012 

(0.027) 

Boys 0.910 0.022 0.050 0.011 0.019 

(0.015) (0.033) (0.018) (0.029) 

Child Has 5+ Friends [SR] (n=4556) 

All children (ages 8 to 19) 0.520 0.029 0.064 0.032 0.052 

(0.022) (0.048) (0.024) (0.039) 

Girls 0.472 0.048 

(0.030) 

0.103 

(0.065) 

0.039 

(0.032) 

0.063 

(0.052) 

Boys 0.567 0.009 0.022 0.025 0.042 

(0.030) (0.070) (0.032) (0.054) 

[SR] (n=2723) 

Youth Lives In Same Neighborhood Or Visits With Friends From Old 

Neighborhood 

All youth (ages 12 to 19) 0.683 -0.108* -0.246* -0.117* -0.207* 

(0.028) (0.063) (0.030) (0.052) 

Girls 0.687 -0.120* 

(0.037) 

-0.273* 

(0.083) 

-0.149* 

(0.040) 

-0.251* 

(0.066) 

Boys 0.679 -0.096* -0.219* -0.086* -0.161* 

(0.038) (0.086) (0.041) (0.075) 

Youth Lives in Same Neighborhood as Random Assignment [SR] (n=2735) 

All youth (ages 12 to 19) 0.461 -0.133* -0.302* -0.156* -0.275* 

(0.028) (0.064) (0.029) (0.051) 

Girls 0.468 -0.152* 

(0.036) 

-0.344* 

(0.082) 

-0.188* 

(0.039) 

-0.316* 

(0.065) 

Boys 0.454 -0.114* -0.260* -0.124* -0.231* 

(0.038) (0.088) (0.039) (0.072) 

Has Brothers, Sisters, Cousins or Friends Who Belong to a Gang [SR] (n=4409)

All children (ages 8 to 19) 0.115 -0.003 -0.007 -0.014 -0.023 

(0.013) (0.029) (0.014) (0.022) 

Girls 0.109 0.005 

(0.018) 

0.011 

(0.037) 

-0.027 

(0.017) 

-0.044 

(0.027) 

Boys 0.122 -0.013 -0.030 -0.001 -0.002 

(0.020) (0.045) (0.021) (0.035) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult, Child and Youth surveys 

Sample: All children ages 5 to 19 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

c) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations).

Appendix E – Detailed Estimation Results – Mediating Factors E-5 
EXHIBIT E5.3 

MEDIATORS: ADULT SOCIAL NETWORKS AND SOCIAL CAPITAL 

Experimental Section 8 

Control vs. Control vs. Control 

Mediator Mean ITTa TOTa ITTa TOTa 

0.274 -0.015 -0.032 0.022 0.037 

Friendship and Social Networks [SR] 

Has diffuse network of 

friends, in which only a (0.019) (0.041) (0.022) (0.037) 

few friends know each 

other (n=3510) 

0.590 0.023 

(0.022) 

0.048 

(0.046) 

0.045 

(0.024) 

0.076 

(0.040) 

Has no friends who live in 

neighborhood (n=3517) 

Visits friends/relatives in 0.426 -0.021 -0.044 -0.022 -0.038 

their home at least once a (0.022) (0.047) (0.024) (0.041) 

week (n=3514) 

hallway at least once a 

week (n=3510) 

0.490 0.020 

(0.022) 

0.043 

(0.047) 

0.015 

(0.024) 

0.025 

(0.041) 

0.427 -0.033 -0.069 0.011 0.018 

Stops to chat with 

neighbor in street or 

Attended church or 

religious services at least (0.021) (0.045) (0.024) (0.039) 

once a month in past year 

(n=3509) 

Social Capital [SR] 

Likely or very likely 

neighbors would do 

something about kids 

doing graffiti on local 

0.541 0.109* 

(0.022) 

0.235* 

(0.047) 

0.057* 

(0.024) 

0.096* 

(0.041) 

Likely or very likely 

neighbors would do 

something about kids 

corner (n=3270) 

0.366 0.106* 

(0.023) 

0.229* 

(0.049) 

0.068* 

(0.025) 

0.115* 

(0.042) 

building (n=3349) 

skipping school or 

hanging out on street 

People can be trusted 0.099 0.010 0.020 0.009 0.016 

(n=3486) (0.014) (0.029) (0.015) (0.026) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Source: Adult survey

Sample: All child ages as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations).

E-6 Appendix E – Detailed Estimation Results – Mediating Factors 
EXHIBIT E5.4a 

MEDIATORS: TIME USE 

TV, SPORTS & CHURCH 

Control 

Experimental 

vs. Control 

Section 8 

vs. Control 

Mediator Mean ITTa TOTa ITTa TOTa 

Child Watches More Than 5 Hours of TV on Typical Weekday [SR] (n=1771)

All children (ages 8 to 11) 0.274 -0.022 -0.048 -0.002 -0.003 

(0.029) (0.063) (0.033) (0.050) 

Girls 0.275 -0.017 

(0.041) 

-0.035 

(0.084) 

0.003 

(0.050) 

0.005 

(0.078) 

Boys 0.272 -0.026 -0.063 -0.007 -0.011 

(0.038) (0.092) (0.042) (0.062) 

[PR] 

(n=2491) 

Child Participated in Sport /Physical Activity on Given Weekday After School 

All children (ages 5 to 11) 0.049 0.002 0.005 0.005 0.008 

(0.013) (0.027) (0.014) (0.021) 

Girls 0.045 -0.001 

(0.018) 

-0.002 

(0.035) 

0.002 

(0.017) 

0.002 

(0.027) 

Boys 0.054 0.006 0.013 0.009 0.013 

(0.017) (0.038) (0.019) (0.028) 

[SR] 

(n=2704) 

Youth Participated in Sport /Physical Activity on Given Weekday After School 

All youth (ages 12 to 19) 0.082 0.028 0.062 0.021 0.036 

(0.015) (0.034) (0.017) (0.030) 

Girls 0.031 0.051* 

(0.018) 

0.111* 

(0.038) 

0.030 

(0.019) 

0.049 

(0.031) 

Boys 0.132 0.005 0.010 0.012 0.023 

(0.025) (0.057) (0.029) (0.054) 

Youth Attended Youth Activities at Church, at Least Once a Month [SR] (n=2778)

All youth (ages 12 to 19) 0.374 -0.005 -0.010 -0.011 -0.019 

(0.028) (0.061) (0.031) (0.054) 

Girls 0.397 0.025 

(0.039) 

0.054 

(0.084) 

-0.033 

(0.043) 

-0.054 

(0.071) 

Boys 0.351 -0.036 -0.081 0.010 0.018 

(0.037) (0.084) (0.042) (0.078) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult, Child, and Youth surveys 

Sample: All children ages 5 to 19 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations).

Appendix E – Detailed Estimation Results – Mediating Factors E-7 
EXHIBIT E5.4b 

MEDIATORS: YOUTH TIME USE 

STRUCTURED ACTIVITY, AGES 12 TO 17 

Experimental Section 8 

Control vs. Control vs. Control 

Mediator Mean ITTa TOTa ITTa TOTa 

[SR] 

(n=2174) 

Youth Spent Time in a Structured Activity on Given Weekday at 3:45p.m. 

All youth (ages 12 to 17) 0.177 0.033 0.070 0.032 0.055 

(0.023) (0.048) (0.025) (0.043) 

Girls 0.166 0.051 

(0.032) 

0.108 

(0.067) 

0.041 

(0.036) 

0.067 

(0.058) 

Boys 0.186 0.015 0.033 0.024 0.044 

(0.032) (0.069) (0.035) (0.064) 

[SR] 

(n=2175) 

Youth Spent Time in a Structured Activity on Given Weekday at 5:30p.m. 

All youth (ages 12 to 17) 0.106 0.036 0.078 0.001 0.002 

(0.019) (0.042) (0.020) (0.034) 

Girls 0.092 0.063* 

(0.027) 

0.133* 

(0.058) 

0.017 

(0.029) 

0.027 

(0.046) 

Boys 0.118 0.011 0.024 -0.013 -0.024 

(0.028) (0.061) (0.029) (0.052) 

[SR] 

(n=2186) 

Youth Spent Time in a Structured Activity on Given Weekday at 7:30p.m. 

All youth (ages 12 to 17) 0.060 0.019 0.042 0.015 0.026 

(0.015) (0.032) (0.017) (0.028) 

Girls 0.069 0.030 

(0.022) 

0.064 

(0.047) 

0.017 

(0.026) 

0.027 

(0.040) 

Boys 0.051 0.008 0.018 0.014 0.025 

(0.020) (0.044) (0.022) (0.039) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult, Child, and Youth surveys 

Sample: All child ages as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations).

E-8 Appendix E – Detailed Estimation Results – Mediating Factors 
EXHIBIT E5.4c 

MEDIATORS: YOUTH TIME USE 

SUPERVISED OR STRUCTURED ACTIVITY, AGES 12 TO 17 

Experimental Section 8 

Control vs. Control vs. Control 

Mediator Mean ITTa TOTa ITTa TOTa 

3:45p.m. [SR] (n=2190) 

Youth Spent Time in a Supervised or Structured Activity on Given Weekday at 

All youth (ages 12 to 17) 0.843 -0.013 -0.028 -0.014 -0.024 

(0.021) (0.045) (0.025) (0.042) 

Girls 0.861 -0.039 

(0.030) 

-0.082 

(0.064) 

-0.001 

(0.033) 

-0.002 

(0.053) 

Boys 0.827 0.014 0.030 -0.028 -0.050 

(0.030) (0.065) (0.035) (0.063) 

5:30p.m. [SR] (n=2188) 

Youth Spent Time in a Supervised or Structured Activity on Given Weekday at 

All youth (ages 12 to 17) 0.871 -0.010 -0.022 -0.018 -0.031 

(0.020) (0.043) (0.022) (0.037) 

Girls 0.882 -0.014 

(0.027) 

-0.028 

(0.057) 

-0.007 

(0.031) 

-0.011 

(0.048) 

Boys 0.861 -0.007 -0.014 -0.029 -0.052 

(0.029) (0.064) (0.032) (0.056) 

7:30p.m. [SR] (n=2190) 

Youth Spent Time in a Supervised or Structured Activity on Given Weekday at 

All youth (ages 12 to 17) 0.900 0.001 0.003 0.001 0.002 

(0.018) (0.040) (0.020) (0.033) 

Girls 0.930 -0.029 

(0.026) 

-0.062 

(0.054) 

-0.017 

(0.027) 

-0.027 

(0.043) 

Boys 0.873 0.031 0.068 0.018 0.033 

(0.027) (0.058) (0.028) (0.051) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult, Child, and Youth surveys 

Sample: All children ages 12 to 17 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations).

Appendix E – Detailed Estimation Results – Mediating Factors E-9 
EXHIBIT E5.4d 

MEDIATORS: CHILD TIME USE 

STRUCTURED ACTIVITY, AGES 5 TO 11 

Experimental Section 8 

Control vs. Control vs. Control 

Mediator Mean ITTa TOTa ITTa TOTa 

[PR] 

(n=2442) 

Child Spent Time in a Structured Activity on Given Weekday at 3:45p.m. 

All children (ages 5 to 11) 0.182 -0.039 -0.084 0.001 0.002 

(0.023) (0.048) (0.026) (0.040) 

Girls 0.214 -0.076* 

(0.030) 

-0.152* 

(0.060) 

-0.041 

(0.036) 

-0.063 

(0.055) 

Boys 0.151 -0.003 -0.006 0.045 0.067 

(0.028) (0.064) (0.033) (0.050) 

[PR] 

(n=2471) 

Child Spent Time in a Structured Activity on Given Weekday at 5:30p.m. 

All children (ages 5 to 11) 0.052 0.000 0.000 0.021 0.032 

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

Girls 0.069 -0.034 

(0.017) 

-0.066 

(0.035) 

-0.010 

(0.019) 

-0.015 

(0.029) 

Boys 0.037 0.033* 0.075* 0.052* 0.078* 

(0.017) (0.037) (0.020) (0.030) 

[PR] 

(n=2491) 

Child Spent Time in a Structured Activity on Given Weekday at 7:30p.m. 

All children (ages 5 to 11) 0.013 0.002 0.005 0.010 0.015 

(0.008) (0.016) (0.009) (0.013) 

Girls 0.008 0.008 

(0.009) 

0.016 

(0.018) 

0.010 

(0.008) 

0.015 

(0.013) 

Boys 0.017 -0.004 -0.008 0.010 0.015 

(0.010) (0.022) (0.013) (0.020) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult, Child, and Youth surveys 

Sample: All children ages 5 to 11 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations).

E-10 Appendix E – Detailed Estimation Results – Mediating Factors 
EXHIBIT E5.4e 

MEDIATORS: CHILD TIME USE 

SUPERVISED OR STRUCTURED ACTIVITY, AGES 5 TO 11 

Experimental Section 8 

Control vs. Control vs. Control 

Mediator Mean ITTa TOTa ITTa TOTa 

3:45p.m. [PR] (n=2485) 

Child Spent Time in a Supervised or Structured Activity on Given Weekday at 

All children (ages 5 to 11) 0.967 -0.008 -0.016 -0.008 -0.012 

(0.011) (0.024) (0.013) (0.020) 

Girls 0.970 0.002 

(0.015) 

0.004 

(0.029) 

-0.001 

(0.016) 

-0.002 

(0.024) 

Boys 0.964 -0.018 -0.040 -0.015 -0.022 

(0.015) (0.035) (0.018) (0.027) 

5:30p.m. [PR] (n=2488) 

Child Spent Time in a Supervised or Structured Activity on Given Weekday at 

All children (ages 5 to 11) 0.965 0.000 0.000 0.005 0.008 

(0.011) (0.023) (0.013) (0.019) 

Girls 0.960 0.018 

(0.015) 

0.035 

(0.030) 

0.006 

(0.019) 

0.010 

(0.029) 

Boys 0.971 -0.017 -0.039 0.005 0.008 

(0.013) (0.030) (0.014) (0.022) 

7:30p.m. [PR] (n=2497) 

Child Spent Time in a Supervised or Structured Activity on Given Weekday at 

All children (ages 5 to 11) 0.990 -0.009 -0.018 0.001 0.002 

(0.007) (0.014) (0.007) (0.010) 

Girls 0.986 0.005 

(0.008) 

0.009 

(0.016) 

0.013 

(0.007) 

0.021 

(0.011) 

Boys 0.994 -0.022* -0.050* -0.011 -0.017 

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

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult, Child, and Youth surveys 

Sample: All children ages 5 to 11 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations).

Appendix E – Detailed Estimation Results – Mediating Factors E-11 
EXHIBIT E5.5 

MEDIATORS: CHILD EXPOSURE TO VIOLENCE AND VICTIMIZATION, AGES 8 TO 19 

Control 

Experimental 

vs. Control 

Section 8 

vs. Control 

Mediator Mean ITTa TOTa ITTa TOTa 

Existence of Gangs in Neighborhood or School [SR] (n=4256)

All children (ages 8 to 19) 0.538 -0.059* -0.131* -0.024 -0.039 

(0.022) (0.049) (0.025) (0.041) 

Girls 0.515 -0.068* 

(0.031) 

-0.147* 

(0.066) 

-0.027 

(0.033) 

-0.044 

(0.054) 

Boys 0.560 -0.050 -0.116 -0.021 -0.035 

(0.030) (0.069) (0.035) (0.059) 

Past Month [SR] (n=4402) 

Saw People Selling/Using Illegal Drugs in Neighborhood at Least Once a Week in 

All children (ages 8 to 19) 0.351 -0.045* -0.098* -0.046 -0.076 

(0.022) (0.048) (0.024) (0.040) 

Girls 0.367 -0.097* 

(0.029) 

-0.205* 

(0.062) 

-0.097* 

(0.032) 

-0.156* 

(0.051) 

Boys 0.336 0.007 0.016 0.004 0.007 

(0.029) (0.066) (0.033) (0.057) 

Heard Gunshots in Neighborhood at Least Once a Week in Past Month [SR] 

(n=4411) 

All children (ages 8 to 19) 0.120 -0.030* -0.065* -0.034* -0.055* 

(0.014) (0.030) (0.015) (0.025) 

Girls 0.109 -0.022 

(0.019) 

-0.046 

(0.040) 

-0.024 

(0.020) 

-0.038 

(0.032) 

Boys 0.130 -0.038* -0.086* -0.043* -0.073* 

(0.019) (0.043) (0.021) (0.035) 

E-12 Appendix E – Detailed Estimation Results – Mediating Factors 
EXHIBIT E5.5 (CONT.) 

MEDIATORS: CHILD EXPOSURE TO VIOLENCE AND VICTIMIZATION, AGES 8 TO 19

Control 

Experimental 

vs. Control 

Section 8 

vs. Control 

Mediator Mean ITTa TOTa ITTa TOTa 

Saw Someone Shoot/Stab Another Person in Past Year [SR] (n=4575)

All children (ages 8 to 19) 0.128 -0.010 -0.023 -0.018 -0.029 

(0.015) (0.032) (0.016) (0.026) 

Girls 0.116 -0.014 

(0.021) 

-0.029 

(0.044) 

-0.032 

(0.021) 

-0.052 

(0.033) 

Boys 0.141 -0.007 -0.017 -0.004 -0.006 

(0.021) (0.048) (0.024) (0.040) 

Someone Pulled a Knife/Gun on Child in Past Year [SR] (n=4587) 

All children (ages 8 to 19) 0.089 -0.010 -0.021 -0.012 -0.020 

(0.013) (0.028) (0.013) (0.022) 

Girls 0.056 -0.014 

(0.015) 

-0.029 

(0.033) 

-0.014 

(0.016) 

-0.022 

(0.026) 

Boys 0.122 -0.005 -0.012 -0.011 -0.018 

(0.020) (0.046) (0.021) (0.034) 

Someone Cut, Shot, Stabbed Child in Past Year [SR] (n=4595) 

All children (ages 8 to 19) 0.035 -0.012 -0.026 -0.010 -0.016 

(0.009) (0.019) (0.009) (0.014) 

Girls 0.025 -0.012 

(0.011) 

-0.025 

(0.024) 

-0.014 

(0.011) 

-0.023 

(0.018) 

Boys 0.046 -0.012 -0.027 -0.006 -0.010 

(0.013) (0.030) (0.013) (0.022) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Child and Youth surveys 

Sample: All child ages as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations).

Appendix E – Detailed Estimation Results – Mediating Factors E-13 
EXHIBIT E6.1 

ADDITIONAL CHARACTERISTICS OF THE SCHOOLS ATTENDED BY MTO CHILDREN 

Experimental Section 8 

Control vs. Control vs. Control 

Mean ITTa TOTa ITTa TOTa 

[PR] (n=4876) 

Current school 0.007 -0.000 -0.000 0.008 0.013 

Charter School 

(0.003) (0.007) (0.005) (0.008) 

Average school 0.008 -0.001 

(0.003) 

-0.002 

(0.006) 

0.002 

(0.004) 

0.003 

(0.006) 

[PR] (n=4903) 

Current school 0.039 -0.005 0.010 -0.008 -0.013 

Private School 

(0.008) (0.017) (0.008) (0.013) 

Average school 0.037 -0.002 

(0.007) 

-0.004 

(0.015) 

-0.005 

(0.007) 

-0.009 

(0.011) 

Title I School [PR] (n=2069) 

Current school 0.847 -0.047* -0.099* -0.029 -0.044 

(0.024) (0.050) (0.023) (0.036) 

Average school 0.790 -0.025 

(0.022) 

-0.051 

(0.046) 

-0.012 

(0.021) 

-0.018 

(0.032) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Source: U.S. Department of Education, Common Core of Data 1993 to 2001, National School-Level State 

Assessment Score Database, 2000 to 2001.

Sample: All children ages 5 to 19 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – arrest data, PR – parental

report, SR – self-report, OBS—interviewer observations). 

E-14 Appendix E – Detailed Estimation Results – Mediating Factors 
EXHIBIT E6.2 

MEDIATORS: SCHOOL CLIMATE, AGES 8 TO 17 

Control 

Experimental 

vs. Control 

Section 8 

vs. Control 

Mediator Mean ITTa TOTa ITTa TOTa 

There is a Lot of Cheating in Tests & Assignments [SR] (n=3875)

All children (ages 8 to 17) 0.416 0.029 0.063 -0.010 -0.016 

(0.023) (0.051) (0.025) (0.039) 

Girls 0.416 0.047 

(0.031) 

0.099 

(0.066) 

-0.008 

(0.034) 

-0.013 

(0.054) 

Boys 0.414 0.011 0.025 -0.011 -0.018 

(0.033) (0.074) (0.034) (0.056) 

Discipline in School is Fair [SR] (n=3943) 

All children (ages 8 to 17) 0.724 -0.007 -0.015 -0.003 -0.004 

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

Girls 0.687 0.036 

(0.028) 

0.075 

(0.059) 

0.050 

(0.031) 

0.078 

(0.048) 

Boys 0.757 -0.046 -0.105 -0.051 -0.084 

(0.028) (0.064) (0.030) (0.049) 

Disruptions From Other Students Inhibit Learning [SR] (n=3984)

All children (ages 8 to 17) 0.640 0.035 0.076 0.008 0.013 

(0.021) (0.045) (0.024) (0.038) 

Girls 0.626 0.059* 

(0.029) 

0.123* 

(0.061) 

-0.017 

(0.036) 

-0.026 

(0.056) 

Boys 0.652 0.010 0.024 0.033 0.054 

(0.029) (0.066) (0.032) (0.053) 

Child Feels Safe in School [SR] (n=3976) 

All children (ages 8 to 17) 0.775 0.015 0.032 0.019 0.030 

(0.018) (0.038) (0.019) (0.031) 

Girls 0.780 0.014 

(0.025) 

0.028 

(0.051) 

0.012 

(0.027) 

0.018 

(0.042) 

Boys 0.771 0.016 0.036 0.025 0.042 

(0.025) (0.057) (0.027) (0.045) 

Appendix E – Detailed Estimation Results – Mediating Factors E-15 
EXHIBIT E6.2 (CONT.) 

MEDIATORS: SCHOOL CLIMATE 

Control 

Experimental 

vs. Control 

Section 8 

vs. Control 

Mediator Mean ITTa TOTa ITTa TOTa 

Teachers are Interested in Students [SR] (n=3966) 

All children (ages 8 to 17) 0.810 -0.010 -0.021 0.017 0.027 

(0.018) (0.039) (0.019) (0.030) 

Girls 0.805 0.001 

(0.025) 

0.001 

(0.053) 

0.012 

(0.027) 

0.019 

(0.042) 

Boys 0.815 -0.020 -0.045 0.022 0.036 

(0.025) (0.056) (0.026) (0.042) 

[SR] 

(n=1770) 

Child Has Own Math Textbook and Can Take it Home To Do Homework 

All children (ages 8 to 11) 0.821 0.002 0.004 -0.005 -0.008 

(0.026) (0.057) (0.029) (0.043) 

Girls 0.865 -0.008 

(0.030) 

-0.016 

(0.062) 

-0.039 

(0.037) 

-0.060 

(0.057) 

Boys 0.779 0.010 0.025 0.028 0.041 

(0.040) (0.095) (0.043) (0.064) 

School Climate Index on 5 School Quality Items [SR] (n=3996)

All children (ages 8 to 17) 0.650 -0.011 -0.024 0.009 0.014 

(0.011) (0.024) (0.012) (0.019) 

Girls 0.644 -0.010 

(0.015) 

-0.020 

(0.032) 

0.022 

(0.017) 

0.034 

(0.027) 

Boys 0.655 -0.012 -0.028 -0.004 -0.006 

(0.015) (0.034) (0.015) (0.025) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Child and Youth surveys 

Sample: All children ages 8 to 17 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations).

E-16 Appendix E – Detailed Estimation Results – Mediating Factors 
EXHIBIT E6.3 

MEDIATORS: CHILD ABSENTEEISM, TARDINESS, OR PROBLEMS AT SCHOOL 

Control 

Experimental 

vs. Control 

Section 8 

vs. Control 

Mediator Mean ITTa TOTa ITTa TOTa 

Child is Late for School Once a Month or More [SR] (n= 4529) 

All children (ages 8 to 19) 0.532 -0.010 -0.021 -0.021 -0.035 

(0.022) (0.049) (0.025) (0.042) 

Girls 0.529 -0.022 

(0.028) 

-0.047 

(0.060) 

-0.021 

(0.032) 

-0.034 

(0.052) 

Boys 0.535 0.003 0.007 -0.022 -0.037 

(0.031) (0.072) (0.036) (0.060) 

Youth is Absent More Than 5% of the School Year [SR] (n=2621)

All youth (ages 12 to 19) 0.367 -0.039 -0.084 -0.032 -0.055 

(0.026) (0.056) (0.029) (0.050) 

Girls 0.383 -0.077* 

(0.035) 

-0.165* 

(0.074) 

-0.058 

(0.039) 

-0.095 

(0.064) 

Boys 0.352 0.000 0.001 -0.006 -0.010 

(0.037) (0.082) (0.041) (0.076) 

Proportion of Days in the School Year That Youth is Absent [SR] (n=2621)

All youth (ages 12 to 19) 0.059 -0.001 -0.002 -0.003 -0.005 

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

Girls 0.066 -0.016* 

(0.006) 

-0.033* 

(0.014) 

-0.015* 

(0.007) 

-0.024* 

(0.011) 

Boys 0.052 0.014* 0.031* 0.009 0.016 

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

[PR] (n=4730) 

School Asked Someone to Come in and Talk About Problems Child Was Having 

With Schoolwork or Behavior in Past 2 Years 

All children (ages 5 to 17) 0.299 0.015 0.031 0.008 0.013 

(0.018) (0.038) (0.020) (0.032) 

Girls 0.231 -0.013 

(0.025) 

-0.027 

(0.051) 

-0.018 

(0.027) 

-0.029 

(0.042) 

Boys 0.368 0.042 0.096 0.034 0.056 

(0.026) (0.059) (0.030) (0.049) 

Appendix E – Detailed Estimation Results – Mediating Factors E-17 
EXHIBIT E6.3 (CONT.) 

MEDIATORS: CHILD ABSENTEEISM, TARDINESS, OR PROBLEMS AT SCHOOL

Control 

Experimental 

vs. Control 

Section 8 

vs. Control 

Mediator Mean ITTa TOTa ITTa TOTa 

Child Was Suspended/Expelled From School in Past 2 Years [PR] (n= 5334) 

All children (ages 5 to 19) 0.166 -0.002 -0.005 -0.010 -0.017 

(0.013) (0.029) (0.015) (0.025) 

Girls 0.108 -0.007 

(0.016) 

-0.015 

(0.032) 

-0.009 

(0.017) 

-0.014 

(0.028) 

Boys 0.224 0.003 0.006 -0.012 -0.020 

(0.021) (0.048) (0.024) (0.039) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult, Child and Youth surveys 

Sample: All children ages 5 to 19 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations).

E-18 Appendix E – Detailed Estimation Results – Mediating Factors 
EXHIBIT E6.4 

MEDIATORS: CHILD’S FUTURE EXPECTATIONS, AGES 8 TO 19 

Control 

Experimental 

vs. Control 

Section 8 

vs. Control 

Mediator Mean ITTa TOTa ITTa TOTa 

[SR] 

(n=4521) 

Child Thinks Chances are High/Very High He/She Will Complete College 

All children (ages 8 to 19) 0.645 0.007 0.016 0.004 0.007 

(0.020) (0.045) (0.023) (0.037) 

Girls 0.688 0.043 

(0.027) 

0.093 

(0.058) 

0.019 

(0.032) 

0.030 

(0.051) 

Boys 0.602 -0.029 -0.068 -0.009 -0.015 

(0.029) (0.068) (0.031) (0.051) 

Job As An Adult [SR] (n=4534) 

Child Thinks Chances are High/Very High He/She Will Find a Well-Paid, Stable 

All children (ages 8 to 19) 0.780 0.002 0.005 -0.005 -0.009 

(0.017) (0.038) (0.020) (0.032) 

Girls 0.804 0.036 

(0.022) 

0.075 

(0.047) 

0.001 

(0.026) 

0.002 

(0.043) 

Boys 0.755 -0.031 -0.072 -0.011 -0.019 

(0.027) (0.061) (0.028) (0.048) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Child and Youth surveys 

Sample: All children ages 8 to 19 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations).

Appendix E – Detailed Estimation Results – Mediating Factors E-19 
EXHIBIT E6.5 

MEDIATORS: PARENTAL MONITORING 

Control 

Experimental 

vs. Control 

Section 8 

vs. Control 

Mediator Mean ITTa TOTa ITTa TOTa 

Parental Monitoring Index [SR] (n=3996) 

All children (ages 8 to 17) 0.417 0.018 0.039 0.014 0.023 

(0.018) (0.039) (0.020) (0.032) 

Girls 0.470 0.018 

(0.024) 

0.038 

(0.051) 

-0.001 

(0.026) 

-0.001 

(0.041) 

Boys 0.367 0.017 0.038 0.030 0.049 

(0.024) (0.055) (0.026) (0.042) 

Adult Knows Child's Friends [SR] (n=4560) 

All children (ages 8 to 19) 0.309 -0.014 -0.032 0.001 0.001 

(0.020) (0.044) (0.022) (0.036) 

Girls 0.329 0.007 

(0.028) 

0.015 

(0.059) 

0.014 

(0.030) 

0.023 

(0.049) 

Boys 0.289 -0.036 -0.082 -0.012 -0.020 

(0.027) (0.061) (0.029) (0.049) 

Adult Knows Who Child Is With When He/She Is Not Home [SR] (n=4557) 

All children (ages 8 to 19) 0.458 0.025 0.056 0.000 0.000 

(0.022) (0.048) (0.024) (0.039) 

Girls 0.524 0.025 

(0.029) 

0.053 

(0.062) 

-0.002 

(0.031) 

-0.003 

(0.050) 

Boys 0.392 0.025 0.059 0.003 0.004 

(0.029) (0.067) (0.033) (0.055) 

Adult Knows Child's Teacher [SR] (n=3960) 

All children (ages 8 to 17) 0.462 0.020 0.044 0.016 0.026 

(0.022) (0.049) (0.026) (0.042) 

Girls 0.517 0.008 

(0.030) 

0.018 

(0.064) 

-0.032 

(0.035) 

-0.050 

(0.055) 

Boys 0.409 0.030 0.067 0.063 0.105 

(0.030) (0.069) (0.036) (0.059) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Child and Youth surveys 

Sample: All children ages 8 to 19 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations).

E-20 Appendix E – Detailed Estimation Results – Mediating Factors 
EXHIBIT E6.6 

MEDIATORS: PARENTING AND PARENTAL SUPPORT 

Experimental Section 8 

Control vs. Control vs. Control 

Mediator Mean ITTa TOTa ITTa TOTa 

Saw Father At Least Once a Week In Past Year [SR] (n=4521)

All children (ages 8 to 19) 0.356 0.023 0.051 0.025 0.042 

(0.022) (0.049) (0.025) (0.041) 

Girls 0.325 0.055 

(0.029) 

0.116 

(0.061) 

0.020 

(0.032) 

0.032 

(0.052) 

Boys 0.388 -0.009 -0.020 0.031 0.052 

(0.030) (0.068) (0.034) (0.057) 

Mother or Child's Primary Caregiver Is Very Supportive [SR] (n=4567)

All children (ages 8 to 19) 0.830 0.008 0.018 -0.010 -0.016 

(0.016) (0.035) (0.019) (0.030) 

Girls 0.799 0.019 

(0.022) 

0.041 

(0.047) 

0.010 

(0.025) 

0.015 

(0.040) 

Boys 0.860 -0.003 -0.006 -0.029 -0.048 

(0.021) (0.049) (0.025) (0.042) 

Father Is Very Supportive [SR] (n=4471) 

All children (ages 8 to 19) 0.345 0.018 0.040 -0.015 -0.025 

(0.021) (0.047) (0.024) (0.039) 

Girls 0.336 0.027 

(0.029) 

0.058 

(0.061) 

-0.032 

(0.032) 

-0.052 

(0.052) 

Boys 0.354 0.008 0.019 0.002 0.004 

(0.030) (0.069) (0.033) (0.056) 

Parenting [OBS] 

Parental warmth scale 0.689 -0.002 -0.004 0.012 0.019 

(n=1711) (0.017) (0.034) (0.019) (0.029) 

(n=1733) 

0.061 0.003 

(0.011) 

0.005 

(0.023) 

-0.004 

(0.011) 

-0.007 

(0.018) 

Parental hostility scale 

Parental verbal skills scale 0.970 -0.017* -0.036* -0.016 -0.024 

(n=1978) (0.008) (0.017) (0.011) (0.016) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Child and Youth surveys and Interviewer observations. 

Sample: All children ages 8 to 19 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations). 

Appendix E – Detailed Estimation Results – Mediating Factors E-21 
EXHIBIT E6.7 

MEDIATORS: ENGAGEMENT IN SCHOOL AND PARENTAL INVOLVEMENT 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

Engagement in School [SR] 

Always pays attention in 0.553 0.021 0.046 0.029 0.047 

class (0.023) (0.051) (0.025) (0.040) 

homework (

0.497 -0.022 

(0.030) 

-0.049 

(0.065) 

-0.029 

(0.033) 

-0.048 

(0.056) 

0.579 -0.004 -0.008 0.007 0.012 

(n=4064) 

 Always completes 

n=2228) 

Works hard in school 

(n=4062) (0.023) (0.050) (0.024) (0.039) 

homework (

4.835 0.582 

(0.309) 

1.270 

(0.673) 

0.582 

(0.374) 

0.969 

(0.623) 

Hours per week spent on 

n= 2250) 

Hours per week reading 3.611 -0.069 -0.155 0.016 0.026 

(n= 4630) (0.206) (0.465) (0.227) (0.372) 

Parental Involvement [PR] 

Number of days family 4.442 0.278* 0.589* 0.017 0.029 

(0.110) (0.234) (0.122) (0.203) 

at child’s school in past 

year 

0.552 0.030 

(0.023) 

0.064 

(0.048) 

0.023 

(0.024) 

0.038 

(0.041) 

eats together (n=3482) 

Adult attended an event 

(n=3284) 

Adult attended a meeting 0.72.5 0.02.5 0.052 0.020 0.033 

at child’s school in past (0.020) (0.042) (0.022) (0.037) 

year

Adult volunteered at 

year

0.34 -0.02.2 

(0.021) 

-0.047 

(0.044) 

0.008 

(0.023) 

0.014 

(0.038) 

 (n=3286) 

child’s school in past 

 (n=3284) 

Adult worked with youth 0.197 -0.04.4 -0.094 -0.043 -0.071 

group, sports team or (0.018) (0.038) (0.019) (0.032) 

club in past year (n=3286) 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Source: Woodcock Johnson-Revised tests. 

Sample: All children ages 5 to 19 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – arrest data, PR – parental

report, SR – self-report, OBS—interviewer observations). 

E-22 Appendix E – Detailed Estimation Results – Mediating Factors 
EXHIBIT E7.1 

MEDIATOR: JOB TRAINING, ADULT AND YOUTH 

Experimental Section 8 

Control vs. Control vs. Control 

Outcome Mean ITTa TOTa ITTa TOTa 

Adult Job Training [SR] (n=3511) 

Adult participated in job- 0.180 -0.017 -0.035 0.016 0.026 

related training since (0.016) (0.035) (0.019) (0.031) 

September 2000 

Youth Job Training [SR] (ages 17 to 19) (n=901) 

Youth participated in job- 0.218 -0.019 -0.044 -0.009 -0.017 

related training since (0.037) (0.090) (0.042) (0.076) 

September 2000 

* = p<.05 on t-test. Robust standard errors are shown in parentheses. 

Sources: Adult and Youth surveys 

Sample: All children ages 17 to 19 as of May 31, 2001. 

Notes: a) ITT = Intent-to-Treat; TOT = Treatment-on-Treated. See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted with robust standard errors. 

c) Subgroup impacts were estimated in a single equation using interactions with treatment indicators. 

See appendix B for detailed explanation of estimation procedures. 

d) Abbreviations in brackets indicate the source of the outcome information (ADMIN – administrative records, PR 

– parental report, SR – self-report, M – direct measurement, OBS – interviewer observations).

Appendix E – Detailed Estimation Results – Mediating Factors E-23 

Appendix F 

Tests for Nonresponse Bias 

Appendix F 

Tests for Nonresponse Bias 

Many of the outcomes analyzed in this report were measured with survey data. As in all surveys, 

there was some nonresponse in the MTO interim evaluation surveys. In this appendix, we use 

administrative data on employment, earnings, and welfare data, which are available for all sample 

members, to analyze the extent to which the impacts on these outcomes differed between adult survey 

respondents and the entire sample. While not a definitive test for nonresponse bias in other outcomes, 

we believe that this analysis provides a strong indication of the likely severity of nonresponse bias in 

the impact estimates based on survey data. It also provides measures of the effectiveness of the steps 

taken in the interim evaluation survey to reduce nonresponse bias. 

As described in appendix B, after completion of the main field period for the interim evaluation 

survey, we implemented a subsampling procedure to reduce nonresponse bias. Our strategy was to 

continue to work 3 in 10 of the cases that had not been completed during the main field period. By 

focusing survey resources on a random subsample of cases, we were able to achieve a higher effective 

response rate than if we had used the same resources to continue to work the full sample.1

 This was 

expected to reduce bias due to nonresponse. 

During the main field period, 80 percent of the adult sample was interviewed (this group of 

respondents is hereafter denoted the “main sample”). Among the 3 in 10 subsample, the completion 

rate was 48 percent, yielding an effective response rate of 89.6 percent. The analyses of outcomes 

based on survey data presented in this report are based on the sample of all respondents, with weights 

reflecting their sampling probabilities—i.e., weights of 1 for those in the main sample and 3.33 for 

respondents in the survey subsample. 

In addition to measuring the bias in impact estimates based on this survey respondent sample, we also 

test the effectiveness of the subsampling approach in reducing nonresponse bias. To do so, we 

estimated impacts on outcomes measured with administrative data, which are available for all sample 

members, for three different adult samples:2 

• the main survey sample, 

1 The effective response rate is equal to the main sample response rate (MRR) plus the subsample response 

rate (SRR) multiplied by one minus the main sample response rate: MRR+ SRR*(1 – MRR). 

2 These impact estimates were derived from a single regression, in which the treatment indicator was 

interacted with dummy variables for the main survey sample, respondents in the subsample, and all 

nonrespondents. If we denote the coefficients of these three interactions m, s, and n, respectively, the 

impact on the main sample equals m, the impact on the full sample is (w1m + w2s + w3n), and the impact on 

the weighted survey sample is (w4m + w5s), where w1, w2, and w3 are the unweighted proportions of the 

overall sample in each of the three subgroups and w4 and w5 are the sampling weights of the main survey 

sample and the survey subsample, respectively. (See appendix B for details of the subsampling procedure 

and the construction of sampling weights.) 

Appendix F – Tests for Nonresponse Bias F-1 
• the survey respondent sample (i.e., the combination of the main sample and respondents in the 

subsample), and 

• the full sample. 

Under random assignment, the estimated impacts on the full sample are unbiased estimates of the true 

impacts. The differences between these estimates and those for the main sample are estimates of the 

nonresponse bias that would have occurred if we had not attempted to interview the hard-to-interview 

cases still outstanding at the end of the main field period.3

 Similarly, the differences between the 

estimated impacts on all sample members and those in the weighted survey respondent sample are 

estimates of the nonresponse bias in the estimates based on the sample used in this report. The 

difference between these two estimates of bias measures the contribution of the subsample in 

reducing nonresponse bias. 

Exhibit F.1 shows, for each of six outcomes, the estimated ITT impacts on the main sample, the 

weighted survey respondent sample, and the full sample. Exhibit F.2 shows the estimated bias in the 

estimates based on the main sample and the weighted survey respondent sample, derived as the 

difference between the impact estimates for these subgroups and the corresponding estimates for the 

full sample. 

These tests provide somewhat mixed evidence of the effectiveness of the subsample in reducing 

nonresponse bias. For 7 of the 8 estimated impacts on welfare outcomes, the bias in the estimates 

based on the weighted survey respondent sample is in fact smaller than in the estimates based on the 

main sample. Moreover, for these outcomes, 5 of the 6 biases that were statistically significantly 

different from zero in the main sample are no longer statistically significant in the weighted survey 

respondent sample. The opposite pattern holds for the employment and earnings outcomes, however. 

For all 4 of these estimates, the magnitude of the bias in the weighted survey respondent sample is 

larger than that in the main sample, and in 2 of the 4 cases a bias that was insignificant in the main 

sample becomes statistically significant in the weighted survey respondent sample. 

These results suggest that the effectiveness of the subsample in reducing nonresponse bias probably 

varied across outcomes, although the consistency of the tests within each of the two domains 

represented here suggests that among highly correlated outcomes the effect may have been relatively 

uniform. Unfortunately, the administrative data required to carry out these tests are only available for 

a small number of outcomes and are not available at all for the outcomes measured with survey data. 

It is important to note that even where the bias in the estimates based on the survey respondent 

sample is statistically significant, it is relatively small—less than 5 percent of the control mean. Thus, 

these results provide some confidence that, whatever the effectiveness of the subsample in reducing 

bias, nonresponse bias in the estimates based on survey data is not a serious concern for this study. 

It is important to recognize that the measures of bias presented here are only estimates, based on a single 

draw from the sampling distribution. The true bias, which is the mean of this difference across all draws 

from the sampling distribution, cannot be measured in any given sample. We use tests of statistical 

significance to take account of the sampling variability of this estimate of bias. 

F-2 Appendix F – Tests for Nonresponse Bias 

3 
EXHIBIT F.1 

ESTIMATED IMPACTS ON SELECTED OUTCOMES, FOR MAIN SAMPLE, WEIGHTED SURVEY 

RESPONDENT SAMPLE, AND FULL SAMPLE 

Experimental Section 8 

Vs. Control vs. Control 

Weighted Weighted 

Control Main Survey Full Main Survey Full 

Mean Sample Sample Sample Sample Sample Sample 

] 

adult, year 5 

.258 .010 -.001 -0.007 .046 .030 .028 

adult, year 5 

1288 37 15 -36 89 49 50 

Outcome 

Welfare Benefits [Admin

TANF receipt, sample 

TANF amount, sample 

Food stamp receipt, .440 .037 .029 0.010 .053 .044 .032 

1272 44 15 -27 109 91 112 

] 

sample adult, year 5 

Food stamp amount, 

sample adult, year 5 

Employment and Earnings [Admin

Fraction of quarters .413 -.002 .005 -.006 .005 .021 .002 

employed, sample adult, 

years 1 to 4 

4 

 Annualized earnings, 5302 -151 248 10 -115 192 -69

sample adult, years 1 to 

* = p<.05 on t-test. 

Source: State UI, TANF, and food stamp administrative records. 

Sample: TANF and food stamps: All sample adults. Employment and earnings: All sample adults in California, 

Illinois, Maryland, and New York. 

Notes: a) All estimates are ITT (intent-to-treat). See Section 1.4 and appendix B for details. 

b) Control means and impact estimates are regression-adjusted, with robust standard errors. 

c) Abbreviations in brackets indicate the source of the outcome information (ADMIN = administrative records). 

Appendix F – Tests for Nonresponse Bias F-3 
EXHIBIT F.2 

ESTIMATED BIAS, SELECTED OUTCOMES: MAIN SAMPLE AND WEIGHTED 

SURVEY RESPONDENT SAMPLE 

Experimental Section 8 

vs. Control vs. Control 

Est’d Bias, Est’d Bias, Est’d Bias, Est’d Bias, 

Control Main Wt’d Survey Main Wt’d Survey 

Mean Sample Sample Sample Sample 

] 

year 5 

.258 .017* .006 .018* .002 

adult, year 5 

1288 73* 51 39 1 

Outcome 

Welfare Benefits [Admin

TANF receipt, sample adult, 

TANF amount, sample 

Food stamp receipt, sample .440 .027* .019* .021* .012 

adult, year 5 

1272 71* 42 -3 -21 

] 

Food stamp amount, 

sample adult, year 5 

Employment and Earnings [Admin

Fraction of quarters .413 .004 .011* .003 .019* 

employed, sample adult, 

years 1 to 4 

 Annualized earnings, 5302 -161 238 -46 261

sample adult, years 1 to 4 

* = p<.05 on t-test of null hypothesis that bias = 0. 

Source: State UI, TANF, and food stamp administrative records. 

Sample: TANF and food stamps: All sample adults. Employment and earnings: All sample adults in California, 

Illinois, Maryland, and New York. 

Notes: a) Bias estimates based on ITT (intent-to-treat) impact estimates. See Section 1.4 and appendix B for 

detailed discussion of ITT estimates and their derivation. 

b) Control means and impact estimates are regression-adjusted, with robust standard errors. 

c) Abbreviations in brackets indicate the source of the outcome information (ADMIN = administrative records). 

F-4 Appendix F – Tests for Nonresponse Bias 
Appendix G 

Assessment of the Size and Significance of the 

Impact Estimates 

Appendix G 

Assessment of the Size and Significance of the 

Impact Estimates 

In this appendix we examine several questions related to the size and significance of the estimated 

impacts of MTO and the likelihood that more and/or larger effects will be observed in the future: 

• Do the findings provide evidence of real effects on family outcomes? 

• Were the estimated effects of MTO large enough to be relevant for policy? 

• Might we have missed some effects that were large enough to be relevant for policy? 

• How different would the results have been if the families who moved with program vouchers had 

stayed in low-poverty areas longer? 

• Can larger effects be expected in the longer term? 

We examine each of these questions in turn. 

Do the findings provide evidence of real effects on family outcomes? 

A number of the estimated effects of MTO were statistically significant. For an individual estimate, 

statistical significance at the .05 level means that the chance of obtaining an estimate that large or 

larger when the actual effect is zero is less than 5 in 100. This is generally regarded as a low enough 

chance of a false positive to be disregarded and to treat the estimate as convincing evidence of a real 

effect. But when large numbers of estimates are derived, the chance that some of them will exceed the 

.05 significance threshold by chance alone may be substantially higher. If, for example, we derived 

100 estimates, we would expect 5 of them to be significant by chance alone. The question therefore 

arises, given the number of estimates presented here, how much credence should be placed in those 

that were statistically significant? 

Unfortunately, there is no simple answer to this question. For any given number of estimates, the 

number that would be expected to be significant by chance alone is easily calculated. It is 0.05 times 

the number of tests. But, as with any expected value, the actual number of false positives in any given 

sample can be greater or less than the expected value. As usual, we tend to regard large deviations 

from the mean (many more statistically significant estimates than would be expected by chance) as 

evidence that the results were not simply due to chance sampling error with zero actual effects. There 

are several problems with this. First, it is difficult to say what large means in this case. If, to give an 

extreme example, all of the outcomes were perfectly correlated, then there would be a 0.05 chance 

that all would be statistically significant if the true effects were all zero. Even so, we can say 

something about how likely it is to get the observed number of statistically significant effects by 

chance alone and we can use this to help judge the likelihood of getting the observed results if there 

actually were no real effects. Second, even if the number of statistically significant effects is no 

greater than would be expected by chance alone, it is always possible that some of them reflect real 

Appendix G – Assessment of the Size and Significance of the Impact Estimates G-1 
effects. If we try to take account of the additional error introduced by looking at large numbers of 

tests, we always run the risk that a few real effects may be masked by large numbers of tests where 

there are no effects. Further, the number of statistically significant estimates presented in the report is 

affected by the apparent significance of the summary measure for a domain, such as when we present 

the components of the risky behavior index and not the delinquency index in Chapter 5 because the 

former index has a significant treatment effect and we explore its components in greater detail. 

Nevertheless, we believe that there is some information to be gained by examining the numbers and 

patterns of statistically significant estimates across the domains and subdomains analysed. 

In several domains, for example, the number of statistically significant estimates is actually less than 

would be expected by chance alone. These include the employment and earnings domain (one 

significant estimate out of 46), the subdomain of educational performance (two significant estimates 

out of 58), and the subdomain of household income, food security, and self-sufficiency (zero 

significant estimates out of 18).1

 In these cases, it is clear that the interim evaluation provides little or 

no evidence of real effects of MTO on the outcomes of interest. 

Conversely, there are some domains and subdomains where the number of significant estimates 

substantially exceeds the number that would be expected by chance alone. These include the housing 

conditions and housing assistance domain (17 significant outcomes out of 24) and the subdomain of 

school characteristics (16 significant estimates out of 24). It seems unlikely that this many estimates 

would be significant by chance alone.2 

The situation is less clear in the remaining domains—health; delinquency, crime, and risky behavior; 

and public assistance. In these domains, about 15 percent of the estimates were statistically 

significant, as compared with the 5 percent that would be expected by chance alone. This could 

simply reflect sampling variability within this particular sample.3

 We leave it to the reader to assess 

the validity of these estimates. 

Were the estimated effects of MTO large enough to be relevant for policy? 

As detailed in the previous section, MTO had statistically significant effects on a number of 

outcomes. Even if we accept these estimates as evidence of real effects of MTO, the fact that they 

were statistically significant does not necessarily mean that they are large enough to be of practical 

significance. To assess the importance of these effects for policy, we compare a number of these 

estimated impacts with the mean outcomes that would have been experienced in the absence of the 

1 These counts, and those given below, are for the ITT estimates presented in the text. 

2 By Markoff’s Inequality, the probability of obtaining 16 or more significant estimates out of 24, as we did 

in the school characteristics subdomain, when there are no true impacts on these outcomes, is less than 

.075, regardless of the correlation among the impacts. In judging this, it may be useful to note that 

Markoff’s Inequality never gives a probability less than 0.05, even if all of the tests are significant. This 

reflects the fact, mentioned earlier, that if all of the measures are perfectly correlated, then either all of them 

will be significant or none of them will be. With a 0.05 test, there would be a 0.05 chance of all being 

significant. 

3 By Markoff’s Inequality, the chance of 15 percent or more of the estimates being statistically significant 

when all true effects are zero is less than .33. 

G-2 Appendix G – Assessment of the Size and Significance of the Impact Estimates 
demonstration by families who leased up with program vouchers and, where available, with other 

benchmark values of the outcomes. Exhibit G.1 shows these comparisons. 

The first two columns of Exhibit G.1 show the estimated impact on those families in the MTO 

experimental group who leased up with program vouchers (the treatment on treated or TOT effect) 

and the mean outcome that these families would have experienced in the absence of the 

demonstration, which we term the “counterfactual”. We estimate the counterfactual by subtracting the 

estimated impact on these families from the actual mean outcome for this subgroup. This provides a 

better benchmark against which to compare the TOT impacts than the control mean shown in the text 

tables because the control mean includes families who did not lease up with program vouchers. The 

third and fourth columns show the corresponding impact estimates and counterfactuals for the Section 

8 group. We confine our attention to the TOT estimates here because they represent the effect of the 

demonstration on those families who actually leased up in the two treatment groups. The final column 

shows other benchmark values of these outcomes, where available. 

As can be seen in the exhibit, most of the estimated impacts are quite large by these standards. For 

example, among adults in the experimental group who moved with program vouchers, MTO 

increased the proportion who felt safe at night by two-thirds and the fraction who rated their housing 

good or excellent by 40 percent, while reducing the proportion who saw drugs being sold in their 

neighborhood by nearly 60 percent. The impacts on these measures in the Section 8 group were only 

about half as large, but still substantial. 

The 11 percentage point reduction in obesity among adults in the experimental group represents more 

than a 20 percent reduction in the obesity rate for this group. Another way to gauge the size of this 

effect is to note that 11 percentage points is larger than the difference in obesity rates between the 

poor and the nonpoor in the U.S. population overall (see Other Benchmark in exhibit G.1). Similarly, 

MTO reduced the psychological distress index by 20 percent among sample adults. This effect is 

more than half as large as the poor/nonpoor differential in the U.S. population. 

The effects on youth mental health were similarly large. MTO reduced the rate of generalized anxiety 

disorder by more than two thirds, relative to what it would have been in the absence of the 

demonstration, for girls who moved with program vouchers in both the experimental group and the 

Section 8 group, and for youth overall in the Section 8 group. 

In the delinquency and risky behavior domain, both the favorable effects for girls and the unfavorable 

effects for boys were also quite substantial relative to the counterfactual. MTO reduced the rate of 

arrests for violent crimes by three-quarters among girls in the Section 8 group who moved with 

program vouchers, and reduced marijuana use and smoking by about half in the experimental group. 

Among boys who moved with program vouchers in the experimental group, MTO increased the 

behavior problem index by two-thirds, tripled the rate of arrests for property crimes, and quadrupled 

the incidence of smoking. In the Section 8 group, the demonstration raised the behavior problem 

index by one-third and tripled the proportion who smoked. 

Appendix G – Assessment of the Size and Significance of the Impact Estimates G-3 
-- 

-- 

Appendix G – Assessment of the Size and Significance of the Impact Estimates 

EXHIBIT G.1 

SELECTED 

STATISTICALLY 

SIGNIFICANT IMPACTS, RELATIVE TO 

COUNTERFACTUAL AND 

OTHER 

BENCHMARKS 

Experimental Section 8 

Counter Counter 

Outcome TOT Est. factuala TOT Est. factuala Other Benchmark (source) 

Housing and Neighborhood Conditions 

Feel safe at night .303* .498 .156* .509 

-.248* .427 -.171* .446 Saw drugs past 30 days 

Rate housing good/excellent .210* .479 .119* .484 Adult Health [SR] Obesity -0.108* 0.507 Ns 0.493 Poor: 24.7% obese

Not poor: 19.0% obese(Pleis & Cole, 2002, Table 31, p.79) 

0.368 Ns 0.323 Poor: 

) 

[SR] )

Psychological distress index for past month -0.073* 0.223 on psych index 

Not poor: 0.098 on psych index (Pleis & Cole, 2002, Tables 13 and 15, p. 37 and 41

Youth Mental Health (ages 12 to 19Generalized anxiety disorder (all youth) Ns 0.096 -0.061* 0.090 Generalized anxiety disorder (girls) -0.092* 0.149 -0.078* 0.117 Delinquency and Risky Behavior (youth ages 15 to 19) [SR] Behavior problems (boys self-report) .169* .249 .095* .301 Difference between median and 70th percentile is .18 

among MTO Controls 

.115* .062 Ns .127 Ever arrested for property crime (boys) Boy/girl difference is .06 among MTO Controls 

Ever smoke cigarettes (boys) .314* .078 .305* .147 Difference between age 12 and age 18 is .11 among 

MTO Controls. 

) Ns -.155* .199 Ever arrested for violent crime (girls Boy/girl difference is .060 among MTO Controls 

Ever use marijuana (girls) -.276* .501 Ns -- Difference between age 14 and age 18 is .30 among 

MTO Controls 

ls) -.184* .398 Ns Ever smoke cigarettes (gir

a

Difference between age 15 and age 18 is .26 among MTO Controls 

Counterfactual = estimated mean outcome that families who leased up with program vouchers would have experienced in the absence of the demonstration; estimated as actual mean 

for this group minus estimated impact ns = not statistically significant 

G-4 
In viewing these results it should be recognized that while the counterfactual is useful as a 

benchmark, the size of the impact as a proportion of the mean is not always a good measure of the 

importance of the effect. This is particularly true when the mean is small or the incidence of the 

problem is low in the population. In these cases, even effects that are large relative to the mean may 

be small in relation to the overall population and, therefore, of only marginal importance for policy. 

For example, MTO reduced the incidence of generalized anxiety disorder among girls in the Section 8 

group by over two-thirds, but because this is a problem that affects less than 1 girl in 10, this was an 

improvement in the lives of only about 6 percent of the girls in the sample. 

This caveat notwithstanding, we conclude that virtually all of the statistically significant impacts of 

MTO were substantial enough to be important for policy. 

Might we have missed some effects that were large enough to be relevant for policy? 

Random assignment provides unbiased estimates of treatment effects, but it does not guarantee that 

all real effects will be detected. The likelihood of detecting an effect of any given size depends on 

sample sizes, the variability of the outcome of interest, and, in this case, the leaseup rates in the 

treatment group. For any given combination of these factors there is some minimum effect that we 

can be confident of detecting—the minimum detectable effect (MDE) for that design. If the true 

effect is substantially smaller than the MDE, there is a good chance that it will not be detected as 

statistically significant—i.e., it will not be distinguishable from random noise in the data generated by 

sampling error.4 

Exhibit G.2 compares the MDEs for a number of outcomes for which statistically significant impacts 

were not found with the estimated mean of those outcomes in the absence of the demonstration (the 

counterfactual), for families who leased up with program vouchers and, where available, other 

benchmark values of the outcome. 5

This comparison shows how large the true impact would have to 

have been for us to be confident of detecting it. 

The MDEs for most of these measures are quite large. For example, for adults and youth in both the 

experimental group and the Section 8 group, MTO would have to have improved general physical 

health by an amount that is over half the difference between the poor and nonpoor populations in the 

U.S. at large for us to be confident of detecting the effect. For girls or boys taken separately, the effect 

would have to have been even larger to be detectable with high confidence. Similarly, to be confident 

that we could detect effects on asthma, MTO would have to have reduced its incidence among adults 

by nearly half and among youth ages 12 to 19 by over two-thirds. To be reasonably sure of detecting 

an effect among boys or girls separately, the demonstration would have had to virtually eliminate the 

condition. The same is true of generalized anxiety disorder for boys. 

4 The minimum detectable effects presented in this section are based on 80 percent power at the .05 

significance level (two-tailed test)—i.e., there is an 80 percent chance that a true effect as large or larger 

than the MDE would yield an estimate that would be statistically significant at the .05 level. 

5 As noted above, we estimate the counterfactual by subtracting the estimated impact on the families who 

leased up with program vouchers from the actual mean outcome for this subgroup. 

Appendix G – Assessment of the Size and Significance of the Impact Estimates G-5 
Appendix G – Assessment of the Size and Significance of the Impact Estimates 

EXHIBIT G.2 

MINIMUM 

DETECTABLE 

EFFECTS FOR 

SELECTED 

STATISTICALLY INSIGNIFICANT TOT ESTIMATES, 

RELATIVE TO 

COUNTERFACTUAL AND 

OTHER 

BENCHMARK 

VALUES

Experimental Section 8 

Counter Counter 

Outcome MDE factuala MDE factuala Other Benchmark (source) 

Adult Health [SR]

General health is currently good or better 0.11 0.702 0.10 0.683 Difference in proportions of poor and nonpoor with 

good or better health is .194 c

(Pleis & Cole, 2002, Table 21, p. 57) 

Had asthma attack or wheezing during 0.10 0.210 0.09 0.207 

past year

Youth Physical Health [SR] (ages 12 to 19) 

General health is currently very good or 0.15 0.678 0.12 0.671 Difference in proportions of poor and nonpoor with better (all youth self-report) c good or better health (according to proxy report) is 

.213(Blackwell & Tonthat, 2002, Table 6, p. 17) 

) 

0.21 0.591 0.15 0.624 General health is currently very good or 

better (girlsGeneral health is currently very good or 0.21 0.771 0.17 0.722 better (boys)

0.11 0.143 0.09 0.136 Had asthma attack or wheezing during 

past year (all youth) Had asthma attack or wheezing during 0.18 0.157 0.15 0.191 past year (girls)Had asthma attack or wheezing during 0.16 0.128 0.15 0.076 

past year (boys) 

Youth Mental Health [SR] (ages12 to 19) 

Generalized anxiety disorder (boys) 0.11 0.037 0.08 0.060 

G-6 
Appendix G – Assessment of the Size and Significance of the Impact Estimates 

EXHIBIT G.2 (CONT.)

MINIMUM 

DETECTABLE 

EFFECTS FOR 

SELECTED 

STATISTICALLY INSIGNIFICANT TOT ESTIMATES, 

RELATIVE TO 

COUNTERFACTUAL AND 

OTHER 

BENCHMARK 

VALUES

Experimental Section 8 

Counter Counter 

Outcome MDE factuala MDE factuala Other Benchmark (source) 

[SR] 

.048 0.083 0.05 0.089 

− Girls .050 0.050 0.05 0.056 − Boys 0.05 0.120 0.08 0.124 

0.08 0.442 0.08 0.444 

Delinquency and Risky Behavior (youth ages 15 to 19) 

Delinquency Index (all youth) Risky Behavior Index (all youth) 

− Boys 0.05 0.382 0.20 0.445 

Education

Pupil-teacher ratio, current school 1.40 16.17 1.00 16.08 [ADMIN] 

5.79 497 4.85 497 

4.90 501 3.89 501 

] 1,456 4905 1,285 4417 ] 1,842 7159 1,537 6255 

882 801 605 1,068 

Broad reading score - WJR Broad math score - WJR 

Adult Earnings 

Annual earnings, years 1 and 2 [ADMINAnnual earnings, years 3 and 4 [ADMIN

Youth Earnings 

Total earnings, 2001 [ADMIN] 

Income and Public Assistance 

Total TANF benefits, year 5 [ADMIN] 784 1472 590 1238 2001 [SR] 

.10% .95% .09% .87% Household income as % of poverty line in 

% Households food insecure with hunger .05% .13% .03% .11% [SR]

G-7 
Notes: 

a Counterfactual = estimated mean outcome that families who leased up with program vouchers would have experienced in the absence of the demonstration; 

estimated as actual mean for this group minus estimated impact. 

na = not available 

c Benchmark source: Pleis JR, Coles R. Summary health statistics for U.S. adults: National Health Interview Survey, 1998. National Center for Health Statistics. 

Vital Health Stat. 10(209). 2002. “Poor” was defined as below the poverty line and “Not poor” as 200 percent of the poverty line or greater (third category is ‘near poor”).

d To calculate the benchmarks for the psychological distress index, we used the NHIS summary statistics on the number of individuals who experienced each of 

the six items “all or most of the time” or “some of the time” and then divided this by 6 times the total number of persons 18 and over in that income category. 

The formula is: (#-sad-all-of-time + #-sad-some-of-time + #-hopeless-all-of-time + #-hopeless-some-of-time + #-worthless-all-of-time + #-worthless-some-oftime+ #-everything-effort-all-of-time + #-everything-effort -some-of-time+ #-nervousness-all-of-time + #-nervousness-some-of-time+ #-restlessness-all-of-time + 

#- restlessness-some-of-time) / (6*#-persons-18-and-over). Note that since missing values are included in the denominator but not in the numerators, missing 

values are essentially treated as not exhibiting a particular symptom. 

e Benchmark source for depression: Kessler, R. C., K. A. McGonagle, et al. (1994). "Lifetime and 12-Month Prevalence of Dsm-Iii-R Psychiatric- Disorders in the 

United-States - Results from the National- Comorbidity-Survey.” Archives of General Psychiatry 51(1): 8-19. 

f Note that the MTO depression outcome is based on the short form of the CIDI-SF consistent with the DSM-IV whereas the benchmark value is for the full CIDI 

measure and was based on the DSM-III-R and therefore uses a slightly different criteria for weight loss (10 pounds for DSM-IV versus 15 pounds for DSM-IIIR).

ns = not significant 

c Benchmark source: Blackwell DL, Tonthat L. Summary Health Statistics for U.S. Children: National Health Interview Survey, 1998. National Center for Health 

Statistics. Vital Health Stat 10(208). 2002. Poor was defined as below the poverty line and Not poor as 200% of the poverty line or greater (third category is 

near poor). Very good or better was calculated as the sum of the percent reporting excellent and the percent reporting very good health (due to rounding, value may not be precise). Note that the NHIS uses the report of a proxy for children. Also note that these benchmarks are for all children rather than just ages 12 to 

19. 

G-8 Appendix G – Assessment of the Size and Significance of the Impact Estimates 
Similar results hold for the delinquency and risky behavior outcomes. The demonstration would have 

to have reduced the delinquency index by half or more for us to be confident of detecting the effect. 

We would be likely to detect somewhat smaller, though still large, effects on the risky behavior index. 

We could have been confident of detecting a 6 to 8 percent reduction in the student-teacher ratio or a 

4 to 6 point change in test scores. The latter, though only about 1 percent of the mean test score, 

corresponds to a fairly substantial movement in the test score distribution. For a 12-year-old, for 

example, a change in the Broad Reading score from 500 to 505 represents a movement from the 33rd 

percentile to the 42nd percentile. 

For the earnings, household income, and public assistance outcomes, we are only likely to detect 

large effects with this sample. Impacts on adult earnings would have to be at least 25 percent of the 

counterfactual to be confidently detected and effects on youth earnings would have to be somewhat 

larger. To be 80 percent confident of detecting impacts on welfare benefits, those impacts would have 

be roughly half as large as the benefits that would have been received in the absence of the 

demonstration. Similarly, MTO would have to reduce the incidence of food insecurity with hunger by 

30 to 40 percent for us to be confident of detecting the effect. These are all relatively large impacts. 

Larger samples and/or higher leaseup rates would almost certainly have yielded more statistically 

significant estimates. In an attempt to obtain some indication of the effect that a larger sample might 

have, we estimated impacts on the two treatment groups combined, thereby roughly doubling the size 

of the treatment group. The estimates produced by the pooled sample are, of course, hybrids of the 

effects on the locationally constrained MTO experimental families and those on the unconstrained 

Section 8 families.6

 Unless the heterogeneity of the two groups outweighs the added precision 

obtained from the increased sample size, however, this should yield more precise estimates of effects. 

Because the locations of the two treatment groups have converged a good deal over the followup 

period, for many outcomes the two impacts may not be all that different. Exhibit G.3 shows the 

results of this exercise, in comparison with the estimated impacts for the two separate groups, for the 

outcomes included in the previous exhibit. As can be seen, the pooled estimates are somewhat more 

precise, but none rise to the level of statistical significance. 

On the basis of these results, we conclude that the MTO impact estimates are sufficiently imprecise 

that we may have missed some impacts that are large enough to be relevant for policy, but not large 

enough to pass the test of statistical significance. To further investigate this possibility, we examined 

the 95 percent confidence intervals around the estimated impacts—i.e., the range that, in repeated 

sampling, would be expected to contain the true value of the impact 95 percent of the time. 

These confidence intervals, along with the TOT estimates and the counterfactuals for each outcome, 

are shown in exhibit G.4. For several outcomes it does appear that substantial impacts are unlikely. 

For example, these results suggest that the true impact on the delinquency index is highly unlikely to 

be more than 10 to 15 percent of the counterfactual (i.e., the lower bounds of the 95 percent 

confidence interval for the experimental and Section 8 groups are -.05 and -.06, respectively, 

The estimated impacts on the two groups combined are in fact weighted averages of the separate impact 

estimates. 

Appendix G – Assessment of the Size and Significance of the Impact Estimates G-9 

6 
EXHIBIT G.3 

ESTIMATED IMPACTS ON THE EXPERIMENTAL AND SECTION 8 TREATMENT GROUPS, 

SEPARATELY AND COMBINED (TOT ESTIMATES) – OUTCOMES FOR WHICH IMPACTS WERE NOT 

STATISTICALLY SIGNIFICANT 

Combined 

MTO Treatment 

Outcome Section 8 Groups 

Adult Health 

Experimental 

General health good or better [SR] -.033 -.013 -.023 

(.042) (.036) (.034) 

Asthma or wheezing attack [SR] -.031 -.016 -.024 

(.037) (.032) (.031) 

Children’s Health (ages 12 to 19) 

Gen health very good/excel [SR] .001 -.010 -.004 

(.054) (.048) (.045) 

Asthma or wheezing attack [SR] .065 .022 .044 

(.042) (.036) (.033) 

Delinquency and Risky Behavior (ages 15 to 19) 

Behavior problems index [PR] .042 .006 .025 

(.032) (.026) (.025) 

Risky behavior index [SR] -.019 .030 .005 

(.053) (.047) (.044) 

Education (children ages 5 to 19) 

Student-teacher ratio, current school .52 -.26 .12 

[ADMIN] (.50) (.36) (.37) 

2.04 

(2.07) 

.74 

(1.73) 

1.44 

(1.62) 

WJR broad reading score 

WJR broad math score 

Employment and Earnings—Adults 

Annualized Earnings, years 1 and 2 

[ADMIN] 

Annualized Earnings, years 3 and 4 

(1.75) 

.50 

-552 

(520) 

-360 

6 

(280) 

(1.39) 

-1.74 

-251 

(459) 

73 

(1.35) 

-.64 

-408 

(434) 

-153 

[ADMIN] 

Income and Public Assistance 

Total TANF benefits, year 5 [ADMIN] 

(315) 

(658) 

Employment and Earnings—Youth (ages 15 to 19) 

Earnings, 2001 [ADMIN] 173 

(216) 

(549) 

277 

64 

(210) 

(336) 

(538) 

401 

2 

(158) 

line [SR] 

.032 

(.036) 

.006 

(.031) 

.034 

(.055) 

-.046 -.004 .027 

Ratio of household income to poverty 

% households food insecure with 

hunger [SR] (.028) (.025) (.024) 

G-10 Appendix G – Assessment of the Size and Significance of the Impact Estimates 
Appendix G – Assessment of the Size and Significance of the Impact Estimates 

EXHIBIT G.4 

95 PERCENT 

CONFIDENCE INTERVALS (TOT ESTIMATES) – OUTCOMES FOR WHICH IMPACTS WERE NOT 

STATISTICALLY 

SIGNIFICANT 

MTO Experimental Section 8 

TOT Confidence Interval TOT Confidence Interval 

Impact Lower Upper Counter Impact Lower Upper Counter 

Outcome (std err) Bound Bound factual (std err) Bound Bound factual 

Adult Health

General health good -.033 -0.12 

or better [SR] (.042) 

Asthma or wheezing -.031 -0.10 attack [SR] (.037) 

0.05 0.70 -.013 -0.08 0.06 0.68 

(.036) 

0.04 0.21 -.016 -0.08 0.05 0.21 

(.032) 

Children’s Health (ages 12 to 19) 

Gen health very .001 -.10 

good/excel [SR] (.054) 

Asthma or wheezing .065 -0.02 

attack [SR] (.042) 

.11 .68 -.010 -0.10 0.08 .67 

(.048) 

0.15 .14 .022 -0.05 0.09 .14 

(.036) 

Delinquency and Risky Behavior (ages 5 to 19) 

Delinquency index .008 -0.03 

[SR] (.017) Risky behavior index -.019 -0.05 

[SR] (.053) 

0.04 .08 .012 -0.02 0.04 .09 

(.016) 

0.04 .44 .030 -0.06 0.12 .44 

(.047) 

Education (children ages 5 to 19) 

Student-teacher ratio, .51 -0.47 1.49 16.17 -.26 -0.97 0.45 16.08 current school (.50) (.36) 

[ADMIN]

score 

2.04

(2.07) 

-2.02 6.10 497 .74

(1.73) 

-2.65 4.13 497 WJR broad reading 

WJR broad math .50 -2.93 3.93 501 -1.74 -4.46 0.98 501 

score (1.75) (1.39) 

G-11 
Appendix G – Assessment of the Size and Significance of the Impact Estimates 

EXHIBIT G.4 (CONT.) 

95 PERCENT 

CONFIDENCE INTERVALS (TOT ESTIMATES) – OUTCOMES FOR WHICH IMPACTS WERE NOT 

STATISTICALLY 

SIGNIFICANT 

MTO Experimental Section 8 

TOT Confidence Interval TOT Confidence Interval 

Impact Lower Upper Counter Impact Lower Upper Counter 

Outcome (std err) Bound Bound factual (std err) Bound Bound factual 

Employment and Earnings—Adults 

Annualized Earnings, -552 -1,571 467 4,905 -251 -1,151 649 4,417 

years 1 and 2 (520) (459) 

[ADMIN]

[ADMIN] 

-360

(658) 

-1,650 930 7,159 73

(549) 

-1,003 1,149 6,255 Annualized Earnings, 

years 3 and 4 

Employment and Earnings—Youth (age 15-19) 

Earnings, 2001 173 -444 790 801 277 -146 700 1,068 [ADMIN] (315) (216) 

Income and Public Assistance 

Total TANF benefits, 6 -473 331 1,472 65 -239 393 1,238 year 5 [ADMIN] (280) (210) line [SR] 

.032

(.036) 

-0.04 0.10 .95 .006

(.031) 

-0.05 0.07 .87 Ratio of household 

income to poverty % households food -.046 -0.10 0.01 .13 -.021 -0.07 0.03 .11 

insecure with hunger (.028) (.025) 

[SR]

G-12 
compared with counterfactuals of .44 for both groups). Similarly the largest likely true impacts on the 

student-teacher ratio, the broad reading and math scores, and the ratio of household income to the 

poverty line are all relatively small for both the experimental and Section 8 groups. For the remaining 

outcomes, however, on the basis of the current estimates, we cannot rule out moderate to large true 

impacts. This includes two-thirds of the outcomes shown in the exhibit. 

In summary, then, the available evidence suggests that the estimates produced by this sample are 

sufficiently imprecise that we may have missed some impacts that are large enough to be relevant for 

policy. 

How different would the results have been if the families who moved with program vouchers 

had stayed in low-poverty areas longer? 

The vouchers issued to families in the MTO experimental group were only valid in census tracts with 

poverty rates below 10 percent in 1990, but these families were only required to stay in such areas for 

1 year to keep the voucher. Many of these families moved to higher poverty areas after the first year. 

Many initially moved to areas where the poverty rate rose between 1990 and 2000, so that even if 

they stayed in their initial location they were not necessarily in a low-poverty area at the time of the 

interim evaluation. And, of course, families in the Section 8 group were not constrained in where they 

could use the voucher. 

As a result, even those families in the MTO experimental group who moved with program vouchers 

spent, on average, only about 20 percent of the followup period in areas with poverty levels below 10 

percent, and only about 60 percent of the followup period in areas with poverty rates below 20 

percent (see exhibit 2.13). At the same time, many families in the control group left public housing 

and moved to areas with lower poverty rates. On average, control families spent about 11 percent of 

the followup period in areas with poverty rates below 20 percent (see exhibit 2.13). 

These are substantial differences in the proportion of time spent in low-poverty areas, and one might 

reasonably expect them to result in a number of positive effects for the families who moved with 

program vouchers. But the MTO demonstration was not a pure test of the effects of living in lowpoverty areas versus living in public housing, even for families in the MTO experimental group. We 

cannot, therefore, infer those effects from these results with any confidence. We can, however, obtain 

some suggestive evidence on this question by comparing the results for the experimental group and 

the Section 8 group. 

Because these two groups were randomly assigned from the same pool of applicants, they are 

comparable in all respects except that one was offered locationally restricted vouchers and the other 

was offered unrestricted vouchers. Therefore, the resulting differences in impacts on the outcomes of 

these two groups can be interpreted as the result of that difference in the vouchers they were offered. 

Furthermore, if we believe this difference is captured by the difference in poverty rates in the areas 

where these two groups lived during the followup period, we can estimate a relationship between 

Appendix G – Assessment of the Size and Significance of the Impact Estimates G-13 
impacts and the proportion of time they spent in low-poverty areas that can then be extrapolated to 

obtain estimates of the effects on a family that spent the entire followup period in low-poverty areas.7 

An example may clarify the approach. The experimental group as a whole spent 34 percent of the 

followup period in areas with poverty rates below 20 percent.8

 The Section 8 group spent 24 percent 

of the followup period in such areas. If the impact on some outcome for the Section 8 group is .8 and 

the impact on the same outcome for the experimental group is 1.0, a linear extrapolation to a 

hypothetical group that spent the entire followup period in areas with poverty rates below 20 percent 

yields an estimated impact of 2.0 (= (1.0 - .8)/(.34 - .24)). 

The first two columns of exhibit G.5 show the TOT estimates for the experimental and Section 8 

groups. The third column shows extrapolated estimates obtained in this fashion for each of the 

outcomes for which the TOT impact was statistically significant for either the experimental or Section 

8 group.9

 Each of the extrapolated estimates in column 3 represents the impact of spending the entire 

followup period in areas with poverty rates below 20 percent, based on linear extrapolation of the 

difference in impacts on the experimental and Section 8 groups and the proportion of time they spent 

in such areas. As can be seen, these estimates are as much as 8 times as large as the impact on the 

experimental group, with over half falling into the range of 1 to 5 times as large as the experimental 

group estimates. Four are smaller than the estimated impacts for the experimental and Section 8 

groups. This reflects the fact that the estimated impact on the Section 8 group is larger than that on 

the experimental group, despite the fact that the Section 8 group spent a smaller proportion of the 

followup period in low-poverty areas. 

This extrapolation makes the very specific assumption that the impacts are proportional to the fraction 

of time spent in areas with poverty rates below 20 percent. A different assumption about the 

functional form of this relationship or a different measure of low poverty would yield somewhat 

different estimates. For example, we might set the poverty threshold at 10 or 15 percent or we might 

extrapolate based on the mean poverty level of areas in which the family has lived, rather than the 

proportion of time spent in low-poverty areas. 

The fourth column of exhibit G.5 shows the estimates obtained if one assumes the impact is 

proportional to the square root of the proportion of time spent in areas with poverty rates below 20 

percent. With this specification, the extrapolated impacts are generally much smaller, with all but 

7 For this purpose, we define low-poverty as less than 20 percent poor, because neither treatment group spent 

a large enough proportion of the followup period in areas with poverty rates below 10 percent to support 

reliable extrapolations. 

8 This figure differs from the 60 percent figure cited at the beginning of this section because it applies to the 

entire experimental group, whereas the 60 percent figure applies only to those families in the experimental 

group who moved with program vouchers. In this analysis it is necessary to work with poverty rates and 

impact estimates for the entire treatment group to maintain the comparability of the experimental and 

Section 8 groups. 

9 The fact that one or both of these estimates is statistically significant does not imply that the difference 

between the two estimates is statistically significant. 

G-14 Appendix G – Assessment of the Size and Significance of the Impact Estimates 
Appendix G – Assessment of the Size and Significance of the Impact Estimates EXHIBIT G.5 ESTIMATED EFFECTS OF SPENDING THE ENTIRE FOLLOWUP PERIOD IN LOW-POVERTY AREAS, EXTRAPOLATED FROM TOT IMPACTS ON EXPERIMENTAL AND SECTION 8 GROUPS, SELECTED OUTCOMES Extrapolated Effect of Spending Entire Followup Period in Low-Poverty Area—Effect Assumed ITT Impacts Proportional to: Proportion of Time Square Root of Spent in Low- Proportion of Time Spent Outcome Section 8 Experimental Poverty Areas in Low-Poverty Areas Housing and Neighborhood Conditions Feel safe at night 0.142 0.093 0.49 0.36 -0.117 -0.103 -0.14 -0.18 Saw drugs past 30 days Rate housing good/excellent 0.099 0.071 0.28 0.22 Obesity c -0.051 -0.047 -0.04 -0.07 Adult Health [SR] past month c, d -0.034 -0.012 -0.22 -0.13 [SR] ) Psychological distress index for Youth Mental Health (ages 12 to 19 Generalized anxiety disorder (all -0.016 -0.035 0.19 0.07 youth) ) -0.042 -0.047 0.05 -0.02 Generalized anxiety disorder (girls

G-15 
Appendix G – Assessment of the Size and Significance of the Impact Estimates 

EXHIBIT G.5 (CONT.) 

ESTIMATED 

EFFECTS OF 

SPENDING THE 

ENTIRE 

FOLLOWUP 

PERIOD IN 

LOW-POVERTY 

AREAS, EXTRAPOLATED FROM TOT IMPACTS ON 

EXPERIMENTAL AND 

SECTION 8 GROUPS, SELECTED 

OUTCOMES

Extrapolated Effect of Spending Entire Followup 

Period in Low-Poverty Area—Effect Assumed 

ITT Impacts Proportional to: 

Proportion of Time Square Root of

Spent in Low- Proportion of Time Spent 

Outcome Section 8 

[SR]

Experimental Poverty Areas in Low-Poverty Areas 

Delinquency and Risky Behavior (youth ages 15 to 19) 

Behavior problems (boys self- 0.075 0.052 0.23 0.18 report) (boys) 

0.060 0.059 0.01 0.06 Ever arrested for property crime 

Ever smoke cigarettes (boys) 0.125 0.152 -0.27 0.00 

) 

-0.031 -0.075 0.44 0.17 Ever arrested for violent crime 

(girlsEver use marijuana (girls) -0.129 -0.079 -0.5 -0.35 

ls) -0.085 -0.015 -0.7 -0.40 Ever smoke cigarettes (gir

G-16 
three being less than four times the impact on the experimental group. Again, in the four cases where 

the estimated impact on the Section 8 group exceeds the estimated impact on the experimental group, 

the extrapolated impacts are smaller than the estimates for the two MTO treatment groups. As this 

suggests, the estimates obtained by extrapolation are very sensitive to the assumed functional form. 

Nevertheless, this exercise does suggest that the impacts of living continuously in low-poverty areas 

might be much more substantial than those observed in the demonstration. 

Can larger effects be expected in the longer term? 

This is an interim evaluation. A final evaluation of the MTO demonstration is planned in roughly 5 

years, 9 to 12 years after random assignment. One potential reason why impacts were not observed 

for some outcomes is that those impacts have not yet had time to develop. If that is the case, we might 

expect the final evaluation to find more and larger impacts. 

The existing literature provides little guidance on this question because few impact studies follow 

their samples for more than 5 years. The most relevant precedent is the study of the Gatreaux 

Program by Rosenbaum (1992), discussed in chapter 1. That study found that 1 to 6 years after their 

families moved to the suburbs, many children “were still struggling to catch up, and it was not clear if 

they would succeed.” But 7 years later Rosenbaum found substantial, statistically significant impacts 

on eight of nine education- and employment-related outcomes for the same children. 

There are fairly strong theoretical reasons why it may take many years for the full effects of 

neighborhood to manifest themselves. Developmental outcomes like educational performance almost 

certainly reflect the cumulative experience of the child from an early age. Children who spend their 

first 10 years in an environment that does not facilitate educational achievement may never fully 

overcome that disadvantage, even if they then move to an environment that supports educational 

achievement. The interim evaluation youth sample is composed of children who moved out of public 

housing at ages 5 to 15. In the final evaluation, the youth sample will have left public housing at ages 

birth to 10. These youth will have spent a much larger proportion of their formative years outside the 

concentrated poverty of public housing and may, therefore, show much greater gains in educational 

achievement and other developmental outcomes. 

It is also true that the move from high-poverty areas to lower poverty neighborhoods is likely to be 

disruptive and require some adjustment period during which positive behavioral effects may not 

appear and, in fact, negative effects may be observed. There is some evidence of such transitional 

effects in the negative behavioral effects observed for male youth in the interim evaluation. If these 

effects indicate that the first 4 to 7 years after random assignment has been an adjustment period for 

these youth, we may observe different impacts in the longer term once that transition is complete. 

We cannot, of course, predict the impacts that will be observed 5 years after our data were collected. 

We can, however, examine the interim findings for evidence that impacts are related to time since 

random assignment. 

The most direct evidence on this question is provided by the time path of impacts on those outcomes 

for which we have longitudinal data: the employment, earnings, and public assistance outcomes 

measured with administrative data. Exhibit G.6 shows impacts in years 1 and 2, years 3 and 4, and 

year 5 after random assignment for each of the main outcomes measured with these data. (Note that 

Appendix G – Assessment of the Size and Significance of the Impact Estimates G-17 
TANF and food stamp data are only available for the first 4 years after random assignment for 

Boston, Chicago, and New York. Earnings data are available for 4 years after random assignment in 

all sites.) 

EXHIBIT G.6 

IMPACTS ON OUTCOMES MEASURED WITH LONGITUDINAL ADMINISTRATIVE RECORDS, BY TIME 

SINCE RANDOM ASSIGNMENT 

TOT Impact, Experimental Group TOT Impact, Section 8 Group 

Years 1 Years 3 Years 1 Years 3-

Outcome and 2 and 4 Year 5 and 2 and 4 Year 5 

Adult -552 -360 Na -251 73 na 

earnings (520) (658) (459) (549) 

TANF 485* 184 2 249 74 37 

benefits (201) (191) (107) (217) (206) (121) 

Food stamp 

benefits 

768* 

(306) 

532 

(362) 

253 

(214) 

329 

(235) 

460 

(268) 

334* 

(158) 

*= p<.05 on t-test 

na= not available 

Source: State administrative records. 

Sample: Earnings: all sample adults. TANF and food stamp benefits: all sample adults in Boston, Chicago, and 

New York. 

The earnings impacts show only a very weak upward trend over the four-year period. The estimated 

impacts on AFDC/TANF, however, show a distinct downward trend over time, especially in the 

experimental group. The estimated impact for that group starts with a statistically significant increase 

in welfare dependency in years 1 and 2, falling to an insignificant effect whose point estimate is near 

zero. The food stamp estimates show a similar downward trend for the experimental group, but no 

discernible pattern for the Section 8 group. Overall, then, these estimates provide at most modest 

support for the hypothesis of increasingly favorable (i.e., less unfavorable) effects over time. 

For outcomes measured in the survey, we cannot estimate impacts at different points in time after 

random assignment for a given sample. The best we can do is to compare the impacts for those who 

were randomly assigned early and who have therefore been exposed to the treatment longer, with 

those who were randomly assigned later. This comparison must be viewed with caution, however, 

because there may be compositional differences between these two groups that would cause their 

impacts to differ, or moving at different times may affect their response to the intervention (e.g., 

because of differences in the state of the housing market that affected where they moved), 

independently of duration of exposure to the new environment. 

As a check for such differences between the early and late random assignment cohorts, we examined 

the estimated TOT impacts in year 5 within each of the two cohorts on outcomes measured with 

administrative data. If the two cohorts are similar, they should show similar impacts in a given year 

after random assignment. Because some sites started random assignment earlier in calendar time than 

others, a simple split of the sample on date of random assignment would heavily concentrate sample 

members from the early sites in one group and those from the later sites in the other. This would 

G-18 Appendix G – Assessment of the Size and Significance of the Impact Estimates 
present an unacceptably high risk that any differences in impacts between the two groups reflected 

site effects, not the influence of length of exposure to the treatment. Therefore, we split the sample 

into early and late random assignment cohorts within each site. This reduces somewhat the difference 

in length of exposure between the two groups, but results in groups that are balanced on site. Defined 

this way, the length of time between random assignment and the interim survey was approximately 81 

months for the early cohort and 62 months for the later cohort, a difference of 19 months. 

As shown in exhibit G.7, there were large differences in the impact estimates between the two 

cohorts. In the experimental group, the early cohort showed large negative impacts on both TANF 

and food stamp benefits, while the late cohort showed equally large positive impacts. In the case of 

food stamps, both estimates were significantly different from zero and from each other. The 

differences between the two cohorts were less striking in the Section 8 group, but still substantial. 

We take these results to indicate that there are important differences that are unrelated to duration of 

exposure to the treatment between the families who were randomly assigned early in each site and 

those assigned later in the same site (recall that these cohorts are balanced on site). Therefore, we 

cannot interpret differences in impacts between the two cohorts as indicative of the effect of length of 

exposure to the treatment. 

EXHIBIT G.7 

YEAR 5 TOT IMPACTS ON OUTCOMES MEASURED WITH LONGITUDINAL ADMINISTRATIVE 

RECORDS, EARLY VS. LATE RANDOM ASSIGNMENT COHORTS 

Experimental Section 8 

Outcome Early Cohort Late Cohort Early Cohort Late Cohort 

TANF benefits, year 5 -484 617 -252 628* 

(251) (335) (218) (236) 

(173) 

464* 

(226) 

20 

(153) 

388* 

(178) 

Food stamp benefits, year 5 -421* 

*= p<.05 on t-test 

Source: State administrative records. 

Sample: All sample adults. 

There are many reasons why the families assigned early might be systematically different from those 

assigned later, even within the same site. Most of the sites were affected at some point by HOPE VI, 

demolition, or revitalization projects. To the extent that the impact of these activities was greater on 

one cohort than the other, this would cause systematic differences in both the type of families 

recruited and in the control experience and, therefore, in treatment-control differences between the 

two cohorts. There were also some shifts in the geographic focus of recruitment in some sites over the 

course of the intake period. For example, in July 1996 New York began recruitment in the South 

Bronx among families that tended to be poorer and newer to public housing than those in the projects 

from which earlier families came. In Boston, later recruitment focused on less distressed 

developments in the South End. In Chicago, later recruitment tended to be in more distressed projects 

such as Robert Taylor Homes. Even in those projects that were relatively stable throughout the intake 

period, it may well be that the first families to volunteer were systematically different from those who 

volunteered after the demonstration had been ongoing for some time and was more familiar to them. 

Appendix G – Assessment of the Size and Significance of the Impact Estimates G-19 
For all of these reasons, the two cohorts appear to differ substantially in ways other than exposure to 

the treatment. 

G-20 Appendix G – Assessment of the Size and Significance of the Impact Estimates 
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