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Evaluating Contradictory Experimental and Non-Experimental Estimates of Neighborhood Effects on Economic Outcomes for Adults
... This resulted in an exogenous source of variation in location, and is linked with long-term improvements in outcomes for children who moved (Chetty, Hendren, and Katz 2016). That said, the effects were much smaller than observational differences in outcomes between children across neighborhoods (Harding et al. 2021). ...
This chapter provides new evidence on educational inequality and reviews the literature on the causes and consequences of unequal education. We document large achievement gaps between children from different socio-economic backgrounds, show how patterns of educational inequality vary across countries, time, and generations, and establish a link between educational inequality and social mobility. We interpret this evidence from the perspective of economic models of skill acquisition and investment in human capital. The models account for different channels underlying unequal education and highlight how endogenous responses in parents' and children's educational investments generate a close link between economic inequality and educational inequality. Given concerns over the extended school closures during the Covid-19 pandemic, we also summarize early evidence on the impact of the pandemic on children's education and on possible long-run repercussions for educational inequality.
... For example, several studies have used non-experimental methods to study adult movers and found large effects on labor market outcomes (Fauth, Leventhal and Brooks-Gunn, 2004;Weinberg, Reagan and Yankow, 2004;Clampet-Lundquist and Massey, 2008). Although selection bias is not the only explanation for the discrepancy between earlier observational studies and more recent work based on experiments and plausible quasi-experiments, Harding et al. (2021) provide evidence suggesting that selection bias plays a large role in driving the findings of traditional non-experimental studies of neighborhood effects on adult economic outcomes. ...
... For example, several studies have used non-experimental methods to study adult movers and found large effects on labor market outcomes (Fauth, Leventhal, and Brooks-Gunn 2004;Weinberg, Reagan, and Yankow 2004;Clampet-Lundquist and Massey 2008). Although selection bias is not the only explanation for the discrepancy between earlier observational studies and more recent work based on experiments and plausible quasi-experiments, Harding et al. (2021) provide evidence suggesting that selection bias plays a large role in driving the findings of traditional non-experimental studies of neighborhood effects on adult economic outcomes. ...
How does one's place of residence affect individual behavior and long-run outcomes? Understanding neighborhood and place effects has been a leading question for social scientists during the past half-century. Recent empirical studies using experimental and quasi-experimental research designs have generated new insights on the importance of residential neighborhoods in childhood and adulthood. This paper summarizes the recent neighborhood effects literature and interprets the findings. Childhood neighborhoods affect long-run economic and educational outcomes in a manner consistent with exposure models of neighborhood effects. For adults, neighborhood environments matter for their health and well-being but have more ambiguous impacts on labor market outcomes. We discuss the evidence on the mechanisms behind the observed patterns and conclude by highlighting directions for future research.
In this Master Thesis, a holistic evaluation of the theory of Megaprojects is carried out
at an economic, social, environmental and institutional level. The aim is to record the
contemporary trends in the bibliography of Major Projects, in order to demonstrate that
it is not a panacea to evaluate them in strictly economic terms, but it is important to
expand the evaluation in terms of the impact they have on society, the environment and
institutions.
In order to achieve this, a Framework for the Description and Evaluation of
Megaprojects as a whole is developed, which includes the four main criteria for the
evaluation of Megaprojects, each criterion being described by 5 key indicators, and the
Framework is applied for Stavros Niarchos Foundation Cultural Center as a Case Study.
We analyze data from a natural experiment involving Denver public housing that quasirandomly assigns low-income Latino and African American youth to neighborhoods. Intent-to-treat and treatment-on-treated models reveal substantial effects of neighborhood socioeconomic status, ethnicity, and safety domains on youth and young adult educational, employment, and fertility outcomes. Effects are contingent on when a youth was first assigned to public housing and the neighborhood characteristic in question. Benefits from neighbors of higher occupational prestige are stronger if a child begins experiencing them at a younger age, whereas negative consequences of neighborhood crime are only manifested for teens. Neighborhood effect sizes apparently depend on the interaction among exposure duration, disruption effects of mobility, and developmental stage-specific differences in vulnerability to the given neighborhood effect mechanism operative. Our results hold powerful and provocative implications for where assisted housing should be developed and how applicants should be assigned to neighborhoods.
Ex-prisoners tend to be geographically concentrated in a relatively small number of neighborhoods within the most resource deprived sections of metropolitan areas. Furthermore, many prisoners return “home” to the same criminogenic environment with the same criminal opportunities and criminal peers that proved so detrimental prior to incarceration. Yet estimating the causal impact of place of residence on the likelihood of recidivism is complicated by the issue of selection bias. In this study, I use a natural experiment as a means of addressing the selection issue and examine whether the migration of ex-prisoners away from their former place of residence will lead to lower levels of recidivism. In August 2005, Hurricane Katrina ravaged the Louisiana Gulf Coast, damaging many of the neighborhoods where ex-prisoners typically reside. The residential destruction resulting from Hurricane Katrina is an exogenous source of variation that influences where a parolee will reside upon release from prison. Findings reveal that moving away from former geographic areas substantially lowers a parolee's likelihood of re-incarceration.
We examine long-term neighborhood effects on low-income families using data from the Moving to Opportunity (MTO) randomized housing-mobility experiment, which offered some public-housing families but not others the chance to move to less-disadvantaged neighborhoods. We show that 10-15 years after baseline MTO improves adult physical and mental health; has no detectable effect on economic outcomes, youth schooling and youth physical health; and mixed results by gender on other youth outcomes, with girls doing better on some measures and boys doing worse. Despite the somewhat mixed pattern of impacts on traditional behavioral outcomes, MTO moves substantially improve adult subjective well-being.
Adjustment methods such as propensity scores and analysis of covariance are often used for estimating treatment effects in nonexperimental data. Shadish, Clark, and Steiner used a within-study comparison to test how well these adjustments work in practice. They randomly assigned participating students to a randomized or nonrandomized experiment. Treatment effects were then estimated in the experiment and compared to the adjusted nonexperimental estimates. Most of the selection bias in the nonexperiment was reduced. The present study replicates the study of Shadish et al. despite some differences in design and in the size and direction of the initial bias. The results show that the selection of covariates matters considerably for bias reduction in nonexperiments but that the choice of analysis matters less.
Linking data from the Panel Study of Income Dynamics with the 1970 and 1980 censuses, the author finds that, in accordance with the social isolation theory, neighborhood conditions greatly affect the likelihood of exiting the Aid to Families with Dependent Children (AFDC) program. Neighborhood conditions are especially strong predictors of leaving the program for African Americans, high school dropouts, and for those who marry. Members of each of these groups stay on welfare significantly longer when they live in the most economically depressed neighborhoods of neighborhoods with a relatively high proportion of manufacturing relative to professional and executive workers. I test two additional theories of neighborhood effects: relative deprivation and epidemic theories.
We estimate the causal effect of each county in the United States on children’s incomes in adulthood. We first estimate a fixed effects model that is identified by analyzing families who move across counties with children of different ages. We then use these fixed effect estimates to (i) quantify how much places matter for intergenerational mobility, (ii) construct forecasts of the causal effect of growing up in each county that can be used to guide families seeking to move to opportunity, and (iii) characterize which types of areas produce better outcomes. For children growing up in low-income families, each year of childhood exposure to a one standard deviation (std. dev.) better county increases income in adulthood by 0.5%. There is substantial variation in counties’ causal effects even within metro areas. Counties with less concentrated poverty, less income inequality, better schools, a larger share of two-parent families, and lower crime rates tend to produce better outcomes for children in poor families. Boys’ outcomes vary more across areas than girls’ outcomes, and boys have especially negative outcomes in highly segregated areas. Areas that generate better outcomes have higher house prices on average, but our approach uncovers many “opportunity bargains”—places that generate good outcomes but are not very expensive.
This paper estimates neighborhood effects on adult labor market outcomes using the Moving to Opportunity (MTO) housing mobility experiment. We propose and implement a new strategy for identifying transition-specific effects that exploits identification of the unobserved component of a neighborhood choice model. Estimated Local Average Treatment Effects (LATEs) are large, result from moves between the first and second deciles of the national distribution of neighborhood quality, and pertain to a subpopulation of nine percent of program participants.
We show that the neighborhoods in which children grow up shape their earnings, college attendance rates, and fertility and marriage patterns by studying more than 7 million families who move across commuting zones and counties in the United States. Exploiting variation in the age of children when families move, we find that neighborhoods have significant childhood exposure effects: the outcomes of children whose families move to a better neighborhood-as measured by the outcomes of children already living there-improve linearly in proportion to the amount of time they spend growing up in that area, at a rate of approximately 4% per year of exposure. We distinguish the causal effects of neighborhoods from confounding factors by comparing the outcomes of siblings within families, studying moves triggered by displacement shocks, and exploiting sharp variation in predicted place effects across birth cohorts, genders, and quantiles to implement overidentification tests. The findings show that neighborhoods affect intergenerational mobility primarily through childhood exposure, helping reconcile conflicting results in the prior literature. © The Author(s) 2018. Published by Oxford University Press on behalf of the President and Fellows of Harvard College. All rights reserved.
Several recent studies have concluded that residential segregation by income in the United States has increased in the decades since 1970, including a significant increase after 2000. Income segregation measures, however, are biased upward when based on sample data. This is a potential concern because the sampling rate of the American Community Survey (ACS)—from which post-2000 income segregation estimates are constructed—was lower than that of the earlier decennial censuses. Thus, the apparent increase in income segregation post-2000 may simply reflect larger upward bias in the estimates from the ACS, and the estimated trend may therefore be inaccurate. In this study, we first derive formulas describing the approximate sampling bias in two measures of segregation. Next, using Monte Carlo simulations, we show that the bias-corrected estimators eliminate virtually all of the bias in segregation estimates in most cases of practical interest, although the correction fails to eliminate bias in some cases when the population is unevenly distributed among geographic units and the average within-unit samples are very small. We then use the bias-corrected estimators to produce unbiased estimates of the trends in income segregation over the last four decades in large U.S. metropolitan areas. Using these corrected estimates, we replicate the central analyses in four prior studies on income segregation. We find that the primary conclusions from these studies remain unchanged, although the true increase in income segregation among families after 2000 was only half as large as that reported in earlier work. Despite this revision, our replications confirm that income segregation has increased sharply in recent decades among families with children and that income inequality is a strong and consistent predictor of income segregation.
Given the widespread use of nonexperimental (NE) methods for assessing program impacts, there is a strong need to know whether NE approaches yield causally valid results in field settings. In within-study comparison (WSC) designs, the researcher compares treatment effects from an NE with those obtained from a randomized experiment that shares the same target population. The goal is to assess whether the stringent assumptions required for NE methods are likely to be met in practice. This essay provides an overview of recent efforts to empirically evaluate NE method performance in field settings. We discuss a brief history of the design, highlighting methodological innovations along the way. We also describe papers that are included in this two-volume special issue on WSC approaches and suggest future areas for consideration in the design, implementation, and analysis of WSCs.
This paper provides new evidence on the effects of moving out of disadvantaged neighborhoods on the long-run outcomes of children. I study public housing demolitions in Chicago, which forced low-income households to relocate to less disadvantaged neighborhoods using housing vouchers. Specifically, I compare young adult outcomes of displaced children to their peers who lived in nearby public housing that was not demolished. Displaced children are more likely to be employed and earn more in young adulthood. I also find that displaced children have fewer violent crime arrests. Children displaced at young ages have lower high school dropout rates.
Background:
It is unclear whether propensity score analysis (PSA) based on pretest and demographic covariates will meet the ignorability assumption for replicating the results of randomized experiments.
Purpose:
This study applies within-study comparisons to assess whether pre-Kindergarten (pre-K) treatment effects on achievement outcomes estimated using PSA based on a pretest and demographic covariates can approximate those found in a randomized experiment.
Methods:
Data-Four studies with samples of pre-K children each provided data on two math achievement outcome measures with baseline pretests and child demographic variables that included race, gender, age, language spoken at home, and mother's highest education. Research Design and Data Analysis-A randomized study of a pre-K math curriculum provided benchmark estimates of effects on achievement measures. Comparison samples from other pre-K studies were then substituted for the original randomized control and the effects were reestimated using PSA. The correspondence was evaluated using multiple criteria.
Results and conclusions:
The effect estimates using PSA were in the same direction as the benchmark estimates, had similar but not identical statistical significance, and did not differ from the benchmarks at statistically significant levels. However, the magnitude of the effect sizes differed and displayed both absolute and relative bias larger than required to show statistical equivalence with formal tests, but those results were not definitive because of the limited statistical power. We conclude that treatment effect estimates based on a single pretest and demographic covariates in PSA correspond to those from a randomized experiment on the most general criteria for equivalence.
We use a novel dataset and research design to empirically detect the effect of social interactions among neighbors on labor market outcomes. Specifically, using Census data that characterize residential and employment locations down to the city block, we examine whether individuals residing in the same block are more likely to work together than individuals in nearby but not identical blocks. We find significant evidence of social interactions operating at the block level: residing on the same versus nearby blocks increases the probability of working together by over 33 percent. The results also indicate that this referral effect is stronger when individuals are similar in sociodemographic characteristics (e.g., both have children of similar ages) and when at least one individual is well attached to the labor market. These findings are robust across various specifications intended to address concerns related to sorting and reverse causation. Further, having determined the characteristics of a pair of individuals that lead to an especially strong referral effect, we provide evidence that the increased availability of neighborhood referrals has a significant impact on a wide range of labor market outcomes including employment and wages.
Objective:
In this article, we examine whether a well-executed comparative interrupted time series (CITS) design can produce valid inferences about the effectiveness of a school-level intervention. This article also explores the trade-off between bias reduction and precision loss across different methods of selecting comparison groups for the CITS design and assesses whether choosing matched comparison schools based only on preintervention test scores is sufficient to produce internally valid impact estimates.
Research design:
We conduct a validation study of the CITS design based on the federal Reading First program as implemented in one state using results from a regression discontinuity design as a causal benchmark.
Results:
Our results contribute to the growing base of evidence regarding the validity of nonexperimental designs. We demonstrate that the CITS design can, in our example, produce internally valid estimates of program impacts when multiple years of preintervention outcome data (test scores in the present case) are available and when a set of reasonable criteria are used to select comparison organizations (schools in the present case).
The Moving to Opportunity (MTO) experiment offered randomly selected families housing vouchers to move from high-poverty housing projects to lower-poverty neighborhoods. We analyze MTO's impacts on children's long-term outcomes using tax data. We find that moving to a lower-poverty neighborhood when young (before age 13) increases college attendance and earnings and reduces single parenthood rates. Moving as an adolescent has slightly negative impacts, perhaps because of disruption effects. The decline in the gains from moving with the age when children move suggests that the duration of exposure to better environments during childhood is an important determinant of children's long-term outcomes. (JEL I31, I38, J13, R23, R38).
American metropolitan areas have experienced rising residential segregation by income since 1970. One potential explanation for this change is growing income inequality. However, measures of residential sorting are typically mechanically related to the income distribution, making it difficult to identify the impact of inequality on residential choice. This paper presents a measure of residential segregation by income, the Centile Gap Index (CGI), which is based on income percentiles. Using the CGI, I find that a one standard deviation increase in income inequality raises residential income segregation by 0.4-0.9 standard deviations. Inequality at the top of the distribution is associated with more segregation of the rich, while inequality at the bottom and declines in labor demand for less-skilled men are associated with residential isolation of the poor. Inequality can fully explain the rise in income segregation between 1970 and 2000.
Do places make a difference to people's health and well-being? This book demonstrates how the physical and social characteristics of a neighborhood can shape the health of its residents. Researchers have long suspected that where one lives makes a difference to health in addition to who one is. Almost everyone understands that smoking, unhealthy eating, lack of exercise can compromise longevity and good health, but can a person's ability to maintain a healthy lifestyle be affected by the smoking habits of other people close by, or access to grocery stores, or the existence of safe parks and recreational space? The answers to this question and other similar ones require new ways of thinking about the determinants of health as well as new analytical methods to test these ideas. This book brings together these ideas and new methods. The book contains various parts. The first part deals with methodological complexities of undertaking neighborhood research. The second part showcases the empirical evidence linking neighborhood conditions to health outcomes. The last part tackles some of the major cross-cutting themes in contemporary neighborhood research.
This article examines the relationship between access to jobs, work activity, and welfare receipt. We hypothesize that welfare recipients who live in closer proximity to employment opportunities are more likely to work and less likely to remain on welfare than those who live farther away.To test this hypothesis, we analyze two unique data sets. The first has information on the residences and characteristics of welfare recipients; the second contains a list of the location of jobs in the three‐county Detroit metropolitan area in the late 1990s. We find that, after controlling for individual characteristics, greater proximity to employment opportunities is associated with both a higher probability of working and a higher probability of leaving welfare.
This study examines the influence of neighborhood poverty and social networks on labor market experiences of less-educated urban job seekers. Data come from the Multi-City Survey of Urban Inequality and the 1990 decennial census. Results indicate that relatively small differences in job search strategies among residents of high- and low-poverty neighborhoods are magnified in the hiring process and that supply- and demand-side factors have qualitatively different effects on earnings within these residential contexts. These results refine our understanding of social isolation by clarifying the points at which "neighborhood effects" manifest themselves in the job-matching process and suggest that social isolation is often accompanied by labor market insulation characterized by an increasing reliance upon neighbors and personal contacts for securing formal employment.
This article revisits the Moving to Opportunity housing mobility experiment, which heretofore has not provided strong evidence to support the hypothesis of neighborhood effects on economic self-sufficiency among adults. The authors undertake a conceptual and empirical analysis of the study's design and implementation to gain a better understanding of the selection processes that occur within the study. The article shows that the study is potentially affected by selectivity at several junctures: in determining who complied with the program's requirements, who entered integrated versus segregated neighborhoods, and who left neighborhoods after initial relocation. Furthermore, previous researchers have not found an experimental treatment effect on adult economic self-sufficiency, relative to controls. The authors propose an alternative approach that involves measuring the cumulative amount of time spent in different neighborhood environments. With this method, they find evidence that neighborhood is associated with outcomes such as employment, earnings, TANF receipt, and use of food stamps.
In an ongoing evaluation of post-secondary financial aid, we use random assignment to assess the causal effects of large privately-funded aid awards. Here, we compare the unbiased causal effect estimates from our RCT with two types of non-experimental econometric estimates. The first applies a selection-on-observables assumption in data from an earlier, nonrandomized cohort; the second uses a regression discontinuity design. Selection-on-observables methods generate estimates well below the experimental benchmark. Regression discontinuity estimates are similar to experimental estimates for students near the cutoff, but sensitive to controlling for the running variable, which is unusually coarse.
The literature on neighborhood effects frequently is evaluated or interpreted in relation to the question, "Do neighborhoods matter?" We argue that this question has had a disproportionate influence on the field and does not align with the complexity of theoretical models of neighborhood effects or empirical findings that have arisen from the literature. In this article, we focus on empirical work that considers how different dimensions of individuals' residential contexts become salient in their lives, how contexts influence individuals' lives over different timeframes, how individuals are affected by social processes operating at different scales, and how residential contexts influence the lives of individuals in heterogeneous ways. In other words, we review research that examines where, when, why, and for whom do residential contexts matter. Using the large literature on neighborhoods and educational and cognitive outcomes as an example, the research we review suggests that any attempt to reduce the literature to a single answer about whether neighborhoods matter is misguided. We call for a more flexible study of context effects in which theory, measurement, and methods are more closely aligned with the specific mechanisms and social processes under study.
One long-standing motivation for low-income housing programs is the possibility that housing affordability and housing conditions
generate externalities, including on children’s behavior and long-term life outcomes. We take advantage of a randomized housing
voucher lottery in Chicago in 1997 to examine the long-term impact of housing assistance on a wide variety of child outcomes,
including schooling, health, and criminal involvement. In contrast to most prior work focusing on families in public housing,
we focus on families living in unsubsidized private housing at baseline, for whom voucher receipt generates large changes
in both housing and nonhousing consumption. We find that the receipt of housing assistance has little, if any, impact on neighborhood
or school quality or on a wide range of important child outcomes. JEL Codes: D10, H23, I38.
The Moving to Opportunity (MTO) housing experiment has proven to be an important intervention not just in the lives of the poor, but in social science theories of neighborhood effects. Competing causal claims have been the subject of considerable disagreement, culminating in the debate between Clampet-Lundquist and Massey (2008) and Ludwig et al. (2008). This paper assesses the debate by clarifying analytically distinct questions posed by neighborhood-level theories, reconceptualizing selection bias as a fundamental social process worthy of study in its own right rather than as a statistical nuisance, and reconsidering the scientific method of experimentation, and hence causality, in the social world of the city. I also analyze MTO and independent survey data from Chicago to examine trajectories of residential attainment. Although MTO provides crucial leverage for estimating neighborhood effects on individuals, as proponents rightly claim, I demonstrate the implications imposed by a stratified urban structure and how MTO simultaneously provides a new window on the social reproduction of concentrated inequality.
European research attempting to quantify neighbourhood effects has relied almost exclusively on analyses of observational data. No consensus has emerged, perhaps because a variety of statistical procedures have been employed. We investigate this by exploring the degree to which alternative, non-experimental statistical methods yield different estimates of the relationship between neighbourhood income mix and individual work income when applied to the same longitudinal database. We find that results are highly sensitive to the statistical approach employed. Methods controlling for geographic selection bias generally reduce the negative association between low-income neighbours and individual earnings, but substantial differences across models remain. Controlling for both selection and endogeneity produces larger associations and evidence of non-linearity, something that is hidden in models only controlling for selection. All methods suffer shortcomings, so we argue for multi-method investigations to identify robust findings, with instrumental variables and fixed effects on non-mover samples being preferred. In our case, we find a substantial neighbourhood effect, regardless of the method employed.
The Moving to Opportunity (MTO) for Fair Housing demonstration provided an opportunity for low-income renters to move to low-poverty neighborhoods. Many of these renters, however, did not move with their vouchers, and many of those who moved did not stay in low-poverty neighborhoods. In this article, we explore the mechanisms behind these residential outcomes and what they mean for housing policy. First, we review evidence suggesting that MTO families wanted to live in low-poverty "opportunity areas." We then describe how some aspects of the Housing Choice Voucher Program, the structural features of the housing market, and the beliefs and coping mechanisms of low-income renters — shaped by years of living in extreme poverty — prevented these families from achieving their goals of residential mobility. Finally, we consider the negative consequences on the life chances of the poor if housing policy does not address constraints to mobility and identify potential policy solutions that might lead to opportunities for low-income renters to live in low-poverty neighborhoods.
Over 20 years of scholarship suggests that living in America's poorest and most dangerous communities diminishes the life course development of children and adults. In the 1990s, the dire conditions of some of these neighborhoods, especially those with large public housing developments, prompted significant policy responses. In addition to the demolition and redevelopment of some of the projects, the federal government launched an experiment to help families leave poor neighborhoods through an assisted housing voucher program called Moving to Opportunity (MTO). While families who moved through this program initially relocated to census tracts with poverty rates almost four times lower than their original projects, many returned to communities of moderate to high poverty. Why? We use mixed methods to explore the patterns and the decision-making processes behind moves among MTO families. Focusing on the Baltimore MTO site, we find that traditional theories for residential choice did not fully explain these outcomes. While limited access to public transportation, housing quality problems, and landlords made it hard for families to move to, or stay in, low-poverty neighborhoods, there were also more striking explanations for their residential trajectories. Many families valued the low-poverty neighborhoods they were originally able to access with their vouchers, but when faced with the need to move again, they often sacrificed neighborhood quality for dwelling quality in order to accommodate changing family needs. Having lived in high-poverty neighborhoods most of their lives, they developed a number of coping strategies and beliefs that made them confident they could handle such a consequential trade-off and protect themselves and their children from the dangers of poorer areas.
Youth in high-poverty neighborhoods have high rates of emotional problems. Understanding neighborhood influences on mental health is crucial for designing neighborhood-level interventions.
To perform an exploratory analysis of associations between housing mobility interventions for children in high-poverty neighborhoods and subsequent mental disorders during adolescence.
The Moving to Opportunity Demonstration from 1994 to 1998 randomized 4604 volunteer public housing families with 3689 children in high-poverty neighborhoods into 1 of 2 housing mobility intervention groups (a low-poverty voucher group vs a traditional voucher group) or a control group. The low-poverty voucher group (n=1430) received vouchers to move to low-poverty neighborhoods with enhanced mobility counseling. The traditional voucher group (n=1081) received geographically unrestricted vouchers. Controls (n=1178) received no intervention. Follow-up evaluation was performed 10 to 15 years later (June 2008-April 2010) with participants aged 13 to 19 years (0-8 years at randomization). Response rates were 86.9% to 92.9%.
Presence of mental disorders from the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) within the past 12 months, including major depressive disorder, panic disorder, posttraumatic stress disorder (PTSD), oppositional-defiant disorder, intermittent explosive disorder, and conduct disorder, as assessed post hoc with a validated diagnostic interview.
Of the 3689 adolescents randomized, 2872 were interviewed (1407 boys and 1465 girls). Compared with the control group, boys in the low-poverty voucher group had significantly increased rates of major depression (7.1% vs 3.5%; odds ratio (OR), 2.2 [95% CI, 1.2-3.9]), PTSD (6.2% vs 1.9%; OR, 3.4 [95% CI, 1.6-7.4]), and conduct disorder (6.4% vs 2.1%; OR, 3.1 [95% CI, 1.7-5.8]). Boys in the traditional voucher group had increased rates of PTSD compared with the control group (4.9% vs 1.9%, OR, 2.7 [95% CI, 1.2-5.8]). However, compared with the control group, girls in the traditional voucher group had decreased rates of major depression (6.5% vs 10.9%; OR, 0.6 [95% CI, 0.3-0.9]) and conduct disorder (0.3% vs 2.9%; OR, 0.1 [95% CI, 0.0-0.4]).
Interventions to encourage moving out of high-poverty neighborhoods were associated with increased rates of depression, PTSD, and conduct disorder among boys and reduced rates of depression and conduct disorder among girls. Better understanding of interactions among individual, family, and neighborhood risk factors is needed to guide future public housing policy changes.
This article examines the impact of neighborhood welfare participation on individual welfare participation, that is, the endogenous neighborhood effects. Endogenous neighborhood effects generate social multipliers. Few existing empirical studies on neighborhood effects distinguish between endogenous neighborhood effects and exogenous neighborhood effects, that is, the effects of neighborhood characteristics. This article constitutes an early attempt to identify and estimate endogenous and exogenous neighborhood effects separately. I construct an instrumental variable for neighborhood welfare participation rate based on the variation in welfare benefits and neighborhood demographic composition to address the reflection problem and the omitted neighborhood variables problem. A two-step method is proposed to separately estimate endogenous and exogenous neighborhood effects. The results show that neighborhood welfare participation plays an important role in a woman’s welfare participation both before and after the welfare reform in 1996.
Individuals participating in the HUD Housing Choice Voucher program, formerly Section 8, can rent units in the private market and are not tied to public housing projects in a specific neighborhood. We would expect vouchers to help poor families leave the ghetto and move to more diverse communities with higher socioeconomic opportunity, but many voucher holders remain concentrated in poor, segregated communities. We use longitudinal qualitative data from one hundred low-income African American families in Mobile, Alabama, to explore this phenomenon, finding that tenants’ limited housing search resources, involuntary mobility, landlord practices, and several aspects of the voucher program itself limit families’ ability to escape disadvantaged areas. We also find that the voucher program’s regulations and funding structures do not incentivize housing authorities to promote neighborhood mobility and residential choice. This combination of forces often keeps voucher recipients in neighborhoods with high concentrations of poor and minority residents.
The ability of nonexperimental estimators to match impact estimates derived from random assignment is examined using data from the evaluation of two interdistrict magnet schools. As in previous within-study comparisons, nonexperimental estimates differ from estimates based on random assignment when nonexperimental estimators are implemented without pretreatment measures of academic performance. With comparison groups consisting of students drawn from the same districts or districts with similar student body characteristics as the districts where treatment group students reside, using pretreatment test scores reduces the bias in nonexperimental methods between 64 and 96 percent. Adding pretreatment test scores does not achieve as much bias reduction when the comparison group consists of students drawn from districts with different student body characteristics than the treatment group students’ districts. The results suggest that using pretreatment outcome measures and comparison groups that are geographically aligned with the treatment group greatly improves the performance of nonexperimental estimators. © 2012 by the Association for Public Policy Analysis and Management.
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According to a cohesiveness perspective, neighborhood stability is good for communities and the individuals who live in them, and may be especially beneficial in poor neighborhoods. In contrast, a social isolation perspective proposes that neighborhood stability has negative effects on residents' psychological well-being in economically disadvantaged neighborhoods. This analysis of multilevel data-data in which survey information from a representative sample of Illinois residents is linked to census-tract information about poverty and stability in their neighborhood-supports a social isolation perspective. In affluent neighborhoods, stability is associated with low levels of distress; under conditions of poverty the opposite is true. In part this occurs because residents of poor, stable neighborhoods face high levels of disorder in their neighborhoods. Stability does not reduce perceived disorder under conditions of poverty, as it does in more affluent neighborhoods, which leaves residents feeling powerless to leave a dangerous place. Finally, the negative effects of poor, stable neighborhoods on residents' psychological well-being do not stem from a lack of social ties among neighbors.
This study uses within-study comparisons to assess the relative importance of covariate choice, unreliability in the measurement of these covariates, and whether regression or various forms of propensity score analysis are used to analyze the outcome data. Two of the within-study comparisons are of the four-arm type, and many more are of the three-arm type. To examine unreliability, simulations of differences in reliability are deliberately introduced into the 2 four-arm studies. Results are similar across the samples of studies reviewed with their wide range of non-experimental designs and topic areas. Covariate choice counts most, unreliability next most, and the mode of data analysis hardly matters at all. Unreliability has larger effects the more important a covariate is for bias reduction, but even so the very best covariates measured with a reliability of only .60 still do better than substantively poor covariates that are measured perfectly. Why regression methods do as well as propensity score methods used in several different ways is a mystery still because, in theory, propensity scores would seem to have a distinct advantage in many practical applications, especially those where functional forms are in doubt.
Cutler and Glaeser (1997) show that urban residential segregation has a strong adverse effect on labor market and social outcomes of young African-Americans relative to whites. We show that this effect is a fairly recent historical phenomenon. There is little evidence of such an effect from 1940 to 1970; rather, it emerged between 1970 and 1980, and it grew stronger during the 1980s.
Using multilevel data, I find that residents of poor, mother-only neighborhoods have higher levels of depression than residents of more advantaged neighborhoods. My data are from the 1995 Community, Crime and Health survey, a prob-ability sample of 2,482 adults in Illinois with linked information about the respondents ' census tract. Adjustment for individual-level race, ethnicity, sex, age, education, employment, income, household structure, and urban residence indicates that more than half of apparent contextual effect is really compositional, due to the fact that residents of disadvantaged neighborhoods tend to be disadvantaged themselves; however, a significant contextual effect survives. All of the distressing effects of female headship and poverty in the neighborhood are mediated by perceived neighborhood disorder. The daily stress of living in a neighborhood where social order has broken down is associated with depression.
