Neighborhood Effect Heterogeneity by Family Income and Developmental Period

Wodtke, Geoffrey T.; Harding, David J.; Elwert, Felix

doi:10.1086/684137

Cited by 101 publications

(106 citation statements)

References 73 publications

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“…Sizable neighborhood inequality is particularly troubling when high neighborhood poverty is associated with race (6). If exposure to poverty adversely affects child development and educational attainment, as recent research suggests (7)(8)(9)(10)(11), then disparities in the neighborhood poverty environments of blacks versus more advantaged groups may be an important factor in the persistence of racial inequality across generations (12).…”

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confidence: 99%

For blacks in America, the gap in neighborhood poverty has declined faster than segregation

Firebaugh

Acciai

2016

Proc. Natl. Acad. Sci. U.S.A.

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Black residential segregation has been declining in the United States. That accomplishment rings hollow, however, if blacks continue to live in much poorer neighborhoods than other Americans. This study uses census data for all US metropolitan areas in 1980 and 2010 to compare decline in the neighborhood poverty gap between blacks and other Americans with decline in the residential segregation of blacks. We find that both declines resulted primarily from narrowing differences between blacks and whites as opposed to narrowing differences between blacks and Hispanics or blacks and Asians. Because black-white differences in neighborhood poverty declined much faster than black-white segregation, the neighborhood poverty disadvantage of blacks declined faster than black segregationa noteworthy finding because the narrowing of the racial gap in neighborhood poverty for blacks has gone largely unnoticed. Further analysis reveals that the narrowing of the gap was produced by change in both the medians and shapes of the distribution of poverty across the neighborhoods where blacks, whites, Hispanics, and Asians reside.on-Hispanic blacks (hereafter "blacks") generally live in poorer neighborhoods than other Americans. In metropolitan areas in 2010, for example, blacks were nearly four times more likely than other residents to live in neighborhoods where the poverty rate was 40% or higher. The racial divide persists even when comparing households of the same income (1). One study finds that the average black household with annual earnings of $75,000 resides in a higher-poverty neighborhood than the average white household with annual earnings of less than $40,000 (2). However, racial differences in neighborhood environments were even greater in the past (3). In this article, we document a significant narrowing of the black-nonblack gap in neighborhood poverty levels since 1980 and compare that decline with the decline in black residential segregation over the same period.The residential clustering of rich and poor in America is important because of the diminished life chances for residents of high-poverty neighborhoods (4, 5). In addition to more noise and congestion and the absence of green space, high-poverty neighborhoods in America often are characterized by poor schools, reduced access to healthy food, high crime rates, and weak social institutions. Sizable neighborhood inequality is particularly troubling when high neighborhood poverty is associated with race (6). If exposure to poverty adversely affects child development and educational attainment, as recent research suggests (7-11), then disparities in the neighborhood poverty environments of blacks versus more advantaged groups may be an important factor in the persistence of racial inequality across generations (12).The term "black neighborhood disadvantage" is used in this article as shorthand for the difference in poverty rates of the neighborhoods where blacks and other Americans live. Although black neighborhood disadvantage requires black residential segrega...

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confidence: 99%

For blacks in America, the gap in neighborhood poverty has declined faster than segregation

Firebaugh

Acciai

2016

Proc. Natl. Acad. Sci. U.S.A.

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“…() and Wodtke et al . ()). In studies of neighbourhood effects, parental income is likely to be affected by prior neighbourhood conditions and also likely to affect both future residential choices and child educational outcomes.…”

Section: Introductionmentioning

confidence: 94%

“…For example, we consider whether living in a disadvantaged neighbourhood throughout childhood and adolescence affects academic achievement (e.g. Sampson et al (2008) and Wodtke et al (2011Wodtke et al ( , 2016). In studies of neighbourhood effects, parental income is likely to be affected by prior neighbourhood conditions and also likely to affect both future residential choices and child educational outcomes.…”

Section: Introductionmentioning

confidence: 99%

Regression-With-Residuals Estimation of Marginal Effects: A Method of Adjusting for Treatment-Induced Confounders That may also be Effect Modifiers

Wodtke

Alaca

Zhou

2019

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary When making causal inferences, treatment‐induced confounders complicate analyses of time‐varying treatment effects. Conditioning on these variables naively to estimate marginal effects may inappropriately block causal pathways and may induce spurious associations between the treatment and the outcome, leading to bias. Although several methods for estimating marginal effects avoid these complications, including inverse probability of treatment weighted estimation of marginal structural models as well as g‐ and regression‐with‐residuals estimation of highly constrained structural nested mean models, each suffers from a set of non‐trivial limitations, among them an inability to accommodate effect modification. In this study, we adapt the method of regression with residuals to estimate marginal effects with a set of moderately constrained structural nested mean models that easily accommodate several types of treatment‐by‐confounder interaction. With this approach, the confounders at each time point are first residualized with respect to the observed past, which involves centring them at their estimated means given prior treatments and confounders. The outcome is then regressed on all prior variables, including a set of treatment‐by‐confounder interaction terms, with these residuals substituted for the untransformed confounders both as ‘main effects’ and as part of any interaction terms. Through a series of simulation experiments and empirical examples, we show that this approach outperforms other methods for estimating the marginal effects of time‐varying treatments.

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“…Finally, the effects of school, classroom, and teacher characteristics on student achievement are often assumed to be a function of student abilities, where future gains are built upon foundations laid down earlier (Heckman 2006; Sanders et al 1997). In fact, treatment effect heterogeneity is endemic to nearly all social contexts (Xie 2007; Xie et al 2012), and it has important implications for social theory, research, and policy (Brand and Xie 2010; Heckman et al 2006; Wodtke et al 2016). …”

Section: Introductionmentioning

confidence: 99%

“…Causal contrasts compare the same subjects in two counterfactual states, but this question implies a comparison of different subjects because the children in families who would continue to have lower incomes had they continuously been exposed to poor neighborhoods and the children in families who would continue to have lower incomes had they been continuously exposed to non-poor neighborhoods are not the same set of subjects. When prior treatments help to create the subgroup of interest in the future, crafting coherent estimands for moderated causal effects is a difficult conceptual challenge (Wodtke et al 2016). …”

Section: Introductionmentioning

confidence: 99%

Estimating Moderated Causal Effects with Time-varying Treatments and Time-varying Moderators: Structural Nested Mean Models and Regression with Residuals

Wodtke

Almirall

2017

Sociological Methodology

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Individuals differ in how they respond to a particular treatment or exposure, and social scientists are often interested in understanding how treatment effects are moderated by observed characteristics of individuals. Effect moderation occurs when individual covariates dampen or amplify the effect of some exposure. This article focuses on estimating moderated causal effects in longitudinal settings where both the treatment and effect moderator vary over time. Effect moderation is typically examined using covariate by treatment interactions in regression analyses, but in the longitudinal setting, this approach may be problematic because time-varying moderators of future treatment may be affected by prior treatment—for example, moderators may also be mediators—and naively conditioning on an outcome of treatment in a conventional regression model can lead to bias. This article introduces to sociology moderated intermediate causal effects and the structural nested mean model for analyzing effect moderation in the longitudinal setting. It discusses problems with conventional regression and presents a new approach to estimation that avoids these problems (regression-with-residuals). The method is illustrated using longitudinal data from the PSID to examine whether the effects of time-varying exposures to poor neighborhoods on the risk of adolescent childbearing are moderated by time-varying family income.

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Neighborhood Effect Heterogeneity by Family Income and Developmental Period

Abstract: TitleNeighborhood effect heterogeneity by family income and developmental period

Cited by 101 publications

References 73 publications

For blacks in America, the gap in neighborhood poverty has declined faster than segregation

For blacks in America, the gap in neighborhood poverty has declined faster than segregation

Regression-With-Residuals Estimation of Marginal Effects: A Method of Adjusting for Treatment-Induced Confounders That may also be Effect Modifiers

Estimating Moderated Causal Effects with Time-varying Treatments and Time-varying Moderators: Structural Nested Mean Models and Regression with Residuals

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