It Is Surprisingly Difficult to Measure Income Segregation.

Recent studies have shown that U.S. Census- and American Community Survey (ACS)-based estimates of income segregation are subject to upward finite sampling bias (Logan et al. 2018; Logan et al. 2020; Reardon et al. 2018). We identify two additional sources of bias that are larger and opposite in sign to finite sampling bias: measurement error-induced attenuation bias and temporal pooling bias. The combination of these three sources of bias make it unclear how income segregation has trended. We formalize the three types of bias, providing a method to correct them simultaneously using public data from the decennial census and ACS from 1990 to 2015-2019. We use these methods to produce bias-corrected estimates of income segregation in the United States from 1990 to 2019. We find that (1) segregation is on the order of 50% greater than previously believed; (2) the increase from 2000 to the 2005-2009 period was much greater than indicated by previous estimates; and (3) segregation has declined since 2005-2009. Correcting these biases requires good estimates of the reliability of self-reported income and of the year-to-year volatility in neighborhood mean incomes.

School racial segregation and long-term cardiovascular health among Black adults in the US: A quasi-experimental study.

BACKGROUND: Cardiovascular disease (CVD) disproportionately affects Black adults in the United States. This is increasingly acknowledged to be due to inequitable distribution of health-promoting resources. One potential contributor is inequities in educational opportunities, although it is unclear what aspects of education are most salient. School racial segregation may affect cardiovascular health by increasing stress, constraining socioeconomic opportunities, and altering health behaviors. We investigated the association between school segregation and Black adults' CVD risk. METHODS AND FINDINGS: We leveraged a natural experiment created by quasi-random (i.e., arbitrary) timing of local court decisions since 1991 that released school districts from court-ordered desegregation. We used the Panel Study of Income Dynamics (PSID) (1991 to 2017), linked with district-level school segregation measures and desegregation court order status. The sample included 1,053 Black participants who ever resided in school districts that were under a court desegregation order in 1991. The exposure was mean school segregation during observed schooling years. Outcomes included several adult CVD risk factors and outcomes. We fitted standard ordinary least squares (OLS) multivariable linear regression models, then conducted instrumental variables (IV) analysis, using the proportion of schooling years spent in districts that had been released from court-ordered desegregation as an instrument. We adjusted for individual- and district-level preexposure confounders, birth year, and state fixed effects. In standard linear models, school segregation was associated with a lower probability of good self-rated health (-0.05 percentage points per SD of the segregation index; 95% CI: -0.08, -0.03; p < 0.001) and a higher probability of binge drinking (0.04 percentage points; 95% CI: 0.002, 0.07; p = 0.04) and heart disease (0.01 percentage points; 95% CI: 0.002, 0.15; p = 0.007). IV analyses also found that school segregation was associated with a lower probability of good self-rated health (-0.09 percentage points; 95% CI: -0.17, -0.02, p = 0.02) and a higher probability of binge drinking (0.17 percentage points; 95% CI: 0.04, 0.30, p = 0.008). For IV estimates, only binge drinking was robust to adjustments for multiple hypothesis testing. Limitations included self-reported outcomes and potential residual confounding and exposure misclassification. CONCLUSIONS: School segregation exposure in childhood may have longstanding impacts on Black adults' cardiovascular health. Future research should replicate these analyses in larger samples and explore potential mechanisms. Given the recent rise in school segregation, this study has implications for policies and programs to address racial inequities in CVD.

Has Income Segregation Really Increased? Bias and Bias Correction in Sample-Based Segregation Estimates.

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.

Educational Opportunity in Early and Middle Childhood: Using Full Population Administrative Data to Study Variation by Place and Age.

I use standardized test scores from roughly forty-five million students to describe the temporal structure of educational opportunity in more than eleven thousand school districts in the United States. Variation among school districts is considerable in both average third-grade scores and test score growth rates. The two measures are uncorrelated, indicating that the characteristics of communities that provide high levels of early childhood educational opportunity are not the same as those that provide high opportunities for growth from third to eighth grade. This suggests that the role of schools in shaping educational opportunity varies across school districts. Variation among districts in the two temporal opportunity dimensions implies that strategies to improve educational opportunity may need to target different age groups in different places.

States as Sites of Educational (In)Equality: State Contexts and the Socioeconomic Achievement Gradient.

Socioeconomic achievement gaps have long been a central focus of educational research. However, not much is known about how (and why) between-district gaps vary among states, even though states are a primary organizational level in the decentralized education system in the United States. Using data from the Stanford Education Data Archive (SEDA), this study describes state-level socioeconomic achievement gradients and the growth of these gradients from Grades 3 to 8. We also examine state-level correlates of the gradients and their growth, including school system funding equity, preschool enrollment patterns, the distribution of teachers, income inequality, and segregation. We find that socioeconomic gradients and their growth rates vary considerably among states, and that between-district income segregation is positively associated with the socioeconomic achievement gradient.

From Census Tracts to Local Environments: An Egocentric Approach to Neighborhood Racial Change.

Most quantitative studies of neighborhood racial change rely on census tracts as the unit of analysis. However, tracts are insensitive to variation in the geographic scale of the phenomenon under investigation and to proximity among a focal tract's residents and those in nearby territory. Tracts may also align poorly with residents' perceptions of their own neighborhood and with the spatial reach of their daily activities. To address these limitations, we propose that changes in racial structure (i.e., in overall diversity and group-specific proportions) be examined within multiple egocentric neighborhoods, a series of nested local environments surrounding each individual that approximate meaningful domains of experience. Our egocentric approach applies GIS procedures to census block data, using race-specific population densities to redistribute block counts of whites, blacks, Hispanics, and Asians across 50-meter by 50-meter cells. For each cell, we then compute the proximity-adjusted racial composition of four different-sized local environments based on the weighted average racial group counts in adjacent cells. The value of this approach is illustrated with 1990-2000 data from a previous study of 40 large metropolitan areas. We document exposure to increasing neighborhood racial diversity during the decade, although the magnitude of this increase in diversity-and of shifts in the particular races to which one is exposed-differs by local environment size and racial group membership. Changes in diversity exposure at the neighborhood level also depend on how diverse the metro area as a whole has become.

Beyond the Census Tract: Patterns and Determinants of Racial Segregation at Multiple Geographic Scales.

The census tract-based residential segregation literature rests on problematic assumptions about geographic scale and proximity. We pursue a new tract-free approach that combines explicitly spatial concepts and methods to examine racial segregation across egocentric local environments of varying size. Using 2000 census data for the 100 largest U.S. metropolitan areas, we compute a spatially modified version of the information theory index H to describe patterns of black-white, Hispanic-white, Asian-white, and multi-group segregation at different scales. The metropolitan structural characteristics that best distinguish micro-segregation from macro-segregation for each group combination are identified, and their effects are decomposed into portions due to racial variation occurring over short and long distances. A comparison of our results to those from tract-based analyses confirms the value of the new approach.

Differences in onset and persistence of substance abuse and dependence among whites, blacks, and Hispanics.

OBJECTIVE: This article describes patterns of onset, persistence, and cessation of substance abuse among whites, blacks, and Hispanics that are masked in cross-sectional prevalence data. METHODS: The authors analyzed longitudinal data from a sample of 1,004 white, black, and Hispanic respondents from Chicago to investigate processes of onset, persistence, and cessation of substance abuse and dependence for two age cohorts, 15 and 18 at baseline and 17 and 20 at follow-up. RESULTS: The data show few racial or ethnic differences in the prevalence of alcohol and marijuana abuse and dependence at age 15. Rates of onset of alcohol abuse and dependence among whites between ages 15 and 17 were significantly higher than for blacks and Hispanics, and the rates of onset of marijuana abuse and dependence among blacks between ages 18 and 20 were significantly higher than for whites and Hispanics of the same age group. There were few significant differences among the three groups in the persistence rates of abuse and dependence. CONCLUSION: By age 20 the rates of marijuana abuse and dependence are significantly higher among blacks than among whites and Hispanics.

Income inequality and income segregation.

This article investigates how the growth in income inequality from 1970 to 2000 affected patterns of income segregation along three dimensions: the spatial segregation of poverty and affluence, race-specific patterns of income segregation, and the geographic scale of income segregation. The evidence reveals a robust relationship between income inequality and income segregation, an effect that is larger for black families than for white families. In addition, income inequality affects income segregation primarily through its effect on the large-scale spatial segregation of affluence rather than by affecting the spatial segregation of poverty or by altering small-scale patterns of income segregation.

Race and space in the 1990s: changes in the geographic scale of racial residential segregation, 1990-2000.

We use newly developed methods of measuring spatial segregation across a range of spatial scales to assess changes in racial residential segregation patterns in the 100 largest U.S. metropolitan areas from 1990 to 2000. Our results point to three notable trends in segregation from 1990 to 2000: (1) Hispanic-white and Asian-white segregation levels increased at both micro- and macro-scales; (2) black-white segregation declined at a micro-scale, but was unchanged at a macro-scale; and (3) for all three racial groups and for almost all metropolitan areas, macro-scale segregation accounted for more of the total metropolitan area segregation in 2000 than in 1990. Our examination of the variation in these trends among the metropolitan areas suggests that Hispanic-white and Asian-white segregation changes have been driven largely by increases in macro-scale segregation resulting from the rapid growth of the Hispanic and Asian populations in central cities. The changes in black-white segregation, in contrast, appear to be driven by the continuation of a 30-year trend in declining micro-segregation, coupled with persistent and largely stable patterns of macro-segregation.

The geographic scale of metropolitan racial segregation.

This article addresses an aspect of racial residential segregation that has been largely ignored in prior work: the issue of geographic scale. In some metropolitan areas, racial groups are segregated over large regions, with predominately white regions, predominately black regions, and so on, whereas in other areas, the separation of racial groups occurs over much shorter distances. Here we develop an approach-featuring the segregation profile and the corresponding macro/micro segregation ratio-that offers a scale-sensitive alternative to standard methodological practice for describing segregation. Using this approach, we measure and describe the geographic scale of racial segregation in the 40 largest U.S. metropolitan areas in 2000. We find considerable heterogeneity in the geographic scale of segregation patterns across both metropolitan areas and racial groups, a heterogeneity that is not evident using conventional "aspatial" segregation measures. Moreover, because the geographic scale of segregation is only modestly correlated with the level of segregation in our sample, we argue that geographic scale represents a distinct dimension of residential segregation. We conclude with a brief discussion of the implications of our findings for investigating the patterns, causes, and consequences of residential segregation at different geographic scales.

Retail tobacco outlet density and youth cigarette smoking: a propensity-modeling approach.

OBJECTIVES: We examined whether retail tobacco outlet density was related to youth cigarette smoking after control for a diverse range of neighborhood characteristics. METHODS: Data were gathered from 2116 respondents (aged 11 to 23 years) residing in 178 census tracts in Chicago, Ill. Propensity score stratification methods for continuous exposures were used to adjust for potentially confounding neighborhood characteristics, thus strengthening causal inferences. RESULTS: Retail tobacco outlets were disproportionately located in neighborhoods characterized by social and economic disadvantage. In a model that excluded neighborhood confounders, a marginally significant effect was found. Youths in areas at the highest 75th percentile in retail tobacco outlet density were 13% more likely (odds ratio [OR]=1.13; 95% confidence interval [CI]=0.99, 1.28) to have smoked in the past month compared with those living at the lowest 25th percentile. However, the relation became stronger and significant (OR=0.21; 95% CI=1.04, 1.41) after introduction of tract-level confounders and was statistically significant in the propensity score-adjusted model (OR = 1.20; 95% CI = 1.001, 1.44). Results did not differ significantly between minors and those legally permitted to smoke. CONCLUSIONS: Reductions in retail tobacco outlet density may reduce rates of youth smoking.

Estimating Multi-Level Discrete-Time Hazard Models Using Cross-Sectional Data: Neighborhood Effects on the Onset of Adolescent Cigarette Use.

Investigating the effects of social context (e.g., neighborhood or school context) on the timing of behaviors (such as cigarette use initiation) requires both multi-level modeling and eventhistory analysis, and often requires the construction of a retrospective person-period data set from cross-sectional data. In this article we describe procedures for constructing such a data set and discuss modeling strategies for estimating multi-level discrete-time event history models. We show that the estimation of two-level discrete-time models involves three distinct modeling assumptions (the assumptions that individual- and neighborhood-level covariates have the same effect at all time points and the assumption that the baseline logithazard curves in each neighborhood are parallel) and discuss methods of relaxing and empirically testing each of these assumptions. Estimation can be simplified in some cases if we additionally assume that the shape of the baseline logit-hazard curve in each neighborhood can be approximated by a simple functional form. The methods described here are applicable to a wide variety of questions where the dependent variable of interest is either onset or cessation. Here we apply these methods to the analysis of cigarette use initiation in a sample of 1,979 11- to 18-year-olds drawn from 79 neighborhoods of Chicago. We find that the racial composition of a neighborhood accounts for roughly half of the difference in age of smoking initiation between Black and White teenagers. Specifically, we find that living in a neighborhood with a large percentage of Black residents is associated with a lower hazard of adolescent cigarette use initiation than is living in neighborhoods with few Black residents.

How beliefs about substance use differ by socio-demographic characteristics, individual experiences, and neighborhood environments among urban adolescents.

In this article, we tested a series of Item Response Theory (IRT) models to examine the individual and neighborhood variation in perceived risk along dimensions of substance use (alcohol, marijuana, and hard drugs) and usage patterns (light/experimental use, moderate use, heavy/regular use). Data were gathered from 2266 adolescents aged 9, 12, and 15 residing in 79 Chicago neighborhoods. Developmental patterns for age and amount of use were observed whereby older respondents rated alcohol and marijuana as less harmful compared to the younger respondents, but rated hard drugs as more harmful. Risk perceptions were found to be more closely tied to one's direct experience with drugs rather than a general constellation of beliefs. Neighborhood variation in risk perceptions was also observed for hard drugs and three patterns of use, controlling for characteristics of individual residents. Neighborhoods did not vary in risk perceptions toward alcohol use. Individual-level factors rather than characteristics of the neighborhoods explained the observed neighborhood variation in perceptions toward marijuana use. These findings illustrate the complex links between individual and contextual factors in the development of beliefs about the health risks associated with substance use.