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ABSTRACT: 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.
Demography 04/2012; 45(3):489-514. · 1.93 Impact Factor
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ABSTRACT: 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.
American Journal of Sociology 01/2011; 116(4):1092-153. · 3.17 Impact Factor
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ABSTRACT: 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.
Social Science Research 04/2009; 38(1):55-70. · 1.27 Impact Factor
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ABSTRACT: 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.
Demography 09/2008; 45(3):489-514. · 1.93 Impact Factor
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ABSTRACT: In many surveys, responses to earlier questions determine whether later questions are asked. The probability of an affirmative response to a given item is therefore nonzero only if the participant responded affirmatively to some set of logically prior items, known as “filter items.” In such surveys, the usual conditional independence assumption of standard item response models fails. A weaker “partial independence” assumption may hold, however, if an individual's responses to different items are independent conditional on the item parameters, the individual's latent trait, and the participant's affirmative responses to each of a set of filter items. In this paper, we propose an item response model for such “partially independent” item response data. We model such item response patterns as a function of a person-specific latent trait and a set of item parameters. Our model can be seen as a generalized hybrid of a discrete-time hazard model and a Rasch model. The proposed procedure yields estimates of (1) person-specific, interval-scale measures of a latent trait (or traits), along with person-specific standard errors of measurement; (2) conditional and marginal item severities for each item in a protocol; (3) person-specific conditional and marginal probabilities of an affirmative response to each item in a protocol; and (4) item information and total survey information. In addition, we show here how to investigate and test alternative conceptions of the dimensionality of the latent trait(s) being measured. Finally, we compare our procedure with a simpler alternative approach to summarizing data of this type.
Sociological Methodology 11/2006; 36(1):257 - 300. · 3.00 Impact Factor
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ABSTRACT: We examined whether retail tobacco outlet density was related to youth cigarette smoking after control for a diverse range of neighborhood characteristics.
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.
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.
Reductions in retail tobacco outlet density may reduce rates of youth smoking.
American Journal of Public Health 05/2006; 96(4):670-6. · 3.93 Impact Factor
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ABSTRACT: A number ofpublic school districts in the United States have adopted income-based integrationpolicies-policies that use measures offamily income or socioeconomic status-in determining school assign-ment. Some scholars and policymakers contend that such policies will also reduce racial segregation. In this article this assumption is explored by computing upper and lower bounds on the possible and probable levels of racial segregation that would result from race-neutral income-based school as-signment policies. The article finds that, in general, income integration is no guarantee of even mod-est racial desegregation. In particular, the extent of ancillary racial integration produced by an in-come-integration policy will depend on the size of racial income disparities within a given district, the specifics of an income-integration policy, and the patterns of racial and socioeconomic residential segregation in a school district. Data on racial income inequality and income segregation in urban districts throughout the United States indicate that very high levels of racial segregation are possible under any practical income-integration policy. The authors conclude that, given the extent of resi-dential racial segregation in the United States, it is unlikely that race-neutral income-integration poli-cies will significantly reduce school racial segregation, although there is reason to believe that such policies are likely to have other beneficial effects on schooling.
01/2006; 28:49-75.
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Sean F. Reardon
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ABSTRACT: This set of commands enables users to invoke and run the HLM v.6 software from within Stata (v. 8.2). HLM v. 6 must be installed on the computer, and the directory where the HLM software is located must be specified in the PATH variable (in Windows). The hlm.ado commands enable users to create HLM MDM files from within stata, to specifiy and to fit multilevel regression models using HLM v. 6 from within Stata.
01/2005;
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ABSTRACT: The measurement of residential segregation patterns and trends has been limited by a reliance on segregation measures that do not appropriately take into account the spatial patterning of population distributions. In this paper we define a general approach to measuring spatial segregation among multiple population groups. This general approach allows researchers to specify any theoretically based definition of spatial proximity desired in computing segregation measures. Based on this general approach, we develop a general spatial exposure/isolation index (P̃*), and a set of general multigroup spatial evenness/clustering indices: a spatial information theory index (H̃), a spatial relative diversity index (R̃), and a spatial dissimilarity index (D̃). We review these and previously proposed spatial segregation indices against a set of eight desirable properties of spatial segregation indices. We conclude that the spatial exposure/isolation index P̃*—which can be interpreted as a measure of the average composition of individuals’ local spatial environments—and the spatial information theory index H̃—which can be interpreted as a measure of the variation in the diversity of the local spatial environments of each individual—are the most conceptually and mathematically satisfactory of the proposed spatial indices.
Sociological Methodology 11/2004; 34(1):121 - 162. · 3.00 Impact Factor
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Sociological Methodology 12/2002; 32(1):85 - 101. · 3.00 Impact Factor
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ABSTRACT: In this paper we derive and evaluate measures of multigroup segregation. After describing four ways to conceptualize the measurement of multigroup segregation—as the disproportionality in group (e.g., race) proportions across organizational units (e.g., schools or census tracts), as the strength of association between nominal variables indexing group and organizational unit membership, as the ratio of between–unit diversity to total diversity, and as the weighted average of two–group segregation indices—we derive six multigroup segregation indices: a dissimilarity index (D), a Gini index (G), an information theory index (H), a squared coefficient of variation index (C), a relative diversity index (R), and a normalized exposure index (P). We evaluate these six indices against a set of seven desirable properties of segregation indices. We conclude that the information theory index H is the most conceptually and mathematically satisfactory index, since it alone obeys the principle of transfers in the multigroup case. Moreover, H is the only multigroup index that can be decomposed into a sum of between– and within–group components.
Sociological Methodology 12/2002; 32(1):33 - 67. · 3.00 Impact Factor
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ABSTRACT: 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.
Journal of Drug Education 02/2002; 32(4):319-42. · 0.28 Impact Factor
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ABSTRACT: 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.
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.
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.
By age 20 the rates of marijuana abuse and dependence are significantly higher among blacks than among whites and Hispanics.
Public Health Reports 02/2002; 117 Suppl 1:S51-9. · 1.27 Impact Factor
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ABSTRACT: 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 logit hazard 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.
Multivariate Behavioral Research 01/2002; 37(3):297-330. · 1.41 Impact Factor
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Annual Meeting of the American Sociological Association; 08/2000
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Sean F. Reardon
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ABSTRACT: SEG calculates a variety of indices to measure diversity and mutual segregation among multiple groups. A 'by' option allows the calculation of the indices within different organizational levels, and a Unit option allows calculation of the indices between different organizational levels. Generate and File options allow the indices to be output to either the current data set or a new data set. This is version 2.00 of the software, and runs under Stata 6 or Stata 7.
05/1999;
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09/2002;
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ABSTRACT: 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.
Social Science Research.
