Glenn Firebaugh

Measures of Multigroup Segregation

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.

Empirics of World Income Inequality

This article employs a common general formula for inequality indexes to answer several basic questions about intercountry income inequality in recent decades: Has inequality across nations increased or declined (and why have earlier studies yielded mixed results)? Have different rates of population growth played a significant role in the trend? Have large nations dominated the trend? Are the results robust over different inequality measures and different income series? Two findings stand out. First, different rates of population growth in rich and poor nations played the predominant role in determining change in the distribution of per capita income across nations. Second, the centuries‐old trend of rising inequality leveled off from 1960 to 1989. The dependency theory thesis of a polarizing world system receives no support.

Trends in Antiblack Prejudice, 1972-1984: Region and Cohort Effects.

The 1972-84 trend in antiblack prejudice in the United States is decomposed with the use of a better method for isolating the cohort replacement component of a trend. Three major findings emerge. First, antiblack prejudice (as measured by questions in the General Social Survey) has declined in recent years; this is true for the South, for the non-South, and for the United States as a whole. Second, the decline in prejudice stems from cohort replacement (the replacement of older, more prejudiced birth cohorts with younger, less prejudiced ones) as well as from attitude change itself; in fact, in some instances cohort replacement is the more important source of the decline. Third, antiblack prejudice is declining more rapidly in the South; as a result, the regional difference in antiblack prejudice is shrinking.

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 multigroup segregation at different scales. We identify the metropolitan structural characteristics that best distinguish micro-segregation from macro-segregation for each group combination, and we decompose their effects into portions due to racial variation occurring over short and long distances. A comparison of our results with those from tract-based analyses confirms the value of the new approach.

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.

Does Your Neighbor's Income Affect Your Happiness?

The relative income or income status hypothesis implies that people should be happier when they live among the poor. Findings on neighborhood effects suggest, however, that living in a poorer neighborhood reduces, not enhances, a persons happiness. Using data from the American National Election Study linked to income data from the U.S. census, the authors find that Americans tend to be happier when they reside in richer neighborhoods (consistent with neighborhood studies) in poorer counties (as predicted by the relative income hypothesis). Thus it appears that individuals in fact are happier when they live among the poor, as long as the poor do not live too close.

Assessing Group Effects A Comparison of Two Methods

This article examines the relationship between two methods for detecting group effects in nonexperimental data: covariance analysis and contextual analysts. The examination shows that contextual effects are a special case of the group effect obtained in covariance analysis. This finding implies that (1) the group effect of covariance analysis provides an upper limit for contextual effects, (2) covariance analysis is more directly applicable in exploratory work, while contextual analysis is more directly applicable in causal analysis, and (3) both contextual and covariance analysis are required for a complete accounting of group effects.

Racial Variation in the Effect of Incarceration on Neighborhood Attainment

Each year, more than 700,000 convicted offenders are released from prison and reenter neighborhoods across the country. Prior studies have found that minority ex-inmates tend to reside in more disadvantaged neighborhoods than do white ex-inmates. However, because these studies do not control for pre-prison neighborhood conditions, we do not know how much (if any) of this racial variation is due to arrest and incarceration, or if these observed findings simply reflect existing racial residential inequality. Using a nationally representative dataset that tracks individuals over time, we find that only whites live in significantly more disadvantaged neighborhoods after prison than prior to prison. Blacks and Hispanics do not, nor do all groups (whites, blacks, and Hispanics) as a whole live in worse neighborhoods after prison. We attribute this racial variation in the effect of incarceration to the high degree of racial neighborhood inequality in the United States: because white offenders generally come from much better neighborhoods, they have much more to lose from a prison spell. In addition to advancing our understanding of the social consequences of the expansion of the prison population, these findings demonstrate the importance of controlling for pre-prison characteristics when investigating the effects of incarceration on residential outcomes.

Structural determinants of urbanization in Asia and Latin America 1950-1970

Why is the world becoming increasingly urban? The primary reason is economic development but economic development alone is inadequate for explaining urbanization in the Third World. Theoretical arguments and fragmentary empirical evidence suggest that in the underdeveloped regions of Asia and Latin America urbanization is caused by adverse rural conditions as well as by economic development. Data for 27 Asian and Latin American nations in 1960 and 1970 provide evidence that two rural conditions high agricultural density and plantation agriculture spur urbanization in underdeveloped regions independent of the effects of economic development and prior urbanization in these regions. (authors)

Fixed Effects, Random Effects, and Hybrid Models for Causal Analysis

Longitudinal data are becoming increasingly common in social science research. In this chapter, we discuss methods for exploiting the features of longitudinal data to study causal effects. The methods we discuss are broadly termed fixed effects and random effects models. We begin by discussing some of the advantages of fixed effects models over traditional regression approaches and then present a basic notation for the fixed effects model. This notation serves also as a baseline for introducing the random effects model, a common alternative to the fixed effects approach. After comparing fixed effects and random effects models – paying particular attention to their underlying assumptions – we describe hybrid models that combine attractive features of each. To provide a deeper understanding of these models, and to help researchers determine the most appropriate approach to use when analyzing longitudinal data, we provide three empirical examples. We also briefly discuss several extensions of fixed/random effects models. We conclude by suggesting additional literature that readers may find helpful.