Unifying Micro-level and Macro-level Analyses of Segregation
Casting segregation indices in the difference of means framework provides a valuable option previously not available to researchers. It enables them to seamlessly connect macro-level segregation – as measured by the index score for a city – to micro-level
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Unifying Micro-level and Macro-level Analyses of Segregation
Casting segregation indices in the difference of means framework provides a valuable option previously not available to researchers. It enables them to seamlessly connect macro-level segregation – as measured by the index score for a city – to micro-level processes of residential attainment. At the simplest level the value of any index placed in the difference of means framework can be obtained by performing an individual-level attainment analysis that predicts index-relevant residential outcomes (y, scored from area group proportion p) for individuals with a dummy variable (0,1) for racial group membership. The regression coefficient for race will exactly equal the index score obtained by standard computing formulas. This introduces a new interpretation of segregation index scores; their values reflect the effect of race on the attainment of residential outcomes that determine the segregation index score for the city. Establishing the equivalence of between macro-level measures of segregation and the effect of race on residential attainments in a bivariate individual-level regression model paves the way for at least three important new options for segregation analysis. The first is to give researchers the ability to extend and elaborate bivariate models to investigate segregation in more detail using multivariate analyses. These models make it possible for researchers to address fundamental questions that previously could not be directly investigated. For example, researchers can assess whether or not the impact of race on segregation-determining residential outcomes seen in the bivariate analysis continues to persist when controls are introduced for other relevant individual- and household-level social characteristics (e.g., age, education, income, marital status, household composition, nativity, etc.) that may exert independent influence on residential outcomes. A second new option for segregation analysis is to give researchers the opportunity to quantitatively dissect the underpinnings of segregation in more detail than has previously been possible. Specifically, researchers can use familiar tools of standardization and decomposition analysis to assess how the index score for a city is
© The Author(s) 2017 M. Fossett, New Methods for Measuring and Analyzing Segregation, The Springer Series on Demographic Methods and Population Analysis 42, DOI 10.1007/978-3-319-41304-4_9
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9 Unifying Micro-level and Macro-level Analyses of Segregation
quantitatively linked to group differences in the resources each group brings to the residential attainment process and to group differences in the parameters of the attainment process where resources (inputs) are converted to residential outcomes. Thus, one can develop improved answers to questions such as “Does segregation arise primarily because groups differ on income and other resources that affect residential contact with the reference group?” Or, “Does segregation arise primarily because groups dif
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