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New Methods for Measuring and Analyzing Segregation (Hardcover, 1st ed. 2017)
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New Methods for Measuring and Analyzing Segregation (Hardcover, 1st ed. 2017)
Series: The Springer Series on Demographic Methods and Population Analysis, 42
Expected to ship within 12 - 17 working days
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This book is open access under a CC BY-NC 2.5 license. This book
introduces new methods for measuring and analyzing residential
segregation. It begins by placing all popular segregation indices
in the "difference of group means" framework wherein index scores
can be obtained as simple differences of group means on
individual-level residential attainments scored from area racial
composition. Drawing on the insight that in this framework index
scores are additively determined by individual residential
attainments, the book shows that the level of segregation in a
given city can be equated to the effect of group membership (e.g.,
race) on individual residential attainments. This unifies separate
research traditions in the field by joining the analysis of
segregation at the aggregate level with the analysis of residential
attainments for individuals. Next it shows how segregation analysis
can be extended by using multivariate attainment models to assess
the impact of group membership (i.e., the level of segregation for
a city) while including controls for other relevant individual
characteristics (e.g., income, education, language, nativity,
etc.). It then illustrates how one can use these models to
quantitatively assess the extent to which segregation traces to
impacts of group membership on residential attainments versus other
factors such as group differences in income. The book then shows
how micro-level attainment models can be used to study macro-level
variation in segregation; specifically, by estimating multi-level
models of individual residential attainments to assess how the
effect of group membership (i.e., segregation index scores) vary
with city characteristics. Finally, the book introduces refined
versions of popular indices that are free of the vexing problem of
upward bias. This improves the quality of segregation measurement
directly at the level of individual cases and expanding the number
of cases that can be safely included in empirical studies.
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