Interpolating Population Distributions using Public-use Data: An Application to Income Segregation using American Community Survey Data
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by
Matthew Simpson, Scott H. Holan, Christopher K. Wikle, Jonathan R. Bradley
2021
Abstract
Income segregation measures the extent to which households choose to live
near other households with similar incomes. Sociologists theorize that income
segregation can exacerbate the impacts of income inequality, and have developed
indices to measure it at the metro area level, including the information theory
index introduced in <cit.>, and the divergence index
presented in <cit.>. To study their differences, we
construct both indices using recent American Community Survey (ACS) estimates
of features of the income distribution. Since the elimination of the decennial
census long form, methods of computing these estimates must be updated to use
ACS estimates and account for survey error. We propose a model-based method to
interpolate estimates of features of the income distribution that accounts for
this error. This method improves on previous approaches by allowing for the use
of more types of estimates, and by providing uncertainty quantification. We
apply this method to estimate U.S. census tract-level income distributions
using ACS tabulations, and in turn use these to construct both income
segregation indices. We find major differences between the two indices in the
relative ranking of metro areas, as well as differences in how both indices
correlate with the Gini index.
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