Interpolating Population Distributions using Public-use Data: An Application to Income Segregation using American Community Survey Data

TL;DR

This paper introduces a new model-based method to interpolate income distribution features from ACS data, enabling more accurate income segregation measurement and revealing significant differences between segregation indices across U.S. metro areas.

Contribution

It develops a novel interpolation approach that accounts for survey error and enhances the estimation of income distribution features from ACS data.

Findings

Major differences in metro area rankings between the two income segregation indices.

The indices show different correlations with the Gini index.

The method improves uncertainty quantification in income distribution estimates.

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 \citet{reardon2011income}, and the divergence index presented in \citet{roberto2015divergence}. 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.