It is Surprisingly Difficult to Measure Income Segregation

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Recent studies have shown that Census- and ACS-based estimates of income segregation are subject to upward finite sampling bias (Logan et al. 2018, 2020; Reardon et al. 2018). We identify two additional sources of bias that are larger and opposite in sign to finite sampling bias: measurement error-induced attenuation bias and temporal pooling bias. The combination of these three sources of bias render estimates of the trend in income segregation unclear. We formalize the three types of bias, providing a method to correct them simultaneously using public Census and ACS data from 1990 to 2015-2019. We use these methods to produce bias-corrected estimates of income segregation in the U.S. from 1990 to 2019. We find that (1) segregation is on the order of 50 percent greater than previously believed; (2) the increase from 2000 to the 2005-09 period was much greater than indicated by previous estimates; and (3) segregation has declined since 2005-09. Correcting these biases requires good estimates of the reliability of self-reported income and of the year-to-year volatility in neighborhood mean incomes.

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APA Citation

Leung-Gagne, J., & Reardon, S.F. (2022). It is Surprisingly Difficult to Measure Income Segregation.