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Should We Trust Regression to Measure Social Disparities?

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  • Wu, Christopher

Abstract

When investigating socioeconomic disparities in exposure to social ills, it is common to use (semi-)parametric regression, and interpret the parameter estimates as measures of inequality. However, disparity researchers often know little about the data generating process; therefore, not only are models misspecified, but it becomes difficult to even know which model is `better'. Under misspecification, common interpretations of regression coefficients can break down. Without a `right' model, model selection can be arbitrary, leading to credibility concerns as estimates are often sensitive to model selection. Predictive model selection can attenuate the measure of inequality if irrelevant predictors explain away the inequality. Examples are presented to illustrate key pitfalls.

Suggested Citation

  • Wu, Christopher, 2022. "Should We Trust Regression to Measure Social Disparities?," SocArXiv 4znu5, Center for Open Science.
    • Handle: RePEc:osf:socarx:4znu5
    DOI: 10.31219/osf.io/4znu5
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    1. Alberto Abadie & Susan Athey & Guido W. Imbens & Jeffrey M. Wooldridge, 2020. "Sampling‐Based versus Design‐Based Uncertainty in Regression Analysis," Econometrica, Econometric Society, vol. 88(1), pages 265-296, January.
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    3. Brett Baden & Douglas Noonan & Rama Mohana Turaga, 2007. "Scales of justice: Is there a geographic bias in environmental equity analysis?," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 50(2), pages 163-185.
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