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The estimation of racial disparities in health care, financial services,voting, and other contexts is often hampered by the lack of individual-levelracial information in administrative records. In many cases, the law prohibitsthe collection of such information to prevent direct racial discrimination. Asa result, many analysts have adopted Bayesian Improved Surname Geocoding(BISG), which combines individual names and addresses with the Census data topredict race. Although BISG tends to produce well-calibrated racialpredictions, its residuals are often correlated with the outcomes of interest,yielding biased estimates of racial disparities. We propose an alternativeidentification strategy that corrects this bias. The proposed strategy isapplicable whenever one's surname is conditionally independent of the outcomegiven their (unobserved) race, residence location, and other observedcharacteristics. Leveraging this identification strategy, we introduce a newclass of models, Bayesian Instrumental Regression for Disparity Estimation(BIRDiE), that estimate racial disparities by using surnames as ahigh-dimensional instrumental variable for race. Our estimation method isscalable, making it possible to analyze large-scale administrative data. Wealso show how to address potential violations of the key identificationassumptions. A validation study based on the North Carolina voter file showsthat BIRDiE reduces error by up to 84% in comparison to the standard approachesfor estimating racial differences in party registration. Open-source softwareis available which implements the proposed methodology.

Estimating Racial Disparities When Race is Not Observed