Estimating Racial Disparities When Race is Not Observed

• 成果类型: Article; Early Access
• 署名作者: McCartan, Cory; Fisher, Robin; Goldin, Jacob; Ho, Daniel E.; Imai, Kosuke
• 署名单位: Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; United States Department of the Treasury; University of Chicago; National Bureau of Economic Research; Stanford University; Stanford University; Harvard University; Harvard University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2025.2526695
• 发表日期: 2025
• 关键词: ecological inference; Race/ethnicity; variables; BIAS

摘要

Estimating racial disparities without access to individual-level racial information is a common challenge in economic and policy settings. We develop a statistical method that relaxes the strong independence assumption of common race imputation approaches like Bayesian-Improved Surname Geocoding (BISG). Our identification assumption is that surname is conditionally independent of the outcome given (unobserved) race, residence location, and other observed characteristics. The proposed approach reduces error by up to 84% relative to BISG when estimating racial differences in political party registration. In our application, we estimate racial differences in who benefits from the home mortgage interest deduction using individual-level tax data from the U.S. Internal Revenue Service. Our analysis reveals that many fewer Black and Hispanic filers claim the HMID than White and Asian filers. We also find that the racial gaps in homeownership rates alone cannot explain this disparity. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.