Work

A calibrated BISG for inferring race from surname and geolocation

Year: 2025

Type: article

Abstract: Bayesian Improved Surname Geocoding (BISG) is a ubiquitous tool for predicting race and ethnicity using an individual’s geolocation and surname. Here we demonstrate that statistical dependence of surname and geolocation within racial/ethnic categories in the US results in biases for minority subpopulations, and we introduce a raking-based improvement. Our method augments the data used by BISG—distributions of race by geolocation and race by surname—with the distribution of surname by geolocation obtained from state voter files. We validate our algorithm on state voter registration lists that contain self-identified race/ethnicity.

Source: Journal of the Royal Statistical Society Series A (Statistics in Society)

DOI: 10.1093/jrsssa/qnaf003

Authors: Philip Greengard, Andrew Gelman

Institution: Columbia University

Language: English

Topic: Census and Population Estimation

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: green

Funders: National Science Foundation, Office of Naval Research