Comparing Methods for Estimating Demographics in Racially Polarized Voting Analyses

Abstract

We consider the cascading effects of researcher decisions throughout the process of quantifying racially polarized voting (RPV). We contrast three methods of estimating precinct racial composition, Bayesian Improved Surname Geocoding (BISG), fully Bayesian BISG, and Citizen Voting Age Population (CVAP), and two algorithms for performing ecological inference (EI), King’s EI and EI:RxC using eiCompare. Using data from two different elections we identify circumstances in which different combinations of methods produce divergent results, comparing against ground-truth data where available. We first find that BISG outperforms CVAP at estimating racial composition, though fully Bayesian BISG does not yield further improvements. Next, in a statewide election, we find that all combinations of methods yield similarly reliable estimates of RPV. However, county-level analyses and results from a non-partisan school board election reveal that BISG and CVAP produce divergent estimates of Black preferences in elections with low turnout and few precincts. Our results suggest that methodological choices can meaningfully alter conclusions about RPV, particularly in smaller, low-turnout elections.