Validating the Applicability of Bayesian Inference with Surname and Geocoding to Congressional Redistricting

Abstract

Ensuring descriptive representation of racial minorities without packing minorities too heavily into districts is a perpetual difficulty, especially in states lacking voter file race data. One advance since the 2010 redistricting cycle is the advent of Bayesian Improved Surname Geocoding (BISG), which greatly improves upon previous ecological inference methods in identifying voter race. In this article, we test the viability of employing BISG to redistricting under two posterior allocation methods for race assignment: plurality versus probabilistic. We validate these methods through 10,000 redistricting simulations of North Carolina and Georgia’s congressional districts and compare BISG estimates to actual voter file racial data. We find that probabilistic summing of the BISG posteriors significantly reduces error rates at the precinct and district level relative to plurality racial assignment, and therefore should be the preferred method when using BISG for redistricting. Our results suggest that BISG can aid in the construction of majority-minority districts during the redistricting process.

The Power of Characters: Evaluating Machine Learning-Modified Bayesian Improved Surname Geocoding Inference of Race in Redistricting

“…Figure 4 presents the results for North Carolina. The results for traditional BISG estimates in panels (a) and (c) are nearly identical to the original work by DeLuca and Curiel (2022). As originally found, the errors for plurality assignment are significant and substantive in all but the three most heavily concentrated racial minority populated districts.…”

“…Figure 5 presents the results for Georgia. We again see nearly identical estimates in panels (a) and (c) as in the original work by DeLuca and Curiel (2022). Within Georgia, the differences in errors between plurality assignment weighted probabilities are significant and substantive in all but two districts for Whites, and the four most heavily concentrated Black districts.…”

“…The scale for our purposes runs from one to three. 14 For the district-level comparison and potential impact on representation, we then replicate the redistricting simulations by DeLuca and Curiel (2022). Specifically, we use the same redistricting simulation results 15 and merge our estimates of the racial composition of precincts in order to compare BISG and ZRP performance.…”

“…Especially in research areas where even slight error can lead to perverse outcomes, such as in redistricting (Hicks et al 2018), any and all attempts to minimize error are critical. BISG is not a perfect method, with the potential for error especially where the geographic component is heterogeneous, is too aggregated a level, or where a lack of distinct naming patterns exists (Clark, Curiel, and Steelman 2022;DeLuca and Curiel 2022;Voicu 2018). One potential innovation in the application of BISG is development in machine learning (ML) methods.…”

“…Where BISG cannot estimate race accuratelyor at allit necessarily leads to specification error. Where scholars might likewise apply BISG to local contexts where individuals are assigned to a single race even amidst such uncertaintypluralistic assignment -BISG applications can start to approach the same type of error that traditionally afflicts normal EI methods (DeLuca and Curiel 2022;King 1997).…”