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Minmaxing of Bayesian Improved Surname Geocoding and Geography Level Ups in Predicting Race

Published online by Cambridge University Press:  29 November 2021

Jesse T. Clark Open the ORCID record for Jesse T. Clark [Opens in a new window] ,
John A. Curiel Open the ORCID record for John A. Curiel [Opens in a new window]  and
Tyler S. Steelman Open the ORCID record for Tyler S. Steelman [Opens in a new window]
Jesse T. Clark
Affiliation:
Postdoctoral Research Associate, Princeton University, Princeton, NJ
John A. Curiel
Affiliation:
Assistant Professor, Ohio Northern University, Ada, OH, USA
Tyler S. Steelman*
Affiliation:
Department of Political Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. E-mail: tsteelman@unc.edu
*
Corresponding author Tyler S. Steelman
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Abstract

Racial identification is a critical factor in understanding a multitude of important outcomes in many fields. However, inferring an individual’s race from ecological data is prone to bias and error. This process was only recently improved via Bayesian improved surname geocoding (BISG). With surname and geographic-based demographic data, it is possible to more accurately estimate individual racial identification than ever before. However, the level of geography used in this process varies widely. Whereas some existing work makes use of geocoding to place individuals in precise census blocks, a substantial portion either skips geocoding altogether or relies on estimation using surname or county-level analyses. Presently, the trade-offs of such variation are unknown. In this letter, we quantify those trade-offs through a validation of BISG on Georgia’s voter file using both geocoded and nongeocoded processes and introduce a new level of geography—ZIP codes—to this method. We find that when estimating the racial identification of White and Black voters, nongeocoded ZIP code-based estimates are acceptable alternatives. However, census blocks provide the most accurate estimations when imputing racial identification for Asian and Hispanic voters. Our results document the most efficient means to sequentially conduct BISG analysis to maximize racial identification estimation while simultaneously minimizing data missingness and bias.

Keywords

Bayesian improved surname geocodingracial identificationgeocodinggeographic information systemZIP codes
Type
Letter
Information
Political Analysis , Volume 30 , Issue 3 , July 2022 , pp. 456 - 462
Copyright
© The Author(s) 2021. Published by Cambridge University Press on behalf of the Society for Political Methodology

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Footnotes

Edited by Jeff Gill

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