Misclassification and Bias in Predictions of Individual Ethnicity from Administrative Records

Lisa P. Argyle and Michael Barber

American Political Science Review, 2024, vol. 118, issue 2, 1058-1066

Abstract: We show that a common method of predicting individuals’ race in administrative records, Bayesian Improved Surname Geocoding (BISG), produces misclassification errors that are strongly correlated with demographic and socioeconomic factors. In addition to the high error rates for some racial subgroups, the misclassification rates are correlated with the political and economic characteristics of a voter’s neighborhood. Racial and ethnic minorities who live in wealthy, highly educated, and politically active areas are most likely to be misclassified as white by BISG. Inferences about the relationship between sociodemographic factors and political outcomes, like voting, are likely to be biased in models using BISG to infer race. We develop an improved method in which the BISG estimates are incorporated into a machine learning model that accounts for class imbalance and incorporates individual and neighborhood characteristics. Our model decreases the misclassification rates among non-white individuals, in some cases by as much as 50%.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.cambridge.org/core/product/identifier/ ... type/journal_article link to article abstract page (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cup:apsrev:v:118:y:2024:i:2:p:1058-1066_33

Access Statistics for this article

More articles in American Political Science Review from Cambridge University Press Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK.
Bibliographic data for series maintained by Kirk Stebbing ().