Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records

Abstract

Edited by Justin GrimmerIn both political behavior research and voting rights litigation, turnout and vote choice for different racial groups are often inferred using aggregate election results and racial composition. Over the past several decades, many statistical methods have been proposed to address this ecological inference problem. We propose an alternative method to reduce aggregation bias by predicting individual-level ethnicity from voter registration records. Building on the existing methodological literature, we use Bayes's rule to combine the Census Bureau's Surname List with various information from geocoded voter registration records. We evaluate the performance of the proposed methodology using approximately nine million voter registration records from Florida, where self-reported ethnicity is available. We find that it is possible to reduce the false positive rate among Black and Latino voters to 6% and 3%, respectively, while maintaining the true positive rate above 80%. Moreover, we use our predictions to estimate turnout by race and find that our estimates yields substantially less amounts of bias and root mean squared error than standard ecological inference estimates. We provide open-source software to implement the proposed methodology.

Tuning In, Not Turning Out: Evaluating the Impact of Ethnic Television on Political Participation

“…Second, it is one of several states that include racial and ethnic demographics in the voter file. Though recent work has improved the estimation of ethnicity in voter files using surname analysis (Imai and Khanna ), we reduce the possibility of measurement error by relying on voters' own reports of ethnic identification . Third, most states with Latino populations comparable to NC already have Spanish‐language stations, and thus, NC is unique in that it possesses a relatively large Latino population and received its first Spanish‐language station in 2003.…”

Last Name Selection in Audit Studies

“…To calculate P(R|S, L), we follow a well-developed health care literature and use Bayes' rule (Elliott et al 2008(Elliott et al , 2009Fiscella and Fremont 2006;Imai and Khanna 2016). 7 This approach provides us a "probabilistic prediction of individual [race or] ethnicity" (Imai and Khanna 2016: 265) for a given surname in a geographic area.…”

“…We also use wru to apply Bayes' rule and generate the predicted probability of a name signaling a race (or ethnicity) (Imai and Khanna 2016) for all 3,142 U.S. counties. We generate these probabilities for three racial (or ethnic) groupsblacks, Hispanics, and whites.…”

“…Relying heavily upon the wru package provided by Imai and Khanna (2016), the software takes a vector of last names, generates a matrix of name and county pairs, uses this matrix to return the probability that a name denotes one of four racial (or ethnic) groups (i.e., Asian, black, Hispanic, and white) for all spatial units, and then plots these values. This allows individuals to visually identify the extent to which the racial information provided by surnames varies across geographic contexts and to identify potentially problematic surnames.…”

Assessing algorithmic fairness with unobserved protected class using data combination

“…Note that the comparative predictiveness of different proxies for protected class labels, such as race, has been thoroughly studied (Consumer Financial Protection Bureau 2014, Dembosky et al 2019, Elliott et al 2008, Imai and Khanna 2016 as it was understood this impacts the quality of corresponding proxy assessments of disparity. The above result highlights that the proxies' predictiveness of outcomes is also crucial, or even sufficient.…”