Voting demographics image

Detecting of Vote Dilution: New tools and methods for protecting voting rights

Project leads: Matt A. Barreto, Professor of Political Science and Chicana/o Studies, and Faculty Director of the Voting Rights Project at University of California, Los Angeles; and Loren Collingwood, Associate Professor in the Department of Political Science at University of California, Riverside

Data scientists: Scott Henderson and Spencer Wood

DSSG fellows: Juandalyn Burke, Ari Decter-Frain, Hikari Murayama, Pratik Sachdeva

Participant bios available here.

Project Summary: Section 2 of the Voting Rights Act allows voters to challenge district boundaries if they believe gerrymandering has been used to dilute their vote and block them from getting candidates of choice for their community elected. To win a VRA lawsuit, plaintiffs must prove that voting patterns in their community are “racially polarized” with Whites and minorities voting in opposite directions for different candidates. However most states do not collect racial data on voters, and the voters ballot is secret. To analyze voting patterns, social scientists use a statistical method called Ecological Inference (EI) to determine how different groups vote. But this method relies on imprecise census data and often creates biased estimates of voting patterns. Recently a new methodology has been developed for estimating voters’ race and ethnicity, which offers great promise for improving voting estimates and being a helpful tool for upholding minority voting rights.

This project uses the existing eiCompare R software package (Collingwood et al. 2016) to update and modernize ecological inference (EI) analysis to be used in voting rights and redistricting efforts. In particular, we propose numerous methodological, programming, and statistical advancements to the EI models in eiCompare to allow for a more accurate and precise model to capture racial voting patterns. In particular we will incorporate Bayesian Improved Surname Geocoding (BISG, see Imai and Khanna 2016) to analyze the surname and address of voters to estimate probabilities of their race or ethnicity, which can then be used in EI models. We will also troubleshoot and address bugs in the various EI model code to ensure that accurate estimates of voter preferences are being calculated and can be better used by state and federal courts when evaluating voting rights claims.

The project will be led by Matt Barreto and Loren Collingwood, two political scientists with experience in voting rights litigation, who have written and developed several software packages in R to assist with voting rights analysis. Barreto and Collingwood are currently involved in multiple efforts across the country to promote and uphold equal voting rights, and team members will engage with voting rights lawyers from the ACLU, NAACP, MALDEF and more about the real world application of statistical analysis to these efforts.

Fellows Juandalyn Burke, Ari Decter-Frain, Hikari Murayama, and Pratik Sachdeva presented the project with the talk “eiCompare: Making Every Vote Count” at the Learning & Doing Data for Good (LDDG) conference at the University of Washington in September 2022. 

The DSSG team published their work in August 2023 in a publication titled “Comparing Methods for Estimating Demographics in Racially Polarized Voting Analyses.”

Learn more via the project website, project summary blog, and fellow Hikari Murayama’s blog post, Impactful Data Science: What I Learned Through Data Science for Social Good, published by the D-Lab at University of California, Berkeley.

Nominating petitions plotted by address and race to identify patterns of support for candidates (2015 School Board Election, Rockland County, NY)