Publications

Working Paper
Maxwell Palmer, Benjamin Schneer, and Kevin DeLuca. Working Paper. “A Partisan Solution to Partisan Gerrymandering: The Define-Combine Procedure”. DraftAbstract
Redistricting reformers have proposed many solutions to the problem of partisan gerrymandering, including non-partisan commissions and bipartisan commissions with members from each party. Redistricting litigation frequently ends with court- or special-master-drawn plans. All of these methods require either bipartisan consensus or the agreement of both parties on the legitimacy of a neutral third party to resolve disputes. In this paper we propose a new method for drawing districting maps, the Define-Combine procedure, that substantially reduces partisan gerrymandering without requiring a neutral third party or bipartisan agreement. First, one party defines a map of 2N equal-population contiguous districts. Then the second party combines pairs of contiguous districts to create the final map of N districts. We use simulations and map-drawing algorithms to show that this procedure reduces the advantage conferred to the party controlling the redistricting process and leads to less biased maps without requiring cooperation or non-partisan actors.
In Preparation
Kevin DeLuca. In Preparation. “Measuring Candidate Quality using Local Newspaper Endorsements”.
2022
Kevin DeLuca and John A. Curiel. 2022. “Validating the Applicability of Bayesian Inference with Surname and Geocoding to Congressional Redistricting.” Political Analysis, Pp. 1-7.Abstract
Ensuring descriptive representation of racial minorities without packing minorities too heavily into districts is a perpetual diiculty, 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.
2020
Kevin DeLuca. 11/3/2020. “Spotlight on Texas: Record Breaking Early Vote Turnout.” Stanford-MIT Healthy Elections Project. Blog Post
Kevin DeLuca, Sam Pauley, and Emerson Webb. 11/2/2020. Georgia Mail Ballot Trends. Stanford-MIT Healthy Elections Project. Online Report
Julia Ansolabehere, Colin McIntyre, Kevin DeLuca, Krithika Iyer, and Diana Cao. 10/27/2020. Voter Registration Summary by State. Stanford-MIT Healthy Elections Project. Online Report
Kevin DeLuca. 9/15/2020. Georgia Primary Election Analysis. Stanford-MIT Healthy Elections Project. Online Report
Kevin DeLuca and Blair Read. 9/14/2020. Texas Primary Election Analysis. Stanford-MIT Healthy Elections Project. Online Report