About

Kevin DeLuca is an Assistant Professor of Political Science at Yale University, specializing in American politics with a research focus on elections and media. During the 2025-2026 academic year, he serves as a Visiting Assistant Professor at MIT and is a Faculty Affiliate of the MIT Election Data and Science Lab. At Yale, he holds the positions of Resident Fellow at the Institution for Social and Policy Studies (ISPS) and Faculty Affiliate at the Center for American Political Studies (CSAP). His previous affiliations include the Center for American Political Studies (CAPS) and the Institute for Quantitative Social Sciences (IQSS) at Harvard University. He has also contributed to the MIT Election Data and Science Lab (MEDSL) and participated in the Stanford-MIT Healthy Elections Project. DeLuca's work centers on the intersection of political science and media, particularly examining the effects of elections, media bias, and the role of local news in political processes.

Research topics

• Computer Science
• Political Science
• Law
• Data Mining
• Artificial Intelligence
• Business
• Public administration
• Public relations
• Engineering
• Geography
• Advertising
• Mathematics
• Cartography
• Econometrics
• Statistics

Selected publications

• Editor's Choice: Measuring Candidate Quality using Local Newspaper Endorsements

  2023 · 4 citations

  1st authorCorresponding
  • Political Science
  • Computer Science
  • Political Science

  I construct a new measure of candidate quality using political endorsements made by local newspapers. Similar to expert opinions, newspaper editorial board endorsements are highly-informed judgements that reflect quality differences between candidates, once accounting for the partisan preferences of the newspapers. Using a new data set of over 22,000 local newspaper endorsements, I simultaneously estimate the quality differences between candidates in thousands of elections between 1950-2022 along with a dynamic measure of the partisan slant of hundreds of local newspapers across the United States. After validating the endorsement-based measures of quality and slant, I use the quality differential measure to assess the effect that candidate quality has on election results and governing performance. I conclude by discussing how the newspaper endorsement-based measures have a strong potential to help advance our understanding of the impact of candidate quality and media bias on political representation.

  DOI

• Validating the Applicability of Bayesian Inference with Surname and Geocoding to Congressional Redistricting

  Political Analysis · 2022 · 15 citations

  1st authorCorresponding
  • Computer Science
  • Political Science
  • Computer Science

  Abstract Ensuring descriptive representation of racial minorities without packing minorities too heavily into districts is a perpetual difficulty, 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.

  DOI

• American election results at the precinct level

  Scientific Data · 2022 · 27 citations

  • Political Science
  • Public administration
  • Political Science

  We describe the creation and quality assurance of a dataset containing nearly all available precinct-level election results from the 2016, 2018, and 2020 American elections. Precincts are the smallest level of election administration, and election results at this granularity are needed to address many important questions. However, election results are individually reported by each state with little standardization or data quality assurance. We have collected, cleaned, and standardized precinct-level election results from every available race above the very local level in almost every state across the last three national election years. Our data include nearly every candidate for president, US Congress, governor, or state legislator, and hundreds of thousands of precinct-level results for judicial races, other statewide races, and even local races and ballot initiatives. In this article we describe the process of finding this information and standardizing it. Then we aggregate the precinct-level results up to geographies that have official totals, and show that our totals never differ from the official nationwide data by more than 0.457%.

  DOI

Frequent coauthors

• John Curiel

  4 shared

• James Dunham

  Georgetown University

  2 shared

• Maxwell Palmer

  1 shared

• Samuel Baltz

  1 shared

• Jennifer Miranda

  1 shared

• Annabel Uhlman

  Massachusetts Institute of Technology

  1 shared

• Marcos Zárate

  1 shared

• Benjamin Schneer

  John F. Kennedy University

  1 shared

Labs

• Kevin DeLuca's LabPI

  Research in American politics, elections, and media

Education

• M.A., Economics

  The University of Texas at Austin

  2015

Similar researchers at Yale University

• Mark Gersteinh-217
• Liangbing Huh-172
• Akiko Iwasakih-145
• Alan Kazdinh-142
• Douglas L. Rothmanh-125