Publications
District competitiveness increases voter turnout: evidence from repeated redistricting in North Carolina
Quarterly Journal of Political Science, 2024, 19(4): 387-432
With Emanuel Garcia Munoz and Andres Munoz Gomez
QPJS article. Data and code archive.
We study whether competitive legislative districts cause higher voter turnout. To do so, we employ rich data on the 2006 to 2020 elections in North Carolina. We make use of variation in district competitiveness due to repeated bouts of redistricting, a process in which district boundaries are redrawn. Specifically, we compare people who share the same districts in each legislative chamber (U.S. House, NC Senate, NC House) before redistricting but who differ in districts after redistricting. We match these people on demographics, party registration, and pre-redistricting turnout. We then track their turnout behavior in post-redistricting elections. For the U.S. House, switching from an uncompetitive “80-20” district to a competitive “55-45” district increases turnout by a rate of 1 percentage point per election of exposure. For the state chambers, the magnitude is 0.6. Effects are highly persistent and sum across chambers. They appear to be explained in part by a learning channel, where living in a competitive district induces people to believe that races can be competitive.
Why do households leave school value added on the table? The roles of information and preferences
American Economic Review, 2023, 113(4): 1049-82
With Rajeev Dehejia, Cristian Pop-Eleches, and Miguel Urquiola
NBER Working Paper No. 28267. VoxEU summary. AER article. Data and code archive.
Romanian households could choose schools with 1 standard deviation worth of additional value added. Why do households leave value added “on the table”? We study two possibilities: (i) information and (ii) preferences for other school traits. In an experiment, we inform randomly selected households about schools’ value added. These households choose schools with up to 0.2 standard deviations of additional value added. We then estimate a discrete choice model and show that households have preferences for a variety of school traits. As a result, fully correcting households’ beliefs would eliminate at most a quarter of the value added that households leave unexploited.
Teaching global public health in the undergraduate liberal arts: a survey of 50 colleges
American Journal of Tropical Medicine and Hygiene, 2012, 87(1): 11-15
With David R. Hill and Uttara Partap
Working papers
District partisan slant shifts voters’ party affiliation: evidence from repeated redistricting in North Carolina
With Carlos Estrada and Emanuel Garcia Munoz. Slides
Does the slant of a legislative district merely reflect the district’s voters, or can it also affect them? To investigate this, we collect rich data on districts and voters from the 2006-2022 elections in North Carolina. We exploit variation in slant due to redistricting, the process in which district boundaries are redrawn. We show that living in a district where one party is powerful causes people to shift their party affiliation toward that party. Effects increase with the number of elections of exposure and sum across legislative chambers. They also seem to stem from changes in preferences, not strategic behavior. An implication of the results is that uncompetitive districts contribute to polarization: under uncompetitive districts, people and places who lean Democratic get put into Democratic-controlled districts and become more Democratic; vice versa for those who lean Republican. As an illustration, we assess the impacts of the districts that were used in North Carolina during the 2010s. Relative to a counterfactual of competitive districts, the 2010s districts led to a sizable increase in geographic polarization—though only a slight increase in polarization at the individual level.
The effects of STEM versus humanities in high school: evidence from admissions cutoffs, administrative data, and large-scale surveys
With Rajeev Dehejia, Andrei Munteanu, Cristian Pop-Eleches, and Miguel Urquiola. Slides
We estimate the impacts of being assigned to a STEM- versus humanities-focused high school curriculum. We use a regression discontinuity design based on cutoffs in Romania’s high school assignment system, together with administrative data (to measure high school enrollment and performance) and large-scale surveys (to measure college enrollment, career plans, high school and college satisfaction, wellbeing, time use, and beliefs and preferences). STEM assignment makes students 67 pp more likely to graduate from high school STEM, 24 pp more likely to enroll in a college STEM program, and 23 pp more likely to plan to pursue a STEM career. Further, it leads students to have greater confidence in their STEM abilities and to enjoy STEM more. During high school, it makes students spend more time on homework and less on social media and reading, have more male and fewer female friends, and report higher wellbeing. Importantly, the STEM curriculum is risky: STEM assignment causes students to score worse on a high-stakes high school exit exam, leading to a lower probability of attending any college and a decline in high school satisfaction by the first year after high school. The risk is especially pronounced for students with low baseline achievement, for whom the reduction in college enrollment is 16 pp. Also, STEM assignment makes females report lower college satisfaction, primarily due to negative experiences with professors and peers—though it does not influence regret over high school or college application choices. Finally, among males, STEM assignment leads to more traditionalist expectations and greater political conservatism.
Measuring gerrymandering by recovering preferences and turnout costs
In this paper, I develop a new method for how to measure whether a legislative map is gerrymandered. My method allows evaluating a map along two key dimensions of map quality. These are proportionality (the alignment between a party’s seat share and its statewide vote share) and competitiveness (the fraction of legislative contests with uncertain winners). The method is designed to account for the main criticism of existing approaches for measuring gerrymandering. In particular, it is commonly argued that existing approaches cannot accurately predict how a map will perform in future elections. This is because future elections will be subject to an unknown electorate and an unknown set of electoral shocks. Importantly, the U.S. Supreme Court recently ruled that this uncertainty is so intractable that the judicial system is unqualified to determine whether a map is gerrymandered. My method responds to this criticism by directly assessing the degree of uncertainty in a map’s quality. The method uses a simulation procedure that is built on top of a structural voting model. The model describes the preference and turnout decisions of a potential voter and decomposes an election into a small number of utility parameters. I fit the model in multiple elections and measure how much the utility parameters vary over time. I then simulate counterfactual elections by drawing from the across-election distribution of these utility parameters. I also allow for demographic changes in the electorate by manually altering the set of potential voters. As an empirical example, I apply the method to rich data from the 2008 to 2020 general elections in North Carolina. I show that the method allows credible and precise evaluations of maps. I also show that it has stronger predictive power than existing approaches.
Works in progress
The effects of students’ incoming information on major choice and major switching
With Damon Clark, David Gill, and Victoria Prowse
Supplementary school funding and residential sorting: RD evidence from property tax referenda in California
With Damon Clark