Quarterly Journal of Political Science, 2024, 19(4): 387-432
With Emanuel Garcia Munoz and Andres Munoz Gomez
QPJS article. Online appendix. Data and code archive.
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.
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.
Malleable minds: The effects of STEM- vs. humanities-focused curricula
With Rajeev Dehejia, Andrei Munteanu, Cristian Pop-Eleches, and Miguel Urquiola
NBER Working Paper No. 34502.
We examine the impacts of assignment to STEM vs. humanities-focused curricula in Romania’s high school system. We apply a regression discontinuity design to administrative and survey data to estimate effects on educational pathways, desired careers, and non-cognitive outcomes. An overarching theme of our findings is the malleability of students to what they study. Assignment to STEM increases STEM college enrollment and technology or engineering career intentions by 25 pp. Exploring mechanisms, we find that STEM assignment changes students’ self-perceived academic abilities and their preferences over academic subjects and job tasks. STEM assignment is risky for low-achieving students, reducing their chances of passing a high school exit exam and enrolling in college. A final finding is that STEM makes boys more conservative, while shifting some of girls' views to the left. Our results identify a strategy for promoting STEM higher education and careers, but also highlight potential tradeoffs.
The spillover effects of affordable housing developments on neighbors’ political participation
Revised and Resubmitted to the Journal of Housing Economics
With Carlos Estrada and Emanuel Garcia Munoz.
This paper examines the impacts of affordable housing developments funded by the Low-Income Housing Tax Credit (LIHTC) on political participation in surrounding neighborhoods. Using voter file data from North Carolina and a near-far ring design, we compare outcomes over time for people who lived closer to versus farther from an LIHTC project in the election before it was finished. We find that LIHTC developments cause a steady decline in both registration and turnout for nearby residents, with each falling by about 0.25 percentage points per post-completion general election. Most of the drop in registration reflects people being de-registered due to persistent inactivity, not moving away, receiving a felony conviction, or dying. A potential explanation for our findings is that LIHTC developments provide an influx of residents with low political engagement, which can reduce participation for existing residents via factors such as negative peer effects, weakened norms, or decreased outreach efforts by parties and campaigns.
Measuring residential sorting responses to local taxes for school spending
With Damon Clark.
Many studies have attempted to estimate the impacts on student outcomes of increased K-12 education spending. These studies are potentially confounded by effects of spending on household migration and thus on student composition within localities and schools. This paper estimates "residential sorting responses” using a regression discontinuity design applied to data from California on elections for parcel taxes---lump-sum taxes levied by a school district that can be used for operational spending. A key innovation is to construct rich measures of an area's residents and in- and out-migrants using longitudinal income tax records for the population of California taxpayers over a twenty-two year period. In line with research on local taxes for operational spending in other states, we find that passing a parcel tax increases operational spending by around 6% and leads to a small decline in the share of students eligible for free or reduced-price meals. However, we find null effects on our sorting measures from the tax data; hence we conclude that any geographic-based sorting responses are small.
With Carlos Estrada and Emanuel Garcia Munoz.
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.
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.
The effects of major restriction policies on college students' academic performance
With Thomas Knight, Macy McLaren, and Yuchen Zhu.
The effects of students’ incoming information on major choice and major switching
With Damon Clark, David Gill, and Victoria Prowse.