Research

Published Work

"Economic Distress and Electoral Consequences: Evidence from Appalachia" (Sole Author), Forthcoming at The Review of Economics and Statistics. [A. Kimball Romney Award] [Manuscript]
Media Coverage: The Boston Globe

"Age Discrimination and Age Stereotypes in Job Ads" (with Ian Burn, Daniel Ladd, and David Neumark), Federal Reserve Bank of San Francisco Economic Letters, March 2023.

"The Impact of Post-Admission Merit Scholarships on Enrollment Decisions and Degree Attainment: Evidence from Randomization" (Sole Author), The Economics of Education Review 84, 102221, February 2022.

Working Papers

"Do Tuition Subsidies Raise Political Participation?"

Abstract: Although civic externalities are a primary motivation for subsidizing education, little research estimates the civic returns to education subsidies. We use data on 16.4 million FAFSAs and a regression discontinuity (RD) design to estimate the impact of the United States' largest tuition-free 4-year college program on political participation. We find that each of the 2.6 million awards increases a student's voter turnout rate by 4 to 12 percentage points in the 2020 election, raising total citizen voter turnout by 1 percentage point. Using detailed location data along with conservative assumptions, we calculate that 1 out of every 66 voters cast a ballot because of the tuition subsidy, with higher rates in politically competitive locations. Evidence from intermediate outcomes and time-variation in treatment effects point to peer socialization mechanisms. We externally validate our estimates with another RD design and data on 2.5 million students subject to a notch in Pell Grant generosity, showing that our results generalize to other policy settings. Our findings demonstrate that the civic externalities of education subsidies can exceed their labor market returns and are large enough to change the outcomes of recent national elections.

"The Effect of Selective Colleges on Student Partisanship"

(Sole Author) [SOCAE 2022 Best Paper Award] [Working Paper]

Media Mentions: The Boston Globe, Marginal Revolution

Abstract: College-educated citizens are trending toward the political left across Western democracies, but the most politically powerful and left-leaning student bodies originate from a smaller number of highly selective institutions. Using data on over 250,000 applicants and a discontinuity in the University of California's admission rules, I estimate the impact of admissions to highly selective colleges in America's largest research university system on partisanship after college. Admissions to highly selective UC campuses reduce Republican registration and increase registration as independents or Democrats. Such admissions likewise raise primary election turnout, mostly through Democratic presidential contests. I use administrative data, surveys, and a poll of in-sample students to show that long-run mechanisms and on-campus peer socialization are plausible, but intentional efforts by faculty to influence their students are unlikely to explain these results.

"Help Really Wanted? The Impact of Age Stereotypes in Job Ads on Applications from Older Workers"

(with Ian Burn, Daniel Ladd, and David Neumark) [NBER Working Paper 30287]

Media Mentions: The Wall Street Journal, Forbes, MarketWatch, Barron's

Pilot Study: "Machine Learning and Perceived Age Stereotypes in Job Ads: Evidence from an Experiment" (with Ian Burn, Daniel Ladd, and David Neumark), Forthcoming at The Journal of Pension Economics and Finance

Abstract: Correspondence studies have found evidence of age discrimination in callback rates for older workers, but less is known about whether job advertisements can themselves shape the age composition of the applicant pool. We construct job ads for administrative assistant, retail, and security guard jobs, using language from real job ads collected in a prior large-scale correspondence study (Neumark et al., 2019a). We modify the job-ad language to randomly vary whether or not the job ad includes ageist language regarding age-related stereotypes. Our main analysis relies on machine learning methods to design job ads based on the semantic similarity between phrases in job ads and age-related stereotypes. In contrast to a correspondence study in which job searchers are artificial and researchers study the responses of real employers, in our research the job ads are artificial and we study the responses of real job searchers. We find that job-ad language related to ageist stereotypes, even when the language is not blatantly or specifically age-related, deters older workers from applying for jobs. The change in the age distribution of applicants is large, with significant declines in the average and median age, the 75th percentile of the age distribution, and the share of applicants over 40. Based on these estimates and those from the correspondence study, and the fact that we use real-world ageist job-ad language, we conclude that job-ad language that deters older workers from applying for jobs can have roughly as large an impact on the hiring of older workers as direct age discrimination in hiring.

Works in Progress

"The Impact and Incidence of Middle Class Tuition Relief" (Sole Author)

"The Impact of Online Learning on Human Capital Investment and Attainment: Evidence from Virginia Community Colleges" (with Di Xu and Xiaoyang Ye)