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Published Papers


Working Papers

"The Effect of Research Universities on Student Partisanship and Turnout"

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

Media Mentions: The Boston Globe, Marginal Revolution

Abstract: Higher  education  is  a  strong  predictor  of  party  support  and  voter  turnout  in  Western  democracies,  but  endogeneity  in  college  enrollment  makes  it  difficult  to  identify if  the  association  is  causal.   Using  data  on  over  a  quarter  million  applicants  and  a discontinuity in the University of California’s admission rules, I estimate the impact of admissions to America’s largest research university system on applicants’ subsequent partisanship and turnout, finding significant effects on both.  In terms of partisanship, admissions reduce Republican registration and increase registration as independents or Democrats.  In terms of turnout, admissions raise participation in primary elections, mostly through Democratic presidential primaries.  I use administrative data, surveys, and a proprietary poll of in-sample students to evaluate causal pathways.  Suggestive evidence is consistent with long-run mechanisms and on-campus peer socialization, but contradicts intentional efforts by faculty to influence their students.

"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: ForbesMarketWatch, Barron's 

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.

"Machine Learning and Perceived Age Stereotypes in Job Ads: Evidence from an Experiment"

(with Ian Burn, Daniel Ladd, and David Neumark) [NBER Working Paper 28328]

Abstract: We explore whether ageist stereotypes in job ads are detectable using machine learning methods measuring the linguistic similarity of job-ad language to ageist stereotypes identified by industrial psychologists. We then conduct an experiment to evaluate whether this language is perceived as biased against older workers. We find that language classified by the machine learning algorithm as closely related to ageist stereotypes is perceived as ageist by experimental subjects. The scores assigned to the language related to ageist stereotypes are larger when responses are incentivized by rewarding participants for guessing how other respondents rated the language. These methods could potentially help enforce anti-discrimination laws by using job ads to predict or identify employers more likely to be engaging in age discrimination.

Works in Progress

"Financial Aid and Civic Participation" (with Igor Geyn)

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

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