Economic Distress and Electoral Consequences: Evidence from Appalachia
Revise & Resubmit at The Review of Economics and Statistics
(Sole Authored) [A. Kimball Romney Award for Outstanding Graduate Paper] [Manuscript]
Information about inequality can change political attitudes in lab and survey experiments. I use a RDD and Appalachian Regional Commission (ARC) data to test whether salient information about local poverty can impact voter behavior outside such settings. I find that when the poorest decile of counties is labeled "economically distressed", the Democratic share of the Presidential and House popular vote rises in subsequent elections. The effect appears tied to local news coverage, not spending or other outcomes. My results are robust to bandwidth selection, additional covariates, and more flexible controls for the running variable.
The Impact of Post-Admission Merit Scholarships on Enrollment Decisions and Degree Attainment: Evidence from Randomization
(Sole Authored) [Manuscript]
Most colleges offer post-admission merit scholarships to attract academically-talented students ahead of enrollment deadlines. I use administrative data on one such program which was randomly assigned at a large public university to test for effects on college choice and degree completion. I find a large recruitment impact on disadvantaged students at the expense of similar in-state public colleges, with no effect on graduation rates. By contrast, there are minor impacts on the enrollment decisions of advantaged students, with marginally negative effects on degree attainment. This heterogeneity may justify limiting eligibility for such scholarships by socioeconomic status.
Machine Learning and Perceived Age Stereotypes in Job Ads: Evidence from an Experiment
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.