Research

The Returns to STEM Programs for Less-Prepared Students. (2024). American Economic Journal: Economic Policy, 16 (2): 37-77.

We examine how returns to enrolling in science, technology, engineering, and math (STEM) programs vary with students’ academic preparation. We match data on STEM admissions at a Colombian flagship university to nationwide college and earnings records. Our identification strategy combines a regression discontinuity design with variation in admission quotas. We find that less-prepared students were less likely to complete a STEM degree than their more able peers, but they had larger earnings returns to enrolling. Our results suggest that policies that encourage less-prepared students to enroll in STEM programs can yield large but unevenly distributed earnings gains.

The Effects of Teacher Tenure on Productivity and Selection. (2024). Economics of Education Review, 1-14.

This study examines the productivity and selection effects of K-12 teacher tenure by leveraging variation from New Jersey’s TEACHNJ Act, which extended the pretenure period. Using a difference-in-differences design, I evaluate the productivity effects of tenure by comparing fourth-year tenured and pretenured teachers. I find math value-added declines but English language arts value-added and ratings remain unchanged. Focusing on labor market effects, I compare teachers hired before and after TEACHNJ within the same district and experience level. TEACHNJ disproportionately increased male and Black teacher turnover, as the policy was tied to subjective evaluation criteria. TEACHNJ did not impact the quality of the teacher labor market as measured by value-added, though higher rated teachers often filled new vacancies. This matches principal-agent models where schools only use ratings to guide personnel decisions. Overall, tenure generates small declines in math value-added, while reforms tied to subjective evaluations disproportionately increase male and Black teacher turnover.

Identifying High-Quality Teachers. (2023). Education Economics, 1-28.

This study evaluates techniques to identify high-quality teachers. Since tenure restricts dismissals of experienced teachers, schools must predict productivity and dismiss those expected to perform ineffectively prior to tenure receipt. Many states rely on evaluation scores to guide these personnel decisions without considering other dimensions of teacher performance. I use predictive models to rank teachers based on expected value-added and summative ratings. I then simulate revised personnel decisions and test for changes in average retained teacher performance. In this exercise, I adjust two factors that impact the quality of the predictions: the number of predictors and the length of the pretenure period. Both factors impact the precision of the predictions, though extended pretenure periods also negatively impact selection into teaching. I estimate optimal weights on each performance measure to maximize measures of teacher quality using a range of utility parameters. These improvements are a product of using additional information (value-added) rather than advanced algorithms, as OLS regressions and advanced machine learning techniques produce similar gains. In comparison, prediction models that extend the pretenure period beyond one year do not provide enough additional information to significantly improve average retained teacher performance unless dismissal rates increase dramatically.

Analyzing Major League Baseball Player’s Performance Based on Age and Experience. (2017). Journal of Sports Economics & Management, 7(2), 78-100.

This study models player performance as a function of age, experience, and talent. The unbalanced panel includes 5,754 seasons spread among 562 batters and 4,767 seasons spread among 489 pitchers. Peak physical age for hitters and pitchers are 26.6 years and 24.5 years, respectively, when holding experience constant. With increased experience, batters peak near age 29, while pitchers peak near age 28. Also, batters encounter greater fluctuations in performance over their careers than pitchers. This model is designed for use by MLB teams to predict future performance based on a player’s first six years of statistics.