Research

The Effects of Teacher Tenure on Productivity and Selection, Revise and Resubmit, Economics of Education Review

This study examines the productivity and selection effects of K-12 teacher tenure by leveraging variation from New Jersey’s TEACHNJ Act. This law extended the pretenure period from three to four years and allowed districts to dismiss consistently low-performing teachers. I use multiple identification strategies to estimate the productivity effects of tenure across a teacher’s career. I evaluate the productivity effects at tenure receipt using a difference-in-differences design, which compares fourth-year tenured and pretenured teachers. At tenure receipt, math value-added declines but English language arts value-added and summative ratings remain unchanged. To estimate the productivity effects later in the career, I use a regression discontinuity design relying on discontinuities in job security around summative rating thresholds. Later in the career, tenure has no impact on productivity. Thus, tenure induces a transitory decline in math value-added without impacting other dimensions of teacher performance. Focusing on the labor market effects, I compare teachers hired before and after TEACHNJ within the same district and experience level. The TEACHNJ Act disproportionately increased male and Black teacher turnover rates. TEACHNJ did not impact the quality of the teacher labor market as measured by value-added, though higher rated teachers often filled new vacancies. Since the TEACHNJ Act only relies on summative ratings to make personnel decisions, this result aligns with a multitask principal-agent model where only one of several measures of performance is used to evaluate the employee.

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.

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.