Research

Job Market Paper


The Effects of Teacher Tenure on Productivity and Selection

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 principal-agent model where only one of several measures of performance is used to evaluate the employee.

Working Papers


The Returns to STEM Programs for Less-Prepared Students

with Evan Riehl
Revise & resubmit, American Economic Journal: Economic Policy


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

Public schools seek to retain high-quality teachers to improve student achievement. However, teacher tenure restricts dismissals of experienced teachers. Prior to tenure receipt, schools must predict future teacher performance to dismiss those expected to perform ineffectively. This study estimates the relationship between pretenure teaching performance and future effectiveness. I leverage value-added and summative rating data from the New Jersey Department of Education to accomplish two goals. First, I use machine learning techniques to rank teachers based on predicted performance. I simulate revised personnel decisions using this system to determine whether schools are optimally utilizing the information available to them. Second, I simulate the effects of pretenure period length on personnel decision-making. This analysis addresses optimal pretenure length.

Publications


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.