Research

Job Market Paper

Instructor Value-added in Post-secondary Education (with Jacob Light and Anthony Yim)

Estimating instructors’ value-added is challenging in post-secondary education because students can select their courses and instructors. In the absence of sound measures of value-added, universities use subjective student evaluations to make important decisions. We develop a method to estimate instructor value-added at any university which groups students together based on their past courses taken. We show that our non-experimental method controls for selection just as well as methods that exploit conditional random assignment of students to courses, using a unique policy change at a large public university in Indiana. We then apply our methods to nearly all Texas public universities and use standard forecast bias tests to demonstrate that our method controls for selection at a wide variety of post-secondary institutions. We find that individual instructors matter for students' future grades and post-college earnings in many subjects and courses. On average, moving to a 1 standard deviation better instructor would increase a student's next semester GPA by 0.13 points, and earnings six years after college entry by 17%. Strikingly, value-added is only weakly correlated with student evaluations, so an institution that made personnel decisions based on value-added rather than evaluations would improve student outcomes.

Publications

A Design-Based Perspective on Synthetic Control Methods (with Lea Bottmer, Guido Imbens, and Jann Spiess), Journal of Business & Economic Statistics, 2023 (alt link)

Why Have College Completion Rates Increased? (with Jeffrey T. Denning, Eric Eide, Kevin Mumford, and Rich Patterson), AEJ: Applied, 2023.

Divisibility Properties of Coefficients of Modular Functions in Genus Zero Levels (with Victoria Iba, and Paul Jenkins), Integers, 2019.

Working Papers

The Distributional and Long-Term Effects of Grade Inflation (with Jeff Denning, Rachel Nesbit, and Nolan Pope) 

Teachers have discretion over how they map student achievement into grades, and their leniency in doing so may positively or negatively impact student achievement. In this paper we construct two measures of grading leniency: "mean grade inflation" which measures how much higher grades are than would be expected, and "passing grade inflation" which measures leniency in receiving a passing grade. We show that these measures represent related but distinct grading practices of teachers. Grading leniency is not very correlated with other well-established teacher characteristics such as test score and non-cognitive value-added, which suggests that teachers may face tradeoffs in classroom practices. We show that more lenient teachers reduce performance on tests in subsequent years, and that leniency also has persistent effects, decreasing some students' likelihood of taking the SAT and graduating. However, while mean grade inflation negatively affects outcomes, we find that passing grade inflation is positively associated with grade progression, especially for lower-performing students.

Research in Progress

The Importance of Non-Major Courses in Predicting College Students' Post-Graduation Outcomes (with Jacob Light)

Performance-Based Funding and Grading Standards in Higher Education