Merrill Warnick, Jacob Light and Anthony Yim:

Estimating post-secondary instructors’ value-added is challenging because college students select their courses and instructors. In the absence of sound measures of value-added, universities use subjective student evaluations to make personnel decisions. In this paper, we develop a method to estimate instructor value-added at any university.

The method groups together students who have previously taken similar courses and estimates value-added based on differences in outcomes for students in the same group and same course who have different instructors. Using a unique policy at a large public university in Indiana, we show that our non-experimental method controls for selection just as well as methods that exploit conditional random assignment of students to courses. We next show that our method reduces forecast bias in a wider variety of institutions using data from nearly all public universities in Texas. 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. An instructor retention policy based on value-added would result in 2.7% higher earnings for students attending Texas universities.