Labor Economics, Economics of Education.
My research centers on Labor Economics with a specific focus on the Economics of Higher Education. I am particularly interested in how universities make decisions and the implications of these decisions for students. From a policy perspective, it is important to understand these “supply side” decisions and their implications so that policymakers can account for the strategic responses of universities when evaluating potential policies.
“The effects of Greek affiliation on academic performance,” with Andrew De Donato. Economics of Education Review, 57 (April, 2017): 41-51. (Working paper version)
“What do course offerings reveal about university preferences?” (Job Market Paper)
This paper asks: what do observed course offerings reveal a university’s preferences for the type of courses students choose and the utility they derive from these choices? I develop a method for inferring these preferences which is based on the idea that the set of available courses affects students’ choice probabilities and expected utility in a standard discrete choice framework. As such, one can solve for university preference parameters which best explain why observed course offerings were preferred to all feasible alternatives. I use these methods to analyze the introductory course offerings of the University of Central Arkansas and find that introductory course offerings at this university do not maximize student utility. The university sacrifices student utility to draw students out of humanities and arts courses and into STEM courses. I find that it would take a 16.6% increase in the cost of offering an introductory STEM course and a 13.2% decrease in the cost of offering an introductory humanities or arts course to price out these institutional preferences and induce the university to offer courses which maximize student utility.induce the university to offer courses which maximize student utility.
“Equilibrium Grade Inflation with Implications for Female Interest in STEM Majors,” (with Thomas Ahn, Peter Arcidiacono, and Amy Hopson).
We estimate an equilibrium model of grading policies where professors set both an intercept and a returns to studying and ability. Professors value enrollment, learning, and student study time and set their policies taking into the account the policies of the other professors. Students respond to grading policies in their selection of courses and how much to study conditional on enrolling. Men and women are allowed to have different preferences over course types, the benefits associated with higher grades, and the cost of exerting more effort. Two decompositions are performed. First, we separate out how much of the differences in grading policies across fields is driven by differences in demand for courses in those fields and how much is due to differences in professor preferences across fields. Second, we separate out differences in female/male course taking across fields is driven by i) differences in cognitive skills, ii) differences in the valuation of grades, iii) differences in the cost of studying, and iv) differences in field preferences. We then use the structural parameters to evaluate restrictions on grading policies. Restrictions on grading policies that equalize grade distributions across classes result in higher (lower) grades in science (non-science) fields but more (less) work being required. As women are willing to study more than men, this restriction on grading policies results in more women pursuing the sciences and more men pursuing the non-sciences.
“The signal quality of grades across academic fields.” Revise and Resubmit, Journal of Applied Econometrics.
Grades are an important mechanism for college students to learn about their academic abilities; however, there is limited evidence on which grades reveal abilities most efficiently. This paper estimates a Bayesian model of correlated learning to measure the overall signal quality of grades across academic fields. Grades in one academic field signal ability in all other fields allowing me to measure both `own category’ signal quality and `spillover’ signal quality. I estimate this model using transcript data from Duke University and an adaptation of the Expectation-Maximization algorithm. Estimates reveal a clear division between information rich Science, Engineering, and Economics grades and less informative Humanities and Social Science grades. I find information spillovers across academic fields are substantial—in many specifications, spillovers are so powerful that precise Science, Engineering, and Economics grades are more informative about Humanities and Social Science abilities than Humanities and Social Science grades. A brief descriptive analysis shows information revelation has important implications for course choices.
Works in Progress
“Admission decisions and University Preferences for Student Body Composition,” with Peter Arcidiacono and Arnaud Maurel.
“The Labor Market Returns to Spending on College Instruction,” with Joseph Altonji and Seth Zimmerman.