Essays on the Economics of Higher Education

At least since the seminal works of Jacob Mincer, labor economists have sought to understand how students make higher education investment decisions. Mincer’s original work seeks to understand how students decide how much education to accrue; subsequent work by various authors seeks to understand how students choose where to attend college, what field to major in, and whether to drop out of college.

Broadly speaking, this rich sub-field of literature contributes to society in two ways: First, it provides a better understanding of important social behaviors. Second, it helps policymakers anticipate the responses of students when evaluating various policy reforms.

While research on the higher education investment decisions of students has had an enormous impact on our understanding of society and has shaped countless education policies, students are only one interested party in the higher education landscape. In the jargon of economists, students represent only the `demand side’ of higher education---customers who are choosing options from a set of available alternatives. Opposite students are instructors and administrators who represent the `supply side’ of higher education---those who decide which options are available to students.

For similar reasons, it is also important to understand how individuals on the supply side of education make decisions: First, this provides a deeper understanding of the behaviors of important social institutions. Second, it helps policymakers anticipate the responses of instructors and administrators when evaluating various reforms. However, while there is substantial literature understanding decisions made on the demand side of education, there is far less attention paid to decisions on the supply side of education.

This dissertation uses empirical evidence to better understand how instructors and administrators make decisions and the implications of these decisions for students.

In the first chapter, I use data from Duke University and a Bayesian model of correlated learning to measure the signal quality of grades across academic fields. The correlated feature of the model allows grades in one academic field to signal ability in all other fields allowing me to measure both ‘own category' signal quality and ‘spillover' signal quality. Estimates reveal a clear division between information rich Science, Engineering, and Economics grades and less informative Humanities and Social Science grades. In many specifications, information 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. This suggests students who take engineering courses during their Freshman year make more informed specialization decisions later in college.

In the second chapter, I use data from the University of Central Arkansas to understand how universities decide which courses to offer and how much to spend on instructors for these courses. Course offerings and instructor characteristics directly affect the courses students choose and the value they receive from these choices. This chapter reveals the university preferences over these student outcomes which best explain observed course offerings and instructors. This allows me to assess whether university incentives are aligned with students, to determine what alternative university choices would be preferred by students, and to illustrate how a revenue neutral tax/subsidy policy can induce a university to make these student-best decisions.

In the third chapter, co-authored with Thomas Ahn, Peter Arcidiacono, and Amy Hopson, we use data from the University of Kentucky to understand how instructors choose grading policies. In this chapter, we estimate an equilibrium model in which instructors choose grading policies and students choose courses and study effort given grading policies. In this model, instructors set both a grading intercept and a return on ability and effort. This builds a rich link between the grading policy decisions of instructors and the course choices of students. We use estimates of this model to infer what preference parameters best explain why instructors chose estimated grading policies. To illustrate the importance of these supply side decisions, we show changing grading policies can substantially reduce the gender gap in STEM enrollment.

Missing data in randomized clinical trials for weight loss: scope of the problem, state of the field, and performance of statistical methods.

BACKGROUND: Dropouts and missing data are nearly-ubiquitous in obesity randomized controlled trails, threatening validity and generalizability of conclusions. Herein, we meta-analytically evaluate the extent of missing data, the frequency with which various analytic methods are employed to accommodate dropouts, and the performance of multiple statistical methods. METHODOLOGY/PRINCIPAL FINDINGS: We searched PubMed and Cochrane databases (2000-2006) for articles published in English and manually searched bibliographic references. Articles of pharmaceutical randomized controlled trials with weight loss or weight gain prevention as major endpoints were included. Two authors independently reviewed each publication for inclusion. 121 articles met the inclusion criteria. Two authors independently extracted treatment, sample size, drop-out rates, study duration, and statistical method used to handle missing data from all articles and resolved disagreements by consensus. In the meta-analysis, drop-out rates were substantial with the survival (non-dropout) rates being approximated by an exponential decay curve (e(-lambdat)) where lambda was estimated to be .0088 (95% bootstrap confidence interval: .0076 to .0100) and t represents time in weeks. The estimated drop-out rate at 1 year was 37%. Most studies used last observation carried forward as the primary analytic method to handle missing data. We also obtained 12 raw obesity randomized controlled trial datasets for empirical analyses. Analyses of raw randomized controlled trial data suggested that both mixed models and multiple imputation performed well, but that multiple imputation may be more robust when missing data are extensive. CONCLUSION/SIGNIFICANCE: Our analysis offers an equation for predictions of dropout rates useful for future study planning. Our raw data analyses suggests that multiple imputation is better than other methods for handling missing data in obesity randomized controlled trials, followed closely by mixed models. We suggest these methods supplant last observation carried forward as the primary method of analysis.