How do parental resources affect the choice of college major? Using restricted tax and survey data, I provide novel descriptive evidence on the relationship between parental income and major-choice. I then use the plausibly exogenous variation in parental income loss due to the 2008 Great Recession along with variation in cohort year-of-birth to estimate the causal effect of parental resource loss on major choice. I find that the loss of parental resources shifts children away from Liberal Arts majors and into higher paying STEM (Science, Technology, Engineering, and Math) majors. Children of income-losers choose majors with higher lifetime earnings that exceed the increase in student loans resulting from parental income loss. My results are consistent with a model of major choice in which college students trade off college consumption with future pecuniary returns, with the loss of parental resources increasing the attractiveness of high-return majors.
Why do some college majors have much higher returns? I ask if differences in returns are due to differences in quality of education across majors. I use a novel dataset where college students rate courses for academic difficulty, and show that academic standards are highly heterogeneous by major. In Molecular Biosciences 84% of professors are rated as average difficulty or higher, while just 36% are in Sociology. Major difficulty explains the majority of variation in survey measured study hours, suggesting grade inflation has negative effects on learning, and supporting the use of difficulty as a proxy for learning. I show that major difficulty predicts earnings in the ACS. Using an event study approach in the NLSY97 and university-major fixed effects with the College Scorecard, I give evidence that the effect is causal. I estimate that one-third of the variation in major premiums can be explained by differences in learning.
Garage Entrepreneurs or Self-Employed? An Investigation of Nonemployers by Legal Form of Organization (w/Adela Luque) - Draft Available Soon
Who are nonemployer businesses and how important are they to the US economy? Given their high start-up and exit rates, what can they teach us about the start-up process? We describe owner demographics of nonemployers and characterize their performance by the four most common legal forms: C-corporation, S-corporation, Partnership, and Sole Proprietorship. Building on the work of Davis et al. (2007), we use the Census’ recently improved (Nonemployer) Integrated Longitudinal Business Database linked to the (Employer) Longitudinal Business Database to examine migration rates to employer status and contribution to employment of firms originating as nonemployers for all sectors and broad time periods. While S-corporation nonemployers are the most likely to migrate, nonemployers who start as sole-proprietors contribute the most jobs and payroll to the economy. We also examine the correlates of migration to employer status and performance in the employer universe using the 2012 Survey of Business Owners. Our findings shed light on the dynamics of entrepreneurship, as well as the role of nonemployers in the economy.
Randomizing Professor Difficulty - Effects on Major Choice and Student Academic Behavior (w/Ahmed Rahman and Alexander McQuoid) - In Progress
Randomly assigned difficult college professors raise test scores (Insler et al 2021) and a major's difficulty is a key predictor of its financial returns (Novik 2022). Yet course difficulty and its effects are still poorly understood. This paper combines US Naval Academy (USNA) administrative data and the USNA's process of randomized professor assignment along with information from a detailed survey we will implement. We seek to answer how difficult professors effect choice of major, study time, and study intensity. This paper will show how students trade-off difficulty of courses in college with after-college monetary returns to better understand the conflicting results in the literature on education as consumption or a "bad" (Gong et al 2021, Lazear 1977). We also aim to shed light on what the precise effects of difficult professors are on group study time, assignment study time, time spent studying independently, and so on.