As the workforce has become more educated, educational decisions are no longer just about whether to acquire more, but rather what type of education to pursue. In college, individuals somewhat specialize through their choice of college major. Further specialization occurs in graduate school. This chapter investigates how majors and graduate school affect labor market outcomes as well as how the individuals make these potentially important decisions. To do so, we develop a dynamic model of educational decision-making. In light of the model, we examine the estimation issues associated with obtaining causal effects of educational choices on earnings. We then examine ways that authors have overcome the selection problem as well as the approaches authors have taken to estimate the process by which these educational decisions are made.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. CEREQ, Marseille). Our econometric approach is based on a semi-structural three-equations model, which is identified thanks to some exclusion restrictions. We exploit in particular exogenous variations in the earnings returns associated with the majors across the business cycle, in order to identify the causal effect of expected earnings on the probability of choosing a given major. Relying on a threecomponent mixture distribution, we account for correlation between the unobserved individual-specific terms affecting the preferences for the majors, the unobserved individualspecific factors entering the equation determining the length of studies within each major, and that affecting the labor market earnings equation. Following Arcidiacono and Jones (2003), we use the EM algorithm with a sequential maximization step to produce consistent parameter estimates. Simulating for each given major a 10 percent increase in the expected earnings suggests that expected earnings have a statistically significant but quantitatively small impact on the allocation of students across majors. Terms of use: Documents in D I S C U S S I O N P A P E R S E R I E SJEL Classification: J24, C35, D84

This paper investigates the role played by informational frictions in college and the workplace. We estimate a dynamic structural model of schooling and work decisions, where individuals have imperfect information about their schooling ability and labor market productivity. We take into account the heterogeneity in schooling investments by distinguishing between two-and four-year colleges, graduate school, as well as science and non-science majors for four-year colleges. Individuals may also choose whether to work full-time, part-time, or not at all. A key feature of our approach is to account for correlated learning through college grades and wages, whereby individuals may leave or re-enter college as a result of the arrival of new information on their ability and productivity. Our findings indicate that the elimination of informational frictions would increase the college graduation rate by 9 percentage points, and would increase the college wage premium by 32.7 percentage points through increased sorting on ability.

We show that data on subjective expectations, especially on outcomes from counterfactual choices and choice probabilities, are a powerful tool in recovering ex ante treatment effects as well as preferences for different treatments. In this paper we focus on the choice of occupation, and use elicited beliefs from a sample of male undergraduates at Duke University. By asking individuals about potential earnings associated with counterfactual choices of college majors and occupations, we can recover the distribution of the ex ante monetary returns to particular occupations, and how these returns vary across majors. We then propose a model of occupational choice which allows us to link subjective data on earnings and choice probabilities with the non-pecuniary preferences for each occupation. We find large differences in expected earnings across occupations, and substantial heterogeneity across individuals in the corresponding ex ante returns. However, while sorting across occupations is partly driven by the ex ante monetary returns, non-monetary factors play a key role in this decision. Finally, our results point to the existence of sizable complementarities between college major and occupations, both in terms of earnings and non-monetary benefits.