JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. University of Wisconsin Press andThe Board of Regents of the University of Wisconsin System are collaborating with JSTOR to digitize, preserve and extend access to The Journal of Human Resources. ABSTRACTThe paper uses a survey to examine undergraduates' knowledge of salaries by type of education. Students' beliefs varied systematically with their year of study and personal background. The median student made (estimated) absolute errors of approximately 20 percent, but the mean signed error was only -6 percent. Regression analysis revealed links between students' knowledge of the labor market, and year of study, proximity of the occupation to the student's own field and parents' income. Over half of learning occurred during the fourth year. Logit analyses of students' use of information sources supported this conclusion. Implications for human capital theory are considered. I. IntroductionHow do people choose whether to attend college? Once in college, how do they choose a field? Despite the pivotal importance of education in labor economics, we know surprisingly little about how people make these decisions about schooling. Our ignorance is reflected by the fact that many empirical models of earnings still treat education as an exogenous regressor.A central tenet of human capital theory is that people choose the optimal level and type of schooling based in part on the market returns to education. This raises the question of whether people do in fact have an accurate perception of the role that education plays in the determination of earnings.Similarly, we know little about how young workers form expectations about the future returns to different levels of schooling. In a series of publications, Freeman (1971, 1975a, 1975b, 1976a, 1976b) applied the cobweb model, with its inefficient enrollment response to wage shocks, to enrollment in numerous fields.1 More recently, other researchers have argued that if workers form rational expectations about future earnings in different fields, then observed volatility in college enrollment may in fact reflect highly efficient supply responses to shocks in labor demand. Examples include Siow (1984) and Zarkin (1983, 1985), who find that the rational and adaptive expectations models fit the data about equally well, despite their radically different policy implications. The rational expectations models assume that workers at time t forecast future earnings based on vtI the current information set. It is plausible that this information set will include present salaries by field and degree.2 Thus one indirect way to assess the credibility of the rational expectations formulation is to study the accuracy of each student's set of information about c...

The paper examines the impact of the business cycle on enrollments and finances at individual community colleges between the late 1960's and the mid 1980's. We find that 1 percent increases in the unemployment rates of recent high school graduates and of all adults are associated with rises in full-time attendance of about 0.5 percent and 4 percent respectively. Part-time enrollment exhibits similar anticyclical patterns. This link carries over in large part to degrees obtained. In contrast, state and local appropriations per student are procyclical. We interpret this funding pattern as a failure to integrate education policy sufficiently closely with labor-market policy.

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We estimate the effect that six types of high school math courses have on students' earnings nearly a decade after graduation. We use High School and Beyond transcript data to differentiate courses at a more detailed level than in previous research. This enables us to show that more-advanced courses have larger effects than less-advanced ones. We also provide evidence that math courses can help close the earnings gap between students from low-income and middle-income families. Finally, by incorporating other academic subjects, we demonstrate how specific course combinations can explain the earnings premium related to an additional year of school. © 2004 President and Fellows of Harvard College and the Massachusetts Institute of Technology.