In this paper we revisit two well-known facts regarding lifecycle expenditures. The first is the familiar “hump ” shaped lifecycle profile of nondurable expenditures. The second is that cross-household consumption inequality increases steadily throughout the life cycle. We document that the behavior of total nondurables masks surprising heterogeneity in the lifecycle profile of individual consumption sub-components. We find that three categories (food, nondurable transportation, and clothing) account for both the entire decline in mean expenditure post-middle age and a substantial amount of the increase in cross sectional dispersion over the life cycle. All other nondurable categories we study show no decline in mean expenditure over the life cycle nor do they show an increase in cross sectional dispersion over the life cycle. We provide evidence that the categories driving life cycle consumption are either inputs into market work (clothing and transportation) or are amenable to home production (food). Changes in the opportunity cost of time will cause movements in expenditures on such goods even if there is no change to lifetime resources. We then discuss how the patterns documented in the paper

Is lifetime inequality mainly due to differences across people established early in life or to differences in luck experienced over the working lifetime? We answer this question within a model that features idiosyncratic shocks to human capital, estimated directly from data, as well as heterogeneity in ability to learn, initial human capital, and initial wealth. We find that, as of age 23, differences in initial conditions account for more of the variation in lifetime earnings, lifetime wealth and lifetime utility than do differences in shocks received over the working lifetime.

Abstract: Due primarily to the difficulty of obtaining ideal data, much remains unknown about how college majors are determined. We take advantage of unique longitudinal data to provide new evidence about this issue, paying particular attention to the choice of whether to major in math and science. The data collection and analysis are based directly on a simple conceptual model which takes into account that, from a theoretical perspective, a student’s final major is best viewed as the end result of a learning process.

This paper studies the determinants of college attendance in Mexico. I use subjective quantitative expectations of future earnings to analyze both the causes and the implications of the steep income gradient in higher-education enrollment. In particular, I examine whether data on individuals ’ expected returns to college as well as on their perceived earnings risk can improve our understanding of heterogeneity in college attendance choices. I find that while expected returns and perceived risk from human capital investment are important determinants, lower returns or higher risk are not sufficient, alone, to explain the poor’s low attendance rates. I also find that poor individuals require significantly higher expected returns to be induced to attend college, implying that they face significantly higher costs than individuals with wealthy parents. I then test predictions of a simple model of college attendance choice in the presence of credit constraints, using parental income and wealth as a proxy for the household’s (unobserved) interest rate. I find that poor individuals with high expected returns are particularly responsive to changes in direct costs such as tuition, which is consistent with credit constraints playing an important role. To evaluate potential welfare implications of introducing a means-tested student loan program, I apply the Local Instrumental Variables approach of Heckman and Vytlacil (2005) to my model of college attendance choice. I find that a sizeable

Abstract: Taking advantage of unique longitudinal data, we provide the first characterization of what college students believe at the time of entrance about their final major, relate these beliefs to actual major outcomes, and, provide an understanding of why students hold the initial beliefs about majors that they do. The data collection and analysis are based directly on a conceptual model in which a student’s final major is best viewed as the end result of a learning process. We find that students enter school quite optimistic about obtaining a science degree, but that relatively few students end up graduating with a science degree. The substantial overoptimism about completing a degree in science can be attributed largely to students beginning school with misperceptions about their ability to perform well academically in science.

We argue that once we take into account the students ’ rational enrollment decisions, mismatch in the sense that the intended beneficiary of affirmative action admission policies are made worse off could occur only if selective universities possess private information about students’ post-enrollment treatment effects. This necessary condition for mismatch provides the basis for a new test. We propose an empirical methodology to test for private information in such a setting. The test is implemented using data from Campus Life and Learning Project (CLL) at Duke. Evidence shows that Duke does possess private information that is a statistically significant predictor of the students ’ post-enrollment academic performance. We also propose strategies to evaluate more conclusively whether the evidence of Duke private information has generated mismatch.

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policy, but the institute itself takes no institutional policy positions.

extraordinary work of Lori Scafidi and the assistance of Diana Stinebrickner, Pam Thomas, and Albert Conley. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

We study Pareto optimal tax and education policies when human capital upon labor market entry is endogenous and individuals face wage uncertainty. Though optimal labor distortions are history-dependent, i.e. depend on income and education, simple policy instruments can yield the desired distortions: a single nonlinear labor income tax schedule combined with income-contingent loans. To take the model to the (US) data, we simplify the model to a binary education decision (graduating from college or not). We find that for low and intermediate incomes the labor supply decision of college graduates should be distorted more heavily than for individuals without a college degree. As a consequence, the optimal student loan repayment schedule increases in income for this range. This result holds along the Pareto frontier. We compare the second best to a situation where loan repayment is restricted to be independent from income and find significant welfare gains.