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We reconsider the role for human capital in accounting for cross-country income differences. Our contribution is to bring to bear new data on the pre- and post- migration labor market experiences of immigrants to the U.S. Immigrants from poor countries experience wage gains that are only 40 percent of the GDP per worker gap, which implies that “country" accounts for 40 percent of income differences, while human capital accounts for 60 percent. Our approach handles selection by comparing the wage of the same individual in two different countries. We also provide evidence on and a correction for skill transfer.
Returns to experience for U.S. workers have changed over the post-war period. This paper argues that a simple model goes a long way towards replicating these changes. The model features three well-known ingredients: (i) an aggregate production function with constant skill-biased technical change; (ii) cohort qualities that vary with average years of schooling; and crucially (iii) time-invariant age-efficiency profiles. The model quantitatively accounts for changes in longitudinal and cross-sectional returns to experience, as well as the differential evolution of the college wage premium for young and old workers.
This paper is motivated by the fact that nearly half of U.S. college students drop out without earning a bachelor’s degree. Its objective is to quantify how much uncertainty college entrants face about their graduation outcomes. To do so, we develop a quantitative model of college choice. The innovation is to model in detail how students progress towards a college degree. The model is calibrated using transcript and financial data. We find that more than half of college entrants can predict whether they will graduate with at least 80% probability. As a result, stylized policies that insure students against the financial risks associated with uncertain graduation have little value for the majority of college entrants.
This paper studies the effect of graduating from college on lifetime earnings. We develop a quantitative model of college choice with uncertain graduation. Departing from much of the literature, we model in detail how students progress through college. This allows us to parameterize the model using transcript data. College transcripts reveal substantial and persistent heterogeneity in students’ credit accumulation rates that are strongly related to graduation outcomes. From this data, the model infers a large ability gap between college graduates and high school graduates that accounts for 54% of the college lifetime earnings premium.