The distribution of returns to education

Abstract

In this work, we revisit the traditional human capital framework and infer that risk as measured by the shape of the returns to education's distributions should be included. While education is often considered to be an investment good, human capital models often ignore the impact of risk on education investment decisions. This thesis has two aims. First, we want to find out how our different measures of risk evolved through time and between different groups. Second, we want to find out if those risks impacted education investment decisions through changes in the expected returns. That is, we investigate whether there exist risk-return trade-offs in education. In the first chapter, we overview nonparametric (spline and kernel) regression methods and illustrate how they may be used in labor economic applications. We focus our attention on issues commonly found in the labor literature such as how to account for endogeneity via instrumental variables in a nonparametric setting. We showcase these methods via data from the Current Population Survey. In the second chapter, we estimate the risk-return trade-off in the context of education. If education is treated like any other investment good, risk could play an important role in individual’s educational decisions. As portfolio theory predicts, there could be a trade-off between returns to education and risks concerning the returns: higher risks are generally associated with higher returns. We contribute to the literature by proposing various measures of risk based on the distribution of returns to education, which are in turn based on nonparametric regression results using the Current Population Survey dataset (1980-2015). We infer that risk-averse individuals prefer distributions with positive skewness and low kurtosis. Our results confirm the findings of the literature, i.e. we observe compensation for variance. We also find statistically significant compensation for the higher moments: skewness and kurtosis. Interestingly, we find that the relationship between expected returns and the higher moments skewness and kurtosis is non-linear. In the third chapter, we build on the second chapter to test two hypothesis: first whether there is heterogeneity in the risk of educational investments and if so whether there is compensation for that risk. We use our individual-level estimated rates of return to education and split them in three different ways: by occupation, by region and race, and by region and education-level. We infer that there is heterogeneity, not only in the expected returns (1st moment), but also in the risk faced by individuals (higher moments). We also add to the second chapter by testing whether risk-return trade-offs exist between occupations, whites and non-whites, and different education-level. We expect, for example, occupations that retain higher risk to be compensated by higher mean returns. Generally, we find risk-return trade-offs exist between states, occupations, whites and non-whites, and different education-level, for all three measures of risk. Surprisingly, we find that kurtosis matters more than skewness as a measure of risk. Moreover, the trade-offs between skewness, kurtosis, and expected returns are not always in the directions predicted by theory on decision making under uncertainty.