Research

In this paper, we estimate a rich model of college major choice using a panel of experimentally derived data. Our estimation strategy combines two types of data: data on self-reported beliefs about future earnings from potential human capital decisions, and survey-based measures of risk and time preferences. We show how to use this data to identify a general life-cycle model, allowing for rich patterns of heterogeneous beliefs and preferences. Our data allow us to separate perceptions about the degree of risk or perceptions about the current versus future payoffs for a choice from the individual's preference for risk and patience. Comparing our estimates of the general model to estimates of models which ignore heterogeneity in risk and time preferences, we find that these restricted models are likely to overstate the importance of earnings to major choice. Additionally, we show that while men are less risk averse and patient than women, gender differences in non-pecuniary tastes, rather than gender differences in risk aversion and patience over earnings, are the primary driver of gender gaps in major choice.

This article reviews the recent literature on the determinants of college major choices. We first highlight long-term trends and persistent differences in college major choices by gender, race, and family background. We then review the existing research in six key areas: expected earnings and ability sorting, learning, subjective expectations, non-pecuniary considerations, peer and family effects, and supply side factors. We examine and compare the various approaches employed by previous research and highlight key areas for future research.

This paper characterizes remote work or work-from-home (WFH) jobs and quantifies the welfare gain from these work arrangements. Using data from the ATUS and CPS, I develop a measure of locational flexibility at work. Motivated by the patterns of sorting in the data, I then develop and estimate a generalized model of sectoral choice with amenities. The structural estimates point to differences by gender in the returns to education and experience, compositional differences as well as preferences. I also find that the gender wage gap persists in remote work at 3.9 dollars (without controlling for selection), most of which is determined by differences in the returns to observable characteristics. With the help of this framework, I estimate the willingness-to-pay (WTP) for these jobs. I find that women have higher valuation (WTP) of these jobs than men. On average, women are willing to pay 3.8 percent of the average hourly wage for locationally flexible jobs whereas men have a low willingness to pay (0.6 percent of hourly wage) for these jobs. Further, college graduates (both men and women) value remote work more than workers without college education. College educated women in particular value remote work the most at 4.3 percent of the hourly wage. The estimates also suggest that there may be specialisation within the household. Mothers who have more children have a higher valuation for remote work whereas fathers have a lower valuation.