UC San Diego

Chapter 1 measures gender differences in college major choice and major switching behavior using a nationally representative sample of beginning postsecondary students. I find that switchers overall have worse graduation outcomes and take longer to earn their degrees than non-switchers. GPA appears to be a driver of switching: for students initially enrolled in a given field, the average GPA of female and male switchers is lower than the overall average for women and men, respectively. However, for most fields, the difference between the average GPA of female switchers and the average overall GPA is small. This suggests that women might interpret the signal sent by GPA in different ways than men. I then show that, conditional on GPA and initial STEM enrollment, women are more likely to change majors than men.

Chapter 2 builds a model of group-based beliefs and human capital specialization. Individuals belonging to a particular group choose to work or study in heterogeneous fields. This maps to the scenario where group type corresponds to gender, and the specialization decision is an individual’s college major choice. Agents form initial beliefs about their unknown abilities based on existing group outcomes, and update these beliefs as they study. Therefore, women may form their initial beliefs about their abilities in each field by considering how many women have specialized in that field in the past. These differences in initial beliefs can cause men and women to respond to signals about their ability in different ways, which can ultimately drive gender gaps in college major choice. This model explores a mechanism for why,conditional on GPA and initial STEM enrollment, women are more likely to change major than men. If women form their initial beliefs about their abilities based on existing group outcomes, then women in historically underrepresented fields (like in STEM) might be more likely to switch than men at a given GPA level.

Chapter 3 explores the literature on technological adoption in agricultural climate change adaptation. Specifically, this chapter reviews two recent approaches to studying climate change adaptation in agriculture: panel data methods and spatial general equilibrium models.