Abstract

This dissertation includes an introduction, three related chapters, and a summary at the end. The primary objective of this dissertation is to explore the factors that influence college enrollment decisions and examine the impact of financial aid policies on these decisions. The thesis focuses on two distinct fields of study: STEM (Science, Technology, Engineering, and Mathematics) and ARTS (non-STEM fields defined within the thesis). STEM fields are generally associated with higher-paying career opportunities, while ARTS fields are often considered to offer relatively lower-paying career paths.

The introduction section of this dissertation begins by examining historical trends in schooling, with a focus on developed countries, notably the U.S. It discusses significant observations from the past decades, including a notable increase in college enrollment rates, the upward trend in college costs, and the presence of generous student financial aid programs. The introduction also sets the stage by posing several key research questions that will be addressed throughout the dissertation. Central to the research is the main question: How do various financial aid policies influence college enrollment decisions, particularly in the context of STEM and ARTS college majors? With these questions in mind, the introduction also outlines the unique contributions this dissertation aims to make to the existing literature.

The first chapter of this dissertation serves as an extensive literature review on educational choice. It offers an in-depth exploration of various perspectives from which scholars examine the main determinants influencing individuals’ educational decisions. Specifically, the chapter delves into how researchers have modelled schooling decisions, focusing on college enrollment and, more specifically, the college major choices, constituting this dissertation’s central theme. Furthermore, this chapter presents various educational policies implemented in the U.S., such as merit-based scholarships and need-based grants.

This chapter explores reduced-form models investigating the relationship between wages and schooling. Next, the chapter introduces the pioneers of structural models, which provide a deeper understanding of the underlying mechanisms driving educational decisions. The discussion then turns to static and dynamic empirical self-selection papers. Another essential strand of literature that this chapter covers is the research that focuses on resolving uncertainty about individuals’ tastes and abilities. Furthermore, the chapter deeply examines the subjective expectations data framework. Researchers in this field use datasets designed to elicit individuals’ expectations about future outcomes.

The first chapter also delves into historical trends in U.S. college attainments and wage premiums in the twentieth century. This chapter discusses the existing research that explains changes in the mean ability of different educational groups over time. Furthermore, the chapter explores how college attendance patterns have reversed over time, specifically examining the role played by family income and academic ability before and after World War II. Another focus aspect of this chapter is the puzzle of the higher trend of college wage premiums compared to the lower rates in college enrollment over the past decades. The literature on the determinants of college or college major choices is categorized, encompassing monetary and non-monetary factors influencing educational decisions. Additionally, the chapter explains studies investigating the impact of various financial aid policies on college enrollment rates. The chapter concludes by describing various educational policy experiments conducted in the U.S. over the past decades.

The second chapter of this dissertation focuses on establishing crucial empirical facts about working individuals in the U.S. economy. These facts will serve as essential inputs for calibrating the life cycle model presented in the following chapter. The information of interest includes income life cycle statistics such as mean, mean/median, and Gini coefficients. These statistics will be derived from the Panel Study of Income Dynamics (PSID) for the survey years 1968-2019. They are vital for mapping the life cycle model to real-world data and finding the relevant distributional moments of the benchmark model economy. These moments are means, standard deviations and cross-correlations of three initial endowments of agents after graduation from high school (learning ability, the initial stock of human capital and the initial assets). Moreover, the chapter estimates the skill price growth rates and human capital depreciation rates for three educational categories. I obtained the growth
rates of skill prices equal to 0.53%, 0.35%, and -0.13% and depreciation rates of human capital as 0.9%, 0.6%, and 0.0% for STEM, ARTS, and no-college individuals, respectively.

Chapter three of this dissertation employs a heterogeneous life cycle, a human capital model, solved using dynamic programming techniques and fitted to the sample data from the PSID. The results from this chapter reveal essential insights into the characteristics of college students and their comparative advantage in learning abilities. Specifically, the analysis shows that, on average, college students have higher learning abilities, allowing them to acquire human capital more efficiently. This comparative advantage in learning ability enables them to accumulate higher levels of human capital, which will receive higher skill prices in the labour market, leading to better career prospects. As a result, college students, especially those in STEM fields, enjoy higher wages, earnings, and consumption paths compared to individuals who do not choose to go to college.

Chapter three presents ten policy experiments based on the real-life education policies discussed. These experiments investigate the potential impact of different policy interventions on college enrollment and major choices. The policy experiments include a 30% increase in merit-based scholarships, a 30% increase in need-based grants, a 30% reduction in tuition and fees, a 35% increase in federal loan limits, three extensions to the merit-based scholarship policy, and three modifications in the need-based grants policy. The extensions entail expanding the maximum aid amount to more eligible agents, extending the financial aid coverage to more individuals, or both.

Among the various policies examined, the reduction in tuition and fees stands out as the most effective in increasing overall college enrollment and enrollment in STEM and ARTS fields. This policy significantly increases 9.9 percentage points for college enrollment, 0.3 percentage points for STEM enrollment, and 9.6 percentage points for ARTS enrollment. However, despite its effectiveness, the reduction in tuition and fees is not considered costefficient. This policy distributes financial aid broadly among all individuals without targeting specific subgroups. This result allows for exploring alternative approaches for increasing college or major-specific enrollments.

The results also underscore the superiority of policies that directly and precisely focus on specific groups of individuals instead of policies lacking a distinct target group. For instance, one highly effective and efficient experiment entails extending the eligibility for merit-based financial aid and expanding the maximum scholarship amount concurrently. This policy substantially increases college enrollment by 5.4 percentage points, demonstrating both efficacy and efficiency. Remarkably, it boosts $4.7 in the present value of students’ lifetime earnings for each additional dollar allocated to this financial aid policy experiment. Another approach that targets low-asset individuals and extends full tuition and fees coverage to a broader range of eligible individuals results in a high increase of 5.3 percentage points in college enrollment. Nonetheless, the efficiency of this policy stands at $3.3, which is lower than a less effective policy that entails widening the threshold for receiving need-based
grants. Despite its relatively modest 1.5% increase in college enrollment, this less-effective policy exhibits higher efficiency, that is, $4.3. These findings highlight the importance of effectiveness and efficiency in addressing financial barriers low-income students face.

By identifying the most effective and efficient financial aid policies, this chapter provides crucial insights for policymakers aiming to promote college access and major choices. The findings highlight the importance of targeting specific subgroups and tailoring financial aid strategies to achieve the desired outcomes in college enrollment and major decisions.

Keywords: Human capital accumulation; Comparative advantage; College, STEM, and ARTS enrollments; Tuition hike; Financial aid policies; Dynamic programming; Simulated annealing method.