An analysis of high school tracking and its effects on labor market outcomes

Abstract

The process of accumulating human capital formally begins when individuals enter the education system. It is widely accepted that tracking students plays an important role in human capital production. This dissertation focuses on the practice and consequences of tracking students at the high school level. I use a variety of methods to analyze how students are assigned to tracks, the effects of tracks on the human capital stock, the flow of services from that stock, and to explore whether tracking affects the decision to drop out of high school. The analysis provides new perspectives in the economics of human capital and has important implications for education policy. Although tracking students by perceived ability is a long-standing practice, its merits have been hotly debated over the years. Chapter 3 explores one of the four tenants of tracking, whether or not it is a fair and accurate process. I analyze the possibility of racial or gender discrimination in track assignment and find that there is evidence of some racial discrimination in the case of African Americans and Latinos. The evidence of discrimination leads me to question whether tracking is indeed an accurate process. This has direct implications for education policy, as accuracy in track assignment is critical for the pedagogical goals of tracking. Chapter 4 considers whether or not tracking students in high school affects their productivity, as measured by their wages, once they enter the labor force. I present the school and work profiles of individuals in the different tracks, develop several stylized facts, and analyze the effect of tracking on the wage rate. I conclude that the value of an additional year of schooling is different across tracks. The decision to drop out of high school is both a private decision and a social decision. In chapter 5, I examine how peer effects can influence the decision to drop out for both high ability and low ability students. The model and evidence suggest that students already at risk for dropping out might be more likely to do so if they are placed in a track with similarly at-risk students.