Minorities in Schools: Three Empirical Essays in Education Economics

This thesis empirically investigates three important topics in the literature on Economics of Education: the evaluation of returns to special education programs for students with special needs in inclusive and segregated education settings, the impact of gifted students on their classroom peers, and, finally, the estimation of peer-effects models that flexibly account for the mutual influence of many peer groups. Chapter 1 addresses the lack of empirical evidence on academic outcomes and labor market returns to special education. I present results from the first ever study to examine short- and long-term returns to special education programs with causal machine learning and computational text analysis methods. I find that special education programs in inclusive settings have positive returns in terms of academic performance as well as labor-market integration. Moreover, I uncover a positive effect of inclusive special education programs in comparison to segregated programs. This effect is heterogeneous: segregation has least negative effects for students with emotional or behavioral problems, and for nonnative students with special needs. Finally, I deliver optimal program placement rules that would maximize aggregated school performance and labor market integration for students with special needs at lower program costs. These placement rules would reallocate most students with special needs from segregation to inclusion. Chapter 2 investigates the causal impact of intellectually gifted students on their nongifted classmates' school achievement, enrollment in post-compulsory education, and occupational choices. Using student-level administrative and psychological data, we find a positive effect of exposure to gifted students on peers' school achievement in both math and language. This impact is heterogeneous: larger effects are observed among male students and high achievers; female students benefit primarily from female gifted students; effects are driven by gifted students not diagnosed with emotional or behavioral disorders. Exposure to gifted students increases the likelihood of choosing a selective academic track and occupations in STEM fields. Chapter 3 is an empirical exploration of peer effects in a systematic way. The majority of peer-effect studies in education have focused on the effect of one particular type of peers on classmates. This view fails to take into account the reality that peer effects are heterogeneous for students with different characteristics, and that there are at least as many peer effect functions as there are types of peers. We develop a general empirical framework that accounts for systematic interactions between peer types and nonlinearities of peer effects. We use machine-learning methods to (i) understand which dimensions of peer characteristics are the most predictive of academic performance, (ii) estimate high-dimensional peer effects functions, and (iii) investigate performance-improving classroom allocation through policy-relevant simulations. First, we find that students' own characteristics are the most predictive of own academic performance, and that the strongest peer effects are generated by students with special needs, low-achieving students, and male students. Second, we show that classroom peer effects reported by the literature likely miss important nonlinearities in the distribution of peer proportions. Third, we determine that classroom compositions that are the most balanced in students' characteristics are the ones that reach maximal aggregated school performance.

This thesis empirically investigates three important topics in the literature on Economics of Education: the evaluation of returns to special education programs for students with special needs in inclusive and segregated education settings, the impact of gifted students on their classroom peers, and, finally, the estimation of peer-effects models that flexibly account for the mutual influence of many peer groups. Chapter 1 addresses the lack of empirical evidence on academic outcomes and labor market returns to special education. I present results from the first ever study to examine short- and long-term returns to special education programs with causal machine learning and computational text analysis methods. I find that special education programs in inclusive settings have positive returns in terms of academic performance as well as labor-market integration. Moreover, I uncover a positive effect of inclusive special education programs in comparison to segregated programs. This effect is heterogeneous: segregation has least negative effects for students with emotional or behavioral problems, and for nonnative students with special needs. Finally, I deliver optimal program placement rules that would maximize aggregated school performance and labor market integration for students with special needs at lower program costs. These placement rules would reallocate most students with special needs from segregation to inclusion. Chapter 2 investigates the causal impact of intellectually gifted students on their nongifted classmates' school achievement, enrollment in post-compulsory education, and occupational choices. Using student-level administrative and psychological data, we find a positive effect of exposure to gifted students on peers' school achievement in both math and language. This impact is heterogeneous: larger effects are observed among male students and high achievers; female students benefit primarily from female gifted students; effects are driven by gifted students not diagnosed with emotional or behavioral disorders. Exposure to gifted students increases the likelihood of choosing a selective academic track and occupations in STEM fields. Chapter 3 is an empirical exploration of peer effects in a systematic way. The majority of peer-effect studies in education have focused on the effect of one particular type of peers on classmates. This view fails to take into account the reality that peer effects are heterogeneous for students with different characteristics, and that there are at least as many peer effect functions as there are types of peers. We develop a general empirical framework that accounts for systematic interactions between peer types and nonlinearities of peer effects. We use machine-learning methods to (i) understand which dimensions of peer characteristics are the most predictive of academic performance, (ii) estimate high-dimensional peer effects functions, and (iii) investigate performance-improving classroom allocation through policy-relevant simulations. First, we find that students' own characteristics are the most predictive of own academic performance, and that the strongest peer effects are generated by students with special needs, low-achieving students, and male students. Second, we show that classroom peer effects reported by the literature likely miss important nonlinearities in the distribution of peer proportions. Third, we determine that classroom compositions that are the most balanced in students' characteristics are the ones that reach maximal aggregated school performance.