Estimating returns to special education: combining machine learning and text analysis to address confounding

Working paper

While the number of students with identified special needs is increasing in developed countries, there is little empirical evidence on academic and labor market returns to special education. By leveraging unique insights into the special education placement process through written individual psychological records, 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 on academic performance in math and language as well as on employment and wages. Moreover, I uncover a positive effect of inclusive special education programs in comparison to segregated programs, and I find that students with emotional or behavioral problems and nonnative students benefit more from segregation than the other students. Finally, I deliver optimal placement rules that increase overall returns for students with special needs and lower special education costs. These placement rules would reallocate most students with special needs from segregation to inclusion.