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

TL;DR

This study uses machine learning and text analysis on psychological records to evaluate the long-term impacts of inclusive versus segregated special education programs, revealing benefits of inclusion and optimal placement strategies.

Contribution

It introduces a novel combination of causal machine learning and computational text analysis to assess special education program outcomes.

Findings

Inclusive programs improve academic and labor market outcomes.

Inclusive programs outperform segregated ones in positive effects.

Optimal placement rules favor inclusive settings, reallocating students from segregation.

Abstract

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 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 heterogenous: 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.