  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.
