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

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  • Aur'elien Sallin

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.

Suggested Citation

  • Aur'elien Sallin, 2021. "Estimating returns to special education: combining machine learning and text analysis to address confounding," Papers 2110.08807, arXiv.org, revised Feb 2022.
    • Handle: RePEc:arx:papers:2110.08807
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    Cited by:

    1. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
    2. Aurélien Sallin & Simone Balestra, 2022. "The Earth is Not Flat: A New World of High-Dimensional Peer Effects," Economics of Education Working Paper Series 0189, University of Zurich, Department of Business Administration (IBW).

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