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Predicting Early Fall Student Enrollment in the School District of Philadelphia

Author

Listed:
  • Sean Tanner
  • Jenna Terrell
  • Emily Vislosky
  • Jonathan Gellar
  • Brian Gill

Abstract

Predicting incoming enrollment is an ongoing concern for the School District of Philadelphia (SDP) and similar districts with school choice systems, substantial student mobility, or both.

Suggested Citation

  • Sean Tanner & Jenna Terrell & Emily Vislosky & Jonathan Gellar & Brian Gill, "undated". "Predicting Early Fall Student Enrollment in the School District of Philadelphia," Mathematica Policy Research Reports 63a18bf538bd41f98d72ff91d, Mathematica Policy Research.
    • Handle: RePEc:mpr:mprres:63a18bf538bd41f98d72ff91dd390339
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    File URL: https://www.mathematica.org/-/media/publications/pdfs/education/2021/rel_2022124_early-enrollment.pdf
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    References listed on IDEAS

    as
    1. Aaron Chalfin & Oren Danieli & Andrew Hillis & Zubin Jelveh & Michael Luca & Jens Ludwig & Sendhil Mullainathan, 2016. "Productivity and Selection of Human Capital with Machine Learning," American Economic Review, American Economic Association, vol. 106(5), pages 124-127, May.
    2. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    3. Gibbons, Stephen & Telhaj, Shqiponja, 2011. "Pupil mobility and school disruption," Journal of Public Economics, Elsevier, vol. 95(9), pages 1156-1167.
    4. Jonah E. Rockoff & Brian A. Jacob & Thomas J. Kane & Douglas O. Staiger, 2011. "Can You Recognize an Effective Teacher When You Recruit One?," Education Finance and Policy, MIT Press, vol. 6(1), pages 43-74, January.
    5. Dana Chandler & Steven D. Levitt & John A. List, 2011. "Predicting and Preventing Shootings among At-Risk Youth," American Economic Review, American Economic Association, vol. 101(3), pages 288-292, May.
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