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Can Gifted and Talented Education Raise the Academic Achievement of All High-Achieving Students?

Author

Listed:
  • Booij, Adam S.

    (University of Amsterdam)

  • Haan, Ferry

    (University of Amsterdam)

  • Plug, Erik

    (University of Amsterdam)

Abstract

We conduct a study under 2,400 third grade students at three large secondary comprehensive schools to evaluate a gifted and talented (GT) program with selective program admission based on past achievement. We construct three complementary estimates of the program's impact on student achievement. First, we use the fragmented GT program implementation (in different tracks at different schools) to get difference-in-differences (DD) estimates for all students above the admission cutoff. Second, we use the GT admission rule to get regression discontinuity (RD) estimates for students near the admission cutoff. And third, we combine the DD and RD designs to estimate how the program's impact varies with past achievement. We find that all participating students do better because of the GT program. Students near the admission cutoff experience a 0.2 standard deviation gain in their grade point average. Students further away from the admission cutoff experience larger gains.

Suggested Citation

  • Booij, Adam S. & Haan, Ferry & Plug, Erik, 2017. "Can Gifted and Talented Education Raise the Academic Achievement of All High-Achieving Students?," IZA Discussion Papers 10836, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp10836
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    References listed on IDEAS

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    1. Sa A. Bui & Steven G. Craig & Scott A. Imberman, 2014. "Is Gifted Education a Bright Idea? Assessing the Impact of Gifted and Talented Programs on Students," American Economic Journal: Economic Policy, American Economic Association, vol. 6(3), pages 30-62, August.
    2. Rachana Bhatt, 2011. "A Review of Gifted and Talented Education in the United States," Education Finance and Policy, MIT Press, vol. 6(4), pages 557-582, October.
    3. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769, April.
    4. Guido Imbens & Karthik Kalyanaraman, 2012. "Optimal Bandwidth Choice for the Regression Discontinuity Estimator," Review of Economic Studies, Oxford University Press, vol. 79(3), pages 933-959.
    5. Yingying Dong & Arthur Lewbel, 2015. "Identifying the Effect of Changing the Policy Threshold in Regression Discontinuity Models," The Review of Economics and Statistics, MIT Press, vol. 97(5), pages 1081-1092, December.
    6. Bertanha, Marinho, 2020. "Regression discontinuity design with many thresholds," Journal of Econometrics, Elsevier, vol. 218(1), pages 216-241.
    7. David Card & Laura Giuliano, 2016. "Can Tracking Raise the Test Scores of High-Ability Minority Students?," American Economic Review, American Economic Association, vol. 106(10), pages 2783-2816, October.
    8. Thomas Buser & Muriel Niederle & Hessel Oosterbeek, 2014. "Gender, Competitiveness, and Career Choices," The Quarterly Journal of Economics, Oxford University Press, vol. 129(3), pages 1409-1447.
    9. David Card & Laura Giuliano, 2015. "Can Universal Screening Increase the Representation of Low Income and Minority Students in Gifted Education?," NBER Working Papers 21519, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    difference-in-differences; secondary education; enrichment program; gifted and talented education; regression discontinuity designs;
    All these keywords.

    JEL classification:

    • I22 - Health, Education, and Welfare - - Education - - - Educational Finance; Financial Aid
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy

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