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School Turnarounds: Evidence from the 2009 Stimulus

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  • Thomas Dee

Abstract

The American Recovery and Reinvestment Act of 2009 (ARRA) targeted substantial School Improvement Grants (SIGs) to the nation’s “persistently lowest achieving” public schools (i.e., up to $2 million per school annually over 3 years) but required schools accepting these awards to implement a federally prescribed school-reform model. Schools that met the “lowest-achieving” and “lack of progress” thresholds within their state had prioritized eligibility for these SIG-funded interventions. Using data from California, this study leverages these two discontinuous eligibility rules to identify the effects of SIG-funded whole-school reforms. The results based on these “fuzzy” regression-discontinuity designs indicate that there were significant improvements in the test-based performance of schools on the “lowest-achieving” margin but not among schools on the “lack of progress” margin. Complementary panel-based estimates suggest that these improvements were largely concentrated among schools adopting the federal “turnaround” model, which compels more dramatic staff turnover.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 17990.

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Date of creation: Apr 2012
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Handle: RePEc:nbr:nberwo:17990

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  1. Cook, Thomas D., 2008. ""Waiting for Life to Arrive": A history of the regression-discontinuity design in Psychology, Statistics and Economics," Journal of Econometrics, Elsevier, vol. 142(2), pages 636-654, February.
  2. David S. Lee & Thomas Lemieux, 2009. "Regression Discontinuity Designs In Economics," Working Papers 1118, Princeton University, Department of Economics, Industrial Relations Section..
  3. Robert Bifulco & William Duncombe & John Yinger, 2005. "Does whole-school reform boost student performance? The case of New York City," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 24(1), pages 47-72.
  4. Wilbert van der Klaauw, 2008. "Regression-Discontinuity Analysis: A Survey of Recent Developments in Economics," LABOUR, CEIS, vol. 22(2), pages 219-245, 06.
  5. Papay, John P. & Willett, John B. & Murnane, Richard J., 2011. "Extending the regression-discontinuity approach to multiple assignment variables," Journal of Econometrics, Elsevier, vol. 161(2), pages 203-207, April.
  6. Alan I. Barreca & Jason M. Lindo & Glen R. Waddell, 2011. "Heaping-Induced Bias in Regression-Discontinuity Designs," NBER Working Papers 17408, National Bureau of Economic Research, Inc.
  7. McCrary, Justin, 2008. "Manipulation of the running variable in the regression discontinuity design: A density test," Journal of Econometrics, Elsevier, vol. 142(2), pages 698-714, February.
  8. Hahn, Jinyong & Todd, Petra & Van der Klaauw, Wilbert, 2001. "Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design," Econometrica, Econometric Society, vol. 69(1), pages 201-09, January.
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Cited by:
  1. Julie Berry Cullen & Steven D. Levitt & Erin Robertson & Sally Sadoff, 2013. "What Can Be Done to Improve Struggling High Schools?," Journal of Economic Perspectives, American Economic Association, vol. 27(2), pages 133-52, Spring.
  2. Lee, Kyung-Gon & Polachek, Solomon, 2014. "Do School Budgets Matter? The Effect of Budget Referenda on Student Performance," IZA Discussion Papers 8056, Institute for the Study of Labor (IZA).

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