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Disruption, Achievement and the Heterogeneous Benefits of Smaller Classes

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  • Graham J. McKee
  • Steven G. Rivkin
  • Katharine R.E. Sims

Abstract

With few exceptions, empirical research investigating the possibility of heterogeneous benefits of class size reduction lacks a conceptual framework about specific dimensions of potential heterogeneity. In this paper we develop a model of education production that incorporates disruption and student achievement and illustrates how these underlying sources of variation may drive heterogeneity in the benefits of class size reductions. We test for results consistent with this model using the Tennessee STAR data. The estimates show that students in higher poverty schools and with greater learning aptitude realize larger benefits from smaller classes.

Suggested Citation

  • Graham J. McKee & Steven G. Rivkin & Katharine R.E. Sims, 2010. "Disruption, Achievement and the Heterogeneous Benefits of Smaller Classes," NBER Working Papers 15812, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:15812
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    References listed on IDEAS

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    1. Sergio Firpo, 2007. "Efficient Semiparametric Estimation of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 75(1), pages 259-276, January.
    2. Ma, Lingjie & Koenker, Roger, 2006. "Quantile regression methods for recursive structural equation models," Journal of Econometrics, Elsevier, vol. 134(2), pages 471-506, October.
    3. Jesse Levin, 2001. "For whom the reductions count: A quantile regression analysis of class size and peer effects on scholastic achievement," Empirical Economics, Springer, vol. 26(1), pages 221-246.
    4. Babcock, Philip & Betts, Julian R., 2009. "Reduced-class distinctions: Effort, ability, and the education production function," Journal of Urban Economics, Elsevier, vol. 65(3), pages 314-322, May.
    5. Krueger, Alan B & Whitmore, Diane M, 2001. "The Effect of Attending a Small Class in the Early Grades on College-Test Taking and Middle School Test Results: Evidence from Project STAR," Economic Journal, Royal Economic Society, vol. 111(468), pages 1-28, January.
    6. Steven Lehrer, 2005. "Class Size And Student Achievement: Experimental Estimates Of Who Benefits And Who Loses From Reductions," Working Paper 1046, Economics Department, Queen's University.
    7. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    8. Farshid Vahid & Pushkar Maitra, 2006. "The effect of household characteristics on living standards in South Africa 1993-1998: a quantile regression analysis with sample attrition," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(7), pages 999-1018.
    9. Caroline M. Hoxby, 2000. "The Effects of Class Size on Student Achievement: New Evidence from Population Variation," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(4), pages 1239-1285.
    10. Djebbari, Habiba & Smith, Jeffrey, 2008. "Heterogeneous impacts in PROGRESA," Journal of Econometrics, Elsevier, vol. 145(1-2), pages 64-80, July.
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    Cited by:

    1. Meghir, Costas & Rivkin, Steven, 2011. "Econometric Methods for Research in Education," Handbook of the Economics of Education, in: Erik Hanushek & Stephen Machin & Ludger Woessmann (ed.), Handbook of the Economics of Education, edition 1, volume 3, chapter 1, pages 1-87, Elsevier.
    2. Anton Bekkerman & Gregory Gilpin, 2011. "Cost-Effective Hiring in U.S. High Schools: Estimating Optimal Teacher Quantity and Quality Decisions," Caepr Working Papers 2011-007, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
    3. Gregory A. Gilpin & Anton Bekkerman, 2012. "Cost-effective hiring in US high schools: estimating optimal teacher quantity and quality decisions," Applied Economics Letters, Taylor & Francis Journals, vol. 19(14), pages 1421-1424, September.
    4. Frederico Gil Sander & Intan Nadia Jalil & Rabia Ali, 2013. "Malaysia Economic Monitor, December 2013 : High-Performing Education," World Bank Publications - Reports 16705, The World Bank Group.

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    More about this item

    JEL classification:

    • I20 - Health, Education, and Welfare - - Education - - - General
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

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