IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/15812.html
   My bibliography  Save this paper

Disruption, Achievement and the Heterogeneous Benefits of Smaller Classes

Author

Listed:
  • 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
    Note: CH ED
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w15812.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Sergio Firpo, 2007. "Efficient Semiparametric Estimation of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 75(1), pages 259-276, January.
    3. 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.
    4. Ma, Lingjie & Koenker, Roger, 2006. "Quantile regression methods for recursive structural equation models," Journal of Econometrics, Elsevier, vol. 134(2), pages 471-506, October.
    5. 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.
    6. Djebbari, Habiba & Smith, Jeffrey, 2008. "Heterogeneous impacts in PROGRESA," Journal of Econometrics, Elsevier, vol. 145(1-2), pages 64-80, July.
    7. 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.
    8. 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.
    9. 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.
    10. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Intan Nadia Jalil & Frederico Gil Sander & Rabia Ali, 2013. "Malaysia Economic Monitor, December 2013 : High-Performing Education," World Bank Publications - Reports 16705, The World Bank Group.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Graham McKee & Katharine Sims & Steven Rivkin, 2015. "Disruption, learning, and the heterogeneous benefits of smaller classes," Empirical Economics, Springer, vol. 48(3), pages 1267-1286, May.
    2. Simone Balestra & Uschi Backes-Gellner, 2014. "Heterogeneous effects of pupil-to-teacher ratio policies - A look at class size reduction and teacher aide," Economics of Education Working Paper Series 0102, University of Zurich, Department of Business Administration (IBW), revised Apr 2017.
    3. Gilpin, Gregory A., 2012. "Teacher salaries and teacher aptitude: An analysis using quantile regressions," Economics of Education Review, Elsevier, vol. 31(3), pages 15-29.
    4. Argaw, Bethlehem A. & Puhani, Patrick A., 2018. "Does class size matter for school tracking outcomes after elementary school? Quasi-experimental evidence using administrative panel data from Germany," Economics of Education Review, Elsevier, vol. 65(C), pages 48-57.
    5. Damian Clarke & Manuel Llorca Jaña & Daniel Pailañir, 2023. "The use of quantile methods in economic history," Historical Methods: A Journal of Quantitative and Interdisciplinary History, Taylor & Francis Journals, vol. 56(2), pages 115-132, April.
    6. Kedagni, Desire & Krishna, Kala & Megalokonomou, Rigissa & Zhao, Yingyan, 2021. "Does class size matter? How, and at what cost?," European Economic Review, Elsevier, vol. 133(C).
    7. Harding, Matthew & Lamarche, Carlos, 2014. "Estimating and testing a quantile regression model with interactive effects," Journal of Econometrics, Elsevier, vol. 178(P1), pages 101-113.
    8. Juliana Guimarães & Breno Sampaio, 2007. "The Influence Of Family Background And Individual Characteristics On Entrance Tests Scores Of Brazilian University Students," Anais do XXXV Encontro Nacional de Economia [Proceedings of the 35th Brazilian Economics Meeting] 092, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    9. Wo[ss]mann, Ludger & West, Martin, 2006. "Class-size effects in school systems around the world: Evidence from between-grade variation in TIMSS," European Economic Review, Elsevier, vol. 50(3), pages 695-736, April.
    10. Ma, Lingjie & Koenker, Roger, 2006. "Quantile regression methods for recursive structural equation models," Journal of Econometrics, Elsevier, vol. 134(2), pages 471-506, October.
    11. Giacomo De Giorgi & Michele Pellizzari & William Gui Woolston, 2012. "Class Size And Class Heterogeneity," Journal of the European Economic Association, European Economic Association, vol. 10(4), pages 795-830, August.
    12. Corak, Miles & Lauzon, Darren, 2009. "Differences in the distribution of high school achievement: The role of class-size and time-in-term," Economics of Education Review, Elsevier, vol. 28(2), pages 189-198, April.
    13. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    14. Callaway, Brantly & Li, Tong & Oka, Tatsushi, 2018. "Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods," Journal of Econometrics, Elsevier, vol. 206(2), pages 395-413.
    15. Sakos, Grayson & Cerulli, Giovanni & Garbero, Alessandra, 2021. "Beyond the ATE: Idiosyncratic Effect Estimation to Uncover Distributional Impacts Results from 17 Impact Evaluations," 2021 Annual Meeting, August 1-3, Austin, Texas 314017, Agricultural and Applied Economics Association.
    16. Chowdhury, Biplob & Jeyasreedharan, Nagaratnam & Dungey, Mardi, 2018. "Quantile relationships between standard, diffusion and jump betas across Japanese banks," Journal of Asian Economics, Elsevier, vol. 59(C), pages 29-47.
    17. Gracious M. Diiro & Abdoul G. Sam & David Kraybill, 2017. "Heterogeneous Effects of Maternal Labor Market Participation on the Nutritional Status of Children: Empirical Evidence from Rural India," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 10(3), pages 609-632, September.
    18. Victor Lavy & Analia Schlosser, 2005. "Targeted Remedial Education for Underperforming Teenagers: Costs and Benefits," Journal of Labor Economics, University of Chicago Press, vol. 23(4), pages 839-874, October.
    19. Christophe Muller, 2019. "Linear Quantile Regression and Endogeneity Correction," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 9(5), pages 123-128, August.
    20. Raj Chetty & John N. Friedman & Nathaniel Hilger & Emmanuel Saez & Diane Whitmore Schanzenbach & Danny Yagan, 2011. "How Does Your Kindergarten Classroom Affect Your Earnings? Evidence from Project Star," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 126(4), pages 1593-1660.

    More about this item

    JEL classification:

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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:15812. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.