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Measurement Matters: Perspectives on Education Policy from an Economist and School Board Member

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  • Kevin Lang

Abstract

One of the potential strengths of the No Child Left Behind (NCLB) Act enacted in 2002 is that the law requires the production of an enormous amount of data, particularly from tests, which, if used properly, might help us improve education. As an economist and as someone who served 13 years on the School Committee in Brookline Massachusetts, until May 2009, I have been appalled by the limited ability of districts to analyze these data; I have been equally appalled by the cavalier manner in which economists use test scores and related measures in their analyses. The summary data currently provided are very hard to interpret, and policymakers, who typically lack statistical sophistication, cannot easily use them to assess progress. In some domains, most notably the use of average test scores to evaluate teachers or schools, the education community is aware of the biases and has sought better measures. The economics and statistics communities have both responded to and created this demand by developing value-added measures that carry a scientific aura. However, economists have largely failed to recognize many of the problems with such measures. These problems are sufficiently important that they should preclude any automatic link between these measures and rewards or sanctions. They do, however, contain information and can be used as a catalyst for more careful evaluation of teachers and schools, and as a lever to induce principals and other administrators to act on their knowledge.

Suggested Citation

  • Kevin Lang, 2010. "Measurement Matters: Perspectives on Education Policy from an Economist and School Board Member," Journal of Economic Perspectives, American Economic Association, vol. 24(3), pages 167-182, Summer.
  • Handle: RePEc:aea:jecper:v:24:y:2010:i:3:p:167-82 Note: DOI: 10.1257/jep.24.3.167
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    References listed on IDEAS

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    1. Kevin Lang, 2007. "Introduction to Poverty and Discrimination," Introductory Chapters,in: Poverty and Discrimination Princeton University Press.
    2. Thomas J. Kane & Douglas O. Staiger, 2008. "Estimating Teacher Impacts on Student Achievement: An Experimental Evaluation," NBER Working Papers 14607, National Bureau of Economic Research, Inc.
    3. Thomas J. Kane & Douglas O. Staiger, 2002. "The Promise and Pitfalls of Using Imprecise School Accountability Measures," Journal of Economic Perspectives, American Economic Association, vol. 16(4), pages 91-114, Fall.
    4. J. R. Lockwood & Daniel F. McCaffrey & Louis T. Mariano & Claude Setodji, 2007. "Bayesian Methods for Scalable Multivariate Value-Added Assessment," Journal of Educational and Behavioral Statistics, , vol. 32(2), pages 125-150, June.
    5. Brian A. Jacob & Lars Lefgren, 2004. "Remedial Education and Student Achievement: A Regression-Discontinuity Analysis," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 226-244, February.
    6. Brian A. Jacob & Lars Lefgren, 2008. "Can Principals Identify Effective Teachers? Evidence on Subjective Performance Evaluation in Education," Journal of Labor Economics, University of Chicago Press, vol. 26, pages 101-136.
    7. Daniel F. McCaffrey & J. R. Lockwood & Daniel Koretz & Thomas A. Louis & Laura Hamilton, 2004. "Models for Value-Added Modeling of Teacher Effects," Journal of Educational and Behavioral Statistics, , vol. 29(1), pages 67-101, March.
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    Cited by:

    1. Elizabeth U. Cascio & Douglas O. Staiger, 2012. "Knowledge, Tests, and Fadeout in Educational Interventions," NBER Working Papers 18038, National Bureau of Economic Research, Inc.
    2. Schwerdt, Guido & West, Martin R. & Winters, Marcus A., 2017. "The effects of test-based retention on student outcomes over time: Regression discontinuity evidence from Florida," Journal of Public Economics, Elsevier, pages 154-169.
    3. Bhalotra, Sonia R. & Karlsson, Martin & Nilsson, Therese & Schwarz, Nina, 2016. "Infant Health, Cognitive Performance and Earnings: Evidence from Inception of the Welfare State in Sweden," IZA Discussion Papers 10339, Institute for the Study of Labor (IZA).
    4. Diego Azqueta Oyarzun & Guillermina Gavaldon, 2014. "The economic assessment of education: Social Efficiency or Social Reconstruction?," Investigaciones de Economía de la Educación volume 9,in: Adela García Aracil & Isabel Neira Gómez (ed.), Investigaciones de Economía de la Educación 9, edition 1, volume 9, chapter 51, pages 969-978 Asociación de Economía de la Educación.
    5. Forbes Silke & Gordon Nora, 2012. "When Educators Are the Learners: Private Contracting by Public Schools," The B.E. Journal of Economic Analysis & Policy, De Gruyter, pages 1-29.
    6. Mariesa A. Herrmann & Jonah E. Rockoff, 2012. "Worker Absence and Productivity: Evidence from Teaching," Journal of Labor Economics, University of Chicago Press, vol. 30(4), pages 749-782.
    7. Nielsen, Eric, 2015. "Achievement Gap Estimates and Deviations from Cardinal Comparability," Finance and Economics Discussion Series 2015-40, Board of Governors of the Federal Reserve System (U.S.).
    8. Nielsen, Eric, 2015. "The Income-Achievement Gap and Adult Outcome Inequality," Finance and Economics Discussion Series 2015-41, Board of Governors of the Federal Reserve System (U.S.).

    More about this item

    JEL classification:

    • H52 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Education
    • H75 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Government: Health, Education, and Welfare
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy

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