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Large-Scale Evaluations of Curricular Effectiveness: The Case of Elementary Mathematics in Indiana

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Abstract

We use data from one of the few states where information on curriculum adoptions is available – Indiana – to empirically evaluate differences in performance across three elementary-mathematics curricula. The three curricula that we evaluate were popular nationally during the time of our study, and two of the three remain popular today. We find large differences in effectiveness between the curricula, most notably between the two that held the largest market shares in Indiana. Both are best-characterized as traditional in pedagogy. We also show that the publisher of the least-effective curriculum did not lose market share in Indiana in the following adoption cycle; one explanation is that educational decision makers lack information about differences in curricular effectiveness.

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  • Cory Koedel & Rachana Bhatt, 2011. "Large-Scale Evaluations of Curricular Effectiveness: The Case of Elementary Mathematics in Indiana," Working Papers 1122, Department of Economics, University of Missouri, revised 31 Jan 2012.
    • Handle: RePEc:umc:wpaper:1122
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    as
    1. Michael Lechner, 2002. "Program Heterogeneity And Propensity Score Matching: An Application To The Evaluation Of Active Labor Market Policies," The Review of Economics and Statistics, MIT Press, vol. 84(2), pages 205-220, May.
    2. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    3. Black, Dan A. & Smith, J.A.Jeffrey A., 2004. "How robust is the evidence on the effects of college quality? Evidence from matching," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 99-124.
    4. Roberto Agodini & Barbara Harris & Melissa Thomas & Robert Murphy & Lawrence Gallagher, "undated". "Achievement Effects of Four Early Elementary School Math Curricula: Findings for First and Second Graders," Mathematica Policy Research Reports 42014cad41884fa9a53007635, Mathematica Policy Research.
    5. Peter R. Mueser & Kenneth R. Troske & Alexey Gorislavsky, 2007. "Using State Administrative Data to Measure Program Performance," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 761-783, November.
    6. repec:mpr:mprres:6192 is not listed on IDEAS
    7. repec:mpr:mprres:7723 is not listed on IDEAS
    8. repec:mpr:mprres:6586 is not listed on IDEAS
    9. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    10. Stephen W. Raudenbush, 1988. "Educational Applications of Hierarchical Linear Models: A Review," Journal of Educational and Behavioral Statistics, , vol. 13(2), pages 85-116, June.
    11. Millimet, Daniel L. & Tchernis, Rusty, 2009. "On the Specification of Propensity Scores, With Applications to the Analysis of Trade Policies," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(3), pages 397-415.
    12. Barbara Sianesi, 2004. "An Evaluation of the Swedish System of Active Labor Market Programs in the 1990s," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 133-155, February.
    13. repec:mpr:mprres:6816 is not listed on IDEAS
    14. Alberto Abadie & Guido W. Imbens, 2008. "On the Failure of the Bootstrap for Matching Estimators," Econometrica, Econometric Society, vol. 76(6), pages 1537-1557, November.
    15. John C. Ham & Xianghong Li & Patricia B. Reagan, 2004. "Propensity Score Matching, a Distance-Based Measure of Migration, and the Wage Growth of Young Men," Working Papers 2004_3, York University, Department of Economics.
    16. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    17. Millimet, Daniel L. & Tchernis, Rusty, 2009. "On the Specification of Propensity Scores, With Applications to the Analysis of Trade Policies," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(3), pages 397-415.
    18. repec:mpr:mprres:7064 is not listed on IDEAS
    19. Roland G. Fryer & Steven D. Levitt, 2006. "The Black-White Test Score Gap Through Third Grade," American Law and Economics Review, American Law and Economics Association, vol. 8(2), pages 249-281.
    20. Brian A. Jacob & Lars Lefgren & David Sims, 2008. "The Persistence of Teacher-Induced Learning Gains," NBER Working Papers 14065, National Bureau of Economic Research, Inc.
    21. Roberto Agodini & Barbara Harris & Sally Atkins-Burnett & Sheila Heaviside & Timothy Novak & Robert Murphy, "undated". "Achievement Effects of Four Early Elementary School Math Curricula: Findings from First Graders in 39 Schools," Mathematica Policy Research Reports 467194cd2d6f4cbaba7e23745, Mathematica Policy Research.
    22. Roberto Agodini, 2009. "Achievement Effects of Four Early Elementary School Math Curricula: Findings from First Graders in 39 Schools (Conference Paper)," Mathematica Policy Research Reports a08d661306a843fa89aff4c30, Mathematica Policy Research.
    23. Dale Ballou, 1996. "Do Public Schools Hire the Best Applicants?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 111(1), pages 97-133.
    24. Zhong Zhao, 2004. "Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 91-107, February.
    25. repec:mpr:mprres:7337 is not listed on IDEAS
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    Cited by:

    1. Quỳnh Tiên Nguyên Lê & Morgan S. Polikoff, 2021. "Do English Language Development Curriculum Materials Matter for Students’ English Proficiency?," SAGE Open, , vol. 11(3), pages 21582440211, July.
    2. Cory Koedel & Diyi Li & Morgan S. Polikoff & Tenice Hardaway & Stephani L. Wrabel, 2016. "Mathematics Curriculum Effects on Student Achievement in California," Working Papers 1612, Department of Economics, University of Missouri.
    3. Bhatt, Rachana & Koedel, Cory & Lehmann, Douglas, 2013. "Is curriculum quality uniform? Evidence from Florida," Economics of Education Review, Elsevier, vol. 34(C), pages 107-121.
    4. David Blazar & Blake Heller & Thomas J. Kane & Morgan Polikoff & Douglas O. Staiger & Scott Carrell & Dan Goldhaber & Douglas N. Harris & Rachel Hitch & Kristian L. Holden & Michal Kurlaender, 2020. "Curriculum Reform in The Common Core Era: Evaluating Elementary Math Textbooks Across Six U.S. States," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 39(4), pages 966-1019, September.
    5. Kim, Dongwoo & Koedel, Cory & Ni, Shawn & Podgursky, Michael, 2017. "Labor market frictions and production efficiency in public schools," Economics of Education Review, Elsevier, vol. 60(C), pages 54-67.

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    More about this item

    Keywords

    curricular effectiveness; math curricula; non-experimental methods; matching methods; education policy;
    All these keywords.

    JEL classification:

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

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