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Estimating Treatment Effects from Contaminated Multi-Period Education Experiments: The Dynamic Impacts of Class Size Reductions

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  • Weili Ding
  • Steven F. Lehrer

Abstract

This paper introduces an empirical strategy to estimate dynamic treatment effects in randomized trials that provide treatment in multiple stages and in which various noncompliance problems arise such as attrition and selective transitions between treatment and control groups. Our approach is applied to the highly influential four year randomized class size study, Project STAR. We find benefits from attending small class in all cognitive subject areas in kindergarten and the first grade. We do not find any statistically significant dynamic benefits from continuous treatment versus never attending small classes following grade one. Finally, statistical tests support accounting for both selective attrition and noncompliance with treatment assignment.

Suggested Citation

  • Weili Ding & Steven F. Lehrer, 2009. "Estimating Treatment Effects from Contaminated Multi-Period Education Experiments: The Dynamic Impacts of Class Size Reductions," NBER Working Papers 15200, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:15200
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    1. O'Connell, Philip J. & Russell, Helen & FitzGerald, John, 2006. "Human Resources," Book Chapters, in: Morgenroth, Edgar (ed.),Ex-Ante Evaluation of the Investment Priorities for the National Development Plan 2007-2013, Economic and Social Research Institute (ESRI).
    2. John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics," Journal of Human Resources, University of Wisconsin Press, vol. 33(2), pages 251-299.
    3. Yau L.H.Y. & Little R.J., 2001. "Inference for the Complier-Average Causal Effect From Longitudinal Data Subject to Noncompliance and Missing Data, With Application to a Job Training Assessment for the Unemployed," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1232-1244, December.
    4. Horowitz, Joel L & Manski, Charles F, 1995. "Identification and Robustness with Contaminated and Corrupted Data," Econometrica, Econometric Society, vol. 63(2), pages 281-302, March.
    5. Michael Lechner, 2004. "Sequential Matching Estimation of Dynamic Causal Models," University of St. Gallen Department of Economics working paper series 2004 2004-06, Department of Economics, University of St. Gallen.
    6. Alan B. Krueger, 1999. "Experimental Estimates of Education Production Functions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(2), pages 497-532.
    7. Ruth Miquel, 2002. "Identification of Dynamic Treatment Effects by Instrumental Variables," University of St. Gallen Department of Economics working paper series 2002 2002-11, Department of Economics, University of St. Gallen.
    8. Becketti, Sean & Gould, William & Lillard, Lee & Welch, Finis, 1988. "The Panel Study of Income Dynamics after Fourteen Years: An Evaluatio n," Journal of Labor Economics, University of Chicago Press, vol. 6(4), pages 472-492, October.
    9. Jeffrey M. Wooldridge, 2002. "Inverse probability weighted M-estimators for sample selection, attrition, and stratification," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 1(2), pages 117-139, August.
    10. James Heckman & Jeffrey Smith & Christopher Taber, 1994. "Accounting for Dropouts in Evaluations of Social Experiments," NBER Technical Working Papers 0166, National Bureau of Economic Research, Inc.
    11. 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.
    12. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    13. James Heckman & Neil Hohmann & Jeffrey Smith & Michael Khoo, 2000. "Substitution and Dropout Bias in Social Experiments: A Study of an Influential Social Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(2), pages 651-694.
    14. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    15. Andrew Chesher, 2003. "Identification in Nonseparable Models," Econometrica, Econometric Society, vol. 71(5), pages 1405-1441, September.
    16. Yoram Ben-Porath, 1967. "The Production of Human Capital and the Life Cycle of Earnings," Journal of Political Economy, University of Chicago Press, vol. 75(4), pages 352-352.
    17. Michael Lechner & Ruth Miquel, 2010. "Identification of the effects of dynamic treatments by sequential conditional independence assumptions," Empirical Economics, Springer, vol. 39(1), pages 111-137, August.
    18. Joshua D. Angrist & Guido W. Imbens & D.B. Rubin, 1993. "Identification of Causal Effects Using Instrumental Variables," NBER Technical Working Papers 0136, National Bureau of Economic Research, Inc.
    19. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-323, May.
    20. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    21. Newey, Whitney K., 1984. "A method of moments interpretation of sequential estimators," Economics Letters, Elsevier, vol. 14(2-3), pages 201-206.
    22. Steven Lehrer & Weili Ding, 2004. "Estimating Dynamic Treatment Effects from Project STAR," Econometric Society 2004 North American Summer Meetings 252, Econometric Society.
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    More about this item

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

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