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Estimating Dynamic Treatment Effects from Project STAR

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  • Steven Lehrer
  • Weili Ding

Abstract

This paper considers the analysis of data from randomized trials which offer a sequence of interventions and suffer from a variety of problems in implementation. In experiments that provide treatment in multiple periods (T>1), subjects have up to 2^{T}-1 counterfactual outcomes to be estimated to determine the full sequence of causal effects from the study. Traditional program evaluation and non-experimental estimators are unable to recover parameters of interest to policy makers in this setting, particularly if there is non-ignorable attrition. We examine these issues in the context of Tennessee's highly influential randomized class size study, Project STAR. We demonstrate how a researcher can estimate the full sequence of dynamic treatment effects using a sequential difference in difference strategy that accounts for attrition due to observables using inverse probability weighting M-estimators. These estimates allow us to recover the structural parameters of the small class effects in the underlying education production function and construct dynamic average treatment effects. We present a complete and different picture of the effectiveness of reduced class size and find that accounting for both attrition due to observables and selection due to unobservables is crucial and necessary with data from Project STAR

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Bibliographic Info

Paper provided by Econometric Society in its series Econometric Society 2004 North American Summer Meetings with number 252.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:nasm04:252

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Keywords: Education; Attrition; Non-Compliance; Sequential Difference in Difference; Class Size reduction;

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References

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  1. Becketti, Sean, et al, 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 472-92, October.
  2. 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.
  3. 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.
  4. Imbens, Guido W & Rubin, Donald B, 1997. "Estimating Outcome Distributions for Compliers in Instrumental Variables Models," Review of Economic Studies, Wiley Blackwell, vol. 64(4), pages 555-74, October.
  5. Alan B. Krueger, 1997. "Experimental Estimates of Education Production Functions," NBER Working Papers 6051, National Bureau of Economic Research, Inc.
  6. John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of income Dynamics," Economics Working Paper Archive 379, The Johns Hopkins University,Department of Economics.
  7. Michael A. Boozer & Stephen E. Cacciola, 2001. "Inside the 'Black Box' of Project STAR: Estimation of Peer Effects Using Experimental Data," Working Papers 832, Economic Growth Center, Yale University.
  8. Caroline M. Hoxby, 2000. "The Effects Of Class Size On Student Achievement: New Evidence From Population Variation," The Quarterly Journal of Economics, MIT Press, vol. 115(4), pages 1239-1285, November.
  9. Sean Becketti & William Gould & Lee Lillard & Finis Welch, 1985. "The Panel Study of Income Dynamics After Fourteen Years: An Evaluation," UCLA Economics Working Papers 361, UCLA Department of Economics.
  10. 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.
  11. James Heckman & Neil Hohmann & Jeffrey Smith, 1998. "Substitution and Dropout Bias in Social Experiments: A Study of an Influential Social Experiment," UWO Department of Economics Working Papers 9819, University of Western Ontario, Department of Economics.
  12. Caroline Hoxby, 2000. "Peer Effects in the Classroom: Learning from Gender and Race Variation," NBER Working Papers 7867, National Bureau of Economic Research, Inc.
  13. Jeffrey M. Wooldridge, 2002. "Inverse probability weighted M-estimators for sample selection, attrition and stratification," CeMMAP working papers CWP11/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  14. Hausman, Jerry A & Newey, Whitney K & Taylor, William E, 1987. "Efficient Estimation and Identification of Simultaneous Equation Models with Covariance Restrictions," Econometrica, Econometric Society, vol. 55(4), pages 849-74, July.
  15. J.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.
  16. 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.
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Cited by:
  1. Lechner, Michael, 2013. "Treatment effects and panel data," Economics Working Paper Series 1314, University of St. Gallen, School of Economics and Political Science.
  2. 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.
  3. 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.
  4. Weili Ding & Steven Lehrer, 2005. "Class Size and Student Achievement: Experimental Estimates of Who Benefits and Who Loses from Reductions," Working Papers 1046, Queen's University, Department of Economics.
  5. Sally Hudson, 2010. "The Effects of Performance-Based Teacher Pay on Student Achievement," Discussion Papers 09-023, Stanford Institute for Economic Policy Research.

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