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New Evidence on Linear Regression and Treatment Effect Heterogeneity

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  • Słoczyński, Tymon

Abstract

In this paper I provide new evidence on the implications of treatment effect heterogeneity for least squares estimation when the effects are inappropriately assumed to be homogenous. I prove that under a set of benchmark assumptions linear regression provides a consistent estimator of the population average treatment effect on the treated times the population proportion of the nontreated individuals plus the population average treatment effect on the nontreated times the population proportion of the treated individuals. Consequently, in many empirical applications the linear regression estimates might not be close to any of the standard average treatment effects of interest.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 39524.

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Date of creation: 27 Jun 2012
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Handle: RePEc:pra:mprapa:39524

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Keywords: treatment effects; linear regression; ordinary least squares; decomposition methods;

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References

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  1. Richard Blundell & Monica Costa Dias, 2002. "Alternative approaches to evaluation in empirical microeconomics," CeMMAP working papers CWP10/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  2. Guido Imbens, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometric Society World Congress 2000 Contributed Papers 1166, Econometric Society.
  3. Barsky R. & Bound J. & Charles K.K. & Lupton J.P., 2002. "Accounting for the Black-White Wealth Gap: A Nonparametric Approach," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 663-673, September.
  4. Crump, Richard K. & Hotz, V. Joseph & Imbens, Guido W. & Mitnik, Oscar A., 2006. "Nonparametric Tests for Treatment Effect Heterogeneity," IZA Discussion Papers 2091, Institute for the Study of Labor (IZA).
  5. Markus Frölich & Blaise Melly, 2007. "Unconditional quantile treatment effects under endogeneity," CeMMAP working papers CWP32/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  6. Joshua D. Angrist, 1995. "Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants," NBER Working Papers 5192, National Bureau of Economic Research, Inc.
  7. Marianne Bitler & Jonah Gelbach & Hilary Hoynes, 2003. "What Mean Impacts Miss: Distributional Effects of Welfare Reform Experiments," NBER Working Papers 10121, National Bureau of Economic Research, Inc.
  8. Nicole Fortin & Thomas Lemieux & Sergio Firpo, 2010. "Decomposition Methods in Economics," NBER Working Papers 16045, National Bureau of Economic Research, Inc.
  9. James J. Heckman, 2001. "Micro Data, Heterogeneity, and the Evaluation of Public Policy: Nobel Lecture," Journal of Political Economy, University of Chicago Press, vol. 109(4), pages 673-748, August.
  10. Roland G. Fryer, Jr. & Steven D. Levitt, 2002. "Understanding the Black-White Test Score Gap in the First Two Years of School," NBER Working Papers 8975, National Bureau of Economic Research, Inc.
  11. Ben Jann, 2008. "The Blinder–Oaxaca decomposition for linear regression models," Stata Journal, StataCorp LP, vol. 8(4), pages 453-479, December.
  12. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-20, September.
  13. Løken, Katrine Vellesen & Mogstad, Magne & Wiswall, Matthew, 2011. "What Linear Estimators Miss: The E ects of Family Income on Child Outcomes," Working Papers in Economics 02/11, University of Bergen, Department of Economics.
  14. Patrick Kline, 2011. "Oaxaca-Blinder as a Reweighting Estimator," American Economic Review, American Economic Association, vol. 101(3), pages 532-37, May.
  15. Djebbari, Habiba & Smith, Jeffrey A., 2008. "Heterogeneous Impacts in PROGRESA," IZA Discussion Papers 3362, Institute for the Study of Labor (IZA).
  16. Dan A. Black & Jeffrey A. Smith & Mark C. Berger & Brett J. Noel, 2003. "Is the Threat of Reemployment Services More Effective Than the Services Themselves? Evidence from Random Assignment in the UI System," American Economic Review, American Economic Association, vol. 93(4), pages 1313-1327, September.
  17. Sergio Firpo, 2007. "Efficient Semiparametric Estimation of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 75(1), pages 259-276, 01.
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Cited by:
  1. Boris Kaiser, 2013. "Decomposing Differences in Arithmetic Means: A Doubly-Robust Estimation Approach," Diskussionsschriften dp1308, Universitaet Bern, Departement Volkswirtschaft.
  2. Sloczynski, Tymon, 2013. "Population Average Gender Effects," IZA Discussion Papers 7315, Institute for the Study of Labor (IZA).

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