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leebounds: Lee’s treatment effect bounds for samples with nonrandom sample selection

  • Harald Tauchmann

    (RWI)

Even if assignment of treatment is purely exogenous, estimating treatment effects may suffer from severe bias if the available sample is subject to nonrandom sample selection/attrition. Lee (Review of Economic Studies, 2009) addresses this issue by proposing an estimator for treatment effect bounds in the presence of nonrandom sample selection. In this approach, the lower and upper bound, respectively, correspond to extreme assumptions about the missing information that are consistent with the observed data. As opposed to conventional parametric approaches to correcting for sample selection bias, such as the classical heckit estimator, Lee bounds rest on very few assumptions, namely, random assignment of treatment and monotonicity. The latter means that treatment affects selection for any individual in the same direction. I introduce the new Stata command leebounds, which implements Lee’s bounds estimator in Stata. The command allows for several options, such as tightening bounds by the use of covariates, confidence intervals for the treatment effect, and statistical inference based on a weighted bootstrap. The command is applied to data gathered from a randomized trial of the effect of financial incentives on weight-loss among obese individuals.

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File URL: http://fmwww.bc.edu/repec/dsug2012/desug12_tauchmann.pdf
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Paper provided by Stata Users Group in its series German Stata Users' Group Meetings 2012 with number 11.

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Date of creation: 04 Jun 2012
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Handle: RePEc:boc:dsug12:11
Contact details of provider: Web page: http://www.stata.com/meeting/germany12/

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  1. Ahn, Hyungtaik & Powell, James L., 1993. "Semiparametric estimation of censored selection models with a nonparametric selection mechanism," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 3-29, July.
  2. David S. Lee, 2009. "Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects," Review of Economic Studies, Oxford University Press, vol. 76(3), pages 1071-1102.
  3. Augurzky, Boris & Bauer, Thomas K. & Reichert, Arndt R. & Schmidt, Christoph M. & Tauchmann, Harald, 2012. "Does Money Burn Fat? – Evidence from a Randomized Experiment," Ruhr Economic Papers 368, Rheinisch-Westfälisches Institut für Wirtschaftsforschung (RWI), Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
  4. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492 National Bureau of Economic Research, Inc.
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