IDEAS home Printed from https://ideas.repec.org/p/boc/dsug12/11.html
   My bibliography  Save this paper

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

Author

Listed:
  • Harald Tauchmann

    (RWI)

Abstract

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.

Suggested Citation

  • Harald Tauchmann, 2012. "leebounds: Lee’s treatment effect bounds for samples with nonrandom sample selection," German Stata Users' Group Meetings 2012 11, Stata Users Group.
    • Handle: RePEc:boc:dsug12:11
    as

    Download full text from publisher

    File URL: http://fmwww.bc.edu/repec/dsug2012/desug12_tauchmann.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Augurzky, Boris & Bauer, Thomas K. & Reichert, Arndt R. & Schmidt, Christoph M. & Tauchmann, Harald, 2012. "Does Money Burn Fat? Evidence from a Randomized Experiment," IZA Discussion Papers 6888, Institute of Labor Economics (IZA).
    3. David S. Lee, 2009. "Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(3), pages 1071-1102.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arndt Reichert & Harald Tauchmann, 2014. "When outcome heterogeneously matters for selection: a generalized selection correction estimator," Applied Economics, Taylor & Francis Journals, vol. 46(7), pages 762-768, March.
    2. Augurzky, Boris & Bauer, Thomas K. & Reichert, Arndt R. & Schmidt, Christoph M. & Tauchmann, Harald, 2014. "Small Cash Rewards for Big Losers – Experimental Insights Into the Fight Against the Obesity Epidemic," Ruhr Economic Papers 530, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    3. Arndt R. Reichert, 2015. "Obesity, Weight Loss, and Employment Prospects: Evidence from a Randomized Trial," Journal of Human Resources, University of Wisconsin Press, vol. 50(3), pages 759-810.
    4. Martin Huber & Giovanni Mellace, 2014. "Testing exclusion restrictions and additive separability in sample selection models," Empirical Economics, Springer, vol. 47(1), pages 75-92, August.
    5. Bo E. Honoré & Luojia Hu, 2020. "Selection Without Exclusion," Econometrica, Econometric Society, vol. 88(3), pages 1007-1029, May.
    6. Arndt Reichert & Harald Tauchmann, 2012. "When Outcome Heterogeneously Matters for Selection – A Generalized Selection Correction Estimator," Ruhr Economic Papers 0372, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    7. Boris Augurzky & Thomas K.Bauer & Arndt R. Reichert & Christoph M. Schmidt & Harald Tauchmann, 2014. "Small Cash Rewards for Big Losers – Experimental Insights Into the Fight Against the Obesity Epidemic," Ruhr Economic Papers 0530, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    8. Guigonan S. Adjognon & Daan van Soest & Jonas Guthoff, 2021. "Reducing Hunger with Payments for Environmental Services (PES): Experimental Evidence from Burkina Faso," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 831-857, May.
    9. Fatouh, Mahmoud & Neamțu, Ioana & van Wijnbergen, Sweder, 2021. "Risk-taking and uncertainty: do contingent convertible (CoCo) bonds increase the risk appetite of banks?," Bank of England working papers 938, Bank of England.
    10. Dalia Ghanem & Sarojini Hirshleifer & Karen Ortiz-Becerra, 2019. "Testing Attrition Bias in Field Experiments," Working Papers 202218, University of California at Riverside, Department of Economics, revised Oct 2022.
    11. Arndt Reichert, 2012. "Obesity, Weight Loss, and Employment Prospects – Evidence from a Randomized Trial," Ruhr Economic Papers 0381, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    12. repec:zbw:rwirep:0381 is not listed on IDEAS
    13. Martin Huber, 2014. "Treatment Evaluation in the Presence of Sample Selection," Econometric Reviews, Taylor & Francis Journals, vol. 33(8), pages 869-905, November.
    14. Mahmoud Fatouh & Ioana Neamtu & Sweder van Wijnbergen, 2022. "Risk-Taking, Competition and Uncertainty: Do Contingent Convertible (CoCo) Bonds Increase the Risk Appetite of Banks?," Tinbergen Institute Discussion Papers 22-017/IV, Tinbergen Institute.
    15. Dalia Ghanem & Sarojini Hirshleifer & Karen Ortiz-Becerra, 2019. "Testing for Attrition Bias in Field Experiments," Working Papers 202010, University of California at Riverside, Department of Economics, revised Mar 2020.
    16. repec:zbw:rwirep:0372 is not listed on IDEAS
    17. Bo E. Honore & Luojia Hu, 2021. "Sample Selection Models Without Exclusion Restrictions: Parameter Heterogeneity and Partial Identification," Working Paper Series WP 2022-33, Federal Reserve Bank of Chicago.
    18. repec:zbw:rwirep:0530 is not listed on IDEAS
    19. Ichimura, Hidehiko & Todd, Petra E., 2007. "Implementing Nonparametric and Semiparametric Estimators," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 74, Elsevier.
    20. Martin Huber, 2012. "Identification of Average Treatment Effects in Social Experiments Under Alternative Forms of Attrition," Journal of Educational and Behavioral Statistics, , vol. 37(3), pages 443-474, June.
    21. McGovern, Mark E. & Canning, David & Bärnighausen, Till, 2018. "Accounting for non-response bias using participation incentives and survey design: An application using gift vouchers," Economics Letters, Elsevier, vol. 171(C), pages 239-244.
    22. Christelis, Dimitris & Messina, Julián, 2019. "Partial Identification of Population Average and Quantile Treatment Effects in Observational Data under Sample Selection," IDB Publications (Working Papers) 9520, Inter-American Development Bank.
    23. Giovanni Mellace & Roberto Rocci, 2011. "Principal Stratification in sample selection problems with non normal error terms," CEIS Research Paper 194, Tor Vergata University, CEIS, revised 02 May 2011.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:boc:dsug12:11. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F Baum (email available below). General contact details of provider: https://edirc.repec.org/data/stataea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.