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Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data

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  • Flores, Carlos A.

    ()
    (University of Miami)

  • Mitnik, Oscar A.

    ()
    (University of Miami)

Abstract

This paper assesses the effectiveness of unconfoundedness-based estimators of mean effects for multiple or multivalued treatments in eliminating biases arising from nonrandom treatment assignment. We evaluate these multiple treatment estimators by simultaneously equalizing average outcomes among several control groups from a randomized experiment. We study linear regression estimators as well as partial mean and weighting estimators based on the generalized propensity score (GPS). We also study the use of the GPS in assessing the comparability of individuals among the different treatment groups, and propose a strategy to determine the overlap or common support region that is less stringent than those previously used in the literature. Our results show that in the multiple treatment setting there may be treatment groups for which it is extremely difficult to find valid comparison groups, and that the GPS plays a significant role in identifying those groups. In such situations, the estimators we consider perform poorly. However, their performance improves considerably once attention is restricted to those treatment groups with adequate overlap quality, with difference-in-difference estimators performing the best. Our results suggest that unconfoundedness-based estimators are a valuable econometric tool for evaluating multiple treatments, as long as the overlap quality is satisfactory.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 4451.

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Length: 53 pages
Date of creation: Sep 2009
Date of revision:
Handle: RePEc:iza:izadps:dp4451

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Keywords: generalized propensity score; nonexperimental estimators; multiple treatments;

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References

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Citations

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Cited by:
  1. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.
  2. Rafael Perez Ribas & Fabio Veras Soares & Clarissa Teixeira & Elydia Silva & Guilherme Hirata, 2011. "Beyond Cash: Assessing Externality and Behaviour Effects of Non-Experimental Cash Transfers," Working Papers PIERI 2011-18, PEP-PIERI.
  3. Taisuke Uchino & Iichiro Uesugi, 2012. "The Effects of a Megabank Merger on Firm-Bank Relationships and Borrowing Costs," Global COE Hi-Stat Discussion Paper Series gd12-233, Institute of Economic Research, Hitotsubashi University.
  4. Olivier Bertrand & Marie-Ann Betschinger, 2011. "Performance of domestic and cross-border acquisitions: empirical evidence from Russian acquirers," Working Papers 129, European Bank for Reconstruction and Development, Office of the Chief Economist.
  5. Choe, Chung & Flores-Lagunes, Alfonso & Lee, Sang-Jun, 2011. "Do Dropouts Benefit from Training Programs? Korean Evidence Employing Methods for Continuous Treatments," IZA Discussion Papers 6064, Institute for the Study of Labor (IZA).
  6. Rodney J. Andrews & Jing Li & Michael F. Lovenheim, 2012. "Quantile Treatment Effects of College Quality on Earnings: Evidence from Administrative Data in Texas," NBER Working Papers 18068, National Bureau of Economic Research, Inc.
  7. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2010. "How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score," IZA Discussion Papers 5268, Institute for the Study of Labor (IZA).

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