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Propensity score matching and variations on the balancing test

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  • Wang-Sheng Lee

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Abstract

Balancing tests are diagnostics designed for use with propensity score methods, a widely used non-experimental approach in the evaluation literature. Such tests provide useful information on whether plausible counterfactuals have been created. Currently, multiple balancing tests exist in the literature but it is unclear which is the most useful. This article highlights the poor size properties of commonly employed balancing tests and attempts to shed some light on the link between the results of balancing tests and bias of the evaluation estimator. The simulation results suggest that in scenarios where the conditional independence assumption holds, a permutation version of the balancing test described in Dehejia and Wahba (Rev Econ Stat 84:151–161, 2002 ) can be useful in applied study. The proposed test has good size properties. In addition, the test appears to have good power for detecting a misspecification in the link function and some power for detecting an omission of relevant non-linear terms involving variables that are included at a lower order. Copyright Springer-Verlag 2013

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Bibliographic Info

Article provided by Springer in its journal Empirical Economics.

Volume (Year): 44 (2013)
Issue (Month): 1 (February)
Pages: 47-80

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Handle: RePEc:spr:empeco:v:44:y:2013:i:1:p:47-80

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Related research

Keywords: Matching; Propensity score; Balancing test; Permutation test; Monte Carlo simulation; C14; C99;

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References

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  1. Jasjeet S. Sekhon, . "Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching package for R," Journal of Statistical Software, American Statistical Association, vol. 42(i07).
  2. Jose C. GALDO & Jeffrey SMITH & Dan BLACK, 2008. "Bandwidth Selection and the Estimation of Treatment Effects with Unbalanced Data," Annales d'Economie et de Statistique, ENSAE, issue 91-92, pages 189-216.
  3. Rajeev H. Dehejia & Sadek Wahba, 1998. "Propensity Score Matching Methods for Non-experimental Causal Studies," NBER Working Papers 6829, National Bureau of Economic Research, Inc.
  4. Busso, Matias & DiNardo, John & McCrary, Justin, 2009. "New Evidence on the Finite Sample Properties of Propensity Score Matching and Reweighting Estimators," IZA Discussion Papers 3998, Institute for the Study of Labor (IZA).
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  6. James J. Heckman, 1989. "Choosing Among Alternative Nonexperimental Methods for Estimating the Impact of Social Programs: The Case of Manpower Training," NBER Working Papers 2861, National Bureau of Economic Research, Inc.
  7. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," NBER Technical Working Papers 0251, National Bureau of Economic Research, Inc.
  8. Daniel Millimet & Rusty Tchernis, 2008. "On the Specification of Propensity Scores: with Applications to the Analysis of Trade Policies," Caepr Working Papers 2006-013_Updated, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
  9. Heckman, James J & Ichimura, Hidehiko & Todd, Petra E, 1997. "Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, Wiley Blackwell, vol. 64(4), pages 605-54, October.
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  11. Zhao, Zhong, 2008. "Sensitivity of propensity score methods to the specifications," Economics Letters, Elsevier, vol. 98(3), pages 309-319, March.
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  13. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
  14. Juan Jose Diaz & Sudhanshu Handa, 2006. "An Assessment of Propensity Score Matching as a Nonexperimental Impact Estimator: Evidence from Mexico’s PROGRESA Program," Journal of Human Resources, University of Wisconsin Press, vol. 41(2).
  15. Koenker, Roger & Yoon, Jungmo, 2009. "Parametric links for binary choice models: A Fisherian-Bayesian colloquy," Journal of Econometrics, Elsevier, vol. 152(2), pages 120-130, October.
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Cited by:
  1. Carlos A. Flores & Oscar A. Mitnik, 2011. "Comparing Treatments across Labor Markets: An Assessment of Nonexperimental Multiple-Treatment Strategies," Working Papers 2011-10, University of Miami, Department of Economics.
  2. Sarah Hamersma & Carolyn Heinrich, 2008. "Temporary Help Service Firms' Use of Employer Tax Credits: Implications for Disadvantaged Workers' Labor Market Outcomes," Southern Economic Journal, Southern Economic Association, vol. 74(4), pages 1123-1148, April.
  3. Sánchez, Elmer, 2013. "Grado de inversión y flujos de inversión directa extranjera a economías emergentes," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 26, pages 61-79.
  4. Sánchez, Elmer, 2013. "Grado de inversión y flujos de inversión directa extranjera a economías emergentes," Working Papers 2013-010, Banco Central de Reserva del Perú.
  5. Dolton, Peter & Smith, Jeffrey A., 2011. "The Impact of the UK New Deal for Lone Parents on Benefit Receipt," IZA Discussion Papers 5491, Institute for the Study of Labor (IZA).
  6. Martin Biewen & Bernd Fitzenberger & Aderonke Osikominu & Marie Paul, 2012. "The Effectiveness of Public Sponsored Training Revisited: The Importance of Data and Methodological Choices," NRN working papers 2012-09, The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria.
  7. Browning, Martin & Crossley, Thomas F., 2008. "The long-run cost of job loss as measured by consumption changes," Journal of Econometrics, Elsevier, vol. 145(1-2), pages 109-120, July.
  8. Francesca MARCHETTA & Simone BERTOLI, 2014. "Migration, remittances and poverty in Ecuador," Working Papers 201407, CERDI.
  9. Gueorgui Kambourov & Iourii Manovskii & Miana Plesca, 2012. "Occupational Mobility and the Returns to Training," Working Papers tecipa-444, University of Toronto, Department of Economics.
  10. Jose C. GALDO & Jeffrey SMITH & Dan BLACK, 2008. "Bandwidth Selection and the Estimation of Treatment Effects with Unbalanced Data," Annales d'Economie et de Statistique, ENSAE, issue 91-92, pages 189-216.

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