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The specification of the propensity score in multilevel observational studies

  • Arpino, Bruno
  • Mealli, Fabrizia

Propensity Score Matching (PSM) has become a popular approach to estimation of causal effects. It relies on the assumption that selection into a treatment can be explained purely in terms of observable characteristics (the “unconfoundedness assumption”) and on the property that balancing on the propensity score is equivalent to balancing on the observed covariates. Several applications in social sciences are characterized by a hierarchical structure of data: units at the first level (e.g., individuals) clustered into groups (e.g., provinces). In this paper we explore the use of multilevel models for the estimation of the propensity score for such hierarchical data when one or more relevant cluster-level variables is unobserved. We compare this approach with alternative ones, like a single level model with cluster dummies. By using Monte Carlo evidence we show that multilevel specifications usually achieve reasonably good balancing in cluster level unobserved covariates and consequently reduce the omitted variable bias. This is also the case for the dummy model.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 17407.

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Date of creation: 2008
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Handle: RePEc:pra:mprapa:17407
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  1. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, 02.
  2. Dehejia, Rajeev H, 2003. "Was There a Riverside Miracle? A Hierarchical Framework for Evaluating Programs with Grouped Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 1-11, January.
  3. Zhao, Zhong, 2008. "Sensitivity of propensity score methods to the specifications," Economics Letters, Elsevier, vol. 98(3), pages 309-319, March.
  4. Wang, Qihua & Lai, Peng, 2011. "Empirical likelihood calibration estimation for the median treatment difference in observational studies," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1596-1609, April.
  5. Wooldridge, Jeffrey M. & Imbens, Guido, 2009. "Recent Developments in the Econometrics of Program Evaluation," Scholarly Articles 3043416, Harvard University Department of Economics.
  6. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-23, May.
  7. Jeffrey Smith & Petra Todd, 2003. "Does Matching Overcome Lalonde's Critique of Nonexperimental Estimators?," University of Western Ontario, CIBC Centre for Human Capital and Productivity Working Papers 20035, University of Western Ontario, CIBC Centre for Human Capital and Productivity.
  8. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
  9. Andrea Ichino & Fabrizia Mealli & Tommaso Nannicini, 2008. "From temporary help jobs to permanent employment: what can we learn from matching estimators and their sensitivity?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(3), pages 305-327.
  10. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, 07.
  11. Aassve, Arnstein & Betti, Gianni & Mazzuco, Stefano & Mencarini, Letizia, 2006. "Marital disruption and economic well-being: a comparative analysis," ISER Working Paper Series 2006-07, Institute for Social and Economic Research.
  12. Leon, Andrew C. & Hedeker, Donald, 2007. "Quintile stratification based on a misspecified propensity score in longitudinal treatment effectiveness analyses of ordinal doses," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6114-6122, August.
  13. Charles Michalopoulos & Howard S. Bloom & Carolyn J. Hill, 2004. "Can Propensity-Score Methods Match the Findings from a Random Assignment Evaluation of Mandatory Welfare-to-Work Programs?," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 156-179, February.
  14. Richard Blundell & Lorraine Dearden & Barbara Sianesi, 2004. "Evaluating the impact of education on earnings in the UK: models, methods and results from the NCDS," LSE Research Online Documents on Economics 19451, London School of Economics and Political Science, LSE Library.
  15. Hong, Guanglei & Raudenbush, Stephen W., 2006. "Evaluating Kindergarten Retention Policy: A Case Study of Causal Inference for Multilevel Observational Data," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 901-910, September.
  16. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, 01.
  17. Rosenbaum, Paul R. & Ross, Richard N. & Silber, Jeffrey H., 2007. "Minimum Distance Matched Sampling With Fine Balance in an Observational Study of Treatment for Ovarian Cancer," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 75-83, March.
  18. Alex Bryson, 2002. "The union membership wage premium: an analysis using propensity score matching," LSE Research Online Documents on Economics 4953, London School of Economics and Political Science, LSE Library.
  19. Alex Bryson & Richard Dorsett & Susan Purdon, 2002. "The use of propensity score matching in the evaluation of active labour market policies," LSE Research Online Documents on Economics 4993, London School of Economics and Political Science, LSE Library.
  20. repec:oup:restud:v:64:y:1997:i:4:p:605-54 is not listed on IDEAS
  21. Aassve, Arnstein & Arpino, Bruno, 2008. "Estimation of causal effects of fertility on economic wellbeing: evidence from rural Vietnam," ISER Working Paper Series 2007-27, Institute for Social and Economic Research.
  22. Jeffrey Smith, 2000. "A Critical Survey of Empirical Methods for Evaluating Active Labor Market Policies," UWO Department of Economics Working Papers 20006, University of Western Ontario, Department of Economics.
  23. Oakes, J. Michael, 2004. "The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiology," Social Science & Medicine, Elsevier, vol. 58(10), pages 1929-1952, May.
  24. repec:oup:restud:v:65:y:1998:i:2:p:261-94 is not listed on IDEAS
  25. Friedlander, Daniel & Robins, Philip K, 1995. "Evaluating Program Evaluations: New Evidence on Commonly Used Nonexperimental Methods," American Economic Review, American Economic Association, vol. 85(4), pages 923-37, September.
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