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Propensity score matching and policy impact analysis - a demonstration in EViews

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Author Info
Essama-Nssah, B.
Abstract

Effective development policymaking creates a need for reliable methods of assessing effectiveness. There should be, therefore, an intimate relationship between effective policymaking and impact analysis. The goal of a development intervention defines the metric by which to assess its impact, while impact evaluation can produce reliable information on which policymakers may base decisions to modify or cancel ineffective programs and thus make the most of limited resources. This paper reviews the logic of propensity score matching (PSM) and, using data on the National Support Work Demonstration, compares that approach with other evaluation methods such as double difference, instrumental variable, and Heckman's method of selection bias correction. In addition, it demonstrates how to implement nearest-neighbor and kernel-based methods, and plot program incidence curves in E-Views. In the end, the plausibility of an evaluation method hinges critically on the correctness of the socioeconomic model underlying program design and implementation, and on the quality and quantity of available data. In any case, PSM can act as an effective adjuvant to other methods.

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Paper provided by The World Bank in its series Policy Research Working Paper Series with number 3877.

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Date of creation: 01 Apr 2006
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Handle: RePEc:wbk:wbrwps:3877

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Keywords: Poverty Monitoring&Analysis; Poverty Impact Evaluation; Statistical&Mathematical Sciences; Scientific Research&Science Parks; Science Education;

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  3. Jalan, Jyotsna & Ravallion, Martin, 2003. "Estimating the Benefit Incidence of an Antipoverty Program by Propensity-Score Matching," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 19-30, January.
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  4. Robert A. Moffitt, 2004. "Introduction to the Symposium on the Econometrics of Matching," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 1-3, 02. [Downloadable!] (restricted)
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  9. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-20, September. [Downloadable!] (restricted)
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  11. Marco Caliendo & Sabine Kopeinig, 2005. "Some Practical Guidance for the Implementation of Propensity Score Matching," IZA Discussion Papers 1588, Institute for the Study of Labor (IZA). [Downloadable!]
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  12. Richard Blundell & Monica Costa Dias, 2000. "Evaluation methods for non-experimental data," Fiscal Studies, Institute for Fiscal Studies, vol. 21(4), pages 427-468, January. [Downloadable!]
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  17. Rajeev H. Dehejia & Sadek Wahba, 1998. "Causal Effects in Non-Experimental Studies: Re-Evaluating the Evaluation of Training Programs," NBER Working Papers 6586, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  20. Ravallion, Martin, 2005. "Evaluating anti-poverty programs," Policy Research Working Paper Series 3625, The World Bank. [Downloadable!]
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  21. Michael Lokshin & Zurab Sajaia, 2004. "Maximum likelihood estimation of endogenous switching regression models," Stata Journal, StataCorp LP, vol. 4(3), pages 282-289, September. [Downloadable!]
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  23. Lokshin, Michael & Yemtsov, Ruslan, 2003. "Evaluating the impact of infrastructure rehabilitation projects on household welfare in rural Georgia," Policy Research Working Paper Series 3155, The World Bank. [Downloadable!]
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  26. Dabalen, Andrew & Paternostro, Stefano & Pierre, Gaelle, 2004. "The returns to participation in the non-farm sector in rural Rwanda," Policy Research Working Paper Series 3462, The World Bank. [Downloadable!]
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