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Matching the gold standard: Comparing experimental and non-experimental evaluation techniques for a geographically targeted program

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Author Info
Sudhanshu Handa
John Maluccio ()
Abstract

We compare non-experimental impact estimates based on matching methods with those from a randomized evaluation to determine whether the non-experimental approach can “match” the so-called gold standard. The social experiment we use was carried out to evaluate a geographically targeted conditional cash transfer antipoverty program in Nicaragua. The outcomes we assess include several components of household expenditure and a variety of children’s health outcomes including breast feeding, vaccinations, and morbidity. We find that using each of the following improves performance of matching for these outcomes: 1) geographically proximate comparison samples; 2) stringent common support requirements; and 3) both geographic- and household-level matching variables. Even for a geographically targeted program, in which the selection is at the geographic-, rather than at the individual- or household-level, and in which it is not possible to find comparison individuals or households in the program locales, matching can perform reasonably well. The results also suggest that the techniques may be more promising for evaluating the more easily measured individual-level binary outcomes, than for outcomes that are more difficult to measure, such as expenditure.

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Paper provided by Middlebury College, Department of Economics in its series Middlebury College Working Paper Series with number 0813.

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Length: 59 pages
Date of creation: 2008
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Handle: RePEc:mdl:mdlpap:0813

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    Other versions:
  2. Guido Imbens & Jeffrey Wooldridge, 2008. "Recent developments in the econometrics of program evaluation," CeMMAP working papers CWP24/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. [Downloadable!]
  3. James Heckman & Salvador Navarro-Lozano, 2004. "Using Matching, Instrumental Variables, and Control Functions to Estimate Economic Choice Models," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 30-57, 08. [Downloadable!] (restricted)
    Other versions:
  4. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    Other versions:
  5. Daniel O. Gilligan & John Hoddinott, 2007. "Is There Persistence in the Impact of Emergency Food Aid? Evidence on Consumption, Food Security, and Assets in Rural Ethiopia," American Journal of Agricultural Economics, American Agricultural Economics Association, vol. 89(2), pages 225-242, 05. [Downloadable!] (restricted)
    Other versions:
  6. Imbens, Guido W. & Wooldridge, Jeffrey M., 2008. "Recent Developments in the Econometrics of Program Evaluation," IZA Discussion Papers 3640, Institute for the Study of Labor (IZA). [Downloadable!]
  7. Jyotsna Jalan & Martin Ravallion, 2000. "Estimating the Benefit Incidence of an Antipoverty Program by Propensity Score Matching," Econometric Society World Congress 2000 Contributed Papers 0873, Econometric Society. [Downloadable!]
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  8. Todd, Petra E., 2008. "Evaluating Social Programs with Endogenous Program Placement and Selection of the Treated," Handbook of Development Economics, Elsevier. [Downloadable!] (restricted)
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  15. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353. [Downloadable!] (restricted)
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  16. David I. Levine & Gary Painter, 2003. "The Schooling Costs of Teenage Out-of-Wedlock Childbearing: Analysis with a Within-School Propensity-Score-Matching Estimator," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 884-900, 06. [Downloadable!] (restricted)
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  18. 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. [Downloadable!] (restricted)
  19. Alberto Abadie & David Drukker & Jane Leber Herr & Guido W. Imbens, 2004. "Implementing matching estimators for average treatment effects in Stata," Stata Journal, StataCorp LP, vol. 4(3), pages 290-311, September. [Downloadable!]
  20. Guido M. Imbens & Jeffrey M. Wooldridge, 2008. "Recent Developments in the Econometrics of Program Evaluation," NBER Working Papers 14251, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  21. Alberto Abadie & Guido W. Imbens, 2008. "On the Failure of the Bootstrap for Matching Estimators," Econometrica, Econometric Society, vol. 76(6), pages 1537-1557, November. [Downloadable!] (restricted)
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  22. Roberto Agodini & Mark Dynarski, 2004. "Are Experiments the Only Option? A Look at Dropout Prevention Programs," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 180-194, 09. [Downloadable!] (restricted)
  23. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2006. "Moving the Goalposts: Addressing Limited Overlap in the Estimation of Average Treatment Effects by Changing the Estimand," NBER Technical Working Papers 0330, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  24. 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, 06. [Downloadable!] (restricted)
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