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The Porous Dialectic: Experimental and Non-Experimental Methods in Development Economics

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  • Dehejia, Rajeev

Abstract

This paper provides a survey of six widely used non-experimental methods for estimating the impact of programmes in the context of developing economies (instrumental variables, regression discontinuity, direct matching, propensity score matching, linear regression and non-parametric methods, and difference-in-differences), and assesses their internal and external validity relative both to each other and to randomized controlled trials. While randomized controlled trials can achieve the highest degree of internal validity when cleanly implemented in the field, the availability of large, nationally representative datasets offers the opportunity for a high degree of external validity using non-experimental methods. Whereas these methods are often presented as competing alternatives, we argue that each method has merits in some context and that experimental and non-experimental methods are complements rather than substitutes.

Suggested Citation

  • Dehejia, Rajeev, 2013. "The Porous Dialectic: Experimental and Non-Experimental Methods in Development Economics," WIDER Working Paper Series 011, World Institute for Development Economic Research (UNU-WIDER).
  • Handle: RePEc:unu:wpaper:wp2013-011
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    1. 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.
    2. Card, David & Krueger, Alan B, 1994. "Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania," American Economic Review, American Economic Association, vol. 84(4), pages 772-793, September.
    3. Esther Duflo, 2001. "Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Policy Experiment," American Economic Review, American Economic Association, vol. 91(4), pages 795-813, September.
    4. Subramanian, Shankar & Deaton, Angus, 1996. "The Demand for Food and Calories," Journal of Political Economy, University of Chicago Press, vol. 104(1), pages 133-162, February.
    5. Hristos Doucouliagos & Martin Paldam, 2005. "Aid Effectiveness on Growth. A Meta Study," Economics Working Papers 2005-13, Department of Economics and Business Economics, Aarhus University.
    6. Markus Frlich, 2004. "Finite-Sample Properties of Propensity-Score Matching and Weighting Estimators," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 77-90, February.
    7. Wilbert van der Klaauw, 2008. "Regression-Discontinuity Analysis: A Survey of Recent Developments in Economics," LABOUR, CEIS, vol. 22(2), pages 219-245, June.
    8. 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, July.
    9. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, January.
    10. 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).
    11. Tseday Jemaneh Mekasha & Finn Tarp, 2013. "Aid and Growth: What Meta-Analysis Reveals," Journal of Development Studies, Taylor & Francis Journals, vol. 49(4), pages 564-583, April.
    12. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    13. Sylvie Moulin & Michael Kremer & Paul Glewwe, 2009. "Many Children Left Behind? Textbooks and Test Scores in Kenya," American Economic Journal: Applied Economics, American Economic Association, vol. 1(1), pages 112-135, January.
    14. Martin Huber, 2011. "Testing for covariate balance using quantile regression and resampling methods," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(12), pages 2881-2899, February.
    15. 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.
    16. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    17. Joshua Angrist & Eric Bettinger & Erik Bloom & Elizabeth King & Michael Kremer, 2002. "Vouchers for Private Schooling in Colombia: Evidence from a Randomized Natural Experiment," American Economic Review, American Economic Association, vol. 92(5), pages 1535-1558, December.
    18. Wilbert van der Klaauw, 2002. "Estimating the Effect of Financial Aid Offers on College Enrollment: A Regression-Discontinuity Approach," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 43(4), pages 1249-1287, November.
    19. Deaton, Angus, 1989. "Rice Prices and Income Distribution in Thailand: A Non-parametric Analysis," Economic Journal, Royal Economic Society, vol. 99(395), pages 1-37, Supplemen.
    20. Jere R. Behrman & Yingmei Cheng & Petra E. Todd, 2004. "Evaluating Preschool Programs When Length of Exposure to the Program Varies: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 108-132, February.
    21. Karthik Muralidharan & Venkatesh Sundararaman, 2011. "Teacher Performance Pay: Experimental Evidence from India," Journal of Political Economy, University of Chicago Press, vol. 119(1), pages 39-77.
    22. Grant Miller & Diana Pinto & Marcos Vera-Hernández, 2013. "Risk Protection, Service Use, and Health Outcomes under Colombia's Health Insurance Program for the Poor," American Economic Journal: Applied Economics, American Economic Association, vol. 5(4), pages 61-91, October.
    23. Ozier,Owen, 2015. "The impact of secondary schooling in Kenya : a regression discontinuity analysis," Policy Research Working Paper Series 7384, The World Bank.
    24. 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.
    25. Shaikh, Azeem M. & Simonsen, Marianne & Vytlacil, Edward J. & Yildiz, Nese, 2009. "A specification test for the propensity score using its distribution conditional on participation," Journal of Econometrics, Elsevier, vol. 151(1), pages 33-46, July.
    26. Martin Ravallion & Emanuela Galasso & Teodoro Lazo & Ernesto Philipp, 2005. "What Can Ex-Participants Reveal about a Program’s Impact?," Journal of Human Resources, University of Wisconsin Press, vol. 40(1).
    27. Adriana Camacho & Emily Conover, 2011. "Manipulation of Social Program Eligibility," American Economic Journal: Economic Policy, American Economic Association, vol. 3(2), pages 41-65, May.
    28. 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, January.
    29. 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).
    30. Thomas Fraker & Rebecca Maynard, 1987. "The Adequacy of Comparison Group Designs for Evaluations of Employment-Related Programs," Journal of Human Resources, University of Wisconsin Press, vol. 22(2), pages 194-227.
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    Keywords

    Economic development; Methodology (Quantitative research); Regression analysis;

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