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On the Implications of Essential Heterogeneity for Estimating Causal Impacts Using Social Experiments

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  • Ravallion Martin

    (Department of Economics, Georgetown University, Washington DC, USA)

Abstract

The standard model of essential heterogeneity, whereby program take up depends on unobserved costs and benefits of take up, is generalized to allow the source of latent heterogeneity to influence counterfactual outcomes. The standard instrumental variables (IV) estimator is shown to still be preferable to the naïve, ordinary least squares (OLS), estimator for mean impact on the treated. However, under certain conditions, the IV estimate of the overall mean impact will be even more biased than OLS. Examples are given for stylized training, insurance and microcredit schemes.

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  • Ravallion Martin, 2015. "On the Implications of Essential Heterogeneity for Estimating Causal Impacts Using Social Experiments," Journal of Econometric Methods, De Gruyter, vol. 4(1), pages 145-151, January.
    • Handle: RePEc:bpj:jecome:v:4:y:2015:i:1:p:7:n:6
    DOI: 10.1515/jem-2013-0009
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    References listed on IDEAS

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    1. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 389-432, August.
    2. James J. Heckman & Vytlacil, Edward J., 2007. "Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 71, Elsevier.
    3. Heckman, James J. & Robb, Richard Jr., 1985. "Alternative methods for evaluating the impact of interventions : An overview," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 239-267.
    4. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    5. Howard S. Bloom, 1984. "Accounting for No-Shows in Experimental Evaluation Designs," Evaluation Review, , vol. 8(2), pages 225-246, April.
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    Cited by:

    1. Mattoo, Aaditya & Cadot, Olivier & Gourdon, Julien & Fernandes, Ana Margarida, 2011. "Impact Evaluation of Trade Interventions: Paving the Way," CEPR Discussion Papers 8638, C.E.P.R. Discussion Papers.
    2. Lant Pritchett & Salimah Samji & Jeffrey S. Hammer, 2012. "It's All about MeE: Using Structured Experiential Learning ('e') to Crawl the Design Space," WIDER Working Paper Series wp-2012-104, World Institute for Development Economic Research (UNU-WIDER).
    3. repec:pri:rpdevs:hammer_its_all_about_me is not listed on IDEAS
    4. Lant Pritchett & Salimah Samji & Jeffrey Hammer, 2012. "It’s All About MeE: Using Structured Experiential Learning (‘e’) to Crawl the Design Space," CID Working Papers 249, Center for International Development at Harvard University.
    5. Chris Elbers & Jan Willem Gunning, 2014. "Evaluation of Non-Governmental Development Organizations," WIDER Working Paper Series wp-2014-026, World Institute for Development Economic Research (UNU-WIDER).
    6. Elbers, Chris & Gunning, Jan Willem, 2014. "Evaluation of non-governmental development organizations," WIDER Working Paper Series 026, World Institute for Development Economic Research (UNU-WIDER).

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    More about this item

    Keywords

    essential heterogeneity; experiments; instrumental variables; randomization; selective compliance;
    All these keywords.

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • H43 - Public Economics - - Publicly Provided Goods - - - Project Evaluation; Social Discount Rate

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