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Evaluating Social Programs with Endogenous Program Placement and Selection of the Treated

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  • Todd, Petra E.

Abstract

This chapter considers methods for evaluating the impact of social programs in the presence of nonrandom program placement or program selection. It first presents the evaluation problem as a missing data problem and then considers various solutions proposed in the statistics and econometrics literature. For ex post evaluation, the following estimation methods are discussed: traditional regression methods, matching, control function methods, instrumental variable and local instrumental variable (LIV) methods, and regression-discontinuity. Alternative estimators are described along with their identifying assumptions, the behavioral implications of those assumptions, and the data requirements for implementation. The chapter also considers methods for ex ante evaluation, which can be used to assess the effects of programs prior to their implementation, for example, in trying to design a program that achieves some desired outcomes for a given cost. Throughout the chapter, numerous examples from the development literature illustrate applications of the different estimation methods and highlight factors affecting estimator performance.

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Bibliographic Info

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This chapter was published in:

  • T. Paul Schultz & John A. Strauss (ed.), 2008. "Handbook of Development Economics," Handbook of Development Economics, Elsevier, edition 1, volume 4, number 5, January.
    This item is provided by Elsevier in its series Handbook of Development Economics with number 5-60.

    Handle: RePEc:eee:devchp:5-60

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    Web page: http://www.elsevier.com/wps/find/bookseriesdescription.cws_home/BS_HE/description

    Related research

    Keywords: program evaluation; self-selection; treatment effects; matching; control function; regression-discontinuity; instrumental variables;

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    Cited by:
    1. Lucia Mangiavacchi & Paolo Verme, 2009. "Evaluating Pro-poor Transfers When Targeting is Weak: The Albanian Ndihma Ekonomike Program Revisited," Working Papers - Economics wp2009_08.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    2. Sudhanshu Handa & John A. Maluccio, 2010. "Matching the Gold Standard: Comparing Experimental and Nonexperimental Evaluation Techniques for a Geographically Targeted Program," Economic Development and Cultural Change, University of Chicago Press, vol. 58(3), pages 415-447, 04.
    3. Henson, Spencer & Masakure, Oliver & Cranfield, John, 2011. "Do Fresh Produce Exporters in Sub-Saharan Africa Benefit from GlobalGAP Certification?," World Development, Elsevier, vol. 39(3), pages 375-386, March.
    4. Labonne, Julien & Chase, Robert S., 2011. "Do community-driven development projects enhance social capital? Evidence from the Philippines," Journal of Development Economics, Elsevier, vol. 96(2), pages 348-358, November.
    5. Rodríguez, Catherine & Sánchez, Fabio & Armenta, Armando, 2010. "Do Interventions at School Level Improve Educational Outcomes? Evidence from a Rural Program in Colombia," World Development, Elsevier, vol. 38(3), pages 415-428, March.
    6. Arimoto, Yutaka, 2010. "Impact of land readjustment project on farmland use and structural adjustment: The case of Niigata, Japan," 2010 Annual Meeting, July 25-27, 2010, Denver, Colorado 61278, Agricultural and Applied Economics Association.
    7. Raghbendra Jha & Katsushi S. Imai & Raghav Gaiha, 2008. "Poverty, Undernutrition and Vulnerability in Rural India: Public Works versus Food Subsidy," ASARC Working Papers 2008-08, The Australian National University, Australia South Asia Research Centre.

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