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Estimating ATT Effects with Non-Experimental Data and Low Compliance

  • Angelucci, Manuela

    ()

    (University of Michigan)

  • Attanasio, Orazio

    ()

    (University College London)

In this paper we discuss several methodological issues related to the identification and estimation of Average Treatment on the Treated (ATT) effects in the presence of low compliance. We consider non-experimental data consisting of a treatment group, where a program is implemented, and of a control group that is non-randomly drawn, where the program is not offered. Estimating the ATT involves tackling both the non-random assignment of the program and the non-random participation among treated individuals. We argue against standard matching approaches to deal with the latter issue because they are based on the assumption that we observe all variables that determine both participation and outcome. Instead, we propose an IV-type estimator which exploits the fact that the ATT can be expressed as the Average Intent to Treat divided by the participation share, in the absence of spillover effects. We propose a semi-parametric estimator that couples the flexibility of matching estimators with a standard Instrumental Variable approach. We discuss the different assumptions necessary for the identification of the ATT with each of the two approaches, and we provide an empirical application by estimating the effect of the Mexican conditional cash transfer program, Oportunidades, on food consumption.

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Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 2368.

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Length: 28 pages
Date of creation: Oct 2006
Date of revision:
Handle: RePEc:iza:izadps:dp2368
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  1. J.D. Angrist & Guido W. Imbens & D.B. Rubin, 1993. "Identification of Causal Effects Using Instrumental Variables," NBER Technical Working Papers 0136, National Bureau of Economic Research, Inc.
  2. Joshua D. Angrist & Guido W. Imbens, 1995. "Identification and Estimation of Local Average Treatment Effects," NBER Technical Working Papers 0118, National Bureau of Economic Research, Inc.
  3. Hoddinott, John & Skoufias, Emmanual, 2003. "The impact of Progresa on food consumption," FCND discussion papers 150, International Food Policy Research Institute (IFPRI).
  4. Jeffrey Smith & Petra Todd, 2003. "Does Matching Overcome Lalonde's Critique of Nonexperimental Estimators?," University of Western Ontario, CIBC Centre for Human Capital and Productivity Working Papers 20035, University of Western Ontario, CIBC Centre for Human Capital and Productivity.
  5. Robert J. LaLonde, 1995. "The Promise of Public Sector-Sponsored Training Programs," Journal of Economic Perspectives, American Economic Association, vol. 9(2), pages 149-168, Spring.
  6. Gertler, Paul & Martinez, Sebastian & Rubio-Codina, Marta, 2006. "Investing cash transfers to raise long term living standards," Policy Research Working Paper Series 3994, The World Bank.
  7. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
  8. James Heckman & Jeffrey Smith & Christopher Taber, 1998. "Accounting For Dropouts In Evaluations Of Social Programs," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 1-14, February.
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