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Estimating ATT Effects with Non-Experimental Data and Low Compliance

Author

Listed:
  • Angelucci, Manuela

    () (University of Texas at Austin)

  • Attanasio, Orazio

    () (University College London)

Abstract

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.

Suggested Citation

  • Angelucci, Manuela & Attanasio, Orazio, 2006. "Estimating ATT Effects with Non-Experimental Data and Low Compliance," IZA Discussion Papers 2368, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp2368
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    References listed on IDEAS

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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. Hoddinott, John & Skoufias, Emmanuel, 2004. "The Impact of PROGRESA on Food Consumption," Economic Development and Cultural Change, University of Chicago Press, vol. 53(1), pages 37-61, October.
    3. 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.
    4. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
    5. 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.
    6. Paul J. Gertler & Sebastian W. Martinez & Marta Rubio-Codina, 2012. "Investing Cash Transfers to Raise Long-Term Living Standards," American Economic Journal: Applied Economics, American Economic Association, vol. 4(1), pages 164-192, January.
    7. 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.
    8. 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.
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    Citations

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    Cited by:

    1. Manuela Angelucci & Orazio Attanasio, 2009. "Oportunidades: Program Effect on Consumption, Low Participation, and Methodological Issues," Economic Development and Cultural Change, University of Chicago Press, vol. 57(3), pages 479-506, April.
    2. Crombrugghe Denis de & Espinoza Henry & Heijke Hans, 2010. "Job-training programmes with low completion rates: The case of Projoven-Peru," ROA Research Memorandum 004, Maastricht University, Research Centre for Education and the Labour Market (ROA).
    3. Cavatassi, Ramina & González-Flores, Mario & Winters, Paul & Andrade-Piedra, Jorge & Espinosa, Patricio & Thiele, Graham, 2016. "Linking smallholders to the new agricultural economy: The case of the plataformas de concertación in Ecuador," IFPRI book chapters,in: Innovation for inclusive value-chain development: Successes and challenges, chapter 12, pages 375-410 International Food Policy Research Institute (IFPRI).
    4. Asadul Islam Author-X-Name-Asadul, 2008. "Who Benefits From Microfinance? The Impact Evaluation Of Large Scale Programs In Bangladesh," Monash Economics Working Papers 29/08, Monash University, Department of Economics.
    5. Shubha Chakravarty & Mattias Lundberg & Plamen Nikolov & Juliane Zenker, 2017. "Vocational Training Programs and Youth Labor Market Outcomes: Evidence from Nepal," Working Papers 2017-056, Human Capital and Economic Opportunity Working Group.
    6. Chakravarty,Shubha & Lundberg,Mattias K. A. & Zenker,Juliane & Nikolov, Plamen V., 2016. "The role of training programs for youth employment in Nepal : impact evaluation report on the employment fund," Policy Research Working Paper Series 7656, The World Bank.
    7. Pettersson, Jan & Wikström, Johan, 2013. "Peeing out of poverty? Human fertilizer and the productivity of farming households," Working Paper Series 2013:1, Uppsala University, Department of Economics.

    More about this item

    Keywords

    program evaluation; treatment effects;

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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