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Accounting for Peer Effects in Treatment Response

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Abstract

When one’s treatment status affects the outcomes of others, experimental data are not sufficient to identify a treatment causal impact. In order to account for peer effects in program response, we use a social network model. We estimate and validate the model on experimental data collected for the evaluation of a scholarship program in Colombia. By design, randomization is at the student-level. Friendship data reveals that treated and untreated students interact together. Besides providing evidence of peer effects in schooling, we find that ignoring peer effects would have led us to overstate the program actual impact.

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  • Rokhaya Dieye & Habiba Djebbari & Felipe Barrera-Osorio, 2014. "Accounting for Peer Effects in Treatment Response," AMSE Working Papers 1435, Aix-Marseille School of Economics, France, revised Jul 2014.
  • Handle: RePEc:aim:wpaimx:1435
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    Cited by:

    1. Nakano, Yuko & Tsusaka, Takuji W. & Aida, Takeshi & Pede, Valerien O., 2018. "Is farmer-to-farmer extension effective? The impact of training on technology adoption and rice farming productivity in Tanzania," World Development, Elsevier, vol. 105(C), pages 336-351.
    2. Felipe Barrera-Osorio & Leigh L. Linden & Juan E. Saavedra, 2019. "Medium- and Long-Term Educational Consequences of Alternative Conditional Cash Transfer Designs: Experimental Evidence from Colombia," American Economic Journal: Applied Economics, American Economic Association, vol. 11(3), pages 54-91, July.
    3. Izaguirre, Alejandro & Di Capua, Laura, 2020. "Exploring peer effects in education in Latin America and the Caribbean," Research in Economics, Elsevier, vol. 74(1), pages 73-86.
    4. Yann Bramoullé & Habiba Djebbari & Bernard Fortin, 2019. "Peer Effects in Networks: a Survey," Working Papers halshs-02440709, HAL.
    5. Michael P. Leung, 2019. "Causal Inference Under Approximate Neighborhood Interference," Papers 1911.07085, arXiv.org, revised Nov 2020.
    6. Boucher, Vincent & Fortin, Bernard, 2015. "Some Challenges in the Empirics of the Effects of Networks," IZA Discussion Papers 8896, Institute of Labor Economics (IZA).
    7. Maria Marchenko, 2019. "Dealing with Endogenous Shocks in Dynamic Friendship Network," Department of Economics Working Papers wuwp291, Vienna University of Economics and Business, Department of Economics.
    8. Áureo de Paula, 2015. "Econometrics of network models," CeMMAP working papers CWP52/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Belhaj, Mohamed & Deroïan, Frédéric, 2019. "Group targeting under networked synergies," Games and Economic Behavior, Elsevier, vol. 118(C), pages 29-46.
    10. Arun Advani & Bansi Malde, 2018. "Methods to identify linear network models: a review," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 154(1), pages 1-16, December.
    11. Gonzalo Vazquez-Bare, 2017. "Identification and Estimation of Spillover Effects in Randomized Experiments," Papers 1711.02745, arXiv.org, revised Nov 2020.
    12. Philip Babcock & Kelly Bedard & Stefanie Fischer & John Hartman, 2019. "Coordination and Contagion: Individual Connections and Peer Mechanisms in a Randomized Field Experiment," Working Papers 1904, California Polytechnic State University, Department of Economics.

    More about this item

    Keywords

    Education; social network; impact evaluation;

    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
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • I22 - Health, Education, and Welfare - - Education - - - Educational Finance; Financial Aid

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