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Accounting for Peer Effects in Treatment Response

Author

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  • Rokhaya Dieye

    (ULaval - Université Laval [Québec])

  • Habiba Djebbari

    (ULaval - Université Laval [Québec], GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - ECM - École Centrale de Marseille - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - AMU - Aix Marseille Université)

  • Felipe Barrera-Osorio

    (Havard Graduate School of Education - Harvard University [Cambridge])

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.

Suggested Citation

  • Rokhaya Dieye & Habiba Djebbari & Felipe Barrera-Osorio, 2014. "Accounting for Peer Effects in Treatment Response," Working Papers halshs-01025680, HAL.
  • Handle: RePEc:hal:wpaper:halshs-01025680
    Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-01025680
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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. Yann Bramoullé & Habiba Djebbari & Bernard Fortin, 2020. "Peer Effects in Networks: A Survey," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 603-629, August.
    3. Gonzalo Vazquez-Bare, 2017. "Identification and Estimation of Spillover Effects in Randomized Experiments," Papers 1711.02745, arXiv.org, revised Jan 2022.
    4. Belhaj, Mohamed & Deroïan, Frédéric, 2019. "Group targeting under networked synergies," Games and Economic Behavior, Elsevier, vol. 118(C), pages 29-46.
    5. Babcock, Philip & Bedard, Kelly & Fischer, Stefanie & Hartman, John, 2020. "Coordination and contagion: Individual connections and peer mechanisms in a randomized field experiment," Journal of Public Economics, Elsevier, vol. 185(C).
    6. Áureo de Paula, 2015. "Econometrics of network models," CeMMAP working papers CWP52/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Boucher, Vincent & Fortin, Bernard, 2015. "Some Challenges in the Empirics of the Effects of Networks," IZA Discussion Papers 8896, Institute of Labor Economics (IZA).
    8. Michael P. Leung, 2022. "Causal Inference Under Approximate Neighborhood Interference," Econometrica, Econometric Society, vol. 90(1), pages 267-293, January.
    9. 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.
    10. 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.
    11. 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.
    12. 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.

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    More about this item

    Keywords

    social network; education; impact evaluation;
    All these keywords.

    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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