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Identification and Estimation of Outcome Response with Heterogeneous Treatment Externalities

This paper studies the identification and estimation of treatment response with heterogeneous spillovers in a network model. We generalize the standard linear-in-means model to allow for multiple groups with between and within-group interactions. We provide a set of identification conditions of peer effects and consider a 2SLS estimation approach. Large sample properties of the proposed estimators are derived. Simulation experiments show that the estimators perform well in finite samples. The model is used to study the effectiveness of policies where peer effects are seen as a mechanism through which the treatments could propagate through the network. When interactions among groups are at work, a shock on a treated group has effects on the non-treated. Our framework allows for quantifying how much of the indirect treatment effect is due to variations in the characteristics of treated peers (treatment contextual effects) and how much is because of variations in peer outcomes (peer effects).

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Paper provided by Center for Policy Research, Maxwell School, Syracuse University in its series Center for Policy Research Working Papers with number 167.

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Length: 39 pages
Date of creation: Apr 2014
Date of revision:
Handle: RePEc:max:cprwps:167
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  1. Stanislav Anatolyev, 2012. "Instrumental variables estimation and inference in the presence of many exogenous regressors," Working Papers w0162, Center for Economic and Financial Research (CEFIR).
  2. H. Kelejian, Harry & Prucha, Ingmar R., 2001. "On the asymptotic distribution of the Moran I test statistic with applications," Journal of Econometrics, Elsevier, vol. 104(2), pages 219-257, September.
  3. Angelucci, Manuela & De Giorgi, Giacomo & Rangel, Marcos A. & Rasul, Imran, 2009. "Family Networks and School Enrolment: Evidence from a Randomized Social Experiment," IZA Discussion Papers 4497, Institute for the Study of Labor (IZA).
  4. Liu, Xiaodong, 2013. "Estimation of a local-aggregate network model with sampled networks," Economics Letters, Elsevier, vol. 118(1), pages 243-246.
  5. Paul Goldsmith-Pinkham & Guido W. Imbens, 2013. "Social Networks and the Identification of Peer Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 253-264, July.
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