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Linear Social Interactions Models

Author Info

  • Blume, Lawrence E.

    (Department of Economics, Cornell University, Ithaca, USA, and Santa Fe Institute and IHS Vienna)

  • Brock, William A.

    (Economics Department, University of Wisconsin-Madison, USA and University of Missouri, Columbia)

  • Durlauf, Steven N.

    (Department of Economics, University of Wisconsin-Madison, USA)

  • Jayaraman, Rajshri

    (European School of Management and Technology, Berlin, Germany)

Abstract

This paper provides a systematic analysis of identification in linear social interactions models. This is both a theoretical and an econometric exercise as the analysis is linked to a rigorously delineated model of interdependent decisions. We develop an incomplete information game that describes individual choices in the presence of social interactions. The equilibrium strategy profiles are linear. Standard models in the empirical social interactions literature are shown to be exact or approximate special cases of our general framework, which in turn provides a basis for understanding the microeconomic foundations of those models. We consider identification of both endogenous (peer) and contextual social effects under alternative assumptions on a priori information about network structure available to an analyst, and contrast the informational content of individual-level and aggregated data. Finally, we discuss potential ramifications for identification of endogenous group selection and differences between the information sets of analysts and agents.

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File URL: http://www.ihs.ac.at/publications/eco/es-298.pdf
File Function: First version, 2013
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Paper provided by Institute for Advanced Studies in its series Economics Series with number 298.

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Length: 55 pages
Date of creation: Jun 2013
Date of revision:
Handle: RePEc:ihs:ihsesp:298
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Keywords: Social interactions; identification; incomplete information games;

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Find related papers by JEL classification:
  • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
  • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
  • 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
  • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
  • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
  • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Social and Economic Stratification

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  1. Robert Bifulco & Jason M. Fletcher & Stephen L. Ross, 2011. "The Effect of Classmate Characteristics on Post-secondary Outcomes: Evidence from the Add Health," American Economic Journal: Economic Policy, American Economic Association, vol. 3(1), pages 25-53, February.
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  3. Ivar Ekeland & James J. Heckman & Lars Nesheim, 2004. "Identification and Estimation of Hedonic Models," Journal of Political Economy, University of Chicago Press, vol. 112(S1), pages S60-S109, February.
  4. repec:att:wimass:9127 is not listed on IDEAS
  5. Ryo Nakajima, 2007. "Measuring Peer Effects on Youth Smoking Behaviour," Review of Economic Studies, Oxford University Press, vol. 74(3), pages 897-935.
  6. Glaeser, Edward L & Sacerdote, Bruce & Scheinkman, Jose A, 1996. "Crime and Social Interactions," The Quarterly Journal of Economics, MIT Press, vol. 111(2), pages 507-48, May.
  7. Bruce Sacerdote, 2001. "Peer Effects With Random Assignment: Results For Dartmouth Roommates," The Quarterly Journal of Economics, MIT Press, vol. 116(2), pages 681-704, May.
  8. Mari Rege & Kjetil Telle & Mark Votruba, 2012. "Social Interaction Effects in Disability Pension Participation: Evidence from Plant Downsizing," Scandinavian Journal of Economics, Wiley Blackwell, vol. 114(4), pages 1208-1239, December.
  9. D'Haultfoeuille, Xavier & Davezies, Laurent & Fougère, Denis, 2006. "Identification of Peer Effects Using Group Size Variation," CEPR Discussion Papers 5865, C.E.P.R. Discussion Papers.
  10. Piero Cipollone & Alfonso Rosolia, 2006. "Social Interactions in High School: Lessons from an Earthquake," Temi di discussione (Economic working papers) 596, Bank of Italy, Economic Research and International Relations Area.
  11. Manski, Charles F, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," Review of Economic Studies, Wiley Blackwell, vol. 60(3), pages 531-42, July.
  12. McManus, Douglas A., 1992. "How common is identification in parametric models?," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 5-23.
  13. Henderson, Vernon & Mieszkowski, Peter & Sauvageau, Yvon, 1978. "Peer group effects and educational production functions," Journal of Public Economics, Elsevier, vol. 10(1), pages 97-106, August.
  14. Timothy G. Conley & Christopher R. Udry, 2010. "Learning about a New Technology: Pineapple in Ghana," American Economic Review, American Economic Association, vol. 100(1), pages 35-69, March.
  15. Epple, Dennis & Romano, Richard E, 1998. "Competition between Private and Public Schools, Vouchers, and Peer-Group Effects," American Economic Review, American Economic Association, vol. 88(1), pages 33-62, March.
  16. Lee, Lung-fei, 2007. "Identification and estimation of econometric models with group interactions, contextual factors and fixed effects," Journal of Econometrics, Elsevier, vol. 140(2), pages 333-374, October.
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