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Adoption Curves and Social Interactions

Author Info

  • William A. Brock
  • Steven N. Durlauf

Abstract

This paper considers the observational implications of social influences on adoption decisions for an environment of perfect foresight adopters. We argue that social influences can produce two observable effects: (1) discontinuities in unconditional adoption curves and (2) pattern reversals in conditional adoption curves, in which earlier adoption is found for one group of actors versus another when fundamentals suggest the reverse ordering should occur; in turn the presence of either of these features can, under weak assumptions, be interpreted as evidence of social influences. As such, these properties are robust implications of social effects. (JEL: C40, D01, O33) (c) 2010 by the European Economic Association.

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Article provided by MIT Press in its journal Journal of the European Economic Association.

Volume (Year): 8 (2010)
Issue (Month): 1 (03)
Pages: 232-251

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Handle: RePEc:tpr:jeurec:v:8:y:2010:i:1:p:232-251
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Find related papers by JEL classification:
  • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
  • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles
  • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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  1. Rodolfo E. Manuelli & Ananth Seshadri, 2013. "Frictionless technology diffusion: the case of tractors," Working Papers 2013-022, Federal Reserve Bank of St. Louis.
  2. Bandiera, Oriana & Rasul, Imran, 2002. "Social Networks and Technology Adoption in Northern Mozambique," CEPR Discussion Papers 3341, C.E.P.R. Discussion Papers.
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  4. 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.
  5. Brock, William A. & Durlauf, Steven N., 2001. "Interactions-based models," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 54, pages 3297-3380 Elsevier.
  6. de Paula, Áureo, 2009. "Inference in a synchronization game with social interactions," Journal of Econometrics, Elsevier, vol. 148(1), pages 56-71, January.
  7. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
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  14. Ait-Sahalia, Yacine, 2004. "Disentangling diffusion from jumps," Journal of Financial Economics, Elsevier, vol. 74(3), pages 487-528, December.
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Cited by:

  1. Costas Azariadis & Yannis M. Ioannides, 2014. "Thinking About Corruption in Greece," Discussion Papers Series, Department of Economics, Tufts University 0783, Department of Economics, Tufts University.
  2. Tumen, Semih & Zeydanli, Tugba, 2014. "Is Happiness Contagious? Separating Spillover Externalities from the Group-Level Social Context," MPRA Paper 53184, University Library of Munich, Germany.
  3. ÖZGÜR, Onur & BISIN, Alberto, 2011. "Dynamic Linear Economies with Social Interactions," Cahiers de recherche 04-2011, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  4. Steven N. Durlauf & Daniel S. Nagin, 2010. "The Deterrent Effect of Imprisonment," NBER Chapters, in: Controlling Crime: Strategies and Tradeoffs, pages 43-94 National Bureau of Economic Research, Inc.
  5. Georgarakos, Dimitris & Haliassos, Michael & Pasini, Giacomo, 2012. "Household debt and social interactions," CFS Working Paper Series 2012/05, Center for Financial Studies (CFS).
  6. M. Ali Khan & Kali P. Rath & Yeneng Sun & Haomiao Yu, 2011. "On Large Games with a Bio-Social Typology," Economics Working Paper Archive 585, The Johns Hopkins University,Department of Economics.
  7. She, Chih-Min, 2015. "What determines the technology adoption of firms under optimal tax?," International Review of Economics & Finance, Elsevier, vol. 37(C), pages 274-289.
  8. Michael D. Koenig & Xiaodong Liu & Yves Zenou, 2014. "R&D Networks: Theory, Empirics and Policy Implications," Discussion Papers 13-027, Stanford Institute for Economic Policy Research.
  9. M. Ali Khan & Kali P. Rath & Yeneng Sun & Haomiao Yu, 2012. "Large Games with a Bio-Social Typology," Working Papers 035, Ryerson University, Department of Economics.
  10. Bet Caeyers, 2014. "Exclusion bias in empirical social interaction models: causes, consequences and solutions," CSAE Working Paper Series 2014-05, Centre for the Study of African Economies, University of Oxford.
  11. repec:oxf:wpaper:wps/2014-05 is not listed on IDEAS
  12. repec:zur:econwp:142 is not listed on IDEAS
  13. Paolo Zeppini & Koen Frenken & Luis R. Izquierdo, 2013. "Innovation diffusion in networks: the microeconomics of percolation," Working Papers 13-02, Eindhoven Center for Innovation Studies, revised Feb 2013.
  14. William R. Kerr & Martin Mandorff, 2015. "Social Networks, Ethnicity, and Entrepreneurship," NBER Working Papers 21597, National Bureau of Economic Research, Inc.
  15. Muliere, Pietro & Suverato, Davide, 2014. "Income and Wealth Distributions in a Population of Heterogeneous Agents," Discussion Papers in Economics 20928, University of Munich, Department of Economics.
  16. Olugbenga Ajilore, 2015. "Identifying peer effects using spatial analysis: the role of peers on risky sexual behavior," Review of Economics of the Household, Springer, vol. 13(3), pages 635-652, September.
  17. R. Bentley & Michael O’Brien & Paul Ormerod, 2011. "Quality versus mere popularity: a conceptual map for understanding human behavior," Mind & Society- Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 10(2), pages 181-191, December.

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