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Homophily and transitivity in dynamic network formation

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  • Bryan S. Graham

Abstract

In social and economic networks linked agents often share additional links in common. There are two competing explanations for this phenomenon. First, agents may have a structural taste for transitive links – the returns to linking may be higher if two agents share links in common. Second, agents may assortatively match on unobserved attributes, a process called homophily. I study parameter identifiability in a simple model of dynamic network formation with both effects. Agents form, maintain, and sever links over time in order to maximize utility. The return to linking may be higher if agents share friends in common. A pair-specific utility component allows for arbitrary homophily on time-invariant agent attributes. I derive conditions under which it is possible to detect the presence of a taste for transitivity in the presence of assortative matching on unobservables. I leave the joint distribution of the initial network and the pair-specific utility component, a very high dimensional object, unrestricted. The analysis is of the 'fixed effects' type. The identification result is constructive, suggesting an analog estimator, whose single large network properties I characterize.

Suggested Citation

  • Bryan S. Graham, 2016. "Homophily and transitivity in dynamic network formation," CeMMAP working papers 16/16, Institute for Fiscal Studies.
  • Handle: RePEc:azt:cemmap:16/16
    DOI: 10.1920/wp.ifs.2016.1607
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    References listed on IDEAS

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    1. Bryan S. Graham, 2015. "Methods of Identification in Social Networks," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 465-485, August.
    2. Matthew O. Jackson & Tomas Rodriguez-Barraquer & Xu Tan, 2012. "Social Capital and Social Quilts: Network Patterns of Favor Exchange," American Economic Review, American Economic Association, vol. 102(5), pages 1857-1897, August.
    3. Ahn, Hyungtaik & Powell, James L., 1993. "Semiparametric estimation of censored selection models with a nonparametric selection mechanism," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 3-29, July.
    4. 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.
    5. Áureo de Paula, 2015. "Econometrics of network models," CeMMAP working papers 52/15, Institute for Fiscal Studies.
    6. Jackson, Matthew O. & Wolinsky, Asher, 1996. "A Strategic Model of Social and Economic Networks," Journal of Economic Theory, Elsevier, vol. 71(1), pages 44-74, October.
    7. Bo E. Honoré & Elie Tamer, 2006. "Bounds on Parameters in Panel Dynamic Discrete Choice Models," Econometrica, Econometric Society, vol. 74(3), pages 611-629, May.
    8. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 225-238.
    9. Manski, Charles F., 1985. "Semiparametric analysis of discrete response : Asymptotic properties of the maximum score estimator," Journal of Econometrics, Elsevier, vol. 27(3), pages 313-333, March.
    10. Matthew O. Jackson & Brian W. Rogers & Yves Zenou, 2017. "The Economic Consequences of Social-Network Structure," Journal of Economic Literature, American Economic Association, vol. 55(1), pages 49-95, March.
    11. Manski, Charles F, 1987. "Semiparametric Analysis of Random Effects Linear Models from Binary Panel Data," Econometrica, Econometric Society, vol. 55(2), pages 357-362, March.
    12. Steven Goodreau & James Kitts & Martina Morris, 2009. "Birds of a feather, or friend of a friend? using exponential random graph models to investigate adolescent social networks," Demography, Springer;Population Association of America (PAA), vol. 46(1), pages 103-125, February.
    13. Áureo de Paula & Seth Richards-Shubik & Elie Tamer, 2015. "Identification of preferences in network formation games," CeMMAP working papers CWP29/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Jackson, Matthew O. & Watts, Alison, 2002. "The Evolution of Social and Economic Networks," Journal of Economic Theory, Elsevier, vol. 106(2), pages 265-295, October.
    15. Nicholas Christakis & James Fowler & Guido Imbens & Karthik Kalyanaraman, 2010. "An empirical model for strategic network formation," CeMMAP working papers CWP16/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. E. Charlier & B. Melenberg & A. H. O. van Soest, 1995. "A smoothed maximum score estimator for the binary choice panel data model with an application to labour force participation," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 49(3), pages 324-342, November.
    17. Watts, Alison, 2001. "A Dynamic Model of Network Formation," Games and Economic Behavior, Elsevier, vol. 34(2), pages 331-341, February.
    18. Matous, Petr & Todo, Yasuyuki, 2016. "Energy and resilience: The effects of endogenous interdependencies on trade network formation across space among major Japanese firms," Network Science, Cambridge University Press, vol. 4(2), pages 141-163, June.
    19. Bo E. Honoré & Ekaterini Kyriazidou, 2000. "Panel Data Discrete Choice Models with Lagged Dependent Variables," Econometrica, Econometric Society, vol. 68(4), pages 839-874, July.
    20. Bryan S. Graham, 2014. "An econometric model of link formation with degree heterogeneity," NBER Working Papers 20341, National Bureau of Economic Research, Inc.
    21. Manski, Charles F., 1975. "Maximum score estimation of the stochastic utility model of choice," Journal of Econometrics, Elsevier, vol. 3(3), pages 205-228, August.
    22. Almquist, Zack W. & Butts, Carter T., 2013. "Dynamic Network Logistic Regression: A Logistic Choice Analysis of Inter- and Intra-Group Blog Citation Dynamics in the 2004 US Presidential Election," Political Analysis, Cambridge University Press, vol. 21(4), pages 430-448.
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    Cited by:

    1. Bryan S. Graham & Andrin Pelican, 2023. "Scenario Sampling for Large Supermodular Games," Papers 2307.11857, arXiv.org.
    2. Kevin Dano, 2023. "Transition Probabilities and Moment Restrictions in Dynamic Fixed Effects Logit Models," Papers 2303.00083, arXiv.org, revised Dec 2023.
    3. Braun, Martin & Verdier, Valentin, 2023. "Estimation of spillover effects with matched data or longitudinal network data," Journal of Econometrics, Elsevier, vol. 233(2), pages 689-714.
    4. Bryan S. Graham & Andrin Pelican, 2023. "Scenario sampling for large supermodular games," CeMMAP working papers 15/23, Institute for Fiscal Studies.
    5. Gao, Wayne Yuan & Li, Ming & Xu, Sheng, 2023. "Logical differencing in dyadic network formation models with nontransferable utilities," Journal of Econometrics, Elsevier, vol. 235(1), pages 302-324.
    6. Fan Zhou & Kunpeng Zhang & Bangying Wu & Yi Yang & Harry Jiannan Wang, 2021. "Unifying Online and Offline Preference for Social Link Prediction," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1400-1418, October.

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