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Methods of Identification in Social Networks

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  • Bryan S. Graham

Abstract

Social and economic networks are ubiquitous, serving as contexts for job search, technology diffusion, the accumulation of human capital and even the formulation of norms and values. The systematic empirical study of network formation - the process by which agents form, maintain and dissolve links - within economics is recent, is associated with extraordinarily challenging modeling and identification issues, and is an area of exciting new developments, with many open questions. This article reviews prominent research on the empirical analysis of network formation, with an emphasis on contributions made by economists.

Suggested Citation

  • Bryan S. Graham, 2014. "Methods of Identification in Social Networks," NBER Working Papers 20414, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:20414
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    Citations

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    Cited by:

    1. Gaudeul, Alexia & Giannetti, Caterina, 2015. "Privacy, trust and social network formation," Center for European, Governance and Economic Development Research Discussion Papers 269, University of Goettingen, Department of Economics.
    2. Konstantin Buechel, Maximilian von Ehrlich, 2016. "Cities and the Structure of Social Interactions: Evidence from Mobile Phone Data," Diskussionsschriften credresearchpaper13, Universitaet Bern, Departement Volkswirtschaft - CRED.
    3. Bryan S. Graham, 2014. "An econometric model of link formation with degree heterogeneity," NBER Working Papers 20341, National Bureau of Economic Research, Inc.
    4. Bryan S. Graham, 2016. "Homophily and transitivity in dynamic network formation," CeMMAP working papers CWP16/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Fernando Linardi & Cees (C.G.H.) Diks & Marco (M.J.) van der Leij & Iuri Lazier, 2017. "Dynamic Interbank Network Analysis Using Latent Space Models," Tinbergen Institute Discussion Papers 17-101/II, Tinbergen Institute.
    6. Ioannides, Yannis M., 2015. "Neighborhoods to nations via social interactions," Economic Modelling, Elsevier, vol. 48(C), pages 5-15.
    7. Luisa Corrado & Salvatore Di Novo, 2018. "Estimating Models with Dynamic Network Interactions and Unobserved Heterogeneity," CEIS Research Paper 439, Tor Vergata University, CEIS, revised 06 Nov 2018.
    8. Quintana-Domeque, Climent & Wohlfart, Johannes, 2016. "“Relative concerns for consumption at the top”: An intertemporal analysis for the UK," Journal of Economic Behavior & Organization, Elsevier, vol. 129(C), pages 172-194.
    9. Patacchini, Eleonora & Rainone, Edoardo & Zenou, Yves, 2017. "Heterogeneous peer effects in education," Journal of Economic Behavior & Organization, Elsevier, vol. 134(C), pages 190-227.
    10. Hsieh, Chih-Sheng & König, Michael D. & Liu, Xiaodong & Zimmermann, Christian, 2018. "Superstar Economists: Coauthorship Networks and Research Output," IZA Discussion Papers 11916, Institute for the Study of Labor (IZA).
    11. Bryan S. Graham, 2017. "An Econometric Model of Network Formation With Degree Heterogeneity," Econometrica, Econometric Society, vol. 85, pages 1033-1063, July.
    12. Chih-Sheng Hsieh & Michael D. König & Xiaodong Liu, 2012. "Network formation with local complements and global substitutes: the case of R&D networks," ECON - Working Papers 217, Department of Economics - University of Zurich, revised Feb 2017.
    13. Kummer, Michael E. & Saam, Marianne & Halatchliyski, Iassen & Giorgidze, George, 2016. "Centrality and content creation in networks - The case of economic topics on German wikipedia," Information Economics and Policy, Elsevier, vol. 36(C), pages 36-52.
    14. Boucher, Vincent & Fortin, Bernard, 2015. "Some Challenges in the Empirics of the Effects of Networks," IZA Discussion Papers 8896, Institute for the Study of Labor (IZA).
    15. repec:eee:phsmap:v:489:y:2018:i:c:p:102-111 is not listed on IDEAS
    16. Mingli Chen & Iv'an Fern'andez-Val & Martin Weidner, 2014. "Nonlinear Factor Models for Network and Panel Data," Papers 1412.5647, arXiv.org, revised Jun 2018.
    17. Jackson, Matthew O. & Rogers, Brian & Zenou, Yves, 2016. "Networks: An economic perspective," CEPR Discussion Papers 11452, C.E.P.R. Discussion Papers.
    18. Ida Johnsson & Hyungsik Roger Moon, 2017. "Estimation of Peer Effects in Endogenous Social Networks: Control Function Approach," Papers 1709.10024, arXiv.org, revised Jan 2019.
    19. repec:eee:jeborg:v:141:y:2017:i:c:p:233-253 is not listed on IDEAS
    20. Firmin Doko Tchatoka & Robert Garrard & Virginie Masson, 2017. "Testing for Stochastic Dominance in Social Networks," School of Economics Working Papers 2017-02, University of Adelaide, School of Economics.
    21. George Judge, 2016. "Econometric Information Recovery in Behavioral Networks," Econometrics, MDPI, Open Access Journal, vol. 4(3), pages 1-11, September.
    22. repec:bla:jecsur:v:32:y:2018:i:4:p:1016-1044 is not listed on IDEAS
    23. Patacchini, Eleonora & Arduini, Tiziano, 2016. "Residential choices of young Americans," Journal of Housing Economics, Elsevier, vol. 34(C), pages 69-81.
    24. repec:spr:sjecst:v:154:y:2018:i:1:d:10.1186_s41937-017-0011-x is not listed on IDEAS

    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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