IDEAS home Printed from https://ideas.repec.org/p/cpr/ceprdp/13911.html
   My bibliography  Save this paper

A Structural Model for the Coevolution of Networks and Behavior

Author

Listed:
  • Koenig, Michael
  • Hsieh, Chih-Sheng
  • Liu, Xiaodong

Abstract

This paper introduces a structural model for the coevolution of networks and behavior. The microfoundation of our model is a network game where agents adjust actions and network links in a stochastic best-response dynamics with a utility function allowing for both strategic externalities and unobserved heterogeneity. We show the network game admits a potential function and the coevolution process converges to a unique stationary distribution characterized by a Gibbs measure. To bypass the evaluation of the intractable normalizing constant in the Gibbs measure, we adopt the Double Metropolis-Hastings algorithm to sample from the posterior distribution of the structural parameters. To illustrate the empirical relevance of our structural model, we apply it to study R&D investment and collaboration decisions in the chemicals and pharmaceutical industry and find a positive knowledge spillover effect. Finally, our structural model provides a tractable framework for a long-run key player analysis.

Suggested Citation

  • Koenig, Michael & Hsieh, Chih-Sheng & Liu, Xiaodong, 2019. "A Structural Model for the Coevolution of Networks and Behavior," CEPR Discussion Papers 13911, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:13911
    as

    Download full text from publisher

    File URL: https://cepr.org/publications/DP13911
    Download Restriction: CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at subscribers@cepr.org
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Lawrence E. Blume & William A. Brock & Steven N. Durlauf & Rajshri Jayaraman, 2015. "Linear Social Interactions Models," Journal of Political Economy, University of Chicago Press, vol. 123(2), pages 444-496.
    2. Bramoullé, Yann & Djebbari, Habiba & Fortin, Bernard, 2009. "Identification of peer effects through social networks," Journal of Econometrics, Elsevier, vol. 150(1), pages 41-55, May.
    3. Angelo Mele, 2017. "A Structural Model of Dense Network Formation," Econometrica, Econometric Society, vol. 85, pages 825-850, May.
    4. à ureo de Paula & Seth Richards†Shubik & Elie Tamer, 2018. "Identifying Preferences in Networks With Bounded Degree," Econometrica, Econometric Society, vol. 86(1), pages 263-288, January.
    5. Nicholas Bloom & Mark Schankerman & John Van Reenen, 2013. "Identifying Technology Spillovers and Product Market Rivalry," Econometrica, Econometric Society, vol. 81(4), pages 1347-1393, July.
    6. Michael D. König & Xiaodong Liu & Yves Zenou, 2019. "R&D Networks: Theory, Empirics, and Policy Implications," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 476-491, July.
    7. 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.
    8. Coralio Ballester & Antoni Calvó-Armengol & Yves Zenou, 2006. "Who's Who in Networks. Wanted: The Key Player," Econometrica, Econometric Society, vol. 74(5), pages 1403-1417, September.
    9. Chih‐Sheng Hsieh & Lung‐Fei Lee & Vincent Boucher, 2020. "Specification and estimation of network formation and network interaction models with the exponential probability distribution," Quantitative Economics, Econometric Society, vol. 11(4), pages 1349-1390, November.
    10. Manuel Trajtenberg & Gil Shiff & Ran Melamed, 2009. "The "Names Game": Harnessing Inventors, Patent Data for Economic Research," Annals of Economics and Statistics, GENES, issue 93-94, pages 67-77.
    11. Hall, Bronwyn H. & Jaffee, Adam & Trajtenberg, Manuel, 2000. "Market Value and Patent Citations: A First Look," Department of Economics, Working Paper Series qt1rh8k6z2, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    12. Blume Lawrence E., 1993. "The Statistical Mechanics of Strategic Interaction," Games and Economic Behavior, Elsevier, vol. 5(3), pages 387-424, July.
    13. Jackson, Matthew O. & Zenou, Yves, 2015. "Games on Networks," Handbook of Game Theory with Economic Applications,, Elsevier.
    14. 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.
    15. Liu, Xiaodong & Lee, Lung-fei, 2010. "GMM estimation of social interaction models with centrality," Journal of Econometrics, Elsevier, vol. 159(1), pages 99-115, November.
    16. Hagedoorn, John, 2002. "Inter-firm R&D partnerships: an overview of major trends and patterns since 1960," Research Policy, Elsevier, vol. 31(4), pages 477-492, May.
    17. Melissa A. Schilling, 2009. "Understanding the alliance data," Strategic Management Journal, Wiley Blackwell, vol. 30(3), pages 233-260, March.
    18. Eric Auerbach, 2019. "Identification and Estimation of a Partially Linear Regression Model using Network Data," Papers 1903.09679, arXiv.org, revised Jun 2021.
    19. Hunter, David R. & Goodreau, Steven M. & Handcock, Mark S., 2008. "Goodness of Fit of Social Network Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 248-258, March.
    20. Lori Rosenkopf & Giovanna Padula, 2008. "Investigating the Microstructure of Network Evolution: Alliance Formation in the Mobile Communications Industry," Organization Science, INFORMS, vol. 19(5), pages 669-687, October.
    21. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
    22. Dzemski, Andreas, 2017. "An empirical model of dyadic link formation in a network with unobserved heterogeneity," Working Papers in Economics 698, University of Gothenburg, Department of Economics, revised Apr 2018.
    23. Bryan S. Graham, 2017. "An Econometric Model of Network Formation With Degree Heterogeneity," Econometrica, Econometric Society, vol. 85, pages 1033-1063, July.
    24. repec:adr:anecst:y:2009:i:93-94:p:04 is not listed on IDEAS
    25. Leung, Michael P., 2015. "Two-step estimation of network-formation models with incomplete information," Journal of Econometrics, Elsevier, vol. 188(1), pages 182-195.
    26. Ida Johnsson & Hyungsik Roger Moon, 2017. "Estimation of Peer Effects in Endogenous Social Networks: Control Function Approach," Papers 1709.10024, arXiv.org, revised Jul 2019.
    27. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 401-425, September.
    28. Lung-fei Lee & Xiaodong Liu & Xu Lin, 2010. "Specification and estimation of social interaction models with network structures," Econometrics Journal, Royal Economic Society, vol. 13(2), pages 145-176, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Xi & Qiu, Yun & Shi, Wei & Yu, Pei, 2022. "Key links in network interactions: Assessing route-specific travel restrictions in China during the Covid-19 pandemic," China Economic Review, Elsevier, vol. 73(C).
    2. Markus Kinateder & Luca Paolo Merlino, 2021. "The Evolution of Networks and Local Public Good Provision: A Potential Approach," Games, MDPI, vol. 12(3), pages 1-12, July.
    3. König, Michael D. & Rogers, Tim, 2023. "Endogenous technology cycles in dynamic R&D networks," European Economic Review, Elsevier, vol. 158(C).
    4. Cui Zhang & Dandan Zhang, 2023. "Spatial Interactions and the Spread of COVID-19: A Network Perspective," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 383-405, June.
    5. Chih‐Sheng Hsieh & Lung‐Fei Lee & Vincent Boucher, 2020. "Specification and estimation of network formation and network interaction models with the exponential probability distribution," Quantitative Economics, Econometric Society, vol. 11(4), pages 1349-1390, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bryan S. Graham, 2019. "Network Data," Papers 1912.06346, arXiv.org.
    2. Bryan S. Graham, 2019. "Network Data," CeMMAP working papers CWP71/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Yann Bramoullé & Habiba Djebbari & Bernard Fortin, 2020. "Peer Effects in Networks: A Survey," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 603-629, August.
    4. Chih‐Sheng Hsieh & Lung‐Fei Lee & Vincent Boucher, 2020. "Specification and estimation of network formation and network interaction models with the exponential probability distribution," Quantitative Economics, Econometric Society, vol. 11(4), pages 1349-1390, November.
    5. Lin, Zhongjian & Hu, Yingyao, 2024. "Binary choice with misclassification and social interactions, with an application to peer effects in attitude," Journal of Econometrics, Elsevier, vol. 238(1).
    6. Topa, Giorgio & Zenou, Yves, 2015. "Neighborhood and Network Effects," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 561-624, Elsevier.
    7. Patacchini, Eleonora & Hsieh, Chih-Sheng & Lin, Xu, 2019. "Social Interaction Methods," CEPR Discussion Papers 14141, C.E.P.R. Discussion Papers.
    8. Michael D. König & Xiaodong Liu & Yves Zenou, 2019. "R&D Networks: Theory, Empirics, and Policy Implications," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 476-491, July.
    9. Ida Johnsson & Hyungsik Roger Moon, 2017. "Estimation of Peer Effects in Endogenous Social Networks: Control Function Approach," Papers 1709.10024, arXiv.org, revised Jul 2019.
    10. Alex Centeno, 2022. "A Structural Model for Detecting Communities in Networks," Papers 2209.08380, arXiv.org, revised Oct 2022.
    11. Chih‐Sheng Hsieh & Xu Lin, 2021. "Social interactions and social preferences in social networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(2), pages 165-189, March.
    12. Boucher, Vincent & Fortin, Bernard, 2015. "Some Challenges in the Empirics of the Effects of Networks," IZA Discussion Papers 8896, Institute of Labor Economics (IZA).
    13. Anton Badev, 2021. "Nash Equilibria on (Un)Stable Networks," Econometrica, Econometric Society, vol. 89(3), pages 1179-1206, May.
    14. Yingyao Hu & Zhongjian Lin, 2018. "Misclassification and the hidden silent rivalry," CeMMAP working papers CWP12/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    15. 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.
    16. Zenou, Yves & Lindquist, Matthew & Sauermann, Jan, 2015. "Network Effects on Worker Productivity," CEPR Discussion Papers 10928, C.E.P.R. Discussion Papers.
    17. Patacchini, Eleonora & Rainone, Edoardo & Zenou, Yves, 2017. "Heterogeneous peer effects in education," Journal of Economic Behavior & Organization, Elsevier, vol. 134(C), pages 190-227.
    18. 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.
    19. Leung, Michael P., 2019. "A weak law for moments of pairwise stable networks," Journal of Econometrics, Elsevier, vol. 210(2), pages 310-326.
    20. Eric Auerbach, 2019. "Testing for Differences in Stochastic Network Structure," Papers 1903.11117, arXiv.org, revised Nov 2020.

    More about this item

    Keywords

    Strategic network formation; Network interactions; Stochastic best-response dynamics; Unobserved heterogeneity; Double metropolis-hastings algorithm; R&d collaboration networks; Key players;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • 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
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • L22 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Organization and Market Structure

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cpr:ceprdp:13911. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://www.cepr.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.