[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/27557.html
   My bibliography  Save this paper

A Graphical Lasso Approach to Estimating Network Connections: The Case of U.S. Lawmakers

Author

Listed:
  • Marco Battaglini
  • Forrest W. Crawford
  • Eleonora Patacchini
  • Sida Peng
Abstract
In this paper, we propose a new approach to the estimation of social networks and we apply it to the estimation of productivity spillovers in the U.S. Congress. Social networks such as the social connections among lawmakers are not generally directly observed, they can be recovered only using the observable outcomes that they contribute to determine (such as, for example, the legislators’ effectiveness). Moreover, they are typically stable for relatively short periods of time, thus generating only short panels of observations. Our estimator has three appealing properties that allows it to work in these environments. First, it is constructed for “small” asymptotic, thus requiring only short panels of observations. Second, it requires relatively nonrestrictive sparsity assumptions for identification, thus being applicable to dense networks with (potentially) star shaped connections. Third, it allows for heterogeneous common shocks across subnetworks. The application to the U.S. Congress gives us new insights about the nature of social interactions among lawmakers. We estimate a significant decrease over time in the importance of productivity spillovers among individual lawmakers, compensated by an increase in the party level common shock over time. This suggests that the rise of partisanship is not affecting only the ideological position of legislators when they vote, but more generally how lawmakers collaborate in the U.S. Congress.

Suggested Citation

  • Marco Battaglini & Forrest W. Crawford & Eleonora Patacchini & Sida Peng, 2020. "A Graphical Lasso Approach to Estimating Network Connections: The Case of U.S. Lawmakers," NBER Working Papers 27557, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:27557
    Note: POL
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w27557.pdf
    Download Restriction: no
    ---><---

    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. Falk Armin & Kosfeld Michael, 2012. "It's all about Connections: Evidence on Network Formation," Review of Network Economics, De Gruyter, vol. 11(3), pages 1-36, September.
    3. Sergio Currarini & Massimo Morelli, 2000. "original papers : Network formation with sequential demands," Review of Economic Design, Springer;Society for Economic Design, vol. 5(3), pages 229-249.
    4. Marco Battaglini & Eleonora Patacchini & Edoardo Rainone, 2019. "Endogenous Social Connections in Legislatures," NBER Working Papers 25988, National Bureau of Economic Research, Inc.
    5. 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.
    6. Goeree, Jacob K. & Riedl, Arno & Ule, Aljaz, 2009. "In search of stars: Network formation among heterogeneous agents," Games and Economic Behavior, Elsevier, vol. 67(2), pages 445-466, November.
    7. Marco Battaglini & Eleonora Patacchini, 2018. "Influencing Connected Legislators," Journal of Political Economy, University of Chicago Press, vol. 126(6), pages 2277-2322.
    8. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "Inference on Treatment Effects after Selection among High-Dimensional Controlsâ€," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(2), pages 608-650.
    9. Sanjeev Goyal & Marco J. van der Leij & José Luis Moraga-Gonzalez, 2006. "Economics: An Emerging Small World," Journal of Political Economy, University of Chicago Press, vol. 114(2), pages 403-432, April.
    10. Áureo de Paula & Imran Rasul & Pedro CL Souza, 2018. "Recovering social networks from panel data: identification, simulations and an application," CeMMAP working papers CWP58/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Venkatesh Bala & Sanjeev Goyal, 2000. "A Noncooperative Model of Network Formation," Econometrica, Econometric Society, vol. 68(5), pages 1181-1230, September.
    12. Pietro Bonaldi & Ali Hortaçsu & Jakub Kastl, 2015. "An Empirical Analysis of Funding Costs Spillovers in the EURO-zone with Application to Systemic Risk," NBER Working Papers 21462, National Bureau of Economic Research, Inc.
    13. 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.
    14. Sida Peng, 2019. "Heterogeneous Endogenous Effects in Networks," Papers 1908.00663, arXiv.org.
    15. Lam, Clifford & Fan, Jianqing, 2009. "Sparsistency and rates of convergence in large covariance matrix estimation," LSE Research Online Documents on Economics 31540, London School of Economics and Political Science, LSE Library.
    16. 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.
    17. McCarty, Nolan & Poole, Keith T. & Rosenthal, Howard, 2001. "The Hunt for Party Discipline in Congress," American Political Science Review, Cambridge University Press, vol. 95(3), pages 673-687, September.
    18. Mehmet Caner & Xu Han & Yoonseok Lee, 2018. "Adaptive Elastic Net GMM Estimation With Many Invalid Moment Conditions: Simultaneous Model and Moment Selection," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 24-46, January.
    19. Marco Battaglini & Valerio Leone Sciabolazza & Eleonora Patacchini, 2020. "Effectiveness of Connected Legislators," American Journal of Political Science, John Wiley & Sons, vol. 64(4), pages 739-756, October.
    20. Angelo Mele, 2017. "A Structural Model of Dense Network Formation," Econometrica, Econometric Society, vol. 85, pages 825-850, May.
    21. Fowler, James H., 2006. "Connecting the Congress: A Study of Cosponsorship Networks," Political Analysis, Cambridge University Press, vol. 14(4), pages 456-487, October.
    22. Lingzhou Xue & Shiqian Ma & Hui Zou, 2012. "Positive-Definite ℓ 1 -Penalized Estimation of Large Covariance Matrices," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1480-1491, December.
    23. Lauren Cohen & Christopher J. Malloy, 2014. "Friends in High Places," American Economic Journal: Economic Policy, American Economic Association, vol. 6(3), pages 63-91, August.
    24. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
    25. Goyal, Sanjeev & Vega-Redondo, Fernando, 2007. "Structural holes in social networks," Journal of Economic Theory, Elsevier, vol. 137(1), pages 460-492, November.
    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. Stefano Pietrosanti & Edoardo Rainone, 2023. "Connecting the dots: the network nature of shocks propagation in credit markets," Temi di discussione (Economic working papers) 1436, Bank of Italy, Economic Research and International Relations Area.

    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. de Paula, Aureo & Rasul, Imran & Souza, Pedro, 2018. "Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition," CEPR Discussion Papers 12792, C.E.P.R. Discussion Papers.
    2. Alex Centeno, 2022. "A Structural Model for Detecting Communities in Networks," Papers 2209.08380, arXiv.org, revised Oct 2022.
    3. Sida Peng, 2019. "Heterogeneous Endogenous Effects in Networks," Papers 1908.00663, arXiv.org.
    4. Bryan S. Graham, 2019. "Network Data," Papers 1912.06346, arXiv.org.
    5. Mariya Teteryatnikova & James Tremewan, 2020. "Myopic and farsighted stability in network formation games: an experimental study," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 69(4), pages 987-1021, June.
    6. Boucher, Vincent, 2020. "Equilibrium homophily in networks," European Economic Review, Elsevier, vol. 123(C).
    7. Patacchini, Eleonora & Hsieh, Chih-Sheng & Lin, Xu, 2019. "Social Interaction Methods," CEPR Discussion Papers 14141, C.E.P.R. Discussion Papers.
    8. Pramod C. Mane & Kapil Ahuja & Nagarajan Krishnamurthy, 2020. "Stability, efficiency, and contentedness of social storage networks," Annals of Operations Research, Springer, vol. 287(2), pages 811-842, April.
    9. Matthew O. Jackson & Brian W. Rogers & Yves Zenou, 2016. "Networks: An Economic Perspective," Papers 1608.07901, arXiv.org.
    10. Rong, Rong & Houser, Daniel, 2015. "Growing stars: A laboratory analysis of network formation," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 380-394.
    11. Mariya Teteryatnikova & James Tremewan, 2015. "Stability in Network Formation Games with Streams of Payoffs: An Experimental Study," Vienna Economics Papers 1508, University of Vienna, Department of Economics.
    12. Mariya Teteryatnikova & James Tremewan, 2015. "Stability in Network Formation Games with Streams of Payoffs: An Experimental Study," Vienna Economics Papers vie1508, University of Vienna, Department of Economics.
    13. 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.
    14. Choi, S. & Goyal, G. & Moisan, F., 2020. "Large Scale Experiments on Networks: A New Platform with Applications," Cambridge Working Papers in Economics 2063, Faculty of Economics, University of Cambridge.
    15. Isabel Melguizo, 2023. "Group representation concerns and network formation," Bulletin of Economic Research, Wiley Blackwell, vol. 75(1), pages 151-179, January.
    16. Goeree, Jacob K. & Riedl, Arno & Ule, Aljaz, 2009. "In search of stars: Network formation among heterogeneous agents," Games and Economic Behavior, Elsevier, vol. 67(2), pages 445-466, November.
    17. Boris van Leeuwen & Theo Offerman & Arthur Schram, 2020. "Competition for Status Creates Superstars: an Experiment on Public Good Provision and Network Formation," Journal of the European Economic Association, European Economic Association, vol. 18(2), pages 666-707.
    18. Marco Mantovani & Georg Kirchsteiger & Ana Mauleon & Vincent Vannetelbosch, 2011. "Myopic or Farsighted? An Experiment on Network Formation," Working Papers 2011.45, Fondazione Eni Enrico Mattei.
    19. 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).
    20. Deng, Liuchun & Sun, Yufeng, 2017. "Criminal network formation and optimal detection policy: The role of cascade of detection," Journal of Economic Behavior & Organization, Elsevier, vol. 141(C), pages 43-63.

    More about this item

    JEL classification:

    • D7 - Microeconomics - - Analysis of Collective Decision-Making
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

    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:nbr:nberwo:27557. 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://edirc.repec.org/data/nberrus.html .

    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.