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Homophily in Collaborations among US House Representatives, 1981 -- 2018

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  • Neal, Zachary P.
  • Domagalski, Rachel
  • Yan, Xiaoqin

Abstract

Effective lawmaking requires collaboration among legislators, who form coalitions to advance their legislative agendas. In the US House of Representatives, these collaborations develop in a context of shifting political party control. In this paper we explore how legislators' party and gender identities simultaneously influence whom they choose as collaborators by examining differential party and gender homophily during a period of shifting party control and increasing representation of women. We introduce new methods for inferring legislative collaboration networks from bill co-sponsorship data, then estimate cross-sectional logistic regression models on these networks from 1981 -- 2015. We find evidence of differential homophily by both party and gender: Republicans and women tend to prefer same-party and same-gender political collaborators more than Democrats and men. However, party homophily (i.e. partisanship) is stronger than gender homophily, suggesting that party is a more salient identity for legislators than gender.

Suggested Citation

  • Neal, Zachary P. & Domagalski, Rachel & Yan, Xiaoqin, 2020. "Homophily in Collaborations among US House Representatives, 1981 -- 2018," OSF Preprints qwdxs_v1, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:qwdxs_v1
    DOI: 10.31219/osf.io/qwdxs_v1
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    References listed on IDEAS

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    1. Craig Volden & Alan E. Wiseman & Dana E. Wittmer, 2013. "When Are Women More Effective Lawmakers Than Men?," American Journal of Political Science, John Wiley & Sons, vol. 57(2), pages 326-341, April.
    2. Ringe, Nils & Victor, Jennifer Nicoll & Gross, Justin H., 2013. "Keeping Your Friends Close and Your Enemies Closer? Information Networks in Legislative Politics," British Journal of Political Science, Cambridge University Press, vol. 43(3), pages 601-628, July.
    3. Fowler, James H., 2006. "Connecting the Congress: A Study of Cosponsorship Networks," Political Analysis, Cambridge University Press, vol. 14(4), pages 456-487, October.
    4. Eduardo Alemán & Ernesto Calvo, 2013. "Explaining Policy Ties in Presidential Congresses: A Network Analysis of Bill Initiation Data," Political Studies, Political Studies Association, vol. 61(2), pages 356-377, June.
    5. Zhang, Yan & Friend, A.J. & Traud, Amanda L. & Porter, Mason A. & Fowler, James H. & Mucha, Peter J., 2008. "Community structure in Congressional cosponsorship networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(7), pages 1705-1712.
    6. Hong, Yili, 2013. "On computing the distribution function for the Poisson binomial distribution," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 41-51.
    7. Moody, James & Mucha, Peter J., 2013. "Portrait of Political Party Polarization – ERRATUM," Network Science, Cambridge University Press, vol. 1(2), pages 251-251, August.
    8. Hunter, David R. & Handcock, Mark S. & Butts, Carter T. & Goodreau, Steven M. & Morris, Martina, 2008. "ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i03).
    9. Moody, James & Mucha, Peter J., 2013. "Portrait of Political Party Polarization1," Network Science, Cambridge University Press, vol. 1(1), pages 119-121, April.
    10. Stefano Gagliarducci & M Daniele Paserman, 2022. "Gender Differences in Cooperative Environments? Evidence from The U.S. Congress," The Economic Journal, Royal Economic Society, vol. 132(641), pages 218-257.
    11. Sarah F. Anzia & Christopher R. Berry, 2011. "The Jackie (and Jill) Robinson Effect: Why Do Congresswomen Outperform Congressmen?," American Journal of Political Science, John Wiley & Sons, vol. 55(3), pages 478-493, July.
    12. Kessler, Daniel & Krehbiel, Keith, 1996. "Dynamics of Cosponsorship," American Political Science Review, Cambridge University Press, vol. 90(3), pages 555-566, September.
    13. Bruce A Desmarais & Skyler J Cranmer, 2012. "Statistical Inference for Valued-Edge Networks: The Generalized Exponential Random Graph Model," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-12, January.
    14. Clio Andris & David Lee & Marcus J Hamilton & Mauro Martino & Christian E Gunning & John Armistead Selden, 2015. "The Rise of Partisanship and Super-Cooperators in the U.S. House of Representatives," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-14, April.
    15. Fabio Saracco & Mika J. Straka & Riccardo Di Clemente & Andrea Gabrielli & Guido Caldarelli & Tiziano Squartini, 2016. "Inferring monopartite projections of bipartite networks: an entropy-based approach," Papers 1607.02481, arXiv.org, revised May 2017.
    16. Fabio Saracco & Riccardo Di Clemente & Andrea Gabrielli & Tiziano Squartini, 2015. "Randomizing bipartite networks: the case of the World Trade Web," Papers 1503.05098, arXiv.org, revised Jun 2015.
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