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Mapping Political Communities: A Statistical Analysis of Lobbying Networks in Legislative Politics

Published online by Cambridge University Press:  23 November 2020

In Song Kim Open the ORCID record for In Song Kim [Opens in a new window]  and
Dmitriy Kunisky
In Song Kim*
Affiliation:
Associate Professor, Department of Political Science, Massachusetts Institute of Technology, Cambridge, MA02139, USA. Email: [email protected], URL: http://web.mit.edu/insong/www/
Dmitriy Kunisky
Affiliation:
Ph.D. Student, Department of Mathematics, Courant Institute of Mathematical Sciences, New York University, New York, NY10012, USA. Email: [email protected], URL: http://www.kunisky.com/
*
Corresponding author In Song Kim
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Abstract

We propose a new methodology for inferring political actors’ latent memberships in communities of collective activity that drive their observable interactions. Unlike existing methods, the proposed Bipartite Link Community Model (biLCM) (1) applies to two groups of actors, (2) takes into account that actors may be members of more than one community, and (3) allows a pair of actors to interact in more than one way. We apply this method to characterize legislative communities of special interest groups and politicians in the 113th U.S. Congress. Previous empirical studies of interest group politics have been limited by the difficulty of observing the ties between interest groups and politicians directly. We therefore first construct an original dataset that connects the politicians who sponsor congressional bills with the interest groups that lobby on those bills based on more than two million textual descriptions of lobbying activities. We then use the biLCM to make quantitative measurements of actors’ community memberships ranging from narrow targeted interactions according to industry interests and jurisdictional committee membership to broad multifaceted connections across multiple policy domains.

Keywords

network analysisbipartite networklobbyingideal point estimationscalingstochastic block modellink community modelcommunity detection
Type
Article
Information
Political Analysis , Volume 29 , Issue 3 , July 2021 , pp. 317 - 336
Copyright
© The Author(s) 2020. Published by Cambridge University Press on behalf of the Society for Political Methodology

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Footnotes

Edited by Jeff Gill

References

Airoldi, E. M., Blei, D. M., Feinberg, S. E., and Xing, E. P.. 2008. “Mixed Membership Stochastic Blockmodels.” Journal of Machine Learning Research 9:1981–2014.Google ScholarPubMed
Austen-Smith, D., and Wright, J. R.. 1992. “Competitive Lobbying for a Legislator’s Vote.” Social Choice and Welfare 9(3):229–257.CrossRefGoogle Scholar
Ball, B., Karrer, B., and Newman, M. E. J.. 2011. “An Efficient and Principled Method for Detecting Communities in Networks.” Physical Review E 84:036103.CrossRefGoogle ScholarPubMed
Barberá, P. 2014. “Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data.” Political Analysis 23(1):76–91.CrossRefGoogle Scholar
Bond, R., and Messing, S.. 2015. “Quantifying Social Media’s Political Space: Estimating Ideology from Publicly Revealed Preferences on Facebook.” American Political Science Review 109(1):62–78.CrossRefGoogle Scholar
Bonica, A. 2013. “Mapping the Ideological Marketplace.” American Journal of Political Science 58(2):367–386.CrossRefGoogle Scholar
Box-Steffensmeier, J. M., and Christenson, D. P.. 2014. “The Evolution and Formation of Amicus Curiae Networks.” Social Networks 36:82–96.CrossRefGoogle Scholar
Carpenter, B. et al. 2017. “Stan: A Probabilistic Programming Language.” Journal of Statistical Software 76(1).CrossRefGoogle Scholar
Dempster, A. P., Laird, N. M., and Rubin, D. B.. 1977. “Maximum Likelihood from Incomplete Data Via the EM Algorithm (with Discussion).” Journal of the Royal Statistical Society, Series B, Methodological 39(1):1–37.Google Scholar
Desmarais, B. A., La Raja, R. J., and Kowal, M. S.. 2015. “The Fates of Challengers in US House Elections: The Role of Extended Party Networks in Supporting Candidates and Shaping Electoral Outcomes.” American Journal of Political Science 59(1):194–211.CrossRefGoogle Scholar
Fenno, R. F. 1966. The Power of the Purse: Appropriations Politics in Congress. Boston, MA: Little and Brown.Google Scholar
Fenno, R. F. 1973. Congressmen in Committees. Boston, MA: Little and Brown.Google Scholar
Fienberg, S. E., and Wasserman, S. S.. 1981. “Categorical Data Analysis of Single Sociometric Relations.” Sociological Methodology 12:156–192.CrossRefGoogle Scholar
Gray, V., and Lowery, D.. 2000. The Population Ecology of Interest Representation: Lobbying Communities in the American States. Ann Arbor, MI: University of Michigan Press.Google Scholar
Grossman, G. M., and Helpman, E.. 2001. Special Interest Politics. Cambridge, MA: MIT Press.Google Scholar
Hadden, J. 2015. Networks in Contention. Cambridge, MA: Cambridge University Press.CrossRefGoogle Scholar
Heaney, M. T. 2004. “Outside the Issue Niche: The Multidimensionality of Interest Group Identity.” American Politics Research 32(6):611–651.CrossRefGoogle Scholar
Heinz, J. P., Laumann, E. O., Nelson, R. L., and Salisbury, R. H.. 1993. The Hollow Core: Private Interests in National Policy Making. Cambridge, MA: Harvard University Press.Google Scholar
Hertel-Fernandez, A. 2014. “Who Passes Business’s ‘Model Bills’? Policy Capacity and Corporate Influence in US State Politics.” Perspectives on Politics 12(3):582–602.CrossRefGoogle Scholar
Hoff, P. D. 2015. “Multilinear Tensor Regression for Longitudinal Relational Data.” The Annals of Applied Statistics 9(3):1169.CrossRefGoogle ScholarPubMed
Hoff, P. D., Raftery, A. E., and Handcock, M. S.. 2002. “Latent Space Approaches to Social Network Analysis.” Journal of the American Statistical Association 97(460):1090–1098.CrossRefGoogle Scholar
Hojnacki, M. 1997. “Interest Groups’ Decisions to Join Alliances or Work Alone.” American Journal of Political Science 41(1):61–87.CrossRefGoogle Scholar
Jiang, J. 2018. “Making Bureaucracy Work: Patronage Networks, Performance Incentives, and Economic Development in China.” American Journal of Political Science 62(4):982–999.CrossRefGoogle Scholar
Kim, I. S. 2017. “Political Cleavages within Industry: Firm-Level Lobbying for Trade Liberalization.” American Political Science Review 111(1):1–20.CrossRefGoogle Scholar
Kim, I. S. 2018. “LobbyView: Firm-level Lobbying & Congressional Bills Database.” Working Paper. http://web.mit.edu/insong/www/pdf/lobbyview.pdf.Google Scholar
Kim, I. S., and Kunisky, D.. 2020a. “Mapping Political Communities: A Statistical Analysis of Lobbying Networks in Legislative Politics.” https://doi.org/10.7910/DVN/CSAJQY Harvard Dataverse, V1, UNF:6:x1FvS8QGSMmnTQeG6+p3Pw== [fileUNF].Google Scholar
Kim, I. S., and Kunisky, D.. 2020b. “Mapping Political Communities: A Statistical Analysis of Lobbying Networks in Legislative Politics.” Code Ocean. https://doi.org/10.24433/CO.0776811.v1CrossRefGoogle Scholar
Krivitsky, P. N., Handcock, M. S., Raftery, A. E., and Hoff, P. D.. 2009. “Representing Degree Distributions, Clustering, and Homophily in Social Networks with Latent Cluster Random Effects Models.” Social Networks 31:204–213.CrossRefGoogle ScholarPubMed
Larremore, D. B., Clauset, A., and Jacobs, A. Z.. 2014. “Efficiently Inferring Community Structure in Bipartite Networks.” Physical Review E 90:012805.CrossRefGoogle ScholarPubMed
Lauderdale, B. E., and Clark, T. S.. 2014. “Scaling Politically Meaningful Dimensions Using Texts and Votes.” American Journal of Political Science 58(3):754–771.CrossRefGoogle Scholar
Lazer, D., Brewer, D., Christakis, N., Fowler, J., and King, G.. 2009. “Life in the Network: The Coming Age of Computational Social Science.” Science 323(5915):721–723.CrossRefGoogle Scholar
Li, Z., Zhang, S., and Zhang, X.. 2015. “Mathematical Model and Algorithm for Link Community Detection in Bipartite Networks.” American Journal of Operations Research 5:421–434.CrossRefGoogle Scholar
Miller, J. W., and Harrison, M. T.. 2018. “Mixture Models with a Prior on the Number of Components.” Journal of the American Statistical Association 113(521):340–356.CrossRefGoogle ScholarPubMed
Minhas, S., Hoff, P. D., and Ward, M. D.. 2019. “Inferential Approaches for Network Analysis: AMEN for Latent Factor Models.” Political Analysis 27(2):208–222.CrossRefGoogle Scholar
Newman, M. E. J. 2006. “Modularity and Community Structure in Networks.” Proceedings of the National Academy of Sciences 103(23):8577–8582.CrossRefGoogle ScholarPubMed
Nourse, V., and Schacter, J. S.. 2002. “The Politics of Legislative Drafting: A Congressional Case Study.” New York University Law Review 77:575–623.Google Scholar
Olivella, S., Pratt, T., and Imai, K.. 2018. “Dynamic Stochastic Blockmodel Regression for Social Networks: Application to International Conflicts.” Technical report. http://santiagoolivella.info/wp-content/uploads/2018/07/dSBM_Reg.pdf.Google Scholar
Peixoto, T. P. 2015. “Model Selection and Hypothesis Testing for Large-scale Network Models with Overlapping Groups.” Physical Review X 5(1):011033.CrossRefGoogle Scholar
Poole, K. T., and Rosenthal, H. L.. 2011. Ideology and Congress, vol. 1. Abingdon, UK: Transaction Publishers.Google Scholar
Porter, M. A., Mucha, P. J., Newman, M. E. J., and Warmbrand, C. M.. 2005. “A Network Analysis of Committees in the US House of Representatives.” Proceedings of the National Academy of Sciences 102(20):7057–7062.CrossRefGoogle Scholar
Potters, J., and Van Winden, F.. 1992. “Lobbying and Asymmetric Information.” Public Choice 74(3):269–292.CrossRefGoogle Scholar
Slapin, J. B., and Proksch, S.-O.. 2008. “A Scaling Model for Estimating Time-series Party Positions from Texts.” American Journal of Political Science 52(3):705–722.CrossRefGoogle Scholar
Tallberg, C. 2004. “A Bayesian Approach to Modeling Stochastic Blockstructures with Covariates.” Journal of Mathematical Sociology 29(1):1–23.CrossRefGoogle Scholar
Ward, M. D., Stovel, K., and Sacks, A.. 2011. “Network Analysis and Political Science.” Annual Review of Political Science 14:245–264.CrossRefGoogle Scholar
Wright, J. R. 1990. “Contributions, Lobbying, and Committee Voting in the US House of Representatives.” American Political Science Review 84(2):417–438.CrossRefGoogle Scholar
Wright, J. R. 1996. Interest Groups and Congress. Boston, MA: Allyn & Bacon.Google Scholar
Yan, X. et al. 2014. “Model Selection for Degree-corrected Block Models.” Journal of Statistical Mechanics: Theory and Experiment 2014(5):P05007.CrossRefGoogle ScholarPubMed
Zhang, Y., Friend, A. J., Traud, A. L., Porter, M. A., Fowler, J. H., and Mucha, P. J.. 2008. “Community Structure in Congressional Cosponsorship Networks.” Physica A: Statistical Mechanics and its Applications 387(7):1705–1712.CrossRefGoogle Scholar
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Kim and Kunisky Dataset

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https://doi.org/10.7910/DVN/CSAJQY
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