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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

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Kim and Kunisky Dataset

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