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

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

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sword-2021-11-15T17:21:28.original.xml (130 B)
Original SWORD entry document
Author(s)
Kim, In Song
•
Kunisky, Dmitriy
Date Issued
2021
Journal
Political Analysis
Publisher
Cambridge University Press (CUP)
Citation
Kim, I., & Kunisky, D. (2021). Mapping Political Communities: A Statistical Analysis of Lobbying Networks in Legislative Politics. Political Analysis, 29(3), 317-336.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Political Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
http://creativecommons.org/licenses/by-nc-sa/4.0/
Persistent DSpace Link
https://hdl.handle.net/1721.1/138141.2
DOI of Published Version
https://doi.org/10.1017/PAN.2020.29
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