Mapping Political Communities: A Statistical Analysis of Lobbying Networks in Legislative Politics. (23rd July 2021)

• Record Type: Journal Article
• Authors: Kim, In Song; Kunisky, Dmitriy
• Is Part Of: Political analysis. Volume 29:Number 3(2021)
• Page Start: 317
• Page End: 336
• Publication Date: 2021-07-23
• Subjects: network analysis, -- bipartite network, -- lobbying, -- ideal point estimation, -- scaling, -- stochastic block model, -- link community model, -- community detection; Political science -- Methodology -- Periodicals; Electronic journals; 320.011
• DOI: 10.1017/pan.2020.29
• Languages: English
• ISSNs: 1047-1987

Abstract

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.