Helping a Friend or Supporting a Cause? Disentangling Active and Passive Cosponsorship in the U.S. Congress
38691Authors
nan
Abstract
AbstractIn the U.S. Congress, legislators can use active and passive cosponsorship to support bills. We show that these two types of cosponsorship are driven by two different motivations: the backing of political colleagues and the backing of the bill’s content. To this end, we develop an Encoder+RGCN based model that learns legislator representations from bill texts and speech transcripts. These representations predict active and passive cosponsorship with an F1-score of 0.88.Applying our representations to predict voting decisions, we show that they are interpretable and generalize to unseen tasks.
Keywords
Full Content
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers, pages 2952–2969 July 9-14, 2023 ©2023 Association for Computational Linguistics Helping a Friend or Supporting a Cause? Disentangling Active and Passive Cosponsorship in the U.S. Congress Giuseppe Russo Christoph Gote Laurence Brandenberger ETH Zurich ETH Zurich ETH Zurich Sophia Schlosser FrankSchweitzer ETH Zurich ETH Zurich {russog, cgote, lbrandenberger, schlosser, fschweitzer}@ethz.ch Abstract In the U.S. Congress, legislators can use ac- tive and passive cosponsorship to support bills. We show that these two types of cosponsorship are driven by two different motivations: the backing of political colleagues and the backing of the bill’s content. To this end, we develop an Encoder+RGCN based model that learns legislator representations from bill texts and speech transcripts. These representations pre- dict active and passive cosponsorship with an F1-score of 0.88. Applying our representations to predict voting decisions, we show that they are interpretable and generalize to unseen tasks. 1 Introduction Expressing political support through the cosponsor- ship of bills is essential for the proper execution of congressional activities. In the US Congress, legislators can draft bills and introduce them to the congress floor, after which they are referred to a committee for assess- ment. Once a legislative draft passes the commit- tee, it is discussed in the plenary. Here, legislators defend their stance and debate the bill’s merits. Fi- nally, a bill is voted on. Throughout the entire process—from a bills’ conception until the final vote—legislators can cosponsor the bill. Cosponsorship has a critical role in studies rela- tive to legislative activities. For instance, cospon- sorship is used to investigate alliance formation (Fowler, 2006; Kirkland, 2011; Kirkland and Gross, 2014; Lee et al., 2017), the effect that such expres- sion of support has on bill’s approval (Browne, 1985; Woon, 2008; Sciarini et al., 2021; Dock- endorff, 2021), and how it signals the positions of legislators on a specific political issue (Kessler and Krehbiel, 1996; Wilson and Young, 1997). In the US Congress, cosponsorship can be differ- entiated between active and passive. As illustrated in Figure 1, the timing of cosponsorship determines this differentiation. Active cosponsorship entails Conception Introduction Vote Drafting Bill Text Gathering Support / Lobbying Committee Assessment Plenary Discussion Sponsor Active Cosponsorship Bill Text Passive Cosponsorship Speeches Roll-Call Votes FORMATION OF TEAM PROPOSING THE BILL EXPRESSION OF SUPPORT THROUGH SIGNATURE COSPONSORSHIP LEGISLATIVE WORK DATA TIME Figure 1: Distinction between active and passive cospon- sorship in time and their relation to the legislative work. Active cosponsorship □ occurs at the initial phase, be- fore the bill is introduced. Passive cosponsorship □ occurs during the deliberation phase of the bill. involvement —together with the legislator intro- ducing the bill (sponsor)—in the bill’s creation in its initial stages. In contrast, passive cosponsorship can be issued after the introduction of a bill to the Congress floor. So far, most studies analyzing cosponsorship have not differentiated between active and passive cosponsorship. These two actions have been quali- tatively distinguished with respect to their effort …
Content truncated to 500 words
Labeled Text
AbstractIn the U.S. Congress, legislators can use active and passive cosponsorship to support bills. We show that these two types of cosponsorship are driven by two different motivations: the backing of political colleagues and the backing of the bill’s content. To this end, we develop an Encoder+RGCN based model that learns legislator representations from bill texts and speech transcripts. These representations predict active and passive cosponsorship with an F1-score of 0.88.Applying our representations to predict voting decisions, we show that they are interpretable and generalize to unseen tasks.
Publication Details
- Paper ID:
- 38691
- Year:
- 2023
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan