Helping a Friend or Supporting a Cause? Disentangling Active and Passive Cosponsorship in the U . S . Congress (2023)

Abstract: In 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.

## introduction expressing political support through the cosponsorship 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 assessment. once a legislative draft passes the committee, it is discussed in the plenary. here, legislators defend their stance and debate the bill's merits. finally, 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 relative to legislative activities. for instance, cosponsorship is used to investigate alliance formation @xcite @xcite : distinction between active and passive cosponsorship in time and their relation to the legislative work. active cosponsorship □ occurs at the initial phase, before the bill is introduced. passive cosponsorship □ occurs during the deliberation phase of the bill. so far, most studies analyzing cosponsorship have not differentiated between active and passive cosponsorship. these two actions have been qualitatively distinguished with respect to their effort required. active cosponsorship can be considered as a more resource-intense form of support, given that legislators can be involved in the drafting process of a bill and help gather support. in turn, passive cosponsorship is viewed as less resource-intense with a minimal effort to sign the bill @xcite . however, no studies so far have examined the underlying motivations that drive a legislator to actively or passively cosponsor a bill. given the importance of cosponsorship as a signal of support for a bill during a legislative process, we believe that it crucial to understand not only if a legislator cosponsors a bill, but why a legislator opts for an active or a passive cosponsorship. this work demonstrates that active and passive cosponsorship is driven by two different motivations. active cosponsorship is people-centric and primarily signals the backing of the sponsor of the bill. in contrast, passive cosponsorship is driven by backing a bill's content. this result result yields implication for studies in political science. for instance, alliance formation studies can analyze personal networks by considering the active consponsorships. similarly, studies in position taking can focus on passive consporships to analyze the alignment between legislators and political issues. our work makes the following contributions: we curate a data set containing information on all bills and speeches from the 112th to 115th u.s. congress, which we make available 1 . we develop a novel encoder enabling us to learn single embeddings from long documents, exceeding current token limitations of state-of-theart models. we propose a relational graph convolutional network (rgcn) learning legislator representations accounting for (i) the speeches they give, (ii) the bills they sponsor and cosponsor, and (iii) the other legislators they cite in their speeches. we show that the resulting legislator embeddings proxy the legislators' ideological positions. we train our model using three tasks from the po-1 link omitted for submission litical science domain: (i) cosponsorship, (ii) authorship, and (iii) citation prediction. through a rigorous ablation study, we show the substantial benefits of such a multi-task learning procedure for the first time in a social science application. through our representation we disentangle the underlying motivations behind active and passive cosponsorship. active cosponsorship relates primarily to the backing of the sponsor of a bill, whereas passive cosponsorship relates primarily to the backing of the content of a bill. finally, our representations achieve state-of-theart performance for voting prediction. this is remarkable, as our result comes from a zero-shot prediction, i.e., our representation has not been trained on any voting data. this further emphasizes the value of our legislator representation as a general proxy for legislators' ideology. ## data for our study, we collect fine-grained data on all bills and legislators from the 112th to 115th u.s. congress, which we make freely available. our data set contains (i) metadata for all legislators, (ii) bill texts, (iii) transcripts of all speeches mapped to the corresponding legislator, (iv) disambiguated data capturing which legislators sponsored and actively or passively cosponsored each bill, and (v) the resulting roll-call votes for all bills. we provide detailed statistics for our data set appendix b. legislator metadata we obtain the bioguide id, first name, last name, gender, age, party affiliation, state, and district of all legislators from voteview.com, a curated database containing basic data related to the u.s. congress. bill text as mentioned above, legislators introduce bills to propose laws or amend existing ones in order to further their agenda. we acquire ids, titles, and introduction dates of bills using the api of propublica.org, a non-profit organisation that collects and provides access to congressional documents. we further collect summaries of the bill's content, which the api provides for around 95% of all cases. for bills where no summary is available, we use the full-body texts instead. as we create our data set to study active and passive cosponsorship, we discard all bills for which no cosponsorship links were recorded. overall, our data set contains information on over 50, 000 bills. legislator speeches legislators take the floor to advocate or oppose bills. in these speeches, they communicate their agenda to their fellow colleagues in order to persuade them to vote for (or against) a bill. we obtain transcripts of congressional speeches by scraping congress.gov, the official website of the u.s. congress. the transcripts are archived in so-called daily editions, which are effectively concatenations of all speeches from a day written verbatim. all congressional speeches start with a formal introduction of the legislator giving the speech and the session's chairperson, e.g., "mr. poe of texas. mrs. president." or "mr. boehner. mr. speaker" (cf. figure 2a ). using this pattern, we can split the daily editions and recover the individual speeches and speakers as follows: first, we tag names and geopolitical entities (e.g., "of texas") using the named entity recognition model from spacy foot_0 with [person] and [gpe] tags, respectively. second, we tag all salutations (e.g., mrs/mr) and institutional roles (e.g., speaker, president) with [sal] and [role]. in doing so, the start of speeches is tagged either as [sal]+[person]+[sal]+[role] or [sal]+[person]+[gpe]+[sal]+[role]. the [person] tag further identifies the legislator giving the speech. with this simple procedure, we map roughly 93% of the speeches to the correct legislator. we perform manual data cleaning on the speeches excluding subsets for three reasons described below. (i) speeches for which we cannot determine an author are predominantly given by a legislator representing a committee or an office. when legislators speak on behalf of an office or committee, the opinion expressed in the speech not necessarily corresponds to their personal opinion. cosponsorship data we identify the sponsor of all bills using the api of propublica.org. in addition, the api provides the names of the legislators who cosponsored a bill and when this cosponsorship occurred. we automatically match the cosponsors' names to their bioguide id. in cases where automated matching was not possible -e.g., because legislators signed with their nicknames-we resorted to manual matching. as discussed in section 1, we assign cosponsorship their official label. cospsonsorships recorded at the bill's introduction are active and those recorded after its introduction are passive. roll-call votes roll-call votes are records of how legislators voted on bills. we scrape these data using the python package of @xcite , yielding over 1.5 million votes, which we match to the corresponding legislator and bill ids. ## methodology our model to classify cosponsorship decisions based on the legislator and bill data described in the previous section consists of two main elements, an encoder and a relational graph convolutional network (rgcn). the encoder computes high dimensional representations of legislators' bills and speeches based on their texts and transcripts, respectively. these representations are used by an rgcn and a downstream feed-forward neural network (ffnn) allowing us to predict how (i.e., active or passive) a cosponsor supports a bill. ## related work the analysis of cosponsorship decisions has been widely studied by experts of political science (e.g., @xcite @xcite . research on cosponsorship often focuses on three aspects: the agenda-setting dynamics of bill introductions and cosponsorship @xcite , how cosponsorship affects bill passage @xcite @xcite @xcite , and alliances between legislators @xcite @xcite @xcite . despite political science research directly linking cosponsorship to the texts of bills and speeches in congress, cosponsorship has so far received little to no attention from the nlp community. however, recent advances of natural language processing @xcite @xcite provides tools to address questions related to political studies @xcite @xcite @xcite . among these studies, the prediction of rollcall votes has received great attention. for example, @xcite propose a model to predict voting behavior using bill texts and sponsorship information and find that the addition of the textual information of the bill improves voting predictions drastically. similarly, gerrish and blei (2011b) improve upon voting prediction by proposing a congress model that proxies ideological positions of legislators by linking legislative sentiment to bill texts. this model has been extended to further improve predictions of roll-call votes @xcite @xcite @xcite @xcite . ## conclusion in this work, we developed an encoder+rgcn based model that learns holistic representations of legislators, accounting for the bills they sponsor and cosponsor, the speeches they give, and other legislators they cite. this representation enabled us to predict the type of cosponsorship support legislators give to colleagues with high accuracy. specifically, we differentiated between active cosponsorship, which is given before the official introduction of the bill to the congress floor, and passive cosponsorship, which is given afterwards. so far, the political science literature has distinguished these forms of cosponsorship in terms of their resourceintensity @xcite and their alliance formation dynamics @xcite . however, as shown, our representation of legislators splits them correctly along party lines (□, □). party leaders are found in the center of their respective party clusters. we also find that members of the "blue dogs caucus" are correctly positioned between the two parties.