M odeling U.S. State-Level Policies by Extracting Winners and Losers from Legislative Texts (2022)

Abstract: Decisions on state-level policies have a deep effect on many aspects of our everyday life, such as health-care and education access. However, there is little understanding of how these policies and decisions are being formed in the legislative process. We take a data-driven approach by decoding the impact of legislation on relevant stakeholders (e.g., teachers in education bills) to understand legislators’ decision-making process and votes. We build a new dataset for multiple US states that interconnects multiple sources of data including bills, stakeholders, legislators, and money donors. Next, we develop a textual graph-based model to embed and analyze state bills. Our model predicts winners/losers of bills and then utilizes them to better determine the legislative body’s vote breakdown according to demographic/ideological criteria, e.g., gender.

## introduction state-level legislation is the cornerstone of national policies and has long-lasting effects on residents of us states. thus, decoding the processes that shape state bills is crucial yet involved. state legislatures vote on 23 times more bills than federal legislatures, exceeding 120k bills per year @xcite . in addition, these state bills cover a broader range of local problems as each state possesses lawmaking power effective within its boundaries. e.g., the state of washington health care committee addresses health service issues including licensing and regulation of health care facilities and providers. moreover, it regulates pharmacies, pharmaceutical drugs, state public health programs, and private/public insurance markets @xcite . we argue that recent nlp architectures can provide new insights into the state-level legislative efforts. in particular, contextualized graph and text embedding can better represent policies within and across states via a shared political context. however, most of the prior efforts are focused on analyzing congressional bills with traditional techniques, e.g., @xcite . a few state-level studies @xcite took great steps in predicting the progression of state bills towards a vote on the floor and the breakdown of votes based on demographic metrics (e.g., gender). but their main downside is they evaluate policies in a limited context and do not capture cross-state patterns. medical malpractice actions. permits a patient to bring an action against a health care provider without submitting the complaint to the medical review board if: (1) the amount of the claim is not more than $15,000; (2) the cause of action is based on the removal of the wrong body part cleavage yea nay figure 1 : a health bill leading to voting cleavage based on the party metric primarily due to its specific losers (red) and winners (green). winners-losers analysis. in this work, we take a new data-driven approach to analyzing state legislation. our key insight is that each state bill inevitably produces some winners and losers to provide practical solutions to specific in-state and local problems. thus, we argue that it is important to examine state bills in the larger context of their impact on different population segments as well as commercial and professional stakeholders. to help clarify this idea, consider the example in figure 1 . this state bill makes it easier for patients (winners) to take legal actions against healthcare providers (losers). this analysis of winners and losers (wls) can foster transparency in legislative efforts in each state, while interconnecting different states through common stakeholders and revealing cross-state patterns. in addition, the context of wls can enable a new category of nlp models for predicting the roll-call behavior of legislators. downstream bill classification tasks. for instance, the political science community sees tremendous value in predicting voting cleavages, based on the ideological and demographic identities of legislators (section 2). each of such metrics (e.g., party, gender, district, ideology) splits legislators into groups. measuring lack of consensus within and across these groups, which has political and social benefits, can be done using two classification tasks (section 5): for a given metric, we say a bill is competitive (figure 2 ) if the majority vote of legislators from a group (e.g., democrat, male, urban, liberal) is different from that of the opposite group (e.g., republican, female, rural, conservative). similarly, a bill is inverse-competitive if there is a tie in votes of members of the same group (e.g., liberals). for instance, the health bill in figure 1 resulted in a party competitive vote. another example is a state bill on abortion that "requires... physician performing an abortion to admitting privileges at a hospital in the county" resulted in a gender competitive vote. we show the context of winners/losers of these bills could hint at such cleavages prior to voting (sections 4, 6). ## modeling we first provide an overview of key players in the state-level legislative process. then, we model them using an efficient text-based graph abstraction (figure 3 ), which will enable us to embed and evaluate state policies in a broad context and perform the stakeholder and roll-call analysis on them. ## data-driven stakeholders analysis next, we describe how we build up the legislative graph, by collecting data on legislators, bills, and contributors. us states do not record the impact of bills on relevant stakeholders. thus, we explain how to derive stakeholders from bill nodes, perform winners-losers analysis on them, and interconnect different us states by forming common stakeholder nodes. we highlight how our analysis can be used (1) to inform the public about the dynamic and direction of state policies, and (2) to determine legislators' roll-call behavior with different demographic and ideological profiles. ## embedding & prediction architecture the stakeholder analysis based on human data annotation is expensive and time-consuming. to automate the analysis and better leverage its results in other applications, we build up a contextualized embedding architecture and define two classification tasks on the legislative graph: ## experiments we first evaluate the efficiency of our legislative graph abstraction and text+graph embedding model in the winners-losers prediction. then, we show the benefits of our combined inference of stakeholders and roll-calls in decoding state bills. ## conclusion we took a new data-driven approach to analyze state legislation in the us. we showed that identifying the winners/losers of state bills can (1) inform the public on the directions of state policies, and (2) build a nationwide context for a better understanding of legislators' roll-call behaviors. thus, we proposed a text-based graph abstraction to model the interplay of key players in the state legisla-tive process, e.g., bills, stakeholders, legislators, and donors to legislators' campaigns. next, to automate our analysis, we developed a shared text and graph embedding architecture to jointly predict winners/losers of bills and legislators' votes on them. we created a new dataset using different data sources and human annotation and evaluated the strength of our architecture against existing models. we hope this work will provide a starting point for further studies examining the impact of policy decisions on individuals and groups, an important step towards making the democratic process more transparent.