In Article 1 of the US Constitution, the founders laid out the powers of Congress: taxation and regulation of commerce, the ability to declare war, and creating all laws necessary and proper to uphold their responsibility to the citizenry. Over the span of US history, hundreds of thousands of bills have been introduced in Congress, dealing with issues ranging from foreign policy to healthcare services. Since Congress has the power to change the foundational laws of the nation, we think it is worthwhile to analyze how likely a particular bill is to be enacted by Congress and what possible factors could account for these decisions.
Voting Blocs and Political Polarization
One way we can gauge the likelihood of a bill passing through Congress is by looking at voting blocs within both chambers. According to Merriam-Webster, a voting bloc is a group of legislators who act together for some common purpose. In the context of our project, these are groups of Congress members that tend to vote together on bills, regardless of party affiliation. From data on voting blocs in Congress and how they have historically voted on certain types of bills, we can infer how likely a similar bill is to pass Congress in the future.
One of the main goals in this project is to visualize these voting blocs through a graph or network diagram. An example of a visualization that effectively captures this idea comes from an article written by Christopher Ingram of the Washington Post. He cited researchers who created a network of over 5 million pairs of representatives and created the visualization picture below to illustrate their finding that partisanship in the U.S. Congress has been increasing exponentially for over 60 years. While voting blocs and political polarization and voting blocs are closely intertwined, the distinction between them is, perhaps, a key in understanding the significance of voting blocs in Congress.
Political polarization is one of the defining topics in modern American politics. It’s no secret that polarization has been on the rise since the 1980s. In fact, just earlier this year, the US government endured the longest shutdown in history, totaling 35 days. According to researchers and political analysts, the largest reason why the shutdown persisted was due to party polarization and the lack of incentives to reach a compromise. Initial observations indicate that throughout history, political party hasn’t always been such a strong indicator of voting coalitions. With this in mind, we aim to gain an understanding of the interaction between voting blocs and political polarization through a network analysis of Congressional roll call votes.
The Goals and Methods of Our Project
The end goal of our project is to build an open source package for visualizing and analyzing Congressional voting patterns. We are taking a graph-based approach to modeling Congress, treating every member of Congress as a node on a graph where an edge represents connectivity between members. Once we’ve finished our visualizations, we will have an idea of the “ideological distance” of members of Congress.
The first step will be implementing a baseline level of functionality — simply generating visualizations for votes on specific bills.
The group will then move on to analyzing our model of Congress, with the intention of identifying voting blocs through graph clustering or similar means.
Below is a preliminary graph, in which the similarity between every 2 congress members is calculated as the number of mutually sponsored bills. Each cluster is given a unique color, and the thickness of edges indicates the strength of similarity between clusters.
Identifying what metrics will be most useful in assembling our network will be the next step. Starting by using the roll call votes on a large set of bills, we hope to eventually incorporate more qualitative factors, such as party membership, subcommittee membership, campaign contribution/lobbyist influence, constituent demographics, etc. This may pose a challenge in terms of gathering data; for example, it’s very difficult to isolate the impact of campaign contributions on day-to-day policy decisions made by Congressional members.
Our next step in the project will include learning more about clustering algorithms and data visualization methods. The clustering algorithms will help us select the most relevant voting blocs out of all the graph clusters. The tricky part here will be to match these selected clusters with real-world ideological or political stances. Additionally, we will explore different visualization techniques on top of the base graph in order to depict votes on different bills. This will allow users to get an idea of Congressional polarization with respect to particular bills or issues.
It will be difficult to make conclusions of causation through our analysis on how likely a particular bill is to be enacted by Congress and what possible factors could account for these decisions. At the very least we hope to make it easier for regular people to learn more about their own government.
Bloc. (n.d). In Merriam-Webster.com Online. Retrieved Mar 8, 2011, from https://www.merriam-webster.com/dictionary/bloc
Ingraham, C. (Apr 2015). A Stunning Visualization of Our Divided Congress. The Washington Post.