The ideology analysis assigns a left–right score to each Member of Congress based on their pattern of cosponsorship. The left–right score reflects the dominant ideological difference or differences among Members of Congress, which changes over time.

In a nutshell, Members of Congress who cosponsor similar sets of bills will get scores close together, while Members of Congress who sponsor different sets of bills will have scores far apart. Members of Congress with similar political views will tend to cosponsor the same set of bills, or bills by the same set of authors, and inversely Members of Congress with different political views will tend to cosponsor different bills.

You can find this analysis on the pages for current Members of Congress and in the charts to the right which plot the ideology score on the horizontal axis and the leadership score on the vertical axis.

Overview

The data that goes into this analysis is a list of who sponsored or cosponsored which bills. The process doesn’t look at the content of the bills or the party affiliation or anything else about the Members of Congress, but it is able to infer underlying behavioral patterns, some of which correspond to real-world concepts like left-right ideology.

You’ll see in the charts on the right that the ideology analysis does a good job at separating the Democrats from the Republicans, and within each party the moderates from the extremes. If you wanted to know how your representatives stood in relation to their peers ideologically, this chart is a good place to start.

We first began publishing this analysis in 2004, then calling it a political spectrum. A similar analysis by Professor Keith Poole using voting records rather than cosponsorship produces similar results: see voteview.com. (As far as we know, we were the first to apply this sort of analysis to cosponsorship behavior.)

Methodology

The statistical method behind this analysis is Principal Components Analysis, also known as dimensionality reduction. Principal Components Analysis is a statistical technique that reveals underlying patterns in data.

Here’s how it works: Form a matrix (a grid of numbers) with columns representing Members of Congress and rows also representing Members of Congress. Do this for the House and Senate separately. We include (co)sponsorship from the current and previous two Congresses, so between four and six years of data. For the Senate, you have a 100x100 table. In each cell of the table, put the number of times the senator for the row cosponsored a bill introduced by the senator for the column. Or if it's the same senator in the row and column, put in the number of bills he or she introduced. Then compute the singular value decomposition of the matrix (which is how Principal Components Analysis is often done).

Every square matrix has a singular value decomposition which can be interpreted as a set of sets of scores for each Member of Congress, each set a ranking on different dimension. The dimensions are themselves ranked in order by how much of the original data they explain. We have found that the second dimension best corresponds with what people generally consider political ideology. We use the scores from that dimension in our charts.

The analysis is blind to actual information like what the legislation is about or what party each Member of Congress is affiliated with. In fact, there’s no guarantee that the scores have anything to do with liberal- or conversative-ness or any other standard frame for political ideology. All it tells us is how to spread Members of Congress out along a spectrum in a way that explains their record of cosponsorship. But in practice it captures left-right ideology very well.

Data

The ideology scores can be found in two CSV files sponsorshipanalysis_h.txt and sponsorshipanalysis_s.txt (House and Senate) over here.

Source Code

Running this analysis is pretty simple in Python. It is literally two lines. Assuming you have the cosponsorship matrix in P :

u, s, vT = numpy.linalg.svd(P) ideology = vT[1,:]

The full source code for this analysis can be found on github.

Citation

To cite our methodology and results, we recommend either of these:

GovTrack.us. 2013. Ideology Analysis of Members of Congress. Accessed at https://www.govtrack.us/about/analysis.

Tauberer, Joshua. 2012. Observing the Unobservables in the U.S. Congress, presented at Law Via the Internet 2012, Cornell Law School, October 2012. [text | slides | video]

References

For more on how to use singular value decomposition, check out:

Wall, Rechtsteiner, and Rocha. “Singular value decomposition and principal component analysis.” in A Practical Approach to Microarray Data Analysis. D.P. Berrar, W. Dubitzky, M. Granzow, eds. pp. 91-109, Kluwer: Norwell, MA (2003). LANL LA-UR-02-4001.