Be careful with the idea of polarization

I updated some old graphs on U.S. House polarization last night. I did this because I use the measure as a ‘system sensing’ device. I also did it because I use the graphs in my classes, partly because students have heard of polarization — and therefore need to think critically about it.

There are lots of critiques of the polarization idea lately. I wrote a not-great one back in February 2021. I say “not great” because 90 percent of it conceded the terms of debate.

Here is one more full critique, which I highly recommend. I was honored to meet one of the authors at the 2022 APSA meeting.

Here is another. I include it on my Congress syllabus.

Updating some U.S. House graphics

Now seems like a good time to remake some graphics I use often. All the underlying data are from Voteview.

Below is a party-blind measure of polarization in the U.S. House, from the beginning of that chamber through Wednesday of last week. The party-blind measure is the mean of all pairwise distances. (I once calculated it here for states and first came across it here.) This lets us go back to before the major parties were Republicans and Democrats. It also does not force us to make arbitrary decisions about handling minor parties, independents, and others with less common ways of affiliating/identifying.

Below is the more familiar measure: distance between the median members of the major parties, back to the end of Reconstruction. This plot includes the median Southern Democrat (conventionally the 11 Confederate states plus Kentucky and Oklahoma).

The last two plots answer the question: “How well does an N-dimensional model fit the data?” I used the optimal classification algorithm to recover ideal points for each House. I did this one Congress at a time; see here and here for other options. The black line comes from one-dimensional models. The gray line comes from two-dimensional models. The first plot gives the percentage of votes correctly classified, by Congress.

The second plot gives the aggregate proportional reduction in error.

Here is a CSV of the fit measures above. The first two columns are for the one-dimensional models. The next two are for two dimensions.

For me, “voting system” implies the wrong question

I recently expressed frustration with something I called “voting-system-speak.” This is my attempt to define (or at least engage) that and say why I find it frustrating. The crux, I think, is that many (but not all) who think about electoral reform are coming at the subject from a social-choice perspective. Another key point, which is related, is that we may be able to sort reform proposals based on their handling of the problem of social choice. One class of reforms turns to parties. Another class turns to voters. And the former strikes me as more realistic, based on insights from the field of political behavior.

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