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The reform wave in context

This post is on the connection between oversized majorities and waves of political reform. Here I am thinking about ranked-choice voting in historical context, though one might say the same about direct primaries. I think reforms like this take off when:

1) Most people lean to one side of the ideological spectrum;

2) But that side of the spectrum has serious, internal cleavages.

The basic idea is that the logic of minimum-winning coalition is not holding in some way. The political majority is oversized, so much of the action is inside it. That fighting finds expression first as party splits, then as reforms to foster coordination. I have floated this hypothesis before. Others are starting to touch on it. Let’s look at some data.

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Oversized majorities and RCV appeal

I mean instant-runoff voting, which goes these days as “single-winner ranked-choice voting.” As readers of this blog know well, IRV manufactures a majority. If no candidate has a majority of first-choice votes, the last-placed candidate is eliminated. Ballots for the eliminated person flow to next-ranked picks on each. Rinse, repeat.

Two developments now catch the eye.

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Eleven Maine Democrats

At the same time that they use it in party primaries, Maine voters this June will vote a second time on retaining ranked-choice voting. This second referendum is the next stage of a people’s veto, a citizen-initiative process that can overturn acts of the legislature. The first stage was collecting more than 66,000 valid signatures, or 10 percent of votes cast in the last gubernatorial election.

What brought about the people’s veto was a classic, legislative roll. Last October 23, in a special session, eleven Maine Democrats joined their Republican colleagues to scuttle ranked-choice voting. This behavior was strange because the Democratic Party is poised to benefit, at least as public sentiment now stands.

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Scholarly work on RCV

I have started a bibliography of recent, empirical, and peer-reviewed work on ranked-choice voting.

My hope is that the page will be useful to policymakers and researchers. So far, it covers burdens on voters, burdens on election officials, effects on candidates and parties, causes of adoption/repeal, and book-length historical accounts.

Please reach out if you have something to add.


Scaling the House through Jan. 2018

The most recent post on Poole’s Voteview blog warned of “possible big trouble ahead.” Cause for this warning was an “unprecedented collapse of the long-term structure of Congressional Voting during the past 20 years.” To back up the claim, Hare and Poole graphed model-fit statistics for optimal-classification scalings of each House session, 1789-2015. At the time of the original post, the need for more than one dimension had steadily declined since the mid-1970s. In earlier years, a second dimension improved model fit for certain issues, recent examples being gun control and abortion. One interpretation is that some Republicans had been liberal, at least on certain issues, and some Democrats conservative — and their roll-call voting reflected this.

This post updates the earlier one through January 22, 2018. I am pleased to report (depending on your preference for party government) that a one-dimensional model better explains voting in the House of Representatives than at any point in American history.

Here you see the percentage of votes correctly classified in 1, 2, and 10 dimensions, per the original post. One-dimensional correct classification was 96 percent as of 1/22/18, an all-time high.

And here you see the aggregate proportional reduction in error (APRE), a model-fit statistic that weights classifier performance by ease of the task (i.e., the lopsidedness of a roll-call vote). One-dimesional APRE was 0.9 as of 1/22, another all-time high.

Two years ago, some wondered whether 2016 would become a high-water mark for polarization. Strictly speaking, these fit statistics don’t measure polarization (as a distance between parties), but they are consistent with party coalitions that continue to be rigid.

Thanks to Jeff Lewis at Voteview for making the underlying data available, and thanks to Chris Hare for the earlier analysis.