Is D. C. Statehood a matter of civil rights?, by Andrew Giambrone in The Atlantic
I know, what does this have to do with math?
Well, you could read Chris Wilson’s article for Slate on Puerto Rico statehood back in 2010, in which he writes about possible flag designs; we’d probably end up going with alternating rows of nine and eight stars, one of the options Skip Garibaldi identified.
But what I’m actually writing about is what I saw when I followed the link in that article to the govtrack.us page on the New Columbia Admission Act. This gives the following prognosis: “64% chance of getting past committee,
17% chance of being enacted.”
(Disclaimer: govtrack.us is the work of Joshua Tauberer, who I knew in grad school, in the sense that we had some mutual friends and have been in the same room at the same time.)It turns out this comes from logistic regression models, trained on the 2011-2013 Congress. The linked page there explains the models, and gives a list of the features looked at and their weights. There are models for both getting out of committee and being enacted. Somewhat amusingly, the feature with the highest positive weight in both of these “Title starts with ‘To designate the facility of the United States Postal'”, which refers to bills like this one that name post offices. In the particular case of this bill the prognosis comes from some more substantial features, though, having to do with sponsorship and committee membership and the like.
Note that the model doesn’t look at the text of the bill. And it need not – we already have sophisticated textual analysis modules in the guise of Congresspeople and their staffs. In looking at sponsorship data, this is an example of an ensemble model, which combines multiple models (the individual Congresspeople).
govtrack.us also offers analyses of ideology of Congresscritters (based on cosponsorship, using singular value decomposition) and leadership (based on cosponsorship again, using PageRank). As always, it’s good to see these statistical techniques being used to analyze things that matter.