Spoiler: this is a summary of a failed experiment.
There’s a wonkish new measure of gerrymandering that’s influencing the courts called the efficiency gap. It is a measure of partisan bias of a districting plan, trying to quantify “wasted votes”. It’s not too complicated a measure. Suppose a single district election goes 53 voters for candidate D and 47 for candidate R. Then 2 of those D votes were wasted, because they were in excess of the 51 votes needed for a victory. All 47 of the R votes were wasted, since their candidate lost. If you count up all those wasted votes over all the districts you have a measure of how many votes were wasted in a state. For instance if the Rs consistently win 80% of a state’s districts 53/47 and the Ds win 20% of a state’s districts 20/80, then the Ds waste a lot of votes both in the winning districts (80% instead of 51%) and in the losing districts (47%). When you apply this measure to real elections it quantifies just how much the Republicans successfully gerrymandered a partisan advantage in states like Pennsylvania or Ohio.
It’s a simple measure, but it’s still complicated to explain to people. So I took a crack at quantifying something even simpler, the gap between popular vote and congressional seats. For instance in North Carolina last year Republicans took 53% of the popular vote for House of Representatives. However they took 10 of 13 seats, 77%. That’s a pretty remarkable spread of 24%. That outcome spread is a different measure than the efficiency gap, much less subtle, but it’s one I think people can relate to pretty easily.
What happens if I add that spread up over all 50 states’ elections and graph it over time? I scraped data from Wikipedia pages and came up with this:
There you go. It shows that in 2016 the Republicans got 55% of seats with about 50% of the popular vote, a spread of +5%. Back in 1990 they got only 39% of the seats with 46% of the vote, a spread of -7%. It’s the spread between the two that I’m interested in, so let’s graph the spread directly:
Note the graph of the spread is more or less correlated with the blue line above, the % of seats the Republicans got. I’m not sure what that means but it seems important.
Visualization Failure: Oversimplified
My main conclusion from this exercise is it’s a failure. I’ve oversimplified. there’s no obvious story here. I think any fine point about redistricting gets lost in the broader story of swings between Republican and Democrat popularity.
I think it’s a mistake to add up the votes and seats across different states. Every state is a different story. North Carolina had a virulently partisan districting which probably explains its huge gap in favor of Republicans. California has an independent non-partisan commission designing its districts, so a different process, although the outcome in 2016 still seems somewhat to benefit Democrats (62% popular vote, 73% of seats).
It might also be a mistake to assume all Republicans and Democrats are the same. I mean as partisan as the United States has become, Representatives are still elected to represent a local community and in many cases are known to their constituents. Congresspeople have individual agenda and views and don’t just reflect their R/D affiliation.
So back to the drawing board. I should consider just redoing visualizations from the efficiency gap data Stephanapolous and McGhee collected.
There’s some nice visualizations of the efficiency gap in this paper from the Campaign Legal Center
Time series view:
Per-state view. (Darker color = bigger efficiency gap. Larger square = bigger state.)