How to actually measure partisan gerrymandering (Part II of a three-part series)

I. NC plan>>>PA plan
In the previous post, I attempted to show the tradeoffs available to a designer of a gerrymander with this graph (see previous post for an explanation):

This approach would work well in states in which the districts of the favored party are all roughly equal in partisanship, such as North Carolina.

(X-axis is the name of the district, the Y-axis is the two-party Democratic vote share on the presidential level in each district)

However, not all states wanted to create a bunch of Likely Favored Party districts that would give the incumbent party supermajorities in neutral years, but would risk the opposing party gaining every seat in the state in a wave year favorable to the opposing party. An example of this is Pennsylvania’s House districts (note Tom Marino of PA was selected to be the President’s Drug Czar just today, so this post is perfect timing).

Pennsylvania has, in my judgment three tossup districts, all of which are held by Republicans, two Lean R districts, six Likely R districts, and two Safe R districts (one of which is Marino’s). There is also one Lean D heavily Obama-Trump district (PA-17), which actually was close enough in 2012 as to be acceptable for a wiser (or more partisan) Republican state legislature to turn into a Lean R district even then under more aggressive lines, and actually went for Trump by its present boundaries by more than both the Lean R districts and, naturally, all three of the the tossups. The fact it has a Democratic representative now is a huge and inexcusable failure of the Republican state legislature in 2011. The current Pennsylvania plan is, in my judgment, a mess, much inferior to North Carolina’s, and unnecessarily creates opportunities for the Democrats where, by any Republican’s judgment, there should be none. It would be relatively easy to create a redistricting plan for Pennsylvania with four Safe D seats and the other fifteen Likely R. My judgment is that the risk of all seats going to the opposing party in a wave year is worth it, and is much superior to being subject to the whims of “moderates” who are afraid of alienating their district’s swing voters in a general election while the favored party is in power. But how does one judge plans such as PA’s, anyway?

II. How does one measure the utility of tossup seats?

Another question, obviously related to the above one, is how does one measure the utility to a party of redistricting a district from Likely Opposing Party to Lean Opposing Party. For example, here’s my proposed Republican gerrymander of Indiana (no county splits, resulting in some serious malapportionment in Marion, but that’s not important here). The 2016 U.S. Senate race is used as a guide:

IN-01: In green. Two-party vote for Bayh: 52.47%. Rating: Lean D, instead of the current Likely D. This is an Obama-Trump district. The right kind of Republican can definitely get elected here. Certainly it would not be left the only uncontested seat in Indiana, as it actually was.
IN-02: In dark blue. Two-party vote for Bayh: 44.32%. Rating: Likely R.
IN-03: In red. Two-party vote for Bayh: 42.14%. Rating: Likely R.
IN-04: In orange. Two-party vote for Bayh: 38.21%. Rating: Safe R.
IN-05: In light blue. Two-party vote for Bayh: 39.84%. Rating: Safe R.
IN-06: In white. Two-party vote for Bayh: 36.52%. Rating: Safe R.
IN-07: Marion (yellow). Two-party vote for Bayh: 61.87%. Rating: Safe D.
IN-08: In light yellow. Two-party vote for Bayh: 44.35%. Rating: Likely R.
IN-09: In purple. Two-party vote for Bayh: 39.18%. Rating: Safe R.

For the current (actual) Indiana map, see here and here -click on the districts for info about them.

HRC got 47.50% of the two-party vote in my IN-01 and Obama in 2012 got 54.70%. In real life, HRC got 56.57% of the two-party vote in the actual IN-01 in 2016 and Obama in 2012 got 62.01%. Obviously, my redistricting plan significantly improved the GOP’s position in Indiana’s first district while not reducing the utility of the rest of the GOP-held seats for the GOP. The reason my IN-01, which has very similar political demographics to the actual PA-17, is more acceptable in Indiana is because Indiana is a more Republican state. Thus, it would be an improvement over the present plan. But how does one measure that? How does one display that using this curve?

I do not see a way for the above curve to be useful in cases in which districts within each side’s are of heterogeneous partisanship.

After thinking about it, the best advice I can give in regards to measuring gerrymandering is to multiply each seat by the probability of victory of the favored party. The maximization of these seat equivalents should be the measure of the worth of a gerrymander. Relative to the actual Indiana House map, my redistricting plan increases the chance of a GOP victory in IN-01 by more than it increases the risk to the Republican-held districts. I thus consider it a better gerrymander for the GOP than the actual GOP plan.

Measuring the probability of victory of the favored party in each district should be fairly simple (I will do this in the third post of this series), and historical data should be used for this purpose. Of course, the most important variable for determining the probability of victory of a party in a district is the district’s presidential vote. If it’s lower for the favored party, the probability of victory for that party is, all else being equal, lower. Historical swing data for congressional districts for the past few cycles should also be brought into account.

So, the above is how to measure a partisan gerrymander.

III. A fair map

Of course, for every villain -the partisan gerrymander- there necessarily has to be a hero to compare it to -the fair map. Of course, the question now becomes: what is a fair map? Certainly, a fair map cannot be a map in which each district is representative of the state in an identical fashion. Otherwise, Massachusetts’s map would be called a fair map, and Tennessee’s map would be considered as biased toward the Democrats. Obviously, neither is the case. Ideally, a fair map should have its median seat (now it enters into play) be representative of the state, though this is obviously not important (as I’ve shown in the previous post, comparing the median seat to the state is not an important measure of gerrymandering).

Perhaps this could be a fair map for a 60% Democratic state:

Of course, when each district becomes 10% more Democratic, the state as a whole won’t become 10% more Democratic, because districts 1 and 2 in the above graph cannot get any more Democratic. Of course, Dem chance of victory can also be used in place of Dem vote share in the y-axis of the above graph.

IV. Issues with the Princeton Gerrymander Tests

The Princeton Election Consortium developed three tests for gerrymandering. I don’t think the current ones are especially valid or useful.

The first test is right out; Texas is penalized because of Will Hurd’s narrow victory, despite the fact he’s obviously going down in 2018. A streak of luck in closely-contested tossup races is obviously no sign of a gerrymander, it’s just a sign of a side’s better campaigning.

The second test is also barely useful; it only measures the partisanship of one seat, the median seat, and, as the authors of the site admit, isn’t very useful at all in safe states.

The third test is a votes-seats curve. To which extent the specific votes-seats curve used is accurate is debatable.

The biggest problem with the tests is that they use the House vote instead of the presidential vote. The problem with this is that this makes no sense due to candidate heterogeneity. Collin Peterson ain’t Keith Ellison. The presidential vote should be used instead of the House vote to account for such differences.

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