In the wake of the 2012 House elections, it looks like Democrats won a slight majority of the major-party votes (roughly 50.5%) but only about 46% of the seats. A story has gradually developed that pins this gap on redistricting (for example, see here, here, and here), since Republicans controlled the line-drawing process more often than not this time around. Matthew Green pushes back a little on this narrative, arguing that, if anything, House seat share and vote share correspond more closely today than they once did. But he doesn’t necessarily deny that redistricting is to blame for the gap this year.
We have looked at this question several times before (here, here, and here) and concluded that redistricting is a wash. But we based this conclusion on a multi-year model with both incumbency and the partisanship of the constituency (as measured by the presidential vote in each district). What if our model missed something important about the national climate this year? Not crazy, since it was too bearish on Democrats. For that matter, what if all this talk about incumbency is nonsense and it’s all about the district?
Let’s work with those assumptions. We’ll drop our regular model and go bare bones. Two steps: 1) identify the relationship between this year’s actual election returns and the 2008 presidential vote in each district (calculated by Daily Kos), 2) use this relationship plus the 2008 presidential vote in the old districts to estimate what would have happened under the old lines. No incumbency, no assumptions about national climate. For the redistricting story to hold, this exercise must eliminate the discrepancy between Democratic vote share and seat share. Otherwise, something else is going on.
Results are in the graph below. Democrats do gain more seats under this simulation–seven more total–but fall far short of matching their predicted vote share. The point should be clear: even under the most generous assumptions, redistricting explains less than half the gap between vote share and seat share this election cycle.
And it’s worth noting just how generous these assumptions are. This bare-bones model misses more individual outcomes than any handicapper or other forecasting model. It ignores one of the most important ways that a gerrymandering party tries to stick it to the other side (i.e., by moving incumbents to more difficult territory), as well as one of the most important ways that a gerrymander doesn’t work (i.e., incumbents beat expectations based on district partisanship alone). It’s not really a bad model. But it’s not really a good one, either.
If redistricting doesn’t explain the discrepancy, what does? We have argued that incumbency is a likely culprit, but as Dan Hopkins recently pointed out, Democrats also do worse because they are more concentrated in urban areas. They “waste” votes on huge margins there, when the party could put many of those votes to better use in marginal seats. (See this paper by Jowei Chen and Jonathan Rodden for more evidence on this point.)
It matters to get this right. If it’s redistricting, then Republicans rigged themselves control of a key lever of federal policymaking. This casts fundamental doubt on the legitimacy of the Republican majority, and bolsters calls for redistricting reform as a way to fix the problem (for the record, I’m not exactly an opponent of such reform). But if the explanation is incumbency or political geography, all it means is that our single-member district majoritarian system distorts outcomes in ways that are difficult to eradicate without moving to a different system entirely.
*For the sake of this exercise, we estimate our model without uncontested races and then add them back in for our predictions. A gerrymandering party can’t anticipate which races will be uncontested, so such races are properly considered outside the realm of redistricting–especially for an exercise devoid of other candidate effects like this one. At any rate, this fact explains why the Democratic vote share is a little better in this exercise than in the actual result.