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The Bias Of The Generic Ballot

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Henry Olsen at National Review Online has a smart critique up on my observation that the generic Congressional ballot may underestimate Democrats’ standing in House races this year. Let me warn you up front: this post is going to get into some fairly technical issues.

The most important question raised by Mr. Olsen’s article actually doesn’t have to do with the generic ballot per se, but rather boils down to whether polls of individual races tend to underestimate the standing of candidates who have relatively poor name recognition (as is often the case when, for instance, a non-incumbent is matched up against an incumbent in a U.S. House race). This is a real effect, and Mr. Olsen is right that it is something to be mindful of. Early in an election cycle, in fact, it’s something to be very cognizant of — candidates who are poorly known to voters generally have some upside in their numbers. By the time we reach Election Day, however, the bias against these candidates pretty much disappears — if nobody knows who you are by the morning of the election, well then, it’s probably too late.

Our House forecasting model has some ways to account for this: for instance, it tends not to look very much at polls of individual districts early in the election cycle, but tends to place more emphasis on them (at the expense of the generic ballot) as Election Day draws nearer. Also, looking at the number of undecideds in a poll can sometimes be informative: a 40-30 lead in the polls is not as solid as a 50-40 lead.

I owe Mr. Olsen a longer response on some of these points (I actually don’t think we disagree on very much). Perhaps more important, I owe FiveThirtyEight readers a more comprehensive overview of our House model in general.

But, for the time being, I want to focus on one particular comment that Mr. Olsen made. He writes:

But, as one noted prognosticator observed earlier this year, “On average the generic ballot has overestimated the Democrats’ performance in the popular vote by 3.4 points since 1992.” When this is applied to the AAF data Nate cites, it appears that the Democratic problem is not better than it appears; it’s worse.

The “noted prognosticator” that Mr. Olsen refers to is yours truly! As I wrote in April, there’s some history of the generic ballot overestimating the Democrats’ performance in the national House popular vote.

The House popular vote is what you get if you simply add up the votes for the Democratic and Republican candidates, respectively, across all 435 congressional districts. For instance, in 1998, Democratic candidates received a total of 31,490,298 votes for the House, while Republicans received 32,233,067, and candidates from other political parties, 2,154,221 votes. That translates into a Republican win of about 1 percentage point in the national popular vote. Most of the generic ballot polls that year, by contrast, had shown Democrats with a slight advantage, so this was one of those years in which it somewhat overestimated their performance.

This is only relevant, however, to the extent that you care about the aggregate House popular vote — which you might, for instance, if you were using the popular vote to back into an estimate of the number of seats that a particular party might gain or lose. (That’s what I was trying to do back in April.) If you’re working with this type of model, you have a decision to make about whether to apply a correction for the fact that the generic ballot has tended to overestimate the Democrats’ popular vote performance in the past. (For a variety of reasons — like the fact that the effect seems to have become less profound in recent elections, and that other types of polls haven’t shown a systematic bias toward one or the other party — it’s not quite so straightforward a decision as it seems, but it’s certainly something a forecaster has to wrestle with.)

That is, however, not the type of model we’re working with now, mainly because it isn’t very precise. Instead, we’re working from the ground up, trying to make calls on each of the 435 individual House races, and then aggregating those projections in a careful way to figure out how many seats each party is likely to control overall. So what we’re really concerned with is

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