Hibbs writes:

The 3rd quarter data posted by the BEA yesterday are so dire that I [Hibbs] changed my tune and generated a new forecast based on the latest data.

October 31, 2008 update of Presidential Vote Forecast: Preliminary estimates of 2008q3 national income data released on October 30 by the BEA indicate that the economy has weakened so much that I have updated my 2008 election forecast: The Bread and Peace model now predicts a 2-party vote share for McCain of 46.25%, implying Obama will win by a margin of 7.5 percentage points.

Just as a reminder, here’s the graph of Hibbs’s model applied to earlier elections:

As you can see, the incumbent party sometimes loses but they never have gotten really slaughtered. In periods of low economic growth, the incumbent party can lose, but a 53-47 margin would be typical; you wouldn’t expect the challenger to get much more than that. Such things can happen (see, for example, Eisenhower’s performance against Stevenson in 1952) but it wouldn’t be expected.

How is the 2008 election different from 2004, beyond the (currently predicted) national swing of about 4 percentage points (enough to move from Kerry’s 49% of the vote to 53% for Obama)?

When I spoke yesterday, someone asked about the changes in the electoral map since 2004 and what did it all mean, are the red states being painted blue etc?  My quick answer is that the relative positions of the 50 states aren’t changing much, it’s the whole country that’s shifting.  This was my impression based on looking at the map and also based on generally seeing uniform partisan swing in votes and attitudes.

But what do the data say? Here’s a graph of Obama’s predicted share of the two-party vote in each state (based on Nate Silver’s recent poll aggregation) compared to Kerry’s in 2004:

I then fit a simple linear regression; here’s a map of the residuals, showing where Obama is doing particularly well or poorly, compared to last time:

I used regression residuals rather than simply plotting or map the differences because of “regression to the mean”: the predictable pattern that the Democratic vote will go down (relatively speaking) in places where they did particularly well last year, and go up where they did particularly poorly. Instead, the regression residuals show changes that are unexpected (relative to the linear model, that is; of course it’s not unexpected that McCain is doing relatively well in Arizona, but the simple linear regression of 2008 on 2004 doesn’t know about home states).

2000/2004

How big are these changes? One way to calibrate is to look at changes from 2000 to 2004. These will be close to the smallest changes we’ll ever see, since 2004 was really a replay of the 2000 election. Here’s the scatterplot:

And here’s the map of residuals:

So, the relative changes of states in 2008 seem greater than the changes in 2004. On the other hand, the 2008 estimates are based on fallible poll data; maybe the election outcomes will be less variable.

Sometimes you hear discussion of how the red states get more from the government than they pay in taxes while the blue states get less and pay more.  This is slightly misleading because the blue states are richer and rich people pay a higher rate of income tax, but it does raise the interesting question of the national effects of the graduated income tax.

For some perspective on where this is coming from, here’s a fun bit from chapter 9 of our book:

In our office is a map from 1924 titled “Good Roads Everywhere” that shows a proposed system of highways spanning the country, “to be built and forever maintained by the United States Government.” The map, made by the National Highways Association, also includes the following explanation for the proposed funding system:  “Such a system of National Highways will be paid for out of general taxation.  The 9 rich densely populated northeastern States will pay over 50 per cent of the cost. They can afford to, as they will gain the most.  Over 40 per cent will be paid for by the great wealthy cities of the Nation. . . . The farming regions of the West, Mississippi Valley, Southwest and South will pay less than 10 per cent of the cost and get 90 per cent of the mileage.” Beyond its quaint slogans (“A paved United States in our day”) and ideas that time has passed by (“Highway airports”), the map gives a sense of the potential for federal taxing and spending to transfer money between states and regions.

I’ll see if I can get someone to take a picture of this amazing map so I can post it on the blog.

Politics doesn’t stop at the shower’s edge.

Drew Linzer writes:

I read your paper with some interest, as within the last week or so I’ve started analyzing the state tracking polls available on pollster.com using a simple Bayesian mean model, and posting my results here.

The model updates the Obama share of the Obama-McCain vote as new polls come in, and then calculates the posterior probability that that proportion is greater than 0.5.

I’m doing this as more of a hobby than anything else…was frustrated with analyses I’ve seen that strike me as overly complex, unstable, totally opaque, and frankly, fairly unbelievable — there’s a real chance Obama could get EVs in the 400s? come on. The R code I use to generate my graphs and predictions are also posted if you click the Data tab.

My goal was to create a model that was very simple, but made reasonable predictions. So, for example, I don’t have any time component — the model assumes the true state level proportion for Obama is constant. Maybe this is a good assumption, probably not, but also probably the actual within-state support numbers are not actually fluctuating as much as some of the predictions out there make it seem. The first time I set up the model, I just used the posterior from one poll as the prior on the next. Problem was that the variance of the priors got so small after a while that there didn’t seem to be enough flexibility to capture trends when they did seem to arise. So then I added a multiplier to increase the variance of the prior in proportion to how many days old the last poll was. I tinkered around with it a bit and came up with this that seemed reasonable.

sd.prior.flex <- sd.prior.flex * (1+(0.05*log(dat$daysold[i]+1)))

Changing the 0.05 makes the trend line more or less sensitive to new polls. It's really just kind of acting as a smoother as the polls appear.

The other thing the model doesn't have is any sort of cross-state correlation structure built in. Every state is treated as its own independent entity. This probably isn't very realistic either, but in these battleground states there also seems to be enough state-level polling going on to get decent enough within state estimates. Where I would like to take account of cross-state correlation is when I simulate election results. Doesn't seem like that would be too hard to estimate in a second stage after the trendlines have been calculated (or simultaneously in a more complicated model), I just haven't gotten around to it.

Anyway, so that's basically it. the "predicted electoral vote" adds up the EVs for each candidate who my trend line has above 50%. And the simulation that produces the "probability of winning" and the histogram just draws from each state's most current posterior distribution 1 million times and adds up the number of Obama EVs. As I said, I don't know if the model is "good" but it is clean and transparent, relatively stable, and, to my mind, produces predictions that accord better with the available polling data.

My thoughts: First, I don’t think it’s impossible that Obama could get EV’s in the 400s, given the uncertainties in national forecasts. It’s not likely but it’s possible. Second, I think poll aggregation is fine (whether it be Drew’s method or Realclearpolitics or 538.com or whatever), but when it comes to forecasts, I think the best thing is a weighted average of polls and model-based predictions, with the model having two parts: (1) the national popular vote and (2) the states relative to each other.

A reporter asked me the other day why political scientists are so sure that people vote based on the economy. I told him there are three big pieces of evidence:

1. Comparing elections. As Doug Hibbs and others have shown, there’s a strong correlation between the incumbent party’s share of the vote and economic conditions in the year preceding the election.

2. Comparing voters within an election. As Ansolabehere, Rodden, and Snyder, and others have shown, individuals’ views of the national economy are a good predictor of their votes.

3. Voters’ stated concerns. When asked what are the most important issues, voters lead with “job creation/economic growth” (23%) and “energy/gas prices” (20%), with, in total, 59% of voters identifying economic issues as most important, as compared to 16% mentioning Iraq, 8% mentioning terrorism, 8% immigration, and 5% environment. The economy isn’t the only issue, but it’s central.

If there’s a factor that predicts elections, individual votes, and it’s what people say that it matters to them–then we’re inclined to believe them.

Here’s a picture from my paper with Jonathan and Gary a few years ago that appeared in the volume Rethinking the Vote, but it’s actually a graph that Gary and I made around 1994 or so. The graph shows the share of the two-party popular vote that the Democrats needed to have a 5%, 50%, or 95% chance of winning the electoral college in each year:

In most years, things were pretty balanced (each party has some close states and some not-so-close states, each party wins some big states and some small states). We haven’t done 2008 but I suspect it would be similar. That’s the conclusion of this paper by Phillip Ardoin and Bryan Parsons:

Our [Ardoin and Parsons's] research provides a direct analysis of the multiple sources of bias within the electoral college and examines their individual impact on each party’s electoral fortunes over the last eleven elections (1964-2004) with particular attention on the 2000 and 2004 presidential elections. Our results are in line with previous analyses indicating no significant bias within the electoral college.

A reporter asked me, “Do people run for VP, who in the past, how, has it worked or failed?”

My reply: I haven’t looked at this recently, but I recall when studying election forecasting 15 years ago, that the estimated effect of VP choice was something like +3 percentage points in the VP’s home state, so nothing huge.

What about national effects? In 1988, I recall that polls found that Bush alone (in a Bush vs. Dukakis matchup) did about 2 points better than Bush-Quayle vs. Dukakis-Bentsen. But this is probably an upper bound:
- Quayle was a horrible candidate
- And probably, when it came down to the voting booth, it’s my guess that less than 2% of people decided not to vote for Bush on the basis of Quayle.
See also Nate Silver’s thoughts on the topic.

So probably the biggest effect of VP is that this is a person who’s likely to become president. (I don’t have the stats on this, but the total probability must be pretty high.) If I were choosing, I’d pick the person I’d most like as a future president and probably not worry so much about electoral calculations, fun though they are to think about.

To follow up on the previous entry on McCain and Obama’s chances, here’s Bob Erikson and Chris Wlezien’s 2008 forecast based on economic conditions and the polls. They write:

On the eve of the election, the presidential vote can be seen fairly clearly from trial-heat polls. Earlier in the election year, the polls off much less information about what will happen on Election Day. The polls capture preferences to the moment and do not—because they cannot—anticipate how preferences will evolve in the future, as the campaign unfolds. Various things ultimately impact on the final vote. The standing of the sitting president is important. The economy is too. Both can change as the election cycle evolves. To make matters worse, late-arriving economic shocks have a bigger impact on the electoral verdict than those that arrive earlier. This complicates forecasting the vote well in advance. Our [Erikson and Wlezien's] solution to the problem of early forecasting has been to turn to the index of leading economic indicators. In previous papers we have shown that the growth in these indicators through the Spring of the election year—quarter 13 of the election cycle—is a strong predictor of the vote.

They fit the model to past election sand go on to plug in some numbers to get a point prediction with uncertainty.

The paper will appear in the journal PS (in the same special issue that will contain my article with Kastellec and Chandler).

The economy is going badly, Bush is unpopular, so why is Obama up only slightly in the polls? This is the question that is (currently) puzzling the political world. A quick answer: the economy isn’t going so badly by historical standards, and Bush’s popularity isn’t so relevant given that he’s not running for reelection. Forecasting models based on past elections predict Obama to get something like 53% of the two-party vote–but these forecasts aren’t perfect; they have a margin of error of a few percentage points. (This is not the margin of error of the polls, arising from sampling variation; rather, it’s the uncertainty reflected by the imperfections of the forecasting model as applied retrospectively to past elections.) In short: macro conditions for the Republicans are not so bad as all that: Obama is a legitimate favorite but there’s no reason to expect that there would be a landslide. Things are going about how one might suspect based on historical patterns of the economy, incumbency, and presidential elections.

To think about it another way, consider this graph adapted from Doug Hibbs of incumbent party’s vote share and economic growth, for all presidential elections since 1952.

As you can see, the incumbent party sometimes loses but they never have gotten really slaughtered. In periods of low economic growth, the incumbent party can lose, but a 53-47 margin would be typical; you wouldn’t expect the challenger to get much more than that. Such things can happen (see, for example, Eisenhower’s performance against Stevenson in 1952) but it wouldn’t be expected.