Political campaigns are commonly understood as random walks, during which, at any point in time, the level of support for any party or candidate is equally likely to go up or down. Each shift in the polls is then interpreted as the result of some combination of news and campaign strategies.

A completely different story of campaigns is the mean reversion model in which the elections are determined by fundamental factors of the economy and partisanship; the role of the campaign is to give voters a chance to reach their predetermined positions.

The popularity of the random walk model for polls may be partially explained via analogy to the widespread idea that stock prices reflect all available information, as popularized in Burton Malkiel’s book, A Random Walk Down Wall Street. Once the idea has sunk in that short-term changes in the stock market are inherently unpredictable, it is natural for journalists to think the same of polls. For example, political analyst Nate Silver wrote in 2010:

In races with lots of polling, instead, the most robust assumption is usually that polling is essentially a random walk, i.e., that the polls are about equally likely to move toward one or another candidate, regardless of which way they have moved in the past.

But, as I discussed then in the context of remarks by Silver and Justin Wolfers, polls are not stock markets: for many races, a forecast from the fundamentals gives a pretty good idea about where the polls are going to end up. For example, in the 1988 presidential election campaign, even when Michael Dukakis was up 10 points in the polls, informed experts were pretty sure that George Bush was going to win. Congressional races can have predictable trends too. Political scientists Erikson, Bafumi, and Wlezien have found predictable changes in the generic opinion polls in the year leading up to an election, with different patterns in presidential years and off years. Individual polls are noisy, though, and predictability will generally only be detectable with a long enough series.

Noah Kaplan, David Park, and I have a paper on the topic (to appear in Presidential Studies Quarterly) reviewing the literature and analyzing data from polls during the 2000, 2004, and 2008 elections. We show that, as the campaign progresses, vote preferences become more predictable based on fundamental variables such as political ideology and party identification. This is consistent with a “mean reversion” model in which the campaign serves to solidify latent preferences, but it is not consistent with a random walk model in which a campaign is an accretion of unpredictable shocks.

To many of the readers of this blog, the above is not news. Political scientists have been talking about “the fundamentals” for awhile, to the extent that journalists and other observers sometimes overestimate the importance of the economy in determining the election (for example, here’s a clueless history professor likening the predictability elections to “the law of gravity”). As John Sides explained reasonably, you have to be careful when translating economic numbers into vote predictions.

Still, a bit of old-fashioned random-walk thinking remains in the old-fashioned news media. For example, Michael Kinsley recently wrote:

In 1988, Michael Dukakis, who was ahead in the polls just after the Democratic convention, declared in his acceptance speech: “This election isn’t about ideology. It’s about competence.” Then he proceeded to blow a large lead and lose to George Bush the Elder, who turned out to be a tougher old bird than anyone suspected.

This sort of understanding of campaigns was pretty standard a few decades ago, back when Kinsley was editor of the New Republic, but nowadays we wouldn’t frame Dukakis as having “blown a large lead” but rather that he lost a lead that was effectively unsustainable, given the economic and political conditions of 1988. Nor would we need to characterize Bush Senior as a “tough old bird” for winning this election; it was more about being in the right place at the right time.

To say that Dukakis blew a lead is not quite to buy into a random-walk model, but I think it is close. Given what we know about elections, I think it would be more accurate to say that the 1988 election was Bush’s to lose (and he didn’t).

Anyway, that Kinsley quote is an example of why I think this blog post could be helpful. I’m hoping that, by explicitly stating the random-walk and mean-reversion scenarios, I can make people more aware of the implicit models that underly their stories about campaigns and elections.

[Cross-posted at The Monkey Cage]

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Andrew Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University.