We have been bombarded with poll results and election models designed to predict winners and losers. I think it is useful to explain how these work without a lot of technical jargon. I begin with polling.
Conducting voter polls has become more difficult in recent years, despite (or maybe because of) a spate of new technological developments that should have made it easier. Simply getting in touch with voters should be easier due to the ubiquity of cellphones. Unfortunately for pollsters, human behavior has adjusted and many of us no longer pick up cellphone calls from unknown numbers.
At the same time, more and more families (including mine) have disconnected their landline telephone. This immediately means a number of potential voters are largely unreachable by phone. If these people were randomly distributed across the population spectrum it wouldn’t bias a poll, but they are not. Moreover, how much they differ from the population as a whole is not well known for the same reasons we cannot contact them to ask about elections. We do generally know that owners of landlines tend to be older and perhaps more affluent.
We also know that a higher share of younger people have cellphones than do older folks. So, when we do call a few thousand people in order to speak to a few hundred, we don’t really know if they are a good sample of the population as a whole. This generates the first real polling problem: getting a representative sample of the population.
The second problem faced by pollsters is determining which of the folks who eventually are contacted actually will vote. Predictions were that half of the eligible public would vote Election Day. Getting this even slightly wrong can make the predictions of a poll very wrong.
As with the cellphone problem, if the likelihood of voting were evenly distributed across the population, this wouldn’t bias the poll results. However, the propensity to vote is affected by age, income, recent moves, enthusiasm for a particular candidate and/or the belief your candidate will win. These factors tend to favor one candidate or another, and so this makes a pollsters job a tough one.
Finally, it should be noted that people lie about whom they will vote for, for a variety of reasons.
In light of these types of problems, economists (and increasingly political scientists) long have favored observing what people do, rather than asking them about it. This is what statistical vote models do, albeit mostly using historical data.
A number of researchers have built models of vote predictions that include candidate favorability ratings, national and local economic conditions, consumer sentiment and the like. The best of these models look at state or sub-state regions and predict voter turnout and winners based upon historical relationships between these conditions and votes.
Michael J. Hicks is the director of the Center for Business and Economic Research and an associate professor of economics in the Miller College of Business at Ball State University. Send comments to firstname.lastname@example.org.