Yale University economist Ray Fair’s presidential election model suggested that Mitt Romney would capture more votes than President Barack Obama. The vote didn’t pan out that way, but Mr. Fair says that’s no sign his model is broken.
The model relies on just three pieces of information: The per capita growth rate of gross domestic product in the three quarters before the election, inflation over the entire presidential term and the number of quarters during the term when GDP per capita growth exceeded 3.2%.
Plug those data points into Mr. Fair’s model, and it says that Mr. Romney should have taken 51% of the two party vote to President Obama’s 49%. Instead, it looks like it was 51.3% for President Obama to Mr. Romney’s 48.7%. It was only the third time since 1916 that the model failed to predict the popular-vote winner.
But the standard error on Mr. Fair’s model — a measure of the average size of the typical error it is likely to make — is 2.5 percentage points. Since the results fell within the standard error, from the perspective of an academic economist like Mr. Fair, the model worked.
That the error was positive for President Obama, rather than negative, could be due to any number of things, according to Mr. Fair, like a better ground game or a failure of Mr. Romney to connect. “There are a dozen different stories you could tell,” he says. “You can do that, but it’s not testable.”
It may be that Mr. Fair’s model turns out to have been more accurate than it now appears to be. Recent economic data suggest that GDP grew at a 2.9% annual rate in the third quarter rather than the 2% that the Commerce Department initially reported. That would nudge President Obama’s vote share in the model to 49.2%. And revisions to prior quarters may show they, too, were stronger than the current data show.
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