The more data points, the better the economic model

Forget about Obama versus Romney. The real contest in the 2012 election was about analytics.
Politics is just the latest front in a war being fought in businesses and consulting firms around the world, with this round pitting “quants” such as The New York Times blogger Nate Silver against the intuition of pundits like James Carville or Karl Rove. The quants bring data, computers and formal models. The pundits – though they do use data – rely more on gut feelings, industry experience and personal contacts.
In the latest skirmish, the quants won. They predicted the election outcome far more accurately than the pundits did. Therein lies a lesson for executives and policymakers alike: Wisdom and intuition may actually be hurting your firm or organization.
Consider the Federal Reserve, arguably the most powerful economic institution on the planet. The Fed’s staff economists do for economic statistics what Silver does for poll numbers, crunching piles of data into a very accurate forecast. By contrast, the members of the Federal Open Market Committee are more like Carville, wisened by years of experience and equipped with anecdotes from their industry contacts.
A surprising study by economists Christina Romer and David Romer found that we would be better served if the members of the FOMC simply withheld their judgment.
This doesn’t mean the quants can declare victory. As it turns out, there is an even better forecaster: crowds. Political prediction markets, such as Intrade, which rely on the wisdom of crowds rather than any individual uber-pundit, have historically done a better job of predicting elections than even very sophisticated statistical models do.
The implications go far beyond elections. Research has shown that prediction markets can forecast economic, business and sporting outcomes better than relevant experts can. What these markets do is efficiently aggregate many different types of information pertinent to the forecast at hand – not merely the parts that are quantifiable and thus easy to subject to the quants’ analytical tools. The quant may be smarter than any other person in the room, but he’s not smarter than the room as a whole.
In recent research, David Rothschild and Justin Wolfers analyzed the results of election polls that asked random samples of Americans who they thought would win. They found that polls of voters’ expectations consistently outperformed standard polls, which asked voters whom they plan to vote for.
Asking the crowd for its forecast yields useful insights because it treats poll respondents as mini-anthropologists, asking them to report back not just on their own thoughts and feelings, but also on those of their friends, neighbors and co-workers. This boosts a poll’s effective sample size, as each respondent may end up speaking for the preferences of 10 or 20 others.
The same logic that works for political forecasts might work well in other contexts, for economic indicators, for instance. Economists interested in tracking employment might ask whether the firm where people work is hiring or firing. Gallup Inc. has recently started asking precisely this question, and some preliminary analysis suggests that it can help explain labor- market developments.
The 2012 election had a clear winner: Analytics beat intuition. This is threatening both to the likes of Carville and Rove and to their intuition-driven counterparts in the corporate world. But the quants also have to respect the crowd. The success of prediction markets and expectations polls tells us something truly humbling – that knowledge doesn’t just reside in the executive suite or in a quantitative model. •


Betsey Stevenson is an associate professor of public policy at the University of Michigan. Justin Wolfers is an associate professor of business and public policy at the University of Pennsylvania, and a nonresident senior fellow of the Brookings Institution. Both are Bloomberg View columnists.

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