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By Ethan Wolff-Mann
September 14, 2015

Every month the government publishes the number of jobs added or subtracted from the economy, and when the stats are disappointing, analysts often blame the weather. A snowy winter, a gross spring, crazy heat—the weather can easily slow down construction in particular, cooling the economy and job growth.

This is nothing new or surprising. For some time, the Bureau of Labor Statistics has used a technique it calls “seasonal adjustment” to smooth out seasonal fluctuations based on a normal year. But why not adjust the numbers for the actual weather in a given month?

Economists Michael Boldin and Jonathan H. Wright have proposed doing just that. Last week, they published a paper with the Brooking Institution think tank detailing a weather adjustment method that factors in data points like snowfall and temperature.

The impact, they argue, could be quite large. “We find that weather effects can be important, shifting the monthly payrolls change number by more than 100,000 in either direction,” the pair write in the study. Since the month might have 200,000 jobs added, an unusually warm winter can easily turn a slow month into a strong one, while a blizzard could have the opposite effect.

Strip out the random noise of weather patterns, and we could all get a clearer view of the country’s underlying economic health.

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