Support our non-partisan non-profit newsroom 💜 Donate now

Snow-zilla could have been Job-zilla — but it wasn’t.

Mitchell Hartman Feb 5, 2016
A man jumps over a snow and puddle filled street two days after a massive snow storm covered the East Coast in snow on January 25, 2016 in New York City.  Andrew Burton/Getty Images

Snow-zilla could have been Job-zilla — but it wasn’t.

Mitchell Hartman Feb 5, 2016
A man jumps over a snow and puddle filled street two days after a massive snow storm covered the East Coast in snow on January 25, 2016 in New York City.  Andrew Burton/Getty Images

Economists are generally underwhelmed by this morning’s monthly jobs report, which came in below expectations, with 151,000 jobs added to the economy, after a stellar end to 2015. Meanwhile, new research published by the  Brookings Institution suggests that seasonal weather effects reduced January’s job creation figure by about 25,000.

In their research, Johns Hopkins University economist Jonathan Wright and  Michael Boldin of the Federal Reserve Bank of Philadelphia attempt to incorporate the effects of unusual and extreme weather events on economic data gathered and published by the government. The Bureau of Labor Statistics, for instance, incorporates standard seasonal adjustments in its monthly surveys of employers and households  — the basis for the monthly employment report. These account for colder weather’s effect on construction activity, for instance, as well as the return of teachers to school payrolls in September, and the severe drop-off in temporary retail and service workers after the holiday shopping season in January.

But those adjustments don’t account for severe one-time weather events that occur during a particular economic report’s pre-designated data-gathering period, said Wright. For instance, the monthly employment report is based on a survey of employers and households, counting the number of people working in the pay-period that incorporates the twelfth day of each month. Wright and Boldin attempt to develop a method for further adjusting data for non-regular seasonal factors, such as big storms, extreme cold and heat waves, etc., using meteorological analysis tools. For instance, said Wright, they’ve used a snowfall index: “It’s very cool, it takes account of facts such as, in Atlanta two inches of snow are going to be more damaging than in Buffalo.” 

Based on this methodology, Wright said they analyzed the massive snowstorm that hit the U.S. East Coast from January 21-23, and found it had no effect on January’s job numbers as reported by the Labor Department — because the reporting period was completed before the storm hit. In fact, the storm did have an effect on January’s actual job totals, wage averages, etc. — because it reduced the number of days people were able to work during the month, and the income they earned, in hard-hit areas.

The research finds that if Snowzilla had hit in the BLS’s January 12 reporting-period sweet spot–today’s jobs report would have been a wash. The storm would have wiped out 156,000 jobs from the data-set, leading to a net loss of 4,000 jobs in January. 

There’s a lot happening in the world.  Through it all, Marketplace is here for you. 

You rely on Marketplace to break down the world’s events and tell you how it affects you in a fact-based, approachable way. We rely on your financial support to keep making that possible. 

Your donation today powers the independent journalism that you rely on. For just $5/month, you can help sustain Marketplace so we can keep reporting on the things that matter to you.