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A man shovels snow during a winter storm in Ottawa, Canada, February 16, 2016. REUTERS/Chris Wattie      TPX IMAGES OF THE DAY      - RTX279LK
Brookings on Job Numbers

Colder-than-normal December suppresses jobs growth

The final employment report of the Obama presidency released this morning shows that 156,000 new jobs were added in December 2016. He will leave office after 75 straight months of positive jobs growth dating back to October 2010, which is the longest such streak in American history.

Monthly job gains and losses can indicate how the economy is doing once they are corrected to account for the pattern we already expect in a process called seasonal adjustment. The approach for this seasonal adjustment that is presently used by the Bureau of Labor Statistics (BLS) puts very heavy weight on the current and last two years of data in assessing what are the typical patterns for each month.

In my paper “Unseasonal Seasonals?” I argue that a longer window should be used to estimate seasonal effects. I find that using a different seasonal filter, known as the 3×9 filter, produces better results and more accurate forecasts by emphasizing more years of data. The 3×9 filter spreads weight over the most recent six years in estimating seasonal patterns, which makes them more stable over time than the current BLS seasonal adjustment method.

I calculate the month-over-month change in total nonfarm payrolls, seasonally adjusted by the 3×9 filter, for the most recent month. The corresponding data as published by the BLS are shown for comparison purposes. According to the alternative seasonal adjustment, the economy added 173,000 jobs in December (column Wright SA), 17,000 more than the official BLS total of 156,000 (column BLS Official).

In addition to seasonal effects, abnormal weather can also affect month-to-month fluctuations in job growth. In my paper “Weather-Adjusting Economic Data” I and my coauthor Michael Boldin implement a statistical methodology for adjusting employment data for the effects of deviations in weather from seasonal norms. This is distinct from seasonal adjustment, which only controls for the normal variation in weather across the year. We use several indicators of weather, including temperature and snowfall.

Temperatures in December were colder than normal, following a warmer-than-normal November. This depressed job gains in the month. Controlling for weather yields a higher season-and-weather adjusted estimate of 181,000 new jobs added (column Boldin-Wright SWA). We find that the cold weather reduced employment by 25,000 jobs (column Weather Effect).

Both my alternative seasonal adjustment and our weather adjustment show higher job gains than reported by the BLS.

Perhaps most noteworthy, the calculations that control for weather effects on payroll employment show that job growth in December was very similar to job growth in November.

Thousands of jobs added BLS Official Wright SA Boldin-Wright SWA Weather Effect
2016-December  156  173  181  -25
2016-November 204 246 194 +10
2016-October 135 146 142 -7
2016-September 208 186 212 -4
2016-August 176 177 175 +1
2016-July 252 272 246 +6
2016-June 271 267 255 +16
2016-May 24 10 22 +2
2016-April 144 146 207 -63
2016-March 186 153 175 +11
2016-February 233 256 202 +31
2016-January 168 135 216 -48
2015-December 271 283 225 +46

Notes:

Changes in previous months’ numbers reflect revisions to the underlying data.

Column labeled Wright SA applies a longer window estimate of seasonal effects (see Wright 2013).

Column labeled Boldin-Wright SWA includes seasonal and weather adjustments, where seasonal adjustments are estimated using the BLS window specifications (see Boldin & Wright 2015). The incremental weather effect in the last column is the BLS official number less the SWA number.

Author

 


The author did not receive financial support from any firm or person for this article or from any firm or person with a financial or political interest in this article. He is currently not an officer, director, or board member of any organization with an interest in this article.

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