In October of 2010, the economy saw its first month of positive jobs growth since coming out of the Great Recession. The employment situation released this morning shows that 161,000 new jobs were added in October 2016, marking 6 straight years of positive jobs growth. This is the last jobs report that will be released before the presidential election on November 8.
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 found 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 174,000 jobs in October (column Wright SA), 13,000 more than the official BLS total of 161,000 (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.
We find a negligible effect of weather on employment in October, lowering employment by only 4,000 jobs (column Weather Effect). Our weather-adjusted total is thus slightly higher than the official BLS total at 165,000 jobs (column Boldin-Wright SWA).
Our weather adjustment does not take into account the effects of hurricanes, such as Hurricane Matthew that struck the southeastern coast of the United States on October 8. In earlier work, we could not find a systematic relationship between hurricanes and employment, and Hurricane Matthew likely had only a small negative effect.
a. Applies a longer window estimate of seasonal effects (see Wright 2013).
b. 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.
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.