Alternative seasonal adjustments show even stronger jobs growth than the BLS official number

The Bureau of Labor Statistics (BLS) employment report released today shows that 313,000 jobs were gained in February. This was a very strong report, with the best monthly job growth since summer 2016, owing in part to the demand effects of fiscal stimulus. Labor force participation moved up, despite continued demographic tailwinds, and measured wage growth was slow. In this blog post, I report results from three alternative projections, each of which was calculated using methodology outlined in my past research. Applying a more stable seasonal adjustment to the raw data, and accounting for the effects of weather on employment yields 332,000 jobs, which is 19,000 above the already-strong BLS Official number.

Calculating the Alternative Seasonal Adjustment

Monthly job gains and losses can indicate how the economy is doing once they are corrected to account for the pattern the BLS already expects in a process called seasonal adjustment. The approach for this seasonal adjustment that is presently used by the 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 2013 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.

To produce the Alternative Seasonal Adjustment, I calculate the month-over-month change in total nonfarm payrolls, seasonally adjusted by the 3×9 filter, for the most recent month, which you can see in table below. The corresponding data as published by the BLS are shown for comparison purposes. According to the Alternative Seasonal Adjustment, the economy added 341,000 jobs in February, even stronger than the official BLS total of 313,000. The reason why the Alternative Seasonal Adjustment produces an even stronger number is that the BLS methodology allows a strong month’s data to pull up the estimated seasonal factor substantially. The Alternative Seasonal Adjustment does this to a lesser extent. Hence, the Alternative Seasonal Adjustment shows an even stronger acceleration in seasonally adjusted jobs numbers.

Calculating the Seasonal and Weather Adjustment

In addition to seasonal effects, abnormal weather can also affect month-to-month fluctuations in job growth. In my 2015 paper with Michael Boldin “Weather-Adjusting Economic Data,” we 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 February were about 1°C warmer than historical averages, and there were no major snowstorms that could have affected employment. Thus, the overall weather effect is small, at +4,000, and the Seasonal and Weather Adjusted estimate for February is 309,000 jobs added.

Combining the Alternative Seasonal and Weather Adjustments

Both my Alternative Seasonal Adjustment and Seasonal and Weather Adjustment show higher employment gains than reported by the BLS. One can indeed do the two adjustments jointly—both adjusting for weather effects and using a longer window. This is shown in the far-right column of the table labeled Combined Alternative Seasonal and Weather Adjustment. Making both adjustments, the employment change in February was an increase of 332,000 jobs, which is 19,000 above the BLS Official number.

The bottom line is that this is yet another strong report. Weather was not a story this month, as February was in line with seasonal norms.

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

^[1] Applies a longer window estimate of seasonal effects (see Wright 2013).

^[2] Includes seasonal and weather adjustments, where seasonal adjustments are estimated using the BLS window specifications (see Boldin & Wright 2015).

^[3] BLS Official number less the Seasonal and Weather Adjustment number.

^[4] Includes seasonal and weather adjustments, where seasonal adjustments are estimated using a longer window estimate.

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.