A reader calls my attention to this article arguing that the large upside surprise in employment growth reported for January 2022 is due to seasonal adjustment. It takes 10 seconds to find the requisite not-seasonally-adjusted data on FRED, and another 10 seconds to load it into a decent software package as simple as Excel, and another 10 seconds (at most) to type in the command to take a 12 month log difference to see seasonal adjustment issues are not the reason for the big job growth number (there might very well be other reasons, but that ain’t it).
If seasonal adjustment were the issue, as David Goldman* argues, then one would expect the 12 month growth rate in not-seasonally adjusted data to diverge from the 12 month growth rate in seasonally adjusted data. At the aggregate level (nonfarm payroll employment), it doesn’t.
Figure 1: 12 month growth rate in nonfarm payroll employment, seasonally adjusted, FRED series PAYEMS (black), not seasonally adjusted, FRED series PAYNSA (red), calculated as log differences. Source: BLS via FRED, and author’s calculations.
I provide the FRED series so people can do their own calculations without accusing me of manipulating the data and/or hiding the “raw” data, as in this case.
Figure 2: 12 month change in nonfarm payroll employment, seasonally adjusted, FRED series PAYEMS (black), not seasonally adjusted, FRED series PAYNSA (red), both in thousands. Source: BLS via FRED, and author’s calculations.
This is not to say at the sectoral level, where samples are smaller, one might not see issues. In manufacturing (less than 10% of total NFP) and leisure and hospitality services (slightly more than 10%), one sees the following
Figure 3: 12 month growth rate in manufacturing employment, seasonally adjusted, FRED series PAYEMS (black), not seasonally adjusted, FRED series PAYNSA (red), calculated as log differences. Source: BLS via FRED, and author’s calculations.
Figure 4: 12 month growth rate in leisure and hospitality services employment, seasonally adjusted, FRED series PAYEMS (black), not seasonally adjusted, FRED series PAYNSA (red), calculated as log differences. Source: BLS via FRED, and author’s calculations.
So, if Mr. David Goldman has spent the 30 seconds to download the data into Excel, he might’ve found out that his long essay on seasonal adjustment for the aggregate number was for naught. But of course, that was not his purpose; rather it was to cast down on the validity of the data itself (and I would be the last one to day there aren’t problems with tabulating the data during a time of pandemic, but I don’t think the one he is pointing to is the right one).
To see how a real economist deals with issues surrounding seasonal adjustment, when one cares about levels and might not have a seasonally unadjusted series to compare against, see Jonathan Wright’s BPEA article (in other words, I do take seasonal adjustment issues seriously — I just want to do it in a serious way).
By the way, about 20 years ago, various conservative commentators argued the establishment series undercounted the amount of employment; the BLS developed a research series which adjusted the household survey data to an NFP concept. I plot the level, and the 12 month growth rates of the official and research series below:
Figure 5: Nonfarm payroll employment series (black), and household survey adjusted to NFP concept (red), both in 000’s, s.a., on log scale. NBER defined recession dates peak-to-trough shaded gray. Source: BLS via FRED, BLS, and NBER.
Figure 6: 12 month growth rate in nonfarm payroll employment series (black), and household survey adjusted to NFP concept (red), both s.a. NBER defined recession dates peak-to-trough shaded gray. Source: BLS via FRED, BLS, and NBER.
The official series on NFP is growing 6.5% y/y, while the research series is growing 6.6% (both calculated as log differences).
For more on data paranoia, see [1], [2], [3], [4].
* David P. Goldman, “American economist, music critic, and author”, BA, Columbia (1973), MA Music Theory, CUNY (see Wikipedia).
