Courtesy of Lee Adler of the Wall Street Examiner
I have regularly ranted on the employment charts page and elsewhere that the BLS monthly jobs data does not accurately reflect actual jobs trends. Over the past year the monthly payrolls and household data seem to have come unhinged from the trend of improvement reflected in the real time data on the withholding taxes which the Federal Government collects. I even tried estimating whether the monthly payrolls data would beat or miss consensus expectations on the basis of the withholding data. In most cases the BLS numbers didn’t make any sense in relation to the trend of withholding taxes, so I suspended that exercise.
Then there was the incident just last month where ADP’s private payrolls data showed unexpected strength, which the withholding tax data had foreshadowed, but the BLS data came out a couple of days later showing weaker than expected employment. Everybody assumed that there was something wrong with the ADP data, but based on the withholding taxes I thought that the ADP data was on the mark.
Now comes the annual benchmark revision of the BLS data, where they will now restate all of the data from April 2011 to March 2012 based on actual unemployment tax returns submitted by employers which give an actual count of total employment. Lo and behold, the BLS underestimated total jobs by 386,000 and private sector jobs by 453,000.
Here’s how the BLS explains it.
In accordance with usual practice, the Bureau of Labor Statistics (BLS) is announcing the preliminary estimate of the upcoming annual benchmark revision to the establishment survey employment series. The final benchmark revision will be issued on February 1, 2013, with the publication of the January 2013 Employment Situation news release.
Each year, the Current Employment Statistics (CES) survey employment estimates are benchmarked to comprehensive counts of employment for the month of March. These counts are derived from state unemployment insurance (UI) tax records that nearly all employers are required to file. For National CES employment series, the annual benchmark revisions over the last 10 years have averaged plus or minus three-tenths of one percent of Total nonfarm employment. The preliminary estimate of the benchmark revision indicates an upward adjustment to March 2012 Total nonfarm employment of 386,000 (0.3 percent).
Table 1 shows the March 2012 preliminary benchmark revisions by major industry sector. As is typically the case, many of the individual industry series show larger percentage revisions than the Total nonfarm series, primarily because statistical sampling error is greater at more detailed levels than at an aggregated level.
Here’s a chart of the new preliminary benchmark revisions.
This does not include the annual seasonal adjustment revisions, where data is restated for 5 years after the fact to bring the seasonally adjusted data trend more into line with the actual trend. The seasonal adjustment models are re run after the benchmarking each year. For most of this year, the seasonally adjusted numbers have generally understated the degree of improvement in the actual trend.
Here’s how the BLS describes the benchmarking process.
Annual CES benchmark revisions are published along with January first preliminary estimates in February of each year. Benchmark revisions reflect a re-anchoring of CES sample-based estimates to incorporate near universe counts of employment. These comprehensive counts of employment, or benchmarks, are derived primarily from employment counts reported on UI tax reports that nearly all employers are required to file with State Workforce Agencies.
The benchmark revision is the difference between the universe count of employment for March and its corresponding sample-based estimate. A table of benchmark revisions from 1979 forward:
Each annual benchmark revision affects 21 months of data for not seasonally adjusted series and five years of data for seasonally adjusted series as described below. BLS revises employment from the current March benchmark month back to the previous year’s March benchmark using a simple linear wedge procedure. Revised estimates for post-benchmark months are derived by applying the previously calculated over-the-month sample changes to the revised March levels for April through October. Revised business birth/death estimates also are incorporated into the post-benchmark months. Additionally, seasonal adjustment models are rerun, and seasonally adjusted estimates are replaced for five years back.
November and December estimates revise due to both impacts of benchmarking and additional sample. Additionally, new sample units are rotated into the survey starting with November.
As an example of benchmark effects, the March 2011 benchmark revisions (published in February 2012) resulted in revised series from April 2010 through December 2011 on a not seasonally adjusted basis and revised series from January 2007 through December 2011 on a seasonally adjusted basis.
See www.bls.gov/web/empsit/cesbmart.htm for more details on the benchmarking process.
The table speaks for itself. While economists are trying to guess the monthly number with their ridiculous garbage-in garbage-out econometric models, they are aiming at an illusory bullseye, using Rube Goldberg formulas extracted from bad data. Their pretense of hitting a number to within a few thousand is absurd. Their target is an ephemeral mirage, constantly shifting. The inputs they are using to construct their estimates are also mirages. Not even the FOMC, with all the tools at its disposal, has been able to guess right on employment, ever. So what’s the point? The monthly economists’ payrolls estimates are theater of the absurd. On the rare occasions when they come close to the reported number, it’s a lucky accident, which simply means that ultimately both the economists’ guesses and the government’s headline numbers are wrong.
For what it’s worth, I’ll stick to analyzing the actual real time tax data, and the weekly actual (not seasonally adjusted) first time jobless claims data, which is based on actual counts, not small survey samples subject to sampling error and bogus seasonal adjustments to get a handle on whether employment trends are consistent with the stock market’s trend.
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