OK, our title is false advertising. The Bureau of Labor Statistics crew is not allowed to surf the Net making comments on blogs, nor do they have a blog of their own. It might be a good idea, but it is an unlikely move given budget constraints.
This is too bad, since there is near-universal criticism of their methodology. Many go much further. A Google search will reveal plenty of aggressive name-calling critics. The criticism has been so loud and pervasive that hardly anyone in the blogosphere or trading worlds believes in the monthly non-farm payroll report. Many sites routinely mention the birth/death adjustment so that the reader can mentally subtract these "phantom" or "magical" jobs.
This presents an interesting situation. What if the BLS approach is correct and accurate? Those understanding this would do better in gauging economic changes.
Since the BLS is not going to respond directly to critics, we propose to use their existing results and words to address some of the key points. In this article, we will show the strength of the BLS methods with only indirect references to the many critics. In future articles we will directly analyze and expose pervasive errors on this topic. Reader questions are invited.
We have three steps: Showing the accuracy of the birth/death adjustment, explaining the b/d role in job creation, and showing how the research design effectively captures economic changes.
This article takes up the first of these issues.
Estimating the number of jobs and the monthly change in jobs is a daunting challenge. There is a way of keeping score. As we wrote last October:
Each year the BLS makes a "benchmark revision" to the payroll employment series based on the establishment survey. The purpose of this is to make sure the survey data are consistent with the actual count of jobs from state unemployment insurance tax records.
The state data is much better, of course, but it is not available in a timely fashion. The benchmarking is a reality check. It allows the BLS to see how well they did with the monthly estimates. Each October, along with the report on September employment, the BLS releases the preliminary version of these benchmark revisions.
This is the report card for the BLS.
This should be a non-controversial test, since it relies upon actual state data, not projections. No employer is going to pay extra taxes, so this count does not include any "phantom jobs."
The better the BLS methods, the smaller the benchmark revisions. If the Birth/Death adjustment is effective, it makes the revisions smaller.
And it does!!
Here is a nice chart showing the effects.
The blue line is the actual count. Just compare the red line to the green line. The red line shows what the estimate would have reported without any birth/death adjustment. The green line shows the effect of birth/death.
The birth/death adjustment improves the job change estimate in every quarter since it has been introduced.
UPDATE on EXPLANATION
While readers got the point of the original article, our original explanation of the chart was not very good. The BLS takes the actual count from unemployment statistics and makes a benchmark revision. The smaller the revision, the better the original estimate. The original estimates use the birth/death adjustment.
With this in mind, the red bars show the revisions using the adjustment. The green bars show what the revision would have been without the adjustment. Since the red bars represent a smaller adjustment in all cases, this shows that the B/D adjustment improved the real-time estimate. The blue bars show, for reference, the amount of the B/D adjustment.
Conclusion to Part One
Most of the BLS critics have been offering the same complaints for many years, but no one ever asks whether they were correct. The closest the BLS team will come is the paper they published last October.
In this article we have emphasized that something about the birth/death adjustment is good, very good. It improves the job change estimates in every quarter.
This seems counter-intuitive. How can we have new job creation in such difficult economic times? Most people believe their intuition rather than the data.
In the next article in this series we will explain this mystery.