There has been some debate recently in the outskirts of the public discourse, where economists and financial analysts have expressed concern over the significant revisions that U.S. statistical agencies have made in their publications of updates on key economic variables.
Although the size of the revisions has been a little bit higher recently, it was nothing dramatic until the Bureau of Labor Statistics, BLS, issued a significant revision on August 21st. In a series called Current Employment Statistics, the BLS had to walk back its employment numbers by a margin that was five times larger than usual:
For National CES employment series, the annual benchmark revisions over the last 10 years have averaged plus or minus one-tenth of one percent of total nonfarm employment. The preliminary estimate of the benchmark revision indicates an adjustment to March 2024 nonfarm employment of -818,000 (-0.5 percent).
In the days following this release, the debate spread beyond professional circles. I encountered numerous allegations of statistical fraud. People on the Right accused the Biden administration of inflating the number of employed Americans in the statistics to make its economic policy look better than it is.
I am extremely reluctant to even entertain such allegations, primarily because I know in detail how sophisticated these statistical systems are. The integrity of the process from data collection all the way to publication is meticulous; any purposeful forgery would require a conspiracy of impressive proportions.
Furthermore, anyone with reasonably good training in economics can use parallel statistics as an analytical checks-and-balances control function. If, e.g., the BLS over an extended period of time reports an unusually high level of employment, we can check this trend break against many different variables, including economic output, GDP. If the employment trend is not matched by a similar change in the trend of economic output, we have a legitimate reason to question both agencies and ask them to solve the discrepancy.
In the case of the 818,000 ‘lost’ jobs in the BLS employment data for March this year, this does not amount to a break in a trend that should raise suspicions of data tampering. It is a revision for one month only, which the BLS says it uses as a “benchmark” to assess the integrity of its own data. Now that they have discovered an unusually large error, they will conduct a deeper analysis of the Current Employment Statistics series and publish their full findings in February next year.
If this was a deliberate attempt by the BLS to artificially inflate the jobs numbers for the Biden economy, i.e., if the BLS was in the business of carrying water for Kamala Harris, they would not have released their preliminary findings. There would have been no revision to report—maybe not even a benchmark report in the first place.
It is also worth noting that it is impossible to avoid errors in any collection, analysis, and publication of statistical data. With that said, the question remains: how could the BLS—one of the world’s premier statistical institutions—make an error in their statistics that is five times the size of their usual mistake?
There are two likely explanations that are not mutually exclusive. The first one is that there may have been budget cuts to the BLS; recently, the Bureau of Economic Analysis announced the discontinuation of a number of data series due to budget constraints. Although the BLS and the BEA belong under different federal overlords, the Department of Labor vs. the Department of Commerce, they could both have been targeted in some inter-departmental effort to restrain federal spending.
It may be hard to believe that any federal agency operates under budget constraints, but we should still trust the BEA’s announcement of these limitations. So far, BLS has not announced any limitations on its budget but that does not mean they have not happened. If the BLS finds a strong correlation between its appropriated resources and its ability to maintain the integrity of its statistical production, we will probably hear about it in February next year.
In other words, if the BLS has been forced to understaff some parts of its operations, we could now have the first piece of evidence of what that means for the agency’s reliability.
The other likely explanation is that the inflow of illegal immigrants to the United States under President Joe Biden and his border czar Vice President Kamala Harris has been big enough to distort official labor market statistics. If this has happened while the BLS has experienced staffing problems and resource constraints, it is no wonder that the error in published statistics has grown as big as it has.
Now that the BLS has made its big error official, we need to watch out for revisions in related statistical series. The most important one is the one for gross domestic product, GDP. It is part of the database on national accounts under the Bureau of Economic Analysis. There is a long-term stable statistical relationship between GDP and employment: in 2023, e.g., the amount of GDP per employed person was $43,821.
If we get an unusually strong jobs number from the BLS and we calculate this GDP-to-jobs ratio, and if, in doing so, we find that the ratio falls when the BLS numbers go up—again over a certain period—then we have a checks-and-balances indication that something is wrong with one of the two numbers in the ratio.
Likewise, there is a relationship between the tax revenue that governments collect and the economic activity on which they levy those taxes. I recently made a point about this ratio, where I noted that the federal government’s tax revenue is unusually slow for an economy that is doing as well as the BEA has been reporting. I pointed specifically to the fact that:
In the three quarters Q3 2023, Q4 2023, and Q1 2024, total federal revenue was almost one percent lower than in Q3 2022, Q4 2022, and Q1 2023.
I added that, with an economy as strong as the one we have now, the federal collection of taxes should be notably higher than it is today. It does not have to keep pace with the 4.5% by which federal spending went up during the aforementioned period. But revenue did not go up. It fell.
There are likely statistical and economic explanations for this tax revenue anomaly, but the anomaly is big enough that I would suggest the BEA go back and review its GDP data for the past year.
Whatever the explanation is for the statistical error in the BLS employment database, it is essential that we sort it out. Statistics is an indispensable—not to say irreplaceable—tool for many key decisions that we make in our lives. Most of all, though, statistical information held to the highest standards of integrity helps us all hold our government accountable.
