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Recent economic data produced conflicting stories: total employment has fully recovered all pandemic losses, but the labor force participation rate has not fully recovered. Resolution of the statistical conflict yields important insight into the status of the economy and also a lesson about interpreting economic data.

In February 2020, the number of jobs peaked at 152,504,000. It dropped sharply—down 14%—in just two months, then slowly grew. In July 2022 it finally regained its peak, hitting 152,536,000. But what does that mean? The job count comes from the Bureau of Labor Statistics Current Employment Statistics report. It counts jobs, not people working. So the person working two part-time jobs is counted twice, once for each job.

The labor force participation rate comes from the Current Population Survey, which counts people. People are asked if they are working. The fellow with two jobs is counted once. The small business owner is counted as working even though she does not have a payroll job but rather profits from her company.

One final bit of statistics before getting to the big picture: The data sources are wildly different. The employment count comes from every large corporation and a sample of small and medium businesses. It’s pretty accurate but runs into a problem when big changes occur in the economy. The sample of small and medium businesses is scaled up to match the entire universe of small and medium businesses. So if the government samples 5% of businesses, it would multiply its number by 20 to cover all businesses. But the government does not know, at least for many months, if its sample covers 5% or 4%, or 6% of small businesses. That’s because new businesses are being formed all the time, and some businesses close up every day. Sometime next year all of the businesses will be counted, but in the meantime, the Bureau of Labor Statistics is making an educated guess.

But the survey problem is even greater for the other data. Government workers get on the phone with a sample of households and ask questions such as, “Did you work last week?” and if the answer is no, the follow-up question is asked: “Did you look for work in the last four weeks?” Labor force participation means the person was either working, looking for work, or waiting for a job to start. This survey does not impact a person’s eligibility for unemployment insurance benefits, but some people are probably suspicious. They may answer that they have been looking when they have not been looking at all. This issue highlights the risks of using surveys of people.

In my first few years of work as a professional economist, I learned that a researcher never gets happier delving into the source of the data.

For the comparison of employment recovery and labor force participation recovery, do not think of two very thin, precise lines; think, instead, of two fuzzy, blurry lines. We can’t really be sure at this time whether they are above or below their February 2020 level, but we know for sure that they have been improving over the past two years. We can say with high confidence that both measures are in the ballpark of their previous peak, especially if we are vague about what “in the ballpark” means.

The greater lesson from these statistics is that business leaders should not be focused on such hard lines as “exceeding the previous peak” “two-quarters of negative growth” or “20% decline in stock prices.” These are journalists’ expressions with little import to managers of enterprises. They imply false precision of the underlying data, and they imply greater meaning than the data warrant. The economy that has regained 100.1% of its past decline isn’t much better off than one which has regained 99.9% of its decline.

An even greater lesson for business leaders would be to ignore most of the headlines and focus on the handful of economic factors that are most relevant to the company, as with an economic dashboard. Looking at a consistent set of data that is known to be closely connected to the company will help business leaders focus on what’s important and avoid the rabbit holes of economic statistics.

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