Four numbers, not as easy as it seems
Speedometer charts: love or hate

Another reminder that aggregate trends hide information

The last time I looked at the U.S. employment situation, it was during the pandemic. The data revealed the deep flaws of the so-called "not in labor force" classification. This classification is used to dehumanize unemployed people who are declared "not in labor force," in which case they are neither employed nor unemployed -- just not counted at all in the official unemployment (or employment) statistics.

The reason given for such a designation was that some people just have no interest in working, or even looking for a job. Now they are not merely discouraged - as there is a category of those people. In theory, these people haven't been looking for a job for so long that they are no longer visible to the bean counters at the Bureau of Labor Statistics.

What happened when the pandemic precipitated a shutdown in many major cities across America? The number of "not in labor force" shot up instantly, literally within a few weeks. That makes a mockery of the reason for such a designation. See this post for more.


The data we saw last time was up to April, 2020. That's more than two years old.

So I have updated the charts to show what has happened in the last couple of years.

Here is the overall picture.


In this new version, I centered the chart at the 1990 data. The chart features two key drivers of the headline unemployment rate - the proportion of people designated "invisible", and the proportion of those who are considered "employed" who are "part-time" workers.

The last two recessions have caused structural changes to the labor market. From 1990 to late 2000s, which included the dot-com bust, these two metrics circulated within a small area of the chart. The Great Recession of late 2000s led to a huge jump in the proportion called "invisible". It also pushed the proportion of part-timers to all0time highs. The proportion of part-timers has fallen although it is hard to interpret from this chart alone - because if the newly invisible were previously part-time employed, then the same cause can be responsible for either trend.

_numbersense_bookcoverReaders of Numbersense (link) might be reminded of a trick used by school deans to pump up their US News rankings. Some schools accept lots of transfer students. This subpopulation is invisible to the US News statisticians since they do not factor into the rankings. The recent scandal at Columbia University also involves reclassifying students (see this post).

Zooming in on the last two years. It appears that the pandemic-related unemployment situation has reversed.


Let's split the data by gender.

American men have been stuck in a negative spiral since the 1990s. With each recession, a higher proportion of men are designated BLS invisibles.


In the grid system set up in this scatter plot, the top right corner is the worse of all worlds - the work force has shrunken and there are more part-timers among those counted as employed. The U.S. men are not exiting this quadrant any time soon.

What about the women?


If we compare 1990 with 2022, the story is not bad. The female work force is gradually reaching the same scale as in 1990 while the proportion of part-time workers have declined.

However, celebrating the above is to ignore the tremendous gains American women made in the 1990s and 2000s. In 1990, only 58% of women are considered part of the work force - the other 42% are not working but they are not counted as unemployed. By 2000, the female work force has expanded to include about 60% with similar proportions counted as part-time employed as in 1990. That's great news.

The Great Recession of the late 2000s changed that picture. Just like men, many women became invisible to BLS. The invisible proportion reached 44% in 2015 and have not returned to anywhere near the 2000 level. Fewer women are counted as part-time employed; as I said above, it's hard to tell whether this is because the women exiting the work force previously worked part-time.


The color of the dots in all charts are determined by the headline unemployment number. Blue represents low unemployment. During the 1990-2022 period, there are three moments in which unemployment is reported as 4 percent or lower. These charts are intended to show that an aggregate statistic hides a lot of information. The three times at which unemployment rate reached historic lows represent three very different situations, if one were to consider the sizes of the work force and the number of part-time workers.


P.S. [8-15-2022] Some more background about the visualization can be found in prior posts on the blog: here is the introduction, and here's one that breaks it down by race. Chapter 6 of Numbersense (link) gets into the details of how unemployment rate is computed, and the implications of the choices BLS made.

P.S. [8-16-2022] Corrected the axis title on the charts (see comment below). Also, added source of data label.


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This is super-interesting and lots to think through! Is the Y axis of these charts mis-labeled, though? It makes more sense to me if the label is "Not In Workforce as % of Civilian Pop 16+".

It'd be interesting to layer in disability rates. I've heard that the number of people with disabling Long COVID is high enough to be visible in data sets like this, and I'm curious how that ties into the economy...


HL: Thanks for the note. The y-axis is indeed mislabeled. Will fix them soon.
Note that the time series have been smoothed by splines before plotting in this scatter plot, otherwise it's too noisy to see any patterns. (This was explained in previous blogs.) The outliers related to Covid-19 are better seen in a line chart with actual data plotted on them. Those numbers are hard to interpret because (a) the changes were extreme (b) there were changes to counting rules during that period of time (ostensibly to deal with the unexpected event but the effect is to make it harder to interpret the trend).

Cody Custis

While these trends and graphs are useful, there's not enough information to determine if the underlying processes are "good" or "bad."

For one example, consider that the percentage of older Americans in the labor force increased from 1990 to 2022. That reflects: demographic impacts of the Baby Boom, healthier workers with longer life expectancy, low income workers who are unable to leave the workforce at traditional retirement are, high income workers who choose to continue earning.

Likewise, fewer Americans in the labor force may reflect: more Americans choosing higher education, Americans choosing not to work due to income from a partner, Americans discouraged or disabled from working.

Economics struggles with "ceterus paribus" factors overwhelming those which are modeled.


CC: Your point is obviously correct. I'd add another crucial factor: manipulation of statistics.
I don't want to let this off the hook. A very important insight from these charts is that we were not looking at gradual shifts but a series of abrupt sharp shifts timed to recessions.


I would be curious to see the charts for male and female superimposed on each other to give context to any comparisons.

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