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Moonlighting in the spotlight

Trends for multiple jobholders

Today we’ll try to better understand moonlighting—that is, holding multiple jobs. The Bureau of Labor Statistics records the number of multiple jobholders, and FRED has the data all the way back to 1994. What can we learn from the graph?

Most multiple jobholders hold a full-time plus a part-time job (blue line in the graph), and this group now makes up about 3% of the working population in the U.S. The percentage of workers with this particular work arrangement has declined since at least 1994, when it was over 3.5%.

Those in the next-largest group hold two part-time jobs (red line). The percentage of workers with this arrangement is significantly lower than the first group—a little less than 1.5% of all employees—and has been quite stable over time.

Finally there’s a small group of workers with two full-time jobs (green line), which accounts for about 0.25% of workers. The percentage for this group has also been quite stable since 1994.

We can also see that recessions don’t seem to have a significant impact on these groups of workers with multiple jobs.

How this graph was created: Search for and select the monthly series “Multiple Jobholders, Primary Job Full Time, Secondary Job Part Time.” From the “Edit Graph” panel, use the “Edit Line 1” tab’s “Customize data” section to search for and add an additional series: “All Employees: Total Nonfarm Payrolls” (not seasonally adjusted option). Then type “a/b*100” into the formula box and click “Apply.” Repeat this process for lines two and three, with “Multiple Jobholders, Primary and Secondary Jobs Both Part Time” for line two and “Multiple Jobholders, Primary and Secondary Jobs Both Full time” for line three. All series should be not seasonally adjusted. Use the “Format” tab to select alternative colors for the lines.

Suggested by Makenzie Peake and Guillaume Vandenbroucke.

View on FRED, series used in this post: LNU02026625, LNU02026628, LNU02026631, PAYNSA

Is household wealth overvalued?

Fluctuations in household net worth relative to income

When economists look at household wealth, they’re often concerned about the individual assets that make up that wealth—specifically, that they may be overvalued. Thankfully, FRED has an indicator to help us evaluate household wealth: The ratio of household net worth to disposable personal income. This ratio remained nearly constant for 50 years before the dot-com bubble (roughly 1994-2000), when it started increasing. And an increasing ratio may signal that the assets underlying net worth are overvalued. Since 2017, household net worth relative to income (dashed blue line) generally has been above its previous record level from the year before the Great Recession.

We also include the value of financial assets, a component of net worth, relative to income (solid green line). Most variations in net worth relative to income are associated with changes in the value of financial assets, which is indicated by the way the two lines track closely together in the graph. Even when housing values were collapsing and net worth fell (from $6.64 billion to $5.25 billion, or 1.4 times disposable income), the decline in the value of financial assets was 50% of that decline in net worth (from $5.07 billion to $4.37 billion). In terms of the recovery of net worth relative to income, which didn’t start until late 2012, we see again that the majority (65%) is accounted for by the rise in the value of financial assets.

How this graph was created: Search for “Household net worth” and select  “Households and nonprofit organizations; net worth, Level.” From the “Edit Graph” panel, use the “Customize Data” option to search for “Disposable Personal Income” and select the quarterly series in billions of dollars. After adding this series, enter “a/b” in the “Formula” box. This will show the ratio of household net worth to disposable income. To add the second line, use the “Add Line” option to search for and add the series “Households and nonprofit organizations; total financial assets, Level.” Then repeat the same process of dividing by disposable personal income.

Suggested by Ryan Mather and Juan Sánchez.

View on FRED, series used in this post: DPI, TFAABSHNO, TNWBSHNO

Taking the time to measure money

A closer look at broad money in the U.K.

The FRED graph above, which tracks broad money in the U.K. over the past 172 years, makes it look like the Bank of England has let the money supply go completely out of control since 1970. But not so fast! Two important effects are at play here. The first is the power of compounding: Any statistic that increases at a constant rate will look like it is accelerating, especially if the sample period is long. That’s why FRED graphs offer the option of taking the natural logarithm, as shown in the second graph, below.

If broad money had increased at a constant rate, the graph would show a straight line. That’s not the case, though, as broad money reacts to economic conditions, which is the second effect at play here. Consider that the money supply follows the general evolution of prices. Or the reverse: Prices follow increases in the money supply. In any case, we deflate broad money by the consumer price index, as shown in the third graph, below.

This new statistic is still skyrocketing. But that’s because the U.K. economy has actually grown during most of the period. In our fourth graph, show below, we divide broad money by nominal GDP, which takes into account inflation, population growth, and increases in productivity in one fell swoop. Our final statistic is less dramatic, but it still shows some sort of effect that keeps propelling broad money upward. What could it be?

Let’s stop and define what broad money actually is. As you may have guessed, it’s the broadest possible definition of money, which encompasses all forms of assets that could possibly be used for transactions: from currency all the way to savings accounts and large time deposits. (In the U.S., we call it M3.) And, as an economy becomes more financially developed, broad money grows more than what nominal GDP would account for. This is what we see here.

How these graphs were created: Search for and select “broad money United Kingdom” and you have the first graph. Use the “Edit Graph” panel to create the others: For the second, choose units “Natural Logarithm.” For the third, add a series to the line by searching for and selecting the “United Kingdom CPI” (in levels, with a long sample) and apply formula a/b. For the fourth, replace the CPI series with “nominal GDP United Kingdom.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CPIUKA, MSBMUKA, NGDPMPUKA

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