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The FRED® Blog

Show me older men: A picture of employment cycles and demographics

“It’s a recession when your neighbor loses his job; it’s a depression when you lose yours.”

Good old Harry Truman gets credit for this colorful adage, along with many others.* But as the top graph shows, the probability two persons remain employed depends on who they are. During recessions, the pool of older workers seems less likely to dwindle: Even during the Great Recession, employment of older workers (55 to 64 years old) declined moderately, while employment of prime-age workers declined more severely.

What gives? Older workers are closer to retirement and in good times may retire early. But a shock to their retirement savings, as in the recent financial crisis, may induce them to stay employed. Older workers also tend to work in more cognitively and less physically intensive jobs, which may be less cyclically sensitive. The younger segments of prime-age workers, especially those under 35, may be less attached to their firms and tend to switch jobs more frequently; they’re also more likely to have young children and higher home-production demands. If their employers are adversely affected by the business cycle, they’re more likely to lose their jobs and potentially have trouble finding new ones.

The bottom graph adds a wrinkle to this perspective: Older men and older women have different employment patterns. During the severe 1981 recession, the employment rate for men fell about 3 percentage points but the rate for women didn’t change. The same story played out in the Great Recession, when men’s employment rate fell by about the same magnitude and women’s again stayed constant. Given that most assets are owned jointly within a household (e.g., houses) and most older workers are married, an asset shock should affect both sexes similarly. Men and women, however, have a different occupational mix at all age groups. Clearly, these differences in employment are complicated. In fact, the data seem to follow another of Truman’s dicta: “If you cannot convince them, confuse them.”

* Truman also allegedly asked for a one-armed economist to avoid the typical “on the one hand…on the other hand” hedging of that profession, but we won’t dwell on that here.

How these graphs were created: For both graphs, search for “employment rate United States”; choose the series you want and click on “Add to Graph.” If you’re overwhelmed by the search results, narrow them by adding the search term “aged” or by playing with the tags in the side bar.

Suggested by David Wiczer.

View on FRED, series used in this post: LREM25TTUSM156S, LREM55FEUSM156S, LREM55MAUSM156N, LREM55TTUSM156S

FRED in North Korea

North Korea is likely the most isolated and secretive place in the world right now. Yet, at the time of this writing, FRED has 52 data series related to this country: Half the series are from the Bureau of the Census and relate to exports to North Korea from 26 states; the other half are from the World Bank.

Some series have zeroes for all observations, such as net migration and Internet users, which seems accurate given the conditions in North Korea. Some series look relatively normal, just as they do for other countries. And some series are just plain peculiar: Above is the youth unemployment rate, which we did not expect to be so high in this mostly command economy. Below is the net interest margin for banks, which is negative by a large margin, indicating a financial sector dominated by non-market forces.

How these graphs were created: Search for North Korea and explore the choices.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: DDEI01KPA156NWDB, SLUEM1524ZSPRK

You can build a house on paper, but you don’t always make it brick

In other words, housing permits don’t always equal housing starts.

So, say you want to build a house… You’ll need to plan for financing and contractor schedules, among other things. But first and foremost, you must apply for and be granted a building permit before you can start to build. FRED has time series for both building permits granted and housing starts. Given that permits and actual construction go hand in hand, you might expect the two series to follow each other closely if not exactly, with possibly a small delay between the two.

As the graph shows, the two series are well connected during booms, when there’s an upswing in construction. But the two series aren’t nearly as well connected when building activity contracts. The lesson here is that a building permit doesn’t guarantee a house will be built. If economic conditions worsen, for example, between the time you apply for a permit and the time you plan to build, you might decide to postpone or even scrap an approved project. It’s during those times when the housing starts series falls faster than the permits series.

How this graph was created: Search for and add the “housing permits” series to the graph. Then open the “Edit Graph” panel to add a line: Search for and add the “housing starts” series. Finally, shorten the sample period to allow for more detail—in this case, starting 2000-01-01.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: HOUST, PERMIT

Engel’s law is still good food for thought

If your income rose by 15%, would your spending also rise by 15%? Maybe. But would all your spending rise by that amount? Ernst Engel surveyed households and published his results in 1857: He found that spending on food did not rise in proportion to a rise in income. Food is clearly a necessity; we all need some. And households that become wealthier will likely increase spending on food to some degree. But the increase in food consumption will be proportionately less than the increase in income.

Engel’s law is remarkably consistent. For the U.S., we can simply take food expenditures in the national account and divide it by GDP. This ratio is pretty much in continuous decline, with the exception of recessionary periods when incomes drop more than usual from unemployment or reduced work time. Engel’s law has held steady for 160 years.

A primer on income elasticity of demand: Food in general is a “normal good,” which means its consumption rises as income rises. It’s a specific type of normal good, though—a “necessity good”—which rises as income rises, but less than one for one. A more formal description is that food has an income elasticity of demand between 0 and 1. Another type of normal good is a “luxury good”—for example, a yacht. Its consumption rises more than one for one as income rises, so its income elasticity of demand is above 1. Consumption of an “inferior good”—for example, bus tickets—actually declines as income rises. Its income elasticity of demand is below 0.

How this graph was created: Search for “food expenditure,” and you’ll see many price indices. To speed up your search, click on the “consumption” tag in the side bar. Once you add the series shown here, open the “Edit Graph” panel and another series to Line 1: GDP. Then apply formula a/b.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: DFXARC1Q027SBEA, GDP

A marginal look at bank margins

How have banks performed over recent years in this environment of very low interest rates? Banking can be complex, so it’s difficult to pinpoint exactly how low interest rates affect banks’ bottom lines. But there’s a simple measure in FRED that we can examine: the net interest margin. It calculates the ratio of a bank’s net income from assets to the level of those assets. (Put another way, it’s the interest banks earn on investments minus the interest they pay to their lenders and depositors divided by the total level of their interest-earning assets.) Of course, the devil is in the details, and the note on the FRED series page captures some of those details.

Did the lending rate decline less than the cost of funds? Or are margins being squeezed by the low interest rates? The graph seems to imply the latter, but it also shows a general tendency toward lower margins over the span of two decades, which hints that more may be at play here. Maybe widespread use of computers in the management of deposits and credits allowed banks to reduce costs and thus margins. Maybe there’s been increased competition. Maybe something else entirely…

How this graph was created: Search for “net interest margin” and add it to the graph.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: USNIM

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