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Halloween candy excesses

Halloween begins frenetic candy consumption that continues into the Christmas holidays and New Year’s Day, when people often make (usually short-lived) resolutions to lose weight. But all this consumption first needs production. The graph shows the relevant data from the industrial production index and its stunning seasonality. October, November, and December are the months with the highest production of candy. Thus, it appears producers don’t build up candy stocks much in advance of these festive opportunities to indulge in sugary consumption. For chocolate, this makes complete sense: You don’t want to wait long after it’s been tempered to consume it. Fresh chocolate is best.

How this graph was created: Search for “candy,” and this series should be among the first choices. Click on the monthly, not seasonally adjusted series.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: IPG3113N

Part-time workers: Willing or not?

The evolution of part-time work has come up repeatedly in the public discourse. Let’s look at the data. The top graph shows two types of part-time situations: one for those who voluntarily choose part-time work and one for those who would rather work full-time but can find only part-time work (including those whose jobs were reduced to part-time status). Both lines trend upward in the long run in ways that seem consistent with population growth. The cyclical impact is also noticeable, as recessions typically push more people into part-time work, especially for the “non-volunteers.” (FYI: That shift in 1994 was caused by a change to the survey that re-explained what “part-time for economic reasons” means.)

The bottom graph uses a percentage distribution that may reveal more clues about the reasons behind part-time work: There’s a long-term trend toward more involuntary part-time work (among those who work part-time) but with a recent reversal of that trend. Since 2009, contrary to what’s often portrayed, there’s been no increase in part-time work. Over that same time period, the proportion of involuntary part-time workers hasn’t increased either.

How these graphs were created. Top graph: Search for “part time employment,” check the two series you want, and select “Add to Graph.” Bottom graph: Start with the same graph but restrict the sample to start in 1994, then re-format the graph by selecting graph type “Area” with stacking set to “Percent.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: LNS12032194, LNS12600000

Homeowners slide and renters rise

For five hundred twenty-five thousand six hundred minutes each year, people have to live somewhere. And it looks like renting is becoming more popular.

The graph clearly shows the U.S. homeownership rate has steadily declined and that the rental vacancy rate has declined right along with it. So the two trends seem closely related, especially recently. But does a decline in homeownership mean homeowners are moving out of houses and into apartments? Not necessarily. So what is going on? At least two things. 1. The financial crisis: The recent economic downturn left many households wary of investing (or reinvesting) in a home. 2. Kids today: The younger generation seem less interested in living in the suburbs. In quite a few cities, St. Louis included, they seem to prefer to live where they work and spend leisure time. Urban commercial buildings are being converted to apartments to accommodate this increased flow of renters. The rental vacancy rate has still been declining, which means the pace of rental property construction hasn’t been fast enough to keep the rental vacancy rate steady. Be sure to check back with the FRED Blog in a few years to see where all this stabilizes.

How this graph was created: Search for “rental vacancy” and add the quarterly measure to the graph. Then use the “Edit Graph” section: Add a line by searching for “homeownership rate” and move the y-axis to the right for the second graph. Start the sample in 1965-01-01.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: RHORUSQ156N, RRVRUSQ156N

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


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