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How much cash do banks keep in the vault?

Have you ever wondered how much cash sits in a bank vault? Even if you’re not planning a robbery, you may still be interested in how much liquidity is out there. (In other words, whether your bank is capable of providing you with all the cash for your deposits.) We can’t give details about your bank specifically, but we do have statistics for the banking system as a whole. The graph shows that banks hold about $75 billion in their vaults at any moment, which translates to about $230 for each U.S. resident. This doesn’t seem like a lot, as many people have more than that deposited in an account.

A principal function of banks, of course, is to provide loans; and they use your deposits for this purpose. Under normal times, only a fraction of deposits are claimed at any moment, so the remaining cash in the banks is plenty to cover the demand. Should that not be the case, the Federal Reserve can provide the required cash as a loan, as long as the bank is solvent.

Note that cash holdings are highest in January and February every year, peaking in mid-February. This in part has to do with reserve requirements, as vault cash qualifies as reserves and can fill in when other components are harder to come by. And there is also cash demand management with some precautionary buffers for unexpected withdrawals. All of these may have seasonal patterns.

Finally, it’s impossible not to see the mountain of cash early in 2000. It was also impossible at the time to avoid the discussion of the risks and concerns associated with Y2K, including worries about computer systems failing on January 1, 2000. Both banks and the public wanted to be assured there was enough cash on hand to complete whatever electronic transactions might be interrupted.

How this graph was created: Search for “vault cash,” select the weekly series, and click “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: TLVAULTW

Gold: malleable, ductile, and volatile

Intraday movements in gold prices

Despite appearances, the graph above has two lines. If you look really closely, you can see a second color peeking out here and there. And what two series are these that track each other so closely? One is the daily price of gold in London as of 10:30 a.m. The other is also the daily price of gold in London but as of 3 p.m. You’d expect these prices to be very close to each other, but let’s graph the percentage change between the price at 10:30 a.m. and 3 p.m. to see exactly how similar they are.

In fact, what we have here is a fairly volatile series.*┬áMany of these price changes, which occur within 4.5 hours of each other, are in the range of 1% to 2%. Some even more. This rate of change is about the same as the rate of inflation in the U.S. Such changes in commodity prices aren’t uncommon, of course, even if trading occurs around the clock on world markets. The London market is open 8 a.m. to 5 p.m. (London time). These series are just snapshots during part of that time, but market activity continues in a similar fashion at other times and elsewhere.

How these graphs were created: NOTE: Data series used in these graphs have been removed from the FRED database, so the instructions for creating the graphs are no longer valid. The graphs were also changed to static images.

* NOTE: Because there are many daily observations for the period shown, the graph offers only a sample of them. To see the details, just shorten the sample time.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: GOLDAMGBD228NLBM, GOLDPMGBD228NLBM

The Black Death in the Malthusian economy

A glimmer of wage growth in the Dark Ages

If you’re interested in economic history, does FRED have some data for you! The graph above features some of the oldest data in FRED: population in England and real wages in the United Kingdom, starting in 1086 and 1210, respectively. The big picture shows how dramatic the Industrial Revolution has been in lifting wages and sustaining a much larger population after centuries of stagnation. In fact, the growth has been so strong that we should have used logarithms in the graph (which we’ve recommended in this blog more than once for long time series). Today, though, we focus on a much shorter period: Notice the sharp drop in population in the 14th century? The slider below the graph lets you change the sample period fairly quickly…as does the click-and-drag method within the graph, which we’ve done to create the graph below to highlight this period.

This sudden and massive drop in population is the Black Death, the catastrophic epidemic of bubonic plague that swept through Europe. Notice something else that is quite particular about this period: Real wages went up substantially and clearly stayed higher for a while. This is very different from the period since the Industrial Revolution, where both wages and population have moved in the same direction. One explanation for this deviation is that the earlier period was an era of scant technological progress where population size was constrained by how much the land could produce. Agriculture was not mechanized in any way and suffered from decreasing returns to scale: Each additional agricultural worker was contributing less to total output than the previous one, and thus the average output (mostly food) per person was lower with higher population. This condition leads to a so-called Malthusian equilibrium where population is limited by food availability and famines control population size.

But then the Black Death came and suddenly wiped out a substantial part of the population. Following the above logic, the marginal agricultural worker suddenly is much more productive and wages are higher. Eventually, population increases back to its previous level, and productivity and wages fall back to their initial levels. But for a generation, the survivors of the epidemic enjoyed a higher-than-normal standard of living. It’s only after the technological progress associated with the Industrial Revolution that the economy managed to break out of this vicious cycle.

How these graphs were created: From the FRED homepage, click on the link in the first line of text that displays the number of series in FRED. (At the time of this writing, that number is 528,000.) Then use the sorting feature at the top right and sort the list by starting observation (“Obs Start”). Check the boxes for the population and weekly earnings data series and click on “Add to Graph.” From the “Edit Graph” panel, open the tab for the wage line. Search for “consumer price index in the United Kingdom” and select the oldest series. (The series ID is CPIUKA, which will save you some time searching.) Apply formula a/b. Finally, from the “Format” tab, move the y-axis of one of the series to the right.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: AWEPPUKA, CPIUKA, POPENA

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