Skip to main content

The FRED® Blog

Immigration and the Brexit vote: A look at the data

On June 23, 2016, a majority in the United Kingdom voted to exit (or "Brexit") the European Union, defying most forecasts. One of the reasons cited for this outcome was concern by British citizens about too much immigration from Central Europe and the Baltics.* Is (or was) this concern valid? Let's see what FRED has to show us about the patterns of net immigration for the U.K. and for Central Europe and the Baltics. The graph tracks net migration—total number of immigrants minus total number of emigrants—from the early 1960s to 2017 (the latest available date point). Throughout the late 20th century, net migration for the U.K. was quite low, fluctuating between slightly positive (net immigration) and slightly negative (net emigration). However, there was a sharp increase in the early 2000s. In 2004, the EU expanded to include several countries from Central Europe, and the U.K. was one of three EU nations (along with Ireland and Sweden) to immediately allow migration from these new member states. This policy may have contributed to the 2007 peak in net immigration into the U.K. After that, there's a sharp decline as the Great Recession (2008-09) reduced economic opportunities. The overall picture of more-recent U.K. immigration is mixed: There was a sharp rise with the 2004 EU expansion, but the years immediately preceding the 2016 Brexit referendum show a decrease in net immigration. But, because net immigration adds to the number of foreign born in a nation (i.e., it is a flow), the immigration spike may have contributed to heightened anxiety in the U.K. about the relatively large immigrant population that had accumulated by the time of the 2016 referendum. Central European nations and the Baltics had much more net emigration, with two clear spikes: The first and larger of the two was in the late 1980s/early 1990s, which was related to the breakup of the Soviet Bloc. The second was in the early 2000s, related to the EU's enlargement in 2004. Since that time, migration from these nations seems to mirror U.K.’s experience, although in the opposite direction. This pattern suggests significant emigration from these nations to the U.K. after 2004, perhaps influencing the Brexit vote. Of course, our data aren't detailed enough to provide a definitive connection between U.K.-Europe immigration flows and Brexit. More research may provide more insight. * These nations are Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, and Slovenia. How this graph was created: Search for "United Kingdom migration," check the series, and click "Add to Graph." From the "Edit Graph" panel, use the the "Add Line" option to search for and select "Central Europe migration." Suggested by Asha Bharadwaj and Subhayu Bandyopadhyay.
View on FRED, series used in this post: SMPOPNETMCEB, SMPOPNETMGBR

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.* Many 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: Search for "gold" and the two series should be among the first choices. Select them and click on "Add to Graph." For the second graph, start with the first one and use the "Edit Graph" panel to make adjustments: Open the tab for the second series and delete it (click on the trashcan). Then, in the tab for the first series, search for and add the second series and then apply formula (b-a)/a*100. * 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 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

North American divergence in the work week U.S. manufacturing workers put in more hours than their Canadian counterparts

This graph shows the average weekly hours in manufacturing for two neighboring countries: Canada and the United States. To make the numbers comparable, we made sure to use the same source for both: the Main Economic Indicators of the OECD. (The OECD tries to keep data definitions uniform across member countries, which is often a problem for labor market data.) What's striking is that both countries looked similar early on in the time series but then diverged. One might assume that, as economies become more intertwined, they would also become more similar. So what's going on here? Sadly, we don't have an answer, but we can list some potential answers. First, economic integration across countries doesn't necessarily make countries more similar. Indeed, integration provides opportunities for specialization, thanks to comparative advantage. It could be that Canada has specialized in manufacturing sectors where the standards for work hours are lower. Second, labor market legislation may have changed. Indeed, current laws may give workers more bargaining power in Canada than in the U.S. In particular, unions currently have more say in Canada, and their goal is typically to improve the situation of their members (for example, by reducing work hours). Third, the labor practices of these countries that relate to the use of overtime or undertime may have become more different over the years. If employers prefer to use overtime instead of hiring new people, then average hours increase. The opposite happens when workers are given fewer hours instead of being laid off. How this graph was created: Because the OECD data for the U.S. will not be among the first choices in a typical search, it's better to search through the data sources to find the OECD. Look for the relevant table for "Main Economic Indicators" for the U.S. and then click on the series name (we took the quarterly seasonally adjusted one). From the "Edit Graph" panel, open the "Add Line" tab and search for the Canadian series to add. Suggested by Christian Zimmermann.
View on FRED, series used in this post: HOHWMN02CAQ065N, HOHWMN02USQ065S

Subscribe to the FRED newsletter

Follow us

Twitter logo Google Plus logo Facebook logo YouTube logo LinkedIn logo
Back to Top