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Posts tagged with: "CPIUKA"

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How has the U.K. stock market fared lo these past 300 years?

An historical data literacy lesson on U.K. stock prices

FRED offers some historical time series that the Bank of England has compiled, including the series shown above that tracks the price of stock market shares. The series starts way back in 1709 with the shares of the South Sea Company and the British East India Company. Other companies have been added and subtracted as they’ve entered and left the U.K. stock market. Just glancing at the graph reveals two things: First, the series is close to zero most of the time and then explodes. Second, there are wild fluctuations in recent decades.

For both these “observations,” though, there’s a mirage at work. Don’t worry: This optical illusion is common in very long time series. But let’s clear it up. Back in the 18th century, all prices, including share prices, were much lower  And when a long time series includes regular growth, recent changes are amplified while initial changes aren’t readily visible.

One way to sidestep these visual pitfalls is to use logarithms. Their big advantage is that any change you can measure on a graph, such as those mentioned above, is a change in percentages: Anywhere in the graph, one inch corresponds to the same percentage change. In this graph, the share price doesn’t look explosive at all; actually, it seems quite stable except for growth periods in the mid 19th century and second half of the 20th century. Also, the recent wild fluctuations have been tamed. (But note the blip in 1720, due to the South Sea Bubble.)

An even better way to represent the data is to remove the growth of the general price level so the growth of stock prices themselves is better captured. A tip of the hat to the Bank of England for offering a time series on the consumer price index that, remarkably, begins in 1206. By dividing share prices by this series (shown in the graph below), we can see that there are longer periods where the real share price has actually declined.

That concludes our historical data literacy lesson for today. Your homework? Convert the units in the graph below to logarithms.

How these graphs were created: For the first graph, search for “share price UK.” For the second, take the first, click on “Edit Graph,” and select units “natural logarithm.” For the third, take the first, click on “Edit Graph,” add a series by searching for “CPI UK” (selecting the one with the earliest start date), and then apply formula a/b.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CPIUKA, SPPUKQ

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

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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