Federal Reserve Economic Data

The FRED® Blog

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 stock market is not the economy

Taking a "random walk" through the data

Does the stock market tell us anything about the economy? The stock market seems to react continually to various data and economic news, and many of us follow its day-to-day changes, especially if we’re invested in it. But do fluctuations in the stock market actually reflect economic health?

The best measure we have for measuring total economic activity is GDP. But GDP is measured only quarterly and with a considerable lag. With the help of FRED, though, we can look at a decade’s worth of data to see how closely GDP relates to the stock market.

The graph above looks at quarter-to-quarter percent changes in the Dow Jones Industrial Average (DJIA), deflated to remove general price increases, and real GDP, which is by definition also deflated to remove general price increases. Of course, the stock market is very volatile, but it’s too hard to see any relationship in this line graph. A better way to visualize connections (or a lack of connections) is a scatter plot, shown below, with the same data.

If the two measures were related, we would see the points clustered in the lower left, middle, and upper right. But we don’t see that. One reason may be that the DJIA covers only 30 firms. While they’re large firms, they make up only a fraction of the economy. So we built the same graph (below) with data from the S&P500, which encompasses the 500 largest firms on the stock market. But no luck: We still don’t see any relationship.

So why are GDP and the stock market graphically unrelated? First, it’s important to understand what the value of a stock measures: the sum of discounted expected dividends plus a liquidation value of capital. In other words, what the market thinks the future dividends of the firm will be, evaluated at current prices, and what could be obtained from liquidation if the firm goes bankrupt. Note that dividends are only a small part of the firm’s income; dividends don’t account for any income that’s directed toward taxes, servicing loans and bonds, and (maybe most importantly) wages. The labor income share of total income in the economy is about 60%. And, as recently noted on this blog, the labor income share has decreased. Now, if regulation or laws reduce the bargaining power of labor, for example, labor income decreases, capital income and dividends increase, but total income may not have changed or even decreased.

How these graphs were created: Search for “Dow Jones,” select the Industrial Average series, and click on “Add to Graph.” Click on “Edit Graph,” add the “GDP deflator,” apply formula a/b, and set units to “Percent change.” From the “Add Line” tab, search for and select “real GDP,” and set units to “Percent change.” Once you restrict the sample to the last 10 years, you have the first graph. For the second, take the first, use the “Edit Graph” panel to open the “Format” tab and select type “Scatter.” For the third graph, replace the DJIA with SP500. You can then expand the sample.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: DJIA, GDPC1, GDPDEF, RU3000TR, SP500

Paychecks at the top, at the bottom, and in the middle

A look at the distribution of wage income

Let’s consider the topic of income disparity by looking at some data from our friends at the Bureau of Labor Statistics—or, as we like to call them, the BLS. (Just to clarify: Top incomes are increasing more than others not so much because of regular labor income, but largely because of capital income, various bonuses, and the like. That said, in this post we’ll stick with the distribution of regular wage income.)

The BLS’s Current Population Survey provides weekly wage income data for the U.S. population that can be split into various segments: These segments are ordered by income, from the very top (100%) to the very bottom (1%). The segments we chose, from top to bottom in the graph, are the 90%, 75%, 25%, and 10% levels. (That is, the ninth decile, the third quartile, the first quartile, and the first decile.) The reported income for each of these segments is divided by the median income to show how each segment compares with the wage earner in the middle of the entire distribution.

So, what do we learn from this graph? For one thing, in 2018, the wage earner at the 90% level got 2.4 times what the median wage earner got. The wage earner at the 10% level got half of what the median wage earner got. It appears that the two bottom segments (25% and 10%) are rather stable compared with the median, except for a surprising improvement recently for the 10% level. The 75% level is almost completely flat. The 90% level is showing a gradual increase, about 10% over the 18 years for which we have data. Although wage income disparity isn’t as spectacular as total income disparity, it is increasing.

How this graph was created: From the release table with the wage quantiles, select the series you want and click “Add to Graph.” If necessary, restrict the sample period to include all series. From the “Edit Graph” panel, add to each line the median statistic (series ID LEU0252887700A), applying formula a/b. From the “Format” tab, move the lines so that the order of the legends matches the order of the lines in the graph. Finally, de-select the “Show: Title” option, as the legends take waaaaaay too much space. (The legends are mostly still visible when you hover over the lines, though.)

Suggested by Christian Zimmermann.

View on FRED, series used in this post: LEU0252887700A, LEU0252916000A, LEU0252916100A, LEU0252916200A, LEU0252916300A


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