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How does productivity affect our future?

Since the beginning of 2011, growth in real output in the nonfarm business sector has been slow, averaging just 2.7% percent. And most of the economic growth has been driven by increases in labor inputs and not by increases in labor productivity. The graph shows real output growth (green line) decomposed into growth in labor input (red line) and growth in labor productivity (blue line), where productivity is measured as real output per hour. Given that the output growth rates are only slightly different from—either a little above or a little below—growth in hours, the majority of growth in output has come from increases in hours instead of increases in labor productivity. Labor productivity growth averaged 0.7% over this period, accounting for just 27% percent of real GDP growth.

Labor productivity growth amounts to the average growth of how much goods and services each individual can consume and, thus, is the driving force behind increases in the standard of living. More importantly, a small difference in labor productivity growth leads to a dramatic difference in the standard of living over the long run. For example, if labor productivity growth held steady at 2%, which is the rate seen in the expansion from 2001 to 2007, the living standard would double in 35 years. If labor productivity continues to grow at 0.7%, it would take 99 years to double the standard of living.

How this graph was created: After searching for “nonfarm business sector,” select “Index 2009=100” for the three series and click on “Add to Graph.” Then go to the “Edit Graph” section and select “Percent Change from Year Ago” under “Units.” Finally, click on “Copy to all” and change the starting date to “2011-01-01.”

Suggested by Yili Chien and Paul Morris.

View on FRED, series used in this post: HOANBS, OPHNFB, OUTNFB

Whither the workers?

How the working-age population affects productive capacity

One way to measure the productive capacity of a country is to look at its working-age population. Members of this group are most likely to be available for productive employment that can sustain a country’s economic growth. The age range is generally considered to be 15 to 64, although some statistics start later. The graph above shows this population for the United States, Canada, and Japan.

Japan stands out in this trio: Its working-age population has been declining as a result of declining fertility and little immigration. These conditions make it difficult for Japan’s economy to grow. If sustained positive growth is the objective, Japan would need to improve the productivity of its smaller workforce much more than other countries with larger workforces would need to.

The story is different for the United States and Canada. They show continuous growth. While fertility rates have declined a little, immigration has helped sustain population growth. Immigrants are typically of working age, so immigration can increase the working-age population specifically. Although these populations for the United States and Canada track each other pretty well (they were almost identical in late 2016), a little bit of separation has occurred in recent years.

How this graph was created: Search FRED for “population 15-64,” check the relevant series, and click “Add to Graph.” Then change the sample to start on 1995-01-01. Click “Edit Graph,” choose units “Index” with 1995-01-01, and click on “Apply to All.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: LFWA64TTCAM647N, LFWA64TTJPM647N, LFWA64TTUSM647N

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

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