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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

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

Is the financial sector becoming more productive?

The Great Recession adversely affected employment across all industries. Since the recovery began in 2010, employment has rebounded and the unemployment rate started declining. But this recovery in employment has not been uniform across industries.

Employment in the financial sector has steadily declined as a share of total employment since the onset of the Great Recession. The financial sector averaged around 6.2% of total employment in the ten years preceding the Great Recession, from 1997 to 2007; in the recovery period, from 2010 to 2018, it averaged around 5.7%. It’s also interesting, but perhaps not very surprising, to note that the employment share in financial activities increased through the previous two recessions—in 1991 and in the early 2000s—but fell quite a bit during the Great Recession. And while total employment has grown by nearly 14% in the years spanning the recovery, from 2010 to 2018, financial employment has grown by only 11%.

So the question is, if there are fewer employees in the financial sector relative to the 1990s, how is that impacting output? One way to answer this question is by looking at the value added by the financial industry. The red line on the graph represents value added. It is interesting to note that while value added declined during the recession, it recovered shortly thereafter and has been on an upward trend since then. This implies that industry output has not declined because of slower employment growth, which in turn indicates that other factors must be responsible for this apparent increase in the productivity of labor.

How this graph was created: Search for and select the series USFIRE. From the “Edit Graph” panel, select a quarterly frequency and set the aggregation method to “Average.” Then add the series “PAYEMS” to the same graph and set the formula as a*100/b. Click on the “Add Line” option and search for the series “VAPGDPFI.” In the “Format” tab, scroll down to the formatting options for Line 2 and set the y-axis position to “Right.”

Suggested by Asha Bharadwaj and Miguel Faria-e-Castro.

View on FRED, series used in this post: PAYEMS, USFIRE, VAPGDPFI

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