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Moderately well understood

The Great Moderation is evident, but its causes are complex

For decades, economists have puzzled over the reduction in macroeconomic volatility in the U.S. and world economies after the mid-1980s: In the last 25 years or so of the 20th century (highlighted by the blue bar in the graph), domestic output, employment, and inflation all fluctuated far less than they had in prior years. The data make the stabilization of the economy clear, yet economists haven’t reached consensus over the causes or duration of the Great Moderation.

The Federal Reserve points to monetary policy as a primary cause of the moderation, through orderly responses to inflation and GDP changes after the Great Inflation of the 1980s. Instead of waiting for the signs of economic recession or rising inflation to appear before acting, as was common in the “go-stop” practices of the 1960s and 1970s, the Federal Reserve began following a more systematic, rules-based approach. Another proposed cause is the structural change of the economy and the labor market. In the 1980s, labor patterns shifted away from manufacturing and toward less-volatile sectors. Also, new technology sped up communication and allowed producers to track inventory and demand more easily, leading to more stable output over time.

It’s easy to look to clear economic and policy changes as contributors to the Great Moderation, but there may be another factor at play: luck. Statistical models such as those proposed by Stock and Watson or Galí and Gambetti assert that, although some economic shocks did occur during the early part of the Great Moderation, they were less severe and better handled than those of the 1970s. While others dispute this claim, it still brings to mind the question: If luck is responsible, how long will it last?

There’s no answer yet to the question of whether the Great Moderation ended with the recession of 2007-09 or was merely temporarily disrupted by it. If we assume that structural and policy changes brought about the reduction in volatility, then, as long as those are maintained, the moderation ought to continue. However, if good fortune is responsible, we may still be waiting for the next shock that will bring about another increase in instability.

How this graph was created: Search “Real GDP” and select the relevant series. From the “Edit Graph” tab, click “Add Line” and select “Create use-defined line.” Set the date range from 1983 to 2017 and set both the start and end points as zero.

Suggested by Maria Hyrc and Christian Zimmermann.

View on FRED, series used in this post: A191RL1Q225SBEA

Wage paradox at the industry level

There’s a well-known disconnect between the fluctuations of average employment and of average wages: Employment is volatile and dips during recessions, while wages tend to be quite stable. This is a problem for economic models, which have difficulty reconciling the fluctuations in productivity that must justify the changes in employment levels despite the smoothness of wages. (There are exceptions, of course, such as Rudanko (2009) and Lamadon (2014).)

Averages, however, don’t tell the whole story, as we’ve pointed out here before. So we look a little deeper at the occupational level. In the past 15 years and through two business cycles, different occupations have clearly been affected differently by both long-term and cyclical changes. Manufacturing is a notable example of a long-term decline, punctuated by more rapid change during recessions; construction has had a stark rise and fall. On the other hand, white collar service work has been more stable over time. In the graph above, we see large changes in employment among “production occupations” but see much less volatility in “installation and repair occupations” (two sets of occupations with similar skills). Construction and extraction occupations have been subject to well-known fluctuations in demand associated with the housing bubble and resource boom, and these factors show up in the employment figures. “Administrative support”—a different set of skills but at a similar level—has been relatively stable over the period and the cycle.

The employment situations look vastly different for these different occupations, but wages are starkly similar, as shown in the graph below: Wages for each occupation, after normalizing out the difference in levels, follow almost exactly the same pattern. This is strange because economists often assume that wage changes will guide the shifts of workers from one occupation to another; but it seems these shifts are occurring without wages leading them. Or, to explain it from the other direction: If demand is low in an occupation, restricting workers’ ability to work there (i.e., reducing the number of available jobs) should depress wages; but, again, wages do not seem to follow the changes in the levels of workers in these occupations. There are potential explanations, of course, but these facts challenge our initial beliefs.

How these graphs were created: Go to “Browse data by release” and on the final page is “Weekly and Hourly Earnings from the Current Population Survey.” Choose “Classified by occupation and sex.” For the top graph, choose “number of workers” and for the bottom graph choose “median usual earnings.” Finally, choose quarterly data and then the four occupations we’ve shown using data for both sexes. Normalize the data to be 100 at the trough of the Great Recession, June 2009.

Suggested by David Wiczer.

View on FRED, series used in this post: LEU0254498800Q, LEU0254505100Q, LEU0254509100Q, LEU0254512900Q, LEU0254552200Q, LEU0254558500Q, LEU0254562500Q, LEU0254566200Q

The trouble with food and energy

There are many ways to measure inflation. One popular method used for monetary policy purposes is to look at the price index for personal consumption expenditures excluding food and energy. Why exclude food and energy? Aren’t those important items that matter a great deal to households? The reason is straightforward: These price categories are considered to be excessively volatile, and including them would make it more difficult for policymakers to pin down the inflation trend. The graph above makes this point visually by comparing the PCE inflation rates with and without food and energy.

Usually when you add items to an index, you reduce the volatility of that index. This same premise is at work when you add assets to an investment portfolio—i.e., when you diversify to reduce volatility. But this does not happen when the item you add is excessively volatile. And, again, food and energy are excessively volatile. Food is subject to large price variations due to external shocks, mostly on the supply side, such as weather. Energy is subject to shocks as well: supply shocks such as discoveries, wars, political risk, and infrastructure issues and demand shocks such as climate events. This happens with food and energy much more than it does for other items included in personal consumption expenditures.

How this graph was created: Search for “PCE.” Then go to the “Filter Series by Tags” box to the left and enter “price index.” Select the first two monthly series that appear and add them to the graph. Change the units for both series to “Percent Change From Year Ago.”

Suggested by Christian Zimmermann

View on FRED, series used in this post: PCEPI, PCEPILFE


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