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A plateau for manufacturing? After steady growth, manufacturing productivity seems at a standstill

The Bureau of Labor Statistics’ productivity and costs release provides data that can help us better understand the state of U.S. manufacturing. The graph above shows the evolution of manufacturing output since 1987. Notice the slow but steady growth in output since the Great Recession’s big dip.

What’s behind this slow and steady growth? The first suspect we’ll look at is manufacturing employment. The graph above shows there’s been a strong downward trend, which has accelerated during each recession. Yet, since 2010, manufacturing employment has been slowly making its way back up.

Next we’ll look at how much each worker produces in the manufacturing sector. Here, the story’s different: The general trend has been continuous increases in productivity per worker, but something seems to have broken with the Great Recession. First a major drop in productivity, then some progress getting back to trend, and then no progress since about 2010.

What if, since the Great Recession, manufacturing jobs have offered fewer hours of work or more part-time work? Maybe productivity per hour worked is growing. But the graph above, which shows productivity per hour instead of per person, shows no difference. The cause of this productivity standstill is thus either lack of technological progress or (more likely) a change in the composition of the manufacturing workforce toward lower-productivity work.

How these graphs were created: Search for “manufacturing sector” and each of the discussed series should be among the top choices. Simply choose them and click “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: OPHMFG, OUTMS, PRS30006013, PRS30006163

The high-tech trade balance Importing and exporting U.S. aerospace, nuclear, and weaponry technology

The graph above shows FRED data on U.S. exports and imports of advanced technology products, which include the categories of advanced materials, aerospace, biotechnology, electronics, flexible manufacturing, information and communications technology, life sciences, optoelectronics, nuclear technology, and weapons. A report from the Brookings Institution noted that the advanced technology sector in the U.S. added $143 billion to GDP in 2013-15 and accounted for more than 20 percent of the growth of the economy. Despite the boost from this sector, the graph shows that the U.S. has turned from a net exporter to a net importer of these products. Now, these products are subject not only to market forces but also to export regulations and restrictions. Indeed, U.S. national interests prevent some technologies from being exported to some countries. In any case, this part of the trade deficit is minor compared with the total trade deficit, as shown in the graph below.

How these graphs were created: For the first graph, search for “advanced technology products,” which should give you the two series (exports and imports). Select them and click on “Add to Graph.” For the second graph, start with the first graph, but remove the imports series. Use the “Customize data” section to add that imports series to the first line (the exports series); then apply formula a-b. Add the second line to the graph by searching for and selecting “Trade Balance: Goods and Service, Balance of Payments Basis.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: BOPGTB, EXP0007, IMP0007

Checking up on hospital price inflation Rising medical costs are not a foregone conclusion

Many people are concerned with the persistent rise in medical costs. But as long as medical services are delivered (for the most part) by people, economic theory tells us that rising costs are normal: As technological progress makes the production of goods less expensive, the production of services becomes comparatively more expensive. Of course, technological progress can also occur with the delivery of services. A good example is the introduction of ATMs, which have dramatically reduced the cost of simple bank transactions.

The delivery of medical care has not (yet) seen such cost-saving technological advances; hence, its relative costs continue to generally increase, in line with basic economic theory. But the pace of that increase may differ under different circumstances. In international comparisons, health care delivery operates under vastly different market mechanisms. The graph above shows inflation for hospital stays in four countries: the United States, where health care is largely privately provided and paid for (except for the poorest and for retirees); the U.K. and France, where health care is provided and paid for by the state; and Switzerland, where people must enroll in private, but regulated, health insurance (not unlike Obamacare).

Surprisingly, the inflation experience is remarkably similar in the U.K. and the U.S., despite having health care institutions that are polar opposites. France shows much less inflation, and Switzerland even shows some deflation. Note that general inflation was similar in all these countries over this period, so dividing each hospital price index by the corresponding general price index yields a similar picture—shown in the graph below. But keep in mind that these are just four examples, and many other factors may matter. So, one shouldn’t generalize from such a small sample. But one also shouldn’t say that health prices always go up.

How these graphs were created: Search for “hospital CPI,” check the series you want, and click on “Add to Graph.” From the “Edit Graph” section, open the panel with the U.S. series and set the units to 100 for 2015-01-01 to match the other series. Finally, start the sample period on 2001-01-01. For the second graph, add to each line a second series (the CPI for the U.S., the harmonized consumer price index for all items for the other countries), apply formula a/b, and set the units to 100 for 2015-01-01.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CP0000CHM086NEST, CP0000FRM086NEST, CP0000GBM086NEST, CP0630CHM086NEST, CP0630FRM086NEST, CP0630GBM086NEST, CPIAUCSL, CUSR0000SEMD

Measuring inflation trends Why use different inflation measures for policy analysis?

Congress has instructed the Federal Reserve to pursue monetary policies that promote maximum employment and price stability. The Federal Open Market Committee (FOMC) has determined that “inflation at the rate of 2 percent, as measured by the annual change in the price index for personal consumption expenditures [PCE], is most consistent over the longer run with the Federal Reserve’s statutory mandate” for price stability. As of March 2018, the year-over-year percent change in the PCE was 2.01 percent, or just 1 basis point above the FOMC’s 2 percent target. However, inflation was substantially lower over much of the past year—as low as 1.40 percent in July 2017—and economists were uncertain whether the low readings reflected temporary factors that would soon dissipate or an underlying inflation rate that was below the level consistent with price stability.

Because the inflation rate measured by the headline PCE tends to be volatile from month to month, many observers monitor other measures, such as the PCE excluding food and energy prices (“core PCE”), to gauge underlying inflation trends. The near-term growth in core PCE is among the economic variables that the FOMC includes in its quarterly Summary of Economic Projections. As of March 2018, the year-over-year growth in core PCE was 1.88 percent. Some critics argue that this measure of inflation is “rotten,” however, because it arbitrarily excludes particular categories of goods whose prices affect the cost of living.

An alternative, and somewhat less arbitrary, measure of underlying inflation trends is based on the mean of changes in the prices of the individual goods and services that make up the price index after dropping items with exceptionally large or exceptionally small price changes in a given month. For example, the Federal Reserve Bank of Dallas calculates a trimmed mean PCE inflation measure designed to hew closely to the trend in overall PCE inflation. By omitting price changes for goods and services having the largest or smallest price movements in a given month, extreme values have less impact on the measured inflation rate, which arguably is a better measure of underlying inflation trends than the traditional core measure.

The chart plots the headline, core, and Dallas Fed trimmed mean PCE inflation rates, measured as percent changes over the past 12 months, for the past year. Whereas the headline PCE inflation rate increased from 1.73 percent in February to 2.01 percent in March, and the core rate rose from 1.57 percent to 1.88 percent, the trimmed mean rose only from 1.71 percent to 1.77 percent. Hence, in contrast with the headline and core measures, the trimmed mean indicates little, if any, change in underlying inflation pressures in recent months, suggesting that low inflation readings might be more reflective of underlying trends than temporary special factors.

How this graph was created: Search for “PCE,” check the three series, and click on “Add to Graph.” From the “Edit Graph” menu, change the units to “Percent Change from Year Ago.” Change the frequency to “Monthly” and the starting date to “2017-03-01.”

Suggested by David Wheelock.

View on FRED, series used in this post: PCEPI, PCEPILFE, PCETRIM12M159SFRBDAL

Spectacular recoveries Comparing the strongest economic recoveries in recent U.S. history

In a previous post, we discussed how the economic recoveries from recessions are longer and slower than the downturns that lead to those recessions. Today, we compare the sizes of recoveries across the economic history of the United States. In the graph above, which shows the unemployment rate, the current recovery is clearly remarkable: The drop from unemployment’s high point of 10% down to 3.9% (at the time of this writing) is 6.1 percentage points. The only recovery that comes close (in the time period shown here) is the 1983-89 recovery, with a 5.8-percentage-point decrease (10.8% to 5%). However, this sample is limited to only the ten recoveries since 1948.

To go back farther, we need to use different data. Thank goodness FRED has some historical data compiled by the NBER on the unemployment rate! The NBER methods for compiling the data shown below aren’t entirely comparable to the BLS methods for the data shown above. In fact, the NBER series is a composite of three different series. But as long as we acknowledge which data sets we’re looking at, we should be able to make some generally fair comparisons. Here, the recoveries from the Great Depression stand out: First, the unemployment rate topped at 25.6% and then dropped to 11%. A 14.6-percentage-point drop. The second recovery went from 20% to 0.2%. A 19.8-percentage-point drop!

So, although the most recent recovery seems remarkable after WWII, it’s small compared with the recoveries before WWII. Even if in some sense we’re comparing apples and oranges, the oranges are a lot bigger.

How these graphs were created: Search for “unemployment rate” and choose each series individually: civilian unemployment rate (monthly, seasonally adjusted, starting in January 1948) and unemployment rate for United States (monthly, seasonally adjusted, starting in April 1929).

Suggested by George Fortier and Christian Zimmermann.

View on FRED, series used in this post: M0892AUSM156SNBR, UNRATE


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