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Manufacturing: Up? Down?

Is manufacturing up or down? As economists like to say, it depends. The graph above shows three indicators of U.S. manufacturing activity, and they point in different directions. Manufacturing output is definitively trending up; that is, the number of things produced in this country has increased over time and is currently increasing. This production is accomplished, however, with fewer and fewer employees. It should be no surprise that an economy becomes increasingly better (quicker, more efficient, etc.) at producing things, thanks to increasing productivity per employee through innovations, for example. Recently, though, manufacturing employment is trending up slightly, while productivity has slowed down (as it has in other sectors).

Is this good or bad? Employing people is clearly important. Yet, when an industry needs fewer people because it is better at doing something, this is viewed as a gain by economists: Workers who aren’t needed any more can move on to produce something else. Of course, there are costs in the process if displaced workers cannot find new employment right away. The U.S. has a relatively flexible labor market that has generally managed to respond well to such challenges. In the short-term, though, the gains are not shared by everyone. Manufacturing unemployment is particularly high in recessions, as is seen in the graph below. But consider yet another twist: The current unemployment rate for manufacturing is lower than the rate for the general population.

How these graphs were created: For the first graph, search for “manufacturing sector,” check on the series you want to graph, and click on “Add to Graph.” For the second graph, search for “manufacturing unemployment rate” and “civilian unemployment rate.” Restrict the sample period to start in the year 2000.

Suggested by Christian Zimmermann

View on FRED, series used in this post: LNU04032232, OPHMFG, OUTMS, PRS30006013, UNRATE

Quits are recovering

The dynamism of the U.S. labor market was threatened by the Great Recession. Clearly, the unemployment rate rose and stayed elevated. But flows from one job to another fell, with lower rates of hiring for both the unemployed and the already employed. Like the slow-to-recover unemployment rate, job flows also took a while to bounce back. However, not all job flows are the same. “Quits” are a very good sign for the economy. In principle, a worker quits a job only if she has a better offer elsewhere or has a strong belief she can find another job relatively easily. Also, the threat of quitting puts upward pressure on the wages of the currently employed because firms have to compete to keep the best workers. Even if a worker is replaceable, employers may offer a higher wage to avoid the costs of refilling an empty position.

The graph shows how long quits (as a share of total separations) have remained low. However, they seem to have just now recovered to their pre-recession levels, in line with many other labor market indicators such as wages. On the other hand, “layoffs and discharges” are generally ill signals for the economy. The Great Recession strongly affected layoffs and discharges, but their rise dates to 2006—no doubt, in part, because the housing market began declining before the rest of the economy. Layoffs and discharges aren’t quite back to their nadir, but they have dropped below their level at the start of the recession. For workers, this is good news: Often, those who are laid off take a wage cut on reemployment; but on average, those who quit gain a higher wage with the new job.

How this graph was created: For the blue line, search for “JOLTS” + “quits” and add “Quits: Total Private” (monthly, seasonally adjusted, level in thousands) to the graph. Modify this first series by using “Add Data Series” / “Modify existing series” / “Data series 1”  to add “Total Separations: Total Private” (monthly, seasonally adjusted, level in thousands). Then use the “Create your own data transformation” using the formula a/b. For the red line, do the same but search for and add “Layoffs and Discharges: Total Private” (monthly, seasonally adjusted, level in thousands) series. Modify this series (series 2) by again adding “Total Separations: Total Private” and using the transformation a/b. Convert the frequency for both to “Quarterly” to smooth some of the monthly wiggles.

Suggested by David Wiczer.

View on FRED, series used in this post: JTS1000LDL, JTS1000QUL, JTS1000TSL

Energy demand and supply

It’s no secret that energy prices have dropped dramatically, especially for oil and natural gas. The graph above looks at the consumer side and displays the same series under two concepts: real and nominal. The nominal series for personal expenditures (in red) shows that U.S. households are spending much less for energy. The real series (in blue) shows no significant trend. Indeed, energy is a very inelastic commodity: At least in the short run, consumption hardly changes even as its price changes. Fuel is actually a textbook example of an inelastic good, and recent data have proven it again.

Now let’s turn to the producer side. The graph below shows that consumption hasn’t changed much but that employment has taken quite a dive. It looks like this has to do mostly with prospecting and digging of new wells, as investment in this sector seems to have crashed. If this reduction in investment lasts for a while, production capacity will start to decrease, at least in the U.S. This is textbook economics as well: The relatively high price for oil generated a lot of investment in the sector, which has been producing much more than before. These conditions, at least in part, lead to a reduction in the price. The supply is adapting by not adding new capacity and letting existing capacity slowly wear out. The action here is all on the supply side, as demand is very price-inelastic.

How these graphs were created: For the first graph, search for “energy personal expenditures.” You should find the two series shown here: Check them both and click on “Add to Graph.” Restrict the sample period to the past five years. For the second graph, search for “real investment mining” and add the series to the graph. Then search for “employees mining oil” and add that series; set the y-axis for this second series to “right.” Then restrict the sample period to the past five years.

Suggested by Christian Zimmermann

View on FRED, series used in this post: CES1021100001, DNRGRC1M027SBEA, DNRGRX1M020SBEA, E318RX1Q020SBEA


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