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North American divergence in the work week U.S. manufacturing workers put in more hours than their Canadian counterparts

This graph shows the average weekly hours in manufacturing for two neighboring countries: Canada and the United States. To make the numbers comparable, we made sure to use the same source for both: the Main Economic Indicators of the OECD. (The OECD tries to keep data definitions uniform across member countries, which is often a problem for labor market data.) What's striking is that both countries looked similar early on in the time series but then diverged. One might assume that, as economies become more intertwined, they would also become more similar. So what's going on here? Sadly, we don't have an answer, but we can list some potential answers. First, economic integration across countries doesn't necessarily make countries more similar. Indeed, integration provides opportunities for specialization, thanks to comparative advantage. It could be that Canada has specialized in manufacturing sectors where the standards for work hours are lower. Second, labor market legislation may have changed. Indeed, current laws may give workers more bargaining power in Canada than in the U.S. In particular, unions currently have more say in Canada, and their goal is typically to improve the situation of their members (for example, by reducing work hours). Third, the labor practices of these countries that relate to the use of overtime or undertime may have become more different over the years. If employers prefer to use overtime instead of hiring new people, then average hours increase. The opposite happens when workers are given fewer hours instead of being laid off. How this graph was created: Because the OECD data for the U.S. will not be among the first choices in a typical search, it's better to search through the data sources to find the OECD. Look for the relevant table for "Main Economic Indicators" for the U.S. and then click on the series name (we took the quarterly seasonally adjusted one). From the "Edit Graph" panel, open the "Add Line" tab and search for the Canadian series to add. Suggested by Christian Zimmermann.
View on FRED, series used in this post: HOHWMN02CAQ065N, HOHWMN02USQ065S

Does oil drive inflation? A look at oil's influence over producer prices vs. consumer prices

The price of oil has declined recently, but does that mean prices overall have declined? Let's see if FRED can help us measure how much connection there is between oil prices and the general price level. The graph above compares oil price inflation and overall price inflation in the U.S. over recent decades. The red and blue lines plot the year-to-year inflation rate corresponding to two of the major aggregate price indexes: the producer price index (PPI) and the consumer price index (CPI). The green and purple lines plot the year-to-year percentage change in two of the major global oil price indexes: the price of Brent crude and the price of West Texas Intermediate (WTI) crude. The graph shows a strong positive relationship between oil prices and PPI inflation: That is, higher oil prices are associated with higher producer prices and vice versa. Specifically, the correlation between oil prices and the PPI is 0.71. This strong link likely comes from the importance of oil as an input in the production of goods. In contrast, the graph shows a positive but much weaker relationship between oil prices and CPI inflation: The correlation is 0.27, much lower than for producer prices. This weaker link between oil prices and consumer prices likely comes from the relatively higher weight of services in the U.S. consumption basket, which you'd expect to rely less on oil as a production input. If you know what to look for, this difference in correlation is more clearly visible in the scatter plot below: The red dots (PPI and oil) more or less follow a 45-degree line that rises from left to right, which translates into a strong positive relationship between PPI and oil prices. The stream of blue dots (CPI and oil) doesn't strictly follow a 45-degree line, which reveals a much weaker relationship.
How these graphs were created: Search for “CPI” and click on the series name. From the “Edit Graph” panel, open the “Add Line” tab and search for “PPI,” then click on the series name. Repeat this procedure searching for “oil price” to add the remaining series. From the “Format” tab: Set the “y-axis” position corresponding to the oil price series to “right,” set the “Graph frame” color to white, and set the thickness of each of the lines to 3. For the second graph, from the “Format” tab, change the graph type to "Scatter." Suggested by Fernando Leibovici.
View on FRED, series used in this post: CPIAUCNS, POILBREUSDQ, POILWTIUSDQ, PPIACO

The lowdown on loan delinquencies Rates are lower than pre-recession levels...except for mortgages

We heard a lot about the surge in mortgage delinquencies during the past recession. In fact, many believe this was the origin of the crisis. FRED has delinquency data so let's see how things look now. The delinquency rates in the graph show the proportion of loans from the 100 largest U.S. banks that are more than 30 days past due. Mortgage delinquency is now considerably lower than at the height of the Great Recession, but it is still high compared with the two decades prior. In fact, it's also higher than credit card delinquencies, something that could not have been foreseen before the past recession. The fact that credit card delinquencies are at their lowest recorded levels is part of the explanation, though. Delinquencies on leases and commercial loans are also at their lowest, or close to it, in the past 30 years or so. Thus, the mortgage market still hasn't shaken its problems from the crisis, while other loans types are doing remarkably well. How this graph was created: Search for "loan delinquency" and click on any relevant result. Look in the notes and click on the release table. Check the series you want displayed and click on "Add to Graph." Suggested by Christian Zimmermann.
View on FRED, series used in this post: DRBLACBS, DRCCLACBS, DRLFRACBS, DRSFRMACBS

Are we moving toward a cashless economy?

There's a lot of talk that the U.S. is moving toward a cashless economy...at least in the sense that people are using more and more "plastic" (credit and debit cards) for transactions and that cryptocurrencies are becoming more popular. One test of this theory is to look at currency in circulation. If this measure stops growing while the economy is growing, it would be an indication that other forms of money have become more important and are serving as substitutes for currency. The graph above tells a different story: Currency in circulation is consistently growing more than the economy is. (Note: Both are nominal, not "real" inflation-adjusted measures). One caveat: U.S. dollars are also used quite a bit abroad. But dollar use abroad would have to increase much more than the U.S. economy for it to counteract a reduction in domestic currency demand. So it seems the question remains open. How this graph was created: Search for currency in circulation and click on the series name. From the "Edit Graph" panel, open the "Add Line" tab and search for GDP. Do not select a real GDP measure! Take GDP in current prices. Change units to "Percent Change from Previous Year" and click on "Copy to All." Finally, change the sample period to start in 1948. Suggested by Christian Zimmermann.
View on FRED, series used in this post: CURRCIR

The unusual duration of unemployment The scars of the Great Recession

The graph above shows the unemployment rate (right axis) and the average duration of unemployment (in weeks, left axis). It's well known that the unemployment rate is currently very low. However, the duration of unemployment since the Great Recession has never been longer.* What's going on? The graph below has an answer. The share of long-term unemployment is significantly higher than in any other post-WWII period. Indeed, those unemployed for more than 6 months (in green) still represent over 20% of the unemployed, after a peak of over 45% in 2011. This share increases after recessions, but the most recent recession was deeper and much longer than the others. It's also well-known that the long-term unemployed have a much harder time finding a job, leading to a catch-22 situation for them. And thus their numbers still persist at a high level.
How these graphs were created: Search for unemployment duration and click on the series name. From the "Edit Graph" panel, open the "Add Line" tab and search for "unemployment rate." Open the "Format" tab and place the axis for the second line on the right. For the second graph, look at the notes for the duration series, where there is a link to the release table. From there, check the relevant series, click on and "Add to Graph." From the "Edit Graph" panel, open the "Format" tab, change graph type to "Area, Stacked," and finally move the "less than 5 weeks" series up so that they are all properly ordered. *At least in the postwar era. Suggested by Christian Zimmermann.
View on FRED, series used in this post: LNS13008397, LNS13025701, LNS13025702, LNS13025703, UEMPMEAN, UNRATE


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