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Dips in durables and nondurables Tracking consumption dynamics since the Great Recession

Household consumption contributes about 70% of total U.S. production. So, movements in consumption over time and the make-up of products purchased are essential for understanding GDP.

In general, final consumption is procyclical. That is, in good times people spend more and in bad times they spend less. During the recent 2007-09 Great Recession, real personal consumption expenditures dropped by 2.4% from pre-recession levels. But did people cut back the same amount on spending for all goods? The first graph splits aggregate consumption into durable and nondurable goods: Durable goods include, for example, cars and furniture and nondurable goods include, for example, food and clothing. We can see that consumption of durable goods is more volatile. During the recession, durable goods expenditures dropped by 12%, while nodurable goods expenditures dropped by 3%. (This doesn’t add up to the total 2.4% drop because there’s also the consumption of services, which isn’t shown here.)

The second graph separates durable goods expenditures into finer categories.

Among these durable goods categories, expenditures on motor vehicles and parts dropped the most—by as much as 24%. Expenditures on furnishings and durable household equipment follow, with a drop of 16%, although this category hits bottom at the end of the recession. Both categories recovered to their pre-recession levels only after 2013. In contrast, expenditures on recreational goods and vehicles and on other durable goods decreased only mildly.

Why did vehicle sales drop so much? One reason could be that a car purchase requires a larger payment than other durable goods. Another explanation is that the decline in house prices deteriorated households’ credit scores, making it more costly to finance a car purchase with a loan. The sinking housing market also would have driven down demand for furnishings and durable household equipment. And, simply, credit may have generally been more difficult to obtain.

Expenditures for nondurable goods fell by 4% on average, less than they did for durable goods. The third graph shows four categories. Interestingly, expenditures on gasoline and other energy goods slowly and consistently fell, hitting bottom in late 2012 and gradually recovering by 2016, which is likely due to the falling car sales shown in the previous graph.

Overall, expenditures related to vehicles and houses account for most of the drop in consumption in the 2007-09 recession.

How these graphs were created: All these series can easily be found in FRED using the Release View. From the FRED home page, select Releases from the main search box and click the link for gross domestic product. You’ll then see the sections of the GDP release. Select “Section 2–Personal Income and Outlays” and then “Table 2.3.3. Real Personal Consumption Expenditures by Major Type of Product, Quantity Indexes” (quarterly frequency). The resulting page will be a table with all the series associated with the real personal consumption expenditures release and all graphs can be made from this page.

First graph: Select “Durable goods” and “Nondurable goods” and click “Add to Graph.” Using the scroll bar at the bottom or the date range at the top, adjust the time horizon of the graph to start in 2006:Q1. From the “Edit Graph” menu, adjust the units to be “Index (scale value to 100 for chosen date)” and set the date to be 2007-01-01. Click “Copy to all” to copy these changes to all the series.

Second graph: Select the four subcomponents under the durable goods category and click “Add to Graph.” Follow the same steps as in the first graph to adjust the time and index base value.

Third graph: Select the four subcomponents under the nondurable goods category and click “Add to Graph.” Follow the same steps as in the first graph to adjust the time and index base value.

Suggested by Sungki Hong.

View on FRED, series used in this post: DCLORA3Q086SBEA, DDURRA3Q086SBEA, DFDHRA3Q086SBEA, DFXARA3Q086SBEA, DGOERA3Q086SBEA, DMOTRA3Q086SBEA, DNDGRA3Q086SBEA, DODGRA3Q086SBEA, DONGRA3Q086SBEA, DREQRA3Q086SBEA

Has wage growth been slower than normal in the current business cycle?

You may have read in the popular press that wage growth seems much slower since the Great Recession compared with previous business cycles. Let’s see what FRED data can tell us. The graph above shows wage growth, defined as the annualized percentage change in the average hourly earnings of private production and nonsupervisory employees. To interpret the graph, note the gray bars, which indicate recessions since 1976, and the green vertical lines, which indicate the peaks of each business cycle. A generally U-shaped pattern occurs between the starts of consecutive recessions. At the start of a recession, the rate of wage growth falls for a number of months, then the trend is reversed as wages increase until the next recession, and the cycle repeats.

To better compare how wages behave across business cycles, we graph the wage behavior observed for each of the prior three business cycles and the current business cycle together. Each cycle is centered at zero, which denotes the month with the lowest wage growth for each business cycle. The current business cycle is identified by the purple line. This cycle started at a lower level of wage increases than the prior three cycles. More importantly, the wage increase from the low point has been following a lower trend: In prior cycles, wage increases exceeded 4%; the current cycle’s wage increases still have yet to reach 3%.

In a future blog post, we’ll look into possible reasons why the current business cycle’s wages have been increasing much more slowly.

How these graphs were created (plus some background): For the first graph, search for wages and select “Average Hourly Earnings of Production and Nonsupervisory Employees: Total Private.” From the “Edit Graph” panel, change the units to “Percent Change from a Year Ago.” The business cycles can be accented by adding green lines to the graph corresponding to each peak using the “Create user-defined line” option under the “Add Line” tab. For the second graph, change the units to “Index” and enter the date “1986-12-01.” This was the lowest point in wage growth for the associated business cycle, which had begun 65 months earlier and would last 43 months longer. To capture the entire business cycle with monthly data, check the “Display integer periods…” box and set the range from -65 to 43. If the units under the “Customize data” tab are changed to “Percent Change from a Year Ago,” the resulting graph shows the section of the first graph from July 1981 to July 1990. While this same result could have been achieved more easily by changing the date range of the original graph, an advantage of this approach is that it allows the same series to be plotted from multiple separate date ranges. Use the “Add Line” tab to add this same series to the graph four times. The options for each line will be the same as those for the first line, except that the custom index date and length of the date range will be different: A second low point occurred in September of 1992, 26 months into a cycle that would last 102 months longer, and the next in January 2004, 35 months into a cycle that would last 46 months longer. The present cycle had its low point 58 months in, during October 2004, and the end date of the cycle has yet to be determined. One way to resolve this problem is to set an unnecessarily high integer end date, like 200. FRED will then automatically fill in the latest available data.

Suggested by Ryan Mather and Don Schlagenhauf.

View on FRED, series used in this post: AHETPI

Corporate profits versus labor income Risk and reward versus slow and steady

This FRED graph shows the evolution of two sources of income in our national economy: the compensation of employees through wages and other salary compensation, and the compensation of capital through profits. Both series are adjusted for inflation and both start at the level of 100 in 1954, which is the first year that’s considered “post-war” for economic purposes. (NOTE: The economic impact of the Korean War has essentially vanished.)

Eyeballing the data leads to two major conclusions. First, corporate profits move a lot, especially in response to general business activity. Profits tend to tank during recessions (noted with gray bars), which is understandable. After all, it’s well understood that investing in a business is a risky undertaking that deserves and often acquires compensation. Employee income is much more stable, but still suffers during recessions. Second, the trends of the two series tend to track each other over several decades, reflecting the general growth of the economy. The past decade and a half seems to be different, though. Never have corporate profits outgrown employee compensation so clearly and for so long. Is it because there’s been a particularly risky climate for investment, or is something else afoot?

How this graph was created: From the release table about national income by type of income, check the two series and click on “Add to Graph.” From the “Edit Graph” panel, add a series by searching for and selecting “GDP deflator,” apply formula a/b, and finally set the index value of 100 to 1954-05. Repeat for the second line.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CPROFIT, GDPDEF, WASCUR

Is there a skills gap in the South? Mapping education and unemployment across the U.S.

Much research has been published on the labor market transition from low-skill and routine jobs to high-skill and non-routine jobs at both a national and a local level. But is this job polarization occurring to the same degree across the country? A recent report co-sponsored by the St. Louis Fed looks at the issue of workforce development in light of this changing economy, especially in southern regions of the U.S. that typically rely more on a low-skilled and low-wage labor force. These GeoFRED maps show that the southern states typically have lower levels of educational attainment for both high school (map above) and bachelor’s degrees (map below).

With a currently tight labor market, there’s demand for skilled workers, since these positions are also seeing the most growth. But for some regional economies, especially in southern states, there seems to also be an unmet demand for middle-skill jobs that require more than a high school education but not a four-year college degree.

The FRED graph below shows that the unemployment rate is lower for those with some college and/or an associate’s degree than (i) for those with only a high school diploma and (ii) the overall national unemployment rate.

The southern states trail the rest of the nation in median wages and there are more persistently poor counties in the south. There seems to be an opportunity, then, for investing in that workforce to create a better-skilled labor pool to help grow the regional economy.

How these maps were created: From GeoFRED, click on “Build New Map.” Under the “Tools” menu, select “County” as the region type. For the first map, enter “high school graduate” and select “High School Graduate or Higher (5-year estimate).” For the second map, enter “bachelor’s degree” and select “Bachelor’s Degree or Higher (5-year estimate).” For the third map, enter “associate’s degree” and select “People 25 Years and Over Who Have Completed an Associate’s Degree or Higher (5-year estimate).” For the FRED graph, search for “civilian unemployment rate.” In the “Edit Graph” panel, add two lines by searching for “unemployment rate associate degree” (series ID LNS14027689) and “unemployment rate high school” (series ID LNS14027660).

Suggested by Shuowei Qin and Christian Zimmermann.

View on FRED, series used in this post: LNS14027660, LNS14027689, UNRATE

Alternative money for transactions What if gold or Bitcoin replaced the dollar?

What if U.S. retail prices were not denominated in U.S. dollars, but instead were denominated in gold or Bitcoin? Paying for a loaf of bread with gold wouldn’t be very practical, as you’d need a very small speck of the precious metal. But one can imagine a system of gold substitutes, such as notes giving you ownership of a fraction of an ounce of gold, thereby overcoming the small-change problem. With Bitcoin, it’d be much easier, as a virtual currency can be divided any way you want.

Now, let’s look at actual prices. FRED doesn’t have price data on just a loaf of bread, but it does have the consumer price index for cereals and bakery products, so let’s use that. The blue line shows the evolution of the U.S. dollar price of a basket of baked goods. The red line shows the price in gold, and the green line shows the price in Bitcoin. It’s apparent that the dollar price is much more stable and has slowly increased over time. The gold price has considerable fluctuations from month to month. While the gold price seems to have a tendency to decrease, this isn’t always true, which you can see if you enlarge the sample window. As for Bitcoin, the fluctuations are extreme, even when you restrict the sample period to the past year.

What’s behind the differences? The Fed’s mandate is to stabilize prices as expressed in U.S. dollars, and this is quite apparent in this graph. The Fed does this by adapting to changes in the demand for dollars. That isn’t possible with gold, as its supply is determined by worldwide mining success, which is outside of the control of any institution. The same applies to Bitcoin, with the additional constraint that mining success keeps dwindling.

How this graph was created: For line 1: Search for and select “cereal price cpi” and click on “Add to Graph.” (You can also paste the series ID, CUSR0000SAF111, directly in the search field.) From the “Edit Graph” menu, use the “Add Line” feature to again search for and select the same cereal price CPI series two more times. For line 2: Open the “Edit Line 2” tab, search for and add “Gold Fixing Price 10:30 a.m. (London Time)…” in the “Customize data” section or paste in the series ID (GOLDAMGBD228NLBM). Then apply formula a/b*1000. (Multiplying by 1000 helps place the line within a visible range.) Repeat this for line 3 by searching for and adding “Coinbase Bitcoin” (series ID CBBTCUSD). Finally, restrict the sample period to the past twenty years.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CBBTCUSD, CUSR0000SAF111, GOLDAMGBD228NLBM


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