Federal Reserve Economic Data

The FRED® Blog

Service with a masked smile: How weaker demand reduced employment in 2020

The FRED Blog has discussed how the COVID-19 recession reduced the demand for services and boosted the demand for goods, whereas in previous recessions it was the inverse. Today we examine the same dynamic from a different angle: how these changes in consumption patterns have affected industry-specific employment.

The FRED graph above shows the large initial declines in employment for goods-producing industries (e.g., construction and manufacturing) and service-providing industries (e.g., leisure and hospitality). We changed the units of the data into an index, with a base period set at the start of the latest recession, to make it easier to measure and compare changes over time. (This post from October 2020 also uses an index to track unemployment by age during recessions.)

After the initial double-digit declines, employment in goods-producing and service-providing industries gradually bounced back. At the time of this writing they were 5.6% and 7% below pre-recession levels, respectively. For reference, we also plot employment data in the service-providing government sector. This includes federal, state, and local governments, which had similar but smaller declines.

For comparison, the second FRED graph shows changes in employment during the Great Recession, from December 2007 to June 2009. At that time, the largest losses in employment were registered in the goods-producing industry—specifically, in construction. Employment in service-providing industries declined more gradually and by a smaller amount. And employment in the government sector remained effectively unchanged.

In that recession, of course, there was no pandemic and no widespread use of masks and social-distancing measures. To learn more about how recent changes in work and consumption habits might impact future economic activity, read the Economic Synopses essay by Julian Kozlowski.

How these graphs were created: From FRED’s main page, browse data by “Release.” Search for ”Employment Situation” and select “Current Employment Statistics (Establishment Data) > Table B-1. Employees on nonfarm payrolls by industry sector and selected industry detail, Seasonally adjusted.” Select the series “Government,” “Goods-Producing,” and “Private Service-Providing.” From the “Edit Graph” panel, select the “Edit Lines” tab. In the “Units” drop-down menu, select “Index (Scale value to 100 for chosen date)” and choose “2020-02-01” for the first graph and “2007-12-01” for the second graph. Adjust the date range to mirror the dates shown in the blog post.

Suggested by Diego Mendez-Carbajo.

View on FRED, series used in this post: CES0800000001, USGOOD, USGOVT

A friendly warning: Data aren’t perfect

Graphing data can reveal issues that spreadsheets may not

“The greatest value of a picture is when it forces us to notice what we never expected to see.” – John Tukey

FRED gives you the option of downloading data into a spreadsheet. Of course, it’s also common to present the data in graph form, which is much easier on the eyes. But plotting your data is exceptionally important for other reasons.

A graph can give the numbers a clear and convincing voice. And it can also reveal the unexpected. Because your eyes can quickly catch something that simply looks wrong, you may observe the existence of data issues on a graph that would not be immediately apparent when looking only at the numbers.

The FRED graph above plots three vintages of the Economic Policy Uncertainty Index from January 5 (blue), 6 (red), and 7 (green) of 2021. The plots are indistinguishable until December 9, 2020, where the January 6 vintage jumps to a series maximum of over 1,000 before dropping to a constant value of 10.92. Notice that there are also some minor differences between values in the January 5 and January 7 vintages. While it’s normal for data vintages (even those that are a day apart) to differ slightly, the large discrepancies in the January 6 vintage clearly stem from data issues…

As it turns out, this vintage contains incorrect values. And that only became clear because we plotted the data. Had we not done that, the issue would have remained undetected and may have caused further errors during our application of the data.

Search the FRED Blog for more posts about the Economic Policy Uncertainty Index.

How this graph was created: Browse FRED data by category. Under the category “Academic Data,” select “Economic Policy Uncertainty” and then the not seasonally adjusted version of the series “Economic Policy Uncertainty Index for United States.” From the “Edit Graph” panel, select the vintage for 2021-01-05. From the “Edit Bars” tab, click “Edit Bar 2” and select the vintage for 2021-01-06. Using the “Add Line” tab, create another line with the same series and edit bar 3 to be the vintage 2021-01-07. Change the graph type and colors to taste using the “Format” tab. Last, change the range by using the scroller directly below the graph or choose specific dates by typing them into the white boxes just above the graph.

Suggested by Aaron Amburgey and Michael McCracken.

View on FRED, series used in this post: USEPUINDXD

Changes in the U.S.-China trade deficit

Exports and imports before and after tariffs and the pandemic

Many of the trade policies that began in 2018 were driven by the high and persistent U.S. trade deficit with China. For example, the U.S. announced tariffs on solar panels and washing machines from China in January 2018, which is marked by the first vertical line in the FRED graph above. Several rounds of U.S. tariffs followed, and China enacted retaliatory tariffs.

We start our graph in January 2016 to include data before and during the period when these trade policies were initiated.*

The basic story told by the graph is that U.S. exports to China (in blue) seem to be relatively stable over time, but U.S. imports from China (in red) are more variable and also much larger. So, the bilateral trade deficit (in green), which is the excess of imports over exports, seems to follow the variable path of imports. Despite the trade war, the trade deficit peaked in October 2018. It fell after that for a few months, only to rise again above its January 2016 level by the middle of 2019.

These facts seem to suggest that the trade war didn’t achieve any significant, durable difference in the U.S.-China trade deficit. How the deficit will evolve in the future, of course, will depend on a whole host of factors, including consumption behavior and the evolution of comparative advantage in each nation.

The second vertical line on the graph marks the first COVID-19 case reported in Wuhan in December 2019. In the months that followed, there was a sharp decline in imports from China to the U.S. and also in the bilateral trade deficit.

Interestingly, since March 2020, there has been a sharp turnaround in imports and the U.S.-China trade deficit. Among other factors, this turnaround in imports may be related to imports of essential medical equipment, described in an Economic Synopses essay by Leibovici and Santacreu.

With the easing of lockdowns in the U.S. and growth in China, there also seems to be a recent spurt in U.S. exports to China, which rose to its highest level in October 2020. Indeed, because of this increase in exports, the U.S.-China trade deficit in October of 2020 is a bit lower than its level in July 2020.

Overall, COVID-19’s effect on U.S.-China trade seems somewhat surprising, with a strong rebound of trade in the relatively early months of the COVID-19 crisis in the U.S., followed by a further strengthening of trade in more recent months when the U.S. economy has seemed to be on a path to recovery.

*By the way, the numbers shown here are nominal (i.e., not adjusted for inflation). It’s a good idea to pay attention to this distinction when interpreting data, but we found that “real” numbers based on the U.S. CPI deflator here don’t lead to any qualitative differences.

How this graph was created: Search for and select “U.S. Exports of Goods by F.A.S. Basis to Mainland China.” From the “Edit Graph” menu, use the “Add Line” tab to search for “U.S. Imports of Goods by Customs Basis from China.” For the dotted line, use “Add Line” again to search for and select “U.S. Imports of Goods by Customs Basis from China.” Then in the “Customize data” section, search for “U.S. Exports of Goods by F.A.S. Basis to Mainland China.” Next, create a custom formula to combine the series by typing “a-b” and clicking on “Apply.” To add the vertical lines, refer to these instructions. Finally, use the “Format” tab to adjust the format of the graph.

Suggested by Subhayu Bandyopadhyay and Praew Grittayaphong.

View on FRED, series used in this post: EXPCH, IMPCH


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