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The decline in industrial production: One for the ages

On Tuesday, April 15, the Federal Reserve released the industrial production (IP) index for March. You have to go to the very far right data point in the FRED graph to see it, but industrial activity plunged in March because of the economic effects stemming from social-distancing orders in response to the COVID-19 pandemic. Millions of businesses have closed or been disrupted, and mass layoffs have occurred. But the March IP index of 103.66 is still far higher than the level registered during the depth of the recession and financial crisis, which was 87.07 in June 2009.

The IP index is one of the nation’s longest continuously produced economic indicators, starting in January 1919. It measures production (real output) of manufacturers, mining (e.g., oil and natural gas), and electric and gas utilities and steadily increases over time; but it is highly sensitive to the state of the economy and falls during recessions, generally proportionate to the depth and duration of the recession. The 2007-2009 recession and financial crisis is a prime example.

FRED can help us compare this recent decline in IP against the entire history of the series. And the next graph shows one way to do this: month-to-month percentage changes. Measured from its February level, industrial activity fell 5.4% in March. This percentage decline is the largest in a long time, since January 1946 (-5.6%), when U.S. factories transitioned from producing primarily wartime goods to producing civilian goods for a peacetime economy. And the largest percentage decline in the series was -10.4%, in August 1945.

In fact, as this graph shows, the retooling of the U.S. economy in 1945 produced larger monthly percentage declines in IP than those during the Great Depression and the deep 1937-38 recession. So, March’s COVID-19-driven plunge in activity, while historically large, falls far short of previous declines in activity. End of story? Not quite.

Another way to gauge the historical magnitude of the March decline in IP is to benchmark it against the historical standard deviation of monthly percent changes. The standard deviation is a statistical measure of changes, or dispersion, relative to the mean (average) of the series. Most of the time, monthly percentage changes are within plus or minus one standard deviation. At times, though, large changes are well outside the bounds of one standard deviation. The larger the percentage change outside the series’ standard deviation, the larger its historical significance.

Now, the second graph also shows that the month-to-month percentage changes have become smaller over time. For example, compare the period before and after 1947—effectively, the transition to a post-WWII economy and the post-WWII economy itself. We can see that volatility—the swings between peaks and troughs—was much larger in the earlier period than in the later period. But to verify this, we’ll need to look at some statistics.

The table below shows the largest percentage declines in IP and their respective sample standard deviation over two intervals: February 1919 to December 1946 and January 1947 to March 2020. The standard deviation of monthly percent changes in IP was 3.29% in the first period and 0.96% in the second period. Hence, the standard deviation in the first period was three times as large as the second period. The table’s right-most column shows the ratio of the two statistics. By this metric, the March 2020 decline in industrial production was the biggest decline on record relative to its standard deviation.

Thus, in this sense, the March decline in IP was one for the ages.


Statistics on Monthly Percentage Changes in IP

  Minimum Standard Deviation Minimum/Stnd. Dev.
1919 to 1946 -10.38 3.29 -3.16
1947 to Present -5.40 0.96 -5.61


How these graphs were created: For the first graph, search for “Industrial Production” and it should be your first choice. For the second graph, start with the first and use the “Edit Graph” panel to change units to “Percent Change.”

Suggested by Kevin Kliesen.

View on FRED, series used in this post: INDPRO

Things to know about initial claims data

A deeper look at initial unemployment insurance claims

Initial claims for unemployment benefits have spiked to historic levels over the past few weeks. For the week ending April 4, over 6.6 million claims were made. As of this morning, for the week ending April 11, this number is 5.2 million claims.

The initial claims series is an important economic indicator for several reasons.

  • First, it’s weekly. Many other indicators are updated much less frequently: For example, nonfarm payroll data are monthly, and gross domestic product data are quarterly.
  • Second, there’s a short collection lag. One week’s data become public information only five days after that week is complete. The number for April 4 was made available April 9, and the number for April 11 was made available today (April 16).
  • Third, because it’s based on government administrative data, it’s more reliable than statistics based on surveys.
  • Fourth and finally, it’s available not only at a national level but also at a state level. Both national and state-level series are available in this FRED release table.

Since the economic shutdown, the initial claims number has been getting even more attention from economists and media outlets. So let’s review a few details about its construction and interpretation.

The underlying data are tabulated and released by the U.S. Department of Labor from reports provided by each state’s unemployment insurance program office. The data reach back to January 1967. Data typically reported in the news have been filtered by the Department of Labor to remove “seasonal effects.” Unseasonalized data usually peak with the start of each new year, in part because many seasonal holiday jobs have come to an end. The raw, unseasonalized data are also available on FRED.

Now, a person’s claim for UI doesn’t necessarily mean that person will receive unemployment benefits, but only that they are seeking benefits. According to the U.S. Department of Labor, “An initial claim is a claim filed by an unemployed individual after a separation from an employer. The claimant requests a determination of basic eligibility for the UI program. When an initial claim is filed with a state, certain programmatic activities take place and these result in activity counts including the count of initial claims.”

The unemployment rate and initial UI claims are often discussed together. The unemployment rate is calculated from a different data set, which is based on monthly surveys of households. To be considered unemployed, one must not have worked in a number of weeks but must be seeking employment. Thus, one could be considered unemployed even if they had not applied for UI benefits for their current unemployment spell, perhaps because they did not have a job covered by UI insurance.

One timely question is how recent government policy, notably the CARES Act signed into law in late March, is likely to affect the initial claims numbers. There are two channels. First, the CARES Act includes the Payroll Protection Program. Through this program, banks issue loans to small businesses that are fully guaranteed by the federal government. Moreover, if the loan-receiving business uses a sufficiently large amount of its loan to pay its workers, that loan will be forgiven. As such, this part of the act should reduce the number of new claims in the coming weeks.

Second, the CARES Act expands benefits for those receiving UI by $600 per week beyond what existing state programs already provide. In some situations, unemployed individuals may receive more in UI benefits than they were earning at their previous job. An employer (not participating in the Payroll Protection Program) might feel more comfortable laying off or furloughing workers, in order to reduce costs, with the knowledge of these expanded benefits. This part of the act may increase the number of new claims.

How this graph was created: Search for “initial claims” and click the series name. Shorten the time period shown in the graph to more clearly view the spike beginning at the March 21 data point.

Suggested by Bill Dupor.

View on FRED, series used in this post: ICSA

Coronavirus effects on exchange rates

This FRED graph shows several exchange rates relative to the U.S. dollar. We start with the date January 6, 2020, where we set the index values equal to 100 for all these exchange rates so we can compare their relative changes since then. Through the end of February, most of these exchange rates have remained relatively stable; however, they began to increase in March. Brazil’s and Mexico’s exchange rates spiked, and their currencies have depreciated nearly 30% since the beginning of January.

In March, the COVID-19 pandemic became more severe, affecting overall economies as well as exchange rates. Reduced global demand for commodities such as oil has sent commodities prices crashing; Mexico and Brazil are major commodities-exporting countries. Canada’s and the U.K.’s exchange rates spiked at the beginning of March, but they seem to have recovered by the end of March. The euro seems to be unaffected by the global pandemic, at least compared with the U.S. dollar. China’s exchange rate, on the other hand, has remained constant despite the coronavirus originating there. Unlike the rest of the countries in our sample, which have floating exchange rates, China has a fixed exchange rate pegged to the U.S. dollar.

Large exchange rate movements can have consequences for economic growth, inflation, trade, and sovereign risk. Commodity-dependent economies and developing countries are most susceptible to this risk. It will be important to monitor the impacts of the coronavirus on international markets and economies because they will impact our interconnected, global economy.

How this graph was created: Start with a graph of any exchange rate. From the “Edit Graph” panel, use the “Add Line” tab to search for each exchange rate by its code and add it to the graph. Under “Edit Lines,” go to the dropdown box “Units” and select “Index (Scale value to 100 for chosen date).” Immediately below, under the heading “Select a date that will equal 100 for you custom index,” type in “2020-01-06.” Do this for all lines. For the euro and the pound, the original series have the U.S. dollar in the numerator, so we have to transform them to make them comparable to the other series. To do this, go to “Formula” and type 1/a and click “Apply.”

Suggested by Brian Reinhold and Yi Wen.


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