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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.

View on FRED, series used in this post: DEXBZUS, DEXCAUS, DEXCHUS, DEXMXUS, DEXUSEU, DEXUSUK

Oil prices and expected inflation

Since the end of the Great Recession, market-based measures of long-run inflation expectations have seemed highly correlated with the spot price of oil. To see what we mean, consider the FRED graph above, where we plot the price of oil (West Texas Intermediate) against the 5-year, 5-year forward expected inflation rate. This measure of expected inflation is calculated using measured yield differentials between nominal and inflation-protected Treasury securities (TIPs) at 10- and 5-year maturities. (To further highlight the correlation, consider the scatter plot of the same data below.)

The 5-year, 5-year forward rate is meant to capture the bond market’s 5-year average forecast of inflation beginning 5 years from now. In this way, anything expected to affect the economy over the next 5 years should not factor prominently in a long-run forecast made 5 years from now. But then, why should the contemporaneous price of oil correlate so highly with the long-run inflation rate which is, or should be, anchored by monetary and fiscal policy?

One possibility is that because the stock of oil is an asset, its price is likely to include a forward-looking element. If the long-run outlook for global growth weakens, the value of this asset should decline. In the event of a long-run forecast of low growth, low interest rates, and low inflation, investors will move away from private sector securities into safe assets, such as U.S. Treasury securities. If so, the value of the stock of oil declines along with expected inflation.

How these graphs were created: Search for “5-year, 5-year Forward Expectation Rate.” From the “Edit Graph” panel, use the “Add Line” tab to search for and add the “Crude Oil Prices: West Texas Intermediate” series. With the “Format” tab, change the “Y-axis position” option to “Right” for “LINE 2.” For the second graph, use the “Format” tab to select plot type “Scatter.”

Suggested by David Andolfatto and Mahdi Ebsim.

View on FRED, series used in this post: DCOILWTICO, T5YIFR


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