Tag: Covid-19 effects

We Will Never See Anything Like It Again: Movements in Real GDP during the Covid-19 Recession

There are a number of ways in which the Covid-19 pandemic was unlike anything the United States has experienced since the 1918 influenza pandemic. Most striking from an economic perspective were the extraordinary swings in real GDP. The following figure shows quarterly changes in real GDP seasonally adjusted and calculated at an annual rate. There were three recessions during this period (shown by the shaded areas).

The first of these recessions occurred during 2001 and was similar to most recessions in the United States since 1950 in being short and relatively mild. Real GDP declined by 1.5 percent during the third quarter of 2001. The recession of 2007–2009 was the most severe to that point since the Great Depression of the 1930s. The worst periods of the 2007–2009 were the fourth quarter of 2008, when real GDP declined by 8.5 percent—the largest decline to that point during any quarter since 1960—and the first quarter of 2009, when real GDP declined by 4.6 percent. 

Turning to the 2020 recession, during the first quarter of 2020, only at the end of which did Covid-19 begin to seriously affect the U.S. economy, real GDP declined by 5.1 percent. Then in the second quarter a collapse in production occurred unlike anything previously experienced in the United States over such a short period: Real GDP declined by 31.2 percent. But that collapse was followed in the next quarter by an extraordinary recovery in production when real GDP increased by 33.8 percent—by far the largest increase in a single quarter in U.S. history.

The following figure shows the changes in the components of real GDP during the second and third quarters of 2020. In the second quarter of 2020, consumption spending declined by about the same percentage as GDP, but investment spending declined by more, as many residential and commercial construction projects were closed. Exports declined by nearly 60 percent and imports declined by nearly as much as many ports were temporarily closed. In the third quarter of 2020, many state and local governments relaxed their restrictions on business operations and the components of spending bounced back, although they remained below their levels of late 2019 until mid-2021. 

Even when compared with the Great Depression of the 1930s, the movements in real GDP during the Covid-19 pandemic stand out for the size of the fluctuations. The official U.S. Bureau of Economic Analysis data on real GDP are available only annually for the 1930s. The following figure shows the changes in these annual data for the years 1929 to 1939. As severe as the Great Depression was, in 1932, the worst year of the downturn, real GDP declined by less than 13 percent—or only about a third as much as real GDP declined during the worst of the 2020 recession.

We have to hope that we will never again experience a pandemic as severe as the Covid-19 pandemic or fluctuations in the economy as severe as those of 2020.

Source: U.S. Bureau of Economic Analysis. Note: Because the BEA doesn’t provide an estimate of real GDP in 1928, our value for the change in real GDP during 1929 is the percentage change in real GDP per capita from 1928 to 1929 using the data on real GDP per capita compiled by Robert J. Barro and José F. Ursúa. LINK

The Demographics of Covid-19 Mortality

Few diseases affect all demographic groups equally.  For example, the 1918–1919 influenza pandemic killed an unusually large number of young adults. Estimates are that half of deaths in the United States during that pandemic occurred among people aged 20 to 40. In recent flu seasons, the elderly have much higher mortality rates than do other age groups. For instance, during the 2018–2019 flu season, people 65 and older died at a rate more than 10 times greater than people 18 to 49 years old.  The very young also have comparatively high mortality rates from the flu. In 2018–2019, children 0 to 4 years-old died at a rate six times higher than children 5 to 17 years-old.

When the Covid-19 virus began to spread widely in the United States in the spring of 2020, some epidemiologists expected that it would affect different demographic groups in about the same way that the flu does. In fact, though, while people 65 and older were particularly at risk, young children were less affected by Covid-19 than they are by the flu. The following chart prepared by the Centers for Disease Control and Prevention (CDC) displays for the United States data on Covid deaths by age group as of early November 2021.

The blue bars show the percentage of total deaths from Covid since the beginning of the pandemic represented by that age group and the gray bars show the percentage that group makes up of the total U.S. population. Therefore, an age group that has a gray bar longer than its blue bar was proportionally less affected by the virus and an age group that has a blue bar longer its gray bar was proportionally more affected by the virus. The chart shows that people over age 65 experienced particularly high mortality rates. Strikingly, people over age 85 accounted for nearly 30 percent of all deaths in the United States, while making up only 2 percent of the U.S. population. 

The following chart displays data on Covid deaths by gender. Men account for about 49 percent of the U.S. population but have accounted for about 54 percent of Covid deaths.

Finally, the following chart displays data on Covid deaths by race or ethnicity. Hispanic, Black, and American Indian or Alaskan Native people have experienced proportionally higher Covid mortality rates than have Asian or white people.

What explains the disparity in mortality rates across demographic groups? With respect to age, we would expect older people to have weaker immune systems and therefore be more likely to die from any illness. In addition, early in the pandemic many older people in nursing homes died of Covid before it was widely understood that the disease spread through aerosols and that keeping people close together inside unmasked made it easy for the virus to spread. The very young have immature immune systems, which might have made them particularly susceptible to Covid, but for reasons not well understood, they turned not to be.

There continues to be debate over why men have experienced a higher mortality rate from Covid than have women. Vaccination rates among men are somewhat lower than among women, which may account for part of the difference. In an opinion column in the New York Times, Dr. Ezekiel  Emanuel of the University of Pennsylvania noted that researchers at Yale University have observed “that there are well-established differences in immune responses to infections between men and women.” But why this pattern should be reflected in Covid deaths is unclear at this point.

Medical researchers and epidemiologists have also not arrived at a consensus in explaining differences in mortality rates across racial or ethnic groups. Groups with higher mortality rates have had lower vaccination, which explains some of the difference. Groups with higher mortality rates are also more likely to suffer from other conditions, such as hypertension, that have been identified as contributing factors in some Covid deaths. These groups are also less likely to have access to health care than are the groups with lower mortality rates. The CDC notes that: “Race and ethnicity are risk markers for other underlying conditions that affect health, including socioeconomic status, access to health care, and exposure to the virus related to occupation, e.g., frontline, essential, and critical infrastructure workers.”

Sources: Ezekiel J. Emanuel, “An Unsolved Mystery: Why Do More Men Die of Covid-19?” nytimes.com, November 2, 2021; Daniela Hernandez, “Covid-19 Vaccinations Proceed Slowly Among Older Latino, Black People,” wsj.com, March 2, 2021; Anushree Dave, “Half-Million Excess U.S. Deaths in 2020 Hit Minorities Worse,” bloomberg.com, October 4, 2021; Centers for Disease Control and Prevention, “Hospitalization and Death by Race/Ethnicity,” cdc.gov, September 9, 2021; Centers for Disease Control and Prevention, “Demographic Trends of COVID-19 cases and deaths in the US reported to CDC,” cdc.gov, November 5, 2021 Centers for Disease Control and Prevention, “2018–2019 Flu Season Burden Estimates,” cdc.gov; and Jeffery K. Taubenberger and David M. Morens, “1918 Influenza: the Mother of All Pandemics,” Emerging Infectious Diseases, Vol. 12, No. 1, January 2006, pp. 15-22.

Sticker Shock in the Market for Used Cars

The term “sticker shock” was first used during the 1970s to describe the surprise car buyers experienced when seeing how much car prices had risen.  Because inflation during that decade was so high, anyone who hadn’t bought a car for several years was unprepared for the jump in car prices. During 2020 and 2021, sticker shock returned, particularly to the used car market. Prices were increasing so rapidly that even people who had purchased a car a year or two before were surprised by the increases. 

The following graph shows U.S. Bureau of Labor Statistics (BLS) data on inflation in the market for used cars in the months since January 2015. Inflation is measured as the percent change from the same month in the previous year in the used cars and trucks component of the Consumer Price Index (CPI). The CPI is the most widely used measure of inflation. Used car prices began rising in August 2020, peaking at a 45 percent increase in June 2021. Inflation at such rates over a period longer than a year is very unusual in any of components of the CPI. 

What explains the extraordinary burst of inflation in used car prices during 2020 and 2021? Three factors seem to have been of greatest importance:

  1. A decline in the supply of new cars resulting from a shortage in semiconductors caused an increase in new car prices. Rising new car prices led some consumers who would otherwise have bought a new car to enter the used car market, increasing the demand for used cars.
  2. Because of the Covid-19 pandemic, some people became reluctant to ride buses and other mass transit, increasing the demand for both new and used cars.
  3. As the pandemic increased in severity in the spring of 2020, most rental car companies decided to purchase fewer new cars for their fleets. After keeping a car in its fleet for one year, rental car companies typically sell the car to used car dealers for resale. Because rental car companies were selling them fewer cars, used car dealers had fewer cars on their lots. So the supply of used cars declined. 

We can use the demand and supply model to explain the jump in used car prices. As shown in the following figure, the demand curve for used cars shifted to the right from D1 to D2, as some consumers who would otherwise have bought new cars, bought used cars instead, and as some people swithced from public transportation to driving their cars to work. At the same time, the supply of used cars shifted to the left from S1 to S2 because used car dealers were able to buy fewer used cars from rental car companies. The result was that the price of used cars rose from P1 to P2 at the same time that the quantity of used cars sold fell from Q1 to Q2.

Sources: Yueqi Yang, “U.S. Used-Car Prices, Key Inflation Driver, Surge to Record,” bloomberg.com, October 7, 2021; Nora Naughton, “Looking to Buy a Used Car? Expect High Prices, Few Options,” wsj.com, May 10, 2021; Cox Automotive, “13-Month Rolling Used-Vehicle SAAR,” coxautoinc.com, October 15, 2021; and Federal Reserve Bank of St. Louis.

How the Effects of the Covid-19 Recession Differed Across Business Sectors and Income Groups

The recession that resulted from the Covid-19 pandemic affected most sectors of the U.S. economy, but some sectors of the economy fared better than others. As a broad generalization, we can say that online retailers, such as Amazon; delivery firms, such as FedEx and DoorDash; many manufacturers, including GM, Tesla, and other automobile firms; and firms, such as Zoom, that facilitate online meetings and lessons, have done well. Again, generalizing broadly, firms that supply a service, particularly if doing so requires in-person contact, have done poorly. Examples are restaurants, movie theaters, hotels, hair salons, and gyms.

The following figure uses data from the Federal Reserve Economic Data (FRED) website (fred.stlouisfed.org) on employment in several business sectors—note that the sectors shown in the figure do not account for all employment in the U.S. economy. For ease of comparison, total employment in each sector in February 2020 has been set equal to 100.

Employment in each sector dropped sharply between February and April as the pandemic began to spread throughout the United States, leading governors and mayors to order many businesses and schools closed. Even in areas where most businesses remained open, many people became reluctant to shop in stores, eat in restaurants, or exercise in gyms. From April to November, there were substantial employment gains in each sector, with employment in all goods-producing industries and employment in manufacturing (a subcategory of goods-producing industries) in November being just 5 percent less than in February. Employment in professional and business services (firms in this sector include legal, accounting, engineering, legal, consulting, and business software firms), rose to about the same level, but employment in all service industries was still 7 percent below its February level and employment in restaurants and bars was 17 percent below its February level.

Raj Chetty of Harvard University and colleagues have created the Opportunity Insights website that brings together data on a number of economic indicators that reflect employment, income, spending, and production in geographic areas down to the county or, for some cities, the ZIP code level. The Opportunity Insights website can be found HERE.

In a paper using these data, Chetty and colleagues find that during the pandemic “spending fell primarily because high-income households started spending much less.… Spending reductions were concentrated in services that require in-person physical interaction, such as hotels and restaurants …. These findings suggest that high-income households reduced spending primarily because of health concerns rather than a reduction in income or wealth, perhaps because they were able to self-isolate more easily than lower-income individuals (e.g., by substituting to remote work).”

As a result, “Small business revenues in the highest-income and highest-rent ZIP codes (e.g., the Upper East Side of Manhattan) fell by more than 65% between March and mid-April, compared with 30% in the least affluent ZIP codes. These reductions in revenue resulted in a much higher rate of small business closure in affluent areas within a given county than in less affluent areas.” As the revenues of small businesses declined, the businesses laid off workers and sometimes reduced the wages of workers they continued to employ. The employees of these small businesses, were typically lower- wage workers. The authors conclude from the data that: “Employment for high- wage workers also rebounded much more quickly: employment levels for workers in the top wage quartile [the top 20 percent of wages] were almost back to pre-COVID levels by the end of May, but remained 20% below baseline for low-wage workers even as of October 2020.”

The paper, which goes into much greater detail than the brief summary just given, can be found HERE.