the effect of covid 19 on unemployment essay brainly

An Unemployment Crisis after the Onset of COVID-19

Nicolas Petrosky-Nadeau and Robert G. Valletta

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FRBSF Economic Letter 2020-12 | May 18, 2020

The COVID-19 pandemic has upended the U.S. labor market, with massive job losses and a spike in unemployment to its highest level since the Great Depression. How long unemployment will remain at crisis levels is highly uncertain and will depend on the speed and success of coronavirus containment measures. Historical patterns of monthly flows in and out of unemployment, adjusted for unique aspects of the coronavirus economy, can help in assessing potential paths of unemployment. Unless hiring rises to unprecedented levels, unemployment could remain severely elevated well into next year.

The wave of initial job losses during the coronavirus disease 2019 (COVID-19) pandemic has been massive, with more than 20 million jobs swept away between March and April. This is much larger than losses recorded during similar time frames in any other postwar recession. As a result, the April unemployment rate spiked to the highest level recorded since the Great Depression of the 1930s.

In this Economic Letter , we assess possible paths for unemployment through 2021. Although the initial scale of the crisis is clear, substantial uncertainty surrounds the future path of unemployment. This uncertainty primarily revolves around the success of virus containment measures and how quickly economic activity can recover. Fundamental measurement challenges are also likely to affect the official unemployment rate: some laid-off workers cannot actively search for new jobs because of shelter-in-place restrictions and hence may be counted as out of the labor force, rather than unemployed.

To assess the possible path of the measured unemployment rate through next year, we focus on the underlying monthly flows in and out of unemployment, accounting for historical patterns and unique aspects of the coronavirus economy; our approach and results are described in detail in Petrosky-Nadeau and Valletta (2020). Our analysis suggests that returning to pre-outbreak unemployment levels by sometime in 2021 would require a significantly more rapid pace of hiring than during any past economic recovery.

Initial wave of job losses and unemployment

Even before the Bureau of Labor Statistics (BLS) released April employment and unemployment numbers on May 8, the unprecedented scale of job losses due to coronavirus containment measures was clear. About 25 million new unemployment insurance (UI) claims were filed between mid-March, when U.S. containment measures started to spread widely and the BLS monthly survey was conducted, and mid-April when the next month’s BLS survey was conducted. During periods of intensive job loss, weekly reports on new UI claims provide a good measure of job losses because most laid-off workers are eligible for UI benefits. However, the current massive scale of new claims has swamped state UI agencies and likely delayed processing of many claims. As such, the recent surge should be interpreted as a loose lower-bound estimate of initial job losses.

A comparison with the Great Recession of 2007-09 starkly illustrates the severity of the current situation (Figure 1). Initial UI claims during the first month of the COVID-19 crisis were about 10 times larger than claims during the worst periods of the Great Recession.

Figure 1 Monthly initial unemployment insurance claims

Note: Data from the U.S. Department of Labor, not seasonally adjusted (last two data points rounded to nearest thousand; April data through May 2). Gray bar indicates NBER recession dates.

These initial job losses, combined with a likely pronounced reduction in hiring activity, imply a sharp increase in the unemployment rate. Before the April BLS report was released, we projected that the unemployment rate was likely to rise nearly 15 percentage points, from 4.4% in March to 19.0% in April.

Other recent projections of the April unemployment rate span a very wide range (Faria-e-Castro 2020, Wolfers 2020, Coibion, Gorodnichenko, and Weber 2020, and Bick and Blandin 2020). The wide range partly reflects the challenge of measuring unemployment when shelter-in-place restrictions prevent active job search in much of the country. This is evident in the estimates by Coibion et al. (2020) and Bick and Blandin (2020), which differed substantially despite their reliance on careful surveys designed to approximate the official BLS approach.

The official April employment report released on May 8 showed that unemployment rose to 14.7%, a huge increase but below our projection. However, the report also noted a large increase in the number of workers on unpaid absences, likely reflecting virus-related business closures. Counting these workers as unemployed would push the unemployment rate much closer to our 19% projection. We therefore have not modified our prior projections.

Unemployment projections based on labor market flows

Our approach to projecting the unemployment rate relies on the monthly flows between unemployment, employment, and out of the labor force (nonparticipation), similar to Şahin and Patterson (2012). In particular, the monthly change in the unemployment rate reflects the difference between the number who enter unemployment (inflows) and the number who exit unemployment (outflows), with employment and nonparticipation as possible initial or subsequent status. This framework accounts for the key determinants of pandemic-related unemployment, with initial UI claims (inflows through job loss) and depressed hiring (outflows) determining the initial spike in unemployment. Using this approach, we explore different scenarios for unemployment through the end of 2021. For all scenarios, we assume that job losses are most severe in April (about 25 million), then ease substantially in May (7.8 million) and June (2.6 million), before returning to their historical trend in July (1.4 million).

The path of the unemployment rate afterward depends on unemployment outflows, primarily reflected in the pace of hiring among the pool of unemployed individuals. Tremendous uncertainty surrounds the timing and strength of the hiring surge as the economy recovers. If the virus is contained quickly and the economic recovery is vigorous, hiring could rapidly resume, particularly if many businesses and workers have maintained their connections. However, hiring could be slow if virus outbreaks or continued containment measures make employers hesitant based on low demand for their products. We therefore explore a range of hiring scenarios over the coming months.

The first scenario, “historical outflow dynamics,” assumes that the pace of hiring corresponds statistically to the typical recovery from past recessions. Because hiring tends to bounce back slowly following recessions, and given the severity of the current downturn, this scenario is relatively adverse.

Our second scenario, “hiring bounce,” incorporates very strong hiring activity following an assumed end of COVID-19 restrictions in July 2020. This scenario provides a baseline for assessing the pace of hiring required to reverse the initial labor market shock. It assumes a return to pre-outbreak hiring rates by the end of the third quarter of 2020. However, the pace of hiring implied by this scenario is extremely high by historical standards given the vast pool of unemployed individuals. In particular, this scenario requires around 9 million hires from unemployment per month during the third quarter, nearly four times faster than the most robust hiring rate during the recovery from the Great Recession.

Our third scenario, “GDP/hiring forecast,” bases hiring projections on the historical relationship between GDP growth and overall exit rates from unemployment to employment or nonparticipation. This requires a GDP forecast. We rely on a recent San Francisco Fed forecast of GDP growth for 2020-21, specifically the more favorable of two alternatives discussed in qualitative terms in Leduc (2020). It assumes that growth bounces back in the second half of this year and continues at a strong pace next year.

Figure 2 shows the unemployment paths for these scenarios. In the historical outflow dynamics scenario (dark blue line), unemployment quickly peaks around 20% and then stays in double digits through early 2021. By contrast, the hiring bounce scenario (light blue line) reflects a stronger recovery in hiring activity, so the unemployment rate drops much more rapidly. At the end of 2020 most of the job losses have been reversed, and unemployment approaches pre-outbreak levels. For the GDP/hiring forecast scenario (yellow line), unemployment peaks above 18% in the second quarter of 2020, followed by a rapid decline in the third quarter due to underlying limited changes in the hiring rate implied by its historical relationship with GDP growth.

Figure 2 Unemployment rate paths under different scenarios

Incorporating unemployment and nonparticipation ambiguities

As noted earlier, widespread shelter-in-place restrictions may preclude active job searches among laid-off workers, causing them to report themselves as out of the labor force rather than unemployed. Consistent with this, the official labor force participation rate fell 2.5 percentage points to 60.2% in April. We explore the potential impact of these measurement challenges through alternative assumptions about flow rates between different labor market states.

In particular, historical patterns of worker flows from employment to nonparticipation then back into employment during recoveries suggest that nearly half of those workers laid off during the pandemic could leave the labor force upon suffering a job loss. This moderates the initial rise in unemployment, shown as the lower participation scenario (red line) in Figure 2. As individuals return to the labor market during the recovery, lifting the labor force participation rate back toward its previous trend, the pace of return to a pre-outbreak unemployment rate is also muted. In fact, the historical outflow dynamics and lower participation scenarios converge at 8% unemployment in mid-2021. However, these two scenarios imply vastly different trajectories for the labor force participation rate. Figure 3 shows the paths for these scenarios over an extended time frame relative to the trend projected by the Congressional Budget Office (2020).

Figure 3 Labor force participation rate under different scenarios

Conclusions: An uncertain road to recovery

The COVID-19 pandemic has created tremendous labor market disruptions and profound hardship throughout the United States and the world. This is partly reflected in the sudden unprecedented increase in the U.S. unemployment rate in April, the first month for which the full effects of coronavirus containment measures are evident. To get a handle on the severity of the labor market disruption, we assess possible paths for unemployment through the end of 2021. Tremendous uncertainty surrounds unemployment projections over the next few years, so we do not claim that any specific scenario qualifies as “likely.” On the pessimistic side, absent a historically unprecedented burst of hiring, the unemployment rate could remain in double digits through 2021. From a more optimistic perspective, if shutdowns are lifted quickly and employers capitalize on the large pool of available workers by ramping up hiring, the unemployment rate could be back down near its pre-outbreak level by mid-2021.

Uncertainty about the path of the unemployment rate also reflects measurement challenges arising from the ambiguous labor force status of laid-off workers whose active job search is limited by shelter-in-place measures. This may temper the official unemployment rate, but at the expense of a lower labor force participation rate, which is an alternative indicator of labor market dislocation and hardship. Given the implied uncertainty about the measurement of future labor market conditions, it is imperative to closely monitor a wide range of indicators to assess how the U.S. labor market is evolving in response to the COVID-19 shock.

Nicolas Petrosky-Nadeau is a vice president in the Economic Research Department of the Federal Reserve Bank of San Francisco.

Robert G. Valletta is a senior vice president in the Economic Research Department of the Federal Reserve Bank of San Francisco.

Bick, Alexander, and Adam Blandin. 2020. “Real Time Labor Market Estimates during the 2020 Coronavirus Outbreak.” Manuscript, Arizona State University, April 15.

Coibion, Olivier, Yuriy Gorodnichenko, and Michael Weber. 2020. “Labor Markets During the COVID-19 Crisis: A Preliminary View.” BFI Working Paper, Becker Friedman Institute for Economics, University of Chicago, April 13.

Congressional Budget Office. 2020. “The Budget and Economic Outlook: 2020 to 2030.” Report 56020, January 28.

Faria-e-Castro, Miguel. 2020. “Back-of-the-Envelope Estimates of Next Quarter’s Unemployment Rate.” On the Economy, FRB St. Louis blog, March 24.

Leduc, Sylvain. 2020. “FedViews.” FRB San Francisco, April 6.

Petrosky-Nadeau, Nicolas, and Robert G. Valletta. 2020. “Unemployment Paths in a Pandemic Economy.” FRB San Francisco Working Paper 2020-18, May.

Şahin, Ayşegül, and Christina Patterson. 2012. “The Bathtub Model of Unemployment: The Importance of Labor Market Flow Dynamics.” Liberty Street Economics, FRB New York blog, March 28.

Wolfers, Justin. 2020. “The Unemployment Rate Is Probably Around 13%.” New York Times (The Upshot), April 16.

Opinions expressed in FRBSF Economic Letter do not necessarily reflect the views of the management of the Federal Reserve Bank of San Francisco or of the Board of Governors of the Federal Reserve System. This publication is edited by Anita Todd and Karen Barnes. Permission to reprint portions of articles or whole articles must be obtained in writing. Please send editorial comments and requests for reprint permission to [email protected]

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The unemployment impacts of COVID-19: lessons from the Great Recession

Subscribe to the economic studies bulletin, stephanie aaronson and stephanie aaronson senior associate director, division of research and statistics - federal reserve board francisca alba francisca alba former research analyst - economic studies.

April 15, 2020

• 12 min read

Efforts to stop the spread of the novel coronavirus—particularly the closure of nonessential businesses—are having an unprecedented impact on the U.S. economy. Nearly 17 million people filed initial claims for unemployment insurance over the past three weeks, suggesting that the unemployment rate is already above 15 percent [1] —well above the rate at the height of the Great Recession.

However, these aggregate statistics mask substantial variation across the country. Some cities, such as New York, are already experiencing full blown pandemics and non-essential business activity has been substantially halted. In other areas economic activity has slowed less. This variation represents the degree of spread of the virus, the timing and extent of the state and local response, and the sectoral mix of economic activity. Work by our colleagues suggests that metropolitan areas dependent on energy, tourism, and leisure and hospitality are likely to suffer greater slowdowns, while those that depend more on industry, agriculture, or professional services will suffer less.

Figure 1 [2] displays the sum of initial claims for unemployment insurance filed during the weeks ending March 21, March 28, and April 4 for selected states as a share of the labor force [3] . As can be seen, in the hardest hit areas, the number of initial claims as a share of the labor force was double or triple that of the least affected areas. While some of the differential likely reflects variation in unemployment insurance systems across states, this explanation is unlikely to explain the entire differential. Since, as can be seen, the states with relatively more claims include those dependent on tourism (Nevada and Hawaii) and those which have been hard hit by the virus ( Rhode Island, Pennsylvania, and Michigan ), while those with few claims have low incidence of the virus. Hence, it does appear, at least to start, there has been an idiosyncratic aspect to how states, and implicitly metropolitan areas, are affected by the pandemic. Eventually, however, a shock of the magnitude of the novel coronavirus will certainly result in a national recession, affecting the entire country to a greater or lesser degree.

In this post, we examine how shocks to the economy, like the one we are experiencing now with the coronavirus, play out at the metropolitan level, with a specific focus on the unemployment rate. We use as our laboratory the Great Recession, which started in metropolitan areas that were most affected by the housing bubble and bust, but then spread nationally. In line with previous research, we find that there is persistence in the unemployment rate across metropolitan areas. Idiosyncratic shocks disrupt these persistent differentials, but over time local economies adjust, and metropolitan areas tend to re-sort back to their previous place in the distribution. Our results also suggest that negative macroeconomic shocks tend to affect high-unemployment rate areas most harshly, and that strong macroeconomic performance helps to ameliorate not only the aggregate shocks, but also the differences across metropolitan areas.

Metropolitan Areas Tend to Have Similar Unemployment Rates Over Time

As has been well documented, the economies of metropolitan areas vary in structural ways, for instance based on their industrial mix, geography , demographics , and infrastructure. These structural differences result in persistent differences in labor market outcomes, including unemployment rates [4] .

In Figure 2, we examine the persistence of the unemployment rate by metropolitan area. Each dot represents a metropolitan area, and dots are color coded according to their quartile in the distribution of unemployment rates in 2006. The x-axis denotes the metropolitan area’s unemployment rate in 2006 and the y-axis the area’s unemployment rate in 2018. These are both years at which the economy was near, but not at its peak.

Figure 2 shows a clear, positive relationship between unemployment rates in 2006 and 2018: lower unemployment rates in 2006 are associated with lower unemployment rates in 2018. Notably this relationship holds across the entire sample, and also within the unemployment rate quartiles. Our results suggest that a 1 percentage point higher unemployment rate in 2006 is associated with a 0.6 percentage point higher unemployment rate in 2018. Moreover, the unemployment rate in 2006 explains 44 percent of the variation in the unemployment rate in 2018.

Although Metropolitan Areas Experiencing Idiosyncratic Shocks Undergo Large Changes in Their Unemployment Rates, They Tend to Revert Back to Their Previous Place in the Distribution:

In addition to the persistent characteristics that shape the economies of metropolitan areas over long periods, idiosyncratic events specific to metropolitan areas can also have a significant impact. Examples of these types of shocks include storms, like Hurricane Katrina, which reshaped New Orleans, or technical changes such as hydraulic fracturing, which made it possible to extract oil and gas from areas where they were previously inaccessible. These idiosyncratic shocks may or may not have long-lasting impacts.

Figure 3 shows the distribution of metropolitan area unemployment rates over a fourteen-year period. The figure highlights five metropolitan areas. In 2006 these highlighted areas were in the first quartile of the distribution; meaning that these areas had lower levels of unemployment than 75 percent of the metropolitan areas displayed in the figure. By 2009, these five areas had unemployment rates that were in the top quartile of the distribution that year. While it is true that the unemployment rate on aggregate was also rising during this period (as can be seen by the fact that the unemployment rates of all the other metropolitan areas, represented by the light gray bars, move up), these areas were affected earlier and by more—a function of the fact that they were hit by a specific, negative idiosyncratic shock: the bursting of the housing bubble. These metropolitan areas are located in Florida and Nevada, states with large housing bubbles, and the specific metropolitan areas highlighted experienced large drops in local housing prices when the bubble burst in 2007 [5] .

Like the financial crisis, the current crisis also has an idiosyncratic component. As noted in the introduction, metropolitan areas first affected by the virus closed non-essential businesses earlier. Moreover, the economies of metropolitan areas reliant on tourism, leisure and hospitality, and energy slowed quickly as travel restrictions were imposed and global demand declined. Other areas with fewer cases of the virus and those with economies dependent on industry, agriculture, or professional services appear so far to have been less impacted.

Interestingly, Figure 3 also illustrates that by 2018 these metropolitan areas that faced a negative shock from the bursting of the housing bubble had largely recuperated, with unemployment rates returning to levels similar to 2005/2006. This finding is in line with Blanchard and Katz (1992) who show that state-level unemployment rates tend to recover approximately five to seven years after experiencing a negative shock to employment. Note, this isn’t to say that adjustment is automatic—indeed specific policies geared at addressing idiosyncratic shocks may be necessary to help local areas cope when they face a crisis.

A Strong National Economy Helps All Metropolitan Areas, Even Those with Persistently High Unemployment Rates

Figure 4 plots the distribution of the unemployment rate by metropolitan area from 2005 to 2018, with dots of different colors and sizes identifying the quartiles of the unemployment rate distribution in 2006, as in Figure 2. (We make the dots different sizes to make it possible to follow the movements in the unemployment rates of the metropolitan areas from year to year.)

There are several phenomena that can be observed in this graph. One is the central tendency of the metropolitan area unemployment rates—as a whole, are the unemployment rates relatively high or low in a given year—which reflects the state of the business cycle. The second is how disperse the unemployment rates are—are the unemployment rates across the metropolitan areas relatively similar (are they clumped together) or are they spread out, with some areas having high rates and others relatively low rates. And the third is the relative position of the unemployment rates of specific metropolitan areas—do metropolitan areas that have high or low unemployment rates to start remain in those positions over the entire time period. To help elucidate these points, we also show the mean, range, and variance of the unemployment rates for groups of years in Table 1.

The first thing to note in Figure 4 is the impact of the Great Recession across metropolitan areas. As the recession gained full force in 2009, metropolitan unemployment rates as a whole began to increase. Second, the differences in unemployment rates across metropolitan areas widened in years in which the economy was underperforming. And, metropolitan areas that started off relatively disadvantaged tended to experience the highest unemployment rates during the recession. This information is summarized in Table 1, where we can see that the mean, variance, and range of the unemployment rate all increase substantially during the recession from the pre-recession period.

Table 1: Spread of the Unemployment Rate

Of course, this aggregate phenomenon is being laid on top of the idiosyncratic shocks we discussed previously, in particular, the bursting of the housing bubble. For instance, the metropolitan areas that we identified as having been particularly hard hit by the bursting of the housing are among those metropolitan areas captured by the yellow dots, which rise much more than average during the financial crisis and recession. But, as the economy recovered, and the aggregate unemployment rate fell, metropolitan area unemployment rates began to converge again. Many areas that saw the largest deterioration in their unemployment rates during the financial crisis and the Great Recession experienced substantial improvement. This finding is consistent with prior research demonstrating that strong macroeconomic conditions are particularly beneficial for workers that are disadvantaged in the labor market.

Notably, the distribution of unemployment rates in 2018 looks fairly similar to that of 2005 and 2006. By this we mean that metropolitan areas with the lowest unemployment rates prior to the Great Recession (the yellow dots) tend to have lower unemployment rates in 2018 and metropolitan areas with the highest unemployment rates (the purple dots) tend to have higher unemployment rates. This is just another way of illustrating the result in Figure 2, showing the persistence of the unemployment rate across metropolitan areas over time, even in the face of significant idiosyncratic and macroeconomic shocks.

Policy Implications for COVID-19:

Metropolitan areas have high (or low) unemployment rates for different reasons. First, there are structural causes—such as average education levels or industry mix—which mean that some areas tend to have high or low unemployment rates over time. Second, there are local idiosyncratic shocks that might cause metropolitan areas to see large but typically transitory increases or decreases in their unemployment rates. Finally, metropolitan areas are buffeted by the business cycle—aggregate shocks that play out similarly, although not identically, across metropolitan areas.

The current crisis in which we find ourselves is no different. Before the pandemic reached our shores, metropolitan areas had distinct capacities to respond based on their structural differences. The impact of the virus will vary across metropolitan areas depending on their exposure and industrial mix. Finally, all metropolitan areas will experience the spillovers from the deep recession as economic activity is curtailed.

Policymakers should take into account these different types of shocks that are buffeting localities, because they suggest different policies. Our results indicate that policies aimed at ensuring liquidity in financial markets now and stimulating aggregate demand once it becomes safe to engage in non-essential economic activity will have a broad positive impact on economic outcomes across metropolitan areas and will reduce disparities between them. However, some localities will require more help, either because they face a particularly pernicious impact from the pandemic or because long-standing structural factors make it particularly difficult for them to weather the economic headwinds we face. Our colleagues Louise Sheiner and Sage Belz show that state tax revenues declined by about 9 percent during the Great Recession and argue that recently passed legislation—such as CARES Act and FFCRA—does not provide enough funding to prevent states and localities from cutting spending. Similarly, our colleague Matt Fiedler and Wilson Powell III make the case for increasing the federal match rate for Medicaid in proportion to the amount that the state’s unemployment rate exceeds some threshold. And the Metropolitan program discuss policies that would bolster metropolitan areas by supporting small businesses.

Becca Portman contributed to the graphics/data visualization for this blog.

[1] This is a back-of-the-envelope calculation which assumes all initial claims translate into spells of unemployment. We take the number of initial claims from the weeks ending in April 4, March 28, and March 21 (16,780 thousand); add the number of unemployed people in March 2020 (7140 thousand); and divide by the March 2020 labor force: (16,780 + 7140)/162913 = 14.68%. Although it is not always the case that initial claims translate into spells of unemployment, this calculation is, nonetheless, most likely an underestimate of the unemployment rate as not all people who become unemployed are eligible to receive benefits and not everyone who is eligible for unemployment insurance applies. Moreover, this estimate likely understates the number of people who have tried to file claims in recent weeks, due to limitations with the state unemployment insurance systems which have been overwhelmed. That said, there is currently less certainty about the relationship between insured unemployment and aggregate unemployment because of changes in the unemployment insurance eligibility rules. [2] Note that the ratios in this graph should be interpreted with caution. We choose the total labor force as the denominator because recent legislation has changed the types of workers covered by unemployment insurance. However, this denominator likely overstates the number of people covered by unemployment insurance. The numerator is not without issues either. As mentioned above, it is likely to understate the number of people who have attempted to file claims, due to limitations with the unemployment insurance systems. [3] Claims data by metropolitan area aren’t readily available. [4] Katheryn Russ and Jay Shambaugh show that the persistence of the unemployment rate is related to the average level of education in a county. They find that counties with lower levels of education have higher levels of persistence. In other words, areas with lower, average education are more likely to get “stuck” with a high unemployment rate over time. [5] We also examine metropolitan areas that were in the fourth quartile of the distribution in 2006 and subsequently moved to near the bottom of the distribution in 2009. We find that these areas are mostly located in places with positive energy shocks.

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Unemployment during the pandemic: How to avoid going for broke

Key takeaways.

• Without significant policy changes, employers will be hit with hefty tax increases to pay for mounting unemployment insurance (UI) claims.
• Thinning tax bases make financing UI more challenging.
• Having state UI trust funds in the red may make it much harder for job markets to recover.

Since the onset of the COVID-19 pandemic in late February, tens of millions of Americans have lost their jobs. Anxiety among many employers and consumers is still high — suggesting little hope of a rapid recovery.

This leaves state and local governments with gaping budget shortfalls amid falling income and sales tax revenues while demand for public services rises. A particularly fast-growing area of state expenditure is the payment of unemployment insurance (UI) benefits.

There has been extensive discussion among policymakers and the media regarding the trade-offs of more generous or longer-lasting UI benefits, such as the federal government’s provision of an additional $600 per week that expired July 31. But there has been very little talk about the tax hikes they will incur.

Many states have depleted their UI trust funds in the current crisis and have started to borrow from the federal government to pay their residents’ UI benefits. In the absence of additional policy changes, employers will be hit with significant UI tax increases over the next few years. And that will likely prevent some of the jobs that were lost from coming back.

In this policy brief, we explain how state unemployment insurance programs are financed and the threats to their solvency. We also discuss two reforms: one to relieve employers faced with crippling payroll tax increases in the coming years, and another to ensure that state UI trusts have enough money for future payouts.

Understanding unemployment insurance

Unemployment insurance is one of the largest social insurance programs in the United States, with each state running its own UI program to pay benefits to people laid off from their jobs. In most states, UI replaces about half of a worker’s earnings up to a weekly benefit maximum ($443 in the median state) for a maximum of 26 weeks (6 months).

While providing a needed cushion to workers, UI leaves policymakers with a difficult balancing act. As benefits become more generous, many recipients reduce their efforts to find and maintain jobs, reducing total income and burdening other workers (Johnston and Mas 2018). But if benefits become stingier, the cushion provides less support leaving some unemployed vulnerable to fall behind on their bills or lose their housing (Ganong and Noel 2019). [1]

Benefits are generally paid to people with relatively low saving rates, so the money that is distributed is quickly spent, providing short-term stimulus for consumer goods. This leads economists to refer to UI as an “automatic stabilizer.” Without the need for additional legislation, states  automatically  spend more money on unemployment benefits when economic conditions deteriorate, and spending naturally retracts as the economy recovers.

During the strong labor market leading up to the pandemic, just 220,000 workers filed new UI claims in the typical week. In late February, the unemployment rate was at 3.5 percent — a 60-year low — ­and about 1.7 million Americans were receiving UI benefits.

But two months later, the pandemic’s sudden and massive shock to the economy vaulted the U.S. unemployment rate to 14.7 percent — an 80-year-high. This April, rates varied substantially across states, from a high of 28.2 percent in Nevada to a low of 8.3 percent in Nebraska.

During the last week of March, 6.9 million Americans filed new claims for UI benefits. As demonstrated in Figure 1, this was  10 times higher  than the corresponding peak in new UI claims during the depths of the Great Recession more than a decade ago. By early May of this year, more than 25 million Americans were receiving UI payments and in every week since early March, new UI claims have exceeded the Great Recession peak of 660,000.

Figure 1: Weekly Initial Unemployment Insurance Claims (Thousands)

From March through the end of July, the federal CARES (Coronavirus Aid, Relief, and Economic Security) Act increased unemployment benefits for each recipient by $600 per week. That meant the average UI recipient was paid one-third  more  in unemployment than she earned while working (Ganong et al. 2020).

This raised concerns that workers had little incentive to return to work or find a new job, a condition necessary for labor market restructuring and recovery. [2]  This additional UI funding expired at the end of July after lawmakers were unable to agree on another round of federal spending. President Trump attempted to provide a $300-dollar weekly “top-up” by executive order (with states given the option to provide an additional $100). Whether and when that happens is unclear given that states have to apply for the funding. [3]

UI benefits are financed by a payroll tax on employers. Unlike other taxes, UI tax rates are “experience-rated,” which means that an employer’s future tax rate rises if its employees claim UI benefits, and its tax rate falls when the firm avoids layoffs. This gives employers a strong incentive to balance the demand for layoffs with the cost that they impose on the UI system.

One consequence of experience-rating UI taxes is that tax rates increase as the economy begins to recover from recession. This significantly raises the cost of hiring new workers or retaining old ones, likely weighing down recovery of the labor market.

As shown in Figure 2, the average UI tax rate increased by more than 50 percent from 2009 to 2012 as the recovery was haltingly underway.  This increase was especially high in middle-class industries — like construction and manufacturing — that were hit hardest during the Great Recession.  As this same figure shows, average tax rates were more than 2.5 times as high among employers in construction as among all employers in the years following the three most recent recessions.

Figure 2: Average UI Tax Rate on Total Wages (1990-2018)

Surviving firms have to cover the UI costs generated by the employers that went out of business — causing them to be doubly burdened. Given the much larger increase in UI claims during the current recession relative to previous ones and the likely greater rate of firm exit, the increase in UI taxes could be substantially higher over the next few years than in the years following the Great Recession. This will encourage outsourcing and automation, induce some firms to shut down, and impede employment.

Softening the blow to businesses

Unless employment recovers with impressive speed, each claim will draw an average of $7,000 in payments from state UI trust funds. Those payments will transform into an estimated $270 billion dollars in payroll tax increases on firms over the next few years, reducing the ability of firms to resume normal hiring and employment and further stalling a labor market comeback. [4]

In March and April of this year, 20 states suspended experience rating to shield their employers from an avalanche of additional UI taxes in the upcoming years. These states span the political spectrum as well as geography, including Arizona, Georgia, Idaho, Maine, Maryland, Ohio, Texas, and Washington. [5]

While this policy change will — all else equal — hasten the labor market recovery in these states, it may also lead to a substantial increase in layoffs since it removes firms’ financial incentives to retain workers. Consistent with this, a comparison of five states that suspended experience rating with five neighboring states that did not reveals that layoff rates (defined as new UI claims divided by the workforce) were 30 percent higher in the five that shut down experience rating. [6]

States are therefore in a bind. By maintaining experience rating, a wave of future tax increases may hamper the economic recovery and prolong unemployment. But suspending experience rating may induce additional layoffs today, when things are most dire.

To soften the blow over the next few years while maintaining the incentives for employers to retain their workforce, states could adjust each company’s UI costs so that they are temporarily evaluated based on conditions in their industry — reducing the scope for tax increases that were out of the firm’s control.

For the next few years, employers would essentially be graded on a curve, comparing their layoff history with industry peers rather than a non-existent perfect firm. For example, since restaurants have been hit especially hard during the pandemic while the average technology firm has thrived, a restaurant that laid off 10 percent of its workers would face a smaller tax increase than a computer software company that did the same.  Employers would have essentially equal incentives to maintain their workforce, but would not face crushing tax increases if they happen to be in an industry that was differentially hit by the COVID pandemic and the resulting lockdowns.

The benefits of such a policy could be substantial. Research suggests that employment is highly sensitive to UI tax increases in part because they hit firms that are already on the proverbial ropes. Anderson and Meyer (1997) find that a 1 percent increase in costs from UI taxes reduces employment by 2 percent. More recent research by Johnston (2020) finds even larger effects.

Shoring up the trust funds

The pandemic has shed light on the vulnerability of UI financing. Better maintenance of UI trust funds is vital to prepare states for the next economic downturn and improve prospects for future recoveries.

There is a large and growing gap in UI tax costs across jurisdictions. States like California and Florida have a low maximum tax rate and an annual tax base of around $7,000 — the lowest allowed by federal law — resulting in maximum potential UI taxes of about $400 per worker. In contrast, states like Washington and Oregon maintain large tax bases ($52,700 and $42,100, respectively) resulting in potential UI taxes of more than $2,000 per worker. [7]

In good times, states store revenues from UI taxes in a trust fund and that fund is drawn down in the depth of recessions. In recent years, however, state trust funds have been low even in good times — a function of benefits that are more generous than their financing (von Wachter 2016). The Department of Labor’s 2020 Solvency Report shows that despite a 10-year economic expansion, 21 state UI trust funds were below the minimum recommended reserve, just prior to the pandemic (U.S. Department of Labor 2020). [8]  As of August 2020, 11 states have already depleted their UI trust funds and have started to receive loans from the federal government to pay UI benefits. [9]

These deficits may contribute to lethargic recoveries. When trust funds are low, states must steeply raise rates to recover their costs and pay benefits. The timing of these increases could not be worse. Weak trust funds also undermine experience rating. When a state trust fund is in debt to the federal government, federal UI taxes rise on all firms in that state until the federal loan is repaid, regardless of the firm’s layoffs.

In California, for instance, the large loan balance accrued during the 2008 recession was not repaid in full until 2018, hiking payroll taxes for employers across the board. This weakens the intended incentives of experience rating to encourage employment stability and curb abuse of the UI system. According to the same Labor Department Solvency Report cited above, California’s UI trust fund was in the worst position of all 50 states just prior to the pandemic (Appendix Figure 1). [10]

The thinning tax base is a leading cause of low UI reserves. States choose how much of a worker’s earnings are exposed to UI taxation, but the federal government can “update” the minimum requirement to keep pace with inflation and the rise in average earnings. The current federal requirement of $7,000 has —remarkably — not been updated since 1982, eroding the tax base unless states have legislated increases or proactively linked their taxable UI earnings base to inflation or wage growth.

Another important consequence of a small tax base is that UI taxes become much more regressive. This can reduce the employment opportunities for part-time workers or those with low earnings since firms essentially pay an equal tax for each worker (Guo and Johnston 2020). In a state like California, an employer would pay the same UI tax for a worker who earned $8,000 annually as for one who earned $40,000.

But the latter worker is eligible for a weekly UI benefit that is five times larger ($400 per week versus just $80 per week for the lower-paid worker). Expanding the UI program’s taxable wage base in states like California would reduce the implicit penalty on hiring low-wage earners (principally seasonal and part-time workers as well as students).

To restore the health of UI trust funds, governments should expand their tax bases to be proportional to the level of benefits in their state. A basic reform to shore up trust funds could be to require states to have taxable wage bases at least half as large as their annual insurable earnings.  

Figure 3 plots the ratio of insured wages to taxable wages across the country, with larger values indicating greater insurance than funding.

Figure 3: Ratio of Annual Insured Wages to Taxable Wages (2015)

In California the UI-insurable income is $47,000, more than six times greater than the tax base of only $7,000. This reform would naturally link revenues to the generosity of the state’s UI system, allow states to lower tax rates, and bring in sufficient revenues to cushion workers the next time there is an economic shock. Harmonizing tax bases across states would also reduce the incentive for multi-state firms to reallocate jobs and operations based on state UI tax differences (Guo 2020).

Time for action

The COVID-19 crisis has put unemployment insurance at center stage of American politics and economic policy. It has provided a lifeline for tens of millions of workers who have lost their jobs since the pandemic’s onset six months ago, while at the same time exposing the system’s vulnerabilities. Given the complexity of UI financing and the scarcity of empirical evidence on which to rely, this is an important area for additional work and exploration.

Unless policymakers take steps to reform how the states’ unemployment insurance trust funds are financed, tax hikes will hurt labor market recoveries across the country — and with them, the American worker.

Mark Duggan is the Trione Director of SIEPR and the Wayne and Jodi Cooperman Professor of Economics at Stanford. Audrey Guo is an assistant professor of economics at Santa Clara University’s Leavey School of Business. Andrew C. Johnston is an assistant professor of economics, as well as applied econometrics at the University of California at Merced.

The authors are grateful to Isaac Sorkin for his helpful feedback.

1  States differ in where they choose to fall on that trade-off. The maximum weekly benefit varies substantially across states, from a low of $235 in Mississippi to a high of $790 in Washington.  Some states also have a maximum duration of less than 26 weeks.

2  Recent research suggests that, at least in the short term, the disincentive effects of the increases in UI benefits (caused by the CARES Act) were minimal (Altonji et al. 2020).

3  More than half of states had applied or signaled their intention to apply as of August 21. Only South Dakota announced that it would not be applying (Iacurci 2020).  States that are approved are guaranteed just three weeks of federal funding for the enhanced UI benefits, though more federal funding may be available.

4  For this calculation, we extrapolate weekly UI claims through the end of the year and assume that half of those claims become benefit spells. We use data on average weekly benefit amounts and average UI spell durations to calculate the typical cost of a UI benefit spell at a little over $7,000. The product of these two values is an estimate of the UI benefit costs that will factor into UI taxes over the coming years. The actual average value could be substantially higher if the recovery is slow, as this would lead to longer and more costly average UI benefit periods.

5  These 20 states are Alabama, Arizona, Georgia, Idaho, Iowa, Louisiana, Maine, Maryland, Minnesota, Missouri, Montana, Nebraska, North Carolina, North Dakota, Ohio, Pennsylvania, South Carolina, Texas, Utah, Washington, and the District of Columbia.

6  The matched pairs are — with the states that suspended experience rating listed first — Alabama and Mississippi, Ohio and Indiana, North Dakota and South Dakota, Arizona and New Mexico, and Idaho and Oregon.

7  Appendix Table 1 lists the UI tax base in each state in 2020 along with each state’s maximum per-worker tax and maximum weekly UI benefit.

8  The Department of Labor recommends that states have reserves in their trust funds that are at least as large as the highest recent years of UI benefit payout.

9  As of August 25, 2020, 11 states have borrowed $24.4 billion from the federal unemployment account. California, New York, and Texas account for 82% of that borrowing .

10  As shown in Appendix Figure 1, California’s solvency ratio of 0.21 was lower than the other 49 states, the District of Columbia, and Puerto Rico.

Altonji, Joseph, Zara Contractor, Lucas Finamor, Ryan Haygood, Ilse Lindenlaub, Costas Meghir, Cormac O’Dea, Dana Scott, Liana Wang, and Ebonya Washington. “Employment Effects of Unemployment Insurance Generosity during the Pandemic.”  Working Paper (2020).

Anderson, Patricia M., and Bruce D. Meyer. "The effects of firm specific taxes and government mandates with an application to the U.S. unemployment insurance program."  Journal of Public Economics  65, no. 2 (1997): 119-145.

Ganong, Peter, and Pascal Noel. "Consumer spending during unemployment: Positive and normative implications."  American Economic Review 109, no. 7 (2019): 2383-2424.

Ganong, Peter, Pascal Noel, and Joseph S. Vavra.  U.S. Unemployment Insurance Replacement Rates During the Pandemic , no. w27216. National Bureau of Economic Research (2020).

Guo, Audrey. "The effects of unemployment insurance taxation on multi-establishment firms." Working Paper (2020).

Guo, Audrey, and Andrew C. Johnston. "The Finance of Unemployment Compensation and its Consequence for the Labor Market." Working Paper (2020).

Iacurci, Greg. “ This Map Shows Where States Stand on the Extra $300 Weekly Unemployment Benefits. ” CNBC, August 21, 2020. 

Johnston, Andrew C. “Unemployment Insurance Taxes and Labor Demand: Quasi-experimental Evidence from Administrative Data.” Forthcoming at  American Economic Journal: Economic Policy  (2020).

Johnston, Andrew C., and Alexandre Mas. "Potential unemployment insurance duration and labor supply: The individual and market-level response to a benefit cut."  Journal of Political Economy  126, no. 6 (2018): 2480-2522.

U.S. Department of Labor. State Unemployment Insurance Trust Fund Solvency Report 2020.  February 2020.  

Von Wachter, Till. “ Unemployment Insurance Reform: A Primer. ” Washington Center for Equitable Growth. October 2016.   

Appendix  Table A

Source:  US Dept of Labor Significant Provisions of State Unemployment Insurance Laws 2019

*For single workers. Some states offer additional dependent allowances

Appendix Figure 1 - State UI Trust Fund Solvency (as of 1/1/2020)

Source: U.S. Department of Labor Trust Fund Solvency Report 2020

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Overcoming roadblocks to california’s public ev charging infrastructure, nar settlement, house prices, and consumer welfare, the impacts of covid-19 illnesses on workers.

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• Copy URL https://www.pbs.org/newshour/economy/3-charts-reveal-how-the-covid-19-unemployment-crisis-isnt-over

3 charts reveal how the COVID-19 unemployment crisis isn’t over

While some politicians have suggested the number of U.S. coronavirus cases has peaked, the prognosis for employment and the economy isn’t good, economists say.

More than 41 millions of Americans have filed for unemployment insurance since the spread of COVID-19, according to data the U.S. Department of Labor released Thursday. Those job losses show signs of slowing down, but the pandemic’s lingering long-term effects on the economy could play out for decades.

Federal data showed 2.1 million people had filed claims in one week.

That number suggests a possible slowdown in U.S. layoffs compared to recent weeks. In late March, more than 3.3 million Americans filed jobless claims , quadrupling a previous record. That number was quickly eclipsed with 4.4 million claims filed in one week by mid-April. But it “remains incredibly high,” said Stephanie Aaronson, an economist who is vice president and director of economic studies at the Brookings Institution.

Here are three ways to look at how the nation is losing jobs and if reopening could slow this unprecedented plunge.

Chart by Megan McGrew/PBS NewsHour

Job losses during the pandemic quickly dwarfed the Great Recession

“Two million claims a week is just hard to imagine,” said Aaronson, who previously served on the Federal Reserve Board. “The numbers of claims we’re seeing now are outside of historical experience.”

For perspective, she pointed to the rise in unemployment between 2007 and 2009 during the Great Recession. Since the pandemic started, the nation lost twice as many jobs in two months as it did during the entire Great Recession, Aaronson said.

In early April, 42 states had some kind of stay-at-home order in place, and jobless claims ballooned. Now, as more states move to allow businesses to reopen, the increase has slowed. But it’s still growing, suggesting that the kickstart for commerce after strict social distancing isn’t enough to get things back to normal.

And there could be “a second wave of layoffs,” said Till von Wachter, economist and professor at University of California at Los Angeles.

“Businesses that hit the ‘pause’ button may find that business is not as good as they had hoped” when they return, he said.

How long will jobless benefits last?

Before COVID-19 came into the picture, unemployment insurance varied greatly state to state. Capping out at 12 weeks, Florida’s was the shortest benefit. Massachusetts offers the longest time period that people can collect — up to 30 weeks — unless federal benefits or ample jobs are available. That means if you are covered through a federal program, or if you lose your job during a period of low unemployment when it’s conceivably easier to find a new job, Massachusetts doesn’t offer the full benefit period. Most states typically offer up to 26 weeks of benefits, according to the Center on Budget and Policy Priorities.

The CARES Act enabled all states to choose to offer 13 more weeks of benefits, called Pandemic Emergency Unemployment Assistance . Thirty states have accepted those benefits, which are funded by the federal government and expire on Dec. 31.

But there’s a lag in how many people have applied for unemployment insurance and how many people are actually getting it. So far, von Wachter said, roughly 20 million Americans have received unemployment insurance.

Right now, people in lower-wage jobs are more at risk of becoming unemployed, Friedman said. While federal legislation has buoyed people who’ve been left without work because of COVID-19, particularly in restaurants and health care (as a result of people steering clear of doctor’s offices for regular check-ups and elective procedures), von Wachter said there is concern that the supplemental money allotted is not enough to sustain them because rent and bills ate up a higher proportion of their wages than those with higher incomes. That capsized lower-wage workers’ ability to set aside savings and shore up resources that could have helped them during this economic downturn.

Younger generations already hit by COVID-19 Recession

But the cohort most affected by the pandemic-fueled economic crisis may be those who haven’t yet joined the labor market, said John Friedman, an economist and professor at Brown University.

“If you graduate into a bad economy, there’s a permanent impact” on earning potential and lost wages, Friedman said.

Already in California, younger generations of workers have been particularly hard-hit, according to an analysis of state jobless claims conducted by the California Policy Lab . In their study, a third of Generation Z workers and a quarter of Millennials have filed for unemployment insurance, compared to roughly one out of five Baby Boomers and Generation X workers.

Recessions are difficult for everyone, but especially so for high school graduates, von Wachter said. For many, he said, the recession unfolding now “will scar them” for the next decade of their lives. Among those in California who completed their high school education but did not graduate from college, the recent study shows half have applied for jobless benefits.

It is too soon to fully understand what the long-term effects of COVID-19 may hold for the U.S. economy, Friedman said. People are spending money differently with more online purchases and fewer things done in person, like eating out or getting a haircut, but it’s unclear if those changes are permanent. And even when the economy does rebound, and more people are back to work, Friedman said those shifts “may come at large personal costs.”

“People may find a job, but it’s at much lower pay than what they did before,” he said.

Laura Santhanam is the Health Reporter and Coordinating Producer for Polling for the PBS NewsHour, where she has also worked as the Data Producer. Follow @LauraSanthanam

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Impact of COVID-19 on people's livelihoods, their health and our food systems

Joint statement by ilo, fao, ifad and who.

The COVID-19 pandemic has led to a dramatic loss of human life worldwide and presents an unprecedented challenge to public health, food systems and the world of work. The economic and social disruption caused by the pandemic is devastating: tens of millions of people are at risk of falling into extreme poverty, while the number of undernourished people, currently estimated at nearly 690 million, could increase by up to 132 million by the end of the year.

Millions of enterprises face an existential threat. Nearly half of the world’s 3.3 billion global workforce are at risk of losing their livelihoods. Informal economy workers are particularly vulnerable because the majority lack social protection and access to quality health care and have lost access to productive assets. Without the means to earn an income during lockdowns, many are unable to feed themselves and their families. For most, no income means no food, or, at best, less food and less nutritious food. 

The pandemic has been affecting the entire food system and has laid bare its fragility. Border closures, trade restrictions and confinement measures have been preventing farmers from accessing markets, including for buying inputs and selling their produce, and agricultural workers from harvesting crops, thus disrupting domestic and international food supply chains and reducing access to healthy, safe and diverse diets. The pandemic has decimated jobs and placed millions of livelihoods at risk. As breadwinners lose jobs, fall ill and die, the food security and nutrition of millions of women and men are under threat, with those in low-income countries, particularly the most marginalized populations, which include small-scale farmers and indigenous peoples, being hardest hit.

Millions of agricultural workers – waged and self-employed – while feeding the world, regularly face high levels of working poverty, malnutrition and poor health, and suffer from a lack of safety and labour protection as well as other types of abuse. With low and irregular incomes and a lack of social support, many of them are spurred to continue working, often in unsafe conditions, thus exposing themselves and their families to additional risks. Further, when experiencing income losses, they may resort to negative coping strategies, such as distress sale of assets, predatory loans or child labour. Migrant agricultural workers are particularly vulnerable, because they face risks in their transport, working and living conditions and struggle to access support measures put in place by governments. Guaranteeing the safety and health of all agri-food workers – from primary producers to those involved in food processing, transport and retail, including street food vendors – as well as better incomes and protection, will be critical to saving lives and protecting public health, people’s livelihoods and food security.

In the COVID-19 crisis food security, public health, and employment and labour issues, in particular workers’ health and safety, converge. Adhering to workplace safety and health practices and ensuring access to decent work and the protection of labour rights in all industries will be crucial in addressing the human dimension of the crisis. Immediate and purposeful action to save lives and livelihoods should include extending social protection towards universal health coverage and income support for those most affected. These include workers in the informal economy and in poorly protected and low-paid jobs, including youth, older workers, and migrants. Particular attention must be paid to the situation of women, who are over-represented in low-paid jobs and care roles. Different forms of support are key, including cash transfers, child allowances and healthy school meals, shelter and food relief initiatives, support for employment retention and recovery, and financial relief for businesses, including micro, small and medium-sized enterprises. In designing and implementing such measures it is essential that governments work closely with employers and workers.

Countries dealing with existing humanitarian crises or emergencies are particularly exposed to the effects of COVID-19. Responding swiftly to the pandemic, while ensuring that humanitarian and recovery assistance reaches those most in need, is critical.

Now is the time for global solidarity and support, especially with the most vulnerable in our societies, particularly in the emerging and developing world. Only together can we overcome the intertwined health and social and economic impacts of the pandemic and prevent its escalation into a protracted humanitarian and food security catastrophe, with the potential loss of already achieved development gains.

We must recognize this opportunity to build back better, as noted in the Policy Brief issued by the United Nations Secretary-General. We are committed to pooling our expertise and experience to support countries in their crisis response measures and efforts to achieve the Sustainable Development Goals. We need to develop long-term sustainable strategies to address the challenges facing the health and agri-food sectors. Priority should be given to addressing underlying food security and malnutrition challenges, tackling rural poverty, in particular through more and better jobs in the rural economy, extending social protection to all, facilitating safe migration pathways and promoting the formalization of the informal economy.

We must rethink the future of our environment and tackle climate change and environmental degradation with ambition and urgency. Only then can we protect the health, livelihoods, food security and nutrition of all people, and ensure that our ‘new normal’ is a better one.

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Editor in Chief's Introduction to Essays on the Impact of COVID-19 on Work and Workers

On March 11, 2020, the World Health Organization declared that COVID-19 was a global pandemic, indicating significant global spread of an infectious disease ( World Health Organization, 2020 ). At that point, there were 118,000 confirmed cases of the coronavirus in 110 countries. China had been the first country with a widespread outbreak in January, and South Korea, Iran and Italy following in February with their own outbreaks. Soon, the virus was in all continents and over 177 countries, and as of this writing, the United States has the highest number of confirmed cases and, sadly, the most deaths. The virus was extremely contagious and led to death in the most vulnerable, particularly those older than 60 and those with underlying conditions. The most critical cases led to an overwhelming number being admitted into the intensive care units of hospitals, leading to a concern that the virus would overwhelm local health care systems. Today, in early May 2020, there have been nearly 250,000 deaths worldwide, with over 3,500,000 confirmed cases ( Hopkins, 2020 ). The human toll is staggering, and experts are predicting a second wave in summer or fall.

As the deaths rose from the virus that had no known treatment or vaccine countries shut their borders, banned travel to other countries and began to issue orders for their citizens to stay at home, with no gatherings of more than 10 individuals. Schools and universities closed their physical locations and moved education online. Sporting events were canceled, airlines cut flights, tourism evaporated, restaurants, movie theaters and bars closed, theater productions canceled, manufacturing facilities, services, and retail stores closed. In some businesses and industries, employees have been able to work remotely from home, but in others, workers have been laid off, furloughed, or had their hours cut. The International Labor Organization (ILO) estimates that there was a 4.5% reduction in hours in the first quarter of 2020, and 10.5% reduction is expected in the second quarter ( ILO, 2020a ). The latter is equivalent to 305 million jobs ( ILO, 2020a ).

Globally, over 430 million enterprises are at risk of disruption, with about half of those in the wholesale and retail trades ( ILO, 2020a ). Much focus in the press has been on the impact in Europe and North America, but the effect on developing countries is even more critical. An example of the latter is the Bangladeshi ready-made-garment sector ( Leitheiser et al., 2020 ), a global industry that depends on a supply chain of raw material from a few countries and produces those garments for retail stores throughout North America and Europe. But, in January 2020, raw material from China was delayed by the shutdown in China, creating delays and work stoppages in Bangladesh. By the time Bangladeshi factories had the material to make garments, in March, retailers in Europe and North American began to cancel orders or put them on hold, canceling or delaying payment. Factories shut down and workers were laid off without pay. Nearly a million people lost their jobs. Overall, since February 2020, the factories in Bangladesh have lost nearly 3 billion dollars in revenue. And, the retail stores that would have sold the garments have also closed. This demonstrates the ripple effect of the disruption of one industry that affects multiple countries and sets of workers, because consider that, in turn, there will be less raw material needed from China, and fewer workers needed there. One need only multiply this example by hundreds to consider the global impact of COVID-19 across the world of work.

The ILO (2020b) notes that it is difficult to collect employment statistics from different countries, so a total global unemployment rate is unavailable at this time. However, they predict significant increase in unemployment, and the number of individuals filing for unemployment benefits in the United States may be an indicator of the magnitude of those unemployed. In the United States, over 30 million filed for unemployment between March 11 and April 30 ( Bureau of Labor Statistics, 2020 ), effectively this is an unemployment rate of 18%. By contrast, in February 2020, the US unemployment rate was 3.5% ( Bureau of Labor Statistics, 2020 ).

Clearly, COVID-19 has had an enormous disruption on work and workers, most critically for those who have lost their employment. But, even for those continuing to work, there have been disruptions in where people work, with whom they work, what they do, and how much they earn. And, as of this writing, it is also a time of great uncertainty, as countries are slowly trying to ease restrictions to allow people to go back to work--- in a “new normal”, without the ability to predict if they can prevent further infectious “spikes”. The anxieties about not knowing what is coming, when it will end, or what work will entail led us to develop this set of essays about future research on COVID-19 and its impact on work and workers.

These essays began with an idea by Associate Editor Jos Akkermans, who noted to me that the global pandemic was creating a set of career shocks for workers. He suggested writing an essay for the Journal . The Journal of Vocational Behavior has not traditionally published essays, but these are such unusual times, and COVID-19 is so relevant to our collective research on work that I thought it was a good idea. I issued an invitation to the Associate Editors to submit a brief (3000 word) essay on the implications of COVID-19 on work and/or workers with an emphasis on research in the area. At the same time, a group of international scholars was coming together to consider the effects of COVID-19 on unemployment in several countries, and I invited that group to contribute an essay, as well ( Blustein et al., 2020 ).

The following are a set of nine thoughtful set of papers on how the COVID-19 could (and perhaps will) affect vocational behavior; they all provide suggestions for future research. Akkermans, Richardson, and Kraimer (2020) explore how the pandemic may be a career shock for many, but also how that may not necessarily be a negative experience. Blustein et al. (2020) focus on global unemployment, also acknowledging the privileged status they have as professors studying these phenomena. Cho examines the effect of the pandemic on micro-boundaries (across domains) as well as across national (macro) boundaries ( Cho, 2020 ). Guan, Deng, and Zhou (2020) drawing from cultural psychology, discuss how cultural orientations shape an individual's response to COVID-19, but also how a national cultural perspective influences collective actions. Kantamneni (2020) emphasized the effects on marginalized populations in the United States, as well as the very real effects of racism for Asians and Asian-Americans in the US. Kramer and Kramer (2020) discuss the impact of the pandemic in the perceptions of various occupations, whether perceptions of “good” and “bad” jobs will change and whether working remotely will permanently change where people will want to work. Restubog, Ocampo, and Wang (2020) also focused on individual's responses to the global crisis, concentrating on emotional regulation as a challenge, with suggestions for better managing the stress surrounding the anxiety of uncertainty. Rudolph and Zacher (2020) cautioned against using a generational lens in research, advocating for a lifespan developmental approach. Spurk and Straub (2020) also review issues related to unemployment, but focus on the impact of COVID-19 specifically on “gig” or flexible work arrangements.

I am grateful for the contributions of these groups of scholars, and proud of their ability to write these. They were able to write constructive essays in a short time frame when they were, themselves, dealing with disruptions at work. Some were home-schooling children, some were worried about an absent partner or a vulnerable loved one, some were struggling with the challenges that Restubog et al. (2020) outlined. I hope the thoughts, suggestions, and recommendations in these essays will help to stimulate productive thought on the effect of COVID-19 on work and workers. And, while, I hope this research spurs to better understand the effects of such shocks on work, I really hope we do not have to cope with such a shock again.

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Inequality, Unemployment, and Poverty in the COVID-19 Era in Eastern Cape Province, South Africa

• First Online: 05 May 2024

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• Tafadzwa Maramura 4 ,
• Peter Makaye 5 &
• Torque Mude   ORCID: orcid.org/0000-0002-9885-6637 6  

Part of the book series: The Anthropocene: Politik—Economics—Society—Science ((APESS,volume 37))

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Just like other Third World countries (3WCs), South Africa is faced with the triple threat of inequality, unemployment, and poverty, a burden which, though inherited from apartheid, has been intensified by the COVID-19 pandemic. Using the case of the Eastern Cape Province, which was one of the COVID-19 hotspots, this paper examines the challenges of inequality, unemployment, and poverty in low-income areas during a pandemic. Using methodological triangulation of purposively sampled secondary data and physical observations, this qualitative paper argues that more than two decades into democracy, socio-economic development is still a far cry from what it should be in South Africa, particularly in previously marginalised demographics. Against this background, this chapter maintains that the unprecedented economic implications of the COVID-19 pandemic have widened the inequality, unemployment, and poverty gaps in the Eastern Cape. The chapter further exposes how the accompanying by-products of the inequality, unemployment, and poverty nexus have particularly afflicted the young population, as evident in the high rates of crime, drug abuse and teenage pregnancies in the COVID-19 era.

Corresponding author: Dr Clementine Maramura Tafadzwa holds a PhD in Public Management and Governance from the North-West University, a Master of Public Policy, Honours Public Policy (Cum Laude) and a B. Soc. Sci (Cum Laude) from the University, of Fort Hare. Email: [email protected]

Dr Peter Makaye is a Zimbabwean academic who holds a PhD in Development Studies from Nelson Mandela University in South Africa, Master of Arts Degree in Economic History and Bachelor of Arts Honours Degree in Economic History. Email: [email protected]

Dr Torque Mude (PhD) is a Postdoctoral Research Fellow at the Centre for Africa-China Studies of the University of Johannesburg. His research engages African politics, international relations, governance, peacebuilding, international law and development. Email: [email protected]. ORCID: https://orcid.org/0000-0002-9885-6637

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Maramura, T., Makaye, P., Mude, T. (2024). Inequality, Unemployment, and Poverty in the COVID-19 Era in Eastern Cape Province, South Africa. In: Kiyala, J.C.K., Chivasa, N. (eds) Climate Change and Socio-political Violence in Sub-Saharan Africa in the Anthropocene. The Anthropocene: Politik—Economics—Society—Science, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-031-48375-2_12

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The Pandemic's Impact on Unemployment and Labor Force Participation Trends

Following early 2020 responses to the pandemic, labor force participation declined dramatically and has remained below its 2019 level, whereas the unemployment rate recovered briskly. We estimate the trend of labor force participation and unemployment and find a substantial impact of the pandemic on estimates of trend. It turns out that levels of labor force participation and unemployment in 2021 were approaching their estimated trends. A return to 2019 levels would then represent a tight labor market, especially relative to long-run demographic trends that suggest further declines in the participation rate.

At the end of 2019, the labor market was hotter than it had been in years. Unemployment was at a historic low, and participation in the labor market was finally increasing after a prolonged decline. That tight labor market came to an abrupt halt with the COVID-19 pandemic in the spring of 2020.

Now, two years later, the labor market has mostly recovered from the depths of the pandemic recession. The unemployment rate is close to pre-pandemic lows, and job openings are at record highs. Yet, participation and employment rates have remained persistently below pre-pandemic levels. This suggests the possibility that the pandemic has permanently reduced participation in the economy and that current participation rates reflect a new normal. In this article, we explore how the pandemic has affected labor markets and whether a new normal is emerging.

What Is "Normal"?

One way to define the normal level of a variable is to estimate its trend and compare the observed data with the estimated trend values. Constructing a trend essentially means drawing a smooth line through the variations in the actual data.

But this means that constructing the trend for a point in time typically involves considering what happened both before and after that point in time. Thus, constructing the trend at the end of a sample is especially hard, since we do not yet know how the data will evolve.

We construct trends for three aggregate labor market ratios — the labor force participation (LFP) rate, the unemployment rate and the employment-population ratio (EPOP) — using methods described in our 2019 article " Projecting Unemployment and Demographic Trends ."

First, we estimate statistical models for LFP and unemployment rates of demographic groups defined by age, gender and education. For each gender and education, we decompose its unemployment and LFP into cyclical components common to all age groups and smooth local trends for age and cohort effects.

Second, we aggregate trends from the estimates of the group-specific trends. Specifically, we construct the trend for the aggregate LFP rate as the population-share-weighted sum of the corresponding estimated trends for demographic groups. We construct the aggregate unemployment rate and EPOP trends from the group-specific LFP and unemployment trends and the groups' population shares.

In our previous work, we estimated the trends for the unemployment rate and LFP rate of a gender-education group separately using maximum likelihood methods. The estimates reported in this article are based on the joint estimation of LFP and unemployment rate trends using Bayesian methods.

We separately estimate the trends using data from 1976 to 2019 (pre-pandemic) and from 1976 to 2021 (including the pandemic period). Figures 1, 2 and 3 display annual averages for the three aggregate labor market ratios — the LFP rate, the unemployment rate and EPOP, respectively — from 1976 to 2021.

In each figure, the solid black line denotes the observed values, and the blue and pink lines denote the estimated trend using data from 1976 up to and including 2019 and 2021, respectively. The estimated trends are subject to uncertainty, and the plotted trends represent the median estimate of the trend.

For the estimates based on data up to 2021, we also include the 90 percent coverage area shown as the shaded pink area. According to the statistical model, there is a 90 percent probability that the trend is contained in the coverage area. The blue and pink dotted lines represent our projections on how the labor market ratios will evolve until 2031, again based on the estimated trend up to and including 2019 and 2021. The shaded gray vertical areas highlight recessions as defined by the National Bureau of Economic Research (NBER).

Pre-Pandemic Trends: 1976-2019

We start with the pre-pandemic trends for the LFP rate and unemployment rate estimated for data from 1976 through 2019. After a long recovery from the 2007-09 recession, the LFP rate was 63.1 percent in 2019 (slightly above the estimated trend value of 62.8 percent), and the unemployment rate was 3.7 percent (noticeably below its estimated trend value of 4.7 percent).

The LFP rate being above trend and the unemployment rate being below trend reflects the characterization of the 2019 labor market as "hot." But note that even though the LFP rate exceeded its trend value in 2019, it was still lower than during the 2007-09 period. This difference is accounted for by the declining trend in the LFP rate.

As noted in our 2019 article , LFP rates and unemployment rates differ systematically across demographic groups. Participation rates tend to be higher for younger, more-educated workers and for men. Unemployment rates tend to be lower for men and for the older and more-educated population.

Thus, changes in the population composition over time — that is, the relative size of demographic groups — will affect the aggregate LFP and unemployment rates, in addition to changes in the LFP and unemployment rate trends of the demographic groups.

As also noted in our 2019 article, the hump-shaped trend of the aggregate LFP rate reflects a variety of forces:

• Prior to 1990, the aggregate LFP rate was boosted by an upward trend in the LFP rate of women. But after 1990, the LFP rate of women began declining. Combining this with declining trend LFP rates for other demographic groups has reduced the aggregate LFP rate.
• Changes in the age distribution had a limited impact prior to 2005, but the aging population since then has lowered the aggregate LFP rate substantially.
• Increasing educational attainment has contributed positively to aggregate LFP throughout the period.

The steady decline of the unemployment rate trend reflects mostly the contributions from an older and more-educated population and, to some extent, a decline in the trend unemployment rates of demographic groups.

Pre-Pandemic Expectations of Future LFP and Unemployment Trends

Our statistical model of smooth local trends for the LFP and unemployment rates of demographic groups has the property that the best forecast for future trend values of demographic groups is their last estimated trend value. Thus, the model will only predict a change in the trend of aggregate ratios if the population shares of its constituent groups are changing.

We combine the U.S. Census Bureau population forecasts for the gender-age groups with an estimated statistical model of education shares for gender-age groups to forecast population shares of our demographic groups from 2020 to 2031 (the dotted blue lines in Figures 1 and 2).

As we can see, the changing demographics alone imply further reductions of 1 percentage point and 0.2 percentage points in the trend LFP rate and unemployment rate, respectively. This projection is driven by the forecasted aging of the population, which is only partially offset by the forecasted higher educational attainment.

Based on data up to 2019, the same aggregate LFP rates in 2021 as in 2019 would have represented a substantial cyclical deviation upward from the pre-pandemic trends.

It is notable that the unemployment rate is much more volatile relative to its trend than the LFP rate is. In other words, cyclical deviations from trend are much more pronounced for the unemployment rate than for the LFP rate.

In fact, in our estimation, the behavior of the unemployment rate determines the common cyclical component of both the unemployment rate and the LFP rate. Whereas the unemployment rate spikes in recessions, the LFP rate response is more muted and tends to lag recessions. This feature will be important for interpreting how the estimated trend LFP rate changed with the pandemic.

Finally, Figure 3 combines the information from the LFP rate and unemployment rate and plots actual and trend rates for EPOP. On the one hand, given the relatively small trend decline of the unemployment rate, the trend for EPOP mainly reflects the trend for the LFP rate and inherits its hump-shaped path and the projected decline over the next 10 years. On the other hand, EPOP inherits the volatility from the unemployment rate. In 2019, EPOP is notably above trend, by about 1 percentage point.

Unemployment and Labor Force Participation During the Pandemic

The behavior of unemployment resulting from the pandemic-induced recession was different from past recessions:

• The entire increase in unemployment between February and April 2020 was accounted for by the increase in unemployment from temporary layoffs. This differed from previous recessions, when a spike in permanent layoffs led the bulge of unemployment in the trough.
• The recovery started in May 2020, and the speed of recovery was also much faster than in previous recessions. After only seven months, unemployment declined by 8 percentage points.
• The behavior of the unemployment rate is reflected in the 2020 recession being the shortest NBER recession on record: It lasted for two months (March to April 2020).

To summarize, the runup and decline of the unemployment rate during the pandemic were unusually rapid, but the qualitative features were not that different from previous recessions after properly accounting for temporary layoffs, as noted in the 2020 working paper " The Unemployed With Jobs and Without Jobs . "

The decline in the LFP rate was sharp and persistent. The LFP rate dropped from 63.4 percent in February 2020 to 60.2 percent in April 2020, an unprecedented drop during such a short period of time. After a rebound to 61.7 percent in August 2020, the LFP rate essentially moved sideways and remained below 62 percent until the end of 2021.

The large drop in the aggregate LFP rate has been attributed to, among others:

• More people — especially women — leaving the labor force to care for children because of school closings or to care for relatives at increased health risk, as noted in the 2021 work " Assessing Five Statements About the Economic Impact of COVID-19 on Women (PDF) " and the 2021 article " Caregiving for Children and Parental Labor Force Participation During the Pandemic "
• An increase in retirement due to health concerns, as noted in the 2021 working paper " How Has COVID-19 Affected the Labor Force Participation of Older Workers? "
• Generous pandemic income transfers and unemployment insurance programs, as noted in the 2021 article " COVID Transfers Dampening Employment Growth, but Not Necessarily a Bad Thing "

All of these factors might impact the participation trend, but by how much?

The Pandemic's Effect on Trend Estimates for LFP and Unemployment

The aggregate trend assessment for the LFP and unemployment rates has changed considerably as a result of 2020 and 2021. Repeating the estimation of trend and cycle for our demographic groups using data from 1976 up to 2021 yields the pink trend lines in Figures 1 and 2.

The updated trend estimates now put the positive cyclical gap in 2019 for LFP at 0.5 percentage points (rather than 0.3 percentage points) and the negative cyclical gap for the unemployment rate at 1.4 percentage points (rather than 1 percentage point). That is, by this estimate of the trend, the labor market in 2019 was even hotter than by the estimates from the 1976-2019 period.

In 2021, the actual LFP rate is essentially at trend, and the unemployment rate is only slightly above trend. That is, by this estimate of the trend, the labor market is relatively tight.

Notice that even though the new 2021 trend estimates for both the LFP and the unemployment rates differ noticeably from the trend values predicted for 2021 based on data up to 2019, the trend revisions for the LFP rate are limited to more recent years, whereas the trend revisions for the unemployment rate apply to the whole sample.  

The difference in revisions is related to how confident we can be about the estimated trends. The 90 percent coverage area is quite narrow for the LFP rate for the entire sample up to the last four years. Thus, there is no need to drastically revise the estimated trend prior to 2017.

On the other hand, the 90 percent coverage area for the trend unemployment rate is quite broad throughout the sample. That is, a wide range of values for trend unemployment is potentially consistent with observed unemployment values. Consequently, the last two observations lead to a wholesale reassessment of the level of the trend unemployment rate.

Another way to frame the 2020-21 trend revisions is as follows. The unemployment rate is very cyclical, deviations from trend are large, and though the sharp increase and decline of the unemployment rate in 2020-21 is unusual, an upward level shift of the trend unemployment rate best reflects the additional pandemic data.

The LFP rate, however, is usually not very cyclical, and it is only weakly related to the unemployment rate. Since the model assumes that the cyclical response does not change over the sample, it then attributes the large 2020-21 drop of the LFP rate to a decline in its trend and ultimately to a decline of the trend LFP rates of most demographic groups.

Finally, the EPOP trend is again mainly determined by the LFP trend, seen in Figure 3. Including the pandemic years noticeably lowers the estimated trend for the years from 2017 onwards. The cyclical gap in 2019 is now estimated to be 1.4 percentage points, and 2021 EPOP is close to its estimated trend.

What Does the Future Hold?

In our framework, current estimates of trend LFP and the unemployment rate for demographic groups are the best forecasts of future rates. Combined with projected demographic changes, this implies a continued noticeable downward trend for the LFP rate and a slight downward trend for the unemployment rate.

The trend unemployment rate is low, independent of how we estimate the trend. But given the highly unusual circumstances of the pandemic, the model may well overstate the decline in the trend LFP rate. Therefore, it is likely that the "true" trend lies somewhere between the trends estimated using data up to 2019 and data up to 2021.

That being a possibility, it remains that labor markets as of now have been unusually tight by most other measures, such as nominal wage growth and posted job openings relative to hires. This suggests that the true trend is closer to the revised 2021 trend than to the 2019 trend. In other words, the LFP rate and unemployment rate at the end of 2021 relative to the 2021 estimate of trend LFP and unemployment rate are consistent with a tight labor market.

Andreas Hornstein is a senior advisor in the Research Department at the Federal Reserve Bank of Richmond. Marianna Kudlyak is a research advisor in the Research Department at the Federal Reserve Bank of San Francisco.

To cite this Economic Brief, please use the following format: Hornstein, Andreas; and Kudlyak, Marianna. (April 2022) "The Pandemic's Impact on Unemployment and Labor Force Participation Trends." Federal Reserve Bank of Richmond Economic Brief , No. 22-12.

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