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Unemployment, after dropping in 2020, teen summer employment may be poised to continue its slow comeback.

Last summer, businesses trying to come back from the COVID-19 pandemic hired nearly a million more teens than in the summer of 2020.

COVID-19 Pandemic Pinches Finances of America’s Lower- and Middle-Income Families

Nearly one-in-five middle-income families report receiving unemployment benefits in 2020.

Most in the U.S. say young adults today face more challenges than their parents’ generation in some key areas

About seven-in-ten say young adults today have a harder time when it comes to saving for the future, paying for college and buying a home.

Some gender disparities widened in the U.S. workforce during the pandemic

Among adults 25 and older who have no education beyond high school, more women have left the labor force than men.

Immigrants in U.S. experienced higher unemployment in the pandemic but have closed the gap

With the economic recovery gaining momentum, unemployment among immigrants is about equal with that of U.S.-born workers.

College graduates in the year of COVID-19 experienced a drop in employment, labor force participation

The challenges of a COVID-19 economy are clear for 2020 college graduates, who have experienced downturns in employment and labor force participation.

U.S. labor market inches back from the COVID-19 shock, but recovery is far from complete

Here’s how the COVID-19 recession is affecting labor force participation and unemployment among American workers a year after its onset.

Long-term unemployment has risen sharply in U.S. amid the pandemic, especially among Asian Americans

About four-in-ten unemployed workers had been out of work for more than six months in February 2021, about double the share in February 2020.

A Year Into the Pandemic, Long-Term Financial Impact Weighs Heavily on Many Americans

About a year since the coronavirus recession began, there are some signs of improvement in the U.S. labor market, and Americans are feeling somewhat better about their personal finances than they were early in the pandemic.

Unemployed Americans are feeling the emotional strain of job loss; most have considered changing occupations

About half of U.S. adults who are currently unemployed and are looking for a job are pessimistic about their prospects for future employment.

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Economic Brief

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.

research paper on the unemployment

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.

This article may be photocopied or reprinted in its entirety. Please credit the authors, source, and the Federal Reserve Bank of Richmond and include the italicized statement below.

V iews expressed in this article are those of the authors and not necessarily those of the Federal Reserve Bank of Richmond or the Federal Reserve System.

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Estimating Natural Rates of Unemployment

Brandyn Bok and Nicolas Petrosky-Nadeau

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FRBSF Economic Letter 2022-14 | May 31, 2022

Before the pandemic, the U.S. unemployment rate reached a historic low that was close to estimates of its underlying longer-run value and the short-run level associated with an absence of inflationary pressures. After two turbulent years, unemployment has returned to its pre-pandemic low, and the estimated underlying longer-run unemployment rate appears largely unchanged. However, economic disruptions appear to have pushed up the short-run noninflationary rate substantially, as high as 6%. Examining these different measures of the natural rate of unemployment can provide useful insights for policymakers.

The U.S. unemployment rate in March 2022 was 3.6%, near its pre-pandemic 50-year low of 3.5% recorded in February 2020. Despite these similarly low levels, the economic environment now is very different than before the pandemic. The low unemployment at the end of the expansion following the Great Recession coincided with a period of very low inflation: personal consumption expenditures (PCE) price inflation hovered around 1.5% for much of 2019, below the Federal Reserve’s 2% average inflation goal. By contrast, recent low unemployment is associated with much higher inflation: in recent months, PCE inflation has exceeded 5%.

With this contrast in mind, policymakers often rely on two different unemployment benchmarks, or so-called natural rates of unemployment, to assess appropriate monetary policy (Crump, Nekarda, and Petrosky-Nadeau 2020). A first benchmark, the longer-run unemployment rate, provides a guide for normal economic activity in the longer run, after all the shocks that are thought to cause a current business cycle, either an expansion or a contraction, have dissipated. While there is no consensus on the time horizon for this longer run, a general guidepost is 10 or more years in the future. The second benchmark assesses the degree of economic slack and inflationary pressures in the short run and medium run over the next few months through a few years. This “noninflationary rate of unemployment” associated with price stability provides a guide to how likely current labor market conditions are to be connected with inflationary pressures. In sum, these two concepts of the natural rate of unemployment help policymakers address separate concerns when assessing the current state of the economy.

This Economic Letter discusses some common approaches to estimating the unobserved longer-run and noninflationary benchmarks for the natural rate of unemployment following the discussion in Crump et al. (2020). The two benchmarks coincide at times, as they did in late 2019. At other times, there can be a sizable gap, as is the case today, with the noninflationary rate of unemployment well above its longer-run level. This divergence provides useful context for the recent Federal Open Market Committee (FOMC) decision to begin tightening policy to bring inflation back towards its longer-run goal for price stability.

Unemployment rates expected to prevail in the longer run

The structure of the economy and the underlying dynamics of the labor market—factors that change slowly over time—are thought to determine the natural rate of unemployment in the longer run. Researchers use a wide range of approaches to estimate the longer-run natural rate; we focus first on the Congressional Budget Office (CBO) estimate of the “noncyclical rate of unemployment.”

The CBO follows a broad approach that mainly relies on changes in the composition of the labor force. According to Shackleton (2018), the longer-run or noncyclical rate of unemployment is based on an assumption that the U.S. labor market was at its longer-run state during the second half of 2005, and that this was true for different populations grouped according to age, sex, race and ethnicity, and educational attainment. Using the second half of 2005 as a long-run benchmark for each demographic group’s unemployment rate, the CBO constructs an aggregate longer-run rate of unemployment for the United States, adjusting to reflect each group’s actual share of the labor force at different dates over time. As a result, all movements in the CBO’s estimate of the longer-run rate of unemployment come from slow-moving changes in the makeup of the workforce.

Figure 1 shows the noncyclical rate of unemployment (dashed blue line) from 1985 through 2021, along with a range of alternative estimates (shaded area), some of which we describe next. In general, the longer-run estimates change very gradually over time, in contrast to the higher-frequency cyclical fluctuations in the actual unemployment rate (red line).

Figure 1 Estimates of U.S. longer-run rate of unemployment

Estimates of U.S. longer-run rate of unemployment

Note: Shaded area represents a range of estimates described in text and in Crump et al. (2020); quarterly data. Sources: Bureau of Labor Statistics (BLS), CBO, and authors’ calculations using CPS micro data.

A second related approach uses statistical methods to estimate the longer-run trends for different population groups’ unemployment rates from historical experience before aggregating them into an overall longer-run natural rate of unemployment. This approach, which can be categorized as “longer-run trends,” tends to imply higher longer-run rates of unemployment than the CBO estimate. This is especially true around the prolonged period of relatively elevated unemployment in the aftermath of the 2007–08 financial crisis. Our own application of this approach, shown as the blue solid line in Figure 1, draws on monthly microdata from the Current Population Survey and a technique called a bandpass filter to extract the changes in each population group’s unemployment rates over multiple decades. Our approach yields an estimate for the longer-run rate of unemployment of 6.0% in the second half of 2005, compared with the CBO’s 5.0% estimate. By the fourth quarter of 2021, the two approaches result in essentially identical estimates of 4.5%.

A third approach seeks to infer the “potential minimum” rates of unemployment for different demographic groups based on recent business cycle peaks (blue dotted line). Adapting a methodology used by DeLong and Summers (1988) to measure the economy’s level of potential output, this approach results in the lowest contour (dotted red line) in the range of estimates in Figure 1. The approach suggests a longer-run rate of unemployment of 4.3% in the second half of 2005, slightly below the CBO’s estimate during its reference period. For the fourth quarter of 2021, this approach suggests a longer-run rate of unemployment of 3.4%.

Unemployment rates associated with no inflationary pressures

The second benchmark rate is meant to assess the degree of economic slack and inflationary pressures in the short and medium run. It is usually derived from an assumed relationship between price inflation and deviations of actual unemployment from this benchmark, a relation referred to as the Phillips curve.

The most common approach to estimate this benchmark rate of unemployment is to follow a statistical representation known as a state-space model (see Laubach 2001). This method relies on statistical assumptions about the dynamics of an unobserved variable, in this case the noninflationary rate of unemployment. The values of this “state variable” are then determined by the movements of observed unemployment and inflation rates via the Phillips curve, while simultaneously accounting for other factors, such as changes in production costs and currency exchange rates, that affect inflationary pressures in the economy.

Figure 2 plots a range (shaded area) of alternative state-space model estimates of the noninflationary rate of unemployment from 1985 through 2021. The solid blue line highlights our preferred approach to addressing the unique challenges from the COVID-19 pandemic, which we will discuss in greater detail. The figure also includes the CBO’s estimate of the longer-run rate of unemployment (blue dashed line) for reference. The figure highlights the degree to which estimates of the noninflationary rate of unemployment fluctuate with the actual rate of unemployment. It also shows that, over longer periods, the noninflationary rate tends to converge back towards the level of the longer-run rate of unemployment.

Figure 2 Estimates of U.S. stable-price rate of unemployment

Estimates of U.S. stable-price rate of unemployment

Note: Shaded area represents the full range of estimates from set of sources; quarterly data. Sources: BLS and authors’ calculations using CPS micro data and estimates reviewed in Crump et al. (2020).

The noninflationary rate of unemployment during the pandemic

Estimating the noninflationary rate of unemployment has been challenging due to the exceptionally large and rapid movements in the unemployment rate during the second quarter of 2020, reaching nearly 15% within two months. In the Phillips curve framework for a given level of the noninflationary rate of unemployment, such a rise in the unemployment rate warrants a more pronounced slowdown in inflation than actually occurred. As a result, models that use the period just before the onset of the pandemic as a baseline imply a sharp increase in the noninflationary rate of unemployment to fit the sharp increase in actual unemployment without a commensurately large decline in price pressures. This is illustrated by the dashed blue line in Figure 3, where the noninflationary rate of unemployment rises sharply to just over 8% in the second quarter of 2020.

Figure 3 Estimates of stable-price unemployment through pandemic

Estimates of stable-price unemployment through pandemic

However, much of the rise in unemployment during this period was driven by people on temporary layoff who were expected to return to work. Indeed, the share of unemployed people on temporary layoff rose from 14% before the pandemic to 78% in April 2020 (see Wolcott et al. 2020), only to return to its pre-pandemic level by mid-2021. This contrasts with past recessions, when the share on temporary layoff did not play a large role.

Temporary layoffs do not contribute to inflationary pressures in the same way as permanent job losses: employers tend to maintain ties with these workers so they can quickly bring them back and ramp up production as demand returns. Following this insight, our preferred estimate (solid blue line in Figure 3) controls for the spike in temporary layoffs and results in a limited increase in the noninflationary rate of unemployment at the start of the pandemic. That said, as the share of temporary layoffs reverted to its historical level and PCE price inflation gained momentum in 2021, our estimated noninflationary rate of unemployment progressively rises to 6% in the fourth quarter of 2021, equaling the model that does not control for temporary layoffs (dashed blue line).

Conclusions

Two benchmark natural rates of unemployment can serve as useful guides in assessing the current state of the labor market, particularly relative to the Federal Reserve’s goals of maximum employment and price stability. This Economic Letter outlines various approaches for estimating both the longer-run rate of unemployment and the rate of unemployment associated with price stability. The unprecedented economic conditions during the pandemic created unique challenges for estimating the latter benchmark. Though longer-run and noninflationary rates of unemployment typically do not coincide at a point in time, any gap between the two benchmark rates tends to close over time. As such, the current sizable gap following the disruptions to the economy from the pandemic is likely to close as the FOMC follows an expected path of removing policy accommodation, intended to slow inflation to levels consistent with its price stability goals.

Brandyn Bok is a research associate in the Economic Research Department of the Federal Reserve Bank of San Francisco.

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

Crump, Richard K., Christopher J. Nekarda, and Nicolas Petrosky-Nadeau. 2020. “ Unemployment Rate Benchmarks .” Federal Reserve Board, Finance and Economics Discussion Series 2020-72.

DeLong, J. Bradford, and Lawrence H. Summers. 1988. “How Does Macroeconomic Policy Affect Output?” Brookings Papers on Economic Activity 1988(2), pp. 433–480.

Laubach, Thomas. 2001. “Measuring the NAIRU: Evidence from Seven Economies.” Review of Economics and Statistics 83(May), pp. 218–231.

Shackleton, Robert. 2018. “ Estimating and Projecting Potential Output Using CBO’s Forecasting Growth Model .” Congressional Budget Office, Working Paper Series 2018-03.

Wolcott, Erin, Mitchell G. Ochse, Marianna Kudlyak, and Noah A. Kouchekinia. 2020. “ Temporary Layoffs and Unemployment in the Pandemic .” FRBSF Economic Letter 2020-34 (November 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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How Did Workers with a History of Long-Term Unemployment Fare during the COVID Recession? Evidence From Applicants To The Ready To Work Partnership Grant Program (Issue Brief)

Publication info, research methodology, country, state or territory, description, other products.

In 2014 the Employment and Training Administration awarded four-year grants totaling $170 million to 24 grantees for the Ready to Work Partnership (RTW) Grant Program. RTW partnerships of workforce agencies, training providers, employers, and other local organizations established programs to prepare workers for employment, particularly in occupations and industries being filled by foreign workers through the H-1B visa program. Grant funds were used to provide a range of customized services to the long-term unemployed. In addition to the impact evaluation, the study produced several brief reports, including an examination of the effect of the COVD-19 pandemic on the long-term unemployed.

During 2021 the economy largely recovered, and the unemployment rate returned nearly to levels that preceded the COVID-19 pandemic. However, workers’ employment experiences during the pandemic and its corresponding economic shutdown varied depending on participant characteristics. This brief explores the employment and earnings of applicants to the RTW Partnership Grant program before and during the COVID pandemic. When the RTW program began offering services in 2015, it targeted workers who lost their jobs during or after the 2007-2009 recession, remained long-term unemployed or underemployed, and yet had sufficient education and experience to become re-employed in higher-paying middle- or high-skill jobs. By 2019, before the pandemic emerged, most of these workers had returned to employment at higher earnings than they experienced before they applied to the RTW program.

This study found that, during the COVID-19 pandemic, a sample of previously long-term unemployed workers who were relatively older and educated experienced the following:

  • Employment fell by 10 percentage points and never fully recovered through late 2021. Although earnings fell in mid-2020, earnings began rising again by late 2020.
  • Rising Unemployment Insurance benefits offset falling earnings, such that the sum of earnings and benefits remained stable in 2020.
  • Changes in employment between 2019 and 2021 did not vary by education level, race and ethnicity, or gender.
  • Changes in earnings between 2019 and 2021 did not vary by race and ethnicity or gender, but workers without a bachelor’s degree had a substantially larger decrease in earnings in 2020 and a smaller increase in earnings in 2021.

OPINION article

Future directions in the research on unemployment: protean career orientation and perceived employability against social disadvantage.

\nChiara Panari

  • 1 Department of Economics and Management, University of Parma, Parma, Italy
  • 2 Department of Humanities, Social Sciences and Cultural Industries, University of Parma, Parma, Italy

Introduction

The level of uncertainty and fear introduced by COVID-19 pandemic has threatened the relationships, work and meanings of existence.

From the point of view of the labor market, the COVID-19 crisis has undermined the illusion of security at work, leading to a massive career shock and accentuating the existing inequities in the labor market, with severe economic and societal implications in terms of career experiences, job opportunities and career paths ( Akkermans et al., 2020 ). During a pandemic, the loss of employment opportunities represents a source of fear which aggravates the intense concerns and anxieties about health and death.

According to a preliminary report from the International Labor Organization ( ILO., 2020 ) estimating between 5.3 and 24.7 million unemployed, the most negative impact will be felt by low-wage and low-skill employees. Jobless individuals tend to be those who have had precarious jobs in fields that typically do not offer long-term contracts, decent wages, and health benefits ( ILO., 2020 ).

Since the individuals' work-lives represents a source of motivation, expression of personal believes and high-quality interpersonal interaction ( Crayne, 2020 ), reconstructing life after this pandemic will need to consider a new perspective of work as a core value in creating decent and decorous work, which has been limited by COVID-19 crisis ( Blustein and Guarino, 2020 ).

This situation has leading researchers to ask questions about the processes by which individuals cope with a job loss experience and the mechanisms triggering attitudes of resilience and exploration of sustainable careers that would imply seeing oneself either in a constantly evolving path, or developing additional skills, or retooling for other jobs and building new career networks ( Hite and McDonald, 2020 ). Studying these aspects will help direct active labor policy interventions aimed at promoting and supporting the employability of people looking for work.

The Literature on Unemployment

Most literature has focused on the negative effects of job loss on well-being, such as physiological symptoms, depression and suicide ( McKee-Ryan et al., 2005 ; Paul and Moser, 2009 ; Wanberg, 2012 ), limited to the examination of the influence of stress, response, and coping with the results of one's job loss ( Gowan, 2014 ). This is also reflected in the social negative evaluation of being unemployed and the stigmatization of personal weaknesses of the unemployed, which in turn lead to less sympathy, and finally to disadvantaged hiring decisions ( Monteith et al., 2016 ).

In fact, from the point of view of the dominant outgroup represented by employed persons, the stigmatization of unemployment status influences recruiters, hiring managers, and interview panelists in the decision to not hire an unemployed worker. Unemployment status as a social identity is shamed, as with other stigmatized social groups, and psychological processes associated with social identity and stigma contribute to the discrimination ( Norlander et al., 2020 ). Particularly, people who possess system-justifying beliefs are more likely to judge unemployed and their deservingness negatively. Beliefs in a just world are likely to affect negative judgments of an unemployed person's competences ( Monteith et al., 2016 ).

From the point of view of the unemployed themselves, the social stigma of the unemployed as being unmotivated, depressed and without professional abilities or personal resources can generate feelings of weakness and blushing on jobless people, and may in turn negatively impact social connections ( Grimmer, 2016 ). McFadyen (1995) argued that the coping processes used by unemployed people to face this stigma could be influenced by whether they categorized themselves as unemployed or adopt some other categorization.

The social identity approach sustained that social image that arises from group memberships has important consequences for how people view and feel about themselves, and also how they are viewed and evaluated by others. If social identities do not provide positive resources for group members, this negatively reflects on individual self-esteem and well-being ( Jetten et al., 2017 ).

The researches that have focused on the attributional processes used by jobless individuals to explain their condition are heterogeneous and also COVID-19 crisis seems to have altered these processes.

On the one side, unemployment is an undesirable and uncontrolled event and there is an ample literature focused on this view. In this sense, from the unemployed person's point of view on his/her perception of social disadvantage, some studies showed that jobless individuals generally show a greater empathy with unemployed people and attribute unemployment to environmental, rather than personal, factors ( Furåker and Blomsterberg, 2003 ; Van Oorschot and Meuleman, 2014 ). They seem to justify their situation as a painful experience beyond their control. Consistent with a social identity theory perspective, some authors underlined that jobless individuals use both intragroup and intergroup comparisons and these processes were related to their self-esteem. In period of very high unemployment, like the current one where the stigmatization is less pronounced because external causes are attributed to unemployment, the perception of being similar to the unemployed group (at the intra-group level) enhanced feelings of self-worth. However, greater perceived differences between unemployed people and employers were associated with reduced self-esteem ( Sheeran et al., 1995 ). This finding supports the view that feelings of self-worth are contingent, at least in part, on the perceived status of one's own group relative to other groups ( Sheeran et al., 1995 ).

On the other side, there is also evidence that unemployed people do not share similar experience of unemployment ( Creed et al., 2001 ). ( Creed and Evans, 2002 ) highlight the importance of individual differences when considering the psychological impact of unemployment. In fact, some researchers have found that jobless people hold a stronger prejudices and stigma on unemployed individuals than do employed individuals, especially regarding overall value, ability, motivation, and mental health ( Takahashi et al., 2015 ).

In addition, a few studies on the process of in-group identification showed that the unemployed identified little with their own disadvantaged category, which was perceived as a group to distance themselves from ( Wahl et al., 2013 ). In this sense, unemployed could carry out a process defined by literature as self-group distancing that represents an individual mobility response to dissociate from their stigmatized in-group and avoid the negative experience of being stigmatized ( Van Veelen et al., 2020 ).

Other studies have underlined that the process of in-group identification seemed to be more related to the personal stress one experienced ( Ybema et al., 1996 ), or to family-extended employment ( Curtis et al., 2016 ), or to length of time they are unemployed ( Cassidy., 2001 ), rather than to a comparison between social categories characterized by different statuses. In terms of effects of self-categorization on social support, locus of control and problem-solving, previous experience of unemployment plays a crucial role ( Cassidy., 2001 ). In a Danish study ( Pultz and Mørch, 2015 ), researchers showed that some jobless individuals challenge the traditional representation of the unemployed and describe them as innovative, skilled and able to cope with economic insecurity even though it is stressful. These authors take up the concept of strategic self-management, which refers to a pro-active career orientation.

The identity of “unemployed” can be perceived as flexible and transient, and how person adopts this identity has implications for the person's core cognitive beliefs that influence person's ability to adapt to career events ( Thompson et al., 2017 ). The possibility of perceiving one's unemployment status only as a phase of one's working career and not as a condition of a stigmatized social group could be due to the perception of the permeability of the boundaries between groups of unemployed and employed people. Probably even today, in a situation of large-scale emergency crisis, the boundaries between employed and unemployed people are still much less clear and the perception of failings, poor competencies and welfare stigma previously attributed to the unemployed has changed consistently. In fact, from the out-group point of view, in the HR selection process evaluators tend to have less bias toward unemployed individuals because unemployment has become today a vast and global scale phenomenon ( Suomi et al., 2020 ).

Also from the in-group perspective, unemployment is now much more seen as a temporary phase of the career path rather than a fixed social category. Rather than justifying the system that excluded them from the productive world, which is an attributional process that usually characterizes employed workers in their perception of unemployed category ( Monteith et al., 2016 ), some employees who have lost their job seem to be more engaged in coping with the resulting change and the discontinuity of their working life.

The framework of a career planning concept and career paths over time ( Wanberg, 2012 ) could be considered as yet another approach through which it is possible to examine job loss, by pointing out the dynamic career planning activities over the course of one's unemployment. Furthermore, research focusing on career exploration during the unemployment conditions following a job loss, has the potential to reconsider and change the meaning of job loss to individuals ( Zikic and Klehe, 2006 ).

Our contribution moves in this direction, as it explores some constructs that can influence the perception of unemployment directly from people experiencing job loss, and could be the precursor to a more realistic interpretation of the condition of social disadvantage, thus promoting a more proactive attitude toward job reintegration.

Particularly, we will focus on protean career orientations that play a pivotal role in the search for growth opportunities within the job loss transition and that help people to face, not only the negative factors associated with their situation of uncertainty in connection with the crisis of their professional project, but also to re-evaluate their wider life goals and career paths ( Waters et al., 2014 ).

The protean career concept is strictly related to the employability that refers to “individual's beliefs about the possibilities of finding new, equal, or better employment” ( De Cuyper et al., 2011 ). It arises from a combination of knowledge, practical skills and abilities that individuals develop over the course of their working life in order to achieve their career path, allowing them to make sense of their previous professional experiences and to explore new opportunities ( Fugate and Kinicki, 2008 ).

The Protean Career Orientation and Perceived Employability as Key Strategies for Work Reintegration

Current literature on unemployment emphasizes how the success of one's job search depends on the sense of individual responsibility and the desire for self-fulfillment in guiding one's career choices, as well as individual beliefs about the possibility of achieving one's goals. In this sense, the concept of Protean Career Orientation (PCO) refers to one's attitude toward career choices, based on the search for self-realization. This attitude implies that an individual is responsible for managing his/her own career and for making career-related decisions shaped on personal values, rather than labor market demands ( Briscoe and Hall, 2006 ).

The two aspects of a protean career orientation are: being self-directed and being value-driven. Self-direction refers to the degree to which an individual has control over his/her own career ( Mirvis and Hall, 1994 ). The aspect of value-driven places career decisions as closely linked to one's own personal values, rather than one being driven by categories of the social system ( Briscoe and Hall, 2006 ). As underlined by Lysova et al. (2015) , the sense of meaning that workers derive from work, however, is impacted by work values, understood as the end states people desire and feel they ought to be able to realize through working ( Nord et al., 1990 ). People who show a high level of intrinsic values, as freedom and self-growth, has an higher protean career orientation and defines career success in terms of psychological factors as compared with traditional career; protean career orientation is also focused on continuous learning in professional development ( Hall, 2004 ).

One of the critical aspects connected with the state of unemployment is the perception of uncontrollability, which can lead one to focus on external factors and to feel closer to other social disadvantaged groups ( Bukowski et al., 2019 ), rather than to focus on internal motivational resources. On the other hand, in the context of unemployment, the protean career orientation activates a reverse process of reworking one's career path, offering a different interpretation of one's social condition, because the person focuses on his/her aspirations and goes back to feeling like he/she still has the personal resources to invest in a new professional project. The prerequisite for a protean career attitude is the overcoming of the categorization and evaluation imposed by the external social world, because those values are founded on the notion of career actors—as opposed to organizations—who take responsibility of their own careers ( Hall, 2002 ). Protean people seem to have more internal control over their career path and this is in line with unemployment research, that underlined the role of internal LOC in predicting reemployment ( Meyers and Houssemand, 2010 ). Applying the perspective of the social determination theory to unemployment, some authors ( Vansteenkiste et al., 2005 ) found that perception of being forced to search for a job, moving by controlled motivation accompanied by stressful and pressuring experiences, negatively predicted their general health. On the contrary, if unemployed perceive the search for a job as an autonomous and personal choice because employment is seen as an opportunity to develop their skills, they have an internal motivation that enhance behavioral effectiveness, greater volitional persistence, and enhanced subjective well-being. This motivational process is the basis of the perception of controllability of the protean orientation. Also social cognitive career theory highlighted the importance of self-regulatory efficacy, which involves beliefs about controlling motivational aspects of the job search, and personal goals, as behavioral intentions to act in ways that produce desired outcomes, in predicting reemployment success ( Thompson et al., 2017 ).

In this sense, when considering re-employment, Waters et al. (2014) emphasized that a protean career orientation helped individuals to clarify and express their goals during unemployment and to find a sense of positive identity ( Zafar et al., 2017 ).

Secondly, another core aspect is related to the loss of self-esteem ( Kanfer et al., 2001 ), that represents a psychological consequence of unemployment. During unemployment PCO may help unemployed people to maintain a positive self-esteem. Protean orientation could be interpreted as a mechanism through which unemployed feel much more similar to people who belong to the world of work and activate a self-group distancing process also for the type of careers that characterize working life. In fact, there were disruptive and macroeconomic factors in the labor market that have changed how individuals conceptualize their careers more fragmented and discontinuous compared to the past ( Briscoe et al., 2012 ).

People who manage their careers from a protean orientation do not link their career identity to the organization and loss. This perception does not lead to the lack of the sense of identity that sometimes occurs after the job loss ( Waters et al., 2014 ). Instead, people with low PCO levels will be less proactive in finding resources for the enhancement of their skills, and their level of self-esteem will likely be lower during the period of unemployment. This can discourage people from looking for a new job, as it affects the belief that they can find it ( Hirschi et al., 2017 ).

Thirdly, people with a high protean career level become more independent and flexible in managing their career opportunities in response to social changes in work organization ( Wiernik and Kostal, 2018 ). In the literature, the concept of protean career has been associated with the concept of boudaryless career which refers to a career characterized by different levels of physical and psychological movement among organizations ( Sullivan and Arthur, 2006 ), which metaphorically recalls the permeability of the boundaries between workers and unemployed. Consequently, high-PCO individuals are in charge of their own career development ( Hall et al., 2018 ) and can adjust to the current dynamic labor market. People with a high PCO tend to: be more learning-oriented; have high self-esteem and clearer goals; and formulate specific career plans ( Li et al., 2019 ).

This proactive attitude translates to a more effective job search during unemployment ( Waters et al., 2014 ). In fact, adopting a protean self-directed approach may lead individuals to regularly explore the situation of work environment in order to increase their chances of finding a job that will help them achieve their personal projects.

Self-managing one's career leads people to become more aware of their acquired professional skills but also increases the knowledge and competencies required in the labor market ( Bozionelos and Bozionelos, 2015 ).

In this sense, recent studies have shown that people oriented toward a protean career are likely to have a high level of perceived employability ( Baruch et al., 2019 ; Cortellazzo et al., 2020 ).

The perceived employability is the second key construct that plays a central role in managing one's work history in unemployment conditions.

When considering changes in career development and paths, increasing one's employability is an important task for both the unemployed and those seeking new employment, as their career may depend on perceived employability.

Employability has been studied mainly from three perspectives. Fugate and Kinicki (2008) proposed a dispositional approach to employability which identifies a range of traits (for example, openness to change, proactivity, and resilience), that facilitates proactivity in adapting to work and career environments. Van Der Heijde et al. (2006) elaborated a competence-based conceptualization of employability, in which the dimension of occupational expertise is complemented with four general competences: anticipation and optimization, personal flexibility, corporate sense and balance. The authors distinguish between two different types of adaptation to changes in the internal and external labor market, the first one that is referred to as anticipation and optimization, and one more passive variant entitled personal flexibility. The concept of corporate sense refers to participation and performance in different workgroups, such as the department, working teams, occupational community or other networks. Finally, balance is defined as compromising between opposing employers' interests as well as one's own opposing work, career and private interests. Finally, the third perspective focuses on perceptions of employability which Vanhercke et al. (2014) define as the individual's perceptions of possibilities of obtaining and maintaining employment.

In the field of unemployment, we refer to the third perspective concerning external perceived employability, that has been also defined by Berntson et al. (2006) as the subjective individual perception of the ability to evaluate one's skill at getting a job. In this sense, employability represents the perception of employment opportunities with the current employer or with another employer ( Rothwell and Arnold, 2007 ; De Cuyper and De Witte, 2008 ). The subjective perception, in fact, of being able to relocate to the professional world had a strong motivational impact, which in turn affected the implementation of realistic assessments of one's actual possibility of relocation and the use of functional strategies to achieve one's professional goals ( Van den Broeck et al., 2010 ), such as skill development ( De Vos et al., 2011 ; Vanhercke et al., 2014 ).

Furthermore, perceived employability increases the feelings of control over careers and job search activities, and it is related to a minor duration of unemployment, and to re-employment ( Consiglio et al., 2021 ).

Research also showed that perceived employability could help mitigate the negative effects of job loss, such as emotional implications ( Hodzic et al., 2015 ; Consiglio et al., 2021 ).

In the context of job loss, individuals who are more employable will perceive less impairment from the job loss, will engage in more job search activity and will achieve higher quality reemployment ( Fugate et al., 2004 ). Koen et al. (2013) showed that employability also increased long-term reemployment opportunities ( McKee-Ryan et al., 2005 ; Paul and Moser, 2009 ; Lim et al., 2016 ; Lo Presti and Pluviano, 2016 ). Perceived employability could represent an individual's belief that reduces the differences with the people who are in the job market because it focuses on the perception of one's personal skills and opportunities for change affecting proactive behaviors and cognitive reinterpretation of job loss. According to social identity theory, especially if boundaries between groups are perceived more permeable, protean career orientation and perceived employability could be seen as an individual mobility strategy to distance from a devalued social group and achieve more positive social identities.

Protean individuals who see themselves as more employable are less likely to feel as they are part of a stigmatized category allowing to protect themselves from social stigma, even if the stigma consciousness of employment does not always have negative consequences in terms of proactivity ( Krug et al., 2019 ) especially in in the context of the COVID-19 health crisis. A high levels of protean career orientation and perceived employability allow to evaluate the experience of unemployment differently and this approach leads jobless individuals to believe in the future. In fact, their perception of available opportunities in the labor market may be selective and more engaged in targeted research ( Zakkariya and Nimmi, 2021 ).

As a career shock, the COVID-19 crisis has led us to develop new studies to identify and implement targeted actions that could contribute not only to improving the general well-being of unemployed persons, but also to increasing their likelihood of finding work.

In the actual socio-economic context characterized by a general lack of job opportunities, and considering the diffusion of new career paths characterized by frequent work changes and transitions, our question is: “Are the unemployed still stigmatized or do they perceive themselves to be a disadvantaged category today?”.

Following the economic consequences of the pandemic, the social perception of unemployment has changed, limiting prejudices against jobless people by employed individuals. This could have an impact on the unemployed perception of their work condition. Unemployed people should therefore suffer a lesser loss of the sense of self-esteem and self-efficacy and rely on their own proactivity to find a new job. To be successful in finding employment a person must believe they have the skills and abilities to do so. In this sense, gaining deeper understanding of the role of a protean career orientation and of perceived employability can offer unemployed people new ways to create change for themselves. In fact, people with a high level of protean career and employability are less likely to feel that they are part of a disadvantaged category, have a high self-esteem and self-efficacy, as they evaluate their experience of unemployment differently and this approach activates proactive behavior in preparatory and active job search.

Even in the case of unemployed individuals seeking guidance and advice to support their return to the labor market, protean people with a high level of perceived employability tended to better estimate their skills and better define their professional goals by identifying possible perspectives for getting out of the unemployed group in which they do not recognize themselves.

In terms of career counseling, working with unemployed clients should focus on building positive perspectives in connection with the clients' career goals and their sense of self direction and responsibility in order to promote control over their career paths. In fact, people with high levels of PCO are less identified in a disadvantaged social category, and this aspect could be used during the counseling to modify the cognitive interpretation of the unemployment status and promote proactivity and agency. In this sense, a counseling centered on protean career orientation and perceived employability should be compared to the develop of proactive coping strategies. Counselors should help people to evaluate the period of unemployment as an opportunity to redefine professional goals in a flexible way and develop a plan for achieving them. For example, starting by the reflection on the pandemic situation in terms of changed traditional working methods and roles, counseling can be viewed as a chance to invest in training and updating one's skills, to respond to a significantly changed labor market, especially from the point of view of digital skills. High PCO and perceived employability represent a great motivational and emotional investment in job search that can help to reach job goals, but it may happen that unemployed have to face difficulties and failures in job search. In this sense, a high PCO allows people to collect informations and reflect about their skills, and make plans based on realistic and objective opportunities. Through this step of research and evaluation, people should gain self-awareness and define achievable goals and evaluate alternatives in case of failure, protecting themselves, partially, from emotional negative consequences.

Furthermore, when the protean career orientation is adopted, employability is more effectively used in job searching, because unemployed become more aware of their values, projects, technical and soft skills and develop proactive career strategies ( Panari et al., 2020 ). This perspective can maintain a positive sense of personal professional identity whilst focusing on solutions to get out of the social disadvantage, rather than on the causes of the unemployment situation.

Author Contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

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Akkermans, J., Richardson, J., and Kraimer, M. (2020). The Covid-19 crisis as a career shock: Implications for careers and vocational behavior. J. Vocat. Behav . 119:103434. doi: 10.1016/j.jvb.2020.103434

PubMed Abstract | CrossRef Full Text | Google Scholar

Baruch, Y., Bhaskar, A. U., and Mishra, B. (2019). Career dynamics in India: a two-wave study of career orientations and employability of graduates. Pers. Rev . 49, 825–845. doi: 10.1108/PR-10-2018-0429

CrossRef Full Text | Google Scholar

Berntson, E., Sverke, M., and Marklund, S. (2006). Predicting perceived employability: human capital or labour market opportunities? Econ. Ind. Dem . 27, 223–244. doi: 10.1177/0143831X06063098

Blustein, D. L., and Guarino, P. A. (2020). Work and unemployment in the time of COVID-19: the existential experience of loss and fear. J. Hum. Psychol 60, 702–709. doi: 10.1177/0022167820934229

Bozionelos, G., and Bozionelos, N. (2015). Employability and key outcomes in times of severe economic crisis: the role of career orientation. Zarzadzanie Zasobami Ludzkimi . 6, 11–32.

Google Scholar

Briscoe, J. P., and Hall, D. T. (2006). The interplay of boundaryless and protean careers: Combinations and implications. J. Vocat. Behav . 69, 4–18. doi: 10.1016/j.jvb.2005.09.002

Briscoe, J. P., Henagan, S. C., Burton, J. P., and Murphy, W. M. (2012). Coping with an insecure employment environment: the differing roles of protean and boundaryless career orientations. J Voc. Behav. 80, 308–316. doi: 10.1016/j.jvb.2011.12.008

Bukowski, M., de Lemus, S., Rodríguez-Bailón, R., Willis, G. B., and Alburquerque, A. (2019). When lack of control enhances closeness to others: the case of unemployment and economic threat. Eur. J. Soc. Psychol. 49, 1144–1160. doi: 10.1002/ejsp.2563

Cassidy. (2001). Self-categorization, coping and psychological health among unemployed mid-career executives. Counsel. Psychol. Q. 14, 303–315. doi: 10.1080/09515070110102800

Consiglio, C., Menatta, P., Borgogni, L., Alessandri, G., Valente, L., and Caprara, G. V. (2021). How youth may find jobs: the role of positivity, perceived employability, and support from employment agencies. Sustain 13:9468. doi: 10.3390/su13169468

Cortellazzo, L., Bonesso, S., Gerli, F., and Batista-Foguet, J. M. (2020). Protean career orientation: behavioral antecedents and employability outcomes. J. Vocat. Behav. 116:103343. doi: 10.1016/j.jvb.2019.103343

Crayne, M. P. (2020). The traumatic impact of job loss and job search in the aftermath of COVID-19. Psychol Trauma 12, S180–S182. doi: 10.1037/tra0000852

Creed, P. A., and Evans, B. M. (2002). Personality, well-being and deprivation theory. Personal Individ. Differ 33, 1045–1054. doi: 10.1016/S0191-8869(01)00210-0

Creed, P. A., Muller, J., and Machin, M. A. (2001). The role of satisfaction with occupational status, neuroticism, financial strain and categories of experience in predicting mental health in the unemployed. Personal Individ. Differ. 30, 435–447. doi: 10.1016/S0191-8869(00)00035-0

Curtis, E., Gibbon, P., and Katsikitis, M. (2016). Group identity and readiness to change unemployment status. J. Employ. Couns. 53, 50–59. doi: 10.1002/joec.12027

De Cuyper, N., and De Witte, H. (2008). “Job insecurity and employability among temporary workers: a theoretical approach based on the psychological contract,” in The Individual in the Changing Working Life , eds K. Naswall, J. Hellgren, and M. Sverke (Cambridge: Cambridge University Press), 88–107.

De Cuyper, N., Mauno, S., Kinnunen, U., and Makikangas, A. (2011). The role of job resources in the relation between perceived employability and turnover intention: a prospective two-sample study. J. Vocat. Behav . 78, 253–263. doi: 10.1016/j.jvb.2010.09.008

De Vos, A., De Hauw, S., and van der Heijden, I. J. M. (2011). Competency development and career success: the mediating role of employability. J. Vocat. Behav. 79, 438–447. doi: 10.1016/j.jvb.2011.05.010

Fugate, M., and Kinicki, A. J. (2008). A dispositional approach to employability: development of a measure and test of implications for employee reactions to organizational change. J. Occup. Organ. Psychol. 81, 503–527. doi: 10.1348/096317907X241579

Fugate, M., Kinicki, A. J., and Ashforth, B. E. (2004). Employability: a psycho-social construct, its dimensions, and applications. J. Vocat. Behav . 65, 14–38. doi: 10.1016/j.jvb.2003.10.005

Furåker, B., and Blomsterberg, M. (2003). Attitudes towards the unemployed. An analysis of Swedish survey data. Int. J. Soc. Welf. 12, 193–203. doi: 10.1111/1468-2397.t01-1-00005

Gowan, M. A. (2014). Moving from job loss to career management: the past, present, and future of involuntary job loss research. Hum. Resour. Manag. Rev . 24, 258–270. doi: 10.1016/j.hrmr.2014.03.007

Grimmer, B. (2016). “Being long-term unemployed in germany: social contacts, finances and stigma,” in Experiencing Long-Term Unemployment in Europe , eds. C. Lahusen, and M. Giugni (London: Palgrave Macmillan), 39–72.

Hall, D. T. (2002). Careers in and Out of Organizations . Thousand Oaks, CA: Sage Publications.

Hall, D. T. (2004). The protean career: a quarter-century journey. J. Vocat. Behav . 65, 1–13. doi: 10.1016/j.jvb.2003.10.006

Hall, D. T., Yip, J., and Doiron, K. (2018). Protean careers at work: Self-direction and values orientation in psychological success. Ann. Rev. Organ. Psychol. Org. Behav. 5, 129–156. doi: 10.1146/annurev-orgpsych-032117-104631

Hirschi, A., Jaensch, V. K., and Herrmann, A. (2017). Protean career orientation, vocational identity, and self-efficacy: an empirical clarification of their relationship. Europ. J. Work Organ. Psychol. 26, 208–220. doi: 10.1080/1359432X.2016.1242481

Hite, L. M., and McDonald, K. S. (2020). Careers after COVID-19: challenges and changes. Hum. Resour. Dev. Int. 23, 427–437. doi: 10.1080/13678868.2020.1779576

Hodzic, S., Ripoll, P., Lira, E., and Zenasni, F. (2015). Can intervention in emotional competences increase employability prospects of unemployed adults? J. Vocat Behav . 88, 28–37. doi: 10.1016/j.jvb.2015.02.007

ILO. (2020). How will COVID-19 Affect the World of Work? Available online at: https://www.ilo.org/global/topics/coronavirus/impacts-and-responses/WCMS_739047/lang-en/index.htm (accessed September 15, 2021).

Jetten, J., Haslam, S. A., Cruwys, T., Greenaway, K. H., Haslam, C., and Steffens, N. K. (2017). Advancing the social identity approach to health and well-being: progressing the social cure research agenda. Eur. J. Soc. Psychol. 47, 789–802. doi: 10.1002/ejsp.2333

Kanfer, R., Wanberg, C., and Kantrowitz, T. (2001). Job search and employment: A personality-motivational analysis and meta-analytic review. J. Applied Psychol. 86, 837–855. doi: 10.1037/0021-9010.86.5.837

Koen, J., Klehe, U., Van, V., and Annelies, E. M. (2013). Employability among the long-term unemployed: a futile quest or worth the effort? J. Vocat. Behav. 82, 37–48. doi: 10.1016/j.jvb.2012.11.001

Krug, G., Drasch, K., and Jungbauer-Gans, M. (2019). The social stigma of unemployment: consequences of stigma consciousness on job search attitudes, behaviour and success. J. Labour Market Res. 53:11. doi: 10.1186/s12651-019-0261-4

Li, H., Ngo, H.-y., and Cheung, F. (2019). Linking protean career orientation and career decidedness: the mediating role of career decision self-efficacy. J. Vocat. Behav . 115:103322. doi: 10.1016/j.jvb.2019.103322

Lim, Y. M., Lee, T. H., Yap, C. S., and Ling, C. C. (2016). Employability skills, personal qualities, and early employment problems of entry-level auditors: perspectives from employers, lecturers, auditors, and students. J. Educ. Bus. 91, 185–192. doi: 10.1080/08832323.2016.1153998

Lo Presti, A., and Pluviano, S. (2016). Looking for a route in turbulent waters: employability as a compass for career success. Organ. Psychol. Rev. 6, 192–211. doi: 10.1177/2041386615589398

Lysova, E. I., Richardson, J., Khapova, S. N., and Jansen, P. G. (2015). Change-supportive employee behavior: a career identity explanation. Career Devel. International . 20, 38–62. doi: 10.1108/CDI-03-2014-0042

McFadyen, R. G. (1995). Coping with threatened identities: unemployed people's self-categorizations. Curr. Psychol . 14, 233–256. doi: 10.1007/BF02686910

McKee-Ryan, F., Song, Z., Wanberg, C. R., and Kinicki, A. J. (2005). Psychological and physical well-being during unemployment: a meta-analytic study. J. Appl. Psychol . 90, 53–76. doi: 10.1037/0021-9010.90.1.53

Meyers, R., and Houssemand, C. (2010). Socioprofessional and psychological variables that predict job finding. Eur. Rev. Appl. Psychol . 60, 201–219. doi: 10.1016/j.erap.2009.11.004

Mirvis, P. H., and Hall, D. T. (1994). Psychological success and the boundaryless career. J. Organ. Behav . 15, 365–380. doi: 10.1002/job.4030150406

Monteith, M. J., Burns, M. D., Rupp, D. E., and Mihalec-Adkins, B. P. (2016). Out of work and out of luck? Layoffs, system justification, and hiring decisions for people who have been laid off. Soc. Psychol. Personal. Sci. 7, 77–84. doi: 10.1177/1948550615599827

Nord, W. R., Brief, A. P., Atieh, J. M., and Doherty, E. M. (1990). “Studying meanings of work: the case of work values,” in Meanings of Occupational Work , eds A. P. Brief, and W. R. Nord (Lexington, VA: Free Press), 21–64.

Norlander, P., Ho, G. C., Shih, M., Walters, D. J., and Pittinsky, T. L. (2020). The role of psychological stigmatization in unemployment discrimination. Basic Appl. Soc. Psychol. 42, 29–49. doi: 10.1080/01973533.2019.1689363

Panari, C., Tonelli, M., and Mazzetti, G. (2020). Emotion regulation and employability: the mediational role of ambition and a protean career among unemployed people. Sustain 12:9347. doi: 10.3390/su12229347

Paul, K. I., and Moser, K. (2009). Unemployment impairs mental health: meta-analyses. J. Vocat. Behav. 74, 264–282. doi: 10.1016/j.jvb.2009.01.001

Pultz, S., and Mørch, S. (2015). Unemployed by choice: young creative people and the balancing of responsibilities through strategic self-management. J Youth Stud. 18, 1382–1401. doi: 10.1080/13676261.2014.992318

Rothwell, A., and Arnold, J. (2007). Self-perceived employability: development and validation of a scale. Pers. Rev. 36, 23–41. doi: 10.1108/00483480710716704

Sheeran, P., Abrams, D., and Orbell, S. (1995). Unemployment, self-esteem, and depression: a social comparison theory approach. Basic Appl. Soc. Psychol . 17, 65–82. doi: 10.1207/s15324834basp1701andamp;2_4

Sullivan, S. E., and Arthur, M. B. (2006). The evolution of the boundaryless career concept: examining physical and psychological mobility. J. Vocat. Behav . 69, 19–29. doi: 10.1016/j.jvb.2005.09.001

Suomi, A., Schofield, T. P., and Butterworth, P. (2020). Unemployment, employability and COVID19: how the global socioeconomic shock challenged negative perceptions toward the less fortunate in the Australian context. Front. Psychol. 11:2745. doi: 10.3389/fpsyg.2020.594837

Takahashi, M., Morita, S., and Ishidu, K. (2015). Stigma and mental health in Japanese unemployed individuals. J. Employ. Couns . 52, 18–28. doi: 10.1002/j.2161-1920.2015.00053.x

Thompson, M. N., Dahling, J. J., Chin, M. Y., and Melloy, R. C. (2017). Integrating job loss, unemployment, and reemployment with social cognitive career theory. J. Career Assess . 25, 40–57. doi: 10.1177/1069072716657534

Van den Broeck, A., Vansteenkiste, M., Lens, W., and De Witte, H. (2010). Unemployed individuals' work values and job flexibility: an explanation from expectancy value theory and self-determination theory. Appl. Psychol. Int. Rev . 59, 296–317. doi: 10.1111/j.1464-0597.2009.00391.x

Van Der Heijde, C. M., Van der Heijden, B. I. J. M., and Schyns, B. (2006). A competence-based and multidimensional operationalization and measurement of employability. Human Res. Manag. 45, 449–476. doi: 10.1002/hrm.20119

Van Oorschot, W., and Meuleman, B. (2014). “Popular deservingness of the unemployed in the context of welfare state policies, economic conditions and cultural climate,” in How Welfare States Shape the Democratic Public , eds. S. Kumlin and I. Stadelmann-Steffen (Cheltenham, MD: Edward Elgar Publishing), 244–262.

Van Veelen, R., Veldman, J., Van Laar, C., and Derks, B. (2020). Distancing from a Stigmatized Social Identity: State of the Art and Future Research Agenda on Self-Group Distancing. Eur. J. Soc. Psychol. 50, 1089–1107. doi: 10.1002/ejsp.2714

Vanhercke, D., De Cuyper, N., Peeters, E., and De Witte, H. (2014). Defining perceived employability: a psychological approach. Pers. Rev. 43, 592–605. doi: 10.1108/PR-07-2012-0110

Vansteenkiste, V., Lens, W., De Witte, H., and Feather, N. T. (2005). Understanding unemployed people's job search behaviour, unemployment experience and well-being: a comparison of expectancy-value theory and self-determination theory. Br. J. Soc. Psychol. , 44, 269–287. doi: 10.1348/014466604X17641

Wahl, I., Pollai, M., and Kirchler, E. (2013). Status, identification and in-group favouritism of the unemployed compared to other social categories. J. Socio. Econ. 43, 37–43. doi: 10.1016/j.socec.2013.01.005

Wanberg, C. R. (2012). The individual experience of unemployment. Annu. Rev. Psychol . 63, 369–396. doi: 10.1146/annurev-psych-120710-100500

Waters, L., Briscoe, J. P., Hall, D. T., and Wang, L. (2014). Protean career attitudes during unemployment and reemployment: a longitudinal perspective. J. Vocat. Behav. 84, 405–419. doi: 10.1016/j.jvb.2014.03.003

Wiernik, B. M., and Kostal, J. W. (2018). Protean and boundaryless career orientations: A critical review and meta-analysis. J. Couns. Psychol. 66, 280–282. doi: 10.31234/osf.io/ftm2k

Ybema, J. F., Buunk, B. P., and Heesink, J. A. (1996). Affect and identification in social comparison after loss of work. Basic Appl. Soc. Psych. 18, 151–169. doi: 10.1207/s15324834basp1802_3

Zafar, J., Farooq, M., and Quddoos, M. U. (2017). The relationship between protean career orientation and perceived employability: a study of private sector academics of Pakistan. J. Manag. Sci. 4, 133–145. doi: 10.20547/jms.2014.1704201

Zakkariya, K. A., and Nimmi, P. M. (2021). Bridging job search and perceived employability in the labour market–a mediation model of job search, perceived employability and learning goal orientation. J. Int. Educ. Bus. Vol . 14, 179–196. doi: 10.1108/JIEB-01-2020-0008

Zikic, J., and Klehe, U. C. (2006). Job loss as a blessing in disguise: the role of career exploration and career planning in predicting reemployment quality. J. Vocat. Behav . 69, 391–409. doi: 10.1016/j.jvb.2006.05.007

Keywords: unemployment, protean career orientation, employability, career planning, job search strategies

Citation: Panari C and Tonelli M (2022) Future Directions in the Research on Unemployment: Protean Career Orientation and Perceived Employability Against Social Disadvantage. Front. Psychol. 12:701861. doi: 10.3389/fpsyg.2021.701861

Received: 28 April 2021; Accepted: 30 December 2021; Published: 24 January 2022.

Reviewed by:

Copyright © 2022 Panari and Tonelli. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Chiara Panari, chiara.panari@unipr.it

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Analysis of the COVID-19 impacts on employment and unemployment across the multi-dimensional social disadvantaged areas

This is the study of economic impacts in the context of social disadvantage. It specifically considers economic conditions in regions with pre-existing inequalities and examines labor market outcomes in already socially vulnerable areas. The economic outcomes remain relatively unexplored by the studies on the COVID-19 impacts. To fill the gap, we study the relationship between the pandemic-caused economic recession and vulnerable communities in the unprecedented times. More marginalized regions may have broader economic damages related to the pandemic. First, based on a literature review, we delineate areas with high social disadvantage. These areas have multiple factors associated with various dimensions of vulnerability which existed pre-COVID-19. We term these places “ multi-dimensional social disadvantaged areas ”. Second, we compare employment and unemployment rates between areas with high and low disadvantage. We integrate geospatial science with the exploration of social factors associated with disadvantage across counties in Tennessee which is part of coronavirus “red zone” states of the US southern Sunbelt region. We disagree with a misleading label of COVID-19 as the “great equalizer”. During COVID-19, marginalized regions experience disproportionate economic impacts. The negative effect of social disadvantage on pandemic-caused economic outcomes is supported by several lines of evidence. We find that both urban and rural areas may be vulnerable to the broad social and economic damages. The study contributes to current research on economic impacts of the COVID-19 outbreak and social distributions of economic vulnerability. The results can help inform post-COVID recovery interventions strategies to reduce COVID-19-related economic vulnerability burdens.

1. Introduction: social disadvantage

Pandemics create severe disruptions to a functioning society. The economic and social disruptions intersect in complex ways and affect physical and mental health and illness ( Wu et al, 2020 ). Additionally, loss of jobs, wages, housing, or health insurance, as well as disruption to health care, hospital avoidance, postponement of planned medical treatment increase mortality, e.g., premature deaths ( Kiang et al., 2020 ; Petterson et al., 2020 ). The COVID-19, misleadingly labelled the “great equalizer” implies everyone is equally vulnerable to the virus, and that the economic activity of almost everyone is similarly impacted regardless of social status ( Jones & Jones, 2020 ). We set out to answer whether economic vulnerability is equally distributed during the COVID-19-caused economic recession or whether is it based on structural disadvantages? Is the social distribution of economic vulnerability magnified in regions with pre-existing social disparities, thus, creating new forms of inequalities? Knowledge of what areas experience the greater economic burden will help identify the most economically vulnerable communities relevant to post-COVID recovery interventions ( Qian and Fan, 2020 ).

Current studies on the impacts of COVID-19 largely focus on medical aspects including the COVID diagnosis and treatment ( Cai et al., 2020 ; Kass et al., 2020 ; O’Hearn et al., 2021 ; Price-Haywood et al., 2020 ). Non-medical urban research primarily concentrates on the impact of COVID on cities by studying factors related to environmental quality including meteorological parameters, and air and water quality ( Sharifi and Khavarian-Garmsir, 2020 ). COVID-related socio-economic impacts on cities are relatively less well studied, especially during the later stages of the recession.

Many pre-pandemic disparities unfold during COVID-19. To illustrate, residents of Black and Latino communities are suffering disproportionately higher unemployment rates, greater mortality due to the COVID-19 ( Thebault, Tran, & Williams, 2020 ; Wade, 2020 ), higher hospitalizations ( O’Hearn et al., 2021 ) and financial troubles. In contrast, some attributes make persons and communities more resilient. In China’s context, these include higher worker education and family economic status, membership in Communist Party, state-sector employment, and other traditional markers. These factors protect people from the pandemic-related financial stress and diminish its adverse economic effects ( Qian and Fan, 2020 ). Building on these recent studies on economic impacts, this social justice research focuses on areas with pre-existing social disadvantages. We study the role of social disadvantage and its impact on labor market during the COVID.

The distribution of economic vulnerability may potentially be related to COVID-19 conditions including those of economic burdens for people living in the pandemic epicenters ( Creţan and Light, 2020 ). Similarly, socio-economic disruptions create “a characteristic mosaic pattern in the region” ( Krzysztofik et al., 2020 , p. 583). The disruptions are strongly correlated with the spatial distribution of the COVID-19-related health effects. This study is set in Tennessee which is part of coronavirus “red zone” states of the US southern Sunbelt region. It is among the U.S. states with the highest rates of cases per capita, with 137,829 cases per 1 million people, or the 6th highest as of August 13, 2021 ( Worldometers, 2020 ; https://www.worldometers.info/coronavirus/country/us/ ). The study seeks to explore the impacts of social disadvantage on economy. The impact is measured by employment and unemployment in unprecedented times in the US context of prolonged disruptions to the health system, society, and economy intersecting in complex ways ( Kiang et al., 2020 ). We answer the following questions: (1) Do communities with high social disadvantage already burdened pre-COVID-19 by the lack of income, healthcare access, lacking resources, have less jobs available during the COVID-19 pandemic? (2) Do these areas simultaneously experience higher unemployment compared with other areas in the context of the pandemic?

The paper is organized as follows: Section 1 introduces the topic, provides the background information on social disadvantage and a brief description of the study implementation. It further discusses the links between employment and unemployment, and coronavirus, respectively, and introduces the study area. Section 2 describes in detail materials and methods used in the study. Section 3 provides the theory and calculations. Section 4 reports the results, and Section 5 offers a discussion. Finally, the paper concludes with conclusions found in Section 6 .

1.1. Background

Certain socio-economic and demographic conditions burden some communities more than others including racial and ethnic minorities, lower-income groups, and rural residents. The conditions include lacking economic opportunities and other inequalities ( Petterson et al., 2020 ) caused by social environment. Prior to the pandemic, it was challenging to live in areas with high social disadvantage where residents already have increased vulnerability to poor health due to greater psychosocial stress such as discrimination, unhealthy behaviors, and poorer health status ( Hajat et al., 2015 ). This is true for poor, marginalized communities elsewhere as spatial segregation of disadvantaged and marginalized communities decreases life opportunities for their members who have limited relationships with broader communities ( Méreiné-Berki et al., 2021 ). Within the context of studying disadvantaged urban communities, a recent work by Creţan et al. (2020) focused on the everyday manifestations of contemporary stigmatization of the urban poor using the case study of the Roma people who have been historically subject to state discrimination, ghettoization, inadequate access to education, housing, and the labor market for many decades in the past in multicultural urban societies of Central and Eastern Europe. The inequalities may persist and even increase if left unaddressed during pandemics ( Wade, 2020 ) leading to stark COVID-19-related health and economic disparities. Indeed, during the COVID-19, economic impacts of the pandemic disproportionately affect marginalized groups. The impact of coronavirus was harsh for those people as many of the already existing disparities unfold during COVID-19: black communities in the United States are disproportionately affected by higher death rates due to the COVID-19 virus ( Thebault et al., 2020 ), unemployment, and financial stress. Other growing COVID-19 research similarly suggests that elsewhere outside of the United States, areas that were disadvantaged prior to the pandemic with high rates of poverty and unemployment tended to be affected the strongest by the COVID-19 with the largest concentration of cases, while other spatially segregated ethnicity-based communities (e.g., the Roma) that have been vulnerable decades prior to COVID-19, saw an increase in the existing discrimination and stigmatization experiencing greater marginalization even during the current COVID-19 pandemic period ( Crețan & Light, 2020 ).

To achieve greater economic stability, and secure a dynamic labor market, countries in the global north and south for several decades have been increasing service employment much of which is low wage. The recent book Corona and Work around the Globe ( Eckert and Hentschke, 2020 ) describes the tremendous impact of the pandemic on human life and livelihoods as it sheds light on various experiences of workers during COVID-19 in various countries. Among the dramatically different cases worldwide, Germany which for decades has been promoting the low-wage sector to combat unemployment, provides a good example. The official approach to handling a disease differed substantially depending on whether the infected individuals were working people from the low- or upper-wage sector of the economy: applying a strict lockdown to the entire high-rise building where ethnic workers lived and preventing them from going to work in the former case and granting permission to work from home in the latter ( Mayer-Ahuja, 2020 ). The plight of the agricultural migrant workers who come to Germany from Eastern and Southeastern Europe, subjected during the pandemic to low wages or no payments and poor working and living conditions, however, is shared among the workers of low-wage sector across all countries who are more likely to get infected due to higher exposure and direct contact, but often experience unfair treatment based on ethnicity, migration and class status.

In yet another case set in the U.K., disadvantaged households have experienced intensified disadvantage during the COVID-19 as they could not access vital necessities, already stretched for resources pre-COVID-19. As provision of services or employment was discontinued due to their closure, disadvantaged households had significant impacts on their income level, mental health and wellbeing, education, nutrition, and domestic violence. In the absence of the key support of public institutions including schools, community centers, and social services, care for the most vulnerable members such as elderly, children, the disabled, have been absorbed by households ( Bear et al., 2020 ).

Another aspect experienced by workers during the pandemic is the total loss of earnings which is especially harsh in places with precarious employment even under normal circumstances. Informal workers in India who represent the vast majority of working population (over 93%), with no social security benefits and absent job security, experienced prolonged periods of time of no work due to lockdown and suspended transport services preventing them from getting to their workplaces, many on the verge of starvation ( Banerjee, 2020 ). This study looks into this aspect of COVID-19 economic impacts and confirms the findings of the growing COVID-19 research.

However, not only the poorest and marginalized people, but also marginalized regions are more likely to suffer from broader social and economic damages related to the pandemic compared with more privileged areas ( Creţan and Light, 2020 ; Krzysztofik et al., 2020 ). When disadvantages combine, it may lead to environment-driven COVID-19-related disparities in health. Besides a direct health effect, disadvantaged communities are disproportionally experiencing other side effects of COVID-19 such as negative labor market outcomes including forced unemployment, loss of income and social isolation. Studies found the extreme vulnerability of cities and urban areas exposed during the global pandemic ( Batty, 2020 ; Gössling et al., 2020 ). We argue that rural areas may be equally vulnerable to the broad range of social and economic damages if there is a spatial concentration of factors related to various dimensions of vulnerability.

This study is situated in the context of social disadvantage. Prior studies developed the methodology of the delineation of disadvantaged residential communities proxied by low-income workers ( Antipova, 2020 ). Disadvantaged low-income workers can be defined as those with inadequate access to material and social resources in the study area. However, this is a narrow approach which uses only a single dimension of a disadvantage, that of worker low earnings and misses other social inequality indicators. Accordingly, an approach adopted in this study identifies areas where socio-economic and demographic attributes each associated with multiple dimensions of social disadvantage are spatially co-locating. Spatial segregation of disadvantaged and marginalized communities decreases life opportunities for their members who have limited relationships with wider communities ( Méreiné-Berki et al., 2021 ). We identify these attributes based on a thorough literature review. Thus, we simultaneously consider multiple factors associated with disadvantage capturing a multi-dimensional social disadvantage. To meet the objective, we integrate geospatial science with the exploration of predictive geographic and social factors associated with disadvantage across counties in TN. The geospatial analysis includes point interpolation within the Geographic Information System (GIS) environment for the generation of a surface from a sample of social disadvantage values. This allowed us to visualize the spatial extent of disadvantaged communities. The focus is on labor market outcomes which are important indicators of society well-being. We study the association between pre-existing inequalities and COVID-19-related employment and unemployment rates. Thus, we identify the role of social disadvantage on labor market conditions in the context of the ongoing pandemic-caused economic recession.

Prior research determined the key metrics of social disadvantage. Conditions contributing to various aspects of disadvantage include poverty, occupations with low earnings, low rent, segregation and discrimination-related residential concentrations of minorities, and exposure to poor air quality ( Bullard, 2000 ). The recent COVID-19-related literature focuses on the separate effect of minorities, Hispanics, crowded households, dense areas, obesity, poverty, air pollution exposure and identifies those as important COVID-19 health risk factors ( Finch & Hernández Finch, 2020 ; Golestaneh et al., 2020 ; Han et al., 2020 ; Millett et al., 2020 ). These community-level variables result in neighborhood disadvantage comprising sub-standard housing quality, crowded conditions, poverty- and violence-caused stress which combined increase the risk of disease and other negative outcomes in life among socially disadvantaged groups ( Malhotra et al., 2014 ). The demographic and socio-economic attributes selected to represent the various aspects of social disadvantage in this research include minorities and ethnicities, poverty, housing crowdedness, educational attainment, underlying population health conditions, and pre-COVID-19 unemployment which may collectively drive a greater vulnerability to the COVID-19 infection and mortality as well as loss in employment and higher unemployment. It is challenging to isolate the separate effects of the multiple risk factors. By “critically analyzing the theoretically intended meaning of a concept” ( Song et al., 2013 ), a composite variable can be created to logically represent a multi-dimensional social disadvantage .

The following subsection briefly describes study implementation. First, we locate areas of disadvantage where multiple factors associated with various aspects of disadvantage co-locate spatially and term these places “multi-dimensional social disadvantaged areas”. Then, we examine how employment and unemployment were impacted in these already socially vulnerable areas. We map geographical inequalities in employment and unemployment rates during the period of COVID-19-related economic recession. For the first objective, we identify socially disadvantaged counties within TN which is part of coronavirus “red zone” states of the US southern Sunbelt region applying consistent criteria. For the second objective, we compare employment and unemployment outcomes between areas with high and low disadvantage.

1.1.1. Employment and coronavirus

This subsection discusses the role of employment and how it was impacted by the COVID-19-caused economic recession. The literature recognizes the complex interrelationship between employment and overall health and well-being. Negative COVID-19 impacts on urban economy include loss of citizens' income, while movement restrictions and ‘stay home’ measures adversely impacted tourism and hospitality and small- and medium sized businesses due to the closure of markets, food outlets and social spaces ( Wilkinson et al., 2020 ).

Millions of essential or blue-collar workers are still doing their jobs out of necessity and because they cannot telecommute and work jobs that cannot be done from home and have higher exposure to the virus. Some racial groups disproportionally have jobs that do not allow them to work from home and where social distancing is a challenge. Prior studies find that workplaces of low-income individuals tend to be close to their residential spaces, and disproportionately concentrated in lower-wage industries such as hospitality and retail services ( Antipova, 2020 ). These industries commonly represent essential services experiencing higher exposure to the COVID virus through workplaces. At the same time, minorities and lower-income groups often live in inner-ring suburbs with older housing and aging infrastructure ( Antipova, 2020 ) in multiunit structures and in multigenerational households which inhibit the ability to practice social distancing increasing the risks of disease occurrence and deaths ( Qualls et al., 2017 ). In addition, minorities and lower-income groups have fewer options for protecting both their health and economic well-being ( Gould and Wilson, 2020 ). Nearly two-thirds of Hispanic people (64.5%) considered at high risk for coronavirus live with at least one person who is unable to work from home, compared to 56.5% of black and less than half (47%) of white Americans, according to a recent study ( Selden and Berdahl, 2020 ).

Despite the pandemic-induced layoffs, job hires have occurred by major retailers such as Walmart and e-commerce giant Amazon, and takeout and delivery-based services such as Domino’s Pizza and Papa John’s which may become permanent positions. These workplaces may match the job skill sets of low-income residents of vulnerable communities. However, oftentimes many low-income workers benefitted less, even when jobs were created during the COVID-19. To illustrate, big technology companies (i.e., communication services: Netflix, Tencent, Facebook, T-Mobile; information technology: Microsoft, Nvidia, Apple, Zoom Video, PayPal, Shopify; consumer discretionary: Amazon, Tesla, Alibaba, etc.) prospered in the pandemic with the financial success measured by equity value added ( Financial Times, 2020 ). Workers who lost jobs in low-income segment such as hospitality sector may be hired by retailers such as Kroger or CVS. However, many others from the communities with high social disadvantage may not have a skill set needed at technology firms that benefit from the working from home trend and hire skilled workers including software engineers and product designers. Cross-industry employment shifts plays a minor role in total job creation, while employer-specific factors primarily account for job reallocation ( Barrero et al., 2020 ).

1.1.2. Unemployment and coronavirus

This subsection discusses how unemployment was impacted by the COVID-19-caused economic recession. An economic recession occurs when there is a substantial drop in overall economic activity diffused throughout the economy for longer than a few months. While past recessions were driven by an inherently economic or financial shock, the current recession is caused by a public health crisis ( Weinstock, 2020 ). COVID-19 caused a drop in consumer demand across all industrial sectors resulting in economic recession and massive unemployment where not only hourly workers but salaried professionals lost their jobs ( Petterson et al., 2020 ). A range of factors contributed to the spatial variation in economic damage including the share of jobs in industries delivering non-essential services to in-person customers ( Dey and Loewenstein, 2020 ), declines in personal consumption caused by individual fears of contracting COVID-19 ( Goolsbee and Syverson, 2020 ), and the implementation of social policies including stay-at-home orders and business shutdowns ( Gupta et al., 2020 ).

Unemployment rate is defined as a percentage of unemployed workers in the total labor force. The rate is published monthly by the Bureau of Labor Statistics (BLS) which uses both the establishment data (captured by the Current Employment Statistics program) and household surveys (Current Population Survey) to generate the labor market data ( Bureau of Labor Statistics (BLS), 2020b ). A person is unemployed if they were not employed during the survey’s reference week and who had actively searched for a job in the 4-week period ending with the reference week, and were presently available for work ( BLS, 2020b ).

Caused by the COVID-19, the unemployment rate reached a peak in April 2020 at 14.7% nationwide, an unprecedented joblessness amount since employment data collection started in 1948. It exceeded the previous peaks during the Great Recession and after ( Falk et al., 2020 ). The official unemployment rate may have been over 20%, since the actual level of joblessness could have been understated due to local unemployment rate measurement errors ( Coibion et al., 2020 ). In addition, the unemployment rate was understated due to a geographically widespread misclassification of those who was not at work but considered employed and non-inclusion of labor force non-participants who still counted as employed ( Bureau of Labor Statistics (BLS), 2020a ). Further, the COVID-19 caused the rapid rate of change in unemployment at the national level challenging accurate forecast of the monthly unemployment rate ( Weinstock, 2020 ).

Overall, current unemployment (using the most recently available county-level data at the time of writing for December 2020) is still elevated and is almost twice as high as it was back in February 2020 which represented the business cycle peak with the peak of payroll employment. March 2020 was the first month of the subsequent current economic recession as declared by The National Bureau of Economic Research (NBER, 2020) caused by the COVID-19 pandemic which turned out the worst downturn after the Great Recession. As Fig. 1 shows using the Current Population Survey data (Series ID: LNS14000000) from the BLS, during the prior recessions the unemployment rate rose gradually reaching its peak, and in the pandemic-caused recession it increased unprecedentedly to its peak over one month, from March 2020 to April 2020 by 10.3% (from 3.5% in February 2020 to 4.4% in March 2020 to 14.7% in April). After that, the rate declined as workers continued to return to work to 6.3% in December 2020.

Fig. 1

U.S. Historical unemployment rate for workers 16 years and over, January 1948 to December 2020, % (seasonally adjusted).

Some communities can absorb the impact of economic downturns due to more favorable economic and social factors protecting residents from adversity. Yet other communities are witnessing the effect of rising unemployment in the time of COVID-19. Loss of income and livelihood has further effects: as wages drop, more people are forced into poverty while simultaneously people's health is impacted. Unemployment impacts all-cause mortality. Fig. 2 presents the dynamics of unemployment distribution across counties in TN for the selected months. Shown are pre-COVID-19 unemployment rates as of August 2019 ( Fig. 2 a), followed by May 2020 ( Fig. 2 b) where even the lowest levels of unemployment exceed the highest rates of the pre-pandemic period even in wealthy counties around Nashville (seen in the legend entries), August 2020 ( Fig. 2 c), and September 2020 ( Fig. 2 d). The overall unemployment abates somewhat during the later stage, and the general spatial pattern resembles that of the pre-COVID-19 period with higher unemployment concentrated in the southwestern corner of the state around Memphis.

Fig. 2

Dynamics of unemployment rate across counties in TN for selected months: (a) August 2019, (b) May 2020; (c) August 2020; (d) September 2020.

1.1.3. Study area

Tennessee is home to large cities including Nashville (the county seat), Memphis, Knoxville and Chattanooga. Despite urban diversified economy, there was a steep decline in the number of international and domestic tourists impacting urban economy. Among cities listed above, Memphis, located in Shelby County, is a shrinking city with a declining population base. Urban shrinkage makes cities more vulnerable due to very negative impacts on urban economy. Shrinking cities are characterized by higher unemployment rates, depopulation (as people with higher economic and social status leave elsewhere), and a higher share of older people (increasing a share of individuals with underlying health conditions) ( Haase et al., 2014 ; Hartt 2019 ; Hoekveld 2012 ; Krzysztofik et al., 2020 ). The shrinking cities have higher exposure to extreme socioeconomic phenomena, including financial stress due to the decreases in the city’s budget. Decreasing budget in its turn has further urban development implications since implementation of some plans deemed of lesser priority such as environmental and cultural may be delayed and cancelled altogether ( Kunzmann, 2020 ; Sharifi and Khavarian-Garmsir, 2020 ).

Tennessee is one of the US southern Sunbelt states which had infection surges since summer 2020 due to the aggressive push for economy opening by then-President Trump administration. The pandemic has affected unemployment for every state in the United States ( Falk et al., 2020 ). Fig. 3 portrays selected industries impacted by the economic recession in Tennessee using seasonally adjusted data on employees on nonfarm payrolls for November 2019 (as a base period), September–November 2020. Unemployment rates concentrate disproportionately in sectors providing in-person non-essential services where some demographic groups are overrepresented. This results in substantially higher unemployment rates for those workers ( Cortes and Forsythe, 2020 ; Fairlie, 2020 ). Accordingly, it can be seen in Fig. 3 that in Tennessee, among the reported industries, leisure and hospitality has suffered the most, followed by jobs in government, education and health services, professional and business services, and trade, transportation, utilities. There was a slight increase in jobs in financial activities from 2019 to 2020 ( Bureau of Labor Statistics (BLS), 2020a ). The hardest hit industries tend to employ demographic groups such as women, minorities, low-income workers, and younger workers who have experienced greater job losses ( Murray and Olivares, 2020 ).

Fig. 3

Employees on nonfarm payrolls by selected industry sector, seasonally adjusted, in TN.

2. Materials and methods

In the absence of fine-scale monthly data on employment and unemployment, we sourced county-level data from the Bureau of Labor Statistics (BLS) to track monthly changes in employment and unemployment in Tennessee (retrieved from https://www.bls.gov/lau/ ). Labor force data were extracted from this official primary source.

We used a comparative assessment approach to analyze the COVID-19-based labor market outcomes including the rates of COVID-19-related employment and unemployment attributable to social disadvantage conditions. For this, we stratify data based on community disadvantage status, and combine data in a comparative assessment framework. We proceed and identify disadvantaged communities using the methodology described below. Next, we test the hypothesis that in areas with high social disadvantage where more essential workers are more likely to reside, the unemployment is higher while employment opportunities are lower by comparing unemployment and employment rates within these communities to those of more privileged communities.

3. Theory/calculation

We focus on the areas where the multiple risk factors identified in the recent literature co-locate spatially and term these places “ multi-dimensional social disadvantaged areas ”. We carried out a rigorous literature review of the variables to stand in for social disadvantage in this research. The following demographic and socio-economic factors have been selected to represent community’s vulnerability: (1) Minorities and ethnicity; (2) Crowded households; (3) Poverty; (4) Education; (5) Underlying medical conditions (obesity); and (6) Unemployment. For the 1st variable, minorities and ethnicity , we used percent minority population and Hispanic ethnicity as studies commonly use race and ethnicity as vulnerability metrics (as explained in Section 2 Background information). For the 2nd variable, crowded households , we used percent households that are multigenerational as an indicator of crowdedness, and thus, indicating area’s disadvantage with a high share of such households. For the 3rd variable, poverty , we chose percent of households below 100% of federal poverty level which is also known as the poverty line. It is an economic measure of income. The poverty guidelines are updated annually by the US Department of Health and Human Services to indicate the minimum income needed by a family for housing, food, clothing, transportation, and other basic necessities and to determine eligibility for certain welfare benefits. This measure was used because less affluent and less privileged households have fewer means and less access to various resources to cope with the effects of financial crises ( Pfeffer et al., 2013 ). Low-income households may be especially vulnerable to wage losses during the outbreak ( Qian and Fan, 2020 ). For the 4th variable, education , we used percent of population with less than high school diploma since lower educational attainment is an indicator of poverty and thus captures social disadvantage, while workers with better education have higher economic resilience when challenged with a large-scaled social shock ( Cutler et al., 2015 ; Kalleberg, 2011 ). For the 5th variable, underlying medical conditions , we used percent population with obesity as the top risk for COVID-19-related hospitalization. Supported by several lines of evidence, both domestically and internationally, obesity may predispose to more severe COVID-19 outcomes ( O’Hearn et al., 2021 ). Finally, for the 6th variable, unemployment , unemployment rate (averaged from August 2019 to January 2020 to adjust for seasonality) was used as a marker of overall vulnerability as it is linked to overall mortality. Further, regions with higher unemployment are more susceptible to business-cycle fluctuations, and thus, are more socially and economically vulnerable.

These socio-economic and demographic attributes (minority population, Hispanic ethnicity, federal poverty level, crowded households, adult obesity, lower educational attainment, and unemployment) have been used in this research to create a composite variable to represent a multi-dimensional social disadvantage (also referred to as vulnerability). Due to different variances in the original variables, we standardized them to prevent a disproportionate impact which may be caused by any one original variable with a large variance. The z-score transformation was applied by averaging the original variables and computing z scores with a mean of 0 and values ranging from negative to positive numbers ( Song et al., 2013 ).

Thus, the original variables were converted to z-scores to preserve the distribution of the raw scores and to ensure the equal contributions of the original variables. Next, we created a composite variable capturing a multi-dimensional social disadvantage. It was calculated by summing standardized z-scores of the original risk factors. The higher value can be interpreted as higher disadvantage while the lower value means more privileged communities. Based on the frequency distribution of values of the composite variable, we established a cut-off value for the composite variable to designate communities with high or low exposure to social disadvantage. We used the following method to determine the cut-off value of the composite variable. The values greater than 3.38 correspond to 1 standard deviation above the mean (or, the 88th percentile in the value distribution) indicating communities in the top 12 percent of social disadvantage and therefore, a higher share of factors contributing to disadvantage. This value was used to differentiate communities according to their disadvantage status. We identified twelve counties with high social disadvantage (N high  = 12), and other counties represent more privileged communities (N low  = 83). To test whether the taken approach correctly identifies disadvantaged communities, we conducted a Wilcoxon two-sample test for the variables of interest ( Table 1 ). We report the results of the estimates in the following section. The above socio-economic and demographic population characteristics come from the 2018 American Community Survey (ACS) 5-year data, an annual nationwide survey conducted by the US Census Bureau, available for various geographic units and applied for areal units within the study area ( U. S. Census Bureau, 2020 ).

Descriptive statistics.

The basic descriptive demographic and socio-economic characteristics of the TN population are shown in Table 1 . It includes the summaries for communities with high and low social disadvantage allowing to compare the variables of interest between these communities. The following variables are reported: percent African American, percent Hispanic, median income, percent of people over 25 years who are less than high school graduates, estimated percent of obese adults, percent households below 100% of federal poverty level, and percent of multi-generation households. The factors comprising social disadvantage were statistically significantly different than those extant in more privileged counties. Compared with the general TN population, the disadvantaged cohort was generally more likely to be of non-Hispanic Black race; more impoverished; with less educational attainment, more obese, and had more households with crowded conditions.

To visualize social disadvantage and show how it varies across the space, we used our sample of social disadvantage measurements and created a surface of social disadvantage within the study area using the Geographic Information System (GIS). The interpolated surface was derived from an Inverse Distance Weighted technique ( Watson and Philip, 1985 ). Fig. 4 presents the surface illustrating that both urban and rural counties in Tennessee are subject to social disadvantage.

Fig. 4

Social disadvantage within the study area.

We examined how unemployment changed from August 2019 to December 2020. Currently, all counties have substantially higher unemployment compared with that prior to COVID. Fig. 5 presents the results of the Nonparametric One-Way ANOVA test showing the distribution of Wilcoxon scores for unemployment rate for all counties in Tennessee combined, regardless of social disadvantage status, for 17 months. A statistically significant difference is found for unemployment rates between the pre-COVID period and the period since April 2020, with current unemployment rates although decreased but still significantly higher compared with those prior to the recession.

Fig. 5

Nonparametric One-Way ANOVA and distribution of Wilcoxon scores for unemployment rate for all counties combined for 17 months (August 2019–October 2020), regardless of social disadvantage status.

We compared employment and unemployment rates for Tennessee counties stratified by the type of social disadvantage separately for each month. Fig. 6 presents the average employment and unemployment rates by community disadvantage from August 2019 to December 2020 in a graphical form. The results of the non-parametric Wilcoxon test for employment and unemployment rates are presented in Table 2 . Pre-COVID and before the unemployment peak in April 2020, communities with high social disadvantage consistently had less jobs and greater unemployment, which we tested statistically and found a significant difference for both outcomes of the labor market between communities by their disadvantage status ( Table 2 ). Shown in Table 2 , in April and May 2020, during the peak of unemployment and immediately after, unemployment rates observed in both types of communities were high with no statistical difference. In June, the differences again became prominent, when there were more jobs available in more advantaged areas and employment rate remained consistently greater in areas with less disadvantage. Also in June, unemployment rate remained consistently greater in areas with higher disadvantage. This month saw the greater difference in both outcomes since the COVID-19 than pre-pandemic (supported by higher p-values). Compared with all TN population, residents of disadvantaged counties had less jobs available and were more likely to be unemployed during all periods except for April and May.

Fig. 6

Mean employment and unemployment stratified by community disadvantage status.

Wilcoxon Two-Sample Test: Distribution of Wilcoxon scores in employment and unemployment rates by community disadvantage status by month (August 2019–December 2020).

We examined the percent change in both labor market outcomes. Fig. 7 presents the percent change in mean employment ( Fig. 7 a), and mean unemployment by community disadvantage ( Fig. 7 b). The percent change in employment and unemployment was relatively small in both types of community during the pre-COVID period. However, the overall fluctuations in both conditions were greater in communities with high social disadvantage (evidenced by a greater range between ups and downs for disadvantaged communities shown with the black-colored symbols). On the other hand, employment and unemployment were more stable in more privileged communities (shown with the grey-colored symbols in the Fig. 7 ). During the unemployment peak in April 2020, the change in percent employment was −11.5 points from the previous month even in more advantaged counties, while the unemployment in April increased by 10.42 percentage points in disadvantaged counties.

Fig. 7

Percent change in (a) mean employment; (b) mean unemployment by community disadvantage.

We show how various factors of social disadvantage intersect and combined impact economic vulnerability measured by unemployment rate. Fig. 8 reports the link between unemployment and social disadvantage pre-COVID (unemployment rate was averaged over August 2019–January 2020 in Fig. 8 a), and during COVID (unemployment rate for November 2020 is shown in Fig. 8 b). During the COVID pandemic, its impact is even stronger as evidenced by a greater slope of the line of fit, larger coefficients, and a greater R-squared value ( Fig. 8 b). The strong relationship between these factors of social disadvantage and economic outcomes in COVID-19 might inform post-COVID recovery intervention strategies to reduce COVID-19-related economic vulnerability burdens. For example, in the light of findings on socio-economic and demographic subpopulations at a higher risk for economic damages, prioritization of economic relief distribution might be based on community disadvantage status targeting individuals from areas with existing inequalities to increase economic resilience of marginalized communities.

Fig. 8

Unemployment and Social disadvantage: (a) pre-COVID (averaged August 2019–January 2020); (b) during COVID (November 2020).

5. Discussion

Current studies on the impacts of COVID-19 tend to focus on medical aspects while non-medical urban research mostly analyzes the role of environmental quality. To better understand the full effects of pandemics on communities and minimize the various impacts as well as to improved response, other aspects need to be examined. This includes studying less researched themes including socio-economic impacts consisting of both social impacts and social factors making individuals and communities less resilient and more vulnerable to the effects of the COVID. Additionally, economic impacts of the pandemic-caused recession so far remain relatively underexplored and need to be investigated ( Sharifi and Khavarian-Garmsir, 2020 ).

Communities are often severely segregated along wealth and social lines in developing and developed world ( Wilkinson et al., 2020 ). We study the role of social factors and the impact of the COVID on labor market conditions in Tennessee. Specifically, we studied the impacts of social environment on employment and unemployment through the concept of a multi-dimensional social disadvantage by using geospatial science.

A recent study identified factors which can make a community more vulnerable to the pandemic’s effects using as a case study the province of Silesia in Poland, one of the largest industrial and mining regions in Europe. Specialized functions such as mining-oriented industries, large care centers, polycentricity, and urban shrinkage make communities most at risk due to very negative impacts on urban economy ( Krzysztofik et al., 2020 ). Since vulnerability is always very context-specific, we found a combination of different causal factors of social disadvantage captured by a composite variable making communities most at risk during the COVID reflected in broader social and economic outcomes. In creating a composite variable to capture social disadvantage logically and meaningfully, the following variables were used: % African American, % Hispanic, % below 100% federal poverty level, % population with less than high school diploma (an indicator of poverty), % multi-generation households (an indicator of crowdedness), % estimated obese adults reporting to be obese with the BMI 30 or greater, % unemployed. The proposed method can be generalized beyond the study area and used as a tool by policy makers using consistent criteria for the delineation of areas carrying a greater risk for the more severe impact by the pandemic due to co-existence and co-location of the multi-dimensional social disadvantage factors which are more likely to experience further socio-economic disruptions.

Current urban research on COVID economic impacts found that some cities are more vulnerable than others and are most at risk. Cities with an undiversified economic structure with industries where a large number of workers are shoulder-to-shoulder share cramped spaces for a prolonged time and where social distancing is challenging (e.g., meat-packing and poultry processing plants), cities relying on tourism as well as cities that have large care centers, polycentric cities, and shrinking cities are the most vulnerable to negative impacts on urban economy. The urban hotel market, city tax revenues, citizens' income, tourism and hospitality, small- and medium sized firms, urban food supply chain, and migrant workers are all impacted ( Krzysztofik et al., 2020 ). Other recent studies similarly concluded that the COVID has revealed the extreme vulnerability of cities and urban areas disrupting tourism and affecting supply chains in cities ( Batty, 2020 ; Gössling et al., 2020 ). We support this statement but also find that rural areas can experience a broad range of social and economic damages related to COVID.

Before and during the COVID-19 period, money laundering, limitations of economic development, environmental pollution and uncontrolled deforestation, population displacement, institutional incompetence, and corruption of political elites have been debated including corruption and conflagration in Bucharest before the pandemic ( Creţan & O’Brien, 2020 ), as well as other contestations on selling masks and different medical products highlighted in different countries during the pandemic period. Following catalytic events, the affected community may respond to long-held concerns with demands to address these problems bringing about important changes to the systems. Marginalized stigmatized minorities may effectively overcome discriminatory laws, higher poverty and other constraints and influence public opinion and politics in their favor through collective action via various strategies including protests against corruption and the inaction of the political leaders in Romania in 2015 forcing the resignation of the Government, and protests in the US in the aftermath of police violence against black people have been documented ( Creţan & O’Brien, 2020 ; Fryer, 2019 ). During the COVID-19, the non-payment of wages and poor working and living conditions caused seasonal workers in Germany to protest against this unfair treatment, however, generating low coverage in the national press ( Mayer-Ahuja, 2020 ).

6. Conclusions

Some socio-economic and demographic conditions consistently and significantly impact some communities more often than others, particularly based on ethnic minority status, low income, and rural location. The conditions include systemic issues such as fragmented health care system (within which some individuals do not get health care in a timely fashion), racism and structural disparities in education, income, wealth, a consistent lack of economic opportunity, environmental factors, transportation and housing ( Petterson et al., 2020 ). These factors interact in complex ways resulting in persisting social environment-driven health and other inequalities which if left unaddressed will only increase.

Respectively, among policies goals across the Global North enhancing wellbeing and social mobility for disadvantaged and marginalized families, creating socially mixed, heterogeneous neighborhoods (that is, desegregation) is promoted to avoid spatial segregation based on racial and ethnic membership and class while supporting social cohesion ( Méreiné-Berki et al., 2021 ). Importantly, a marginalized community is not a homogeneous group as the lived experience of disadvantage within the communities is variegated: respectively, policies to improve socio-spatial integration and addressing the various causes of extreme poverty including social, economic, and cultural that improve social equity have been suggested since desegregation on its own is insufficient (( Méreiné-Berki et al., 2021 ). Sustainable planning may mitigate consequences of urban sprawl noted in the urban studies literature including urban blight which is the greatest in poorest areas entrapping the low-income residents in the inner city where they have only limited regional mobility and access to job opportunities at the urban edge. Understanding the links between a development of a metropolitan-wide blight remediation strategy toward a sustainable urban form and welfare enhancing among the disadvantaged populations needs to be further investigated.

During public health crises, the importance of the central role of the community has been highlighted especially when some state-based social services may be less available due to lockdown. Rather than inventing new solutions, voluntary informal social networks that have been generated by communities utilize local assets and resources ( Bear et al., 2020 ). Community-based initiatives may rely on the voluntary sector, faith- and charities-based organizations, and social enterprises for various services including help with visiting housebound people, or using them as a distribution hub for food distribution to families in need.

In conclusion, in this study, we situated the research on economic impacts of the COVID in the broader context of social disadvantage with findings both domestically and from other countries in line with those in our study. The earlier misleading view of the global epidemic representing a systematic disadvantage that may affect and limit everyone’s economic activity, with any socioeconomic status or from any geographic location, was rejected. Our finding indicates that certain factors may increase people's vulnerability to the financial stress related to COVID-19. We find support that the social distribution of economic vulnerability is magnified in regions with pre-existing social disparities, creating new forms of disparity ( Qian and Fan, 2020 ).

This work was supported by the UTHSC/UofM SARS-CoV-2/COVID-19 Research CORNET (Collaboration Research Network) Award.

CRediT authorship contribution statement

Anzhelika Antipova: Conceptualization, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review & editing.

Declaration of competing interest

The author declares no conflict of interest.

  • Antipova A. Analysis of Commuting Distances of Low-Income Workers in Memphis Metropolitan Area. TN. Sustainability. 2020; 12 (3):1209. doi: 10.3390/su12031209. [ CrossRef ] [ Google Scholar ]
  • Banerjee S. In: Corona and work around the Globe. Eckert A., Hentschke F., editors. De Gruyter; Berlin, Boston: 2020. Skill, informality, and work in pandemic times: Insights from India; pp. III–IX. 2020. [ CrossRef ] [ Google Scholar ]
  • Barrero J.M., Bloom N., Davis S.J. COVID-19 also a reallocation shock. 2020. https://bfi.uchicago.edu/wp-content/uploads/BFI_WP_202059.pdf Working paper NO. 2020-59. At:
  • Batty M. The coronavirus crisis: What will the post-pandemic city look like? Environ. Plan. B: Urban Anal. City Sci. 2020; 47 (4):547–552. [ Google Scholar ]
  • Bear L., James D., Simpson N., Alexander E., Bhogal J.K., Bowers R.E., Cannell F., Lohiya A.G., Koch I., Laws M., Lenhard J.F., Long N.J., Pearson A., Samanani F., Wuerth M., Vicol O., Vieira J., Watt C., Whittle C., Zidaru-Barbulescu T. In: Corona and work around the Globe. Eckert A., Hentschke F., editors. De Gruyter; Berlin, Boston: 2020. Changing care networks in the United Kingdom; pp. VVIII–VVX. [ CrossRef ] [ Google Scholar ]
  • Bullard R. Westview Press; 2000. Dumping in Dixie: Race, class, and environmental quality, third edition: Bullard, Robert D.: 9780813367927: Amazon.com: Books. [ Google Scholar ]
  • Bureau of Labor Statistics (BLS) Economic news release. State employment and unemployment —NOVEMBER 2020. December 18, 2020. 2020. https://www.bls.gov/news.release/laus.nr0.htm USDL-20-2267. At:
  • Bureau of Labor Statistics (BLS) Frequently asked questions: The impact of the coronavirus (COVID-19) pandemic on the Employment Situation for April 2020. 2020. https://www.bls.gov/cps/employment-situation-covid19-faq-april-2020.pdf May 8, 2020. At:
  • Cai Q., et al. Obesity and COVID-19 severity in a designated hospital in shenzhen, China. Diabetes Care. 2020; 43 (7):1392–1398. [ PubMed ] [ Google Scholar ]
  • Coibion O., Gorodnichenko Y., Weber M. NBER working paper No. 27017. 2020. Labor markets during the COVID-19 crisis: A preliminary view. April 2020. [ Google Scholar ]
  • Cortes G.M., Forsythe E. Upjohn Institute Working Paper; May 2020. The heterogeneous labor market impacts of the Covid-19 pandemic. [ Google Scholar ]
  • Creţan R., Light D. COVID-19 in Romania: Transnational labour, geopolitics, and the Roma ‘outsiders. Eurasian Geography and Economics. 2020; 61 (4–5):559–572. [ Google Scholar ]
  • Creţan R., Málovics G., Méreiné-Berki B. On the perpetuation and contestation of racial stigma: Urban Roma in a disadvantaged neighbourhood of Szeged. Geographica Pannonica. 2020; 24 (4):294–310. [ Google Scholar ]
  • Creţan R., O’Brien T. Corruption and conflagration: (in)justice and protest in bucharest after the colectiv fire. Urban Geography. 2020; 41 (3):368–388. doi: 10.1080/02723638.2019.1664252. [ CrossRef ] [ Google Scholar ]
  • Cutler D.M., Huang W., Lleras-Muney A. When does education matter? The protective effect of education for cohorts graduating in bad times. Social Science & Medicine. 2015; 127 :63–73. 2015. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Dey M., Loewenstein M.A. How many workers are employed in sectors directly affected by COVID-19 shutdowns, where do they work, and how much do they earn? Monthly Labor Review. April 2020 https://www.bls.gov/opub/mlr/2020/article/covid-19-shutdowns.htm [ Google Scholar ]
  • Eckert A., Hentschke F. Andreas Eckert and Felicitas Hentschke. De Gruyter; Berlin, Boston: 2020. Introduction: Corona and work around the Globe". Corona and work around the Globe; pp. XVII–XXII. 2020. [ CrossRef ] [ Google Scholar ]
  • Fairlie R. NBER working paper No. 27309, June 2020. 2020. The impact of covid-19 on small business owners: Evidence of early-stage losses from the April 2020 current population survey. [ Google Scholar ]
  • Falk G., Carter J.A., Nicchitta I.A., Nyhof E.C., Romero P.D. Unemployment rates during the COVID-19 pandemic. 2020. https://fas.org/sgp/crs/misc/R46554.pdf Brief. Nov. 2020. Prepared by the Congressional Research Service (CRS). CRS Report R46554. At:
  • Financial Times “Prospering in the pandemic: The top 100 companies,” 18 June. 2020. https://www.ft.com/content/844ed28c-8074-4856-bde0-20f3bf4cd8f0 At:
  • Finch W.H., Hernández Finch M.E. Poverty and covid-19: Rates of incidence and deaths in the United States during the first 10 Weeks of the pandemic. Front. Sociol. 2020; 5 :1–10. June. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Fryer R.G.J. An empirical analysis of racial differences in police use of force. Journal of Political Economy. 2019; 127 (3):1210–1261. [ Google Scholar ]
  • Golestaneh L., et al. The association of race and COVID-19 mortality. EClinicalMedicine. 2020; 25 :100455. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Goolsbee A., Syverson C. NBER working paper No. 27432. June 2020. Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Gössling S., Scott D., Hall C.M. Pandemics, tourism and global change: A rapid assessment of COVID-19. Journal of Sustainable Tourism. 2020:1–20. [ Google Scholar ]
  • Gould E., Wilson V. 2020. Black workers face two of the most lethal preexisting conditions for coronavirus — racism and economic inequality. [ Google Scholar ]
  • Gupta S., et al. NBER working paper No. 2780. May 2020. Effects of social distancing policy on labor market outcomes. [ Google Scholar ]
  • Haase A., Rink D., Grossmann K., Bernt M., Mykhnenko V. Conceptualizing urban shrinkage. Environment and Planning A. 2014; 46 (7):1519–1534. doi: 10.1068/a46269. [ CrossRef ] [ Google Scholar ]
  • Hajat A., Hsia C., O’Neill M.S. Vol. 2. Springer; 2015. Socioeconomic disparities and air pollution exposure: A global review; pp. 440–450. (Current environmental health reports). 4. 01-Dec. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Han J., Meyer B.D., Sullivan J.X. 2020. Income and poverty in the COVID-19 pandemic. [ Google Scholar ]
  • Hartt M. The prevalence of prosperous shrinking cities. Annals of the Association of American Geographers. 2019; 109 (5):1651–1670. doi: 10.1080/24694452.2019.1580132. [ CrossRef ] [ Google Scholar ]
  • Hoekveld J.J. Time-space relations and the differences between shrinking regions. Built Environment. 2012; 38 (2):179–195. doi: 10.2148/benv.38.2.179. [ CrossRef ] [ Google Scholar ]
  • Jones B.L., Jones J.S. Gov. Cuomo is wrong, covid-19 is anything but an equalizer. Washington Post. 2020 https://www.washingtonpost.com/outlook/2020/04/05/gov-cuomo-is-wrong-covid-19-is-anything-an-equalizer/ Accessed from. [ Google Scholar ]
  • Kalleberg A.L. Russell Sage Foundation; New York, NY: 2011. Good jobs, bad jobs: The rise of polarized and precarious employment systems in the United States, 1970s-2000s. 2011. [ Google Scholar ]
  • Kass D.A., Duggal P., Cingolani O. Obesity could shift severe COVID-19 disease to younger ages. Lancet. 2020; 395 (10236):1544–1545. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Kiang M.V., Irizarry R.A., Buckee C.O., Balsari S. Every body counts: Measuring mortality from the COVID-19 pandemic. Annals of Internal Medicine. Sep. 2020:M20–M3100. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Krzysztofik R., Kantor-Pietraga I., Spórna T. Spatial and functional dimensions of the COVID-19 epidemic in Poland. Eurasian Geography and Economics. 2020; 61 (4–5):573–586. 10.1080/15387216.2020.1783337. [ Google Scholar ]
  • Kunzmann K.R. Smart cities after covid-19: Ten narratives. disP - Plan. Rev. 2020; 56 (2):20–31. [ Google Scholar ]
  • Malhotra K., Baltrus P., Zhang S., Mcroy L., Immergluck L.C., Rust G. Geographic and racial variation in asthma prevalence and emergency department use among Medicaid-enrolled children in 14 southern states. Journal of Asthma. 2014; 51 (9):913–921. Nov. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Mayer-Ahuja N. In: Corona and work around the Globe. Eckert A., Hentschke F., editors. De Gruyter; Berlin, Boston: 2020. Solidarity’ in times of Corona? Of migrant Ghettos, low-wage heroines, and empty public coffers; pp. XVII–XXII. 2020. [ CrossRef ] [ Google Scholar ]
  • Méreiné-Berki B., Málovics G., Crețan R. “You become one with the place”: Social mixing, social capital, and the lived experience of urban desegregation in the Roma community. Cities. 2021; 117 :103302. [ Google Scholar ]
  • Millett G.A., et al. Assessing differential impacts of COVID-19 on black communities. Annals of Epidemiology. 2020; 47 :37–44. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Murray S., Olivares E. 2020. Job losses during the onset of the COVID-19 pandemic: Stay-at-home orders, industry composition, and administrative capacity (June 18, 2020) https://ssrn.com/abstract=3633502 Available at SSRN: [ CrossRef ] [ Google Scholar ]
  • O’Hearn M., Liu J., Cudhea F., Micha R., Mozaffarian D. Coronavirus disease 2019 hospitalizations attributable to cardiometabolic conditions in the United States: A comparative risk assessment analysis. Journal of the American Heart Association. 2021 doi: 10.1161/JAHA.120.019259. https://www.ahajournals.org/doi/abs/10.1161/JAHA.120.019259 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Petterson S., Westfall J.M., Miller B.F. Well Being Trust; 2020. Projected deaths of despair from COVID-19. Well being trust. [ Google Scholar ]
  • Pfeffer T., Danziger S., Schoeni R.F. Wealth disparities before and after the great recession. The Annals of the American Academy of Political and Social Science. 2013; 650 (1):98–123. 2013. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Price-Haywood E.G., et al. Hospitalization and mortality among black patients and white patients with Covid-19. New England Journal of Medicine. 2020; 382 (26):2534–2543. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Qian Y., Fan W. Research in social stratification and mobility. JAI Press; 2020. Who loses income during the COVID-19 outbreak? Evidence from China. [ Google Scholar ]
  • Qualls N., et al. Community mitigation guidelines to prevent pandemic influenza — United States, 2017. MMWR. Recomm. Reports. Apr. 2017; 66 (1):1–34. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Selden T.M., Berdahl T.A. COVID-19 and racial/ethnic disparities in health risk, employment, and household composition. Health Affairs. Sep. 2020; 39 (9):1624–1632. [ PubMed ] [ Google Scholar ]
  • Sharifi A., Khavarian-Garmsir A.R. The COVID-19 pandemic: Impacts on cities and major lessons for urban planning, design, and management. The Science of the Total Environment. 2020; 749 :1–3. 2020. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Song M.K., Lin F.C., Ward S.E., Fine J.P. Composite variables: When and how. Nursing Research. Jan. 2013; 62 (1):45–49. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • The National Bureau of Economic Research https://www.nber.org/cycles.html At:
  • Thebault Reis, Tran Andrew Ba, Williams Vanessa. “African Americans are at higher risk of deathfrom coronavirus”, The Washington Post, 07-Apr-2020. The Washington Post. 2020 https://www.washingtonpost.com/nation/2020/04/07/coronavirus-is-infecting-killing-black-americans-an-alarmingly-high-rate-post-analysis-shows/ [ Google Scholar ]
  • U. S. Census Bureau 2018 American Community Survey (ACS 2018 5-year) 2020. https://www.census.gov/programs-surveys/acs/data.html [Online]. Available:
  • Wade L. An unequal blow. Science. 2020; 368 (6492):700–703. [ PubMed ] [ Google Scholar ]
  • Watson D.F., Philip G.M. A refinement of Inverse distance weighted interpolation. Geo-Processing. 1985; 2 :315–327. [ Google Scholar ]
  • Weinstock L.R. Prepared by the congressional research service (CRS). CRS report IN11460. 2020. COVID-19: How quickly will unemployment recover? https://crsreports.congress.gov/product/pdf/IN/IN11460 AT: [ Google Scholar ]
  • Wilkinson A., Ali H., Bedford J., Boonyabancha S., Connolly C., Conteh A., Dean L., Decorte F., Dercon B., Dias S., Dodman D., Duijsens R., D’Urzo S., Eamer G., Earle L., Gupte J., Frediani A.A., Hasan A., Hawkins K.…Whittaker L. Local response in health emergencies: Key considerations for addressing the COVID-19 pandemic in informal urban settlements. Environment and Urbanization. 2020; 32 (2):503–522. https://journals.sagepub.com/doi/full/10.1177/ [ Google Scholar ]
  • Worldometers COVID-19 coronavirus pandemic. 2020. https://www.worldometers.info/coronavirus/country/us/ At:
  • Wu D., Yu L., Yang T., Cottrell R., Peng S., Guo W., Jiang S. The Impacts of Uncertainty Stress on Mental Disorders of Chinese College Students: Evidence From a Nationwide Study. Frontiers in Psychology. 2020; 11 :243. [ PMC free article ] [ PubMed ] [ Google Scholar ]

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Title: large language models: a survey.

Abstract: Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks, since the release of ChatGPT in November 2022. LLMs' ability of general-purpose language understanding and generation is acquired by training billions of model's parameters on massive amounts of text data, as predicted by scaling laws \cite{kaplan2020scaling,hoffmann2022training}. The research area of LLMs, while very recent, is evolving rapidly in many different ways. In this paper, we review some of the most prominent LLMs, including three popular LLM families (GPT, LLaMA, PaLM), and discuss their characteristics, contributions and limitations. We also give an overview of techniques developed to build, and augment LLMs. We then survey popular datasets prepared for LLM training, fine-tuning, and evaluation, review widely used LLM evaluation metrics, and compare the performance of several popular LLMs on a set of representative benchmarks. Finally, we conclude the paper by discussing open challenges and future research directions.

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research paper on the unemployment

Green Chemistry

Environmental impact of different scenarios for the pyrolysis of contaminated mixed plastic waste †.

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* Corresponding authors

a Department of Chemical Engineering, Faculty of Sciences, University of Granada, 18071 Granada, Spain E-mail: [email protected] , [email protected]

b Department of Agrifood Chain Economics, Institute of Agricultural and Fisheries Research and Training (IFAPA), Centre ‘Camino de Purchil’, 18080 Granada, Spain

Every day, large amounts of plastic are disposed of all over the world. Most of it is not recycled and ends up polluting the environment. Therefore, waste collection and management must be improved to reduce the environmental impact caused by plastic waste. Pyrolysis has been explored as an alternative to treat contaminated mixed plastic waste and obtain valuable materials, such as oil and char. These materials can effectively substitute fuel and activated carbon, respectively. However, the pyrolysis process also has a significant environmental impact, mainly due to gas emissions. It is important to quantify this environmental impact and compare it with alternative treatment methods to identify the best management strategy for contaminated mixed plastic waste. This study applies the Life-Cycle Assessment methodology to evaluate the environmental impact and compare it with the conventional practice of landfilling. Three different pyrolysis scenarios are considered: one in which the char is used as fuel and therefore combusted, and two in which the char is activated by carbon dioxide and potassium hydroxide, respectively, to be used as an adsorbent. Our results show that pyrolysis is environmentally superior to landfilling for the treatment of contaminated mixed plastic waste. This is mainly due to the production of oil, which substitutes commercial diesel, the production of which has a high environmental impact. Pyrolysis followed by char combustion has the lowest environmental impact of all pyrolysis scenarios considered.

Graphical abstract: Environmental impact of different scenarios for the pyrolysis of contaminated mixed plastic waste

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research paper on the unemployment

Environmental impact of different scenarios for the pyrolysis of contaminated mixed plastic waste

G. Garcia-Garcia, M. Á. Martín-Lara, M. Calero and G. Blázquez, Green Chem. , 2024, Advance Article , DOI: 10.1039/D3GC04396G

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Data, Privacy Laws and Firm Production: Evidence from the GDPR

By regulating how firms collect, store, and use data, privacy laws may change the role of data in production and alter firm demand for information technology inputs. We study how firms respond to privacy laws in the context of the EU’s General Data Protection Regulation (GDPR) by using seven years of data from a large global cloud-computing provider. Our difference-in-difference estimates indicate that, in response to the GDPR, EU firms decreased data storage by 26% and data processing by 15% relative to comparable US firms, becoming less “data-intensive.” To estimate the costs of the GDPR for firms, we propose and estimate a production function where data and computation serve as inputs to the production of “information." We find that data and computation are strong complements in production and that firm responses are consistent with the GDPR, representing a 20% increase in the cost of data on average. Variation in the firm-level effects of the GDPR and industry-level exposure to data, however, drives significant heterogeneity in our estimates of the impact of the GDPR on production costs.

We thank Guy Aridor, James Brand, Alessandro Bonatti, Peter Cihon, Jean Pierre Dubé, Joe Doyle, Ben Edelman, Liran Einav, Sara Ellison, Maryam Farboodi, Samuel Goldberg, Yizhou Jin, Garrett Johnson, Gaston Illanes, Markus Mobius, Devesh Raval, Dominik Rehse, Tobias Salz, Bryan Stuart, Taheya Tarannum, Joel Waldfogel, and Mike Whinston for helpful comments, and Abbie Natkin, Taegan Mullane, Doris Pan, Ryan Perry, Bea Rivera for excellent research assistance. We are also grateful to Han Choi for copyediting assistance. We gratefully acknowledge the support of the National Institute on Aging, Grant Number T32- AG000186 (Li) and the National Science Foundation Graduate Research Fellowship under Grant No 214106 (Li). The views expressed herein are those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Chicago, the Federal Reserve System, or the National Bureau of Economic Research.

Mert Demirer is a former paid postdoctoral researcher at Microsoft (a firm active in the cloud market, which this paper studies).

Diego Jiménez Hernández is a former paid postdoctoral researcher at Microsoft.

Dean Li is a former intern at Microsoft.

Sida Peng is a paid employee and minority equity holder at Microsoft.

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Science | Eureka! How a Stanford study revealed the success of research failures

Faced with experiments that can’t be reproduced, academia seeks to improve and “future proof” research.

Stanford neuroscientist Dr. Thomas Südhof, winner of the 2013 Nobel Prize in Physiology or Medicine, co-authored a paper with postdoc Kif Liakath-Ali that revealed flaws in previous research. Faced with a disturbing flurry of experiments that can't be reproduced, academia is ramping up efforts to “future proof” experiments.

An important paper recently published by an esteemed Stanford research team reported an unusual result: An experiment went wrong.

Usually, scientists seek to burnish their reputations by announcing positive news of a discovery that solves a problem or transforms how we view the world.

But this negative news — which revealed that earlier neuroscience research was flawed — can also be positive. It builds a stronger scientific foundation and helps restore public trust, according to a growing consensus of scientists and journal editors.

Faced with a disturbing flurry of experiments that can’t be reproduced, academia is ramping up efforts to “future proof” its research.

While headlines are dominated by fraud or research misconduct cases, including a scandal that led to the resignation of Stanford University President Dr. Marc Tessier-Lavigne , these instances are relatively rare. A bigger problem is experimentation that lacks robust design, methodology, analysis and interpretation of results — so arrives at the wrong conclusions.

“Our efforts highlight the importance of experimental rigor,” said Stanford postdoctoral neuroscientist Kif Liakath-Ali , who conducted the work with Nobel Laureate Thomas Südhof.

His revelation — that sometimes a negative can be a positive — came while he was trying to reproduce and build upon a 2017 study about the behavior of brain cells. He wanted to understand the regulation of brain cells, with major implications for memory, behavior and neurological disease. He discovered that the previous approach in the lab had killed cells, leading to “a skewing of results and biased conclusions,” he said.

I t was a professional setback for Liakath-Ali, who had aimed to build on this research to make a new and meaningful discovery.

A junior scientist, he worried that his insight, based on almost two years of work, would not advance his career. Instead, Liakath-Ali has been honored by the School of Medicine ’s new Program on Research Rigor and Reproducibili ty with an award for his integrity.

His finding has emboldened other research teams to come forward to describe their own failed attempts, he said. Although those teams had stayed silent, “they had seen the same thing.”

“That’s what good science is about,” said Dr. Steven Goodman , who leads Stanford’s Program on Research Rigor and Reproducibility. “It detected that some really important findings … were just wrong.”

“We want to reward how people do science,” he said, “and if they do it better than the last person.”

Science is famed for its “Eureka” moments. We love the tale of Scottish microbiologist Alexander Fleming, who came home from vacation to discover a mold producing penicillin, the world’s first antibiotic, growing inside a neglected Petri dish..

Real experimentation takes many twists and turns and doesn’t always deliver the expected outcome.

“I have not failed,” inventor Thomas Edison famously said. “I’ve just found 10,000 ways that won’t work.”

But modern science is competitive. In today’s “publish or perish” academic culture, careers are advanced by insights, so scientists are incentivized to announce only positive findings.

In the worst cases, this can foster fraudulent or sloppy practices. Tessier-Lavigne resigned his post after an independent review found multiple errors in five papers he had overseen , concluding that “multiple members of (his) labs over the years appear to have manipulated research data and/or fallen short of accepted scientific practices.”

Journals also favor papers that are “hot,” with impact that will be widely cited and elevate a journal’s reputation.

“A challenge for scientists has been that (experimental) repetitions are difficult to publish, no matter whether they are positive or negative,” said Südhof, who won the 2013 Nobel Prize in Physiology or Medicine. “This is bad because it creates a disincentive for repeating experiments.”

Journals are trying to change, said Holden Thorp, editor of the prominent journal Science, at last week’s Stanford conference on research integrity. “It’s a very, very challenging problem, because all of the emphasis is on novelty and ‘being first…We don’t take a lot of papers that say, ‘We tested this hypothesis and we found that it’s still correct.’”

But repetition may reveal problems, and “ensures that people have the full story,” said Emily Chenette, editor-in-chief of PLOS ONE , published by the Public Library of Science, which evaluates research on scientific validity, methodology and ethical standards — not perceived significance. PLOS ONE publishes a collection called “Missing Pieces,” which lists studies that present inconclusive, null findings or demonstrate failed replications of other published work.

A negative finding can suggest promising new directions, approaches and hypotheses. It may warn other investigators to steer clear, saving time and money, Chenette said.

“It has real life implications for people,” she said.

Early in the COVID-19 pandemic, use of the malaria drug hydroxychloroquine was spurred by anecdotal reports from China and France of patients who seemed to improve and laboratory findings of a possible antiviral effect. But a rigorous study found that the drug didn’t work — a discovery that saved many lives.

In the prestigious British journal Lancet, a doctor linked vaccines to autism, a claim that has led to clusters of resistance to inoculation. It was refuted by multiple studies, and a subsequent investigation showed his work to be bunk. 

This week, in a surprise announcement, a precious 280-million-year-old fossilized lizard turned out to be mostly … black paint. An Italian team had hoped to make history by using high-tech tools — electron microscopy, spectroscopy and micro x-rays — to reveal the cellular structure of one of the world’s oldest reptiles. Instead, they found forgery. But their revelation could lead to a rethinking of ancient taxonomy.

Stanford’s Liakath-Ali sought to better understand how brain cells, called neurons, communicate via trillions of synapses — and how things go wrong. Synapses connect using a vast network of molecules, governed by genes whose function may change if subjected to stress, causing devastating ailments like schizophrenia, autism and other neurological disorders.

He based his work on a 2017 report by scientists from China’s Tsinghua University published in the journal Nature Neuroscience. They had found that when we learn something, brain cells change in a way that helps us remember it better. But brain cells’ regulatory mechanism can be altered by stress.

He took a closer look at the Chinese research and found fault with their technique, which caused the cells to be so stressed — “hammered,” he said — that they died. This skewed their results.

“Liakath-Ali did what no one else had done: He took the care to look at the cells,” said Goodman.

Nobel Laureate Südhof commended his perseverance.

“Science operates by a trial-and-error process in which scientists, like all other humans, also make mistakes,” he said. “To distinguish valid results from erroneous ones, it is necessary to repeat experiments independently.”

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The moon is littered with wreckage from failed landings. Some missions never even got that far. Another U.S. company — Astrobotic Technology — tried to send a lander to the moon last month, but it didn't get there because of a fuel leak. The crippled lander came crashing back through the atmosphere, burning up over the Pacific.

World News | Private US spacecraft enters orbit around the moon

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