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A Crisis of Long-Term Unemployment Is Looming in the U.S.

• Ofer Sharone

How biases trap qualified job seekers in a cycle of rejection — and how to help them break free.

The stigma of long-term unemployment can be profound and long-lasting. As the United States eases out of the Covid-19 pandemic, it needs better approaches to LTU compared to the Great Recession. But research shows that stubborn biases among hiring managers can make the lived experiences of jobseekers distressing, leading to a vicious cycle of diminished emotional well-being that can make it all but impossible to land a role. Instead of sticking with the standard ways of helping the LTU, however, a pilot program that uses a wider, sociologically-oriented lens can help jobseekers understand that their inability to land a gig isn’t their fault. This can help people go easier on themselves which, ultimately, can make it more likely that they’ll find a new position.

Covid-19 has ravaged employment in the United States, from temporary furloughs to outright layoffs. Currently, over 4 million Americans have been out of work for six months or more , including an estimated 1.5 million workers in white-collar occupations, according to my calculations. Though the overall unemployment rate is down from its peak last spring, the percent of the unemployed who are long-term unemployed (LTU) keeps increasing and is currently at over 40%, a level of LTU comparable to the Great Recession but otherwise unseen in the U.S. in over 60 years.

• Ofer Sharone is an expert on long-term unemployment and the author of the book Flawed System/Flawed Self: Job Searching and Unemployment Experiences (University of Chicago Press). Sharone received his PhD in sociology from the University of California Berkeley, his JD from Harvard Law School, and is currently an associate professor of sociology at the University of Massachusetts Amherst.

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

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

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

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

What Is "Normal"?

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

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

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

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

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

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

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

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

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

Pre-Pandemic Trends: 1976-2019

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

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

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

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

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

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

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

Pre-Pandemic Expectations of Future LFP and Unemployment Trends

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

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

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

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

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

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

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

Unemployment and Labor Force Participation During the Pandemic

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

What Does the Future Hold?

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

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

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

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

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

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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Covid-19, unemployment, and health: time for deeper solutions?

Read our latest coverage of the coronavirus outbreak.

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• Peer review
• Martin Hensher , associate professor of health systems financing and organisation 1 2
• 1 Deakin Health Economics, Deakin University, 221 Burwood Highway, Burwood, VIC 3125, Australia
• 2 Menzies Institute for Medical Research, University of Tasmania
• Correspondence to: martin.hensher{at}deakin.edu.au

As covid-19 drives unemployment rates around the world to levels unseen in generations, once radical economic policy proposals are rapidly gaining a hearing. Martin Hensher examines how job guarantee or universal basic income schemes might support better health and better economics

Covid-19 has been a dramatic global health and economic shock. As SARS-CoV-2 spread across nations, economic activity plummeted, first as individuals changed their behaviour and then as government “lockdowns” took effect. 1 Macroeconomic forecasters foresee a major recession continuing through 2020 and into 2021. 2 Although the governments of many nations have taken novel steps to protect workers, unemployment has risen dramatically in many countries ( box 1 , fig 1 ); poverty and hunger are on the rise in low and middle income countries. 5 Covid-19 has directly caused illness and death at a large scale, and further threatens health through disruption of access to health services for other conditions.

Covid-19 and unemployment

Although unemployment soared in response to covid-19 in some nations, the policy measures undertaken by others have prevented many workers from becoming technically unemployed. In the United Kingdom, the headline rate of unemployment for April-June 2020 was 3.9%—only slightly higher than the 3.89% rate in April-June 2019. Yet in June 2020 9.3 million people were in the coronavirus job retention scheme (“furlough”) and another 2.7 million had claimed a self-employment income support scheme grant; there had been the largest ever decrease in weekly hours worked; 650 000 fewer workers were reported on payrolls in June than in March; and the benefit claimant count had more than doubled from 1.24 million to 2.63 million people. 3 The Australian Bureau of Statistics has produced an adjusted estimate of Australian unemployment that includes all those temporarily stood down or laid off, to allow a closer comparison with US and Canadian statistics ( fig 1 ). As emergency support measures are wound back, concern is growing that the downwards trend from the April peak might not be maintained in coming months.

Unemployment rates in Australia, Canada, and the United States from March to July 2020. 4

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The pandemic continues to spread, and hopes for a rapid “return to normal” look increasingly unfounded. The economic consequences of covid-19 have the potential to further damage human health if not managed effectively—even after the pandemic has faded. Even with the most rose tinted views of recovery, the effects of covid-19 on unemployment are likely to be substantial and long lived. Ambitious responses to the imminent scourge of mass unemployment are being discussed. Two such proposals—a job guarantee and universal basic income—might protect and promote health as well as prosperity. Governments around the world should consider radical plans to safeguard their citizens’ livelihoods and wellbeing.

Unemployment and health in the time of covid-19

Decades of accumulated evidence show a strong and consistent association between unemployment and a range of adverse health outcomes, including all cause mortality, death from cardiovascular disease and suicide, and higher rates of mental distress, substance abuse, depression, and anxiety. 6 7 8 Job insecurity is similarly associated with poorer self-assessed health status, mental distress, depression, and anxiety. 9 Unemployment and economic adversity are intimately related with despair and lack of hope, which have increasingly been linked with mortality and the rise and severity of the US opioid epidemic. 10 11 Whether recessions and mass unemployment increase aggregate mortality is less clear; historical studies indicated improvements in mortality during the Great Depression in the 1930s, 7 but more recent US research found that older workers (aged 45-66) who lose their jobs in a recession have higher mortality than those who lose their jobs in boom times. 12 Insecurity, precariousness, and austerity harmed both unemployed and employed people during the protracted economic crisis in Greece after 2008-09. 13 Meanwhile, differing welfare state institutions and unemployment insurance arrangements directly limit or amplify health inequalities in a society. 7 14

These factors could adversely affect the health of growing numbers of unemployed workers after covid-19. 15 16 Governments, business lobbyists, and civil society advocates around the world are debating how economies might best recover from the covid recession. Although governments currently acknowledge the need to spend freely during the crisis, experience suggests that pressure to pursue misguided austerity policies might grow, threatening subsequent recovery. Options on the table range from “green new deal” programmes to build a post-carbon economy and national industrial strategies to bring globalised manufacturing back onshore through to calls for reducing wages and labour protections to “free up” labour markets. Yet these are all indirect approaches to the effects of unemployment. Proposals for a job guarantee or a universal basic income seek to act more directly to support individual citizens.

The job guarantee

The idea of a right to employment can be traced back to the US New Deal in the 1930s, and to Article 23 of the 1948 United Nations Universal Declaration of Human Rights. More recently, in the contest for the Democratic Party’s 2020 candidate for US president, senators Bernie Sanders, Kirsten Gillibrand, and Cory Booker all included a job guarantee in their platforms, as did Alexandria Ocasio-Cortez’s green new deal resolution. More than one detailed proposal for a Federal Job Guarantee has been published in the US 17 18 and in Australia. 19 In one US proposal, 18 a federally funded public service employment programme would provide a standing offer of work at a living wage ($15 (£12; €13) an hour), along with key benefits including healthcare coverage. Employees of this programme would be deployed on a wide range of public works and community development activities, delivered through federal, state, local, and non-profit agencies. The proposal argues that this would effectively eliminate unwanted joblessness and underemployment and would rapidly force the private sector to increase wages to match this “living wage” alternative, lifting millions out of poverty and greatly improving the incomes of working poor people. 18 Proponents argue that the job guarantee is the most efficient “automatic stabiliser” for the economy throughout the business cycle, able to adjust up and down to reflect the changing economic health of the private sector. In economic downturns, it would provide guaranteed employment to stop people falling into poverty and losing “employability,” while also supporting aggregate demand to lift the economy out of recession. In boom times, workers will simply exit the programme for the private sector, as firms offer higher wages to secure the additional labour they need.

In the US, the job guarantee has been proposed as not only a key tool for recovery from covid-19, 20 but also a mechanism to ensure that this recovery breaks down historically entrenched racial inequalities in wealth. 21 Similarly, an emerging job guarantee proposal for Australia could rectify decades of welfare policy failures that have disproportionately affected indigenous Australians. 22 Proponents point to successful past or present international experiences with full or targeted employment guarantee programmes, including Argentina’s Plan Jefes, South Africa’s Expanded Public Works Programme, India’s National Rural Employment Guarantee Act, Belgium’s Youth Job Guarantee, the US Youth Incentive Entitlement Pilot Projects, and the UK’s Future Jobs Fund. 20

Universal basic income

Over the past few years, there has been a global explosion of interest in the concept of universal basic income. 23 24 25 Andrew Yang, another former contender for the 2020 Democrat presidential nomination, made universal basic income a central plank of his platform. Such proposals share key characteristics: they are a transfer of income (from the state to individuals) that is provided universally (to everyone, with no targeting), unconditionally (with no requirements, for example to work), and in cash (with no controls on what the money can be spent on). 25 Proposals also typically specify an income that is sufficiently generous that it can fully cover a basic level of living expenses. 23 Universal basic income is a direct means of reducing poverty, by ensuring that all in society receive enough to live with dignity; it could reduce income inequality; it could radically simplify current social welfare systems and remove poverty traps and disincentives to move from welfare into work; it could improve the ability of workers to refuse poorly paid, insecure, exploitative or unsafe jobs, through a reduced fear of loss of income; and it could be a buffer against technological unemployment, as automation and artificial intelligence replace human labour. 23 25 Universality is the key difference from today’s welfare systems; everyone should receive universal basic income as a right of citizenship, and its receipt by all should build the solidarity and legitimacy that will sustain this right. Universal basic income could improve health and reduce health inequities through direct action on various social determinants of health. 26 27 This variety of aims leads to the concept being simultaneously supported by those on the left as a radical, anti-capitalist policy, often viewed as an essential component of the ecological degrowth agenda, and by libertarian, tech capitalists as an efficient solution to the risk that ever expanding digital automation will destroy more jobs than it creates, and as a vital measure to help capitalism survive mass technological unemployment in the future. 28

In the wake of the covid-19 economic shock, universal basic income has been discussed as a potentially powerful policy solution to unprecedented economic dislocation. It has specifically been suggested as a tool for limiting the economic, social, and psychological trauma of covid-19. 29 The Spanish government has just introduced a nationwide, means tested minimum income programme (not universal) as a direct response to covid related unemployment. 30 The US government has made unconditional, one-off economic impact payments to most (but not all) American households. Near universal and unconditional universal basic income programmes have only operated at nationwide scale in two countries, Mongolia and Iran. The Mongolian programme has since ceased, and the Iranian programme is no longer strictly universal (the richest people are no longer eligible). Partial schemes and regional pilots, however, have been run successfully in a wide range of nations. 25 A recent trial that provided universal basic income to 2000 recipients in Finland found that employment outcomes, health, and wellbeing measures were better in the universal basic income group than in the comparison group, 31 and the Scottish government has been contemplating a three year trial of universal basic income in an experimental group of recipients. 32

Potential health benefits

Given the substantial evidence linking unemployment to poor health, proponents of both job guarantee and universal basic income schemes point to their potential health benefits as major arguments in their favour ( table 1 ). 20 26 These measures could be expected to positively affect health through four main pathways: direct effects for individual beneficiaries; knock-on effects improving labour market conditions for all workers; the macroeconomic and distributive benefits of more widespread prosperity; and more localised community effects unlocked by these programmes.

Health effects of job guarantee (JG) and universal basic income (UBI) programmes

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Multiple mechanisms would work through these four pathways to deliver potential health benefits, including reduced mortality and improved physical and mental health status. Key mechanisms include reducing poverty, improving economic security, improving the quality of jobs and work, and rebuilding stronger local communities. Unsurprisingly, pathways that link unemployment with poorer health will be more reliably affected by job guarantee programmes than by universal basic income. But universal basic income offers alternative pathways for better health through informal caring and non-market activities. Both types of programme could help resolve one of the problems that the covid-19 pandemic has brought into sharp focus—that low paid, insecure, and casualised workforces cannot afford to self-isolate or stay at home when sick or potentially infected because they lack access to paid sick leave. This problem has proved especially disastrous for those who care for elderly people.

Controversies and choices

Supporters of job guarantee or universal basic income programmes typically have different priorities and view them as two alternative options, not as complementary programmes that could co-exist. Most job guarantee proposals see it as not only a means to fight unemployment, but also an explicit instrument of macroeconomic policy 38 ; universal basic income would not function as an “automatic stabiliser” in the same way. Critics of job guarantee and universal basic income schemes primarily question their affordability and potential macroeconomic consequences ( box 2 ).

Economic controversies

Implementing a job guarantee or universal basic income programme would be a major economic reform in any nation and a decisive break with the economic orthodoxy that has prevailed since the Thatcher-Reagan revolution of the 1980s. It would undoubtedly be controversial. Most obviously, some would question them on cost and affordability grounds. A job guarantee programme would incur a substantial net cost to governments—modelling of proposed programmes indicates a net cost to the federal budget equivalent to 1.5% of annual general domestic product (GDP) in the US 18 and 2.6% in Australia (based on a net budgetary cost of A$51.7bn). 19 By comparison, the Australian government is spending A$70bn, or 3.6% of its GDP, on its emergency JobKeeper employment protection programme this year—budget costs of these magnitudes are not unheard of. The gross costs of a universal basic income programme would be substantially larger: income of $12 000 (close to the 2017 US poverty line) for every US adult would cost the federal budget about $3tn, or nearly 14% of GDP. 23 Yet this gross cost estimate is arguably misleading, 39 not only because universal basic income would be partially offset by large savings from current welfare programmes, but because so many recipients would return much or all of it in the form of tax payments. One estimate of the net cost of such a programme indicates that it could be as low as 2.95% of US GDP. 39 These proposals emerge as a growing number of economists are saying that the governments of countries in possession of their own sovereign currency can never “run out of money” and can always purchase whatever goods and services are for sale in the currency they issue. 38 40 They also suggest that inflation—the other risk often pointed to by critics of job guarantee or universal basic income—is currently highly unlikely, with a general fear that the covid-19 recession will prove to be deflationary rather than inflationary.

For those concerned with health, however, philosophical differences might be of more interest. Social determinants and socioeconomic inequalities are well understood to be powerful forces driving health outcomes at both individual and population levels. Universal basic income seeks to reduce poverty and inequality by putting in place an absolute floor—a minimum income provided to everyone in society. A job guarantee seeks to affect poverty by ensuring that anyone who wants to work can work, for a living wage in a decent job. But in so doing, a job guarantee also explicitly increases the relative power of workers, ensuring that a larger share of national income flows to labour, rather than to the owners of capital—potentially reducing some of the extreme inequalities in income and wealth distribution that have arisen over the past four decades. One criticism of universal basic income is that it might (whether inadvertently or by design) become a “plutocratic, philanthropic” programme 28 —scraps from the table of the ultra wealthy, which might cement dependence and powerlessness in a future of technological unemployment. Equally, a job guarantee might be criticised as being a mid 20th century solution to a 21st century problem, which will reinforce social hierarchies by insisting on participation in paid employment as the solution to poverty.

The unemployment triggered by covid-19 in so many countries is a clear and present danger to individual and population health. Tinkering around the margins of current welfare systems, exhortations for yet more labour market “flexibility,” or an unwillingness to maintain public spending through a potentially long and drawn out downturn all offer a fast track to poor outcomes. The scale of the covid economic shock demands more radical action. The substantial health harms of unemployment might be mitigated by a universal basic income programme, but if unemployment is the problem, then employment seems likely to deliver more effective mitigation along the many and complex pathways by which these harms are transmitted. If so, implementing national job guarantee programmes should be a more urgent priority for governments in the immediate aftermath of covid-19. A successful job guarantee scheme would avert the harms of unemployment, strengthen the position of ordinary working people, and deliver a more broadly distributed prosperity in the short to medium term. This would be a much better position from which to then debate and trial universal basic income, allowing it to be correctly framed as a strategic, long term solution to the changing future of work, rather than simply as a response to the current economic crisis.

Key messages

Covid-19 has triggered economic recession and unprecedented rapid rises in unemployment in many countries

Mass unemployment has the potential to cause grave harm to individual and population health if not effectively mitigated

The scale of the crisis means that radical solutions might need to be considered, such as a job guarantee or universal basic income programmes

These policies have the potential to protect human health and dignity, but would mark a significant break with economic orthodoxy

Acknowledgments

I acknowledge the Wurundjeri people of the Kulin Nation as the traditional owners of the land on which this work was undertaken.

Contributors and sources: MH has worked on health financing, planning, and economics as a senior policy maker and researcher in the UK, South Africa, and Australia and as a consultant for the World Bank, World Health Organization and the European Commission. His research on the ecological and economic sustainability of healthcare systems has included examining a number of emerging heterodox economic approaches, two of which are gaining in significance: ecological economics and modern monetary theory. Members of these schools have promoted universal basic income and a job guarantee, respectively, over many years. This article builds on the existing academic literature to consider very recent policy proposals that are emerging in response to the threat of mass unemployment in the wake of covid-19.

Patient involvement: No patients were involved.

Competing interests: I have read and understood BMJ policy on declaration of interests and have the following interests to declare: this research was supported by an Australian Government Research Training Scholarship.

• ↵ Goolsbee A, Syverson C. Fear, lockdown, and diversion: comparing drivers of pandemic economic decline 2020. National Bureau of Economic Research, June 2020. https://www.nber.org/papers/w27432
• ↵ International Monetary Fund. World economic outlook update: a crisis like no other, an uncertain recovery. June 2020. https://www.imf.org/en/Publications/WEO/Issues/2020/06/24/WEOUpdateJune2020
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OPINION article

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

• 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

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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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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• Research article
• Open access
• Published: 29 April 2019

Does unemployment contribute to poorer health-related quality of life among Swedish adults?

• Fredrik Norström   ORCID: orcid.org/0000-0002-0457-2175 1 ,
• Anna-Karin Waenerlund 1 ,
• Lars Lindholm 1 ,
• Rebecka Nygren 1 ,
• Klas-Göran Sahlén 1 &
• Anna Brydsten 2  

BMC Public Health volume  19 , Article number:  457 ( 2019 ) Cite this article

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Previous studies have shown that unemployment has negative impacts on various aspects of health. However, little is known about the effect of unemployment on health-related quality of life. Our aim was to examine how unemployment impacts upon health-related quality of life among Swedish adults, and to investigate these effects on population subgroups defined by education level, marital status, previous health, and gender.

As part of a cross-sectional study, a questionnaire was sent to 2500 randomly selected individuals aged 20 to 64 years living in Sweden in 2016. The questionnaire included the EuroQol 5 dimensions (EQ-5D) instrument and was answered by 967 individuals (39%). Quality-adjusted life year (QALY) scores were derived from the EQ-5D responses. Of the respondents, 113 were unemployed and 724 were employed. We used inverse probability-weighted propensity scores in our analyses to estimate a risk difference. Gender, age, education level, marital status, and previous health were used as covariates in our analyses.

There was a statistically significant lower QALY score by 0.096 points for the unemployed compared to the employed. There were also statistically significant more problems due to unemployment for usual activities (6.6% more), anxiety/depression (23.6% more), and EQ-5D’s Visual Analogue Scale (7.5 point lower score). Grouped analyses indicated a larger negative health effect from becoming unemployed for men, those who are married, and young individuals.

Conclusions

In our study, we show that the health deterioration from unemployment is likely to be large, as our estimated effect implies an almost 10% worse health (in absolute terms) from being unemployed compared to being employed. This further highlights that unemployment is a public health problem that needs more focus. Our study also raises further demands for determining for whom unemployment has the most negative effects and thus suggesting groups of individuals who are in greatest need for labor market measures.

Peer Review reports

Unemployment is in general something that has a negative effect on health [ 1 , 2 , 3 ], not only at the time of becoming unemployed, but also in a longer time span [ 4 , 5 , 6 , 7 , 8 ]. This relationship has previously been shown for different dimensions of self-assessed, mental and physical health and for depressive symptoms [ 2 , 3 ]. In previous research, priorities have often been focused on determining the magnitude of the effect at the population level, e.g. through the meta-analyses by McKee-Ryan et al. [ 1 ] and by Paul and Moser [ 3 ]. However, the study context is highly important to consider because the health consequences from unemployment vary between groups of individuals, e.g. men and women, but also between and within countries and over time [ 2 ]. For some groups of individuals, e.g. for Spanish women [ 9 ] and for Swedes with only a primary-school education [ 10 ], there are even indications of a positive or no effect on health from unemployment. Therefore, policy decisions attempting to reduce unemployment from a health perspective need to consider for whom it is most important to create new job opportunities. Despite a growing literature about the health aspects from becoming unemployed, there is still not much knowledge about how unemployment affects groups of individuals in relation to education level, marital status, previous health, and gender [ 2 ].

Despite many health measures being used to study the effect on health from unemployment, studies using health-related quality of life are lacking, and probably the most commonly used instrument in public health research, the EuroQol 5 dimensions (EQ-5D) [ 11 ], has as far as we know, not previously been used in unemployment research. The EQ-5D instrument consists of a descriptive system with five questions, a visual analogue scale (EQ-VAS), and a value set. An advantage with the EQ-5D is that its value set, which is derived from responses to the descriptive system, allows it to be translated to so-called Quality Adjusted Life-Year (QALY) scores [ 12 , 13 ]. The QALY score has two anchor points, 0 (death) and 1 (full health), which makes it possible to compare the magnitude of different public health problems. QALYs also enable comparisons between conducted and potential interventions in a systematic manner, which makes them attractive and commonly used in cost-effectiveness studies of new interventions.

In studies of unemployment and health, unemployment might at least partly be due to poor health, and for this reason one or more variables related to health before unemployment are therefore important as part of the statistical analyses. It is also important to add other potentially confounding variables. In previous research, gender, age, education level, marital status, household income, geographic location, and social network/support have been used most frequently in the statistical models and in presentations of stratified estimates [ 2 ]. Gender might be the most important variable to stratify for based on previous research and there are studies that show that women are more affected than men by unemployment as well as studies showing the opposite [ 2 ].

Our aim was to examine how unemployment impacts upon health-related quality of life among Swedish adults, and investigate these effects on population subgroups defined by education level, marital status, previous health, and gender.

Study design and participants

A cross-sectional survey consisting of a questionnaire combined with register data from Statistics Sweden was conducted during 2016. From the 5,671,149 individuals who were between 20 and 64 years old and lived in Sweden at the time of the survey, 2500 individuals were selected with simple random sampling and were invited to participate in the study. The questionnaire, which was administered by Statistics Sweden, was sent to the home addresses of the invited individuals on May 2 with two reminders (May 18 and June 1). Participants consented to participate in the study when they returned their questionnaire. There were 967 individuals (39%) who responded to the questionnaire.

The questionnaire consisted of questions related to the participant’s labor market position, health status, health care consumption, and socio-economic and demographic conditions (including education level and marital status). We used self-reported questions related to labor market status to define exposure to unemployment, the EQ-5D as the outcome variable, and gender, age, education level, marital status, and previous health as potentially confounding variables.

Questionnaire responses were scanned and merged with register data that Statistics Sweden administers, and thereafter de-identified, by Statistics Sweden. Register data included, among other things, information about historical unemployment and demographics of the study participants. In the current study, we only used register data to validate the age and gender of the participants. Our choice of variables were consistent with variables in studies similar to ours, with previous health as the only exception [ 2 ]. The Regional Ethical Board in Umeå, Sweden, approved the survey.

Definition of labor market status

The study participants’ labor market status was determined based on the questions: “Which is your main employment” – with ten response alternatives – and “How long have you been unemployed in the last three years?” – with five response alternatives. Those who responded that they had at least six months of unemployment during the last three years were categorized as unemployed ( n  = 113), and those among the other respondents who had responded that their main employment was “gainfully employed” ( n  = 720) or “labor market activity” ( n  = 4) were defined as employed ( n  = 724). Labor market activity refers to a job that is subsided by the state to give the unemployed work experience in order to establish themselves in the labor market. We defined those engaged in labor market activities as employed because we argue that they, in line with what Jahoda has proposed, have a time structure for their waking day, regular contacts with people outside the nuclear family, a purpose of the day transcending their own, and an enforced activity [ 14 ]. Thus, their situation is similar to the gainfully employed. The 837 employed and unemployed participants were defined as active in the labor market. The remaining 130 individuals were excluded from our analyses because they had either a different main employment than “gainfully employed” or “labor market activity” or had not responded to this question. There were 52 participants who had experienced unemployment of less than six months, and of these 36 were defined as employed and 16 were excluded.

Health-related quality of life variables

The EQ-5D was used to measure health-related quality of life [ 11 ]. We used the descriptive system with five questions that measure different dimensions of health (mobility, self-care, usual activities (such as work, studies, housework, family, and leisure activities), pain/discomfort, and anxiety/depression) and three response alternatives (corresponding to no, some, or extreme problems). Responses to these questions were translated to QALY scores based on the United Kingdom value set for the EQ-5D, which was derived using linear regression and based on the responses to the EQ-5D descriptive system [ 12 ]. The main emphasis in our study was on the QALY scores, but we also present results for the other parts of the EQ-5D instrument, i.e. the dimensions themselves, and EQ-VAS. With the EQ-VAS, respondents valued their current health on a visual analogue scale ranging from 0 to 100. Responses to the EQ-5D dimensions were dichotomized into two groups for analyses of the dimensions themselves, with some and extreme problems combined into one of the two groups, while all three levels were used for QALY calculations.

Other variables

For gender, man was used as the reference group. We used age as a continuous variable. We also tested age categorized into three age groups (20–34, 35–49, and 50–64), but this did not provide results that seemed to improve the statistical model. Education level was divided into three groups based on the question “What is your highest education?” – 9 years at public school or less was categorized as “primary education”, “secondary education”, and university or college studies was categorized as “university” – with primary education being the reference group. For marital status, the response “living with wife/husband/cohabitant/partner” to the question “How do you live?” was coded as “married” and was used as the reference group, while other responses to the question were defined as “single” and used as the exposure group. Previous health was defined from the question “How was, in general, your health five years ago?”, where the responses “very good” and “fairly good” were defined as “good” and used as the reference group, while the other responses (“fair”, “fairly bad”, and “very bad”) were defined as “poor”.

In our analyses, propensity score weighting was used [ 15 ]. Propensity scores were introduced in 1983 by Rosenbaum and Rubin [ 16 ], and they correspond to the conditional probability of being assigned to the exposure group based on baseline covariates. A more thorough description and explanation of the use of propensity scores in the current study is available in Norström et al. [ 6 ].

We used logistic regression with our potential confounders (gender, age, education level, marital status, and previous health) as covariates in order to estimate the propensity scores. In our study, the propensity score corresponds to the probability of being unemployed given his or her characteristics. Comparisons between an exposed and unexposed individual with the same propensity measure is therefore similar to analyzing exposure in a randomized controlled trial. Using the propensity score approach for observational studies is a quasi-experimental approach.

We used propensity scores weighting, as we favored this approach above matching, and stratification, which are other popular propensity score approaches [ 17 ]. We expected the other approaches to perform poorer as they would include fewer unemployed participants because of matching problems with employed individuals. However, there is still a lack of consensus about recommendations on when to use the different approaches. For further discussion about the propensity score approaches, see, for example, Schroeder et al. [ 17 ].

In our results, we used the risk difference, which corresponds to the marginal effect of becoming unemployed, with counterfactual arguments. We used an inverse probability weight estimator, as suggested by Lunceford and Davidian [ 15 ], to estimate the risk difference

where Y refers to the outcome (health-related quality of life). The marginal effect from this estimator corresponds to the average treatment effect [ 18 ]. The standardized difference was calculated, both with and without a weight, to assess the balance of covariates between the employed and unemployed groups for each potential confounder [ 6 , 19 ].

To be part of our analyses of the descriptive system and the QALY scores, it was required that participants had responded to all variables, including the five health dimensions, with a valid response. Of the 837 individuals who were active in the labor market, 788 were part of our analyses. Of the 49 excluded participants, 14 had not answered at least one of the EQ-5D questions, 14 had no response to education level, 3 had no response to marital status, and 20 had no response to previous health. Two of them had no response to at least two of the variables. Valid responses were also required for all variables for the EQ-VAS analyses. There were 771 valid responses for the EQ-VAS analyses.

Descriptive statistics were used to present the characteristics of the sample, and stratified results were derived for each covariate for the outcome variables. Analyses were carried out for QALY scores, EQ-VAS, and three of the dimensions of the EQ-5D separately as outcome variables. For the first two dimensions of the EQ-5D (mobility and self-care), too few participants reported any problem, and these results were therefore only presented descriptively. Pearson’s χ 2 -test was used to test if the exposure variable (labor market status) was associated with potential confounders. Student’s t-test was used to test differences in age with respect to QALYs between the employed and unemployed.

There are some potential problems with low QALY scores, such as a large gap between QALY scores if responding with some or extreme problems, low QALY scores potentially being related to poor employability, and a low QALY score potentially implying poor health already ahead of unemployment. We therefore performed two different sensitivity analyses. In scenario 1, we chose to exclude those who had answered extreme problems to any of the first three EQ-5D questions (mobility, self-care, and usual activities), and in scenario 2 we excluded participants who had answered extreme problems to any of the EQ-5D questions. Sensitivity analyses were not performed for EQ-VAS.

For logistic regression, it is recommended that the number of individuals of the least occurring event, in our case unemployed, divided by the number of explanatory variables should be at least 10 [ 20 ]. This condition was not fulfilled in many of our stratified analyses, which we have indicated in our tables. Also, for other grouped analyses the interpretations should be handled with care due to the small number of unemployed. Interactions between variables were not considered in any of our analyses. We did not experience problems due to collinearity between variables, and hence all candidate variables were kept in the analyses.

R Studio was used for statistical analyses (R Studio, Boston, MA), with its GLM procedure used for logistic regression, where confidence intervals were derived with the profile likelihood [ 21 ]. The Bootstrap technique with replacement was used to derive the mean square error from 10,000 replicates. Confidence intervals corresponded to the 2.5 and 97.5% percentiles of the Bootstrap simulations [ 22 ]. Based on the Bootstrap simulations, p -values were derived. Statistical significance was defined at the 5% level.

General characteristics

The proportion of unemployed (12.9%) after removal of missing data was similar to the proportion for the full data set (13.5%). In comparison with the employed, the unemployed reported to a greater extent previous poor health (52% compared to 22%), were more commonly single (48% compared to 22%), and were younger (mean age of 41 compared to 47), and these differences between the groups were statistically significant (Table  1 ). There was also a statistically significant association between education level and labor market status, with a higher proportion of unemployed than employed having only a primary education.

Effect of unemployment on health

The responses to the EQ-5D questions are presented in Table  2 . The mean QALY scores were higher among employed than unemployed when no adjustments were made for potentially confounding variables. The propensity scores of the main model gave evidence, through statistical significance in the logistic regression, of a higher likeliness of being unemployed for those with only primary education, those who are single, and those with poor previous health, while the likelihood of unemployment was lower with increased age (Additional file  1 : Table S1).

There was a statistically significant negative effect on QALY scores from becoming unemployed, and the QALY score was 0.096 points lower for the unemployed compared to the employed. Also, for EQ-VAS there was a statistically significant negative effect on health from becoming unemployed (7.5 point lower scores compared to the employed) (Table  3 ). For the EQ-5D questions, there were statistically significant negative health effects due to unemployment for usual activities (6.6% more estimated to have problems among the unemployed than the employed) and anxiety/depression (23.6% more estimated to have problems among the unemployed than the employed). There was also a greater proportion of unemployed individuals with pain/discomfort problems, but this difference was not statistically significant. For scenario 1 of the sensitivity analyses, the effects differed by at most 0.014, i.e. a marginal difference compared to the main analyses (Table 3 ). Results for scenario 2 showed different results than the main analysis with some results for the scenario having a substantially smaller negative, and non-significant, effect due to unemployment compared to the main analyses.

The balance of the covariates was improved with the propensity scores. The standardized difference ranged from 3.7 to 57% when weights for the QALY score estimates were not applied and from 1.5 to 12% when such weights were applied (Additional file  3 : Table S3). The balance was not optimal for gender because the standardized difference was above 10%, the level that according to Austin and Stuart is considered by some experts to be a negligible imbalance [ 19 ]. For other variables, the imbalance in observed baseline covariates could be considered as negligible according to the literature.

Effect of unemployment on health in groups of individuals

Stratified results are presented for QALY score in Table  4 , for EQ-VAS in Table  5 and for EQ-5D dimensions in Table  6 .

For gender, there was a statistically significant negative effect from unemployment on health for both men and women for the QALY score and for the EQ-VAS scale. For anxiety/depression, there was only a statistically significant difference for women. However, the estimated negative effect due to unemployment was greater for men than women and was almost significant. For all EQ-5D questions, there was a greater negative effect for men than women related to unemployment, but it was only for usual activities that men had a statistically significant effect. For the sensitivity analyses, there was no statistical evidence for a negative effect on QALY score for either men or women (Additional file  2 : Table S2). Interestingly, the QALY score, which is a summary measure of the five EQ-5D questions, for men was lower than for women in the main analysis despite the results on any of the EQ-5D questions showing a larger, though not in all cases statistically significant, negative effect for men than for women due to unemployment. In the second scenario for the sensitivity analysis, however, the estimate of the negative effect due to unemployment was greater even for the QALY scores for men.

For results on age, there was a statistically significant negative effect on health due to unemployment for the youngest age group (20–34 years old) for the QALY score, EQ-VAS, and the anxiety/depression dimension, but not for the other EQ-5D dimensions. For the other two age groups, the estimated effects were mostly smaller than for the 20–34 year olds but were not statistically significant. For these two age groups, the number of unemployed was too few to fulfill the criterion for the number of events per variable. For education level, no group fulfilled this criterion. For education level, the negative health effect from becoming unemployed was statistically significant for QALY score, EQ-VAS, and the anxiety/depression dimension for university studies, and for secondary education the negative health effect from becoming unemployed was statistically significant for EQ-VAS and usual activities.

For marital status, becoming unemployed was negative for health for those who were married, with statistical significances for QALY score, EQ-VAS, usual activities, and anxiety/depression, while there were no evidence of negative effects for participants who were single, except for EQ-VAS. Previously poor health was a negative factor for becoming unemployed, with a statistically significant estimated QALY decrement of 0.24 compared to being employed, and there was a statistical significance for all of the presented EQ-5D dimensions. For those with good previous health, there were no statistically significant differences. However, the estimated negative effect was rather large and close to significant for the anxiety/depression dimension ( p  = 0.054).

Sensitivity analyses for other variables than gender showed similar results in most cases (Additional file 2 : Table S2). It was mainly results for the second scenario that had inconsistent results, e.g. no significance for QALY or usual activities for the youngest if they became unemployed. Usual activities for single persons and women even showed signs of an improved health if becoming unemployed.

In current study we have explored the impact on health-related quality of life among Swedish adults on unemployment. We show that unemployment is strongly related to a poorer health-related quality of life. The magnitude of the effect is large, with an absolute loss of QALY of 10% from unemployment according to our estimate. The effect on QALY is mainly explained by an increase in problems with anxiety/depression due to unemployment. In our study, 24% more of the unemployed than the employed had problems with anxiety/depression, despite there being as many as 35% of the employed having at least some problem. Also, for usual activities unemployment was shown to affect health negatively, while evidence of an increase in problems with pain/discomfort could not be statistically supported, though these were numerically more common among the unemployed than employed. For grouped analyses, after becoming unemployed married individuals, young individuals, and those who already had poorer health before their unemployment showed greater problems than people did in general.

That unemployment is negative for health has been shown in most previous studies [ 2 , 3 ], and our study is well in line with these studies. Our study uses QALYs, which through its construction makes it possible to present the magnitude of health effects in both a comparable way with other public health problems and in terms of health-related quality of life [ 13 ]. For instance would our estimated effect of 0.096 mean that 9.1 years of employed years would be valued above living 10 years as unemployed. Other studies, such as meta-analyses have presented effect sizes [ 1 , 3 ], but also effect sizes are difficult to translate in a similar way as QALYs.

It is interesting that it is mainly due to feeling anxious and/or depressed that unemployment causes significant problems in our study. In previous research on unemployment and health, the health measures and the health dimension in focus have varied to a large degree [ 2 ]. In the meta-analysis by Paul and Moser, which was published almost 10 years ago [ 3 ], effect sizes were presented for different aspects of mental health. Most of these measures showed a similarly sized negative health effect due to unemployment. It is thus difficult to draw firm conclusions about which health aspects are mainly affected by unemployment based on the literature. That our results clearly indicate more extensive problems related to anxiety/depression than for pain/discomfort might be explained by the fact that unemployment has both economic and social consequences. At the time of unemployment, some unemployed might have reduced their bodily burden after becoming unemployed and thus had lower problems with work-related pain/discomfort. Thus, pain/discomfort might be related to more of a problem than our study suggests, and it is advisable that longitudinal and qualitative studies investigate these health problems more in depth in relation to unemployment.

The results we present on the group level indicate, despite a higher estimated QALY for women, that health-related quality of life might be more negatively affected in men than in women by unemployment. Previous results have shown diverging results in a global perspective, as well as in Swedish studies [ 2 ]. Among previous Swedish studies, those using the General Health Questionnaire, which focuses on psychological symptoms, including anxiety, have indicated more problems due to unemployment for men than for women [ 23 , 24 ], while those studies that have shown more problems for unemployed women than men have mainly used a question about self-rated health with 3 or 5 responses [ 6 , 10 , 25 , 26 , 27 ]. Thus, our results that indicate that men are more affected by problems with anxiety and depression than women when they become unemployed are in line with previous studies. It can be speculated that unemployment causes different health consequences for men and women, highlighting the need for looking at health in a broader view for determining who is most strongly affected by unemployment among men and women, but also for other groups of individuals. More research is needed to better understand differences in unemployment experiences for men and women because our study does not provide strong enough evidence to draw firm conclusions.

Our study suggests more problems for young people than others due to unemployment in the short-term perspective. Previous studies have shown varied results from the short-term perspective [ 2 ], and it is therefore difficult to draw firm conclusions. It has also been shown that there is a long-term effect on health from youth unemployment [ 4 , 7 , 8 ]. Even if the short-term negative health aspects might not be larger for the younger age groups, still, from the full time spectrum, the largest health consequences are among the youngest, at least if being long-term unemployed. Also, long-term consequences of unemployment have been shown for middle-aged persons despite being re-employed [ 6 ].

Few previous studies have presented results for different education levels [ 2 ]. Our study could not add much to this knowledge because the numbers of unemployed in the three education groups were too few for stable results. Our study suggests that married people who become unemployed fare worse than single people who become unemployed. Also for this group, the evidence base is weak mainly because results are rarely presented on the group level for marital status.

It is difficult to get good estimates for the effect on health from becoming unemployed, and this is mainly due to problems with health selection. In a cross-sectional study design such as ours, this is an even bigger hurdle to overcome than in the recommended longitudinal studies, which also face problems with showing causality. To improve the estimates, we asked respondents about their previous health status in order to get an idea about the contribution to health deterioration that is from the unemployment episode. Interestingly, despite expressing poor previous health, which was not to a greater extent likely to be related to unemployment, health was more affected for these individuals than for those who had good health before their unemployment. Targeting people with already poor health for labor market measures might therefore be even more important than previously considered, at least in the Swedish context. To our knowledge it is rare that results are presented stratified on a proxy for health before unemployment, such as in our case health 5 years prior to participation in the study. Our study focus is on the short-term perspective. A previous Swedish study, which focused on the long-term perspective of being unemployed, showed a similar long-term effect for those with good and poor health when becoming unemployed [ 6 ], thus, also indicating that those with poor health already at the time of unemployment were being affected by unemployment.

As already mentioned, our study has the limitation of being a cross-sectional study. In our study, we had a low response rate which also makes it difficult to draw firm conclusions. However, the number of responders was still large and conclusions for at least the group as a whole should therefore be valid. We defined unemployment as at least six months of unemployment during the past three years. Using current unemployment would have meant too few cases, and it would not have guaranteed that the person had an unemployment episode that would have meant a problem for him/her. Our definition of unemployment is common in similar previous studies, and we believe that it is a good way of capturing the exposure of being unemployed in both the short and long-term perspective.

A strength with our study is that it is the first to study health-related quality of life extensively. Our results on the group level might be weak, but they still provide important information that can be used by decision makers. They also provide important information to be used for future studies.

In our sensitivity analysis, we could show that those who responded as having extreme problems affected the results for gender. For the first scenario we had only small differences with our main scenario where we did not censor extreme responses. For our second scenario, we found some interesting differences, but because these depended on the fourth and fifth dimension where we had a large proportion of individuals, we think that this scenario should be seen as more of an illustration of how sensitive the results are for these responses, and not as a sign that the results from the main analysis are misleading. Still, our results highlight that the EQ-5D should rather be seen as an indicator of health and not as a measure that can produce a fine-tuned picture of an individual’s health status. In our view, no other set of questions than the EQ-5D, which measures self-assessed health, can more precisely estimate health-related quality of life on a scale from 0 to 1, so the EQ-5D should still be strongly advisable to use.

We used the United Kingdom value set for EQ-5D for our analyses [ 12 ]. There is a recent Swedish value set for EQ-5D, but it has rarely been used and this makes it difficult to compare QALYs with other populations [ 28 ], which is important from a health economics perspective. Our results could therefore be biased due to being based on a non-Swedish population. The United Kingdom value set for EQ-5D is over 20 years old, which might also bias results. However, we find no reason to believe that this would have more than a marginal effect on our findings.

In our study, we would have liked to have split the employed into permanent and temporary employees. However, our small sample size did not allow for such a discrimination of the respondents. Splitting the group of employed might have resulted in a larger gap between the permanently employed and unemployed than we observed in our study, which would, if true, have further emphasized the need for permanent positions instead of unemployment. The debate about stable positions in relation to non-stable positions is a hot topic politically, at least in Sweden, and there is evidence suggesting that job insecurity is as bad for health as unemployment. [ 29 ] Knowing more about this is important, but this was outside the scope of the present study.

In our study, we present a QALY difference of 0.096 between the unemployed and employed. As previously touched upon in the discussion, the interpretation of this QALY difference from the health economics theory is that 9.1 years of employment should be valued above 10 years of unemployment, i.e. that it is better to live 9% less lifetime if unemployment can be avoided. This highlights the importance of policy makers prioritizing from the public health perspective rather than from an economic growth perspective. However, the latter tends to dominate discussions about lowering unemployment. We might have overestimated the negative health effect of unemployment, but even so the effect is nevertheless likely to be negligible. In line with a previous review [ 2 ], we recommend that more focus should be put on group-wise analyses in order to determine for whom it is most important to prioritize efforts to avoid unemployment, both in regard to political priorities and for future research.

An added value with our study is that, besides increased knowledge about how unemployment relates to health-related quality of life, our results can be used for health economic studies because QALYs are the most common measure used in cost-effectiveness analyses. We also intend to use our results for a future health economic evaluation where we study the cost-effectiveness of increased staffing within home care.

Our study was performed in Sweden. The results are likely to be representative for other countries, e.g. other Nordic countries, that have a similar labor market. Our results should add valuable information also for other European countries even if their labor market system more or less differ from the Swedish.

In our study, we show that the health deterioration from unemployment is likely to be large, as our estimated effect implies almost 10% poorer health-related quality of life (in absolute terms) from being unemployed compared to being employed, and the problems are most apparent for the anxiety/depression scale of the EQ-5D instrument. Our results show, just like previous research, that unemployment hits groups of individuals differently, and measures directed to specific groups of individuals are therefore recommended. For future research, it is important to put more emphasis on groups of individuals to get a better basis for prioritizations within labor market measures in order to lower the public health impact of unemployment.

Abbreviations

• EuroQol 5 dimensions

EuroQol Visual Analogue Scale

Quality-Adjusted Life-Year

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Acknowledgements

The authors would like to thank all of the participants of the study.

This work was funded by the Swedish Research Council for Health, Working Life and Welfare (dnr 2015–00647). The funder had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

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The datasets generated and/or analyzed during the current study are not publicly available because the Swedish Data Protection Act (1998:204) does not permit sensitive data from humans (like in our interviews) to be freely shared. The datasets are available based on ethical permission from the Regional Ethical board in Umeå, Sweden, from corresponding author (Fredrik Norström).

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FN designed the study in collaboration with AKW, LL, KS and AB. FN performed the analyses in collaboration with RN with support from AKW and AB. All co-authors supported FN in interpretations of results. FN drafted the paper, and AKW, LL, RN, KS and AB contributed actively. All authors read and approved the final manuscript.

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Table S1. Logreg coefficients prop score. (DOCX 13 kb)

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Table S2. Sensitivity analyses. (DOCX 20 kb)

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Norström, F., Waenerlund, AK., Lindholm, L. et al. Does unemployment contribute to poorer health-related quality of life among Swedish adults?. BMC Public Health 19 , 457 (2019). https://doi.org/10.1186/s12889-019-6825-y

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

The coronavirus outbreak that began in February 2020 sent shock waves through the U.S. labor market, pushing the unemployment rate to near record highs and causing millions to leave the workforce . A year later, a full recovery for the labor market appears distant. Employment in February 2021 was 8.5 million less than in February 2020, a loss that could take more than three years to recoup assuming job creation proceeds at roughly the same monthly rate as it did from 2018 to 2019. But a faster recovery is possible if the job gains seen in March 2021 are sustained in the coming months.

As it rippled through the economy, the COVID-19 downturn affected some Americans more than others. Unemployment climbed more sharply among women than men, a reversal from the trend in the Great Recession. Young adults, those with less education, Hispanic women and immigrants also experienced greater job losses . Unpartnered mothers saw a bigger drop in the share at work than other parents, and low-wage workers saw a particularly sharp decrease in employment .  

Here are six facts about how the COVID-19 recession is affecting labor force participation and unemployment among American workers a year after its onset.

The U.S. labor market continues to feel the effects of the coronavirus outbreak of February 2020. With millions still out of work, Pew Research Center conducted this analysis to look at how different groups of workers have been affected during the first year of the economic downturn induced by the pandemic.

The main data source for this analysis is the  Current Population Survey (CPS) . The CPS is the U.S. government’s official source for monthly estimates of unemployment. In this report, monthly CPS files from each year were analyzed separately to generate monthly estimates for 2020 and 2021. The Census Bureau incorporates updated population estimates in the CPS each January, and this may affect the comparability of some statistics over time. Most of the CPS microdata files used in this report are the Integrated Public Use Microdata Series ( IPUMS-CPS ) provided by the University of Minnesota. Estimates in this report are not seasonally adjusted.

The COVID-19 outbreak has  affected data collection efforts  by the U.S. government in its surveys, limiting in-person data collection and affecting the response rate. It is possible that some measures of labor market activity and how they vary across demographic groups are affected by these changes in data collection. For example, in February 2021, the not seasonally adjusted unemployment rate may have been as high as 7.0%, instead of the 6.6% officially reported, if an adjustment is made for measurement errors,  per BLS reports .

Our analysis includes an “adjusted” unemployment rate which accounts for the measurement error reported by the BLS. In addition, we adjust the unemployment rate to account for the drop in the labor force participation rate in a given month compared with the same month the previous year. For example, the labor force in February 2021 is estimated two ways – once by applying the participation rate in February 2020 to the working-age population in February 2021 and again by applying the participation rate in February 2021 to the same working-age population. The difference in the two numbers is added to the unemployed population for February 2021 to compute the adjusted unemployment rate.

More women than men quit the labor force in the first year of the COVID-19 recession. From February 2020 to February 2021, a net 2.4 million women and 1.8 million men left the labor force – neither working nor actively looking for work – representing drops of 3.1% and 2.1%, respectively. Women accounted for a majority of the decrease in the labor force in the first year of the downturn even though they make up less than half of the U.S. workforce.

Looked at another way, the shares of women and men (ages 16 and older) participating in the labor force – at work or actively looking for work – have fallen notably during the pandemic. For women, the labor force participation rate in February 2021 was 55.9%, compared with 57.9% a year earlier. For men, the rate fell from 69.0% to 67.1% over this period. The decrease in the labor force participation rate for workers overall – from 63.3% to 61.3% – exceeds that seen in the Great Recession and ranks among the largest 12-month declines in the post-World War II era, according to Bureau of Labor Statistics data.

Although lower than a year ago, the labor force participation rate has risen in recent months. The rate for women had fallen as low as 54.4% in April 2020, and the rate for men had dipped to 65.9% in the same month. Since then, the recovery appears to have been somewhat sharper for women.

The changes in labor force participation in the COVID-19 downturn stand in sharp contrast to the Great Recession, when men were more deeply affected. From December 2007 to December 2009, the number of women who left the labor force (84,000) was modest in comparison with the number of men who did the same (929,000). Also, the labor force participation rate for women decreased by 1 percentage point over this period of the Great Recession, compared with 2 points for men.

The key difference between the two recessions is that job losses in the pandemic have been concentrated in service sectors in which women account for the majority of employment, such as leisure and hospitality and education and health services. More typically,  job losses in recessions , including  the Great Recession , have centered around goods-producing sectors, such as manufacturing and construction, in which men account for the greater share of employment.

Hispanic and Black women accounted for much of the decrease in labor force participation among women. The net 2.4 million women who left the labor force from February 2020 to February 2021 included 582,000 Hispanic women and 511,000 Black women. Collectively, Hispanic and Black women accounted for 46% of the total decrease among women but represent less than one-third of the female labor force in the U.S.

This was also reflected in the changes in the labor force participation rates. From February 2020 to February 2021, the decrease in the rate among Hispanic and Black women was 3.6 and 3.4 percentage points, respectively. For Asian women it was 1.9 points, while for White women it was 1.3 points.

One reason Hispanic women may have been more likely to leave the labor force is that they have a greater presence than other women or men in the leisure and hospitality sector. This sector has shed more jobs than any other sector in the economy from February 2020 to February 2021. Pandemic-driven pressures on parents may also have affected Hispanic, Black and Asian women more than White women. Compared with other women with children at home, Hispanic and Black women are more likely to be unpartnered parents .  

There is little difference in how the labor force participation rate changed among White, Black, Hispanic and Asian men. White and Asian men experienced a similar drop in the labor force participation rate as White and Asian women – about 2 percentage points or less. But the decrease in the rate among Black and Hispanic men – roughly 1.5 points each – appears to have been less than the decrease among Black and Hispanic women, about 3.5 points each.

The decrease in labor force participation suggests that the official unemployment rate understates the share of Americans who are out of work. Workers who left the labor force during the pandemic are not counted among the unemployed, as per usual practice . As the economy improves, many of these workers may reenter the labor market, adding to the number currently counted as unemployed and in want of work. For that reason, Jerome Powell, chair of the Federal Reserve board of governors, recently suggested that those who left the labor force since February 2020 should be counted among the unemployed to gain a better understanding of the slump in the labor market.

Adjusting the unemployment rate for labor force exits, and also making a correction for measurement challenges that have affected government surveys in the pandemic, shows that the U.S. unemployment rate in February 2021 may have been as high as 9.9%, instead of 6.6% as officially reported .

The difference between the official and the adjusted unemployment rates was highest in April 2020. In that month, the official rate stood at 14.4%, compared with an adjusted rate of 22.7%. The labor force participation rate had dipped to 60.0% in April, the lowest rate recorded in 2020, and measurement issues also loomed large in the government surveys.

Both the official and the adjusted unemployment rates have trended downward since April 2020. However, a gap of about 3 percentage points has persisted between the two measures since June 2020. It should be emphasized that the adjusted rate assumes that all workers who left the labor force during the pandemic will return in search of work in the near future. Other researchers have proposed that a more realistic unemployment rate may be closer to 8% at the moment.

After a sharper increase earlier in the pandemic, the unemployment rate for women likely was on par with the rate for men in February 2021. The initial wave of the pandemic sent the unemployment rate for women soaring from 3.4% in February 2020 to 15.7% in April 2020, as officially reported. Men also experienced a spike, but less so than women, as their unemployment rate increased from 4.1% to 13.3% over this period. 

By February 2021, the official unemployment rate for women (6.1%) had fallen below the rate for men (7.0%), not seasonally adjusted. However, since labor force participation fell more among women than men, the adjusted unemployment rate for women (9.8%) was similar to the rate for men (9.9%) in February 2021.

Unemployment remained more elevated among Black and Hispanic workers. Roughly one-in-ten Black and Hispanic workers, women or men, were unemployed in February 2021, based on the official unemployment rate. Black men (11.6%) were unemployed at a higher rate than other men or women. By comparison, only about 6% of White and Asian workers or fewer, women or men, were unemployed in February 2021.

As business operations ramped up more recently, the unemployment rate decreased for all groups of workers. Among Black women, the unemployment rate dropped from a peak of 17.3% in May 2020 to 9.2% in February 2021. Among Black men, the rate fell from a high of 16.1% in June 2020 to 11.6% in February 2021.

White women saw a decrease in their unemployment rate from a peak of 14.2% in April 2020 to 4.7% in February 2021. Over the same period, White men’s unemployment rate decreased from a peak of 11.6% to 5.6% in February 2021. Asian men and women also saw significant reductions from their peak unemployment rates of roughly 9 or more percentage points each. In February 2021, Asian women had an unemployment rate of 5.9%, while the rate for Asian men was 4.5%.

In April 2020, Hispanic women had a peak unemployment rate of 20.5%, while Hispanic men had an unemployment rate of 16.9%. But Hispanic women (8.9%) and men (9.0%) had unemployment rates similar to each other in February 2021.

Despite recent improvements, unemployment rates for all major racial and ethnic groups of workers were substantially higher in February 2021 than in February 2020. For example, while the unemployment rate for White women (4.7%) was lower than among other women, it was nearly double the rate they experienced in February 2020. That was also the case among Asian women, whose unemployment rate increased from 2.8% in February 2020 to 5.9% in February 2021.

Workers in low-wage jobs experienced the greatest drop in employment. From February 2020 to February 2021, employment among low-wage workers fell by 11.7%, from 28.1 million to 24.8 million. This compares with a loss of 5.4% among middle-wage workers, whose employment fell by 5.5 million over the period. Meanwhile, employment among high-wage workers was roughly unchanged, at slightly more than 28 million.

The reason for this pattern is that the COVID-19 recession is centered in the services sector, especially in the leisure and hospitality industry, which has been  hit hardest  in the pandemic and accounts for many of the low-wage jobs. The trend in the current recession stands in contrast with the Great Recession, which saw middle-wage occupations shed jobs at a higher rate than other occupations.

• Coronavirus (COVID-19)
• COVID-19 & the Economy
• Recessions & Recoveries
• Unemployment

Rakesh Kochhar is a former senior researcher at Pew Research Center .

Jesse Bennett is a former research analyst focusing on social and demographic trends research at Pew Research Center .

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• Original Article
• Open access
• Published: 08 March 2018

Unemployment among younger and older individuals: does conventional data about unemployment tell us the whole story?

• Hila Axelrad 1 , 2 ,
• Miki Malul 3 &
• Israel Luski 4  

Journal for Labour Market Research volume  52 , Article number:  3 ( 2018 ) Cite this article

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In this research we show that workers aged 30–44 were significantly more likely than those aged 45–59 to find a job a year after being unemployed. The main contribution is demonstrating empirically that since older workers’ difficulties are related to their age, while for younger individuals the difficulties are more related to the business cycle, policy makers must devise different programs to address unemployment among young and older individuals. The solution to youth unemployment is the creation of more jobs, and combining differential minimum wage levels and earned income tax credits might improve the rate of employment for older individuals.

1 Introduction

Literature about unemployment references both the unemployment of older workers (ages 45 or 50 and over) and youth unemployment (15–24). These two phenomena differ from one another in their characteristics, scope and solutions.

Unemployment among young people begins when they are eligible to work. According to the International Labor Office (ILO), young people are increasingly having trouble when looking for their first job (ILO 2011 ). The sharp increase in youth unemployment and underemployment is rooted in long-standing structural obstacles that prevent many youngsters in both OECD countries and emerging economies from making a successful transition from school to work. Not all young people face the same difficulties in gaining access to productive and rewarding jobs, and the extent of these difficulties varies across countries. Nevertheless, in all countries, there is a core group of young people facing various combinations of high and persistent unemployment, poor quality jobs when they do find work and a high risk of social exclusion (Keese et al. 2013 ). The rate of youth unemployment is much higher than that of adults in most countries of the world (ILO 2011 ; Keese et al. 2013 ; O’Higgins 1997 ; Morsy 2012 ). Official youth unemployment rates in the early decade of the 2010s ranged from under 10% in Germany to around 50% in Spain ( http://www.indexmundi.com/g/r.aspx?v=2229 ; Pasquali 2012 ). The youngest employees, typically the newest, are more likely to be let go compared to older employees who have been in their jobs for a long time and have more job experience and job security (Furlong et al. 2012 ). However, although unemployment rates among young workers are relatively higher than those of older people, the period of time they spend unemployed is generally shorter than that of older adults (O’Higgins 2001 ).

We would like to argue that one of the most important determinants of youth unemployment is the economy’s rate of growth. When the aggregate level of economic activity and the level of adult employment are high, youth employment is also high. Footnote 1 Quantitatively, the employment of young people appears to be one of the most sensitive variables in the labor market, rising substantially during boom periods and falling substantially during less active periods (Freeman and Wise 1982 ; Bell and Blanchflower 2011 ; Dietrich and Möller 2016 ). Several explanations have been offered for this phenomenon. First, youth unemployment might be caused by insufficient skills of young workers. Another reason is a fall in aggregate demand, which leads to a decline in the demand for labor in general. Young workers are affected more strongly than older workers by such changes in aggregate demand (O’Higgins 2001 ). Thus, our first research question is whether young adults are more vulnerable to economic shocks compared to their older counterparts.

Older workers’ unemployment is mainly characterized by difficulties in finding a new job for those who have lost their jobs (Axelrad et al. et al. 2013 ). This fact seems counter-intuitive because older workers have the experience and accumulated knowledge that the younger working population lacks. The losses to society and the individuals are substantial because life expectancy is increasing, the retirement age is rising in many countries, and people are generally in good health (Axelrad et al. 2013 ; Vodopivec and Dolenc 2008 ).

The difficulty that adults have in reintegrating into the labor market after losing their jobs is more severe than that of the younger unemployed. Studies show that as workers get older, the duration of their unemployment lengthens and the chances of finding a job decline (Böheim et al. 2011 ; De Coen et al. 2010 ). Therefore, our second research question is whether older workers’ unemployment stems from their age.

In this paper, we argue that the unemployment rates of young people and older workers are often misinterpreted. Even if the data show that unemployment rates are higher among young people, such statistics do not necessarily imply that it is harder for them to find a job compared to older individuals. We maintain that youth unemployment stems mainly from the characteristics of the labor market, not from specific attributes of young people. In contrast, the unemployment of older individuals is more related to their specific characteristics, such as higher salary expectations, higher labor costs and stereotypes about being less productive (Henkens and Schippers 2008 ; Keese et al. 2006 ). To test these hypotheses, we conduct an empirical analysis using statistics from the Israeli labor market and data published by the OECD. We also discuss some policy implications stemming from our results, specifically, a differential policy of minimum wages and earned income tax credits depending on the worker’s age.

Following the introduction and literary review, the next part of our paper presents the existing data about the unemployment rates of young people and adults in the OECD countries in general and Israel in particular. Than we present the research hypotheses and theoretical model, we describe the data, variables and methods used to test our hypotheses. The regression results are presented in Sect.  4 , the model of Business Cycle is presented in Sect.  5 , and the paper concludes with some policy implications, a summary and conclusions in Sect.  6 .

2 Literature review

Over the past 30 years, unemployment in general and youth unemployment in particular has been a major problem in many industrial societies (Isengard 2003 ). The transition from school to work is a rather complex and turbulent period. The risk of unemployment is greater for young people than for adults, and first jobs are often unstable and rather short-lived (Jacob 2008 ). Many young people have short spells of unemployment during their transition from school to work; however, some often get trapped in unemployment and risk becoming unemployed in the long term (Kelly et al. 2012 ).

Youth unemployment leads to social problems such as a lack of orientation and hostility towards foreigners, which in turn lead to increased social expenditures. At the societal level, high youth unemployment endangers the functioning of social security systems, which depend on a sufficient number of compulsory payments from workers in order to operate (Isengard 2003 ).

Workers 45 and older who have lost their jobs often encounter difficulties in finding a new job (Axelrad et al. 2013 ; Marmora and Ritter 2015 ) although today they are more able to work longer than in years past (Johnson 2004 ). In addition to the monetary rewards, work also offers mental and psychological benefits (Axelrad et al. 2016 ; Jahoda 1982 ; Winkelmann and Winkelmann 1998 ). Working at an older age may contribute to an individual’s mental acuity and provide a sense of usefulness.

On average, throughout the OECD, the hiring rate of workers aged 50 and over is less than half the rate for workers aged 25–49. The low re-employment rates among older job seekers reflect, among other things, the reluctance of employers to hire older workers. Lahey ( 2005 ) found evidence of age discrimination against older workers in labor markets. Older job applicants (aged 50 or older), are treated differently than younger applicants. A younger worker is more than 40% more likely to be called back for an interview compared to an older worker. Age discrimination is also reflected in the time it takes for older adults to find a job. Many workers aged 45 or 50 and older who have lost their jobs often encounter difficulties in finding a new job, even if they are physically and intellectually fit (Hendels 2008 ; Malul 2009 ). Despite the fact that older workers are considered to be more reliable (McGregor and Gray 2002 ) and to have better business ethics, they are perceived as less flexible or adaptable, less productive and having higher salary expectations (Henkens and Schippers 2008 ). Employers who hesitated in hiring older workers also mentioned factors such as wages and non-wage labor costs that rise more steeply with age and the difficulties firms may face in adjusting working conditions to meet the requirements of employment protection rules (Keese et al. 2006 ).

Thus, we have a paradox. On one hand, people live longer, the retirement age is rising, and older people in good health want or need to keep working. At the same time, employers seek more and more young workers all the time. This phenomenon might marginalize skilled and experience workers, and take away their ability to make a living and accrue pension rights. Thus, employers’ reluctance to hire older workers creates a cycle of poverty and distress, burdening the already overcrowded social institutions and negatively affecting the economy’s productivity and GDP (Axelrad et al. 2013 ).

2.1 OECD countries during the post 2008 crisis

The recent global economic crisis took an outsized toll on young workers across the globe, especially in advanced economies, which were hit harder and recovered more slowly than emerging markets and developing economies. Does this fact imply that the labor market in Spain and Portugal (with relatively high youth unemployment rates) is less “friendly” toward younger individuals than the labor market in Israel and Germany (with a relatively low youth unemployment rate)? Has the market in Spain and Portugal become less “friendly” toward young people during the last 4 years? We argue that the main factor causing the increasing youth unemployment rates in Spain and Portugal is the poor state of the economy in the last 4 years in these countries rather than a change in attitudes toward hiring young people.

OECD data indicate that adult unemployment is significantly lower than youth unemployment. The global economic crisis has hit young people very hard. In 2010, there were nearly 15 million unemployed youngsters in the OECD area, about four million more than at the end of 2007 (Scarpetta et al. 2010 ).

From an international perspective, and unlike other developed countries, Israel has a young age structure, with a high birthrate and a small fraction of elderly population. Israel has a mandatory retirement age, which differs for men (67) and women (62), and the labor force participation of older workers is relatively high (Stier and Endeweld 2015 ), therefore, we believe that Israel is an interesting case for studying.

The Israeli labor market is extremely flexible (e.g. hiring and firing are relatively easy), and mobile (workers can easily move between jobs) (Peretz 2016 ). Focusing on Israel’s labor market, we want to check whether this is true for older Israeli workers as well, and whether there is a difference between young and older workers.

The problem of unemployment among young people in Israel is less severe than in most other developed countries. This low unemployment rate is a result of long-term processes that have enabled the labor market to respond relatively quickly to changes in the economic environment and have reduced structural unemployment. Footnote 2 Furthermore, responsible fiscal and monetary policies, and strong integration into the global market have also promoted employment at all ages. With regard to the differences between younger and older workers in Israel, Stier and Endeweld ( 2015 ) determined that older workers, men and women alike, are indeed less likely to leave their jobs. This finding is similar to other studies showing that older workers are less likely to move from one employer to another. According to the U.S. Bureau of Labor Statistics, the median employee tenure is generally higher among older workers than younger ones (BLS 2014 ). Movement in and out of the labor market is highest among the youngest workers. However, these young people are re-employed quickly, while older workers have the hardest time finding jobs once they become unemployed. The Bank of Israel calculated the chances of unemployed people finding work between two consecutive quarters using a panel of the Labor Force Survey for the years 1996–2011. Their calculations show that since the middle of the last decade the chances of unemployed people finding a job between two consecutive quarters increased. Footnote 3 However, as noted earlier, as workers age, the duration of their unemployment lengthens. Prolonged unemployment erodes the human capital of the unemployed (Addison et al. 2004 ), which has a particularly deleterious effect on older workers. Thus, the longer the period of unemployment of older workers, the less likely they will find a job (Axelrad and Luski 2017 ). Nevertheless, as Fig.  1 shows, the rates of youth unemployment in Israel are higher than those of older workers.

(Source: Calculated by the authors by using data from the Labor Force survey of the Israeli CBS, 2011)

Unemployed persons and discouraged workers as percentages of the civilian labor force, by age group (Bank of Israel 2011 ). We excluded those living outside settled communities or in institutions. The percentages of discouraged workers are calculated from the civilian labor force after including them in it

We argue that the main reason for this situation is the status quo in the labor market, which is general and not specific to Israel. It applies both to older workers and young workers who have a job. The status quo is evident in the situation in which adults (and young people) already in the labor market manage to keep their jobs, making the entrance of new young people into the labor market more difficult. What we are witnessing is not evidence of a preference for the old over the young, but the maintaining of the status quo.

The rate of employed Israelis covered by collective bargaining agreements increases with age: up to age 35, the rate is less than one-quarter, and between 50 and 64 the rate reaches about one-half. In effect, in each age group between 25 and 60, there are about 100,000 covered employees, and the lower coverage rate among the younger ages derives from the natural growth in the cohorts over time (Bank of Israel 2013 ). The wave of unionization in recent years is likely to change only the age profile of the unionization rate and the decline in the share of covered people over the years, to the extent that it strengthens and includes tens of thousands more employees from the younger age groups. Footnote 4

The fact that the percentage of employees covered by collective agreement increases with age implies that there is a status quo effect. Older workers are protected by collective agreements, and it is hard to dismiss them (Culpepper 2002 ; Palier and Thelen 2010 ). However, young workers enter the workforce with individual contracts and are not protected, making it is easier to change their working conditions and dismiss them.

To complete the picture, Fig.  2 shows that the number of layoffs among adults is lower, possibly due to their protection under collective bargaining agreements.

(Source: Israeli Central Bureau of Statistics, 2008, data processed by the authors)

Dismissal of employees in Israel, by age. Percentage of total employed persons ages 20–75 and over including those dismissed

In order to determine the real difference between the difficulties of older versus younger individuals in finding work, we have to eliminate the effect of the status quo in the labor market. For example, if we removed all of the workers from the labor market, what would be the difference between the difficulties of older people versus younger individuals in finding work? In the next section we will analyze the probability of younger and older individuals moving from unemployment to employment when we control for the status quo. We will do so by considering only individuals who have not been employed at least part of the previous year.

3 Estimating the chances of finding a job and research hypotheses

Based on the literature and the classic premise that young workers are more vulnerable to economic shocks (ILO 2011 ), we posit that:

H 1 : The unemployment rate of young people stems mainly from the characteristics of the labor market and less from their personal attributes.

Based on the low hiring rate of older workers (OECD 2006 ) and the literature about age discrimination against older workers in labor markets (Axelrad et al. 2013 ; Lahey 2005 ), we hypothesis that:

H 2 : The difficulty face by unemployed older workers searching for a job stems mainly from their age and less from the characteristics of the labor market.

To assess the chances of younger and older workers finding a job, we used a logit regression model that has been validated in previous studies (Brander et al. 2002 ; Flug and Kassir 2001 ). Being employed was the dependent variable, and the characteristics of the respondents (age, gender, ethnicity and education) were the independent variables. The dependent variable was nominal and dichotomous with two categories: 0 or 1. We defined the unemployed as those who did not work at all during the last year or worked less than 9 months last year. The dependent variable was a dummy variable of the current employment situation, which received the value of 1 if the individual worked last week and 0 otherwise.

3.1 The model

i—individual i, P i —the chances that individual i will have a full or part time job (at the time of the survey). \(\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\text{X}}_{\text{i}}\) —vector of explanatory variables of individual i. Each of the variables in vector \(\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{X}_{i}\) was defined as a dummy variable with the value of 1 or 0. β—vector of marginal addition to the log of the odds ratio. For example, if the explanatory variable was the log of 13 years or more of schooling, then the log odds ratio refers to the marginal addition of 13 years of education to the chances of being employed, compared with 12 years of education or less.

The regression allowed us to predict the probability of an individual finding a job. The dependent variable was the natural base log of the probability ratio P divided by (1 − P) that a particular individual would find a job. The odds ratio from the regression answers the question of how much more likely it is that an individual will find a job if he or she has certain characteristics. The importance of the probability analysis is the consideration of the marginal contribution of each feature to the probability of finding a job.

3.2 The sample

We used data gathered from the 2011 Labor Force Survey Footnote 5 of the Israeli Central Bureau of Statistics (CBS), Footnote 6 which is a major survey conducted annually among households. The survey follows the development of the labor force in Israel, its size and characteristics, as well as the extent of unemployment and other trends. Given our focus on working age individuals, we excluded all of the respondents under the age of 18 or over the age of 59. The data sample includes only the Jewish population, because structural problems in the non-Jewish sector made it difficult to estimate this sector using the existing data only. The sample does not include the ultra-Orthodox population because of their special characteristics, particularly the limited involvement of men in this population in the labor market.

The base population is individuals who did not work at all during the past year or worked less than 9 months last year (meaning that they worked but were unemployed at least part of last year). To determine whether they managed to find work after 1 year of unemployment, we used the question on the ICBS questionnaire, “Did you work last week?” We used the answer to this question to distinguish between those who had succeeded in finding a job and those who did not. The data include individuals who were out of the labor force Footnote 7 at the time of the survey, but exclude those who were not working for medical reasons (illness, disability or other medical restrictions) or due to their mandatory military service. Footnote 8

3.3 Data and variables

The survey contains 104,055 respondents, but after omitting all of the respondents under the age of 18 or above 59, those who were outside the labor force for medical reasons or due to mandatory military service, non-Jews, the ultra-Orthodox, and those who worked more than 9 months last year, the sample includes 13,494 individuals (the base population). Of these, 9409 are individuals who had not managed to find work, and 4085 are individuals who were employed when the survey was conducted.

The participants’ ages range between 18 and 59, with the average age being 33.07 (SD 12.88) and the median age being 29. 40.8% are males; 43.5% have an academic education; 52.5% are single, and 53.5% of the respondents have no children under 17.

3.4 Dependent and independent variables

While previous studies have assessed the probability of being unemployed in the general population, our study examines a more specific case: the probability of unemployed individuals finding a job. Therefore, we use the same explanatory variables that have been used in similar studies conducted in Israel (Brander et al. 2002 ; Flug and Kassir 2001 ), which were also based on an income survey and the Labor Force Survey of the Central Bureau of Statistics.

3.5 The dependent variable—being employed

According to the definition of the CBS, employed persons are those who worked at least 1 h during a given week for pay, profit or other compensation.

3.6 Independent variables

We divided the population into sub-groups of age intervals: 18–24, 25–29, 30–44, 45–54 and 55–59, according to the sub-groups provided by the CBS. We then assigned a special dummy variable to each group—except the 30–44 sub-group, which is considered as the base group. Age is measured as a dummy variable, and is codded as 1 if the individual belongs to the age group, and 0 otherwise. Age appears in the regression results as a variable in and of itself. Its significance is the marginal contribution of each age group to the probability of finding work relative to the base group (ages 30–44), and also as an interaction variable.

3.6.2 Gender

This variable is codded as 1 if the individual is female and 0 otherwise. Gender also appears in the interaction with age.

3.6.3 Marital status

Two dummy variables are used: one for married respondents and one for those who are divorced or widowed. In accordance with the practice of the CBS, we combined the divorced and the widowed into one variable. This variable is a dummy variable that is codded as 1 if the individual belongs to the appropriate group (divorced/widowed or married) and 0 otherwise. The base group is those who are single.

3.6.4 Education

This variable is codded as 1 if the individual has 13 or more years of schooling, and 0 otherwise. The variable also appears in interactions between it and the age variable.

3.6.5 Vocational education

This variable is codded as 1 if the individual has a secondary school diploma that is not an academic degree or another diploma, and 0 otherwise.

3.6.6 Academic education

This variable is codded as 1 if the individual has any university degree (bachelors, masters or Ph.D.) and 0 otherwise.

3.6.7 Children

In accordance with similar studies that examined the probability of employment in Israel (Brander et al. 2002 ), we define children as those up to age 17. This variable is a dummy variable that is codded as 1 if the respondents have children under the age of 17, and 0 otherwise.

3.6.8 Ethnicity

This variable is codded as 1 if the individual was born in an Arabic-speaking country, in an African country other than South Africa, or in an Asian country, or was born in Israel but had a father who was born in one of these countries. Israel generally refers to such individuals as Mizrahim. Respondents who were not Mizrahim received a value of 0. The base group in our study are men aged 30–44 who are not Mizrahim.

We also assessed the interactions between the variables. For example, the interaction between age and the number of years of schooling is the contribution of education (i.e., 13 years of schooling) to the probability of finding a job for every age group separately relative to the situation of having less education (i.e., 12 years of education). The interaction between age and gender is the contribution of gender (i.e., being a female respondent) to the probability of finding a job for each age group separately relative to being a man.

To demonstrate the differences between old and young individuals in their chances of finding a job, we computed the rates of those who managed to find a job relative to all of the respondents in the sample. Table  1 shows that the rate of those who found a job declines with age. For example, 36% of the men age 30–44 found a job, but those rates drop to 29% at the age of 45–54 and decline again to 17% at the age of 55–59. As for women, 31% of them aged 30–44 found a job, but those rates drop to 20% at the age of 45–54 and decline again to 9% at the age of 55–59.

In an attempt to determine the role of education in finding employment, we created Model 1 and Model 2, which differ only in terms of how we defined education. In Model 1 the sample is divided into two groups: those with up to 12 years of schooling (the base group) and those with 13 or more years of schooling. In Model 2 there are three sub-groups: those with a university degree, those who have a vocational education, and the base group that has only a high school degree.

Table  2 shows that the probability of a young person (age 18–24) getting a job is larger than that of an individual aged 30–44 who belongs to the base group (the coefficient of the dummy variable “age 18–24” is significant and positive). Similarly, individuals who are older than 45 are less likely than those in the base group to find work.

Women aged 30–44 are less likely to be employed than men in the same age group. Additionally, when we compare women aged 18–24 to women aged 30–44, we see that the chances of the latter being employed are lower. Older women (45+) are much less likely than men of the same age group to find work. Additionally, having children under the age of 17 at home reduces the probability of finding a job.

A university education increases the probability of being employed for both men and women aged 30–44. Furthermore, for older people (55+) an academic education reduces the negative effect of age on the probability of being employed. While a vocational education increases the likelihood of finding a job for those aged 30–44, such a qualification has no significant impact on the prospects of older people.

Interestingly, being a Mizrahi Jew increases the probability of being employed.

In addition, we estimated the models separately twice—for the male and for the female population. For male and female, the probability of an unemployed individual finding a job declines with age.

Analyzing the male population (Table  3 ) reveals that those aged 18–24 are more likely than the base group (ages 30–44) to find a job. However, the significance level is relatively low, and in Model 2, this variable is not significant at all. Those 45 and older are less likely than the base group (ages 30–44) to find a job. Married men are more likely than single men to be employed. However, divorced and widowed men are less likely than single men to find a job. For men, the presence in their household of children under the age of 17 further reduces the probability of their being employed. Mizrahi men aged 18–24 are more likely to be employed than men of the same age who are from other regions.

Table  3 illustrates that educated men are more likely to find work than those who are not. However, in Model 1, at the ages 18–29 and 45–54, the probability of finding a job for educated men is less than that of uneducated males. Among younger workers, this might be due to excess supply—the share of academic degree owners has risen, in contrast to almost no change in the overall share of individuals receiving some other post-secondary certificate (Fuchs 2015 ). Among older job seeking men, this might be due to the fact that the increase in employment among men during 2002–2010 occurred mainly in part-time jobs (Bank of Israel 2011 ). In Model 2, men with an academic or vocational education have a better chance of finding a job, but at the group age of 18–24, those with a vocational education are less likely to find a job compared to those without a vocational education. The reason might be the lack of experience of young workers (18–24), experience that is particularly needed in jobs that require vocational education (Salvisberg and Sacchi 2014 ).

Analyzing the female population (Table  3 ) reveals that women between 18 and 24 are more likely to be employed than those who are 30–44, and those who are 45–59 are less likely to be employed than those who are 30–44. The probability of finding a job for women at the age of 25 to 29 is not significantly different from the probability of the base group (women ages 30–44).

Married women are less likely than single women to be employed. Women who have children under the age of 17 are less likely to be employed than women who do not have dependents that age. According to Model 2, Mizrahi women are more likely to be employed compared to women from other regions. According to both models, women originally from Asia or Africa ages 25–29 have a better chance of being employed than women the same age from other regions. Future research should examine this finding in depth to understand it.

With regard to education, in Model 1 (Table  3 ), where we divided the respondents simply on the question of whether they had a post-high school education, women who were educated were more likely to find work than those who were not. However, in the 18–29 age categories, educated women were less likely to find a job compared to uneducated women, probably due to the same reason cited above for men in the same age group—the inflation of academic degrees (Fuchs 2015 ). These findings become more nuanced when we consider the results of Model 2. There, women with an academic or vocational education have a better chance of finding a job, but at the ages of 18–24 those with an academic education are less likely to find a job than those without an academic education. Finally, at the ages of 25–29, those with a vocational education have a better chance of finding a job than those without a vocational education, due to the stagnation in the overall share of individuals receiving post-secondary certificate (Fuchs 2015 ).

Thus, based on the results in Table  3 , we can draw several conclusions. First, the effect of aging on women is more severe than the impact on men. In addition, the “marriage premium” is positive for men and negative for women. Divorced or widowed men lose their “marriage premium”. Finally, having children at home has a negative effect on both men and women—almost at the same magnitude.

5 Unemployment as a function of the business cycle

To determine whether unemployment of young workers is caused by the business cycle, we examined the unemployment figures in 34 OECD countries in 2007–2009, years of economic crisis, and in 2009–2011, years of recovery and economic growth. For each country, we considered the data on unemployment among young workers (15–24) and older adults (55–64) and calculated the difference between 2009 and 2007 and between 2011 and 2009 for both groups. The data were taken from OECD publications and included information about the growth rates from 2007 to 2011. Our assessment of unemployment rates in 34 OECD countries reveals that the average rate of youth unemployment in 2007 was 13.4%, compared to 18.9% in 2011, so the delta of youth unemployment before and after the economic crisis was 5.55. The average rate of adult unemployment in 2007 was 4% compared to 5.8% in 2011, so the delta for adults was 1.88. Both of the differences are significantly different from zero, and the delta for young people is significantly larger than the delta for adults. These results indicate that among young people (15–24), the increase in unemployment due to the crisis was very large.

An OLS model of the reduced form was estimated to determine whether unemployment is a function of the business cycle, which is represented by the growth rate. The variables GR2007, GR2009 and GR2011 are the rate of GDP growth in 2007, 2009 and 2011 respectively ( Appendix ). The explanatory variable is either GR2009 minus GR2007 or GR2011 minus GR2009. In both periods, 2007–2009 and 2009–2011, the coefficient of the change in growth rates is negative and significant for young people, but insignificant for adults. Thus, it seems that the unemployment rates of young people are affected by the business cycle, but those of older workers are not. In a time of recession (2007–2009), unemployment among young individuals increases whereas for older individuals the increase in unemployment is not significant. In recovery periods (2009–2011), unemployment among young individuals declines, whereas the drop in unemployment among older individuals is not significant (Table  4 ).

6 Summary and conclusions

The purpose of this paper was to show that while the unemployment rates of young workers are higher than those of older workers, the data alone do not necessarily tell the whole story. Our findings confirm our first hypothesis, that the high unemployment rate of young people stems mainly from the characteristics of the labor market and less from their personal attributes. Using data from Israel and 34 OECD countries, we demonstrated that a country’s growth rate is the main factor that determines youth unemployment. However, the GDP rate of growth cannot explain adult unemployment. Our results also support our second hypothesis, that the difficulties faced by unemployed older workers when searching for a job are more a function of their age than the overall business environment.

Indeed, one limitation of the study is the fact that we could not follow individuals over time and capture individual changes. We analyze a sample of those who have been unemployed in the previous year and then analyze the probability of being employed in the subsequent year but cannot take into account people could have found a job in between which they already lost again. Yet, in this sample we could isolate and analyze those who did not work last year and look at their employment status in the present. By doing so, we found out that the rate of those who found a job declines with age, and that the difficulties faced by unemployed older workers stems mainly from their age.

To solve both of these problems, youth unemployment and older workers unemployment, countries need to adopt different methods. Creating more jobs will help young people enter the labor market. Creating differential levels for the minimum wage and supplementing the income of older workers with earned income tax credits will help older people re-enter the job market.

Further research may explore the effect of structural and institutional differences which can also determine individual unemployment vs. employment among different age groups.

In addition to presenting a theory about the factors that affect the differences in employment opportunities for young people and those over 45, the main contribution of this paper is demonstrating the validity of our contention that it is age specifically that works to keep older people out of the job market, whereas it is the business cycle that has a deleterious effect on the job prospects of younger people. Given these differences, these two sectors of unemployment require different approaches for solving their employment problems. The common wisdom maintains that the high level of youth unemployment requires policy makers to focus on programs targeting younger unemployed individuals. However, we argue that given the results of our study, policy makers must adopt two different strategies to dealing with unemployment in these two groups.

6.1 Policy implications

In order to cope with the problem of youth unemployment, we must create more jobs. When the recession ends in Portugal and Spain, the problem of youth unemployment should be alleviated. Since there is no discrimination against young people—evidenced by the fact that when the aggregate level of economic activity and the level of adult employment are high, youth employment is also high—creating more jobs in general by enhancing economic growth should improve the employment rates of young workers.

In contrast, the issue of adult unemployment requires a different solution due to the fact that their chances of finding a job are related specifically to their age. One solution might be a differential minimum wage for older and younger individuals and earned income tax credits (EITC) Footnote 9 for older individuals, as Malul and Luski ( 2009 ) suggested.

According to this solution, the government should reduce the minimum wage for older individuals. As a complementary policy and in order to avoid differences in wages between older and younger individuals, the former would receive an earned income tax credit so that their minimum wage together with their EITC would be equal to the minimum wage of younger individuals. Earned income tax credits could increase employment among older workers while increasing their income. For older workers, EITCs are more effective than a minimum wage both in terms of employment and income. Such policies of a differential minimum wage plus an EITC can help older adults and constitute a kind of social safety net for them. Imposing a higher minimum wage exclusively for younger individuals may be beneficial in encouraging them to seek more education.

Young workers who face layoffs as a result of their high minimum wage (Kalenkoski and Lacombe 2008 ) may choose to increase their investment in their human capital (Nawakitphaitoon 2014 ). The ability of young workers to improve their professional level protects them against the unemployment that might result from a higher minimum wage (Malul and Luski 2009 ). For older workers, if the minimum wage is higher than their productivity, they will be unemployed. This will be true even if their productivity is higher than the value of their leisure. Such a situation might result in an inefficient allocation between work and leisure for this group. One way to fix this inefficient allocation without reducing the wages of older individuals is to use the EITC, which is actually a subsidy for this group. This social policy might prompt employers to substitute older workers with a lower minimum wage for more expensive younger workers, making it possible for traditional factories to continue their domestic production. However, a necessary condition for this suggestion to work is the availability of efficient systems of training and learning. Axelrad et al. ( 2013 ) provided another justification for subsidizing the work of older individuals. They found that stereotypes about older workers might lead to a distorted allocation of the labor force. Subsidizing the work of older workers might correct this distortion. Ultimately, however, policy makers must understand that they must implement two different approaches to dealing with the problems of unemployment among young people and in the older population.

For example, in the US, the UK and Portugal, we witnessed higher rates of growth during late 1990 s and lower rates of youth unemployment compared to 2011.

Bank of Israel Annual Report—2013, http://www.boi.org.il/en/NewsAndPublications/RegularPublications/Research%20Department%20Publications/BankIsraelAnnualReport/Annual%20Report-2013/p5-2013e.pdf .

http://www.boi.org.il/en/NewsAndPublications/RegularPublications/Research%20Department%20Publications/RecentEconomicDevelopments/develop136e.pdf .

The Labor Force Survey is a major survey conducted by the Israeli Central Bureau of Statistics among households nationwide. The survey follows the development of the labor force in Israel, its size and characteristics, as well as the extent of unemployment and other trends. The publication contains detailed data on labor force characteristics such as their age, years of schooling, type of school last attended, and immigration status. It is also a source of information on living conditions, mobility in employment, and many other topics.

The survey population is the permanent (de jure) population of Israel aged 15 and over. For more details see: http://www.cbs.gov.il/publications13/1504/pdf/intro04_e.pdf .

When we looked at those who had not managed to find a job at the time of the survey, we included all individuals who were not working, regardless of whether they were discouraged workers, volunteers or had other reasons. As long as they are not out of the labor force due to medical reasons or their mandatory military service, we classified them as "did not manage to find a job."

Until 2012, active soldiers were considered outside the labor force in the samples of the CBS.

EITC is a refundable tax credit for low to moderate income working individuals and couples.

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HA, MM and IL conceptualized and designed the study. HA collected and managed study data, HA and IL carried out statistical analyses. HA drafted the initial manuscript. MM and IL reviewed and revised the manuscript. All authors read and approved the final manuscript.

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Axelrad, H., Malul, M. & Luski, I. Unemployment among younger and older individuals: does conventional data about unemployment tell us the whole story?. J Labour Market Res 52 , 3 (2018). https://doi.org/10.1186/s12651-018-0237-9

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The long-lasting impact of unemployment on life satisfaction: results of a longitudinal study over 20 years in East Germany

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Economic disruption in East Germany at the time of reunification (1990) resulted in a noticeable increase in unemployment. The present study provides data from a German cohort for over 20 years. The aim was to examine how the frequency of experiencing unemployment affects life satisfaction and whether their relationship changes over time.

In the Saxon Longitudinal Study, an age-homogeneous sample was surveyed annually from 1987 to 2016. Since 1996, 355 people (54% female) have been examined for issues related to unemployment. Life satisfaction was measured with both the Global Satisfaction with Life Scale and the Questions on Life Satisfaction Modules questionnaire.

In 1996, the participants were 23 years old and 50% of the sample was affected by unemployment. At all 16 different measuring points, participants who were never unemployed indicated higher life satisfaction than those who were once unemployed. The repeatedly unemployed consistently reported the lowest values of life satisfaction. In each year, there were significant differences with small to medium effect sizes.

Our results support the notion that the adverse effects of unemployment on life satisfaction increase with the time spent unemployed. In 2016, only 2% of the cohort were currently unemployed, but differences between people with and without unemployment experience still exist. This indicates that the negative effect of the unemployment experience will last for a very long time. To the best of our knowledge, this is the first study that demonstrates the effect so persistently at so many measurement points for over 20 years.

Unemployment is strongly associated with an increased risk of morbidity, mortality, mental health problems, and lower life satisfaction levels. The topic of unemployment has evoked a growing interest in 2020. The COVID-19 pandemic, which began in spring 2020, led to a collapse of the global economy and a massive increase in the unemployment rate [ 1 ]. A recent analysis of more than 1 million health insured individuals in Germany showed that the long-term unemployed had a more than 80 percent higher likelihood of hospitalization due to COVID-19 infection than the employed [ 2 ]. There is also evidence of a link between unemployment and suicides. A longitudinal analysis between 2000–2011 indicated that the relative risk of suicide associated with unemployment has increased by about 20–30% [ 3 ]. There was a massive increase in unemployment after the German reunification in 1990. The restructuring of the former East German economy led to the closure of many state-owned enterprises, resulting in massive job losses. The following unemployment rates were much higher than in West Germany. Despite considerable political efforts, these differences remain a social reality to this day. According to federal statistics, over 2 million people (5.3%) were unemployed in February 2017 in Germany. The differences between the East German states (7.0%) and the old West German states (4.9%) are significant [ 4 ].

Since 1990, almost all citizens of the East German states had experienced unemployment either themselves or within their family and acquaintances. The experience of unemployment in East Germany was a mass phenomenon with profound consequences [ 5 ]. A large number of studies have reported various links between unemployment and adverse psychological reactions, such as a higher number of mental disorders or more global aspects of daily life, such as impaired life satisfaction [ 6 , 7 ]. Earlier studies concluded that the non-monetary effect of unemployment is much higher than the impact of the associated loss of income [ 8 ]. The consequences of unemployment include loss of social contact and identity, along with reduced self-esteem.

Life satisfaction can be defined as the cognitive aspect of subjective well-being and refers to the global assessment of the quality of life [ 9 ].

Hahn et al. [ 10 ] examined 908 individuals from 3 years before until 3 years after becoming unemployed. The results showed that experiencing unemployment leads to a significant decline in life satisfaction. Even when people found employment again, life satisfaction stagnated at a low level for many years after the period of being unemployed [ 11 , 12 ].

While the effects of unemployment have been extensively investigated in cross-sectional studies, only a few longitudinal studies on the association of unemployment and life satisfaction have been conducted [ 13 ]. The present study provides data from a German cohort for over 20 years. We annually examined the link between life satisfaction and the frequency of experienced unemployment by using a large cohort from the Saxon longitudinal study. We want to investigate how the frequency of experienced unemployment affects life satisfaction and whether this relationship changes over time. Furthermore, we performed stepwise linear regression analyses in 1996 and 2016. We want to investigate whether life satisfaction is more strongly affected by current unemployment and its consequences or by the experience of past unemployment. Besides, we were interested if there were differences between the years 1996 and 2016 regarding the prediction of life satisfaction.

The Saxon Longitudinal Study (“Sächsische Längsschnittstudie”) [ 14 , 15 , 16 , 17 , 18 ] started in 1987 in the former German Democratic Republic (GDR). A sample ( N  = 1281) of 14-year-old students was selected as a representative group for the East German cohort of 1973. The sample was age-homogeneous because all the participants were in the eighth grade.

They were interviewed repeatedly until spring 1989. After the third survey in spring 1989, 587 of these participants agreed to continue participating in the study. The study continues after the German reunification until today. The Ethics Committee of the Technische Universität Dresden, Germany, approved the study protocol (No. EK8012011). The main topics of the study were political and social questions; for example, questions related to the long-term development of the GDR citizens’ socialization, their experience of the German reunification, and the changes in their living conditions. Since 1996, research into the consequences of unemployment has been a further focus of the study [ 19 , 20 ].

In 2016, the 29th survey was conducted. On average, the 270 respondents in the 29th survey were 43 years old (53% female) and 77% had children. In 2016 the response rate was 46% based on the 587 people who had agreed to continue participating in 1989. Most of the respondents completed their vocational training; only 2% had no completed vocational training. Further information on the participants in the 12th (1996) and 29th (2016) surveys is provided in Table 1 .

Questionnaires

Besides the socio-demographic parameters in the study, we have also collected a great deal of information on peoples’ experiences of the reunification and the transformation of East Germany [ 14 , 15 , 16 , 17 ]. Unemployment data were recorded by asking: “How many times have you been unemployed since 1990?”. Possible answers were “none”, “once”, and “several times”.

Furthermore, since 1996, the life satisfaction of the respondents has been measured annually with a self-developed, single-item scale—the Global Satisfaction with Life Scale (G-SLS). In the G-SLS, the respondents were asked to provide an answer to the question, “Taking all together, how do you assess your current life situation? With my life situation, I am.…” (in the original German: “Wie schätzen Sie—Alles in allem—Ihre gegenwärtige Lebenssituation ein? Mit meiner Lebenssituation bin ich…”). Participants answered on a symmetrical 5-point Likert scale ranging from 1 “very satisfied” to 5 “not satisfied at all”.

The FLZ M (Questions on Life Satisfaction Modules , in the original German: Fragen zur Lebenszufriedenheit Module )) is a valid and widely used questionnaire to assess general life satisfaction. This questionnaire covers eight domains of daily life (e.g., family, work, health), and the respondents were asked to rate the subjective importance and their immediate satisfaction with each domain [ 21 ]. The Saxon longitudinal study has many different topics. For economic reasons, we therefore only used the single-item scale (G-SLS) to measure life satisfaction at all points in time. The longer, well-established questionnaire FLZ M was only used at three points in time (2003, 2005, 2016). To check the validity of the G-SLS, we calculated the Pearson correlation with the well-established FLZ M . Table 2 shows the correlations of G-SLS with the other method of measuring life satisfaction. In our sample, the G-SLS correlates strongly with the well-established questionnaire FLZ M , which indicates a high convergent validity. The correlations are negative because the single-item scale (G-SLS) is inversely coded, with higher scores indicating lower life satisfaction. Whereas in the FLZ M , higher scores indicate higher life satisfaction.

Statistical analysis

The data were analyzed using SPSS software version 25 [ 22 ]. The mean values and the standard deviations ( SD ) for life satisfaction measures (G-SLS, FLZ M ) between groups (times of unemployment) were reported.

First, the data were analyzed using the Shapiro–Wilk test to determine whether they followed a normal distribution. The study found that the data did not follow a normal distribution. Therefore, the results were further analyzed nonparametrically using the Mann–Whitney-U-test and Kruskal–Wallis rank test. The Kruskal Wallis test is conservative and does not assume population normality, nor homogeneity of variance, and requires at least ordinal scaling of the dependent variable [ 22 ].

We calculated differences in respondents’ life satisfaction dependent on different frequencies of experienced unemployment (several times, once, none). Effect size eta squared ( η 2 ) was calculated for H -statistic to describe the magnitude of the effect [ 23 ]. Eta squared is defined as the proportion of variance in scores on the outcome variable that is predictable from group membership [ 24 ]. An effect size (ES) of η 2  = 0.01 was defined as small, η 2  = 0.06 as medium and η 2  = 0.14 as large [ 25 ]. We set the significance level at p  < 0.05 (two-tailed).

Two multiple linear regression analyses with stepwise inclusion were carried out. Depend variable was life satisfaction (G-SLS) in 1996 and 2016, regressed on gender, have children, type of occupation as dummy variables (unemployed, student, home keeper, blue-collar worker, white-collar worker, self-employed, public servant), and frequencies of experienced unemployment as dummy variables (none, once, several times). The stepwise inclusion method provided a measure of the relative effect of each predictor variable upon life satisfaction. These analyses started with the strongest predictor and added additional predictors if they explained significant additional variance in the dependent variable. The entered predictors were deleted in subsequent steps if they no longer contributed considerable unique predictive power to the regression. We set the inclusion criterion to p  = 0.05 and the exclusion criterion to p  = 0.10. The method terminated when no further variables were eligible for inclusion or exclusion. This minimized the possibility of entering two highly correlated predictor variables into the model. This method was used to identify the optimal set of predictors [ 26 ]. We reported standardized regression coefficients ( β ), which can be interpreted in the same way as regression coefficients.

Gender aspects

In 1996, the participants were 23 years old and were surveyed on their unemployment experiences for the first time. Even then, 50% of the sample was affected by unemployment, 17% of the respondents several times and 33% once. Until 1990, the respondents were unemployed for a total of 6.5 months on average. In 1996, significant gender differences were found: The cumulative duration of unemployment was 4.9 months for men and 7.8 months for women since 1990 ( Z  = − 2.60; p  = 0.01).

By 2016, 70% of the remaining sample was affected by unemployment, 40% several times, and 30% one time. On average, the cumulative duration of unemployment since 1990 was 10.9 months. In 2016 only descriptive differences in the duration of unemployment between men (9.0 months) and women (13.2 months) could be found, but those were no longer significant ( Z  = − 0.54; p  = 0.59).

The effect of unemployment periods and differences in life satisfaction

Table 3 summarizes the results in terms of life satisfaction (G-SLS) between the groups of never-, once- and several times unemployed from 1996 to 2015. At all 16 different points in time, people who were never unemployed reported higher life satisfaction than people who were once unemployed. Several times unemployed participants always reported the lowest values of life satisfaction.

In each year of data collection, there were significant differences between the three groups. The differences between the groups were highly significant almost every year ( p  < 0.001), all of small to medium effect sizes ( η 2  = 0.014–0.113).

Table 4 presents the results of the years 2003, 2005, and 2016, in which life satisfaction was measured with both the single-item scale G-SLS and the well-established questionnaire FLZ M . At all different points in time, there were differences in life satisfaction measured by G-SLS and also FLZ M between the people who were never, once and several times unemployed ( p  < 0.02) with small to medium effect sizes [ η 2 (G-SLS) = 0.055–0.099; η 2 (FLZ M ) = 0.023–0.093]. The significance levels and effect sizes showed a high descriptive degree of convergence in their level between the two questionnaires.

Predictors of life satisfaction

Two stepwise linear multiple regression analyses were performed to identify which variables explained variance in life satisfaction in 1996 and 2016. The G-SLS score was used as a criterion variable of life satisfaction. Predictive variables were: gender, having children, different types of occupation, and the frequency of unemployment. The two multiple regression analyses showed that in 1996 and 2016, there was only one significant and consistent predictor of life satisfaction in each case. In 1996, currently unemployed as type of occupation ( β  = 0.81, p  < 0.001) and in 2016, a repeated experience of unemployment ( β  = 0.24, p  = 0.009) was associated with lower life satisfaction. The remaining predictor variables (gender, having children, and the different types of occupations) did not explain any additional variance in life satisfaction scores in our analysis.

The results of the study show that people who have experienced unemployment in their occupational biography reported lower life satisfaction. This negative effect of unemployment is robust and persists for many years; it could be measured convergently with small to medium effect sizes by using two different questionnaires to assess life satisfaction. To the best of our knowledge, this is the first study that demonstrates the effect so persistently at so many measurement points for over 20 years.

Lucas et al. [ 11 ] and Winkelmann and Winkelmann [ 8 ] analyzed data from the German Socio-Economic Panel and found a similar pattern among people who have experienced unemployment, but only over a few measurement points. The negative effect was observed during unemployment and even during the period of re-employment. Our results support the assumption that the negative influence of unemployment increases with its frequency, as several times, the unemployed reported the lowest and the never unemployed the highest levels of life satisfaction. In order to avoid many multiple comparisons and alpha error inflation, we presented these differences only descriptively. The highly significant differences between people with and without the experience of unemployment could be shown consistently in all 16 surveys, both with the G-SLS and FLZ M questionnaires for measuring life satisfaction. Our analyses showed that this effect has a small to medium effect sizes. This magnitude of the effect was also found in other analyses. A meta-analysis revealed that unemployment has a strong negative effect on the self-reported global life satisfaction with a mean medium effect size ( d  = − 0.44) [ 6 ]. Clark et al. [ 12 ] showed that life satisfaction is lower not only among people who reported a higher degree of previous unemployment but also among people who were currently unemployed (relative to the employed). We also found this association.

Our stepwise regression analysis showed that being currently unemployed was the only predictor for lower life satisfaction in 1996. It suggests that unemployment has a strong negative effect because gender, having children, frequency of experienced unemployment, or different types of occupation explained no further incremental variance. Other studies have shown that the other predictors from our regression may affect life satisfaction. An analysis of the World Value Survey of 34 countries revealed that people who had children showed significantly higher life satisfaction [ 27 ]. McKee-Ryan, Song, Wanberg, & Kinicki [ 6 ] reported in their meta-analysis that women are slightly less satisfied with their lives during unemployment than men. In Germany, unemployment rates have decreased significantly over the last 20 years. Since 1996, the unemployment rate in East Germany has almost been halved [ 28 ]. Unlike in 1996, in the regression analysis, being currently unemployed was not a significant predictor of life satisfaction in 2016. Only the repeatedly unemployed were significantly associated with life satisfaction in the stepwise regression. One possible explanation for the changed predictors could be that a five times smaller proportion of our sample was currently unemployed in 2016 than in 1996 (2% vs. 11%). In 2016, 98% of the cohort was employed, but differences between people with and without the experience of unemployment still existed. It indicates that the negative effect of unemployment experience will last for a very long time. Our results are constrained by some methodological limitations that should be considered in future studies. First, the Saxon longitudinal study is an investigation with more than 30 surveys by now. Due to non-compliance, not every person responded at all times, which leads to a different sample for different surveys. With the known limitation, we analyzed the data in the study only cross-sectionally. Due to a large number of different topics in the Saxon longitudinal study, we are only able to analyze the changes in life satisfaction by using a one-item scale in most surveys. It would be interesting to know whether similar results were found for other components of subjective well-being, for example, quality of life. Overall, our results support the idea that the adverse effects will cumulatively increase with the time spent unemployed and are persistent for many years. Therefore, it is crucial to see unemployment as a potential pathogenic factor. Future studies could investigate whether these effects still occur in older people, even if they are already receiving a retirement pension.

Availability of data and materials

The datasets generated and analyzed during the current study are available in the GESIS – Leibniz-Institut für Sozialwissenschaften in Mannheim (German) repository, https://www.bit.ly/sls-gesis and from the corresponding author on reasonable request.

Change history

05 march 2021.

The original article has been revised to include a funding note.

Abbreviations

Effect size

German Democratic Republic

Global Satisfaction with Life Scale

Questions of Life Satisfaction

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Acknowledgements

We acknowledge support by the Open Access Publication Funds of the SLUB/TU Dresden.

Open Access funding enabled and organized by Projekt DEAL. The study was funded by the German Research Foundation, the Hans Böckler Foundation, the Otto Brenner Foundation, the Rosa Luxemburg Foundation, the Friedrich Ebert Foundation, the Federal Foundation for the Reappraisal of the SED Dictatorship, the Saxon State Ministry for Higher Education, Research and Arts, the University of Applied Science Erfurt and the University of Applied Science Zittau-Görlitz.

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HB, YSR, MZ, and EB constructed the study design. HB, EB, and YSR had been involved in the acquisition of data. EPR and HB wrote the first draft of the manuscript and revised it. EPR, MZ, YSR, and contributed to statistical analysis, data interpretation, and manuscript drafting. All authors read and approved the final manuscript.

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Richter, E.P., Brähler, E., Stöbel-Richter, Y. et al. The long-lasting impact of unemployment on life satisfaction: results of a longitudinal study over 20 years in East Germany. Health Qual Life Outcomes 18 , 361 (2020). https://doi.org/10.1186/s12955-020-01608-5

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Unemployment in the time of COVID-19: A research agenda ☆

David l. blustein.

a Boston College, United States of America

b University of Florida, United States of America

Joaquim A. Ferreira

c University of Coimbra, Portugal

Valerie Cohen-Scali

d Conservatoire National des Arts et Métiers, France

Rachel Gali Cinamon

e University of Tel Aviv, Israel

Blake A. Allan

f Purdue University, United States of America

This essay represents the collective vision of a group of scholars in vocational psychology who have sought to develop a research agenda in response to the massive global unemployment crisis that has been evoked by the COVID-19 pandemic. The research agenda includes exploring how this unemployment crisis may differ from previous unemployment periods; examining the nature of the grief evoked by the parallel loss of work and loss of life; recognizing and addressing the privilege of scholars; examining the inequality that underlies the disproportionate impact of the crisis on poor and working class communities; developing a framework for evidence-based interventions for unemployed individuals; and examining the work-family interface and unemployment among youth.

This essay reflects the collective input from members of a community of vocational psychologists who share an interest in psychology of working theory and related social-justice oriented perspectives ( Blustein, 2019 ; Duffy, Blustein, Diemer, & Autin, 2016 ). Each author of this article has contributed a specific set of ideas, which individually and collectively reflect some promising directions for research about the rampant unemployment that sadly defines this COVID-19 crisis.

Our efforts cohere along several assumptions and values. First, we share a view that unemployment has devastating effects on the psychological, economic, and social well-being of individuals and communities ( Blustein, 2019 ). Second, we seek to build on the exemplary research on unemployment that has documented its impact on mental health ( Paul & Moser, 2009 ; Wanberg, 2012 ) and its equally pernicious impact on communities ( International Labor Organization, 2020b ). Third, we hope that this contribution charts a research agenda that will inform practice at individual and systemic levels to support and sustain people as they grapple with the daunting challenge of seeking work and recovering from the psychological and vocational fallout of this pandemic.

The advent of this period of global unemployment is connected causally and temporally to considerable loss of life and illness, which is creating an intense level of grief and trauma for many people. The first step in developing a research agenda for unemployment during the COVID-19 era is to describe the nature of this process of loss in so many critical sectors of life. A major research question, therefore, is to what extent does this unemployment crisis vary from previous bouts of unemployment which were linked to economic fluctuations? In addition, exploring the role of loss and trauma during this crisis should yield research findings that can inform psychological and vocational interventions as well as policy guidance to support people via civic institutions and communities.

1. Recognizing and channeling our own privilege

In Joe Pinker's (2020) Atlantic essay entitled, “ The Pandemic Will Cleave America in Two”, he highlights two distinct experiences of the pandemic. One is an experience felt by those with high levels of education in stable jobs where telework is possible. Lives are now more stressful, work has been turned upside down, childcare is challenging, and leaving the house feels ominous. The other is an experience felt by the rest of the working public – those who cannot work from home and thus are putting themselves at risk every day, whose jobs have been either lost or downsized, and who are wondering not only if they will catch the virus but whether they have the means and resources to survive. As psychologists and professors, the vast majority of “us” (those writing this essay and those reading it) are extremely fortunate to be in the first group. The pandemic has only served to exacerbate the extent of this privilege.

Given our relative position of power, what are ways we can change our research to be more meaningful and impactful to those outside of our bubble? We propose that the recent work on radical healing in communities of color – where the research is often done in collaboration with the participants and building participant agency is an explicit goal - can inform our path forward ( French et al., 2020 ; Mosley et al., 2020 ). Work has always been a domain where individuals experience distress and marginalization. However, in the current pandemic and into the unforeseeable future, this will only exponentially increase. Sure, we can do surveys about people's experiences and provide incentives for their time. And of course qualitative work will allow us to more directly connect with participants and hear their voices. But what is most needed is research where participants receive tangible benefits to improve their work lives. We, as privileged scholars, need to think about how we can use our expertise in studying work to infuse our studies with real world benefits. We see this as occurring on a spectrum in terms of scholars' time and resources available – from information sharing about resources to providing job-seeking or work-related interventions. In our view, now is the time to truly commit to using work-related research not just as a way to build scholarly knowledge, but as a way to improve lives.

2. Inequality and unemployment

Focusing research efforts on real-world benefits means acknowledging how the COVID-19 pandemic has exposed and exacerbated existing inequities in the labor market. Millions of workers in the U.S. have precarious jobs that are uncertain in the continuity and amount of work, do not pay a living wage, do not give workers power to advocate for their needs, or do not provide access to basic benefits ( Kalleberg, 2009 ). Power and privilege are major determinants of who is at risk for precarious work, with historically marginalized communities being disproportionately vulnerable to these job conditions ( International Labor Organization, 2020a ). In turn, people with precarious work experience chronic stress and uncertainty, putting them at risk for mental health, physical, and relational problems ( Blustein, 2019 ). These risk factors may further worsen the effects of the COVID-19 crisis while simultaneously exposing inequities that existed before the crises.

The COVID-19 pandemic is an opportunity for researchers to define and describe how precarious work creates physical, relational, behavioral, psychological, economic, and emotional vulnerabilities that worsen outcomes from crises like the COVID-19 pandemic (e.g., unemployment, psychological distress). For example, longitudinal studies can examine how precarious work creates vulnerabilities in different domains, which in turn predict outcomes of the COVID-19 pandemic, including unemployment and mental health. This may include larger scale cohort studies that examine how the COVID-19 crisis has created a generation of precarity among people undergoing the school-to-work transition. Researchers can also study how governmental and nonprofit interventions reduce vulnerability and buffer the relations between precarious work and various outcomes. For example, direct cash assistance is becoming increasingly popular as an efficient way to help people in poverty ( Evans & Popova, 2014 ). However, dominant social narratives (e.g., the myth of meritocracy, the American dream) blame people with poor quality work for their situations. Psychologists have a critical role in (a) documenting false social narratives, (b) studying interventions to provide accurate counter narratives (e.g., people who receive direct cash assistance do not spend money on alcohol or drugs; most people who need assistance are working; Evans & Popova, 2014 ), and (c) studying how to effectively change attitudes among the public to create support for effective interventions.

3. Work-family interface

Investigating the work-family interface during unemployment may appear contradictory. It can be argued that because there is no paid work, the work-family interface does not exist. But ‘work’ is an integral part of people's lives, even during unemployment; for example, working to find a job is a daunting task that is usually done from home. Thus, the work-family interface also exists during unemployment, but our knowledge about this is limited. Our current knowledge on the work-family interface primarily focuses on people who work full-time and usually among working parents with young children ( Cinamon, 2018 ). As such, focusing on the work-family interface during periods of unemployment represents a needed research agenda that can inform public policy and scholarship in work-family relationships.

The rise in unemployment due to COVID-19 relates not only to the unemployed, but also to other family members. Important research questions to consider are how are positive and negative feelings and thoughts about the absence of work conveyed and co-constructed by family members? What family behaviors and dynamics promote and serve as social capital for the unemployed and for the other members of the family? Do job search behaviors serve as a form of modeling for other family members? What are the experiences of unemployed spouses and children, and how do these experiences shape their own career development? These issues can be discerned among unemployed people of different ages, communities, and cultures.

Several research methods can promote this agenda. Participatory action research can enable vocational researchers to be proactive and involved in increasing social solidarity. This approach requires mutual collaboration between the researcher and families wherein one of the parents is unemployed. By giving them voice to describe their experiences, thoughts, ideas, and suggested solutions, we affirm inclusion of the individuals living through the new reality, thereby conveying respect and acknowledgment. At the same time, we can bring ideas, knowledge, and social connections to the families that can serve as social capital. In addition, longitudinal quantitative studies among unemployed families that explore some of the issues noted above would be important as a means of exploring how the new unemployment experience is shaping both work and relationships. We also advocate that meaningful incentives be offered to participants in all of these studies, such as online job search workshops and career education interventions for adolescents.

4. Strategies for dealing with unemployment in the pandemic of 2020

Forward-looking governments and organizations (such as universities) should begin thinking about how to deal with the immediate and long-term consequences of the economic crisis created by COVID-19, especially in the area of unemployment. Creating meaningful interventions to assist the newly unemployed will be difficult because of the unprecedented number of individuals and families that are affected and because of the diverse contextual and personal factors that characterize this new population. Because of this diversity of contextual and personal factors, different interventions will be required for different patterns of individual/contextual characteristics ( Ferreira et al., 2015 ).

In broad outline, a research program to address the diversity of issues identified above could be envisioned to consist of several distinct phases: First, it would be necessary to carefully assess the external circumstances of the unemployed individual's job loss, including the probability of re-employment, financial condition, family composition, and living conditions, among others. Second, an assessment should be made of the individual's strengths and growth edges, particularly as they impact the current situation. These assessments could be performed via paper or online questionnaire. Based on these initial assessments, the third phase would involve using statistical analyses such as cluster analysis to form distinct groups of unemployed individuals, perhaps based in part on the probability of re-employment following the pandemic. The fourth phase would focus on determining the types (and/or combinations) of intervention most appropriate for each group (e.g., temporary government assistance; emotional support counseling; retraining for better future job prospects; relocation, etc.). Because access to specific types of assistance is frequently a serious challenge, especially for underprivileged individuals, the fifth phase should emphasize facilitating individuals' access to the specific assistance they need. Finally, the sixth phase of research should evaluate the efficacy of this approach, although designing such a large research program in a crisis situation requires ongoing process evaluation throughout the design and implementation stages of the research program.

5. Unemployment among youth

As reflected in a recent International Labor Organization (2020a) report on the impact of the COVID-19 crisis, youth were already vulnerable within the workforce prior to the crisis; the recent advent of massive job losses and growing precarity of work is having particularly painful impacts on young people across the globe. The COVID-19 economic crisis with vast increases in unemployment (and competition between workers) and the probable growth of digitalization may result in a major dislocation of young workers from the labor market for some time ( International Labor Organization, 2020b ). To provide knowledge to meet this daunting challenge, researchers should develop an agenda focusing on two major components—the first is a participatory mode of understanding the experience of youth and the second is the development of evidence-based interventions that are derived from this research process.

The data gathering aspect of this research agenda optimally should focus on understanding unemployed youths' perception of their situation (opportunities, barriers, fears, and intentions) and of the new labor market. We propose that research is needed to unpack how youth are constructing this new reality, their relationship to society, to others, and to the world. This crisis may have changed their priorities, the meaning of work, and their lifestyle. For example, this crisis may have led to an awareness of the necessity of developing more environmentally responsible behaviors ( Cohen-Scali et al., 2018 ). These new life styles could result in skills development and increased autonomy and adaptability among young people. In addition, the focus on understanding youths' experience, which can encompass qualitative and quantitative methods, should also include explorations of shifts in youths' sense of identity and purpose, which may be dramatically affected by the crisis. The young people who are without work should be involved at each step of the research process in order to improve their capacities, knowledge, and agency and to ensure that the research is designed from their lived experiences.

Building on these research efforts, interventions may be designed that include individual counseling strategies as well as systemic interventions based on analyses of the communities in which young people are involved (for example, families and couples and not only individuals). In addition, we need more research to learn about the process of collective empowerment and critical consciousness development, which can inform youths' advocacy efforts and serve as a buffer in their career development ( Blustein, 2019 ).

6. Conclusion

The research ideas presented in this contribution have been offered as a means of stimulating needed scholarship, program development, and advocacy efforts. Naturally, these ideas are not intended to be exhaustive. We hope that readers will find ideas and perspectives in our essay that may stimulate a broad-based research agenda for our field, optimally informing transformative interventions and needed policy interventions for individuals and communities suffering from the loss of work (and loss of loved ones in this pandemic). A common thread in our essay is the recommendation that research efforts be constructed from the lived experiences of the individuals who are now out of work. As we have noted here, their experiences may not be similar to other periods of extensive unemployment, which argues strongly for experience-near, participatory research. We are also advocating for the use of rigorous quantitative methods to develop new understanding of the nature of unemployment during this period and to develop and assess interventions. In addition, we would like to advocate that the collective scholarly efforts of our community include incentives and outcomes that support unemployed individuals. For example, online workshops and resources can be shared with participants and other communities as a way of not just dignifying their participation, but of also providing tangible support during a crisis.

In closing, we are humbled by the stories that we hear from our communities about the job loss of this pandemic period. Our authorship team shares a deep commitment to research that matters; in this context, we believe that our work now matters more than we can imagine.

☆ The order of authorship for authors two through six was determined randomly; each of these authors contributed equally to this paper.

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US unemployment claims fall 7,000 to 227,000 in sign of resiliency in job market

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WASHINGTON (AP) — The number of Americans applying for unemployment benefits fell last week, another sign that the job market remains resilient in the face of high interest rates.

Jobless claims dropped by 7,000 to 227,000 last week, the Labor Department reported Thursday. The four-week average of claims, which smooths out week-to-week ups and downs, fell by 4,500 to 236,500.

In the week that ended Aug. 3, 1.86 million Americans were collecting jobless benefits, down by 7,000 from the week before.

Weekly filings for unemployment benefits, which are a proxy for layoffs, remain low by historic standards. From January through May, claims averaged a rock-bottom 213,000 a week. But they started rising in May, hitting 250,000 in late July and adding to evidence that high interest rates are taking a toll on the U.S. job market.

But claims have since fallen two straight weeks, dispelling worries that the job market was deteriorating rapidly rather than just slowing.

“Claims calmed down and their recent rise appears to be just a blip, not a fundamental shift in the labor market,’' said Robert Frick, economist at the Navy Federal Credit Union.

The Federal Reserve, fighting inflation that hit a four-decade just over two years ago, raised its benchmark interest rate 11 times in 2022 and 2023, taking it to a 23-year high. Inflation has come down steadily — from 9.1% in June 2022 to a three-year low of 2.9% last month. Despite higher borrowing costs, the economy and hiring kept cruising along, defying widespread fears that the United States would sink into recession.

The economy is weighing heavily on voters as they prepare for November’s presidential election. Despite a solid job market and decelerating inflation, Americans are still exasperated that consumer prices are 19% higher than they were before inflation started to take off in 2021. Many blame President Joe Biden, though it’s unclear whether they will hold Vice President Kamala Harris responsible as she seeks the presidency.

Lately, higher rates have finally seemed to be taking a toll. Employers added just 114,000 jobs in July, well below the January-June monthly average of nearly 218,000. The unemployment rate rose for the fourth straight month in July, though it remains low at 4.3%. Monthly job openings have fallen steadily since peaking at a record 12.2 million in March 2022. They were down to 8.2 million in June.

As signs of an economic slowdown accumulate and inflation continues to drift down toward its 2% target, the Fed is expected to start cutting rates at its next meeting in September.

Impacts of Early Youth Unemployment Self-Esteem and Quality of Life: Moderating Effects of Career Unemployment

• Published: 26 August 2024

Cite this article

• KonShik Kim   ORCID: orcid.org/0000-0002-9817-576X 1  

This study examines the effects of early youth unemployment and career unemployment on young adults’ self-esteem and quality of life using the survey data from the Youth Panel of the Korea Employment Information Service collected from 2007 to 2020. The study found a stigma effect that increases the probability of career unemployment as the duration of early unemployment experienced by young adults in the entry into the labor market increases. The duration of unemployment negatively affects self-esteem, confirming a psychological stigma effect that alters the psychosocial status of individuals. In addition, unemployment duration harms the quality of life of young adults, confirming that joblessness leads to increased dissatisfaction and a decline in quality of life in many domains. Further, subsequent unemployment experiences of young adults later in their careers exacerbate the negative impact of early unemployment on self-esteem and quality of life. This study demonstrates that youth unemployment is not only a temporary setback for individuals who are not economically active but also a mechanism that exacerbates the subsequent significant socioeconomic costs of unemployment.

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Kim, K. Impacts of Early Youth Unemployment Self-Esteem and Quality of Life: Moderating Effects of Career Unemployment. Applied Research Quality Life (2024). https://doi.org/10.1007/s11482-024-10360-7

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Mixed Story: What the Revision to the Jobs Data Means

• ARTICLE Beat the Press

August 21, 2024

I don’t have time to do an exhaustive analysis of the implication of the downward revisions to the jobs numbers today, but I will make a few quick points.

First, the people complaining that this downward revision exposes cooked jobs data in prior months need to get their heads screwed on straight. Let’s just try a little logic here.

If the Biden-Harris administration had the ability to cook the job numbers, do we think they are too stupid to realize that they should keep cooking them at least through November? Seriously, do we think they are total morons? If you’ve been cooking the numbers for twenty months, wouldn’t you keep cooking them until Election Day?

Okay, but getting more serious here, the staff of the Bureau of Labor Statistics (BLS) is a professional outfit that does exactly what we want it to do. They produce the data about the economy as best they can in a completely objective way. And they use methods that are completely transparent.

People can go to the BLS website and read as much as they like about the nature of the survey that produces the monthly jobs number and large revision we saw today. The basic story is that the monthly survey goes to hundreds of thousands of employers who are supposed to be representative of the millions of employers in the economy. Generally, the survey gives us a pretty accurate job count, but it will never be exact.

Every year the BLS adjusts the data from the survey based on state unemployment insurance (UI) filings which have data from nearly every employer in the country. These UI filings are a near census for all payroll employment. If the UI data gives a different picture than the survey of employers, then the filings are almost certainly right and BLS revises it data accordingly.

For reasons that we can only speculate about, there was an unusually large gap this year. Many economists and statisticians will spend many hours trying to figure out why this is the case. But one thing we should be confident of is that no one cooked the data. BLS did the best they could in structuring their survey of employers. If they can find ways to improve it, they will, as they have in the past.

What Does This Tell Us About the Economy?

Turning briefly to the substance of the revision, I realize many people will be quick to say that this is bad news for our picture of the economy. That is not clear at all.

First, we should be clear that even with the revision the economy still created jobs at a very rapid pace in the period covered, from March 2023 to March 2024. While BLS had previously reported that we created 2.9 million jobs over this period, or 242,000 a month. The revision means we created 2.1 million jobs or 172,000 jobs a month. By comparison, in the three years prior to the onset of the pandemic, we created jobs at a rate of 179,000 a month. Even with the downward revision, we were still creating jobs at a very healthy pace.

To put these numbers in a larger context, it is necessary to have a broader picture of the labor market. Every month we look at two independent surveys in the BLS Employment Report. One is the establishment survey which will be revised based on today’s report from the UI filings. The other is the survey of households, the Current Population Survey (CPS), which tells us the percentage of the workforce that is employed and unemployed.

The CPS also has a measure of employment, but we generally pay it much less attention, since on a monthly basis it is highly erratic. For example, in October of 2017, when the economy seemed to be growing at a healthy pace, the CPS showed a loss of 677,000 jobs. It is implausible there actually was a job loss anything like that in the month, just as it was implausible there was a job gain like the 783,000 reported for the prior month.

Over longer periods of time, the erratic jumps and plunges tend to average out, but even here there are problems. The CPS gives us ratios for the percent of people who are employed and unemployed and the characteristics of their employment, such as whether their job is full-time or part-time, the industry they are employed in, their race, gender, education, and other factors.

However, the total employment figure depends on population controls that come indirectly from the decennial Census. The population controls take the total number of people found in the Census and then adjust based on estimates of births, deaths, immigration, and emigration. This process is imperfect and can often lead to large errors.

For example, at the end of the decade of the 1990s, more than two million people were added into the population controls for the survey because of an underestimate of population growth in the decade. In recent years, the employment growth in the CPS has seriously lagged job growth in the CES. This is true even after today’s downward revision to the CES.

The most plausible explanation for the large gap between the two surveys is that the population controls are underestimating the impact of immigration. A recent paper from the Brookings Institution calculates that undercounting immigration may have led the CPS to understate employment growth by more than 1 million a year.

While we may not be able to use the CPS to get a more accurate count of the actual number of jobs generated by the economy, what it does give us is actually more important. It tells us the share of the population that is employed, unemployed, employed part-time, and even gives us data on wages.

These factors are more important because these factors tell us more directly how well people are doing. The number of jobs in itself doesn’t answer that question. It is not like runs in a baseball game. We care about jobs because people who want to work should be able to get a job. If it turns out that we are generated somewhat fewer jobs than we thought, but most people who want jobs have jobs, then this is a pretty good story.

That looks like the situation today. The unemployment rate was 4.3 percent in July. That is higher than the 3.4 percent low hit in April of last year, but it is still quite low by historical standards. It was only this low for three months of the George W. Bush administration, it only got down to 4.3 percent or lower for the last two years of the late 1990s boom. It never got close to 4.3 percent during the Reagan “boom” years.

To be clear, the rise in the unemployment rate since last April is cause for concern. If that continues, unemployment will definitely be a serious issue. But if we just take the snapshot for the unemployment rate for July of this year, it is hard to see much cause for complaint.

We can also flip this over and ask the opposite question of what share of the population is employed. Here we have a very good story. If we look at the prime age population, people between the ages of 25 and 54, the employment to population ratio stood at 80.9 percent in July. We would have to go back to April of 2001 to find a higher rate.

It is reasonable to look at prime-age employment to control for the effect of the aging of the population. Most of us would not consider it a bad thing that people in their sixties and seventies opt to retire rather than work. As the huge baby boom cohorts age, we are seeing a growing share of the population in retirement. Looking just at the prime age population allows us to see the share of the population that we think would want to work, who can actually get jobs.

We can also look at other factors like the share of the population who are working part-time, but want full-time jobs, which is now very low . And we can look at the growth rate of real wages (wages adjusted for higher prices), which has been good overall and especially for those at the lower end of the wage distribution.     

How Do the Revisions Change the Economic Picture?

Nothing about the revisions to the CES change the story of an economy where jobs are relatively plentiful, and workers are seeing wage increases in excess of inflation. What they tell us is that the economy is creating fewer jobs than we had previously believed. But if we have high levels of employment with fewer jobs, what exactly is the problem?

In fact, there is a very positive side to this story. Assuming that we have accurately measured output (there are issues here too), if we generated this output with fewer jobs than we had previously estimated, this means productivity growth has been faster than had previously been estimated.

Productivity growth is the change in output per hour of work. If the economy created fewer jobs than we thought, then the growth in hours of work was less than we had previously believed. This means that productivity growth was stronger than had been reported.

This is a big deal since productivity growth ultimately determines how rapidly living standards can improve. There are huge issues of distribution, and also measurement (much of what affects living standards will not be picked up in productivity), but if productivity is growing at a 2.0 percent annual rate, this means that we can in principle see more rapid gains in living standards than if it is growing at just a 1.5 percent annual rate.

The one-year change doesn’t matter much, but over time this difference is substantial. In the case of a gap between a 2.0 percent growth rate versus a 1.5 percent growth rate, after a decade it would be almost 6.0 percent. For a worker near the median wage this would be the difference between taking home $53,000 a year versus $50,000 a year.

After 20 years, the gap would grow to almost 14 percent. This would amount to a wage gap of close to $7,000 for a worker earning near the median annual wage.

The lower job growth resulting from the revisions today would imply that hours grew by 0.5 percent less from the first quarter of 2023 to the first quarter of 2024. Productivity growth for this period is currently reported as 2.9 percent. A reduction in hours growth of 0.5 percent would mean that productivity growth is 0.5 percentage points faster than had been reported, or 3.4 percent over this period.

Before celebrating this extraordinary rate of productivity growth (we had averaged just 1.1 percent in the decade prior to the pandemic) it is important to realize that the data are highly erratic, especially in the period following the pandemic. Productivity had actually fallen 0.5 percent in the prior year.

So, it’s far too early to celebrate a pickup in productivity growth, but the downward revision to hours growth implied by today’s revision to the jobs data unambiguously raises the pace of productivity growth over the last year. In that sense, it is good news. But as with all economic data, it’s part of a big picture, and we can’t make too much of any specific data release in isolation.

Take Away from Revision—More Fears of Weakness and Hope for Faster Productivity

The rise in unemployment since its recovery low has caused many to fear a new recession. This is definitely a cause for concern, but most other data, including very current data on items like road and air travel and restaurant reservations, do not give evidence of a recession. Nonetheless, the report showing that we were creating jobs at a slower pace than previously reported, albeit still very rapid, points in the direction of weakness.

On the other hand, the revision is encouraging in that it bolsters the case for a productivity upturn. It is far too early to declare the upturn is here, but it’s great to get another data point in the right direction. For this reason, the slower job growth indicated by this revision should be seen as more good than bad.

Dean Baker | Senior Economist

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Published 27 August 2024

China’s unemployment crisis underscores the broader challenges facing the country’s economic model. The combination of a deteriorating economy at home, falling consumption, overcapacity, excessive domestic competition in the labor market, and inadequate policy intervention now threaten China's long-term macroeconomic stability. These factors are stoking trade tensions for China abroad.

How bad exactly is unemployment in China? Consider these examples: An increasing number of fresh college graduates are joining the gig economy by taking low-skilled jobs such as delivering food as they struggle to find jobs commensurate with their degrees. The number of people under the age of 25 who applied for manual jobs in the first quarter of 2024 surged 165% compared with the same period in 2019. A memo from an airport in Wenzhou city indicated that the airport had hired architects and engineers as ground managers and bird controllers. Since December 2022, over 10 protests occurred at Carrefour stores across the country due to store closures and unpaid wages. Alibaba, China’s e-commerce giant, cut 20,000 jobs, or 12.8% of the total employment in the 2023 fiscal year, following a 7% cut in the previous year.

China’s official unemployment rate in urban areas has remained flat at around 5% for decades, but it has never reflected the reality on the ground, especially when economic gloom is weighing on Chinese manufacturing and service industries. Since the end of its draconian COVID-19 restrictions, China’s economy has been struggling to rebound amid insufficient demand, excess savings, debt crises, and falling property prices and investments. In the absence of significant fiscal or monetary stimulus, China’s disappointing economic recovery continues to depress employment through its impact on the country’s “three engines” of growth – investment, exports, and consumption. While the persistent pessimism prevailing in the job market is a global phenomenon to some extent, the downward trend of the Chinese economy worsened the situation significantly in a country where the government does not have much experience or make sufficient preparations in coping with the scale of the challenge.

Download China’s unemployment conundrum and its implications for global trade by Chen Gang:

China’s unemployment crisis underscores the broader challenges facing the country’s economic model. Its job market struggles are deeply embedded in a state capitalism system driven by mercantile national strategies that aim at export expansion and trade surpluses. China has been relying on exports to boost its economy amid weak domestic demand, but the country’s foreign trade is facing significant headwinds due to supply chain disruptions, the US-China decoupling process, and high tariffs levied by major trading partners including the United States and European Union.

The slowdown in exports has a direct impact on manufacturing jobs, with lower demand for Chinese goods abroad leading to reductions in workforce and production volumes in export-oriented industries. China’s exports fell for the first time since 2016 as global demand for Chinese-made goods slowed. The interplay between overcapacity, excessive competition, and policy interventions now threaten the stability of China's overall economic health. To address these challenges, China must implement comprehensive reforms that balance domestic consumption with production, adopt measures to mitigate the impact of global trade tensions, and provide targeted support to affected industries and workers. Without such reforms, the pressures from prolonged economic stagnation, loss of trade competitiveness, and social discontent cannot stay contained much longer, writes Chen Gang , deputy director of the National University of Singapore’s East Asian Institute.

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Dr. Chen Gang is Deputy Director and Senior Research Fellow of the East Asian Institute (EAI), National University of Singapore. Since he joined the EAI in 2007, he has been tracing China’s politics, foreign policy, environmental and energy policies and publishing extensively on these issues.

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