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After dropping in 2020, teen summer employment may be poised to continue its slow comeback

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

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

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

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

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

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

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

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


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Unemployment Scarring Effects: An Overview and Meta-analysis of Empirical Studies

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  • Published: 17 May 2023

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  • Mattia Filomena   ORCID: 1 , 2  

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This article reviews the empirical literature on the scarring effects of unemployment, by first presenting an overview of empirical evidence relating to the impact of unemployment spells on subsequent labor market outcomes and then exploiting meta-regression techniques. Empirical evidence is homogeneous in highlighting significant and often persistent wage losses and strong unemployment state dependence. This is confirmed by a meta-regression analysis under the assumption of a common true effect. Heterogeneous findings emerge in the literature, related to the magnitude of these detrimental effects, which are particularly penalizing in terms of labor earnings in case of unemployment periods experienced by laid-off workers. We shed light on further sources of heterogeneity and find that unemployment is particularly scarring for men and when studies’ identification strategy is based on selection on observables.

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Estimating the mental health effects of social isolation, data availability.

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

Moreover, further outcomes discussed by the literature on scarring are family formation, crime and negative psychological implications in terms of well-being, life satisfaction and mental health (see e.g. Helbling and Sacchi 2014 ; Strandh et al. 2014 ; Mousteri et al. 2018 ; Clark and Lepinteur 2019 ).

A further strand of the recent literature focuses on the effect of adverse labor market conditions at graduation, for example focusing on the effect of local unemployment rate or graduating during a recession (see e.g. Raaum and Roed, 2006 ; Kahn 2010 ; Oreopoulos et al. 2012 ; Kawaguchi and Murao 2014 ; Altonji et al. 2016 ). The consequences of economic downturns on wages, labor supply and social outcomes for young labor market entrants have been recently surveyed by Cockx ( 2016 ), Von Wachter (2020) and Rodriguez et al. ( 2020 ).

The stigma effect means that individuals who have been unemployed face lower chances of being hired because employers may use their past history of unemployment as a negative signal.

Thus, papers using traditional multivariate descriptive analysis, duration models, or OLS regressions with a reduced number of controls which do not properly address endogeneity issues and are unlikely to have a causal interpretation (endogeneity issues are discussed in SubSect.  3.2 ).

For intergenerational scars we mean that studies focused on the effect of parents’ unemployment experiences on the children’ future employment status (see e.g. Karhula et al. 2017 ). For macroeconomic conditions at graduation we mean that we excluded that literature focused on the local unemployment rate at graduation or other local labor market conditions, rather than on individual unemployment experience and state dependence (see e.g. Oreopoulos et al. 2012 ; Raaem and Roed, 2006 ).

When we could not directly retrieve the t -statistics because not reported among the study results, we computed them as the ratio between the estimated unemployment effects ( \({\beta }_{i}\) ) and their standard errors. If studies only displayed the estimated effects and their 95% confidence intervals, the standard error can be calculated by SE  = ( ub − lb )/(2 × 1.96), where ub and lb are the upper bound and the lower bound, respectively.

We removed from the meta-regression analysis 8 articles because they did not contain sufficient information to compute the t -statistic of the estimated scarring effect. They are reported in italics in Tables 5 and 6 .

For employment outcomes we mean the likelihood of experiencing future unemployment, the probability to have a job later (employability), the fraction of days spent at work or the hours worked during the following years (labor market participation), the call-backs from employers in case of field experiment. Earning outcomes include hourly wages, labor earnings, income, etc.

Since many studies did not provide precise information on the number of covariates, we approximated \({dk}_{i}\) with the number of observations minus 2. Indeed, given that in microeconometric applications the sample sizes are very often much larger than the number of the parameters, the calculation of the partial correlation coefficient is quite robust to errors in deriving \({dk}_{i}\) (Picchio 2022 ).

The publication bias is the bias arising from the tendency of editors to publish more easily findings consistent with a conventional view or with statistically significant results, whereas studies that find small or no significant effects tend to remain unpublished (Card and Krueger 1995 ).

We employed the Precision Effect Estimate with Standard Error (PEESE) specification because its quadratic form of the standard errors has been proven to be less biased and often more efficient to check for heterogeneity than the FAT-PET specification when there is a nonzero genuine effect (Stanley and Doucouliagos 2014 ). Nevertheless, the results from the FAT-PET specification are very similar to the ones from the PEESE model.

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The author acknowledges financial support from the Cariverona Foundation Ph.D. research scholarship. He is particularly grateful to Matteo Picchio and Claudia Pigini for their comments and suggestions on a preliminary version of this paper. He also thanks the Associate Editor Massimiliano Bratti and two anonymous reviewers, whose comments were very useful for an important improvement of the paper.

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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.

research essay on unemployment

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

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

Pre-Pandemic Trends: 1976-2019

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

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

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

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

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

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

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

Pre-Pandemic Expectations of Future LFP and Unemployment Trends

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

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

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

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

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

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

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

Unemployment and Labor Force Participation During the Pandemic

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

What Does the Future Hold?

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

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

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

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

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

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

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

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Unemployment, employability and covid19: how the global socioeconomic shock challenged negative perceptions toward the less fortunate in the australian context.

\r\nAino Suomi,*

  • 1 Research School of Population Health, The Australian National University, Canberra, ACT, Australia
  • 2 Institute of Child Protection Studies, The Australian Catholic University, Melbourne, VIC, Australia
  • 3 Melbourne Institute of Applied Economic and Social Research, The University of Melbourne, Melbourne, VIC, Australia

Unemployed benefit recipients are stigmatized and generally perceived negatively in terms of their personality characteristics and employability. The COVID19 economic shock led to rapid public policy responses across the globe to lessen the impact of mass unemployment, potentially shifting community perceptions of individuals who are out of work and rely on government income support. We used a repeated cross-sections design to study change in stigma tied to unemployment and benefit receipt in a pre-existing pre-COVID19 sample ( n = 260) and a sample collected during COVID19 pandemic ( n = 670) by using a vignette-based experiment. Participants rated attributes of characters who were described as being employed, working poor, unemployed or receiving unemployment benefits. The results show that compared to employed characters, unemployed characters were rated substantially less favorably at both time points on their employability and personality traits. The difference in perceptions of the employed and unemployed was, however, attenuated during COVID19 with benefit recipients perceived as more employable and more Conscientious than pre-pandemic. These results add to knowledge about the determinants of welfare stigma highlighting the impact of the global economic and health crisis on perception of others.


The onset of COVID19 pandemic saw unemployment climb to the highest rate since the Great Depression in many regions globally 1 . Over just one month, from March to April 2020 unemployment rate in the United States increased from 4.4% to over 14.7% and in Australia the effective rate of unemployment increased from 5.4 to 11.7% ( Australian Bureau of Statistics, 2020 ) 2 . In Australia, a number of economic responses were rapidly introduced including a wage subsidy scheme (Jobkeeper) to enable employees to keep their employees connected to the workforce, one-off payments to many welfare recipients, and a doubling of the usual rate of the unemployment benefits (Jobseeker payment) through a new Coronavirus supplement payment. At the time of writing in July 2020, many countries, including Australia remain in the depths of a health and economic crisis.

A rich research literature from a range of disciplines has documented the pervasive negative community views toward those who are unemployed and receiving unemployment benefits, with the extent of this “welfare stigma” being particularly pronounced in countries with highly targeted benefit systems such as the United States and Australia ( Fiske et al., 2002 ; Baumberg, 2012 ; Contini and Richiardi, 2012 ; Schofield and Butterworth, 2015 ). The stigma and potential discrimination associated with unemployment and benefit receipt are known to have negative impacts on health, employability and equality (for meta-analyses, see Shahidi et al., 2016 ). In addition, the receipt of unemployment benefits co-occurs with other stigmatized characteristics such as poverty and unemployment ( Schofield and Butterworth, 2018a ). The changing context related to the COVID19 crisis provides a novel opportunity to better understand the determinants of stigmatizing perceptions of unemployment and benefit receipt.

Negative community attitudes and perceptions of benefit recipients are commonly explained by the concept of “deservingness” ( van Oorschot and Roosma, 2017 ). The unemployed are typically seen as less deserving of government support than other groups because they are more likely to be seen as responsible for their own plight, ungrateful for support, not in genuine need ( Petersen et al., 2011 ; van Oorschot and Roosma, 2017 ), and lacking reciprocity (i.e., seen as taking more than they have given – or will give – back to society; van Oorschot, 2000 ; Larsen, 2008 ; Petersen et al., 2011 ; Aarøe and Petersen, 2014 ). Given the economic shock associated with COVID19, unemployment and reliance on income support are less likely to seen as an outcome within the individuals control and may therefore amplify perceptions of deservingness. Prior work has shown that experimentally manipulating perceived control over circumstances does indeed change negative stereotypes ( Aarøe and Petersen, 2014 ).

A number of experimental paradigms have been used to investigate perceptions of “welfare recipients” and the “unemployed.” The stereotype content model (SCM; Fiske et al., 2002 ), for example, represents the stereotypes of social groups on two dimensions: warmth, relating to being friendly and well–intentioned (rather than ill–intentioned); and competence, relating to one’s capacity to pursue intentions ( Fiske et al., 2002 ). Using this model, the “unemployed” have been evaluated as low in warmth and competence across a variety of welfare regime types ( Fiske et al., 2002 ; Bye et al., 2014 ). The structure of stereotypes has also been studied using the Big Five personality dimensions ( Schofield and Butterworth, 2018b ; Schofield et al., 2019 ): Openness, Conscientiousness, Extraversion, Agreeableness, and Emotional Stability (for background on the Big Five see: Goldberg, 1993 ; Hogan et al., 1996 ; Saucier and Goldberg, 1996 ; McCrae and Terracciano, 2005 ; Srivastava, 2010 ; Chan et al., 2012 ; Löckenhoff et al., 2014 ). There are parallels between the Big Five and the SCM: warmth relating to the dimension of Agreeableness, and competence relating to Conscientiousness ( Digman, 1997 ; Ward et al., 2006 ; Cuddy et al., 2008 ; Abele et al., 2016 ) and these constructs have been found to predict employability and career success ( Barrick et al., 2001 ; Cuesta and Budría, 2017 ). Warmth and agreeableness have also been linked to the welfare-specific characteristics of deservingness ( Aarøe and Petersen, 2014 ).

The term “employability” has been previously defined as a set of achievements, skills and personal attributes that make a person more likely to gain employment and leading to success in their chosen career pathway ( Pegg et al., 2012 ; O’Leary, 2017 , 2019 ). While there are few studies examining perceptions of others, perceptions of one’s own employability have been recently studied in university students, jobseekers ( Atitsogbe et al., 2019 ) and currently employed workers ( Plomp et al., 2019 ; Yeves et al., 2019 ), consistently showing higher levels of perceived employability being linked to personal and job-related wellbeing as well as career success. Examining other’s perceptions of employability may be more relevant to understand factors impacting on actual employment outcomes. A majority of studies examining other’s perceptions of employability have focused on job specific skills study ( Lowden et al., 2011 ; Dhiman, 2012 ; Saad and Majid, 2014 ).

Building on this previous work, our own research has focused on the effects of unemployment by drawing on frameworks of Big Five, SCM and employability in pre-COVID19 samples ( Schofield and Butterworth, 2018b ; Schofield et al., 2019 ). Our studies consistently show that unemployed individuals receiving government payments are perceived as less employable (poorer “quality” workers and less desirable for employment) and less Conscientious. We found similar but weaker pattern related to Agreeableness, Emotional Stability, and the extent that a person is perceived as “uniquely human” ( Schofield et al., 2019 ). Further, we found that vignette characters described as currently employed but with a history of welfare receipt were indistinguishable from those described as employed and with no reference to benefit receipt ( Schofield et al., 2019 ). Findings such as this provide experimental evidence that welfare stigma is malleable and can be challenged by information inconsistent with negative stereotype ( Schofield and Butterworth, 2018b ; Schofield et al., 2019 ; see also Petersen et al., 2011 ).

The broad aim of the current study was to extend this previous work by examining the impact of COVID19 on person perceptions tied to employment and benefit recipient status. It repeats a pre-COVID19 study of an Australian general population sample in the COVID19 context, drawing on the same sampling frame, materials and study design to maximize comparability. The study design recognizes that the negative perceptions of benefit recipients may reflect a combination of difference sources of stigma: poverty, lack of work, and benefit receipt. Therefore, the original study used four different conditions to seek to differentiate these different sources: (1) Employed ; (2) Working poor ; (3) Unemployed ; and (4) Unemployed benefit recipient . Finally, for the COVID19 sample we added a novel fifth condition: (5) Unemployment benefit recipient also receiving the “Coronavirus” supplement . We except that the reference to a payment specifically applicable to the COVID19 context may lead to more favorable perceptions (more deserving) than the other unemployed and benefit receipt characters.

The study capitalizes on a major exogenous event, the COVID19 crisis, which we hypothesize will alter perceptions of deservingness by fundamentally challenging social identities and perceptions of one’s own vulnerability to unemployment. The study tests three hypotheses, and in doing so makes an important empirical and theoretical contribution to understanding how deservingness influences person perception, and understanding of the potential “real world” barriers experienced by people seeking employment in the COVID19 context.

Hypothesis 1

The pre-COVID19 assessment uses a subset of data from a pre-registered study, but this reuse of the data was not preregistered 3 . We hypothesize that, at Time 1 (pre-COVID19 assessment) we will find that employed characters will be rated more favorably than characters described as unemployed and receiving unemployment benefits, particularly on dimensions of Conscientiousness, Worker and Boss suitability. Moreover, we expect a gradient in perceptions across the four experimental conditions, from employed to working poor, to unemployed to unemployed receiving benefits and to show a similar trend for the other outcome measures included in the study.

Hypothesis 2

We hypothesize that the character in the unemployed condition(s) would be rated less negatively relative to the employed condition(s) at Time 2, compared to Time 1. We predict a two-way interaction between time and condition for the key measures (Conscientiousness, Worker and Boss suitability) and a similar trend on other outcomes.

Hypothesis 3

We expect that explicit reference to the unemployed benefit character receiving the “Coronavirus supplement” payment will increase the salience of the COVID19 context and lead to more positive ratings of this character relative to the standard unemployed benefit condition in the pre-COVID19 and COVID19 occasions.

Materials and Methods


Two general population samples (pre-COVID19 and COVID19) were recruited from the same source: The Australian Online Research Unit (ORU) panel. The ORU is an online survey platform that provides access to a cohort of members of the general public who are interested in contributing to research. The ORU randomly selects potential participants who meet study eligibility criteria, and provides the participant with an incentive for their participation. The sample for the Time 1 (pre-COVID19) occasion was part of a larger study (768 participants) collected in November 2018. From this initial dataset, we were able to use data from 260 (50.1% female, M Age = 42.1 [16.7] years, range: 18–82) participants who were presented with the one vignette scenario that we could replicate at the time of the social restrictions applicable in the COVID19 context (i.e., the vignette character was not described as going out and visiting friends, as these behaviors were illegal at Time 2). The sample for Time 2 (COVID19) was collected in May–June 2020, at the height of the lock down measures in Australia and included 670 participants (40.5% female, M Age = 51.0 [15.8] years, range: 18–85). The two samples were broadly similar (see below), though the proportion of male participants at Time 2 was greater than at Time 1.

The pre-COVID assessment at Time 1 was restricted to those participants who completed the social-distancing consistent vignette in the first place to avoid potential order/context effects. This provided, on average, 65 respondents in each of the four experimental conditions. Using the results from our previous published studies as indicators of effect size ( Schofield and Butterworth, 2018b ; Schofield et al., 2019 ). Monte Carlo simulation was used to identify the Time 2 sample size that would provide 90% power to detect an interaction effect that represented a 50% decline in the difference between the two employment and two unemployment conditions on the three-key measures at the COVID occasion relative to the pre-COVID difference. This sample size of 135 per condition also provided between 60 and 90% power to detect a difference of a similar magnitude between the employed and unemployment benefit conditions across the two measurement occasions. Given previous evidence that the differences between employed and unemployed/welfare conditions is robust and large for Conscientiousness and Worker suitability ( Schofield and Butterworth, 2018b ), the current study is also adequately powered to detect the most replicable effects of unemployment and welfare on perceptions of a person’s character (even in the absence of the hypothesized interaction effect).

Materials and Procedure

The procedures were identical on both study occasions. Participants read a brief vignette that described a fictional character, and then rated the character on measures reflecting personality dimensions, their suitability as a worker or boss, morality, warmth, and competence, and the participant’s beliefs the character should feel guilt and shame, or feel angry and disgusted. At Time 1 (pre-COVID19 context) participants then repeated this process with a second vignette, but we do not consider data from the second vignette.


The key experimental conditions were operationalized by a single sentence embedded within the vignette that was randomly allocated to different participants (employed: “S/he is currently working as a sales assistant in a large department store”; working poor: “S/he is currently working as a sales assistant, on a minimum-wage, in a large department store”; unemployed: “S/he is currently unemployed”; and receipt of unemployment benefits: “S/he is currently unemployed, and is receiving government benefits due to his/her unemployment”). The four experimental conditions were identical at both time points. At Time 2, an additional COVID19-specific condition was included (to maximize the salience of the COVID19 context): “S/he is currently unemployed and is receiving government benefits, including the Coronavirus supplement, due to his/her unemployment.”

All three study conditions will imply poverty/low income. In Australia, few minimum-wage jobs are supplemented by tips, and so a minimum-wage job indicates a level of relative poverty. A full-time worker in a minimum wage job is in the bottom quartile of income earners ( Australian Bureau of Statistics, 2017 ). Prior to the COVID19 crisis and the increase in payment level, a single person with no dependents receiving unemployment benefits received approximately 75% of the minimum-wage in cash assistance. During COVID19 and at the time of the data collection, the rate of pay exceeds the minimum-wage.

Several characteristics of the vignette character, including age and relationship status, were balanced across study participants. Age was specified as either 27 or 35 years, relationship status was either “single” or “lives with his/her partner.” The character’s gender was also varied and names were stereotypically White.

For Time 1, manipulated characteristics yielded 32 unique vignettes, comprised of four key experimental conditions (employed, working poor, unemployed, and unemployment benefits) × 2 ages × 2 genders × 2 relationship statuses. For Time 2, manipulated characteristics yielded 40 unique vignettes, comprised of five key experimental conditions (employed, working poor, unemployed, unemployment benefits, and unemployed + coronavirus supplement) × 2 ages × 2 genders × 2 relationship statuses. The vignette template construction is presented in Figure 1 including each component of the vignette that was randomly varied.

Figure 1. Outline of vignette construction in 4 parts. Bullet pointed options replace the underlined text, with gendered pronouns in each option selected to match character name.

Comprehension Checks

In both studies, participants were required to affirm consent after debriefing or had their data deleted. Participant comprehension of the vignettes was checked via three free-response comprehension questions about the character’s age and weekend activities. Participants who did not answer any questions correctly were not able to continue the study.

Outcome Measures

Personality, employability (suitability as a worker or boss), communion and agency, cognitive and emotional moral judgments, and dehumanization were included as the study outcomes. While not all personality or character dimension measures can be considered as negative or positive, higher scores were used in the study to indicate more “favorable” perceptions by the participants of the characters.


The Ten Item Personality Inventory was used to measure the Big Five ( Gosling et al., 2003 ) and adapted to other–oriented wording (i.e., “I felt like the person in the story was…”) ( Schofield et al., 2019 ). Two items measured each trait via two paired attributes. One item contained positive attributes and one contained negative attributes. Participants indicated the extent to which “I think [Name] is [attributes]” from 1 (strongly disagree) to 7 (strongly agree). The order of these 10 items was randomized. Agreeableness (α = 0.54) was assessed from “sympathetic, warm” and “critical, quarrelsome” (reversed); Extraversion (α = 0.50) was assessed from “extraverted, enthusiastic” and “reserved, quiet” (reversed); Conscientiousness (α = 0.76) was assessed from “dependable, self-disciplined” and “disorganized, careless” (reversed); Openness to experience (α = 0.36) was assessed from “open to new experiences, complex” and “conventional, uncreative” (reversed); Emotional stability (α = 0.65) was assessed from “calm, emotionally stable.” and “anxious, easily upset” (reversed). The order of these 10 items was randomized.


Single item measures: “I think [Name] would be a good worker” ( Worker suitability ) and “I think [Name] would be a good boss” ( Boss suitability ) were rated on the same scale as the personality measure. The order of these two items was randomized. Higher scores indicated better employability.

Communion and Agency

Communion and agency was assessed using Bocian et al. (2018) adaptation of Abele et al. (2016) scale that measures the fundamental dimensions of communion and agency using two-subscales for each dimension. The morality and warmth subscales are seen as measures of communion (referred to as warmth in SCM; Fiske, 2018 ); while the competence and assertiveness subscales measure agency (what Fiske refers to as competence in SCM; Fiske, 2018 ). This subscale structure has been identified in multiple samples. Participants indicated the extent to which “I think [Name] [attributes]” from 1 (not at all) to 5 (very much so). Morality (α = 0.92) was measured with six items, e.g., “is just,” “is fair”; Warmth (α = 0.96) with six items, e.g., “is caring,” “is empathetic”; Competence (α = 0.90) with five items, e.g., “is efficient,” “is capable”; and Assertiveness (α = 0.83) with six items, e.g., “is self-confident,” “stands up well under pressure.” These items were presented in a random order.


Dehumanization was measured with a composite scale of two-items drawn from Bastian et al. (2013) . Based on prior research, we measured dehumanization with two items: “I think [Name] is mechanical and cold, like a robot” and “I think [Name] lacked self-restraint, like an animal” order of these two items was randomized. We reverse coded the two items for the analyses for consistency for the other variables, so that higher scores were indicative of more favorable perceptions.

Moral Emotions

Moral emotions were measured by four items that asked about emotional responses to the character that were framed as self-condemning or other-condemning ( Haidt, 2003 ; Giner-Sorolla and Espinosa, 2011 ). Two other-condemning items asked the participant about their own emotional response to the character in the vignette (Anger: “[Name]’s behavior makes me angry”; Disgust: “I think [Name] is someone who makes me feel disgusted,” α = 0.92). The two self-condemning items asked about the character’s emotional response (Guilt: “[Name] should feel guilty about [his/her] behavior”; Shame: “I think [Name] should feel ashamed of [him/her]self”; α = 0.95). We reverse coded the two scales to ensure consistency with other variables, with higher scores indicative of more favorable perceptions.

Analytical Strategy

With the exception of the Moral emotion (and Communion and Agency) scales that are new to this study and the previously tested Openness to Experience, our previous research has demonstrated differences between the ratings of employed and unemployed characters on the included outcome measures ( Schofield and Butterworth, 2018b ; Schofield et al., 2019 ). We undertake the analysis using a four-step process. We use mixed-effects multi-level models, with the 14 outcome measures nested within participants, and predicted by fixed (between-person) terms representing the experimental “Condition,” “Time” (pre-/COVID19) and their interaction, and controlling for measure differences and allowing for random effects at the participant level: i) We initially assessed the effect of condition in the pre-COVID19 occasion to establish the baseline pattern of results; ii) we then evaluated the interaction term and, specifically, the extent to which the baseline difference observed between employment and unemployment conditions is attenuated at Time 2 (COVID19 occasion); iii) we tested the three-way interaction between condition, occasion and measure to assess whether this two-way interaction varies across the outcome measures; and if significant iv) repeated the modeling approach using separate linear regression models for each outcome measure. Our initial model contrasts the two employed (employed and working poor) and unemployed (unemployed and benefit receipt) conditions. The second model examines the four separate vignette conditions separately, differentiating between unemployed and unemployed benefit conditions. Finally, we contrast the three unemployment benefit conditions: (1) unemployment benefit recipients at Time 1; (2) unemployment benefit recipients at Time 2; and (3) unemployment benefit recipients receiving the Coronavirus payment at Time 2. For all models, we consider unadjusted and adjusted results (controlling for participant demographics). To address a potential bias from gender differences between samples, post-stratification weights were calculated for the COVID19 sample to reflecting the gender by age distribution of the pre-COVID19 sample. All models were weighted.

The two samples from Time 1 (pre-COVID19) and Time 2 (COVID19) were comparable on all demographic variables, except for gender (χ 2 [1, 923] = 7.04, p < 0.001) and employment (χ 2 [1, 910] = 27.66, p < 0.001): The gender distribution was more balanced at Time 1 with 49.8% of males, compared to 59.5% of males at Time 2. There was also a significant increase in unemployment with 20.9% of Time 1 participants out of work compared to 39.3% of the Time 2 participants. This was likely reflective of the employment rate nearly doubling in Australia during COVID19 crisis. Bivariate correlations showed significant positive correlations between all 14 outcomes ( p ’s < 0.001), except for Extraversion that was only positively correlated with Emotional Stability, boss suitability, warmth, assertiveness, and competence ( p’ s < 0.05).

Contrasting Employed and Unemployed Characters

The results, both adjusted and unadjusted, from the initial overall multilevel model using a binary indicator of whether vignette characters were employed (those in the employed or working poor conditions) or unemployed (unemployed or welfare) and testing the interaction between vignette Condition and Time (pre-COVID19 vs COVID19) are presented in the Supplementary Table S1 . The adjusted results (holding participant age, gender, employment, and education constant) indicated that the unemployed characters were rated lower than the employed characters at Time 1 ( b = −0.57). This difference in the ratings of employed and unemployed characters was reduced in the COVID19 assessment at Time 2, declining from 0.57 to 0.26, across all the outcome measures. The addition of the three-way interaction between Condition, Time and outcome measure significantly improved overall model fit, χ 2 (52) = 482.94, p < 0.001, indicating the interaction between Condition and Time varied over measures.

A series of separate regression models considering each outcome separately (see Supplementary Table S2 ) showed a significant effect of Condition (employment rated higher than unemployment) at Time 1 (pre-COVID) for all outcomes except Openness and Extraversion. The lower ratings for unemployed relative to employed characters were significantly moderated at Time 2 on the Competence, Worker and Boss suitability, and Guilt/Shame outcomes ( p’ s < 0.05).

COVID19 and Perceptions of Unemployment Benefit Recipients

The next set of analyses consider the four separate vignette conditions, differentiating between the unemployed and unemployed benefit recipient conditions. The overall mixed-effects multilevel model incorporating the four distinct vignette conditions provided evidence of significant effects for Condition and Condition by Time in both adjusted and unadjusted models. The result for the adjusted model ( Table 1 ), averaged across the various outcomes, replicated the previous finding of a difference in ratings of employed and unemployed characters at Time 1 (pre-COVID19): relative to the employed condition, there was no difference in ratings of the working poor, but the unemployed and the unemployed benefit recipient characters were rated less favorably. There was some evidence of a gradient across the unemployed characters: the average rating of the unemployed condition was higher than the unemployed benefit condition, though this difference was not statistically significant. In the presence of the interaction effect, the non-significant effect of Time shows that, averaged across all the outcome measures, there was no difference in the rating of the characters in the employed condition on the pre-COVID19 and COVID19 occasions. We tested for the effect of sociodemographic characteristics as covariates in the adjusted models (employment and benefit receipt status, education, age, and gender) but found no main effects of any of the covariates except for gender: females tended to rate characters higher ( b = 0.13, 95% CI [0.04, 0.21]) compared to males. Testing the heterogeneity of these patterns across outcomes via the inclusion of a three-way interaction between vignette condition, occasion and measure significantly improved overall model fit, χ 2 (104) = 533.40, p < 0.001, prompting analysis of each outcome separately.

Table 1. Adjusted fixed effects estimates of outcomes as a function of interactions between condition and time.

The separate linear regressions for each outcome measure ( Supplementary Table S3 ) show that ratings of unemployed benefit recipients at the Time 1 (pre-COVID19) were significantly lower than the employed characters for all outcomes except Openness and Extraversion. Statistically significant Condition by Time terms indicated that the unemployed benefit effect was moderated at Time 2 (COVID19) for the three key outcome measures identified in previous research (Conscientiousness, Worker and Boss suitability) and for the measure of Guilt and Shame. Figure 2 depicts this interaction for these four outcomes. These occurred in two profiles. For Conscientiousness, Worker and Boss suitability, COVID19 attenuated the negative perceptions of unemployed relative to employed characters, providing support for Hypothesis 2. By contrast, COVID19 has induced a new difference, such that participants thought employed characters should feel higher levels Guilt and Shame at Time 2, compared to Time 1. While the “working poor” condition was not central to the COVID19 hypotheses, we note that we found no evidence that ratings of these characters on any outcome differed from the standard employed character, or that this difference was changed in assessment at Time 2 (COVID19 occasion).

Figure 2. Interaction effect of Time (COVID19) by Condition marginal mean ratings on four outcomes: Conscientiousness, Worker suitability, Boss suitability, and Guilt and Shame (reversed).

The Impact of COVID19 on Perceptions of Unemployment Benefit-Recipients

The inclusion of the fifth COVID19-specific unemployment benefit condition did not generate more positive (or different) ratings than the standard unemployment benefit condition. Overall mixed-effects multilevel models, both adjusted and unadjusted, indicated that participants in the Coronavirus supplement condition (adjusted model: b = 0.26, 95% CI [0.06, 0.45]) and the general unemployed benefit recipient condition at Time 2 (adjusted model: b = 0.28, 95% CI [0.08, 0.48]) were both rated more favorably in comparison to unemployed benefit recipients at Time 1. There was no difference between these two Time 2 benefit recipient groups ( b = 0.03, 95% CI [−0.12, 0.19]). These results did not support hypothesis 3.

Previous research has demonstrated that people who are unemployed, and particularly those receiving unemployment benefits, are perceived more negatively and less employable than those who are employed. However, the economic shock associated with the COVID19 crisis is likely to have challenged people’s sense of their own vulnerability and risk of unemployment, and altered their perceptions of those who are unemployed and receiving government support. The broad aim of the current study was to examine the potential effect of this crisis on person perceptions tied to employment and benefit recipient status. We did this by presenting brief vignettes describing fictional characters, manipulating key experimental conditions related to employment status, and asking study participants to rate the characters’ personality and capability. We contrasted results from two cross-sectional general population samples collected before and during the COVID19 crisis.

The pre-COVID19 assessment replicated our previous findings (e.g., Schofield and Butterworth, 2018b ) showing that employed characters are perceived more favorably than those who were unemployed and receiving government benefits on measures of Conscientiousness and suitability as a worker. These findings supported Hypothesis 1. In comparison, the assessment conducted during the COVID19 crisis showed that unemployed and employed characters were viewed more similarly on these same key measures, with a significant interaction effect providing support for Hypothesis 2. Our third hypothesis, suggesting that n reference to the Coronavirus Supplement (an additional form of income support introduced during the pandemic) would enhance ratings of unemployed benefit recipients at the second assessment occasion, was not supported. We found that benefit recipients at Time 2 were rated more favorably than the benefit group at Time 1, irrespective of whether this COVID19-specific payment was referenced. This suggests the broader context in which the study was conducted was responsible for the change in perceptions.

We sampled participants from the same population, used identical experimental procedures, and found no difference over time in the ratings of employed characters on the key outcome measures of employability (Worker and Boss suitability) and Conscientiousness. The more favorable ratings of unemployed and benefit receiving characters at Time 2 is likely to reflect how the exogenous economic shock brought about by the COVID19 crisis challenged social identities and the stereotypes held of others 4 . The widespread impact and uncontrollable nature of this event are inconsistent with pre-COVID19 views that attribute ill-intent to those receiving to unemployment benefits ( Fiske et al., 2002 ; Baumberg, 2012 ; Contini and Richiardi, 2012 ; Bye et al., 2014 ). We suggest the changing context altered perceptions of the “deservingness” of people who are unemployed as unemployment in the context of COVID19 is less indicative personal failings or a result of one’s “own doing” ( Petersen et al., 2011 ; van Oorschot and Roosma, 2017 ). It is important to recognize, however, that the negative perceptions of unemployed benefit recipients were attenuated in the COVID19 assessment, but they continued to be rated less favorably than those who were employed on the key outcome measures.

In contrast to our findings on the key measures of employability and Conscientiousness, the previous and current research is less conclusive for the other outcome measures. The current study showed a broadly consistent gradient in the perception of employed and unemployed characters for all outcome measures apart from Openness and Extraversion. Findings on these other measures have been weaker and inconsistent across previous studies ( Schofield and Butterworth, 2018b ; Schofield et al., 2019 ), and the current experiment was not designed with sufficient power to demonstrate interaction effects for these measures. There was, however, one measure that showed significant divergence from the expected profile of results. A significant interaction term suggested that study participants at the Time 2 (COVID19) assessment reported that the employed characters should feel greater levels of Guilt and Shame than those who participated in the pre-COVID19 assessment. In contrast, there was consistency in the ratings of unemployed characters on this measure across the two assessment occasions. While not predicted, these results are also interpretable in the context of the pervasive job loss that accompanied the COVID19 crisis. Haller et al. (2020) , for example, argue that the highly distressing, morally difficult, and cumulative nature of COVID19 related stressors presents a perfect storm to result in a guilt and shame responses. The context of mass job losses may leave “surviving” workers feeling increasingly guilty.

The main findings of the current study are consistent with previous experimental studies that show that the stereotypes of unemployed benefit recipients are malleable ( Aarøe, 2011 ; Schofield et al., 2019 ). These previous studies, however, have demonstrated malleability by providing additional information about unemployed individuals that was inconsistent with the unemployed benefit recipient stereotype (e.g., the external causes of their unemployment). In contrast, the current study did not change how the vignette characters were presented or the experimental procedures. Rather, we assessed how the changing context in which study participants were living had altered their perceptions: suggesting the experience of COVID19 altered stereotypical views held by study participants rather than presenting information about the character that would challenge the applicability of the benefit recipient stereotype in this instance.

Perceptions and stereotypes of benefit recipients can be reinforced (and potentially generated) by government actions and policies. Structural stigma can be used as a policy tool to stigmatize benefit receipt as a strategy to reduce dependence on income support and encourage workforce participation ( Moffitt, 1983 ; Stuber and Schlesinger, 2006 ; Baumberg, 2012 ; Contini and Richiardi, 2012 ; Garthwaite, 2013 ). In the current instance, however, the Australian government acted quickly to provide greater support to Australians who lost their jobs (e.g., doubling the rate of payment, removing mandatory reporting to the welfare services) and this may have reduced the stigmatizing structural features of the income support system and contributed to the changed perceptions of benefit recipients identified in this study.


The current study took advantage of a natural experimental design and replicated a pre-COVID19 study during the COVID19 crisis. The study is limited by the relatively small sample size at Time 1, which was not designed for current purposes but part of another study. We were not able to include most of the participants from the original Time 1 study as most of the experimental conditions described activities that were illegal/inconsistent with recommend activity at the time of the COVID19 lockdown and social restriction measures. Finally, the data collection for the current study occurred very quickly after the initial and sudden COVID19 lockdowns and economic shock, which is both a strength and a limitation for the generalizability of the results. The pattern of results using the same sampling frame offers compelling support for our hypothesis that the shared economic shock and increase in unemployment attenuates stigmatizing community attitudes toward those who need to receive benefits. Our current conclusions would be further strengthened by a subsequent replication when the public health and economic crises stabilize, to test whether pre-COVID perceptions return.

The current study provides novel information about impact of the COVID19 health and economic crisis, and the impact of the corresponding policy responses on community perceptions. This novel study shows how community perceptions of employment and benefit recipient status have been altered by the COVID19 pandemic. These results add to knowledge about the determinants of welfare stigma, particularly relating to employability, highlighting societal level contextual factors.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Melbourne University Human Research Ethics Committee. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

AS led the review conceptualized by TS and PB. AS and PB conducted the analyses and wrote up the review. TS led the data collection, reviewed and edited the manuscript, and provided data management support. This manuscript is based on previous extensive work by TS and PB on stereotypes toward the unemployed and welfare benefit recipients. All authors contributed to the article and approved the submitted version.

This study was funded by the Australian Research Council (ARC) grant # DP16014178.

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.

Supplementary Material

The Supplementary Material for this article can be found online at:

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  • ^ The Australian figure includes individuals working zero hours who had “no work, not enough work available or were stood down.” The US Bureau of Labor Statistics noted that some people on temporary layoff were not classified as such and the unemployment rate could have been almost 5 percentage points higher.
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Keywords : COVID19, employability, personality, Big Five, public policy, unemployment

Citation: Suomi A, Schofield TP and Butterworth P (2020) Unemployment, Employability and COVID19: How the Global Socioeconomic Shock Challenged Negative Perceptions Toward the Less Fortunate in the Australian Context. Front. Psychol. 11:594837. doi: 10.3389/fpsyg.2020.594837

Received: 14 August 2020; Accepted: 22 September 2020; Published: 15 October 2020.

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Copyright © 2020 Suomi, Schofield and Butterworth. 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: Aino Suomi, [email protected]

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.

  • Original Article
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  • 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 ( ; 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, . .

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: .

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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Authors’ contributions

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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Hila Axelrad

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Department of Public Policy & Administration, Guilford Glazer Faculty of Business & Management, Ben-Gurion University of the Negev, Beer Sheva, Israel

Department of Economics, The Western Galilee College, Akko, Israel

Israel Luski

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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).

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  • Unemployment
  • Older workers

JEL Classification

research essay on unemployment

Unemployment Essay


Unemployment can be defined as the condition where citizens of a country are jobless and have in the recent past been involved in searching work without a success. Unemployment rate can be defined as the prevalence of unemployment opportunities in a country. The unemployment index is calculated by dividing the number of unemployed individuals by the number of individuals in the labour force (Arestis & John 15).

I have chosen the unemployment situation because of the increasing and prevalent unemployment rates in various countries.

Recent statistics indicate that, the rate of unemployment is on the increase and there is a lot of information to cover the complex area of unemployment. There are various reasons which cause unemployment in a country. The following are some of the reasons that cause unemployment rate in a country (Arestis & John 20).

  • Advances in new technologies. New technologies to a large extent replace the human labour force which renders most human beings as unemployed.
  • Population increase. The level of unemployment is believed to go up as population in a country increases. Increase in population leads to an increased pressure on the available resources. These limited resources are few and cannot accommodate the increasing demands of the population
  • National policies. Some countries have stringent national policies that favour the increase in the unemployment levels in a country. These national policies will always restrict the participation of certain gender groups in specific employment sectors. For example, some countries restricts the participation of women in many employment sectors especially the building, and construction sector.
  • Political environments also play a major role towards increasing the rate of unemployment. Political environments that increase the rate of unemployment are quite dominant especially in the developing countries. Such environments will enhance political vices such as nepotism and corruption which undermines the possibility of having fair distribution of employment opportunities to citizens.
  • Economic depression. Economic depression is a form of economic recession that is long-term which is characterized by a downturn in the various economic activities in a country.

In analyzing this complex situation of unemployment, it is of vital importance to consider the application of various system analyzing tools such as “tools, methods, methodology and many techniques”. This will make the analysis of the complex situation being analyzed to be understood easily.

In my research I will analyze the complex process of unemployment with the aim of creating a sustainable environment in the employment sector.

The research will encompass the various causes of unemployment rates, the challenges being encountered in the process of reducing unemployment rates. The research will critically analyze the various types of unemployment and, the mechanisms which can be adopted in order to reduce the prevalent rates of unemployment.

Unemployment is a complex problem facing many countries presently. The process of reducing unemployment rates can be a daunting process fraught with disappointments. As a summary of the major findings of the research, the major cause of unemployment is lack of information among citizens, and poor governance policies in a country (Arestis & John 30).

Various citizens are ignorant on how to effectively utilize the available natural and human resources to create employment. They lack a sense of creativity which could possibly create employment opportunities. Also, most citizens are ignorant on how to effectively participate in democratic governance process.

This ignorance eventually creates autocratic governance regimes which fosters nepotism and corruption. These political vices reduce the availability of employment opportunities in a country, leading to unemployment (Arestis & John 15).

One key requirement of a good system practitioner is the ability of the system practitioner to apply the various models, methods and theories of system practitioner into the real life.

My undertaking of this project will assist me to apply the various system practitioner concepts learnt in class to the real life complex scenario of unemployment (Jacques & Stephen, 1994, pg. 21). In analyzing the complexity associated with unemployment, I will consider the various theories of complex management which I will discuss along with the development of this paper.

Applying hard systems method

Before analyzing the complex process of unemployment, it is important to make a distinction between hard and soft systems. Hard systems can be defined as those problems that deal with the “how” questions. For example, the question of how to increase the rate of employment is an example of a hard problem.

A hard problem is always characterized by the fact that there is a distinct solution, and there are a number of defined goals that are well defined which should be accomplished. On the other hand, a soft system is a problem that encompasses both the “how” and “what” questions (Jacques & Stephen, 1994, pg. 27)

The complex process of unemployment could be well analyzed using the hard system method of approach. The hard system method tries to analyze a complex problem through many stages which will be discussed in this paper.

There are various advantages that are associated with the use of the hard system methodology of concept analysis. The following are some of the advantages of using the hard system method (Jacques & Stephen, 1994, pg. 35)

  • It provides a deeper understanding and analysis of the problem of unemployment and answers the question of how to mitigate the unemployment problem.
  • Hard system analysis provides answers to other complex problems related to unemployment like how to use technology to increase the rate of employment.

The following diagram indicates the application of hard system in the analysis of unemployment.

the application of hard system in the analysis of unemployment

The above diagram indicates the various processes that will be undertaken in the analysis of the unemployment problem.

Stage 1. System description

System description will always indicate the current position and status of the problem being analyzed. Currently we are experiencing a high rate of unemployment which is characterized by the increasing population rate. The world population is estimated at 7 billion people.

The available limited employment resources are not sufficient to carter for the demands of the 7 billion individuals. This eventually piles up pressure on the resources that could be used to create employment opportunities, hence leading to an increased level of unemployment (Steger, Maznevski & Wolfgang 39).

The following table illustrates the world population by continent by 2011.

Population by continent

The alarming increase in the level of unemployment creates a need for analyzing the complex concept of unemployment with the aim of unmasking the strategies to be adopted in order to reduce unemployment rates (Arestis & John 55).

The following table indicates the world top ten countries by unemployment rate

The above data directly underpins the fact that unemployment is a complex prevalent issue. If much is not done to contain the issue, then, the issue will become out of control and cause adverse effects to the limited human, and natural resources available (Arestis & John 60).

Stage 2. Identification of constraints and objectives

The main objective of this sturdy is to identify the various causes of unemployment and any relevant measure that can be adopted to mitigate the problem of unemployment. Also, the sturdy is aimed at identifying the reasons as to why there is a huge gap between the unemployment rates in developed countries and the developing countries (Steger, Maznevski & Wolfgang 40).

The major projected constrain is the political environments and government policies and ideologies governing the utilization of resources, and the creation of employment opportunities.

Stage 3. Generation of routes to objectives

  • Governments and relevant stakeholders should ensure that, there is gender equity and equality in the allocation of employment opportunities.
  • Governments should adopt various strategies that will involve the citizens in the creation of employment opportunity for self-sustainability. A self-sustenance economy should be adopted which can be achieved through promotion of innovation and creativity.
  • Removal of political and governance ideologies that promote nepotism, and corruption.
  • Creation of public awareness and increased public participation in the governance process.

Stage 4. Formulating measures for performance

Measures of performance will measure to what extent has the research objectives been met. In order to measure the performance, the unemployment index will be recorded for the next five years after the activities stipulated in the routes to objectives have been undertaken.

The unemployment index obtained will then be compared to unemployment index done before undertaking this research. The comparison will give vital information as to whether there is been an improvement in the unemployment index after the adoption of the routes to objectives (Zimmer & Jake 44).

Stage 5. Modeling

The modeling process will involve those activities that are geared towards determining the outcomes of the research (Zimmer & Jake 51). In order to identify the outcomes, a survey will be carried out after every year for the next five years to find out the rate of unemployment.

This will be calculated by dividing the number of employed individuals by the number of unemployed individuals. The index obtained will then be compared in order to determine whether there is an improvement or a decline in the unemployment rate (Arestis & John 31).

Stage 6. Evaluation

The evaluation stage is the most important stage in the analysis of the complex issue. Evaluation will involve the analysis of the outcomes obtained from the modelling stage. The evaluation will involve the analysis of the disparities that will be recorded in the research.

This will involve the sturdy of what factors are causing the disparity and how to re-align and reconfigure the process routes in order to achieve the research objectives. Evaluation process might also include the prototyping technique where the routes are tested, and retested in order to determine their viability before being fully implemented (Zimmer & Jake 71).

Stage 7. Selecting the best routes to objectives

After the evaluation process, the best route towards achieving the objectives should be selected. The route chosen should ensure that the research objectives have been met to a large extent. In the analysis of unemployment, the best route that was identified was the creation of awareness and involving the individuals in the governance process (Steger, Maznevski & Wolfgang 59).

This route will increase the level of democracy in a country hence creating equal employment opportunities for both women and men. Also, creating public awareness will ensure that citizens are well equipped with knowledge of how to effective utilize resources and create employment opportunities.

Also, public participation in the governance process will ensure that the governance policies adopted foster democracy which is a key ingredient towards reducing the rate of unemployment (Arestis & John 75).

Stage 8. Implementation of the selected routes

The implementation process will involve the process of adopting and enrolling the best selected route. In the case of unemployment, the selected route of creating awareness and increasing public participation in the governance process will be adopted.

Creating awareness will involve conducting of seminars to enlighten the public on how to effectively use the available resources, and how to create employment opportunities through innovation (Steger, Maznevski & Wolfgang 69).

Public participation in the process of policy formulation will be achieved through promotion of civic education among citizens on how to carefully vote and chose leaders with integrity.

Also, the civic education will be aimed at increasing public participation in government related projects, and governance processes starting from the grassroots government structures. Also, the civic education will aim at educating citizens on how they can get access to public funds and amenities.

Stakeholders involved

Stakeholders can be described as those people who are in one way or the other affected by the problem of unemployment. Also, stakeholders in one way or the other affect the entire problem of unemployment. Stakeholders can negatively or positively be affected by the unemployment concept.

On the other hand, stakeholders can positively or negatively influence the prevalence of unemployment concept (Jacques & Stephen, 1994, pg. 75)

The following tables indicates a summary of the how stakeholders are affected/affect the unemployment concept

Ethicality statement

As a system practitioner I fully commit myself to the various ethical guidelines that should be followed whenever undertaking any research work. I will consider the following ethical consideration I my research undertaking:

  • The data collected will be solely used for the purpose of the research, and no client data will be used for any other purposes not stipulated in the research.
  • The clients will be fully informed on the purpose of the research, and the duration the research is going to take.
  • The participation of subjects in the research will be voluntary, and out of consent. Where approval is required, then, the relevant approving bodies will be sought.

Conclusion and recommendations

In conclusion, it is evident from the research that unemployment is a complex issue that can be solved abstractly. With the increasing levels of unemployment, much has to be done in order to mitigate and reduce the rate of unemployment. This calls for public awareness, and participation in the entire process of creating employment. Such a complex issue should not be entrusted in the hands of greedy and selfish leaders.

Project log

The entire project will be spread over a period of six weeks with the first two weeks dealing with the preparation process and the last four weeks dealing with the data collection and analysis. The five weeks have been broken into three phases.

The following table indicates the project log phases.

Week 1 and week 2

During the first two weeks, I was involved in the process of consulting my colleagues and tutor to try and sought out their opinion about the topic. This gave a chance to discuss the various available methodologies that could be used in the sturdy. Also, this gave a chance to identify whether unemployment is a complex process or not.

Also, during the first week, I was able to undertake a literature review in order to determine what other researchers have done about the unemployment concept. The literature review sufficed me with relevant information about the unemployment concept.

Also, the information obtained was useful in avoiding mistakes done by previous researchers. The literature review involved researching the relevant literature materials like the internet, books, journals, and articles. I also obtained a chance of visiting various libraries in order to find out more information about the unemployment concept.

Week 3, 4 and 5

Most of the research work was conducted during the third, fourth and the fifth week. Various data was collected about the unemployment rate of individuals. Also, clients were required to fill in a survey form and a questionnaire in order to determine the causes of unemployment. Also, the subjects were required to give their individual opinions about what could be done in order to reduce the rate of unemployment.

Various data collections methods were employed in the process of data collection which includes the following methods; survey forms, questionnaires, and interviews.

Subjects were required to fill in questionnaires which sought to find out what were the causes of unemployment and what could be done to reduce the unemployment rates. Subjects were also required to fill in a survey form to determine whether the government is doing much to contain the problem of unemployment.

A series of interviews were also conducted with the aim of finding more first-hand information about the problem of unemployment. A total of three interviews were conducted during the entire period of the project. The following are the interviews that were conducted during the time of research.

The sixth week of the research was purely dedicated to data analysis and the sturdy of the findings. The data collected was analyzed and compared to previous data that was collected by other researchers on the same subject. The data analysis stage involved the application of the hard system on the complex process of unemployment. Various stages of the hard system methodology were studied in respect to unemployment.

Also, the compilation of the results obtained was done on the sixth week. This was the last week of the project undertaking, and due consultation was made to ensure that the project is up to date and with the relevant requirements.

I also, spent some time with my project supervisor in order to discuss the application of TMA in the complex process of unemployment. My supervisor advice helped a lot in the development of the project in the sense that, the information I was given largely assisted in the ensuring the realization of the research objectives.

Summary of the project log

In this section of the report, I will cover a brief summary of what I have been able to undertake during my six weeks of undertaking the project.

Undertaking the T306 course has largely helped build more on the concept that I learnt in my previous course, T205-An approach to system thinking. Managing complexity has equipped me with knowledge of how to apply various managing complexity theories, systems, and methodology in analyzing complex situations.

During the first five weeks of my project undertaking, I was extensively involved in the process of data collection and literature review. This introduced me to a number of literature and concept regarding the problem of unemployment. It also introduced me to a wide range of knowledge regarding data collection methods like interviews, questionnaires, and surveys.

The last week of the project was dedicated to data analysis. During this period of data analysis, various data analysis techniques were employed to analyze the data. This introduced me to a wide range of scientific data analysis methods of analyzing data.

Client report

The major client in the above research is the unemployed citizen or individual. They are the ones who are largely affected by the rising unemployment rate. Most of the unemployed individuals or citizens have the common ideology that it is the responsibility of government and private sectors to create employment.

Such an ideology is wrong because the process of creating employment opportunities is neither a government responsibility nor the responsibility of the private sector. It is a collective responsibility that has to be done by the collaboration of the citizens, the private sector, and the government.

There are various forms of employment that currently exists. One can be self employed which means that, they are their own employers. This form of employment fosters renovation and a spirit of entrepreneurship among citizens. This spirit eventually promotes innovation and creativity which eventually creates employment opportunities.

Also, citizens should be made to understand that, democracy plays a major role towards creation of employment opportunities. Democracy ensures accountability and transparency towards the use of resources, hence creating avenues for more employment opportunities. On the other hand, corruption, nepotism, and violence lead to misuse of resources which eventually blocks avenues for creating employment opportunities.

In order to reduce the adverse effects associated with unemployment, citizens should engage in innovation and creative activities. This will enhance the proper utilization of resources and eventually creating employment opportunities. One major desirable aspect of a good economy is the ability to be self sustainable.

A self sustainable economy will ensure that citizens have the services and products they require. One way of ensuring a self sustaining economy is by having citizens engage in innovative and creative activities. Such activities will lead to specialization, and creation of more job, and employment opportunities.

Works Cited

Arestis, Philip & McCombie, John. Unemployment: Past and Present . Basingstoke: Palgrave Macmillan, 2009. Print.

Jaques, Elliott, and Stephen, Clement. Executive Leadership: A Practical Guide to Managing Complexity . Malden, Mass: Blackwell, 1994. Print.

Steger, Ulrich & Maznevski, Martha & Wolfgang, Amann. Managing Complexity in Global Organizations . Chichester, West Sussex, England: John Wiley & Sons, 2007. Print.

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7 Things We Learned About Unemployment Insurance During the Pandemic

When COVID-19 hit the United States in March 2020, major sectors of the economy shut down. By April, 23 million U.S. workers were out of a job, the largest loss of employment since the Great Depression. [1] Black and Latinx workers faced the highest rates of unemployment, and women, especially mothers of color, were disproportionately pushed out of the labor force by a lack of childcare and shift to remote school. [2] As working people across the country demanded relief, Congress passed the Coronavirus Aid, Relief, and Economic Security (CARES) Act and additional legislation that significantly expanded eligibility for unemployment insurance (UI), increased benefit amounts, and extended the maximum duration of benefits. [3]

This brief assesses the nation’s experience with expanded unemployment insurance during the pandemic, finding that expanded programs powerfully supported families and the larger economy, and advanced racial and gender equity. At the same time, the rush of pandemic claims exposed the poor condition of states’ unemployment insurance infrastructure. Unfortunately, Congress allowed pandemic benefits to expire, leaving a broken system still in need of permanent reform.

Below are seven conclusions about unemployment insurance during the pandemic.

1. unemployment insurance is a powerful anti-poverty tool and improves household well-being..

At the height of the COVID-19 unemployment crisis, roughly one in six U.S. adults (about 40 million people) received unemployment insurance benefits. [4] According to the Census Bureau, access to unemployment insurance reduced the official poverty rate from 12.9% to 11.4%, preventing 4.7 million people, including 1.4 million children, from falling into poverty in 2020. [5]

Unemployment insurance lifted Black and Latinx workers and their families out of poverty: An estimated 1.1 million Black people and 1.2 million Latinx people avoided poverty as a result of unemployment insurance benefits. According to a different indicator of poverty, the Supplemental Poverty Measure, unemployment insurance benefits had an even greater anti-poverty impact, preventing 5.5 million people from experiencing poverty in 2020. [6]

Beyond curtailing poverty, unemployment benefits substantially reduced hardship and broadly improved the well-being of households, including recipients’ financial stability and mental health. The Bureau of Labor Statistics (BLS) compared households that received unemployment benefits at some point in the pandemic with households where a worker applied for benefits but did not receive them. The BLS found that successful unemployment benefits recipients were significantly less likely to experience food insecurity, have difficulty with household expenses, fall behind on their mortgage or rent, or report symptoms of anxiety or depression. [7] These disparities remained significant even after controlling for pre-pandemic differences in household income, education, and demographics.

2. Unemployment benefits sustain state and local economies.

The expansion of unemployment insurance benefits under the Families First Coronavirus Response Act and CARES Act sent $664.9 billion in federal funds to workers and families experiencing unemployment as of October 2021. [8] These well-targeted federal dollars were pumped into slumping state and local economies, where they boosted consumer spending and supported local businesses, preventing further layoffs.

One way to measure the positive impact of expanded unemployment benefits is to examine what happens when benefits are eliminated. A recent study of real-time anonymous banking data in states that cut off expanded unemployment benefits early finds that for every $1 of reduced benefits, household spending fell by 52 cents. [9] On average, workers whose federal benefits were cut off lost $264 net income per week and spent $145 less per week. Researchers at the Economic Policy Institute extrapolated from these trends, finding that the elimination of federal unemployment expansions caused an estimated $144.3 billion loss of income and a $79.2 billion decline in consumer spending nationwide. [10] The loss of personal income from curtailing pandemic unemployment benefits contributed to slowing national GDP growth in the third quarter of 2021, according to the U.S. Department of Commerce Bureau of Economic Analysis. [11]

At its peak in August 2020, PUA effectively doubled the reach of unemployment insurance, supporting 14.6 million workers, half of all unemployment insurance recipients.

Meanwhile, neither states nor the nation as a whole saw a dramatic resurgence in employment when expanded unemployment benefits were eliminated: for example, employment fell in Iowa after the state eliminated expanded benefits early, even as job growth picked up significantly in Nevada and New Mexico, which retained benefits. [12] When expanded unemployment benefits expired nationwide in September 2021, job growth was lackluster, [13] disappointing the expectations of many business observers who had mistakenly believed unemployment benefits played a major role in holding back job growth. [14]

3) Self-employed workers, caregivers, misclassified independent contractors, part-timers, and underpaid workers need unemployment benefits, too. Expanding eligibility made the system more equitable.

The federally funded Pandemic Unemployment Assistance (PUA) program temporarily expanded eligibility for unemployment benefits to many groups of workers who were excluded from, or who had exhausted all right to other forms of unemployment insurance. For example, PUA included self-employed workers, caregivers, workers misclassified as independent contractors, [15] part-timers, and underpaid workers. At its peak in August 2020, PUA effectively doubled the reach of unemployment insurance, supporting 14.6 million workers—half of all unemployment insurance recipients. [16] Without PUA’s expanded eligibility, millions of workers would have lacked any income support when their paychecks disappeared.

Expanded eligibility for unemployment benefits made the system more equitable by making support available to workers excluded by many state unemployment insurance laws. For example, women and people with disabilities are more likely to seek part-time work, [17] yet many states mandate that workers must be able and available to work full time to qualify for unemployment benefits. [18] As a result of occupational segregation that is fueled by structural racism and sexism, workers of color—and especially women of color—disproportionately work in low-paying positions [19] that make them ineligible for unemployment insurance in some states. [20] Black and Latinx workers are also overrepresented in app-based jobs where they are frequently misclassified as independent contractors and wrongfully excluded from unemployment benefits in many states. [21]

Policymakers in states with a higher share of Black residents are more likely to have imposed more stringent restrictions on eligibility for unemployment insurance.

The racist impact of these exclusions is worsened by the fact that policymakers in states with a higher share of Black residents are more likely to have imposed more stringent restrictions on eligibility for unemployment insurance. [22] As a result, when PUA and other federal unemployment programs expanded eligibility, they disproportionately benefitted workers in states with high Black populations. [23]

By providing unemployment benefits to workers who do not fit the model of a white, male full-time worker, PUA succeeded in supporting millions of additional jobless workers during the pandemic. Yet, now that PUA has expired, the unemployment insurance system is again shutting out broad categories of workers who need benefits.

4) It takes time to find a good job—especially during an economic crisis.

Recognizing that securing a new job during a pandemic can be a lengthy process, Congress enacted Pandemic Emergency Unemployment Compensation (PEUC), which extended unemployment support for workers who had exhausted their state benefits. As a result of several benefit extension policies, workers could ultimately qualify for up to 53 weeks of benefits. These federal extensions were greatly needed: in September 2020, more than three million workers had exhausted all of their state unemployment benefits. [24] By March 2021, 6.2 million workers depended on PEUC to get by as they searched for work. [25]

The creation of PEUC was an acknowledgement that the existing Extended Benefits program—intended to automatically provide additional weeks of jobless benefits during periods of high unemployment when it is harder to find a job—was inadequate. Extended Benefits fail to “trigger on” when unemployment rises, fail to add sufficient weeks of additional benefits, and often “trigger off” when unemployment rates remain high.

Additionally, Extended Benefits triggers often fail to account for high unemployment rates in particular regions of a state or in particular communities. Congress created the temporary PEUC program to make up for these shortcomings. Yet, the short-term fix ultimately left workers stranded: more than 3.6 million workers were still relying on PEUC when it abruptly terminated on September 6, leaving most of these workers without any further means of income support. [26]

The reality is that job searches take time, no matter what the nation’s economic conditions are. In 2019, when the pandemic had not yet hit and the economy was widely seen as strong, more than one in five unemployed workers were out of work for 27 weeks or longer. [27] Yet, most state benefits terminate after just 26 weeks—and several states have cut their budgets by providing even fewer weeks of benefits. [28] A shorter maximum duration of benefits reduces unemployed workers’ financial security and increases pressure on workers to accept jobs that do not match their skills and needs. [29]

Cuts in weeks of benefits may particularly harm workers who confront systemic discrimination in the labor market due to their race or ethnicity, gender or gender expression, age, sexual orientation, or other factors that prolong the search for suitable work. Black workers—Black men in particular—suffer longer periods of unemployment than white workers because of systemic discrimination and are more likely to exhaust state unemployment benefits. [30] Providing additional weeks of standard benefits and improving policies that extend benefits for extra weeks when jobs are scarce would give all jobless workers sufficient time to find suitable employment.

5) Regular unemployment benefit amounts aren’t sufficient for job seekers to get by.

With millions of people unable to work due to the pandemic, Congress established Federal Pandemic Unemployment Compensation (FPUC), which provided a temporary $600 federal supplement to weekly unemployment benefits (later scaled back to $300). At various points during the pandemic, federal officials instituted additional supplements. [31] FPUC alone pumped $438 billion [32] into the pockets of unemployed workers. When the $300 supplement was halted on September 6, more than 11 million workers suddenly lost this critical source of income. [33]

In 2019, when the pandemic had not yet hit and the economy was widely seen as strong, more than one in five unemployed workers were out of work for 27 weeks or longer. Yet, most state benefits terminate after just 26 weeks—and several states have cut their budgets by providing even fewer weeks of benefits.

The FPUC benefit supplement is a major reason why unemployment benefits played such an important role in reducing poverty and financial hardship and in supporting the consumer spending that sustained state and local economies. In the absence of this supplement, regular unemployment insurance benefit amounts are not sufficient for unemployed workers to make ends meet while searching for a job. In August 2021, for example, the national average unemployment benefit without FPUC was $347.86 a week—approximately $1,511 a month. [34] Meanwhile, the nation’s average fair market rent for a two-bedroom rental home was $1,295 a month, [35] illustrating the struggle that unemployed workers who lack a financial cushion face in affording food, utilities, and other necessities without falling behind on rent. And some states paid much lower benefits: Louisiana offered an average weekly benefit of only $180.67 a week, while Mississippi provided $201.22 and Tennessee paid $223.66. [36]

To make matters worse, many workers receive benefits far below the average. Because most states determine benefit amounts as a proportion of a worker’s prior wages, women of color, who are dramatically overrepresented in low-paying jobs, typically receive lower benefits. [37] Workers with dependents, single parents whose sole income supports a family, tipped workers, and part-time workers all confront significant obstacles to surviving on unemployment benefits. Black and Latinx workers disproportionately experience greater hardship when they receive low levels of benefits because they typically have less personal wealth than white households to fall back on as a result of the systematic exclusion from wealth-building opportunities over generations. [38]

Simply reinstating FPUC may not be the most effective way of addressing the inadequacy of unemployment benefit amounts. NELP and allied organizations recommend that replacement rates be raised to at least 50% of a worker’s wages, with a maximum no less than two-thirds of the state’s average weekly wage. [39] Low-paid workers, who live paycheck to paycheck, should have 85% of their wages replaced by UI benefits.  In addition, states should set a mandatory minimum benefit of at least 30% of the state’s average weekly wage or $250 per week, whichever is greater. Additional reforms, such as establishing a dependent allowance to give additional assistance to unemployed workers who are supporting children, loved ones with disabilities, or aging parents,  would also improve the adequacy of benefits.

6) States must improve their handling of unemployment claims in the future—and avoid penalizing workers for the pandemic chaos.

The COVID-19 pandemic produced an unprecedented volume of unemployment claims: in a single week in March 2020, claims shot up 1,000%, [40] and millions of newly laid-off workers confronted jammed phone lines, crashing websites, and long delays to access the benefits they were entitled to and desperately needed. [41] Overwhelmed state unemployment agencies struggled to handle not only the deluge of new claims but also the intricacies of new federal unemployment programs with standards that were constantly evolving. Working with antiquated computer systems, deficient staffing and resources, and outdated administrative practices, state agencies were ill-equipped to respond to the need.

The challenges of the pandemic exposed an unemployment insurance infrastructure in disrepair. [42] Even before COVID struck, many states failed to determine workers’ eligibility, pay benefits, or decide administrative appeals in timely manner, [43] leaving recently laid-off workers to navigate extensive backlogs and delays—including administrative hurdles that policymakers in some states deliberately erected to reduce workers’ access to benefits. [44]

Black and Latinx workers disproportionately experience greater hardship when they receive low levels of benefits because they typically have less personal wealth than white households to fall back on as a result of the systematic exclusion from wealth-building opportunities over generations.

The federal Department of Labor has recently offered state unemployment insurance agencies several rounds of grants to modernize their technology, improve administration, and enhance equity. [47] States should take advantage of the funds to better serve unemployed workers, while recognizing that without a focus on disadvantaged workers, technology upgrades alone will not be sufficient to improve access to unemployment insurance. In fact, when states such as Florida modernized their unemployment computer systems, they created new barriers for workers, delayed payments, and increased false accusations of fraud. [48] Obstacles to receiving unemployment benefits have impacted Black workers most severely, with Black applicants significantly more likely to be denied benefits (27.2%) than white applicants (21.8%) in the first nine months of the pandemic. [45] One reason for this disparity, as economist William Spriggs points out, is that implementation of pandemic unemployment benefits lagged the most in the states where Black workers disproportionately live. [46]

Workers still trying to get back on their feet should not be forced to pay more for the shortcomings of the unemployment system.

As states work to improve their systems, they must not penalize working people for errors that agencies made during the chaos of the pandemic. In many states, workers who were already harmed by payment delays are now being sent large bills for overpayments of unemployment benefits that were made with no fault on the worker’s part. [49] Months later, with any unemployment benefit payments long ago spent on necessities, workers still trying to get back on their feet should not be forced to pay more for the shortcomings of the unemployment system. Just as importantly, states should not seek to block their own economic recovery by draining funds from struggling workers and communities. And while the pandemic turmoil was extreme, bills for overpayment errors have always been a harsh burden on working people. Moving forward, states should always avoid penalizing workers when there is no evidence of fraud.

7) Workers need permanent reform, not just temporary programs with arbitrary expiration dates.

When pandemic unemployment benefits were abruptly curtailed on September 6, 2021, 11.2 million jobless workers were left stranded. [50] This includes more than 8.5 million unemployed people who saw support completely cut off, and another 2.7 million who continued to be eligible for state unemployment benefits but lost the vital $300 federal supplement.

Yet, the economy had not fully recovered and the crisis didn’t end as scheduled: the September unemployment rate of 4.8% remained substantially above the pre-pandemic rate of 3.5%. [51] And Black and Latinx workers continued to be sidelined: Black unemployment rates were still nearly twice as high as the rates for white workers, with Black men confronting especially high unemployment at 8.0%, compared to 4.2% for white men. The unemployment rate for Latinx workers (6.3%) also remained elevated. As noted above, the benefit cutoff also produced an estimated $144.3 billion loss of income nationwide and a $79.2 billion decline in consumer spending. [52]

The sudden disappearance of benefits at a time when workers still needed aid highlights the limitations of applying short-term fixes to a system in need of permanent reform.

The sudden disappearance of benefits at a time when workers still needed aid highlights the limitations of applying short-term fixes to a system in need of permanent reform. Pandemic unemployment insurance programs expanded eligibility, increased benefit amounts, and enabled workers to get additional weeks of support—greatly benefitting workers and their communities and advancing racial equity. Then these benefits were allowed to expire, leaving the same inadequate system that has failed unemployed workers for years and remains badly unprepared for the next economic downturn.

Conclusion and recommendations

Congress should learn from experience and build on the success of pandemic unemployment benefits. Lawmakers can make immediate advances by including Senator Ron Wyden’s Unemployment Insurance Improvement Act in the Build Back Better reconciliation bill. This legislation begins to address the significant ways that the unemployment insurance system disproportionately excludes Black and Latinx workers, women workers, and workers with disabilities by mandating that states provide at least 26 weeks of unemployment benefits, increasing coverage for part-time workers, and expanding eligibility by requiring states to consider a workers’ most recent earnings and standardizing earning requirements. These reforms lay the groundwork for a transformed unemployment insurance system that will enable all workers to thrive.

State lawmakers and agency officials must also act on the lessons of the pandemic: recognizing that expanded unemployment insurance greatly benefitted state residents and local economies; acting to ensure that state unemployment laws and policies exclude as few jobless workers as possible; guaranteeing workers sufficient weeks of support to find a new job; and raising benefit levels to an amount that enables workers and their families to survive. States must also take advantage of federal funding that will enable them to improve their handling of unemployment claims to better serve workers.

The expansion and enhancement of unemployment insurance during the pandemic temporarily improved a broken system. Policymakers must learn from the experience to build a more equitable and comprehensive unemployment insurance system for the long term.

[1] Elizabeth Weber Handwerker, et. al. “Employment Recovery in the Wake of the COVID-19 Pandemic,” Monthly Labor Review , U.S. Bureau of Labor Statistics, December 2020, .

[2] “The Employment Situation: April 2020,” U.S. Bureau of Labor Statistics, May 8, 2020, ; Daniel Aaronson, Luojia Hu, and Aastha Rajan, “Did Covid-19 Disproportionately Affect Mothers’ Labor Market Activity?” Chicago Fed Letter , No. 450, Federal Reserve Bank of Chicago, January 2021, .

[3] During the pandemic, policymakers also acted to create more flexibility within unemployment insurance programs in a variety of ways, including creating incentives for states to waive the waiting week that many states impose when applying for benefits, waiving work search requirements, imposing more parameters around suitable work environments, and other COVID-19 allowances such as exceptions for caregivers. While analyzing each of these provisions is outside the scope of this report, they all increased economic security for workers.

[4] Patrick Carey, et. al., “Applying for and Receiving Unemployment Insurance Benefits During the Coronavirus Pandemic,” Monthly Labor Review , U.S. Bureau of Labor Statistics, September 2021, .

[5] Frances Chen and Em Shrider, Expanded Unemployment Insurance Benefits During Pandemic Lowered Poverty Rates Across All Racial Groups, U.S. Census Bureau,  September 14, 2021, .

[6] Liana E. Fox and Kalee Burns, The Supplemental Poverty Measure: 2020 , Current Population Reports P60-275, September 2021, .

[7] Carey, “Applying for and Receiving Unemployment Insurance Benefits.”

[8] “Families First Coronavirus Response Act and Coronavirus Aid, Relief, and Economic Security (CARES) Act Funding to States through October 23, 2021,” Office of Unemployment Insurance Employment and Training Administration

U.S. Department of Labor, October 2021, .

[9] Kyle Coombs, et. al. “Early Withdrawal of Pandemic Unemployment Insurance: Effects on Earnings, Employment and Consumption,” working paper, August 20, 2021, .

[10] Asha Banerjee and Ben Zipperer, “All Pain and No Gain,” Working Economics Blog, Economic Policy Institute, September 17, 2021, .

[11] “Gross Domestic Product, Third Quarter 2021 (Advance Estimate),” US Department of Commerce Bureau of Economic Analysis, October 28, 2021, .

[12] “State Employment and Unemployment: August 2021,” U.S. Bureau of Labor Statistics, September 17, 2021, .

[13] “The Employment Situation: September 2021,” U.S. Bureau of Labor Statistics, October 8, 2021,

[14] Charisse Jones and Michael Collins, “With Unemployment Benefits Cut Off in Many States, Americans Returned to Work, Right?” USA Today , October 4, 2021, ; Michael Hiltzik, “Unemployment Benefits Weren’t What Kept Workers Home. The Latest Crummy Jobs Report Proves It,” Los Angeles Times , October 12, 2021,

[15] For more on the costs of misclassifying app-based workers and other employees as independent contractors, see: Independent Contractor Misclassification Imposes Huge Costs on Workers and Federal and State Treasuries, National Employment Law Project, October 26, 2020, .

[16] Unemployment Insurance Weekly Claims, Office of Unemployment Insurance Employment and Training Administration U.S. Department of Labor, September 10, 2020, .

[17] U.S. Bureau of Labor Statistics, Current Population Survey, 2020,

[18] Comparison of State Unemployment Insurance Laws , U.S. Department of Labor, 2020, p 5-28,

[19] “A Profile of the Working Poor,” BLS Reports, U.S. Bureau of Labor Statistics, 2020, .

[20] Comparison of State Unemployment Insurance Laws , p 3-4, 3-5.

[21] Rebecca Smith, Independent Contractors and Covid-19: Working Without Protections, National Employment Law Project, March 24, 2020, .

[22] Josh Bivens, et. al., Reforming Unemployment Insurance: Stabilizing a System in Crisis and Laying the Foundation for Equity , Economic Policy Institute, National Employment Law Project, Center for American Progress, Center for Popular Democracy, Groundwork Collaborative, National Women’s Law Center and Washington Center for Equitable Growth, 2021, p 50, .

[23] Sebastian Martinez Hickey, “New Personal Income Data Show the Need for Broad And Permanent Unemployment Insurance Reform,” Working Economics Blog , Economic Policy Institute, April 23, 2021, .

[24] Unemployment Insurance Data Dashboard , The Century Foundation, 2021, .

[25] Unemployment Insurance Weekly Claims, Office of Unemployment Insurance Employment and Training Administration U.S. Department of Labor, April 1, 2021, .

[26] Note that states were still paying backlogged PEUC claims after 9/6 and that some workers were able to transition to their state’s extended benefits program. Unemployment Insurance Weekly Claims, Office of Unemployment Insurance Employment and Training Administration U.S. Department of Labor, September 30, 2021, .

[27] U.S. Census Bureau, Current Population Survey, 2020 .

[28] Policy Basics: How Many Weeks of Unemployment Compensation Are Available?, Center on Budget and Policy Priorities, October 26, 2021, .

[29] Bivens, Reforming Unemployment Insurance , p 72.

[30] U.S. Census Bureau, Current Population Survey, 2020, .

[31] Additional supplement programs during the pandemic included the Mixed Earner Unemployment Compensation (MEUC) program, providing a $100 supplement for certain workers who had income both from W-2 employment and self-employment and who lost their livelihood during the pandemic and the Lost Wages Assistance Program (LWAP), funded by the Federal Emergency Management Agency.

[32] “Families First Coronavirus Response Act and Coronavirus Aid, Relief, and Economic Security (CARES) Act Funding to States.”

[33] Unemployment Insurance Weekly Claims, Office of Unemployment Insurance Employment and Training Administration U.S. Department of Labor, September 30, 2021, .

[34] Monthly Program and Financial Data, Employment and Training Administration, U.S. Department of Labor, 2021,

[35] Andrew Aurand, et. al., Out of Reach: The High Cost of Housing 2021 , National Low-Income Housing Coalition, 2021, .

[36] Monthly Program and Financial Data, Employment and Training Administration.

[37] Jasmine Tucker and Julie Vogtman, When Hard Work Is Not Enough: Women in Low-Paid Jobs , National Women’s Law Center, April 2020, .

[38] Amy Traub, et. al., The Asset Value of Whiteness: Understanding the Racial Wealth Gap , Demos and the Institute on Assets and Social Policy at Brandeis University, 2017, .

[39] Bivens, Reforming Unemployment Insurance , p 90-95.

[40] Unemployment Insurance Weekly Claims, Office of Unemployment Insurance Employment and Training Administration U.S. Department of Labor, March 26, 2020, .

[41] Megan Cassella and Katy Murphy, “States Overwhelmed by Previously Unimaginable Layoff Numbers,” Politico , April 1, 2020, .

[42] For further detail, see: Rebecca Dixon, From Disrepair to Transformation: How to Revive Unemployment Insurance Information Technology and Infrastructure , National Employment Law Project, July 15, 2020, .

[43] Unemployment Insurance Core Measures, Office of Unemployment Insurance Employment and Training Administration U.S. Department of Labor, ; Julia Simon-Mishel et. al., Centering Workers: How to Modernize Unemployment Insurance Technology , The Century Foundation, the National Employment Law Project and Philadelphia Legal Assistance, September 17, 2020, .

[44] George Wentworth, Closing Doors on the Unemployed: Why Most Jobless Workers Are Not Receiving Unemployment Insurance and What States Can Do About It , National Employment Law Project, December 19, 2017, .

[45] Carey, “Applying for and Receiving Unemployment Insurance Benefits.”

[46] William E. Spriggs, “The Unemployment Pandemic: Addressing America’s Jobs Crisis,” Testimony before the US House of Representatives Select Subcommittee on the Coronavirus Crisis, June 18, 2020, .

[47] U.S. Department of Labor Employment and Training Agency Grants,

[48] George Wentworth and Claire McKenna, Ain’t No Sunshine: Fewer Than One in Eight Unemployed Workers in Florida Is Receiving Unemployment Insurance , National Employment Law Project, September 21, 2015, . For more on how states can modernize their unemployment insurance systems in a way that centers the needs of workers, see Simon-Mishel, Centering Workers .

[49] See for example: “States Can Forgive Unemployment Overpayments, But Missouri Demands People Pay Up,” National Public Radio , February 15, 2021, ; “Idaho Residents Asked to ‘Pay Back’ Unemployment Relief Funds,” CBS2 Idaho News , October 1st 2021, ; “DUA bid to recoup overpayments should be DOA,” Lowell Sun, October 24, 2021, .

[50] Unemployment Insurance Weekly Claims, Office of Unemployment Insurance Employment and Training Administration U.S. Department of Labor, September 30, 2021, .

[51] “The Employment Situation: September 2021,” U.S. Bureau of Labor Statistics, October 8, 2021, .

[52] Banerjee, “All Pain and No Gain.”

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137 Unemployment Essay Topics

🏆 best essay topics on unemployment, 🔎 easy unemployment research paper topics, 👍 good unemployment essay topics to write about, 🎓 most interesting unemployment research titles, 💡 simple unemployment essay ideas, ❓ unemployment research questions.

  • Causes of Youth Unemployment
  • The Impact of Unemployment on Crime Rates
  • Artificial Intelligence and Unemployment
  • Economics: Unemployment, Its Causes and Types
  • Unemployment Rates Among Young College Graduates
  • Unemployment, Its Types and Government Intervention
  • Natural Rate of Unemployment
  • Unemployment’ Nature and Possible Causes Unemployment rate refers to the percentage of people within the available labour force who do have jobs and are actively looking for one. Unemployment rates cannot be reduced to zero.
  • Social Problems and Policy: Youth Unemployment and Mental Health In the history of the US, the federal and state governments have been at the forefront to facilitate effective social programs.
  • “Unemployment and Terrorism” TED Talk by Mohamed Ali In this TED talk, Mohamed Ali explores the relationship between unemployment and terrorism. Ali incorporates stories from his native country to support his arguments.
  • AI Development, Unemployment, and Universal Basic Income The theme of AI-human relationships takes an important place in science fiction literature, movies, and video games, but it is not limited by them.
  • Social and Economic Aspects of Unemployment in the UAE Despite the UAE having the lowest level of unemployment in the world, the number of foreign workers exceeds its native employees.
  • Homelessness Due to Unemployment During COVID-19 This paper is a research on how unemployment resulting from the Covid-19 pandemic has left many homeless in the United States.
  • Building a Business to Address Youth Unemployment An opportunity to build a business based on the youth unemployment problem has both strengths and weaknesses, also opportunities for further development.
  • Domestic Violence in Melbourne: Impact of Unemployment Due to Pandemic Restrictions The purpose of this paper is to analyze to what extent does unemployment due to pandemic restrictions impact domestic violence against women in Melbourne.
  • Unemployment and Its Macroeconomic Implications In the process of learning about macroeconomic trends, one obtains an opportunity to expand their knowledge about particular factors and their outcomes for the economy.
  • Counter-Terrorism and Unemployment Approaches A more novel approach to unemployment that considers the needs of a disenchanted youth is vital to reducing the draw towards terrorist activities.
  • The Long-Term Unemployment Positive Tendency The article argues that the level of long-term unemployment has fallen significantly compared to the previous years, reaching the lowest point in 9 years.
  • The Unemployment and Inflation Causes in Australia The change in the Australian 2021 indicator of unemployment is the representation of cyclical unemployment since it lasted less than a year.
  • Why the Unemployment Rate Needs Fixing in the US The article Latest Jobs Report Shows Why the Unemployment Rate Needs Fixing regards the issue of the inadequateness of the currently established formal indicator in the US.
  • Unemployment After the US Industrial Revolution Since the commencement of the industrial revolution, the process of automation, or more broadly the replacement of human employees by machines, has piqued widespread interest.
  • Unemployment and Political Regime Unemployment should be considered one of the critical factors influencing the economy of states and political stability. This paper discusses unemployment and political regime.
  • The Hispanic Unemployment Issue in the US A Hispanic person in the US is more likely to be unemployed than an average American. People of color have historically been one of the most discriminated groups.
  • The US Fiscal Policy and Unemployment Rate The problem to be discussed will be centered around the relationship between fiscal policies in regard to the unemployment rate in the United States.
  • Unemployment Rates in the State of Georgia In this essay, the author will present the current unemployment statistics and job outlook in the state of Georgia.
  • The High Unemployment Rate as a Most Serious Threat to Americans Although the United States has one of the highest economic indicators globally, thousands of Americans are unemployed across the country.
  • Unemployment Rates in the United States due to COVID-19 The increase in unemployment in the United States is associated with the country’s epidemiological situation and the tightening of quarantine measures taken by states.
  • Unemployment Rate During COVID-19 COVID-19 and subsequent lockdown measures significantly affected the civilian labor force participation and unemployment rates.
  • An Article Review: “Metropolitan Area Employment and Unemployment” The U.S. Bureau of Labor Statistics published a short article that reports the results of the analysis of the changes in the “nonfarm payroll employment” in metropolitan areas.
  • The Effects of the Minimum Wage on Overall Unemployment The raised minimum wage would create more jobs for low-wage workers, as this rise would prompt the goods and services demand of such workers who would now be able to afford more.
  • Unemployment Rates in the United States Unemployment is unevenly distributed across the US population, with regards to race, age, gender, and education.
  • Inflation and Unemployment in Bavaria Considering the normal state of the economy and the existing level of employment close to full, the President of Bavaria is not recommended to pursue an expansionary fiscal policy.
  • Economic Issues: Factors of Production Growth and Unemployment Rates Assessing the factors such as the rates of production growth, the selected financial systems, and the rates of unemployment is essential for determining the threat to the state economy.
  • Federal Poverty, Welfare, and Unemployment Policies In the paper, the federal policies regarding the above mentioned areas of public interest will be scrutinized and discussed at length.
  • Unemployment: Types And Factors Unemployment is one of the greatest social evils in our society today. This is because of the unfriendly impacts it has on the economy.
  • Unemployment and Rosenberg’s Self-Esteem Scale The concept of self-esteem is derived from self–theory. A basic assumption of self-theory is the need to appreciate oneself and be appreciated by others.
  • “Unemployment Checks: Keep ‘Em Coming” by Owens and Stettner: Article Review In the article, Owens and Stettner underline that current unemployment affects both the economy and employers, the government financial resources, and the jobless population.
  • Youth Unemployment in the United Kingdom Over the years, there have been remarkable unemployment rates among the youths all across the globe as compared to the age brackets that are regarded as adults.
  • Economics for Management. Unemployment in Spain Spain has the potential to reduce the unemployment rate, especially since it has already decreased significantly from 2016.
  • The Relationship Between Unemployment and Economic Growth Among the factors that define economic growth and development, human resources and unemployment are considered to be the most vital.
  • Unemployment Rates in the US The state of the American economy is getting closer to full employment, whereas the unemployment rates (as of 2017) remain to be approximately 4.4%.
  • Frictional Unemployment and Hyperinflation Frictional unemployment is also known as voluntary unemployment. It cannot be eliminated from the economy. There are some economic benefits associated with it.
  • Offshoring, Risks, and Unemployment The offshoring is fairly simple, yet this phenomenon has affected a range of companies across the globe, making it possible to enhance the quality of end products and services.
  • Youth Unemployment Rates in Canadian Society The problem under investigation is the fact that the unemployment rate among people in the 18-25 age group is higher than any other age group in Canadian society.
  • 2008 Great Recession, Unemployment and Stagnation This paper is looking into the case of the financial crisis, which results in an economic recession and the further sustained effects.
  • Unemployment and the Labour Market in Australia The paper studies forces of supply and demand in the Australian labor market, the labor force participation rate and the trends in labour force participation of older workers.
  • Reduced Unemployment in the UK In order to understand why there has been a decline in unemployment rate in the UK, it is essential to understand the reasons affecting UK unemployment.
  • Earnings-Related Unemployment Security, Employment and Lifetime Income
  • Employment, Unemployment and Real Economic Growth
  • Business Cycles and Compositional Variation in US Unemployment
  • Crime, Earnings Inequality, and Unemployment in England and Wales
  • European Unemployment: Cause and Cure
  • Demographic and Education Effects on Unemployment in Europe: Economic Factors and Labour Market Institutions
  • Centralized Wage Bargaining and Regional Unemployment
  • Capital Shortages and Asymmetries in UK Unemployment
  • Disarmament, Unemployment, Budgets, and Inflation
  • Demography, Capital Flows, and Unemployment
  • Duty-Free Zone, Unemployment, and Welfare a Note
  • Factors Affecting the Adjustments to Unemployment
  • Capital, Wages, and Structural Unemployment
  • Earnings, Unemployment, and Housing in Britain
  • Canada’s Interwar Unemployment From 1919 Until 1939
  • Aging and the Labor Market: Age Structure, Cohort Size, and Unemployment
  • Community Unemployment and Immigrants’ Health in Montreal
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  • Correlation Between Crime and Unemployment
  • Equilibrium Labor Turnover, Firm Growth and Unemployment
  • Changing Identity: Retiring From Unemployment
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  • Embodied Technical Change and the Fluctuations of Wages and Unemployment
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  • Age Structure and the UK Unemployment Rate
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  • Broadband Infrastructure and Unemployment – Evidence for Germany
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  • Construction Industry Growth Economic Unemployment
  • Agglomeration, Job Flows, and Unemployment
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  • Economic Freedom and Unemployment in Emerging Market Economies
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  • Banking Crises, Labor Reforms, and Unemployment
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  • Demographic Evolutions and Unemployment: An Analysis of French Labour Market With Workers Generations
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  • Child Support and Involuntary Unemployment
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  • Addressing Education, Inequality, and Unemployment in Uganda
  • Economic Freedom and Unemployment Around the World
  • Dual Labor Markets, Urban Unemployment, and Multicentric Cities
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  • Employment and Unemployment Effects of Unions
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  • Economic Policy, Industrial Structure, and Unemployment in Russia’s Regions
  • Capital Stock, Unemployment and Wages in the UK and Germany
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  • Why Did Unemployment Persist Despite the New Deal?
  • Can More FDI Solve the Problem of Unemployment in the EU?
  • What Is the Current Rate of Unemployment in the UK 2022?
  • Who Can Get Unemployment Benefits in Germany?
  • What Are Relationships Between Short-Term Unemployment and Inflation?
  • Does Broadband Internet Reduce the Unemployment Rate?
  • Are Education Systems Modern as Well as Practical Enough to Eliminate Unemployment, and Thus Poverty?
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  • Why Is the Us Unemployment Rate So Low?
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  • Are Interest Rates Responsible for Unemployment in the Eighties?
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  • What Are the Solutions to Unemployment?
  • Can Google Econometrics Predict Unemployment?
  • How Far Was Unemployment the Most Important Reason for the Rise of the Nazis in Germany Between 1918 and 1933?
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  • What Is China’s Unemployment Rate?
  • What Are the Five Causes of Unemployment?
  • What Are the Main Causes of Unemployment in an Economy?
  • What City Has the Lowest Unemployment Rate?
  • Can Insider-Outsider Theories Explain the Persistence of Unemployment?

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These essay examples and topics on Unemployment were carefully selected by the StudyCorgi editorial team. They meet our highest standards in terms of grammar, punctuation, style, and fact accuracy. Please ensure you properly reference the materials if you’re using them to write your assignment.

This essay topic collection was updated on December 27, 2023 .

Essay on Unemployment Rate


The essay is focused on examining the issue of unemployment among people in various societies and countries around the world, the reasons for unemployment, types of unemployment, ways to overcome the issue of unemployment and examine the costs associated with unemployment. Unemployment is a major social issue among people in the economic market today, there are several reasons for unemployment in economics depending on the type of unemployment available, these can be directed to government, employer, supply of labor, among others. The types of unemployment can be directed to frictional unemployment, structural unemployment, cyclical unemployment, and seasonal unemployment. There is a need to solve the unemployment problem in the social economies, this can be done in several ways including enhancing political stability, promoting education, controlling the growth of population, among others. The issue of unemployment in the social economies has several costs ranging from loss of income among individuals, low standard of living among unemployed people, low production, and economic growth, among other costs.

Unemployment and reasons for unemployment

Unemployment is a major problem in various countries around the world, this is a situation whereby people do not have any work that can generate income and are actively searching for a job (Hauser & Burrows, 2018). Individuals in this situation are mostly employable and are actively searching for jobs. This can also include people who are working in jobs that are below their qualifications, experience, and satisfaction. The situation where there is a high level of unemployment will result in several economic distress and problems. The situation of unemployment hinders people to engage in productive work to facilitate development in the country. This will result in a decrease in economic productivity and development (Hauser & Burrows, 2018).

The two categories of unemployment are voluntary and involuntary unemployment, the situation where one moves out from a job to search for another is known as voluntary unemployment, and a situation where a person is laid off and now looking for a job is known as involuntary unemployment. Wolcott et al., (2020) in 2020, the coronavirus pandemic affected Singapore and the world, causing a high level of involuntary unemployment. There are several reasons for unemployment, inadequate job opportunities in a country can cause unemployment in the country. The slow economic development issue can result in unemployment, especially when the population growth is increasing.

The high growth of the population can result in unemployment challenges when the country is not developing and when fewer job opportunities are in the country. Also, people who depend on agriculture production work can become unemployed when the season is not favorable for plant growth. A decrease in industrialization and investment can account for unemployment in several countries that are not developing, the situation where there are few industries and investments are not available will have a severe influence on the employment of people in a developing country. Improper economic planning can also result in unemployment challenges, situations, where there is a gap between supply and demand of labor, which will result in unemployment.

Various types of unemployment

Hauser & Burrows, (2018) Types of unemployment can include frictional, cyclical, structural, institutional, and seasonal unemployment. These types of unemployment are based on voluntary and involuntary unemployment. Frictional unemployment is a situation where a person voluntarily moves from one job to another in a country. The time spent to get another job can be linked to unemployment, it may take some time before the person gets another job. Some people move to another location to search for a new job which can take some time before getting the desired job. When fresh graduate students’ transit to search for a job, the time spent before getting a job is also linked to frictional unemployment. The time can be very short or longer depending on the availability of the job the applicant is seeking (Hwang, 2019).

Hwang, (2019) cyclical unemployment is another type of unemployment where the decrease in production and demand for products results in a decrease in employment of new workers. The situation where a company is not getting enough purchases of goods from the market will decrease income level and ability to pay workers. This can lead to a decrease in salary, and an increase in savings. People will not be able to buy enough goods when their salary is decreased and savings are increased. The situation is cyclical in nature but can be resolved when economic growth and investment are facilitated in the country.

Structural unemployment is when there is a new change in the operation of a business, technology, and production. The situation whereby a company adapts to a new technological system or process will result in laying off workers who do not have the relevant skills for the new changes. There will be a need for retraining of staffs or workers to understand how to use new systems in the company (Wolcott et al., 2020).

Institutional unemployment occurs when there are changes in policies, institutional incentives, and interference of the free market condition in a country. The change in employment policies and strategies can result in laying off some workers in order to regulate the new policies.

Seasonal unemployment is a situation whereby workers that depend on jobs that operate in different seasons are not having jobs due to the unfavorable season or weather conditions to engage in such jobs. An example is farming, among others.

Various ways to overcome unemployment

Education is very important in solving the problem of unemployment, educating people will help increase their skills and ability to be employed and start their own businesses. Education will enable workers to advance their skills and employability level. The education system has to be improved in order to enhance the admission of students into higher studies in colleges and universities. The availability and improvement of vocational institutions can also facilitate the development of skills among students in the higher level of education. Students from these training institutions can start their own businesses after completion and reduce the unemployment rate in the country. The industrial technique can be enhanced to identify the employment needs of the country and improve labor-intensive technology in order to employ more people instead of using machines and other fully technological systems (Sari, 2019).

There is a need to design a policy to solve seasonal unemployment issues, mostly in the agriculture sector, the provision of alternative job opportunities for workers who depend on seasonal jobs will help reduce the unemployment rate during unfavorable seasons. Also, the use of multiple cropping can be encouraged in order to facilitate the production of crops favorable in any given season. The addition of varied plantations, animal husbandry, food processing, and other farming practices will enhance employment.

The use of communication and employment exchanges can be provided to enhance the dissemination of information regarding job opportunities available anytime. This will alert the public in order to enhance the employment rate and reduce frictional unemployment. There is a need to assist self-employed individuals in order to facilitate their work and production. This can be in a form of financial support, technical training, and raw material support in order to improve self-employed businesses.

The development and implementation of policies that encourage the development of more jobs in a country will help reduce unemployment. There is a need to increase development of jobs in a country and increase employment of labor in order to help them gain benefits and contribute towards societal development. There can also, be enhanced employment program facilitation, this is done by setting plans to develop infrastructure, power supply, roads, and other facilities that encourage production (Sari, 2019).

Costs associated with unemployment

There are some costs associated with unemployment which include individual and societal costs; the major cost to unemployed individuals is income lost, this is the situation where a person does not get any income due to unemployment. Unemployment affects people’s ability to earn money. The situation can hinder people’s ability to finance and take good care of themselves. The well-being of people becomes disrupted due to a lack of income to support themselves. The cost to society is a reduction in productivity through a decrease in goods and services, the people do not have money to purchase goods and services, this also affects the performance of companies and organizations in the society. The situation also causes waste of resources including labor, since the economy does not utilize these resources efficiently. The situation, when people take up jobs below their qualifications, brings about inefficient use of labor. This also results in the loss of skills and labor capital in the economy (Alrasheedy, 2019).

The situation of unemployment can affect the health of people in the country, this includes the mental and physical wellbeing of individuals in the country. There is an incidence of family and social unrest, an increase in the crime rate, and other bad practices in society. The situation of unemployment can cause disruption on confidence and self-esteem among people who are unemployed (Alrasheedy, 2019). The situation can drastically deplete the skills, motivation, and well-being of people in the country. The situation of unemployment also causes loss of tax and revenue to the government, development of the country will be slowed and hindered. The expenditure incurred by the government increases as a result of providing support to unemployed people in the country. The situation of a high rate of unemployment will bring about severe problems to individuals, the situation where the unemployment problem takes longer duration, the country resort to borrowing from other countries and becoming indebted to those countries.

Unemployment is a major social issue in many countries in the world, the situation of unemployment is a challenge to the government, employers, and individuals in the society. The types of unemployment can be directed to frictional unemployment, structural unemployment, cyclical unemployment, and seasonal unemployment. The situation of unemployment can be identified when a person is actively seeking a job but does not get any available. This can also include people who are working in jobs that are below their qualifications, experience, and satisfaction. The situation where there is a high level of unemployment will result in several economic distress and problems. The situation of unemployment hinders people to engage in productive work to facilitate development in the country. This will result in a decrease in economic productivity and development.

The two categories of unemployment are voluntary and involuntary unemployment, the situation where one moves out from a job to search for another is known as voluntary unemployment, and a situation where a person is laid off and now looking for a job is known as involuntary unemployment. There are several causes of unemployment, including population growth, slow economic development, people who depend on seasonal jobs such as agriculture production work can become unemployed. A decrease in industrialization and investment can account for unemployment, improper economic planning can also result in unemployment challenges. The situation of unemployment can result in social unrest, an increase in the crime rate, and other bad practices in society. The situation of unemployment can cause disruption on confidence and self-esteem among people who are unemployed. Unemployment can be solved when there is education, development of new jobs, investment, and support of self-employed workers.

Alrasheedy, A. (2019). The cost of unemployment in Saudi Arabia.  International Journal of Economics and Finance, 11 (11), 1-30.

Hauser, M. M., & Burrows, P. (2018).  The Economics of Unemployment Insurance  (1st ed.). London: Routledge.

Hwang, G. J. (2019). How fair are unemployment benefits? The experience of East Asia.  International Social Security Review, 72 (2), pp.49-73.

Sari, A. I. (2019). Social Entrepreneurs and Innovation for the Unemployment.  International Journal of Economics and Management, 1 (3), pp.72-79.

Wolcott, E., Ochse, M. G., Kudlyak, M., & Kouchekinia, N. A. (2020). Temporary Layoffs and Unemployment in the Pandemic.  FRBSF Economic Letter , p.34.

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