Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

  • Publications
  • Our Methods
  • Short Reads
  • Tools & Resources

Read Our Research On:

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

Jace Gentry was planning to move back from Carlsbad, New Mexico, to Louisiana in May 2020 after losing his job in the oil fields. (Paul Ratje/AFP via Getty Images)

The U.S. economy abruptly plunged into a recession roughly a year ago, as the rapid spread of the coronavirus and ensuing shutdowns and stay-at-home orders dealt a devastating blow to many businesses and industries. This put in motion a dramatic spike in unemployment between March and April of 2020, which was unprecedented in the post-World War II era – peaking at 14.8% in April (seasonally adjusted).

Unemployed adults have mixed views about their future job prospects; most say they’ve thought seriously about changing their field or occupation

The unemployment rate has come down significantly since last spring, falling to 6.3% in January 2021. But labor market disruption remains a hallmark of the COVID-19 recession .

A new Pew Research Center survey finds that about half of U.S. adults who are currently unemployed, furloughed or temporarily laid off and are looking for a job are pessimistic about their prospects for future employment, and most say they’ve seriously considered changing fields or occupations since they’ve been unemployed. Many say they’ve experienced more emotional or mental health issues during the time they’ve been out of work.

The U.S. labor force has been hit hard by the COVID-19 recession. Pew Research Center conducted this study to understand how the recession has affected employment among major demographic groups as well as the experiences and outlook of people who are unemployed.

The survey analysis is based on 715 U.S. adults who are currently unemployed, furloughed or temporarily laid off and who are currently looking for work. The data was collected as a part of a larger survey of 10,334 adults conducted Jan. 19-24, 2021. Everyone who took part is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way, nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the  ATP’s methodology .

Here are the questions used for this report, along with responses, and its methodology .

The main data source for the analysis of labor force trends is the Current Population Survey (CPS) . The CPS is the U.S. government’s official source for monthly estimates of unemployment. Read more here about the methodology used in this analysis. 

The COVID-19 outbreak has affected data collection efforts by the U.S. government in its surveys, limiting in-person data collection and affecting the response rate. It is possible that some measures of labor market activity and how they vary across demographic groups are affected by these changes in data collection. For example, in April 2020, the unemployment rate may have been as high as 19.2%, instead of the 14.4% officially reported on a nonseasonally adjusted basis, if an adjustment is made for measurement errors, per BLS reports .

References to unemployed adults include those who are unemployed, furloughed or temporarily laid off and who are currently looking for work.

The unemployment rate is the number of jobless workers actively seeking work as a share of workers either at work or actively seeking work. Seasonally adjusted figures are used for the overall trend in unemployment; unemployment by demographic groups uses nonseasonally adjusted figures.

Job losses during the pandemic have hit workers in low-wage occupations particularly hard – something that distinguishes this downturn from the Great Recession, according to a new Pew Research Center analysis of government data. From December 2019 to December 2020, the percentage decrease in employment in low-wage occupations was more than twice as great as in middle-wage occupations (-12.5% vs. -5.3%). At the same time, employment in high-wage occupations increased marginally over this period.

The Center’s survey, conducted Jan. 19-24, finds that 49% of adults who are unemployed and looking for work say they are pessimistic they will find a job in the near future: 18% are very pessimistic about this and 31% are somewhat pessimistic. A similar share (51%) are optimistic, with 15% saying they are very optimistic and 36% saying they are somewhat optimistic.

For some, that positive outlook comes with a caveat. Among those who say they’re optimistic about finding a job, a substantial minority – 37% – say they are not too or not at all confident they will find a job that pays as much and provides the same benefits they had in their last job. Among all unemployed adults, 55% say they are not confident they’ll find a job with the same income and benefits; 45% say they are somewhat or very confident this will happen.

Not only are many unemployed adults feeling discouraged about their future job prospects, two-thirds say that, since losing their jobs, they have seriously considered changing their occupation or field of work. This sentiment is shared by lower-income unemployed adults, as well as those with middle or upper incomes. (Incomes are based on 2019 earnings.) A third of unemployed adults say they have already taken steps to retool their skills by pursuing job retraining programs or educational opportunities.

Even so, most unemployed adults (70%) believe they have the education and training they need to get a job. Those with a bachelor’s degree or more education (87%) are more likely to say this than those with less education (66%).

Majorities of unemployed workers have felt more stressed, had more mental health issues since being unemployed

The psychological toll of job loss is apparent in the survey findings. Seven-in-ten unemployed adults say, as a result of being unemployed, they have felt more stressed than usual, and 56% say they have experienced more emotional or mental health issues, such as anxiety or depression. Some 53% say they have felt like they lost a piece of their identity, while 41% say they’ve had more conflicts or arguments than usual with family and friends. Overall, roughly eight-in-ten unemployed adults (81%) say they have experienced at least one of these negative consequences since they have been unemployed.

Unemployed adults with a bachelor’s degree or more education (65%) are more likely than those without a four-year college degree (54%) to say they have experienced more emotional or mental health issues than usual as a result of being unemployed. Middle- and upper-income unemployed adults (65%) are more likely than those with lower incomes (46%) to say they’ve felt like they lost a piece of their identity.

On the positive side, many see advantages to the change in their employment situation. A majority of unemployed adults (63%) say they have spent more time on hobbies or interests since being unemployed and 55% say they have enjoyed not having to work for a while.  

Rise in unemployment has been more pronounced for Hispanic workers, younger workers and those without a bachelor’s degree

In December 2020, the U.S. unemployment rate stood at 6.5% (nonseasonally adjusted), roughly 3 percentage points higher than the rate in December 2019 (3.4%), before the coronavirus outbreak. Initially, the increase in unemployment fell disproportionately on certain demographic groups, such as Hispanic women, immigrants, young adults and those with lower levels of education. Going into the economic downturn, more workers in these groups than their share of the workforce overall had jobs in industries that were most vulnerable to the economic shock that was coming.

Almost one year later, similar demographic patterns of job loss persist. Hispanic workers (both women and men), younger workers and those with less education have seen larger percentage point increases in unemployment compared with other workers.

COVID-19 recession has accentuated unemployment gaps by education and age

While men and women overall have experienced roughly equal increases in the unemployment rate, there are some gender differences within racial and ethnic groups. Among Black and Hispanic workers, men experienced a greater increase in unemployment from December 2019 to December 2020 than women. Among Asian workers, women fared worse than men.

There are also notable differences by age and education. The unemployment rate among the youngest workers (ages 16 to 24) rose by 4.2 percentage points from December 2019 to December 2020. For other age groups, the increase was closer to 3 points.

A bachelor’s degree provided some level of protection for workers during the COVID-19 recession. Unemployment increased by only 2 points for this group, while it went up by about 4 points for those with less education.

From 2019 to 2020, employment fell more sharply in low-wage jobs

Looking at the patterns of job loss by occupation underscores the disproportionate impact the pandemic has had on the financial lives of lower-income Americans . In 2020, the percentage loss in employment was greatest among low-wage occupations. The gaps were most dramatic in the early months of the recession. Employment in low-wage occupations was down 33.9% in April 2020 from April 2019. The loss was 14.1% in middle-wage occupations. Employment in high-wage occupations was 2.6% higher in April 2020 than in April 2019.

Low-wage occupations have seen the steepest job losses during the pandemic

From December 2019 to December 2020, employment in low-wage occupations decreased by 12.5%, compared with a loss of 5.3% in middle-wage occupations and an increase of 0.4% in high-wage jobs.

This pattern is in stark contrast to what happened during the Great Recession. From December 2007 to December 2009, job losses were most severe among middle-wage occupations. Employment in low- and high-wage occupations was only modestly affected during that period.

The key difference between the two recessions is that the Great Recession hit the construction and manufacturing sectors the hardest . These sectors paid much higher wages than leisure and hospitality, the industry hit hardest in the COVID-19 recession. Within low-wage occupations, job losses during the current recession have been highest among waiters and waitresses, cashiers, chefs and cooks, retail salespersons and maids and housekeeping cleaners. In a striking contrast, among low-wage occupations, seven of the 10 job categories that have let go of the most workers in the COVID-19 recession hired the most workers during the Great Recession.

Among middle-wage occupations, those leading in job losses in the COVID-19 recession have been a mixed bag: taxi drivers and chauffeurs, secretaries and administrative assistants, accountants and auditors, driver/sales workers and truck drivers, and laborers and material movers. Teachers have also lost large numbers of jobs.

Jesse Bennett, research assistant, contributed to this analysis.

Note: Here are the questions used for this report, along with responses, and its methodology .

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

Kim Parker's photo

Kim Parker is director of social trends research at Pew Research Center .

Ruth Igielnik's photo

Ruth Igielnik is a former senior researcher at Pew Research Center .

Rakesh Kochhar's photo

Rakesh Kochhar is a senior researcher at Pew Research Center .

How Americans View the Coronavirus, COVID-19 Vaccines Amid Declining Levels of Concern

Online religious services appeal to many americans, but going in person remains more popular, about a third of u.s. workers who can work from home now do so all the time, how the pandemic has affected attendance at u.s. religious services, mental health and the pandemic: what u.s. surveys have found, most popular.

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

  • Age & Generations
  • Economy & Work
  • Family & Relationships
  • Gender & LGBTQ
  • Immigration & Migration
  • International Affairs
  • Internet & Technology
  • Methodological Research
  • News Habits & Media
  • Non-U.S. Governments
  • Other Topics
  • Politics & Policy
  • Race & Ethnicity
  • Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

Copyright 2024 Pew Research Center

Terms & Conditions

Privacy Policy

Cookie Settings

Reprints, Permissions & Use Policy



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

  • Review article
  • Published: 17 May 2023

Cite this article

research title about job loss

  • Mattia Filomena   ORCID: 1 , 2  

858 Accesses

3 Altmetric

Explore all metrics

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

research title about job loss

Similar content being viewed by others

research title about job loss

Experiencing Long-Term Unemployment in Europe: A Conclusion

research title about job loss

The Effects of Unemployment on Non-monetary Job Quality in Europe: The Moderating Role of Economic Situation and Labor Market Policies

research title about job loss

The effects of unemployment assistance on unemployment exits

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.

Abebe DS, Hyggen C (2019) Moderators of unemployment and wage scarring during the transition to young adulthood: evidence from Norway. In: Hvinden B, Oreilly J, Schoyen MA, Hyggen C (eds) Negotiating early job insecurity. Elgar Publishing, Cheltenham

Google Scholar  

Adascalitei D, Morano CP (2016) Drivers and effects of labour market reforms: evidence from a novel policy compendium. IZA J Labor Policy 5(1):1–32

Article   Google Scholar  

Ahmad N (2014) State dependence in unemployment. Int J Econ Financ Issues 4(1):93

Altonji JG, Kahn LB, Speer JD (2016) Cashier or consultant? Entry labor market conditions, field of study, and career success. J Law Econ 34(1):S361–S401

Arranz JM, García-Serrano C (2003) Non-employment and subsequent wage losses. Instituto de Estudios Fiscales, No. 19-03

Arranz JM, Davia MA, García-Serrano C (2005) Labour market transitions and wage dynamics in Europe. ISER Working Paper Series, No. 2005-17

Arulampalam W (2001) Is unemployment really scarring? Effects of unemployment experiences on wages. Econ J 111(475):F585–F606

Arulampalam W, Booth AL, Taylor MP (2000) Unemployment persistence. Oxf Econ Pap 52(1):24–50

Arulampalam W, Gregg P, Gregory M (2001) Introduction: unemployment scarring. Econ J 111(475):F577–F584

Ayllón S (2013) Unemployment persistence: not only stigma but discouragement too. Appl Econ Lett 20(1):67–71

Ayllón S, Valbuena J, Plum A (2021) Youth unemployment and stigmatization over the business cycle in Europe. Oxford Bull Econ Stat 84(1):103–129

Baert S, Verhaest D (2019) Unemployment or overeducation: which is a worse signal to employers? De Econ 167(1):1–21

Baumann I (2016) The debate about the consequences of job displacement. Springer, Cham, pp 1–33

Becker, Gary. 1975. Human capital: a theoretical and empirical analysis, with special reference to education. Second Edition. National Bureau of Economic Research, Inc.

Biewen M, Steffes S (2010) Unemployment persistence: is there evidence for stigma effects? Econ Lett 106(3):188–190

Birkelund GE, Heggebø K, Rogstad J (2017) Additive or multiplicative disadvantage? The scarring effects of unemployment for ethnic minorities. Eur Sociol Rev 33(1):17–29

Borland J (2020) Scarring effects: a review of Australian and international literature. Aust J Labour Econ 23(2):173–188

Bratberg E, Nilsen OA (2000) Transitions from school to work and the early labour market experience. Oxford Bull Econ Stat 62:909–929

Brodeur A, Cook N, Heyes A (2020) Methods matter: p-hacking and publication bias in causal analysis in economics. Am Econ Rev 110(11):3634–3660

Brodeur A, Lé M, Sangnier M, Zylberberg Y (2016) Star wars: the empirics strike back. Am Econ J Appl Econ 8(1):1–32

Burda MC, Mertens A (2001) Estimating wage losses of displaced workers in Germany. Labour Econ 8(1):15–41

Burdett K (1978) A theory of employee job search and quit rates. Am Econ Rev 68(1):212–220

Böheim R, Taylor MP (2002) The search for success: do the unemployed find stable employment? Labour Econ 9(6):717–735

Cameron CA, Gelbach JB, Miller DL (2008) Bootstrap-based improvements for inference with clustered errors. Rev Econ Stat 90(3):414–427

Card D, Krueger AB (1995) Time-series minimum-wage studies: a meta-analysis. Am Econ Rev 85(2):238–243

Chamberlain G (1984) Panel data. Handb Econ 2:1247–1318

Clark AE, Lepinteur A (2019) The causes and consequences of early-adult unemployment: evidence from cohort data. J Econ Behav Organ 166:107–124

Cockx B, Picchio M (2013) Scarring effects of remaining unemployed for long-term unemployed school-leavers. J R Stat Soc A Stat Soc 176(4):951–980

Cockx B (2016) Do youths graduating in a recession incur permanent losses? IZA World Labor

Couch KA (2001) Earnings losses and unemployment of displaced workers in Germany. ILR Rev 54(3):559–572

Deelen A, de Graaf-Zijl M, van den Berge W (2018) Labour market effects of job displacement for prime-age and older workers. IZA J Labor Econ 7(1):3

Dieckhoff M (2011) The effect of unemployment on subsequent job quality in Europe: a comparative study of four countries. Acta Sociol 54(3):233–249

Doiron D, Gørgens T (2008) State dependence in youth labor market experiences, and the evaluation of policy interventions. J Econometr 145(1–2):81–97

Dorsett R, Lucchino P (2018) Young people’s labour market transitions: the role of early experiences. Labour Econ 54:29–46

Doucouliagos H (1995) Worker participation and productivity in labor-managed and participatory capitalist firms: a meta-analysis. ILR Rev 49(1):58–77

Eicher TS, Papageorgiou C, Raftery AE (2011) Default priors and predictive performance in Bayesian model averaging, with application to growth determinants. J Appl Economet 26(1):30–55

Eliason M, Storrie D (2006) Lasting or latent scars? Swedish evidence on the long-term effects of job displacement. J Law Econ 24(4):831–856

Eriksson S, Rooth D-O (2014) Do employers use unemployment as a sorting criterion when hiring? Evidence from a field experiment. Am Econ Rev 104(3):1014–1039

Fallick BC (1996) A review of the recent empirical literature on displaced workers. ILR Rev 50(1):5–16

Farber HS, Herbst CM, Silverman D, Von Watcher T (2019) Whom do employers want? The role of recent employment and unemployment status and age. J Law Econ 37(2):323–349

Farber HS, Silverman D, Von Watcher T (2016) Determinants of callbacks to job applications: an audit study. Am Econ Rev 106(5):314–318

Farber HS, Silverman D, Von Watcher T (2017) Factors determining callbacks to job applications by the unemployed: an audit study. RSF Russell Sage Found J Soc Sci 3(3):168–201

Filomena M, Picchio M (2022) Retirement and health outcomes in a meta-analytical framework. J Econ Surv (forthcoming)

Fraja De, Gianni SL, Rockey J (2021) The wounds that do not heal: the lifetime scar of youth unemployment. Economica 88(352):896–941

Gangji A, Plasman R (2008) Microeconomic analysis of unemployment persistence in Belgium. Int J Manpow 29(3):280–298

Gangji A, Plasman R (2007) The Matthew effect of unemployment: how does it affect wages in Belgium. DULBEA Working Papers 07-19.RS, ULB—Universite Libre de Bruxelles

Gangl M (2004) Welfare states and the scar effects of unemployment: a comparative analysis of the United States and West Germany. Am J Sociol 109(6):1319–1364

Gangl M (2006) Scar effects of unemployment: an assessment of institutional complementarities. Am Sociol Rev 71(6):986–1013

Gartell M (2009) Unemployment and subsequent earnings for Swedish college graduates. A study of scarring effects. Arbetsrapport 2009:2, Institute for Futures Studies

Gaure S, Røed K, Westlie L (2008) The impacts of labor market policies on job search behavior and post-unemployment job quality. IZA Discussion Papers 3802, Institute of Labor Economics (IZA)

Ghirelli C (2015) Scars of early non-employment for low educated youth: evidence and policy lessons from Belgium. IZA J Eur Labor Stud 4(1):20

Gibbons R, Katz LF (1991) Layoffs and lemons. J Law Econ 9(4):351–380

Gregg P (2001) The impact of youth unemployment on adult unemployment in the NCDS. Econ J 111(475):F626–F653

Gregg P, Tominey E (2005) The wage scar from male youth unemployment. Labour Econ 12(4):487–509

Gregory M, Jukes R (2001) Unemployment and subsequent earnings: estimating scarring among British men 1984–94. Econ J 111(475):607–625

Guvenen F, Karahan F, Ozkan S, Song J (2017) Heterogeneous scarring effects of full-year nonemployment. Am Econ Rev 107(5):369–373

Hamermesh DS (1989) What do we know about worker displacement in the US? Ind Relat J Econ Soc 28(1):51–59

Havránek T, Horvath R, Irsova Z, Rusnak M (2015) Cross-country heterogeneity in intertemporal substitution. J Int Econ 96(1):100–118

Havránek T, Stanley TD, Doucouliagos H, Bom P, Geyer-Klingeberg J, Ichiro Iwasaki W, Reed R, Rost K, van Aert RCM (2020) Reporting guidelines for meta-analysis in economics. J Econ Surv 34(3):469–475

Heckman JJ (1979) Sample selection bias as a specification error. Econometrica 47(1):153–161

Helbling LA, Sacchi S (2014) Scarring effects of early unemployment among young workers with vocational credentials in Switzerland. Emp Res Voc Educ Train 6(1):12

Heylen V (2011) Scarring, the effects of early career unemployment. In ECPR General conference, 2011/08/24–2011/08/27, University of Iceland, Reykjavik

Hämäläinen K (2003) Education and unemployment: state dependence in unemployment among young people in the 1990s. VATT Institute for Economic Research, No. 312

Jacobson LS, LaLonde RJ, Sullivan DG (1993) Earnings losses of displaced workers. Am Econ Rev 83(4):685–709

Jovanovic B (1979a) Firm-specific capital and turnover. J Polit Econ 87(6):1246–1260

Jovanovic B (1979b) Job matching and the theory of turnover. J Polit Econ 87(5, Part 1):972–990

Kahn LB (2010) The long-term labor market consequences of graduating from college in a bad economy. Labour Econ 17(2):303–316

Karhula A, Lehti H, Erola J (2017) Intergenerational scars? The long-term effects of parental unemployment during a depression. Res Finn Soc 10:87–99

Kawaguchi D, Murao T (2014) Labor-market institutions and long-term effects of youth unemployment. J Money Credit Bank 46(S2):95–116

Kletzer LG (1998) Job displacement. J Econ Perspect 12(1):115–136

Kletzer LG, Fairlie RW (2003) The long-term costs of job displacement for young adult workers. ILR Rev 56(4):682–698

Knights S, Harris MN, Loundes J (2002) Dynamic relationships in the Australian labour market: heterogeneity and state dependence. Econ Record 78(242):284–298

Kroft K, Lange F, Notowidigdo MJ (2013) Duration dependence and labor market conditions: evidence from a field experiment. Q J Econ 128(3):1123–1167

Kuchibhotla M, Orazem PF, Ravi S (2020) The scarring effects of youth joblessness in Sri Lanka. Rev Dev Econ 24(1):269–287

Lazear EP (1986) Raids and offer matching. In: Ehrenberg R (ed) Research in Labor Economics, vol 8. JAI Press, Greenwich

Lockwood B (1991) Information externalities in the labour market and the duration of unemployment. Rev Econ Stud 58(4):733–753

De Luca G, Magnus JR (2011) Bayesian model averaging and weighted-average least squares: equivariance, stability, and numerical issues. Stata Journal 11(4):518–544

Lupi C, Ordine P (2002) Unemployment scarring in high unemployment regions. Econ Bull 10(2):1–8

Magnus JR, De Luca G (2016) Weighted-average least squares (WALS): a survey. J Econ Surv 30(1):117–148

Magnus JR, Powell O, Prüfer P (2010) A comparison of two model averaging techniques with an application to growth empirics. J Econometr 154(2):139–153

Manzoni A, Mooi-Reci I (2011) Early unemployment and subsequent career complexity: a sequence-based perspective. Schmollers Jahrbuch J Appl Soc Sci Stud ZeitschrFür Wirtschaftsund Sozialwissenschaften 131(2):339–348

Mavromaras K, Sloane P, Wei Z (2015) The scarring effects of unemployment, low pay and skills under-utilization in Australia compared. Appl Econ 47(23):2413–2429

Mincer J (1974) Schooling, experience, and earnings. National Bureau of Economic Research Inc, Cambridge

Mooi-Reci I, Ganzeboom HB (2015) Unemployment scarring by gender: human capital depreciation or stigmatization? Longitudinal evidence from the Netherlands, 1980–2000. Soc Sci Res 52:642–658

Mortensen DT (1987) Job search and labor market analysis. In: Ashenfelter O, Layard R (eds) Handbook of labor economics, vol 2, Chapter 15, pp 849–919, Elsevier

Mortensen DT (1988) Wages, separations, and job tenure: on-the-job specific training or matching? J Law Econ 6(4):445–471

Mousteri V, Daly M, Delaney L (2018) The scarring effect of unemployment on psychological well-being across Europe. Soc Sci Res 72:146–169

Mroz TA, Savage TH (2006) The long-term effects of youth unemployment. J Hum Resour 41(2):259–293

Mundlak Y (1978) On the pooling of time series and cross section data. Econometrica 69–85

Möller J, Umkehrer M (2015) Are there long-term earnings scars from youth unemployment in Germany? Jahrbücher Für Nationalökon Und Stat 235(4–5):474–498

Nekoei A, Weber A (2017) Does extending unemployment benefits improve job quality? American Economic Review 107(2):527–561

Nickell S, Jones P, Quintini G (2002) A picture of job insecurity facing British men. Econ J 112(476):1–27

Nilsen ØA, Reiso KH (2014) Scarring effects of early-career unemployment. Nord Econ Policy Rev 1:13–46

Nordström Skans O (2011) Scarring effects of the first labor market experience. IZA Discussion Papers 5565, Institute of Labor Economics (IZA)

Nunley JM, Pugh A, Romero N, Alan Seals R (2017) The effects of unemployment and underemployment on employment opportunities: results from a correspondence audit of the labor market for college graduates. ILR Rev 70(3):642–669

Nüß P (2018) Duration dependence as an unemployment stigma: evidence from a field experiment in Germany. Technical report, Economics Working Paper.

Oberholzer-Gee F (2008) Nonemployment stigma as rational herding: a field experiment. J Econ Behav Organ 65(1):30–40

Omori Y (1997) Stigma effects of nonemployment. Econ Inq 35(2):394–416

Ordine P, Rose G (2015) Educational mismatch and unemployment scarring. Int J Manpower 36(5):733

Oreopoulos P, Von Watcher T, Heisz A (2012) The short- and long-term career effects of graduating in a recession. Am Econ J Appl Econ 4(1):1–29

Pastore F, Quintano C, Rocca A (2021) Some young people have all the luck! The duration dependence of the school-to-work transition in Europe. Labour Econ 70:101982

Petreski M, Mojsoska-Blazevski N, Bergolo M (2017) Labor-market scars when youth unemployment is extremely high: evidence from Macedonia. East Eur Econ 55(2):168–196

Picchio M, van Ours JC (2013) Retaining through training even for older workers. Econ Educ Rev 32(1):29–48

Picchio M (2022) Meta-analysis. In: Zimmermann KF (eds) Handbook of labor, human resources and population economics. Springer, Cham. (forthcoming)

Pissarides CA (1992) Loss of skill during unemployment and the persistence of employment shocks. Q J Econ 107(4):1371–1391

Plum A, Ayllón S (2015) Heterogeneity in unemployment state dependence. Econ Lett 136:85–87

Raaum O, Røed K (2006) Do business cycle conditions at the time of labor market entry affect future employment prospects? Rev Econ Stat 88(2):193–210

Rodriguez JS, Colston J, Wu Z, Chen Z (2020) Graduating during a recession: a literature review of the effects of recessions for college graduates. Centre for College Workforce Transitions (CCWT), University of Wisconsin

Schmillen A, Umkehrer M (2017) The scars of youth: effects of early-career unemployment on future unemployment experience. Int Labour Rev 156(3–4):465–494

Shi LP, Wang S (2021) Demand-side consequences of unemployment and horizontal skill mismatches across national contexts: an employer-based factorial survey experiment. Soc Sci Res 104:102668

Spence M (1973) Job market signaling. Q J Econ 87(3):355–374

Spivey C (2005) Time off at what price? The effects of career interruptions on earnings. ILR Rev 59(1):119–140

Stanley TD (2005) Beyond publication bias. J Econ Surv 19(3):309–345

Stanley TD (2008) Meta-regression methods for detecting and estimating empirical effects in the presence of publication selection. Oxford Bull Econ Stat 70(1):103–127

Stanley TD, Doucouliagos H (2014) Meta-regression approximations to reduce publication selection bias. Res Synth Methods 5(1):60–78

Stewart MB (2007) The interrelated dynamics of unemployment and low-wage employment. J Appl Economet 22(3):511–531

Strandh M, Winefield A, Nilsson K, Hammarström A (2014) Unemployment and mental health scarring during the life course. Eur J Pub Health 24(3):440–445

Tanzi GM (2022) Scars of youth non-employment and labour market conditions. Italian Econ J (forthcoming)

Tatsiramos K (2009) Unemployment insurance in Europe: unemployment duration and subsequent employment stability. J Eur Econ Assoc 7(6):1225–1260

Tumino A (2015) The scarring effect of unemployment from the early’90s to the great recession. ISER Working Paper Series, No. 2015-05

Ugur M (2014) Corruption’s direct effects on per-capita income growth: a meta-analysis. J Econ Surv 28(3):472–490

Verho J (2008) Scars of recession: the long-term costs of the Finnish economic crisis. Working Paper Series 2008:9, IFAU—Institute for Evaluation of Labour Market and Education Policy

Vishwanath T (1989) Job search, stigma effect, and escape rate from unemployment. J Law Econ 7(4):487–502

Von Wachter T (2020) The persistent effects of initial labor market conditions for young adults and their sources. J Econ Perspect 34(4):168–194

Vooren M, Haelermans C, Groot W, van den Brink HM (2019) The effectiveness of active labor market policies: a meta-analysis. J Econ Surv 33(1):125–149

Webb MD (2014) Reworking wild bootstrap based inference for clustered errors. Queen’s Economics Department Working Paper No. 1315, Kingston, Canada

Wooldridge JM (2005) Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity. J Appl Economet 20(1):39–54

Xue X, Cheng M, Zhang W (2021) Does education really improve health? A meta-analysis. J Econ Surv 35(1):71–105

Download references


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.

Author information

Authors and affiliations.

Department of Economics and Social Sciences, Marche Polytechnic University, Piazzale Martelli 8, 60121, Ancona, Italy

Mattia Filomena

Department of Public Economics, Masaryk University, Lipová 41a, 60200, Brno, Czech Republic

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Mattia Filomena .

Ethics declarations

Conflict of interest.

The author has no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

See Tables 5 and 6 .

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Filomena, M. Unemployment Scarring Effects: An Overview and Meta-analysis of Empirical Studies. Ital Econ J (2023).

Download citation

Received : 09 April 2022

Accepted : 15 April 2023

Published : 17 May 2023


Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Unemployment scarring effects
  • State dependence
  • Earning penalties
  • Causal effect
  • Meta-analysis

JEL Classification

  • Find a journal
  • Publish with us
  • Track your research
  • Open supplemental data
  • Reference Manager
  • Simple TEXT file

People also looked at

Original research article, job loss or income loss: how the detrimental effect of unemployment on men's life satisfaction differs by immigration status.

research title about job loss

  • MZES, University of Mannheim, Mannheim, Germany

Driven by the ongoing debate of job loss vs. income loss in understanding the detrimental effect of unemployment, this study examines how perceptions of unemployment and the resulting levels of life satisfaction differ by immigration status. Based on a countrywide longitudinal dataset in the UK, findings show that immigrant men's life satisfaction suffers more from the detrimental effect of job loss per se , whereas that of native-born men suffers more in the pecuniary respect, which is mainly driven by perceived financial strain, instead of objective income loss. By further examining the heterogeneity among immigrant men themselves, we find similar differences between recent non-EU immigrant men and the rest of the group. While job loss causes a deeper decline in life satisfaction for recent non-EU immigrant men, income loss causes a deeper decline in life satisfaction for recent EU and established immigrant men. We attribute those differences to the extent to which one's legal status in the country is vulnerable to unemployment.


Based on robust evidence drawn from the German Socio-Economic Panel Study (1990–2014), in their recent publication in Demography Leopold et al. (2017) have argued that unemployment hurts life satisfaction of immigrant men more than that of their native-born counterparts. However, explanations about why this is the case remain unclear. This is particularly because the immigrant-native gap in life satisfaction cannot be explained by commonly used mediators of the relationship between unemployment and subjective well-being (SWB hereafter), such as the differences in socio-demographic and socioeconomic characteristics, as well as cultural values between immigrants and the native-born ( Leopold et al., 2017 , p. 239). The authors thus speculate that immigrant and native-born men may perceive costs of unemployment differently. To date, however, no study has directly touched upon in which exact respects perceptions of unemployment differ by immigration status.

Existing discussion about the detrimental effect of unemployment on SWB has mainly focused on two aspects: the detachment from a workplace due to job loss and the deprivation of economic resources due to the accompanying income loss ( Björklund, 1985 ; Clark and Oswald, 1994 ; Korpi, 1997 ; Winkelmann and Winkelmann, 1998 ; Creed and Reynolds, 2001 ; Ervasti and Venetoklis, 2010 ; see also a review by McKee-Ryan et al., 2005 ). To date, much of the debate is still centered on the question “Which aspect is more hurtful to one's SWB, between job loss and income loss?” Answers to this question remain controversial, because the level of one's SWB involves complicated comparison mechanisms, so that perceptions of unemployment and the resulting levels of SWB—e.g., indicated by life satisfaction—vary from individual to individual ( Campbell et al., 1976 ; Michalos, 1985 ).

Previous studies have shown that the detrimental impact of unemployment on SWB varies with individual characteristics. Scholars have generally agreed that the psychological costs of unemployment are higher for men ( Lucas et al., 2004 ), the highly educated ( Clark and Oswald, 1994 ), those with poorer health ( Wilson and Walker, 1993 ), those with religious beliefs ( Shen and Kogan, 2019 ), as well as among the middle aged compared to the young and old ( Clark et al., 1996 ; Winkelmann and Winkelmann, 1998 ; Shields and Wailoo, 2002 ). How one perceives and feels about being unemployed is also contingent on environmental factors. For example, unemployment would be a more stressful event among those with unemployed partners compared to those with working partners ( Clark, 2003 ), those with more, as compared to with less, dependent family members ( McClelland, 2000 ), and those who are not or poorly protected by unemployment benefits ( Clark and Oswald, 1994 ). To the best of our knowledge, however, there has not yet been a study focused on how the effect of unemployment on individual SWB varies in the dimension of immigration status.

We must emphasize that we will take an exclusive focus on men in the labor force, since men's labor market participation is a relatively universal phenomenon across societies. By contrast, there is a much larger degree of heterogeneity in labor force participation and its contributions to women's SWB ( Leana and Feldman, 1991 ; Clark et al., 1996 ; Clark, 2003 ; Fahey and Smyth, 2004 ). Moreover, employment shifts do not seem to have a differentiated impact on life satisfaction of immigrant and native-born women ( Leopold et al., 2017 ). All the existing findings have made it clear that the impact of unemployment on women's SWB would require a separate investigation.

Immigration status matters for men's perceptions of unemployment, because the extent to which a man's legal residence is vulnerable to unemployment directly affects in which respect(s) and to what extent he considers unemployment detrimental. Based on the social comparison theory ( Campbell et al., 1976 ; Michalos, 1985 ), these subjective evaluations do not simply mirror one's factual status, but instead, are formed in comparison with relevant others. In this study, we therefore ask the following research question: Between job loss and income loss, which aspect of unemployment hurts life satisfaction of immigrant men more, in comparison with their native-born counterparts and among themselves, respectively?

Job Loss vs. Income Loss: Detrimental Consequences of Unemployment

Scholars have long agreed that unemployment deprives an individual of multiple needs that can only be obtained through work. The term “deprivation” has become the most well-known in Jahoda's (1982) paper, which refers to distress resulting from the deprivation of five latent functions of work during unemployment; namely, time structure, social contact, collective purpose, status, and activity. As only employment can sufficiently provide these latent functions in modern societies, unemployment would unavoidably deprive the person of self-identity in a broader social setting beyond the household, subsequently causing a decrease in SWB [see also the review by Paul and Moser (2009) ]. Similarly, Sirgy et al. (2001) have identified seven major needs related to work: health and safety needs, family needs, social needs, esteem needs, actualization needs, knowledge needs, and aesthetic needs. Job loss restricts one's possibilities to fulfill these needs, causing a decline in SWB. In addition, Fryer's (1986 , 1995) agency theory, in which individuals are considered social actors trying to reach desirable goals, and Ezzy's (1993) theory of status package, which posits employment as a channel for one to give meaning to objective social relationships, are also influential in this line of research.

Empirical evidence from this approach has generally supported that the detrimental effect of unemployment is mainly due to job loss per se , and that income loss is only of secondary importance. In their studies based on the German Socio-Economic Panel (GSOEP), Winkelmann and Winkelmann (1995 , 1998) decomposed the total well-being costs of unemployment into these two parts in fixed effect models. Their findings show that well-above 75 percent of the detrimental effect of unemployment was non-pecuniary resulting from job loss itself, while below 25 percent was due to income loss ( Winkelmann and Winkelmann, 1995 , p. 293). Also drawing data from the GSOEP, Knabe and Rätzel (2011) altered income measures by distinguishing permanent income from current income. Although the non-pecuniary costs of unemployment are reduced this way, results by and large support the importance of work in increasing life satisfaction, as the decline in life satisfaction resulting from job loss itself is still significantly larger than that due to income loss for both unemployed men and women. In addition, high costs of job loss, at the given income level, have generally been found in the United States ( Helliwell and Huang, 2011 ; Young, 2012 ), the United Kingdom ( Blanchflower and Oswald, 2004 ) and among EU citizens ( Pittau et al., 2010 ).

In contrast with job loss, the other aspect of unemployment, income loss has remained controversial in existing literature. The loss of a stable income source cuts off one's access to sufficient food, shelter, heat, and ability to pay bills, and such worsening socioeconomic conditions would reasonably impact one's SWB negatively ( McKee-Ryan et al., 2005 ). However, by merely highlighting the material costs of unemployment, early investigations seem to show a tendency to equate unemployment with income loss, so that there are policy suggestions aiming to reduce unemployment rates by cutting down unemployment benefits [see the review by Clark and Oswald (1994) ]. Those policies are driven by the assumption of monetary returns being the only incentive for people to work. Derived from this assumption, one may intuitively think that individuals do not necessarily perceive unemployment negatively, but instead, even stay unemployed voluntarily, as long as their financial needs are satisfied. Based on the British Household Panel Study, Clark and Oswald (1994) have tested this opinion, and found that despite the financial compensation, the unemployed still have much lower levels of SWB than their employed counterparts, which suggests that the detrimental effect of unemployment cannot solely be explained by objective income loss.

The recent development of the literature has deepened scholarly understanding about income loss due to unemployment, by shifting the focus from one's objective income loss to subjective perception of income loss. In their study based on the European Social Survey from 21 countries, Ervasti and Venetoklis (2010) criticize that the detrimental effect of the financial aspect of unemployment has largely been underestimated, as previous studies took only the objective measure of income loss into account. Perceived financial strain or hardship, which indicates the extent to which one is worried about his or her financial situation and feels difficult to make ends meet, plays an important role in SWB ( Ullah, 1990 ; Vinokur and van Ryn, 1993 ; McKee-Ryan et al., 2005 ). This relationship is independent from objective financial resources, as perceived financial strain has been found to be only moderately correlated with objective financial resources ( Ervasti and Venetoklis, 2010 ). When objective income loss and subjective perception of financial well-being are both included in the analysis, perceived financial strain is found to explain the negative effect of unemployment on SWB much more effectively than the objective measure ( Ullah, 1990 ). For example, financial strain is found to be the key stressor during unemployment, and one's perceptions of the current as well as future financial well-being account for 50–90 percent of psychological impact of unemployment, measured by the GHQ (General Health Questionnaire) Likert scale or other mental health problems ( Kessler et al., 1988 ; Price et al., 2002 ). In Ervasti and Venetoklis's (2010) study, the inclusion of perceived financial strain reduces the negative effect of unemployment to a level of non-significance in some European countries.

In short, existing literature about unemployment has mainly focused on the debate between job loss and income loss, with the latter being further distinguished between objective income loss and perceived financial strain. The purpose of this study is thus to clarify the relative importance between the two aspects of psychological costs of unemployment on men's life satisfaction, and more importantly, how the relative importance differs between immigrant and native-born men, as well as among immigrant men themselves.

Comparison Between Immigrant and Native-Born Men

One direct consequence of unemployment lies in the loss of economic resources to sustain a man himself and his dependents. Such a detrimental effect resulting from the loss of the major income source applies to every unemployed man, regardless of one's immigration status. However, we expect that the extent to which an adverse income change has a negative impact on life satisfaction differs between immigrant and native-born men. Immigrants are usually fully aware of difficulties of job obtainment in the host-country labor market. For example, they are often unfamiliar with labor market institutions, lack formal credentials that are recognizable in the host country, struggle to build informal ties that may lead to better jobs, and are often geographically constrained into a certain area with limited job opportunities ( Elliott, 2001 ; Aguilera and Massey, 2003 ; Kogan, 2004 , 2011 ). Thus, when unemployed, immigrants are more likely to attribute their failure in the labor market to disadvantageous circumstances associated with their immigrant status. By contrast, native-born men do not encounter many of the obstacles facing immigrants, as they are at a relatively privileged status in the socioeconomic hierarchy. This means that, when unemployed, they have fewer external reasons to draw upon to justify their income drop. As the attribution theory ( Cohn, 1978 ) posits, the more a man is able to attribute his adverse status change to external reasons, the less painful he would perceive this change to be. On the contrary, the lack of channels of externalization naturally means an increasing tendency of internalizing the cause of the status change, which subsequently increases mental stress resulting from the change. Thus, we hypothesize that, other covariates being equal:

Hypothesis 1: The adverse income change due to unemployment has a greater detrimental effect on life satisfaction of native-born men than that of immigrant men .

Moving beyond objective income loss, we take a further look at how immigrant and native-born men evaluate their own financial well-being under unemployment. The sense of financial well-being is only moderately related to the objective income status as aforementioned, and it is always in the relative sense based on comparisons with one's own past experience and social comparisons with a desirable reference group ( Shen and Kogan, 2019 ). Due to the pervasive existence of labor market segregation, the native-born, relative to their immigrant counterparts, usually possess higher-status occupations associated with higher income ( Bosanquet and Doeringer, 1973 ; Wilson and Portes, 1980 ; Angrist and Adriana, 2003 ). Using the British Labor Force Survey, Brynin and Güveli (2012) have demonstrated that due to occupational segregation, there is a significant pay gap in favor of white British workers, who are dominantly native-born, vs. ethnic workers, among whom immigration background is not uncommon. Thus, native-born and immigrant men may hold different starting points, which serve as distinctive baselines in the evaluation of their own economic situations. When unemployed, native-born men evaluate their income loss based on their relatively privileged status in the past and in comparison with their friends, neighbors, and colleagues who remain in their job positions, so as to perceive a deeper drop in their income status. On the contrary, immigrant men are, on average, socioeconomically disadvantaged even when they are employed 1 ( Kogan, 2004 , 2011 ; Brynin and Güveli, 2012 ). When out of jobs, they are likely to perceive a less strong contrast between their current economic situation and that in the past or that of their friends in similarly disadvantaged job positions. In short, other covariates being equal,

Hypothesis 2: When being unemployed, native-born men tend to perceive their financial well-being more negatively than immigrant men, which contributes to a larger decline in their life satisfaction, compared to that of their immigrant counterparts .

In terms of job loss, existing literature has implied the assumption about higher psychological costs among minority groups as compared to the mainstream population ( Shields and Wailoo, 2002 ). We apply this argument to the comparison between immigrant men and their native-born counterparts. First, immigrant men are likely to place particular importance on work, due to their expectations prior to migration and intentions to form new self-identity after migration. Pursuing economic well-being is often the strongest motive for migration, and for the majority of immigrant men, work is the only channel to achieve an economic improvement in the host society ( Bartram, 2011 ). To them, unemployment is not just income loss, but a challenge to their decision of migration. The disillusion of the expectation of improving economic well-being through work in the host society subsequently causes mental harm far more than income loss itself. Second, based on the deprivation approach, immigrant men tend to attach their needs to work more than the native-born, as work is likely to be the foremost arena where the majority of the immigrant population interact with mainstream society, particularly after schooling is completed. For an immigrant man, thus, unemployment is a major disruption of the connection with mainstream society. Very often, an immigrant man's feeling of disconnection from the host society is intertwined with that of frustration due to the disillusion of the original expectation of economic prosperity, subsequently causing a greater degree of distress and decline in life satisfaction that cannot be attributed to income loss alone. Therefore, we hypothesize that other covariates being equal,

Hypothesis 3: The negative impact of job loss on life satisfaction is greater among immigrant men as compared to native-born men .

Comparison Among Immigrant Men Themselves

Needless to say, immigrant men are by no means a homogenous group, which means that their perceptions of unemployment vary. With a focus on the distinction between job loss and income loss, in the present study we mainly discuss the heterogeneity in terms of vulnerability to job loss and income loss, respectively, among immigrant men.

Reasonably, if one's legal status in the host country is tied to employment status, job loss would deprive an immigrant of the legitimacy of residing in the host country. It is thus expected that the more vulnerable an immigrant's status in the host country is to unemployment, the more likely job loss hurts the immigrant for non-economic reasons. On the contrary, the more secure an immigrant's legal status is in the host country, the more similarly he perceives job loss to his native-born counterparts. This is because when one's legal status in the host country is less tied to employment, one can be selective about job options so as to achieve higher income. Upon job loss, therefore, an immigrant with higher socioeconomic status prior to unemployment would suffer more for economic reasons than his counterparts with less bargaining power in the labor market. We therefore hypothesize that job loss should be perceived as more hurtful by immigrant men whose legal status in the host country is more vulnerable to unemployment, whereas income loss would be more hurtful for those whose legal status in the host country is relatively secure. Namely, other covariates being equal,

Hypothesis 4a: Job loss reduces life satisfaction more for immigrant men whose legal status in the host country depends more on employment status . And,

Hypothesis 4b: Income loss reduces life satisfaction more for immigrant men whose legal status in the host country depends less on employment status .

In terms of income loss, we hypothesize that, similar to the native-born population, less vulnerable immigrant men would also suffer more from subjective financial strain than objective income loss. Thus,

Hypothesis 4c: A deeper drop in life satisfaction among immigrant men whose legal status is less vulnerable to unemployment is mainly due to perceived financial strain than objective income loss itself .

Data, Measurements, and Methods

Data used in this study were drawn from Understanding Society: the UK Household Longitudinal Study (UKHLS) (waves 1–5 2 ) between 2009 and 2015 ( University of Essex, 2015 ). The UKHLS incorporates an ethnic minority boost sample, which significantly improves heterogeneity of the immigrant sample concerning countries of origin, migration histories, and other individual characteristics ( Knies et al., 2016 ). We exclusively focused on the active male labor force, aged between 18 and 65, who are either employed, or self-employed, or unemployed but actively seeking employment. Observed individuals include 3,550 immigrant and 16,069 native-born men, with 8,456 and 46,578 individual-wave observations, respectively.

The dependent variable, life satisfaction, refers to an overall assessment of an individual's quality of life according to his or her personal judgment and criteria, and a longer-term state of contentment and well-being ( Diener, 1984 ; Amit, 2010 ). In the recent development of the SWB literature, life satisfaction has increasingly been used as the proxy of SWB. The measurement of life satisfaction came from a single question in the UKHLS: “Please choose the number which you feel best describes how dissatisfied or satisfied you are with your life overall.” Responses were captured by a seven-point scale ranging from “completely dissatisfied” to “completely satisfied.”

Main independent variables pertain to different aspects of the costs of unemployment. Income loss was measured both objectively and subjectively. In the objective measure, one's position in income distribution, based on household income per capita adjusted by the modified OECD equivalence scale, 3 was used (coded as 0 = the median 20%, 1 = lowest 20%, 2 = low-median 20%, 3 = median-high 20%, and 4 = the highest 20%). In the subjective measure, perceived financial well-being was captured by one's perceptions of the current and future financial situations. Both measures were coded in the same scale, with the perception of the current financial situation categorized as “just getting by,” “doing all right or well,” and “finding it quite difficult or very difficult,” and the perception of the future financial situation categorized as “about the same,” “better off,” and “worse off.”

Job loss was directly recoded from the “current labor force” in the questionnaire, with being unemployed coded 1 while being paid-employed or self-employed coded 0 4 . We must emphasize that the detrimental effect of unemployment was estimated on the basis of employment status change in two directions—from being employed to unemployed, and from unemployment to reemployment. The majority of existing studies have focused on either of the directions of the employment status change and are unable to address the issue of endogeneity. In terms of the status change into unemployment, individuals with lower levels of life satisfaction are those who have higher risks of being laid off ( Leopold et al., 2017 ). On the contrary, regarding the status change from unemployment to reemployment, individuals with higher levels of life satisfaction tend to be optimistic and proactive in adverse situations, so as to increase their chances of getting reemployed and landing in relatively good positions ( McArdle et al., 2007 ). This means that if self-selection drives estimation biases, it does so in opposite directions for changes from being employed to unemployed and from unemployment to reemployment. Thus, we consider estimating the employment status change in both directions an effective strategy to alleviate the challenge of endogeneity, as estimation biases in opposite directions would more or less cancel each other out at the population level.

Immigrant men were distinguished from native-born men by a dichotomous measure of the immigration status (native born coded 0, including born in England, Wales, Scotland, or Northern Ireland, and non-UK born coded 1, including all other countries). Among immigrants, we further considered the heterogeneity in their vulnerability to unemployment. We first distinguished between recent and established immigrant men. Reasonably, as newcomers, recent immigrant men have a much more vulnerable status in the host country and their self-sustainment is more likely to be tied to employment, compared to their established counterparts. Since this classification among immigrants was not directly available in the questionnaire, we adopted the conventionally used threshold of living in the host country for 10 years to define recent immigrants (duration of residence no more than 10 years, coded 1) and established immigrants (duration of residence more than 10 years, coded 0). This threshold is often used to differentiate between temporary and permanent immigrants across societies. The validity of this measurement has been demonstrated by a recent study about life satisfaction of recent immigrants in Canada ( Frank et al., 2016 ). Whereas the majority of immigrants who plan to leave their countries of residence would do so within 10 years after their first arrival, immigrants who remain in their countries of residence for more than 10 years are more likely to stay permanently ( Statistics Canada, 2006 ; Kone and Sumption, 2019 ). In a report issued by the Canadian government, established immigrants who live in the country for more than 10 years share similar collective identities with native-born Canadians, while recent immigrants who live in the country for no more than 10 years are significantly less likely to strongly agree with various Canadian identities ( Gilkinson and Sauvé, 2010 ). By utilizing the UKHLS data, we also experimented measuring the duration of residence as either a continuous variable or a categorical variable with a 5-year gap between every two groups. Findings support a significant difference between immigrants residing in the UK for no more than 10 years and those residing in the UK for more than 10 years. Other group differences are negligible. Relevant results are not shown in the paper, but are available upon request.

Among recent immigrants, second, we differentiated immigrants originating from EU countries from those from non-EU countries. During the observational period covered by this study, immigrants with European Economic Area (EEA) nationalities were entitled to the residence right, regardless of their employment status in the UK. This is not the case for non-EU immigrants, whose residence rights are strictly tied to immigration channels through which their entries to the country were initially granted. For those who came for economic, rather than family, reasons, having a job is thus crucial to remain their legal status in the UK. Therefore, we further categorized recent immigrants as recent EU 5 immigrants and recent non-EU immigrants, based on the original coding of immigrants' countries of origin in the questionnaire 6 .

Other individual characteristics that have commonly been examined as factors influencing how one feels about unemployment were controlled, including age and its quadratic form (due to a non-linear relationship shown by existing literature as aforementioned), having a religion (yes = 1; no = 0), marital status in combination with the partner's employment status (single = 0, never married = 1; having a partner who is not unemployed = 2; having an unemployed partner = 3; widowed and divorced = 4), educational qualification, physical well-being, household composition, and access to unemployment benefits. Educational qualification was measured by six dummy categories: having a degree, having other degrees, A-level, GCSE, other qualifications, and no qualification, with the group of “no qualification” used as a reference group. Physical well-being was measured by a score between 0 and 100, calculated based on a series of self-reported questions on health issues and physical activities 7 . Type of household composition included eight categories: a working couple without any child (used as a reference group), a one-person household, a lone-parent household, a senior couple (referring to couples with at least one side retired) without any child, a couple with one child, a couple with two children, a couple with three children, and others. The variable “unemployment benefits” was measured by a dichotomous measure with “getting any kind(s) of unemployment benefit(s)” coded 1 and “not getting any” coded 0 8 . Descriptive statistics are shown as the Appendix .

Analyses were carried out by using fixed-effect modeling. Subjective measures such as life satisfaction are often faced with challenges of endogeneity. For example, individuals with optimistic personalities may view the unemployment experience more positively than those who are more pessimistic. The personality difference would consequently cause a smaller estimated effect of unemployment for optimistic individuals, whereas a larger one for pessimistic individuals. Such issues would not exist in fixed-effect modeling. By estimating only within-individual variations, the fixed effect model can effectively address unobserved, individual-specific, and time-invariant disturbances. In all models about the immigrant population, standard errors were estimated by using the countries of origin as the cluster variable, with the consideration that the shape of the distribution of each independent variable may be country-specific across immigrants. The model specification is: y it = x ′ it β + ε it , where i = 1, …, n (individuals), t = 1, …, T (waves), and x ′ it β = β 0 + β 1 x it ,1 + … + β K x it , K ( Rabe-Hesketh and Skrondal, 2008 ). The dependent variable “life satisfaction” was treated as a continuous variable. In their methodological comparison, Ferrer-i-Carbonell and Frijters (2004) have shown that assuming ordinality or cardinality of SWB (such as happiness) scores did not make significant differences in estimations on the changes in satisfaction and the corresponding standard errors. Under this condition, more parsimonious estimations by using life satisfaction as a continuous variable were preferred.

Differences in Perceptions of Unemployment Between Immigrant and Native-Born Men

In Table 1 , Models 1 through 4 present fixed-effect estimations for the whole sample of men in the labor force. We first estimated the coefficient of unemployment, without controlling for any financial measures. We subsequently controlled for objective income status and subjective financial well-being, separately and together, to observe the extent to which the coefficient of unemployment can be reduced by taking into account objective and subjective measures of income loss. Covariates were controlled in all models.

Table 1 . Fixed-effect estimations on men's life satisfaction by job loss and income loss, the United Kingdom, 20019–2015.

Model 1 shows that without controlling for income loss, life satisfaction of unemployed men is 0.25 points lower than that of employed men, and this detrimental effect of unemployment on life satisfaction does not significantly differ by immigration status, as shown by the non-significant interaction term. Model 2 includes objective income status and its interaction with immigration status. Compared to Model 1, controlling for objective income status slightly reduces the detrimental effect of unemployment on life satisfaction, from 0.25 to 0.23 points. The coefficient of each income status refers to the effect of income status change, because fixed-effect modeling only estimates over-time changes occurring on each individual, namely, within-individual variations. For example, the coefficient of “bottom 20%” means that comparing to those moving to the median 20%, those who have dropped to the bottom 20% report life satisfaction by 0.107 points lower, whereas those who have moved to the upper-middle and upper tiers report life satisfaction by 0.017 and 0.029 points higher, respectively, other covariates being equal. Interaction terms show that the immigrant-native gap in life satisfaction is significant only among those who have dropped to the bottom of the income distribution, with immigrants being 0.163-point more satisfied. In other words, native-born men suffer more than immigrant men from an adverse income status change.

In Model 3, perceived financial well-being is included, which greatly reduces the detrimental effect of unemployment on life satisfaction, from 0.25 to 0.15 points. This negative impact of unemployment differs by immigration status, though with marginal significance. The impact of perceived financial well-being on life satisfaction varies between immigrant and native-born men significantly. Other covariates being equal, positive perceptions of one's financial situation boost whereas negative perceptions hinder life satisfaction, with perceptions of the current situation playing a greater role than those of the future situation. Interaction terms show that for those who hold negative perceptions of the current financial situation, immigrant men report higher life satisfaction than their native-born counterparts by 0.214 points. Namely, perceived current financial hardship hurts life satisfaction of native-born men more than that of immigrant men.

Model 4 is the full model with job loss as well as both objective and subjective measures of income loss taken into account. Other covariates being equal: unemployment reduces life satisfaction—for native-born men—by 0.14 points, and it further reduces life satisfaction of unemployed immigrant men by additional 0.12 points. When income loss is measured by both objective and subjective terms, one can see that the effects of objective income status become less salient—in terms of statistical significance and magnitudes of coefficients, compared to corresponding coefficients in Model 2. The effects of subjective financial well-being remain by and large similar to those in Model 3. While the difference in objective income loss is no longer significant between immigrant and native-born men, the subjective perception of income loss, indicated by perceiving the current situation being worse off, is still significant, with immigrant men feeling more positive than their native-born counterparts.

To summarize, findings in Models 1 through 4 show that: (1) unemployment indeed has a detrimental impact on the level of men's life satisfaction; (2) a part of the detrimental effect of unemployment is attributed to pecuniary reasons; (3) perceived financial strain or hardship plays a more important role than objective income loss in affecting unemployed men's life satisfaction; and (4) with both objective and subjective measures of income loss taken into account, jobs loss by itself hurts life satisfaction more for immigrant men than their native-born counterparts. Namely, Hypotheses 2 and 3 are supported. Hypothesis 1 is supported only when perceived financial well-being is not taken into account.

To gain a further understanding about how perceptions of unemployment and the resulting consequences on life satisfaction differ by immigration status, we subsequently ran separate models for immigrant and native-born men as presented by Table 2 . By comparing coefficients of unemployment in Models 5 through 8 and Models 9 through 12, it is clear that unemployment has a generally larger negative impact on immigrant men's life satisfaction than native-born men's. Moreover, while the inclusion of objective and subjective financial measures does not reduce the negative effect of unemployment considerably for immigrant men, it does so for native-born men. Comparing the full models (Models 8 and 12), one can see that the effect of unemployment (job loss) is much larger for immigrant than native-born men (−0.32 vs. −0.13). In terms of pecuniary costs, objective income status has no significant impact on immigrant men's life satisfaction, and has only a slight effect on life satisfaction of native-born men at the bottom 20 percent of the income hierarchy (relative to their counterparts at the middle 20 percent). Subjective income loss—one's perception of being worse off, particularly about the current situation—presents a much larger negative impact on life satisfaction among native-born men relative to immigrant men (−0.38 vs. −0.18).

Table 2 . Fixed-effect estimations on life satisfaction by job loss and income loss for immigrant and native-born men, the United Kingdom, 2009–2015.

We further calculate the composition of the detrimental effect of unemployment, based on estimations from Table 2 . As shown by Figure 1 , for immigrant men, 95 percent of the detrimental effect of unemployment is non-pecuniary, namely, due to job loss per se , and subjective financial strain explains the remaining 5 percent. For native-born men, by contrast, only 55 percent of the negative impact of unemployment is due to non-pecuniary costs, whereas 45 percent is pecuniary, in which the contribution of subjective income loss—perceived financial strain—is 4 times as large as that of objective income loss. In short, the detrimental effect of job loss is higher for immigrant men compared to native-born men. This is in contrast with the higher detrimental effect of income loss for native-born men, which is mainly due to a larger negative impact of perceived financial strain among native-born men compared to immigrant men.

Figure 1 . Composition of the detrimental effect of unemployment on life satisfaction for immigrant and native-born men. Authors' own calculations based on Table 1 . Data source: Understanding Society: the UKHLS, 2009–2015 ( University of Essex, 2015 ).

Differences in Perceptions of Unemployment Among Immigrant Men Themselves

In this section, we narrow down the analysis to the immigrant subsample. Table 3 presents the same modeling strategies used in the previous two tables with a focus on the distinction between recent and established immigrants. From Models 13 through 16, unemployment significantly reduces life satisfaction for all immigrant men in the subsample, but more so for recent immigrants, as shown by the significantly negative interaction coefficients between unemployment and the recent immigrant status. The full model (Model 16) shows that other covariates being equal: recent immigrant men are generally more satisfied with their lives than their established counterparts (coef. = 0.17). This finding is consistent with existing literature about the declining trend of life satisfaction among immigrants, as the duration of residence in the host country increases and across generations ( Safi, 2010 ; Bartram, 2011 ). However, once unemployed, recent immigrant men's life satisfaction suffers more than established immigrants' due to job loss, as the negative impact of unemployment is 0.34 points larger for recent immigrants compared to established immigrants.

Table 3 . Fixed-effect estimations on life satisfaction by job loss and income loss between recent and established immigrant men, the United Kingdom, 2009–2015.

In terms of income loss, an anticipation of future financial hardship decreases established immigrant men's life satisfaction by 0.22 points, compared to established immigrant men who foresee no financial change in the future. However, it does not seem to decrease recent immigrants' life satisfaction. More precisely, even with the perception of future financial hardship, recent immigrants' life satisfaction is still 0.06 points (= 0.28–0.22) higher than established immigrants who anticipate no financial change in the future. Due to a trivial role objective income loss plays in explaining unemployment costs among the employed immigrant men as shown by previous two tables, the difference between recent and established immigrants is negligible. Therefore, unemployed established immigrant men bear higher psychological costs of income loss than unemployed recent counterparts, due to their stronger perception of future financial strain. Meanwhile, we find strong evidence to support a significantly larger negative impact of job loss on life satisfaction for recent immigrant men, compared to established immigrant men.

In Table 4 , we further differentiate recent immigrant men by the EU status of their countries of origin and report results in comparison with those shown by Table 3 . By comparing Models 13 and 17, one can see that without controlling for measures of income loss, coefficients of unemployment are similar, and that a greater decline in life satisfaction of unemployed recent immigrant men mainly exists among those from non-EU countries. While Model 14 reports non-significant coefficients of objective income status change, Model 18 shows that when income drops to the bottom 20% of the distribution, recent EU immigrant men perceive this change more positively than their established counterparts (coef. = 0.41). Model 19 shows that higher life satisfaction of recent immigrant men with the perception of future financial hardship is mainly driven by the positive attitude held by those from non-EU countries. Perceived financial hardship hurts life satisfaction of recent EU immigrant men significantly more than that of their established counterparts, whether in terms of the current or future situation. The full model (Model 20) confirms all the above findings.

Table 4 . Fixed-effect estimations on life satisfaction by job loss and income loss among recent EU, recent non-EU and established immigrant men, the United Kingdom, 2009–2015.

Overall, findings from Tables 3 , 4 show that job loss reduces life satisfaction more for recent than established immigrant men, and this gap mainly exists between recent immigrant men from non-EU countries and their established counterparts. Perceived financial well-being plays a bigger role than objective income status change in life satisfaction of all immigrant men. As shown by the final model (Model 20), a positive perception of the current financial situation increases, whereas a negative perception of the future financial situation decreases, life satisfaction of established immigrant men. Recent immigrant men show a higher level of life satisfaction than established counterparts when perceiving future financial hardship, but this is solely driven by the pattern observed among those from non-EU countries. By contrast, recent EU immigrant men feel significantly unsatisfied, and their life satisfaction drops much further compared to that of the established counterparts, when they perceive either current or future financial hardship.

In short, our results confirm that job loss leads to higher psychological costs for those whose legal status in the host country depends more on employment status, i.e., recent non-EU immigrant men. Hypothesis 4a is supported. We also find that compared to recent non-EU immigrants, established immigrant men bear higher psychological costs of perceived income loss, indicated by the perception of future financial hardship. When comparing established immigrant men with their recent EU counterparts, one can observe significantly lower levels of life satisfaction among the latter group, particularly with perceptions of financial hardship. As shown in the descriptive statistics ( Appendix ), the majority of established immigrant men originate from non-EU countries. This means that by sharing equal rights of employment and residence with the native-born, recent EU immigrant men may possess a legal status even less vulnerable to unemployment. The comparison between established and recent EU immigrant men further confirms that when an immigrant's legal status in the host country is less contingent on employment, individuals would put more emphasis on the pecuniary aspect of work and consequently feel more stressed when perceiving financial hardship. Namely, Hypotheses 4b and 4c are supported.

Conclusions and Discussion

Existing literature about the detrimental effect of unemployment on life satisfaction has mainly been centered on the debate about the relative importance of job loss and income loss. By drawing data from a countrywide longitudinal dataset in the UK, this study provides new evidence to the debate. Moreover, this study contributes to the literature by examining to what extent men's immigration status moderates the effects of unemployment on life satisfaction.

Our findings confirm that for native-born men, both job loss and income loss play significant roles in the decline of life satisfaction, and that the detrimental effect of income loss is mainly due to perceived financial strain, rather than objective income loss. Among immigrant men, job loss by itself is the dominant reason for the decline in life satisfaction during unemployment. Only a small proportion of the detrimental effect of unemployment is pecuniary, and this proportion can only be explained by subjective perceptions of financial strain rather than objective income loss. Our results also show that the total detrimental effect of unemployment on life satisfaction is much larger for immigrant than native-born men, and this is mainly due to the greater negative impact of job loss, rather than income loss.

The above findings suggest that immigrant and native-born men perceive unemployment differently. While native-born men consider work primarily a means of economic independence, immigrant men gain greater life satisfaction from the non-pecuniary aspect of work. We speculate that this is because native-born men's self-evaluation about their position in the society is more vulnerable to income loss, whereas the legal status of immigrant men in the country is more vulnerable to job loss. Namely, the more secure one's legal status is in the society, the more likely one would emphasize the pecuniary aspect over the non-pecuniary aspect of work. Our further investigation within the group of immigrant men has confirmed this speculation. With a focus on the extent to which an immigrant man's legal status in the host country is vulnerable to unemployment, we distinguished between established and recent immigrant men, and for the latter group, we made a further distinction between recent EU and non-EU immigrant men. Findings show that job loss causes a deeper decline in life satisfaction for those whose legal status in the host country depends more on employment status, i.e., recent non-EU immigrants, whereas income loss causes a deeper decline in life satisfaction for those whose residence right in the host country is not or less attached to employment, i.e., recent EU and established immigrants.

Above all, comparisons between immigrant and native-born men and among immigrant men themselves reflect a similar pattern: People whose residence right in the society is attached to employment emphasize more on the non-pecuniary aspect of work and thus suffer more from job loss. On the contrary, those whose residence right is less attached to employment emphasize more on the pecuniary aspect of work and thus suffer more from income loss accompanying unemployment. This may be because people with vulnerable status in the society, i.e., recent non-EU immigrants, are likely to consider work the foremost channel to build social connections and to avoid isolation in the host country. Others, including native-born men, established and recent EU immigrant men, are likely to consider work the dominant channel of upward mobility in the socioeconomic hierarchy in mainstream society. This divergence may fundamentally be driven by the assimilation argument, with recent non-EU immigrant men being less assimilated whereas recent EU and established immigrant men being more assimilated into the norms of mainstream society. Empirical demonstration of the assimilation argument is beyond the scope of the present study and should be explored in future research.

Another possible explanation is self-selection. Immigrants moving for economic reasons are often driven by their ambitions and motivations to achieve better economic lives. Compared to their counterparts staying in their countries of origin, economic immigrants are likely to be able to take higher risks for greater career success. The self-selection argument may well-explain why unemployed immigrants suffer more from job loss per se . However, it cannot explain why recent EU and non-EU immigrant men perceive and feel about unemployment differently, particularly because the portion of economic immigrants is larger within the EU than non-EU group ( Vargas-Silva and Rienzo, 2019 ). It is possible that the immigration screening process applied to immigrants from non-EU countries drives a much stronger positive self-selection mechanism. For one, economic immigrants from non-EU countries could be more career-driven than their counterparts from EU countries, so as to make extra efforts to go through the immigration procedure. For the other, people who manage to move to the UK from non-EU countries are likely to be the advantaged in their countries of origin. For example, our results show that when employed, recent non-EU immigrant men report a higher level of life satisfaction than native-born men, while the level of life satisfaction does not significantly differ among native-born men, recent EU and established immigrant men. Future research is thus faced with the challenge of estimating immigrants' perceptions of and evaluations about unemployment with different extents of self-selection among various immigrant groups taken into account.

It is necessary to restate the exclusion of women in this study. Women's labor force participation is a multi-faceted phenomenon, due to their reproductive roles and family obligations. Great variations in perceptions of labor market participation exist among women. By contrast, men's participation in the labor market is a relatively universal phenomenon and their perceptions of unemployment are much less heterogeneous compared to women's, as the social expectation of men being providers is very much consistent across societies ( Cohn, 1978 ). For this reason, factors causing heterogeneity in women's perceptions of unemployment would be less significant in the men subsample. This naturally calls for a new task in future research, which is to carry out an analysis of women in the labor force to complete the picture of the impact of unemployment on life satisfaction. Women's subjective reactions to unemployment are expected to be significantly different from men's. The inclusion of immigration status would further complicate the scenario. Therefore, proper strategies that can capture factors of the heterogeneity of women's—particularly immigrant women's—perceptions of work as well as feelings about unemployment will make significant contributions to the literature.

Future work notwithstanding, this paper is one of the very few studies analyzing the effect of unemployment on the immigrant men. We find that in addition to individual characteristics discussed in existing literature, investigations about the negative impact of unemployment on life satisfaction should take into account immigration status in general and the extent to which one's legal status in the society is vulnerable to unemployment in particular.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Author's Note

The authors confirm that this study is original research. It has not been previously published or been under consideration for publication elsewhere, either in whole or in part.

Author Contributions

JS has completed the data analyses, drafted the complete paper and revised the paper based upon the IK's comments. IK has supervised the process of data analyses, reviewed, and commented on various versions of the paper.

This study was conducted under the project Inside Integration and Acculturation—Migrants' Life Satisfaction in Europe led by IK and funded by the German Research Foundation (DFG). The grant number is KO 3601/9-1. This study was published under the support of the Open Access Publication fund provided by the Library of the University of Mannheim (Universitätsbibliothek Mannheim). We thank both DFG and the University of Mannheim for their generous financial support.

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:

1. ^ It is true that highly educated and highly skilled immigrants have much better chances to be employed in high-status positions in the host-country labor market. However, this only constitutes a small portion of the immigrant population. Furthermore, studies show that the immigrant-native wage gap is wider, rather than narrower, at the higher end of the income distribution ( Chiswick and Miller, 2008 ; Dell'Aringa et al., 2015 ).

2. ^ Wave 6 was not available during the completion of data analysis for this study.

3. ^ For details, please refer to the variable “ieqmoedc_dv”—Modified OECD equivalence scale in UKHLS Codebook. Available at: .

4. ^ The original variable in the questionnaire “current labor force status” includes the following categories: (1) self-employed, (2) paid employment (either full time or part time), (3) unemployed, (4) retired, (5) on maternity leave, (6) family care or home, (7) full-time student, (8) long-term sick, or disabled, (9) governmental training scheme, (10) unpaid family business, (11) on apprenticeship and (12) doing something else. Category (3) does not distinguish between short-term and long-term unemployment. Respondents inactive in the labor market, namely, categories (4–12) were excluded from the analysis.

5. ^ There is no country that is an EEA country but does not belong to the EU in the original coding of immigrants' source countries in the questionnaire. For convenience, we thus equated EEA countries to EU countries in this study.

6. ^ Categories of immigration, e.g., economic immigrants and those immigrating for family reasons, were not distinguished in the original UKHLS data. However, our exclusive focus on men in the labor force in this study has significantly alleviated this limitation. Men's labor market participation is consistently higher than women's in UK society, and this gender gap is even more salient among immigrants ( Office for National Statistics, 2019 ). Namely, an immigrant man has a much greater chance than an immigrant women to actively participate in the labor market, regardless of the channel of his entry into the UK.

7. ^ More details can be seen in the UKHLS Codebook, University of Essex 2015.

8. ^ For those who moved from employment into unemployment, this variable would not vary, as no unemployment benefits would apply to employed individuals. This variable was meaningful only for those who moved from unemployment to reemployment. Compared to results presented in the main text, results without controlling for unemployment benefits show a much more consistent level of life satisfaction across waves for each respondent (indicated by the much larger interclass correlation ρ), regardless of the employment status change (results are not presented in the paper but are available upon request). That is to say, having unemployment benefits or not indeed made a difference in one's perception of being unemployed and the subsequent level of life satisfaction. We therefore took into account this variable, despite the fact that it was meaningful only for one direction of the employment status change.

Aguilera, M., and Massey, D. S. (2003). Social capital and the wages of Mexican migrants: new hypotheses and tests.” Soc. Forces. 82, 671–701. doi: 10.1353/sof.2004.0001

CrossRef Full Text | Google Scholar

Amit, K. (2010). Determinants of life satisfaction among immigrants from western countries and from the FSU in Israel. Soc. Indicat. Res. 96, 515–534. doi: 10.1007/s11205-009-9490-1

Angrist, J. D., and Adriana, K. (2003). Protective or counter-productive? Labor market institutions and the effect of immigration on EU natives. Eco. Jour. 113, F302–F331. doi: 10.1111/1468-0297.00136

Bartram, D. (2011). Economic migration and happiness: comparing immigrants' and natives' happiness gains from income. Soc. Indicat. Res. 103, 57–76. doi: 10.1007/s11205-010-9696-2

Björklund, A. (1985). Unemployment and mental health: some evidence from panel data. J. Hum. Res. 10, 469–483. doi: 10.2307/145679

Blanchflower, D. G., and Oswald, A. J. (2004). Well-being over time in Britain and the USA. J. Pub. Eco. 88, 1359–1386. doi: 10.1016/S0047-2727(02)00168-8

Bosanquet, N., and Doeringer, P. B. (1973). Is there a dual labor market in Great Britain? Eco. J. 83, 421–435. doi: 10.2307/2231178

Brynin, M., and Güveli, A. (2012). Understanding the ethnic pay gap in Britain. Wor. Emp. Soc. 26, 574–587. doi: 10.1177/0950017012445095

Campbell, A., Converse, P. E., and Rodgers, W. L. (1976). The Quality of American Life. Perceptions, evaluations, and satisfactions. New York, NY: Russell Sage Foundation.

PubMed Abstract | Google Scholar

Chiswick, B., and Miller, P. (2008). Why is the payoff to schooling smaller for immigrants? Lab. Eco. 15, 1317–1340. doi: 10.1016/j.labeco.2008.01.001

Clark, A. (2003). Unemployment as a social norm: psychological evidence from panel data. J. Lab. Eco. 21, 289–322. doi: 10.1086/345560

Clark, A., and Oswald, A. (1994). Unhappiness and unemployment. Eco. J. 10, 648–659. doi: 10.2307/2234639

Clark, A., Oswald, A., and Warr, P. (1996). Is job satisfaction U-shaped in age? J. Occu. Orga. Psy. 69, 57–81. doi: 10.1111/j.2044-8325.1996.tb00600.x

Cohn, R. M. (1978). The effect of employment status change on self-attitudes. Soc. Psy. 41, 81–93. doi: 10.2307/3033568

Creed, P., and Reynolds, J. (2001). Economic deprivation, experiential deprivation and social loneliness in unemployed and employed youth. J. Comm. App. Soc. Psychol. 11, 167–178. doi: 10.1002/casp.612

Dell'Aringa, C., Lucifora, C., and Pagani, L. (2015). Earnings differentials between immigrants and natives: the role of occupational attainment.” IZA J. Migra. 4, 1–18. doi: 10.1186/s40176-015-0031-1

Diener, E. (1984). Subjective well-being. Psyc. Bull. 95, 542–575. doi: 10.1037/0033-2909.95.3.542

PubMed Abstract | CrossRef Full Text | Google Scholar

Elliott, J. (2001). Referral hiring and ethnically homogeneous jobs: how prevalent is the connection and for whom? Soc. Sci. Res. 30, 401–425. doi: 10.1006/ssre.2001.0704

Ervasti, H., and Venetoklis, T. (2010). Unemployment and subjective well-being: an empirical test of deprivation theory, incentive paradigm and financial strain approach. Acta Soci. 53, 119–138. doi: 10.1177/0001699310365624

Ezzy, D. (1993). Unemployment and mental health: a critical review. Soc. Sci. Med. 37, 41–52. doi: 10.1016/0277-9536(93)90316-V

Fahey, T., and Smyth, E. (2004). Do subjective indicators measure welfare? Evidence from 33 European societies. Eur. Soc. 6, 5–27. doi: 10.1080/1461669032000176297

Ferrer-i-Carbonell, A., and Frijters, P. (2004). How important is methodology for the estimates of the determinants of happiness? Eco. J. 114, 641–659. doi: 10.1111/j.1468-0297.2004.00235.x

CrossRef Full Text

Frank, K., Hou, F., and Schellenberg, G. (2016). Life satisfaction among recent immigrants in Canada: comparisons to source-country and host-country populations. J. Hap. Stud. 17, 1659–1680. doi: 10.1007/s10902-015-9664-2

Fryer, D. (1986). Employment deprivation and personal agency during unemployment. Soc. Behav. 1, 3–23.

Google Scholar

Fryer, D. (1995). Benefit agency? Labor market disadvantage, deprivation and mental health. Psychology 8, 265–272.

Gilkinson, T., and Sauvé, G. (2010). Recent Immigrants, Earlier Immigrants and The Canadian-Born: Personal and Social Trust . Research Report from the Immigrant, Refugees and Citizenship Canada. Retrieved from: (accessed December 20, 2019).

Helliwell, J., and Huang, H. F. (2011). New Measures of the Costs of Unemployment: Evidence From the Subjective Well-Being of 2.3 Million Americans. NBER Working Paper16829 . Cambridge: National Bureau of Economic Research.

Jahoda, M. (1982). Employment and Unemployment: A Socio-Psychological Analysis . Cambridge: Cambridge University Press.

Kessler, R., Turner, B., and House, J. (1988). Effects of unemployment in a community sample: main, modifying and mediating effects. J. Soc. Iss. 44, 69–85. doi: 10.1111/j.1540-4560.1988.tb02092.x

Knabe, A., and Rätzel, S. (2011). Quantifying the psychological costs of unemployment: the role of permanent income. App. Eco. 43, 2751–2763. doi: 10.1080/00036840903373295

Knies, G., Nandi, A., and Platt, L. (2016). Life satisfaction, ethnicity and neighborhoods: is there an effect of neighborhood ethnic composition on life satisfaction? Soc. Sci. Res. 60, 110–124. doi: 10.1016/j.ssresearch.2016.01.010

Kogan, I. (2004). Last hired, first fired? The unemployment dynamics of male immigrants in Germany. Eur. Soc. Rev. 20, 445–461. doi: 10.1093/esr/jch037

Kogan, I. (2011). New immigrants—Old disadvantage patterns? Labor market integration of recent immigrants into Germany. Inter. Migrat. 49, 91–117. doi: 10.1111/j.1468-2435.2010.00609.x

Kone, Z., and Sumption, M. (2019). Briefing – Permanent or Temporary: How Long Do Migrants Stay in the UK? The Migration Observatory at the University of Oxford . Available online at: (accessed December 15, 2019).

Korpi, T. (1997). Is utility related to employment status? Unemployment, labor market policies and the psychological well-being of youth. Lab. Eco. 4, 125–146. doi: 10.1016/S0927-5371(97)00002-X

Leana, C., and Feldman, D. (1991). Gender differences in responses to unemployment. J. Vocat. Behav. 38, 65–77. doi: 10.1016/0001-8791(91)90018-H

Leopold, L., Leopold, T., and Lechner, C. (2017). Do immigrants suffer more from job loss? Unemployment and subjective well-being in Germany. Demogra 54, 231–257. doi: 10.1007/s13524-016-0539-x

Lucas, R., Georgellis, Y., and Diener, E. (2004). Unemployment alters the set point for life satisfaction. Psyc. Sci. 15, 8–13. doi: 10.1111/j.0963-7214.2004.01501002.x

McArdle, S., Waters, L., Briscoe, J., and Hall, D. (2007). Employability during unemployment: adaptability, career identity and human and social capital. J. Vocat. Behav. 71, 247–264. doi: 10.1016/j.jvb.2007.06.003

McClelland, A. (2000). Effects of unemployment on the family. Eco. Lab. Rel. Rev. 11, 198–212. doi: 10.1177/103530460001100204

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

Michalos, A. (1985). Multiple discrepancies theory (MDT). Soc. Indi. Res. 16, 347–413. doi: 10.1007/BF00333288

Office for National Statistics (2019). Labor Market Overview, UK: May 2019 . Available online at: (accessed December 15, 2019).

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

Pittau, M. G., Zelli, R., and Gelman, A. (2010). Economic disparities and life satisfaction in European regions. Soc. Ind. Res. 96, 339–361. doi: 10.1007/s11205-009-9481-2

Price, R., Choi, J. N., and Vinokur, A. (2002). Links in the chain of adversity following job loss: How financial strain and loss of personal control lead to depression, impaired functioning, and poor health. J. Occup. Heal. Psyc. 7, 302–312. doi: 10.1037/1076-8998.7.4.302

Rabe-Hesketh, S., and Skrondal, A. (2008). Multilevel and Longitudinal Modeling Using Stata . STATA press.

Safi, M. (2010). Immigrants' life satisfaction in Europe: between assimilation and discrimination. Euro. Soci. Rev. 26, 159–176. doi: 10.1093/esr/jcp013

Shen, J., and Kogan, I. (2019). Immigrants' relative income and life satisfaction: comparison groups from a multi-generational perspective. Acta Sociol. 63, 82–102. doi: 10.1177/0001699319859397

Shields, M., and Wailoo, A. (2002). Exploring the determinants of unhappiness for ethnic minority men in Britain. Scot. J. Policy Econ. 49, 445–466. doi: 10.1111/1467-9485.00241

Sirgy, J. M., Efraty, D., Siegel, P., and Lee, D. J. (2001). A new measure of quality of work life (QWL) based on need satisfaction and spillover theories. Soc. Ind. Res. 55, 241–302. doi: 10.1023/A:1010986923468

Statistics Canada (2006). Study: Immigrants Who Leave Canada. The Daily, March 1 . Available online at: (accessed December 19, 2019).

Ullah, P. (1990). The association between income, financial strain and psychological well-being among unemployed youths. J. Occup. Psyc. 63, 317–330. doi: 10.1111/j.2044-8325.1990.tb00533.x

University of Essex (2015). Institute for Social and Economic Research, NatCen Social Research and Kantar Public, [Producers]: Understanding Society: Waves 1-5, 2009-2015, 7th Edn . Colchester: Data Service [distributor]. SN: 6614.

Vargas-Silva, C., and Rienzo, C. (2019). Briefing – Migrants in the UK: An Overview. The Migration Observatory at the University of Oxford . Available online at: (accessed December 19, 2019).

Vinokur, A., and van Ryn, M. (1993). Social support and undermining in close relationships: their independent effects on the mental health of unemployed persons. J. Perfect. Soc. Psyc. 65, 350–359. doi: 10.1037/0022-3514.65.2.350

Wilson, K. L., and Portes, A. (1980). Immigrant enclaves: an analysis of the labor market experiences of Cubans in Miami. Am. J. Soc. 86, 295–319. doi: 10.1086/227240

Wilson, S., and Walker, G. M. (1993). Unemployment and health: a review. Pub. Heal. 107, 153–162. doi: 10.1016/S0033-3506(05)80436-6

Winkelmann, L., and Winkelmann, R. (1995). Happiness and unemployment: a panel data analysis for Germany. Appl. Econ. Quar. 41, 293–307.

Winkelmann, L., and Winkelmann, R. (1998). Why are the unemployed so unhappy? Evidence from panel data. Economica 65, 1–15. doi: 10.1111/1468-0335.00111

Young, C. (2012). Losing a job: the nonpecuniary cost of unemployment in the United States. Soc. For. 91, 609–34. doi: 10.1093/sf/sos071

Keywords: unemployment, job loss, objective income loss, perceived financial strain, immigrant, native-born, men, life satisfaction

Citation: Shen J and Kogan I (2020) Job Loss or Income Loss: How the Detrimental Effect of Unemployment on Men's Life Satisfaction Differs by Immigration Status. Front. Sociol. 5:10. doi: 10.3389/fsoc.2020.00010

Received: 30 September 2019; Accepted: 11 February 2020; Published: 28 February 2020.

Reviewed by:

Copyright © 2020 Shen and Kogan. 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: Jing Shen,

This article is part of the Research Topic

Migration and European Societies

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access


Research Article

Job loss and mental health during the COVID-19 lockdown: Evidence from South Africa

Roles Conceptualization, Formal analysis, Methodology, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation School of Economics and Finance, University of the Witwatersrand, Johannesburg, South Africa

ORCID logo

Roles Formal analysis, Methodology, Writing – review & editing

  • Dorrit Posel, 
  • Adeola Oyenubi, 
  • Umakrishnan Kollamparambil


  • Published: March 30, 2021
  • Peer Review
  • Reader Comments

Table 1

Existing literature on how employment loss affects depression has struggled to address potential endogeneity bias caused by reverse causality. The COVID-19 pandemic offers a unique natural experiment because the source of unemployment is very likely to be exogenous to the individual. This study assessed the effect of job loss and job furlough on the mental health of individuals in South Africa during the COVID-19 pandemic.

Data and methods

The data for the study came from the first and second waves of the national survey, the National Income Dynamics-Coronavirus Rapid Mobile Survey (NIDS-CRAM), conducted during May-June and July-August 2020, respectively. The sample for NIDS-CRAM was drawn from an earlier national survey, conducted in 2017, which had collected data on mental health. Questions on depressive symptoms during the lockdown were asked in Wave 2 of NIDS-CRAM, using a 2-question version of the Patient Health Questionnaire (PHQ-2). The PHQ-2 responses (0–6 on the discrete scale) were regrouped into four categories making the ordered logit regression model the most suited for assessing the impact of employment status on depressive symptoms.

The study revealed that adults who retained paid employment during the COVID-19 lockdown had significantly lower depression scores than adults who lost employment. The benefits of employment also accumulated over time, underscoring the effect of unemployment duration on mental health. The analysis revealed no mental health benefits to being furloughed (on unpaid leave), but paid leave had a strong and significant positive effect on the mental health of adults.


The economic fallout of the COVID-19 pandemic resulted in unprecedented job losses, which impaired mental wellbeing significantly. Health policy responses to the crisis therefore need to focus on both physical and mental health interventions.

Citation: Posel D, Oyenubi A, Kollamparambil U (2021) Job loss and mental health during the COVID-19 lockdown: Evidence from South Africa. PLoS ONE 16(3): e0249352.

Editor: Gabriel A. Picone, University of South Florida, UNITED STATES

Received: December 8, 2020; Accepted: March 16, 2021; Published: March 30, 2021

Copyright: © 2021 Posel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data are held in a public repository, and can be accessed at: .

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.


It is well documented that the COVID-19 pandemic has resulted in large increases in unemployment in many countries [ 1 ]. South Africa is no exception: studies estimate that between 2.2 and 2.8 million adults in the country lost their jobs from February to April 2020, following the lockdown and the wide-scale suspension of economic activity [ 2 – 4 ]. This loss of employment had significant implications for people’s access to economic resources [ 4 , 5 ]; and it may also be an important reason for why elevated depressive symptoms were reported among adults during the first months of the pandemic [ 6 ].

It is increasingly being recognized that the health costs of COVID-19 are not limited to physical health but include the effects of the pandemic on the individual’s mental or psychological well-being [ 7 – 10 ]. This study explores how job loss affects people’s mental health using longitudinal micro-data collected after the introduction of the COVID-19 lockdown in South Africa.

The COVID-19 pandemic offers a unique opportunity to analyze the implications of job loss for mental health, because the source of unemployment is very likely to have been exogenous to (or beyond the control of) the individual. There is a large literature which investigates how the loss of employment affects depression or anxiety, where studies compare the mental health of the employed and the unemployed [ 11 – 14 ]. However, testing the relationship between unemployment and depression typically is complicated by methodological problems associated with causality, which arise even with longitudinal data. This is because it is often not possible to establish the temporal ordering of events: are changes in depressive symptoms caused by, or do they precede, changes in activity status? For example, people who experience job loss may exhibit more depressive symptoms because of their unemployment; but it is also possible that those who are depressed are significantly less likely to search for, or maintain, employment [ 15 – 17 ].

The national lockdown in response to the COVID-19 pandemic, and the associated loss of employment, provide a natural experiment that removes these problems of causality. In addition, the labor market implications of the COVID-19 lockdown are unique because most economic activity was suspended in anticipation that (at least some) activity would resume once the lockdown was eased. Some workers therefore retained jobs to return to, but for the duration of the lockdown, they were neither working nor earning an income. For example, among adults who reported being employed during South Africa’s lockdown, a sizeable share (approximately 17 percent in April) also reported that they were currently not working any hours and had not received payment, but that they had a job to return to. Of these furloughed workers, half were back at work by June, but nearly 40 percent fell into unemployment [ 2 ].

These unusual characteristics of the COVID-19 crisis make it possible to distinguish between job loss and job furlough when investigating the implications of activity status for mental health. This is an interesting distinction to draw because it offers insight into whether expectations of a job in the future provide psychological protection against the loss of current earnings and work activity.

South Africa is also an important country in which to explore the effects of job loss on mental health. There have been many decades of research, particularly in developed countries, on the psychological implications of unemployment [ 12 , 14 , 18 – 20 ]. However, although South Africa has had persistently high rates of unemployment since the transition to democracy [ 21 , 22 ], there are few studies which interrogate how this unemployment affects levels of depression and anxiety in the population [ 23 ].

Existing research that assesses psychological health during the COVID-19 pandemic has relied on cross-sectional data that have been collected through online questionnaires, biasing samples against people with limited access to the internet [ 8 ]. This type of selection bias is likely to be particularly important in developing countries such as South Africa, where access to the internet varies significantly and systematically by socio-economic status and location [ 24 , 25 ].

In this study, we analyze unique longitudinal data from two waves of a rapid mobile survey, where participants were drawn from a nationally stratified sample, and information was collected using computer-assisted telephonic interviews. We use these data to investigate the extent to which job loss undermined the mental health of adults who were employed before the COVID-19 lockdown, if this effect was compounded as unemployment persisted, and whether job furlough provided any protection against the distress caused by losing a job altogether.

Data source

The data for the study come from the National Income Dynamics-Coronavirus Rapid Mobile Survey (NIDS-CRAM). NIDS-CRAM was developed by a consortium of more than 30 academics (of which one author was part), from universities across South Africa. It was introduced to track the socio-economic and health effects of the COVID-19 pandemic and the associated lockdown. It is expected that the survey will span one year, by which time, five waves will have been conducted [ 26 ]. By October 2020, two waves of NIDS-CRAM had been completed. Ethical clearance for the study was obtained from the University of Cape Town Commerce Ethics Committee (REC 2020/04/2017), with reciprocal ethics from the University of Stellenbosch. The data, which are in the public domain, are available at: .

To obtain a sample that was as nationally representative as possible under the circumstances, participants for NIDS-CRAM were drawn from South Africa’s national household survey, the National Income Dynamics Study (NIDS). NIDS was conducted by the Southern African Labour and Development Research Unit, and the last wave was undertaken in 2017. The NIDS-CRAM sample was selected from the 2017 national sample using a stratified design but with ‘batch sampling’. Sampling in batches offered flexibility in adjusting the sample rate as the surveying progressed, and as information about stratum response became available [ 27 ].

The first wave of NIDS-CRAM, which was conducted from 7 May to 27 June 2020, surveyed 7073 adults aged 18 years and older. In the second wave, which was undertaken from 13 July to 13 August 2020, 5676 adults were successfully re-interviewed, yielding a response rate of 80.2 percent [ 28 ]. Attrition from Wave 1 to Wave 2 of NIDS-CRAM is estimated to be random based on observed covariates, when measured using goodness-of-fit statistics [ 28 ]. A test of attrition using probit models [ 29 ] also shows that there is no relationship between mental health and the probability of not being interviewed in NIDS-CRAM Wave 1, or of not remaining in the sample from Wave 1 to Wave 2.

All interviews for NIDS-CRAM have been conducted telephonically by call-center agents, and the instrument has been designed to take no longer than twenty minutes per interview [ 26 ]. Consequently, the questionnaire is far shorter than typical household questionnaires undertaken in South Africa, including the instrument for NIDS 2017. The Wave 1 questionnaire was translated into 10 of the 11 official languages in South Africa, while the Wave 2 questionnaire was conducted in all 11 languages.

All participants in NIDS-CRAM were informed verbally before they were interviewed that participation in the study was voluntary, and that their participation could be stopped at any time. Consent and the telephonic interview were recorded, but participants were advised that all information collected would be kept confidential and that the information released in the datasets would be anonymized.

Depressive symptoms

In order to increase the scope of information collected in short interviews, not all modules in the NIDS-CRAM questionnaire are repeated across waves. Of interest to this study are the questions on mental health, which were included in the Wave 2 questionnaire, but not in Wave 1. However, information on mental health was also collected in NIDS 2017.

NIDS 2017 included the ten questions which make up the Center for Epidemiologic Studies Short Depression Scale (CES-D 10). Individuals were asked about their emotional health over the past week, including whether they felt “hopeful”, “fearful” “lonely” and “happy”. In the far shorter questionnaire for NIDS-CRAM Wave 2, information on depressive symptoms was collected using a 2-question version of the Patient Health Questionnaire (PHQ-2) [ 6 ]. Respondents were asked whether over the previous two weeks, they “had little interest or pleasure in doing things” (question G11); and whether they had “been feeling down, depressed or hopeless” (question G12). Response options included “not at all”, “several days”, “more than half the days” and “nearly every day” (which we have coded from 0 to 3). The PHQ-2 is a shortened version of the widely used PHQ-9 [ 24 ], and both the PHQ-9 and the CES-D 10 have been validated as reliable screening measures of depression, including for South Africa [ 30 ].

Given differences in the information collected, measures of mental health in NIDS and NIDS-CRAM are not directly comparable. The study is therefore unable to use individual fixed effects models (or intra-individual comparisons) to control for any unobserved time-invariant factors (such as personality) that influence both depressive symptoms and activity status. It is also not possible to draw robust conclusions about how mental health has changed from 2017 (pre-COVID) to 2020 (COVID). However, the CESD-D 10 scores from 2017 are included as a covariate in the multivariate regression analysis of depressive symptoms in 2020, to offer some control both for variation in the individual propensity to exhibit depressive symptoms [ 23 ] and for possible anchoring effects in how respondents assess their symptoms [ 31 ].

The PHQ-2 scale ranges from 0 to 6, and the CES-D 10 scale, from 0 to 30, with both increasing in depressive symptoms. Both scales are employed as a continuum of distress [ 23 , 32 – 34 ], rather than imposing a threshold to identify depression, because the appropriate cut-off has been found to vary across different language groups in South Africa [ 30 ].

Sample and variables

The focus of the study is on the relationship between employment status and mental health during COVID-19. The first wave of NIDS-CRAM established whether adults had been working in February, prior to the start of the ‘hard’ lockdown in South Africa (referred to as alert level 5) when all non-essential economic activity was suspended. Detailed information was also collected on whether adults had been working in April, the number of hours worked in a typical week and whether (and what) earnings had been received. The second wave of NIDS-CRAM collected information on labor market activity in June, by which time South Africa had progressed to alert level 3 of the lockdown, and many businesses were able to re-open.

The sample for the study is all adults who were employed in the month before the COVID-lockdown started. Of these 3408 adults, 2213 were interviewed in both Waves 1 and 2 of NIDS-CRAM and have complete (non-missing) information for all the main variables included in the study. The study does not use survey weights to generate population estimates partly because the available weights are benchmarked to a sample in 2017, which as a fifth wave of the NIDS panel, was itself not nationally representative. Further, our sample is restricted to those who were employed before the lockdown, and the weights are not stratified by employment status. We therefore consider a model-based approach more suitable [ 35 ], and we refer to our estimates as sample estimates.

For the empirical analysis, we first identify adults who reported having a job in April and a job in June. Although the time span is short, distinguishing the two periods may shed light on whether the negative effects of job loss are compounded as the duration of joblessness increases [ 36 ]. We then differentiate among the employed in April and June, identifying: adults who were working and earning a non-zero income; adults who were not working but still earning an income (and therefore most likely on paid leave); and adults who were neither working nor earning an income but who identified that they had a job to return to (whom we refer to as furloughed).

The multivariate analysis also includes a range of variables that are commonly adopted in empirical studies of depression, and which may moderate the relationship between activity status and depressive symptoms [ 23 , 33 , 34 ]. These are first, the adult’s demographic characteristics: age and age squared; sex (female); marital status (partnered); educational attainment (tertiary education); race (African, where the omitted category, non-African, includes the three other race categories always identified in South African surveys, viz., Colored (of mixed race), Indian (of Asian descent) and white); and whether the individual has a chronic health condition. We also control for the adult’s geographical location (urban); the type of dwelling (formal dwelling such as a house or a flat, informal dwelling or a shack, with a traditional dwelling as the omitted category); and household composition (living in a household with children aged 17 or younger). To avoid endogeneity between employment status and household income, socio-economic status is captured with information collected in NIDS on the adult’s net worth in 2017, and whether at least one child support grant or older persons grant (the two most common social grants in South Africa) was received in the household in April and then in June 2020. Finally, we identify people’s attitudes to COVID-19 with a binary variable for whether the respondent believed that it was possible to avoid being infected by the coronavirus.

Statistical analysis

research title about job loss

A key assumption underlying the ordered logit regression is the proportional odds or parallel regression assumption, viz. that the same relationship exists between all the categories of the ordinal scale. The assumption is tested using the Stata post-estimation command ‘oparallel’ that compares the ordered logit model with a full generalized ordered logit model, which relaxes the parallel regression assumption on all explanatory variables. The null hypothesis, that there is no difference in the coefficients between models, is tested using the Wald test, Wolfe-Gould test, Likelihood ratio test, Brant test and Score test [ 37 ]. An insignificant outcome indicates that there is not enough strong evidence against the parallel regression assumption.

The ordered logit regressions with the PHQ-2 scale from 0 to 6 violated the parallel regression assumption. We therefore regrouped the scale into four categories: 0; 1 (1 or 2 of the original scale); 2 (3 or 4 of the original scale); and 3 (5 or 6 of the original scale). These regressions satisfied the parallel regression assumption and the estimated coefficients remained robust for both the original scale and the regrouped scale. (The main marginal effects from the regressions with the original scale, and the tests of the parallel regression assumption, are reported in the Tables 6–9 in S1 Appendix .)

Among the sample of adults who were employed before the implementation of the hard lockdown in South Africa ( Table 1 ), the modal PHQ-2 score was 0 (respondents had not experienced any depressive symptoms in the previous 2 weeks), accounting for 47% of adults. However, if a PHQ-2 score of 3 or larger is taken as the cut-off for depression [ 38 ], then almost a quarter (24%) of adults in the sample would be classified as depressed. If a CES-D 10 score of 10 or more is considered indicative of depression [ 33 ], then among this same group of adults, 17% were depressed in 2017.


  • PPT PowerPoint slide
  • PNG larger image
  • TIFF original image

In the first month following the lockdown, 30% of adults had lost their jobs, while a further 12% were furloughed. Only 41% of all adults who had been employed before the COVID-19 crisis were still actively working and earning an income, and 17% were on paid leave. Two months later, after the lockdown conditions had eased, the share of adults who were actively working had increased to 57%, and only 6% were on paid leave. The percentage of adults who were furloughed also dropped to 5%, but the share who was unemployed increased slightly to 32%.

Compared to adults who lost their job over the lockdown period, PHQ-2 scores were significantly lower among adults who retained employment (Tables 2 and 3 ). Moreover, the protection from depression associated with employment, or the risk of depression among those who lost their jobs, was compounded over time. Adults who retained their jobs in Wave 1 were 5.1% more likely than those who did not have jobs to report no depressive symptoms (Regression 1, Table 3 ) and a further 6% more likely if they also retained their job in Wave 2 (Regression 2, Table 3 ).



However, the employed were not all equally protected against adverse mental health. There is no significant relationship between PHQ-2 scores and being furloughed. Adults who were neither working any hours nor earning any income were therefore no more likely than adults who had lost their job to have low PHQ-2 scores on average, even if they reported having a job to return to (Tables 2 and 3 ).

In each wave, adults who had been actively working were 5–6% more likely to report no depressive symptoms than those who had lost employment. There was at most a weak negative relationship between having had paid leave in Wave 1, and depression scores in Wave 2. But adults who were on paid leave in the wave that depression scores were collected reported significantly lower scores, even compared to adults who were actively working in that month (χ 2 = 8.02, p < 0.02). Adults on paid leave in Wave 2 were also 10% less likely than adults who had lost their job to report no depressive symptoms ( Table 4 ).


These results remain robust when the set of control variables is expanded to include a measure of the individual’s net wealth (three years prior) and their assessment of whether contracting the coronavirus can be avoided (although the sample size was considerably reduced because of large numbers of non-response to these questions) (Regression 4 Tables 2 and 5 ).


As the lagged depression score from 2017 is measured using a different instrument and therefore captures depressive symptoms on a more extensive scale, we also tested the robustness of the findings to alternative specifications. First, we converted both the PHQ-2 and CES-D 10 scores to binary variables using the threshold that is often adopted in studies from other countries (a score of 3 or higher for the PHQ-2 and of 10 or more for the CES-D) and estimated logit regressions. Second, we ran the ordered logit regressions without the depression score from 2017; and third, we normalized both the PHQ-2 and the CES-D 10 scores and estimated ordinary least squares regressions. The results from these tests are reported in the Tables 10a-10c in S1 Appendix . Overall, the findings are consistent with the original estimations and all variables retain significance in the latter two sets of regressions, although some of the activity status variables lose significance in the binary specification.

Although employment is typically far less secure in developing countries, there has been little research on the relationship between mental health, employment and joblessness in these countries [ 14 ], with studies focusing more on the association between mental health and poverty [ 17 , 39 ]. In South Africa, there is a growing body of empirical literature which has estimated the correlates of depression or depressive symptoms [ 15 , 23 , 33 , 34 , 40 ]; but despite South Africa’s very high unemployment rate, there is no work that has specifically explored how job loss, or the lack of employment, affects an adult’s vulnerability to depression.

This study analysed longitudinal micro-data collected in 2020, during the COVID-19 lockdown in South Africa, from a sample of adults who had been previously interviewed in a national household survey in 2017. The analysis was restricted to adults who were employed shortly before the introduction of the hard lockdown and the subsequent wide-spread loss of employment. Although employment started to recover as the lockdown conditions eased, corresponding to the second wave of the data collected, adults remained considerably less likely to be employed than before the lockdown started.

We used ordered logit models to investigate the relationship between depressive symptoms and job loss during the COVID-19 crisis. As the source of job loss following the nation-wide lockdown was exogenous to the individual, the relationship between depression scores and activity status was not biased by selection issues; viz. that individuals with poor mental health were more likely to lose their jobs. In addition, prior depression scores (the adult’s CES-D score from the 2017 data) were included as a covariate in the regression models, to control for unobserved differences in personality or genetic endowments, which may have affected not only vulnerability to depression but also how symptoms were recalled and reported.

Consistent with what would be expected from studies on unemployment and depression, adults who retained employment during the COVID-19 lockdown reported significantly lower depression scores than adults who lost employment. The benefits of employment also accumulated over time, as employment in each wave resulted in significantly lower scores. This finding is consistent with studies that show how the duration of unemployment is associated with increasing negative effects on mental health. A distinction is often drawn between short-term unemployment (< 6 months), and long-term unemployment (≥ 6 months) [ 14 ], but in this study, the trend was evident also over the first few months of unemployment.

The estimations included a historical measure of the individual’s economic status (their individual net worth in 2017), but because earnings are the largest source of income in the household, household income was not included as a covariate. The association between unemployment and mental health therefore arises partly because job loss threatens the economic security of the individual (and the household) [ 41 ], and also because of the psychological trauma associated with a loss of identity, purpose and structure of time [ 19 ].

When the employed were disaggregated into three groups (actively working and earning, on paid leave, not working or earning) the analysis revealed no mental health benefits to being furloughed. Any protective effect of ‘having a job to return to’ was likely undermined by the loss of current income, and anxiety over when and whether work would resume. In contrast, the analysis identified strong mental health benefits of recently taken paid leave (in Wave 2), even if this leave was spent during times of COVID-19.

The regression analysis also suggested that social grants (or cash transfers) may provide some protection against the incidence of depressive symptoms. Social grants are an integral part of the livelihood strategies of poor households in South Africa. This is the case even in households where adults have employment, because much of this employment involves low-waged work [ 5 ]. Most adults in the study’s sample lived in a household where at least one social grant for a child (the child support grant) or the elderly (the older persons grant) was received. Depression scores were lower when social grants were received but the association was only weakly significant (at the 10% level) and only for social grants received in Wave 2 i.e. there is no suggestion that any protective effects of social grants endure beyond a month. The value of social grants is insufficient to lift most households above the poverty line [ 42 ]; but the expansion of the social grant system has been associated with a large decline in the incidence of hunger reported in households [ 43 ], and the importance of social grant receipt would have been amplified during the COVID-19 crisis.

The coefficients on the other covariates included in the regression models were mostly aligned with findings from South African studies which have analyzed (pre-COVID-19) national micro-data [ 23 , 34 , 44 ]. Vulnerability to depression increased non-linearly with age; and it was significantly higher among adults who reported a chronic health condition and who lived in an urban area (relative to a rural area). Contrary to other studies, however, our results consistently showed that on average, Africans reported significantly lower levels of depression than non-Africans. One possible explanation for this unexpected finding is that it reflects a “steeling effect” [ 45 ] among Africans, who likely have experienced much more past adversity than non-Africans, and who may therefore have acquired more resilience in dealing with negative events.

In the first months of the pandemic, COVID-19 was sometimes presented as the ‘great equalizer’, in that the people who travelled (and who therefore may have had higher socio-economic status) were initially more likely to be infected [ 46 ]. The development of the pandemic has shown that COVID-19 is not blind to socio-economic status [ 5 , 46 ]; but it is also not a pandemic that is confined only to the poor or disadvantaged. Although Africans were significantly more likely than non-Africans to experience job loss during the lockdown in South Africa [ 2 , 43 ], it also exposed non-Africans to far greater economic shocks, on average, than they were likely to have experienced previously. In comparison to Africans, who have suffered high rates of poverty and unemployment as a legacy of apartheid and racial exclusion, non-Africans therefore may not have developed as effective coping strategies to overcome the difficult circumstances associated with the COVID-19 crisis.

In contrast to research on the mental health implications of COVID-19 in the UK, there is no evidence that the effects of job loss on mental health were gendered (an interactive term for African and female did not yield significant results in any of the estimations) [ 10 ].

The lockdown in response to the COVID-19 pandemic resulted in sizeable job losses in South Africa (and around the world). This exogenous shock provided a natural experiment to investigate how job loss affects mental health. The labor market implications of the COVID-19 lockdown were also unique because many workers retained jobs to return to, but for the duration of the lockdown, they were neither working nor earning an income.

This study showed that among a sample of adults who were employed before the lockdown in South Africa, those who lost their jobs or whose jobs were furloughed reported significantly more vulnerability to depression than those who retained employment. It is also possible that the severity of depressive symptoms has been underestimated in the PHQ-2 measures analyzed in the study. The shortened version of the Patient Health Questionnaire is an attractive measure of depression when there are stringent constraints on data collection (as has been the case during COVID-19). However, as it based on only two questions, it is less sensitive to variation in, or the severity of, depressive symptoms in contrast to more comprehensive measures such as the PHQ-9 and the CES-D 10 [ 47 ].

After HIV and other infectious disorders, mental health and nervous system disorders are the third highest contributor to the burden of disease in South Africa [ 48 ]. However, mental disorders are far less likely to be treated than physical disorders [ 49 ]. The provision of mental health services has been decentralized and moved to communities and districts hospitals; but the scale of services remains inadequate [ 49 , 50 ] and mental health services in South Africa have been significantly underfunded [ 51 ]. One of the stated objectives of the South African Declaration on the Prevention and Control of Non-Communicable Diseases is to increase the number of people screened and treated for mental illness by 30 percent by 2030 [ 52 ]. The effects of the COVID-19 crisis on mental health make this objective even more salient.

Mental health interventions and support by themselves cannot solve the underlying problem of job loss as a result of a widespread event like the pandemic; but they can help the individual stay confident and motivated to persevere with job search when the economy rebounds. Apart from this, more specialized programmes that address the needs of job seekers through, for example, retraining initiatives and skills development, including those related to job search and dealing with rejection, need to be put in place to enhance the probability of re-employment. These interventions are relevant not only in response to the COVID-19 pandemic but also more generally, in the context of South Africa’s persistently high rate of unemployment.

Supporting information

S1 appendix..


The authors thank two anonymous reviewers for their helpful comments.

  • 1. Blustein DL, Duffy R, Ferreira JA, Cohen-Scali V, Cinamon RG, Allan BA. Unemployment in the time of COVID-19: A research agenda. Elsevier; 2020.
  • View Article
  • Google Scholar
  • 3. Statistics South Africa. Quarterly Labour Force Survey Quarter 2: 2020. Statistical Release P0211. Pretoria: Statistics South Africa.—Google Search. 2020.
  • PubMed/NCBI
  • 18. Jahoda M. Employment and unemployment. Cambridge Books. 1982.
  • 20. Warr P. Work, unemployment, and mental health. Oxford University Press; 1987.
  • 32. Steffick DE. Documentation of affective functioning measures in the Health and Retirement Study. Ann Arbor, MI: University of Michigan. 2000.
  • 37. Buis M, Williams R. Using simulation to inspect the performance of a test, in particular tests of the parallel regressions assumption in ordered logit and probit models. German Stata Users’ Group Meetings 2013. Stata Users Group; 2013.
  • 43. Posel, D, Hall, K. Families and household formation in South Africa. In: Oqubay, A, Tregenna F and Valodia I. 2021.
  • 47. Zuvekas SH. Health Care Demand, Empirical Determinants Of. Culyer AJ (ed) Encyclopedia of health economics Amsterdam, Elsevier; 2014. pp. 343–354.

IRLE logo

The Rising Stakes of Job Loss

By Sylvia A. Allegretto and Andrew Stettner • May 1, 2005

No one wants to lose his or her job. Families face grave difficulties when a worker is jobless, especially for an extended period. Workers receiving unemployment insurance (UI) benefits receive less than 40% of their prior wages.1 Typically, after six months out of work, the worker has exhausted unemployment benefits and has significantly or completely depleted savings. It is at this point that unemployment can have lasting effects such as elevated levels of debt, diminished retirement and savings accounts (tapped to meet daily expenses), or relocation from secure housing and communities to unfamiliar places in order to find employment.

Recent research has examined how unrelenting high rates of long-term unemployment were spawned by the lack of job creation that followed the 2001 recession. In this report, we examine this unprecedented period of long-term unemployment and compare it with the most recent economic downturn of the 1990s. We conclude that a different picture of long unemployment spells has emerged.

  • Women represented 43% of long-term jobless workers, on average, from 2001-04, up from 35% compared to the 1990-93 period. Such long-term unemployment has a direct impact on children and families, especially families with single mothers.
  • After experiencing historic labor market gains during the late 1990s, African Americans represented a greater share of the long-term jobless in this economic cycle.
  • Long-term unemployment is expanding beyond blue collar workers: higher levels of education and white collar jobs are no longer providing insulation against severe joblessness.

These consequences make the recent and persistent problem of long-term unemployment a critical labor market problem requiring policymakers’ attention, and assistance for the long-term unemployment was a major issue in the 108th Congress. Changing dynamics should cause law makers to rethink how policy can more effectively support family income while helping those who experience long-term joblessness return to work.

Related Publications

research title about job loss

Parental Labor Supply

November 10, 2021

Read More Right Arrow

research title about job loss

Should the federal minimum wage be increased?

November 4, 2021

Comments on “Economic Impact Evaluation of the City of Minneapolis’s Minimum Wage Ordinance”

November 3, 2021

NYU Scholars Logo

  • Help & FAQ

Job loss and unemployment

  • Strategies to Reduce Inequality

Research output : Chapter in Book/Report/Conference proceeding › Chapter

ASJC Scopus subject areas

  • Economics, Econometrics and Finance(all)
  • General Business, Management and Accounting

Other files and links

  • Link to publication in Scopus
  • Link to citation list in Scopus

T1 - Job loss and unemployment

AU - Hout, Michael

AU - Levanon, Asaf

AU - Cumberworth, Erin

PY - 2011/1/1

Y1 - 2011/1/1

UR -

UR -

M3 - Chapter

AN - SCOPUS:84978945904

SN - 9780871544216

BT - The Great Recession

PB - Russell Sage Foundation

Congressional Budget Office logo

Refine Results By

Losing a job during a recession.

This issue brief reviews the research on the short- and long-term effects of involuntary job loss for reasons other than poor performance or misconduct on people’s future employment and earnings.

Economic and Budget Issue Brief

Each year, even when the economy is growing, millions of people lose a job for reasons other than poor performance or misconduct. The ability of employers to quickly adjust the size of their workforces in response to changes in demand is generally considered a source of strength for the U.S. economy over the long term, because it prompts a shift of labor resources toward areas of higher productivity. Some people, however, bear substantial costs from employers’ flexibility—particularly during recessions, when many people lose jobs and new opportunities are relatively scarce.

This issue brief reviews the research on the short- and long-term effects of involuntary job loss for reasons other than poor performance or misconduct on people’s future employment and earnings. In light of the recession that began in December 2007 and CBO’s projection that, under current law, the unemployment rate will remain elevated for a number of years, the brief focuses on the effects of involuntary job loss during periods of weak economic activity. The brief also summarizes some of the government programs that help people who have lost their job.


Job Openings and Hiring Are at a 3-Year Ebb

March data showed a cooling labor market, but layoffs remain low. The overall trend is likely to be welcomed by Federal Reserve policymakers.

  • Share full article

Two men shake hands next to a table with a yellow tablecloth and leaflets. They are surrounded by other people and corporate signage.

By Ben Casselman

  • May 1, 2024, 11:20 a.m. ET

The red-hot labor market cooled somewhat in March, government data showed on Wednesday.

Employers had 8.5 million unfilled job openings on the last day of March, the fewest since early 2021, according to data released by the Labor Department . They also filled the fewest jobs in nearly four years, suggesting that employers’ seemingly insatiable demand for workers might finally be abating.

A slowing labor market would be welcome news for policymakers at the Federal Reserve, who are concluding a two-day meeting on Wednesday amid signs that inflation is proving difficult to stamp out. Fed officials have said they see falling job openings as a sign that supply and demand are coming into better balance.

For workers, however, that rebalancing could mean a loss of the bargaining power that has brought them strong wage gains in recent years. The number of workers voluntarily quitting their jobs fell to 3.3 million, the lowest level in more than three years and a far cry from the more than four million a month who were leaving their jobs at the peak of the “great resignation” in 2022.

“This continued moderation is largely positive for the market and the economy overall, and is mostly sustainable for the time being,” Nick Bunker, economic research director for the Indeed Hiring Lab, wrote in a note on Wednesday. But, he added, “if job openings continue to decline for much longer, hiring of unemployed workers will eventually retreat enough to drive unemployment up.”

There is little sign of that so far, however. Despite high-profile job cuts at a few large companies, layoffs remain low overall, and fell in March. And while job openings have fallen, there are still about 1.3 available positions for every unemployed worker. Data released by the Labor Department on Tuesday showed that wage growth picked up in the first three months of the year, suggesting workers retain some leverage.

The data released Wednesday came from the Labor Department’s monthly survey of job openings and labor turnover. Economists will get a more timely snapshot of the labor market on Friday, when the government releases its monthly jobs report.

Forecasters expect that data to show that employers added about 240,000 jobs in April and that the unemployment rate remained below 4 percent for the 27th consecutive month.

Ben Casselman writes about economics with a particular focus on stories involving data. He has covered the economy for nearly 20 years, and his recent work has focused on how trends in labor, politics, technology and demographics have shaped the way we live and work. More about Ben Casselman

A woman said her tattoos got her rejected for a job, but experts say personality is far more important

  • A tattooed content creator sparked a debate about hiring biases after being rejected by T.J. Maxx.
  • Experts said tattoos could influence hiring decisions, especially in customer-facing roles.
  • But overall, personality and cultural fit are more important, they said.

Insider Today

A TikToker, Ash Putnam, was frustrated after T.J. Maxx denied her application — and she said she thought her tattoos were to blame.

Some of her designs that are visible when she's dressed are a skull with horns on her neck, solid black patches on her arms, and a pattern on her forehead. Putnam, 23, also has multiple facial piercings , including a large silver ring hanging from her septum.

"I hate that my tattoos are such a defining factor for me getting a job or not," she said in a recent TikTok. "Just because I have tattoos doesn't mean I'm not going to be a good worker."

Putnam, from California, said she went into the store to ask why she hadn't gotten the job and that the hiring manager told her she didn't have enough experience. The hiring manager also denied that her tattoos played any role in the rejection, she said. T.J. Maxx did not respond to a request for comment.

She wasn't convinced and took to TikTok to complain. Many commentators claimed her attitude may have been to blame, rather than her tattoos. Others said they thought her body art likely played a role in the rejection.

While the jury is out over whether tattoos can damage your prospects of being hired, experts told BI that the personality of a candidate was likely more important for recruiters.

Putnam's story went viral

Putnam's video amassed 7.4 million views, and it struck a nerve.

"HR supervisor here," one person commented. "There is no way any company would put you in front of customers like T.J. Maxx."

Another commenter, who said they used to be a hiring manager for the store, said: "I will tell you it's the facial piercings and tattoos."

@ashxobrien I want to know who is also having a hard time finding a job right now! #jobs #jobmarket ♬ original sound - Ash🖤

Some fellow content creators criticized Putnam's approach.

Ivy Johnson, for example, who also has many tattoos, said she worked in corporate America as a hiring manager before starting her apothecary business.

"Your tattoos are very aggressive," she said. With customer-facing positions, she said, "that doesn't always go over well."

Johnson said she also thought Putnam had "a really bad attitude."

"If you had come into my business after an interview, or even applying and chatting on the phone, even if I didn't even know that you're a heavily tattooed person, I'd be like, 'Yo, bye, there's the door,'" she said.

Related stories

"You have to put your best foot forward in an interviewing circumstance, no matter what you do, what you're applying for, or what you look like," she added.

@svvampfae #stitch with @Ash🖤 #heavilytattooed #tattoo #facetattoo #hiring #jobtips #job ♬ original sound - svvampfae

It depends on the role

Almost one-third (32%) of US workers in a 2023 Pew Research Center survey said they had a tattoo, and 22% said they had more than one.

Some studies have suggested that tattoos can affect someone's career progression. In a 2018 LinkedIn survey, 40% of respondents said they had rejected a candidate for a job because they had a visible tattoo. Eighty-eight percent of recruiters and human-resources professionals who responded said they thought tattoos limited a candidate's prospects.

However, research from the University of Miami that same year found tattooed job seekers were no less likely to be employed than those without.

The stigma of tattoos is lessening every day, with many employers no longer having an issue with hiring tattooed employees, according to Indeed .

There may still be a line, though, and some of Putnam's viewers argued that she crossed it. Putnam declined to comment for this article, but she told the UK publication The Daily Star : "I am not going to change who I am for minimum-wage jobs."

Adam Collins, the founder and CEO of Ignite SEO, told BI that as someone who hired people to work at his company, he thought "tattoos can make a big impact on how a candidate is perceived."

"I wouldn't say that tattoos make or break an interview because it depends on the role," he said. "A candidate applying to be an account manager for our clients and is supposed to speak to our clients directly should definitely appear trustworthy and clean-cut, so face and neck tattoos would affect that."

On the other hand, with someone who isn't directly working with clients, appearance is less important.

In technical and operational roles, for example, "it's not a big deal," Collins said.

Michelle Enjoli, a career coach, told BI the visibility and type of tattoos someone has could make a difference.

"Tattoos are personal and typically represent something for that person," she said. "People represent companies, and therefore if a tattoo represents something that a company would not want to be associated with, it can definitely be an issue for a hiring manager."

How likely it is that a tattoo will determine the course of an interview depends on how visible they are and what they may represent, Enjoli added. Tattoos are nowhere near as much of a taboo as they used to be, but some people still hold judgment over them.

In Putnam's case, her tattoos were considered extreme, Enjoli said, and "seemed to be a big part of her identity."

"In other cases, where someone might have a smaller tattoo on their arm or visible area, it might not matter as much as it is less obvious," Enjoli said.

"I think a company demanding that an employee not have any tattoos regardless of visibility or meaning is definitely outdated as they have become a big part of the modern culture."

Personality matters more

Justina Raskauskiene, the HR team lead at Omnisend, told BI as tattoos had become more common, it's likely recruiters and hiring managers barely paid attention to them "unless they are offensive or distracting."

"Sometimes hiring managers may even prefer an employee with a tattoo because it can be evidence of an interesting personality," Raskauskiene said.

"Discriminating against those people would mean missing out on some talented people in the industry."

Rachel Pelta, a hiring expert who is the head writer at the virtual-work-experience platform Forage, told BI that overall, hiring managers were looking at skills and abilities.

"The thing is, everyone who's interviewing probably has the skills and abilities I'm looking for," she said. "So then it comes down to, how well are you selling yourself in the interview? Are you making the case for why you're the best person for the role? If you're not doing that, you won't get the job."

As for tattoos, piercings, or anything else that could be considered unusual, such as bright hair colors, hiring managers "shouldn't evaluate a candidate on their appearance," Pelta added.

But some companies are traditional or conservative, and for them, these things could be a "big deal."

"Unless you're willing to cover or remove them, you'll have to keep searching until you find a company that accepts you as you," she said. "And they are out there. It just may take you a bit longer to find one."

Watch: I got faux freckles tattooed

research title about job loss

  • Main content

Research Area Specialist Inter

The selected candidate for this position will serve as a key research member for the Nutrition, Exercise and Phenotype Testing Core (NExT).   NExT is a collaborative network of laboratory and research facilities on campus, which provide expanded support for clinical and translational research studies using nutrition interventions and/or focused on obesity or obesity-related metabolic disorders in humans.   The immediate program needs for this position are focused on managing the Physical Activity Lab ( PAL ) daily operations, conducting metabolic testing, body composition and assist with physical activity design.   This position will also be expected to cross coverage the Nutrition Assessment Lab ( NAL ) with nutrition research studies.  

Mission Statement

Michigan Medicine improves the health of patients, populations and communities through excellence in education, patient care, community service, research and technology development, and through leadership activities in Michigan, nationally and internationally.  Our mission is guided by our Strategic Principles and has three critical components; patient care, education and research that together enhance our contribution to society.


Physical Activity Lab:

  • Collaborate and coordinate with research teams to conduct high quality research; maintain knowledge of specific study and regulatory guidelines to help ensure compliance.
  • Participate in the development and implementation of new research projects.
  • Conduct metabolic and exercise tests  e.g., VO2max, Resting Metabolic Rate, fitness assessments, body composition.  Oversee/coordinate exercise training studies. 
  • Manage project data, including data collection, organization and storage, as well as ensure data consistency/security.
  • Assist in data analysis and interpretation of data findings e.g., lifestyle behavior data collected from wearable devices.
  • Supervise, orient, coordinate and manage other members of the research team including graduate/undergraduate students and other trainees.
  • Manage monthly totals, billing and assure deadlines are met.
  • Prepare material for research team meetings and summarize meeting notes.

Nutrition Assessment Lab:

  • Research and implement nutrition procedures related to dietary interventions
  • Coordinate and operate metabolic kitchen to produce research meals
  • Perform dietary assessment and analysis with NDSR computer software
  • Perform body composition measurements
  • Coordinate day to day operations of Nutrition Assessment Laboratory
  • Collaborate with study coordinators, ancillary personnel and Principal Investigators

Required Qualifications*

  • Bachelors degree in Exercise Physiology or a related field and have experience in a research setting
  • 4-5 years of experience in a related field
  • Experience with clinical exercise research, various types of exercise testing, and diverse populations are preferred
  • Familiarity with proper collection of data and will possess at least basic knowledge of study design and data management
  • Familiarity with lifestyle behavior data collected from wearable devices
  • Strong interpersonal skills and the ability to effectively interact with investigators, collaborators, other project staff, students and study subjects are necessary
  • The candidate must be highly organized and possess excellent oral and written communication skills
  • Basic knowledge of eResearch is preferred
  • Nutritional research studies experience
  • Computer proficiency
  • Excellent interpersonal skills

Desired Qualifications*

Desired Qualifications:

?          A Masters degree in Exercise Physiology

Additional Information:

This position may have possible evening/weekend work.   This position will be expected to provide in person coverage at Domino Farms and Cardiovascular Center and some duties can be performed from home.

Background Screening

Michigan Medicine conducts background screening and pre-employment drug testing on job candidates upon acceptance of a contingent job offer and may use a third party administrator to conduct background screenings.  Background screenings are performed in compliance with the Fair Credit Report Act. Pre-employment drug testing applies to all selected candidates, including new or additional faculty and staff appointments, as well as transfers from other U-M campuses.

Application Deadline

Job openings are posted for a minimum of seven calendar days.  The review and selection process may begin as early as the eighth day after posting. This opening may be removed from posting boards and filled anytime after the minimum posting period has ended.

U-M EEO/AA Statement

The University of Michigan is an equal opportunity/affirmative action employer.


  1. Job Loss & Unexpected Unemployment

    research title about job loss

  2. Research paper on job description pdf in 2021

    research title about job loss

  3. How to Deal with a Job Loss

    research title about job loss

  4. Job loss by wage

    research title about job loss

  5. HakiPensheni: Use job loss numbers for candid enterprise review

    research title about job loss

  6. (PDF) The Cost of Job Loss

    research title about job loss


  1. Common questions for research title defense #speechwithgia #research #thesis #philippines

  2. QUALITATIVE RESEARCH TITLES FOR STEM STUDENTS #researchtitle #qualitativeresearch #stem

  3. These People Have Chances of Job Loss || ఈ రాశుల వారు ఉద్యోగం కోల్పోయే ప్రమాదం ఉంది జాగ్రత్త


  5. IMF Report: AGI destroys all jobs within 5 to 20 years! Frontier of Automation expands beyond humans

  6. Writeshop 1. Crafting Research Title and Statement of the Problem


  1. The Far-Reaching Impact of Job Loss and Unemployment

    Abstract. Job loss is an involuntary disruptive life event with a far-reaching impact on workers' life trajectories. Its incidence among growing segments of the workforce, alongside the recent era of severe economic upheaval, has increased attention to the effects of job loss and unemployment. As a relatively exogenous labor market shock, the ...

  2. What helps the unemployed to stay healthy? A qualitative study of

    A research gap exists regarding salutogenic factors and successful coping strategies to master involuntary job loss and unemployment with the least damage to health. ... Job loss coping strategies have most often been conceptualized as either problemfocused or emotion-focused coping strategies. 26 Problem-focused coping strategies involve all ...

  3. The toll of job loss

    The mental health impacts of today's job losses are likely to be significant, given a large body of research showing that unemployment is linked to anxiety, depression and loss of life satisfaction, among other negative outcomes. Similarly, underemployment and job instability—two additional results of the coronavirus pandemic—create ...

  4. Unemployed Americans are feeling the emotional strain of job loss; most

    The Center's survey, conducted Jan. 19-24, finds that 49% of adults who are unemployed and looking for work say they are pessimistic they will find a job in the near future: 18% are very pessimistic about this and 31% are somewhat pessimistic. A similar share (51%) are optimistic, with 15% saying they are very optimistic and 36% saying they ...

  5. What Helps the Unemployed to Stay Healthy? A Qualitative Study of

    Job loss coping strategies have most often been conceptualized as either problemfocused or emotion-focused coping strategies. 26 Problem-focused coping strategies involve all own actions, ... Job loss and unemployment research from 1994 to 1998: A review and recommendations for research and intervention. J Vocat Behav 1999;55:188-220. Crossref.

  6. The traumatic impact of job loss and job search in the aftermath of

    Title. The traumatic impact of job loss and job search in the aftermath of COVID-19. Publication Date. Aug 2020. Publication History. First Posting: Jun 1, 2020. ... Hanisch, K. A. (1999). Job loss and unemployment research from 1994 to 1998: A review and recommendations for research and intervention. Journal of Vocational Behavior, 55(2), 188 ...

  7. The Effect of Job Loss on Health: Evidence from Biomarkers

    Abstract. We estimate the effect of job loss on objective measures of physiological dysregulation using biomarker measures collected by the Health and Retirement Study in 2006 and 2008 and longitudinal self-reports of work status. We distinguishing between mass or individual layoffs, and business closures.

  8. Moving from job loss to career management: The past, present, and

    1. Introduction to job loss and job loss research. Job loss continues to be a pervasive issue for millions of workers around the globe. In the "new normal" economy, many individuals can expect to lose a job through a downsizing, company closure, or restructuring, and expect to be unemployed for a longer period of time (Censky, 2011).Research has documented the negative impact of the ...

  9. Unemployment Scarring Effects: An Overview and Meta-analysis of

    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 ...

  10. What helps the unemployed to stay healthy? A qualitative study of

    negative effects of unemployment on health. A research gap exists regarding salutogenic factors and successful coping strategies to master involuntary job loss and unemployment with the least dam-age to health. Hence, this study aims at generating a deeper under-standing of coping with unemployment and maintaining health.

  11. PDF Knowledge of Future Job Loss and Implications for Unemployment Insurance

    job loss. Regressing the subjective probability elicitations on the job loss indi) - cator suggests 80percent of the information about job loss is revealed in the last year prior to unemployment. I show that one can divide the 7-10percent first difference estimate by this first stage of 0.8 to arrive at the average causal effect of 8-13 ...

  12. Frontiers

    Driven by the ongoing debate of job loss vs. income loss in understanding the detrimental effect of unemployment, this study examines how perceptions of unemployment and the resulting levels of life satisfaction differ by immigration status. Based on a countrywide longitudinal dataset in the UK, findings show that immigrant men's life satisfaction suffers more from the detrimental effect of ...

  13. Gender Gaps in Employment and Earnings after Job Loss

    Women also experience a larger relative loss in earnings (panel b). In the first year after job displacement, the gender gap in earnings loss is 44 percent, as men lose on average 19.6 percent of their earnings while women lose 28.2 percent of theirs. In the fourth year following displacement, the gender gap disappears.

  14. PDF Understanding and Predicting Job Losses due to COVID-19

    Jordan's female participation rate (13.4 percent in 2019)8 is among the lowest in the world and female unemployment — 19 percent at the end of Q4 2019 was also relatively high just before the COVID-19 pandemic. Georgia's female participation and unemployment rates, in contrast, were 54.5% and 10.2%, 9 respectively.

  15. Job loss and psychological distress during the COVID‐19 pandemic

    Job loss has been found to lead to large decreases in self-reported health, ... (2000, 2003) approaches using state-level economic conditions, and the marginal worker who loses their job. Still, other research has found the opposite. During the Great Recession, there was an overall increase in mental illness; ...

  16. Job loss and mental health during the COVID-19 lockdown: Evidence from

    Objectives Existing literature on how employment loss affects depression has struggled to address potential endogeneity bias caused by reverse causality. The COVID-19 pandemic offers a unique natural experiment because the source of unemployment is very likely to be exogenous to the individual. This study assessed the effect of job loss and job furlough on the mental health of individuals in ...

  17. Unemployment, Behavioral Health, And Suicide

    The most recent US-based studies have generally focused on the Great Recession from 2007 to 2009. This research finds that a 1-percentage-point increase in unemployment is associated with a 1 ...

  18. The Rising Stakes of Job Loss

    The patterns of job creation following the last two recessions have raised the stakes of job loss for a broadening segment of American families. Women represented 43% of long-term jobless workers, on average, from 2001-04, up from 35% compared to the 1990-93 period. Such long-term unemployment has a direct impact on children and families ...

  19. Recovering from a traumatic job loss

    The belief in one's personal competence. (confidence in one's sense of self and one's abilities) and receiving emotional support. were reported to be critical factors for an individual to recover from a traumatic job loss. Thus, emphasis on providing these essential support mechanisms should be a priority in.

  20. Job loss and unemployment

    Hout, Michael; Levanon, Asaf ; Cumberworth, Erin./ Job loss and unemployment.The Great Recession. Russell Sage Foundation, 2011. pp. 59-81

  21. Experiences of unemployment and well-being after job loss during

    The psychological consequences of unemployment have been reported mainly from quantitative research, which has consistently shown that loss of work and changes in one's financial situation are associated with decreased mental health. 18,48-50 For instance, in a meta-analysis of 237 cross-sectional and 87 longitudinal studies, Paul & Moser ...

  22. Losing a Job During a Recession

    Losing a Job During a Recession. April 22, 2010. Report. This issue brief reviews the research on the short- and long-term effects of involuntary job loss for reasons other than poor performance or misconduct on people's future employment and earnings. View Document. 631.55 KB.

  23. Job Openings and Hiring Are at a 3-Year Ebb

    Despite high-profile job cuts at a few large companies, layoffs remain low overall, and fell in March. And while job openings have fallen, there are still about 1.3 available positions for every ...

  24. A Woman Said Her Tattoos Got Her Rejected for a Job, Sparked Debate

    A TikToker, Ash Putnam, was frustrated after T.J. Maxx denied her application — and she said she thought her tattoos were to blame.. Some of her designs that are visible when she's dressed are a ...


    Michigan Medicine conducts background screening and pre-employment drug testing on job candidates upon acceptance of a contingent job offer and may use a third party administrator to conduct background screenings. Background screenings are performed in compliance with the Fair Credit Report Act.

  26. Identifying key indicators of job loss trends during COVID-19 and

    We identify the ethnic groups and job sectors that are affected by the pandemic and demonstrate that Gross Domestic Product (GDP), race, age group, lockdown severity and infected count are the key indicators of post-COVID job loss trends. Keywords: COVID-19, Economy, Job loss, Policymaking, Regression. 1. Introduction.

  27. Clinical Research Coord Inter

    Additional Information. This position can be filled at 80-100% effort. This is a time-limited position with funding secured until August 2026. The Physical Medicine and Rehabilitation Department within Michigan Medicine is firmly committed to advancing inclusion, diversity, equity, accessibility, and belonging, which are core to the culture and values of the Medical School Office of Research.

  28. Research Area Specialist Inter

    The selected candidate for this position will serve as a key research member for the Nutrition, Exercise and Phenotype Testing Core (NExT). NExT is a collaborative network of laboratory and research facilities on campus, which provide expanded support for clinical and translational research studies using nutrition interventions and/or focused ...