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

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).

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%).

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.

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.

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 is director of social trends research at Pew Research Center .

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

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

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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 .

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Investigating the Impact of Job Loss and Decreased Work Hours on Physical and Mental Health Outcomes Among US Adults During the COVID-19 Pandemic

Guerin, Rebecca J. PhD; Barile, John P. PhD; Thompson, William W. PhD; McKnight-Eily, Lela PhD; Okun, Andrea H. DrPH

Division of Science Integration (DSI), National Institute for Occupational Safety and Health (NIOSH), Centers for Disease Control and Prevention (CDC), Cincinnati, Ohio (Dr Guerin, Dr Okun); Social Science Research Institute, College of Social Sciences, University of Hawaii at Manoa, Honolulu, HI (Dr Barile); Division of Viral Hepatitis, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia (Dr Thompson); COVID 19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia (Dr McKnight-Eily).

Address correspondence to: Rebecca J. Guerin, PhD, CDC/NIOSH/DSI, 1090 Tusculum Avenue, Mailstop C-10, Cincinnati, OH 45226 ( [email protected] ).

Funding sources: N/A.

Conflicts of interest: None declared.

Ethical considerations and disclosures: N/A.

Disclaimer: The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention.

Clinical significance: Study results indicate the need for mental health supportive services for people—including those with lower levels of household income—experiencing adverse changes in employment status during the COVID-19 pandemic. Potential, deleterious effects of longer unemployment duration and worse mental health outcomes for some groups (eg, females) should be assessed.

Objective: 

To investigate associations between adverse changes in employment status and physical and mental health among US adults (aged 18 years or older) during the COVID-19 pandemic.

Methods: 

Data from participants ( N = 2565) of a national Internet panel (June 2020) were assessed using path analyses to test associations between changes in self-reported employment status and hours worked and physical and mental health outcomes.

Results: 

Respondents who lost a job after March 1, 2020 (vs those who did not) reported more than twice the number of mentally unhealthy days. Females and those lacking social support had significantly worse physical and mental health outcomes. Participants in the lowest, pre-pandemic household income groups reported experiencing worse mental health.

Conclusions: 

Results demonstrate the importance of providing economic and social support services to US adults experiencing poor mental and physical health during the COVID-19 pandemic.

INVESTIGATING THE IMPACT OF JOB LOSS DURING THE COVID-19 PANDEMIC ON PHYSICAL AND MENTAL HEALTH OUTCOMES AMONG US ADULTS

The significant mental health impacts of the COVID-19 pandemic on individuals living in the United States is becoming more apparent. 1,2 According to one study, among US adults, national estimates of self-reported symptoms of anxiety disorder were approximately three times higher during the pandemic in the second quarter of 2020 compared with the second quarter of 2019 (25.5% vs 8.1%); the prevalence of depressive disorder symptoms was approximately four times higher (24.3% vs 6.5%). 3 Researchers have also demonstrated that the pandemic has exacerbated existing mental health disparities by sociodemographic factors. 1,3 Results from a national survey of US adults fielded from March 10 to 16, 2020 indicate that people who were younger, female, or not married were significantly more likely to experience greater levels of mental distress, while people with higher household incomes or those currently employed were less likely to experience significant mental distress. 1

To date, limited evidence exists on the mental-health related impact of unemployment (and underemployment) during the current public health crisis and efforts in this area have been called for. 4 Our study addresses these gaps in the literature by exploring how changes in employment status and hours worked resulting from the COVID-19 pandemic have affected people's physical and mental health in the United States. We were guided by multiple theoretical perspectives that support the notion that unemployment and underemployment have a negative impact on physical and mental health. 5–14 We analyzed data from a large, national sample weighted to United States adult population statistics during the first wave of the COVID-19 pandemic, when businesses were shutting down and/or laying off or furloughing workers. We investigated (pre-pandemic) income as a moderator of the association between changes in employment and physical and mental health outcomes and explored whether these changes were differentially experienced by respondents across varying levels of household income. Social support is an important resource that may attenuate the effects of loss-related events, 8 and thus we included this variable in our analysis. We also explored the extent to which sociodemographic variables—including age, gender/sex, race/ethnicity, educational attainment, and marital status—are associated with physical and mental health outcomes. Identifying groups that are disproportionately affected by the COVID-19 pandemic can assist in prioritizing populations for interventions, now and in future public health emergencies.

THEORY DEVELOPMENT

Unemployment.

Substantial evidence from systematic reviews and meta-analyses reveals the link between unemployment and adverse mental health outcomes, supporting a possible causal relationship, 5,11,12,14 as well as poorer self-reported functional health and more chronic diseases. 13 Mental health effects of adverse employment changes have been shown to be mediated by both the loss of income and social-psychological supports such as collective purpose, social contact, and social status. 6,9,10,13 Theory also suggests that people are motivated to foster, protect or attain resources, and stress occurs when key resources are threatened to be lost, are actually lost, or new resources are not gained. 15,16

During the COVID-19 pandemic, communities have suffered as a result of job/economic loss, work and school closures, and inadequate health and safety resources. 17 These effects have translated into a range of emotional disturbances. 1,18 We therefore propose:

Hypothesis 1

Losing a job after March 1, 2020, during the COVID-19 pandemic in the United States, controlling for other factors, will be associated with worse physical and mental health.

Underemployment

Underemployment (defined in part as working less than full-time or at a lower wage than would be expected) has also been shown to have a detrimental effect on mental health. 6,19 Findings from a longitudinal study by Friedland and Price 19 indicate that underemployed (eg, having inadequate wages, hours, or status) compared with adequately employed workers reported lower levels of health and well-being. Dooley et al 6 found in a longitudinal survey of US youth (aged 14–22 years) that underemployment was associated with more depressive symptoms (controlling for prior depression). Research from Australia of a cohort of working people indicates that declines in mental health were associated with decreasing hours of employment. 20 We therefore propose:

Hypothesis 2

Having work hours reduced after March 1, 2020, controlling for other factors, will be associated with worse physical and mental health.

Social Support

People receiving more social support have been reported to have better physical and mental health. 21–23 Social support is an important resource that may attenuate the effects of loss-related events. 8 National survey research from Holingue et al 1 indicates that being separated or divorced, or being never married, during COVID-19 were significantly associated with greater levels of psychological distress. Cao et al 24 report that social support was negatively correlated with anxiety among Chinese college students during the pandemic. Having adequate social support appears to be critical for physical and mental health during public health emergencies and natural disasters. 25,26 Based on this evidence, we propose:

Hypothesis 3

Lack of social support, controlling for other factors, will be associated with more physically and mentally unhealthy days and more depressive and anxiety symptoms.

Research by Weissman et al 27 indicates that people in higher income brackets experience lower rates of serious mental distress. This is consistent with theory suggesting that people with compromised resources are most susceptible to additional resource loss, while those with higher socioeconomic status are more likely to have resource buffers that facilitate coping. 16 Evidence suggests that unemployment is harmful to both health and well-being, in itself and because it results in the loss of income. 28 Furthermore, unemployment has been shown to contribute to significantly poorer mental health among groups with low (previous) wages. 29 It has been posited that being in a socially disadvantaged position is by itself a risk factor for mental distress, amplifying the negative impact of unemployment on mental health. 29 During the COVID-19 pandemic, research indicates that higher household income and being employed are protective against mental distress. 1 These results are partially supported by McGinty et al, 30 who found that US adults ( N = 1468) with household income of less than $35,000 annually had among the highest prevalence rates of psychological distress (19.3%; [95% CI, 14.2% to 25.6%]) in April 2020 of the pandemic compared with the other subgroups included in the analysis. Given that unexpected household income reduction causes distress for individuals, job loss and wage cuts on the scale occurring during the pandemic are of substantial concern for public health. 31 Therefore, we propose:

Hypothesis 4

The association between losing a job after March 1, 2020, and poor mental health will be dependent upon household income. We also hypothesize that these associations will be the same or similar to those revealed for respondents having had their work hours reduced.

Previous evidence demonstrates that the following sociodemographic variables are associated with mental health outcomes and thus were included in our analytical models:

Prior studies indicate that among adults, mental health improves and physical health declines as individuals age. 32,33 A recent US study reports elevated rates of psychological distress among adults (in April 2020, compared with National Health Interview Survey data from 2018), with the relative increase being the greatest among young adults aged 18 to 29 years (24.0% [95% CI, 16.3% to 33.8%]) compared with other subgroups examined. 30 Analyses using a large sample ( N > 15,000) from the UK Household Longitudinal Study panel assessing mental health with the 12-item General Health Questionnaire (GHQ-12) in April 2020 indicated that mean GHQ-12 scores were higher among younger respondents (aged 16 to 24 years: 14.7 [95% CI, 14.1 to 15.3]) than in older ones (aged 70 years or older: 10.9 [95% CI 10.6 to 11.1]). 34 In a representative sample of the Austrian population 4 weeks after a national lock-down due to COVID-19, Pieh et al 35 report that adults (aged 65 years or older) in their sample were demonstrating better mental health outcomes than younger adults (aged 18 to 24 years). These results are supported by those from a national survey conducted during the beginning of the COVID-19 pandemic in the United States reporting that older (vs younger) adults scored lower on measures of both depression and anxiety. 36

In the United States, extensive evidence indicates that more depressive symptoms are commonly reported among females than males. 27,37 Holingue et al 1 also demonstrate (from results of a national survey fielded in March 2020) that females compared with males are experiencing significantly higher depression and anxiety during the pandemic, results that align with those reported by McGinty et al. 30 The study by Pierce et al 34 described previously, indicates that UK females are among the groups experiencing the greatest increase in poor mental health symptoms during the pandemic. Pieh et al 35 report that females compared with males in Austria during COVID-19 had statistically significant increased mean scores for symptoms of depression (PHQ-9) and anxiety (GAD-7).

Race/Ethnicity

Being a member of a racial or ethnic minority group in the United States has been shown to be associated with social and health inequities, but, paradoxically, with a similar or lower incidence of some negative mental health outcomes. 38–40 However, racial disparities in mental health have also been observed. 41 Racial and ethnic minority group identity and cultural values may foster some behavioral health resilience. 42 However, the COVID-19 pandemic may be contributing to disproportionate, adverse mental health outcomes for racial/ethnic groups. 43 For example, Czeisler et al 3 found that, during the pandemic, poor mental health is being disproportionately experienced by Hispanic persons and non-Hispanic Black persons. Research from McGinty et al 30 reveals Hispanic adults (April 2020) had among the highest prevalence rates of psychological distress (18.3% [95% CI, 11.2% to 28.3%]) compared with other subgroups examined.

Educational Attainment

A national survey of African American adults ( N = 3570) living in the United States indicates that higher levels of educational attainment were associated with lower depressive symptoms and psychological distress. Findings from a recent survey of US respondents conducted during the early stages of the COVID-19 pandemic, indicate that people with higher educational attainment experienced a greater increase in depressive symptoms compared with those with lower education levels. 2 Conversely, Donnelly and Farina 31 report in an analysis of a large national dataset (more than 580,000 respondents, April to July 2020) that the odds for depression were higher among respondents with lower (vs higher) levels of education.

Participants and Procedure

Data were collected by Porter Novelli Services for their SummerStyles survey 44 fielded from June 10 through June 25, 2020 via an online panel weighted to US adult population statistics from the 2019 U.S. Current Population Survey. 45 Porter Novelli adheres to professional standards and codes of conduct set forth by the Insights Association [ https://www.insightsassociation.org/issues-policies/ insights-association-code-standards-and-ethics-market-research-and-data-analytics-0 ]). Panel members were randomly recruited by mail using probability-based sampling by address from a pool of approximately 60,000 eligible respondents. Participants earned cash-equivalent reward points (worth approximately $10). The survey was sent to 6463 households and non-responders received up to three email reminders. Survey completion times were approximately 38 minutes (median). Surveys with fewer than half of the questions completed or those that were completed within 5 minutes were removed from the dataset ( n = 5). A total of 4053 adults met the inclusion criteria and completed the survey for a response rate of 62.7%.

Survey proportions weight for sex (male/female), age (18 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 64, 65+), pre-pandemic household income (21 categories, range [less than $5000, less than or equal to $250,000]), race/ethnicity (White, Non-Hispanic persons; Black, Non-Hispanic persons; all other races, Non-Hispanic persons; more than one race, Non-Hispanic persons; Hispanic/Latino persons), household size (one, two, three, four, more than or equal to five), educational attainment (less than high school; high school; some college; Bachelor's degree and higher) census region (Northeast, Midwest, South, West), and metro status (as defined by the US Office of Management and Budget [OMB] Core-Based Statistical Areas) (Non-metro, Metro).

Physical and Mental Health Outcome Variables

The outcomes of interest include two items from the U.S. Centers for Disease Control and Prevention (CDC) Healthy Days measures, 33,46,47 which have previously demonstrated content, construct, and criterion validity. 48 For physically unhealthy days, respondents assess how many days during the past 30 days their physical health was not good. For mentally unhealthy days, respondents assess how many days during the past 30 days their mental health—which includes stress, depression, and problems with emotions—was not good. 46 Additional outcome measures include the two-item version of the Patient Health Questionnaire (PHQ-2) 49 and the two-item Generalized Anxiety Disorder scale (GAD-2), 50 both of which have demonstrated high levels of sensitivity and specificity. 49,51 The PHQ-2 is a measure of depressive symptoms and anhedonia and specifically asks about (1) loss of interest and (2) feeling down, depressed, or hopeless, over the past 2 weeks. Response options include: not at all (0); several days (1); more than half the days (2); and nearly every day (3). The GAD-2 measures general anxiety disorders and asks about, (1) feeling nervous, anxious, or on edge, and (2) not being able to stop or control worrying, over the past 2 weeks. Response options are identical to those for the PHQ-2.

Predictor Variables

To assess changes in employment status and hours worked resulting from the pandemic, respondents reported: Since March 1, 2020, did you lose a job, get furloughed, or have your hours or work schedule changed due to public health closures or social distancing measures? (Response choices [check all that apply]: I lost a job; I was temporarily laid off or furloughed; I had my hours reduced; I had my hours increased; I had to work a different schedule; None of these). To assess levels of social support respondents were asked: How often do you get the social and emotional support you need? (Response choices: Always; Usually; Sometimes; Rarely; Never; I don’t know how to get what I need).

Sociodemographic Variables

Sociodemographic items assessed include age (in years, as a continuous variable), gender/sex (male/female), race/ethnicity (White, Non-Hispanic; Black, Non-Hispanic; all other races, Non-Hispanic; more than one race, Non-Hispanic; and Hispanic/Latino), educational attainment (Bachelor's, Graduate, or Professional degrees vs < Bachelor's degree), married or living with a partner (yes/no), and pre-pandemic household income (21 categories, range: less than $5000 to more than or equal to $250,000, modeled as a continuous variable).

Statistical Analyses

We used SPSS version 26 (SPSS Inc., Chicago, IL) to compare unadjusted mean scores between physical and mental health outcomes by employment status to determine which categories merited further examination. We tested two path models using full information maximum likelihood estimation with robust standard errors (MLR) in Muthén's & Muthén's Mplus 8.4 software (Muthén and Muthén, Los Angeles, CA). In the analyses, lost a job (model 1) or reduced work hours (model 2) were the independent variables, the four physical and mental health outcomes served as dependent variables, and pre-pandemic household income was a moderator of these associations. 52 Both models included and were adjusted for social support and the sociodemographic variables. We used the Johnson–Neyam technique 53 to determine whether the strength of the association between employment status (lost a job and hours reduced) and the physical and mental health outcomes were significant across all levels of household income.

Participants included 2565 US adults who indicated that they were either employed at the time of the survey or who had lost their job since March 1, 2020 during the COVID-19 pandemic. Table 1 provides demographic and social variable characteristics for the sample population. The average age of respondents was 47.7 years (range: 18 to 94 years).

Evaluation of unadjusted mean scores between physical and mental health outcomes by employment category ( Table 2 ) indicates that having lost a job since March 1, 2020 (at the time the survey was conducted) was significantly associated with poorer mental health outcomes (mentally unhealthy days, p < 0.05; PHQ-2, p < 0.05; GAD-2; p < 0.05), but was not significantly associated with physically unhealthy days. On average, respondents who lost a job reported more than twice the number of mentally unhealthy days compared with those who did not lose a job (5.24 days vs 2.41 days out of the last 30, respectively). Respondents who were temporarily laid off /furloughed after March 1, 2020 (at the time the survey was conducted) had higher unadjusted mean scores across all physical and mental health outcomes compared with those not experiencing these changes, but only reached significance on the PHQ-2 ( p < 0.01). The same pattern was observed for respondents who had to work a different schedule after March 1, 2020.

Similar to respondents losing a job, having reduced work hours was significantly associated with poorer mental health (mentally unhealthy days, p < 0.01; PHQ-2, p < 0.05; GAD-2, p < 0.05), and with poorer physical health (physically unhealthy days, p < 0.01). Having hours reduced resulted in poorer physical and mental health than did being laid off or furloughed. Respondents having hours increased had higher unadjusted mean scores across the physical and mental health outcomes compared with those not having hours increased, but only reached significance for GAD-2 scores ( p < 0.05). Overall, those who reported not having any changes to either employment status, hours worked, or schedule since March 1, 2020, had better physical and mental health compared with respondents’ who did experience such changes. Based on the findings presented above and in Table 2 and to investigate our hypotheses, we analyzed separate path models for the “lost a job” (model 1) and the “hours reduced” (model 2) categories.

Table 3 presents results for model 1 ( Table 4 presents the bivariate correlation matrix) indicating that losing a job after March 1, 2020 (reported at the time the survey was conducted in June 2020) was associated with more mentally unhealthy days ( b = 7.06; standard error [SE] = 2.82; p = 0.012), worse depression (PHQ-2; b = 0.91; SE = 0.44; p = 0.038), and more anxiety (GAD-2; b = 1.08; SE = 0.43; p = 0.013). These results support Hypothesis 1. The results for model 2 ( Table 5 ) indicate that having work hours reduced was significantly associated with more anxiety (GAD-2; b = 0.88; SE = 0.30; p = 0.003), providing partial support for Hypothesis 2.

For Model 1 ( Table 3 ), not being able to obtain social support was significantly associated with poorer physical health (physically unhealthy days: b = 0.57; SE = 0.11; p < 0.001) and mental health (mentally unhealthy days: b = 1.33; SE = 0.13; p < 0.001; PHQ-2: b = 0.36; SE = 0.03; p < 0.001; and GAD-2: b = 0.28; SE = 0.03; p < 0.001). Results for the social support variable in model 2 ( Table 5 ), are nearly identical to those for model 1 (physically unhealthy days [ b = 0.57; SE = 0.11; p < 0.001]; mentally unhealthy days [ b = 1.34, SE = 0.13; p < 0.001]; depression [PHQ-2: b = 0.36; SE = 0.03; p < 0.001]; and anxiety [GAD-2: b = 0.28, SE = 0.03; p < 0.001]). These results provide support for Hypothesis 3.

As illustrated by model 1 ( Table 3 ), pre-pandemic household income was significantly associated with physically unhealthy days ( b = –0.11; SE = 0.03; p = 0.001), depression (PHQ-2: b = –0.03; SE = 0.01; p < 0.001) and anxiety (GAD-2: b = –0.02; SE = 0.01; p = 0.008), with respondents from lower (vs higher) income households reporting more physically unhealthy days and worse depression and anxiety. A significant interaction effect was observed between pre-pandemic household income and “lost a job” for mentally unhealthy days ( b = –0.36; SE = 0.17; p = 0.037), indicating that having lost a job after March 1, 2020 (at the time the study was conducted) and having more mentally unhealthy days was moderated by the respondents’ household income. These results provide partial support for Hypothesis 4. To further investigate this interaction, we explored whether there were differential effects between having lost a job and mentally unhealthy days for each of the 21 income categories (range: less than $5000 to more than or equal to $250,000). This analysis revealed that the strength of the association between losing a job and a higher number of mentally unhealthy days was dependent upon household income. Respondents in the lowest income groups experienced the strongest associations between losing a job and mentally unhealthy days, up to a household income of $100,000 ( b = 1.70; SE = 0.76; p = 0.024), after which this association is no longer significant (see Fig. 1 ).

Although the overall interactions did not reach significance for the other mental health outcomes, at the lower income levels, significant effects were found between those who lost a job and PHQ-2 and GAD-2. For PHQ-2, individuals in the lowest income groups experienced the strongest associations between “lost a job” and worse symptoms of depression, up to a household income of $85,000 ( b = 0.28; SE = 0.13; p = 0.028), when this association is no longer significant. As illustrated in Fig. 1 , for those who lost a job, the associations with worse anxiety symptoms were significant for levels of household income up to $125,000 ( b = 0.28; SE = 0.14; p = 0.043).

For model 2, having work hours reduced (results presented in Table 5 ), pre-pandemic household income was significantly associated with physically unhealthy days ( b = –0.10; SE = 0.03; p = 0.002), and depression (PHQ-2: b = –0.03; SE = 0.01; p = 0.001), and approached significance for mentally unhealthy days ( b = –0.07; SE = 0.04; p = 0.052). These findings indicate that respondents who had their hours reduced from lower income households reported more physically unhealthy days and worse depression. We found a significant interaction between “hours reduced” and income for GAD-2 ( b = –0.05; SE = 0.02; p = 0.019), providing partial support for Hypothesis 4. When probing the interaction further, findings indicate that the associations between reduced work hours and GAD-2 were significant across the levels of household income up to $100,000 b = 0.19; SE = 0.07; p = 0.008).

Increased age (model 1, results presented in Table 3 ) was significantly associated with more physically unhealthy days ( b = 0.02; SE = 0.01; p = 0.010) but better mental health (mentally unhealthy days: b = –0.05; SE = 0.01; p ≤ 0.001); PHQ-2: b = –0.01; SE < 0.001; p < 0.001; GAD-2: b = –0.02; SE < 0.001; p < 0.001). The results for the age variable in model 2 ( Table 5 ) were nearly identical to those reported for model 1.

Being female (model 1) was significantly associated with more physically unhealthy days ( b = 0.65; SE = 0.21; p = 0.002), more mentally unhealthy days ( b = 1.28; SE = 0.23; p < 0.001) and worse depressive and anxiety symptoms (PHQ-2: b = 0.17; SE = 0.05; p < 0.001; GAD-2: b = 0.36; SE = 0.05; p < 0.001). The results for the gender/sex variable in model 2 were nearly identical to those reported for model 1, with females having worse outcomes across all measures.

Hispanic compared with non-Hispanic White participants had significantly fewer physically unhealthy days ( b = –0.58; SE = 0.25; p = 0.019) and mentally unhealthy days ( b = –0.71; SE = 0.36; p = 0.047) (see model 1). When considering the effects by race/ethnicity in model 2, the only significant association was for Hispanic (compared with non-Hispanic White) participants having significantly fewer physically unhealthy days ( b = –0.60; SE = 0.25; p = 0.015).

Educational attainment or being married or living with a partner were not significantly associated with any of the physical or mental health outcomes assessed in either model.

Recent research demonstrates that the COVID-19 pandemic has negatively affected people's, including workers’, 54 health and well-being. 1–3,17,26,30,34 Losing a job or having work hours reduced can further exacerbate the negative impacts of this public health crisis. 55 In the current study, respondents who reported losing a job had more than twice the number of mentally unhealthy days (over the previous 30 days) compared with those who did not lose a job. These findings are consistent with a substantial body of evidence that unemployment and underemployment are detrimental to people's mental health. 5,6,11–13,19,20 Consistent with theory, 9,10 unemployment (or underemployment) during the pandemic may be depriving people of critical supports, potentially contributing to negative mental health outcomes. 17,18,26 Previous research also indicates that longer term unemployment is harmful to subsequent mental health. 13,56 Thus, it will be important to monitor on-going changes in/disruptions to employment among US adults due to the COVID-19 pandemic. 4

Although respondents who were temporarily laid off or furloughed after March 1, 2020 during the pandemic (at the time the survey was conducted) reported increased levels of anxiety symptoms, overall, these individuals had better physical and mental health outcomes than those who reported losing a job or having hours reduced. A possible explanation may be that those who were laid off or furloughed had the expectation of being rehired, and therefore perceive their non-working status as temporary. 28 These workers may have also received unemployment benefits or compensation that could have mitigated the negative impacts of being laid off or furloughed, 4 and/or they may sense relief at not having to report to a workplace where the risk of virus transmission is potentially increased.

We report that not being able to obtain social support was significantly associated with poorer physical and mental health outcomes, which theory posits is a critical resource that ameliorates the effects of loss-related events, and that not having adequate social support undermines mental health. 6,8,17,22,23,57,58 Cao et al 24 found that social support during the COVID-19 pandemic was negatively correlated with anxiety among Chinese college students. Moreover, results from Holingue et al 1 indicate that being separated or divorced, or being never married, during COVID-19 were significantly associated with greater levels of psychological distress. The American Psychological Association 59 promotes the importance of social support during the COVID-19 pandemic. 26 Broadly speaking, having adequate social support is critical for fostering good physical and mental health during public health emergencies and natural disasters. 25,26

Findings from the current analyses reveal that the impact of job loss and reduced work hours on mental health outcomes was differentially experienced by respondents at various income levels, with those in the lowest household income groups experiencing worse mental health outcomes during the COVID-19 pandemic. These findings are consistent with research from China 24 demonstrating that having a less stable family income increased the risk of mental distress during the pandemic. Pieh et al 35 reported that people in lower income groups in Austria experienced a higher burden of mental health problems when compared with survey respondents in higher income groups. In the United States, a recent study reports that a higher income was protective against mental distress during the pandemic. 1 As Donnelly and Farina 31 note, experiencing a job loss or a reduction in work hours is a stressful event and high unemployment during the pandemic raises concerns about the mental health of the US public.

Our analyses indicate that increased age was associated with more physically unhealthy days but better mental health, results supported by pre-pandemic research. 32,33 Our findings are consistent with recent US and international studies 1,3,30,34–36 reporting increased prevalence of poor mental health during the pandemic among younger compared with older adults, with poor mental health outcomes decreasing as age increases.

In our study, being female was associated with worse physical and mental health outcomes. These results are consistent with meta-analytic evidence indicating that, broadly speaking, females in the United States experience more depressive symptoms than do males ( d = 0.27). 37 Our findings are similar to those reported by Pieh et al, 35 where females compared to males during the pandemic had statistically significant increased depression and anxiety symptoms. Research from the United Kingdom 34 demonstrates that being female, being young, and living with preschool-aged children during the pandemic, have contributed substantially to increases in mental distress. Holingue et al 1 and McGinty et al 30 report findings from survey research demonstrating that females compared with males in the United States during the pandemic are experiencing increased mental distress. One possible explanation for these reported differences is that females are performing a disproportionate share of household work and childcare. 60 Research is needed to investigate the physical and mental health impacts associated with additional household and family burdens during the COVID-19 pandemic.

The current study demonstrates that Hispanic compared with non-Hispanic White participants had fewer physically unhealthy and fewer mentally unhealthy days, results supported by previous research indicating that racial and ethnic minority group identity and cultural values may foster some behavioral health resilience. 39,40,42,61 However, our results do not align with those reported by Czeisler et al 3 who found that, during the pandemic, poor mental health outcomes are disproportionately affecting Hispanic persons and Black persons. Moreover, McGinty et al 30 found in their analysis of national survey data that Hispanic adults during the pandemic have among the highest prevalence rates of psychological distress compared with other subgroups examined. As Purtle 43 notes, although racial/ethnic minorities (compared with non-Hispanic Whites in the United States) demonstrate lower lifetime prevalence rates of mood and anxiety disorders, 38 specific aspects of the current crisis, including financial insecurity due to a job loss, could have disproportionate, adverse, mental health effects on racial/ethnic minorities and low-income groups.

Findings from the current research indicate that educational attainment, measured as having at least a college degree versus having less than a college degree, was found to have no significant association with increased depressive and anxiety symptoms or more mentally unhealthy days. These results are inconsistent with those reported by Wanberg et al, 2 who report results from their research that people with higher educational attainment experienced a greater increase in depressive symptoms during the early stages of the pandemic, in comparison to those with lower education levels. More research is needed on the association between educational attainment, unemployment, and mental health during the COVID-19 crisis.

Limitations of our study include its cross-sectional design, 34 which does not allow for making causal inferences. Additionally, internet surveys vary in methodology and quality and lower response rates from diverse socioeconomic and racial/ethnic minority groups are common. 62 Future research would benefit from a longitudinal design that measures outcomes at different time points, and a closer examination of sociodemographic and regional differences.

The COVID-19 pandemic has substantial implications for individual and collective physical and mental health. 17 The current study contributes to the unemployment/underemployment and public health literature by exploring how changes in employment status and hours worked resulting from the pandemic have affected US adults’ physical and mental health. Limited evidence exists on the impacts of unemployment (or underemployment) resulting from the COVID-19 pandemic on mental health and efforts in this area have been called for. 4 Experiencing a job loss or a reduction in work hours is a stressful event and high unemployment during the pandemic raises concerns about the mental health of the US public. 31 Our analyses also address research gaps related to how associations between mental health and un/underemployment vary for respondents of different income levels. The results highlight the need for mental health supportive services for those experiencing changes to their employment status or hours worked due to the pandemic, especially among those with household incomes of less than $100,000. The potential, deleterious effect of longer unemployment duration, 13,53 and indications from this study and that some groups (eg, females, lower income households, and those not being able to obtain social support) are experiencing worse mental health outcomes, suggest the critical importance for the public health community to monitor the on-going impact of the COVID-19 pandemic on people's health and wellbeing, including and perhaps especially for those disproportionately affected by the current crisis.

Acknowledgments

The authors thank Devin Baker, Dr. Michael Blank, Dr. Tom Cunningham, Hannah Free, Dr. Matt Groenewold, and Dr. Taylor Shockey, for their thoughtful reviews of this manuscript.

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Open Access

Peer-reviewed

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

Roles Formal analysis, Methodology, Writing – review & editing

• Dorrit Posel, 
• Adeola Oyenubi, 
• Umakrishnan Kollamparambil

• Published: March 30, 2021
• https://doi.org/10.1371/journal.pone.0249352
• Peer Review
• Reader Comments

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.

Conclusions

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. https://doi.org/10.1371/journal.pone.0249352

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: https://cramsurvey.org .

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

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

Introduction

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: https://cramsurvey.org .

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

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.

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https://doi.org/10.1371/journal.pone.0249352.t001

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 ).

https://doi.org/10.1371/journal.pone.0249352.t002

https://doi.org/10.1371/journal.pone.0249352.t003

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 ).

https://doi.org/10.1371/journal.pone.0249352.t004

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 ).

https://doi.org/10.1371/journal.pone.0249352.t005

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..

https://doi.org/10.1371/journal.pone.0249352.s001

Acknowledgments

The authors thank two anonymous reviewers for their helpful comments.

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Speaking of Psychology: How job loss and economic stress affect workers and their families, with Anna Gassman-Pines, PhD

Episode 204.

For many Americans, the past two-and-a-half years have been a time of economic turmoil. Anna Gassman-Pines, PhD, of Duke University talks about how job loss, unstable work schedules, and other hardships affect workers, their families, and even entire communities, and about how working families–particularly low-wage workers–fared through the pandemic.

About the expert: Anna Gassman-Pines, PhD

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Kim Mills: For many American families, the past two-and-a-half years have been a time of economic turmoil. At the beginning of the pandemic, millions of workers lost their jobs and hourly workers in service industries were especially hit hard. Now unemployment is down, but inflation is up and so are fears about a possible recession. This kind of instability and economic hardship affects more than just workers themselves. When people lose their jobs or have to cope with unstable work schedules and incomes, the effects spill over to their families, their children, even entire communities. Research has linked job loss to everything from mental health problems to children's lower test scores in school.

So how have workers, especially low wage workers and their families, fared over the past two and a half years? How are they doing now compared with March 2020? Did government interventions such as expanded unemployment insurance and other programs make a difference in people's lives? What is needed now? More broadly, what have researchers learned from the pandemic that could inform employment and economic policies going forward?

Welcome to Speaking of Psychology , the flagship podcast of the American Psychological Association that examines the links between psychological science and everyday life. I'm Kim Mills.

My guest today is Dr. Anna Gassman-Pines, a professor of public policy, psychology and neuroscience at Duke University. Her research focuses on how work and employment and welfare policies affect families, family life, and wellbeing, particularly for low wage and hourly workers. Since the start of the COVID-19 pandemic, Dr. Gassman-Pines has also been closely following how job loss, childcare interruptions and other pandemic related disruptions have affected these families. Her research has been supported by grants from the APA, the National Science Foundation, the National Head Start Association and the National Institute of Mental Health among others. 

Thank you for joining me today, Dr. Gassman-Pines.

Anna Gassman-Pines, PhD: Thank you so much for having me.

Mills: For the past couple of years, you've been studying how the COVID-19 pandemic has affected low-wage working families. In a lot of ways this work started because you were in the right place at the right time. Can you tell us about that? Who are the people you've been following over the course of the pandemic and what are the research questions you've been asking them?

Gassman-Pines: Sure. So the work that I've been doing during the pandemic actually started way before the pandemic was even a glimmer in anyone's eye. I had been planning a study, really trying to understand both how common unpredictable and unstable work schedules are for low wage workers in the service sector, the consequences of those unpredictable work schedules for family wellbeing, and whether policy changes that aim to regulate work schedules could improve working conditions and possibly improve family wellbeing at the same time. So to do that work, my colleague Elizabeth Ananat and I recruited a sample of about a thousand hourly service workers who were working in retail jobs, in the food service sector and in hotels, all of whom who had a young child between the ages of two and seven. Because that's the time for parents when navigating and negotiating and balancing work and family is particularly challenging.

The goal was to follow those workers over time to ask and understand those research questions. We did this work in the city of Philadelphia because the city of Philadelphia was about to implement a new policy called the Fair Workweek Standard that was going to start regulating work schedules for hourly service workers in April of 2020. We went back into the field shortly before then, in February of 2020, to do another round of data collection with these families. So we were actually in the field surveying these parents about work, about their wellbeing, about family life, right at the moment that so many of us remember so well when everything changed. When schools closed, when stay at home orders were issued, when non-essential businesses were closed.

We were really able to see with those survey responses almost in real time as our own lives as working parents were turned upside down, just how quickly these families lives were turned upside down, how quickly people lost connections to work and how much family wellbeing suffered in the immediate aftermath of all of those closures and changes.

Mills: So what are the factors that were most salient as you were looking at all of this? What were the outcomes that you found?

Gassman-Pines: We asked parents about their own mood. How are they feeling? Are they feeling anxious, depressed, worried? We asked them how their children were doing. So for young children, sometimes if they're feeling anxious or worried, they might cry or be clingy the way that adults might be. But sometimes when young children are feeling stressed or worried, they act out, so they might be uncooperative for example. So we asked parents about whether their children had been more uncooperative or whether they had seemed more worried or sad. What we saw is that in the early phase of the pandemic, both parents' mood and mental health was worse and so was children. So parents were telling us their children were more uncooperative than usual. They were seeming more sad and worried.

That was particularly true for families who were really hard hit in those early days of the pandemic. So families where the parents were doing more care work, whether for children or older adults, where parents had lost jobs, where families had lost income, where people in that family had been feeling sick. Those things really accumulated. When families experienced multiple hardships related to the pandemic, both parents and children's mental health was much worse in those early days.

Mills: Over the course of the pandemic, government programs tried to buffer people against some of the effects that you're talking about. Programs like expanded unemployment insurance and SNAP benefits, which help people buy food. Did the people in your study actually benefit from these programs? Did the programs make a difference that you could really measure and see?

Gassman-Pines: We were able to ask folks about a lot of different supports that they might have received from the government during the pandemic. So you mentioned some of them. Expanded eligibility and generosity of unemployment insurance, SNAP benefits, single stimulus payments. There were several times during 2020 and 2021 when the government simply sent checks to people around the country. Also the child tax credit where eligibility was expanded and it went from a lump sum payment to a monthly payment for the last six months of 2021.

Taken together what we see is a few things. So number one, those policy supports did make a huge difference for families, especially in terms of buffering very large income losses. So this was a time when many of the parents in our sample had been laid off, or those who were still working had their hours reduced. So those set of government supports definitely buffered large income losses. They also reduced material hardships. So things like reporting that your family doesn't have enough money for food or that you're worrying that you're going to run out of money for food or worrying that you can't pay enough rent. So the set of government supports reduced material hardship as well. We also have some evidence that especially the more generous policies like the expanded unemployment insurance, also improved mental health, especially for workers who had been laid off.

Mills: What about the timing of these benefits and what did you find with respect to when people are getting certain things? The fact that one of these benefits was the child benefits that you were talking about, it used to be a lump sum, and then it was given on a monthly basis. Did that make a big difference? Could you look at what was happening before when it was a lump sum and then what happened as it was being doled out on a monthly basis?

Gassman-Pines: So we're still working on that specific question, but other researchers, including my colleague and collaborator, Elizabeth Ananat, have also been looking at that using other data sources. What we're learning from across the psychological science and social science of this issue is that those monthly child tax credit payments definitely reduced financial distress and material hardship for families with children.

Mills: Now you've looked at the timing of SNAP payments and when a family gets that money. If it comes at the beginning of the month and what happens to the children over the course of that month, how are they faring as the money is being spent?

Gassman-Pines: So one of the things I found in my work is that SNAP benefits are a really crucial support for low income families, but they don't last the whole month. So they're designed to last for a whole month, but they tend to run out and they run out for most families after about two weeks. So what that means is in that second half of the month when those benefits have run out, things are actually really different from those families than they are in the first half of the month. So first of all, parents are much more worried about having enough money for food. Actually in some cases report eating less or eating different types of food than they might prefer if they had more money available. Parents report relying more on other sources of nutrition assistance, like borrowing money from friends and family or using other non-government supports like backpack programs at their child's school.

Finally, I've also shown that these things can together accumulate and actually affect children's test scores. So when children sit down at the end of a SNAP month to take an end of the year exam, like a reading or math achievement test that public school students across the country take, if they're sitting down to take that test at the end of the SNAP month, they actually do slightly worse on that test than if they would've sat down to take that test right after their family got those benefits.

Mills: Some of these kids were relying on getting free lunches at school, and then they weren't in school anymore. So what happens to the money? You've got to cover meals that were being paid for elsewhere.

Gassman-Pines: That's right. So what we've found in other work that I've been doing is that when schools close, that was a particularly vulnerable time for families who were relying on school meals and other kinds of nutritional supports that were provided through schools like backpack programs, where food gets sent home on the weekend. My colleagues and I have actually shown that right when schools closed at that same moment in mid-March for low income families who are relying on school meals, food insecurity increased substantially after those schools were closed.

Now I should say that there have been policy changes during the pandemic that have also sought to increase nutritional support for families that were relying on school meals, primarily by essentially paying families out the money that schools would have used to buy the food for school lunch and school breakfast. But of course, those payments didn't come until several months later and right when schools closed families had food need right away.

Mills: So how does your pandemic research fit in with what we know from previous research about what people need from benefits programs? What makes these programs more or less effective?

Gassman-Pines: So there are several ways in which what we've learned during the pandemic isn't necessarily something brand new, but really making even clearer, really shining a light on some things that we had been learning before. So for example, the unemployment insurance system is our main policy response when people are laid off from jobs, but it is set up to be difficult to access. The idea of that is that's a policy choice that many state policy makers have made. The idea is if we make this difficult to access, then only the people who really need it will go through the hoops and the hurdles to get their benefits.

What actually happens in practice is that the folks who are struggling the most, who are facing the most life challenges and therefore need the support the most often have the most trouble accessing benefits. It's really people who are more advantaged, who have more practice and support for navigating complex systems are the ones that can achieve the goal of getting those benefits. That all became much more challenging during COVID. So here's a system that is set up to be difficult to access. All of a sudden there was a huge need in 2020, where so many people were being laid off and the system was just not set up to be user friendly, to be smooth and to be easy for people to get through.

Those barriers already existed before the pandemic and were really made much more challenging during the pandemic. So a lesson is we might reflect on, is this the kind of support that we want to actually make easier? There are ways of reducing so-called administrative burdens to make government programs easier for folks to access. That's a policy choice that we could decide to make.

Mills: Did you see that happening or is that really a case of legislators having to step in and carry the water on this?

Gassman-Pines: Yeah, I mean, I think there are a lot of advocates on the ground who also, I know, do really important work shedding light on some of these issues for especially state legislators, to understand the importance of these supports, to understand the importance of making them easier to access. One thing that I think the pandemic has done in many ways is helped lots of folks to see that sometimes there are circumstances outside control that really change our work lives, for example, or really change our need for supports for caregiving. That can happen to anyone. It's not just poor people. It's not just hourly service workers. Hopefully we'll be able to reflect on that and moving forward, think about ways to make these programs easier for lots of different people to access when they need them.

Mills: You've been studying job loss and economic recessions since before the pandemic. So let's talk a little more broadly about that research. Some of your findings that are especially interesting center around how job loss affects whole communities. You have found that when there's a lot of job loss in a geographic area, it can affect an entire community even the people who haven't lost their jobs and it can show up in very wide ranging ways. Can you talk about what it is that you found?

Gassman-Pines: Sure. So my colleagues and I have done a series of studies that really highlight the ways that large scale layoffs, when lots of people in a community lose jobs. That has all kinds of ripple effects throughout the community that go well beyond the workers themselves who are affected. I think we have all been able to see that so much more during the pandemic, but it was true before the pandemic too. So for example, I'm here at Duke University, I'm in North Carolina. North Carolina is a state that at one time had a tremendous amount of textile manufacturing. There were many communities in North Carolina where lots of adults in the community were working in textile mills.

When those mills closed, lots of people lost work. Now, not every adult in the community, because of course there were still police officers, teachers, nurses. But nevertheless, when there were large scale job losses where a lot of people in the community lost work at the same time, it changed so many things about how youth imagined where they could go in life. So looking around and saying, "Well, my parent is still employed, but I'm seeing my classmate or my friend or my peers parent has just lost a job at that textile mill. That was a job that I thought might always be there and now I don't know. That's not available to me anymore."

A lot of families experience economic strain, even though those adults continue to work. So maybe it's the waitress whose restaurant was across the street from the textile mill and folks aren't coming across the street for lunch anymore. So she's taking home less in tips. Even though she still has a job, her earnings have gone down. That family may be feeling more crunched and be feeling more economic strain and worry. So it's not just the affected workers, but actually these ripple effects that go out from the center and can really be harmful for other adults, but also, especially for youth in those communities.

Mills: I think you also found some impacts on things like college attendance, children's test scores and even suicide rates. It sounds like it's very widespread and complex.

Gassman-Pines: That's right and this is especially true again for adolescents. So adolescents being a time when young people are figuring out so many things about their identity, who are they going to become as an adult, this kind of bridge between childhood and adulthood. So much reflecting on who am I going to be? What's important to me? What are my educational and career goals? What we found in our work is that when there are these community wide job losses, youth in those communities are affected in a range of ways. So we find that youths test scores suffer. So the same kind of end of grade reading and math, achievement tests. Adolescents perform worse on those tests. When they're in a community that's just experienced these job losses, they become less likely to go to college.

That's particularly true for low income adolescents and they have more mental health problems. That's particularly true for girls, but this is a very stressful experience. Again, to be in a community that's going through these kinds of changes where so many adults and peers are affected, we did see that when youth are living in a place that's experienced these large job losses, really serious mental health problems, like considering suicide actually go up.

Mills: Why would girls be more affected? I'm just curious to know about that. Do you have any ideas?

Gassman-Pines: So that's something we've wondered about quite a bit. Is it differences in how much girls are internalizing their feelings and when they're feeling stressed? Because different socialization, a lot of that is getting kept inside. Boys perhaps might be more likely to externalize those feelings in ways that show up, not in depression or suicide, but in other acting out behaviors. So there could be several different reasons.

Mills: Another longstanding area of interest for you has been how unpredictable and chaotic work schedules affect people's family life and their children. Now, as someone who spent years on shift work as a reporter, I can tell you it is physically punishing. But what have you found in your research regarding the effect on families and children?

Gassman-Pines: Several things. I mean, one thing we find is that, especially for service workers, so for folks working for low wages in retail, in food service, in hotels, unpredictable work schedules are incredibly common. So this is just part of what it means to have those jobs. So for example, one thing we do is we ask folks to answer these short surveys every day for a whole month. So we're getting really detailed information about what happened every day. Did you go to work? When did you start and stop? What are those the hours that were originally on your schedule? If you didn't work, were you supposed to work? Had shifts been on the schedule that got taken off? What we find is that on 10% of the days, these workers had some kind of unexpected change to their schedule. Whether it was a change in hours, a canceled shift, a shift that got added on at the last minute.

So 10% might sound low, but that is three days out of the month, every month where something did not go as planned. Then this causes a ripple effect for families in terms of having to rearrange childcare. So if you get a shift added on that wasn't on the original schedule that was posted, and all of a sudden you're told, "We are going to need you to work tomorrow morning." Now there's a scramble for finding childcare, because again, the parents in our sample have young children who cannot be left alone. They need to be in care of some sort. So one thing is, this is incredibly common when it happens, it leads to all kinds of other challenges, especially around finding care for children and it is incredibly stressful for parents. So parents report much worse mood on days when this happens compared to the same parents on days when work goes as planned.

So when we get these detailed survey reports, we're actually able to compare, how do you feel on a day when you work the hours that were on that schedule originally compared to a day when you get told that you need to stay late or you get sent home early? It's incredibly stressful and parents report much worse mood. We're also seeing some emerging evidence and it relates to your own personal experience that parents sleep quality is also affected when their work schedules are changed. On days with these kinds of unexpected changes, parents say that they're sleeping worse.

Mills: You mentioned earlier the Fair Workweek law in Pennsylvania, that you were planning to look at. Some of these laws may address these problems. What is the research into how well these laws work and whether they make a difference in people's lives?

Gassman-Pines: So our work in Philadelphia is ongoing, but I can't tell you, we also did a smaller scale version of this work in the city of Emeryville, California, which was one of the early leaders in passing of Fair Workweek policy change. What we found in Emeryville are several things. So first of all, the Emeryville Fair Workweek ordinance reduced the instances of the kinds of unstable work schedules that I just described. So in Emeryville very, very small businesses, kind of family run businesses weren't covered by the ordinance, but larger businesses were. So what we were able to do is look at differences in work schedules between the small businesses and the larger businesses, both before the law change and after.

What we see is basically that after that law went into effect, those changes to work schedules decreased right away in the large businesses. So they end up looking a lot more like the small businesses. So those instances of unstable work schedules are much lower after the law goes into effect.

Mills: How do those laws work? Can you actually say to a business, "You can't schedule somebody to stop work at seven o'clock in the morning and then come back at seven o'clock at night? Like what airlines do to keep people able to fly and not crash the plane.

Gassman-Pines: That's right. So the way these laws work, and they're a little bit different in the different cities where they are on the books right now, but they have the same sort of general structure. The way the structure works is they say, "Okay, large employers, you need to give your workers a certain amount of advance notice of their work schedule." So sometimes it's 10 days, sometimes it's 14 days and you need to post that schedule in a place that is visible to all employees. If you want to change the schedule within the 10 or 14 days, that's okay, but you need to compensate people for those schedule changes. That's the really crucial difference. So it's not saying you can never make a change to the work schedule or even a last minute change. But what it is saying is that if you need to change the schedule, you have to compensate those employees because they were making their life plans.

They were holding that time based on what you originally posted. Then what the compensation, what that means also again, varies, but it might say, for example, "Look, if you cancel someone's shift, you have to pay them for half of the shift." Because they were holding that time on their schedule and so if you're going to cancel it, they need to be compensated for having held that time. If you want to add hours to a shift, you might need to pay a little bit of extra, almost like overtime. Again, because those are additional hours that the person wasn't originally scheduled. It turns out that employers really don't want to have to pay when they change people's shifts. The emerging evidence is they would actually rather stick with the shifts that are posted in advance.

So for example, in Emeryville the instances of canceled shifts went down because presumably those employers did not want to have to pay that compensation for having canceled a shift.

Mills: One piece of economic news that seems inescapable right now is inflation. I know that's not really an area where you're necessarily working, but I'm just wondering if there's any research, if you're seeing anything regarding the effects of inflation on people's mental health and wellbeing, or is it just too soon to say?

Gassman-Pines: Yeah, I mean, I think there's probably going to be a lot more psychological science and social science coming out about inflation. But what I can say is that for low wage workers, even before the pandemic, they were living day to day with a lot of precarity. So many people have jobs where shifts might get changed, hours might be irregular, there may be other work stressors, but for folks who are in higher wage, higher earning jobs, there's often a lot of different ways to buffer some of the negative effects of that instability or unpredictability.

For low wage workers, that's never been the case. So any changes that make that kind of balancing act more difficult are going to increase stress and financial strength for those families. So I can imagine, for example, that we will learn and I'm sure it's true right now that for people with lower incomes, inflation is really making a difference in terms of their ability to meet their family's basic needs and to balance the competing demands of work and care.

Mills: So what's next in your research? Are you continuing to follow the families you've been following since the beginning of the pandemic?

Gassman-Pines: So we've just finished what I think will be our last round of surveying these folks. We are just so grateful to the families in our study for having stuck with us for two and a half years through a very challenging time. What we're working on now is a book length project, really trying to put it all together, both to understand how difficult the pandemic was, but also the way that these unconventional policy responses really did make a difference in family's lives. The hope is by being able to reflect across all that we've learned over the last two and a half years, this research might really shed light on a different path forward that will provide a stronger safety net and more stability and predictability for low income families into the future.

Mills: Dr. Gassman-Pines, thank you for joining us today. This has been really interesting. Thank you.

Gassman-Pines: Thank you so much for having me.

Mills: You can find previous episodes of Speaking of Psychology on our website or on Apple, Stitcher or wherever you get your podcasts. If you like what you hear, leave us a review. If you have comments or ideas for future podcasts, you can email us at Speaking of Psychology . Speaking of Psychology is produced by Lee Weinman. Our sound editor is Chris Condayan. 

Thank you for listening. For the American Psychological Association, I'm Kim Mills.

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Episode 204: How job loss and economic stress affect workers and their families, with Anna Gassman-Pines, PhD

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• Anna Gassman-Pines, PhD
• The Effects of the Emeryville Fair Workweek Ordinance on the Daily Lives of Low-Wage Workers and Their Families ( The Russell Sage Foundation Journal of the Social Sciences , August 2022)
• Effect of Daily School and Care Disruptions During the COVID-19 Pandemic on Child Behavior Problems ( Developmental Psychology , August 2022)
• Linking job loss, inequality, mental health, and education ( Science , June 2017)

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ORIGINAL RESEARCH article

Job loss or income loss: how the detrimental effect of unemployment on men's life satisfaction differs by immigration status.

• 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.

Introduction

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: https://www.frontiersin.org/articles/10.3389/fsoc.2020.00010/full#supplementary-material

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: https://www.understandingsociety.ac.uk/documentation/mainstage/dataset-documentation .

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.

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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, jing.shen@mzes.uni-mannheim.de

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

The Effects of Job Loss on Older Workers

This article uses data from the Health and Retirement Study to examine the employment patterns of workers aged 50 and above who have experienced an involuntary job loss. Hazard Models for returning to work and for exiting post displacement employment are estimated and used to examine work patterns for 10 years following a job loss. Our findings show that a job loss results in large and lasting effects on future employment probabilities. Four years after job losses at age 55, the employment rate of displaced workers remains 20 percentage points below the employment rate of similar nondisplaced workers.

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Job loss and depression: the role of subjective expectations

Affiliation.

• 1 Washington State University, Pullman, WA 99164, USA. [email protected]
• PMID: 21183267
• PMCID: PMC3684950
• DOI: 10.1016/j.socscimed.2010.11.014

Although the importance of expectations is well documented in the decision-making literature, a key shortcoming of the empirical research into effects of involuntary job loss on depression is perhaps its neglect of the subjective expectations of job loss. Using data from the US Health and Retirement Study surveys we examine whether the impact of job loss on mental health is influenced by an individual's subjective expectations regarding future displacement. Our results imply that, among older workers in the age range of 55-65 year, subjective expectations are as significant predictors of depression as job loss itself, and ignoring them can bias the estimate of the impact of job loss on mental health.

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Please note you do not have access to teaching notes, employability, well‐being and job satisfaction following a job loss.

Journal of Managerial Psychology

ISSN : 0268-3946

Article publication date: 2 November 2012

This paper aims to investigate changes in psychological well‐being over time for individuals who experienced a career disruption in the form of a company closing, and to examine the relationships between employability, well‐being, and job satisfaction. It seeks to expand on previous work of job loss relative to the long‐term impact of the experience and on Fugate et al.'s psycho‐social conceptualization of employability.

Design/methodology/approach

Data were collected at the time of job loss (T1) and six years later (T2). The 73 respondents at T2 represent a stratified random sample of the T1 respondents. Hypotheses were tested with paired sample t ‐tests and hierarchical multiple regression.

Results indicate that the negative psychological impact of job loss diminishes over time. Additionally, employability predicted well‐being and job satisfaction.

Practical implications

The results of the study provide guidance for the design and administration of outplacement and related programs that focus on increasing employability and psychological well‐being, and suggest ways that individuals can shield themselves from the negative consequences associated with a job loss.

Social implications

The results have policy implications for the design of government funded outplacement and retraining programs.

Originality/value

The paper is the first to examine job loss over a six‐year period of time, and the first to examine the impact of employability attributes on multiple indicators of well‐being and on job satisfaction in the job loss context.

• Employability
• Reemployment
• Psychological well‐being
• Unemployment
• Quality of life

Gowan, M.A. (2012), "Employability, well‐being and job satisfaction following a job loss", Journal of Managerial Psychology , Vol. 27 No. 8, pp. 780-798. https://doi.org/10.1108/02683941211280157

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Are “bad jobs” dead ends or steppingstones to better things? New research identifies some pathways to better job quality

Research shows service sector workers do best when they change sectors or find themselves in tight labor markets. But also that the vast majority stay in jobs with low wages, poor benefits, and difficult schedules.

Nearly one in five workers in the United States work in the low-wage retail and food service sector, which has the largest concentration of low-wage workers, and is characterized by poor benefits and unpredictable schedules. Are these jobs dead ends or steppingstones to better careers? And if they are steppingstones, what paths do workers take to those better opportunities?  

New research, which followed thousands of service sector workers over a period of several years that spanned the COVID-19 pandemic and the mass unemployment and then the historically tight labor market that followed, has found that workers do best when they change jobs, and especially when they change sectors; but that wage growth, especially during a tight labor market, can be highest when workers stayed in the same position. 

But the research also came to a sobering conclusion: of the thousands of workers they followed, 92% of those who began in a bad job remained in a bad job 18 months later. 

The researchers were Daniel Schneider, the Malcolm Wiener Professor of Social Policy at HKS, Kristen Harknett, of the University of California-San Francisco, and Tyler Woods and Dylan Nguyen, postdoctoral and predoctoral fellows, respectively, at the Shift Project. The Shift Project , based at HKS and UCSF and led by Schneider and Harknett, documents the economic security, schedules, and health and well-being of hourly workers across the country. 

The research , recently published in Research in Social Stratification and Mobility, followed 8,600 respondents across the United States, with initial interviews conducted between 2017 and 2022, and follow-up interviews conducted up to 18 months later. This allowed the researchers to follow respondents in distinctly different job markets, including the height of the pandemic and its mass lockdowns and spiraling unemployment and the period that followed, known as the “Great Resignation,” which was marked by very low unemployment rates and very high quit rates. 

The authors were also able to develop fuller portraits of their respondents’ job quality, including not just wages, but also benefits—most lack paid sick leave and retirement benefits, for example—and schedule instability, given them a more holistic view of working conditions. Based on this, the authors propose a multi-dimensional measure of jobs quality that includes both a minimum wage ($15 an hour), benefits such as paid sick leave, retirement savings, and health insurance, and, crucially, minimal schedule instability. Previous research by Schneider and Harknett has shown the link between unstable work schedules and a number of poor health and well-being outcomes, including stress, poor sleep, and work-family conflict.  

“Service sector jobs are often described as quintessential ‘bad’ jobs, but this is often forgiven based on the idea that they can be steppingstones or a first rung on the ladder to better jobs. Our work shows that ... that’s far from the reality.”

Daniel schneider.

They found that, “in general, workers who began in bad jobs in the service sector experienced more upward mobility to good jobs when they changed jobs, especially in favor of opportunities in a new sector.” They also found that workers “experienced more upward mobility during the strong economy of the Great Resignation and in states with tighter labor markets.”

But they were surprised to find that the Great Resignation also meant better working conditions—particularly better wages and schedules—for those who remained in their jobs. “For those who remained in their jobs, the rate of upward mobility to good jobs during the Great Resignation period was double that of the prior period in which the labor market was less strong,” the authors write.

However, the authors conclude, “transitioning from a bad to a good job was the exception, not the rule. ... Even in the best-case scenario, during the strongest labor market conditions, mobility to a good job was limited to 8 % of those who began in bad jobs.”

“Service sector jobs are often described as quintessential ‘bad’ jobs, but this is often forgiven based on the idea that they can be steppingstones or a first rung on the ladder to better jobs,” Schneider said. “Our work shows that, for almost all workers, that’s far from the reality. These jobs are all too often poverty traps as workers struggle to move on and up to better jobs. The one bright spot is that when workers have more labor market power, they are much more likely to find their way to better jobs, by leaving the sector, changing employers, and even by demanding better conditions where they work. Tight labor markets are an effective tool for upward mobility.”

The researchers say that their study has three important implications for policy and practice:

The need to move beyond a focus solely on wages, instead considering the importance of factors such as schedule stability and fringe benefits for workers’ job quality. “For example, the passage of secure scheduling laws in cities like Seattle is a strong step in this direction.”

Higher wages, more stable schedules, and more generous benefits may be effective retention strategies for firms in periods of low unemployment, encouraging workers to move ahead in their existing jobs rather than looking elsewhere. 

Workers might be more likely to remain in the service sector if there were stronger internal career ladders. Firms in the service sector should consider creating more opportunities for upward career advancement for hourly workers, such as pathways to management or the corporate workforce. 

— Photography by AP Photo/Nam Y. Huh.

More from HKS

California’s tough labor laws aren’t protecting the majority of hourly workers, for young service sector workers the “great resignation” often led to an upgrade, daniel schneider on how the pandemic reshaped the socioeconomic landscape of america.

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Grief reactions, depression, and anxiety following job loss: patterns and correlates

Reacciones de duelo, depresión y ansiedad luego de la pérdida del empleo: patrones y correlatos, 失业后的哀伤反应, 抑郁和焦虑:模式和相关, janske h. w. van eersel.

a Department of Clinical Psychology, Utrecht University, Utrecht, The Netherlands

Toon W. Taris

b Department of Social, Health and Organizational Psychology, Utrecht University, Utrecht, The Netherlands

Paul A. Boelen

c ARQ National Psychotrauma Centre, Diemen, The Netherlands

Associated Data

The data set is freely retrievable (Van Eersel et al., 2021 ).

Background : Research on grief, depression, and anxiety reactions following job loss is sparse. More insight in this matter could be important for the development of preventive and curative interventions targeting different manifestations of emotional distress following job loss, including grief reactions.

Objective : The aim of this study was to examine job loss-related grief reactions in relation to depression and anxiety symptoms.

Method : A sample of 525 Dutch workers (59.8% women, mean age of 50.6 years) who had lost their job was recruited. Latent class analysis was used to examine whether separate classes could be distinguished based on the endorsement of grief reactions and symptoms of depression of anxiety. We also examined factors associated with class membership.

Results : Four classes were identified, including a so-called ‘mixed’, a ‘grieving’, a ‘depressed’, and a ‘resilient’ class. Job loss circumstances and coping strategies (but not socio-demographic and work characteristics) were associated with class membership.

Conclusion : These results shed light on unique characteristics that might be targeted with specific clinical methods to increase mental health of different subgroups of individuals confronted with job loss.

• Research on grief, depression, and anxiety reactions following job loss is sparse.
• Latent class analysis revealed four distinct classes.
• Job loss circumstances and coping strategies (but not socio-demographic and work variables) were associated with class membership.

Short abstract

Antecedentes : La investigación acerca de las reacciones de duelo, depresión y ansiedad luego de la pérdida del empleo es escasa. Una mayor profundización en esta área podría ser importante para el desarrollo de intervenciones preventivas y curativas dirigidas a las diferentes manifestaciones del sufrimiento emocional que ocurre luego de la pérdida del empleo, incluyendo las reacciones de duelo.

Objetivo : El objetivo de este estudio fue examinar las reacciones de duelo asociadas a la pérdida del empleo en relación a síntomas de depresión y ansiedad.

Método : Se reclutó una muestra de 525 trabajadores holandeses (59,8% mujeres, edad promedio de 50.6 años) que perdieron su empleo. Se utilizó análisis de clases latentes para examinar si se podían distinguir clases separadas basándose en la confirmación de reacciones de duelo y síntomas de depresión y ansiedad. También examinamos factores asociados con la pertenencia a las clases.

Resultados : Se identificaron cuatro clases, que incluyeron una clase llamada ‘mixta’, una ‘en duelo’, una ‘deprimida’ y una ‘resiliente’. Las circunstancias de la pérdida del empleo y las estrategias de afrontamiento (pero no las características del empleo ni las sociodemográficas) se asociaron con la pertenencia a las clases.

Conclusión : Estos resultados revelaron las características únicas que podrían ser blanco de métodos clínicos específicos para mejorar la salud mental de los diferentes subgrupos de personas que enfrentan la pérdida del empleo.

背景 : 对于失业后的哀伤, 抑郁和焦虑反应的研究很少。在此问题上获得更多的见识对于开发包括哀伤反应在内的失业后情绪困扰的不同表现形式的预防和治疗干预可能很重要。

目的 : 本研究旨在考查失业相关的哀伤反应与抑郁和焦虑症状的关系。

方法 :招募了525名失业的荷兰工人 (女性59.8％, 平均年龄50.6岁) 。潜在类别分析用于考查是否可以基于有无哀伤反应和焦虑抑郁症状来区分出不同类别。我们还考查了所属类别的相关因素。

结果 : 确定了四个类别, 包括所谓的‘混合’, ‘哀伤’, ‘抑郁’和‘韧性’类别。失业状况和应对策略 (但不包括社会人口统计学和工作特性) 与所属类别相关。

结论 : 这些结果揭示了针对提高面临失业的不同亚群心理健康特定临床方法的独特特征。

1. Introduction

Employment is more than just a way to make a living; it provides structure to the day, a reason to get up in the morning, goals to pursue, meaning, identity, and status (Jahoda, 1981 ). Hence, it is not surprising that involuntary job loss contributes to a decrease in psychological, physical, and social well-being (e.g. McKee-Ryan, Song, Wanberg, & Kinicki, 2005 ). For example, job loss has been found to be related to increases in depression (Kim & Von Dem Knesebeck, 2016 ), anxiety, and psychosomatic symptoms (Paul & Moser, 2009 ), loss of psychosocial assets, stigmatization, social withdrawal, family disruption (Brand, 2015 ), and increased risk of substance use (Modrek, Stuckler, McKee, Cullen, & Basu, 2013 ). A meta-analysis of longitudinal studies has shown that job loss can cause severe emotional distress (Paul & Moser, 2009 ). Job loss may yield transient reactions of grief (Brewington, Nassar‐mcmillan, Flowers, & Furr, 2004 ; Climent-Rodríguez, Navarro-Abal, López-López, Gómez-Salgado, & García, 2019 ; Diaz et al., 2015 ; cf. Lorenz, Maercker, & Bachem, 2020 , who focused on adjustment disorder after involuntary job loss). However, in a minority of people grief reactions following job loss can become persistently disabling and distressing 1 (Papa & Lancaster, 2016 ; Van Eersel, Taris, & Boelen, 2019 ).

1.1. Grief reactions and symptoms of depression and anxiety following job loss

Loss can be defined as a reduction of resources in which a significant investment has been made (Harvey & Miller, 1998 ). Loss of work can provoke multiple cascading losses (e.g. reduction of social contacts, status, and self-esteem), leading to elevated levels of stress. Interestingly, prior research has shown that a reduction of income following job loss was not significantly related to the intensity of job loss-related grief reactions (Papa & Maitoza, 2013 ; Van Eersel, Taris, & Boelen, 2020a ); apparently, loss of income is not the main driver of such grief reactions. According to conservation of resources theory, the emotional distress resulting from the partial or full loss of a resource depends on a person’s investment in that resource, the number of remaining resources, and the appraisal of possible threats (Hobfoll, Tirone, Holmgreen, & Gerhart, 2016 ). Basic assumptions about the sense of self, the world, the future, and others require reconstruction to incorporate the new reality. This is the case following different kinds of losses, including bereavement, loss of bodily functions, victimization through violence (Harvey & Miller, 1998 ), job loss, divorce (Papa, Lancaster, & Kahler, 2014 ), romantic break-up (Boelen & Reijntjes, 2009 ), and natural disaster (Shear et al., 2011 ).

The intensity of grief reactions has been associated with disruption of a person’s day-to-day life, access to meaningful activities, valuable interactions, social relationships, loss of identity, self-esteem, and self-efficacy (Papa & Lancaster, 2016 ). Grief reactions are characterized by separation distress combined with difficulties accepting the loss, yearning, difficulty finding meaning in life, feeling bitterness over the loss, identity confusion, and difficulty moving on with life, causing severe distress and disability on most days (Prigerson et al., 2009 ). Job loss-related grief reactions can occur in conjunction with symptoms of depression and anxiety, and may also precipitate elevations of symptoms of depression and anxiety over time (Van Eersel, Taris, & Boelen, 2020b ). Nonetheless, recent variable-centred studies have shown that depression, anxiety and grief reactions can be distinguished empirically (Papa & Maitoza, 2013 ; Van Eersel et al., 2019 ).

A variable-centred approach postulates a linear structure which is common for a homogenous population and, as a result, does not allow detecting nuances within the population, such as the existence of latent classes (Meeusen, Meuleman, Abts, & Bergh, 2018 ). However, it may be possible that different subgroups of people can be distinguished among people who have lost their jobs, based on the endorsement job-related grief reactions and symptoms of depression and anxiety. One way to study this notion is by using a person-centred approach which may help to improve insight in the interrelations among job loss-related grief, depression, and anxiety reactions. In addition, it is important to increase knowledge on variables related to class membership (e.g. coping style, demographics, or loss characteristics) to inform theorizing and the development of interventions targeting distress following job loss.

1.2. Latent class analysis

Latent class analysis (LCA) is a person-centred statistical method that identifies subgroups of individuals who share a set of common characteristics (Lanza & Cooper, 2016 ). As a primarily data-driven approach, it is useful to explore a data set and to determine the direction of further theory and research. Since LCA has not previously been used to study grief, depression, and anxiety reactions following job loss, we formulated our expectations concerning the characteristics of latent classes on the basis of earlier research among people confronted with bereavement loss. Several studies have used LCA to identify subgroups of bereaved people, based on symptoms of complicated grief (CG) – often also referred to as prolonged grief disorder, depression, and post-traumatic stress disorder (PTSD). For instance, Djelantik, Smid, Kleber, and Boelen ( 2017 ) examined CG, depression, and PTSD levels among bereaved individuals, and identified three classes: a resilient class, a CG class, and a mixed class of CG and PTSD. Lenferink, De Keijser, Smid, Djelantik, and Boelen ( 2017 ) obtained similar results in their sample with disaster-bereaved individuals: a resilient class, a CG class, and a combined class of CG, depression, and PTSD symptoms. Taking these findings into account, we anticipated that a sample of job loss-related grief, depression, and anxiety reactions would dissolve in perhaps as many as four classes: a resilient class, a job loss-related grief class, a depression class, and a mixed class with job loss-related grief, depression, and anxiety.

1.3. Coping, grief, and resilience

In addition to examining the clustering of job loss-related grief, depression, and anxiety reactions into different classes, it was deemed relevant to explore which variables are related to class membership. LCA studies on emotional responses to bereavement loss have shown that the resilient pattern (i.e. low levels of distress) is the most common response (Bonanno, Boerner, & Wortman, 2008 ; Lenferink, Nickerson, De Keijser, Smid, & Boelen, 2018 ). Resilience can be described as the ability to maintain relatively stable, healthy levels of functioning when confronted with a potentially highly disruptive event (Bonanno, 2004 ). In the case of job loss, Galatzer-Levy, Bonanno, and Mancini ( 2010 ) found that 82% of the participants of their study experienced no long-term effects on life satisfaction in response to their unemployment. Individuals with a resilient response to job loss tended to use more adaptive coping strategies than people with higher levels of emotional distress, while this latter group of people appeared to use maladaptive coping strategies relatively often (Sojo & Guarino, 2011 ). Coping refers to the effort a person undertakes to manage the demands of a situation, when these demands are appraised as taxing or even exceeding the person’s capability to control, reduce, or tolerate the stressful conditions (Folkman & Lazarus, 1988 ). In several studies, the use of maladaptive coping strategies has been linked to diminished well-being during unemployment (Brand, 2015 ; Gowan, 2014 ; McKee-Ryan et al., 2005 ) and persistent job loss-related grief reactions (Papa & Maitoza, 2013 ; Van Eersel et al., 2020a ). Therefore, it was considered conceivable that following job loss, different forms of coping were associated with membership of different classes characterized by different symptom patterns.

1.4. The present study

The current study aimed to identify: (1) subgroups of individuals who involuntary lost their job, and (2) predictors of subgroup membership. Specifically, the first aim was to examine whether subgroups of individuals could be identified, based on their levels of grief, depression, and anxiety reactions following job loss. Based on the results from LCA studies on bereavement cited above and factor analyses of job loss-related grief (Papa & Maitoza, 2013 ; Van Eersel et al., 2019 ), we expected that various subgroups would emerge with distinct and differentiated symptom profiles (e.g. high grief, low depression, low anxiety or low grief, high depression, low anxiety).

The second aim was to investigate socio-demographic and loss-related characteristics associated with the subgroup membership. Little is known about the correlates of subgroups of persons characterized by different patterns of grief, depression, and anxiety symptoms following job loss. However, Brewington et al. ( 2004 ) found that the abruptness of the loss, feeling unprepared for this loss, and an inadequate notice of dismissal were risk factors for developing grief symptoms following job loss. These findings can be linked to Janoff-Bulman’s ( 1999 ) theory that postulates that after experiencing stressful life events, people tend to hold on to their basic assumptions that the world is fair, predictable, and controllable. Events that disrupt these assumptions, such as involuntary job loss, can lead to emotional distress and problems.

Finally, there is some evidence that maladaptive coping strategies are associated with job loss-related grief (Papa & Maitoza, 2013 ), depressive symptoms (Hasselle, Schwartz, Berlin, & Howell, 2019 ), and diminished well-being during employment (Gowan, 2014 ). This might be due to maladaptive coping yielding a decrease in available resources, which can force a person to fall back on avoidant coping strategies to deal with the changed reality (Hobfoll et al., 2016 ). In our study, we anticipated that participants in the relatively more disturbed classes would experience their job loss as more unexpected and more unjustified, and that they would endorse a higher use of maladaptive coping strategies and a lower use of adaptive and social coping strategies.

2.1. Procedure and participants

The study was approved by the Ethical Review Board of the faculty of Social Sciences of Utrecht University (FETC 16–111). The data collection took place between 2016 and 2019. During this period unemployment rates in the Netherlands decreased from 6.0% in 2016 to 3.2% in 2019 (CBS, 2020 ). Dutch individuals who had involuntarily lost their job were recruited via two channels: (1) meetings on the impact of the job loss, and (2) social (media) networks. Potential participants received a short explanation (either in person or in writing) of the goals and general content of the study. If they were interested, the researcher handed out the information letter or they could click on a link to read this letter online. After reading the information letter, people decided whether they wanted to participate in the study. Informed consent was obtained from all participants ( N = 592). After signing the consent form, 88% completed the survey either using paper and pencil ( n = 44) or by completing an online questionnaire in a secured online area ( n = 481). The ‘paper and pencil’ group was recruited via meetings on the impact of job loss, and the ‘online’ group via social (media) networks. Groups did not differ in terms of the variables assessed in the study, except for educational level (χ 2 ( df  = 2, n = 525) = 18.6, p < .001). In the online group more people held a college or university degree (58.8%) than in the paper-and-pencil group (28.9%). A part of the data on grief reactions, depression, and anxiety was used in other parts of our research programme (Van Eersel et al., 2019 , 2020a ).

The data from people who did not complete the grief, depression, and anxiety questionnaires ( N = 37) or who resigned voluntarily from their job ( N = 30) were excluded. The participants in the final sample for this study ( N = 525) were on average 50.6 ( SD  = 9.0) years old and included 211 men (40%) and 314 women (60%). Their level of education varied, with 48 people having completed primary education only (9%), 182 people having completed secondary education (35%), and 295 people holding a college or university degree (56%). Table 1 presents all socio-demographics and work characteristics.

Socio-demographic and loss-related characteristics plus symptom-levels across classes

Grief = job loss-related grief; dep = depression; anx = anxiety. * p < .01. ** p < .001.

2.2. Measures

2.2.1. socio-demographics.

The following socio-demographic data and work characteristics were collected from all participants: gender, age, educational level, income reduction, years of employment, and time passed since the job loss ( Table 1 ).

2.2.2. Job loss grief scale (JLGS)

For the measurement of persistent job loss-related grief reactions, the validated 33-item JLGS (Van Eersel et al., 2019 ) was administered. With their job loss in mind, participants rated the extent to which they experienced the reactions listed (e.g. ‘I can’t accept the loss of my job’ and ‘Memories about the loss of my job upset me’) during the previous month on a 5-point scale (0 =  never to 4 =  always ). Because of the sample size and in an attempt to reduce the complexity of the analysis, only the ten items included in the short version of the JLGS (i.e. the Job Loss Grief Scale–Short Form, JLGS-SF) were used in the analysis. A prior study (Van Eersel et al., 2019 ) showed that the JLGS-SF possessed good psychometric properties, similar to the extended JLGS. For instance, the items formed a unidimensional scale (χ 2  = 75.79; df  = 32; χ 2 / df  = 2.37; CFI = .99; TLI = .99; RMSEA = .07), that could be distinguished from symptoms of anxiety and depression, thus supporting the scale’s discriminant validity. In the present sample Cronbach’s α for these ten items was .94.

2.2.3. Depression anxiety stress scale (DASS-21)

For the measurement of depression and anxiety symptoms, the DASS-21 (Lovibond & Lovibond, 1995 ) was used. Participants rated the extent to which they had experienced the twenty-one symptoms listed during the preceding week (e.g. ‘I had nothing to look forward to’, ‘I felt afraid for no reason’) on a 4-point scale (0 =  never or rarely to 3 =  always or frequently ). In the present sample Cronbach’s α for depression was .93 and for anxiety it was .88.

2.2.4. Job loss circumstances scale (JLCS)

A six-item questionnaire was designed for the current research to tap specific information about circumstances of the job loss, including its perceived suddenness, injustice, and lack of control over the dismissal. These items are based on the notion that a stressful life event can shatter beliefs that the world is fair and predictable (Janoff-Bulman, 1999 ) and prior evidence that inadequate notice of dismissal is associated with job loss-related grief (Brewington et al., 2004 ). The JLCS measures three different aspects, each measured with two items. ‘Suddenness’ was assessed with items (2) ‘Before my dismissal there were signs of my approaching dismissal (e.g. my workload was cut down, advice was given to go look for another job)’, and (3) ‘My dismissal came totally unexpected to me’ (reversed). The ‘unfairness’ of the dismissal was measured with items (4) ‘My consent to my dismissal felt voluntary’ and (5) ‘My dismissal feels unfair’ (reversed). ‘Lack of control’ was measured with items (1) ‘My employer has spoken to me about my approaching dismissal’ and (6) ‘I said goodbye in a way that felt appropriate to me’. Participants rated the extent to which they agreed with each statement (1 =  totally agree to 4 =  totally disagree ).

Exploratory factor analysis revealed that two factors had an eigenvalue that exceeded 1.00 (2.67 and 1.08, respectively). The first factor explained 44.4% and the second factor 17.9% of the variance in the six items. Four items loaded strongly on the first factor: item 1 (.85), item 2 (.84), item 3 (.78), and item 6 (.51). Below this factor is referred to as ‘perceived suddenness and no suitable farewell’ (Cronbach’s α of these items was .75). Two items loaded strongly on the second factor: item 4 (.80) and item 5 (.81). This factor is referred to as ‘perceived injustice’. If a scale consists of two items, the alpha coefficient underestimates the true reliability of the scale, so the Pearson correlation coefficient is recommended instead (Eisinga, Te Grotenhuis, & Pelzer, 2013 ). The Pearson correlation coefficient for these two items was .36 (a medium effect, cf. Cohen, 1988 ). 2

2.2.5. Brief COPE

Coping behaviour was measured with Carver’s ( 1997 ) Brief COPE. Participants rated the extent to which they agreed with the scale’s twenty-eight statements (1 =  never or rarely to 4 =  very frequently ). Since we were mainly interested in maladaptive, adaptive, and social coping, we followed an earlier study from this project (Van Eersel et al., 2020a ) to construct these three factors from the subscales of the Brief COPE. As an index of maladaptive coping we summed the scores of the Brief COPE subscales: denial, behavioural disengagement, and self-blame. As an index of adaptive coping we summed the scores of the subscales: active coping, acceptance, positive reframing, and planning. Finally, as an index of social coping we summed the scores of the subscales: emotional support, instrumental support, and venting of emotions. In the present sample Cronbach’s α for maladaptive coping was .75, for adaptive coping .83, and for social coping .80.

2.3. Statistical analyses

LCA was conducted using Mplus version 8.1 (Muthén & Muthén, 1998–2017 ). To reduce the complexity of the analyses and in keeping with common practice, LCA was performed using dichotomized indicators of job loss-related grief, depression, and anxiety (Clogg & Goodman, 1985 ). For job loss-related grief, items scored as 0 =  never or 1 =  rarely were coded as ‘reaction is (largely) absent’, and items scored as 2 =  sometimes , 3 =  often , or 4 =  always as ‘reaction is (largely) present’. For depression and anxiety, items scored as 0 =  did not apply to me at all or 1 =  applied to me to some degree were coded as ‘reaction is (largely) absent’, and items scored as 2 =  applied to me to a considerable degree or 3 =  applied to me very much , as ‘reaction is (largely) present’.

The following indices were examined to determine the optimal number of classes: the log likelihood, the Akaike information criterion (AIC), the Bayesian information criterion (BIC), the sample size-adjusted Bayesian information criterion (SS-BIC), the Vuong-Lo-Mendell-Rubin likelihood ratio test (VLMR), the Bootstrap likelihood ratio test (BLRt), and the entropy. Lower log likelihood, AIC, BIC, and SS-BIC values indicate better fit (Nylund, Asparouhov, & Muthén, 2007 ); higher entropy values indicate fewer classification errors and lower bias in the determination of class membership (Van de Schoot, Sijbrandij, Winter, Depaoli, & Vermunt, 2017 ). A p- value below .05 for the BLRt and the VLMR indicates a significant improvement of the fit of the model under consideration, compared to the model with one class less (Nylund et al., 2007 ). Nylund et al. ( 2007 ) recommend to rely not solely on statistical indicators for the selection of the optimal class solution; rather, the interpretability of classes, the size of classes (to avoid too few observations within a cell), and consistency with prior research also should be taken into account.

Chi-square tests and analyses of variance (ANOVAs) were conducted in SPSS version 26 to examine whether membership of a particular class was associated with socio-demographics, loss-related characteristics (e.g. age, educational level, time passed since loss, job loss circumstances) and coping. For the measure of income reduction and for the six items of the JLCS, data were missing for 18 participants (3%). There were no data missing for job loss-related grief, depression, and anxiety. To handle missing data, cases were removed pairwise for optimal use of all available data. First, for each variable, we examined whether it was associated with class membership. Next, predictors that were significant in these univariate analyses were included in a multinomial logistic regression analyses to examine which of these variables distinguished best between classes, controlling for the shared variance of these variables. The data set is freely retrievable (Van Eersel, Taris, & Boelen, 2021 ).

3.1. Latent class analyses

Table 2 presents the fit indices of the solutions with one to seven classes. The log likelihood test, AIC, and SS-BIC presented results which are closely related for the 5-class solution, the 6-class solution, and the 7-class solution. The value of the BIC was practically the same for the 4-class solution, the 5-class solution, and the 6-class solution, although the BIC was the lowest for the 5-class solution. In conjunction, these results suggest that overall the 5-class solution had the best fit to the data. However, the VLMR test showed the 2-class solution to have a significantly better fit to the data than the 1-class solution, and the 4-class solution yielded a significant better fit than the 3-class solution. According to this measure, solutions with more than four classes did not improve significantly on the 4-class solution. Finally, the 5-class solution could not be interpreted meaningfully; for instance, there were two classes with almost identical grief symptoms and low scores on depression and anxiety. Therefore, the 4-class model was selected as the optimal solution. Figure 1 (and the supplementary table) present the symptom prevalence in the four classes; values > .50 were considered as indicating a high probability of item endorsement.

Fit indices for best fit model latent class analyses

AIC = Akaike information criterion; BIC = Bayesian information criterion; SS-BIC = sample size adjusted Bayesian information criterion (SS-BIC); VLMR = Vuong-Lo-Mendell-Rubin; BLRt = Bootstrap likelihood ratio test.

Probability estimates of item endorsement for all participants and for the four-class solution

The interpretation of the four classes of this solution was fairly straightforward. The first class (16.6%) was characterized by relatively low probabilities for six anxiety symptoms and high probabilities for all job loss-related grief reactions, all depression symptoms, and one anxiety symptom (‘feeling scared’), and was therefore labelled as the ‘mixed class’. The second class (25.5%) evidenced comparatively low probabilities for all depression symptoms, all anxiety symptoms, and three job loss-related grief reactions (‘feeling numb’, ‘partly vanished’, and ‘shattered view of the world’), as well as relatively high probabilities of seven job loss-related grief reactions. It was therefore labelled as the ‘grieving class’. The third class (12.8%) was characterized by relatively low probabilities of all anxiety symptoms, three depression symptoms, and most job loss-related grief reactions, and high endorsement of four depression symptoms (‘could not seem to get going’, ‘nothing to look forward to’, ‘down-hearted and blue’, and ‘worthlessness’) and two job loss-related grief reactions (‘personal disaster’ and ‘feeling on edge or jumpy’). Consequently, it was named the ‘depressed class’. Finally, the fourth class (45.1%) was characterized by low probabilities of endorsement of all items and was labelled ‘resilient class’. Note that the scores of the members of these four classes were compared between classes, rather than with an external criterion. For instance, the ‘depressed class’ was given this label because individuals in this class reported relatively high levels of depressive symptoms as compared to the other three classes. However, this does not imply that the members of this class are clinically depressed, but only that their scores on this set of symptoms were comparatively high vis-à-vis those of the other three classes. Similar reservations apply to the labels of the other three classes. Figure 1 (and the supplementary table) present the probabilities of endorsement of the symptoms for all four classes.

3.2. Predictors of class membership: univariate analyses

Information on socio-demographical variables, loss-related variables, and indices of the coping strategies of all classes is presented in Table 1 . The means of the socio-demographic and work variables did not differ significantly across classes, except for circumstances of the job loss. These differed across all classes on both aspects assessed by the Job Loss Circumstances Scale: the degree to which the job loss was experienced as ‘unexpected without a suitable goodbye’ and as ‘unfair’. Post-hoc analyses showed that members of the mixed and the grieving classes had higher scores on the index for ‘unexpected without a suitable goodbye’ from the Job Loss Circumstances Scale, compared to the resilient class. Post-hoc analyses indicated that participants in both the mixed and grieving class considered their job loss significantly more often as unfair, compared to the resilient class.

Maladaptive coping differed between groups ( Table 1 ); post-hoc analyses revealed significant differences between almost all classes, except between the grieving and depressed class. Further, all classes differed on adaptive coping; post-hoc analyses showed that the resilient class scored significantly higher than all other classes, with the grieving class scoring significantly higher than the mixed class. Further, social coping also differed between classes; post-hoc analyses revealed that the grieving class employed significantly more social coping strategies than the depressed class.

With respect to differences in overall job loss-related grief reactions (i.e. the summed JLGS scores), post-hoc analyses indicated significant differences among all four classes, with the mixed class having the highest JLGS total score, followed by the grieving class, then followed by the depressed class, and with the lowest JLGS score reported by the resilient class. Similar findings emerged when looking at the summed depression items of the DASS-21, where the mixed class had the highest score followed by the depressed class, the grieving class, and the resilient class. Finally, for the total scores on the anxiety items of the DASS-21, all classes differed significantly as well. Again, Table 1 shows that the mixed class represented the highest score with the depressed class as runner-up, followed by the grieving class, and the resilient class again had the lowest score.

3.3. Predictors of class membership: multinomial logistic regression analysis

A multinomial logistic regression analysis was conducted to examine which of the variables that were significantly associated with class membership in the univariate analyses, were still associated with class membership after controlling for the shared variance between variables. Total scores for job loss-related grief, depression, and anxiety were not included in these analyses. Table 3 summarizes the outcomes of this analysis. Class membership was differentiated by job loss circumstances; participants who experienced their dismissal as unjustified were more likely to be assigned to the grieving class than to the resilient or depressed classes.

Multinomial logistic regression predicting class membership

Values in bold indicate a significant difference between the compared classes.

Class membership also differed as a function of coping ( Table 3 ). The use of maladaptive coping strategies was more strongly endorsed in the mixed, the grieving, and the depressed classes compared to the resilient class; the mixed class showed the highest effect (exp(B) = 1.97) compared to the resilient class. In comparison to the depressed and grieving classes, the mixed class showed a significant higher endorsement of maladaptive coping. The use of adaptive coping was more strongly endorsed by participants included in the resilient class, compared to the mixed, the grieving, and the depressed classes. The strongest significant effect was found for the mixed class as compared to the grieving class (exp(B) = 0.92), with the mixed class making less use of adaptive coping strategies. A similar result was found in comparison to the depressed class, with the mixed class having a lower endorsement of adaptive coping relative to the depressed class. Finally, social coping was more strongly employed by the grieving class compared to the depressed and the resilient classes. Compared to the mixed class, the depressed class showed the strongest effect (exp(B) = 1.18) and a lower endorsement of social coping.

4. Discussion

The aim of this study was to use LCA to examine whether subgroups could be identified among people who involuntarily lost their jobs, based on different patterns of endorsement of reactions of grief, depression, and anxiety. The first main result was that four classes were identified: (i) a mixed class characterized by endorsement of most of the items representing grief, depression, and anxiety reactions, (ii) a grieving class, (iii) a predominantly depressed class, and (iv) a resilient class. These findings indicate that people confronted with involuntary job loss can be distinguished in terms of the dominance of particular emotional reactions, rather than by a graded severity of a general post-loss response. This accords with the notion that these reactions represent multiple dimensions rather than one single dimension of job loss-related distress. The emergence of a class characterized by elevated grief (but not depression and anxiety) aligns with earlier findings that job loss-related grief reactions can be distinguished from depression and anxiety symptoms after involuntary job loss (Papa & Maitoza, 2013 ; Van Eersel et al., 2019 ). We did not find a class that mainly displayed anxiety symptoms. According to Osman et al. ( 2012 ) the items of the DASS-21 have stronger associations with the general distress dimension than with the domain-specific dimensions: depression, anxiety, and stress. This implies a possible lack of sensitivity of the DASS-21 when it comes to distinguishing between depression and anxiety symptoms. The anxiety items of the DASS-21 mainly represent symptoms of physiological hyperarousal, such as ‘I experienced trembling’ and ‘I experienced breathing difficulty’. It might be possible that such physical symptoms are more commonly observed following bereavement loss or psycho-trauma than after job loss.

A second main finding was that a distinct class could be identified that was characterized by the presence of job loss-related grief reactions, but not by elevated reactions of depression and anxiety. This indicates that job loss-related grief is distinct from depressive and anxiety symptoms following job loss which accords with earlier variable-centred research (Papa & Maitoza, 2013 ; Van Eersel et al., 2019 ). However, the first item of the job loss grief scale (‘The loss of my job feels like a personal disaster’) was found to be endorsed across all classes and, as such, does not appear to make a relevant distinction among the classes. Two items (‘I feel bitter about the loss of my job’ and ‘I have felt on edge, jumpy or easily startled since the loss of my job’) appeared to be strongly associated to depression and did not clearly distinguish between the depressed and grieving classes. Items that were related to grief and that were distinctive for depression and anxiety symptoms are: ‘I think about my job so much that it is hard for me to do the things I normally do’, ‘I can’t accept the loss of my job’, and ‘I feel stunned and dazed over the loss of my job’. These symptoms are characteristic of elevated job loss-related grief among those exposed to job loss.

A third main finding was that the resilient class comprised approximately half of the sample (45%). From prior research on bereavement loss, it is known that the majority of people confronted with loss shows no or very few symptoms of distress (Bonanno et al., 2008 ). The size of the resilient class in the present study suggests that the same applies to job loss. This is in line with prior research (Galatzer-Levy et al., 2010 ) in which the majority of the people showed a resilient response after job loss, while a minority developed long-term increased levels of emotional distress (e.g. depression or anxiety symptoms).

A fourth main finding was that class membership was unrelated to most of the socio-demographic variables and work characteristics, including the time passed since dismissal. However, other variables, including aspects of an individual’s experience of his/her dismissal were associated with class membership. If the dismissal was considered as unfair, sudden, involuntary, and when there was no opportunity for an appropriate goodbye to the former job, there was lower probability of being assigned to the resilient class. Note that due to the cross-sectional design of this study no conclusions can be drawn concerning the causal direction of the association between job loss circumstances and class membership. Multinomial regression analyses revealed that, in comparison to the resilient and the depressed class, endorsement of experiencing the dismissal as unfair increased the chance of being assigned to the grieving class. This accords with prior findings that an inadequate notice of dismissal (Brewington et al., 2004 ) and believing that the world is unfair (Papa & Maitoza, 2013 ) can be a risk factor for the development of grief reactions following job loss. The feeling of unfairness might also be fuelled by the loss event itself. This event can shatter an individual’s basic beliefs about the world, others, and the self, which can subsequently change one’s sense of justice and fairness in general (Janoff-Bulman, 1999 ; Park, 2010 ). It would be interesting to further explore the linkage between the perceived degree of unfairness of dismissal and the intensity of emotional distress following job loss over time in longitudinal research, to examine the temporal relationship between job loss circumstances and class membership.

A final main finding was that endorsement of maladaptive coping strategies was highest in the mixed class and lowest in the resilient class, whereas endorsement of adaptive coping strategies was highest in the resilient class and lowest in mixed class. These findings agree with prior research findings showing that maladaptive coping strategies were associated with elevated job loss-related grief reactions (Papa & Maitoza, 2013 ; Van Eersel et al., 2020a ). However, social coping strategies were endorsed strongest in the grieving class and the least in the depressed class. Considering results from bereavement research (Burke, Neimeyer, & McDevitt-Murphy, 2010 ), this could imply that people who mainly experience grief symptoms might have the tendency to reach out to others, where are as people who mainly experience depressive symptoms tend to withdraw from others.

A tendency towards maladaptive coping strategies, and relatively higher levels of job loss-related grief, depression, and anxiety might be provoked through a lack of available resources to deal with the changed reality. According to the conservation of resources theory, emotional distress tends to increase when valuable resources are threatened, like in the case of job loss (Hobfoll, 1989 ). Weak resources (e.g. in terms of money, self-esteem, or social network) make it more difficult to handle stressful events, which can lead to a vicious cycle of further depletion of resources and more stress. In an attempt to maintain resources and minimize the net loss, individuals tend to employ (and possibly drain) other resources to help them in the short run and, as a result, make themselves more vulnerable in the long run (Hobfoll et al., 2016 ). In future research, it would be interesting to examine the direction of the relationship between maladaptive coping, job loss-related grief, depression, anxiety within the theoretical framework of the conservation of resources theory.

4.1. Limitations

The main limitations of this study are the following. First, although we can measure job loss-related grief reactions, much remains unknown about this phenomenon. There are commonalities between grief reactions following bereavement, job loss, divorce (Papa et al., 2014 ), romantic break-ups (Boelen & Reijntjes, 2009 ), and natural disaster (Shear et al., 2011 ). It is also known (and in line with the current study) that job-loss related grief reactions can be distinguished from depression and anxiety symptoms after dismissal (Papa & Maitoza, 2013 ; Van Eersel et al., 2019 ). However, more longitudinal research combined with clinical interviews is necessary to fully comprehend this phenomenon and to provide a solid time-frame during which these job loss-related grief reactions may reflect a ‘normal’ adjustment process and when such reactions become signs of disturbed adjustment. In spite of this limitation, the present study contributes to our limited knowledge about job loss-related grief reactions and on the impact that involuntary job loss can have on an individual’s well-being and mental health.

Second, we have only examined a limited number of possible predictors of class membership: general sociodemographic variables, work characteristics and coping strategies. It would be interesting to further explore other possible predictors, like negative cognitions about the loss event, the self, others, the future and the world. Since these types of cognitions (negative a priori beliefs or negative beliefs activated by the job loss) could be related to the intensity someone experiences grief reactions, depression, and anxiety following involuntary job loss (Papa & Lancaster, 2016 ). The JLCS has not been validated in independent studies, hence the outcomes based on this scale should be considered with caution.

Finally, this study was conducted in the Netherlands, where unemployment benefits are relatively well arranged. Some studies indicate that there is no significant relation between income reduction and job loss-related grief reactions (Papa & Maitoza, 2013 ; Van Eersel et al., 2020a ). However, other studies have shown that higher unemployment benefits were related to higher mental health due to lower financial strain and lower time pressure (Wanberg, Van Hooft, Dossinger, Van Vianen, & Klehe, 2020 ). It is conceivable that the limited income reduction in the present sample did not lead to a substantial increase of financial strain due to specific contextual factors (e.g. the level of unemployment benefits, the presence of savings or a partner earning a good income); that might have influenced our results for the relation between income reduction and class membership. Future research may include specific contextual factors (e.g. financial strain, unemployment benefits, and breadwinnership) to gain more insight into the associations of these factors with reactions of job loss-related grief, depression, and anxiety.

4.2. Implications

The results of this study suggest that both the extent to which individuals experience their dismissal as unfair, and higher use of maladaptive coping strategies are associated with more intense reactions of job loss-related grief, depression, and anxiety or combination of these reactions. This is in line with the research of Ricketson, Dodd, Zion, and Winarnita ( 2020 ); in their sample a third of the people who were laid off experienced their job loss as a negative event and described the process of dismissal as humiliating and insulting. For example, one of their participants stated not getting a farewell from the management, and although time passed by, he/she was still consumed with anger about the way it all went down.

There are often legal and regulatory issues influencing how and when employees are notified about possible redundancy and dismissal. Additionally, there is the need to control access to company resources such as computer databases, and the need to balance sharing information with keeping workers productive. Taking this into account, employers can use this knowledge to their advantage when giving notice, to reduce the level of emotional stress before, during and after the job loss. They could consider involving people more during the termination process, as far as possible within the given context of protecting company resources. Openness in communication, consistent feedback, and being respectful to each other could decrease the degree to which a person experiences the job loss as sudden or unfair. Employers might consider discussing with the person to think about an appropriate way to say goodbye to the company, their colleagues, and customers and, in doing so, provide the opportunity to the person to regain some sense of control. They could also hold an exit interview for remaining questions, closure, appreciation, and achievements.

Screening for reactions of grief, depression, and anxiety after dismissal can yield a better picture of the mental health issues experienced by this group and provides the opportunity for timely and targeted interventions. For instance, depressive symptoms require a different approach to increase positive affect (e.g. scheduling enjoyable activities, cognitive restructuring of negative views of the self and life) than job loss-related grief symptoms (e.g. enhance emotion-affect regulation, cognitive restructuring misinterpretations of the job loss). Alleviating these reactions seems necessary to increase the mental health of individuals confronted with involuntary job loss and their chance of sustainable re-employment.

Supplementary Material

Funding statement.

The authors did not receive any specific grants for this research from funding agencies in the public, commercial, or not-for-profit sectors.

1. In other studies, ‘job loss-related grief reactions’ were called ‘job loss-related complicated grief symptoms’ to described the same phenomenon (Papa & Lancaster, 2016 ; Papa et al., 2014 ; Papa & Maitoza, 2013 ; Van Eersel et al., 2019 , 2020a , 2020b ). In this study, the term ‘job loss-related grief reactions’ was used to clarify that we are not referring to disordered grief as currently defined in DSM-5 and ICD-11 and also to emphasize that we do not argue that ‘job loss-related grief reactions’ or ‘job loss-related complicated grief symptoms’ should be included as a novel disorder in the existing classification systems.

2. The two JLCS scales (perceived suddenness/no suitable farewell and perceived injustice, respectively) were significantly related to job loss-related grief symptoms ( r = .21 and .27), the brief cope subscale ‘denial’ ( r = .31 and .28), and the brief cope subscale ‘acceptance’ ( r = .15 and .15), attesting to the concurrent validity of the JLCS.

Authors contributions

JE and PB designed the study. JE collected the data. JE and TT conducted the statistical analyses. JE wrote the draft of the manuscript. All authors critically reviewed and improved draft versions of the manuscript. All authors read and approved the final version of the manuscript.

Data availability statement

Disclosure statement.

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed here .

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Research: How Losing a High-Paying Job Affects Family Relationships

• Aliya Hamid Rao

Gender norms make unemployment an unequal burden among heterosexual couples in the U.S.

Layoffs and other job losses not only affect people’s career prospects and finances; they affect people’s relationships with family, too. But these affects are quite different depending on the gender of the person out of work. Research on well-off, heterosexual couples with children found that, while men’s job losses were seen as both urgent and shameful, women’s job losses were more often framed as a way for mothers to spend more time with their children. Both of these reactions can be harmful for both the people who lost a job and for their loved ones. To better support families with a person out of work, society needs to start destigmatizing unemployment and decoupling gender with paid and unpaid work.

Businesses often treat layoffs as one of several tools they can deploy to increase profitability. But the toll of layoffs as a routine business practice can be extraordinary for the people who lose their jobs.

• AR Aliya Hamid Rao is an Assistant Professor in the Department of Methodology at the London School of Economics. Her research focuses on precarious work and unemployment. Her book, Crunch Time: How Married Couples Confront Unemployment , is now out with the University of California Press.

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Dow plunges more than 1,000 points amid fears of U.S. economic slowdown

By Aimee Picchi

Edited By Anne Marie Lee

Updated on: August 5, 2024 / 9:22 PM EDT / CBS News

Stocks in the U.S. plunged for a third consecutive trading day, with the Dow Jones Industrial Average tumbling more than 1,000 points amid growing fears of an economic downturn sparked by a slowdown in hiring and consumer spending. 

The S&P 500 slid 160 points, or 3%, to 5,186 on Monday, the index's biggest one-day drop in nearly two years, according to FactSet. The tech-heavy Nasdaq Composite sank 3.4% as investors fled some of the Big Tech players that until recently had powered the U.S. market higher — Apple shed 4.8%, while Meta and Nvidia, fell 2.5% and 6.4%, respectively. 

The Dow Jones Industrial Average tumbled 1,034 points, shedding 2.6% of its value. Earlier in the day, it had lost as more than 1,200 points, but the markets regained some of their early losses as Wall Street digested Monday data from the Institute for Supply Management (ISM) Services index, which showed that service employment picked up in July. 

"The details of the ISM report were encouraging, with business activity, new orders and employment all rebounding markedly in July," Oxford Economics said in a Monday research note. The report "aligns with our view of an economy in transition rather than one on the brink of collapse."

Even with Monday's rout, U.S. stocks still remain in positive territory this year. The S&P 500 has gained 9.4% in 2024, even after including its recent slide, while the Dow remains up by 2.6%.

What's driving down stocks

Stocks  lost ground on Thursday after weak reports on manufacturing and construction, which stoked fears the U.S. economy may finally be buckling under the pressure of high interest rates. 

Then on Friday, government data showed that hiring last month was far weaker than expected , adding to Wall Street's fears that a "soft landing," in which the U.S. economy could avoid a recession despite the highest interest rates in 23 years, could instead become a hard landing. 

"The main factor that has staying power is the economy's slowdown," wrote Wells Fargo head of global investment strategy Paul Christopher in a report. "Investors have been watching household financial stress build for the past two years, but during that time, job growth remained above its December 2009-December 2019 average of 180,000 new jobs per month."

But Friday's jobs report showed that employers added only 114,000 new jobs last month, far fewer than the 175,000 jobs expected by economists, he noted. 

Tech stocks have been hit particularly hard in recent weeks as investors pull back from artificial intelligence companies amid questions about when the emerging sector will deliver profits. 

"It has been a tough few weeks for the AI group as earnings were reported," analysts with Melius Research wrote. 'Microsoft, Meta, Google and Amazon were all asked about payoffs from AI investments. While pretty clear that they all need to keep spending, the market remains skeptical of the pace."

The market rout extended to Asian and European markets, with Japan's benchmark stock index plunging 12.4% on Monday. The Nikkei had dropped 5.8% on Friday, making this its worst two-day decline ever. 

Stocks in Korea and Taiwan also fell sharply, with all three Asian markets damaged as investors pull back from companies focused on artificial intelligence out of concern the sector has been overhyped.

When will the Fed cut rates?

With the disappointing economic data, Wall Street is worried the Federal Reserve may have kept its benchmark interest rate too high for too long, heightening the risk of a recession. The central bank kept the federal funds rate unchanged when it met  on July 31 to discuss economic conditions and whether and when it should begin cutting rates.

A rate cut would make it less expensive for U.S. households and companies to borrow money, but it could take time for the effects to boost the economy. On Monday, some investors called for the Fed to start cutting rates sooner rather than later to stave off an economic downturn.

"The Federal Reserve needs to start easing monetary policy more aggressively than had been anticipated, in order to head off a looming recession in the world's largest economy," said Nigel Green, CEO of deVere Group, an independent financial advisory and asset management firm, in an email. "The Fed was behind the curve at the beginning of the cycle, it cannot afford to be behind the curve this time too."

Economists still don't expect a recession

Although worries over weakness in the U.S. economy and volatile markets have rippled around the world, domestic economic activity remains solid, with many analysts saying that a recession remains unlikely. Stephen Brown, deputy chief North America economist with Capital Economics, still expects a soft landing, while acknowledging that the risk of a sharper downturn is rising. 

The economy has accelerated this year, with the nation's gross domestic product jumping to 2.8% in the second quarter, blowing past forecasts. A recession is typically marked by two consecutive quarters of negative GDP. And although July's jobs report was disappointing, analysts point out that it reflects just one month of data, while also noting that the depressed hiring figures in July could have also been impacted by Hurricane Beryl . 

"It can be a mistake to read too much into a single data release," noted Solita Marcelli, chief investment officer Americas at UBS Global Wealth Management, told investors in a research note. "The number of people who reported being unable to work [in July] due to the weather was 436,000; this compares to an average of 33,000 for July since 2000."

The Associated Press contributed to this report.

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Aimee Picchi is the associate managing editor for CBS MoneyWatch, where she covers business and personal finance. She previously worked at Bloomberg News and has written for national news outlets including USA Today and Consumer Reports.

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Experiences of Unemployment and Well-Being after Job Loss during Economic Recession: Results of a Qualitative Study in East Central Sweden

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COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK

Clinical Research Coordinator

• Obstetrics and Gynecology
• Columbia University Medical Center
• Opening on: Aug 13 2024
• Job Type: Officer of Administration
• Bargaining Unit:
• Regular/Temporary: Regular
• End Date if Temporary:
• Hours Per Week: 35
• Standard Work Schedule:
• Salary Range: $64,000 - $66,000

Position Summary

The primary role of this position is to support clinical research involving Maternal Fetal Medicine Research. The employee will interact with pregnant women, their families and clinical staff as it relates to clinical research protocols and clinical trials, industry funded and grant funded, being implemented in the inpatient and outpatient setting. The inpatient setting includes labor and delivery, ante-partum and post-partum.  The outpatient setting includes ACN clinics, Columbia doctor’s private offices, and ultrasound units.

Responsibilities

• Complete of GCP, HIPAA and applicable regulatory training.
• Complete certification requirements for assigned protocols.
• Screen designated schedules or patient lists for eligible subjects.
• Contact, approach and verify eligibility subjects.
• Screen, enroll and consent eligible subjects.
• Complete research study visits as delineated in assigned protocol and manual of operations set forth by sponsor and supervisor.
• Complete Telephone follow-up and telephone reminder calls for study participants, during these phone calls the person will need to administer study questionnaire as assigned.
• Coordinate the collection of all research data points as assigned, whether through research visits, chart abstraction or telephone.
• Schedule of research visits.
• Enter research data into a centralized database program and other study specific electronic databases.
• Collect through venipuncture, process, track, transport and ship biological specimens (including birth specimens) as assigned and by steps delineated in the protocol or manual of operations.
• Complete study documents and files some examples might include case report forms, worksheets and medical record notes.
• Maintain confidentiality of documents and files such as HIPAA.
• Inform relevant clinical staff regarding subject protocol participation.
• Assist in other research related activities and projects as needed.
• Collaborate regularly with the investigators and other research staff.
• Consult with medical records of outlying clinics and hospitals to obtain pertinent data and patient information.
• Interface with various hospital departments such as medical records, radiology, nursing, information technology services, and others as needed.
• Attend and participate in Investigator, site initiation and monitoring meetings as assigned.
• Participate in on-call activities to cover research activity off- hours as needed.
• Perform other related duties and responsibilities as assigned/requested.

Minimum Qualifications

• Requires a bachelor's degree or equivalent in education and experience.
• Phlebotomy Certified (or other forms of certification in lieu of phlebotomy such as certified medical assistant, nurse degree (LPN, RN) - current or obtained within 3 months.
• Complete proficiency in written and spoken English and Spanish.
• Incumbent must be self-directed and able to make independent decision within the parameters of all federal, state, institutional and departmental guidelines.

Preferred Qualifications

• Experience in a patient care setting and clinical research experience.
• Excellent interpersonal, written/oral communication, and organizational skills.
• Proficiency in Microsoft Office.
• Master’s or another advanced degree may substitute in part for experience.

The Department of Obstetrics and Gynecology is dedicated to the goal of building a multicultural faculty and staff committed to teaching, working and serving in a diverse community, and strongly encourages applications from candidates of traditionally underrepresented backgrounds.

We are continuously seeking to recruit individuals who will enhance the diversity of our workplace and the effectiveness of our organization.

Equal Opportunity Employer / Disability / Veteran

Columbia University is committed to the hiring of qualified local residents.

Commitment to Diversity 

Columbia university is dedicated to increasing diversity in its workforce, its student body, and its educational programs. achieving continued academic excellence and creating a vibrant university community require nothing less. in fulfilling its mission to advance diversity at the university, columbia seeks to hire, retain, and promote exceptionally talented individuals from diverse backgrounds.  , share this job.

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Research specialist.

The Environment Exposure Assessment Laboratory in the Department of Environmental Health and Engineering at the Johns Hopkins Bloomberg School of Public Health is looking for a Research Specialist position who is a self-motivated professional focused on research related to environmental exposures that can work independently and develop new laboratory analysis techniques. We have a special focus on exposures to Secondhand smoke and toxic metals, evaluating exposures to environmental contaminants such as air, water and soil. We also assess potential impacts on human health by analyzing biospecimens including blood, urine, hair, etc. Our lab works with multiple investigators and projects, making this a very dynamic and stimulating environment.

Special Knowledge Skills & Abilities

• Must be able to handle multiple tasks at one time.
• Must be comfortable using Microsoft office software such as Word, Excel, Teams, PowerPoint and Outlook.
• Familiarity with databases, accurate data entry, and report generation.
• Bachelor's Degree in Biology, Chemistry or related field.
• One year laboratory experience performing relevant techniques.
• Master's Degree, with related graduate research, may substitute for experience to the extent permitted by the JHU equivalency formula.
• Familiar with separation techniques and wet chemistry skills.
• Basic lab techniques is a must.
• Excellent organizational skills.
• Excellent oral and written communication skills.
• Research Specialist candidate must be a NON-SMOKER. Analyses of trace level nicotine and assembly of secondhand monitors are essential part of the job.
• Occasionally lifting, carrying, pushing, and/or pulling objects weighing up to 50 lbs., such as compressed gas cylinders.
• Duties may require standing, walking, or sitting for an extended periods of time.
• Requires dexterity to manipulate objects, such as pipetting and other laboratory duties.
• Occasional evenings or weekend on-duty if emergency at the lab.

Classified Title: Research Specialist  Role/Level/Range: ACRP/03/MA   Starting Salary Range: $35,600 - $62,200 Annually ($50,000 targeted; Commensurate with experience)  Employee group: Full Time  Schedule: 8:30am-5pm  Exempt Status: Non-Exempt  Location: School of Public Health  Department name: ​​​​​​​Environmental Health and Engineering   Personnel area: School of Public Health 

Total Rewards The referenced base salary range represents the low and high end of Johns Hopkins University’s salary range for this position. Not all candidates will be eligible for the upper end of the salary range. Exact salary will ultimately depend on multiple factors, which may include the successful candidate's geographic location, skills, work experience, market conditions, education/training and other qualifications. Johns Hopkins offers a total rewards package that supports our employees' health, life, career and retirement. More information can be found here: https://hr.jhu.edu/benefits-worklife/ .

Education and Experience Equivalency Please refer to the job description above to see which forms of equivalency are permitted for this position. If permitted, equivalencies will follow these guidelines: JHU Equivalency Formula: 30 undergraduate degree credits (semester hours) or 18 graduate degree credits may substitute for one year of experience. Additional related experience may substitute for required education on the same basis. For jobs where equivalency is permitted, up to two years of non-related college course work may be applied towards the total minimum education/experience required for the respective job.

Applicants Completing Studies Applicants who do not meet the posted requirements but are completing their final academic semester/quarter will be considered eligible for employment and may be asked to provide additional information confirming their academic completion date.

Background Checks The successful candidate(s) for this position will be subject to a pre-employment background check. Johns Hopkins is committed to hiring individuals with a justice-involved background, consistent with applicable policies and current practice. A prior criminal history does not automatically preclude candidates from employment at Johns Hopkins University. In accordance with applicable law, the university will review, on an individual basis, the date of a candidate's conviction, the nature of the conviction and how the conviction relates to an essential job-related qualification or function.

Diversity and Inclusion The Johns Hopkins University values diversity, equity and inclusion and advances these through our key strategic framework, the JHU Roadmap on Diversity and Inclusion .

Equal Opportunity Employer All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.

EEO is the Law https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf

Accommodation Information If you are interested in applying for employment with The Johns Hopkins University and require special assistance or accommodation during any part of the pre-employment process, please contact the Talent Acquisition Office at [email protected] . For TTY users, call via Maryland Relay or dial 711. For more information about workplace accommodations or accessibility at Johns Hopkins University, please visit https://accessibility.jhu.edu/ .

Vaccine Requirements Johns Hopkins University strongly encourages, but no longer requires, at least one dose of the COVID-19 vaccine. The COVID-19 vaccine does not apply to positions located in the State of Florida. We still require all faculty, staff, and students to receive the seasonal flu vaccine . Exceptions to the COVID and flu vaccine requirements may be provided to individuals for religious beliefs or medical reasons. Requests for an exception must be submitted to the JHU vaccination registry.  This change does not apply to the School of Medicine (SOM). SOM hires must be fully vaccinated with an FDA COVID-19 vaccination and provide proof of vaccination status. For additional information, applicants for SOM positions should visit https://www.hopkinsmedicine.org/coronavirus/covid-19-vaccine/  and all other JHU applicants should visit https://covidinfo.jhu.edu/health-safety/covid-vaccination-information/ .

The following additional provisions may apply, depending upon campus. Your recruiter will advise accordingly. The pre-employment physical for positions in clinical areas, laboratories, working with research subjects, or involving community contact requires documentation of immune status against Rubella (German measles), Rubeola (Measles), Mumps, Varicella (chickenpox), Hepatitis B and documentation of having received the Tdap (Tetanus, diphtheria, pertussis) vaccination. This may include documentation of having two (2) MMR vaccines; two (2) Varicella vaccines; or antibody status to these diseases from laboratory testing. Blood tests for immunities to these diseases are ordinarily included in the pre-employment physical exam except for those employees who provide results of blood tests or immunization documentation from their own health care providers. Any vaccinations required for these diseases will be given at no cost in our Occupational Health office.

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Wildfires Costing The U.S. $89 Billion In Lost Output, Study Finds

TOPSHOT - Vehicles and a home are engulfed in flames as the Dixie fire rages on in Greenville, ... [+] California on August 5, 2021. - Evacuation orders were widened on August 5, 2021 as California's biggest wildfire raged through the state's tinder-dry landscape, laying waste to hundreds of square miles (kilometers). The Dixie Fire is already the sixth biggest in the state's history, and was still spreading thanks to gusting winds and record-low humidity. (Photo by JOSH EDELSON / AFP) (Photo by JOSH EDELSON/AFP via Getty Images)

The current spate of wildfires could cost the economy of the United States more than $89 billion in lost output, according to a new analysis.

According to research by the economic software and analysis company IMPLAN, the wildfires could have a profound effect on the national economy.

The analysis claims the wildfires could cost 466,000 jobs, a $52.2 billion contribution to gross domestic product (GDP) and $89.6 billion in lost economic output for the US.

It also looked at California and Washington specifically, and found in California, the current wildfires could cost almost 12,000 jobs and more than $2.3 million in output, while contributing $1.5 million to GDP.

In Washington, the current wildfires could see a decrease in 5,195 jobs, $389 million in labor income, and $678 million in contribution to GDP.

In an interview, IMPLAN’s vice president of customer success, Candi Clouse, said some of the economic costs will not become immediately apparent, and only be seen over a longer period of time.

Clouse said these costs include property damage and exposure to smoke, which could have long-term health impacts.

But she added there were immediate costs like the electricity and water needed to fight the wildfires, a loss of household income to families who have been directly affected and for the timber sector itself.

The analysis also notes rebuilding after a wildfire and supporting those affected could actually has a positive impact on the U.S. economy.

In October 2023, a separate study by the U.S congress joint economic committee found climate-exacerbated wildfires cost the United States between $394 to $893 billion each year in economic costs and damages.

And wildfires can also have major health impacts as well.

In an interview, the American Lung Association’s national senior director for clean air advocacy, Will Barrett said the increase in exposure to wildfire smoke “has definitely added to the health burdens that people are facing”.

Barrett said exposure to the fine particle pollution in smoke, which is often referred to as PM2.5, can cause a wide range of negative health impacts, including asthma and heart attacks.

He told me other chemicals could also be contained in some wildfire smoke, meaning it could be even more toxic and have longer-term health impacts.

Barrett added the geographical reach of the impacts is also widening.

For example, he said wildfires in Western Canada in recent years led to smoke and unhealthy levels of air pollution reaching as far as Pennsylvania, while fires last year in Canada exposed a wide swath of communities across the eastern United States.

“An increased exposure to wildfire smoke has a wide range of harmful outcomes, both in the near term and the longer term,” said Barrett.

“It also comes with very high price in terms of lives lost and the mental health toll that wildfires can cause when people are forced to evacuate at a moment’s notice, lose their home or even lose a community.”

Researchers at Columbia University and New York University recently announced they have come up with a tool to forecast the risk of forest fires in any particular region of the western U.S. months in advance, and they can do so within minutes as opposed to hours.

According to the tool’s analysis, researchers have warned there is a high level of certainty that the U.S. regions of California and Pacific Northwest are expected to be hit hard with forest fires in the coming months.

The Rockerfeller Foundation also recently released its annual report for its Zero Gap Fund, which highlights the critical role of capital in addressing pressing challenges, like wildfires.

In 2023, the fund made several new investments including on in Blue Forest’s Forest Resilience Bond I, which serves communities and forests within the western U.S. with long-term ecological restoration to reduce the frequency and severity of wildfires and increase forest resilience.

In an email, the Rockefeller Foundation president Dr Rajiv Shah said: “As the world continues to shatter heat records, we need to urgently identify and scale solutions that advance people's well-being even as they help reverse the climate crisis.

“Blue Forest is doing just that, restoring and protecting forests and water supplies in frontline communities, avoiding wildfire carbon emissions, and returning capital to their investors.”