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

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• Published: 17 May 2023

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• Mattia Filomena   ORCID: orcid.org/0000-0002-4099-9168 1 , 2  

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

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Data Availability

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

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

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

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

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

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

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

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

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

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

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

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

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Acknowledgements

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

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Filomena, M. Unemployment Scarring Effects: An Overview and Meta-analysis of Empirical Studies. Ital Econ J (2023). https://doi.org/10.1007/s40797-023-00228-4

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

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

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

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

What Is "Normal"?

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

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

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

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

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

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

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

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

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

Pre-Pandemic Trends: 1976-2019

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

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

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

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

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

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

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

Pre-Pandemic Expectations of Future LFP and Unemployment Trends

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

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

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

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

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

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

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

Unemployment and Labor Force Participation During the Pandemic

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

What Does the Future Hold?

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

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

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

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

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

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

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

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The Far-Reaching Impact of Job Loss and Unemployment *

Jennie e. brand.

University of California – Los Angeles

Job loss is an involuntary disruptive life event with a far-reaching impact on workers’ life trajectories. Its incidence among growing segments of the workforce, alongside the recent era of severe economic upheaval, has increased attention to the effects of job loss and unemployment. As a relatively exogenous labor market shock, the study of displacement enables robust estimates of associations between socioeconomic circumstances and life outcomes. Research suggests that displacement is associated with subsequent unemployment, long-term earnings losses, and lower job quality; declines in psychological and physical well-being; loss of psychosocial assets; social withdrawal; family disruption; and lower levels of children’s attainment and well-being. While reemployment mitigates some of the negative effects of job loss, it does not eliminate them. Contexts of widespread unemployment, although associated with larger economic losses, lessen the social-psychological impact of job loss. Future research should attend more fully to how the economic and social-psychological effects of displacement intersect and extend beyond displaced workers themselves.

A central tradition of research in sociology and economics seeks to identify and take account of the processes shaping socioeconomic outcomes, including the mechanisms that affect mobility and define opportunity structures. A notable strand of this research has assessed the extent to which job loss, often accompanied by a period of unemployment, divides the career achievement of workers. With the recent severe economic upheaval came a precipitous increase in attention to the study of job loss and unemployment. Much of this work has understandably focused on economic outcomes as indicated by employment levels and earnings, but another important body of research has attended to the wider impact of job loss.

A few definitions help fix ideas. Job separation includes both voluntary (worker initiated job separation, or “quitting”) and involuntary job termination. Job loss is generally understood as indicating involuntary separation that occurs when workers are fired or laid off, where layoffs occur as a result of firms downsizing, restructuring, closing plants or relocating. Involuntary job loss may also indicate job separation as a result of health conditions. In this case, the separation may be worker initiated, but nevertheless be considered to some degree involuntary. Job displacement is a specific form of involuntary job loss that does not include workers being fired or termination for health reasons; it is reserved for involuntary job separation that is the result of economic and business conditions that are largely beyond the control of the individual worker and thus presumably less governed by worker performance. Strict definitions include some period of pre-displacement firm-specific tenure, such as three years in the Displaced Worker Survey of the Bureau of Labor Statistics. Some studies on job loss focus attention on involuntary job loss, while others focus more specifically on job displacement. I nevertheless use these terms somewhat interchangeably throughout this review, as the distinctions are not always explicitly made in the literature and are to some degree amorphous.

Individual-level (involuntary) unemployment occurs when individuals are without a job and actively seeking employment; some definitions allow discouraged workers who have dropped out of the labor force to be counted among the unemployed, or at least among the jobless. Unemployment is one potential consequence of job loss. Job loss, as opposed to unemployment, is a discrete event and is not synonymous with unemployment. A period (at times a prolonged period) of unemployment typically, but not necessarily, accompanies job loss. However, unemployment is not necessarily preceded by job loss, and displaced workers are not generally representative of the unemployed population ( Kletzer 1998 ). Job loss is a discrete event, while unemployment is a state, with a great deal of heterogeneity with respect to instigation and duration. Job displacement is more of an exogenous shock than unemployment, or job loss more broadly defined, allowing for better estimates of the consequences of socioeconomic mobility. I spend considerably more time on job displacement than on unemployment, per se, in this review.

This review proceeds as follows. I begin with a description of trends and risk factors associated with job loss, and then consider some methodological and interpretative issues in estimating displacement effects. I then review the economic impact of job loss. Thereafter I thoroughly attend to the wider impact of worker displacement. I conclude with several directions for future research. I focus my review on job loss in the United States.

Trends in and Risk Factors Associated with Job Loss

Widespread job insecurity, waves of job loss, and associated periods of unemployment and income loss have characterized the last several decades in the U.S. ( Farber 2010 ; Farley 1996 ; Kalleberg 2000 , 2009 ; Kletzer 1998 ; Wetzel 1995 ). Most Americans believe that employment stability has declined ( Hollister 2011 ), and job displacement is now considered a common feature of the U.S. labor market. The macroeconomic trends commonly associated with worker displacement include: technological change; foreign trade and the shift to production offshore to take advantage of low-wage foreign workers; immigration; firms’ greater use of outside suppliers, subcontractors, and partners, and the paring down of the activities of the firm; the shift in U.S. consumption from manufactured goods to services; poor firm management; weakened labor unions; and regional and national economic downturn.

High levels of workers displacement marked the last four recessions in the U.S. The early 1980s recession convinced firms to utilize effective new equipment, shift production to modern plants, and lay off thousands of workers ( Farley 1996 ). Wetzel (1995) wrote: “Industrial firms that had prided themselves on lifetime paternalistic commitments to their production workers – largely men with average or below-average educational attainment – slashed employment … The abrupt contraction struck at the heart of the middle class by drastically impacting mature family men with strong labor force attachment, good work histories, and long job tenure” (p. 101). The economic recovery of the 1980s was marked by large employment gains; nevertheless, unemployment persisted at a relatively high rate and newly created jobs were in general of a lower quality than jobs from which workers had lost. The early 1990s recession was marked by the creation of flat organization and elimination of middle management positions. High levels, particularly during economic recessions, of job loss and unemployment characterize the U.S. labor market since 1990. In the 1990s through early 2000s, worker layoffs, once regarded as organizational failure, were increasingly utilized as a labor allocative process available to firms in order to preserve shareholder value. Ensuing waves of downsizing, reorganization, mergers and takeovers rewarded some individuals with great prosperity while others were threatened with displacement, unemployment, and downward mobility ( Baumol et al. 2003 ). The recessionary period from the end of 2007 to mid-2009, the “Great Recession,” was deeper and more extensive than any other since the Great Depression of the 1930s ( Hout, Levanon, and Cumberworth 2011 ). The U.S. unemployment rate hovered around 9 to 10 percent in 2009–2011, the highest rate since the early 1980s recession and roughly twice the pre-crisis rate. The proportion of families with an unemployed member was roughly 12 percent in 2009, up from about 6 percent in 2007. The large increase in long-term unemployment in this most recent recession is suggestive of longer-term structural labor market changes ( Katz 2010 ).

While macroeconomic and firm-level factors influence the incidence of job loss and unemployment, a number of individual-level characteristics also govern the risk of displacement. Men and blacks and Hispanics had a higher probability of being displaced than women and whites in the 1980s; family background disadvantage, blue-collar and manufacturing work, low occupational status, low job tenure, and low levels of education likewise heightened the risk of job loss over this period (Brand 2005; Farber 2005 ). Job loss rates increased for women and for whites in the 1990s, as well as for college-educated and high tenure workers (Couch 1998; Farber 1993b , 1997b , 2005 ). While educated workers maintain a lower risk of displacement, the increased rates have nevertheless aroused public concern that the structure of job loss qualitatively changed over recent decades, increasing vulnerability to job loss across the population ( Fallick 1996 ; Farber 1993a , 1993b , 2010 ).

Estimating Effects of Job Loss

Abrupt changes in socioeconomic conditions provide a sort of “natural experiment” offering a stronger basis for inference than the usual practice of examining the covariation of outcomes with socioeconomic status that may arise from a variety of sources over an indeterminate period of time. The study of job displacement, thus, provides a unique opportunity to assess within individual changes in socioeconomic conditions that are relatively exogenous to individual characteristics. Indeed, scholars often explicitly describe the study of displacement as a purer way to estimate the effects of socioeconomic shocks ( Stevens 2014 ). Nevertheless, the study of displacement does not fully mitigate selection issues, as job loss is clearly conditioned by factors that are also associated with levels of subsequent outcomes. A primary concern in attempting to identify effects of job loss is the potential presence of unobservable characteristics that affect both worker displacement and subsequent outcomes. That is, we are left with the fundamental question of whether workers who were displaced from jobs have outcomes that are different than they otherwise would have been had they not been displaced. If employers make targeted decisions regarding whom to displace, there is a possibility that it is relatively less productive workers (e.g. lower levels of motivation, commitment, and ability), workers with physical or mental health issues, and socially inept workers who both are more likely to lose jobs and have worse economic and social outcomes. Scholars, however, have found few differences across several leading estimators of causal effects (including regression, matching, difference-in-difference and fixed effects models), suggesting a degree of robustness regarding the nature of the observed associations between displacement and life outcomes in the face of various technical assumptions and model specifications ( Brand 2006 ; Coelli 2011 ; Stevens and Schaller 2009).

Yet another strategy to deal with possible selection bias is to adopt a quasi-experimental strategy that tracks the well-being of workers following a plant closure. When an entire organization closes, it is unlikely that a workers’ specific characteristics are responsible for the displacement. Thus if the results for plant closings and more individualized lay-offs are similar, we have a firmer basis for claiming the validity of the effect estimates for the full population of displaced workers. Likewise, job losses occurring during recessionary periods, in which large numbers of individuals lose jobs, may provide better causal estimates of job loss ( Stevens 2014 ). A few caveats as to inferences we can make from mass layoff studies are nevertheless in order. While such studies make strong claims for having eliminated the influence of selection, plant closure studies are typically limited to specific populations (often blue-collar workers) in specific geographic areas, restricting generalizability to the U.S. workforce as a whole. That is, studies of plant closures ostensibly sacrifice external for internal validity. Some closure studies are also lacking a control group of non-displaced workers. Additionally, plant closure studies may still be subject to selection bias, as more qualified and adaptive employees may leave the plant upon word of the impending closure. The same can be said for studies of workers displaced during recessions.

Job losses due to layoffs and those due to plant closings, and job loss occurring in different economic contexts, may also produce different effects because they are potentially different treatment conditions. In the case of layoffs and job loss during economic expansions, the greater likelihood for discretionary dismissal of employees can call into question competency and character and act as a signal of below-average productivity, to the displaced workers, as well as to their families and communities, and in the labor market. If potential employers interpret layoffs as indications of ineptitude, hiring will be discouraged. The resulting difficulty of laid-off workers to secure suitable reemployment may result in greater long-term economic losses. Economic distress, alongside attribution of job loss to one’s own shortcomings, and the stigma of a layoff and resulting strained relations with colleagues, friends, and/or family members, can in turn lead laid off workers to lower self-esteem, anxiety, and depressive symptoms (Leana and Feldman 1992; Miller and Hoppe 1994 ). Individually laid of workers may also lack similarly strained workers to offer a network of support ( Miller and Hoppe 1994 ; Brand, Levy, and Gallo 2008 ). These circumstances contrast with that of job loss due to plant closings and loss occurring in economic recessions, in which clearly external influences, including the health of the macro-economy and firms’ decisions to restructure or relocate business units, provokes separation. Because such factors are clearly beyond the control of individual employees, plant closings do not involve a negative signal that raises transaction costs for displaced workers. Indeed workers displaced due to business closings are victims of an event that could befall anyone, and seldom perceive themselves as responsible for the job loss. Thus, such workers may endure lower economic and social-psychological burdens. 1

Economic Effects of Job Loss

Increasing job insecurity and displacement has motivated a large body of research on effects, beginning with economic losses. The average displaced worker experiences a long period of unemployment ( Brand 2004 ; Chan and Stevens 1999; Fallick 1996 ; Farber 2003, 2005 ; Kletzer 1998 ; Podgursky and Swaim 1987 ; Ruhm 1991 ), but the duration has a high degree of worker variance ( Seitchik 1991 ). Unemployment among displaced workers generally lasts longer during recessions than expansions ( Farber 1993a ; Kletzer 1991 , 1998 ). The impact of job loss on careers is considerable even when workers do not experience long-term unemployment. Displaced workers suffer substantial earnings losses, which are generally more persistent than unemployment effects ( Brand 2004 ; Cha and Morgan 2010 ; Chan and Stevens 1999, 2001 ; Couch 1998; Couch, Jolly, and Placzek 2011 ; Couch and Placzek 2010 ; Davis and von Wachter 2012 ; Fallick 1996 ; Farber 2003, 2005 ; Jacobson, LaLonde, and Sullivan 1993 ; Kletzer 1998 ; Podgursky and Swaim 1987 ; Ruhm 1991 ; Seitchik 1991 ; Stevens 2014 ; von Wachter 2010 ). Couch and Placzek (2010) report an immediate 33 percent earnings loss and as much as a 15 percent loss six years following job separation. The cumulative lifetime earning loss is estimated to be roughly 20 percent, with wage scarring observed as long as 20 years post-displacement ( Brand and von Wachter 2013 ; Davis and von Wachter 2012 ; von Wachter 2010 ; von Wachter, Song, and Manchester 2009). Reemployed displaced workers are more likely than their non-displaced counterparts to be employed part-time, and this likelihood has increased over time, particularly during recessions ( Farber 1993b , 2003, 2005 ). Displaced workers may also find, when reemployed, that their jobs are of lower quality in terms of job authority, autonomy, and employer-offered benefits in comparison to both the jobs they lost and those held by their non-displaced counterparts ( Brand 2004 , 2006 ; Podgursky and Swaim 1987 ). Workers also withstand greater job instability for at least a decade following a displacement event ( von Wachter 2010 ).

While economic losses occur for displaced workers across demographic categories, across industries and throughout the skill distribution ( von Wachter 2010 ), there is nevertheless effect variation by worker characteristics. Displaced workers’ losses reflect both industry-specific decline and the loss of firm- and industry-specific skills ( Kalleberg 2000 ). Older workers with higher pre-displacement tenure, those who change industries, and those who experience multiple job losses thus experience greater earnings losses ( Carrington and Zaman 1994 ; Couch, Jolly, and Placzek 2009; Fallick 1996 ; Jacobson, LaLonde, & Sullivan 1993 ; Stevens 1997; von Wachter 2010 ). As greater skill transferability is expected for educated workers, employment, earnings, and job quality reductions are typically more pronounced for less-educated workers ( Farber 1997 , 2003, 2005 ; Kletzer 1991 , 1998 ; Podgursky and Swaim 1987 ; Seitchik 1991 ). Still, as the incidence of displacement for more educated workers has increased, the transition difficulties for such workers have increased as well.

While displaced workers’ economic costs are substantial during both economic recessions and expansions, losses are cyclical ( Couch, Jolly, and Placzek 2011 ; Davis and von Wachter 2012 ; Fallick 1996 ; Farber 1993a , 1997a , 2005 ; Jacobson, LaLonde, and Sullivan 1993 ; Kletzer 1998 ; von Wachter 2010 ). As few firms hire during economic contractions, displaced workers seeking reemployment are in a poorer negotiating position than during economic expansions. Davis and von Wachter (2012) find that men lose an average of 1.4 years of pre-displacement earnings if displaced in mass-layoff events that occur when the national unemployment rate is below 6 percent, and lose 2.8 years of pre-displacement earnings if displaced when the unemployment rate exceeds 8 percent. Similarly, Couch, Jolly, and Placzek (2011) find that long-term earnings losses for displaced workers during a recessionary period are about 2 to 4 times larger than for those observed during a period of economic expansion.

There is some debate over variation in economic losses by the specific form of job loss. Recent work (Kashinsky 2002; Stevens 1997; von Wachter 2010 ) has questioned the findings of an influential study by Gibbons and Katz (1991) that suggested that layoffs are associated with greater economic losses than are plant closings. Gibbons and Katz (1991) argued that in the case of a layoff, the discretionary dismissal of employees acts as a signal of below average productivity, stigmatizing laid-off workers, resulting in large employment and earnings losses. On the contrary, a plant closing, in which all workers are terminated without discretion, does not carry a comparable performance signal, rendering earnings penalties less severe. Extending this argument to differences in earnings losses by economic context, we might expect countercyclical earnings losses, as the stigma associated with displacement during an economic contraction should be less than that during an economic expansion. However, as I note above, such losses are cyclical. In support of the evidence for cyclicality, we should expect larger earnings losses from job loss due to plant closings as such closures may indicate weak local or macro economic conditions. Krashinsky (2002) argues that the Gibbons and Katz (1991) result is driven by the fact that small plants are more likely to close, and that layoffs that occur from larger, higher-wage employment establishments result in larger earnings losses. 2

Several mechanisms help explicate the large economic losses of displaced workers. Earnings and job quality declines are likely to increase with unemployment duration. Yet it is unclear whether this association is the result of length of unemployment itself, and possible stigma effects, or because those workers facing the greatest challenges in the labor market take longer to find a new job ( von Wachter 2010 ). Workers are also disadvantaged in the market if industries in which they were previously employed shift their operations elsewhere or permanently reduce their employment levels. Relatedly, lower job quality upon reemployment is a function of the loss of a high quality match between the worker and the job ( Fallick 1996 ). While a worker generally only leaves a job voluntarily when he or she believes there are relative gains in career attainment to be made, displaced workers likely feel an urgency to find a new job and are in a poor position to perform a quality job screening ( Newman 1988 ).

Non-Economic Effects of Job Loss

Job loss is a negative, often unpredictable event that entails a sequence of stressful experiences, from job loss notification, anticipation, dismissal, and often unemployment, to (in most cases) job search, re-training and eventual reemployment, often at jobs with lower wages and job quality. Yet the impact of job loss and unemployment is not limited to economic decline; it is also associated with considerable, long-term non-economic consequences for displaced workers, as well as for their families and communities. Displaced workers face psychological and physical distress, personal reassessment in relation to individual values and societal pressures, and new patterns of interaction with family and peers. Much of the work on the non-economic consequences of job loss is consistent with a large literature demonstrating a strong correlation between indicators of socioeconomic status and individual life chances and well-being. However, as displacement is a relatively exogenous labor market shock, its study enables a stronger causal link between socioeconomic circumstances and life outcomes. In this section, I begin reviewing individual worker effects on psychological and physical well-being, and then consider the consequences for families and communities.

Job Loss and Psychological Well-Being

A large literature on mental health has focused on the impact of stressful life events, such as unemployment and job loss. Job loss disrupts more than just income flow; it disrupts individuals’ status, time structure, demonstration of competence and skill, and structure of relations. It carries societal stigma, creating a sense of anxiety, insecurity, and shame ( Newman 1988 ). The loss of a job presents a source of acute stress associated with the immediate disruption to a major social role, as well as chronic stress resulting from continuing economic and social and psychological strain (House 1987; Pearlin et al. 1981 ). Research suggests that displaced workers report higher levels of depressive symptoms, somatization, and anxiety and the loss of psychosocial assets including self-acceptance, self-confidence, self-esteem, morale, life satisfaction, goal and meaning in life, social support, and sense of control ( Brand, Levy, and Gallo 2008 ; Burgard, Brand and House 2007 ; Catalano et al. 2011 ; Dooley, Fielding and Levi 1996 ; Darity and Goldsmith 1996 ; Dooley, Prause, and Ham-Rowbottom 2000 ): Gallo et al. 2000 ; Gallo et al. 2006a ; Hamilton et al. 1990 ; Jahoda 1981 , 1982 ; Jahoda, Lazarsfeld, and Zeisel 1933[1971] ; Kasl and Jones 2000; Kessler, Turner, and House 1988 , 1989 ; Leana and Feldman 1992 ; McKee-Ryan et al. 2005 ; Miller and Hoppe 1994 ; Paul and Moser 2009 ; Pearlin et al. 1981 ; Turner 1995 ; Warr and Jackson 1985 ). 3 The increase in reported symptoms of depression and anxiety among displaced workers compared to non-displaced workers is roughly 15 to 30 percent ( Burgard, Brand, and House 2007 ; Catalano et al. 2011 ; Paul and Moser 2009 ). Leading explanations for why job loss and unemployment negatively impact well-being include lowered self-esteem, sense of purpose, and control; heightened apathy, idleness, isolation, and the breakdown of social personality structure; and a loss of the positive derivatives of participating in a work environment, such as skill use, time structure, economic security, interpersonal socialization, and valued societal position ( Darity and Goldsmith 1996 ; Jahoda 1982 ; Jahoda, Lazarsfeld, and Zeisel 1933[1971] ; McKee-Ryan et al. 2005 ). 4

Although displacement is more of an exogenous shock than other types of job mobility, the possibility of omitted variable bias nevertheless threatens the validity of results associating job loss to subsequent outcomes. Of particular concern in the study of psychological well-being, workers with psychological distress and lacking self-confidence and morale may be those workers most likely to be displaced from jobs. Studies have used various approaches to address this selection problem, most often attempting to control for a range of factors that impact the likelihood of job loss and subsequent well-being. Studies continue to find an association, although often reduced in magnitude. For example, Burgard, Brand, and House (2007) adjust for numerous social background characteristics, including baseline psychological health, and find a significant effect of job loss on depressive symptoms. Moreover, using meta-analytic techniques drawing on over 100 empirical studies, McKee-Ryan et al. (2005) find consistency in results across multiple kinds of studies and hundred of data points suggesting a relationship between job loss and worker well-being. Studies based on plant closures, thought to be less prone to issues of selection, continue to find an increased risk of mental distress among the displaced ( Hamilton et al. 1990 ). 5

As is true with the economic consequences of job loss, the effects of job loss on psychological well-being vary by a range of factors, including demographic characteristics, socio-emotional skills and social support, and the economic context. While more disadvantaged workers may be more vulnerable to financial shocks ( Hamilton et al. 1990 ), such economic adversity is a comparatively normative experience; by contrast, job displacement and socioeconomic decline may instigate an acute sense of deprivation among more advantaged families whose peers tend to be likewise advantaged and for whom displacement is a considerable shock ( Brand and Simon Thomas 2014 ). That is, judgments of disruptive events depend on the experience of similar situations in the past, and higher levels of past adversity may lessen the impact of current adversity ( Clark, Georgellis, and Sanfey 2001 ; Dooley, Prause, and Ham-Rowbottom 2000 ). If the difficulties posed by job loss and unemployment are primarily financial, then reemployment has the potential to remove much of the stress, particularly if the income is comparable to what the worker had been earning. If job loss profoundly alters one’s self-concept and place in society, however, the extent to which reemployment will reverse these effects is unclear. While significant effects of reemployment have been documented among blue-collar workers ( Kessler, Turner, and House 1989 ; Warr and Jackson 1985 ), professionals and upper-level, white-collar workers do not seem to recover as readily. In contrast to the literature on the economic effects, attention has also been paid to variation in the effects of job loss by socio-emotional skills and social support. For example, worker response to displacement varies by individual work-role centrality, or employment commitment, where workers who place more importance on the work role to their sense of self suffer more from job loss. Individuals also vary in their coping resources, i.e. the personal, financial, and social resources they draw on to cope with job loss, and social support, such as social integration, availability of friends, relatives, and co-workers, and marital status and spousal support ( Darity and Goldsmith 1996 ; Leana and Feldman 1988 ; Pearlin et al. 1981 ).

The experience of job loss and unemployment may also vary by the economic context. Displacement that occurs during recessions, in which many workers are laid off, is associated with greater economic losses than displacement that occurs during economic expansions ( Couch, Jolly, and Placzek 2011 ; Davis and von Wachter 2011; Fallick 1996 ; von Wachter 2010 ). However, contexts of widespread unemployment lessen the internalization of blame and social stigma associated with job loss ( Brand, Levy, and Gallo 2008 ; Charles and Stephens 2004 ; Clark 2003 , 2010 ; Miller and Hoppe 1994 ). That is, displaced workers may benefit from a “social norm effect”: as aggregate unemployment increases, one’s own unemployment represents a smaller deviation from the social norm ( Clark 2010 ), and thus displacement effects on social-psychological well-being may be less in contexts of mass layoffs. Turner (1995) shows a three-way interaction, indicating that unemployment effects on psychological well-being are strongest in low unemployment areas, particularly among individuals with a college-level education. While economic burden is greater among workers with lower socioeconomic status and those displaced in higher unemployment contexts, personal attribution is greater among higher status victims of job loss and those displaced in low unemployment contexts ( Kessler, Turner, and House 1988 ; Pearlin et al. 1981 ; Turner 1995 ). These interactions highlight that results are sensitive to the population, geographic location, and time period under study.

Scholars have proposed a number of mechanisms to explain the relationship between job loss and psychological well-being. First, economic deprivation and downward socioeconomic mobility provide leading explanations for the relationship between job loss and psychological distress, as indicated by unemployment duration ( Clark, Georgellis, and Sanfey 2001 ; McKee-Ryan et al. 2005 ) and income loss ( Gallo et al. 2006a ; Kasl and Jones 2000; Kessler, Turner, and House 1988 ; Warr and Jackson 1985 ). Second, job loss and unemployment can dampen self-esteem, aspirations, and time structure; incite resignation, apathy, uncertainty, and stigmatization; and frustrate one’s social identity by replacing a socially approved role with one of markedly lower prestige ( Jahoda 1982 ; Jahoda, Lazarsfeld, and Zeisel 1933[1971] ). Scholars either include these measures within the set of dependent variables of interest, or treat the psychosocial indicators as mediators linking job loss to depressive symptoms. Third, family and social strain help to explain the relationship ( Darity and Goldsmith 1996 ). Fourth, additional stressful life events that occur subsequent to job loss, such as additional job losses, divorce, health shocks, and migration explain some of the effects. Although scholars routinely implicate these mechanisms, few studies rigorously empirically test these mediating influences ( Catalano et al. 2011 ).

Job Loss and Physical Well-Being

Job loss has been linked to both short and long-term declines in physical health, including worse self-reported health, physical disability, cardiovascular disease, a greater number of reported medical conditions, increase in hospitalization, higher use of medical services, higher use of disability benefits, an increase in self-destructive behaviors and suicide, and mortality ( Burgard, Brand, and House 2007 ; Catalano et al. 2011 ; Dooley, Fielding, and Levi 1996 ; Ferrie et al. 1998 ; Gallo et al. 2000 ; Gallo et al. 2004 ; Gallo et al. 2006b ; Gallo et al. 2009 ; Kasl and Jones 2000; Kessler, Turner, and House 1988 ; McKee-Ryan et al. 2005 ; Strully 2009 ; Turner 1995 ). For example, Gallo et al. (2004 , 2006b) found that job loss doubled the risk of subsequent myocardial infarction and stroke among older workers. Sullivan and von Wachter (2009) and von Wachter (2010) found a 50 to 100 percent increase in mortality the year following displacement and a 10 to 15 percent increase in mortality rates for the next 20 years.

Despite a large literature suggesting an association between job loss and ill health, the causal relationship remains contested due to concerns over selection bias. The fundamental concern is whether job loss leads to ill health, or whether at least some or all of the observed association occurs because those individuals who have poor health are more likely to lose jobs. Even with a rich set of pre-displacement covariates, the question remains as to whether models fully adjust for pre-displacement health, personality and psychosocial characteristics, lifestyle, and labor market experiences that may lead to both job loss and ill health. Burgard, Brand, and House (2007) continue to find a significant association between involuntary job loss and overall self-rated health even after adjustment for social background characteristics and baseline health. More nuanced analyses of specific reasons for job loss and the timing of job loss relative to health shocks reveal that those who lose their jobs for health-related reasons have, not surprisingly, the most precipitous declines in health. Effects of job losses for non-health reasons on self-rated poor health are relatively small ( Burgard, Brand, and House 2007 ). Studies of plant closures also show that workers’ health declines following job loss (Arnetz et al. 1991; Beal and Nethercott 1987; Gore 1978; Iversen, Sabroe, and Damsgaard 1989; Kasl and Cobb 1970; Kessler, House, and Turner 1987; Strully 2009 ).

Variation in displacement effects and the mechanisms linking job loss to physical health are similar to psychological effects, including economic loss ( Sullivan and von Wachter 2009 ; von Wachter 2010 ) and erosion of psychosocial assets and social support ( Eliason and Storrie 2009 ) and subsequent adverse life events. Yet a few comments specific to the mediating effects on physical health are merited. The effect of job loss and unemployment on depressive symptoms may manifest itself in physiological outcomes, thus the impact of job loss on psychological well-being can help explain the effect on physical health. In addition, health behaviors, such as greater alcohol and drug use, unhealthy food consumption and less exercise, and sleep quality, may partially mediate the association. On the other hand, for some individuals, the increase in discretionary time due to unemployment may be used to pursue health-promoting behaviors, such as physical activity, that might precipitate weight loss or encourage alcohol temperance ( Catalano et al. 2011 ). Another clear mechanism is the loss of employer-offered health insurance and reduced access to medical care.

Job Loss and Families

As job displacement has significant, long-term effects on workers’ socioeconomic status and psychological and physical well-being, we reasonably expect these consequences to impact the families of displaced workers. The displaced have an increased risk of family tension, and of family disruption ( Attewell 1999 ; Charles and Stephens 2004 ; Jahoda, Lazarsfeld, and Zeisel 1933 [1971] ). Charles and Stephens (2004) considered differences in the mode of displacement on subsequent risk for divorce, reporting increased likelihood of divorce following a layoff but not a plant closing. The authors attributed the higher risk for marital dissolution to the spouse’s negative inference about the worker’s personal role in the layoff, i.e. the discretionary nature of the termination conveys to the spouse certain qualities of the displaced worker which may suggest a lack of marital fitness.

A literature is also emerging which suggests deleterious effects of parental displacement on children, including lower self-esteem and higher likelihood of grade repetition, dropout, and suspension or expulsion from school ( Johnson, Kalil, and Dunifon 2012 ; Kalil and Ziol-Guest 2005 , 2008 ; Stevens and Schaller 2010 ), educational attainment ( Kalil and Wightman 2011 ), and lower income of children in adulthood ( Page, Stevens, and Lindo 2009 ). These studies largely emphasize the deleterious effects of fathers’ loss of financial standing in the family among married couple households. Studies examining differences between paternal and maternal displacement effects among married couples find significant effects of paternal but not maternal displacement ( Kalil and Ziol-Guest 2008 ; Rege, Telle, and Votruba 2011 ). They hypothesize that maternal displacement is not as detrimental to children’s outcomes as paternal due to greater psychological consequences associated with economic loss among fathers who are largely expected to maintain the role of primary provider. Brand and Simon Thomas (2014) , however, focus on displacement among single mothers, and find significant negative effects on children’s educational attainment and social-psychological well-being in young adulthood. Overall, the evidence suggests a significant impact of parental displacement on children’s life outcomes.

Just as worker response to job loss varies, children also respond differently to parental displacement. As I note above, more disadvantaged workers and workers displaced during recessions tend to have greater economic losses than more advantaged workers and workers displaced during economic expansions. However, disadvantaged families may have acquired particular coping skills and support structures as a result of previous experience with economic adversity, while advantaged families lack referents to similarly strained families and a social norm of deprivation. Additionally, contexts of widespread unemployment increase economic losses but lessen the internalization of blame and social stigma associated with job loss, and thus effects on social-psychological well-being among displaced workers and their families are potentially greater in contexts of more individualized layoffs. Some studies suggest that effects are concentrated among disadvantaged families ( Kalil and Wightman 2011 ; Oreopoulos, Page, and Stevens 2008 ; Stevens and Schaller 2011), while others find larger effects among more advantaged families and in low unemployment contexts ( Brand and Simon Thomas 2014 ). 6

Mechanisms linking parental job loss to children’s outcomes are similar to those I discuss above. Fewer parental resources restrict the ability to purchase goods critical for child development, such as schooling, housing, food, and safe and cognitively-enriched learning environments ( Kalil and Ziol-Guest 2008 ). Job loss is also associated with residential mobility, inciting stress as well as a disruption of children’s schooling and social networks. Parental downward mobility can also dampen children’s attitudes about the value of education and work. Displaced parents may foster psychological distress among their children to the extent that they model despondency and despair. Displaced parents’ decreased physical and psychological well-being can inhibit emotional warmth and incite erratic or punitive parenting practices ( Kessler, Turner, and House 1989 ; McLoyd 1990 ; McLoyd et al. 1994 ), and social withdrawal can reduce children’s social capital and collective efficacy.

Job Loss and Communities

Employment and career stability have long been considered important factors for social involvement ( Durkheim 1933 ; Jahoda, Lazarsfeld, and Zeisel 1933 [1971] ; Rotolo and Wilson 2003 ; Wilensky 1961 ; Wilson and Musick 1997 ). Expanding on Durkheim’s (1933) notion that employment performs an integrative role, drawing people into social life, Wilensky (1961) and Wilson and Musick (1997) find that stable employment and an orderly career marked by functionally related, hierarchically-ordered jobs (i.e., the absence of job displacements and downward socioeconomic mobility) is associated with higher levels of social integration. Likewise, Rotolo and Wilson (2003) show that disorderly careers have the potential to undermine social involvement. These studies, however, are restricted to specific populations and careers marked by substantial job movement, whether voluntary or involuntary. In fact, the research on the effects of job loss and unemployment is decidedly limited. Brand and Burgard (2008) , in an analysis most similar to those I review above, find that displaced workers have significant and long-term lower probabilities of involvement in various modes of social participation, including church groups, youth and community groups, charitable organizations, and informal social gatherings with friends. The strain of insecure employment, displacement events, periods of unemployment, reemployment in jobs with lower earnings and quality, psychological distress, geographic mobility, and diminished social trust and the erosion of commitment to social reciprocity indubitably contribute to decreased levels of social involvement among displaced workers ( Putnam 2000 ; Wilson 2000 ; Wilson and Musick 1997 ). Brand and Burgard (2008) find that workers who experience one displacement are significantly less likely to participate socially, while workers experiencing disorderly careers marked by multiple job displacements are no less likely to participate, relative to non-displaced workers. Among workers with high levels of job instability, displacements may be more normalized and less of a shock, and thus less likely to lead to further declines in already lower levels of social involvement.

Effects described above are individual effects on social involvement. The assumption is that social withdrawal will have a meaningful impact upon the aggregate welfare and the distribution of welfare in society, but this impact is not directly estimated in these studies. Another approach is to consider the impact of community-level job loss and unemployment on individual well-being. For example, Ananat, Gassman-Pines, and Gibson-Davis (2011) show that community-level job losses affect the achievement test scores of children, possibly the result of both direct effects on children whose parents lost jobs and indirect peer and teacher effects. The link between individual job loss and unemployment and community well-being, as well as the link between community-level unemployment and individual well-being, is limited ( Dooley, Fielding, and Levi 1996 ).

Conclusions and Directions for Future Research

A job is more than a source of income. It is a fundamental social role providing a source of identity, self-concept, and social relations. Classical social theorists, including Weber and Marx, describe, in diverse ways, the centrality of work to the individual ego and social identity and prestige. Jobs are also an integral component to the process of social stratification, inequality, and mobility, representing a principal outcome of social background resources and individual attainment. The displacement of workers has become a normative feature of the U.S. labor market, commonly assumed to increase economic efficiency. However, the costs of such fluidity are unequally distributed, born largely by displaced workers and those closest to them. Moreover, if lack of regulation negatively impacts worker, family, and community well-being, countervailing effects that decrease overall productivity inevitably follow.

The evidence that job loss matters, that the range of consequences is wide, and that the effects persist long-term, is persuasive. The research literature described above documents nontrivial, short- and long-term observed differences between displaced and non-displaced workers across far-reaching life outcomes. Displacement is associated with significant economic costs, including a period of unemployment, reduced income, lower job quality, loss of health and pension benefits, and interruption of asset accrual. Long-term trends of rising inequality and job market polarization exacerbate adjustment problems the displaced endure. And, as indicated throughout this review, job loss is not limited to economic effects. Worker displacement is associated with: lower levels of self-acceptance, goal and meaning in life, and morale; higher levels of depressive symptoms and poor health; loss of social support and personal reassessment in relation to societal norms and unemployment stigmatization; new patterns of interaction with family members, restriction of socially-supportive collegial relationships, and disruption of social and family ties; and intergenerational effects as indicated by reduced attainment among children of displaced workers. Some of these themes have received considerable empirical investigation, while others, including family and community effects, have received less attention. Future work should attend more fully to the impact of displacement beyond workers themselves.

An intricate intersection of the outcomes of displacement is needed to illuminate any particular estimated effect. When job loss impacts workers’ psychological well-being, for example, human capital depreciates and further restricts displaced workers’ ability to secure comparable reemployment and socioeconomic welfare. Social withdrawal may further impede labor market position, as social and economic resources are embedded in social networks. Likewise, while reemployment mitigates some of the negative effects of job loss on social and psychological well-being, it does not eliminate them. In fact, no single explanation can account for why job loss hurts. Here, also, more work is needed to understand the mechanisms linking displacement to workers’ outcomes, and to the outcomes of the families and communities of the displaced. Scholars have not rigorously attended to the empirical study of these mechanisms, and particularly to the complex issues that underlie a causal analysis of direct and indirect effects ( Morgan and Winship 2014 ).

Effects vary by workers characteristics and contexts in which displacement occurs. Economic consequences seemingly diminish with workers’ relative position in the labor market. Future work would benefit from developing models that explicitly recognize the way in which both opportunity and choice influence employment outcomes, incorporating data on the characteristics of both employees and potential employers [see e.g., Logan (1996) ]. Moreover, while workers with fewer skills and workers displaced in economic recessions have more transition difficulties and suffer greater economic losses, the same cannot be said for the non-economic consequences of displacement. Economic adversity is a comparatively normative experience for disadvantaged workers, while socioeconomic decline may be a greater shock and incite a stronger sense of relative deprivation among more advantaged workers, and consequently have a greater impact upon psychological well-being and social interactions. Likewise, contexts of widespread unemployment, while associated with larger economic losses, lessen the internalization of blame and social stigma associated with job loss. As one’s own unemployment represents a smaller deviation from the social norm, psychological and social effects are potentially lessened. Future research should continue to explore the way the economic and social responses to worker displacement interact with and potentially diverge according to differing economic and social contexts.

Important interactions may exist not only between displacement and the social and economic context, but also between one displacement and another one nearby, between one displaced worker and another competing for a job in the same market ( Fallick 1996 ). Such interference, or dependency, violates the “stable unit treatment value assumption” in the estimation of worker displacement effects, i.e. that the observation on one unit is unaffected by the assignment of treatments to other units ( Morgan and Winship 2014 ). Research to date has focused, understandably, on individuals. But spillover effects are themselves substantively interesting and should be the subject of future study.

The most common response to reduce the burden of job loss is to increase the duration over which eligible workers can receive unemployment benefits. Extended benefits provide workers some income to buffer short-term earnings losses and allow workers time to search for a suitable job. While many express concern that unemployment insurance may reduce recipients’ willingness to work, the aggregate benefits of extended unemployment insurance surely outweigh the possible costs ( von Wacther 2010 ). Additional policy suggestions include prompt reallocation of workers to suitable employment and skill retraining, as well as universal health care ( Farber 2005 ). Reemployment efforts should be focused on getting displaced workers in jobs that offer the prospect of long-term employment, preferably in a job in their pre-layoff industry or one that is a good match to their skills. Most of these policy efforts focus on alleviating the economic burden of displacement. Yet it is unclear if these will have the same impact upon the social and psychological consequences of job loss. For example, assistance with geographic mobility may help workers find jobs, but discounts potential consequences of migration for psychological well-being and for families and communities of displaced workers. Discourse involving social assistance should admit to the widespread consequences of involuntary job separation.

Economists and sociologists have many motivations for studying job loss and unemployment. There is clearly interest in the economic and social difficulties that workers face when they lose their jobs due to reasons beyond their control. Job displacement is an involuntary and often unforeseen disruptive life event that induces abrupt changes in workers’ trajectories, enabling robust estimates of associations between socioeconomic circumstances and life outcomes. The increasing incidence of job displacement among growing segments of the workforce, alongside the recent era of economic upheaval, furthers societal attention to the far-reaching impact of job loss on life chances.

Acknowledgments

This project used facilities and resources provided to the author at the California Center for Population Research at UCLA, which receives core support (R24HD041022) from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). The ideas expressed herein are those of the author.

JENNIE E. BRAND

Jennie E. Brand is Associate Professor of Sociology at the University of California – Los Angeles and Associate Director of the California Center for Population Research (CCPR). Her research centers on inequality and its implications for various outcomes that indicate life chances. This substantive focus accompanies a methodological focus on causal inference and the application and innovation of statistical models for panel data. Current research projects include evaluation of heterogeneity in the effects of education on socioeconomic outcomes and the socioeconomic and social-psychological consequences of disruptive events, such as job loss and family disruption.

1 In addition to the issue of selection bias, measurement error, recall bias, and attrition bias are all of concern in the study of the effects of worker displacement. Most studies of job displacement have used administrative or survey data. Commonly use nationally representative data include the Displaced Worker Survey supplement to the Current Population Survey, the Panel Study of Income Dynamics, and the National Longitudinal Survey. Others have used data from specific geographic areas, or specific establishments. Some of these data are limited for making causal statements because they are cross-sectional, inadequate for constructing a control group of comparable non-displaced workers, or are unable to distinguish displaced workers from those suffering other types of job loss, such as firings.

2 When an entire plant closes, it is unlikely that a worker’s specific characteristics are responsible for his or her displacement; larger differences from layoffs relative to plant closings could thus also be the result of greater selection bias, as I describe above.

3 Some scholars contend that the lowest level of well-being may occur prior to and in anticipation of the job loss, and lessen when the actual loss occurs ( Dooley, Fielding, and Levi 1996 ). Other research suggests that persistent job insecurity may be even more detrimental for psychological well-being than actual job loss ( Burgard, Brand, and House 2009 ).

4 The work cited generally focuses on subclinical symptomatology, as measured by some form of the Center for Epidemiologic Studies-Depression (CESD) battery currently administered in many U.S. surveys. Little work has examined the link between displacement and clinically diagnosable depression and anxiety ( Catalano et al. 2011 ).

5 As I note above, job loss due to layoffs may also have larger effects on psychological well-being than that due to plant closings as the former are more likely to suggest personal deficiencies and thus negatively impact self-concept and social relations ( Miller and Hoppe 1994 ). Few studies explicitly compare effects by form of job loss on psychological distress [although see Brand, Levy, and Gallo (2008) for evidence on older workers].

6 Effects of displacement may also vary by children’s age when parental job displacement occurs. Early childhood is important for development and may be a period especially sensitive to parental displacement and associated economic adversity. Low income can limit families’ ability to provide adequate nutrition, health care, and enriching activities during children’s formative years. Conversely, periods of unemployment allow mothers more time to spend with children. Moreover, young children are likely less conscious of relative status. We may expect larger effects of parental displacement when children are adolescents, as older children are more attuned to social stigma and relative status, such that displacement negatively impacts important life transitions in adolescence. Economic adversity is quite important to adolescents as well, especially for their educational decision making process.

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

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

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

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

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

1 Introduction

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

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

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

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

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

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

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

2 Literature review

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

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

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

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

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

2.1 OECD countries during the post 2008 crisis

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

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

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

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

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

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

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

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

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

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

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

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

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

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

3 Estimating the chances of finding a job and research hypotheses

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

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

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

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

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

3.1 The model

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

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

3.2 The sample

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

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

3.3 Data and variables

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

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

3.4 Dependent and independent variables

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

3.5 The dependent variable—being employed

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

3.6 Independent variables

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

3.6.2 Gender

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

3.6.3 Marital status

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

3.6.4 Education

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

3.6.5 Vocational education

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

3.6.6 Academic education

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

3.6.7 Children

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

3.6.8 Ethnicity

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

5 Unemployment as a function of the business cycle

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

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

6 Summary and conclusions

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

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

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

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

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

6.1 Policy implications

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

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

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

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

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

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

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

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

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

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

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

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

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

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Hila Axelrad

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

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Page One Economics ®

Unemployment insurance: a tried and true safety net.

"Of all the aspects of social misery nothing is so heartbreaking as unemployment." 

—Jane Addams

What happens when workers lose their jobs through no fault of their own? How do they manage with the loss of income? Today, every state has an unemployment insurance (UI) program that provides some income to these unemployed workers. But how did UI begin and how does it work? 

The prosperous times of the 1920s set an expectation of having employment and money to spend. Electricity, radios, refrigerators, and even cars became more affordable. And with employment opportunities, people grew accustomed to the good times of the "Roaring Twenties" and thought they would last forever. 

Figure 1 Civilian Unemployment Rate, 1919-2006

SOURCE: Federal Reserve Bank of St. Louis; https://www.stlouisfed.org/education/great-depression-curriculum-unit .

But beginning in 1929, things changed. The Great Depression brought an economic catastrophe with a challenging crisis. Between 1929 and 1933, the unemployment rate soared to 25 percent of the labor force (Figure 1). The effects were alarming and in stark contrast to the 1920s. Without a safety net, a lost job meant no income. Many became homeless with no means to support their families. And the struggles began. 

Federal-State Partnership

Struggles and crises can bring innovative ideas and reform. And that's exactly what happened during the Great Depression. The ugly picture of severe depres sion with despair and poverty brought a renewed interest in and attention to providing unemployment compensation (UC).  

Programs were tried at the state level. Wisconsin led the way and in 1932 became the first state to enact a UI law. A few other states followed with similar programs, which were funded by a tax on employers . 1 But most states did not. They feared a loss of business and jobs to states that did not tax employers. This created an interstate problem and was, therefore, a direct concern of the federal government. 

In 1935, as part of the Social Security Act, the Federal Unemployment Tax Act (FUTA) created the federal-state unemployment insurance program. 2 This act mirrored the earlier state plans, with the purpose of temporarily replacing a portion of wages for workers who had been laid off and who were looking and available for work. 

The act gave oversight responsibility to the U.S. Depart­ment of Labor (DOL). Within federal guidelines, states were given the freedom and flexibility to set criteria and design their programs. State unemployment offices were created to implement their UI programs and make payments to workers who qualified.

Today, all states have UI programs, but they vary in design. For example, eligibility requirements for receiving benefits vary based on duration of prior employment and whether full-time or part-time status. Also, the normal maximum amount of benefits, the duration of benefits, and claim procedures often differ. And UI program guidelines within each state are periodically subject to change according to economic conditions; this can be done by state governments or by the federal government in times of recession or special conditions. (See boxed insert, "Sample of Normal State Unemployment Benefits, August 2020.") 3,4

Eligibility for Unemployment Insurance

All states require that workers be able to work, available for work, and actively seeking work for UI eligibility. Most states require a work history listing a minimum time worked and a minimum amount of earnings based on a 12-month period. 5

Workers must file claims weekly to maintain eligibility. Any earnings for the period, including job offers accepted or declined, must be reported. This can affect eligibility for receiving benefits and the amount received. Addi­tion­ally, workers must continue to meet state requirements. 6  

In all states, workers can be denied benefits. Knowingly making false statements to obtain benefit payments is one reason for a denial. Other reasons include voluntarily leaving work without good cause and being discharged for misconduct connected with work. And even after being determined eligible for UI, workers can become ineligible if they're unable to or unavailable for work, not actively seeking work, or refusing an offer of suitable work. 7 UI does not cover people who leave their jobs voluntarily or people looking for their first job. Individuals who are self-employed are normally not entitled to regular UC. Also, certain types of employment, such as charitable work, are excluded from UI coverage. 8  

Each state has its own formula for determining unemployment benefits. And the number of unemployed workers receiving UC changes according to economic conditions (Figure 2). 9 Generally, benefits are calculated as a percentage of a worker's income over the past year, up to a certain maximum. And some states pay reduced benefits for part-time work, which provides only a small amount of income. Since payments are capped, UI may replace a smaller share of previous earnings for higher-income workers than for lower-income workers. The earnings and work history requirements can especially affect low-wage workers. They may not qualify for unemployment benefits, because many may not have had previous steady employment. States' eligibility rules may require a certain amount of steady earnings from a worker over the previous year. 10

Figure 2 Percent of Labor Force Getting Unemployment Benefits, August 27, 2020

SOURCE: Stateline analysis of U.S. Department of Labor and U.S. Bureau of Labor Statistics.

Today, in most states the regular program provides up to 26 weeks of benefits to workers who qualify. However, this does not mean all workers receive the benefits for the entire time—only for the time they qualify. 

Benefits can be received on a state-issued, prepaid debit card. Or UC can be directly deposited into a personal bank or credit union account, or onto an existing prepaid card. Some states will send payments by paper check. 11 UC payments are taxable income and must be reported on federal income tax returns. 12 Also, in states that have a state income tax, UC may be subject to this tax.

Extended Benefits

UI programs often change. In times of recession or special economic conditions, workers may receive extended unemployment benefits . For example, a state may pay UC for a longer period of time when the state's unemployment rate increases. Also, Congress can approve additional payment amounts, extend the amount of time people can receive benefits, and expand eligibility to include part-time workers during times of high unemployment. 13 For example, in response to the COVID-19 pandemic, Federal Pandemic Unemployment Compensation (FPUC) was enacted in March 2020. The DOL gave instructions to administer an additional $600 weekly payment to individuals who were collecting regular UC. The additional FPUC benefit payments were fully federally funded. The temporary boost to regular UC was scheduled to end after a person's last week of unemployment before July 31, 2020. 14

Who Pays for Unemployment Insurance?

The regular UI program is generally funded by employers. Most employers pay both federal and state unemployment taxes. 15 The unemployment taxes are based on the amount of wages paid to employees and are determined as a percentage of an employee's wages. The FUTA tax rate is the same for all employers in all states. However, the state employment tax varies from one state to another. 

Employers must pay FUTA tax on a regular basis. And at the end of a year, employers must file a FUTA tax return. This tax return requires filers to record FUTA tax payments made for the year and submit any payment due. FUTA taxes are submitted to the Internal Revenue Service (IRS) and are credited to individual state accounts.

As part of the federal-state partnership, the federal government pays for administrative costs and for setting up employment offices that attempt to match workers with new jobs. 16 Determining benefits and issuing payments are state responsibilities. 

Data Collection

Data collected by the DOL from state unemployment agencies for the number of UI claims filed weekly are helpful. These data can tell something about the job market. For example, when the number of initial claims is steep, it's an indication of an abrupt loss of jobs (Figure 3). Con­tinued claims (Figure 4) indicate the number of unemployed people who have already filed an initial claim and are filing again so that they keep receiving weekly benefits. 

Figure 3 Initial Claims

SOURCE: FRED ® , Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/ICSA#0 .

Figure 4 Continued Claims (Insured Unemployment)

SOURCE: FRED ® , Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CCSA#0 . 

Figure 5 Unemployment Rate

SOURCE: FRED ® , Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/UNRATE#0 . 

However, many workers who lose their job may not file for UC for a while. And many people remain unemployed after benefits expire. So, UI claims data do not precisely show the number of unemployed persons and are not a measure of the unemployment rate. The unemployment rate is measured quite differently by another agency, the Bureau of Labor Statistics (BLS). The BLS does not use the unemployment claims data to figure the unemployment rate. It uses a system of household surveys to collect information from a representative sample of the U.S. population to calculate the unemployment rate (Figure 5). 

Unemployment Insurance Changes

UI programs are subject to ongoing change. For example, over the years the duration of regular UI benefits has increased from 16 weeks to 26 weeks in most states. Also, in some previous UI programs, workers could become eligible for UI after being fired from their job or quitting work. Over the years this became disallowed in all states. 17 Technology has also changed the UI process. In earlier times, workers had to physically go to an unemployment office to file for unemployment benefits. Today, workers can file most everything via cell phone and/or the internet.

The innovative plan for UI in the United States was first tried individually by a few states. FUTA made it a federal-­state partnership. And UI programs have changed according to economic conditions, individual states, unemployment rates, and federal intervention. But some things have remained constant: The UI system helps eligible workers who have lost their jobs by temporarily replacing part of their wages. The benefits are especially important during economic downturns and recessions. For example, during the 2020 COVID-19 pandemic, the unemployment rate spiked sharply, but workers could apply for unemployment benefits. Unlike the days of the Great Depression, workers today have some protection from unemployment. The federal-state unemployment insurance program continues to serve its intended purpose with reasonable results. UI has been tried and has remained true to the hopes and ideas of the founders of FUTA.

1 Price, Daniel. "Unemployment Insurance, Then and Now, 1935-85." Social Security Bulletin , October 1985, 48 (10); https://www.ssa.gov/policy/docs/ssb/v48n10/v48n10p22.pdf .

2 Price, 1985. See footnote 1.

3 Justia. "Coronavirus and Unemployment Benefits: 50-State Resources." August 2020 update; https://www.justia.com/covid-19/50-state-covid-19-resources/coronavirus-and-unemployment-benefits-50-state-resources .

4 States periodically update their maximum weeks of UI available based on changes in a state's unemployment rate and when a state's unemployment rate triggers a high unemployment period.

5 Justia, 2020. See footnote 3.

6 U.S. Department of Labor. "Unemployment Insurance: Fact Sheet"; https://oui.doleta.gov/unemploy/docs/factsheet/UI_Program_FactSheet.pdf .

7 U.S. Department of Labor. See footnote 6.

8 West's Encyclopedia of American Law, Edition 2 , s.v. "Unemployment benefit." Retrieved October 28, 2020, from https://legal-dictionary.thefreedictionary.com/Unemployment+benefit .

9 Henderson, Tim. "Unemployment Likely Rising in 11 States." Stateline , an initiative of The Pew Charitable Trusts, August 28, 2020, update; https://www.pewtrusts.org/en/research-and-analysis/blogs/stateline/2020/08/28/unemployment-likely-rising-in-11-states?utm_campaign=2020-09-02+Rundown&utm_medium=email&utm_source=Pew . 

10 Kovalski, Manuel Alcalá and Sheiner, Louise. "How Does Unemployment Insurance Work? And How Is it Changing During the Coronavirus Pandemic?" Brookings Institution , July 20, 2020, update; https://www.brookings.edu/blog/up-front/2020/07/20/how-does-unemployment-insurance-work-and-how-is-it-changing-during-the-coronavirus-pandemic/ .

11 Malaiyandi, Sangeetha. "You Have Options for How To Receive Your Unemploy­ment Benefits." Consumer Financial Protection Bureau , June 23, 2020; https://www.consumerfinance.gov/about-us/blog/receive-your-unemployment-benefits-options/ . 

12 U.S. Department of Labor. See footnote 6. 

13 U.S. Department of Labor. See footnote 6.

14 U.S. Department of Labor. "U.S. Department of Labor Publishes Guidance on Federal Pandemic Unemployment Compensation"; https://www.dol.gov/newsroom/releases/eta/eta20200404 , accessed September 11, 2020. 

15 Internal Revenue Service. "Federal Unemployment Tax"; https://www.irs.gov/individuals/international-taxpayers/federal-unemployment-tax .

16 West's Encyclopedia of American Law, Edition 2 . See footnote 8.

17 Augustinho, David. "Happy 75th Birthday, Unemployment Insurance." Barnstable Patriot , August 27, 2010; https://www.barnstablepatriot.com/article/20100827/BUSINESS/308279957 .

© 2020, Federal Reserve Bank of St. Louis. The views expressed are those of the author(s) and do not necessarily reflect official positions of the Federal Reserve Bank of St. Louis or the Federal Reserve System.

Depression: A severe and long-lasting economic downturn that is worse and deeper than a recession; a severe reduction in gross domestic product (GDP).

Employee: A person who works for an employer in exchange for a monetary payment.

Employer: A person or business providing a job or work to others and giving a monetary payment in exchange for the work. 

Extended unemployment benefits: Additional weeks of benefits available to workers who have exhausted regular unemployment insurance benefits during periods of high unemployment. 

Labor force: The total number of workers, including both the employed and the unemployed.

Recession: A period of declining real income and rising unemployment; significant decline in general economic activity extending over a period of time.

Taxes: Fees charged on business and individual income, activities, property, or products by governments. People are required to pay taxes. 

Unemployed: People 16 years of age and older who are without jobs and actively seeking work.

Unemployment compensation: A program providing cash benefits for a specified period of time to workers who lose a job through no fault of their own. Also known as unemployment insurance.

Unemployment rate: The percentage of the labor force that is willing and able to work, does not currently have a job, and is actively looking for employment.

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To reduce South Africa’s unemployment, make work more attractive

Jacques morisset.

Originally published by  Brookings and Business Day (South Africa) October 13 and 10, 2023

At over 30%, unemployment is South Africa’s biggest contemporary challenge. Of course, there are diverse reasons behind the incapacity of the South African economy to provide sufficient jobs for its working population but allow me to focus on one: limited financial incentives for a poor South African to look for employment. The key question in South Africa is not why people are unemployed, but rather why they should work.

The financial incentives to work are low

To answer this question, I simply calculate how much money a typical South African worker (Dumisani) entering the formal labour market is expected to bring home in comparison with a Vietnamese worker (Dung). In our example, let us focus on taxes and transport costs (Figure 1). This comparison demonstrates that a low-income worker in South Africa has realistically few incentives to work or at least much less than a worker in Vietnam, a country with rapid job growth.

For the same gross salary, a low-income South African worker will get less than half the money in his pocket than a Vietnamese worker

For the same gross salary of $1,000 per month (approximately R19 140), Dumisani keeps in his pocket less than half of the money that Dung would because of higher taxes and transport costs. Unfortunately, Dumisani is not an exception as the personal income tax is set at 18 % for those earning up to about $1000 per month in South Africa, while it is only 5% (up to $215 / R4115), 10 % (between $215 and $430 / R4115 and R8231), and 15% (between $430 and $760; R8231 and R14548) in Vietnam. The personal allowance, which can be deduced from the tax payment for a single worker, is also slightly lower in South Africa ($400 vs $475; R7657 vs R9093). Other East Asian countries, like Indonesia and Malaysia , also have personal income taxes with lower entry rates than South Africa.

South African workers like Dumisani are also heavily penalized by transport costs due to long commutes between townships and industrial/businesses centers, as a result of the legacy of Apartheid era spatial planning. Two economists from the Harvard Growth Lab ( Shah and Sturzenegger ) estimate that the average transport costs for those who are employed in South Africa is equal to 57% of net wages when time to commute is accounted for. According to their calculations, transport costs could exceed 80 percent for the lowest quintile of workers.  In Vietnam, the same cost is estimated at only 10% of net wages because of shorter distances and more competitive modes of transport, including motorcycles.

In his decision to work, Dumisani will consider not only how much he will earn but also the amount of money that he could have expected from the government if he was not working. Like many low-income families, he or a member of his household would have qualified for a form of social transfers (grants/subsidies) distributed by the government. Today, it is estimated that over half of households are receiving money in one form or another from the State in South Africa. When becoming active in the labour market, Dumisani could lose some of these social benefits, including the unemployment insurance, the unallocated grants for low-income households (e.g., the COVID-grant), and the provision of free public services (electricity, water) to “indigent” households. By contrast, Dung is unlikely to receive any transfers from the government as social support in Vietnam is limited to few specific groups (war heroes, people living with disabilities).

Changing the relative price of work

Boosting employment could be achieved by changing the relative returns to a worker between being active or inactive in the labour market. My argument is this can be implemented by three policy changes or strokes of the pen that mainly require political consensus, not additional money.

• The Minister of Finance could initiate the first policy change. He can lower the entry tax rate of the personal income tax from 18% to, say, 5% or increase the threshold at which a citizen starts to pay the personal income tax. This is the East Asian model described above. While the benefit of this action is evident for low-income workers (they will take home more money), the costs will be minimal to the government as South Africa’s top decile contributes almost 80% of the personal income tax’s revenue .
• The second policy change, which may be more bolder and require concerted political will, would be for the authorities to modify existing social transfers to encourage poor workers to enter the labour market, including through reduced transport costs. A suggestion would be to replace (at least partially and for those ready to engage in work) the Social Relief Distress grant (about US$18.5 / R350per month), which was introduced as a temporary protective measure during COVID-19, by a direct subsidy that will help low-income workers keep more money in their pockets. The authorities could distribute a voucher through a phone application or an electronic card that beneficiaries will use to cover part of their commute costs. The amount of the subsidy could be adjusted to make the reform revenue neutral for the government.
• The third policy change will be to facilitate additional job opportunities closer to the homes of low-income workers – automatically reducing transport costs. This could be achieved by making it easier for existing small businesses to grow and operate, and for new ones to get started. Ecuador, for example, recently introduced a new type of company modality, (“ Simplified Corporation Form ”) available on a digital platform, leading to the creation of 43,000 companies in less than three years. Of course, the development of (small and micro) enterprises and self-employment – the main source of jobs in underserved areas--requires additional measures such as improvement in infrastructure, better access to finance, and skills, but this could be a starting point. This simple administrative change could be accompanied by targeted financial support and training programs as implemented in many low- and middle-income countries with extensive positive impact evaluation evidence.

Longer-term measures are needed to further enhance the mobility of poor workers

These short-term solutions can be attractive because they will encourage poor people to look for jobs and self-employment opportunities by making the relative price of labour more attractive in South Africa. They will also send an immediate signal about the government’s willingness to help disadvantaged workers to get more money in their pockets. However, in the longer run, the country’s ability to generate jobs will be largely determined by workers’ mobility, especially in urban areas where three-quarters of the labour force is concentrated today. The overhaul of the urban public transport systems, which are notoriously unreliable, unsafe and largely unregulated in the case of the taxi industry; and the development of new housing communities closer to industrial and business centers, will therefore have to be in the mind of every policymaker who wants to address the unemployment challenge in South Africa.

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Lead Economist and Program Leader, World Bank

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How do I file a claim for unemployment insurance benefits?

What will the information i provide be used for, when should i file a claim for ui benefits, what are the requirements for filing a valid claim, what is a base period, what is an alternative base period, what is a benefit year, what is the average weekly insured wage, what is the difference between eligibility and qualification, what is an issue, who is the last employer, who is a base period employer, what is a waiting period week, what are wages, what is an earning allowance, in which state should i file a claim if i live in north carolina or another state, what if i work part-time, can i collect benefits while working, what is an attached claim, how are attached claims handled, what wages can be used to establish a claim for benefits, what is a wage transcript and monetary determination, what is a separation payment, what effect do vacation and severance weeks have on eligibility for benefits, if i live in north carolina, how do i file an interstate claim (claim against another state), if i live in another state where i do not have wages, but have qualifying wages in north carolina, may i file an interstate claim, what if i filed a claim, but have now returned to work, how many weeks of regular benefits are available in north carolina, how is my weekly benefit amount determined, what is the maximum weekly benefit amount that i can receive, can i use military service to establish a claim, am i eligible for benefits if i work for a school system, does receiving social security disability payments affect my unemployment benefits, will receipt of workers’ compensation affect my unemployment benefits, am i eligible for benefits during a leave of absence from my job, can i file a claim if i am not a united states citizen, what if i am out of work due to a strike, who pays for the benefits i receive, what does it mean to be monetarily eligible, what is a nonmonetary determination, what will happen if i begin to receive ui benefits and i am later determined to be disqualified, am i required to register for work, i received a message indicating that i have not registered for work at ncworks.gov. what should i do, what is ncworks online, what is resume builder, do i have to look for work while collecting benefits, what kind of work must i accept, what should i do if i lose my job again, does receiving a pension affect my benefits, does receiving social security retirement benefits affect my ui benefits, are my benefits taxable and does des withhold any benefits for taxes, can i change my tax withholding option, can i have my tax withholding returned to me, will i receive a year-end statement, if i repaid an overpayment, will it appear on my certain government payments (form 1099-g), if i did not receive my 1099-g, how can i get a copy, as an employer, what if the claimant is not claiming benefits against my account, the claimant quit without giving me any notice or reason. why should i be held liable for the benefits when my business suffered as a result.

The fastest and most efficient way to file a new claim is to file online . If you don't have access to a computer, you may file over the phone by calling 1-888-737-0259. 

Wage information and other confidential unemployment compensation information may be requested and utilized for other governmental purposes, including, but not limited to, verification of an individual's eligibility for other government programs. 20 C.F.R. § 603.11(b). 

You should file a claim for benefits if you have become unemployed through no fault of your own, you are willing to register for work and actively seek employment, and you are able and available to work if any work is offered to you. NOTE: If you are still employed, but are temporarily laid off due to a decrease in workload, your employer may file an attached claim on your behalf. Attached claims do not require that you register for work or actively seek work. If your employer refuses to file an attached claim on your behalf, you must file your own claim and meet all eligibility requirements.

You must have worked in employment subject to UI tax (known as covered employment) and received wages in at least two (2) quarters of your base period. You must also have been paid wages totaling at least six (6) times the average weekly insured wage during your base period. The exact amount of benefits and the duration of those benefits cannot be determined until you actually file your claim for benefits.

Your base period is the time frame used to determine whether you are monetarily eligible to receive unemployment payments. It normally includes the first four of the last five completed calendar quarters.

If you lack enough base period wages, DES may use an alternative base period to determine whether you are eligible for UI benefits. The alternative base period consists of the last four completed calendar quarters immediately before the first day of your benefit year.

Your benefit year is the 52-week period from the date you register for work and first file a valid claim. If you are still attached to your employer’s payroll, your benefit year begins on the Sunday before your payroll week ends. If you are not attached to an employer’s payroll, your benefit year begins on the Sunday of the calendar week that you file a valid claim and register for work.  

The average weekly insured wage is calculated by dividing the total wages reported by all covered employers for a calendar year by the average monthly number of employees during that year and dividing that number by 52.

Eligibility involves working and earning enough money within your base period to establish a claim for benefits, and meeting the requirements of searching for work and being able and available for work. Eligibility is determined weekly. Ineligibility is a postponement of benefits until you meet the weekly requirements. Qualification involves being separated from work through no fault of your own. Disqualification is a loss of benefits. It is possible to be eligible and disqualified for benefits. It is also possible to be ineligible and qualified for benefits. You must be both qualified and eligible to receive benefits.  

An issue is an act or circumstance, which, by virtue of State or Federal law/regulation, could affect your eligibility for UI benefits. Whenever an issue is discovered, benefit payments may be interrupted until the issue is resolved. There are three possible resolutions to an issue:

• The issue disqualifies you from receiving a portion or all of your UI benefits.
• The issue does not disqualify you from receiving UI benefits.
• The issue is determined to be set in error, thus it is simply removed from the claim.

If you have an issue on your UI claim, while awaiting a resolution, you should continue to file your weekly certifications.

When a new claim is filed, your last employer is the last employer for whom you worked in covered employment (employment subject to UI tax) for an indefinite period, or for more than 30 days before your separation, regardless of whether work was performed each day.

Your base period employer is any covered employer (employer subject to UI tax) who reported wages for you during the base period of your claim. All base period employers are potentially responsible for a proportional share of charges based on the percentage of benefits reported for you during your base period.

Your waiting period week is the first eligible week for benefits under each claim filed. You must serve a waiting period week for each claim filed. You will never receive payment for this first week, but it must be claimed to be counted. It does not mean you should wait a week before filing a UI claim.

Wages are cash or any medium other than cash paid by an employer to, or on behalf of an employee for services rendered.  

Your earning allowance is the amount of money you can earn without reducing your weekly benefit amount. Earnings over this amount are deducted dollar-for-dollar from your weekly benefits. You must always report your gross earnings for any work performed during any week you claim benefits.

You should apply for benefits in the state where your base period wages were reported. If you have base period wages in multiple states, you may choose the state in which you file for benefits. 

If you work part-time, you must report the gross (before taxes) earnings for the weeks that you worked, and you must maintain your eligibility requirements. If your only employment was part-time and you have been separated, you may be eligible to receive benefits. Contact a DES claims representative at 1-888-737-0259 so that a determination can be made.

Yes. You can collect benefits while working in certain limited circumstances. This is generally found in G.S. §96-15.01.

An attached claim is a claim filed by an employer on behalf of an employee who has been temporarily laid off or who has worked less than 60% of the customary scheduled full-time hours for the employee.

An employer may file an attached claim only if the employer has a positive balance in its UI account. If an employer does not have a positive balance, it must pay DES an amount equal to the amount necessary to bring the employer’s negative balance to at least zero. After this happens, there are other restrictions with which the employer must comply:

• An employer may file an attached claim for an employee only once per year; and
• The period of partial unemployment for which the claim is filed may not exceed six consecutive weeks; and
• The employer must also pay DES an amount equal to the full cost of unemployment benefits payable to the employee under the attached claim before the attached claim is filed.

Any wages used to establish eligibility for UI benefits must be earned in employment that is covered by the Employment Security Law. This means that the employer must be subject to UI tax. Employers who are liable under the Employment Security Law are required to post a Certificate of Coverage and Notice to Workers (Form NCUI 524) in their place of business.

A Wage Transcript and Monetary Determination (Form NCUI 550) is a document that itemizes your quarterly wages paid by each base period employer. This form also shows your weekly benefit amount, duration, and effective date of your claim. If monetary eligibility is not established, the reason is shown on this form.

Separation payment is any payment that was made, is being made, or will be made to you as a result of separation from last employment. Separation pay may be in the form of:

• Wages in lieu of notice,
• Accrued vacation pay reported on claims effective prior to July 2, 2017,
• Terminal leave pay,
• Severance pay,
• Separation pay, or
• Dismissal payments or wages (no matter what they are called).

Alert!  Claims effective July 2, 2017 and beyond: Paid Time Off (Vacation and/or Sick Pay) will not be considered separation pay if the payment was issued as a result of the employer's written policy established prior to your separation.

Any worker who receives severance pay is considered to be attached to that employer's payroll during that time and not eligible for UI benefits.

Paid Time Off (Vacation and/or Sick Pay) will not be considered separation pay if the payment was issued as a result of the employer's written policy established prior to your separation. Workers receiving Paid Time Off (Vacation and/or Sick Pay) under these conditions will not be disqualified from receiving benefits.

If you live in North Carolina, but do not have wages in North Carolina, you can file an interstate claim in the state where you have wages. You must contact the agency responsible for UI claims in that state and follow its instructions for filing your claim.

Yes. If you have no wages in the state where you live, but have qualifying wages in North Carolina, you may file an interstate claim in North Carolina. You can file your claim online on the DES website, or by telephone at 888-737-0259.

If you have returned to work full-time, you should immediately stop filing for UI benefits.

You can receive between 12 and 20 weeks of regular unemployment benefits in North Carolina. The actual number of weeks you receive depends on the seasonally adjusted statewide unemployment rate.

Your weekly benefit amount is the amount of money you may receive each week. This is calculated by dividing your total of wages in the last two quarters by 52. That number is then rounded to the next lower whole dollar. In order to receive a payment, the total must equal to or exceed $15. The exact amount of benefits and the length of time that you may get benefits cannot be determined until you actually file your claim for benefits. 

You may receive the maximum amount of $350 per week. Your weekly benefit amount is based on the last two completed quarters in your base period divided by 52 and rounded down to the next whole dollar. The seasonally adjusted statewide unemployment rate used to determine the maximum number of weeks that you may get benefits is calculated on January 1 and July 1.

Military service may be used to establish your claim if that service occurred during your base period. Military wage credits are assigned to the state where the military claimant files a "first" claim. These wage credits may be combined with wage credits from other base period work to establish a claim and pay benefits. In order for military service to be considered, you must provide a copy of your Report of Separation from Active Duty (DD Form 214) or Correction to DD Form 214 (DD-215).

If you work for the school system, you are generally not eligible for UI benefits during non-school periods, as long as you are attached to the school system.

In order to qualify for benefits, you must be able to work. You are not able to work during any week that you are receiving or applying for benefits under any other state or federal law based on your temporary total or permanent total disability.

In order to qualify for benefits, you must be able and available to work. If you are currently receiving workers’ compensation benefits, you must inform DES. DES will determine whether you are eligible to receive benefits based upon the specifics of your workers’ compensation injury.

Generally, you are not eligible for benefits during a leave of absence from your job. In order to be eligible for benefits, you must be unemployed through no fault of your own, be actively seeking work, and be able and available to accept work if it is offered to you. In limited circumstances, an exception may apply to the general rule. DES will make a determination in each case.

If you are not a citizen or national of the United States, you must have legal authority to work in the U.S. You must present either:

• Alien registration documents or other proof of immigration registration from the United States Citizenship and Immigration Service (USCIS) that contains your alien registration number or alien file number; or
• Other document the State determines provides reasonable evidence of satisfactory immigration status. If you have not provided any acceptable form of documentation showing satisfactory immigration status, you will not be eligible for benefits.

You are disqualified from receiving unemployment benefits during an active labor dispute. You are not qualified for unemployment benefits when your partial or total unemployment is due to:

• an active labor dispute at the factory, plant, etc. where you work or last worked; or
• an active labor dispute at another factory, plant, etc., owned by the same employing unit, which causes the materials or services necessary for operation of your factory, plant, etc. to become unavailable.

Benefits are paid from the North Carolina’s Unemployment Insurance Fund, which is funded by a tax paid by employers.

Monetary eligibility simply means that you have worked and earned enough wages within your base period to meet the requirements for establishing a claim. You must have filed a valid claim and met the requirements for eligibility and qualification to receive benefits.

A determination of qualification for benefits based on any consideration that is not monetary is a nonmonetary determination. If you quit a job, get discharged, refuse referral to a job, refuse a job, refuse to enter DWS-approved training, or fail to complete DWS-approved training, you may be disqualified. You and/or your employer may appeal an unfavorable nonmonetary determination. 

For claims filed June 30, 2013 and after, claimants are subject to repayment of benefits received from any administrative or judicial decision that is later reversed on appeal. 

In order to meet your work registration requirement for unemployment insurance benefits, you must:

• Register for work at  www.NCWorks.gov  by creating an online account. Click on the 'Not Registered?' link and then select 'Individual' under Option 3 -Create a User Account. The website will then guide you through the steps to complete your account setup.
• Maintain an active account. If more than 90 days has passed since you last logged in to NCWorks.gov, you must login and verify your information to reactivate your registration.

While you're logged in to NCWorks Online, you may check out the many resources the Division of Workforce Solutions (DWS) has online to assist you with your re-employment efforts. 

If you fail to register for work at  www.NCWorks.gov  by setting up an NCWorks Online account or maintain an active account, your benefits may be delayed or denied.

You must register for work at  www.NCWorks.gov  by creating an account. Click on the 'Not Registered?' link and then select 'Individual' under Option 3 - Create a User Account. The website will then guide you through the steps to complete your account setup.  If you fail to register for work at  www.NCWorks.gov  by setting up an NCWorks Online account or maintain an active account, your benefits may be delayed or denied.  

NCWorks Online is a one-stop online resource for job seekers and employers in North Carolina. Job seekers can search for jobs, create resumes, and find education and training. Employers can find candidates, post jobs, and search labor market information. According to North Carolina law and Federal law, Unemployment Insurance claimants must be registered for work. This registration must be accomplished by going to  www.NCWorks.gov  and building a resume using the Resume Builder tool which details your employment history and occupational skills.  

Resume Builder is a tool on the NCWorks Online site that will provide you step by step assistance in compiling your prior work history and occupational skills to create a resume. 

You must make three valid job contacts with potential employers for each week you claim for unemployment insurance benefits. For more information view the  NCUI 506  Form.

You must accept any suitable work during your first 10 weeks of the benefit period based on such factors as your experience, customary occupation, prior earnings, etc. During the remaining weeks, any suitable work must be considered. NOTE: Suitable work will be any work offered that pays 120% of your weekly benefit amount beginning with the eleventh (11th) week after you file your claim.

If you lose your job again, you may file for unemployment benefits by telephone or at the DES website.  

Yes. If you are receiving a pension from a base period employer, your weekly payment amount will be reduced. You should notify DES immediately to determine the appropriate action to be taken.  

Social Security retirement benefits do not reduce the amount of your weekly payment amount. You are not required to report Social Security retirement benefits.

Yes. You should include the UI payments you received on your federal and state tax filing form. If you request it, DES will withhold state and federal taxes from your benefits.

Yes. You may change your tax withholding option by completing the Request to Change Income Tax Withholding/Direct Deposit (Form NCUI 500TWC ) on the Forms and Documents page , or through your online account.

No. Tax withholdings are immediately transmitted to the NC Department of Revenue and/or the Internal Revenue Service when the payment is issued.

You should receive a year-end statement called a Certain Government Payments (Form 1099-G) for the previous year by January 31 of the current year.

No, there is a line to report repayment of overpayments on your federal tax return form. The repaid amount should be reported on the tax return submitted for the year the repayment was made.

You may download a copy of your current Certain Government Payments (Form 1099-G) from the DES website at no charge. Your current 1099-G remains on the DES website for one (1) year. After its removal from the website, you may request a copy of your 1099-G by submitting a written request by mail, facsimile or email to:

There is no charge for providing a copy of the current 1099-G. However, there is a $15.00 charge for previous years’ 1099-Gs.  

You should respond to all requests for information regarding a claim from DES. Neither claimants nor employers get to choose which employer is charged for a claim. This is determined by the Employment Security Law. All employment within a claimant’s base period and a claimant’s last employment before filing a claim are considered.

If you are a base period employer and your account is potentially liable for a portion of the charges resulting from a claim for UI benefits, you will be mailed a Notice of Initial Claim and Potential Charges To Your Account (Form NCUI 551). You must provide a timely and complete response to this notice so that the reason for separation can be reviewed. Your response will be used to determine an appropriate action regarding liability, if any, on your account.

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The Maryland Department of Labor has created an unemployment insurance hotline for workers affected by the Key Bridge collapse. Call 667-930-5989 Monday to Friday from 8:00 a.m. to 5:00 p.m. and Saturdays from 8:00 a.m. to 12:00 p.m. (noon).

Port of Baltimore Worker Support Program

The Port of Baltimore Worker Support Program provides temporary relief to Port workers who have lost work and income due to the reduction in Port operations following the Francis Scott Key Bridge collapse.

The program gives a $430 weekly payment to individuals who regularly worked at the Port before the bridge collapse and need assistance given a loss of job hours and income since then. To be eligible, applicants need to have worked at the Port of Baltimore at least 25 times or have earned at least $5,000 from Port jobs between January 1 and March 26, 2024. As part of the application, workers need to provide documentation that they regularly worked at the Port of Baltimore and verify their employment.

Here is the online application: https://maryland-dol.submittable.com/submit

Who is eligible for the Worker Support Program?

• Independent contractors
• Individual owner-operators
• Sole proprietors
• Single person Limited Liability Companies (LLC)
• Employees of a Port business or trade association
• Workers who receive a 1099 tax form from a Port business

How much money can I receive and for how many weeks?

Am i eligible if i also apply for and receive unemployment insurance, do i have to recertify every week similar to unemployment insurance, what identification and documents do i need to apply.

• U.S. or state-government-issued photo identification
• A real-time photograph taken by smartphone or webcam during the application process
• A Transportation Worker Identification Credential (TWIC card)
• A terminal ID badge
• A Port employer-provided ID badge
• 2023 W-2 from Port of Baltimore employer
• 2023 1099 from Port of Baltimore contractor
• Copy of paystub(s) from 2024 from a Port of Baltimore employer
• Canceled check(s) from 2024 from a Port of Baltimore employer/contractor
• Evidence of direct deposits(s) from 2024, from a Port of Baltimore employer/contractor
• Interchange ticket(s) from 2024, for containers moved from the Port of Baltimore

How do I apply?

Can i get help over the phone or in person with the application.

A temporary office at 2501 Broening Highway near the Seagirt terminal will open starting Monday, April 22, 2024 at noon to help workers with applications for the Worker Support Program as well as for Unemployment Insurance. Starting Tuesday, April 23, the office will be open weekdays from 8:30 a.m. to 4:30 p.m.

More information and resources are below:

• Application English
• Application Spanish
• FAQs English
• FAQs Spanish
• Guidelines English
• Guidelines Spanish
• Flyer Spanish
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Some gender disparities widened in the U.S. workforce during the pandemic

The COVID-19 recession resulted in a  steep but transitory  contraction in employment, with greater  job losses among women than men. The recovery began in  April 2020 and is not complete. As of the third quarter of 2021, the labor force ages 25 and older remains nearly 2 million below its level in the same quarter of 2019.

The pandemic is associated with an increase in some gender disparities in the labor market. Among adults 25 and older who have no education beyond high school, more women have left the labor force than men. Other disparities have stayed the same or even narrowed: The gender pay gap has remained steady, for example, and the difference in the average hours worked by men and women has slightly diminished.

The COVID-19 recession differed from prior recent recessions in that it disproportionately eliminated jobs employing women. Media accounts often referred to it as a “ shecession .” As the economy and labor market have been recovering for more than 21 months, Pew Research Center conducted this analysis to investigate if there are lingering impacts on the labor market opportunities of women.

Labor force estimates and average hours worked are derived from the monthly Current Population Survey (CPS), sponsored jointly by the U.S. Census Bureau and the U.S. Bureau of Labor Statistics. The CPS is the nation’s premier labor force survey and is the basis for the monthly national unemployment rate released on the first Friday of each month. The CPS is based on a sample survey of about 70,000 households . The estimates are not seasonally adjusted.

For a quarter of the sample each month, the CPS also records data on usual hourly earnings for workers paid by the hour and usual weekly earnings and hours worked for other workers.

The CPS microdata files analyzed were provided by the Integrated Public Use Microdata Series (IPUMS) at the University of Minnesota .

The COVID-19 outbreak has affected data collection efforts by the U.S. government in its surveys, especially limiting in-person data collection. This resulted in about an 8 percentage point dec rease in the response rate for the CPS in September 2021. It is possible that some measures of labor market activity and its demographic composition are affected by these changes in data collection.

Overall, the number of women ages 25 and older in the labor force has fallen 1.3% since the third quarter of 2019, similar to the 1.1% decline of men in the labor force.

But this modest overall change obscures divergent outcomes for labor force members with different levels of education. Women who have no education beyond high school exited the labor force in greater numbers than similarly educated men. However, the pandemic has not interrupted the long-running gains of  women among the college-educated  labor force.

From the third quarter of 2019 to the same quarter of 2021, the number of women in the labor force who are not high school graduates decreased 12.8%, dwarfing the 4.9% contraction among comparably educated men. The pandemic also disproportionately affected women with a high school diploma. The ranks of women in the high-school-educated labor force have declined 6.0% since the third quarter of 2019. The labor force of similarly educated men has fallen only 1.8%.

Among the labor force with at least some amount of education beyond high school, women have fared at least as well as men. The number of men and women in the labor force who have some college experience but not a bachelor’s degree has contracted for both groups, with no strong disparities between the two. Both men and women with at least a bachelor’s degree saw positive gains in the labor force (2.7% and 3.9%, respectively) from 2019 to 2021.

What accounts for the larger labor force  withdrawals among less-educated women  than men during the pandemic? It is complex but there seems to be a consensus that it partly  reflects how women are overrepresented  in certain health care, food preparation and personal service occupations that were sharply curtailed at the start of the pandemic. Although women overall are more likely than men to be able to work remotely, they are  disproportionately employed in occupations  that require them to work on-site and in close proximity to others.

It is less clear whether women’s parental roles and  limited child care  and schooling options have played a large role in forcing them to exit the labor market. The number of mothers and fathers in the labor force has declined in similar fashion over the past two years.

Turning to the number of hours employees work per week, on average, there have been small changes associated with the pandemic and they have occurred among men. In the third quarter of 2021, women ages 25 and older worked 37.5 hours on average in paid employment, unchanged from how much they worked two years earlier. Men ages 25 and older worked 41.6 hours on average in the third quarter of 2021. That is 0.7 fewer hours than they worked pre-pandemic (42.2). So, the disparity in hours of paid employment between women and men workers has somewhat narrowed.

The pandemic is also not associated with a widening of the  gender pay gap . Among full- and part-time workers ages 25 and older, women earned 86% of what men earned based on median hourly earnings in the third quarter of 2021. Two years ago, the estimated gender pay gap was 85%.

The overall pay gap partly reflects that employed women have higher levels of education than employed men. In 2021, 48% of women workers ages 25 and older had completed at least a bachelor’s degree compared with 40% of men. Workers with at least a bachelor’s degree tend to earn more and thus women’s earnings are boosted by their greater educational attainment. The gender pay gap is greater when you look at groups of women and men with equal levels of education. The gap depends on the education level, but in 2021 women ages 25 and older earned closer to 80 cents on the dollar compared with equally educated men.

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Employment in Moscow

At a Glance:

• To find work in Russia’s capital, ask your current employer about a company transfer or check out a Russian job site.
• The work permit process is long and complicated, so start early and be patient!
• Your employer will take care of your social security contributions, although we would recommend acquiring private health insurance in addition to this.
• Russia has double-taxation treaties with a number of countries, so check if this applies to you.

Moscow is Russia’s undisputed economic and financial center. Greater Moscow’s workforce produces over a quarter of Russia’s entire GDP. With an unemployment rate of just 1.3% in 2017, the capital has the lowest unemployment rate in all of Russia.

Many of Russia’s largest companies have their headquarters and the majority of their staff working in Moscow. Nearly all multinational corporations which have entered the Russian market are based in the city as well. This makes Moscow an attractive option for expats from all over the world.

As the city is Russia’s capital and major political center, another large share of the expatriates in Moscow are diplomatic staff, foreign correspondents, and employees of cultural institutions. There is also a high demand for foreign native speakers working as language teachers.

Moscow’s Main Industries

Moscow’s economy has changed drastically since Soviet times, when the city was reliant on its manufacturing and engineering activities. Nowadays, the service sector employs many Muscovites, thanks to the city’s growing tourism and retail industries. Moscow is also Russia’s financial center: it is home to the Moscow Exchange (the national stock exchange) and almost all of the country’s major banks, including Sberbank, which is the largest in Eastern Europe.

Despite the decline in Moscow’s manufacturing sector, the city is still a major industrial center of Russia and home to the national headquarters of many major companies, with mechanical engineering, food processing, and research and development (R&D) being the most prominent sectors.

Looking for a Job: Search Wisely

Depending on your background and qualifications, realizing your dream of working in Moscow may or may not be easy to fulfill. There is a high demand for foreign experts, but it is generally limited to specific sectors. Skills in the fields of construction, business development, IT, and finance are much sought after.

Your most promising option for working in Moscow is to check directly with companies from your home country or multinationals in your field which are doing business in Russia, as these are the most likely to hire expats.

Alternatively, there are many online recruitment consultancies which can help you find a job in the capital to match your qualifications. If you would like to go job-hunting on your own, you might find the following websites useful:

• The Moscow Times Career Center
• SuperJob.ru (website in Russian)
• HeadHunter.ru
• JobsinMoscow

Remember that due to work permit quotas, locally advertised jobs may not always be an option for expats.

Moscow: Work Permits and Social Security

Priorities: work permits.

Getting the necessary work permit for Russia is a complex and time-consuming procedure. The country has a quota regulation for foreign workers. Companies wishing to employ foreign staff have to submit an application specifying the number and nationality of employees they wish to hire a year in advance.

If a potential employer’s request to hire foreign employees is granted, job vacancies have to be registered with the authorities. If no local candidate has been found within a month, the company receives a corporate permit. Now, the application for an individual work permit can be filed. This requires translated evidence of qualifications and a health certificate. In a best-case scenario, this process takes three months.

An exception to this lengthy process, however, is in the highly qualified specialist category, which is not subject to quotas or corporate permit requirements. Highly qualified specialists are foreign professionals in a particular sector, and eligibility for this category depends on their wage. If working in the educational or scientific fields, you need to earn more than 1 million RUB (approx. 17,600 USD as of 2017) per year, and this rises to 2 million RUB (35,300 USD) if working in any other sector. However, if you are planning on working in one of Russia’s Special Economic Zones (SEZs) , you only need to be earning 700,000 RUB (12,300 USD) per year. Visas for highly qualified specialists are issued for up to three years at a time, with an option to extend it for a further three years. The visa simply requires an application to the state application body, and the authorities must consider it within 14 days.

It’s a Different Story for CIS Nationals

Unlike other nationals, workers from Commonwealth of Independent States (CIS) countries do not need to go through such a lengthy and complex process. They need to apply for a work patent within 30 days of their arrival in Russia, and have 30 days in which to confirm their knowledge of the Russian language, history, and legislation in an exam. Only once the exam has been passed can they receive the work patent.

  After receiving the patent, they have 60 days to find local employment. They can then work for up to twelve months, and the patent is renewable once.

Everything You Need to Know about Taxation

All expats working in Moscow are liable to pay Russian income tax. Non-residents are taxed only on their income from Russian sources. In this case, the tax rate for all types of income is 30%.

If you live in Russia for at least 183 days during a 12-month period, you are considered a resident under Russian taxation law. Tax residents are taxed on all their income, including income from non-Russian sources. Since the tax reform of 2001, there is a flat income tax rate of 13% for most types of incomes.

One exception is the abovementioned highly qualified specialist immigration category. Expats who have entered the country on this visa are eligible for the standard personal income tax rate of 13%, even before officially becoming a Russian tax resident. Additionally, Russia has signed double taxation treaties with a number of countries .

The Social Security System in Moscow

Everyone employed in Russia must be insured through the social security system — however, it is up to your employer to pay the contributions, so you do not have to worry about this responsibility. Social security in Russia is fairly comprehensive, covering unemployment, unexpected sickness, and an old-age pension, among other things. However,  we would recommend getting additional private health insurance on top of this, as the country’s state medical facilities leave a lot to be desired.

Professional Qualifications for Moscow

Teaching english as a foreign language.

Teaching English as a foreign language is a very popular option for young people who would like to gain some international experience in Moscow as well as native speakers who cannot find a position in their original profession. In recent years, a large number of private language institutions have sprung up all across the city. The demand for foreign language teachers is continually high, and chances are good for native speakers of languages such as English, French, Spanish, or German to find a teaching position.

On the downside, teacher salaries are usually not the most competitive. Before you accept a teaching post, carefully check the conditions you are offered. Finally, research the reputation of your potential employer — stories of scams are quite frequent.

Language Skills: Don’t Expect Too Much

English skills are a lot less common in Moscow than they are in many other European capitals. The average taxi driver or shop assistant probably knows a couple of English words at the very most. To make daily life easier, it is strongly recommended to learn at least some basic Russian for your life in Moscow.

In the business world, on the other hand, English is more widely spoken. Some positions, especially those in Russian companies, require knowledge of both Russian and English. For those working for one of the many multinationals, however, fluency in just English is often sufficient.

How to Behave in the Moscow Business World

In the Moscow business world, assertiveness and patience are assets in meetings and negotiations. Although meetings should be arranged well in advance, it is not unusual for them to be rearranged with short notice. Punctuality is not as important as elsewhere, and side conversations in meetings are acceptable. If circumstances are favorable, business deals may be concluded extremely spontaneously. Expect things to go a lot slower, though, when dealing with government agencies.

Dress formally and conservatively while in Moscow. Pay attention to your shoes and make sure they are always polished. The shoes are what many Russians will look at first when sizing up a new acquaintance.

A thing which often confuses newcomers is the use of Russian names. Every person in Russia has three names: a first name, a patronymic (a middle name derived from the father’s first name), and a family name. In formal situations, people should be addressed by their title and last name. For closer acquaintances and business relations, however, calling someone by their first name and patronymic is both affectionate and polite.

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