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"I Have a Job, But . . .": A Review of Underemployment

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This article reviews the underemployment literature, providing a comprehensive integrative overview of underemployment research. Underemployment, which occurs when a worker is employed in a job that is inferior by some standard, is linked to a broad range of negative outcomes for employees. This article builds on Feldman’s 1996 model of underemployment and identifies relevant theoretical perspectives and dimensions of underemployment, as well as reviewing the empirical research on the relationships between underemployment’s antecedents and outcomes. Suggestions for future research are offered, with particular attention on career implications, the effects of underemployment on an employee’s identity, and the importance of “choice” for underemployed employees. Finally, recommendations for improving the methodological rigor of underemployment research are provided.
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Psychological and Physical Well-Being During Unemployment:
A Meta-Analytic Study
Frances M. McKee-Ryan
Oregon State University
Zhaoli Song and Connie R. Wanberg
University of Minnesota
Angelo J. Kinicki
Arizona State University
The authors used theoretical models to organize the diverse unemployment literature, and meta-analytic
techniques were used to examine the impact of unemployment on worker well-being across 104 empirical
studies with 437 effect sizes. Unemployed individuals had lower psychological and physical well-being
than did their employed counterparts. Unemployment duration and sample type (school leaver vs. mature
unemployed) moderated the relationship between mental health and unemployment, but the current
unemployment rate and the amount of unemployment benefits did not. Within unemployed samples,
work-role centrality, coping resources (personal, social, financial, and time structure), cognitive apprais-
als, and coping strategies displayed stronger relationships with mental health than did human capital or
demographic variables. The authors identify gaps in the literature and propose directions for future
unemployment research.
Job loss is a life event in which paid employment is involun-
tarily taken away from an individual. Unfortunately, the frequency
of job loss continued to occur during the robust economy of the
1990s and has increased since September 11, 2001. For example,
the unemployment rate in the United States jumped from 4.0% in
2000 to 6.0% in 2003 (U.S. Bureau of Labor Statistics, 2003b), and
the average duration of unemployment went from less than 13
weeks to over 19 weeks (U.S. Bureau of Labor Statistics, 2003c)
during the same period.
1
During the first 9 months of 2003, there
were 5,206 extended mass layoff events in the United States that
led to over a million separations (U.S. Bureau of Labor Statistics,
2003a). According to the U.S. Department of Labor Current Pop-
ulation Study conducted in January 2002, 4.0 million employees
lost long-tenured jobs between January 1999 and December 2001,
and nearly 30% of the reemployed displaced workers took a pay
cut of at least 20% in their new jobs (U.S. Bureau of Labor
Statistics, 2002). These trends highlight the importance of a close
examination of the unemployment experience.
A large number of narrative reviews have been written about the
experience of job loss from the perspective of those who have lost
a job (e.g., Hanisch, 1999; Latack, Kinicki, & Prussia, 1995; Leana
& Feldman, 1994; Platt, 1984; Wanberg, Kammeyer-Mueller, &
Shi, 2001; Winefield, 1995) and portray job loss as a highly
stressful experience that provokes reactions among those who lose
jobs such as anxiety, depression, and lowered physical health. Yet,
quantitative synthesis of the literature concerning the relationship
between unemployment and employee well-being has been ex-
tremely limited. A comprehensive meta-analysis is needed within
this field of research because the vast number of individual studies
that exist are subject to the effects of sampling error and artifacts
that can produce erroneous conclusions (Guzzo, Jackson, & Kat-
zell, 1987). This problem is exacerbated when researchers rely on
narrative reviews of a research literature.
The objectives of this study are to examine, through meta-
analytic methods, four central and important questions stemming
from the literature on job loss.
1. What is the average impact of unemployment on individ-
ual psychological and physical well-being?
2. How do individual levels of psychological and physical
well-being during unemployment relate to the probability
of reemployment?
3. What are the correlates of psychological and physical
well-being during unemployment?
1
Statistic is through November 2003, the latest data available.
Frances M. McKee-Ryan, Department of Management, Marketing, and
International Business, Oregon State University; Zhaoli Song and Connie
R. Wanberg, Department of Human Resources and Industrial Relations,
University of Minnesota; Angelo J. Kinicki, Department of Management,
Arizona State University.
Zhaoli Song is now at the Department of Management and Organiza-
tion, National University of Singapore, Singapore.
This research was supported in part by a grant to Angelo J. Kinicki from
the Dean’s Award for Excellence Summer Grant Program funded by the
Dean’s Council of 100, the Economic Club of Phoenix, and the Alumni of
the W.P. Carey School of Business at Arizona State University. Portions of
this research were presented at the Academy of Management Meeting,
Seattle, Washington, August 2003.
Correspondence concerning this article should be addressed to Frances
M. McKee-Ryan, College of Business, Oregon State University, 200
Bexell Hall, Corvallis, OR 97331–2603. E-mail: fran@bus.oregonstate.edu
Journal of Applied Psychology Copyright 2005 by the American Psychological Association
2005, Vol. 90, No. 1, 53–76 0021-9010/05/$12.00 DOI: 10.1037/0021-9010.90.1.53
53
4. Are study characteristics such as the current unemploy-
ment rate, level of unemployment protection, average
duration of unemployment in the sample, or the type of
sample related to the association found between well-
being and unemployment?
To complement our meta-analytic effort, we provide predictor and
criterion taxonomies to organize the results. Finally, we compare
our findings with past research to identify gaps in the literature and
to propose directions for future research.
Unemployment and Psychological and Physical
Well-Being
Criterion Taxonomy
Researchers have examined a wide variety of psychological
(e.g., hostility, depression, frustration, anger, guilt, worry, anxiety,
psychiatric disorders, suicide, and changes in emotional state or in
life or career satisfaction) and physical (e.g., perceived health and
cardiovascular, immunological, gastrointestinal, biochemical, and
physical disease) health variables in their examinations of the
effects of job loss (DeFrank & Ivancevich, 1986; Hanisch, 1999).
The wealth of constructs examined in past research necessitates a
discussion of criterion taxonomies that can be used to summarize
research using a variety of psychological and physical measures.
To impose order on the vast array of scales and measures of
psychological outcomes used in the unemployment literature, we
relied on Diener, Suh, Lucas, and Smith’s (1999) seminal discus-
sion of the components of subjective well-being. Diener et al.
conceptualized psychological or subjective well-being as a broad
construct, encompassing four specific and distinct components
including (a) pleasant affect or positive well-being (e.g., joy,
elation, happiness, mental health), (b) unpleasant affect or psycho-
logical distress (e.g., guilt, shame, sadness, anxiety, worry, anger,
stress, depression), (c) life satisfaction (a global evaluation of
one’s life), and (d) domain or situation satisfaction (e.g., work,
family, leisure, health, finances, self).
Although measures of pleasant affect (or positive well-being)
and negative affect (or psychological distress) are highly corre-
lated, several studies support the two dimensions as distinct (Lu-
cas, Diener, & Suh, 1996; Veit & Ware, 1983). Complicating the
distinction between positive and negative aspects of well-being,
however, is the fact that a large number of measures contain
aspects of both (Diener et al., 1999). For example, a scale widely
used to assess psychological well-being in the unemployment
literature, the General Health Questionnaire (GHQ–12; Goldberg,
1972), contains items that tap both positive and negative affect
(such as feeling happy and feeling under strain, respectively).
Because of the small number of studies across different psycho-
logical well-being correlates and the use of measures that do not
cleanly distinguish between positive and negative affect, we col-
lapsed across positive and negative well-being measures to portray
a broader, higher order affective dimension of well-being. We
labeled this dimension mental health. Life satisfaction and domain
satisfaction were analyzed separately, where measured.
Physical well-being measures were categorized as being subjec-
tive (involving self-reports of physical symptoms) or objective
(involving measurements of more objective medical indices) in
nature. Subjective physical health assessments typically include
inquiries about the extent that one has either specific (e.g., head-
aches, backaches) or general (e.g., number of days not feeling
well) health complaints (e.g., Schwarzer, Jerusalem, & Hahn,
1994) or diminished physical functioning (e.g., Gallo, Bradley,
Siegel, & Kasl, 2000). Such subjective health assessments are
important, as some symptoms cannot be observed by others and
can only be understood by asking the individual (Sherbourne,
Allen, Kamberg, & Wells, 1992). Objective physical health as-
sessments include assessments of indices such as blood pressure
(e.g., Bailey, 1984), salivary cortisol (Grossi, Ahs, & Lundberg,
1998), or serum uric acid (Cobb, 1974). Although assessments of
subjective physical health are more common in the unemployment
literature, we differentiate when possible between subjective and
objective indices in our meta-analysis.
Past Research
Research conducted at both aggregate and individual levels has
been suggestive that unemployment, on the average, has a negative
impact on individuals’ psychological and physical well-being. The
aggregate-level studies have portrayed a positive relationship be-
tween unemployment rates and indices such as mortality, heart
disease, mental health, heavy drinking, and the use of mental
health services (Jin, Shah, & Svoboda, 1995). These studies have
been criticized on a number of counts, including their inability to
allow generalization to an individual level. For example, if a
positive relationship between suicides and unemployment rate is
observed, it is impossible to determine if it is those who are
unemployed who are committing suicide (for an excellent discus-
sion, see Dooley & Catalano, 1988). Individual-level studies, the
focus of our study, are nevertheless similarly suggestive of the
relationship between unemployment and reduced well-being.
Three types of individual-level studies have been supportive of the
negative impact of unemployment (Wanberg et al., 2001). First,
cross-sectional studies have shown that unemployed groups tend to
have lower levels of psychological and physical well-being than
employed groups. Second, longitudinal studies have followed in-
dividuals over time from unemployment back into employment,
showing increases in psychological and physical well-being
among those who become reemployed. Last, studies have also
followed individuals over time from employment into unemploy-
ment (showing decreases in psychological and physical well-being
among displaced workers).
Cross-sectional comparisons of unemployed and employed in-
dividuals provide highly useful information regarding the associ-
ation between unemployment and individual well-being. We can-
not, however, make causal inferences from this type of data. A
researcher, for example, who finds an unemployed group with
lower mental health than an employed comparison group cannot
conclude that the lower mental health is a consequence of the
unemployment. Instead, it may be the case that individuals with
lower mental health are more likely to lose their jobs or that
individuals with higher mental health are more likely to find new
jobs (e.g., Mastekaasa, 1996; Warr, Jackson, & Banks, 1988). This
concern has been termed “selection bias” (Kessler, Turner, &
House, 1987) or just “selection” (Claussen, Bjørndal, & Hjort,
1993) in the literature. Longitudinal studies have the advantage of
following and comparing the same individuals over time. One
54 MCKEE-RYAN, SONG, WANBERG, AND KINICKI
disadvantage with these studies relates to a different type of
selection effect, subject mortality (Cook & Campbell, 1976). That
is, it is possible that study dropouts are individuals who do not
experience mental health declines during unemployment (Graetz,
1993). It also is plausible that individuals who stay in longitudinal
studies are those experiencing more anxiety and fear and that they
complete surveys because it gives their pain a voice. Finally, it is
possible that longitudinal studies are coincidentally conducted
over times where there are natural, seasonal changes in well-being
(e.g., improvements in well-being could naturally occur as indi-
viduals move from winter months into spring months; e.g., see
Cook & Campbell, 1979). Thus, the findings from most studies of
job loss and unemployment must be tempered by acknowledging
the threats to internal and external validity in their research designs
(cf. Cook & Campbell, 1976). Given these potential weaknesses, it
is important to collect evidence from a variety of research ap-
proaches to uncover patterns and trends regarding the relationship
between mental health and unemployment.
A preliminary meta-analysis based on nine individual-level lon-
gitudinal studies conducted between 1986 and 1996 provided
initial information about the potential effect size of unemployment
on mental health (Murphy & Athanasou, 1999). Specifically, a
weighted effect size of .36 (k5) was calculated for mental health
changes associated with moves from employment to unemploy-
ment, representing a decrease in mental health. A weighted effect
size of .54 (k7) was calculated for moves from unemployment
into employment, demonstrating an increase in mental health. Our
meta-analysis draws on a much more extensive literature base and
has a broader focus than that study. For example, to allow exam-
ination of the relationship between unemployment and well-being,
we include additional longitudinal studies as well as a wealth of
available cross-sectional comparisons between unemployed and
employed individuals. We also investigate correlates of mental
health during unemployment as well as the relationship between
well-being during unemployment and the probability of reemploy-
ment. Our outcome measures across these analyses include, where
available, not only mental health but a broader domain of well-
being outcomes including life satisfaction, domain satisfaction,
and physical health. Finally, unlike Murphy and Athanasou (1999)
who did not correct for measurement error (i.e., unreliability), our
effect size calculations were corrected for unreliability in both the
predictors and criteria.
Warr (1987) and Jahoda (1979) provided theoretical explana-
tions for why unemployment may negatively impact individuals’
well-being. Warr (1987) proposed that unemployment leads to
negative psychological and physical outcomes because unem-
ployed individuals do not experience nine positive benefits asso-
ciated with employment: opportunity for control, opportunity for
skill use, externally generated goals, variety, environmental clar-
ity, availability of money, physical security, opportunity for inter-
personal contact, and valued social position. Jahoda (1982) simi-
larly concluded that job loss spawns negative outcomes because
unemployed individuals are less likely to experience a host of
positive manifest and latent consequences associated with work-
ing. She suggested that employment imposes a time structure on
the day, allows individuals to socialize with others, provides
people with a sense of purpose, allows individuals increased status,
and encourages activity. Research partially supported the above
propositions (Kinicki, Prussia, & McKee-Ryan, 2000; Wanberg,
1995; Winefield, Winefield, Tiggemann, & Goldney, 1991). On
the basis of the discussion above, we expect to find lower levels of
well-being among unemployed individuals in comparison with
employed individuals and strive to document the effect size of this
relationship in (a) cross-sectional comparisons of unemployed and
employed individuals, (b) longitudinal studies that follow individ-
uals from unemployment back into employment, and (c) longitu-
dinal studies that follow individuals from employment into
unemployment.
Psychological and Physical Well-Being and
Reemployment
Many studies have sought to examine the effects of unemploy-
ment on individuals’ psychological well-being, with fewer focused
on physical well-being. A small number of studies have examined
the relationship between displaced workers’ psychological and
physical well-being and their reemployment probability. A narra-
tive assessment of available studies yields mixed findings. Claus-
sen et al. (1993) found that among a random sample of registered
unemployed workers in Norway, those who performed normally
on mental distress tests and medical diagnoses had an increased
chance of reemployment. These results were supported by a recent
study conducted in the Netherlands (Taris, 2002), in which higher
mental health was related to reemployment probability among 98
unemployed adults. Contrasting findings, however, were reported
by Warr and Jackson (1985); Kessler, Turner, and House (1989);
and Schaufeli and van Yperen (1993). Although empirical results
are mixed, theoretical analysis suggests a positive relationship
between well-being during unemployment and reemployment
probability. Taris (2002), for example, explained the selection to
reemployment by using life-span developmental theory, suggest-
ing that poor mental health may deteriorate the capacity of unem-
ployed workers to actively shape their environment and may
reduce their job search intention and behavior, thus lowering their
reemployment probability.
The psychological impact of unemployment may also manifest
itself in physiological outcomes. For example, Grossi et al. (1998)
examined psychological variables and cortisol levels in response to
a stressful activity among a group of long-term unemployed per-
sons. They identified a group of “exhausted” employees who had
high levels of depression, irritability, and anxiety and low mastery
and who exhibited low reactivity to stressors in terms of cortisol
excretions. Moreover, unemployed individuals report greater phys-
ical illness and health complaints (e.g., Schwarzer et al., 1994;
Turner, 1995), and they are more likely to engage in high-risk
health behaviors such as using alcohol (e.g., Catalano, Dooley,
Novaco, Wilson, & Hough, 1993; Claussen, 1999; Rasky, Stro-
negger, & Freidl, 1996; Viinama¨ki, Koskela, & Niskanen, 1993).
We thus make the argument that individuals with poor physical
health may encounter constraints that cause them to have difficul-
ties searching for and obtaining employment. In our meta-analysis,
we compute and report the average relationship between psycho-
logical and physical well-being and reemployment probability
across studies completed to date.
Correlates of Well-Being During Unemployment
Leana and Feldman (1994) noted, “While virtually all termi-
nated employees suffer some negative consequences from job loss,
55
WELL-BEING DURING UNEMPLOYMENT
there is also substantial variance among the unemployed in the
degree to which they respond negatively to a termination” (p. 279).
Indeed, our literature search uncovered over 100 different vari-
ables that were correlated with various indicators of psychological
and physical well-being following job loss. Given both the diver-
sity and the number of predictors of psychological and physical
well-being associated with job displacement, it was necessary to
consolidate the database by using a theoretical taxonomy to create
broad correlate categories (see Kanfer, Wanberg, & Kantrowitz,
2001, and Kinicki, McKee-Ryan, Schriesheim, & Carson, 2002,
for similar approaches).
We derived our taxonomy from McKee-Ryan and Kinicki’s
(2002) life-facet model of coping with job loss. These researchers
sought to explain the variability and process of reactions to job loss
by means of a coping–stress framework. From their model, we
glean five important correlate, or predictor, categories that have
been sufficiently studied to warrant meta-analytic review and
discussion: (a) work-role centrality, (b) coping resources, (c) cog-
nitive appraisal, (d) coping strategies, and (e) human capital and
demographics (see Figure 1). Whereas their model—as well as
general theories of stress and coping—suggests that appraisal and
coping mediate the effect of work-role centrality, coping re-
sources, and human capital and demographics on individual well-
being, we meta-analytically examine the direct relationship be-
tween each variable set and well-being because of the limited
number of studies that include measures of cognitive appraisal or
coping strategies. In the following sections, we describe each of
the correlate categories depicted in Figure 1.
Work-Role Centrality
Work-role centrality—also referred to as work involvement,
employment commitment,employment value, and career commit-
ment—indicates the general importance of the work role to an
individual’s sense of self. The notion of work-role centrality is
conceptually distinct from the constructs of job involvement or
organizational commitment, which denote an individual’s orienta-
tion toward a specific job or organization rather than work in
general (S. P. Brown, 1996; Kanungo, 1982). Work-role centrality
may stem from Protestant-work-ethic socialization or simply from
a belief that work is central to one’s life and satisfaction (Kanungo,
1982). Because individuals with high work-role centrality find the
work role as providing meaning and fulfillment, the absence of
work for these individuals has been proposed by many authors to
lead to lower psychological and physical well-being (e.g., Ash-
forth, 2001; P. R. Jackson, Stafford, Banks, & Warr, 1983; Kin-
icki, 1989).
Coping Resources
Coping resources consist of individual characteristics (internal)
and environmental objects or conditions (external) a person can
Figure 1. Contributing elements to psychological and physical well-being following job displacement.
56 MCKEE-RYAN, SONG, WANBERG, AND KINICKI
draw on to cope with involuntary job loss (Latack et al., 1995;
Lazarus & Folkman, 1984). They represent a repertoire of aids a
person can use in a stressful situation. As such, coping resources
are expected to reduce the negative effects of involuntary job loss.
McKee-Ryan and Kinicki (2002) identified three types of coping
resources (personal, social, and financial) that are particularly
relevant for coping with job displacement. We add to this a fourth
resource—time structure—that has been theoretically and empiri-
cally shown (e.g., see Hepworth, 1980; Wanberg, Griffiths, &
Gavin, 1997) as relevant to well-being during unemployment.
Personal Resources
Personal resources are “internal resources upon which an indi-
vidual may draw to cope” with stressful life events (McKee-Ryan
& Kinicki, 2002, p. 18). Consistent with McKee-Ryan and Kinicki
(2002), our review of the literature revealed that the personal
resources that have been theorized and studied in relation to
psychological and physical well-being during unemployment were
those that related to individuals’ self-perceptions of worth, per-
ceived control over life events, and various affective disposi-
tions—all components of core self-evaluations (Judge, Locke, &
Durham, 1997). Core self-evaluations refer to a highly correlated
constellation of personality traits pertinent to individuals’ funda-
mental evaluations of themselves in comparison to others (Judge et
al., 1997). The specific personality traits that have been concep-
tualized as components of core self-evaluation include self-esteem,
locus of control, generalized self-efficacy, and emotional stability
(represented by low neuroticism or negative affectivity), although
positive affectivity has also loaded on a common core self-
evaluation factor (Judge, Locke, Durham, & Kluger, 1998). Core
self-evaluation components, because they are fundamental to in-
dividuals’ self-appraisals of their worth and capabilities, have been
conceptualized and supported as important to individuals’ psycho-
logical and physical well-being. For example, individuals with
higher self-esteem, higher perceived control, and higher levels of
optimism generally have higher levels of mental health and cope
more effectively with a variety of stressful life events (e.g.,
Armstrong-Stassen, 1994; Aspinwall & Taylor, 1992). Because the
components of core self-evaluation have been shown to be highly
correlated and representative of a general core self-evaluation
factor (Erez & Judge, 2001; Judge et al., 1998), and owing to the
need to collapse across individual core self-evaluation constructs
that have been studied in isolation as correlates of well-being
during unemployment, we collapsed constructs that are conceptu-
ally relevant to core self-evaluation into an overarching core
self-evaluation category.
Social Resources
Social resources are an external coping resource derived through
social interactions and social support. Lazarus and Folkman (1984)
concluded that social resources contribute to psychological and
physical well-being in two different ways. First, social-network
embeddedness helps people feel good about themselves and their
lives, which in turn enhances displaced workers’ propensity to
maintain a positive outlook during unemployment. Second, social
resources serve to buffer stress and its destructive somatic conse-
quences. Two different meta-analyses supported these proposi-
tions. Pinquart and So¨rensen (2000) found that the quality of social
contacts was positively related to subjective well-being, and
Viswesvaran, Sanchez, and Fisher’s (1999) results revealed that
social support mitigated the perceptions of stressors and the re-
ported strains experienced at work. Kinicki et al.’s (2000) findings
further showed that displaced workers’ social resources were de-
pleted during periods of unemployment and were replenished after
becoming satisfactorily reemployed. This finding suggests that
length of unemployment may moderate the relationship between
social resources and psychological and physical well-being.
Social undermining (otherwise known as negative social sup-
port or social hindrance) has also been examined as a negative
social resource that has an impact independent of the absence of
social support (Vinokur, Price, & Caplan, 1996; Vinokur & van
Ryn, 1993). Vinokur and van Ryn (1993) conceptualized social
undermining as involving behaviors toward an individual that
involve anger, dislike, or criticism or that hinder the individual’s
attainment of desired goals. Social undermining has been nega-
tively linked to well-being both within (Vinokur et al., 1996;
Vinokur & van Ryn, 1993) and outside (Abbey, Abramis, &
Caplan, 1985) of the job-loss domain.
Financial Resources
Financial resources refer to the extent to which an individual has
access to adequate household income, cash reserves or savings,
liquid assets, or severance pay following displacement. Jones
(1991–1992) suggested that “availability of income may be the
most important determinant of the expression of psychological and
health symptoms” (p. 50) following job loss. This may be the case
because possessing financial resources improves access to other
important resources, such as social and leisure activities, food,
housing, and general physical security (Hobfoll, Freedy, Green, &
Solomon, 1996; Ullah, 1990).
A construct related to financial resources, but yet distinguish-
able, is perceived financial strain. Perceived financial strain,
sometimes labeled perceived financial hardship, has been exam-
ined by asking respondents to indicate how worried they are about
their financial situation or how difficult it is to meet expenses (e.g.,
see Ullah, 1990; Vinokur & van Ryn, 1993). Perceived financial
strain was moderately correlated with objective financial resources
(e.g., Vinokur et al., 1996, reported a correlation of .39 between
the two types of measures). This may occur because individuals
with the same level of financial resources can vary in terms of
either their financial obligations or their appraisal of the situation.
As might be expected given the positive relationship between
financial resources and well-being, research has portrayed a neg-
ative relationship between perceived financial strain and well-
being during unemployment (e.g., Creed & Macintyre, 2001;
Feather, 1989; Vinokur & Schul, 2002). However, studies that
included assessments of both types of financial measures generally
found that perceived-financial-strain measures were more highly
correlated with well-being than objective measures of financial
resources (Ullah, 1990). Two individuals may have the same level
of objective financial resources but may have varying levels of
financial obligations. In our meta-analysis, we expect measures of
perceived financial strain to be more strongly related to well-being
than are measures of financial resources.
57
WELL-BEING DURING UNEMPLOYMENT
Time Structure
An individual’s level of time structure is another coping re-
source that has been examined in relation to psychological and
physical health during unemployment. Some unemployed individ-
uals, for example, are able to organize their time, keep routines,
feel their time has a sense of purpose, avoid excessive contempla-
tion of the past, and persist at their activities while others are not
(Feather & Bond, 1994). Higher time-structure levels are influ-
enced both by the individual (e.g., through one’s characteristic
approach toward time, routine, and purposeful activity) and by his
or her situation (e.g., through obligations such as child care or
other activities that impute purpose or structure into the day). We
expect to find a positive relationship between time structure and
well-being based on Warr’s (1987) vitamin model and Jahoda’s
(1982) deprivation theory of employment. These authors proposed
that the daily routines and sense of purpose associated with work-
ing provide positive manifest and latent consequences and that
positive health consequences should occur when unemployed in-
dividuals’ lives approximate the employment experience with a
scheduled routine full of purposeful activity.
Cognitive Appraisal
Individuals vary in how they interpret job loss (Warr et al.,
1988), and cognitive appraisal (i.e., an individual’s affective in-
terpretation of being displaced) captures this variation. Cognitive
appraisals evaluate environmental demands in terms of their rele-
vance to an individual’s well-being and are categorized as harm/
loss, threat, or challenge (Lazarus & Folkman, 1984). Stress ap-
praisals signify negative evaluations and are negatively related to
psychological and physical well-being. Further, general models of
stress and coping (Lazarus & Folkman, 1984) and specific models
of coping with job loss (Latack et al., 1995; McKee-Ryan &
Kinicki, 2002) are based on the notion that appraisals partially
mediate relationships between work-role centrality, coping re-
sources, and human capital and psychological and physical
well-being.
Self-attributions about the responsibility held for one’s job loss
as well as an individual’s expectations for reemployment also
represent forms of cognitive appraisal that are expected to be
relevant to well-being during unemployment. A longitudinal study
by Prussia, Kinicki, and Bracker (1993), for example, showed that
internal attributions for job loss were negatively associated with
affective consequences (a latent construct indicated by life satis-
faction and self-esteem), expectations about becoming reem-
ployed, and reemployment. Miller and Hoppe’s (1994) results
similarly revealed that variability in psychological consequences
was predicted by displaced workers’ attributions for being termi-
nated. Finally, Wanberg (1997) found that lower levels of situa-
tional control (expectations for reemployment) were related to
lower levels of mental health among a sample of unemployed
individuals.
Coping Strategies
Coping strategies are defined as cognitive and behavioral efforts
to manage the internal and external demands associated with
person–situation transactions that tax or exceed a person’s re-
sources (Folkman, Lazarus, Dunkel-Schetter, DeLongis, & Gruen,
1986). Occurring after the cognitive appraisal process, coping is
typically classified into two general categories: problem/control-
focused coping and emotion/symptom-focused coping (Latack &
Havlovic, 1992). Problem/control-focused coping attempts to re-
solve the “root” cause of a stressful situation, whereas emotion/
symptom-focused coping is aimed at managing the emotional
response to a stressor (Lazarus & Folkman, 1984).
One form of problem/control-focused coping during job loss—
active job search—is particularly critical for unemployed individ-
uals who wish to obtain a job. Meta-analytic results show that
active job search is an important predictor of reemployment (Kan-
fer et al., 2001). At first blush, it might be assumed that job-
seeking activity is associated with increased psychological health
during unemployment because job-seekers feel as if they are doing
something proactive to become reemployed. Yet, research sug-
gests higher job-seeking activity among laid off individuals is
instead associated with decreased psychological health (e.g., see
Wanberg, 1997). Specifically, job-seeking is often a discouraging
process, replete with rejections and uncertainty. Warr et al. (1988)
noted that job-seeking has a particularly negative impact on psy-
chological health for unemployed individuals who continue to look
for work without success. Those who engage a proactive job
search process may also feel pressure to accept any job they are
offered and may settle for a low-quality job (cf. Kinicki et al.,
2000). On the basis of the preceding discussion, we expect a
negative relationship between job search and well-being.
Other forms of coping include problem/control-oriented strate-
gies, such as working on finances, reviewing job skills and qual-
ifications, relocating, and enrolling in training programs, and
emotion/symptom-related strategies, such as seeking social sup-
port or reminding oneself that job loss is not the end of the world
(e.g., Kinicki & Latack, 1990; Leana & Feldman, 1990, 1992).
These coping strategies have been argued to help reduce stress
during unemployment. Recent research, however, has noted that
the relationship between coping strategies and well-being is com-
plex and reciprocal. For example, an individual’s reduced well-
being may increase a coping response. Conversely, a positive
relationship may be found because the person’s coping behavior
improves well-being over time (cf. Cooper, Dewe, & O’Driscoll,
2001). Put another way, lower well-being may be argued to either
stimulate coping or to stall coping in the case of an individual
impaired enough to be unable to mobilize their coping. Moreover,
the relationship may depend on an individual’s coping goal
(Latack et al., 1995; Prussia, Fugate, & Kinicki, 2001). All told,
however, we draw on Lazarus and Folkman’s (1984) original
notion that coping improves well-being, and we expect that the
coping strategies will be positively related to well-being.
Human Capital and Demographics
Human capital is the productive potential of an individual’s
knowledge and actions (Bartlett & Ghosal, 2002). Dimensions of
human capital (e.g., education, ability, occupational status) have
been examined in relation to psychological and physical well-
being following job loss, most often as control variables. The
reasoning behind their inclusion in job-loss studies is that human
capital is highly relevant to individuals’ chances of reemployment
(Kanfer et al., 2001) and their cognitive appraisals of job loss. For
58 MCKEE-RYAN, SONG, WANBERG, AND KINICKI
example, individuals with higher education may have more posi-
tive expectations about their chances of finding a satisfactory job,
thus easing their anxiety during unemployment (e.g., Price &
Fang, 2002).
Research has included a number of additional demographic
variables in job-loss research, including marital status, number of
dependents, gender, race, and length of unemployment at the time
of the study. These variables are included variously because of
their theoretical relevance to individuals’ coping strategies, well-
being, and probability of reemployment or to other outcomes or
predictors that have been included in job-loss studies. For exam-
ple, marital status and number of dependents have been included as
control variables because they are relevant to unemployed indi-
viduals’ financial situation and support structure and because they
help parse out the explanatory effects of other variables (e.g., see
Vinokur & Schul, 2002; Wanberg, Carmichael, & Downey, 1999),
with the thought being that married individuals experience higher
levels of well-being and that individuals with more dependents
experience lower well-being. Gender also was used as a variable of
interest on the basis of (a) the premise that women rely on different
types of coping behaviors (e.g., may use more symptom-focused
coping) than men or (b) the possibility that women experience job
loss as a less serious blow to their central identity than men (Leana
& Feldman, 1991). Race has been included as a control variable
primarily because of higher unemployment rates, lower average
skill and education levels, and possible discrimination issues
among some minority groups (e.g., see D. R. Brown & Gary, 1985;
Elvira & Zatzick, 2002; Moss & Tilly, 2001). Finally, length of
unemployment has been examined to assess the extent to which
well-being is associated with how long individuals have been
without their job. Conceivably, duration of unemployment is neg-
atively related to mental health because of a cumulative stress
factor (P. R. Jackson & Warr, 1984) or because of anxiety stem-
ming from the limited duration of unemployment benefits. These
demographic variables are often relevant to well-being during job
loss because of their association with other variables.
There are conflicting theoretical propositions regarding the re-
lationship between age and well-being. For example, older indi-
viduals who desire to find employment may face real or perceived
job discrimination or possess outdated skills. Furthermore, while
early retirement may be a possible option for such individuals, a
retirement decision is not an easy one and involves its own
transition (Hanisch, 1999). Older individuals also are at higher risk
for other negative life events that may lower their mental health,
such as poor physical health or deaths of friends (e.g., Pinquart,
2001). In contrast to these arguments, Kunzmann, Little, and
Smith (2000) suggested that older individuals are better able to
accept disappointment and to maximize their positive affect than
younger individuals. They proposed that studies examining age as
a correlate of psychological well-being must recognize that older
individuals’ life experiences vary and that it makes sense to attend
to their situations rather than their age. Pinquart (2001) further
noted that older individuals experience lower levels of objective
physical health without always reporting lower levels of subjective
physical health, owing to health comparisons with their peers.
Consistent with these conflicting theoretical expectations, em-
pirical findings between age and mental health among unemployed
workers are scattered. The relationship was found to be nonsig-
nificant (e.g., Baik, Hosseini, & Priesmeyer, 1989; Creed, 1999;
Creed, Muller, & Machin, 2001; Hepworth, 1980; Ullah, 1990;
Vuori, Silvonen, Vinokur, & Price, 2002; Wanberg, 1997; Wiener,
Oei, & Creed, 1999), negative (e.g., Kemp & Mercer, 1983;
Reynolds & Gilbert, 1991; Wanberg et al., 1999), and positive
(e.g., P. R. Jackson & Warr, 1984; Macky, 1984). The relationship
between age and physical health has rarely been examined in the
unemployment context (for an exception, see P. R. Jackson &
Warr, 1984).
Potential Moderators
On the basis of past theoretical and empirical research, we
uncovered four potential moderator variables for which there was
sufficient information to conduct tests for moderation across stud-
ies of the relationship between unemployment and well-being: the
unemployment rate in the study location, the level of unemploy-
ment insurance protection or benefits provided to unemployed
workers, the average length of unemployment of individuals in the
sample, and the type of sample (school leavers or unemployed
adults).
Unemployment Rate
The current unemployment rate provides an indicator of the
economy’s general well-being (Dooley & Catalano, 1984). Turner
(1995) argued that the “implications of losing a job in an area with
high unemployment rates and minimal reemployment opportuni-
ties are undoubtedly different from those involved with being
jobless during a time in which opportunities for reemployment are
plentiful” (p. 214). On one hand, workers displaced during a time
of high unemployment may experience less stress because they are
able to attribute their bad situation to external causes. These
dislocated workers could have less self-blame, thus diminishing
the impact of unemployment on their morale and well-being.
However, it seems much more plausible that a high unemployment
rate is more stressful for job seekers since it makes the job search
much more difficult and diminishes the likelihood of finding
reemployment. Two studies support this latter conclusion, with
results of lower mental health for unemployed individuals when
unemployment rates were high (Cohn, 1978; Turner, 1995). An-
other study failed to show significant differences for unemployed
individuals but found lower levels of psychological well-being for
all study participants (Dooley, Catalano, & Rook, 1988). This
discussion leads us to expect that results will show that the impact
of job loss is greater with high unemployment rates.
Unemployment Protection
Unemployment benefits are government-subsidized social-security
programs to financially support the living and job search of unem-
ployed individuals for a certain time period. Unemployment-benefit
systems vary considerably throughout the world, in terms of cov-
erage, source of funds, qualifying conditions, benefit amount,
duration of coverage, and the like (Social Security Administration
Division of Research and Statistics, 1999). The economic literature
indicates that indices of unemployment protection—such as
unemployment-insurance wage-replacement ratio and coverage
duration—are related to the duration of unemployment (Atkinson
& Micklewright, 1991; Barron & Gilley, 1979). Though seldom
59
WELL-BEING DURING UNEMPLOYMENT
mentioned in the psychological literature, unemployment protec-
tion systems are a possible factor in explaining differential rela-
tionships between unemployment and well-being observed in stud-
ies conducted in various countries (cf. Murphy & Athanasou,
1999).
For example, Schaufeli and van Yperen (1993) found no differ-
ences in psychological distress among unemployed and employed
workers in the Netherlands (a country with generous unemploy-
ment benefits). In contrast, a study of unemployed women in Hong
Kong (with limited unemployment insurance benefits) found that
over half of the sample could be classified as “probable clinical
cases” (Lai, Chan, & Luk, 1997). These researchers attributed this
level of distress to a lack of unemployment benefits. Therefore, it
seems plausible that unemployed individuals in countries provid-
ing more generous unemployment benefits (higher replacement
rate and longer coverage duration) on average experience less
economic pressure than those in less generous countries and thus
have higher well-being during unemployment. On the basis of the
above discussion, we expect to find that the impact of unemploy-
ment is lower with higher levels of unemployment protection.
Length of Unemployment
How long the average participant in the sample has been unem-
ployed at the time of the study may be another important moder-
ator. Being without a job for longer may allow stress to accumulate
(P. R. Jackson & Warr, 1984) as coping resources are depleted
(e.g., Kinicki et al., 2000) and anxiety and tension to mount from
the prospect of unemployment benefits running out and savings
being exhausted. Thus, the financial detriment of job loss increases
as unemployment duration extends (e.g., Brief, Konovsky, Good-
win, & Link, 1995; Huang & Perrucci, 1994; Kinicki et al., 2000;
Sales, 1995). For example, Kinicki et al. (2000) found that finan-
cial strain increased and that unemployed workers displayed more
coping behaviors as unemployment persisted over time. These
considerations lead us to expect that studies that have individuals
with longer unemployment duration will also have individuals
reporting lower well-being.
Sample Type
Most studies about unemployment and well-being have con-
cerned unemployed adults. However, there are several studies,
most conducted in Australia (e.g., Feather & O’Brien, 1986;
Tiggemann & Winefield, 1984) and the United Kingdom (e.g.,
P. R. Jackson et al., 1983; Layton, 1986), that have involved young
adult “school leavers” (those who are jobless after leaving public
school). Unemployment may have a different impact on school
leavers than on adults (Donovan & Oddy, 1982; O’Brien, 1986).
For example, most school leavers are not married and live with
their parents and subsequently have fewer financial obligations
than their traditional adult unemployed counterparts (O’Brien,
1986). However, because most school leavers have no previous
employment history, they also lack the well-established occupa-
tional identity that most unemployed workers have. The pressure
to establish such an identity is an extra burden for school leavers
when they become unemployed (Donovan & Oddy, 1982), and this
pressure for identity formation may manifest in diminished psy-
chological well-being. We therefore expect that the impact of
unemployment may be lower for adult samples than for young
adult school leavers.
Method
Data Collection
Articles were identified for potential inclusion in the meta-analysis by
conducting electronic searches of computerized databases using the key
words job loss,unemployed,layoff and employee,layoff and unemploy-
ment,laid off worker,job displacement, and dislocated worker in the
ABI/Inform (1985–2002) and PsycINFO (1887–2002) databases. In addi-
tion, we conducted a manual search of our respective files to identify
studies that did not appear in the electronic searches. We also made
attempts to get all relevant non-U.S. articles by reviewing abstracts. Those
written in English or having English translations available were reviewed.
These processes resulted in the identification of approximately 5,010
articles. This set of articles was then screened for relevance to this review
by the first two authors.
Studies were excluded if they were not empirical (e.g., several were
practitioner-oriented or offered advice to unemployed workers), if they
were not published in a refereed journal (i.e., no dissertations or book
chapters), if they were not directly related to job loss or unemployment,
and if they did not examine at least one mental or physical health variable
and report zero-order correlations or a statistic that could be converted
(e.g., F, t,
2
). An article was discussed until a consensus was reached. This
process resulted in the inclusion of 104 studies with 146 independent
samples.
Developing Broad Coding Categories
Our literature search revealed a multitude of variables related to the
unemployment experience, including over 100 potential predictor and
criterion variables and a total of 737 correlations. Given the diversity of
these variables, it was necessary to consolidate the database by developing
broad categories of variables (James & James, 1989; see both Kanfer et al.,
2001, and Kinicki et al., 2002, for similar approaches). To reduce the 100
variables into meaningful homogeneous constructs, we started with the
frameworks outlined in the literature review (i.e., Diener et al., 1999;
McKee-Ryan & Kinicki, 2002) along with past research, underlying the-
ory, and variable measures to develop construct definitions. We then
consulted the original-source articles for construct definitions and to ex-
amine item content and worked collaboratively to categorize variables into
overarching constructs for use in the meta-analysis. If there was not a clear
case for combining variables, they were left independent. Discussions
continued until 100% agreement was reached.
This process resulted in identifying a set of 5 outcome variables (3
psychological and 2 physical well-being outcomes) and 22 predictor vari-
ables for use in the meta-analysis. These categories are summarized in
Table 1. The final variable set included 437 correlations. When there were
multiple measures for a variable within a study, correlations were averaged
to prevent double counting. When necessary, we reversed correlation signs
for consistency purposes. All variables ultimately were coded such that a
higher number reflects more of the variable as defined by a category. For
example, mental health includes measures of depression and anxiety. These
variables were recoded to reflect positive rather than negative mental
states.
Special coding situations were discussed carefully among the authors.
For example, there were a small number of situations with more than one
unemployed group compared with an employed group (e.g., Creed &
Reynolds, 2001). To prevent double counting of the employed sample, we
first weighted means and standard deviations of different unemployed
groups by their corresponding sample sizes to form one set of statistics for
the total unemployed group, then the pooled unemployed group was
60 MCKEE-RYAN, SONG, WANBERG, AND KINICKI
compared with the employed group to create a single effect size. As an
additional example, some studies reported both cross-sectional and longi-
tudinal information about the health status of unemployed and employed
(reemployed) individuals. In this case, we included relevant data for each
type of analysis (e.g., cross-sectional unemployed vs. employed compari-
son, changes in well-being following movement from unemployment into
employment, predicting Time 2 employment status from Time 1 well-
being, etc.).
Meta-Analytic Procedures
Our meta-analytic method was based on Hunter and Schmidt’s (1990)
work. We began by converting effect sizes to a common statistic (dor r;
see Arthur, Bennett, & Huffcutt, 2001, p. 162, for a complete list of
transformation formulas). Then, sample-size weighted mean effect sizes
were computed. The next step was to calculate the corrected mean effect
size by adjusting for measurement error. Because reliabilities were not
reported in all studies, mean reliability for each variable was used to correct
for measurement error. Table 2 presents the reliability distributions of the
measures. No estimates of reliability were reported for objective physical
health, financial resources, demographics, and employment status. The
reliability of each of these variables was assumed to be 1.0, leading to
conservative mean effect-size estimates for relationships with these vari-
ables. No adjustment was made for the two studies (Vinokur, Price, &
Caplan, 1996; Vinokur & van Ryn, 1993) that reported correlations for
social undermining because the authors had already adjusted for measure-
ment error. The final step in the analysis was calculating confidence
intervals and testing for moderated relationships. For the meta-analysis of
r, a formula provided by Osburn and Callender (1992, p. 116, Equation 5)
was used to compute the confidence intervals. A similar formula provided
by Hunter and Schmidt (1990, p. 430) was used for the meta-analysis of d,
which reflects the difference of the group means of the unemployed and the
employed group divided by the pooled standard deviation. These two
formulas are appropriate for both homogeneous and heterogeneous situa-
tions and were similarly used by Huffcutt, Conway, Roth, and Stone
(2001). We judged the magnitude of significant effects using Cohen’s
(1988) categorization: small-sized effects are d
c
.49 or r
c
.29,
medium-sized effects are .50 d
c
.79 or .30 r
c
.49, and large-sized
effects are d
c
.80 or r
c
.50. Finally, Qstatistics were used to test for
moderation (Hedges & Olkin, 1985).
We conducted moderator analysis using the subgroup method suggested
by Hunter and Schmidt (1990). Because of the sample size restriction,
moderator analysis was only conducted for the cross-sectional comparison
of unemployed and employed individuals. Potential moderator variables
were first dichotomized, and then separate meta-analyses were conducted
for each subgroup. Moderators are indicated when the 95% confidence
intervals for the subgroups do not overlap. The average unemployment rate
was dichotomized at the median, forming low and high groups (cf. Hom &
Kinicki, 2001). When the unemployment rate was not reported for the city
and year in which the study was conducted, we used corresponding data by
country (for non-U.S. samples) or state (in the United States) from the
International Labour Organization and/or the U.S. Department of Labor
Web sites. If no data collection time frame was provided, we used the
unemployment rate 2 years before publication or 1 year before submission
acceptance.
Unemployment protection was coded based on the World Labour Report
2000 (International Labour Organization, 2000). This study identified 14
“top level” countries that provide generous benefits to unemployed work-
ers, including a high proportion of replacement income, benefits that
extend at least 12 months, and secondary benefits available when primary
benefits are exhausted. In the current study, Denmark, Finland, Nether-
lands, Sweden, and Norway were coded as “high” unemployment protec-
tion from the list of top-level countries. The “medium-benefit” countries
were those that provided a lower proportion of replacement income,
provided benefits for less than a year, and/or did not have a secondary
benefit set available. Australia, Canada, Hong Kong, Italy, Israel, New
Zealand, the United Kingdom, and the United States were coded as
medium-benefit unemployment protection in the current study. (See Inter-
national Labour Organization, 2000, for more specific information about
this categorization scheme.)
Length of unemployment was dichotomized at 6 months, forming short-
and long-term categories. This convention is commonly used both by the
government (e.g., Ilg, 1994) and academics (e.g., Dooley et al., 1988;
Hammarstro¨m & Janlert, 2002; P. R. Jackson & Warr, 1984) to mark the
transition to long-term unemployment. Finally, unemployed sample type
was coded as either school leaver or adult on the basis of the sample
description provided in the source articles.
Results
Impact of Unemployment on Psychological and Physical
Well-Being
As previously discussed, there are three types of studies that
pertain to assessing the average impact of unemployment on
individual psychological and physical well-being: (a) cross-
sectional comparisons of unemployed and employed individuals,
(b) longitudinal examinations of changes in well-being as individ-
uals moved from unemployment into reemployment, and (c) lon-
gitudinal examinations of changes in well-being as individuals
moved from employment into unemployment. Table 3 presents
results from the separate meta-analysis of these three different
types of studies for those criteria examined in past studies. Our
outcome categories were not available for all types of studies. For
example, domain satisfaction, where we found only marital and
family satisfaction measures, was only measured in cross-sectional
studies comparing unemployed and employed individuals and not
in longitudinal studies.
Cross-Sectional Comparison: Unemployed Versus
Employed
We identified 52 cross-sectional studies containing 64 indepen-
dent samples that compared the well-being among 6,684 unem-
ployed and 15,988 employed individuals. Unemployed workers
had significantly lower mental health (d
c
⫽⫺.57), life satisfaction
(d
c
⫽⫺.48), marital or family satisfaction (d
c
⫽⫺.21), and
subjective physical health (d
c
⫽⫺.45) than their employed coun-
terparts. Though the relationship for objective physical health was
in the expected direction, the 95% confidence interval included
zero (see Table 3).
Longitudinal Effects of Reemployment
Table 3 shows that 15 longitudinal studies involving 19 samples
and 1,911 total participants examined changes in the well-being of
unemployed workers as they became reemployed. Significant im-
provements in mental health (d
c
⫽⫺0.89), life satisfaction (d
c
3.04), and subjective physical health (d
c
⫽⫺.36) were found
when workers became reemployed. The latter two effects should
be interpreted with caution, however, because of the very small
total sample sizes and numbers of included studies.
Longitudinal Effects of Job Loss
Eight studies with 10 independent samples and 660 participants
provided longitudinal data that followed individuals from employ-
61
WELL-BEING DURING UNEMPLOYMENT
Table 1
Variable Category Descriptions and Example Measures
Variable Description Examples of measures used
Outcomes
Psychological
Mental health Psychological well-being and
satisfactory adjustment to society
and to the ordinary demands of
life (Webster’s Dictionary, 1996)
GHQ–12, GHQ–20, GHQ–30, General Health Questionnaire
(Goldberg, 1978)
Beck Depression Inventory (Beck & Beck, 1972)
Center for Epidemiologic Studies–Depression Scale
(Radloff, 1977)
Hopkins Symptom Checklist (Derogatis, Lipmann, Rickels,
Uhlenhuth, & Covi, 1974)
Manifest Anxiety Scale (Taylor, 1953)
State–Trait Anxiety Inventory (Spielberger, Gorsuch, &
Lushene, 1970)
Life satisfaction Global assessment of a person’s
quality of life (Shin & Johnson,
1978)
Satisfaction With Life Scale (Diener, Emmons, Larsen, &
Griffin, 1985)
Life Satisfaction Scale (Quinn & Shepard, 1974)
Present Life Satisfaction Scale (Warr, 1978)
Quality of Life Delighted–Terrible Scale (Andrews &
Withey, 1976)
Domain satisfaction
Marital/family satisfaction Satisfaction with one’s marital life,
partner or spouse, or family
Dyadic Adjustment Scale (Spanier, 1976)
Family Relations Scale (Brand & Pullen, 1991)
Physical
Subjective physical health Self-rated physical health, illness,
symptoms, or health-related
behaviors (e.g., smoking and
alcohol use)
Proxy Measure of Health Status (Kisch, Kovner, Harris, &
Kline, 1969)
Psychophysiological disorders (Brett & Werbel, 1980)
Single item perceived health (Payne & Hartley, 1987)
U.S. National Health Survey (U.S. Department of Health,
Education, and Welfare, 1972)
Physical Symptoms Scale (Spector, 1988)
Objective physical health Objective measures of physical
health
Cortisol levels (e.g., Brenner & Starrin, 1988; Claussen,
1994; Hall & Johnson, 1988)
Predictors
Work-role centrality The importance and significance of
working in an individual’s overall
life or the degree of cognitive
investment into the work role
Employment importance (Feather & Bond, 1983)
Employment Commitment Scale (Banks & Ullah, 1988;
Warr, Cook, & Wall, 1979)
Kanungo Work Involvement Scale (Kanungo, 1982)
Valence of work (Feather & Davenport, 1981)
Protestant Work Ethic Scale (Blood, 1969)
Coping resources
Personal
Core-self-evaluation Overall evaluations that represent
one’s appraisal of people, events,
and things in relation to oneself
(Judge, Locke, & Durham, 1997)
Self-Esteem Inventory (Rosenberg, 1965)
Life Orientation Test (Scheier & Carver, 1985)
Internal–External Locus of Control Scale (Rotter, 1966)
Work Locus of Control Scale (Spector, 1988)
Neuroticism Scale of the Revised Eysenck Personality
Questionnaire (Eysenck & Eysenck, 1991)
Social
Social support Instrumental and emotional aid
exchanged through social
interactions (Latack et al., 1995)
Social Support Scale (Caplan, Cobb, French, Harrison, &
Pinneau, 1975)
Social support (Gore, 1978)
Social Provisions Scale (Cutrona & Russell, 1988)
Positive social support (Abbey, Abramis, & Caplan, 1985)
Brief Social Support Questionnaire (Siegert, Patten, &
Walkey, 1987, as cited in Sarason, Levine, Basham, &
Sarason, 1983)
Inventory of Socially Supportive Behaviours (Barrera &
Ainlay, 1983)
Social undermining Behaviors directed toward a person
that display negative affect,
criticism, and hindrance in
attaining personal goals (Vinokur,
Price, & Caplan, 1996)
Social undermining (Abbey, Abramis, & Caplan, 1985)
62 MCKEE-RYAN, SONG, WANBERG, AND KINICKI
ment into unemployment. Table 3 shows a significant reduction in
mental health following job displacement (d
c
⫽⫺0.38).
Longitudinal Impacts of Well-Being on Reemployment
Our next set of analyses examined the relationship between
well-being during unemployment and the probability of reemploy-
ment. Seven studies involving 5,135 individuals and nine indepen-
dent samples were used in this meta-analysis. The analysis in-
volved comparing the initial psychological well-being of groups of
displaced workers who either became reemployed after being
displaced or remained unemployed over time. An effect size dwas
calculated for each study; this represented the difference between
the average group mental health levels at Time 1 for those who
were reemployed at Time 2 and those who were still unemployed
at Time 2 divided by the pooled standard deviation. The sample
size weighted mean effect size dand the sample size weighted
corrected mean effect size d
c
were calculated. Although those who
found employment by the end of the collected studies had some-
what higher levels of well-being while unemployed than those who
had not found employment (d0.09; d
c
0.10), the 95%
confidence interval included zero (CI ⫽⫺0.07, 0.28).
Correlates of Well-Being During Unemployment
Results regarding the relationships between correlates and the
outcomes of psychological and physical well-being are presented
in Table 4. Fifty-nine studies involving 15,881 individuals pro-
vided 67 independent samples for this meta-analysis. Data were
lacking for the other criteria (domain satisfaction and objective
physical health).
Work-Role Centrality
Unemployed individuals’ work-role centrality had significant
negative relationships with their mental health (r
c
⫽⫺.34) and life
satisfaction (r
c
⫽⫺.14), respectively. There was no relationship
between work-role centrality and subjective physical health during
unemployment.
Table 1 (continued)
Variable Description Examples of measures used
Predictors (continued)
Financial
Financial resources Available material resources (e.g.,
savings, investments, and income
from other sources or family
members)
Average weekly income (Ullah, 1990)
Net and gross financial resources (Gowan, Riordan, &
Gatewood, 1999)
Financial strain Assessment of financial difficulties Financial Concerns Scale (Pearlin & Radabaugh, 1976)
Financial Stress Scale (Feather, 1989)
Financial Strain Scale (Warr & Jackson, 1984)
Time structure Extent to which time is used in a
structured and purposeful way
(Feather & Bond, 1983)
Time Structure Questionnaire (Bond & Feather, 1988)
Structured time use (Feather & Bond, 1983)
Time structure (Rowley & Feather, 1987)
Cognitive appraisal
Stress appraisal Appraisal of job loss as a negative
event or stressor (e.g., Lazarus &
Folkman, 1984)
Perceived need for a job (Feather & Davenport, 1981)
Perceived problems from unemployment (Payne, Warr, &
Hartley, 1984)
Intensity (life disruption caused by job loss; Leana &
Feldman, 1991)
Internal attribution for job
loss/unemployment
Personal responsibility or blame for
job loss and unemployment
Control over becoming unemployed (Cvetanovski & Jex,
1994)
Causal attribution for unemployment (Feather & Davenport,
1981)
Reemployment expectation How likely the person thinks it is
that she/he will be able to find a
suitable new job
Reversibility of job loss (Leana & Feldman, 1991)
Specific employment confidence (Wiener, Oei, & Creed,
1999)
Coping strategies
Job-search effort Degree of engagement in job search
activities
Job Search Scale (Leana & Feldman, 1990)
Proactive Search Scale (Kinicki & Latack, 1990)
Finnish Institute of Occupational Health Job Seeking
Activity Scale (Vuori & Vesalainen, 1999)
Job-seeking behaviors (Vinokur & Caplan, 1987)
Job-search intensity (Shamir, 1986)
Problem-focused coping Efforts to directly manage or
control the stressors related to job
loss and/or unemployment
Proactive Self-Assessment Scale (Kinicki & Latack, 1990)
Seeking Retraining, Seeking to Relocate, and Seeking
Financial Resources scales (Leana & Feldman, 1990)
Emotion-focused coping Efforts to avoid or escape from the
stressors related to job loss and/
or unemployment
Non-Work Organization, Distancing From Loss, and Job
Devaluation scales (Kinicki & Latack, 1990)
Seeking Social Support, Seeking Counseling, and
Community Activism scales (Leana & Feldman, 1990)
63
WELL-BEING DURING UNEMPLOYMENT
Coping Resources
Personal coping resources. Core self-evaluations had signifi-
cant positive relationships with mental health (r
c
.55), life
satisfaction (r
c
.47), and physical well-being (r
c
.14).
Social coping resources. Unemployed workers with greater
social support felt better psychologically than those without such
support. Social support had a positive relationship with mental
health (r
c
.26) and life satisfaction (r
c
.43) and was unrelated
to subjective physical health. Because our analysis collapsed
across various kinds of support (e.g., instrumental support, emo-
tional support, or support from friends), we explored these rela-
tionships further by looking within subsets of social support of
varying types and from various sources. Results revealed that all
facet-level results were consistent with the overall results. With
respect to social undermining, this negative social resource was
associated with significantly lower mental health (r
c
⫽⫺.36).
Data were not available for the relationship between social under-
mining and life satisfaction or physical health.
Financial coping resources. Table 4 shows that financial re-
sources were related to significantly higher mental health (r
c
.11) and life satisfaction (r
c
.41) among unemployed individu-
als. Perceived financial strain was associated with lower mental
health (r
c
⫽⫺.45) and life satisfaction (r
c
⫽⫺.38). Neither
financial variable was significantly related to subjective physical
health, although studies for these relationships were limited (k
4) so this finding is tentative.
We suggested in our literature review that we expected financial
strain to be more strongly related to well-being than financial
resources. ztests partially confirmed this expectation: Financial
strain was more strongly related to mental health than were finan-
cial resources (z15.16, p.05). Contrary to predictions,
however, life satisfaction was more strongly linked to financial
resources than to financial strain, but the difference was not
significant (z1.24, p.05). The relationship was in the
expected direction for subjective physical health, but the ztest was
not significant (z1.07, p.05).
Time structure. Results revealed that unemployed individuals’
time structure was associated with positive mental health (r
c
.31). We cannot draw any conclusions about the relationships
between time structure and life satisfaction or physical well-being
because of a lack of research regarding these outcomes (see
Table 4).
Table 2
Descriptive Statistics for the Reliability Distributions
Variable kMSD
Mental health 53 .83 .10
Life satisfaction 12 .82 .08
Marital/family satisfaction 1 .91
Subjective physical health 1 .82
Work-role centrality 13 .73 .08
Coping resources
Core-self-evaluation 17 .81 .11
Social support 6 .80 .13
Financial strain 8 .85 .06
Time structure 7 .71 .13
Cognitive appraisal
Stress appraisal 2 .75 .31
Internal attribution 2 .50 .22
Reemployment expectation 8 .76 .12
Coping strategies
Job-search effort 12 .80 .07
Problem-focused coping 2 .71 .05
Emotion-focused coping 6 .71 .04
Note. k number of samples; Mmean reliability; SD standard
deviation of reliability.
Table 3
Meta-Analytic Results of the Effect of Unemployment on Psychological and Physical Well-Being Outcomes
Variable kN d
a
d
c
95% CI Q
Cross-sectional comparison: Unemployed vs. employed
Psychological well-being
Mental health 60 21,735 0.52 0.57 (0.65, 0.49) 412.04**
Life satisfaction 7 1,249 0.44 0.48 (0.68, 0.28) 17.61**
Marital/family satisfaction 4 419 0.20 0.21 (0.36, 0.06) 2.12
Physical well-being
Subjective physical health 3 1,136 0.41 0.45 (0.74, 0.16) 15.26**
Objective physical health 3 484 0.89 0.89 (1.96, 0.20) 99.00**
Longitudinal effects of reemployment
Psychological well-being
Mental health 19 1,911 0.82 0.89 (1.08, 0.70) 61.90**
Life satisfaction 2 106 2.79 3.04 (5.86, 0.22) 43.81**
Physical well-being
Subjective physical health 1 162 0.33 0.36
Longitudinal effects of job loss
Psychological well-being
Mental health 10 660 0.35 0.38 (0.60, 0.16) 16.33**
Note. k number of samples; dmean weighted effect size; d
c
mean corrected weighted effect size; 95% CI confidence interval of the d
c
;Q
homogeneity of d
c
.
a
A positive sign of dand d
c
represents that the unemployed group had higher well-being than the employed group, whereas a negative sign of dand d
c
represents that the employed group was better off.
** p.01.
64 MCKEE-RYAN, SONG, WANBERG, AND KINICKI
Cognitive Appraisal
Results generally supported the pattern of relationships between
cognitive appraisal and well-being. Stress appraisals were associ-
ated with lower mental health (r
c
⫽⫺.38), and internal attribu-
tions for job loss were significantly related to both lower life
satisfaction (r
c
⫽⫺.16) and physical health (r
c
⫽⫺.10). Further,
displaced workers with positive reemployment expectations had
higher levels of mental health (r
c
.29) and life satisfaction (r
c
.54). Table 4 shows that two of the remaining correlations had
confidence intervals that included zero and that two were based on
a limited number of studies.
Coping Strategies
Job search effort. Exerting effort in a job search was associ-
ated with lower mental health during unemployment (r
c
⫽⫺.11).
Table 4 also reveals that job search effort was unrelated to life
satisfaction and subjective physical health.
Problem-focused and emotion-focused coping. Higher levels
of problem-focused (r
c
.17) and emotion-focused coping (r
c
.14) during unemployment were weakly associated with higher
levels of mental health (see Table 4). These results collapse across
different forms of problem-focused (e.g., seeking retraining and
seeking relocation are considered together) and emotion-focused
coping (e.g., distancing from loss and seeking social support are
considered together) because of a lack of studies focused on
coping strategies in the literature. Examination of results by sub-
scale level was consistent with the aggregate results, with one
exception: Seeking social support (k1) and seeking counseling
(k1; both forms of emotion-focused coping) were negatively
rather than positively associated with well-being. However, given
that these two relationships were each based on only one study, we
cannot conclusively determine that different forms of emotion-
focused coping are differentially related to well-being.
Human Capital and Demographics
Human capital: Education, ability, and occupational status.
Higher levels of education were weakly associated with mental
health (r
c
.08) and life satisfaction (r
c
.05). It is not possible
to draw clear conclusions about relationships between the criteria
Table 4
Meta-Analytic Results for the Correlates of Well-Being During Unemployment
Variable
Psychological well-being
Physical well-being:
Subjective physical healthMental health Life satisfaction
kN r r
c
kN r r
c
kN r r
c
Work-role centrality 19 4,398 .26 .34
a
3 318 .11 .14
a
3 1,467 .03 .04
Coping resources
Personal
Core-self-evaluation 26 5,186 .45 .55
a
6 793 .38 .47
a
4 623 .11 .14
a
Social
Social support 20 4,858 .21 .26
a
3 347 .34 .43
a
3 451 .00 .01
Social undermining 2 1,700 .36 .36
a
—— — — —
Financial
Financial resources 9 4,393 .10 .11
a
2 142 .37 .41
a
2 1,353 .01 .02
Financial strain 17 5,257 .38 .45
a
3 260 .32 .38
a
2 421 .07 .08
Time structure 12 2,426 .24 .31
a
1 78 .38 .50
Cognitive appraisal
Stress appraisal 4 881 .30 .38
a
1 157 .42 .54 2 556 .20 .25
Internal attribution 6 714 .06 .08 2 329 .11 .16
a
2 329 .07 .10
a
Reemployment expectation 11 4,778 .23 .29
a
4 896 .42 .54
a
1 157 .31 .40
Coping strategies
Job-search effort 20 8,214 .09 .11
a
5 584 .08 .10 2 1,111 .01 .01
Problem-focused coping 3 585 .13 .17
a
2 257 .03 .04 1 157 .09 .12
Emotion-focused coping 7 1,137 .11 .14
a
4 503 .04 .05 1 157 .06 .09
Human capital and demographics
Education 10 4,688 .07 .08
a
2 346 .05 .05
a
—— — —
Ability 2 253 .04 .05 — —
Occupational status 3 742 .09 .10 2 343 .04 .04 1 399 .05 .05
Marital status 4 925 .03 .04 2 422 .15 .17
a
1 157 .16 .18
Gender 14 6,763 .09 .09
a
2 422 .05 .06
a
1 157 .20 .23
Race 5 4,021 .05 .06
a
1 265 .05 .06
Number of dependents 2 1,004 .11 .12
a
1 954 .01 .01
Length of unemployment 23 5,122 .08 .09
a
2 343 .16 .18
a
2 976 .08 .09
a
Age 20 7,091 .03 .03 3 424 .01 .01 1 954 .03 .03
Note. Dashes indicate that data were not available. Occupational status was coded 0 nonprofessional, 1 professional or managerial. Marital status
was coded 0 single, 1 married. Gender was coded 0 female, 1 male. Race was coded 0 non-White, 1 White. knumber of samples; r
mean weighted correlation; r
c
mean corrected weighted correlation.
a
The 95% confidence interval does not include zero.
65
WELL-BEING DURING UNEMPLOYMENT
and ability and occupational status because of the small number of
studies in these cells (see Table 4).
Demographics: Marital status, gender, race, number of depen-
dents, length of unemployment, and age. Those with more de-
pendents had lower mental health (r
c
⫽⫺.12), whereas those who
were married were more satisfied with their lives (r
c
.17).
Longer unemployment duration was weakly linked to lower men-
tal health (r
c
⫽⫺.09), life satisfaction (r
c
⫽⫺.18), and subjective
physical health (r
c
⫽⫺.09). Mental health was slightly lower for
White unemployed workers than for unemployed workers from
other racial groups (r
c
⫽⫺.06), and men had slightly higher levels
of mental health (r
c
.09) and life satisfaction (r
c
.06) than their
female counterparts. As expected— given the conflicting theoret-
ical propositions regarding the relationship between age and well-
being during unemployment—there was no clear pattern of rela-
tionships between age and our study outcomes.
Moderator Analyses
Table 5 summarizes our findings across four potential modera-
tors of the relationship between mental health and employment
status. The results for these moderators are mixed. Specifically, the
unemployment rate at the time of the study did not moderate the
relationship between mental health and employment status. Al-
though there was less distinction between the well-being of the
unemployed and the employed samples when there were generous
unemployment benefits available than under less generous condi-
tions, the difference only approached significance. In contrast,
results revealed that the length of unemployment and study type
both were significant moderators: The long-term unemployed sam-
ples displayed lower well-being to a much greater extent than did
the short-term unemployed samples (d
c
⫽⫺0.97 and 0.43), and
unemployed school leavers had lower well-being than did unem-
ployed adults (d
c
⫽⫺0.82 and 0.53).
Discussion
This study is among the first to comprehensively evaluate the
quantitative relationship between unemployment and well-being.
Our goal was to be as inclusive and broad as possible when
accumulating studies that encompassed multiple disciplines such
as economics, sociology, public health, family studies, and indus-
trial and organizational psychology. To accomplish this integrative
effort, we first needed to identify a taxonomy with which to
evaluate over 100 potential predictor and criterion variables en-
compassing 737 correlations. We identified 5 common outcome
variables, 22 common predictor variables, and 4 moderator vari-
ables by using theoretical frameworks provided by Diener et al.
(1999) and McKee-Ryan and Kinicki (2002) and by relying on
past research, underlying theory, and variable descriptions in pub-
lished studies. Meta-analysis then was used to analyze the data in
pursuit of four primary research questions.
The first research question concerns the average impact of
unemployment on individual well-being. Results underscore three
general conclusions. First, mental health is the most widely studied
outcome in the literature. Across the 27 variables reported in
Tables 3 and 4, about 77% of the correlations included a mental
health variable. That leaves less than one quarter of correlations
across the other four outcome variables (life satisfaction, marital
and family satisfaction, subjective physical health, and objective
physical health). Indeed, many of the meta-analytic relationships
on nonmental health variables could not be computed or could
only be computed on very small sample sizes. Additionally, as in
other literature studying well-being (Myers & Diener, 1995), the
job-loss literature tends to incorporate more negative (e.g., falling
into the domain of unpleasant affect) than positive outcome vari-
ables. Future research might better tap positive affective outcomes,
as well as examine a broader range of well-being variables.
Table 5
Moderator Analysis for Mental Health
Variable kN d
a
d
c
95% CI z
Unemployment rate
Low (median) 30 11,780 0.51 0.56 (0.67, 0.45)
High (median) 30 9,955 0.53 0.58 (0.69, 0.47)
Difference test 0.28
Unemployment protection
High benefit 11 3,361 0.41 0.46 (0.62, 0.29)
Medium benefit 49 18,374 0.54 0.59 (0.68, 0.51)
Difference test 1.54
Length of unemployment
Short-term (6 months) 6 2,645 0.39 0.43 (0.69, 0.17)
Long-term (6 months) 7 1,687 0.89 0.97 (1.26, 0.69)
Difference test 2.75**
School leaver vs. adult unemployed
School leaver 12 2,985 0.75 0.82 (1.07, 0.55)
Adult 48 18,750 0.49 0.53 (0.61, 0.46)
Difference test 2.23*
Note. k number of samples; dmean weighted effect size; d
c
mean corrected weighted effect size; 95%
CI confidence interval of the d
c
.
a
A negative sign of dand d
c
represents that the employed group had higher well-being than the unemployed
group.
*p.05. ** p.01.
66 MCKEE-RYAN, SONG, WANBERG, AND KINICKI
Second, the meta-analytic results are suggestive that unemploy-
ment has, on the average, a negative effect on mental health. How
strong of a causal statement can be made from these data? It is
unrealistic for us to claim we can prove a causal relationship
between unemployment and mental health as there are limitations
to the causal interpretation of each type of study that has examined
this relationship. Yet, we do feel it is appropriate to state that the
evidence is strongly supportive of a causal relationship because
there is consistency in results across multiple kinds of studies and
hundreds of data points. In cross-sectional studies, unemployed
individuals had lower well-being than employed individuals. In
longitudinal studies, well-being declines as individuals move from
employment into unemployment but improves as individuals move
from unemployment into reemployment. Our examination of cor-
relates suggests that there are several aspects of the unemployment
experience (e.g., financial concerns, work-role centrality) that are
the actual factors responsible for reduced well-being during un-
employment, meaning a causal suggestion of a relationship be-
tween unemployment and mental health is molar in nature or exists
at a very broad level (cf. Cook & Campbell, 1979). For example,
Price, Friedland, and Vinokur (1998) suggested that job loss and
unemployment bring about a “cascade” of secondary stressors
such as worry, uncertainty, and financial, family, and marital
difficulties.
Third, unemployed workers’ response to unemployment is not
homogeneous. This heterogeneity is evidenced by the presence of
moderators in seven out of eight relationships reported in Table 3.
Research Question 4 identified four potential moderators for which
we had adequate data to test for moderation. Unfortunately, we
were only able to test for moderation of cross-sectional relation-
ships between employment status and mental health because of the
small number of studies that investigated relationships with other
criteria. Even so, duration of unemployment and life status (i.e.,
unemployed school leaver vs. unemployed adults) appear to be
related to the magnitude of the effect of unemployment on the
mental health of displaced workers. The detrimental effect of job
loss was higher in studies with individuals unemployed longer. It
also appears that school leavers face the extra burden of establish-
ing their occupational identity when faced with early career un-
employment, and these early experiences may manifest in dimin-
ished well-being and employment outcomes over time
(Hammarstro¨m & Janlert, 2002).
In contrast, results reveal that the current unemployment rate at
the time of data collection was not related to the mental health
effects of unemployment. Perhaps this reflects the overall impact
of a strong or poor economy on the psyche of the population as a
whole, masking the effects of unemployment (cf. Dooley et al.,
1988). As an alternative, an individual’s perception of the unem-
ployment rate may have a stronger impact on well-being than the
actual unemployment rate. This explanation is consistent with the
belief that the appraisal of stressors is more important than the
existence of stressors in influencing how people respond to life
events (Lazarus & Folkman, 1984) and specifically job loss
(Latack et al., 1995; McKee-Ryan & Kinicki, 2002). One final
explanation involves the measurement of unemployment. It is
plausible that the unemployment rate for a study does not reflect
the local labor market, as there may have been “pockets” of
unemployment that were higher or lower than country or regional
averages: Statistics used to provide the unemployment rate tend to
be based on entire countries or regions. Moreover, many research-
ers failed to provide the unemployment rate at the time of the
study, leading us to use proxy measures or to estimate the study’s
unemployment rate. Future research is needed to more clearly
examine these alternative explanations, and researchers are en-
couraged to provide detailed information about the current unem-
ployment rate at the time of data collection.
Moreover, the mental health effects of unemployment did not
vary significantly on the basis of the availability of unemployment
protection benefits, although differences approached significance.
Despite the increased replacement wages and length of benefits,
generous unemployment benefits did not protect displaced workers
from the detrimental effects of job loss. This finding is similar to
that of Ouweneel (2002), who found no relationship between
social-security spending and unemployed worker well-being. Al-
though overall national wealth is related to individual well-being
(cf. Diener & Diener, 1995; Veenhoven, 1989), the effect on
well-being does not translate for social security expenditures
(Ouweneel & Veenhoven, 1995). Perhaps our finding is due,
however, to the lack of studies in our meta-analysis that were
conducted in very low benefit countries, such as China, producing
a restriction of range.
Our second research question examined the relationship be-
tween individual well-being during unemployment and subsequent
reemployment outcomes. Contrary to Taris’s (2002) proposition
that poor mental health deteriorates an individual’s capacity to
become reemployed, there was no significant relationship between
mental health and future reemployment, and data were not avail-
able to examine the reemployment outcome of poor physical
health. Given that previous studies found both supportive and
unsupportive results related to Taris’s prediction, it is plausible
that this relationship is moderated by other variables such as
human capital and coping resources. (Our results reflect modera-
tion with a significant Qstatistic of 73.74.) For example, Kinicki
et al.’s (2000) findings revealed that displaced workers possessing
greater coping resources engaged in more emotion-focused than
problem-focused coping following job loss and remained unem-
ployed longer than those displaced workers with low coping re-
sources. It is possible that displaced workers with poorer mental
health and lower levels of coping resources are more likely to
accept the first job opportunity available to them regardless of the
quality of reemployment. In contrast, and similar to Kinicki et al.’s
results, those with higher mental health and high coping resources
may be more likely to persist at finding a job with a high quality
of reemployment. Future research is needed to examine the verac-
ity of this plausible explanation.
These results also point to the important shift occurring in
unemployment research from an ultimate outcome of reemploy-
ment to the outcome of quality of reemployment, because the
positive effects of becoming reemployed may be limited to those
who regain satisfactory new jobs (e.g., Feldman, Leana, & Bolino,
2002; Kinicki et al., 2000; Latack et al., 1995; Leana & Feldman,
1995; Wanberg, 1995). For example, those who are dissatisfacto-
rily reemployed continue to cope with job loss at similar levels to
unemployed workers (Kinicki et al., 2000), and those who become
employed too quickly after being displaced may actually be worse
off psychologically (Leana & Feldman, 1995). Such workers may
face “relative deprivation” or be “underemployed” (Feldman et al.,
2002). Underemployment reflects employment in a poorer quality
67
WELL-BEING DURING UNEMPLOYMENT
job, in terms of level in the organization, wages, or skill utilization
(Feldman et al., 2002), and has been linked to a whole host of
negative outcomes, including diminished job satisfaction, work
commitment, job involvement, internal work motivation, life sat-
isfaction, and psychological well-being (see Feldman, 1996, for a
comprehensive review). Unfortunately, not enough data were
available to summarize this research meta-analytically. Research-
ers are encouraged to adopt a common definition and measure for
the quality of reemployment and to examine the quality of reem-
ployment as a criterion in future research.
Our third research question focused on identifying the correlates
of psychological and physical well-being during unemployment.
Overall, the pattern of relationships highlights the importance of
work-role centrality and the coping variables of coping resources,
cognitive appraisal, and coping strategies over a host of human
capital and demographic variables. There are five important con-
clusions from results pertaining to the correlates of psychological
and physical well-being. First, work-role centrality is associated
with lower mental health and life satisfaction during unemploy-
ment. These findings are consistent with identity theory (e.g.,
Ashforth, 2001). Future research is needed to more fully explore
the relationship between work-role centrality and well-being and
to determine the types of employees more likely to view work as
an important component of their personal identity (cf. Price et al.,
1998; Turner, 1995).
Second, the possession of coping resources plays an important
role in facilitating well-being during unemployment. Results sug-
gest that positive core self-evaluations, the presence of social
support and financial resources, and structured use of one’s time
are related to higher well-being, whereas social undermining and
financial strain are related to lower well-being. Some of the largest
relationships across all three outcome variables were for core
self-evaluations, which include self-esteem, optimism, neuroti-
cism, and an internal locus of control. Having a generally positive
self-view is a protective resource when faced with job loss and
unemployment. This discovery provides a preliminary extension of
Judge and colleagues’ findings that core self-evaluations are an
important component of job-relevant variables, such as job satis-
faction and job performance (e.g., Erez & Judge, 2001; Judge &
Bono, 2001; Judge et al., 1997, 1998). Because these results reflect
an aggregation of studies that measured a single core-self-
evaluation variable, future research is needed to directly examine
the core-self-evaluation construct in an unemployment setting.
Unemployed workers who experience supportive social relation-
ships also fare better than those without such relationships in terms
of both mental health and life satisfaction. These results are con-
sistent with previous meta-analyses that found social support to be
positively related to subjective well-being (Pinquart & So¨rensen,
2000) and to diminish the impact of stressors on experienced
strains (Viswesvaran et al., 1999).
Moreover, financial resources and financial strain are important
components of individual well-being during unemployment. The
magnitude of the effect size between mental health and financial
strain was over three times that of financial resources. This trend
underscores the importance of the appraisal of one’s financial
situation, not the mere presence of savings and investments. Per-
haps all financial resources are not liquid enough to be drawn on
or the unemployed person may face substantial short- or long-term
penalties for using financial resources such as retirement accounts.
The differences may also reflect varying degrees of financial
obligations for different individuals who have lost their job. It is
interesting to note that the trend reverses when the outcome of life
satisfaction is examined. Financial reserves positively relate to
satisfaction among unemployed individuals, whereas financial
strain or worries do so to a lesser degree. These results must be
interpreted with extreme caution, however, because of the small
number of studies included in the analysis. Future research is
needed to unravel the web of relationships between financial
resources and financial strain and their various outcomes.
The final coping resource examined in the current study was
structured time use. This is an important variable because unem-
ployment removes a major part of the familiar routine of daily
living. Though unemployed individuals tend to report lower levels
of time structure (e.g., T. Jackson, 1999), mental health is higher
for those who are able to impose daily routines on their lives, to
remain active, and to use their time in a structured way (e.g.,
Wanberg et al., 1997). Future research is needed to determine how
structured time use relates to personality variables such as being
proactive, as well as its relationship to role demands.
Third, our results suggest those who appraise job loss more
negatively may face diminished well-being and that positive ex-
pectations for future reemployment are related to higher well-
being. These trends highlight the importance of the individual’s
primary and secondary appraisal of job loss (Lazarus & Folkman,
1984). The stress appraisal reflects the individual’s primary ap-
praisal: What does job loss mean to me? Reemployment expecta-
tions reflect secondary appraisal: What can I do to manage the
stress from losing my job? Both are critical components to well-
being during unemployment. The small number of studies in the
unemployment literature incorporating cognitive appraisal mea-
sures (and problem-focused and emotion-focused coping mea-
sures) are strongly suggestive of an opportunity for future re-
searchers to more carefully map their examinations of the
unemployment experience onto contemporary and generalizable
theories of stress (e.g., Edwards, 1992; French, Caplan, & Van
Harrison, 1982; Lazarus & Folkman, 1984).
Fourth, actively engaging in job-search activities is related to
lower mental health for unemployed workers. This negative rela-
tionship reflects the stressful experience of looking for a job and
facing inevitable rejections. In contrast, both other problem-
focused coping strategies and emotion-focused coping were linked
to higher mental health among unemployed workers. Higher levels
of well-being were found among those who sought to manage their
stress level directly (through problem-focused coping other than
proactive job search, such as enrolling in a retraining program,
seeking to relocate to an area of increased employment opportu-
nity, reframing negative events more positively, or engaging in
nonwork activities) and indirectly (through emotion-focused cop-
ing, such as distancing oneself from the job loss, devaluing one’s
former job, seeking social support or financial assistance, or get-
ting involved in the community). One caveat to this conclusion is
that from our data we were unable to examine whether specific
forms of problem- or emotion-focused coping are more or less
effective, given the small number of studies that have examined
coping strategies. This difficulty is confounded by the fact that the
same individual may engage multiple coping strategies in response
to the same stressor (Kinicki & Latack, 1990; Kinicki et al., 2000;
Leana & Feldman, 1995). Our data also did not provide informa-
tion on the impact of these coping strategies on reemployment.
More research is needed to determine if that which makes unem-
68 MCKEE-RYAN, SONG, WANBERG, AND KINICKI
ployed workers feel better in the short-term improves or detracts
from their chances for reemployment in high-quality jobs in the
future. This is particularly important because coping is a dynamic
process that changes over time (cf. Kinicki et al., 2000). Similarly,
the relationship between job search and mental health may be
negative in the near term, but Kanfer et al. (2001) demonstrated its
positive relationship with reemployment: Job-search behavior is
related to a higher likelihood of reemployment and number of job
offers received and to decreased unemployment duration. This
variability highlights the need for additional longitudinal research
into the process of coping with job loss over time. In particular, a
major contribution is available for multiwave, panel studies that
follow a set of respondents over time as the job loss and unem-
ployment experience unfolds. Researchers also need to consis-
tently report the duration of unemployment for their particular
sample.
Finally, human capital and demographic results displayed in-
consistent patterns that must be interpreted with caution because of
very small sample sizes and measurement inconsistencies. Of the
27 cells in the human capital and demographic section of Table 4,
over two thirds (19/27) contain two or less studies; both the
median and the mode for this subset is two. Even so, some
interesting patterns surfaced. Among the human-capital variables,
education emerged as the most often studied and strongest corre-
late of both mental health and life satisfaction during unemploy-
ment. Among the demographic variables, we note important find-
ings for gender and length of unemployment. First, unemployed
women displayed lower mental health and life satisfaction than
their male counterparts. This finding runs counter to traditional
wisdom that suggests that unemployment is more psychologically
damaging to men than women. Two plausible explanations exist. It
may be that gender differences reflect the general finding that
women display more depression and lower mental health than men
(cf. Fujita, Diener, & Sandvik, 1991). A more complex explanation
centers on the changing role of women in the workplace. Recent
research suggests that changing gender roles have allowed work to
take a more central presence in the lives and identities of female
workers (e.g., Lee & Owens, 2002; Waters & Moore, 2002a).
More research is needed to tease out contributing factors to the
differential impacts of unemployment on men and women. Sec-
ond, although we cannot determine causality, both psychological
and physical well-being seem to be lower for individuals with
longer lengths of unemployment, even with very small sample
sizes for life satisfaction and physical health. Moreover, moderator
analysis also demonstrates that mental health differences between
unemployed and employed workers are more dramatic for long-
term versus short-term unemployed workers. Thus, the length of
unemployment emerged as an important variable in unemployment
research. Unfortunately, not all unemployment research reports the
length of unemployment for the study’s sample. The moderator
analysis included only 13 of 60 potential studies. Future research
should include information about the length of unemployment for
the sample.
Taken together, this set of findings provides particular insight
for future research focused on developing job-loss interventions.
Specifically, it suggests that generalizing the impact of job loss
according to particular demographic characteristics is not appro-
priate. The focus should instead be on identifying sets of individ-
uals at risk on the basis of psychological variables. For example,
those with high work-role centrality, low levels of personal coping
resources, and a high degree of stress appraisals or low reemploy-
ment expectations are appropriate populations to target for specific
interventions. These interventions should seek to (a) deal with the
threat to personal identity, (b) bolster dislocated workers’ personal
coping resources, and (c) help minimize the negative appraisal of
job loss. This recommendation is consistent with previous research
demonstrating the effectiveness of specifically targeted interven-
tions (e.g., Caplan, Vinokur, Price, & van Ryn, 1989; Creed,
Hicks, & Machin, 1998; Eden & Aviram, 1993; Vinokur, Price, &
Schul, 1995; Vinokur, Schul, Vuori, & Price, 2000) and provides
a new focus for such interventions.
Research Gaps
There have clearly been many studies focused on well-being
during unemployment. However, there is still a need for additional
research in this area. First, future research should consider an
expanded array of correlates of well-being during unemployment
and develop a more complex understanding of the correlates that
have been studied in terms of their role in the unemployment
experience. For example, Warr (1987) proposed relationships be-
tween several environmental features (e.g., including “valued so-
cial position,” a predominately unstudied feature in the job-loss
domain) and well-being during unemployment. His discussion
suggests that individuals may have varying preferences and needs;
for example, whereas time structure may be important to one
person, it may not be essential to another (see also Edwards &
Cooper, 1988).
Second, research must consider non-mental-health variables as
outcomes, including more attention to physical health and behav-
ioral outcomes such as substance abuse. It is interesting to note that
a recent volume on organizational stress similarly noted a dearth of
research of physiological and behavioral outcomes in the job-stress
literature (Cooper et al., 2001). The role of “proactive coping” in
advance of actual job loss (e.g., Aspinwall & Taylor, 1997) and
positive aspects of job loss that result in the long term—such as
career growth and development (Latack & Dozier, 1986)— could
also use more attention. We also noted a shortage of research on
domain satisfaction, such as family and marital satisfaction, as
outcomes of job loss.
The third issue revolves around the design and implementation
of future job-loss and unemployment research. In particular, stud-
ies need to be designed to strengthen the causal inferences that can
be drawn from them regarding the impact of unemployment on
employee well-being. Greater emphasis must be placed on identi-
fying and explicitly delineating variables that potentially alter the
nature of this relationship. For example, simply providing the date
and geographic location of data collection allows for tracking
potentially critical information, such as seasonal factors that affect
unemployment and well-being and the local unemployment rate.
Additional variables of interest are average unemployment dura-
tion, age, and gender of the sample.
Perhaps most salient, however, is the need for more transac-
tional research on cognitive appraisal, coping strategies, and the
mediating and moderating relationships of these and other vari-
ables on well-being during unemployment. As with the coping-
with-job-stress literature (Cooper et al., 2001; Kinicki, McKee, &
Wade, 1996), there is not enough known about how individuals
cope with job loss, how different forms of coping may be differ-
entially helpful, and why two individuals having the same circum-
69
WELL-BEING DURING UNEMPLOYMENT
stances during job loss may appraise their situation differently.
Despite the inclusion of coping variables in conceptual models of
job loss and unemployment (e.g., DeFrank & Ivancevich, 1986;
Latack et al., 1995; Leana & Feldman, 1988; McKee-Ryan &
Kinicki, 2002), in our review we noticed a striking lack of process-
oriented empirical studies. More research is needed, for example,
examining coping resources leading to cognitive appraisal and
coping strategies—and ultimately outcomes such as reemployment
and subjective well-being, with feedback loops from well-being to
coping. Time-series research with multiple assessments of these
variables would be valuable, and the benefits of qualitative exam-
inations of coping during unemployment should not be overlooked
(Cooper et al., 2001; Latack et al., 1995). Refining measures of
cognitive appraisal and coping-strategy constructs within the job-
loss domain would also be beneficial (Cooper et al., 2001), as
would further explication of the role of the coping goal (McKee-
Ryan & Kinicki, 2002; Prussia et al., 2001).
Contributions and Limitations of the Study
This study contributes to the job-loss and unemployment liter-
ature in at least four important ways. First, the study reflects a
comprehensive review of the unemployment literature, drawing
from diverse fields of inquiry (such as economics, sociology,
psychology, management, family studies, etc.) to portray the
whole spectrum of research on unemployment and well-being.
Second, a quantitative synthesis of available research provides a
more accurate base from which to derive conclusions about the
relationship between unemployment and employee well-being.
This quantification allowed us to identify gaps in the literature and
to suggest avenues for future research. Finally, we examined
multiple outcomes of unemployment, including mental health, life
satisfaction, physical health, and reemployment. Results demon-
strate that the bulk of research is focused on mental health out-
comes, suggesting that unemployment research needs to broaden
the scope of outcome variables.
Despite the strengths of the current study, four potential limita-
tions should be noted. A large number of articles were excluded
from the analysis because they did not contain statistics that could
be converted to usable statistics in the meta-analysis. Second, only
studies that were published in English were included in the anal-
ysis. Third, it is imperative that readers carefully reflect on the
sample sizes available for our reported meta-analyses. Some of our
reported relationships must be interpreted with extreme caution
given that they were based on only a few studies. Finally, as is
common with meta-analytic work, we had to collapse various
measures into construct categories (e.g., see Table 1). We recog-
nize the advantage of more specific meta-analytic work being
conducted as more data become available. Although these limita-
tions should be kept in mind, the meta-analysis as a whole is a
highly informative portrayal of current research on job loss and
well-being.
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Received May 14, 2003
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76 MCKEE-RYAN, SONG, WANBERG, AND KINICKI

Supplementary resource (1)

... Prior research demonstrated that the alignment of graduates' education and their first job can have longterm career effects (e.g., Baert, Cockx, & Verhaest, 2013). Multiple outcomes of the fit between graduates' education and work were investigated in prior studies, which primarily focused on economic outcomes such as career success, operationalised as wages or promotion opportunities (Groot & Maassen van den Brink, 2000;McKee-Ryan & Harvey, 2011). However, although they are mostly neglected in previous research, non-economic outcomes are of key importance to graduates' careers as well. ...
... However, although they are mostly neglected in previous research, non-economic outcomes are of key importance to graduates' careers as well. McKee-Ryan and Harvey (2011) demonstrated beneficial effects of the fit between educational background and the first job, for example, in terms of wellbeing and career satisfaction. ...
... The rising incidence of misfit results in a loss of (potential) resources at an organisational and societal level (e.g., difficulties in selection processes; frictions in the labour market) (Leuven & Oosterbeek, 2011). However, most outcomes of fit were identified at the individual level, in which misfit results in extensive negative outcomes for employees (McKee-Ryan & Harvey, 2011). As such, McKee-Ryan and Harvey (2011) identified several personal outcomes (e.g., wellbeing), job outcomes (e.g., performance), and career outcomes (e.g., wages). ...
... Findings of existing research on the perceived overqualification are mixed. A large number of the studies focused on the harmful consequences of negative psychological effect of perceived overqualification (e.g., McKee-Ryan and Harvey, 2011;. This is because perceived overqualification is widely considered to be contributing to inactive psychology. ...
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... When individuals face resource loss or threat, they will feel tension and pressure, which will lead to negative reactions such as burnout, emotional exhaustion and depression (Hobfoll et al., 2018). From the perspective of resources, qualifications such as knowledge, skills and work experience that exceed job requirements are obtained by overqualified employees through investment methods such as professional learning, skill training or work practice, and are valuable resources for employees (McKee-Ryan et al., 2011). Therefore, the perception that individual resources are wasted caused by the situation of overqualification will stimulate employees' behavior of maintaining, protecting and acquiring resources. ...
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... With focus shifting on improving for the future position, employees will feel overtrained for the current job. H5: Correspondence negatively influences subjective underemployment: Underemployment is likely to occur when there is a poor fit between worker and job (Anderson and Winefield, 2011;Maynard et al., 2006;McKee-Ryan and Harvey, 2011). However, if the job corresponds to the skills and interests of the employee, levels of subjective underemployment are likely to diminish. ...
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