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Scand J Work Environ Health
Online-first -article
https://doi.org/10.5271/sjweh.4131
Published online: 09 Nov 2023
The labor market costs of work-related stress: A longitudinal
study of 52 763 Danish employees using multi-state modeling
by
Pedersen J, Graversen BK, Hansen KS, Madsen IEH
This study shows that work-related stress has a markedly economic
impact on production, sickness absence, and unemployment for
employees, employers, and society. Given this sizeable economic
burden, the study emphasizes that the prevention of work-related
stress is a major occupational health concern and that the
development of effective interventions to achieve this aim should be
given high priority.
Affiliation:
National Research Centre for the Working Environment,
Lersø Parkallé 105, DK-2100 Copenhagen Ø, Denmark. [email protected]
Refers to the following texts of the Journal:
2012;38(6):516-526
2014;40(3):266-277 2017;43(1):5-14 2021;47(7):489-508
2022;48(8):641-650
Key terms:
cost; Denmark; ELMA; labor market cost; longitudinal
study; modeling; multi-state modeling; occupational health; sickness
absence; stress; unemployment; work; work-related stress
Additional material
Please note that there is additional material available belonging to
this article on the
Scandinavian Journal of Work, Environment & Health
-website.
This work is licensed under a
Creative Commons Attribution 4.0 International License.
Print ISSN: 0355-3140 Electronic ISSN: 1795-990X
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O
riginal article
Scand J Work Environ Health – online first: 9 November 2023. doi:10.5271/sjweh.4131
This work is licensed under a Creative Commons Attribution
4.0 International License.
The labor market costs of work-related stress: A longitudinal study of 52 763 Danish
employees using multi-state modeling
by Jacob Pedersen, PhD,
1
Brian Krogh Graversen, PhD,
1
Kristian Schultz Hansen, Professor,
1
Ida Elisabeth Huitfeldt Madsen,
PhD
1, 2
Pedersen J, Graversen BK, Hansen KS, Madsen IEH. The labor market costs of work-related stress: A longitudinal study of 52
763 Danish employees using multi-state modeling.
Scand J Word Environ Health
– online first.
Objective
Work-related stress is an important public health concern in all industrialized countries and is linked
to reduced labor market affiliation and an increased disease burden. We aimed to quantify the labor market costs
of work-related stress for a large sample of Danish employees.
Methods
We linked four consecutive survey waves on occupational health and five national longitudinal registers
with date-based information on wage and social benefits payments. From 2012 to 2020, we followed survey
participants for two year-periods, yielding 110 559 person-years. We identified work stress by combining three
dichotomous stress indicators: (i) self-perceived work stress, (ii) Cohen 4-level perceived stress scale, and (iii)
job strain. Using the multi-state expected labor market affiliation (ELMA) method, we estimated the labor market
expenses associated with work-related stress.
Results
Of the employees, 26–37% had at least one work-stress indicator. Men aged 35–64 years and women
aged 18–64 years with work-related stress had up to 81.6 fewer workdays and up to 50.7 more days of sickness
absence during follow-up than similarly aged men without work stress. The average annual work absenteeism
loss per employee linked to work-related stress was €1903 for men and €3909 for women, corresponding to
3.3% of men’s average annual wages and 9.0% of women’s average annual wages, respectively. The total annual
expenses were €305.2 million for men and €868.5 million for women.
Conclusion
Work-related stress was associated with significant labor market costs due to increased sickness
absence and unemployment. The prevention of work-related stress is an important occupational health concern,
and the development of effective interventions should be given high priority.
Key terms
Denmark; ELMA; occupational health; sickness absence; unemployment; work.
Work-related stress profoundly affects labor market
affiliation in terms of increased risk of employees expe-
riencing sickness absence (1–7), lowered probability of
returning to work (8), and increased risk of an early exit
from the labor market (9–11). Nevertheless, almost all
economic and epidemiologic studies on work-related
stress include only a single labor market outcome (12),
such as the risk of sickness absence. Studies investigat-
ing the impact of multiple labor outcomes and their
interconnectivity are rare (8), thereby omitting essential
knowledge concerning recurrent sickness absence lead-
ing to decreased work participation, unemployment,
and early retirement (8, 13). Moreover, translating the
findings into real-world contexts such as costs can be
challenging for companies and employers facing the
complex behavior of sick listings among employees.
Economic studies that deal with work-related stress
and its associated labor market consequences often use
aggregated portions or results from the research litera-
ture to make assumptions on costs, eg, 11 of 15 studies in
the review by Hassard et al (14). Such studies may have
high macroeconomic relevance, but again, not necessar-
ily precisely the type of specific information applicable
to the individual employer or employee (14). In contrast,
only a few studies use information from individual wage
payments when estimating costs per work-related stress
1 National Research Centre for the Working Environment, Copenhagen, Denmark.
2 National Institute of Public Health, Copenhagen, Denmark.
Correspondence to: Jacob Pedersen, PhD, National Research Centre for the Working Environment, Lersø Parkallé 105, DK-2100 Copenhagen
Ø, Denmark. [E-Mail: [email protected]]
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The labor market costs of work-related stress
case. One Australian incidence-based study, one Swiss
prevalence-based study, and one incidence-based study
from the United Kingdom estimated that work-related
stress costs society €124–529 per afflicted employee
(14),
with an average 2014 exchange rate of US$1 =
€0.7541 (15). The respective annual costs accumulated
to €3.0 and €4.1 billion per year (16–18). Addition-
ally, the Swiss study reported that the highest cost of
work-related stress concerns sickness absence wages
(59.9%), followed by medical service use (31.5%) and
self-medication (8.6%) (17). However, these three stud-
ies are not directly comparable, as they include differ-
ent definitions of work-related stress given by mental
stress, anxiety, and depression. Moreover, the range of
included healthcare and non-healthcare expenses differ
and encompasses, eg, doctor visits, rehabilitation, tax
loss, and insurance costs.
Principally, the work-related stress costs per
employee may differ for many reasons, including coun-
try differences in the labor market system, the health-
care system, healthcare expenses, and non-healthcare
expenses. Additionally, the methodological approach
may influence the results. The top-down approach aggre-
gates the national burden portion of a specific health
problem concerning medical, sick leave, and value of
life costs. In contrast, the bottom-up approach takes the
estimated cost per case and extrapolates it to a national
level. The bottom-up approach typically contains a
higher variety of cost components per case or person
than the top-down approach. However, the bottom-up
approach relies on more detailed data sources, and the
analysis may therefore be more time-consuming (14).
The human capital approach assumes that reductions in
employment of an employee reduce society’s production
value by the reduction in working hours of the employee
times the employee’s productivity per hour of work
measured by the hourly wage rate (19).
This study aimed to quantify the labor market costs
associated with work-related stress for a large sample
of Danish employees. Utilizing the expected labor mar-
ket affiliation (ELMA) method in a prospective study,
we take a human capital and bottom-up approach to the
societal cost of work-related stress concerning reduced
work production value in terms of increased sickness
absence and unemployment. However, since we can
only estimate the actual production value lost while
employees were absent from work, we use the term
‘costs of work absenteeism’ to describe the costs of
any negative difference between the number of work-
ing days deduced from the analysis and the expected
number of working days. The ELMA method has
shown to be a well-founded analytical tool for analyz-
ing multiple labor market outcomes while including
the interconnectivity between multiple outcomes (8,
13, 20).
This study included three indicators of work-related
stress: (i) self-perceived work-related stress, defined
as the degree to which situations in one’s working life
are appraised as stressful (21); (ii) Cohen’s four-level
perceived stress scale (21); and (iii) job strain, defined
as a combination of high quantitative demands and low
influence (22). While the first two indicators concern
work-related stress as reported by the employee, the
third indicator, job strain, is a widely applied operation-
alization of psychosocial stressors, ie, potentially stress-
ful situations at work (23). Job strain is likely to identify
individuals who have not yet developed symptoms of
stress or are unaware of their stress reactions.
Methods
Study design and source population
This longitudinal study analyses survey data on work-
related stress from four successive waves of the Work
Environment and Health in Denmark (WEHD) study
conducted in 2012, 2014, 2016, and 2018 (3, 24). The
WEHD surveys each contain a sample of 18–64 aged
Danish employees. Details on the WEHD surveys are
presented in the supplementary material,
www.sjweh.
fi/article/4131,
part A. The WEHD data were linked to
national registers (25), and WEHD responders were fol-
lowed in registers for two years from the date of survey
response. Individuals who responded to multiple waves
were included for multiple follow-up periods.
The WEHD data were linked with five registers
through Statistics Denmark: (i) the Danish labor market
accountant (LMA), (ii) work absences (RoWA), (iii)
education, (iv) emigration and immigration, and (v) the
death register. We included data from 2010 until the end
of 2020. LMA contains information on all major social
benefits payments, including unemployment, sickness
absence, disability pension, pension, and all wage pay-
ments reported to the tax authorities.
RoWA links the absence and employment register
(FRAN) and the periods of absence register (FRPE),
containing information about sickness absence spells
from the first day of absence and employment infor-
mation (3).
RoWA contains records for all public and
a large yearly sample of private employees, summing
to about 37% coverage, including approximately 2600
private companies with ≥9 employees (26). The educa-
tion register contains records of the highest education
level completion for all Danes. The emigration and
immigration register contains dates on all immigration
and emigrations in Denmark. The death register includes
death dates on all deceased Danes.
2
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Pedersen et al
Study sample and data preparation
We included all respondents from the four WEHD
waves (N=85 271), totaling 124 859 follow-up periods.
The study sample (N=52 763, and 75 537 follow-up
periods) consisted of active employees not receiving a
disability pension and with a follow-up linked only to
the employer registered at the survey. The study sample
was divided into six subsamples by sex and age: 18–34,
35–49, and 50–64 years. Since we included multiple
survey waves, each employee may have had up to four
follow-up periods. A detailed description of the sample
selection process, including a flow chart, is presented in
supplementary material B.
Work-related stress
The study used one work-stress variable defined by
combining three dichotomous (1=yes or 0=no) work-
stress indicators: (i) self-perceived work stress, (ii) the
Cohen four-item perceived stress scale modified to work
stress, and (iii) job strain, high quantitative demands and
low influence/job control at work. Each individual was
classified as having either zero, one, any combination of
two, or all three work-stress indicators during a follow-
up period. For additional details on the three work-stress
indicators, see supplementary material C.
Covariates and weights
The analysis included nine covariates previously used
in studies about work-related stress in relation to long-
term sickness absence and work disability (3, 13).
The covariates were associated with adverse health
outcomes, possibly through selection, eg, selection into
part-time work, or through causation, eg, smoking and
sickness absence.
Five variables were included from WEHD: (i) body
mass index (BMI, kg/m
2
) (underweight: BMI<18.0;
normal weight: 18.5≤BMI< 25.0; overweight:
25.0≤BMI<29.9; and obese: BMI≥29.9). (ii) Smoking
(yes: “daily” and “sometimes”; no: “prior smokers”
and “never”). (iii) Alcohol consumption, defined as the
number of items (15 ml of pure alcohol) per week (none;
moderate: 1–9; high: ≥10). (iv) Physical activity “How
much time on average do you use on each of the fol-
lowing physical activities in the last year?” as “exercise,
heavy gardening or fast walking/cycling where you sweat
and getting short of breath?” with the dichotomizing of
the answering range (yes: >4, 2–4, and <2 hours/week;
no: “Does not practice this activity” and missing). (v)
Disease treatment – dichotomously defined as whether
the individual has had treatment for one of the following
diseases (no/yes): depression, asthma, diabetes, athero-
sclerosis or blood clot in the heart, blood clot in the brain
(cerebral hemorrhage), cancer, back disease, migraine, or
other long-term disease. (vi) Working time arrangement,
ranging by the number of hours recorded at the follow-up
starting state (low: 0–64%; medium: 65–94%; full-time:
≥95%) standardized and compared to a norm working
day of 7.4 hours included from the LMA register. (vii)
Employment sector (private/public) from the FRAN reg-
ister. (viii) Highest accomplished education (low/middle/
high) from the education registers. The variable (ix)
“number of survey waves” was constructed to account for
the number of WEHD survey waves the individual had
attended –“1 of 4”, “2 of 4”, “3 of 4”, and “4 of 4”. Only
variable (viii) was allowed to change during the follow-up
period, while the remaining variables were updated only
at the start of each individual follow-up period.
Labor market affiliation
The labor market affiliation was modeled by seven
mutually exclusive labor market states – four recur-
rent states (work, sickness absence, unemployment,
temporary out) and three absorbing states (retirement,
disability pension, death) as illustrated in figure 1. The
modeling was based on a “long format” arrangement
(27)
of the longitudinal linkage of the LMA and RoWA.
Absorbing states were prioritized over recurrent states,
and prior states overwrite subsequent vacation time;
moreover, neither the LMA nor RoWA contains any
registration of leisure time. If a record contains multiple
payments such as wage and sickness absence benefits,
we prioritized the payment with the most recorded hours
as the labor market state. The follow-up started in any
of the four recurrent states.
The follow-up period was censored at the first occur-
rence of either the end of the two-year follow-up, if
reaching the age of 65, when a new follow-up period
started for the same individual (because the individual
had been interviewed again in a subsequent survey
round), or if a new employer-id was registered, which-
ever came first.
Supplementary material D contains a detailed
description of the states of the model, including a short
introduction to the Danish labor market and social
system.
Statistical analysis
The study used the ELMA method developed by Peder-
sen et al (8),
which relies on estimated transition prob-
abilities between the possible states of the multi-state
model. The ELMA incorporates time-invariant variables,
time-varying variables, and weights. The ELMA uses
Cox proportional hazard regression for establishing
time-dependent transition probabilities for each covari-
ate while incorporating modern survival terms such
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The labor market costs of work-related stress
Figure 1.
The multi-state labor
market affiliation model with
boxes representing labor states
and arrows representing transi-
tions. The lines represent tran-
sitions between the recurrent
and absorbing states, and the
numbers show the general flow
in events per 1000 person-years.
as left and right censoring, time truncation, recurrent
events, and competing events management while ful-
filling a Markov assumption (28). Using numerical
integration, ELMA converts complex patterns of state-
conditioned transition probabilities into overall state
duration estimates (29,
30) before conducting variance
analysis on the duration estimates to find the variable-
specific contributions (8, 13, 20).
For each subsample of sex and age groups, we
estimated the time-dependent baseline transition prob-
ability for each of the 24 arrows in the multi-state model
(see figure 1), using employees with no work-stress
indicators as the reference group. Then, we estimated
the transition probabilities for the non-reference values
by adjusting the baseline transition probabilities with
estimates derived from corresponding transition-specific
Cox proportional hazard regressions. Based on the
Chapman–Kolmogorov equation, we calculated the state
probabilities and estimated the area under the transition
and state probabilities. Then, we combined the area esti-
mates to express the expected time spent in each of the
seven states during the 730 days of follow-up.
We used 1000 normally distributed random resa-
mples of the area estimates to produce the state duration
95% confidence intervals (CI). All variables, except the
work-stress indicator variable, were incorporated into
the model as inverse probability weights, which we
multiplied with the weights from RoWA.
For sensitivity analysis purposes, we compared the
ELMA results with crude estimates on the state dura-
tions. The crude estimates were calculated by the sum of
days for all employees within the state during follow-up,
divided by the total number of employees – grouped by
sex and age.
Cost estimation
We estimated the work-stress-related costs regarding
work absenteeism, sickness absence, unemployment,
and temporary out. This was done using date-based
information on individual gross wage payments and
working hours from the LMA register. The individual
wage payments were standardized to hourly payments,
using the Danish norm of 7.4 working hours per day
(37-hour working week) – truncating extreme payments
to a minimum limit of €6.72 per hour (DKK50) and a
maximum limit of €268.63 per hour (DKK2000). Any
missing information on hourly wages was imputed by
regression using baseline information on sex, age group,
education level, sector, and industry group, and then all
wages were transformed into a 2022 price level. We then
estimated the state-specific annual cost per employee
by multiplying the individual standardized hourly wage
payments with 7.4 hours per day and additionally with
the state-specific durations per year deduced from the
ELMA analysis – estimated as reduced or increased
4
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Pedersen et al
number of days per year. We made the cost estimates
representative of Danish employees by multiplying
them with weights retained from the WEHD data. Then,
we estimated the state-specific annual average cost per
employee and yearly total costs by sex and age groups
with corresponding 95% CI.
All results on costs are presented at the 2022 price
level, as we adjusted all wages using the sex and age-spe-
cific consumer price index from Statistics Denmark (31).
For sensitivity analysis purposes, we compared the
ELMA cost results with (i) the crude estimates, (ii) the
cost with the inclusion of part-time wages, and (iii) the
cost using each of the three stress indicators separately
(supplementary material E). Supplementary material F
contains analyses on the hypothetical reduction potential
regarding the total annual value of work absenteeism
and sickness absence and a top-down estimation of the
society costs. The study was inspired by the Consensus
Health Economic Criteria (CHEC) list for securing the
methodological quality of the study (32).
Results
Table 1 shows that the study sample includes 52%
women (N=23 616) and 48% men (N=22 120). More-
over, more women than men experienced work-related
stress concerning the number of work-stress indicators.
Figure 1 illustrates the multi-state labor market
model, with arrows representing the possible transi-
tions. Transitions from work to sickness absence and
back were most frequent, with over 6700 events per
1000 person-years for women and over 4700 events per
1000 person-years for men. The second most frequent
transitions were between temporary out and work. Tran-
sitions to the absorbing states were infrequent, except
for retirement from work. The model contains 110 559
person-years of follow-up.
Table 2 presents the ELMA results. To find the
expected days for the individual or the combination of
work stress indicators, you add (+) or subtract (-) the
Table 1.
Descriptive characteristics of the study population at baseline of the first follow-up period of the individual employee.
 
 
 
TOTAL
Self-perceived stress
 
 
 
18–34
N (%)
3730 (17)
3331 (89)
399 (11)
3073 (82)
657 (18)
3244 (87)
486 (13)
2633 (71)
743 (20)
263 (7)
91 (2)
20 (1)
1900 (51)
1132 (30)
346 (9)
332 (9)
2631 (71)
787 (21)
312 (8)
646 (17)
1333 (36)
1442 (39)
309 (8)
2052 (55)
1678 (45)
1345 (36)
292 (8)
2093 (56)
3009 (81)
559 (15)
162 (4)
2375 (64)
1355 (36)
395 (11)
1552 (42)
1768 (47)
15 (0)
2912 (78)
559 (15)
150 (4)
109 (3)
Men (age in years)
35–49
N (%)
8593 (39)
7590 (88)
1003 (12)
7096 (83)
1497 (17)
7308 (85)
1285 (15)
6001 (70)
1675 (19)
641 (7)
276 (3)
16 (0)
3265 (38)
3598 (42)
1315 (15)
399 (5)
6622 (77)
1591 (19)
380 (4)
1316 (15)
3310 (39)
3584 (42)
383 (4)
4778 (56)
3815 (44)
3200 (37)
1090 (13)
4303 (50)
7483 (87)
1019 (12)
91 (1)
5791 (67)
2802 (33)
882 (10)
3413 (40)
4245 (49)
53 (1)
6194 (72)
1445 (17)
434 (5)
520 (6)
50–64
N (%)
9797 (44)
8798 (90)
999 (10)
8335 (85)
1462 (15)
8561 (87)
1236 (13)
7270 (74)
1624 (17)
636 (6)
267 (3)
20 (0)
3337 (34)
4529 (46)
1596 (16)
315 (3)
7595 (78)
1931 (20)
271 (3)
995 (10)
2985 (30)
5549 (57)
268 (3)
5639 (58)
4158 (42)
3197 (33)
1637 (17)
4963 (51)
8397 (86)
1274 (13)
126 (1)
6034 (62)
3763 (38)
1517 (15)
4459 (46)
3741 (38)
80 (1)
7307 (75)
1622 (17)
408 (4)
460 (5)
18–34
N (%)
5226 (17)
4344 (83)
882 (17)
4024 (77)
1202 (23)
4320 (83)
906 (17)
3292 (63)
1120 (21)
572 (11)
242 (5)
162 (3)
3055 (58)
1019 (19)
550 (11)
440 (8)
3923 (75)
942 (18)
361 (7)
1540 (29)
2145 (41)
1182 (23)
359 (7)
3091 (59)
2135 (41)
1666 (32)
667 (13)
2893 (55)
3606 (69)
1322 (25)
298 (6)
1752 (34)
3474 (66)
289 (6)
1847 (35)
3054 (58)
36 (1)
3961 (76)
866 (17)
248 (5)
151 (3)
Women (age in years)
35–49
N (%)
12437 (41)
10421 (84)
2016 (16)
9916 (80)
2521 (20)
10544 (85)
1893 (15)
8233 (66)
2480 (20)
1222 (10)
502 (4)
176 (1)
6698 (54)
3235 (26)
1775 (14)
553 (4)
9925 (80)
2082 (17)
430 (3)
3158 (25)
5848 (47)
2986 (24)
445 (4)
7493 (60)
4944 (40)
4410 (35)
2199 (18)
5828 (47)
8208 (66)
3991 (32)
238 (2)
3815 (31)
8622 (69)
710 (6)
4418 (36)
7269 (58)
40 (0)
8605 (69)
2222 (18)
699 (6)
911 (7)
50–64
N (%)
12980 (42)
11051 (85)
1929 (15)
10601 (82)
2379 (18)
11080 (85)
1900 (15)
8933 (69)
2384 (18)
1165 (9)
498 (4)
190 (1)
6732 (52)
3714 (29)
1828 (14)
516 (4)
10156 (78)
2448 (19)
376 (3)
2285 (18)
5353 (41)
4963 (38)
379 (3)
7914 (61)
5066 (39)
4269 (33)
2358 (18)
6353 (49)
8370 (64)
4306 (33)
304 (2)
3342 (26)
9638 (74)
1714 (13)
5171 (40)
6054 (47)
41 (0)
9300 (72)
2355 (18)
601 (5)
724 (6)
 
No
Yes
Cohen four-item stress
No
Yes
Job strain
No
Yes
Number of work-stress indicators 0 of 3
1 of 3
2 of 3
3 of 3
Body mass index
Underweight
Normal weight
Overweight
Obese
Not available
Smoking
Nonsmoker
Smoker
Not available
Weekly alcohol consumption
None
Moderate
High
Not available
Physical activity
No
Yes
Disease treatment
No
Yes
Not available
Status time arrangement
≥95%
of 37 hours/week
65–94% of 37 hours/week
0–64% of 37 hours/week
Employment sector
Private
Public
Highest educational level
Low
Middle
High
Not available
Number of survey waves
1 of 4
2 of 4
3 of 4
4 of 4
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The labor market costs of work-related stress
Table 2.
Estimated labor market affiliation (ELMA) results given by the expected number of days during the two-year follow-up period spent in the
four recurrent labor market states stratified by sex and age groups. Reference value showing the expected days and the additional or subtracted
days (+/-) for employees with 1–3 indicators of work stress. [Ref= Reference value; CI=sample confidence interval].
Number of
work-stress
indicators
Men
18–34 years
Ref 0 of 3
1 of 3
2 of 3
3 of 3
35–49 years
Ref 0 of 3
1 of 3
2 of 3
3 of 3
50–64 years
Ref 0 of 3
1 of 3
2 of 3
3 of 3
Women
8–34 years
Ref 0 of 3
1 of 3
2 of 3
3 of 3
35–49 years
Ref 0 of 3
1 of 3
2 of 3
3 of 3
50–64 years
Ref 0 of 3
1 of 3
2 of 3
3 of 3
a
Work
ELMA
Days (95% CI)
per 2 years
a
Crude
Days per
2 years
 
 
Sickness absence
ELMA
a
Crude
Days (95% CI)
Days per
per 2 years
2 years
 
 
Unemployment
ELMA
a
Crude
Days (95% CI)
Days per
per 2 years
2 years
 
 
Temporary out
ELMA
a
Crude
Days (95% CI)
Days per
per 2 years
2 years
 
 
636.5 (625.9–647.2)
-4.3 (-19.4–10.8)
-12.8 (-27.9–2.3)
+35.2 (20.0–50.3)
b
 
700.8 (696.1–705.5)
-7.2 (-13.9–-0.5)
-15.8 (-22.5–-9.1)
b
-46.3 (-53.0–-39.6)
b
 
667.2 (661.6–672.8)
-8.1 (-16.0–-0.1)
-36.5 (-44.5–-28.6)
b
-32.9 (-40.9–-25.0)
b
 
557.2 (541.1–573.2)
-19.7 (-42.3–3.0)
-19.5 (-42.2–3.2)
-80.5 (-103.1–-57.8)
b
 
672.3 (664.8–679.7)
-19.8 (-30.4–-9.3)
b
-51.5(-62.1–-41.0)
b
-81.6 (-92.2–-71.1)
b
 
661.3 (655.9–666.8)
-17.0 (-24.7–-9.3)
b
-37.9 (-45.6–-30.2)
b
-44.9 (-52.5–-37.2)
b
648.7
-0.9
-15.1
-2.6
 
703.1
-3.0
-23.4
-38.0
 
639.9
+0.3
-7.1
+11.6
 
549.4
-16.2
-23.1
-71.8
 
681.7
-17.1
-43.6
-54.6
 
626.8
-10.4
-27.3
-33.0
15.8 (10.3–21.3)
-2.0 (-9.7–5.8)
+18.0 (10.3–25.8)
b
-1.8 (-9.6–5.9)
 
16.2 (12.8–19.6)
+3.2 (-1.6–8.0)
+6.8 (1.9–11.6)
b
+32.7 (27.9–37.5)
b
 
22.1 (19.4–24.9)
+3.9 (-0.0–7.8)
+23.7 (19.8–27.6)
b
+26.0 (22.1–29.9)
b
 
25.8 (16.8–34.7)
+15.7 (3.0–28.4)
+9.6 (-3.1–22.3)
+46.0 (33.2–58.7)
b
 
31.1 (27.4–34.8)
+11.6 (6.4–16.9)
b
+25.0 (19.8–30.3)
b
+50.7 (45.4–55.9)
b
 
30.0 (26.4–33.6)
+15.0 (9.9–20.2)
b
+32.7 (27.6–37.9)
b
+40.5 (35.3–45.6)
b
13.4
+3.2
+12.8
+7.9
 
14.1
+4.0
+10.9
+21.0
 
18.2
+5.8
+13.9
+18.5
 
27.1
+8.9
+19.7
+34.6
 
26.8
+12.8
+30.0
+40.8
 
26.2
+11.1
+28.0
+41.7
7.0 (3.3–10.8)
+4.7 (-0.6–9.9)
+14.6 (9.4–19.9)
b
+2.5 (-2.7–7.8)
 
3.7 (2.1–5.2)
+0.6 (-1.6–2.8)
+2.2 (0.1–4.4)
+3.6 (1.5–5.8)
b
 
4.5 (0.7–8.3)
+6.8 (1.4–12.2)
+15.7 (10.3–21.1)
b
+30.2 (24.8–35.6)
b
 
7.8 (2.6–13.1)
+3.5 (-3.9–10.9)
+18.0 (10.5–25.4)
b
+15.7 (8.3–23.2)
b
 
6.2 (2.9–9.5)
+1.7 (-3.0–6.3)
+11.5 (6.8–16.1)
b
+33.6 (29.0–38.3)
b
 
7.2 (5.6–8.7)
+3.1 (0.9–5.2)
b
+7.6 (5.4–9.7)
b
+9.6 (7.5–11.8)
b
6.9
+2.8
+11.8
+13.4
 
3.5
+0.1
+3.3
+4.9
 
4.3
+3.8
+10.4
+9.3
 
9.6
+2.2
+10.7
+14.8
 
4.7
+2.3
+8.9
+9.3
 
4.6
+2.7
+4.7
+8.5
68.3 (59.3–77.2)
+4.0 (-8.7–16.7)
-26.9 (-39.7– -14.2)
b
-49.8 (-62.6– -37.1)
b
 
7.7 (5.9–9.6)
+0.0 (-2.5–2.6)
+2.7 (0.2–5.3)
+7.3 (4.7–9.9)
b
 
2.5 (0.6–4.4)
+1.1 (-1.6–3.8)
+7.8 (5.0–10.5)
b
+0.2 (-2.6–2.9)
 
146.1 (138.1–154.2)
-1.7 (-13.1–9.7)
-22.3 (-33.7– -10.9)
b
+1.0 (-10.4–12.4)
 
16.2 (12.0–20.5)
+1.7 (-4.3–7.7)
+21.3 (15.3–27.3)
b
+6.9 (0.9–12.9)
 
2.5 (1.3–3.8)
+1.3 (-0.4–3.1)
+1.5 (-0.3–3.3)
+3.8 (2.0–5.6)
b
60.9
-5.0
-9.4
-18.6
 
9.0
-0.8
+9.5
+8.6
 
2.5
+1.0
+2.0
-0.5
 
143.7
+5.2
-7.2
+22.6
 
15.9
+2.6
+5.5
+5.4
 
2.7
+0.6
+0.8
+2.6
ELMA results are adjusted by inverse probability weights on: body mass index, smoking, weekly alcohol consumption, physical activity, disease treatment, state
time arrangement, employment sector, highest educational level, and number of survey waves.
b
5% significant.
number of days presented for one, two, or three work
stress indicators to the reference value. For men aged
35– 64 years and women aged 18–64 years, an overall
pattern can be seen; for an increasing number of work
stress indicators, the number of work days decreased,
while the number of sickness absence days and unem-
ployment days increased.
For example, for women aged 35–49 years, the num-
ber of work days decreased by 19.8 days at one indica-
tor, 51.5 days at two indicators, and 81.6 days at all three
indicators, with a corresponding increase in sickness
absence of 11.6, 25.0, and 50.7 days. For the smallest
group of young men, no distinct pattern was seen.
The crude estimates in table 2 generally followed the
ELMA results for the reference group but deviate when
compared to the employees experiencing work stress.
Supplementary material H presents the ELMA results
for the absorbing states of retirement, disability pension,
and death. Table H1 shows a postponed retirement (4.2
days to 26.4 days) for older employees having one to
three work-stress indicators – most pronounced for the
men. Supplementary material I presents the results of
the multi-state cox-regressions.
Figure 2 shows that the costs associated with work
stress closely followed the pattern shown for the ELMA
results in table 2. The numbers in figure 2 correspond
to the cost results shown in table 3 (note that the cost
measures in table 3 are annual and not for the two-year
follow-up. Moreover, table 3 contains the population
confidence interval, while figure 2 shows the sample’s
confidence interval).
Table 3 shows the annual average costs per
employee, which was estimated as a weighted average
of the sum of the sex- and age-specific estimates. The
supplementary material table F1 contains the corre-
sponding weighted number of employees and the total
yearly costs. The total weighted sample (N=1 230 754)
represents all Danish employees matching the study
sample, corresponding to 54% coverage of all full-time
employees (N=2 275 785 full-time employees in the
Danish labor force in 2022, aged 18–64 years) (33).
The weighted total average annual cost of work
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Pedersen et al
Figure 2.
Mean annual costs of work
absenteeism per employee at the
euro 2022 price level. By the number
of work-stress indicators – and the
contribution of sickness absence,
unemployment, and temporary
out. Adjusted by inverse probability
weights on body mass index, smok-
ing, weekly alcohol consumption,
physical activity, disease treatment,
state-time arrangement, employ-
ment sector, highest educational
level, and number of survey waves,
including the sample’s 95% confi-
dence interval.
absenteeism was €1903 and €3909 for men and women,
respectively, per employee with one, two, or three
work-stress indicators. Payments of wages to sick-
listed employees constituted 60% of the cost of work
absenteeism for men and 68% for women. For men, the
remaining cost of work absenteeism concerned employ-
ees being unemployed, while for women, 7% of the
remaining cost was due to time spent in the “temporary
out” state concerning maternity leave.
The highest age-divided annual average costs of
work absenteeism per employee were for women aged
35–49 years with three indicators of work stress (10
€802.60). Partly originating from increased sickness
absence (€6704.20), unemployment (€4451.70), and the
temporary out state (€917.20). The lowest annual aver-
age costs of work absenteeism were observed among
young male employees (-€4294.30). Overall, most of the
work absenteeism loss originates from increased sick-
ness absence costs, but increased loss due to employees
becoming unemployed and time spent in the temporary
out state, were also critical.
The supplementary material table F1 shows the
total annual costs for the weighted sample size. The
total annual cost of work absenteeism for men was 35%
(€305.2 million) of the total annual cost for women
(€868.5 million), and the contribution of increased sick-
ness absence was 60% for men (€183.2 million), while
it was 67% for women (€580.7 million).
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The labor market costs of work-related stress
Table 3.
Estimated labor market affiliation (ELMA) results converted to annual standardized (37-hours per week) average costs of work absentee-
ism per full-time employee from increased work stress levels by sex and age group and the contribution of sickness absence, unemployment, and
temporary out. (All priced at EUR 2022 value). [CI=population confidence interval].
Number of work-stress
indicators
Work absenteeism
Average EUR per employee
per year (95% CI)
Sickness absence
Average EUR per employee
per year (95% CI)
Unemployment
Average EUR per employee
per year (95% CI)
Temporary out
Average EUR per employee
per year (95% CI)
Men 
 
 
 
 
18–34 years
 
 
 
 
1 of 3 
527.2 (516.1–538.2)
-242.0 (-247.7– -236.4)
578.6 (574.8–582.5)
499.0 (489.7–508.2)
2 of 3 
1585.6 (1567.4–1603.8)
2238.8 (2229.4–2248.1)
1818.2 (1811.8–1824.5)
-3346.1 (-3361.5– -3330.8)
3 of 3 
-4294.3 (-4326.3– -4262.4)
-223.4 (-239.8– -207.1)
306.6 (295.5–317.7)
-6089.5 (-6116.4– -6062.6)
35–49 years
 
 
 
 
1 of 3 
1150.9 (1145.7–1156.0)
512.2 (508.5–515.9)
92.1 (90.4–93.8)
4.6 (2.6–6.6)
2 of 3 
2512.4 (2504.2–2520.6)
1078.1 (1072.2–1084.1)
355.0 (352.3–357.7)
436.6 (433.5–439.8)
3 of 3 
7047.6 (7036.0–7059.2)
4975.7 (4967.3–4984.0)
552.6 (548.8–556.4)
1109.3 (1104.8–1113.7)
50–64 years
 
 
 
 
1 of 3 
1250.0 (1243.1–1256.9)
601.1 (597.8–604.5)
1052.3 (1047.6–1057.0)
171.3 (168.9–173.7)
2 of 3 
5780.0 (5768.9–5791.1)
3744.8 (3739.3–3750.2)
2483.6 (2476.1–2491.2)
1228.4 (1224.6–1232.2)
3 of 3 
5437.1 (5418.3–5455.9)
4288.7 (4279.5–4297.9)
4988.2 (4975.4–5000.9)
26.2 (19.8–32.6)
Total 
a
1903.0 (1892.3–1913.7)
1141.9 (1134.4–1149.4)
842.4 (837.4–847.5)
-43.3 (-50.1–-36.4)
Women 
 
 
 
 
18–34 years
 
 
 
 
1 of 3 
2106.6 (2093.2–2119.9)
1679.8 (1672.3–1687.3)
374.2 (369.8–378.6)
-180.5 (-187.2–-173.7)
2 of 3 
2074.9 (2056.3–2093.5)
1022.7 (1012.3–1033.1)
1910.8 (1904.8–1916.9)
-2370.4 (-2379.8–-2361.1)
3 of 3 
8865.2 (8835.4–8895.0)
5064.5 (5047.8–5081.2)
1733.6 (1723.9–1743.4)
106.2 (91.2–121.2)
35–49 years
 
 
 
 
1 of 3 
2625.7 (2619.7–2631.6)
1539.5 (1536.5–1542.5)
224.0 (221.4–226.6)
225.3 (221.9–228.7)
2 of 3 
6850.8 (6842.2–6859.4)
3329.6 (3325.3–3333.9)
1523.2 (1519.5–1527.0)
2828.1 (2823.2–2832.9)
3 of 3 
10802.6 (10789.2–10815.9)
6704.2 (6697.5–6710.8)
4451.7 (4445.8–4457.5)
917.2 (909.6–924.8)
50–64 years
 
 
 
 
1 of 3 
2268.2 (2263.3–2273.1)
2005.2 (2001.9–2008.5)
408.4 (407.0–409.8)
179.7 (178.6–180.8)
2 of 3 
5015.5 (5008.5–5022.5)
4331.2 (4326.6–4335.9)
999.3 (997.3–1001.3)
197.1 (195.5–198.7)
3 of 3 
6231.0 (6219.5–6242.4)
5616.9 (5609.2–5624.6)
1337.5 (1334.2–1340.7)
528.9 (526.3–531.6)
Total 
a
3909.0 (3898.1–3919.8)
2613.6 (2606.9–2620.3)
792.0 (787.7–796.3)
274.9 (269.9–279.8)
a
The Total estimate uses a standardized weighted average. Adjusted by inverse probability weights on: body mass index, smoking, weekly alcohol consumption,
physical activity, disease treatment, state time arrangement, employment sector, highest educational level, and number of survey waves.
Supplementary table F1 additionally shows that the
yearly costs of work absenteeism were generally lower
for men than women. Among men, those aged 50–64
with two work-stress indicators had the highest total
annual costs of work absenteeism (€74.3 million). This
was 59% less than the highest costs of work absentee-
ism for women.
The highest total age-divided annual costs of work
absenteeism were for women aged 35–49 years with
two work-stress indicators (€182.9 million). Women
aged 50–64 years with two work-stress indicators had
the highest contribution of sickness absence to costs
of work absenteeism (€90.9 million), and women aged
35–49 years with three work-stress indicators had the
highest contribution of unemployment to costs of work
absenteeism (€48.6 million).
Supplementary material E contains results from vari-
ous sensitivity analyses: (i) an alternative descriptive
presentation of the sample by a dichotomous version of
the combined work-stress indicator, with a comparison
of the annual mean hourly wages by sex, age group, and
work-stress indicator; (ii) separate analyses of the individ-
ual work-stress indicators: Self-perceived stress, Cohen
four-item stress, and job strain; and (iii) cost analysis with
part-time employees and crude duration estimates.
The descriptive supplementary tables E1 and E2
show no general difference in the sample between the
exposed and nonexposed groups. The sensitivity analy-
ses of the single work-stress measurements show that
relying only on one type of work-stress measurement is
insecure, as it gives mixed results concerning the cost
of work absenteeism and sickness absence both across
sexes and ages. Including part-time employees in the
cost analysis reduces the total annual costs by €0.1 bil-
lion. The cost analysis using the crude estimates was
generally lower than the ELMA estimates, which was
most widespread on the costs of work absenteeism and
least widespread on the cost of sickness absence.
The hypothetical reduction potential presented in
supplementary material F shows that the costs of work
absenteeism and sickness absence are reduced by 7%,
>30%, and >60% if the level of work-related stress
within the employees is reduced by 10%, 50%, and
100%, respectively.
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Pedersen et al
Discussion
The aim of the present study was to analyze work-related
stress in a large sample of Danish employees to estimate
labor market-related costs. We observed substantial eco-
nomic costs associated with the number of work-stress
indicators in terms of self-perceived work stress, the
Cohen four-item perceived stress scale, and job strain.
The overall average annual cost of work absenteeism per
employee was €1903 and €3909 for men and women,
respectively. This corresponds to 3.3% of men’s aver-
age annual wages and 9.0% of women’s average annual
wages (34). We observed significantly higher costs for
women than men, and across age ranges, we observed
higher costs for middle- to high-aged employees than
for young employees. For young male employees with a
high level of work stress, we observed both negative and
positive associations with labor market costs, reminding
us that some work-related stress may incline increased
productivity.
The total annual cost of work absenteeism associated
with work-related stress was €1.2 billion or 0.3% of the
Danish GDP in 2022 (35), of which 67% (€0.8 billion)
was for sick-listed employees. The total annual costs of
work absenteeism and sickness absence were reduced
by €0.1 billion when adjusting for part-time employ-
ees. Employees with three work-stress indicators were
generally the costliest concerning the value of work
absenteeism. However, the yearly costs of work absen-
teeism depended highly on the occurrence of work stress
within the age-sex subgroups and whether a clear pattern
of labor market affiliation was evident or not evident, as
was the case for young male employees.
A hypothetical analysis of reducing the work-stress
level within the employees did show a marked potential
for reducing costs of work absenteeism and sickness
absence at all three steps: moderately (10%), across
widespread (50%), to heavy (100%). Moreover, an
analysis comparing the ELMA method with a conven-
tional crude method for estimation of costs of work
absenteeism suggested an extensive underestimation
of the cost of work-related stress when using the crude
measurements.
We did not find any major differences in variable
composition between the group of employees with work
stress and the reference group. This may have changed
if additional explanatory factors were included in the
study. However, the combination of lifestyle, health,
employment type, and educational factors suggests a
strong explanatory basis of variables.
Comparison with previous studies
We did not find many comparable Danish studies. Juel
et al (36)
estimated the total cost of work-related stress
in 2005 to be approximately €2.0 billion per year,
corresponding to €2.9 billion in 2022 (31). The study
included costs of sickness absence, early death, and
health service expenses and was based on 2000 survey
data. Work-related stress was measured solely by job
strain. In comparison, the top-down estimate of the total
costs of the present study (presented in supplementary
material F) was €0.5 billion lower. It is, however, diffi-
cult to make a direct comparison since the two studies do
not include the same costs and take different approaches
to measuring work-related stress.
Making a direct comparison with studies from coun-
tries other than Denmark involves issues that should be
considered, for example, unequal access to reliable data,
differences in wages, health service expenses, and differ-
ences in the composition of the labor force. The analy-
sis design and estimation method may also differ. For
example, the present study used the combination of three
work-stress indicators: self-perceived stress, the Cohen
4-level scale, and job strain, while most of the studies
included in the comprehensive review by Hassard et al
(14)
solely used job strain as the primary measure of
work stress. Several of the studies included by Hassard
et al (14)
reported substantial expenses linked to work
stress despite differences in both study designs and the
prevalence of work stress, ranging from 2% to 27%.
We observed a comparable prevalence of work stress
(13–17%) despite using a slightly more restrictive job
strain version. However, our sensitivity analysis on the
three single work stress measurements did show mixed
results on work absenteeism and sickness absence, sug-
gesting uncertainty regarding the prevalence of work
stress when using the measurements separately.
Despite the unique analytical approach and study
design, we believe our results are comparable to other
European work-stress studies since many European
employees can receive wages during sickness absence,
such as in the UK, The Netherlands, and Scandinavian
countries. Moreover, by appropriately adjusting the
overall estimated annual costs, the results may become
comparable for hypothetical effect comparison with
foreign interventions and policy-making (37).
Strengths and limitations
The study has several strengths. First, by including four
waves of WEHD survey data, we built a large sample
size spanning a long period, increasing the analysis
strength by incorporating both single and repeated mea-
surements. The linkage to multiple longitudinal registers
with date-based records on wage payments and social
benefits is a profound strength, especially when the
analysis preserves the dynamic of the individual labor
transitions in terms of the multi-state modeling and the
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The labor market costs of work-related stress
ELMA method. An additional strength concerns the
multiple-angle detection of work-related stress through
three acknowledged work-stress indicators.
There are also some limitations to the study. First,
the sample population does not include small companies
with <10 employees due to a lack of information on
short-term sickness absence in the registers. Small com-
panies constitute a large part of the Danish labor market.
Second, only a few individuals entered the states of “dis-
ability pension” and “death” despite work-related stress,
which may be related to these outcomes. This was likely
due to the relatively short follow-up period and the age
limit of 64 years. Third, the results cannot be used for
individual predictions of expected costs of work absen-
teeism for a specific employee who experiences work
stress. Instead, the results are of a general character
expressing the mean expected costs of work absenteeism
for groups of employees exposed to work stress. Fourth,
the study included both part- and full-time benefits, as
well as part- and full-time wage payments. If multiple
payments were recorded simultaneously, then we pri-
oritized between the payments made. This prioritization
likely resulted in slightly underestimated durations of
working time and overestimated durations of the other
states. Fifth, any exposure to private life-related stress
was likely to interfere and may trigger stress at work,
and the individual contribution of different sources of
stress types may be difficult to separate. Additionally,
the reference group contains employees reporting a
high level of personal-related stress. Sixth, the lack of
individual-based objective information on medication
and disease may have caused bias if, eg, the use of cer-
tain medications was more frequent within the exposed
group. This may, for example, cause an underestimation
of the work-stress cost if the exposed employees more
frequently used pain medication to reduce headaches,
thereby reducing the risk of sickness absence. Sev-
enth, the restricted two-year follow-up period favors
short-term consequences of work stress and was likely
to underrate long-term consequences such as continu-
ous sickness absence. Moreover, the study concerns a
period with a fairly constant prevalence of work stress
of 28–29%, which may restrict the results to 2012–2020.
Eighth, the cost analysis primarily estimated the work-
stress-related costs of work absenteeism. However, we
expect work stress to have other costs, eg, concerning
specific healthcare services and likewise costs related
to the individual quality of life. Therefore, obtaining a
more solid estimate of the total employer and employee
costs of work stress will require more research.
Concluding remarks
We showed that work-related stress was associated with
substantial labor market costs. This study estimated that
the total annual value of work absenteeism of work-
related stress in Denmark was €305.2 million for men
and €868.5 million for women, or 0.3% of the GDP. The
long-term and social health costs of work-related stress
are likely even higher, depending on the possibility of
quantifying every aspect of the problem. However, given
this already sizeable economic burden, the prevention
of work-related stress is a major occupational health
concern, and the development of effective interventions
to achieve this aim should be given high priority.
Role of the funding source
The Danish National Research Centre for the Working
Environment supported this study. The funder of the
study had no role in the study design, data collection,
data analysis, data interpretation, or writing of the
report. The corresponding author had full access to all
the data and had the final responsibility to submit it for
publication.
Ethics approval
According to Danish law, research studies that use solely
survey and register data do not need approval from the
National Committee on Health Research Ethics (Den
Nationale Videnskabetiske Komité).
Competing interests
The authors declare no conflicts of interest.
Data sharing statement
Data are available in the Researcher access portal at the
Statistics Denmark website:
www.dst.dk/en/TilSalg/
Forskningsservice.
References
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