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The Impact of the Employee’s Personal Characteristics on the Abuse of Sickness Absence: Empirical Evidence From Poland

Łukasz Jurek
Łukasz Jurek
   | 26 ene 2024
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Publicado en línea: 26 ene 2024
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DOI: https://doi.org/10.2478/wrlae-2022-0008
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© 2023 Łukasz Jurek, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1:

Results of the predictive model estimates for the abuse of compulsion sickness absenceSource: Own elaborationNote: The error whisker bars present 95% of the confidence interval for estimate B. Lines that cross one another represent the lack of differences between the predictors in the effect on the level of Compulsion. However, lines that do not cross one another represent important differences in the effect on the level of the Compulsion variable.
Results of the predictive model estimates for the abuse of compulsion sickness absenceSource: Own elaborationNote: The error whisker bars present 95% of the confidence interval for estimate B. Lines that cross one another represent the lack of differences between the predictors in the effect on the level of Compulsion. However, lines that do not cross one another represent important differences in the effect on the level of the Compulsion variable.

Figure 2:

Results of the predictive model estimates for the abuse of escape sickness absenceSource: Own elaborationNote: The error whisker bars present 95% of the confidence interval for estimate B. Lines that cross one another represent the lack of differences between the predictors in the effect on the level of Escape. However, lines that do not cross one another represent important differences in the effect on the level of the Escape variable.
Results of the predictive model estimates for the abuse of escape sickness absenceSource: Own elaborationNote: The error whisker bars present 95% of the confidence interval for estimate B. Lines that cross one another represent the lack of differences between the predictors in the effect on the level of Escape. However, lines that do not cross one another represent important differences in the effect on the level of the Escape variable.

Figure 3:

Results of the predictive model estimates for the abuse of recreation sickness absenceSource: Own elaborationNote: The error whisker bars present 95% of the confidence interval for estimate B. Lines that cross one another represent the lack of differences between the predictors in the effect on the level of Recreation. However, lines that do not cross one another represent important differences in the effect on the level of the Recreation variable.
Results of the predictive model estimates for the abuse of recreation sickness absenceSource: Own elaborationNote: The error whisker bars present 95% of the confidence interval for estimate B. Lines that cross one another represent the lack of differences between the predictors in the effect on the level of Recreation. However, lines that do not cross one another represent important differences in the effect on the level of the Recreation variable.

Abuse of sick leave absence and personal characteristic of respondents (in %)

Characteristic Description Circumstance
CIR1 CIR2 CIR3 CIR4 CIR5 CIR6 CIR7 CIR8 CIR9 CIR10 CIR11
General 19.4 17.7 10.6 8.4 9.2 11.2 22.9 14.3 9.7 18.6 19.4
Gender Female 7.7 18.1 9.0 6.2 6.8 7.9 18.4 10.3 7.0 16.5 19.2
Male 12.7 17.4 12.2 10.7 11.6 14.4 27.3 18.1 12.3 20.6 19.6
Age 18–24 12.2 21.2 12.2 10.6 9.0 12.2 27.0 13.8 11.1 20.6 23.3
25–34 9.0 19.4 8.3 6.8 9.6 8.3 23.8 12.0 9.6 17.6 18.2
35–44 10.6 15.0 12.5 10.3 9.5 11.0 16.9 14.7 9.2 15.4 15.4
45–54 10.1 14.6 10.6 8.5 8.5 14.1 26.6 19.6 10.6 21.1 22.1
55–64 9.8 19.5 9.8 3.7 8.5 13.4 20.7 9.8 6.1 22.0 22.0
Education Primary 20.0 45.0 15.0 25.0 0.0 15.0 25.0 10.0 20.0 15.0 15.0
Secondary 12.3 17.2 13.1 11.5 11.5 11.5 32.8 28.7 12.3 29.5 28.7
Vocational 9.2 17.2 10.7 8.0 8.3 14.1 25.2 14.4 11.7 18.7 19.3
Post-secondary 9.9 13.7 12.2 8.4 9.9 9.2 15.3 10.7 13.0 15.3 15.3
Bechelor 11.2 23.8 14.7 11.2 8.4 11.9 22.4 18.2 9.1 18.9 18.9
Graduate and higher 9.5 15.7 6.8 5.5 9.9 8.3 20.0 8.6 4.9 15.7 18.2
Place of living Village 8.5 21.8 10.6 8.5 11.2 11.2 30.3 23.4 9.6 24.5 27.1
Small town 17.2 24.2 12.5 18.0 15.6 21.9 28.9 17.2 14.8 25.0 26.6
Medium city 11.2 13.4 9.2 7.3 7.0 10.2 19.8 11.2 8.6 15.0 13.7
Big city 7.5 16.3 11.7 7.1 7.9 7.5 19.2 12.9 8.3 13.8 20.4
Metropolis 9.1 18.3 10.2 5.6 8.1 10.2 21.3 10.2 9.6 20.3 15.2
Marital status Single 10.9 20.5 14.1 8.7 9.0 11.5 29.5 15.1 12.5 21.5 22.8
Married 9.6 15.6 9.6 9.0 7.9 11.6 21.8 14.1 8.4 18.2 18.0
Divorced 12.1 19.7 10.6 12.1 10.6 12.1 19.7 16.7 7.6 22.7 24.2
Separation 18.8 25.0 18.8 25.0 25.0 12.5 25.0 25.0 25.0 18.8 31.3
Widowed 6.3 31.3 18.8 0.0 25.0 25.0 12.5 37.5 18.8 25.0 25.0
Partnership 9.5 15.8 5.8 4.7 9.5 7.9 16.3 9.5 6.8 12.6 14.2
Number of children None 9.5 17.1 9.5 6.8 8.3 9.0 19.9 11.1 8.4 16.9 16.7
02-sty 8.9 18.4 10.8 8.7 9.2 12.5 25.4 15.1 8.9 19.3 21.4
04-mar 18.9 20.3 16.2 18.9 16.2 18.9 28.4 31.1 21.6 27.0 25.7
5 and more 36.4 9.1 18.2 9.1 9.1 18.2 36.4 27.3 18.2 18.2 36.4
Financial situation Definitely good 11.0 14.4 10.5 11.0 10.5 10.5 23.4 18.2 10.5 16.3 18.7
Rather good 8.0 15.8 9.4 6.3 7.8 9.4 21.6 12.8 8.4 17.6 18.1
Average 12.8 22.0 11.4 9.9 11.0 14.5 25.5 13.8 12.1 22.0 22.3
Rather bad 16.3 25.6 20.9 9.3 4.7 11.6 16.3 11.6 2.3 14.0 18.6
Definitely bad 11.1 33.3 11.1 22.2 22.2 22.2 33.3 33.3 22.2 44.4 22.2
Health Very good 12.4 18.1 10.2 11.3 9.0 13.6 24.3 15.3 12.4 18.6 14.7
Good 7.5 14.6 11.8 8.2 7.9 11.4 20.4 13.6 9.3 16.1 18.6
Average 9.7 17.1 9.5 7.8 9.9 9.7 23.9 15.2 8.2 20.4 20.0
Bad 13.0 25.9 13.0 7.4 9.3 11.1 21.3 9.3 11.1 14.8 26.9
Very bad 31.3 31.3 12.5 6.3 12.5 25.0 31.3 18.8 18.8 31.3 18.8

Sample characteristics according to a personal characteristics of respondents

Characteristic Description %
Gender Female 49.9
Male 51.1
Age 18–24 17.7
25–34 30.4
35–44 25.6
45–54 18.7
55–64 7.7
Education Primary 1.9
Secondary 11.4
Vocational 30.6
Post-secondary 12.3
Bechelor 13.4
Graduate and higher 30.5
Place of living Village 17.6
Small town (up to 20k citizens) 12.0
Medium city (between 20k and 100k citizens) 29.4
Big city (between 100k and 500k citizens) 22.5
Metropolis (more than 500k citizens) 18.5
Marital status Single 29.2
Married 43.8
Divorced 6.2
Separation 1.5
Widowed 1.5
Partnership 17.8
Number of children None 52.2
1–2 39.8
3–4 6.9
5 and more 1.0
Subjective assessment of own financial situation Definitely good: I have enough for living and I am saving 16.6
Rather good: I have enough for living but I am not saving 26.2
Average: I live frugally, so I can afford to buy everything 45.5
Rather bad: I can afford only the most basic expenses 10.1
Definitely bad: I cannot afford even the most basic expenses 1.5
Subjective assessment of health condition Very good 19.6
Good 49.1
Average 26.4
Bad 4.0
Very bad 0.8

The direction of the effect of personal factors on particular categories of sick leave absence abuse

Predictor Abuse category
COMPULSION ESCAPE RECREATION
Gender: male ↑* ↑* ↑*
Age: mature ↓* - ↓*
Age: old ↑ ↑ ↓
Number of children: with children ↑* ↑* ↑*
Place of living: metropolitan ↑ ↑ ↑
Place of living: provincial ↑* ↑* ↑*
Education: higher ↓ ↓ ↑
Marital status: in a relationship ↓* ↓* ↓*
Health ↑ ↑* ↑*
Financial situation ↑ - ↑

Categories of sick leave absence abuse

Abuse category Circumstances of abuse
RECREATION CIR1. extending the period free from work
CIR2. overtiredness and/or overwork
ESCAPE CIR3. refusal to grant regular leave
CIR5. escape from problematic work tasks and/or cooperation with unliked persons
CIR6. spontaneous escapade
CIR9. other paid work
COMPULSION CIR7. situation of higher necessity
CIR8. renovation or other important work on the home
CIR10. need to arrange an important administrative matter
CIR11. providing care for a loved one or animal

The list and description of independent variables

Characteristic Independent variable
Name Description
Gender gender: male ➢ male
gender: female* ➢ female
Age age: young*

➢ 18–24

➢ 25–34
age: mature ➢ 35–44
age: old

➢ 45–54

➢ 55–64
Number of children number of children: childless* ➢ none
number of children: with children

➢ 1–2

➢ 3-4

➢ 5 and more
Education education: lower*

➢ primary

➢ secondary

➢ vocational

➢ post-secondary
education: higher

➢ bachelor

➢ graduate or higher
Place of living place of living: provincial

➢ village

➢ small town
place of living: medium-city* ➢ medium city
place of living: metropolitan

➢ big city

➢ metropolis
Marital status marital status: single*

➢ single

➢ divorced

➢ separation

➢ widowed
marital status: in a relationship

➢ married

➢ partnership
Subjective assessment of own financial situation financial situation**

➢ definitely good

➢ rather good

➢ average

➢ rather bad

➢ definitely bad
Subjective assessment of health condition health**

➢ very good

➢ good

➢ average

➢ bad

➢ very bad

Results of structural model estimates for the dependent variable according to the three categories of abuse (recreation, escape, and compulsion)

Latent □ Circumstance B s.e. Z DPU GPU R2
COMPULSION -> CIR10 0.63 0.04 15.38*** 0.55 0.72 0.40
COMPULSION -> CIR7 0.57 0.04 15.18*** 0.50 0.65 0.33
COMPULSION -> CIR8 0.58 0.05 12.83*** 0.49 0.67 0.34
COMPULSION -> CIR11 0.53 0.04 13.30*** 0.45 0.60 0.28
ESCAPE -> CIR3 0.38 0.05 7.28*** 0.28 0.49 0.15
ESCAPE -> CIR5 0.43 0.05 8.01*** 0.33 0.54 0.19
ESCAPE -> CIR9 0.52 0.05 9.71*** 0.42 0.63 0.27
ESCAPE -> CIR6 0.47 0.05 9.47*** 0.38 0.57 0.23
RECREATION -> CIR1 0.48 0.06 7.96*** 0.36 0.60 0.23
RECREATION -> CIR2 0.48 0.06 8.65*** 0.37 0.59 0.23
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