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

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

Wroclaw Review of Law, Administration & Economics
Volume 13 (2023): Issue 2 (December 2023)
By:   
Open Access
|Jan 2024
Figures & tables

Figures & Tables

Table 1:

Sample characteristics according to a personal characteristics of respondents

CharacteristicDescription%
GenderFemale49.9
Male51.1
Age18–2417.7
25–3430.4
35–4425.6
45–5418.7
55–647.7
EducationPrimary1.9
Secondary11.4
Vocational30.6
Post-secondary12.3
Bechelor13.4
Graduate and higher30.5
Place of livingVillage17.6
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 statusSingle29.2
Married43.8
Divorced6.2
Separation1.5
Widowed1.5
Partnership17.8
Number of childrenNone52.2
1–239.8
3–46.9
5 and more1.0
Subjective assessment of own financial situationDefinitely good: I have enough for living and I am saving16.6
Rather good: I have enough for living but I am not saving26.2
Average: I live frugally, so I can afford to buy everything45.5
Rather bad: I can afford only the most basic expenses10.1
Definitely bad: I cannot afford even the most basic expenses1.5
Subjective assessment of health conditionVery good19.6
Good49.1
Average26.4
Bad4.0
Very bad0.8

[i] Source: Own elaboration.

Table 2:

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

CharacteristicDescriptionCircumstance
CIR1CIR2CIR3CIR4CIR5CIR6CIR7CIR8CIR9CIR10CIR11
General19.417.710.68.49.211.222.914.39.718.619.4
GenderFemale7.718.19.06.26.87.918.410.37.016.519.2
Male12.717.412.210.711.614.427.318.112.320.619.6
Age18–2412.221.212.210.69.012.227.013.811.120.623.3
25–349.019.48.36.89.68.323.812.09.617.618.2
35–4410.615.012.510.39.511.016.914.79.215.415.4
45–5410.114.610.68.58.514.126.619.610.621.122.1
55–649.819.59.83.78.513.420.79.86.122.022.0
EducationPrimary20.045.015.025.00.015.025.010.020.015.015.0
Secondary12.317.213.111.511.511.532.828.712.329.528.7
Vocational9.217.210.78.08.314.125.214.411.718.719.3
Post-secondary9.913.712.28.49.99.215.310.713.015.315.3
Bechelor11.223.814.711.28.411.922.418.29.118.918.9
Graduate and higher9.515.76.85.59.98.320.08.64.915.718.2
Place of livingVillage8.521.810.68.511.211.230.323.49.624.527.1
Small town17.224.212.518.015.621.928.917.214.825.026.6
Medium city11.213.49.27.37.010.219.811.28.615.013.7
Big city7.516.311.77.17.97.519.212.98.313.820.4
Metropolis9.118.310.25.68.110.221.310.29.620.315.2
Marital statusSingle10.920.514.18.79.011.529.515.112.521.522.8
Married9.615.69.69.07.911.621.814.18.418.218.0
Divorced12.119.710.612.110.612.119.716.77.622.724.2
Separation18.825.018.825.025.012.525.025.025.018.831.3
Widowed6.331.318.80.025.025.012.537.518.825.025.0
Partnership9.515.85.84.79.57.916.39.56.812.614.2
Number of childrenNone9.517.19.56.88.39.019.911.18.416.916.7
02-sty8.918.410.88.79.212.525.415.18.919.321.4
04-mar18.920.316.218.916.218.928.431.121.627.025.7
5 and more36.49.118.29.19.118.236.427.318.218.236.4
Financial situationDefinitely good11.014.410.511.010.510.523.418.210.516.318.7
Rather good8.015.89.46.37.89.421.612.88.417.618.1
Average12.822.011.49.911.014.525.513.812.122.022.3
Rather bad16.325.620.99.34.711.616.311.62.314.018.6
Definitely bad11.133.311.122.222.222.233.333.322.244.422.2
HealthVery good12.418.110.211.39.013.624.315.312.418.614.7
Good7.514.611.88.27.911.420.413.69.316.118.6
Average9.717.19.57.89.99.723.915.28.220.420.0
Bad13.025.913.07.49.311.121.39.311.114.826.9
Very bad31.331.312.56.312.525.031.318.818.831.318.8

[i] Source: own elaboration.

Table 3:

Categories of sick leave absence abuse

Abuse categoryCircumstances of abuse
RECREATIONCIR1. extending the period free from work
CIR2. overtiredness and/or overwork
ESCAPECIR3. refusal to grant regular leave
CIR5. escape from problematic work tasks and/or cooperation with unliked persons
CIR6. spontaneous escapade
CIR9. other paid work
COMPULSIONCIR7. 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

[i] Source: Own elaboration.

Table 4.

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

Latent□CircumstanceBs.e.ZDPUGPUR2
COMPULSION->CIR100.630.0415.38***0.550.720.40
COMPULSION->CIR70.570.0415.18***0.500.650.33
COMPULSION->CIR80.580.0512.83***0.490.670.34
COMPULSION->CIR110.530.0413.30***0.450.600.28
ESCAPE->CIR30.380.057.28***0.280.490.15
ESCAPE->CIR50.430.058.01***0.330.540.19
ESCAPE->CIR90.520.059.71***0.420.630.27
ESCAPE->CIR60.470.059.47***0.380.570.23
RECREATION->CIR10.480.067.96***0.360.600.23
RECREATION->CIR20.480.068.65***0.370.590.23

Note; □ = Direction of effect of latent variable on circumstance; B = Non-standardised factor loading; s.e. = Standard estimation error B; Z = Statistic Z; DPU and GPU = 95% confidence intervals (appropriately lower and higher); β = Standardised factor loading; X2(32) = 57.10; p < 0.01.; CFI = 0.98; TLI = 0.97; NFI = 0.96; IFI =0.98; RMSEA = 0.03; 90%PU[0.02–0.04]; PCLOSE = 1.000; SRMR = 0.02; GFI = 0.99; AGFI =0.98.

*** p < 0.001

** p < 0.01

* p < 0.05

Source: Own elaboration.

Table 5:

The list and description of independent variables

CharacteristicIndependent variable
NameDescription
Gendergender: male➢ male
gender: female*➢ female
Ageage: young*

  • ➢ 18–24

  • ➢ 25–34

age: mature➢ 35–44
age: old

  • ➢ 45–54

  • ➢ 55–64

Number of childrennumber of children: childless*➢ none
number of children: with children

  • ➢ 1–2

  • ➢ 3-4

  • ➢ 5 and more

Educationeducation: lower*

  • ➢ primary

  • ➢ secondary

  • ➢ vocational

  • ➢ post-secondary

education: higher

  • ➢ bachelor

  • ➢ graduate or higher

Place of livingplace of living: provincial

  • ➢ village

  • ➢ small town

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

  • ➢ big city

  • ➢ metropolis

Marital statusmarital status: single*

  • ➢ single

  • ➢ divorced

  • ➢ separation

  • ➢ widowed

marital status: in a relationship

  • ➢ married

  • ➢ partnership

Subjective assessment of own financial situationfinancial situation**

  • ➢ definitely good

  • ➢ rather good

  • ➢ average

  • ➢ rather bad

  • ➢ definitely bad

Subjective assessment of health conditionhealth**

  • ➢ very good

  • ➢ good

  • ➢ average

  • ➢ bad

  • ➢ very bad

* reference category

* ordinal measurement treated as a numerical variable

Source: Own elaboration.

Figure 1:

Results of the predictive model estimates for the abuse of compulsion sickness absence

Source: Own elaboration

Note: 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 absence

Source: Own elaboration

Note: 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 absence

Source: Own elaboration

Note: 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.

Table 6:

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

PredictorAbuse category
COMPULSIONESCAPERECREATION
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↑-↑

Description:

↑ an increase in the factor value represents an increase in abuse in a given category

↓ an increase in the factor value represents a decrease in abuse in a given category

- relation close to zero for the level of abuse in a given category

* statistically significant effect or on the borderline of statistical significance

Source: Own elaboration

DOI: https://doi.org/10.2478/wrlae-2022-0008 | Journal eISSN: 2084-1264
Journal RSS Feed
Language: English
Page range: 60 - 81
Published on: Jan 26, 2024
Published by: University of Wroclaw, Faculty of Law, Administration and Economics
In partnership with: Paradigm Publishing Services
Keywords:
sickness absence,
absenteeism,
welfare abuse,
personal factors
Related subjects:
Law,
Public law,
Public law, other

© 2024 Łukasz Jurek, published by University of Wroclaw, Faculty of Law, Administration and Economics
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 13 (2023): Issue 2 (December 2023)
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