Sickness absenteeism is a complex phenomenon that is the result of three factors: (1) illness, (2) capacity to work, and (3) absence behavior. People with similar health statuses may show different work capacities depending on the type of tasks performed. Illness can be a significant contraindication to one job but not another. Moreover, people with a similar work incapacity may show radically different absence behaviors: some tend to keep working or at least minimise the period of absence, while others tend to take a break and extend it as much as possible.
While disease is a random event, largely independent of the human will, sickness absence is the result of conscious decisions. Such decisions may lead to insufficient absence, if an employee refrains from taking a break despite the illness, or excessive absence, if an employee decides to take a medically unjustified break or extends this break excessively.
Sick leave is a field of potential abuse. Sometimes employees use it inconsistently with purpose. The studies conducted so far indicate that such unethical practices occur in the case of, inter alia, nice weather
Shi & Skuterud, ‘Gone fishing! Reported sickness absenteeism and the weather.’ Skogman Thoursie, ‘Reporting sick: Are sporting events contagious?’ Thoursie, ‘Happy birthday! You’re insured! Gender differences in work ethics.’ Ben Halima, et al. ‘The Effects of the Complementary Compensation on Sickness Absence: an Approach Based on Collective Bargaining Agreements in France.’ Vahtera, et al. ‘The role of extended weekends in sickness absenteeism.’
The tendency to abuse sickness absence depends on the employee’s personal factors and the context
Hensing, et al. ‘How to measure sickness absence? Literature review and suggestion of five basic measures.’ In Steers & Rhodes, ‘Major influences on employee attendance: A process model.’
The subject of interest in this article is the first (micro) level. The research aim is to assess the effect of various factors related to employees’ personal characteristics on the abuse of referred sick leave in Poland. The following factors were taken into account: age, gender, place of living, level of education, marital status, number of children, health, and financial situation. The data used for the statistical analysis is from a survey (CAWI) conducted in 2021.
The propensity to abuse sick leave is a component of personal and environmental characteristics. The environment may create either incentives or constraints to excessive (unjustified) absenteeism. This applies to both the closer environment (workplace) and the further environment (society).
Regarding the workplace, based on the literature to date, there are three main factors that shape absenteeism behaviour. The first is the size of the company - the propensity to overuse sickness absence is much higher among employees of large corporations than small companies
Ahn & Yelowitz, ‘Paid Sick Leave and Absenteeism: The First Evidence from the U.S.’ Melchior, et al. ‘Do psychosocial work factors and social relations exert independent effects on sickness absence? A six year prospective study of the GAZEL cohort.’ Kristensen, ‘Sickness absence and work strain among Danish slaughterhouse workers: An analysis of absence from work regarded as coping behaviour.’ Melchior, et al. ‘Do psychosocial work factors and social relations exert independent effects on sickness absence? A six year prospective study of the GAZEL cohort.’ Marmot, et al. ‘Sickness absence as a measure of health status and functioning: From the UK Whitehall II study.’ Bekker, et al. ‘Sickness absence: A gender-focused review.’ In Chadwick-Jones, ‘Renegotiating absence levels.’
As for the broad environment, the propensity to abuse sick leave is determined by three main factors. The first is cultural conditions: habits and patterns of absenteeism are acquired in the family and are deeply rooted in local community traditions
Virtanen et al., ‘Locality and habitus: The origins of sickness absence practices.’ Pfau-Effinger, ‘Culture and welfare state policies: Reflections on a complex interrelation.’ Prins & De Graaf, ‘Comparison of sickness absence in Belgian, German, and Dutch firms.’ Ziebarth & Karlsson, ‘The effects of expanding the generosity of the statutory sickness insurance system.’ Leigh, ‘The effects of unemployment and the business cycle on absenteeism.’
In this study, the area of interest is personal characteristics of an employee related to the propensity to abuse sick leave. First it should be noted that some of those factors, such as, for example, gender or socioeconomic status, although they are directly related to the level of absenteeism, do not have to be abusive. As for gender, women are generally more likely to be on sick leave than men
Mastekaasa, ‘Parenthood, gender and sickness absence.’ Bekker et al., ‘Sickness absence: A gender-focused review.’ In Kristensen et al., ‘Socioeconomic status and duration and pattern of sickness absence. A 1-year follow-up study of 2331 hospital employees.’ Adler et al., ‘Socioeconomic Status and Health: The Challenge of the Gradient.’
Based on the literature, the two personal characteristics that can be distinguished that influence the propensity to abuse sickness absence are age and psychological conditions.
As for the psychological determinants, over half a century ago P. Taylor (1968) found that naturally pessimistic and unhappy people are most prone to abuse sick leave. Moreover, it was noticed that people with an extrovert personality are most often on sick leave, and people with a neurotic personality are on the most extended leaves. People with introverted personalities use absenteeism the least frequently and for the shortest periods.
As for age, it is widely believed that older workers spend the most time on sick leave. However, this stereotype is not reflected in reality. Employees, depending on age, show a different pattern of absence: younger workers are often on sick leave, but a single break is relatively short, while older workers are rarely on sick leaves, but a single break is relatively long
Slowey & Zubrzycki, Melchior et al., ‘Do psychosocial work factors and social relations exert independent effects on sickness absence? A six year prospective study of the GAZEL cohort.’
The abuse of sick leave is a problem that is still relatively poorly understood. Above all, there is a lack of empirical research on the issue. Research in this area is difficult to conduct due to the blurred distinction between justified and unjustified use of sick leave. Researchers are forced to observe high levels of caution in interpreting the available data, as it is never fully clear whether an absence is forced by an actual illness, or whether it is the effect of other non-health-related causes.
In Poland, the abuse of sick leave has not as yet been the subject of scientific research, and as a result the scale of the phenomenon is not known. The one available source of information on the topic is the results of spot checks carried out by the welfare authorities (ZUS). Unfortunately, the possibility of conclusions based on this data is severely limited as the spot checks are selective and cover only a narrow group of sick leave referrals (long-term sick leave absence).
The lack of reliable and complete data from public sources requires the sourcing of information in another way. One of the potential solutions is to use a survey-based study. Of course, the information gathered in this way does not reflect the actual state of affairs, and merely contains the declarations of respondents, which can to a lesser or greater degree diverge from reality, especially if difficult and/or morally questionable topics are covered
Bostyn et al., ‘Of Mice, Men, and Trolleys: Hypothetical Judgment Versus Real-Life Behavior in Trolley-Style Moral Dilemmas.’
The source material is from a survey study conducted in December 2021 by the research agency BBiAS. The information was gathered using the CAWI method, that is, via an internet survey. The territory covered by the research encompassed the whole of Poland, and the participants were full-time employees covered by national health insurance. The research sample was 1067 respondents. The random sampling was made up of national panels of respondents. It can be assumed that the randomised character of the sample provides grounds for generalisation of the results. The maximum measurement error was +/− 3% with a reliability level of 95%. The structure of the sample due to chosen personal characteristics of respondents is presented in Table 1.
Sample characteristics according to a personal characteristics of respondents
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 |
Source: Own elaboration.
Based on the results of prior research, eleven circumstances were isolated that particularly encourage the abuse of sick leave referrals. These are situations in which employees may feel a particular temptation to partake in unethical behavior. These circumstances are:
CIR1: extending the period away from work during public holidays or long weekends, CIR2: overtiredness and/or overwork (sick leave as additional rest), CIR3: refusal to grant regular leave (sick leave as a form of retaliation), CIR4: demonstrating dissatisfaction with working conditions (sick leave as a form of strike), CIR5: escape from problematic work tasks and/or from cooperation with disliked people, CIR6: a spontaneous escapade (e.g., fishing, mushroom picking, attending a favourite team’s match), CIR7: a situation of higher necessity (e.g., an important family occasion), CIR8: renovation work or other important work on the home, CIR9: carrying out other paid work (e.g., an urgent task), CIR10: the need to arrange an important administrative matter, CIR11: caring for a loved one or an animal.
The respondents were asked to respond to each of these eleven cases and declare if they had ever taken sick leave in such circumstances. The results (in general and due to personal characteristics) are presented in Table 2. Employees in Poland use sick leave the least often (8.4%) to demonstrate dissatisfaction with working conditions, and the most often (22.9%) in situations of higher necessity.
Abuse of sick leave absence and personal characteristic of respondents (in %)
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 |
Source: own elaboration.
The research aimed to assess the influence of various factors related to personal characteristics on the abuse of sick leave in Poland. For structural equation modelling the MLR algorithm was used (maximum likelihood estimation with robust (Huber-White) standard errors), which is recommended when the assumption of a multivariate normal distribution is not met
Lai, ‘Estimating Standardized SEM Parameters Given Nonnormal Data and Incorrect Model: Methods and Comparison.’
Basing on the classic ‘Fraud Triangle’ concept
Cressey,
Categories of sick leave absence 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 |
Source: Own elaboration.
Results of structural model estimates for the dependent variable according to the three categories of abuse (recreation, escape, and compulsion)
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 |
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.
In the compulsion category, the motivation for absence is the pressure related to the need to deal with an important and/or unpredicted matter that is in conflict with working hours. Such pressure is related to an important administrative matter or another situation of higher necessity, renovation work, or the need to provide personal care for a loved one or an animal. In the escape abuse category the motivation is the desire to ‘escape from’ unwanted work tasks, or ‘escape to’ desired activities that collide with working hours. This desire is related to various factors that either push away from work (push factors), such as avoiding unpleasant events and/or people, or attract towards absence (pull factors) such as the wish to participate in a spontaneous escapade (fishing, mushroom picking, attending a favorite team’s match). In the recreation abuse category, the motivation to abuse sick leave is rest and recuperation. These circumstances take place in situations such as extending one’s free time away from work (e.g., a long weekend), or as a reaction to weariness, overtiredness, and/or overwork.
The independent variables were various factors related to the respondents’ personal characteristics. The list and description of those variables is presented in Table 5. Variables related to age, gender, number of children, education, place of living, and marital status are nominal. In statistical analysis they were linked with reference categories. For the age-related variables reference category was ‘young’. For the gender-related variable, reference category was ‘female’. For the variable related to number of children, reference category was ‘childless’. For the education-related category, reference category was ‘lower’. For the variables related to a place of living, reference category was ‘medium city’. For the variable related to marital status, reference category was ‘single’. Variables related to financial situation and health were ordinal and treated as numerical.
The list and description of independent variables
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 |
reference category
ordinal measurement treated as a numerical variable
Source: Own elaboration.
To estimate the effect of the personal characteristic factors on abuse in the compulsion category, a multivariate linear regression analysis was conducted. The obtained model proved to be statistically significant, F (24, 1056) = 9.26; p < 0.001. It explains around 8% (7% after correction) of the variability of the tested variable (R2 = 0.08, adj.R2 = 0.07). The results of the model estimation are presented in Figure 1.
In the model, five of ten analysed predictors were statistically significant: (1) age: mature, (2) number of children: with children, (3) place of living: provincial, (4) marital status: in a relationship, (5) gender: male.
As for age, mature workers less often declare propensity to abuse compulsion absence than young workers. In case of old workers, such propensity is higher in comparison to young ones, but the result was statistically insignificant.
As for number of children, workers with children far more often declare abusing compulsion absence than childless workers.
As for place of living, workers living in both provincial and metropolitan places more often declare abusing compulsion absence than workers living in medium cities. However, the result was statistically significant only in accordance with workers living in the provinces.
As for education, workers with higher education less often declare abusing compulsion absence, however this result is statistically insignificant.
As for marital status, employees in a relationship far less often declare abusing compulsion absence than singles. As for gender, male workers far more often declare abusing compulsion absence than female workers. Both results were statistically significant.
As for financial situation and health, better assessment of those factors is linked with an increase in propensity to abuse compulsion absence. It means, that employees feeling better in terms of health status or financial status, more often declare abusing such absence. Both of those results were, however, statistically insignificant.
Similarly to the previous category of abuse, multivariate linear regression analysis was conducted. The obtained model was shown to be statistically significant, F(24, 1042) = 5,80; p < 0.001. It explains around 5% (4% after correction) of the variability of the tested variable (R2 = 0.05, adj.R2 = 0.04. The results of the model estimation are presented in Figure 2.
In the model, four of ten analysed predictors were statistically significant: (1) number of children: with children, (2) place of living: provincial, (3) marital status: in a relationship, (4) gender: male. In addition, two factors turned out to be on the borderline of statistical significance: health and higher education.
As for age, there was no link between mature and young workers in terms of propensity to abuse escape sickness absence. Old workers more often declare abusing such absence than young ones, however, this result was statistically insignificant.
As for number of children, employees with children far more often declare abusing escape sick leaves than childless employees.
As for place of living, workers from both metropolitan and provincial areas more often declare abusing escape absence than workers living in middle cities, but only in the case of provincial was the result statistically significant. As for education, workers with higher education are less likely to abuse such absence than workers with lower education, and this result is on the borderline of statistical significance. As for marital status, workers in a relationship less often declare abusing escape absence than single ones, and this result is statistically significant. As for gender, males are far more likely to abuse the escape absence than females, and this result is statistically significant.
As for health, a better assessment of own health status is linked with more often abusing escape absence. This result was on the borderline of statistical significance. The financial situation was not related to this kind of abuse.
Similarly to the previous category of abuse, multivariate linear regression analysis was conducted. The obtained model was shown to be statistically significant, F(24, 1042) = 3.17; p < 0.01. It explains around 3% (2% after correction) of the variability of the tested variable (R2 = 0.03, adj.R2 = 0.02). The results of the model estimation are presented in Figure 3.
In the model, four of ten analysed predictors were statistically significant: (1) number of children: with children, (2) place of living: provincial, (3) marital status: in a relationship, (4) gender: male. In addition, two factors turned out to be on the borderline of statistical significance: (1) age: mature and (2) gender: male.
As for age, both mature and old workers less often declare abusing recreation sickness absence than young workers. The result was statistically significant only in the case of mature workers.
As for number of children, workers with children were more likely to abuse recreation absence than childless workers, and this result was statistically significant. As for place of living, workers from both metropolitan and provincial areas more often declare abusing absence than workers from middle cities, but the result was statistically significant only in the case of provincial. As for education, workers with higher education were more likely to abuse absence than less educated ones, but this result was statistically insufficient. The result was significant also for marital status – workers in a relationship were less likely to abuse such absence than single workers. As for gender, males more often declare abusing than females, and this result was on the border of statistical significance.
As for health and financial situation, better assessment of own status in both cases was linked with a higher propensity to abuse recreation absence. This result was statistically significant only for health.
The level of sick leave absence is a complex issue that is dependent on a range of varied factors that come down to individual characteristics of the employee (micro factors), the work environment (meso factors), and the wider social, economic, and institutional environment (macro factors). The subject of interest in this article was factors related to the personal characteristics of employees. However, this does not concern conditions of a ‘health-related’ nature that affect the ability to work, but ‘non-health-related’ factors that are related to absenteeism behavior.
The research confirmed that certain personal characteristics have an important effect on the abuse of sick leave absence. Table 6 contains a summary of the results, including the directions of the effect of the tested factors on particular abuse categories. Factors affecting propensity to abuse are gender, age, number of children, place of living, marital status, and subjective health. We found no impact of education and financial status.
The direction of the effect of personal factors on particular categories of sick leave absence abuse
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
Gender is an important personal characteristic determining all categories of sick leave abuse. As we knew earlier, females are generally more likely to be on sick leave than males
Mastekaasa, ‘Parenthood, gender and sickness absence.’
As for age, young workers more often declare abusing recreation and compulsion sickness absence than mature workers. Surprisingly, it does not apply to escape absence, therefore we cannot confirm the common opinion that young workers particularly often use minor ‘illnesses’ to avoid uncomfortable professional tasks.
We found also that employees positively assessing own health more often declare abusing sickness absence. Taking into account previous findings reveals surprising observations. Namely, sickness absence is most often abused by those who rarely use it due to illness. These are males, young, and in good condition. This suggests that the abuses may constitute a form of ‘compensation’ for fewer days off than those on legitimate layoffs. However, confirmation of this assumption would require further, in-depth research.
The added value of our study is drawing attention to the new personal factors which – according to our best knowledge – were not previously identified as a determinant of abuse of sick leave. These are: the number of children, place of living, and marital status. Workers with children are more likely to abuse sickness absence. It applies to all forms of abuse: recreation, escape, and compulsion. The same with single workers – they often declare abusing sickness absence. As for the place of living, the least likely to abuse sickness leave are workers from medium-sized cities.