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

Abstract

The tendency to abuse sickness absence is a complex problem conditioned by many factors, that can be divided, depending on the level of impact, into three groups: micro (related to personal characteristics), meso (related to the work environment), and macro (related to the broader environment). The subject of interest in the article is the first level (micro). The aim is to evaluate the impact of various personal factors on the declared abuse of sickness leave in Poland.

The data was gathered using the CAWI survey. The research sample consisted of 1067 respondents (full-time employees). Statistical analysis incorporated multivariable linear regression and structural equation modelling.

Some personal characteristics have a significant impact on the declared abuse of sick leave. These factors are: gender, age, number of children, place of living, marital status, and subjective health. We found no impact of education and financial status.

Although abusing sick leave is a serious economic and organisational problem, its causes still remain relatively poorly recognised. Personal characteristics determine the propensity to abuse sick leave, and they need to be taken into account under the process of managing absence behaviours.

The added value of the study is drawing attention to the new personal factors which were not previously identified as a determinant of abuse of sick leave. These are: the number of children, place of living, and marital status.

I.. Introduction

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 weather¹ , major sporting events² , birthdays³ , long weekends⁴ , or simply Wednesday⁵ .

The tendency to abuse sickness absence depends on the employee’s personal factors and the context⁶ . According to the process model of absence⁷ factors are divided into three levels: (1) micro, related to the personal characteristics of the employee, (2) meso, related to the work environment, and (3) macro, related to broad social, cultural and institutional environment.

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.

II.. Literature Review

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 companies⁸ . The second is the quality of work - the propensity to overuse absenteeism increases with deficits in support from colleagues and supervisors⁹ , an increase in job stress and strain¹⁰ , a reduction in career development opportunities and participation¹¹ , and a decrease in overall job satisfaction¹² . And the third is organisational culture – an employee’s absenteeism behaviour to some extent depends on how other people (colleagues, superiors) in the company behave¹³ . Indeed, every organisation creates informal arrangements that define ‘acceptable’ or ‘tolerable’ behaviour. It is for this reason that levels of absenteeism tend to vary more between companies than within them¹⁴ .

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 traditions¹⁵ . More broadly, however, the tendency to abuse absenteeism is an issue of morality (so-called ‘benefit morality’), which is an element of the general welfare culture¹⁶ . The second is the standard of sickness benefits: the more ‘generous’ the benefits, the greater the moral hazard¹⁷ . The level of this ‘generosity’ is expressed in terms of the replacement rate, that is, the ratio of the amount of benefit received during sick leave to the net basic salary¹⁸ . Finally, the third is the labour market situation – as this situation improves, the propensity to abuse sickness absence increases¹⁹ .

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 men²⁰ , but the difference, in this particular case, seems to be mainly determined by health factors²¹ . As for socioeconomic status, the lower it is, the higher the absenteeism²² . In this case, the difference seems to be mainly related to health, as the status (level of education, material situation) affects the health outcomes²³ . However, the impact of a non-health factor (value system, work ethos, etc.) should not be excluded here, but the research results in this field are inconclusive so far.

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 long²⁴ . Since short-term absences are the most abused²⁵ , it can be concluded that they are most often committed by young workers. This is not a new problem. It was noticed over half a century ago by Hill and Trist (1955) that young workers use minor ‘illnesses’ to avoid uncomfortable professional tasks. These observations automatically evoke associations with a pupil who starts to feel sick just before a troublesome challenge in classes (e.g., a test). It suggests that the habits from school may be transferred to the initial period of the professional career and may affect the absenteeism of young employees.

III.. Research Methodology

3.1.. Data sources and sample characteristics

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 covered²⁶ . Nevertheless, this is also a valuable source of information; while it may not present the facts of the problem, does reveal how it is perceived by respondents.

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.

Table 1:

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

Source: Own elaboration.

3.2.. Abuse of sick leave in the light of declarations by respondents

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.

Table 2:

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

Source: own elaboration.

3.3.. Method and research procedure

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 met²⁷ . Next, a series of multivariable linear regression analyses were conducted which were used to assess the influence of the predictors on particular categories of abuse. The statistical analysis was conducted using R software with the ‘Lavaan’ and ‘car’ packages.

3.4.. Dependent variable

Basing on the classic ‘Fraud Triangle’ concept²⁸ , the individual circumstances of abuse were grouped according to the type of motivational element into three intercorrelated subfactors (according to the division presented in Table 3), which were defined as categories of abuse: compulsion, escape, and recreation. At the stage of initial calculations, it was found that circumstance CIR4, that is, sick leave as a form of demonstrating dissatisfaction with working conditions, was not correlated with the other circumstances, and as a result, was excluded from further analysis. The obtained model structural estimates are presented in Table 4.

Table 3:

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

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	□	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

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.

3.5.. Independent variables

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.

Table 5:

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

*

reference category

* ordinal measurement treated as a numerical variable

Source: Own elaboration.

IV.. Results and Discussion

4.1.. Estimate of the predictive model for the abuse of compulsion sickness absence

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.

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.

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.

4.2.. Estimate of the predictive model for the abuse of escape sickness absence

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.

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.

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.

4.3.. Estimate of the predictive model for the abuse of recreation sickness absence

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.

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.

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.

V.. Conclusions

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.

Table 6:

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	↑	-	↑

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 males²⁹ . Our findings suggest that such discrepancy is related to health factors, since men often declare abusing absenteeism.

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.

Footnotes

1. Shi & Skuterud, ‘Gone fishing! Reported sickness absenteeism and the weather.’ Economic Inquiry, 53(1), 2015, 388–405. https://doi.org/10.1111/ecin.12109

2. Skogman Thoursie, ‘Reporting sick: Are sporting events contagious?’ Journal of Applied Econometrics, 19(6), 2004, 809–823. https://doi.org/10.1002/jae.758

3. Thoursie, ‘Happy birthday! You’re insured! Gender differences in work ethics.’ Economics Letters, 94(1), 2007, 141–145. https://doi.org/10.1016/j.econlet.2006.08.013

4. Ben Halima, et al. ‘The Effects of the Complementary Compensation on Sickness Absence: an Approach Based on Collective Bargaining Agreements in France.’ Labour, 32(3), 2018, 353–394. https://doi.org/10.1111/labr.12123

5. Vahtera, et al. ‘The role of extended weekends in sickness absenteeism.’ Occupational and Environmental Medicine, 58(12), 2001, 818–822. https://doi.org/10.1136/oem.58.12.818

6. Hensing, et al. ‘How to measure sickness absence? Literature review and suggestion of five basic measures.’ In Scandinavian Journal of Public Health, Vol. 26, Issue 2, 1998, pp. 133–144). https://doi.org/10.1177/14034948980260020201

7. Steers & Rhodes, ‘Major influences on employee attendance: A process model.’ Journal of Applied Psychology, 63(4), 1978, 391–407. https://doi.org/10.1037/0021-9010.63.4.391

8. Ahn & Yelowitz, ‘Paid Sick Leave and Absenteeism: The First Evidence from the U.S.’ SSRN Electronic Journal. 2016, https://doi.org/10.2139/ssrn.2740366

9. 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.’ Journal of Epidemiology and Community Health, 57(4), 2003, 285–293. https://doi.org/10.1136/jech.57.4.285; North et al. ‘Psychosocial work environment and sickness absence among British civil servants: The Whitehall II study.’ American Journal of Public Health, 86(3), 1996, https://doi.org/10.2105/AJPH.86.3.332; Väänänen et al., ‘Job characteristics, physical and psychological symptoms, and social support as antecedents of sickness absence among men and women in the private industrial sector.’ Social Science and Medicine, 57(5), 2003, 807–824. https://doi.org/10.1016/S0277-9536(02)00450-1)

10. Kristensen, ‘Sickness absence and work strain among Danish slaughterhouse workers: An analysis of absence from work regarded as coping behaviour.’ Social Science and Medicine, 32(1), 1991, https://doi.org/10.1016/0277-9536(91)90122-S; Szubert et al., Stres zawodowy a ryzyko absencji chorobowej na stanowiskach obsługi interesantów. Medycyna Pracy, 60(4), 2009, 259–271.

11. 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.’ Journal of Epidemiology and Community Health, 57(4), 2003, 285–293. https://doi.org/10.1136/jech.57.4.285

12. Marmot, et al. ‘Sickness absence as a measure of health status and functioning: From the UK Whitehall II study.’ Journal of Epidemiology and Community Health, 49(2), 1995, 124–130. https://doi.org/10.1136/jech.49.2.124

13. Bekker, et al. ‘Sickness absence: A gender-focused review.’ In Psychology, Health and Medicine, Vol. 14, Issue 4, 2009, 405–418. https://doi.org/10.1080/13548500903012830

14. Chadwick-Jones, ‘Renegotiating absence levels.’ Journal of Organizational Behavior, 2(4), 1981, https://doi.org/10.1002/job.4030020403

15. Virtanen et al., ‘Locality and habitus: The origins of sickness absence practices.’ Social Science and Medicine, 50(1), 2000. https://doi.org/10.1016/S0277-9536(99)00250-6

16. Pfau-Effinger, ‘Culture and welfare state policies: Reflections on a complex interrelation.’ Journal of Social Policy, 34(1), 2005, 3–20. https://doi.org/10.1017/S0047279404008232; van Oorschot, W. (2007). ‘Culture and social policy: A developing field of study.’ International Journal of Social Welfare, 16(2), 2007. https://doi.org/10.1111/j.1468-2397.2006.00451.x

17. Prins & De Graaf, ‘Comparison of sickness absence in Belgian, German, and Dutch firms.’ British Journal of Industrial Medicine, 43(8), 1986. https://doi.org/10.1136/oem.43.8.529

18. Ziebarth & Karlsson, ‘The effects of expanding the generosity of the statutory sickness insurance system.’ Journal of Applied Econometrics, 29(2), 2014. https://doi.org/10.1002/jae.2317

19. Leigh, ‘The effects of unemployment and the business cycle on absenteeism.’ Journal of Economics and Business, 37(2), 1985, https://doi.org/10.1016/0148-6195(85)90014-1; Shapiro, C., & Stiglitz, J. E. (1984). ‘American Economic Association Equilibrium: Unemployment as a Worker Discipline Device.’ The American Economic Review, 74(3), 1984.

20. Mastekaasa, ‘Parenthood, gender and sickness absence.’ Social Science and Medicine, 50(12), 2000, 1827–1842. https://doi.org/10.1016/S0277-9536(99)00420-7; Melsom, & Mastekaasa, ‘Gender, occupational gender segregation and sickness absence: Longitudinal evidence.’ Acta Sociologica (United Kingdom), 61(3), 2018, 227–245. https://doi.org/10.1177/0001699317691583

21. Bekker et al., ‘Sickness absence: A gender-focused review.’ In Psychology, Health and Medicine, Vol. 14, No. 4, 2009, 405–418). https://doi.org/10.1080/13548500903012830

22. Kristensen et al., ‘Socioeconomic status and duration and pattern of sickness absence. A 1-year follow-up study of 2331 hospital employees.’ BMC Public Health, 10(1), 2010, 1–11. https://doi.org/10.1186/1471-2458-10-643; Marmot et al., ‘Sickness absence as a measure of health status and functioning: From the UK Whitehall II study.’ Journal of Epidemiology and Community Health, 49(2), 1995, 124–130. https://doi.org/10.1136/jech.49.2.124

23. Adler et al., ‘Socioeconomic Status and Health: The Challenge of the Gradient.’ American Psychologist, 49(1), 1994, 15–24. https://doi.org/10.1037/0003-066X.49.1.1; Mackenbach et al., ‘Socioeconomic Inequalities in Health in 22 European Countries.’ New England Journal of Medicine, 358(23), 2008, 2468–2481. https://doi.org/10.1056/nejmsa0707519

24. Slowey & Zubrzycki, Living longer, learning longer – working longer? Implications for new workforce dynamics. Higher Education Research Centre, DCU, 2018. http://heer.qaa.ac.uk/pages/default.aspx

25. 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.’ Journal of Epidemiology and Community Health, 57(4), 2003, 285–293. https://doi.org/10.1136/jech.57.4.285; Ziebarth, ‘Long-term absenteeism and moral hazard-Evidence from a natural experiment.’ Labour Economics, 24, 2013, 277–292. https://doi.org/10.1016/j.labeco.2013.09.004

26. Bostyn et al., ‘Of Mice, Men, and Trolleys: Hypothetical Judgment Versus Real-Life Behavior in Trolley-Style Moral Dilemmas.’ Psychological Science, 29(7), 2018, 1084–1093. https://doi.org/10.1177/0956797617752640

27. Lai, ‘Estimating Standardized SEM Parameters Given Nonnormal Data and Incorrect Model: Methods and Comparison.’ Structural Equation Modeling, 25(4), 2018, 600–620. https://doi.org/10.1080/10705511.2017.1392248

28. Cressey, Other people’s money: A study of the social psychology of embezzlement. 1953, Free Press.

29. Mastekaasa, ‘Parenthood, gender and sickness absence.’ Social Science and Medicine, 50(12), 2000, 1827–1842. https://doi.org/10.1016/S0277-9536(99)00420-7; Melsom, & Mastekaasa, ‘Gender, occupational gender segregation and sickness absence: Longitudinal evidence.’ Acta Sociologica (United Kingdom), 61(3), 2018, 227–245. https://doi.org/10.1177/0001699317691583

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