IPC Sick Leave Trends Survey Report 2019

Sick Leave Trends Survey

Introduction

Absence due to sickness is of great concern and has huge economic and social consequences. The aim of this survey was to find the rate at which employees fake sick leave. Employees were asked questions related to sick leave and the general economic climate.

Executive Summary

In this survey, we investigated sick leave trends for different sectors in Zimbabwe. Most participants were from the financial sector (18.6%) with petro-chemicals having the least participants (0.5%). 26% of the participants said they faked sick leave. The findings show that age, gender, employment level and how much someone earns are correlated with faking sick leave. A logistic regression model was used to determine the factors that predict an employee’s likelihood to fake sick leave. Age and gender were found to be significant predictors in the logistic regression model. Those aged 34 years and below are more likely to fake sick leave compared to those aged 35 years and above. According to this survey, females are more likely to fake sick leave compared to males. Using the same logistic regression model there is a 17% probability that an employee will fake sick leave.

The Zimbabwe National Statistics Agency reported in their 2014 Labour Force Report that 1 003 985 people are formally employed. If we use the same labour force to extrapolate current number of employees faking sick leave it will translates to 261 036 employees (26%) who are faking sick leave nationally. The average remuneration for employees who participated in this survey is $2 310.50 per month. Employees faked on average 9 days per annum as sick leave in 2018. If we use the average salary information and average number of days employees fake sick leave per year to extrapolate current amount of money that is being spend by employers to pay employees not at work after faking sick leave it will translate to $258 479 674.452 . This amount does not include loses incurred as a result of the employer putting together a cover for this employee who is absent in order for work to continue. A full calculation of the cost emanating from employees absenting themselves after faking sick leave is much higher than the estimated cost given above.

Interestingly the majority of those who faked sick leave did so in order to; attend an interview(43.41%), deal with personal issues (60.47%),just tired from overworking(50.39%), just to have time to myself(20.93%) and to deal with alcohol induced handover (5.43%). 63% of those who faked sick leave did not feel guilty at all. Thus implies that this trend of faking sick leave will continue. Our conclusion is that people fake sick leave largely due to poor management system that create a hostile environment for employees. Secondly they fake sick leave because employers are not creating enough programs to deal with work –life balance.

Methodology

A questionnaire with 11 questions was emailed to employees from various organisations on our mailing list. A total of 634 individuals of varied age groups, education levels, gender, employment positions and economic sectors participated in this survey.

Sample Profile

Distribution of participants by economic sector is shown in the graph below:

Financial services and manufacturing are by far the largest industries in this survey contributing 18.6% and 13.9% respectively.

Distribution of participants by age is shown in the graph below:

There is a normal distribution for the participants by age.

Distribution of participants by gender is shown in the graph below:

60% of the participants were males and 40% were females.

Distribution of participants by highest level of education is shown in the graph below:

52% of participants are post graduate degree holders, 36% are undergraduate degree holders, 10% are certificate holders, 1% are O-Level holders and 0.5% are A-Level holders.

Distribution of participants by employment level is shown in the graph below:

60% of participants in this survey are in managerial level and 40% are in non-managerial level.

Results

Exploratory analysis

26% of participants said they have faked sick leave whilst 74% said they haven’t faked sick leave.

Most of the participants faked sick leave to deal with personal issues (60.47%). A larger percentage do not fake sick leave to deal with alcohol induced hangover (94.57%) as only 5.43% said they faked sick leave to deal with alcohol induced hangover.

Most of participants who faked sick leave were from law and legal services (75%) followed by those from professional services (39%) with tourism and hospitality being the least having no one who faked sick leave.

When we consider those who faked sick leave by gender. More females faked sick leave (31%) when compared to males (22%).

22% of managerial staff faked sick leave whilst 31% of non-managerial staff faked sick leave

Most of participants who faked sick leave were undergraduates (30%) followed by those with post graduate degrees.

When age is considered those who faked sick leave more were aged between 21 and 30.

When salary is considered most of participants who faked sick leave have a salary range of between $100 and $999.

Only 37% of people who faked illness to get days off felt a sense of guilt whilst 63% did not feel any sense of guilt and this indicate that when people fake illness most of them do not feel any sense of guilt.

  • In 2018 employees faked sick leave on average 9 days.
  • In 2018 on average employee took 16 days genuine sick leave.

Correlations between the dependent variable and independent variables

KEY:

  • Correlation is significant at the 0.05 level (2-tailed).
  • ** Correlation is significant at the 0.01 level (2-tailed).
  • b Cannot be computed because at least one of the variables is constant.

Age and remuneration were found correlated with faking sick leave at 0.01 level of significance.

Cross Tabulations

1. Have you ever faked sick leave? * Which sector/industry does your organisation operate in?

H0: sector/industry one operate in has no effect on one faking a sick leave. H1: sector/industry one operate in has an effect on one faking a sick leave.

The computed p-value is above the significance level alpha = 0.05. We fail to reject the null hypothesis H0 and conclude that the sector/industry one operate in has no effect on one faking a sick leave.

2. Have you ever faked sick leave? * What is your Gender?

H0: gender has no effect on one faking a sick leave.

H1: gender has an effect on one faking a sick leave

The computed p-value is lower than the significance level alpha = 0.05. We reject the null hypothesis H0 and conclude that gender has an effect on one faking a sick leave.

3. Have you ever faked sick leave? * What is you highest level of education?

H0: highest level of education has no effect on one faking a sick leave.

H1: highest level of education has an effect on one faking a sick leave.

The computed p-value is above the significance level alpha = 0.05. We fail to reject the null hypothesis H0 and conclude that the highest level of education has no effect on one faking a sick leave.

4. Have you ever faked sick leave? * What is your level of employment?

H0: level of employment has no effect on one faking a sick leave.

H1: level of employment has an effect on one faking a sick leave.

The computed p-value is lower than the significance level alpha = 0.05. We reject the null hypothesis H0 and conclude that the level of employment has an effect on one faking a sick leave

Logistic regression

From the table of results above, the logic of the multiple logistic regression model using step 2 is:

g(x) = -1.566 + 0.591*gender + 0.06*age

Therefore, the multiple logistic regression model is:

?(?) = ? ?(?) 1 + ? ?(?)

Where ?(?) is the probability that an employee fakes sick leave

The intercept from the results above is significant in our model and is therefore fitted in the model. The intercept represents the average odds of an employee faking sick leave when all of the explanatory variables are zero. The following holds:

exp(?) = exp(−1.566) ≅ 0.2088790297

To make probability prediction, odds ratio are converted to probability by:

Probability = odds 1 + odds

Substituting the constant we get:

Probability = 0.2088790297 1 + 0.2088790297 = 0.1727873712

This probability result indicates that there is a 17% chance that an employee will fake sick leave without considering any explanatory variable.

Evaluation of the logistic regression model

From the results shown in the figure below, it appeared that significant predictor variables have been included in the model since the Cox and Snell R Square is significant (Cox and Snell R Square = 0.05) and significance for the Hosmer and Lemeshow Test (0.463) which is above 0.05. Therefore, the model is significant in predicting fake sick leave among the employees of Zimbabwe.

Productivity

The Zimbabwe National Statistics Agency reported in their 2014 Labour Force Report that 1 003 985 people are formally employed. If we use the same labour force to extrapolate current number of employees faking sick leave it will translates to 261 036 employees (26%) who are faking sick leave nationally. The average remuneration for employees who participated in this survey is $2 310.50 monthly (approximately $110.023 daily). Employees faked on average 9 days as sick leave in 2018 and if we use this to extrapolate amount of money that is being spend by employers yearly to pay employees when they have faked sick leave it translates to $258 479 674.452.

Conclusion

Faking sick leave can be predicted using gender and age. Employees are not likely to stop faking sick leave since a lot of people do not feel a sense of guilt after faking sick leave therefore employees who are likely to fake sick leave can only be avoided during recruitment.

About the Authors

Memory Nguwi (Registered Psychologist) is the Managing Consultant of Industrial Psychology Consultants (IPC). You may contact him by email at mnguwi@ipcconsultants.com

Taurai Masunda is a Consultant at Industrial Psychology Consultants (IPC). You may contact him by email at taurai@ipcconsultants.com

If you would like to discuss this report, please contact one of the authors.

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