Do Absenteeism and Level of Education Affect Workers’ Productivity?

Reduced work performance results from absenteeism or presenteeism in the workplace. Presenteeism is defined as a loss of work productivity when a worker reports to work despite being sick. The associated reduced work performance contributes to productivity costs bared by the employer. Productivity losses related to presenteeism are more significant than those associated with absenteeism. Loss of productivity due to absenteeism in most studies is calculated by estimating the number of days that an individual is absent (Strömberg et al., 2017). The following study answers the research question on whether absenteeism and level of education affect workers’ productivity. 

The study is presented in four different sections. Section two discusses the theoretical model used to describe the impact of the mentioned factors on worker productivity. Section three includes an analysis of existing literature about the role of absenteeism and level of education on workers’ productivity. The second last section describes the research methodology, discussion, and results. Finally, section four details a summary of the study, a conclusion, and recommendations for future studies. 

Purpose 

This study aims to determine whether a relationship exists between absenteeism, level of education, and worker productivity. A study performed by Zhang et al. (2017) revealed that for small firms, employee absenteeism lowers productivity and wages. The extent of the reduction in productivity is dependent on whether the workers are a team or non-team workers. Forbes et al. (2010) report that to determine the effect of level of education on productivity, hourly wages can be used as an indicator of productivity. People with a higher level of education earn higher salaries than those with lower educational attainment, indicating a higher level of productivity. 

Hypothesis 

Ho: Null Hypothesis 

There is no defined relationship between absenteeism, level of education, and worker productivity. 

Ha: Alternate Hypothesis 

Absenteeism and level of education have an impact on worker productivity. 

Research Question 

The research question considered in this study is whether absenteeism and level of education affect worker productivity. 

Definition of Terms 

Absenteeism 

Absenteeism is defined as the number of workdays missed due to poor health among employed individuals—people suffering from various conditions, such as diabetes, exhibit higher levels of absenteeism. 

Presenteeism 

Presenteeism is defined as a reduction in productivity associated with reporting to work while suffering from an illness or a condition (American Diabetes Association, 2018). 

Theoretical Framework 

Production Model 

The economic theory presents productivity as an output and as a function of capital and labor input. A reduction in labor input due to absenteeism and presenteeism leads to a loss of productivity. Absenteeism is a short period of absence caused by illness, while presenteeism is the attendance of work despite illness. For presenteeism, the sickness would justify absence due to the extent of its impact on worker productivity. Apart from absenteeism and presenteeism, other factors that would affect worker productivity are related to the work environment. Such factors include physical, social, and psychological issues linked to the work environment that might influence work productivity (Strömberg et al., 2017). 

For this theoretical model, it can be assumed that labor can be obtained in exchange for adequate remuneration. Wages paid should reflect worker output. Another assumption is that different jobs have varied demands, where an organization’s output differs from another’s. The production function allows organizations to combine the required resources, capital, and labor to obtain the desired output. Capital is held constant across different job requirements. When the labor requirements exceed available labor, the output is greater than zero (Strömberg et al., 2017). 

Literature Review 

Strömberg et al. (2017) performed a study to determine the cost of productivity loss for employers. The chief data collection tool used in the study was the performance of a survey that included a survey panel of 758 managers. Managers were involved in analyzing the impact that absenteeism, presenteeism, and other work environment-related issues on group productivity and cost. Independent variables considered in the study were presenteeism, absenteeism, job characteristics, and work environment-related issues. Dependent variables included group productivity and productivity costs. The study revealed that the job’s characteristics determine the extent of productivity loss. Therefore, the impact of presenteeism and absenteeism is dependent on job characteristics. The level of the time-sensitivity of output, teamwork, and ease of replacing a worker determine the extent of the loss of productivity. 

The research design used in the study was a cross-sectional study where data was collected in two different phases between 2014 and 2015. Thirty occupations were considered in the study to determine the impact of job characteristics on associated productivity losses. Fifteen respondents were chosen from each occupation. Out of the 3753 samples chosen, 1721 responded to the survey. Inclusion criteria included managers responsible for medium to large organizations, and those responsible for more than 50 workers. Statistical analysis was carried out using ordered probit regression. Data collected showed that 55% of the managers reported that finding a suitable replacement with the same level of productivity as the absent worker is almost impossible. The highest productivity costs were associated with absenteeism (Strömberg et al., 2017). 

Zhang et al. (2017) performed a study to determine the disparities between productivity losses due to absenteeism for the team and non-team workers. The study revealed that team workers exhibit a higher level of productivity and higher remuneration rates than non-team workers. The productivity gap between the team and non-team workers is larger than the wage gap. Small firms experience a loss of productivity due to absenteeism. The extent of productivity loss is greater than wage loss in team workers. Unlike Strömberg et al. (2017), Zhang et al. (2017) did not use wages as a measure for productivity since wages may not fairly represent productivity levels. Using wages as a measure for productivity reduces the accuracy of the determined productivity loss. 

Therefore, the productivity costs associated with absenteeism were measured from the impact of absenteeism on aggregate wages for firm employees. Various payroll and non-wage benefits were also considered in the study. Non-wage benefits included health insurance covers, allowances, and pensions. Measures of absenteeism considered in the study focused on illness-related absences only. Only absences due to paid sick leave were considered in the study. Therefore, the measure of absenteeism was the number of days of paid sick leave for study participants. Other absenteeism measures considered were paid educational leave days, jury duty, disability leave, unpaid leave, bereavement, and marriage. Results showed that productivity loss associated with absenteeism is higher for team workers than non-team workers. Additionally, using wages as a measure for productivity underestimates productivity loss due to absenteeism (Zhang et al., 2017). 

According to Forbes et al. (2010), the human capital theory can be used to support that higher levels of productivity are exhibited by people with higher education attainment levels and low incidence of chronic illness. One measure of productivity that can be used is hourly wages. However, using wages as a measure of productivity could lead to overestimating or underestimating the adverse effects of illness on productivity. Higher levels of education are associated with higher levels of productivity based on the wages earned. For instance, people with 12 years of education earn 13% more than those with less than 11 years of education. People with university-level education earn 40% more in comparison to those without a college education. Additionally, people suffering from chronic illnesses earn fewer wages than those with optimum health status (Forbes et al., 2010). 

Gaps in the Literature 

Existing studies use wages as a measure of productivity. However, this approach causes overestimation or underestimation of productivity levels and the impact of absenteeism due to illness. Existing literature does not define the most suitable and accurate measure of productivity. Additionally, the level of education is determined by wages, which is not necessarily accurate since people with lower education levels could earn higher wages than those with higher education levels. 

Significance of the Study 

The United States (US) continues to record a reduction in productivity growth since 2004. Labor productivity declined from 2.85 in 2004 to 1.27 in 2015. It is defined as the measure of real outputs to labor inputs. Various theories are describing the cause of the decline in labor productivity. This study attempts to identify the impact of absenteeism and the level of education on productivity. Understanding the impact of these factors on productivity could help formulate business and legislative properties to increase work productivity. 

Limitations of the Study 

A sample size of 11 participants was chosen for the study, which reduces the accuracy of the results. It also reduces the likelihood of using inferential statistics to expand study results to other settings. 

Methodology 

For the study, 10 participants were conveniently selected based on their education levels. The main data collection tool used for the study is a questionnaire composed of 7 items. It is divided into two sections. The first section includes prompts used to collect background information about study participants, specifically their education attainment levels. The second section includes prompts used to collect data about work performance, productivity, and absenteeism. Appendix 1 shows the questionnaire used for data collection, while appendix 2 the data collected from study participants. 

Data Analysis and Discussion of Results 

For data analysis, a Chi-square test for independence was used to determine the existence or absence of a relationship between absenteeism and productivity, and education level and productivity. The following formula was used for the Chi-square test; 

Where; 

O i = Observed frequency 

E i = Expected frequency 

The Chi-test results are summarized in the table below. 

Table 1 . Chi-square test p values 

The p values obtained from the Chi-square test lie between 1% and 5% for both relationship tests. Therefore, the null hypothesis should be rejected. There is a significant relationship between the dependent variable (work productivity) and the two independent variables (absenteeism and education level). Therefore, efforts and policies targeting an increase in work productivity should consider ways to reduce absenteeism and increase the level of education for employees. The following scatter plot shows the relationship between the level of education and work performance. Work performance is presented on a scale of 1 to 10, where 1 indicates poor performance, and 10 represents superior performance. 

Figure 1 . Work Performance in Relation to Education Level 

Summary of Findings 

The findings obtained from data analysis using the Chi-square test indicate that the null hypothesis should be rejected. The findings support the alternate hypothesis that there is a significant relationship between absenteeism, education level, and work productivity. 

Conclusion and Recommendations 

According to results obtained from the study, absenteeism and education level have an impact on work productivity. The study was performed on a sample of 11 participants with varied work experience between two and twelve years. There was no relationship between the number of years worked with the company and work performance. The level of education determined work performance. 

Further studies are recommended to determine alternative approaches to measuring work productivity. Additionally, more studies should be performed to determine productivity costs associated with various causes of absenteeism, ranging from illness to working from home. The results can be used to determine the efficiency of working from home in comparison to working from the office. 

References 

American Diabetes Association. (2018). Economic costs of diabetes in the US in 2017.  Diabetes care ,  41 (5), 917-928. Retrieved from https://care.diabetesjournals.org/content/diacare/41/5/917.full.pdf 

Forbes, M., Barker, A., & Turner, S. A. (2010). The effects of education and health on wages and productivity. Retrieved from http://library.bsl.org.au/jspui/bitstream/1/1615/1/education-health-effects-wages.pdf 

Strömberg, C., Aboagye, E., Hagberg, J., Bergström, G., & Lohela-Karlsson, M. (2017). Estimating the effect and economic impact of absenteeism, presenteeism, and work environment–related problems on reductions in productivity from a managerial perspective.  Value in Health ,  20 (8), 1058-1064. https://doi.org/10.1016/j.jval.2017.05.008 

Syverson, C. (2017). Challenges to mismeasurement explanations for the US productivity slowdown.  Journal of Economic Perspectives ,  31 (2), 165-86. 10.1257/jep.31.2.165 

Zhang, W., Sun, H., Woodcock, S., & Anis, A. H. (2017). Valuing productivity loss due to absenteeism: firm-level evidence from a Canadian linked employer-employee survey.  Health economics review ,  7 (1), 3. https://doi.org/10.1186/s13561-016-0138-y 

Appendix 

Appendix 1: Year 2019 TOPIC: Educated employees 

Academic Level, Hours of work at the office and home: Questionnaire 

Purpose: To gather data on the variables (dependent and independent variables) 

Y = Work (dependent variable) 

X1 (Hours of work from the office) 

X2 (Number of absences from office) 

PART I  Background	Demographic Data  Background Information about students
1.	Do you possess a college degree?	Yes	No
2.	If your answer is yes, is it an undergraduate or graduate?  Sample should focus on 2 nd year student for previous semester information)	Undergraduate	graduate
Section A	Sex	Circle
3.	Male = 1 Female = 2
PART II  Information on Variables	Gather information for the most recent employee education.
4	What is your highest education attained?
5.	How many years have you been with the company?
6.	On average, how many hours do you work per week?
7.	On your yearly performance card, what is your rating? Poor,averarge,above average, superior?

D . In office data organization 

You will collect data on 10 employees on three variables: 

Highest level of education attained (Measure of Academic performance) 

Number of absence of from the office to work remotely 

Average number Hours of work per week. 

Dependent variable: level of education or number of years with the company is a numeric measure of an employee performance in this study. 

Independent variable (1) Absences from office due to remote working 

Independent variable (2) level of productivity generated. 

Appendix 2: Collected Data 

employee	Work  Performance	Numbers of Hours of work during the week	Absences from the office due to remote work per week	Level of education	How long have you been with the company	Do you complete more assignment from home than in your office at work?
1 john	Above average	40	16 hours a week	bachelor	2 years	Yes
2 jay	average	40	16 hours a week	High school	5 years	no
3 ray	Superior	40	16 hours a week	master	3 years	yes
4 max	average	40	16 hours a week	associate	3 years	no
5 damien	Above average	40	16 hours a week	bachelor	8 years	yes
6 jackie	Above average	40	16 hours a week	bachelor	12 years	yes
7 joe	Superior	40	16 hours a week	Bachelor	10 years	Yes
8 robinson	Above average	40	16 hours a week	Associate	5 years	yes
9 lue	Average	40	16 hours a week	High school	7 years	no
10 jose	Average	40	16 hours a week	High school	4 years	no
11 johnathan	Above average	40	16 hours a week	Bachelor	6 years	yes
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