Absenteeism: a 3-Step Analytics Approach
You probably hate it when you feel sick – and so does your boss. Absenteeism is a real problem. The U.S. spends $84 billion per annum on absenteeism. This boils down to $560 per employee. The UK spends on average $890 per employee. In the Netherlands, this number is $1,545.
I specifically mention the Netherlands because we recently started an exciting analytics project in the Netherlands. This project solely focuses on the issue of long-term absenteeism. The scope of the project is first to provide accurate and new insights on long-term absenteeism. Secondly, we aim to develop a statistical model to predict future absenteeism.
Since applying analytics to absenteeism is also new to us – and I have seldom heard one of my fellow analytics-enthusiasts talk about this specific application of analytics – I will share our insights and results with you as the project progresses.
Absenteeism Business Case
The relatively high expenditure on absenteeism in the Netherlands is caused by a number of different factors. The Netherlands is characterized by a high level of social security and employee protection. For example, when an employee has a long-term physical or psychological disability, his or her company is obliged to pay the employee’s salary for up to 12 years.
You can imagine that organizations would want to prevent this. A severe burnout can prevent someone from working for a long time. This can potentially cost the organization over $500,000. That’s why long-term disabilities pose an enormous risk for employers, and why it is an excellent incentive to start applying HR analytics.
Absenteeism can’t be avoided entirely (e.g. seasonal flue or a chronic illness). Almost half of the total absenteeism costs in the Netherlands can be attributed to work-related issues. Many of these work-related issues are preventable because they are caused by, among other things, work pressure, stress, and problems with managers or colleagues.
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In order to identify preventable absenteeism, you need to ‘run the numbers’. Next, we will go over the three steps you need to take to apply analytics on your absenteeism data and potentially save thousands, if not millions of dollars.
Step 1: Monitor
The first problem regarding absenteeism is that organizations are not always aware of the financial impact of absenteeism. This is usually caused by a lack of monitoring of the incidence, cause and cost. Not surprisingly, this is exactly the information you need to start analyzing long-term absenteeism.
So, how do we track and prevent absenteeism?
In order to obtain a clear picture of the impact of absenteeism, organizations need to track and monitor it systematically. Depending on the organization and country, detailed data might already be available; as in the case of the Netherlands. The Netherlands is an interesting country to track absenteeism analytics. Because of the country’s strict regulations, Dutch companies are required to track absenteeism, which means that they already have access to more detailed data than a vast amount of companies in other countries.
Closely monitoring absenteeism also helps organizations compare their scores to the rest of the market. Employees working for the best performing companies in the UK – in terms of absenteeism – have 3 days of absence per employee per annum on average. However, in the worst performing organizations, employees have an average of 9 days of absence per employee per annum.
In addition, monitoring also helps to see where in the organization absenteeism is most prevalent (Report by the Confederation of British Industries, 2013).
Step 2: Collect and analyze data
Monitoring is an important first step, but it is not yet enough. Preventable absenteeism is often the consequence of deeper issues, like a conflict with a manager or work stress. Most companies use annual engagement and well-being surveys. Factors such as satisfaction, organizational commitment, work engagement and well-being, play an important role in both voluntary and involuntary absenteeism. In addition, they provide important clues as to what could potentially cause the prevalence of absenteeism in the organization.
The next step is to analyze the absenteeism data in order to find what causes it. Since most organizations already collect both absenteeism and relevant survey data, it is relatively easy to study the interaction of these variables over time. This helps identifying cause-and-effect relationships: e.g. research by Schaufeli, Leiter and Maslach (2009) found that higher levels of engagement were associated with a lower frequency of absence. A similar effect was also reported for commitment to the organization (Somers, 1995). All these factors are often already measured by organizations and help you analyze and, eventually, predict absenteeism.
Step 3: Take action
The moment you are able to identify the causes of absenteeism, you are able to do something about it. This can help you to proactively prevent or lower absenteeism in your company. Different causes require different solutions. Absenteeism caused by work stress requires a different approach than absenteeism caused by conflict with supervisors (this is another major cause for absenteeism that is relatively easy to measure by means of an employee survey; Geurts, Schaufely & Buunk, 1993). For example, research shows that employees cope with stress much better when they have autonomy, social support and opportunities for personal growth (Schaufeli, Bakker & Rhenen, 2009). Absenteeism caused by a conflict with one’s supervisor has to be resolved in an entirely different way.
Monitoring and analyzing absenteeism is not only beneficial to get a grip on absenteeism, it can potentially save an organization millions of dollars. We want to analyze and eventually predict absenteeism in the coming months. First, we will research the most important predictors of absenteeism. We will keep you updated!
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