5.3  Presenteeism (labour force participants)

5.3.1  Methods

Some workers will show up to work even when they do not feel well, perhaps to avoid sick days. As well as creating a heightened risk of injury or spreading of infectious diseases, workers are unlikely to be fully productive, resulting in lost output (Davis et al., 2005). Costs associated with such a concept are very difficult to quantify. Ideally there would be survey information about perceived productivity as a result of ill health. Numerous examples based on such questions can be found in US research. For example, estimates of illness-related presenteeism by Davis et al. (2005) were based around a survey question which asked the number of days they had been unable to concentrate at work because they were not feeling well or were worried about a sick family member. The estimate of lost output was based on the average earnings of these workers and the assumption that, owing to being unable to concentrate, they were working at half capacity. Similarly, research by Goetzel et al. (2004) compared estimates of presenteeism from a number of surveys which asked people, in various ways, how often their work performance had been affected by sickness. They used these results to create an average measure of presenteeism costs. The survey results used included those from surveys specifically undertaken with the aim of quantifying on-the-job productivity losses which result from poor health (Lerner et al., 2001). Other US research by the Milken Institute used the number of sick days reported in a US survey and adjusted this estimate using a factor from the Goetzel et al. research to estimate presenteeism (DeVol and Bedroussian, 2007). There are no questions in SoFIE that directly ask about the number of hours or days of work where productivity is affected owing to ill health or about the perceived productivity level for such hours. Therefore, to estimate presenteeism costs, assumptions have to be made using other questions asked in SoFIE.

To identify those who may be more productive in the absence of ill health the questions asking whether physical or mental health have interfered with daily activities in the past four weeks are used. For those who participate in the period, it is assumed that if a person's daily activities are limited or performed less well, or with less care, work activities will be affected. These “productivity-related” questions are listed in Figure 4 below.

The responses to each question for those participating were used to determine the proportion of working time in a four-week period where productivity was affected (this scale is similar to one of the surveys considered in the work by Goetzel et al., 2004):

• All of the time – 100% of working time affected
• Most of the time – 75% of working time affected
• Some of the time – 50% of working time affected
• A little of the time – 25% of working time affected
• None of the time – 0% of working time affected.

Three methods were used to summarise the responses to these questions into a measure of the proportion of hours worked at reduced productivity in a four-week period for each person:

• Method 1 – Maximum – the highest proportion across all questions was taken to be the proportion of hours that were worked at reduced productivity.
• Method 2 – Proportion – assumes each question response carries an equal weight. That is, the proportion of hours is equal to one-seventh of the response to the first question plus one-seventh of the response to the second question etc. This means that those people with multiple responses of activity being limited have a higher proportion of hours worked at reduced productivity.[38]
• Method 3 – Principal Components Analysis (PCA) – this is a method for aggregating several indicators into a single measure. The method uses components to explain variation in the data. It aims to use as few components as possible to explain as large an amount of variation in the data as possible. This method was used despite the fact that for ordinal variables it means ignoring the discreteness of the variables. Examination of the data indicated that 83% of the variation in the data could be explained using two components. These two components were therefore used to predict the proportion of hours that were less productive for each person.[39] The results from PCA are initially on a standardised scale. These were therefore adjusted to form a continuous measure of the proportion of time that a person worked at reduced productive capacity which lies between zero and 100.[40]

For each method, multiplying the proportion of hours worked at reduced productivity each month by the number of average hours usually worked each month gives the estimated number of hours affected by presenteeism for each person in a month. Multiplying by 12 gives an annual estimate of the hours affected. The total number of hours lost is estimated based on assumptions about the level of reduced productivity owing to ill health.[41]

The level of reduced productivity to assume is difficult to ascertain, given the variation in jobs people undertake and the subjective nature of the questions on role limitations. The level of productivity will not be the same for all respondents even if they have identical responses to the “productivity” questions as it will depend on the job they undertake and the specific illness. The assumptions about level of reduced productivity used in earlier studies vary widely. For example, the US study by Davis et al. (2005) assumes workers are half as productive, while research in Australia used results from a survey to estimate that reduced effectiveness when at work owing to chronic pain was 14.2%, much lower than the assumption used in the US study (Access Economics, 2007).[42] When presenteeism is estimated by Southern Cross Medical Care Society (2009) in the New Zealand context, a reduction in productivity of 50% is assumed. As there does not appear to be a consensus approach, in this paper lost hours will be estimated assuming a range of different levels of productivity reduction based around the estimates from other research in this area. The levels of productivity considered will be:

• 85% of full productivity (Assumption 1)
• 75% of full productivity (Assumption 2)
• 50% of full productivity (Assumption 3).

The number of hours lost owing to presenteeism is then estimated using all combinations of the methods and assumptions in turn. That is, for each person the proportion of hours affected each month is multiplied by the number of usual hours worked each month multiplied by the level of productivity. This estimate is multiplied by 12 to obtain an annual estimate. The resulting estimates are weighted and summed across all people and evaluated at the average hourly full-time rate. The formula can be found in Appendix D.

One limitation of these productivity-related questions is that it is not possible to split out responses that are a result of injury or pregnancy. As a result some of the loss of productivity estimated may be a result of this. To minimise this, a question asking about how much bodily pain interfered with usual daily activities in the last four weeks was not used as a productivity-related question, as this was more highly correlated with injury.

An alternative approach to estimating the reduced productivity owing to ill health may have been to use differences in wages (ie, when all else is equal, what the difference is in hourly pay between those with and without ill health).[43] However, along with the issues of estimating hourly pay from SoFIE that were discussed in Section 5.1.3, there are a number of problems with using pay as a proxy for productivity. Firstly, it assumes that people's pay reflects their performance which may not be the case. Secondly, even if a relationship between pay and health is established it is difficult to estimate the true magnitude of the relationship as there are lots of unobserved variables that may explain variations in pay. As such the differences in “productivity” that may be attributed to differences in pay may be the result of other unmeasured factors (for example, a year out to gain overseas experience, or a choice to work for lower wages, rather than ill health). Further, the reflection of ill health in pay levels may only exist for longer-term conditions, rather than short-term health problems. The shorter-term conditions, or the period after diagnosis of a longer-term condition, may be the least productive period, as for longer-term conditions it will take time to get the condition under control.