How This Data is Presented - The Example of Educational Status and Long Hours

There are a number of key ways of examining the relationships between long hours and a variety of variables. This paper presents three of these ways, including: a) the proportions of long hours workers in each variable category; b) the distribution of each variable amongst long hours workers; and c) the absolute numbers of employees who are working long hours by each variable. It is vital that these analyses be considered together, especially when considering any policy implications of the data. This section illustrates these methods of analysis, using the example of educational status.

Figure 1 depicts the proportions of long hours workers in each educational category, showing that those workers with post-graduate qualifications are the most likely to work long hours.

Figure 1: Percentage of employees who work 50+ hours a week, by highest qualification

Data Table for Figure 1

Figure 2 shows the distribution of qualifications gained by those working long hours, as well as the qualifications of the total working population. This highlights that, while those with post-graduate qualifications are the most likely to work long hours, most of those employees who work long hours have much lower or no qualifications. Furthermore, the proportions of long hours workers with no qualifications do not differ significantly from those of the total workforce.

These two figures show the importance of considering the absolute numbers of respondents in each category, as well as the proportions of respondents. Figure 3 shows the absolute numbers of long hours workers by educational profile, again highlighting the fact that, although those with post-graduate qualifications are the most likely to work long hours (as in Figure 1), relatively few employees hold these qualifications, and thus they represent only a small number of long hours workers.

Figure 2: Educational qualifications, those working 50+ hours per week and total workforce

Data Table for Figure 2

Considering both the distribution and proportion, along with the absolute number, of respondents in the analysis of each variable becomes key when attempting to draw policy conclusions from the data. This is particularly the case with variables such as industry: it is easy to argue that particular industries contain high proportions of long hours workers, but the absolute number of employees in that industry must also be considered when prioritising areas for attention. Similarly, it is also important to consider the distribution of each variable throughout the total working population, in order to explore whether there are groups who are disproportionately under- or over-represented in the total number of long hours workers.

Figure 3: Highest qualifications of those working 50+ hours per week, absolute numbers

Data Table for Figure 3

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