Hot-Desking and the Absence Figures
In January a department of 210 staff moved to hot-desking, keeping 120 desks. Recorded sickness absence fell from 4.8% to 3.9% over the following six months. The director cites this as proof that hot-desking improved wellbeing. In the same period the department ran a flu vaccination clinic, and a vacancy freeze cut headcount to 191 by June; the staff who left were concentrated among those on long-term sick leave. A second office of 180 staff doing similar work kept assigned desks and saw its absence fall from 5.0% to 4.1% over the same six months. No vaccination clinic ran there.
1. State the director's conclusion and the unstated assumption on which it depends.
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Conclusion: hot-desking caused the fall in recorded sickness absence, and by extension improved wellbeing.
The argument runs: change happened, absence fell afterwards, therefore the change caused the fall.
Unstated assumptions:
- Nothing else changed in the period that could plausibly affect absence. The vaccination clinic and the vacancy freeze both changed.
- Recorded absence measures how well staff are, rather than how absence is recorded or who remains on the books.
Both assumptions are load-bearing. If either fails, the figures no longer point to hot-desking.
2. What does the second office's experience do to the argument?
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It substantially weakens it.
The second office saw an almost identical fall (5.0% to 4.1%, a drop of 0.9 points) without hot-desking. So hot-desking cannot be a necessary explanation for a fall of this size; something common to both sites or to the season explains at least as much.
This does not prove hot-desking had no effect. The offices differ in size, work and management, and the second office's fall is not a controlled comparison. But the director's claim was 'proof', and a parallel fall elsewhere is enough to show the evidence is a correlation presented as a cause.
3. Explain how the fall in headcount could produce a lower recorded absence percentage with no change in anyone's health.
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The percentage has a numerator (days lost) and a denominator (staff or days available). A change in the denominator moves the figure on its own.
If staff on long-term sick leave leave the department, their ongoing absence stops being counted against staff who remain. The remaining population is, in effect, filtered for people who were well enough to stay. A 4.8% rate over 210 staff is a different measurement from a 3.9% rate over 191 staff drawn from the same pool.
The absence percentage therefore falls mechanically. Nothing in the report shows any individual recovered, so the figure cannot be read as a wellbeing improvement without separate evidence.