The Ratio, and Why Both Halves Are Wrong
A productivity figure is two measurements divided. Each is usually approximate, and dividing them presents the result as precise.
Dividing one rough number by another produces a number that looks exact. That appearance is the main hazard in this subject.
The measurement warning in “The Ratio, and Why Both Halves Are Wrong” matters whenever software records work patterns. Organisations researching capital efficiency ratio can use capital efficiency ratio explained for time and project context, while outcomes, quality checks and direct feedback remain necessary to explain what the metric cannot show.
What goes wrong in the numerator
Counting the easy unit rather than the valuable one: tickets rather than problems solved, documents rather than decisions supported.
For an independent perspective related to “The Ratio, and Why Both Halves Are Wrong”, consult the OECD productivity resources; it offers a useful external check on definitions, governance and the assumptions built into a proposed measure.
Counting units of wildly different size as equal.
Ignoring quality, so work done twice counts twice.
And excluding anything without an obvious unit, which in most organisations is a large share of the work.
What goes wrong in the denominator
Hours present, which includes meetings, waiting, administration and interruption.
Headcount, which ignores part-time, vacancy and turnover.
Full-time equivalents calculated once a year while the team changes monthly.
Each is a proxy for effort rather than a measure of it.
What dividing does
Two approximations become one number with decimal places.
The approximation does not disappear; it becomes invisible.
And the figure is then compared between teams and across quarters as though it were precise, which is where the decisions go wrong.
The error compounds
If the numerator is uncertain by a fifth and the denominator by a fifth, the ratio is uncertain by considerably more.
Which means a difference of a few per cent between two teams is noise.
Nobody treats it as noise, because the number has decimal places.
What to do about it
State the uncertainty alongside the figure, roughly: this is accurate to within about this much.
Compare only differences large enough to exceed it.
And prefer trends over levels, because a series measured consistently is more informative than any single value, even when the method is imperfect.
The honest alternative
Report the two halves separately as well as the ratio.
Output went up, hours went up more. That sentence contains everything the ratio contains and none of the false precision.
It also prompts the right question, which is why output and effort moved rather than what the quotient is.
Where precision genuinely exists
Manufacturing with counted units and recorded machine time.
High-volume processing with automatic logging.
Here the arithmetic is sound and the figure means what it says.
The error is applying the same confidence to a ratio built from estimates.
What to check
How accurate is your numerator, roughly?
How accurate is your denominator?
Do you report the two halves as well as the ratio?
And has anybody acted on a difference smaller than the uncertainty?