How Can Turnover Be Over 100%?
A common question for those new to HR Analytics is “How can turnover be over 100%?”
Many people think they have done something wrong when they see this for the first time. But indeed turnover CAN actually be over 100%.
To discover how, we’ll first quickly review how turnover is measured. Then we’ll consider two slightly different scenarios: annual turnover and annualized turnover.
As we discussed in a previous post, turnover rate is calculated by dividing the total number of employees leaving by the AVERAGE of the number of employees at the beginning of the period and at the end of the period:
Here is the basic formula:
# Employees leaving in period / mean(# Employees period beginning + # Employees period end)
If we were calculating the annual turnover rate as we often do, then the beginning of the period would be Jan 1st and the end would be Dec 31st.
Note: Click here if you are looking for our primer on developing your own predictive models for employee turnover.
Scenario 1: Annual Turnover Over 100%
Given that background, when will the turnover rate will be greater than 100%?
When the total number of people leaving the company over the year is higher than the overall average headcount for the year.
For example, let’s suppose on January 1, 2015 we have 100 employees. Then, for each of the next 12 months, let’s further suppose we lose 10 employees but also hire 10 new employees.
By Dec 31, 2015 we will have lost a total of 120 employees. Our overall average headcount over this period, however, will be 100 because those loses were offset by new hires.
If we divide 120 by our overall average of 100 employees, we get a turnover rate of 120%.
Just remember that companies both hire and lose employees. Stated informally, you can lose a ton of people and still have employees (and a company) provided you keep hiring more. You’ll just end up with a really high turnover rate…sometimes over 100%.
Scenario 2: Annualized Turnover Over 100%
Annualized turnover calculations are more likely to generate turnover rates over 100%.
Remember that annualized turnover is essentially a loose form of projection in which we take turnover data based on some period of data and answer the question “What would our annual turnover look like if we continued at this same rate?” This conversation allows to compare turnover rates using the same time scale.
But this conversion also means that random or periodic fluctuations in turnover can have an outsized impact on projected turnover rates.
Still, turnover rates over 100% arise for the same basic reason: a high number of employee exits relative to the overall workforce size.
Let’s again suppose that we have 100 employees on January 1st. By the end of the of the month though, we have lost 15 but also hired 10, giving us a headcount of 95 at the end of the month.
The average headcount for his month is (100+95)/2 = 97.5. If we were to calculate the turnover for just this single month, then our turnover rate would be 15/97.5 = 15.4%. Not a good month.
If we annualize our turnover rate based on this single month, however, we would end up with a projected 180 departures (15 X 12 months) for the year against an average headcount of 97.5 ((100 + 95)/ 2 = 97.5).
Dividing that projected 180 by 97.5 and we get a ghastly turnover rate of 184.6%!
That’s bad news to be sure but perfectly correct as far as the calculations go.
Now, the rest of the year is perhaps unlikely to be that horrible but such spikes are more likely to occur when we make projections based on limited data. When your samples are smaller, you are more likely to see extreme events.
There you have it: you can absolutely get turnover rates of more than 100%. But remember that turnover numbers can vary substantially month to month. This is particularly important in the case of annualized turnover.
Before falling into deep despair in the face of turnover rates over 100% (and certainly before reporting those numbers), double check your data and your calculations to be sure everything is in order.
If everything still checks out, do some more homework and try to localize possible sources or the leading contributors. Reporting something is good, but following up with targeted, actionable information is always better and much appreciated.
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