## Turnover Rate and Annual Turnover: What They Mean and How to Calculate Them In today’s tutorial we will discuss the two most prominent metrics in Human Capital Analytics: Monthly Turnover Rate and Annual Turnover Rate. (Note: See also our  our and visualizing employee turnover and movement as a flow within a network.

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By the end of this tutorial you will:

• Understand what a “turnover rate” means
• Know how to calculate a monthly turnover rate
• Know how to calculate annual turnover rates using three different methods
• Know how to perform these calculations in R

### What is a turnover rate?

The turnover rate is the percentage of employees that leave a company in a given period of time and differs importantly from employee retention rate which ignores new hires (see this post for more on retention). Typically companies are most interested in monthly or annual turnover rates, focusing only on those who leave voluntarily.

Note: If you want know how your turnover rate can be over 100% see this post.

### How to Calculate Monthly Turnover Rates

To develop our intuitions, we’ll focus first on monthly turnover.

Example 1: If I start out with 100 employees at the beginning of June but 5 of those employees leave the company before the end of the month, then I might say that my turnover rate for June is 5% (5 out of 100 left).

Typically, though, companies both lose and gain employees. To account for this, turnover rate is normally 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. By averaging the number of employees at the beginning and the end of the month, we get an average employee headcountthat more closely resembles the true workforce size.

(# Employees Leaving in period )/ [(# Employees period beginning + # Employees period end)/2]

More compactly, I could say: # Employees Leaving in period / mean(# Employees period beginning and # Employees period end)

Example 2: At the beginning of June I have 100 employees. During the month, 9 employees left but 6 new employees joined, leaving me with 97 employees at the end.

The total number leaving is 9.

The average headcount (the number of employees) from the beginning to the end of the month works out to 197/2 = 98.5.

Plugging these values into the equation, I would get 9/98.5 = .0914, or a 9.14%.

The following code snippet shows how to calculate monthly turnover rates in R using the values from Example 2.

``````total_leaving <- 9
emp_beginning <- 100
emp_end <- 97
turnover_rate <- total_leaving/mean(c(emp_beginning, emp_end))
turnover_rate # multiple by 100 and add % to change decimal value to percent``````
``##  0.09137056``

If we want to convert this to a percentage, we just multiple by 100 and slap on a “%”. In R, we can do either of the following:

``````# Rounding the values and converting to a % by hand
turnover_rate_perc <- paste0(round(turnover_rate*100,2), "%")
turnover_rate_perc # show the new percent value``````
``##  "9.14%"``
``````# Or converting to % using the percent function from the scales library
# install.packages("scales") # install scales package if needed
library(scales)
turnover_rate_perc_2 <- percent(turnover_rate)
turnover_rate_perc_2 # show the percent value``````
``##  "9.14%"``

### What is the Annual Turnover Rate?

When we calculate our turnover rate over the period of a year, then we are talking about annual turnover rate. In principle, this calculation should be fairly straightforward but it turns out there are at least three different methods for calculating an annual turnover rate.

### How to Calculate Annual Turnover Rate: Method 1

In Method 1, we measure our “average” number of employees using just the number of employees at the beginning of the year and at the end of the year. Here then, the steps for calculating the turnover rate for this period are EXACTLY the same as when we calculated them for the month. We just use the number of employees at the beginning and end of the YEAR when calculating the rate instead of the beginning and end of the month.

Example 3: On Jan 1, we had 100 people. From Jan 1 until Dec 31, 27 people left the company.

However, we also ramped up hiring and expanded the size of our workforce substantially, ending the year with 129 employees.

Using Method 1, the average number of employees over the year using just the beginning and ending values was (100+129)/2 = 114.5.

Plugging these values into our formula, we get 27/114.5, or an annual turnover rate of 23.6%.

``````total_leaving <- 27
emp_beginning <- 100
emp_end <- 129
turnover_rate <- total_leaving/mean(c(emp_beginning, emp_end))
percent(turnover_rate)``````
``##  "23.6%"``

### How to Calculate Annual Turnover Rate: Method 2

In Method 1, we calculated annual turnover using an average employee count based on the just the beginning (Jan 1) and ending (Dec 31) of the year values for the number of employees.

Some companies, though, prefer to calculate the average number of employees over the year by getting the average number of employees from each individual month and then averaging those individual monthly values.

Example 4: For January, we had 106 employees and 109 at the end, an average employee count of 107.5. Let’s suppose we do the same calculation for each month, yielding the following month average headcount for the year: 107.5, 109,111, 123, 127, 127, 133, 136, 132, 131, 133, 131.

Averaging the above monthly headcounts gets us and average of 125.08 employees over the entire year.

Given that a total of 27 employees left over the year, our annual turnover rate is 27/125.08 or 21.6%.

``````# average of the 12 monthly averages to get the average employee count for the year
annual_average_count <- mean(c(107.5, 109, 111, 123.5, 127, 127, 133, 136, 132, 131, 133, 131))
total_leaving <- 27
turnover_rate <- total_leaving/annual_average_count
percent(turnover_rate)``````
``##  "21.6%"``

### How to Calculate Annual Turnover Rate: Method 3

Believe it or not, there is a third way to calculate annual turnover.

1. Calculate the monthly turnover rate for each of the 12 months of the year.

Note the key difference between Method 2 and Method 3. In Method 2, we took the total number of leavers over the whole year and divided it by the average of the average monthly headcounts. We did NOT calculate a monthly turnover rate for each month separately.

By contrast, in Method 3, we actually calculated the turnover rates for each individual month.

Observe also that because this is a measure of annual turnover, you need to make sure that you are only adding 12 months’ worth of individual month data together.

Note too that this same general approach can be used to calculate year-to-date turnover. For example, if it is May 1st and we want to know our year-to-date turnover rate, then we would simply add up the individual monthly turnover rates for January, February, March, and April.

Remember, the key aim here is figure out what percentage of your workforce you are losing over a given period of time. There is nothing magical about being a monthly measure v. a quarterly measure v. an annual measure. Just be sure to keep track of you time units.

Bottom line? Dive in, grow your skills, and see what you learn!

## Which Annual Turnover Calculation Method is Best?

That’s a great question and one without a definitive answer. In a future post, we’ll run some simulations to more thoroughly understand the implications of each (amending the present discussion as necessary). As a rule of thumb, each of the methods should yield broadly similar results. The real key is making sure you use the same calculations every time so you are comparing apples to apples as they say.

For right now, consider the following but be sure to also check what approach your company has used historically. A change in the calculation from one discussion to the the next could yield different results that don’t necessarily reflect a true change in the state of things.

1. Method 1 is the easiest to calculate and the easiest to explain. Both are good things.
2. Method 2 appears to be the most commonly used. It is also more sensitive than Method 1 to the actual size of the workforce and any changes (up or down) throughout the year.
3. Method 3 is similarly more sensitive to the size of the workforce throughout the year. It is also possible, however, that the final annual turnover rate measure will be overly influenced by single month with a flukishly high or low turnover rate more related to noise or the timing of data input than a real change in workforce behavior.

For my money, Method 1 is the simplest and most direct. That said, I would suggest at least working through the other methods. That will help reinforce what you have learned and give you a better feel for the data. In addition, I also suspect you’ll find there isn’t much difference in the outcomes.

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