What Causes Employee Turnover, Part 1: A Different View of Demographics
In a previous post, I provided a basic of list of variables and data sources that help predict employee turnover. In this post, we’ll dig into a handful of demographic factors that might ultimately help us predict turnover, but we’ll take a different view to see how demographic elements might actually reflect other processes and issues beyond the surface.
Why you should read this post
- Learn how to think about the variables you select…and don’t select
- Shed light on possible turnover issues at your company
- Learn how factors related to turnover might reflect other factors
Note: You can find our primer on developing your own predictive models for employee turnover here.
Before we get started, it is important to remember the difference between “predicting” and “understanding”.
Predicting an outcome simply means using currently available information to more accurately estimate the likelihood of something happening. Understanding, on the other hand, means knowing the true “why” behind a prediction.
Future posts will discuss the prediction v. understanding issue further. For now, just remember that we are focusing more on the selection of reasonable variables to look at first, not a deep, systematic dive into true cause.
Ethnicity and Sex
Ethnicity and Sex are typically among the first variables to consider when looking at turnover. The most basic reason is simply that turnover differences tied to these groups would be of interest to leaders.
For example, if people from one particular ethnic group are more likely to voluntarily leave their roles, the HR analytics team should be aware of it and that team should indeed be driving that discussion. In many cases, there won’t be any systematic differences in turnover by Ethinicity or Sex, but it’s better to stay ahead of the issue instead of waiting for issues to surface, creating problems for individual employees and your company.
A second, richer point is that Ethnicity and Sex are quite likely to be related to other things impacting turnover. For instance, higher employee quit rates in Texas and Florida could reflect either a more competitive labor market in those regional economies or professional development headwinds for Hispanic/Latino employees. Those are two completely different issues.
If you don’t account for Ethinicity, you might end up focusing on the wrong thing and creating more problems instead of solving the one you have.
The key thing to remember? Ethnicity and Sex are often systematically tied to other variables. They may help predict turnover but be sure to consider their ties to other factors before leaping to conclusions and declaring victory.
Age: Round 1
What if I told you that the average turnover at ACME Co. was 30% but it was not correlated with Age? On the surface that might seem plausible, especially given that there are some basic statistics tied to it.
What do you see?
A painfully obvious tie between Age and Turnover, but one that simple correlation could not detect because of the up-and-down nature of the relationship.
The lesson? If you don’t plot your Age data (or really any data) first and just look at the cold stats, you might miss something. Lazily relying just on only means and correlations is a mistake.
Age: Round 2 (Hint: Not just a case of Millenialitis)
But there is a second possible insight here too, tied specifically to those between 20 and 40 in this figure. Turnover is clearly higher for the younger crowd and then steadily declines as age approaches 40 in this data.
Can you think of some possible explanations for this without just saying “Millenials!”?
I’m sure you came up with a bunch of good ones. My mind leaps to roles and career opportunities. We need to remember age is tied to experience level, “life season”, demographics, and simple experimentation and job sampling.
Yes, it might be true that younger employees are more likely to bolt for greener pastures, but we are mistaken if we just assign that to generational disloyalty.
First, younger workers are typically in entry-level roles. Are they leaving because they are “Millenials” or because the developmental paths for entry-level jobs are unclear? Is the on-boarding suddenly lousy? Have talent acquisition processes changed in the last few years along with the cohort of applicants?
We can’t tell that just by looking at the figure, but notice that each of these alternatives (and certainly some those you came up with) are actionable and tied to human capital processes. “Millenials” is not an actionable explanation. Recruitment and development are.
Second, for larger companies, do we see regional difference in the age of our employees? Are all the young employees in bustling cities like Atlanta and New York? If there are regional differences, does that tell us something richer beyond “Millenials are leaving in droves!”
What is the recurring theme?
Basic demographic variables can serve as carrier variables for other kinds of data. I am not dismissing the possibility of true differences in generational culture, but I do think there are simpler, more direct explanations of some of the claimed patterns.
Region, City, State: The World is Lumpy
I placed a big emphasis on location in the examples above because many factors tied to turnover might also be related to location.
This possible underlying role for location reflects a more general rule of thumb: the world is lumpy.
Call centers are typically clustered in just a few areas. IT support may be largely located in tech hubs where the right talent is. Most of our sales reps work where most of our customers live.
In a future posts I will look at location factors like population size and local labor markets more carefully. For now, when it comes to thinking about demographics be sure to remember the real estate mantra: location, location, location.
Understanding turnover at your company is not trivial but it’s not impossible either. An important initial step is to look at a few basic demographics variable to get a feel for the data and your people.
But make sure you dig a bit deeper. Ask yourself what other variables might be hiding in Ethnicity, Sex, Age, Region, orother elements you consider. Remember that variables are often intertwined. The impact of one factor may be masquerading as another.
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