When working with data about people, a common demographic variable that I’ve seen used is generations. As in the Baby Boomers, Gen Xers, Millennials, and others. I will vent a bit about a pet peeve of seeing data analysis with generational labels to group people. Just don’t do it. I don’t know of any reason to use it and plenty of drawbacks exist.
To make these drawbacks clearer, let’s use as context the example of analyzing turnover to see if there are differences by generation. Maybe you find differences in turnover by generation. So, what are the problems with using generations?
1. It leads to inconsistent definitions of what we mean
Quick, can you tell me who are the people that make up the Millennial generation? Can you say what years they were born without looking it up? Probably not. And even if you did know the years, you might find different people with different If you want to see some examples, look at this Wikipedia entry for Millennials. Some definitions are as vague as those born in the “early 1980s and the late 1990s”. Other definitions give precise years. The point is, that by using generational labels, you are using a vague term that isn’t always consistent.
In addition to disagreement about what constitutes a generation, the labels are a moving target. Over time, the groups of people in different generations evolve and you have to keep track of what the generations mean at different points of time. And do we know the names of those coming into the workforce today? Gen Y, Gen Z, Gen Alpha, Gen Beta anyone?
Inconsistent definitions wreak havoc on your data quality and increase the efforts for data governance. Instead of spending time arguing or trying to herd people around a nebulous definition, it is better to use something more consistent and easier to define.
2. It perpetuates stereotypes and labels
Have you heard the oft-repeated assumptions we make about generations? For example, the older generation assumes the new kids on the block are lazy, entitled, and selfish. The younger ones assume the old kids on the block are technophobes and stuck in their traditions. Using generational labels makes it easier to fall into these simple characterizations of large swaths of people that are false and incomplete. Assuming that different generations have different motivations ignores the individual diversity across people. As an example, saying that Millennials are more likely to want to communicate by text than in-person interactions ignores the personality differences of people. We need every employee to be productive and stereotypes don’t help in our quest to understand what motivates people and how to engage them.
Read a report like this one and pay attention for stereotypes and assertions without evidence. You’ll find them all over. In our push for greater DEI efforts, why are HR departments shooting themselves in the foot by promoting labels and stereotypes that have little basis in fact?
3. Generational differences are not what they seem
Finally, going back to the example of turnover and Millennials (I’m sorry if it seems like I’m picking on millennials here), you’ll find that apparent generational differences can be explained by other non-generational factors. Google “millennial attrition” and you’ll find numerous articles bemoaning the higher attrition rates, such as this one.
It is more challenging to find the data to answer the key question of “Are Millennials more likely to have higher turnover when they were early in their career compared to Gen Xers/Baby Boomers when they were also early in their career”? If you can do so at your organization, look at your turnover rates for early career employees over the past 25 years. You’ll likely find that the generational differences are not all that different, much like this study did.
In other words, the apparent generational differences in turnover are not unique to each generation. Rather the difference is due to life stages (single, young people without kids are generally more mobile than older people with families and mortgages) rather than anything unique to a generation. Gen Xers did as much job hopping when they were younger as the Millennials and Gen Yrs do today.
As a result, it is more productive to talk about early-career turnover than Millennial turnover. Early-career turnover is a phenomenon that has been going on for generations and is not unique to the latest generation. A focus on early-career turnover leads to more sustainable retention strategies compared to strategies focusing only on Millennials.
The Alternative to Generations
So what would I use instead of generations? Simple. Use age buckets.
There may be different ways to group ages but the one I’ve found the most useful is 10-year buckets with under 25 years of age as the first group. So group those <25 years of age, those 25-35 years of age, those 35-45 years, and so on. It is clear what you mean, you can compare different age groups over time and your labels don’t change over time.
Age groups also have the advantage of allowing you to more easily analyze data relative to certain age related milestones. For example, in the United States, a common retirement age is 65 years ago and certain benefits kick in for certain ages (this is true in other countries although their specific age ranges may differ).
So if we are evaluating our talent and the risk that we will lose talent and experience due to retirement, we can easily use the age groups to determine how many people are close to retirement, especially if we align the age groups to match what we are interested in for a particular country. Knowing that 20% of your workforce is in Gen X gives you little information about your potential talent drain due to retirement. Knowing that 20% of your workforce is eligible to retire in the next 5 years is more information. With age buckets that don’t change as generational labels do, you can also more easily compare the age distribution of your workforce over time.
Building on something I wrote earlier about actionable and non-actionable variables, I find that it is easier to talk about actions related to age groups than it is to talk about actions related to generations. With age groups, you can think more about policies based on age. It will force you to dig deeper to think about what is different about the age groups, without resorting to uninformative stereotypes and labels.
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