I read an article yesterday from the Wall Street Journal entitled “Are Companies Any Good at Picking Stars?” in which the author stated that despite companies having more data than ever (big data!), predicting which employees will be successful is still more an art than a science. Those of us who pay attention to HR technology trends know that seemingly every vendor out there is scrambling to bring anything they can label as ‘predictive analytics’ to the market. Am I right? (You know I am).
As the article reminds us “makers of HR software are beginning to develop their own solutions, claiming that algorithms built using an array of metrics—from an individual’s 401(k) contribution to promotions to connections on the corporate social network—can yield information about high potentials.”
Yeah. I really want to see the employer side attorney who gets to litigate the class action suit that arises when a bunch of employees decide they were passed over for promotion because their employer was analyzing data that included their 401(k) contributions.
Now, were I the attorney for the company, I imagine I could argue that using this data point had absolutely no disparate impact on any class of protected employees; nothing at all to do with race or age or gender, et al. After all, I might argue, according to an oft cited report from the US Social Security Administration’s Office of Policy, 401(k) participation and contribution rates are not related solely to income and age. Rather, participation and contribution rates appear to be related to an individuals’ propensity to plan as well as the existence of a match (and the rate of the match) as well as access to the right mix of funds in the plan.
The research also finds that “Overall, the results confirm earlier findings that age, income, and job tenure increase the probability of participating in a 401(k) plan. Education is not statistically significant, a result that holds regardless of how the education variable is specified. Age has a large impact. An eligible worker between the ages of 25 and 34 has a 14 percent greater probability of participating in a 401(k) plan than a counterpart under age 25, and the probability increases for workers aged 35 to 44. Interestingly, for workers aged 45 and over, the probability of participation is only 11 percent greater than for workers under 25.”
Also… “job tenure has a statistically significant impact; as noted earlier, one additional year of tenure raises the probability of participation by 0.7 percentage point.”
So…we can assume that if Gina is over 40 and has been with the organization 10 years (the mean is 9 years) her 401(k) participation is higher than her coworker who is 34 and has only been with the organization 7 years.
But then we get to contribution rate. According to that SSA report “….age, the presence of a defined benefit plan, and the wealth in that plan are no longer statistically significant, and education remains insignificant. In contrast, a short planning horizon continues to have a statistically significant and important effect: a planning horizon of less than 5 years reduces the contribution rate by roughly 1.2 percentage points. The contribution rate is positively related to wealth, which again suggests that the variable reflects a taste for saving. Household income has a statistically significant negative effect.”
What if Gina, who attended a state university in the Midwest, is competing for promotions against a bunch of Ivy League guys/gals with family wealth? What if Gina, a superstar employee, is not socking away as much money in her 401(k) as Tripp and Buffy? What if Gina, who does not have a trust fund, is supporting her extended family back in the Midwest and thus, at this stage in her life, is not bulking up her personal retirement plan?
Why in the world should ANY of that have anything to do with assessing Gina’s potential?
I’m all for using data to make appropriate decisions; we surely need to do a better job of that in HR. Let’s definitely look at hiring data, performance metrics, employees’ networks and collaborative reach and impact. I want us to pull in organizational data like sales figures and output and production. We need to gather and sync external market and economic data, trends and forecasts.
We don’t need to reach end-ways around our behinds though to make a labored connection that seems to indicate an employee making a 12% contribution to his 401(k) doesn’t have the same leadership chops or mental acuity as the employee who contributes 18% to her 401(k).
Give me a break.