Managers attempt to rate their employees as best they can. The ratings are then calibrated against one another and, if necessary, adjusted by distribution guidelines that are typically bell curves (Gaussian distribution curves). These guidelines assume that the vast majority of employees cluster around the mean and meet expectations, while smaller numbers over- and underperform. […] This logic appeals intuitively (“aren’t the majority of people average by definition?”) and helps companies distribute their compensation (“most people get average pay; overperformers get a bit more, underperformers a bit less”).
But bell curves may not accurately reflect the reality. Research suggests that talent-performance profiles in many areas—such as business, sports, the arts, and academia—look more like power-law distributions. Sometimes referred to as Pareto curves, these patterns resemble a hockey stick on a graph. […] The sample curve emerging from this research would suggest that 10 to 20 percent of employees, at most, make an outsized contribution.
Google has said that this research, in part, lies behind a lot of its talent practices and its decision to pay outsized rewards to retain top performers. […] Companies weighing the risks and rewards of paying unevenly in this way should bear in mind the bigger news about power-law distributions: what they mean for the great majority of employees. For those who meet expectations but are not exceptional, attempts to determine who is a shade better or worse yield meaningless information for managers and do little to improve performance. Getting rid of ratings—which demotivate and irritate employees, as researchers Bob Sutton and Jeff Pfeiffer have shown—makes sense.
Authors: Asmus Komm, Boris Ewenstein, Bryan Hancock
Source: McKinsey Quarterly
Subject: Human Resources
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