The methodology of measuring the gender pay gap is well known and generally accepted.
It is based off a regression model where wages (actually, the log of wages) are explained using bonafide determinants of pay such as job role, education, and performance.
The resulting model highlights differences in pay that cannot be explained by anything else except the gender of the employee.
However, how best to close the pay gap has remained an open question.
In collaboration with a team of academics, we studied each specific employee’s effect on the gap.
And what we found is somewhat counter-intuitive: we can demonstrate that within organizations there may be women who, if given a raise, actually end up increasing the gap.
Similarly, we can find men who, if given a raise, actually decrease the gap.
In fact, one key finding from our research is that there is almost no correlation between the fairness of a raise and the impact of said raise on the gender pay gap.
This means that an organization solely focused on cost efficiency in closing the gap may end up with a skewed and unfair compensation structure.
Data and optimization to the rescue Given the complexity of the interactions between an employee’s impact on the pay gap and the notion of fairness, there is a great opportunity for the effective use of data-driven methods.
Using companies’ data, we can build algorithmic approaches that are more fair and efficient than, say, extending a raise of the same percentage of salary to every female employee.
And data-driven approaches have additional benefits.
For instance, they can highlight manifestations of unconscious bias in the pay structure.
When we were working with our development partner, we saw that female top performers were not being compensated to the same extent as male top performers: while the pay for top performing females was above average, they were not at the top of the pay scale like the top performing males.
Data-driven methods deliver results Our methodology and findings speak for themselves: Reykjavik Energy, our initial development partner, has driven down their gender pay gap to zero percent.
After building a data-driven cloud solution, they can now test salary decisions – their impact on both the salary structure and the pay gap – on the fly before making them.
This ensures that the gap remains closed.
The drivers of demographic pay gaps may be complex, but with the right quantitative tools and buy-in from top-level management, we can eliminate them one raise at a time.
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