What it measures
This is the absolute best metric when it comes to gender pay equality: it computes compensation gaps between men and women on the exact same roles, seniority levels and location.
While on any given role, between 2 persons, there could be differences in pay explained by multiple factors (education level, past experience, on the job performance, etc.), on average the differences between men and women should be in theory nil.
Methodology
For each combination of Role x Seniority Level x Location, we average the total compensation packages of men and compare it to the women's. If there's any gap, we call it an unexplained pay gap that favors men or women (spoiler alert: often men).
We then average those unexplained pay gaps per job family (Job Family Unexplained Gap).
For each Job Family Unexplained Pay Gap, we compute a Job Family Pay Gap Score using the following formula:
Starting at a 100% score, each % of unexplained pay gap favoring men removes 20% from the score and each % of unexplained pay gap favoring women removes 10% from the score.
The total Pay Gap Score is the weighted average of those Job Family Pay Gap Scores (weighted by the number of employees)
Example
Let's say that in Company X, 5 persons have the same role, Senior Growth Hacker, all based in Paris:
Name | Gender | Compensation Package |
Dwight | Male | 55 000 € |
Andy | Male | 53 000 € |
Jim | Male | 58 000 € |
Kelly | Female | 54 000 € |
Pam | Female | 52 000 € |
On this role, the average compensation packages for men is 55 333 €, for women 53 000 €. The unexplained pay gap between men and women is therefore 4,2% (2 333 € is 4,2% of 55,3K) favoring men.
Once all of the unexplained pay gaps of all of the roles in the Marketing Job Family (to which the Growth Hacker role belongs to) have been computed, let's say we get an average unexplained pay gap of 3% favoring men for the entire Job Family.
It means the Pay Gap Score for Marketing is 40% (3% favoring men = 60% penalty. If it was favoring women, it would have been a 30% penalty, therefore a score of 70%).
Let's look at the overall numbers for all of Company X's job families:
Job Family | Average Unexplained Pay Gap | Job Family Pay Gap Score |
Marketing | 3% favoring men | 40% |
Software Engineering | 2% favoring women | 80% |
Product | 2% favoring men | 60% |
Sales | 4% favoring men | 20% |
Company's X total Pay Gap Score is the average of those 4 Job Family Pay Gap Scores, 50%
💡 Note: in reality, the average is weighted by the number of men and women in each job family but for simplicity purposes we removed that variable in the example
Why isn't the Pay gap score computed on the average unexplained pay gap ?
ie. I have a 0,5% unexplained pay gap yet my score is 60%, not 90%!
Because we want to take the variance into consideration. For example if a company has a 10% gap favoring men in Tech, a 20% gap favoring women in Sales&Marketing and a 10% gap favoring men in Support Functions, assuming they each represent a 3rd of the headcount, it means a 0% gap overall. Should it receive a 100% Pay Gap Score?
We don't feel that it should be the case, as there are huge unexplained pay gaps all over the company ! Therefore using an average of each Job Family' means the company receives a score much more representative of its ability to manage an equal pay for equal roles between women and men.