In order for this list to be activating the change we hope for the workplace, we want to be transparent with the approach and the principles that are leading the methodology.
Below are some of the main questions and responses we thought you might have as well as a technical appendix.
As everything we do, this list of questions is alive and will evolve as we are adding on features, data points, and your suggestions. It will be completed by regular posts in our blog section (“Voices”) when we feel like emphasizing on a specific choice or finding of the score.
EVE List believes it is possible to reach a broad consensus based on reliable data and actual quantifiable results achieved by an employer. For that reason, we have prioritized the collection of publicly available employer-level data, to establish an objective diagnosis on the state of gender inequality within each specific firm.
This being said, as our community grows it is our intention to use crowdsourced data (specifically individuals’ perceptions or experiences) and integrate it in our analysis, recognizing it as a very powerful tool to measure (and eventually address) the difference between reality and insiders’ perception. We will do this transparently and highlight the respective contributions of public official data and crowdsourced data when presenting our educated view on the state of gender parity within a company.
Like many social enterprises, EVE List will be standing on the shoulders of giants: the data we seek to use has already been patiently collected and stored by firms themselves, by government organizations, by non-profits and social businesses, by research institutions or by professional data providers. Our added value is to bring it all in one place and our aim is to then present the most accurate and educated view to the EVE List community, thus assisting those who wish to make well-informed decisions about their career.
The EVE Score™ is
In Version 0 the component scores and their respective weight are as follows:
The component score captures differences in gender representation between two specific groups: the highest earners and the lowest earners. If the same gender composition (%female vs %male) is observed amongst the highest and the lowest earners a score of 100 is given.
The component score is driven by the difference between the proportion of male employees receiving a bonus and the proportion of female employees receiving a bonus the same year. The larger the difference (in both directions) the lower the score. A score of 100 is attributed if no difference is observed.
We define HighLowEarnersRatio% = [% of female amongst the highest earners / % of female amongst the lowest earners].
A score of 100 corresponds to HighLowEarnersRatio% = 100%, a score of 0 is given if HighLowEarnersRatio% is equal to 0% or above 200%, and the score is linearly interpolated as HighLowEarnersRatio% varies between 100% and 0% / 200%.
For example, if HighLowEarnersRatio% = 70% (or 130%) the component score will be 70.
For Small and Medium-Sized Enterprises (SMEs), we do not penalize the component score when the HighLowEarnersRatio% is greater than 100%. This is to account for SMEs’ smaller datasets which may be prone to outlier effects.
We define BonusPayGap% = 1 – [average female bonus pay / average male bonus pay].
A score of 100 corresponds to BonusPayGap% = 0, a score of 0 is given if BonusPayGap% is outside of the [-50%, 50%] range, and the score is linearly interpolated as BonusPayGap% varies between 0% and +/-50%.
For example, if BonusPayGap% = 10% (or -10%) the component score will be 80.
We define BonusAttributionGap% = [%proportion of male receiving bonus – %proportion of female receiving bonus].
A score of 100 corresponds to BonusAttributionGap% = 0, a score of 0 is given if BonusAttributionGap% is outside of the [-20%, 20%] range, and the score is linearly interpolated as BonusAttributionGap% varies between 0% and +/-20%.
For example, if BonusAttributionGap% = 10% (or -10%) the component score will be 50.
We define FemaleEmployee% = [# of female employees / # of employees].
A score of 100 corresponds to FemaleEmployee% = 50%, a score of 0 is given if FemaleEmployee% is equal to 0% or 100%, and the score is linearly interpolated as FemaleEmployee% varies between 50% and 0%/100%.
For example, if FemaleEmployee% = 35% (or 65%) the component score will be 70.
We define SeniorMgmtPopulationRatio% = [% of female amongst senior management / FemaleEmployee%].
A score of 100 corresponds to SeniorMgmtPopulationRatio% = 100%, a score of 0 is given if SeniorMgmtPopulationRatio% is equal to 0% or above 200%, and the score is linearly interpolated as SeniorMgmtPopulationRatio% varies between 100% and 0% / 200%.
For example, if SeniorMgmtPopulationRatio% = 70% (or 130%) the component score will be 70.
For Small and Medium-Sized Enterprises (SMEs), we do not penalize the component score when the SeniorMgmtPopulationRatio% is greater than 100%. This is to account for SMEs’ smaller datasets which may be prone to outlier effects.