Soon, most Swiss companies will be faced with the challenge of communicating the results of their equal pay analysis in a credible way for the first time. Credibility, not communication, will be the main challenge to tackle here. This is especially true when the results can scarcely be explained internally and generalised phrasing won’t be sufficient.
The public expects well substantiated, clear statements on such issues. However, the results of the prescribed multiple regression analysis reflect a scientific model and are subject to statistical scattering and significance tests. In an attempt to simplify the result of the equal pay analysis, the federal government’s Logib instrument therefore conveys the results in a traffic light system:
Green – No gender effect was detected.
Amber – There is a gender effect
Red – Large gender effect / tolerance threshold is exceeded.
On closer inspection, however, these classifications may raise more questions than they resolve. Does Green mean that there is no disadvantage at all? How can a calculated disadvantage of 3% still land in the Green category? How can a result of 7.5% end up in the Amber category? What are the exact causes for the positioning within the traffic light system? How far away is our company from Green? What measures should be taken? Are these really causal relationships or merely correlations? Where are the specific cases of discrimination?
How to draw a clear picture
The main goal of the Swiss Gender Equality Act was to encourage companies to address the issue of equal pay proactively. On this path, regressions can only represent an intermediate step. Moreover, one should have a deep understanding of the wage-setting dynamics within the organisation and, for example, break down the effects by level or function. After all, robust equal pay cannot simply be established by using a blanket statistical formula, but must work reliably at a team and department level.
Talk to us before you embark on risky communication exercises and let us support you with suitable tools first. With our log+insights tool, for example, we can visualise the context and help to build up confidence in the results. With our function-based consulting approach, we can help to create role clarity. With our wide-ranging compensation market data, we can put the internal compensation structure into an external context.
Confidence in the robustness of equal pay is too important to be left to steering information derived from a black box.
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