The grading trap
The directive defines equal value work through skills, experience, working conditions, and other objective factors that are gender unspecific. Practically, this means grouping employees into grades that reflect similar work value, but how exactly do you objectively measure skills, working conditions and experience[i]? More importantly, how do you ensure that roles are graded consistently with many different people doing the grading?
Even under ideal circumstances the level of agreement between raters is roughly 70% with real world estimates being in the of 35-45%[ii] range. To address this challenge many organizations adopted a pragmatic solution called 'broad banding': make job grades broader to reduce the risk of grading similar roles differently.
In the context of the pay transparency directive, these broad groupings are liability machines. When you lump together roles of fundamentally different value, you create artificial pay variance that even the best pay model struggles to explain.
Pay factors won't save you
Faced with unexplainable pay gaps, many organizations instinctively reach for more pay factors. If experience and education aren't enough, add performance ratings. Still gaps? Include tenure, certifications, and location as controls. The logic seems sound: more data should provide better explanations.
But this approach fundamentally misunderstands the problem. Adding factors to compensate for poor job groupings creates over-parameterized models that become statistically unstable. Small changes in employee data can produce wildly different gap calculations, making it nearly impossible to determine if gaps are actual gaps or modeling artifacts.
The mathematical reality is unforgiving: If your job grades mix apples and oranges, no amount of additional factors will consistently explain away the noise.
When employees end up in grades that don't reflect their actual work value, what we refer to as grade outliers, they distort the pay distribution for the entire group. A senior professional misclassified with mid-level peers doesn't just affect their own compensation—their presence makes everyone else's pay look potentially problematic. The cumulative effect transforms what should be manageable compliance costs into significant financial burdens that compound with each pay cycle.
Design for stability
Effective job architecture for pay transparency starts with a different mindset. Instead of asking "how can we simplify administration?" the question should "how do we create groups where pay differences can be reliably explained by objective factors?" This requires grades that capture meaningful distinctions in work complexity, impact, and market positioning while maintaining sufficient homogeneity for stable statistical analysis. The goal isn't perfect precision—it's creating defensible groupings that your chosen pay factors can adequately explain.
Successful approaches share common characteristics: they align grade boundaries with clear differences in job value, they maintain consistency in how similar roles are classified across departments, and they consider the statistical requirements of gap analysis rather than treating measurement as an afterthought.
Review your grades
Organizations approaching this challenge strategically start By auditing their current structures against transparency requirements. This means analyzing how much pay variance exists within each grade and whether their planned factors can reasonably explain those differences. Consider carefully whether pay factors actually drive pay in the organization.
The most effective implementations also test their approaches before full deployment, modeling how different grading decisions affect gap calculations and remediation costs. This upfront investment in analysis often reveals classification issues that would otherwise create ongoing compliance problems, ultimately driving up cost and leading to inaccurate and unfair pay adjustments.
Conclusion
The pay transparency directive represents more than a regulatory hurdle—it's forcing organizations to build more sophisticated, equitable compensation frameworks. Companies that approach job architecture thoughtfully will find themselves with stronger foundations for fair pay practices that extend well beyond meeting the 5% threshold.
The organizations that struggle will be those that treat grading as a peripheral administrative task rather than a core component of their pay equity strategy. In an environment where remediation costs can escalate quickly, getting the foundation right isn't just good practice—it's essential risk management.
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References:
[i] EU Pay Transparency Directive.
[ii]Salgado, J. F., & Moscoso, S. (2019). Meta-Analysis of Interrater Reliability of Supervisory Performance Ratings: Effects of Appraisal Purpose, Scale Type, and Range Restrictions
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