Pay equity analytics starts with a compensation dataset that can reproduce the metrics required by Directive (EU) 2023/970 and support deeper investigation where differences appear. Article 9 requires organisation-level gender pay gap, median pay gap, variable-pay gaps, receipt rates for complementary or variable components, quartile distributions and category-level gaps. Article 10 can require average pay information and analysis within categories of workers. Employers therefore need reliable worker, job, pay, hours, variable compensation and category data, together with documented rules for normalising dates, currencies, part-time arrangements and missing values. Statistical methods such as regression can support investigation, but they are analytical tools rather than a substitute for the Directive's legal tests or prescribed reporting metrics.
Jurisdiction: European Union
Pay Equity Analytics Begins With the Legal Metric
The first design question is not which software or statistical model to use. It is which legal or business question the analysis must answer. Article 9 of Directive (EU) 2023/970 requires several distinct outputs, including the gender pay gap, median gender pay gap, equivalent metrics for complementary or variable components, the proportion of women and men receiving those components, quartile pay bands and category-level pay gaps. Those outputs need different combinations of worker, pay and grouping data. A dataset built only for payroll processing may therefore be insufficient for pay transparency because it may not preserve the job, category, hours or component detail needed to reproduce the required measures.
Build One Analytical Record Per Worker and Measurement Period
A practical analytical model usually needs a stable worker identifier linked to the relevant measurement period. The record can then bring together sex, job title, job family, grade, worker category, employment status, location, working time, FTE, base pay, variable pay and other relevant remuneration. The exact design depends on the organisation and national reporting rules, but the principle is consistency. Analysts should know which date each field represents and whether it reflects a point-in-time snapshot, a full calendar year or another period. Mixing current job data with historical pay without documenting the mismatch can distort category comparisons and make later review difficult.
Worker Categories Are a Core Analytical Dimension
The Directive does not treat the organisation as one undifferentiated population. Category-level analysis is central to the framework. Categories of workers are linked to the same work or work of equal value, and Article 9 requires category-level pay-gap information broken down by ordinary basic wage or salary and complementary or variable components. That means the analytics dataset needs a defensible category field or enough job-evaluation information to derive one. A reporting system that can calculate an organisation-wide gap but cannot connect workers to comparable categories may satisfy only part of the analytical need and can leave the employer unable to explain where differences arise.
Mean and Median Answer Different Questions
Article 9 requires both the gender pay gap and the median gender pay gap. The mean reflects the average pay level and can be influenced by very high or very low values. The median identifies the middle pay level and is less sensitive to extreme observations. Neither measure is inherently superior. They describe different features of the pay distribution. Employers should calculate each consistently using the relevant Directive and national methodology, then investigate why the results differ. A large difference between mean and median gaps can be analytically useful because it may indicate concentration of one sex at the upper or lower end of the pay distribution or the influence of unusual pay observations.
Data Cleaning Is Part of the Analytical Method
Cleaning compensation data is not clerical work separate from the analysis. Duplicate worker records, stale job titles, inconsistent currencies, missing FTE values, mixed pay periods and misclassified variable compensation can change the result. Each transformation should therefore be documented. Analysts should preserve the source value where possible, create a transformed analytical value separately and record the rule used. That approach makes the dataset auditable and allows the organisation to rerun the analysis when source data changes. It also reduces the risk that a manual spreadsheet correction becomes an undocumented assumption that cannot be reproduced during reporting, consultation or a later pay dispute.
Adjusted Analysis Is Not the Same as the Reported Gap
An unadjusted pay gap describes the observed difference in pay between groups using the selected measure. An adjusted analysis attempts to account for explanatory variables such as job, grade, location or experience. Regression and similar techniques can help employers investigate whether a difference remains after selected factors are considered. However, an adjusted model should not be substituted for the specific metrics required by Article 9. The legal reporting output and the analytical investigation serve different purposes. Employers should retain the unadjusted required metrics, document any adjusted methodology separately and avoid treating statistical significance as the only test of whether a pay difference requires legal or organisational review.
Repeatability Is the Standard for a Mature Pay Analytics Process
A mature process can be rerun without rebuilding the methodology from memory. It defines source systems, extraction dates, field mappings, inclusion and exclusion rules, worker-category logic, pay-component treatment, currency conversion, annualisation, part-time handling, outlier rules and missing-data decisions. It also records who reviewed the methodology and which version generated a published or internal result. This repeatability matters because pay transparency is not a one-off calculation. Reporting obligations recur, pay structures change and workers may request information or challenge differences. A reproducible process allows the organisation to compare periods, investigate changes and demonstrate how a result was produced.
Frequently Asked Questions
Does the EU Pay Transparency Directive require regression analysis?
No. The Directive specifies reporting and equal-pay requirements, but it does not prescribe regression as a mandatory universal method. Regression can be a useful analytical tool for investigating differences when appropriate.
What is the minimum data needed for pay equity analytics?
The exact fields depend on the question and national rules, but employers commonly need worker identity, sex, job or worker category, pay level, basic pay, variable pay, working-time information and the relevant measurement period.
Should cleaned values replace source payroll data?
Usually the safer analytical practice is to preserve source values and create documented transformed fields. That maintains data lineage and makes the analysis easier to reproduce and audit.
Related Guides
Official Sources
Requirements and practices differ by jurisdiction and organisation. Check current local law, official guidance and professional advice for a specific situation.