EU pay gap reporting data: what employers need to collect
It is crucial to distinguish between the final gender pay gap reporting metrics that organizations report and the detailed source data needed to calculate them. The EU Pay Transparency Directive (EU) 2023/970 specifies seven key areas for reporting. These are outputs, not the raw data you need to gather:
- The gender pay gap.
- The gender pay gap in complementary or variable pay components.
- The median gender pay gap.
- The median gender pay gap in complementary or variable pay components.
- The proportion of female and male workers receiving complementary or variable components.
- The proportion of female and male workers in each quartile pay band.
- The gender pay gap by categories of workers, separated into basic salary and complementary or variable components.
Each of these metrics relies on a foundation of comprehensive employee-level data, job classifications, and detailed pay information. This is where the work truly begins for HR, payroll, and finance teams across Europe.
Who may need to report and when
The EU Pay Transparency Directive sets clear thresholds for reporting obligations:
- Employers with 250 or more workers: First reporting by 7 June 2027 and annually thereafter.
- Employers with 150–249 workers: First reporting by 7 June 2027 and every three years thereafter.
- Employers with 100–149 workers: First reporting by 7 June 2031 and every three years thereafter.
However, it is vital to remember that national legislation implementing the Directive may introduce different or additional requirements, including potentially lower thresholds or more frequent reporting.
Headcount definitions may also vary locally. Even if your organization is below the EU reporting threshold, other national pay transparency obligations might apply. Proactive data preparation can provide valuable internal insights regardless of mandatory reporting schedules.
Employee population data
The first step in any EU gender pay gap reporting exercise is defining who is included in your reporting population. This requires clear and consistent employee data:
- Unique employee identifier
- Employing legal entity
- Country of employment
- Reporting location
- Employment status (e.g., permanent, temporary)
- Start and termination dates
- Full-time or part-time status
- Contracted working hours
- Actual reporting-period coverage
- Temporary absences relevant to methodology
- Worker gender or sex classification (as required by national rules)
Treatments for contractors, agency workers, expatriates, and employees on long-term leave can vary. It’s important to clarify how these groups are included or excluded under local reporting rules.
Job and organizational data
Pay data cannot be meaningfully analyzed without understanding the work employees perform. To create valid comparisons and interpret differences, you need strong job and organizational data. This is essential for categories of workers’ pay transparency.
- Current job title
- Job description
- Job family (e.g., Marketing, Engineering)
- Job level (e.g., Junior, Senior, Lead)
- Department or function
- Management responsibility
- Country or location
- Relevant collective agreement or classification
- Category of workers performing equal work or work of equal value
Simply using job titles is often insufficient. Categories of workers should be based on objective, gender-neutral criteria, not existing salary levels. This avoids perpetuating biases in your reporting.
Complementary and variable pay
This is often a complex area because many components sit outside of base salary. The variable pay gender pay gap can reveal significant disparities. Employers must track a wide range of components:
- Annual bonuses
- Performance bonuses
- Sales commission
- Overtime payments
- Allowances (e.g., car, housing)
- Shift premiums
- Retention payments
- Signing bonuses
- Company shares or equity-related rewards
- Benefits in cash or in kind
- Other direct or indirect employment-related remuneration
It’s important to define each component clearly and map local payroll codes to central reporting categories. Establish rules for one-off payments and for payments earned in one year but paid in another. This requires careful attention to detail for complete payroll data for pay gap reporting.
Categories of workers
One of the most challenging data requirements is grouping workers who perform “the same work” or “work of equal value.” This requires objective and gender-neutral criteria, moving beyond simple job titles. Employers may need:
- Job family information
- Level definitions
- Role scope
- Skills requirements
- Responsibility
- Effort
- Working conditions
- A documented job-evaluation methodology
Creating these categories should not be an exercise to minimize reported differences. The goal is accurate, fair, and explainable groupings based on the nature of the work itself.
Common data-quality problems
Poor data quality is a significant hurdle for pay transparency reporting requirements. Common issues include:
- Missing gender information
- Duplicate employee records
- Incorrect start or termination dates
- Outdated job titles or no consistent job levels
- Bonuses stored outside payroll, or benefits not valued consistently
- Different payroll codes for the same pay element across countries
- Gross and net amounts being mixed inadvertently
- Full-time and part-time pay not normalized correctly
- Currency conversions using different dates or methodologies
- Employees assigned to the wrong legal entity
- Salary changes without effective dates
- Missing information from acquired businesses
- Manually edited spreadsheets without an audit trail
Addressing these challenges is critical for achieving strong HR and payroll data quality.
Multi-country complexity
International employers face unique challenges when preparing for EU gender pay gap reporting across multiple countries. This involves dealing with:
- Different payroll providers and local pay codes
- Various currencies and exchange rate practices
- Different working-hour standards
- Thirteen-month or fourteen-month salary structures
- Local allowances and benefits
- Collective agreements influencing pay structures
- Different employee identifiers across systems
- Different definitions of reporting populations
- Varied national submission formats and languages
- Local privacy and access requirements
For effective multi-country payroll reporting, central definitions and governance, supported by local HR and payroll expertise, are essential. Do not try to force all local payroll data into one structure without understanding what the local fields genuinely represent.
A practical data-readiness process
Achieving pay data readiness is a structured process. Here’s a recommended sequence:
- Map applicable reporting obligations by country and legal entity.
- Define the employee population for each reporting entity.
- List the reportable metrics (the seven outputs).
- Identify the source data required for each calculation.
- Map data owners and systems for each data field.
- Agree central definitions and conversion methodologies.
- Collect and clean the data, addressing quality issues.
- Create categories of workers based on objective criteria.
- Run a test calculation to identify gaps.
- Investigate anomalies and missing data, making corrections.
- Validate the methodology and results locally with stakeholders.
- Document decisions and establish recurring ownership for future cycles.
“The final report may look simple, but the preparation rarely is. Employers first need to know where their pay data comes from, whether local payroll fields mean the same thing and who is responsible for validating the result.”
– Ceren, Parakar’s Head of HR services.
Reliable pay-gap reporting starts with complete payroll data, consistent definitions and clear ownership across HR, Payroll and Finance.
Download our EU Pay Transparency Directive guide for an overview of reporting thresholds, required pay-gap measures, variable-pay considerations and the latest implementation status across Parakar’s ten markets. It also covers common compliance mistakes, potential consequences and a practical 16-point checklist to assess your data, pay structures and internal governance.
Need support preparing your multi-country HR and payroll data? Book a free 30-minute Pay Transparency Readiness Call to discuss your reporting obligations, data gaps and the actions needed before your first reporting deadline.
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