ESRS S1-6 Workforce Diversity
Beyond basic headcount, ESRS S1-6 requires a comprehensive diversity breakdown of the workforce — by age group, gender, and employee category. These metrics reveal the structural composition of the workforce and, when tracked over time, show whether diversity strategies are producing measurable change at all levels of the organisation.
Beyond basic headcount, ESRS S1-6 requires a comprehensive diversity breakdown of the workforce — by age group, gender, and employee category. ESRS S1-6 requires a three-dimensional workforce breakdown:.
The ESRS S1-6 diversity metrics
ESRS S1-6 requires a three-dimensional workforce breakdown:
Gender breakdown: Number and percentage of employees by gender — male, female, and where applicable and legally permissible, non-binary or other gender identities. Apply the same gender categories consistently across all S1 disclosures — S1-6, S1-12 (gender pay gap), S1-13 (training by gender), S1-15 (parental leave by gender).
Age group breakdown: Number and percentage of employees in three age bands: under 30; 30–50; over 50. These three bands are prescribed by ESRS — do not substitute different age bands. The under 30 category reveals the graduate and early career pipeline; the over 50 category reveals succession risk and workforce ageing trends.
Employee category breakdown: Number and percentage of employees in each employee category — management, professional, administrative, operational/production, or your company-specific categorisation. The category definition must be consistent with S1-12 (pay gap by category), S1-13 (training by category), and S1-14 (H&S by category).
Combined breakdowns: The most analytically powerful disclosures combine dimensions — women in management (gender × category); young employees in senior roles (age × category); age distribution in production vs professional categories (age × category). These intersectional views reveal structural diversity patterns that single-dimension metrics miss.
Defining employee categories consistently
Employee category definition is one of the most consequential methodology decisions in ESRS S1 — because the same categories must be used consistently across S1-6 (diversity), S1-12 (pay gap), S1-13 (training), and S1-14 (H&S). Inconsistent categorisation creates internal contradictions across the report.
Standard category approaches: The most common categorisation systems for large companies are ISCO (International Standard Classification of Occupations) — an internationally recognised system used by GRI and many national labour statistics offices; internal job grading — using the company's own pay bands or job levels; functional category — management, professional, technical, administrative, operational; and hybrid — combining functional category with seniority (senior management, middle management, professional, technical, clerical, production).
For ESRS S1 purposes: choose a categorisation that: is meaningful for your business model (a professional services firm has different meaningful categories than a manufacturer); enables analysis of pay equity and diversity at the levels where structural inequality occurs (management concentration is the key insight for diversity); is stable year-on-year to enable trend analysis; and aligns with your HRIS categorisation to enable automated data extraction.
Management definition: The management category deserves particular attention — consistent definition is critical for meaningful diversity trend analysis. Define management clearly: does it include all line managers (people with direct reports)? Only senior management (above a certain salary band)? Only the executive committee and direct reports?
For global companies: ensure category definitions translate across different country contexts. A 'senior manager' in the US may have a different scope and seniority than a 'senior manager' in Germany or Japan. Anchor categories to job grade levels that are internally consistent rather than to job titles that vary by country.
Diversity trend analysis and target-setting
Single-year diversity snapshots are far less valuable than multi-year trends — and S1-6 data is most powerful when combined with explicit diversity targets and progress tracking.
Women in management trend: The most watched diversity metric for investors and proxy advisers is the percentage of women in senior management roles. EU directive on gender balance on corporate boards (2022) mandates that 40% of non-executive directors at EU-listed companies must be the underrepresented gender by June 2026. S1-6 management gender breakdown tracks progress toward this and equivalent aspirational targets.
Age diversity and succession: The over 50 percentage reveals workforce ageing risk. Where more than 30–35% of a critical skill category is over 50, the company faces significant succession and knowledge transfer challenges over the next 10–15 years. S1-6 age data triggers meaningful workforce planning discussions — what is the pipeline of younger employees being developed for senior roles vacated by older workers retiring?
Target disclosure under ESRS S1-5: ESRS S1-5 includes targets for diversity and equal opportunities. Where diversity targets exist — 'achieve 40% women in management by 2027', '20% increase in under-30 hires in technical roles' — the S1-6 diversity metrics are the progress tracking mechanism. Aligning S1-5 targets with S1-6 metrics creates a closed-loop accountability system.
Intersectionality: The most advanced diversity analysis combines gender, age, and category simultaneously — revealing compound effects that single-dimension metrics miss. A company may show good overall gender diversity but close inspection of senior management age × gender breakdown may reveal that women in senior management are significantly younger than male counterparts — potentially indicating a glass ceiling effect at higher age and seniority combinations.
Frequently asked questions
How do we collect gender data in compliance with GDPR?
GDPR allows processing of sensitive personal data (which includes gender beyond binary categories) where there is a legitimate purpose and appropriate safeguards. Equal opportunity monitoring is a recognised legitimate purpose in most EU jurisdictions — documented in your HR data processing policy and GDPR privacy notice. Collect gender data through voluntary self-identification in onboarding and periodic HR data verification processes. Where employees decline to provide gender data, this creates a 'not disclosed' category — report the proportion who declined alongside the male/female/other breakdown.
Should we break down diversity metrics for temporary and fixed-term employees separately from permanent employees?
ESRS S1-6 requires disclosure by employment contract type (permanent vs temporary) in addition to diversity breakdowns. Where temporary employees have significantly different diversity profiles from permanent employees — for example, seasonal agricultural workers who are predominantly from specific nationalities or demographics — the breakdown reveals structural patterns worth disclosing and addressing.
Our company has only 3 employees in the over-50 management category — do we still disclose the number?
ESRS S1-6 requires the disclosure. Where specific numbers are very small and could identify individuals (privacy risk), consider disclosing at a less granular level — for example, disclosing the management gender split without also breaking down by age for the management category where numbers are too small. Note the aggregation and reason in your methodology disclosure. Privacy protection of small groups is a legitimate basis for limited aggregation.