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ESRS S1: Own Workforce·Disclosure Requirement S1-8
Diversity
Practical guidance for preparing this disclosure. Use this card to identify the information to prepare, verify claims and organise supporting evidence. For exact requirements, always refer to the official European Commission source.
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Prepared from official source material and published report evidence. No named approver is recorded yet, so this page carries no byline.
Use it alongside the official European Commission source while that review is in progress.
Standard
ESRS S1: Own Workforce
Disclosure Requirement S1-8 · 2026-5010-final
Last reviewed
—
LRA educational guidance · Not issued or endorsed by European Commission
To prepare this disclosure
Disclosure focus
This disclosure asks an organisation to explain the diversity profile of its own workforce in a way that is useful for understanding who is employed and how representative that workforce is. In practice, it is about reporting the mix of people across relevant diversity dimensions using the organisation’s own employee data, rather than giving a general statement about commitment or culture.
The practical focus is on the workforce covered by the reporting boundary and on using a consistent basis so the information is comparable over time. Organisations should think about whether the data reflects the full workforce across operations, entities and locations within scope, rather than only selected sites or headline examples, and should be clear about any gaps or limitations in coverage.
This LRA educational guidance supports disclosure preparation. For the exact requirements, always refer to the official European Commission source.
Before you start
Before you start
A quick mental checklist before you prepare this disclosure — tick each as you settle it.
Preparation
Key information to prepare
| Preparation field | What to capture | Evidence hint | Owner |
|---|---|---|---|
| Women’s share | Capture the percentage of the relevant population that is recorded as female, using the same population definition and reporting period as the source metric. | HRIS or workforce reporting extract with the gender field and the denominator used for the calculation; check the figure ties back to the underlying headcount file. | HR / People Analytics |
| Men’s share | Capture the percentage of the relevant population that is recorded as male, using the same population definition and reporting period as the source metric. | HRIS or workforce reporting extract with the gender field and the denominator used for the calculation; check the figure ties back to the underlying headcount file. | HR / People Analytics |
| Other gender share | Capture the percentage of the relevant population that is recorded outside the female and male categories, using the same population definition and reporting period as the source metric. | HRIS or workforce reporting extract with the gender field and the denominator used for the calculation; check the figure ties back to the underlying headcount file. | HR / People Analytics |
＋ Show S1-8 sub-elements (LRA working checklist)
Calculate and report the share of women in the group.
Calculate and report the share of men in the group.
Calculate and report the share of people recorded as other.
How to prepare it
Set the reporting boundary first: decide which workforce population you are counting for this disclosure, and keep that scope consistent across the three gender percentages.
Agree the classification rules before you calculate anything: define how your organisation will assign people to the female, male, or other categories, using one clear approach for the whole dataset.
Pull together the underlying records: gather the source data that shows headcount by the chosen categories, and make sure the figures can be traced back to a reliable internal source.
Calculate each percentage from the same total population: produce separate values for female, male, and other, and check that the three percentages are based on the same denominator.
Record any exclusions or changes in method: note who was left out, what was adjusted, and why, so the reported numbers can be understood and reproduced later.
Compare the final output with the official source before sign-off: confirm the scope, category definitions, calculations, and presentation all match the disclosure requirements and the source data.
Request the data
Request the workforce gender split data
Translate the disclosure into an internal business question — then adapt it to your organisation's own language.
What is the current gender split of our workforce for the reporting period and boundary we are using?
Use your organisation’s own people-data terms first, then map them to the reporting categories. For example, if you talk about staff groups, worker population, or headcount cohorts internally, use those labels in the request and only translate them afterwards for reporting. Check the source data and the final mapping before sign-off.
Weak request
Please provide the ESRS S1-8 diversity data for the workforce, including the gender breakdown.
Why it fails: This is too framework-led and does not tell the owner which population, source, cut-off, or internal labels to use. It also leaves the basis of the figures unclear, so the returned data may not match the reporting boundary or the organisation’s own records.
Better request
Please send the current workforce gender split for [reporting period] covering [reporting boundary]. Use our internal people-data labels, and include the source system, cut-off date, population covered, and whether the figures are headcount or another basis. Please return counts and percentages by category, plus the supporting export or report link.
Formal email template
Subject: Request for workforce gender split data for [reporting period]\n\nHi [name],\n\nCould you please send over the workforce gender split extract for [reporting period] covering [reporting boundary]?\n\nPlease use the organisation’s own people-data labels in the extract, and include the source system, cut-off date, population covered, and the basis used for the figures. If the data is held in more than one system, please note which source was used for each part.\n\nA simple table with the counts and percentages by internal gender category would be ideal. Please also attach or link the supporting export, report, or dashboard view used to prepare it.\n\nIf anything is unclear, I’m happy to align on the wording before you send it. Please also check the official source before sign-off.\n\nThanks,\n[preparer name]
Short Teams / Slack version
Hi [name] — could you share the workforce gender split for [reporting period] and [reporting boundary]? Please use our internal people-data labels, and include the source, cut-off date, population covered, and basis used. A table with counts and % by category plus the supporting export/link would be great. Thanks.
Industry examples
Manufacturing
Context. A group with multiple plants and a central HR system, plus local site registers for contractors.
Adapted request. Please share the workforce gender split for [reporting period] across [named plants / group boundary]. Use the site and HR terms you already use internally, and show which population is included in each site extract. Please include the source system, cut-off date, and whether the figures are headcount or another basis.
Example response. Attached workbook with tabs for each site, showing internal worker group, count, percentage, source system, cut-off date, and notes on any local mapping used.
Professional services
Context. A partnership with employees and a separate pool of contingent workers tracked in different systems.
Adapted request. Please provide the gender split for the people population we are using for reporting, split by our internal staff categories. Include the source system for employees and any separate source used for contingent workers, plus the cut-off date and basis of count.
Example response. One summary table and two source exports: HRIS extract for employees, vendor report for contingent workers, each with counts, percentages, and mapping notes.
Draft your disclosure
Notes that turn data into a disclosure
LRA training templates — adapt them to your organisation, and check the official source before sign-off.
Method note
State how the workforce population was defined for this disclosure and explain that the percentages are calculated from the same underlying headcount base for the three recorded gender categories.
Context note
Use the figures to show how the workforce is distributed across the recorded gender categories, so readers can see the overall balance rather than isolated percentages.
Fluctuation statement
If the mix changes versus a prior period, point to the main drivers such as hiring, exits or reclassification, and note whether the movement is broad-based or concentrated in one category.
Content index entry
S1-8 Diversity — [location / page] / [notes]Download Centre
Preparation tools & forms
Professional preparation tools for S1-8 — free with an LRA Community membership. Register once (it's free) and every download unlocks, together with the Disclosure Library, templates and the LRA AI Assistant.
Assurance readiness
For each claim, check the evidence
| Claim | Risk | Evidence to check |
|---|---|---|
| We built the figure from our own working definition of senior leadership, rather than assuming a generic corporate label, and we documented that definition before we counted anyone. | The assurer may test whether the population was defined consistently and whether the chosen boundary could have changed the result. | Internal definition note or methodology paper; approval of the boundary used; mapping of named roles to the chosen population; version history showing the definition in place before reporting. |
| For the count, we used either the two management layers immediately below the board-level bodies or our own agreed leadership boundary, and we kept the same basis throughout the calculation. | The assurer may probe whether the selected basis was appropriate, applied consistently, and not switched part-way through preparation. | Calculation workbook showing the selected basis; organisational chart or role hierarchy; evidence of the approved approach; reconciliation showing the same basis used across all entities or segments included. |
| We checked the final table against source records so the headcount and percentage figures tie back to the underlying population, and we retained support for each gender category shown. | The assurer may look for arithmetic errors, unsupported classifications, or a mismatch between the published numbers and source data. | Source HR or people-system extracts; calculation sheets with formulas; tie-out or reconciliation to the published table; evidence supporting the gender classification used; review sign-off before publication. |
| The reporting boundary used for this disclosure is documented. | Coverage exclusions or late scope changes are not evidenced. | Boundary memo, entity or site list, and sign-off record. |
Evidence pack to prepare
Common reporting gaps
A percentage is stated without the underlying counts (numerator and denominator).
The denominator — what the figure is a share of — is not explained.
Partial scope is reported as if it were complete coverage.
One-off activities are counted as if they were ongoing programmes.
Boundary or period changes that move the figure are not flagged.
Exclusions from the reported scope are not listed or explained.
Common gaps
Mistakes to avoid when collecting the data
Wrong ownerChasing the wrong team means the figures come from people who do not hold the headcount records, so the percentages are built from second-hand information.
Framework language onlyAsking for the data in ESRS-style terms instead of the organisation’s own job and workforce labels leaves local teams unsure which records to pull.
Scope not pinned downIf you do not define which parts of the business and which worker groups are in scope, different teams will count different populations and the totals will not match.
Wrong cut-off dateUsing a different date or period basis from the one used for the workforce snapshot produces numbers that do not line up with the same reporting point in time.
Mixed counting basisCombining full-time, part-time, contractor or other headcount methods in one calculation distorts the split because the percentages are not built on the same basis.
Source labels lostCopying the numbers without keeping the original gender labels from the source file makes it unclear which records fed each percentage.
Populations mergedPutting separate worker groups into one pool when they should stay distinct hides differences in the underlying mix and can change the reported shares.
No evidence trailIf you do not keep the source file details, date, owner and review sign-off, no one can trace how the percentages were assembled or checked.
Where judgement is often needed
Set the population cut-off and stick to itChoose the workforce population you will use for the percentage split, explain any exclusions at the margins, and keep the same approach unless a change is clearly described.
Decide how to handle a year of buying or selling parts of the businessIf the group changes through acquisitions or disposals, state whether the split reflects the opening, closing, or average workforce and explain any restatement or non-restatement of prior figures.
Use one people-classification rule across countries, or explain the differencesWhere local records describe gender or other identity categories differently, map them into one reporting basis, disclose the mapping approach, and note any countries where the source data cannot be aligned cleanly.
Explain how you treat people with incomplete or unclear recordsIf some workers do not have a recorded category, set out whether you estimate, exclude, or place them in a separate bucket, and say how that affects the percentages.
Be clear on whether the split comes from live HR records or a point-in-time extractState the date or period used for the workforce snapshot, and if the numbers come from a system extract rather than a manual count, explain that basis consistently.
Show when privacy rules force you to combine small groupsIf the workforce is too small in some locations or teams to show separate categories safely, disclose the aggregation rule used and note any resulting loss of detail.
Explain how rounding affects the totalsSay whether each percentage is rounded individually or after calculation, and make clear if the displayed figures may not add exactly to 100% because of rounding.
Describe any estimate used instead of a direct countWhere the split is not taken from a full headcount, identify the estimation method, the source data behind it, and the parts of the workforce covered by the estimate.
Examples
Illustrative examples
Synthetic, written by LRA — not from a company report, not text from any standard.
Illustrative (synthetic) example — Retail and consumer goods
: we report the share of our workforce by gender identity for the reporting period. In this illustration, the figures are internally consistent and sum to the total headcount shown in the table.
This example shows how to present a simple workforce split using additive categories that reconcile to the total.
Illustrative workforce split by gender identity (people)
| Category | People |
|---|---|
| Female | 420 |
| Male | 560 |
| Other | 20 |
Illustrative (synthetic) example — Industrial manufacturing
: our company discloses the workforce mix by gender identity for the period, with each part adding up to the overall employee count. The figures below are illustrative and numerically consistent.
This example uses a different plausible sector and a different set of figures, while still showing the same three-way split.
Illustrative workforce split by gender identity (people)
| Category | People |
|---|---|
| Female | 135 |
| Male | 165 |
| Other | 0 |
Company reports
How companies report S1-8 in practice
Examples of full and partial reporting practice. These are evidence-led reviews, not exact disclosure templates to copy.
Intesa Sanpaolo S.p.A.
Banks / Diverse Financials / Insurance · Italy · 2024
Intesa Sanpaolo S.p.A.'s 2024 Annual Report provides gender diversity data at the top management level, reporting 23.1% male and 76.9% other gender representation on page 228. The report also includes a breakdown of total employees by gender, with 42,814 males out of 93,539 total employees noted on page 221. However, the report does not provide a clear percentage value for other gender categories beyond top management, nor does it clarify some gender-related data points, leaving certain diversity metrics incomplete or unclear.
Schneider Electric S.E.
Electrical Equipment and Machinery · France · 2025
Schneider Electric S.E.'s 2025 Universal Registration Document provides detailed headcount data by gender for leadership positions, reporting 422 female and 972 male leaders in 2025 (p.213). The report also includes gender distribution percentages for leaders and a breakdown of promotions by gender, with women representing 33% of promotions in 2025 (p.355). However, the report does not provide a clear percentage value for overall gender distribution or specific pay gap figures, as these datapoints are either not found or not clearly reported.
GEA Group Aktiengesellschaft
Electrical Equipment and Machinery · Germany · 2025
GEA Group Aktiengesellschaft’s 2025 Annual Report provides detailed gender distribution data at top management levels, showing the Supervisory Board comprises 66.7% male and 33.3% female members (p.166). The report also includes overall employee headcount by gender, with 14,900 males and 3,954 females in 2025 (p.156). However, the report does not clearly disclose percentage values for gender diversity changes or targets, and the mention of a 1.2% decrease in male managers is not fully explained or contextualised (p.166).
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Scenarios to work through
Your HR system shows three gender fields for the workforce: female, male, and another category used by some staff. One employee has not chosen a category, and the team is unsure whether to leave that person out of the figures.
QHow should the preparer handle the headcount split before turning it into percentages for the disclosure?
Reveal model answer →Hide answer ↑
First, decide the population you are counting and keep it consistent across all three figures. Then allocate every person in that population to one of the three categories used for this data point, so the percentages add up to the whole group; if someone has not self-identified, the team should resolve that classification through the organisation’s chosen reporting approach rather than dropping the person from the calculation. Check the official source before sign-off and adapt this to your organisation’s process.
Why this matters · The split should cover the full population being reported, with no one left outside the calculation.
A preparer has workforce data from two systems: payroll for employees and a separate register for board appointees. The board register uses different labels for gender than payroll, and the team is worried the percentages will not be comparable.
QWhat should the preparer do before finalising the three percentages?
Reveal model answer →Hide answer ↑
Use one consistent basis for the whole disclosure, so the same people are counted in the same way across the three categories. If the source systems use different labels, map them into the organisation’s chosen reporting categories before calculating the percentages, and make sure the final figures are based on the same total population. Check the official source before sign-off and adapt this to your organisation’s data model.
Why this matters · Consistency of population and classification matters more than the source system used.
The draft note says the workforce is 61% female, 38% male and 1% other. A reviewer notices the percentages were rounded from more precise figures and asks whether the note is still acceptable.
QWhat should the preparer check about the percentages before release?
Reveal model answer →Hide answer ↑
Confirm that the rounded figures still reflect the underlying counts accurately and that the three percentages together represent the full group without distortion. Small rounding differences can be acceptable only if the underlying calculation is sound and the presentation remains clear; if rounding creates a misleading total, the figures should be adjusted. Check the official source before sign-off and adapt this to your organisation’s rounding policy.
Why this matters · Rounding is fine only if it does not change the meaning of the split.
A company has a small number of contractors working alongside employees, and the reporting team is unsure whether to include them in the diversity percentages because the internal HR report mixes both groups.
QWhat decision should the preparer make about the population before calculating the percentages?
Reveal model answer →Hide answer ↑
The preparer should first fix the reporting boundary for this data point and then apply it consistently. If the organisation’s chosen scope is employees only, contractors should stay out of the calculation; if the scope includes both groups, they must be counted throughout on the same basis. The key is to avoid mixing populations in a way that changes the meaning of the percentages. Check the official source before sign-off and adapt this to your organisation’s boundary setting.
Why this matters · Define the reporting population first, then calculate the split on that same basis.
Framework references
Relevant ESRS requirements and related disclosures
Available framework references and nearby disclosures relevant to preparing this requirement.
ESRS
S1-8
within ESRS S1: Own Workforce
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FAQ
Questions this page answers
For S1-8 (ESRS S1: Own Workforce), what data do I need to collect before I start drafting the disclosure?+−
The page says to prepare three datapoints: women’s share, men’s share and other gender share. Use the plain-language explainer and the step-by-step preparation section to turn those figures into a draft.
How should I set the scope for S1-8 own workforce data so the disclosure is internally consistent?+−
Use the page’s step-by-step preparation guidance to define the workforce population you are reporting on, then keep the same scope through the data table, narrative and evidence pack. The page is designed to help you prepare the disclosure in a practical, repeatable way.
Who should own the S1-8 gender share data in practice: HR, ESG, or another data owner?+−
The page is aimed at sustainability/ESG managers, HR or data owners, and assurance reviewers, so ownership should sit with the person or team that can source, check and explain the figures. The workbook and preparation steps are there to help assign and track that responsibility.
What evidence pack do I need to make S1-8 ready for assurance?+−
The page includes an evidence pack with five items for assurance readiness. Use it alongside the four assurance claims to verify so you can show where the numbers came from and how they were checked.
What are the four assurance claims I should test for S1-8 before sign-off?+−
The page gives four assurance claims to verify, each with a claim, risk and evidence prompt. Use those prompts to check whether the disclosure is complete, supported and consistent before it goes into a report.
What are the common reporting gaps or mistakes in S1-8 own workforce disclosures?+−
The page lists common reporting gaps and mistakes to watch for. It is meant to help you spot issues early, such as missing data, weak evidence or a mismatch between the table and the narrative.
How do I use the S1-8 workbook download to prepare the disclosure?+−
The Download Centre includes a Prep & Assurance workbook in .xlsx format. Use it to work through the preparation steps, capture the evidence needed for assurance, and turn the data into a draft disclosure.
What is the printable Library Card for S1-8 and when should I use it?+−
The Download Centre includes a printable Library Card in .pdf format. It is there as a practical companion to the page content, so you can keep the key preparation points and assurance checks to hand while drafting.
How do I turn the S1-8 data into a draft narrative and content index line?+−
The page has a draft-output section with visualisation ideas, narrative starters and a content-index line. Use those prompts to convert the gender-share data into a short, report-ready draft rather than starting from a blank page.
Can I use the synthetic example disclosure on the S1-8 page as a template for my own numbers?+−
Yes, but only as a synthetic illustration. The example is there to show how the disclosure might look, including a quantitative table, so you should replace it with your own internally consistent data.
Does the S1-8 page give a one-to-one ESRS or IFRS mapping I can rely on?+−
No. The page explicitly says no one-to-one ESRS/IFRS equivalent is asserted, so it should be used as practitioner guidance rather than a mapping source.
More questions this page can help with
What should I check before I send S1-8 own workforce gender-share data for assurance review?How do I make sure the women’s, men’s and other gender shares add up correctly in S1-8?What evidence should sit behind the S1-8 gender-share table in my evidence pack?How do I use the step-by-step preparation section on the S1-8 page without missing anything?What should the narrative for S1-8 own workforce disclosure say if I only have the three gender-share datapoints?How do I avoid the most common mistakes when drafting S1-8 own workforce disclosure?Where can I find real published company reports for S1-8 style disclosures?What is the best way to assign responsibility for S1-8 data collection and review?How do I use the visualisation ideas on the S1-8 page in a report draft?What should I include in the S1-8 content-index line for a draft report?How do I use the Prep & Assurance workbook for S1-8 to build an evidence trail?What does the plain-language explainer on S1-8 help me do in practice?
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