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 EFRAG source.
Before you start
A quick mental checklist before you prepare this disclosure — tick each as you settle it.
Preparation
Key datapoints to prepare
＋ 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
1Set the reporting boundary first: decide which workforce population you are counting for this disclosure, and keep that scope consistent across the three gender percentages.
2Agree 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.
3Pull 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.
4Calculate 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.
5Record 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.
6Compare 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.
Industry examples
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
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Assurance readiness
For each claim, check the evidence
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 owner
Chasing 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 only
Asking 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 down
If 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 date
Using 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 basis
Combining 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 lost
Copying the numbers without keeping the original gender labels from the source file makes it unclear which records fed each percentage.
Populations merged
Putting 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 trail
If 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 it
Choose 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 business
If 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 differences
Where 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 records
If 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 extract
State 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 groups
If 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 totals
Say 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 count
Where 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 (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.
How companies report S1-8 in practice
Real reports where this topic is disclosed. These are report practice, 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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Check your understanding
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
Related framework references
How this disclosure maps across the major reporting frameworks.
Primary
Related & explore
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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