Disclosure focus
The practical focus is on coverage across the organisation as a whole, not just a few flagship sites or headquarters teams. The information should be presented consistently enough to show the workforce structure across operations, so differences between locations, business units or employee groups are visible where relevant.
This LRA educational guidance supports disclosure preparation. For the exact requirements, always refer to the official European Commission source.
Prepare
What to collect before drafting
Tick each field as the evidence lands. Owners are the teams that usually hold the source files.
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Workforce count
HR / People OperationsThe total number of people in scope at the reporting date, using one agreed headcount basis.
Evidence HRIS headcount extract, payroll summary, or workforce register reconciled to the reporting cut-off.
Watch for Headcount is mixed with FTE or a different cut-off date, so the figure does not reconcile to the HRIS period-end count.
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Gender breakdown
HR / People AnalyticsThe workforce split by gender categories used for reporting, with each person counted once on the same basis as the total.
Evidence HRIS demographic fields, employee self-identification records, and a reporting extract showing the category mapping.
Watch for Categories are not aligned with employees' self-identification, so the split does not reconcile to the source demographic data.
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Workforce by country
HR / Global MobilityThe distribution of employees across countries of work or employment, using a consistent country basis for every person.
Evidence HRIS location fields, legal entity records, or payroll country mapping used in the reporting extract.
Watch for The country basis is mixed between work location and employing entity, so the distribution does not reconcile to the location master.
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Open-ended staff
HR / People OperationsThe number of employees on ongoing contracts, counted on the same workforce basis as the total.
Evidence HRIS contract-type field, employee register, and payroll status report at the reporting date.
Watch for Permanent staff are counted using a different contract definition, so the figure does not reconcile to the HRIS employment-status field.
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Fixed-term staff
HR / People OperationsThe number of employees on time-limited contracts, counted on the same workforce basis as the total.
Evidence HRIS contract-type field, employee register, and payroll status report at the reporting date.
Watch for Temporary staff are pulled from a contract field that also includes interns or agency workers, so the category does not reconcile to the HRIS contract master.
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Zero-hours staff
HR / Workforce PlanningThe number of workers whose hours are not guaranteed, counted on the same workforce basis as the total.
Evidence HRIS contract terms, scheduling records, and payroll status report for the reporting date.
Watch for Workers with no guaranteed hours are omitted or grouped with casual staff, so the category does not reconcile to the contract-type source.
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People who left
HR / People OperationsThe number of employees who exited during the reporting period, using the same leaver definition throughout.
Evidence HRIS termination report, payroll leaver file, and exit records for the period.
Watch for Leavers are counted outside the reporting period or with a different exit definition, so the number does not reconcile to the termination report.
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Staff turnover rate
HR / People AnalyticsThe percentage of the workforce that left during the reporting period, calculated from the agreed leaver count and the agreed workforce base.
Evidence Leaver count, period workforce base, and calculation workbook showing the formula used.
Watch for The numerator and denominator use different periods or bases, so the rate does not reconcile to the leaver and headcount source data.
Before you start
- Have you fixed which people count as part of your core workforce for this table, and which groups sit outside it?
- Are you using one clear cut-off date or reporting period for every figure, so the headcount and turnover line up?
- Can each number be traced back to a source you can explain, such as payroll, HR records, or another internal system?
- Have you agreed how to split the workforce by sex and by country, using the same basis across the whole disclosure?
- For the employment-status breakdown, have you settled how to classify open-ended, fixed-term, and variable-hours arrangements in your own records?
- When you count people who left and work out the turnover percentage, are you using the same population and time basis throughout?
How to prepare it
Request the data
Request the workforce headcount and movement data from HR
Translate the disclosure into an internal business question — then adapt it to your organisation's own language.
What is our core workforce mix and movement at the reporting date, and how many people joined or left during the period?
Use your organisation’s own people-data terms first, then map them to the reporting categories. For example, if you use ‘staff’, ‘colleagues’, ‘crew’, ‘field teams’ or ‘contracted workers’ internally, keep those labels in the request and only translate them when you prepare the disclosure pack. Check the source data and the reporting cut-off before sign-off.
Weak request
Please provide the ESRS S1:S1-5 employee structure data for the period, including total employees, gender split, country distribution, permanent, temporary and non-guaranteed-hours workers, leavers and turnover.
Why it fails: This uses framework language that many operational teams do not use day to day, and it does not tell the owner which internal file, population, cut-off, or counting basis to use. It also leaves the split basis for country, gender and employment type unclear, so the returned data may not reconcile to the reporting pack.
Better request
Could you send the latest workforce extract for [period/cut-off]? Please include the people file your team uses for headcount reporting, with total headcount, gender field, country or location split, permanent / temporary / non-guaranteed-hours split, leavers, and turnover rate. Also include the source system, population covered, and any definition notes so we can map your labels into the reporting pack.
Formal email template
Subject: Request for workforce headcount and movement data Hi [Name], Could you please share the latest workforce file for [reporting period / cut-off date] so we can prepare the people-data pack for reporting? Please include: - total headcount at the cut-off date - split by gender using the field your team normally uses - split by country or location, using the basis you normally report on - split by employment type, including permanent, temporary and any non-guaranteed-hours group you track - number of leavers during the period - turnover rate for the period, if you already calculate it Please also include the file date, source system, population covered, and any notes on definitions or exceptions. If it is easier, you can send this as a table or export from [system name]. Please use your team’s usual terms and we will map them for the reporting pack. Please check the official source before sign-off. Thanks, [Your name]
Short Teams / Slack version
Hi [Name] — could you send the latest workforce extract for [period/cut-off]? We need headcount, gender split, country/location split, permanent/temporary/non-guaranteed-hours split, leavers, and turnover rate, plus the source system and any definition notes. Please use your normal internal labels and we’ll map them. Thanks.
Industry examples
Retail / hospitality
Context. The business uses store teams, head office staff and seasonal staff, with some locations on variable-hours arrangements.
Adapted request. Please share the workforce file for [period/cut-off] covering store teams, head office and seasonal staff. Include total people on the books, gender split, country or region split, permanent, fixed-term and variable-hours groups, leavers, and turnover rate, plus the source system and any notes on how you count seasonal starters and leavers.
Example response. A table with one row per internal worker group and country, showing headcount at cut-off, leavers in period and turnover rate, plus a note that seasonal staff are included in the variable-hours group and counted by employing entity country.
Manufacturing
Context. The business tracks plant operators, maintenance staff and office staff, with some temporary labour recorded separately in payroll.
Adapted request. Please provide the latest workforce extract for [period/cut-off] covering plant, maintenance and office staff. Include headcount, gender split, site or country split, permanent and temporary staff, any no-guarantee-hours group you track, leavers, and turnover rate. Please also state whether the file is based on payroll, HRIS or site rosters.
Example response. A spreadsheet exported from HRIS and payroll, with columns for worker group, site country, gender, contract type, headcount, leavers and turnover rate, plus a note that agency labour is excluded from the workforce file.
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 figures were counted, including the basis used for headcount, the definitions applied to each worker category, and whether the data covers the full reporting period or a point in time.
Context note
Explain what the figures show about the size, make-up and location of the workforce, and how the leaver count and turnover rate help readers understand staff movement during the period.
Fluctuation statement
If numbers changed materially, note the main drivers in plain terms, such as hiring, departures, changes in contract mix or shifts in country coverage, and link those changes to the reported totals.
Content index entry
S1-5 Employees (Core workforce structure) — [location / page] / [notes]Download Centre
Preparation tools & forms
Download preparation tools for S1-5 with free verified email access for 24 hours. Source-report links remain public.
Check
For each claim, check the evidence
We checked that the country-level employee count covers every jurisdiction that met our internal threshold for inclusion, and that no qualifying location was left out.
Risk An assurer may test whether any eligible country was omitted, whether the threshold was applied consistently, and whether the inclusion list matches the underlying population used for the figure.
Evidence to check Country-by-country population extract; documented inclusion threshold and application notes; consolidation workbook showing all included jurisdictions; review sign-off confirming completeness against the source population.
For staff working across more than one country, we used the relevant local legal definitions to classify employee types, then rolled the country figures up into the reported totals.
Risk An assurer may challenge whether local definitions were applied correctly, whether cross-border workers were treated consistently, and whether the roll-up from local files to the reported total is traceable.
Evidence to check Method note on local-law definitions used; country source files; mapping or reconciliation from local classifications to the reported totals; evidence of review of edge cases and exceptions.
We calculated the turnover percentage for permanent staff using the agreed leaver count over the average workforce for the period, and we reported it for the same reporting window as the rest of the data.
Risk An assurer may probe the numerator definition, the averaging method, the period boundary, and whether the calculation was performed on a consistent basis across the dataset.
Evidence to check Calculation workbook with formulae; source leaver report; average headcount support; period cut-off rules; management review evidence and recalculation checks.
We presented the workforce information in a format that fits the reporting framework, using either a table or narrative where permitted, and kept the layout consistent with the rest of the report.
Risk An assurer may assess whether the chosen presentation is allowed, whether the same data appear consistently across sections, and whether the format obscures or distorts the underlying numbers.
Evidence to check Final report draft; formatting decision note; cross-reference to source schedules; editorial review comments; sign-off confirming the presentation matches the approved data set.
We documented how the employee data were compiled, including the main assumptions, data sources, and any context needed to interpret the figures.
Risk An assurer may look for gaps in the method description, unsupported assumptions, missing context, or a lack of clarity over how the numbers were assembled.
Evidence to check Methodology paper; data lineage or process map; assumptions log; contextual narrative used in the report; evidence of internal review of the method disclosure.
We verified the total workforce count by headcount and included a split by gender, using the same source population throughout.
Risk An assurer may test whether the total and the gender split reconcile, whether the population basis is consistent, and whether any staff were double-counted or excluded.
Evidence to check Headcount extract; gender breakdown report; reconciliation showing totals equal the sum of the split; source system screenshots or exports; review and approval 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.
Where drafts go wrong
Mistakes to avoid and where judgement is needed
The left column is what preparers get wrong while collecting; the right column is where you will have to make and explain a judgement.
Mistakes to avoid
- Wrong data ownerPeople teams sometimes ask the wrong function for the figures, instead of the group that actually holds headcount and joiner-leaver records.
- Framework language used in the requestA vague ask in reporting jargon can leave the business unsure whether you mean the payroll view, HR roster, or another internal headcount source.
- Scope not fixed up frontTeams can start collecting without agreeing which entities, locations, and worker groups sit inside the population, so the final set is inconsistent.
- Timing basis not alignedData gets pulled from different dates or cut-off points, which makes the employee counts and movement figures impossible to compare cleanly.
- Counting methods mixed togetherOne team may use a snapshot count while another uses an average or period total, so the numbers do not sit on the same basis.
- Source labels stripped awayWhen extracts are copied into spreadsheets, the original field names and category labels are lost, making it hard to trace what each number came from.
- Groups merged that should stay separatePermanent staff, fixed-term staff, and zero-hours or similar arrangements can be blended into one bucket, which hides the split needed for the disclosure.
- Evidence trail incompleteThe pack may contain numbers but no supporting metadata, such as source system, extract date, or version, so the figures cannot be checked later.
- No review sign-off recordedA draft can move forward without a named reviewer confirming the figures, leaving no clear trail of who checked the data before it was used.
Judgement needed
- Set the workforce boundary when businesses join or leave mid-yearChoose a clear cut-off for adding or removing people after a purchase, sale, or closure, then explain whether the headcount and leaver figures reflect the position at period end or a different agreed date basis.
- Align local job categories before counting permanent, fixed-term, and variable-hours staffWhere country HR systems use different labels, map them to one internal classification rule and disclose the mapping so readers can see how local contract types were grouped for the report.
- Decide how to treat people on the edge of the workforce perimeterState whether you include agency workers, contractors, interns, apprentices, or other borderline groups only when they sit inside your own people records and explain any exclusions or partial inclusion rules.
- Choose one timing basis for the year-end workforce snapshotIf staffing moves around the reporting date, explain whether the totals come from a single date, an average, or another consistent cut-off, and keep the same basis across the related workforce breakdowns.
- Separate measured counts from estimates where records are incompleteWhen some locations cannot provide a full count, use a documented estimate method, identify which figures are estimated, and explain the main assumptions and any known limits on accuracy.
- Keep leaver and turnover calculations on the same population basisMake clear whether departures are counted from the same people set used for the headcount, and disclose the formula used so the turnover percentage can be reconciled to the leaver number.
- Handle privacy limits by grouping small country or gender cellsIf a split would expose individuals, combine small groups or suppress a cell and explain the aggregation rule so the published breakdown still protects personal data.
- Round consistently and explain any small differencesApply one rounding rule across totals and splits, then note that minor mismatches may arise from rounding so readers do not expect every subtotal to tie exactly.
Examples
How it reads on the page
Synthetic, written by LRA — not from a company report, not text from any standard.
we would show our workforce by sex, contract type and country, alongside joiners and exits for the period. The figures below are internally consistent and are presented as a simple example of how we might report this information.
Illustrative workforce profile and movement (people / %)
| Category | Women | Men |
|---|---|---|
| Total workforce | 420 | 380 |
| Permanent staff | 360 | 310 |
| Fixed-term staff | 40 | 50 |
| Staff on variable-hours arrangements | 20 | 20 |
| Leavers during the period | 30 | 20 |
| Turnover rate (%) | 7 | 5 |
This example shows a compact workforce snapshot split by gender, employment status and location, with movement through the year shown as leavers and the resulting churn rate.
we would present our headcount split by sex, contract form and country, then show how many people left and the related churn percentage. The numbers below are made up for training purposes and are internally consistent.
Illustrative workforce profile and movement (people / %)
| Category | Women | Men |
|---|---|---|
| Total workforce | 260 | 340 |
| Permanent staff | 210 | 260 |
| Fixed-term staff | 30 | 50 |
| Staff on variable-hours arrangements | 20 | 30 |
| Leavers during the period | 18 | 22 |
| Turnover rate (%) | 7 | 6 |
This example uses a different sector and a different workforce mix, but still covers the same required data points in a single quantitative table.
Quality ladder
- Basic
We have 240 employees.
- Better
We have 240 employees, split 132 women and 108 men, with 180 in our home market and 60 across three other countries.
- Best
At year-end, I reported 240 employees in our core workforce: 180 permanent, 40 fixed-term and 20 on variable-hours contracts, with 132 women and 108 men across four countries; we had 18 leavers and a 7.5% turnover rate, mainly because we closed one site and moved work to other locations.
Check your understanding
Scenarios to work through
A group HR team is closing the year-end people data pack. One subsidiary has 120 staff on open-ended contracts, 18 on fixed-term contracts, and 7 people on zero-hours arrangements; the team also has location data for each person.
A business has 84 employees in total. Of these, 50 are women and 34 are men, but 6 people work across two countries during the year and the payroll team is unsure whether to show them once or twice.
A retailer has 260 employees at year-end. It also uses 40 agency workers, but they are paid by the agency and are not on the company payroll; meanwhile, 22 employees are on fixed-term contracts and 12 are on zero-hours arrangements.
A manufacturer has 310 employees at the reporting date. During the year, 28 employees left and 14 new people joined, but the HR system also shows 9 internal transfers between sites; the team is unsure whether the turnover figure should include the transfers.
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Advice, not assurance
Framework references
Relevant ESRS requirements and related disclosures
ESRS
S1-5
within ESRS S1: Own Workforce
Guides that settle this question
Related & explore
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FAQ
Questions this page answers
Use the page’s datapoint list as your starting checklist: workforce count, gender breakdown, workforce by country, open-ended staff, fixed-term staff, zero-hours staff, people who left, and staff turnover rate. The page also includes a step-by-step preparation section to help you organise the work.
The page points you to workforce count, country breakdowns, and contract-type splits, so scope should be set around those data cuts before drafting. Use the preparation steps and the workbook to make sure the same scope is applied consistently across the disclosure.
The page is designed for sustainability/ESG managers, HR or data owners, and assurance reviewers, so ownership should be assigned to the team that can evidence the underlying workforce data and explain the method. The workbook and evidence-pack sections are there to help you coordinate that ownership.
The page includes an evidence pack with five items and six assurance claims to verify, each framed around claim, risk, and evidence. Use those sections to build a file that shows where the numbers came from and how they were checked.
The page says there are six assurance claims to verify, organised by claim, risk, and evidence. It does not list them in the source provided here, so you should use the page’s assurance section directly rather than relying on memory.
The Download Centre includes a Prep & Assurance workbook in .xlsx format, which is intended to support preparation and assurance readiness. Use it alongside the step-by-step guidance, evidence pack, and common-gaps section to turn raw workforce data into a draft disclosure.
The Download Centre includes a printable Library Card in .pdf format, so it is meant as a quick reference companion to the main page. Use it to keep the key datapoints, assurance points, and drafting prompts to hand while you work.
The page has a draft-output section with visualisation ideas, narrative starters, and a content-index line to help you move from data to text. Start with the quantitative table, then use the narrative prompts to explain the main movements and any notable patterns.
The page includes a list of common reporting gaps and mistakes, so it is meant to help you spot issues before finalising the disclosure. Use that section as a pre-submission check against your data, scope, and evidence pack.
Yes, the page includes synthetic illustrative examples with a quantitative table to show how the disclosure can look in practice. Treat them as examples only and make sure any figures you prepare are internally consistent and based on your own data.
More questions this page can help with