This disclosure asks an organisation to describe the make-up of its core workforce in a way that helps readers understand who its employees are and how that workforce is structured. In practice, the report should give a clear picture of the employee base, rather than a selective snapshot, so users can see the main characteristics of the people the organisation employs.
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 EFRAG source.
A quick mental checklist before you prepare this disclosure — tick each as you settle it.
Key datapoints to prepare
How to prepare it
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.
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.
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.
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.
Notes that turn data into a disclosure
LRA training templates — adapt them to your organisation, and check the official source before sign-off.
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.
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.
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.
Preparation tools & forms
Professional preparation tools for S1-5 — 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.
For each claim, check the evidence
Evidence pack to prepare
Common reporting gaps
Mistakes to avoid when collecting the data
Where judgement is often needed
Illustrative examples
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.
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.
This example uses a different sector and a different workforce mix, but still covers the same required data points in a single quantitative table.
How companies report S1-5 in practice
Real reports where this topic is disclosed. These are report practice, not exact disclosure templates to copy.

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.
Related framework references
How this disclosure maps across the major reporting frameworks.
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.
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