This disclosure asks an organisation to report the average amount of training each employee receives over a year. In practice, it is about showing how much learning and development time is being provided, rather than listing every course or training event. The figure should be presented in a way that lets readers understand the overall level of training support across the workforce.
The practical focus is on how broadly training is covered across the organisation. A useful question is whether the average reflects all employees and all relevant parts of the business, or only selected sites, functions, or programmes. The reporting should make clear the scope used so readers can judge whether the number represents training across operations or only a more limited set of activities.
This LRA educational guidance supports disclosure preparation. For the exact requirements, always refer to the official GRI 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 training-hours data from People Analytics
Translate the disclosure into an internal business question — then adapt it to your organisation's own language.
Use your organisation’s own labels first, then map them to the reporting categories. For example, if you use job family, grade, population, or workforce segment internally, ask for those terms and translate them later into the disclosure wording. Keep the request in the language the data owner already uses.
Please provide the GRI 404-1 data showing the evidence needed for GRI 404:GRI 404-1, split by gender and employee category.
Why it fails: It uses framework language rather than the organisation’s own terms, and it does not tell the owner which system, population, grouping labels, or calculation basis to use. That makes the response harder to prepare and harder to check.
Please send the training-hours extract for [period] from [system], using your usual workforce group labels. Include gender, employee category, total training hours, average hours per employee, the population covered, the calculation method, and any exclusions or assumptions. We will map your labels later. This is a possible LRA training template only; please adapt it to your organisation and check the official source before sign-off.
Notes that turn data into a disclosure
LRA training templates — adapt them to your organisation, and check the official source before sign-off.
Define the employee groups and gender categories used, and state that the figures show the average training time per person during the reporting period.
These figures show how much learning time different parts of the workforce received on average, helping readers see whether development time is spread evenly or concentrated in particular groups.
If the averages moved materially from the prior period, explain whether this was driven by changes in headcount mix, training availability, programme design, or other operational factors.
Preparation tools & forms
Professional preparation tools for GRI 404-1 — 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.
*Synthetic example for illustration only.* During the year, we tracked learning time by gender and by staff group, and we report the average number of training hours per person for each slice. - Women: managers 18 hours, specialists 14 hours, support staff 10 hours. - Men: managers 16 hours, specialists 12 hours, support staff 9 hours.
This example shows how a reporter can break learning time down by gender and employee group, while keeping the figures internally consistent and clearly labelled as illustrative.
*Synthetic example for illustration only.* We measured development time across our workforce and present the mean hours per employee, split by gender and job level. - Women: senior leaders 22 hours, supervisors 15 hours, production operatives 11 hours. - Men: senior leaders 20 hours, supervisors 13 hours, production operatives 10 hours.
This example demonstrates a second plausible way to present the same information, using a different sector and different staff categories while still showing the average training time by gender.
How companies report GRI 404-1
Real reports where this topic is disclosed. These are report practice, not exact disclosure templates to copy.

Scenarios to work through
A preparer is compiling the learning and development note for a group with three staff segments: senior managers, office staff, and field staff. The training log shows different totals for women and men in each segment, and some people joined part-way through the year.
A company has a mix of permanent employees, fixed-term staff, and agency workers. The HR system records training for everyone, but the reporting team is unsure whether to combine all of them into one average.
The training register shows 120 employees in one category. Ninety employees each completed 8 hours of training, while 30 employees completed none. The team is unsure whether the zero-hour group should be left out because it makes the average look lower.
A preparer has separate spreadsheets for women and men, but one category has only two employees and the other has 200. The team wonders whether it is acceptable to report only the larger group because the smaller one is easy to identify.
Related framework references
How this disclosure maps across the major reporting frameworks.
Questions this page answers
The page says to prepare three core datapoints: gender split, worker category and average training hours. Use those as the starting set for your data request and check they are available for the same reporting period and scope.
Use it as a working checklist to move from the plain-language explainer to the datapoints, then to methodology, evidence and draft output. It is designed to help you prepare the disclosure rather than just describe it.
The page is set up to help you define what population is included, how worker categories are treated and how the average training hours are calculated. Keep those choices consistent across the datapoints and explain them clearly in the draft.
The page is aimed at sustainability/ESG managers, HR or data owners, and assurance reviewers, so ownership should sit with the person who can confirm the source data and methodology. In practice, that usually means one named owner for the data and one reviewer for assurance readiness.
The page includes an evidence pack with five items and five assurance claims to verify. Use those to show the source data, the calculation approach and the checks that support the final figures.
The page says there are five claims to verify, each with a claim, risk and evidence prompt. Use them as a control list to test whether the disclosure is supported, where it could go wrong and what documents prove it.
The page lists common reporting gaps and mistakes so you can check for missing scope, unclear methodology or weak evidence before drafting. Use that section as a pre-submission quality check.
The page includes draft-output support with visualisation ideas, narrative starters and a GRI content-index line. Use those to turn the prepared data into a short, readable draft and then tailor the wording to your organisation.
The Download Centre includes a Prep & Assurance workbook in .xlsx format. Use it to organise the datapoints, evidence and assurance checks before you finalise the draft.
The Download Centre also includes a printable Library Card in .pdf format. It is a quick reference aid for the disclosure, useful when you want the key points in one place while you work through the data and evidence.
The page notes ESRS S1 (Own Workforce) as the closest correspondence, so the same underlying data may be reusable. Treat that as a practical link rather than assuming the reporting asks are identical.
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