GRI 404: Training and Education 2016 · Topic Standard · Cross-sectoral
Disclosure GRI 404-1
Average hours of training per year per employee
Practical guidance for preparing this disclosure. Use this card to identify datapoints, verify claims and organise supporting evidence. For exact requirements, always refer to the official GRI source.
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Reviewed by
Dr Ross Kurinko · GRI Certified Trainer
To prepare this disclosure
3 datapoints ↓
5 claims to verify ↓
5 evidence areas ↓
3 report examples ↓
Disclosure focus
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.
Before you start
A quick mental checklist before you prepare this disclosure — tick each as you settle it.
Preparation
Key datapoints to prepare
| Datapoint | What to capture | Evidence hint | Owner |
|---|---|---|---|
| Gender split | Capture the gender categories used for the workforce disclosure, using the same definitions and labels as the underlying people data. | HRIS workforce extract, diversity reporting definitions, and any self-identification guidance used to classify staff. | HR / People Analytics |
| Worker category | Capture the employee groupings used for reporting, with each person assigned to the correct workforce category under the organisation’s own classification rules. | HRIS employee master data, workforce segmentation rules, and payroll or contract records used to assign categories. | HR / People Operations |
| Training hours average | Capture the average number of training hours completed by employees during the reporting period, based on the same employee set and time window used in the training records. | Learning management system reports, training attendance logs, and the calculation workbook showing the average and the employee count used. | L&D / HR Analytics |
Show GRI 404-1 sub-elements (LRA working checklist)
- Calculate the mean number of training hours completed by employees over the reporting period.
- Group employees by category.
- Group employees by gender.
LRA working checklist - paraphrased; see official source
How to prepare
- Set the reporting boundary first: decide which employee groups and locations are in scope for this disclosure, and keep that scope consistent across the figures or narrative you will present.
- Define the categories you will use before collecting data. Use clear internal labels for gender and employee groupings so the same terms are applied throughout the calculation or explanation.
- Gather the supporting records for the period you are reporting on. Pull together the source evidence that shows the training time for employees, along with the underlying workforce breakdowns needed to present the disclosure.
- Work out the reported result from the evidence, or prepare the narrative if the disclosure is being explained in words. Make sure the final output reflects the average training time for employees during the reporting period.
- Record any exclusions, restatements, or changes in method. Explain what was left out, what changed from the prior period if anything did, and why the reported approach is the one being used.
- Check the draft against the official source before sign-off. Confirm that the scope, category labels, evidence trail, and final wording all match the underlying requirement and that nothing material has been missed or added.
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.
What training time did each employee group record during the reporting period, split by gender and employee category?
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.
Weak request
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.
Better request
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.
Formal email template
Subject: Request for training-hours data for sustainability reporting Hi [Name], Could you please share the training-hours extract for [reporting period] from [source system], using the workforce groups you normally report internally? We need the data broken down by: - gender - employee category / workforce segment - total training hours recorded in the period - average hours per employee for each group Please also include: - the population covered - the definition of training used in the extract - the calculation method - the extract date and source system - any exclusions, assumptions, or data quality notes If your team uses different internal labels, please send those as-is and we will map them later. This is a possible LRA training template only; please adapt it to your organisation and check the official source before sign-off. Thanks, [Your name]
Short Teams / Slack version
Hi [Name] — could you send the training-hours extract for [period] from [system], split by your usual workforce groups plus gender? Please include the method, population covered, exclusions, and extract date. We’ll map your internal labels later. This is a possible LRA training template only; please adapt it to your organisation and check the official source before sign-off.
Industry examples
Retail
Context. A store-based workforce with full-time, part-time, and seasonal staff recorded in an LMS and HR system.
Adapted request. Please send the learning-hours extract for [period] from [LMS/HR system], split by your usual staff groups such as store, warehouse, and head office, plus gender. Include total learning hours, average hours per person, the population covered, and any exclusions such as agency staff or one-off induction sessions. This is a possible LRA training template only; please adapt it to your organisation and check the official source before sign-off.
Example response. Returned table shows: gender; staff group; headcount; total learning hours; average hours per person. Notes state that agency staff were excluded and that induction was included for new starters only.
Manufacturing
Context. A site-based workforce with production, maintenance, and office teams, where training is tracked through a learning platform and local spreadsheets.
Adapted request. Please provide the training-time extract for [period] from [system], using the site’s normal workforce categories such as production, maintenance, and support teams. Split by gender and include total hours, average hours per employee, the calculation method, and any items not counted, such as toolbox talks or external conferences. This is a possible LRA training template only; please adapt it to your organisation and check the official source before sign-off.
Example response. Returned table shows: gender; workforce category; headcount; total training hours; average hours per employee. Notes explain that toolbox talks were excluded and that only completed courses in the learning platform were counted.
The full request pack — response form, data table, evidence metadata and sign-off — is in the Download Centre.
Draft your disclosure
LRA training templates — adapt them to your organisation, and check the official source before sign-off.
Method note
Define the employee groups and gender categories used, and state that the figures show the average training time per person during the reporting period.
Context note
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.
Fluctuation statement
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.
Content index entry
GRI 404-1 Average hours of training per year per employee — [location / page] / [notes]
Download Centre
Professional preparation tools and forms for GRI 404-1. Each download includes a concise “How to use” guide.
Assurance readiness
For each claim, check the evidence
| Claim | Risk | Evidence to check |
|---|---|---|
| We built the coverage figure from the employee records we held for the reporting year, and we split the workforce by sex and by job group using our internal HR coding. | The assurer may test whether the grouping logic was applied consistently, whether any staff were left out, and whether the categories used in the published figure match the source records. | HR headcount extract for the reporting period; workforce coding guide or data dictionary showing sex and job-group fields; reconciliation from source records to the published figure; sample employee files or system screenshots confirming the assigned codes. |
| For the disclosed workforce breakdown, we used the same cut-off date and the same employee population across all tables, and we excluded only people outside the defined reporting boundary. | The assurer may probe boundary decisions, duplicate counting, treatment of leavers and joiners, and whether the same population was used consistently across the disclosure. | Reporting boundary memo; population definition used for the disclosure; dated headcount report; joiner/leaver logs; reconciliation showing how the final population was derived; evidence of any exclusions and the reason for them. |
| We calculated the training-hours figure from attendance logs and learning-system records, then checked the total and the average before publication. | The assurer may question whether the underlying hours were complete and accurate, whether the average was calculated correctly, and whether the published number was rounded or adjusted without support. | Training attendance records; learning-management-system export; calculation workbook showing total hours and average; rounding policy or calculation note; review sign-off from the preparer and checker; exception log for missing or corrected records. |
| Where records were incomplete, we used documented estimates or replacements only after review, and we kept a clear audit trail from the original source to the final figure. | The assurer may test whether estimates were justified, whether substitutions were applied consistently, and whether the audit trail is strong enough to support the published result. | Data-quality issue log; estimate methodology or replacement rule; approval evidence for any manual adjustments; version history of the calculation file; source-to-report traceability schedule. |
| Before release, we ran a final consistency check against prior-period data, internal management reports, and the narrative in the report so the published figure agreed with the supporting schedules. | The assurer may look for unexplained movements, mismatches between tables and narrative, and weak review controls before sign-off. | Pre-publication review checklist; variance analysis versus prior period and management reporting; final proof of the report; sign-off emails or approval form; evidence of any corrections made after review. |
Evidence pack to prepare
- The governing policy or written commitment behind this disclosure
- A methodology / definition note setting out how the disclosure was scoped and prepared
- Source-system exports the figures or facts were drawn from
- The internal approval / sign-off record for the disclosure before publication
- Minutes or records evidencing the relevant engagement or consultation
Common reporting gaps
- The information is presented without a date or as-at point.
- The scope or boundary of the statement is left undefined.
- Key terms are used inconsistently across the report.
- Material changes since the previous period are not disclosed.
- Assertions are made without supporting detail or a source record.
- Boilerplate is used that does not actually answer what is asked.
Mistakes to avoid when collecting the data
- Wrong owner
Chasing the learning team alone can miss the payroll, HRIS, or local people lead who actually holds the headcount and training records.
- Framework language first
Asking for the metric in reporting jargon can confuse the business owner, so they send a different training summary than the one used internally.
- Scope left vague
If you do not pin down which worker groups are in or out, teams may include contractors, interns, or leavers by mistake.
- Period basis mixed up
Using a calendar-year training log for a financial-year disclosure can pull in the wrong months and distort the result.
- Counting basis not aligned
Combining course hours, booked hours, and completed hours in one file can make the average inconsistent across sites or systems.
- Source labels stripped out
Copying figures into a new sheet without the original field names or system tags makes it hard to trace what each number came from.
- Groups merged too early
Rolling all staff into one pool before separating the agreed people groups can hide differences between categories that should stay apart.
- Evidence details missing
Saving only the final number, without the extract date, source file, or owner, leaves no way to check how the figure was built.
- No sign-off trail
If the draft is not reviewed by the data owner before submission, errors can pass through without anyone confirming the numbers.
Where judgement is often needed
- Set the people base before you calculate the average
Decide which workers sit in the headcount for the period, explain any inclusions or exclusions at the margins, and keep the same basis across the training total and the people count.
- Handle joiners and leavers on a consistent time basis
Choose whether to count only time actually employed in the period or to annualise part-year service, then state that approach so the average is not distorted by mid-year starts or exits.
- Agree one training-hour definition across countries and functions
Where local records treat learning time differently, map those differences to one group-wide rule, describe the conversion used, and note any country-specific exceptions.
- Decide how to treat outsourced, agency, and other non-standard workers
If some labour sits close to the workforce boundary, explain whether those people are inside or outside the calculation and why that treatment matches your reporting scope.
- Use a clear rule for acquisitions, disposals, and restructures
State whether the period includes only the business as owned for part of the year or a full-year view for acquired or sold operations, and explain any restatements or exclusions.
- Choose measured data or a reasonable estimate where records are incomplete
If learning logs are missing or inconsistent, disclose the estimation method, the source data used, and the extent of any judgement applied.
- Round in a way that does not change the story
Set and disclose a rounding rule for hours and averages, and make sure the rounded figures still reconcile sensibly to the underlying totals.
- Protect personal data by grouping small populations
Where a small team or a narrow category could identify individuals, combine groups or suppress detail as needed, and explain the aggregation approach used.
Examples
Illustrative examples
Synthetic, written by LRA — not from a company report, not text from any standard.
Retail banking · synthetic · written by LRA
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. 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.
Food manufacturing · synthetic · written by LRA
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.
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.
Draft output & visualisation ideas
How to turn the collected data into a draft disclosure. Suggested visuals and a GRI content-index line generated from this disclosure's datapoints.
Suggested visuals
- Training hours by gender and employee group — stacked bar: Average training time split by gender within each employee category, so readers can compare patterns across workforce groups.
- Average training time by employee category — bar: How the average hours differ across staff groups, with gender used as a secondary split if the reporter wants a more detailed view.
- Gender comparison of average training hours — bar: A side-by-side view of average training time for women, men and any other reported gender categories.
- Workforce training profile table — table: A compact matrix showing each employee category, the gender split, and the corresponding average hours of training.
From a number to a disclosure
What separates a figure from a disclosure.
Basic
We averaged 12 training hours per employee this year.
Better
We averaged 12 training hours per employee this year, split by gender and staff group.
Best
We averaged 12 training hours per employee in the reporting period, split by gender and staff group, and the figure was higher than last year because we added more role-based learning.
Real reports where this topic is disclosed. The confidence label shows how closely each match maps to GRI 404-1 — these are report practice, not exact disclosure examples.
| Company | Sector · Country | Year | Match | Page | Report | Assurance | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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Indra Sistemas, S.A. | Software and Services · Spain | 2025 | Partial | p. 114 →p. 115 →p. 206 → | Sustainability Report 2025 → | — | ||||||||||||
Evidence in Indra Sistemas, S.A.’s reportWhat the report shows Indra Sistemas, S.A.'s Sustainability Report 2025 provides several relevant data points, including the calculation of the gender pay gap as "[average male salary - average female salary] / [average male salary]" on page 118, average remuneration by employee category with figures from 25,950 to 35,103 on page 119, and the average number of training hours per employee ranging from 16.0 to 22.8 on page 114. The report also includes employee numbers by category and gender (p.93) and part-time employee percentages by category and gender (p.266). However, it does not clearly present a comprehensive gender pay gap figure or detailed analysis beyond the formula, and some contract type data remain unclear (p.94).
Evidence-based summary of this company’s own report — not a disclosure template to copy, and not a compliance verdict. Datapoint coverage
Source trail
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Firstsource Solutions Limited | Professional Services · India | 2025 | Partial | p. 102 →p. 107 →p. 128 → | ESG Report FY 2024-25 → | BSI | ||||||||||||
Evidence in Firstsource Solutions Limited’s reportWhat the report shows Firstsource Solutions Limited’s ESG Report FY 2024-25 provides detailed data on employee distribution by age and management level, showing numbers for permanent employees across categories such as top, middle, junior, and non-management on page 124. The report also includes gender breakdowns of permanent employees, with figures for male, female, and others on page 90, and training hours data indicating an average of 17.27 learning hours per associate on page 103. However, some data points such as internal hires by gender and training hours by gender are mentioned in the source trail but not clearly detailed in the main report pages provided, leaving some aspects of workforce composition and development less explicit.
Evidence-based summary of this company’s own report — not a disclosure template to copy, and not a compliance verdict. Datapoint coverage
Source trail
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Temenos AG | Software and Services · Switzerland | 2025 | Partial | p. 83 →p. 91 →p. 98 → | Sustainability Report 2025 → | PwC | ||||||||||||
Evidence in Temenos AG’s reportWhat the report shows Temenos AG’s Sustainability Report 2025 provides detailed data on employee turnover rates by gender and region, with turnover defined according to GRI standards (p.54). The report also includes workforce composition by employee category and gender (p.51), as well as average training hours by gender during the 2025 talent review cycle (p.55). However, while there is demographic data on diversity groups (p.52), the report does not clearly present turnover rates broken down by diversity categories or provide comprehensive training participation rates across all employee groups.
Evidence-based summary of this company’s own report — not a disclosure template to copy, and not a compliance verdict. Datapoint coverage
Source trail
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Check your understanding
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.How should the preparer present the figures so the note is usable and not misleading?
Model answer. Break the information out by both sex and staff segment, then show the average training time for each combination using the hours actually completed in the reporting period. If someone was employed for only part of the year, include only the training they took during that period; do not blend in time outside the reporting window.
Why this matters. Use the period’s actual training activity and separate the data in the ways the disclosure asks for, so readers can see differences across groups.
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.Which people should be included when calculating the average training hours for this disclosure?
Model answer. Limit the calculation to the organisation’s employees, then group them by sex and by employee category. Do not fold in agency workers if they are not part of the employee population being reported; the average should reflect the training taken by employees in each category during the year.
Why this matters. Keep the calculation tied to the employee population covered by the disclosure, and do not mix in other worker groups unless they are part of that population.
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.Should employees with no training be included in the average, and how is the figure worked out?
Model answer. Yes. The average should reflect all employees in the relevant group, including those with no training. In this example, the total training hours are 720 hours (90 × 8), and dividing by 120 employees gives an average of 6 hours per employee for that category.
Why this matters. Include zero-training employees in the denominator so the average shows the full picture for the group.
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.What should the preparer do when a group is very small but still part of the employee breakdown?
Model answer. Report the average for every relevant sex-and-category combination that exists in the workforce data, even if one group is small. If a group has only a few people, the preparer should still calculate the average from the training hours completed by that group during the year and then consider whether the presentation needs careful handling to avoid confusion or unnecessary detail.
Why this matters. Do not drop a valid group from the breakdown just because it is small; the disclosure is about the averages for each relevant employee segment.
Analyse this disclosure across real reports
See how companies actually report GRI 404-1 — drawn from their own published reports, with the exact pages, and an LRA AI-assistant that works through it with you. Available to LRA Community members and to students throughout their platform access.
Related framework references
How this disclosure maps across the major reporting frameworks.
→
ESRSRelated
ESRS S1
Own Workforce — closest topical match (post-Omnibus ESRS catalogue).
→
IFRSNo equivalent
—
No direct IFRS S1/S2 topical equivalent.
Related & explore
Questions this page answers
For GRI 404-1, what data points should I gather before I start drafting the disclosure?
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. ↑ section
How do I use the step-by-step preparation section for GRI 404-1 in practice?
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. ↑ section
What should I decide about scope and methodology for the GRI 404-1 training and education page?
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. ↑ section
Who should own the GRI 404-1 data collection and sign-off process?
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. ↑ section
What evidence should I include in the GRI 404-1 assurance pack?
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. ↑ section
What are the five assurance claims on the GRI 404-1 page and how do I use them?
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. ↑ section
What are the common reporting gaps or mistakes on the GRI 404-1 page?
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. ↑ section
How do I turn the GRI 404-1 data into a draft disclosure?
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. ↑ section
How should I use the GRI 404-1 workbook download to prepare the disclosure?
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. ↑ section
What is the printable Library Card for GRI 404-1 used for?
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. ↑ section
Can I reuse GRI 404-1 data for ESRS S1 (Own Workforce)?
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. ↑ section
More questions this page can help with
- GRI 404-1 training and education: what should I ask HR for first?
- How do I check whether my GRI 404-1 average training hours are ready for assurance?
- What evidence do I need for GRI 404-1 gender split and worker category data?
- How do I avoid common mistakes when drafting GRI 404-1 training and education?
- What should go into a GRI 404-1 evidence pack?
- How do I use the GRI 404-1 narrative starters in a report draft?
- What does the GRI 404-1 plain-language explainer help me decide?
- How do I build a GRI 404-1 content index line from the page?
- Where can I find real company report examples for GRI 404-1?
- How do I use the synthetic example disclosure on the GRI 404-1 page?
- What is the closest ESRS reference for GRI 404-1 and can I reuse the data?
- Who should review the GRI 404-1 workbook before submission?
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Sources, status and disclaimer
This LRA assistance tool is designed for educational and internal data-collection purposes. It is not an official interpretation of the GRI Standards, IFRS Sustainability Disclosure Standards or EU CSRD/ESRS requirements. When applying these frameworks in professional practice, users should consult and double-check the official standards, guidance and applicable regulatory sources.
