Preparing for the EU Pay Transparency Directive: Important Pay Analytics Approaches

EU member states must transpose the EU Pay Transparency Directive into their own laws by June 2026. These laws will require global organizations to provide a number of key pieces of pay information to job applicants, employees, labor agencies, and the wider public. In this article, Affirmity Principal Business Consultant Patrick McNiel, PhD, examines the data collection and analysis processes that organizations must undertake to comply.

How to Perform Job Valuations and Create Categories of Workers

The concept of job “value” in the directive isn’t based on market forces or market comparisons, but rather on the intrinsic nature of the job and what is required of employees in that role. You must find and compare roles of similar value across your organization using this particular framework for value.

The criteria you must use for determining when roles involve work of equal value are:

• Skills: The specific skills required for the job.
• Effort: The amount of physical and mental effort required for the job.
• Responsibility: The level of authority, influence, and obligation involved in the job.
• Working Conditions: Aspects of the physical and psychological environment that contribute to job difficulty, personal risk, and overall quality of life.

Teams may decide to split these criteria further into various subfactors as part of their efforts to assign value to each job. The process will likely involve assigning scores to criteria/sub-criteria, or having a set of buckets into which jobs are sorted relative to one another based on the four factors.

If your organization goes the “bucket” route, the buckets in question could be based on grade structures, assuming your grades are already sufficiently refined. Grades can be too wide for this purpose, with potential for large overlaps, and methods such as Radford scales are somewhat antithetical to the directive’s use of “value” (as the scales are too rooted in market value).

Therefore, if you do use grade structures or Radford scales, these systems will have to be modified and adjusted to account for the four criteria. Alternatively, you’ll need to find or create a different system in order to score jobs and sort them into categories of workers based on their value.

Another aspect to consider here is that once you have the scores for a job, or have some idea of the buckets they need to go into, you’ll either have to figure out what range of values go in a specific bucket, or how big your buckets are in terms of the value ranges that are going to be within them.

U.S. workers dealing with U.S. laws may find it unusual to categorize a warehouse worker and an administrative assistant in the same bucket. However, categories are defined by job value according to required levels of skill, responsibility, effort, and working conditions rather than the nature of work done in jobs. Furthermore, one aspect that may have implications for your analysis is that roles in a category of workers may be different in terms of gender predominance, which can be a driver of pay gaps in a category

DISCOVER PAY EQUITY TRENDS IN THIS WHITE PAPER | ‘The Future of Pay Equity and Transparency 2025’

A Preview of Things to Come: Example Cases From UK Retail

So what might this look like in practice? Though, no longer in the EU, the UK has adopted a similar legislative stance and has already seen cases requiring this kind of analysis. A class action lawsuit was brought to tribunal against Next, a fashion retailer. The complaint was that retail consultants in their stores were being paid less than warehouse workers, even though the value of those jobs was the same.

There was a gendered aspect of the discrepancy that factored into the case, with 77.5% of retail consultants being female, while 52.75% of warehouse operators were male. The tribunal upheld the complaint, and couldn’t find a legitimate reason for the discrepancy. 3,500 workers were awarded £39.6 million (around $52 million USD), representing six years of back pay for the plaintiffs.

Though Next is currently in the process of appealing this ruling, the result has kicked off a series of other lawsuits in UK retail, with workers at the country’s largest supermarket chains (Asda, Tesco, Morrisons, Sainsbury’s, and the Co-op) pursuing cases.

Basic Reporting Requirements

Depending on the size of your organization, you’re going to have a date by which you need to produce certain analytics and send them to authoritative bodies, your own employees, and your own employee representatives.

At the basic reporting level, you will have to produce reports on:

1. The gender pay gap
2. The gender pay gap in complementary or variable components
3. The median gender pay gap
4. The median gender pay gap in complementary or variable components
5. The proportion of female and male workers receiving complementary or variable components
6. The proportion of female and male workers in each quartile pay band
7. The gender pay gap between workers by categories of workers, broken down by ordinary basic wage or salary and complementary or variable components.

These are essentially a series of pay gap analyses, using an uncontrolled pay gap. The last of these will require the biggest lift for most organizations: it requires you to perform pay gaps analyses by categories of workers, and you may have hundreds of categories of workers to analyze. Then, there’s the further requirement that you must break each of these categories down by wage type—so base wage, bonuses, incentives. The end result is a significant analytical burden to comply, so it’s imperative that organizations are well-prepared and aren’t caught out.

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Advanced Further Analytics Requirements

The directive also has a second level of analytics you will likely need to conduct for every category of worker for which you uncover a difference of 5% or more:

1. An analysis of the proportion of female and male workers in each category of workers
2. Information on average female and male workers’ pay levels and complementary or variable components for each category of workers
3. Any differences in average pay levels between female and male workers in each category of workers
4. The reasons for such differences in average pay levels, on the basis of objective, gender-neutral criteria, if any, as established jointly by the workers’ representatives and the employer
5. The proportion of female and male workers who benefited from any improvement in pay following their return from maternity or paternity leave, parental leave, or carers’ leave, if such improvement occurred in the relevant category of workers during the period in which the leave was taken

According to the rules of the directive, these analyses will usually involve a joint pay assessment, which will require you to include employee representation in the process. The directive implies that there will be exceptions to this joint pay assessment requirement. Firstly, in the event that the employer can justify the 5% pay differences that the initial analysis uncovers. Or secondly, in the event that the employer is able to close the gap by modifying pay within six months or reporting.

In addition to these required analyses, organizations will be required to:

1. Take measures to address differences in pay if they are not justified on the basis of objective, gender-neutral criteria
2. Perform an evaluation of the effectiveness of measures from previous joint pay assessments

We additionally suggest the following remedial measures:

1. Run regression analyses where possible to see if gender neutral explanatory variables can bring pay differences below 5%
2. Create charts and graphs to help decision makers and committees see how gender neutral explanatory variables relate to pay differences

KEEP READING ABOUT PAY EQUALITY STRATEGIES | ‘How Legal Strategies Address the 4 Principal Causes of Gender Pay Inequality’

Learn More About the EU Pay Transparency Directive

This is an extract from our ebook, ‘Preparing for the EU Pay Transparency Directive: A Guide for Global Businesses.’ The full ebook, co-authored with PeopleFluent, will help you understand the core provisions and requirements of the directive, as well as the key strategies and recommended technologies you’ll need to identify and address pay gaps.

Inside, you’ll find information about:

• Pay equity definitions and an explanation of the current state of play globally
• The six main provisions of the EU Pay Transparency Directive
• The major sticking points your organization will likely encounter
• How compensation planning software can help you comply

Prepare for the incoming directive: download the full ebook. And when you’re ready to build the reports and processes you need, contact Affirmity here.

About the Author

Patrick McNiel, PhD is a Principal Business Consultant for Affirmity. Dr. McNiel specializes in workforce analytics using both qualitative and quantitative methods to analyze employment practices and inform employment decisions. Dr. McNiel holds a PhD in Industrial and Organizational Psychology, and is licensed to practice psychology by the State of Texas. Connect with him on LinkedIn.