Pay Transparency in Practice: Turning Regulation into Fair, Data-Driven Action

Andras Rusznyak

artificial intelligence expert

Ha magyarul szeretnéd olvasni a cikket, kattints ide

Across Europe — and soon across Hungary — pay transparency is transforming HR from policy-driven to data-driven.
The EU Pay Transparency Directive (2023/970) turns the principle of equal pay for equal work into a measurable, auditable obligation.

By June 2026, every EU member state must adopt local laws that:

• Require salary ranges in job ads

• Give employees the right to access average pay data by gender

• Mandate pay gap reporting for employers with 100+ staff

• Trigger joint pay assessments if gaps exceed 5%

This means HR can no longer rely on “good intentions.”
The new baseline is quantified fairness — powered by clean data, consistent job architecture, and transparent analytics.

IMPORTANT NOTE:

We utilized generative AI in the making of this article.

Core legislation: Directive (EU) 2023/970 – “Strengthening the application of the principle of equal pay for equal work or work of equal value.”

Timeline and impact:

• June 2026: Directive transposed into Hungarian law

• 2027 onward: Employers with 100+ employees must report gender pay gaps every 3 years (those with 250+ employees → annually)

• Applies to all employment forms, both private and public

Key obligations:

1. Salary range disclosure: Include pay range or expected level in job postings.

2. Pay transparency for employees: Workers can request average pay data for comparable roles, by gender.

3. Pay reporting: 100+ employers must report gender pay gap data to authorities.

4. Joint pay assessment: Required if >5% gap remains unexplained.

5. Right to compensation: Employees can claim pay adjustments or damages.

In Hungary:
The directive will likely be integrated into the Munka Törvénykönyve (Labour Code) with the Ministry for National Economy (NGM) and the Equal Treatment Authority overseeing enforcement.
Hungarian companies should expect a local reporting framework linked to existing HR and payroll systems, similar to gender equality declarations already used in EU-funded projects.

The Regulatory Background

From Legal Obligation to HR Analytics Opportunity

Most HR leaders see pay transparency as a compliance risk — but with the right analytics mindset, it becomes a strategic lever for trust, retention, and talent brand.

Here’s how analytics enables a structured, measurable approach.

1. Build Your Pay Data Foundation

You can’t manage what you can’t measure.
Start by auditing and cleaning all compensation-related data sources: HRIS, payroll, and bonus systems.

Actions:

• Align job titles and families — eliminate duplicates and legacy naming (e.g. “HR Generalist” vs “People Partner”).

• Add consistent leveling codes (L1–L7) so pay comparisons are fair.

• Validate FTE status, tenure, and location data — common data gaps distort analysis.

• Calculate compa-ratios (employee salary ÷ midpoint of band) to detect outliers.

Data required: Job title, level, base pay, bonus, FTE%, location, gender, tenure.
Tools: Excel, Power BI, or Python (pandas).
Skills: Data cleaning, pivot tables, basic descriptive statistics.

📊 Visualization idea: A heatmap showing compa-ratio distribution by department or gender to identify inequity clusters.

2. Define Fair and Defensible Pay Bands

Publishing ranges isn’t just policy — it’s a data validation exercise.

Actions:

• Define salary bands per role level using statistical spread (e.g. median ± 20%).

• Compare to market benchmarks (salary surveys, Korn Ferry, Hays, PwC).

• Run simulations: how many employees sit outside the range, and what correction cost would bring them within tolerance?

• Prepare candidate-facing ranges (e.g. 85–115% of midpoint) and align messaging with recruiters.

Analytical insight:
Identify whether variability within a level is performance-driven or structural — if two employees with similar tenure differ by >20%, HR must justify why.

📊 Visualization idea: Box plot of pay distribution per level, highlighting median, outliers, and gender splits.

3. Measure Pay Gaps — and Explain Them with Data

Compliance requires more than publishing numbers; it demands understanding why the gaps exist.

Actions:

• Compute mean and median pay gaps (overall and per comparable group).

• Use regression models to control for legitimate factors: level, experience, education, region.

• Quantify the “unexplained” gap — typically anything beyond 5% not justified by data.

• Document explanations clearly (e.g. “Gap driven by tenure skew in new hires”).

Data required: Base pay, job level, tenure, gender, performance rating, education, region.
Tools: Excel Analysis ToolPak, R, or Python (statsmodels).
Skills: Regression interpretation, significance testing.

📊 Visualization idea: Bar chart comparing explained vs. unexplained gender pay gaps by department.

4. Combine Legal Compliance with Analytics Governance

A strong data foundation must link to governance: who owns what and how it’s reviewed.

Actions:

• Assign data owners (HR, Comp & Ben, Legal).

• Set data quality thresholds (e.g. <2% null values in salary fields).

• Define a review cycle: quarterly data validation, annual pay gap audit.

• Involve works councils early to align on method and findings.

• Document models and assumptions — so if audited, your analytics are defensible.

Best practice:
Use data contracts for HR/payroll systems — ensuring schema consistency and audit logs.

5. Communicate with Clarity and Context

Data transparency fails if managers and employees can’t interpret it.

Actions:

• Create simple visuals for internal communication (e.g. how pay bands are built, where fairness checks happen).

• Educate managers on interpreting pay bands and addressing questions.

• Publish summary metrics — not all data — to avoid overload.

• Track changes in employee trust and engagement after rollout using pulse surveys

Why It Matters Now

The EU Pay Transparency Directive is a data governance challenge disguised as an HR policy.
Organizations that treat it analytically — with clear definitions, clean data, and regular measurement — will gain not just compliance, but a reputation for fairness and integrity.

The future of compensation isn’t just about “how much we pay,” but how we explain it — with data, not intuition.

Up next: Thank you for reading this article. We will be posting short snippets on HR Analytics while we are working on season 2 of the larger series. Stay tuned.

Have you read our other articles? Go to Motioo Insights

Do you have any questions or comments? Contact us