How to run a pay-equity analysis
The EU Pay Transparency Directive turns pay equity from a values question into a reporting obligation. Here’s how to measure the gap that actually matters and close it.
What unexamined pay gaps now cost
Pay equity used to be an internal-values exercise. Under the EU Pay Transparency Directive - being transposed into member-state law through 2026 - it becomes a disclosure obligation: employers report gender pay gaps, workers gain the right to pay information, and an unexplained gap above a set threshold forces a joint pay assessment and remediation.
Why pay-equity analysis is hard to get right
- The headline raw gap mixes legitimate factors (role, seniority) with potential bias, so it over- or understates the real problem.
- You have to define comparison groups of “equal work or work of equal value” before any number is meaningful.
- Isolating the gap attributable to a protected class requires multiple regression, not a spreadsheet average.
- Compensation data is messy - inconsistent titles, missing tenure, bonuses and equity treated differently.
- A one-time fix decays: new hires, promotions, and merit cycles reopen gaps within a year if you don’t monitor.
The Pay-Equity Analysis Path
Five steps, in order
- 1
Define comparison cohorts
Group employees by comparable work or work of equal value - job family, level, and function - so you compare like with like rather than the whole company at once.
- 2
Assemble clean compensation data
Pull base pay, bonus, and equity alongside the legitimate explanatory factors - role, level, tenure, location, performance rating - and standardize titles so the regression has honest inputs.
- 3
Measure the raw gap, then the adjusted gap
Compute the raw average gap for context, then run a multiple regression that controls for the legitimate factors to isolate the residual gap attributable to gender or another protected class.
- 4
Investigate flagged outliers
Where the unexplained gap exceeds your threshold - 5% under the Directive - examine individual cases to confirm whether an objective, gender-neutral reason exists or whether it is genuine inequity.
- 5
Remediate and monitor continuously
Adjust pay for confirmed inequities, fix the systemic drivers (starting-offer rules, merit-cycle guardrails), and re-run the analysis on a schedule so gaps don’t silently reopen.
Raw gender pay gap
Raw gap % = (Avg. men’s pay − Avg. women’s pay) ÷ Avg. men’s pay × 100
The raw gap is the reporting headline, but it is only a starting point. The adjusted gap - the regression residual after controlling for role, level, tenure, and performance - is the figure that indicates whether pay differs for equal work.
How Fintra supports each step
| Step | What Fintra does |
|---|---|
| Define cohorts | Workforce data groups employees by job family, level, and function from one shared model. |
| Assemble data | Pay, bonus, equity, tenure, and level already live together in payroll and workforce planning - no cross-tool export. |
| Measure the gap | Pay-equity analysis computes both the raw and regression-adjusted gap per cohort. |
| Investigate outliers | Flags individuals whose unexplained gap exceeds your threshold for human review. |
| Remediate and monitor | Models the cost of remediation and re-runs the analysis on a cadence, with a SentriAI-powered audit trail. |
Because compensation and the explanatory factors already sit in one workforce model, the regression runs on live data instead of a stale spreadsheet extract - and the same platform models what closing each flagged gap costs before you commit.
Your pay-equity checklist
Before your next reporting cycle
- Define comparison cohorts by equal work or work of equal value.
- Standardize job titles and levels so the regression compares like with like.
- Collect legitimate factors: role, level, tenure, location, and performance rating.
- Compute the raw gap for context and the regression-adjusted gap for decisions.
- Flag every unexplained gap above your threshold (5% under the Directive).
- Investigate each flagged case for an objective, gender-neutral justification.
- Model and fund remediation for confirmed inequities.
- Re-run the analysis on a fixed cadence to catch gaps that reopen.
Frequently asked questions
What is the difference between the raw and adjusted pay gap?
The raw (or unadjusted) gap simply compares average pay between groups and mixes legitimate factors with potential bias. The adjusted gap is the residual left after a regression controls for role, level, tenure, location, and performance - it estimates how much pay differs for genuinely equal work. Regulators and courts increasingly focus on the adjusted gap because it isolates the part that may be inequitable.
How does regression analysis detect pay inequity?
You build a multiple-regression model where pay is predicted by legitimate factors - job level, tenure, location, performance - and then add the protected characteristic. If the coefficient on that characteristic is statistically significant after the legitimate factors are accounted for, it signals pay differences that those factors don’t explain, pointing you to the cohorts and individuals that need investigation.
What does the EU Pay Transparency Directive require?
Being transposed into national law across member states through 2026, it requires employers to report gender pay gaps, gives workers the right to information about pay levels for their role, and bans pay secrecy clauses. Where an unexplained gap exceeds a defined threshold (commonly cited as 5%) and cannot be justified by objective, gender-neutral factors, the employer must run a joint pay assessment with worker representatives and remediate.
How often should we run a pay-equity analysis?
Treat it as continuous rather than annual. Gaps reopen every merit cycle, promotion round, and new-hire wave, so many organizations analyze at least twice a year and always before and after compensation reviews. Building the analysis on live workforce data - rather than a once-a-year spreadsheet - lets you catch a drift before it becomes a reportable, remediable gap.
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