1. Introduction
The Pay Equity Analysis Guide provides a comprehensive framework for conducting pay equity analysis in alignment with EU Directive requirements and modern reward principles. The guide explains the purpose of pay equity work, the analytical models used to evaluate pay gaps, and the processes for identifying, explaining, and addressing disparities. It also covers equal work analysis, comparative job analysis, the use of the MIA model in pay setting, and how to document findings for regulatory reporting.
Pay equity analysis helps organizations build transparency, fairness, trust, and compliance. It strengthens employer reputation, supports talent retention, and provides insights that shape long-term compensation strategies.
2. Importance of Pay Equity Analysis
As emphasized on page 3, pay equity has multiple organizational benefits:
Enhances employer reputation by signaling commitment to fairness.
Increases employee satisfaction and trust through transparent reward practices.
Enables data-driven decision-making by identifying root causes of pay gaps.
Supports retention and high-performance cultures through equitable compensation structures.
Organizations can leverage the outcomes of pay equity analysis to drive structural improvements and equitable compensation policies.
3. The MIA Model for Pay Setting
The MIA model—Market, Individual, and Assignment—provides a holistic framework for pay setting (pages 4–7):
Market
Reflects external competitiveness, including sector standards, labor supply and demand, and competition for skills.
Individual
Reflects employee-specific factors such as skills, experience, performance, and unique contributions.
Assignment
Reflects the scope, complexity, and impact of the role—essentially what work is done and how it is performed.
The MIA model helps balance external market forces with internal fairness, supporting both competitiveness and equal pay for equal and comparable work.
4. Equal Work Analysis
Unadjusted Pay Gap
On page 9, the guide describes unadjusted pay gaps using:
Mean gap – preferred because it considers outliers
Median gap – middle salary comparison
A negative percentage indicates women earn more; a positive percentage indicates men earn more.
Using Factors to Interpret Pay Differences
Page 10 explains how analysts can use factors—such as age, tenure, or performance—on the X-axis of scatter plots to identify patterns. Trends may confirm or contradict assumptions about pay setting.
Regression Analysis
Pages 11–12 explain regression and R² (coefficient of determination), showing how mathematical analysis can uncover relationships between factors and salary:
Positive slope: salary increases with the factor
Negative slope: salary decreases with the factor
High R²: strong explanatory power
Low R²: weak relationship
Examples highlight situations such as pay compression, outdated salary structures, or market-driven disparities for recent hires.
Sample Equal Work Cases
Across pages 12–15, the guide presents real-world scenarios illustrating:
Negative slopes indicating newer employees earning more
Outliers explained or unexplained by performance, tenure, or age
When gaps must be further investigated due to unexplained variances
These examples show how to evaluate patterns, investigate pay settings, and identify the root causes of gender gaps.
5. Investigating and Explaining Individual Pay
Page 16 outlines the investigation process for flagged employees:
Select the employee in the graph and choose a reason for analysis.
Gather context from managers and decision-makers.
Tag objective explanations.
Suggest salary adjustments if gaps cannot be justified.
Document reasoning before finalizing the report.
Add an overall analysis in the “Other Analysis” section for each equal job.
A recommended report—Compilation Report: Employees—helps track employees requiring further review.
6. Tags to Explain Salary Situations
On page 17, the guide lists common tags used to document objective reasons behind pay variations:
Market competitiveness
Market scarcity at recruitment
Experience
Historical salary
Competence
Performance
Assignment responsibility variations
Each tag helps create consistent, transparent explanations grounded in legitimate business factors.
7. Using Market Data Responsibly
Pages 18 and 27 explain why market factors cannot justify long-term pay gaps:
Risks of Market-Driven Pay Decisions
Market benchmarks may reflect biased historical pay norms.
Market-driven hiring pay can create inequities if incumbents are not aligned.
Market rates describe external pay but not internal job value.
Guidance for Using Market Data
Use market data only in combination with validated internal job evaluation.
Adjust internal pay structures when market-based hiring changes the baseline.
Apply consistent, gender-neutral criteria when granting market premiums.
Conduct regular internal reviews to prevent structural disparities.
8. Designing Pay Ranges
Page 28 describes narrow vs. wide pay ranges:
Wide Ranges
More flexibility
Fewer hierarchical levels
Easier administration
But: higher risk of internal inequity and hard-to-explain pay variation
Narrow Ranges
Stronger control and reduced inequity
Better alignment with equal pay laws
But: less differentiation for performance or experience, higher risk of becoming uncompetitive
Organizations must balance flexibility with fairness, compliance, and transparency.
9. Comparative Job Analysis (Work of Equal Value)
Comparative work analysis examines pay gaps between female-dominated jobs (>60% women) and non-female-dominated jobs of equal value.
Goals (page 22)
Ensure compliance with equal pay for work of equal value legislation
Remove systemic undervaluation of female-dominated work
Build pay structures that reflect the objective value of roles
Key Metrics (page 23)
Women ratio
Mean salary difference (%)
Identification of higher-paid non-female-dominated jobs doing work of equal value
Sample Comparative Analyses (pages 24–26)
Illustrations show how tenure, age, and other factors influence pay comparisons across groups, and why both comparative and female-dominated jobs must be analyzed to explain gaps and propose corrective actions.
10. Reporting Requirements
Pages 29–31 outline reporting obligations under the EU Pay Transparency Directive:
Required disclosures include:
Mean and median gender pay gaps
Total compensation gaps, including:
bonuses
overtime
allowances
pensions
other benefits
Percentage of men and women receiving variable pay
Pay gaps by categories of workers (equal jobs)
Breakdown by base salary and variable components
Reporting Tools
Data Compilation Report – raw data + gap calculation
Employee Compilation Report – tags, comments, suggested adjustments
Final Report Template – customizable compliance-ready report
Reporting functionalities will evolve in line with Member State implementation of the Directive.
11. Summary
The Pay Equity Analysis Guide provides a complete methodology for identifying, explaining, and addressing pay disparities within organizations. Using the MIA model, statistical analysis, equal work evaluation, and comparative job analysis, organizations can conduct structured, defensible pay equity audits. The guide also outlines how to document findings and comply with EU pay transparency reporting requirements, ensuring both legal compliance and fair compensation practices.