Compensation Equity Analysis

Pay equity is no longer just a moral imperative. It's a legal requirement. Under the EU Pay Transparency Directive, Member States must write it into national law by June 2026, and employer gender pay gap reporting then phases in by company size, starting with the largest employers in 2027 and reaching those with 100+ employees by 2031. Where an unexplained gap of 5% or more in any category of workers is not corrected, employers must carry out a joint pay assessment. In the U.S., California, New York, Colorado, Washington, and Illinois have enacted pay transparency laws. Most HR teams still compare average salaries by gender in a spreadsheet and call it an audit. This analysis does what a proper audit requires: compare compensation across groups while controlling for legitimate pay factors, test whether the remaining gaps are statistically significant, and document the method so it can be reviewed.

Why a Spreadsheet Average Is Not a Pay Equity Audit

The raw pay gap is the simple difference in average pay between two groups. National and EU-wide figures show that women are paid less on average than men (see the EU Council for current EU figures). These numbers are real but misleading for your organization's internal audit, because they don't account for legitimate pay factors.

A proper compensation equity analysis asks a more specific question: after controlling for job role, experience, education, performance, and location, do statistically significant pay gaps remain? The adjusted gap is almost always smaller than the raw gap. But it's the adjusted gap that matters legally and operationally, because it reveals whether the company is paying differently for the same work.

Enterprise pay equity tools (Syndio, Trusaic, PayScale) start at $50,000+ per year and require long implementation cycles. Mid-market companies (200 to 10,000 employees) face a gap: they need a proper statistical analysis but can't justify the enterprise price tag. This analysis fills that gap. You upload a CSV from your HRIS, specify the compensation column and the grouping variables, and get a statistical comparison in under 60 seconds.

What the Law Requires

The regulatory landscape is tightening rapidly:

• EU Pay Transparency Directive: Member States must transpose it into national law by June 7, 2026. Employer reporting is phased by size: employers with 250+ employees report first in 2027 and annually after that, those with 150 to 249 employees first in 2027 and then every three years, and those with 100 to 149 employees first in 2031 and then every three years. If the gap is at least 5% in any category of workers, cannot be justified by objective, gender-neutral criteria and is not remedied within six months, the employer must conduct a joint pay assessment with worker representatives (Ravio, 2026). The burden of proof in pay discrimination cases shifts to the employer.
• U.S. state laws: California (SB 1162) requires employers with 100+ employees to submit pay data by job category, race, ethnicity, and sex. New York, Colorado, Washington, and Illinois have similar disclosure requirements. EEOC scrutiny has increased.
• UK Gender Pay Gap Reporting: employers with 250+ employees must publish median and mean gender pay gaps annually.

The common thread: governments are moving from "we encourage fair pay" to "prove it with data." A spreadsheet of averages does not meet that standard. A statistical analysis with documented methodology, significance testing, and effect sizes does.

How a Proper Pay Equity Analysis Works

A defensible audit has two layers:

Layer 1: Unadjusted group comparison: compare mean and median compensation between groups (male vs. female, or across ethnic groups) without any controls. This is the "headline number" that matches the publicly reported pay gap. You produce it with a t-test (two groups) or ANOVA (three or more groups). Even though it doesn't control for pay factors, this number matters: it's what regulators report, it's what the press cites, and it's what your employees will compare their experience against.

Layer 2: Controlled comparison: the same analysis, but run within comparable groups. Instead of comparing all men to all women, compare men and women within the same job level, department, and experience band. If you have enough granularity in your data (30+ employees per comparable group), this approach controls for legitimate pay factors without requiring multivariate regression. Within each group, a remaining gap is not explained by the factors you stratified on, but it may still reflect factors you did not include.

The first layer tells you what the gap looks like. The second layer tells you how much of it remains within comparable groups. Neither one on its own decides whether pay is fair: when several factors matter at once, use a regression that adjusts for them together, and have legal or compensation specialists review any unexplained gap before drawing conclusions.

What Data Do You Need?

A CSV export from your HRIS (Workday, ADP, BambooHR, SAP SuccessFactors) combining compensation and demographic data. Most systems can produce this as a custom report. Some companies need to merge payroll data with demographics from a separate HRIS export.

Required columns

• base_salary (or annual_compensation, total_comp): the numeric outcome you're testing
• gender (or ethnicity, race): the protected characteristic you're analyzing

Columns for a controlled analysis

• job_title or job_level: defines comparable groups (people doing similar work)
• department: additional grouping to create tighter comparisons
• years_of_experience or seniority: the most important legitimate pay factor
• education_level, if your compensation model considers education
• performance_rating: most recent cycle rating
• location: for companies with geographic pay differentials

Sample size guidance

• Minimum: 200 employees for meaningful overall comparisons
• Per group: roughly 30 or more employees per group (e.g., 30 men and 30 women within the same job level) is a common rule of thumb for t-tests; also check for heavy skew, unequal variances and the size of the gap
• For within-group analysis: you need enough employees per comparable group (job level + department + gender) to run statistical tests. Groups smaller than 5 should be combined or excluded.

How to Read the Report

Group means and medians: the raw numbers. Mean salary for men, mean salary for women, and the difference in both absolute dollars and percentage terms. The median is often more informative than the mean for compensation data, because a few highly paid executives can pull the mean up without reflecting the typical employee's experience.

Box plots: visual comparison of the salary distributions by group. Overlapping boxes suggest similar compensation; clearly separated boxes suggest a gap worth testing. The plot shows the shape of the gap, but significance comes from the statistical test, not from how the boxes look. Look for outliers (the dots beyond the whiskers): a few extremely high or low salaries can distort the means.

ANOVA F-test (for 3+ groups), if you're comparing pay across multiple ethnic groups, departments, or job levels, the F-statistic and p-value tell you whether at least one group differs significantly. The p-value is the probability of seeing differences at least this large if there were no real pay difference between the groups. A p-value below 0.05 is statistically significant evidence against "no difference", not proof that a gap is real or what caused it; a p-value above 0.05 is not proof of equal pay either. Significance alone isn't enough: look at the effect size (eta-squared). An effect size below 0.01 means the gap is statistically significant but practically tiny.

Tukey HSD post-hoc comparisons: after ANOVA finds a significant overall difference, Tukey identifies which specific pairs differ. This is critical for pay equity: "Compensation differs significantly across ethnic groups" is a finding. "Hispanic employees earn significantly less than White employees (difference = $4,200, p = 0.008), while Black-White and Asian-White differences are not significant" is an actionable finding.

Confidence intervals: for each comparison, the report shows the estimated size of the gap with upper and lower bounds. A confidence interval that doesn't cross zero confirms significance. The width tells you about precision: a narrow interval ($3,800 to $5,200) gives you a specific remediation target. A wide interval ($500 to $9,000) means you need more data or tighter comparable groups.

Effect size (Cohen's d): measures practical significance, separate from statistical significance. A d of 0.2 is a small effect, 0.5 is medium, and 0.8 is large. In compensation analysis, even a small effect size can translate to meaningful dollar amounts when multiplied across the workforce.

What to Do With the Results

Immediate

• Document the findings: under the EU directive, you need documented evidence of your pay equity analysis, not just results. Save the report with its methodology, data sources, and date of analysis.
• Flag significant gaps: for each significant comparison, calculate the total cost to close the gap. If 45 women in job level 3 are underpaid by an average of $3,500, the remediation cost is $157,500, a concrete number for budget discussions.
• Check for confounders, if the gender gap is significant overall but not within any single job level, the gap is consistent with occupational segregation (more women in lower-paying roles) rather than unequal pay for equal work, though non-significance within a level is not proof of equal pay. Both are problems, but they require different solutions.

Strategic

• Run annually, at minimum: the EU directive requires periodic reporting. Build pay equity analysis into your annual compensation cycle, ideally before merit increases so you can use the budget to close gaps.
• Layer in regression: the ANOVA-based approach compares groups. For a more granular analysis that controls for multiple factors simultaneously, follow up with a regression analysis using salary as the dependent variable and gender/ethnicity as an independent variable alongside the legitimate pay factors. The coefficient on the protected characteristic is the adjusted gap.
• Explore intersectionality: the gap may be larger for specific intersections (e.g., Hispanic women in technical roles) than for any single dimension. Run separate analyses for key intersections.
• Validate with correlation analysis: use correlation analysis to understand which factors (seniority, education, performance) most strongly predict pay in your organization. If performance ratings barely correlate with compensation, your pay-for-performance policy isn't working as intended.

When to Use Something Else

• Comparing exactly two groups (e.g., male vs. female): A t-test gives a simpler, directional result with a confidence interval for the pay gap. It's mathematically equivalent to ANOVA with two groups but produces cleaner output for a two-group comparison.
• Heavily skewed compensation data: If executive compensation creates extreme outliers, a Kruskal-Wallis test compares medians without assuming a normal distribution. Compensation data is often right-skewed, making this a useful robustness check.
• Controlling for multiple factors simultaneously: ANOVA compares groups one factor at a time. For a proper multivariate analysis (controlling for experience, education, and performance all at once), you need regression. Start with ANOVA for the headline comparison, then follow up with regression for the adjusted gap.
• Full enterprise pay equity platform: If you need continuous monitoring, integration with your HRIS, and automated remediation recommendations, tools like Syndio or Trusaic are purpose-built. This analysis is for organizations that need a proper statistical audit without the enterprise price tag.

References

• Pay Transparency in the EU. Council of the European Union. consilium.europa.eu
• EU Pay Transparency Directive: Complete Legislation Guide. Ravio. ravio.com
• Pay Equity Analysis: The Essential Guide. AIHR. aihr.com
• Statistics 101 for Pay Equity. Equity Methods. equitymethods.com
• EU Pay Transparency Directive: A 4-Stage Readiness Plan. DCI Consulting. dciconsult.com