Your CHRO just asked whether the company has any unexplained pay gaps by gender or race. You have two weeks to produce a defensible answer before the board meeting.
I have fielded that question more than once. The first time, I spent three days pulling data from four systems, realized 22 percent of employees had no job level assigned, and ended up presenting a slide that said “preliminary findings pending data cleanup.”
Not my proudest moment. A structured pay equity analysis would have surfaced the gaps months earlier, with documentation that held up under scrutiny.
This guide walks through exactly how to run one, from scoping to remediation, so you can answer that board question with confidence.
Key Takeaways
- Pay equity analysis reveals unfair pay gaps by gender and race.
- Adjusted pay gaps are critical for compliance and legal protection.
- Poor data quality frequently stalls effective pay equity reviews.
- Remediation plans should emphasize retention benefits and risk reduction.
What Is Pay Equity Analysis?
Pay equity analysis is a statistical examination of whether protected characteristics such as gender, race, or ethnicity are associated with pay differences after controlling for legitimate job-related factors like role, level, tenure, location, and performance.
It is not a simple comparison of average salaries between groups. That raw number, often called the unadjusted gap, tells you what the difference is but not why it exists.
The adjusted gap is what matters for legal and operational purposes. You run a regression model that holds job-related variables constant, then check whether being female or a member of a racial minority still predicts lower pay.
If it does, you have an unexplained gap that warrants remediation.
The EEOC’s guidance on compensation discrimination makes clear that employers must ensure pay decisions do not disadvantage protected groups. Pay equity analysis is how you test whether your decisions pass that standard.
Why Pay Equity Analysis Matters for Employers
Unexplained pay gaps expose your organization to legal liability, accelerate regrettable turnover, and erode the trust that keeps high performers engaged.
The numbers are stark.
Women in the United States earned 83.8 cents for every dollar men earned in 2023, according to Bureau of Labor Statistics data. The Economic Policy Institute found that even after controlling for education, age, race, and geography, women were paid 21.8 percent less than men.
That controlled gap is the territory where lawsuits live, which is why compliance pressure is rising fast.
Pay transparency laws now require salary ranges in job postings across multiple states, and regulators are paying closer attention to equal pay for substantially similar work, not just identical job titles. The documentation habits you build through regular pay equity analysis translate directly to audit readiness.
Beyond compliance, there’s also the retention argument. Employees who discover they are paid below peers for similar work rarely stick around to see if things improve. If you see them updating their LinkedIn profiles, this may be why.
A structured analysis lets you find and fix gaps before your best people find them first.
Core Concepts You Need to Understand
Before we dive into methodology, understanding three fundamental concepts can help shape better pay equity decisions.
First, the unadjusted pay gap compares the raw average earnings between different groups.
For instance, if women at your company earn an average of $85,000 and men earn $100,000, your unadjusted gap stands at 15 percent. While striking, this figure doesn’t reflect job roles, tenure differences, or geographic factors.
Next, the adjusted pay gap refines this analysis by controlling for legitimate variables such as job level, function, location, tenure, and performance.
Using a regression model with pay as the outcome, you add protected characteristics like gender or race. A significant negative coefficient indicates an unexplained gap that suggests potential inequities.
Finally, defining pay analysis groups ensures meaningful comparisons.
Employees performing substantially similar tasks should be grouped together. Comparing a senior engineer with a junior accountant undermines your analysis, diluting accuracy and obscuring genuine disparities.
A Six-Step Methodology for Running Pay Equity Analysis
The following sequence works whether you are running your first analysis or tightening an existing process. I have used variations of this approach at organizations ranging from 200 employees to over 5,000.
Step 1: Define scope and objectives
Clarify which entities, geographies, and protected classes are in scope. Align HR, legal, finance, and executive sponsors on goals before pulling any data. Decide upfront whether the analysis will be conducted under attorney client privilege, which affects how you handle findings and who sees raw results.
Step 2: Gather and clean your data
Extract current base salary, bonus targets, job codes, levels, tenure, performance ratings, and demographic fields from your HRIS and payroll systems. This step consistently takes longer than teams expect. HR practitioners on Reddit describe spending more time cleaning job titles and standardizing levels than running the actual regression. Budget accordingly.
Step 3: Build your pay analysis groups
Define which employees perform substantially similar work. Most organizations use job family combined with level, though highly specialized roles may need custom groupings. Document every grouping decision so future cycles stay consistent. Groups with fewer than 30 employees often lack the statistical power for reliable regression, so you may need to combine adjacent levels or functions.
Step 4: Run the statistical analysis
Start with descriptive statistics: median pay by group, compa ratio distributions, and simple gap calculations. Then build regression models for each pay analysis group, controlling for tenure, performance, location, and any other legitimate factors. Flag coefficients for protected characteristics that are statistically significant and practically meaningful.
Step 5: Interpret findings and plan remediation
Translate model outputs into concrete pay adjustments. Work with finance to quantify the cost of closing identified gaps. Prioritize by risk, gap size, and employee impact. Many organizations phase remediation over two to three compensation cycles rather than absorbing the full cost at once. Frame the business case around risk reduction and retention, not just fairness, because that framing tends to resonate with executive sponsors.
Step 6: Communicate results and build ongoing governance
Prepare executive summaries with clear recommendations. Train managers on how to discuss pay decisions when employees ask questions. Embed pay equity checks into your annual merit and promotion processes so gaps do not reopen. Schedule repeat analyses at least annually and track progress over time.
The Common Pay Equity Analysis Mistakes to Avoid
Even well-designed pay equity processes can falter due to common mistakes. Here are some frequent issues and how to avoid them:
1. Skipping the data freeze
Without a fixed data cutoff, late changes to employee records can disrupt your analysis and reduce confidence in your results. Set a clear, firm cutoff date and adhere strictly to it.
2. Over-controlling variables in regression models
Excessive controls can unintentionally hide actual discrimination. Be cautious about including variables influenced by bias, such as promotion histories shaped by unequal opportunities. Focus only on genuinely legitimate factors.
3. Ignoring small sample sizes
Small groups yield unstable results. Regression analyses involving very few employees produce unreliable outcomes. For these cases, either combine small populations or opt for descriptive analysis instead.
4. Mixing equity adjustments with merit increases
Clearly distinguish between equity corrections and merit-based raises. Communicating them separately ensures employees understand the distinct reasons behind each type of adjustment.
5. Miscommunicating remediation costs
Resistance to remediation can slow progress, especially when executives see adjustments as immediate expenses. Framing these adjustments as phased investments aimed at reducing risk can improve budget approval.
Final Thoughts
Pay equity analysis turns intuition about fairness into defensible, data-driven decisions. The methodology is not complicated, but it requires clean data, thoughtful grouping, and honest interpretation.
Getting this right matters beyond compliance. It shapes whether employees trust that their contributions are valued fairly. That trust is difficult to rebuild once it erodes.
Start this week by auditing your employee data. Pull a sample file and flag any missing job levels, inconsistent titles, or incomplete demographic fields. Then map your existing compensation review process against the six steps above. The gaps will tell you where to focus first.
Pay transparency expectations are accelerating. The organizations that build rigorous pay equity analysis habits now will have a significant advantage when those expectations become regulatory requirements.