Pay Equity Analysis: A Step-by-Step Guide for HR and Compensation Teams

What Is Pay Equity Analysis?

Pay equity analysis—also called a pay equity audit or equal-pay audit—is the systematic process compensation teams use to determine whether employees performing substantially similar work are paid equitably regardless of gender, race, ethnicity, age, or other protected characteristics. The process goes beyond comparing raw averages: it applies statistical controls for legitimate pay factors (job level, tenure, geography, performance, education) to isolate unexplained pay gaps that may indicate discriminatory compensation practices.

This guide is written for HR professionals, compensation analysts, and people operations leaders at U.S.-based organizations—particularly mid-market companies with 200 to 5,000 employees. If you are responsible for setting defensible pay ranges, managing merit cycles, or responding to pay transparency legislation, this article walks through the complete pay equity analysis methodology from scoping through ongoing monitoring.

A well-executed pay equity analysis produces three outputs that compensation teams depend on:

1. A statistical findings report documenting which employee groups, if any, show statistically significant pay disparities after controlling for legitimate factors
2. A remediation plan with specific dollar amounts needed to close unjustified gaps, prioritized by risk and magnitude
3. A monitoring framework that prevents new disparities from emerging as the organization hires, promotes, and adjusts pay over time

Organizations that skip formal pay equity analysis—or conduct it only when litigation threatens—face predictable consequences: regulatory penalties under state and federal equal-pay laws, class-action exposure, difficulty attracting diverse talent, and erosion of internal trust when employees discover unexplained pay differences through pay transparency disclosures.

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The Legal Framework: Why Pay Equity Analysis Is Non-Negotiable

Pay equity analysis is not a voluntary best practice. It is a compliance obligation rooted in multiple overlapping federal and state laws. Compensation teams must understand these legal drivers because they determine the scope, methodology, and documentation requirements of any credible audit.

Federal Law

The Equal Pay Act of 1963 (EPA) prohibits sex-based wage discrimination for employees performing substantially equal work—defined as jobs requiring equal skill, effort, and responsibility performed under similar working conditions. Employers can defend pay differences only through four affirmative defenses: seniority systems, merit systems, systems that measure earnings by quantity or quality of production, or any factor other than sex.

Title VII of the Civil Rights Act of 1964, enforced by the EEOC, extends pay discrimination protections to race, color, religion, national origin, and sex. Unlike the EPA, Title VII does not require a comparator performing "substantially equal" work—broader job groupings can be examined for patterns of discriminatory pay.

Source note: The EEOC enforces both the EPA (29 U.S.C. § 206(d)) and Title VII (42 U.S.C. § 2000e-2). The EPA uses a "substantially equal work" standard; Title VII uses a broader "discrimination in compensation" standard.

State and Local Laws

State-level pay equity and pay transparency laws have expanded rapidly. As of 2026, more than a dozen states and municipalities impose requirements that directly affect how compensation teams must approach pay equity:

• California (SB 1162): Requires salary ranges on all job postings, annual pay data reporting by race/ethnicity and sex, and prohibits using prior salary history in setting compensation
• New York (NYC Int. 134-A and statewide amendments): Salary range disclosure on job postings, with expanding coverage beyond New York City
• Colorado (Equal Pay for Equal Work Act): Salary ranges required on all job postings for roles performable in Colorado, plus promotion and job opportunity disclosures
• Illinois (HB 3129): Pay scale and benefits disclosure requirements with EEO-1-style pay data reporting
• Washington (SB 5761): Salary range disclosure on job postings for employers with 15+ employees

Source note: Pay transparency laws vary by jurisdiction and are evolving rapidly. Several additional states—including Minnesota, Vermont, and Massachusetts—have enacted or proposed pay disclosure requirements. Consult legal counsel for jurisdiction-specific compliance obligations.

Many of these state laws explicitly or implicitly require that published salary ranges be based on a good-faith determination of expected compensation. A documented pay equity analysis is the most straightforward way to demonstrate that good faith—and to ensure that the ranges you publish do not themselves encode historical pay disparities.

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Step-by-Step Pay Equity Analysis Methodology

A defensible pay equity analysis follows a structured sequence. The methodology below is designed for mid-market HR and compensation teams running the process in-house or with the support of compensation intelligence tools.

Step 1: Define Scope, Stakeholders, and Privilege

Before collecting any data, establish the boundaries of the analysis and who owns the process.

Scope decisions:

• Which protected classes? At minimum, analyze gender and race/ethnicity. Depending on your jurisdiction and workforce, also consider age, disability status, and veteran status.
• Which pay elements? Base salary is the starting point, but a comprehensive analysis should also examine total cash compensation (base plus bonus), equity grants, and promotion velocity.
• Which employee populations? Decide whether to include all employees, exempt only, or specific business units. Most mid-market organizations analyze the full workforce.

Stakeholder alignment:

• The compensation analyst or HR director typically owns methodology and execution
• Legal counsel should be involved from the outset—many organizations conduct pay equity analyses under attorney-client privilege to protect findings during the remediation process
• The CFO or CHRO approves remediation budgets
• Document approval workflows to ensure consistency and defensibility

Attorney-client privilege: Many organizations engage outside counsel to direct the pay equity analysis so that findings are protected by attorney-client privilege during remediation. This does not mean hiding problems—it means having the legal space to identify and fix issues before they become litigation targets. Discuss this approach with your legal team before beginning.

Step 2: Build Comparable Job Groups

Pay equity analysis compares employees doing substantially similar work. This requires grouping employees into defensible comparison sets—called similarly situated employee groups (SSEGs) or comparable job groups.

How to construct job groups:

• Start with job families and levels. A strong job classification framework is essential. Group employees by job family (Engineering, Finance, Marketing) and career level (Associate, Senior, Manager, Director).
• Consider functional similarity over title. Two employees with different titles but substantially similar duties, required skills, and scope of responsibility belong in the same group.
• Avoid over-splitting. Creating too many narrow groups reduces statistical power and can obscure real disparities. Conversely, groups that are too broad may combine jobs that are not truly comparable.
• Document grouping rationale. Every grouping decision should be documented so it can withstand legal scrutiny. Courts and regulators will examine whether groups were constructed to minimize or maximize apparent disparities.

A platform like SalaryCube's DataDive Pro—which organizes 17,000+ job titles by job family and level—can accelerate the job matching process and ensure that internal roles are mapped to standardized benchmark titles consistently across the organization.

Step 3: Collect and Validate Compensation Data

Gather comprehensive employee-level data for every individual in your analysis population.

Required data fields:

• Current base salary, bonus/incentive targets and actuals, equity grants
• Job title, job family, job level, department, location
• Protected class demographics: gender, race/ethnicity (from HRIS or EEO-1 data)
• Legitimate pay factors: years of experience, tenure with the organization, education level, performance ratings, relevant certifications, FLSA classification

Data quality checks:

• Verify that demographic data is complete. Missing race/ethnicity data—common in organizations that do not require self-identification—can undermine the analysis. If more than 10-15% of records are missing demographic data, address the gap before proceeding.
• Confirm that job groupings are current. Employees who have changed roles or received promotions should be mapped to their current job group, not their role at time of hire.
• Validate that performance ratings are consistently applied. If some departments use a five-point scale while others use three-point, normalize before including performance as a control variable.

Step 4: Conduct Statistical Analysis

This is the analytical core of the pay equity process. The goal is to determine whether protected class membership (gender, race/ethnicity) has a statistically significant effect on pay after controlling for legitimate, non-discriminatory factors.

Multivariate Regression Analysis

Regression analysis is the most widely accepted statistical method for pay equity analysis. It models compensation as a function of legitimate pay factors and protected class variables:

Compensation = f(job level, tenure, experience, performance, geography, education) + protected class variable

If the coefficient on the protected class variable is statistically significant (typically at the p < 0.05 level), it indicates an unexplained pay gap that warrants investigation and potential remediation.

Key considerations:

• Model specification matters. Including too few control variables may inflate apparent disparities; including too many (such as prior salary, which may itself reflect historical discrimination) can mask real gaps. Work with a statistician or compensation consultant to specify the model appropriately.
• Practical significance vs. statistical significance. A statistically significant gap of $200 per year may not warrant individual remediation, but a pattern of small gaps across an entire protected class may indicate systemic issues.
• Interaction effects. Examine whether disparities differ across intersectional groups (e.g., women of color vs. white women vs. men of color). Intersectional analysis is increasingly expected by regulators and courts.

Cohort Analysis

Cohort analysis complements regression by examining specific employee pairs or small groups with similar profiles. This approach is particularly useful for:

• Small job groups where regression lacks statistical power
• Executive-level roles where each position is unique
• Validating regression findings with individual-level review

Compa-Ratio Analysis

Compa-ratios—the ratio of an employee's actual pay to the midpoint of their salary range—provide a useful supplementary lens. If female employees consistently have lower compa-ratios than male counterparts in the same job group, it may indicate a pattern even if absolute dollar gaps appear small.

Step 5: Identify and Investigate Unexplained Gaps

Statistical analysis will surface gaps. Not all of them indicate discrimination. The investigation phase separates explainable differences from those requiring remediation.

Legitimate explanations to investigate:

• Differences in relevant experience that the regression model did not fully capture
• Geographic pay differentials for remote employees
• Retention adjustments or sign-on bonuses for hard-to-fill roles
• Recently hired employees brought in at higher market rates (which may indicate pay compression rather than discrimination)

Red flags that suggest systemic issues:

• Consistent patterns where a protected class is paid below predicted values across multiple job groups
• Starting salary gaps that persist or widen over time
• Lower bonus payouts for a protected class despite equivalent performance ratings
• Slower promotion velocity for a protected class

Document every investigation and its conclusion. If an explanation exists, record it. If no legitimate explanation can be identified, the gap moves to the remediation phase.

Step 6: Develop and Execute a Remediation Plan

Remediation converts findings into action. The goal is to close unjustified gaps while maintaining internal equity and budget discipline.

Remediation approaches:

• Individual pay adjustments. The most direct approach: increase the pay of underpaid employees to the level predicted by the regression model. Calculate the total remediation cost and present it to leadership as a compliance investment, not a discretionary expense.
• Salary range realignment. If entire job groups have ranges that are below market or based on outdated data, refresh the ranges using current salary benchmarking data before making individual adjustments. SalaryCube's Bigfoot Live provides real-time salary data for 35,000+ roles updated daily, which can anchor remediation to current market conditions rather than stale survey data.
• Policy changes. If the analysis reveals that certain practices create disparities—such as negotiation-based starting salaries, discretionary bonuses, or inconsistent promotion criteria—change the policies, not just the pay.
• Prospective adjustments through merit cycles. For smaller gaps, organizations can close disparities over one or two merit cycles by allocating a portion of the merit increase budget specifically for equity adjustments. SalaryCube's Comp Planning tool supports this approach with pre-populated manager worksheets and real-time budget tracking by department.

Communication considerations:

• Employees receiving adjustments should be told that the change results from a pay equity review—but specific statistical findings are typically not shared broadly
• Avoid reducing any employee's pay as a remediation measure. Courts and regulators view pay cuts as retaliatory, and they destroy trust
• Document every remediation decision for audit readiness

Step 7: Establish Ongoing Monitoring

Pay equity is not a one-time project. New hires, promotions, merit increases, and market adjustments can reintroduce disparities. An effective monitoring framework includes:

• Annual full-cycle analysis. Repeat the complete statistical analysis at least once per year, timed to follow the annual merit cycle so that new adjustments are captured.
• Trigger-based interim checks. Run targeted analyses after events that commonly create new disparities: large-scale hiring, reorganizations, acquisitions, or changes to compensation philosophy.
• New-hire audit. Review starting salaries by protected class quarterly to catch negotiation-driven gaps before they compound. Organizations using a salary banding framework with defined hiring zones can significantly reduce this risk.
• Promotion and bonus equity review. Extend the analysis beyond base pay to examine whether promotion rates and bonus payouts are equitable across protected classes.
• Dashboard tracking. Maintain a pay equity dashboard that tracks key metrics (compa-ratio by demographic group, median pay gap trends, remediation spend) so that leadership can monitor progress without waiting for the annual audit.

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Tools and Platforms for Pay Equity Analysis

The right technology stack makes pay equity analysis repeatable and defensible rather than a one-off consulting engagement.

Compensation Intelligence Platforms

Modern compensation platforms accelerate pay equity work by providing the market data and analytical infrastructure that HR teams need. SalaryCube's DataDive Pro gives compensation analysts access to 17,000+ job titles organized by job family and level, with filters for geography, industry, revenue, and headcount—making it straightforward to validate whether internal pay levels are aligned with external market rates for each job group in the analysis.

For organizations that need to compare their internal pay data against real-time market benchmarks as part of the equity review, Bigfoot Live provides daily-updated salary data across 35,000+ roles drawn from over 800 million data points. This is particularly valuable during remediation, when teams need to determine whether a gap reflects internal inequity, a market misalignment, or both.

Statistical Software

For the regression analysis itself, many organizations use R, Python, or specialized pay equity software. The choice of tool matters less than the methodology—what matters is that the model is properly specified, the results are documented, and the analysis is reproducible.

HRIS Integration

Pay equity analysis requires clean, complete employee data. Organizations that maintain integrated HRIS systems—and can pull compensation, demographic, and job architecture data into a single dataset—will spend far less time on data preparation. SalaryCube integrates with Workday, UKG, and BambooHR via CSV/API, and supports import of survey data from Mercer, Radford, WTW, and other providers.

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Addressing Gender and Racial Pay Equity

While the methodology above applies to all protected classes, gender and race/ethnicity deserve specific attention because they are the most commonly examined dimensions and the most frequent basis for regulatory enforcement and litigation.

Gender Pay Equity

Gender pay equity analysis examines whether male and female employees in substantially similar roles are paid equitably after controlling for legitimate factors. Key considerations:

• Unadjusted vs. adjusted gap. The unadjusted (raw) gender pay gap—often cited in public discourse—compares median earnings of all women to all men without controlling for job level, experience, or other factors. The adjusted gap, which controls for these factors, is the legally relevant metric for pay equity compliance. Both are worth tracking, but they answer different questions.
• Occupational segregation. If women are underrepresented in higher-paying job families or leadership levels, the organization may not have a pay equity problem within job groups but may have an opportunity equity problem that feeds the unadjusted gap. Pay equity analysis can surface this pattern even if it does not directly remediate it.
• Parental leave and re-entry effects. Examine whether employees who take parental leave experience slower pay growth upon return. This is an increasingly scrutinized area under state pay equity laws.

Racial and Ethnic Pay Equity

Racial pay equity analysis follows the same statistical methodology but must account for additional complexity:

• Intersectional analysis. Examine race/ethnicity in combination with gender (e.g., Black women, Hispanic men) rather than in isolation. Intersectional disparities can be masked when race and gender are analyzed separately.
• Data completeness. Race/ethnicity self-identification rates are often lower than gender data completeness. If a significant portion of employees have not self-identified, the analysis may undercount disparities in underrepresented groups.
• Small group sizes. Some racial/ethnic groups may be too small within certain job groups for regression analysis to detect statistically significant gaps. Cohort analysis and descriptive statistics become more important in these cases.

Source note: The EEOC's Compliance Manual on Compensation Discrimination provides detailed guidance on analytical approaches for examining race- and sex-based pay disparities, including the use of regression analysis and the treatment of legitimate, non-discriminatory factors.

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Best Practices for Sustainable Pay Equity

Conducting a one-time pay equity analysis and declaring victory is insufficient. The organizations that maintain equitable pay over time embed equity into their compensation operations.

Embed Equity in Compensation Infrastructure

• Market-based ranges with consistent positioning. When every role has a defensible salary range built from current market data, new-hire offers and merit decisions have guardrails that prevent discretionary disparities. SalaryCube's Range Builder creates defensible salary ranges from real-time market data with full version history and audit trails.
• Structured offer processes. Eliminate or constrain salary negotiation for new hires. Set starting pay based on the candidate's qualifications and the role's salary band, not their prior salary or negotiation skill.
• Standardized merit and promotion criteria. Use consistent criteria for merit increases and promotions across the organization, with manager training on how to apply them equitably.

Build Organizational Accountability

• Executive sponsorship. Pay equity must be owned at the C-suite level, not delegated solely to HR. Include pay equity metrics in executive scorecards.
• Manager training. Managers who make day-to-day pay decisions—hiring offers, merit recommendations, promotion nominations—need training on how bias can enter those decisions and how to use compensation tools to make data-driven choices.
• Transparent communication. Organizations do not need to publish individual salary data, but communicating the existence of a pay equity review process, its methodology, and aggregate findings builds trust and demonstrates commitment.

Document Everything

Every pay equity analysis should produce documentation sufficient for an external auditor or regulator to understand the methodology, findings, and remediation decisions. This includes:

• The statistical model specification and rationale for included/excluded variables
• Job grouping methodology and rationale
• Summary findings by protected class and job group
• Investigation notes for each identified gap
• Remediation actions taken, including dollar amounts and effective dates
• Policy changes implemented as a result of findings

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Moving From Reactive to Proactive Pay Equity

The most mature compensation organizations do not treat pay equity analysis as an annual compliance exercise. They build equity into every compensation decision point—from job architecture and range design through hiring, merit cycles, and promotions—so that the annual analysis confirms equity rather than discovering problems.

This shift requires two things: reliable market data that keeps ranges current (so that market misalignment does not create internal disparities), and compensation technology that gives analysts visibility into pay patterns before they become entrenched.

For mid-market organizations that need compensation intelligence without enterprise-suite complexity, SalaryCube provides the benchmarking data, analytical tools, and planning workflows to make pay equity an operational capability rather than a periodic project. Start with Open Benchmark to see how your compensation data compares to the market—no credit card required.