Master compa-ratio analysis for pay equity. Learn calculations, benchmarking methods, demographic pay gap detection, and remediation strategies.
Somewhere in your organization right now, two people doing substantially similar work are being paid significantly different amounts — and the gap has nothing to do with performance, tenure, or market conditions. It exists because of compounding historical decisions: different starting salaries, inconsistent raise cycles, manager discretion without guardrails, and the slow drift that happens when compensation is managed in spreadsheets instead of systems.
Pay inequity is not just an ethical problem. It is a retention problem, a legal liability, and increasingly a brand risk as salary transparency laws spread across jurisdictions. The good news: you do not need a PhD in statistics to diagnose and fix it. Compa-ratio analysis gives you a simple, powerful lens to evaluate whether your pay practices are fair — and where they are not.
This guide covers everything from calculating compa-ratios to identifying demographic pay gaps to building a remediation plan that closes disparities without blowing up your compensation budget.
Compa-ratio (comparative ratio) measures how an employee's pay compares to the midpoint of their salary range:
Compa-Ratio = (Employee's Actual Pay) / (Midpoint of Pay Range) x 100
A compa-ratio of 100 means the employee is paid exactly at the midpoint. Below 100 means they are paid below midpoint; above 100 means they are above it.
To assess pay positioning for a team, department, or demographic group, calculate the group compa-ratio:
Group Compa-Ratio = (Average Actual Pay of Group) / (Average Midpoint of Group's Ranges) x 100
Group compa-ratios are where pay equity analysis gets powerful. If your female engineers have a group compa-ratio of 89 while male engineers sit at 102, you have a structural gap that individual performance scores cannot explain.
Compa-ratio analysis requires established pay ranges. If your organization does not have formal ranges, building them is the essential first step.
Gather compensation survey data from at least three reputable sources (Radford, Mercer, Payscale, Levels.fyi for tech roles). Match your jobs to survey benchmarks based on role scope, not just title. A "Senior Manager" at a 200-person company may match a "Manager" benchmark at a Fortune 500.
For each job level, set:
A standard range spread of 40-50% between minimum and maximum works for most professional roles. Broader spreads (50-60%) suit roles with long development curves; narrower spreads (30-40%) suit roles where employees promote quickly.
Survey data ages at roughly 3-4% per year due to market movement. Update your ranges annually — or more frequently in hyper-competitive talent markets — to keep compa-ratios meaningful.
Extract from your HRIS: employee name (or anonymized ID for sensitive analyses), current base salary, job code, job level, department, location, and the midpoint of their assigned pay range. Add demographic fields for pay equity analysis: gender, race/ethnicity, age band.
Calculate compa-ratio for every employee. Sort and filter to quickly identify:
import pandas as pd
df['compa_ratio'] = (df['base_salary'] / df['range_midpoint']) * 100
# Summary statistics by department
dept_summary = df.groupby('department')['compa_ratio'].agg(['mean', 'median', 'std', 'min', 'max'])
print(dept_summary.round(1))
A histogram of compa-ratios across the organization reveals whether your pay practices are centered, skewed, or bimodal. A healthy distribution clusters around 95-105 with manageable tails. A wide, flat distribution signals systemic inconsistency in pay decisions.
This is where compa-ratio becomes a pay equity diagnostic tool. The question shifts from "are people paid appropriately?" to "are people paid equitably?"
Calculate group compa-ratios by gender, race/ethnicity, and their intersections. Compare:
An unadjusted gap tells you there is a problem but not why. A 6-point compa-ratio gap between men and women could reflect pay discrimination, different distributions across job levels, tenure differences, or performance rating disparities.
To isolate the effect of demographic characteristics on pay, run a multivariate regression controlling for legitimate pay-determining factors:
import statsmodels.api as sm
# Control for job level, tenure, performance, location, department
controls = ['job_level', 'years_in_role', 'performance_rating', 'location_factor', 'department']
demographic = ['gender_female', 'race_non_white']
X = df[controls + demographic]
X = sm.add_constant(X)
y = df['compa_ratio']
model = sm.OLS(y, X).fit()
print(model.summary())
The coefficients on your demographic variables tell you the adjusted pay gap — the portion of the gap that remains after accounting for all legitimate factors. A statistically significant negative coefficient on gender_female of -3.2 means that women are paid 3.2 compa-ratio points less than men in equivalent roles, tenure, and performance — a gap that demands remediation.
Single-dimension analysis (gender alone, race alone) can mask compounding disparities. An organization might show a 2-point gender gap and a 2-point racial gap, but women of color might face a 6-point gap that is invisible when you only look at one dimension at a time. Always examine intersections.
Pay gaps rarely have a single cause. Common drivers include:
Employees who negotiate at hire often lock in a permanent advantage. If your organization has historically allowed wide negotiation latitude, starting salary differences compound over time as percentage-based raises amplify the original gap.
Managers with different calibration standards apply merit increases unevenly. Without centralized oversight, one team might average 4% annual increases while another averages 2%, creating divergence within the same job level.
If certain demographic groups are promoted more slowly, they spend more time at lower pay ranges, depressing their lifetime earnings trajectory within the organization. Analyze time-in-role before promotion by demographic group.
When ranges are updated to reflect market movement, employees hired before the adjustment often fall behind newer hires who entered at the updated range. This "compression" effect disproportionately impacts tenured employees.
Not every gap can be closed in a single cycle. Prioritize based on:
Sum the cost of bringing all below-target employees to their appropriate compa-ratio. A typical pay equity remediation costs 1-3% of total payroll in the first year, decreasing in subsequent years as structural fixes take hold.
One-time adjustments fix the symptom. Structural changes fix the cause:
Compensation analysis does not exist in isolation. Pay gaps often correlate with engagement gaps, attrition patterns, and learning access disparities.
PeoplePilot Analytics unifies your compensation, engagement, and attrition data so you can see these connections in a single view. When a pay equity analysis reveals that underpaid employees also report lower engagement scores in PeoplePilot Surveys, the case for remediation becomes undeniable. And when PeoplePilot Learning data shows that employees in underpaid segments receive fewer development opportunities, you can address the root cause alongside the pay symptom.
Fair pay is not just a compliance checkbox. It is the foundation of a people strategy that retains talent, builds trust, and earns the discretionary effort that drives business results.
Conduct a comprehensive analysis annually, timed to inform your merit increase and promotion cycles. Run a lighter quarterly check on compa-ratio distributions to catch emerging gaps before they compound. Organizations in heavily regulated industries or those undergoing rapid growth should consider semi-annual full analyses.
For fully competent performers, target a compa-ratio of 95-105. New hires and employees recently promoted into a role typically land at 85-95 and should progress toward midpoint within 18-24 months. Employees consistently above 115 should be evaluated for promotion to the next level rather than continued raises within the current range.
Outdated ranges distort compa-ratios because midpoints no longer reflect market reality. Before running a pay equity analysis, update ranges to current market data. If ranges have not been updated in more than two years, your compa-ratio analysis will likely show artificially high ratios and may mask real gaps relative to market competitiveness.
Frame adjustments as part of a proactive pay equity commitment. Language like "As part of our annual compensation review, we conduct a market and internal equity analysis to ensure all employees are paid fairly for their role, experience, and contribution. Based on this analysis, your compensation has been adjusted to better reflect your value." This positions the adjustment positively without making legal admissions.