Pay Equity Analysis Calculator
The formula
What the pay equity tells you
The measured average-pay difference between two groups doing comparable work, plus the straight arithmetic of what closing it costs.
Calculation: Gap = (A − B) ÷ A; remediation cost = gap × group B payroll. Enter your inputs and every substitution is shown below the result.
Reading the result
Raw gaps above 5% demand a controlled analysis — job level, tenure, location and performance explain most legitimate gaps; whatever survives the controls is the real finding.
Rules of thumb: Well-run programs hold uncontrolled band-level gaps under 2%
Limitations and best practice
Averages hide distributions: parity in the mean with Group B locked out of senior bands is a worse equity story than the headline shows. Run the analysis by band.
This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Frequently asked questions
What gap is legally risky?
Do raises or lump sums fix it better?
Source & methodology
Equal-pay audit practice (ILO conventions; UK gender pay gap methodology).
Pay Equity Analysis explained: formula, worked example and honest limits
You are here because you need a specific number, and you want to see how it was produced before you rely on it. Pay Equity Analysis is the kind of number people argue about from memory. This page ends that argument with arithmetic you can watch happen: enter your figures, get Raw pay gap in real time, and see every substitution printed below.
There is a habit that separates people who get useful answers from HR & Compensation numbers: they decide what question the figure answers before they compute it. Pay Equity Analysis answers “Raw pay gap?” — and nothing else, which is precisely why the result is trustworthy.
People rarely disagree about arithmetic; they disagree about assumptions that were never written down. Printing the substitution and the sensitivity ranking removes that category of disagreement for Pay Equity Analysis.
Every figure in the prose is generated by the engine rather than written beside it — the worked example resolves to raw pay gap = 8.0 % on the default inputs, and the sensitivity section ranks the inputs that move the result — Group A average pay first, at ≈128.6 % of the headline for a ten-percent error either way in that input — which is the thread the rest of this guide pulls on.
Inside the Pay Equity Analysis calculation: formula and every input
The engine implements a single stated model: Gap = (A − B) ÷ A; remediation cost = gap × group B payroll. There is no hidden coefficient and no login-gated precision — the formula printed is the formula executed, which is also what makes the result citable.
Group A average pay ($) (example value 88,000) is a quantity rather than a category — precision here is cheap and worth having. Replace the example with the figure from your own records — the tool recomputes instantly and the printed steps follow your numbers.
Group B average pay ($) (example value 81,000) is a plain measured quantity — the unit shown in the label is the unit the formula expects. The example value is there to demonstrate the mechanics, not to describe your situation — overwrite it.
Group A headcount (example value 123) is a quantity rather than a category — precision here is cheap and worth having. If you are unsure what belongs in this field, look at the printed steps: the substitution shows exactly how the engine uses it.
Group B headcount (example value 89) is a measured number in the unit named by the label; the formula does not convert for you. Replace the example with the figure from your own records — the tool recomputes instantly and the printed steps follow your numbers.
A fully worked Pay Equity Analysis example
The tool prints its own working; this is the same arithmetic on the example values, reproduced so you can follow it by hand. Starting figures: Group A average pay ($), Group B average pay ($), Group A headcount, Group B headcount. Run to the end, the raw pay gap resolves to 8.0 % — Closing to parity costs roughly $623,000/yr before role and experience controls on the bands this tool uses.
- Gap = ($88,000 − $81,000) ÷ $88,000 = 8.0%
- Equalization: $7,000 × 89 people = $623,000 per year
Gap per person — $7,000. If your inputs change, this is usually the first companion figure to move.
Annual cost to equalize Group B at parity — $623,000. Compare it against the main result of 8.0 % before drawing conclusions.
Group B total payroll — $7,209,000. If your inputs change, this is usually the first companion figure to move.
Parity cost as share of combined payroll — 3.45 %. Compare it against the main result of 8.0 % before drawing conclusions.
Re-running with your own figures is instant; re-running by hand once is the best way to know the tool is telling you the truth.
The Pay Equity Analysis corridor: your result across a realistic band
The honest version of any estimate is a corridor, and this one is cheap to produce: Pay Equity Analysis was recomputed at 5 points along the dominant input, with every other field left untouched.
| Group A average pay setting | Raw pay gap |
|---|---|
| 80 % of the example value | -15.1 % |
| 90 % of the example value | -2.3 % |
| example inputs | 8.0 % |
| 110 % of the example value | 16.3 % |
| 120 % of the example value | 23.3 % |
That spread — -15.1 % at the low end, 23.3 % at the high — is what a careful read of Pay Equity Analysis looks like: a band you can state out loud, with the reason for each edge. Reproduce it in the form above by editing Group A average pay alone; the substitution steps will follow your figures exactly.
Sensitivity: which inputs change Pay Equity Analysis the most
This list was computed for this specific engine just now: each numeric input was nudged ±10 % while every other field held its example value, and the raw pay gap was recalculated from scratch. Read it as a map of where Pay Equity Analysis is fragile — drawn for the example inputs, which is the map you want when deciding what to measure carefully.
- Group A average pay: ten percent up gives 16.3 %, ten percent down gives -2.3 % — ≈128.6 % of the raw pay gap either way
- Group B average pay: ten percent up gives -1.3 %, ten percent down gives 17.2 % — ≈115.7 % of the raw pay gap either way
Group A average pay is the dominant lever here: ten percent either way moves the result by about 128.6 %, from -2.3 % to 16.3 %. Measure that input from a document rather than from memory, and treat Group B average pay as the second priority (≈115.7 % per ten percent). The rest of the inputs earn only a rounding-error of attention, and that asymmetry is the practical payoff of measuring rather than asserting.
Is your Pay Equity Analysis good — reading the result bands
A number on its own is trivia; a number against a range is intelligence. On the example inputs the band reads “Closing to parity costs roughly $623,000/yr before role and experience controls”; your own figures place you on a different rung of the same ladder. How much of that band is luck? On Group A average pay alone ten percent either way covers -2.3 % to 16.3 %. The practical reading rules: read the band as a starting hypothesis, then test it: change the two dominant inputs by ten percent and see whether the verdict survives. Verdicts that survive stress are the ones worth acting on.
Set Pay Equity Analysis beside its neighbours and it usually plays a specific role: this figure establishes the size of something, and the neighbouring tools establish whether that size is acceptable. Skipping the first step makes the second one arbitrary.
The errors that quietly break a Pay Equity Analysis figure
Averages hide distributions: parity in the mean with Group B locked out of senior bands is a worse equity story than the headline shows. Run the analysis by band. This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Chasing precision in Group B average pay ($) while Group A average pay ($) is still a guess. The sensitivity ranking above shows exactly where the accuracy budget should go.
This model has its own particular trap: Group A average pay carries ≈128.6 % of the response per ten percent of error, so effort spent elsewhere is largely decorative.
The subtlest error is answering the wrong question precisely. Confirm that raw pay gap is actually the quantity your decision needs — the arithmetic is flawless either way, which is what makes the mistake invisible.
A quick check for any Pay Equity Analysis figure
- State, in one sentence, which convention each rate, unit or period in the form is using.
- Confirm the example values are plausible for your own situation before replacing them.
- Put Group A average pay under a magnifier; the sensitivity table above is the reason why.
- Record the date and the inputs alongside the number so future-you knows what was assumed.
- Re-run the calculation with a ±10 % stress on the two most sensitive inputs.
- Repeat the ±10 % test on the inputs that matter and see whether the decision — not just the number — still holds.
A routine for decision-grade Pay Equity Analysis
Using a calculator well sounds trivial, but the difference between a decision-grade figure and a throwaway one lives entirely in the routine. The routine for Pay Equity Analysis takes a minute to learn and applies to every input above.
Step 1 — collect, then type. Everything this tool asks for is listed above (group an average pay ($), group b average pay ($), group a headcount, group b headcount). Pull each figure from something you can point at — paperwork, a dashboard, a label — before touching a field. Guesswork at the input stage cannot be repaired at the interpretation stage. When two sources disagree, it is almost never the arithmetic that differs.
Step 2 — start from the case you can defend. Enter pessimistic-but-honest figures first; the optimistic run is a ceiling, not a plan. A conclusion that holds on conservative inputs holds everywhere. When in doubt, prefer the boring input: the interesting one is usually the one you misread.
Step 3 — play with the inputs on purpose. Ten percent up, ten percent down, one field at a time. Within a few minutes you will know this calculation better than most people who quote its results. A calculation you cannot explain to somebody else is a calculation you cannot defend.
Step 4 — ask what would have to be true. Take the result you want and work backwards through the inputs until you find the assumption that produces it; then decide honestly whether that assumption is defensible. A figure with a date beside it becomes evidence; without one it stays an anecdote.
Step 5 — record the context, not just the number. Note today’s date and the inputs alongside the 8.0 % output. Six months from now the context is what turns this figure into a trend. Write the assumptions in the same file as the result, and future arguments take minutes instead of meetings.
Who actually uses Pay Equity Analysis — and when
Business & Entrepreneurship arguments end at arithmetic or they do not end. Pay Equity Analysis is the arithmetic: 8.0 % on the example inputs, reproducible by anyone who types the same numbers. If the verdict changes with a ten-percent nudge, report the nudge alongside the number.
The audience for this page is people who need one trustworthy number before their next decision. In professional HR & Compensation practice the pattern is always the same — screen with Pay Equity Analysis, then invest slow analysis only in whatever survives the screen. The input you reach for first is usually the one you measured longest ago.
Re-running this page costs nothing and catches the slow drifts that never announce themselves. In Business & Entrepreneurship the expensive mistakes are rarely sudden; they are small changes in Group A average pay ($) that nobody recalculated for a year. Estimates improve most when the worst case is written down in ink.
People also ask about Pay Equity Analysis
What affects Pay Equity Analysis the most?
The short answer: Group A average pay. The longer answer is the sensitivity table on this page, where every numeric input was probed by ten percent and the raw pay gap recalculated — Group A average pay moved it furthest, by roughly 128.6 % (-2.3 % to 16.3 %).
Which matters more: group b average pay or group an average pay?
By the numbers, Group A average pay: a ten-percent change in it moves the result ≈128.6 %, versus ≈115.7 % for Group B average pay. Spend your measuring effort in that order — precision on the weaker lever buys almost nothing.
What is a typical Pay Equity Analysis?
On the worked example above Pay Equity Analysis comes out at 8.0 %. Treat that as one point on a distribution rather than a norm: the bands, the companion outputs and the sensitivity ranking tell you how wide the plausible range really is.
How do I calculate Pay Equity Analysis by hand?
Three steps: write the formula (Gap = (A − B) ÷ A; remediation cost = gap × group B payroll), insert your values, and simplify. The worked example section shows exactly that chain for the default inputs; replicating it once on paper teaches you more about Pay Equity Analysis than any number of calculator runs.
What happens to Pay Equity Analysis if group an average pay changes by 10 %?
Try it in the form above and the printed substitution will follow you; the pre-computed answer for the defaults is -2.3 % to 16.3 % across the ten-percent probe, i.e. ≈128.6 % sensitivity on the raw pay gap.
Is the Pay Equity Analysis result of 8.0 % good or bad?
The tool answers “what”, not “whether”: 8.0 % carries the verdict “Closing to parity costs roughly $623,000/yr before role and experience controls” for the default case, which is a starting interpretation rather than a verdict on your situation. Re-run with your own figures and compare the bands.
Should I use Pay Equity Analysis before launching?
Yes — a Pay Equity Analysis run on honest inputs before spending is the cheapest consulting an entrepreneur can buy. Re-run at every pricing change; the number moves more often than founders expect.
How often should I check Pay Equity Analysis?
Whenever an input changes by more than a few percent, and otherwise on the rhythm of your decisions — before a purchase, a review, a renewal. Pay Equity Analysis is a snapshot, not a subscription; stale snapshots are the most common silent error in Business & Entrepreneurship planning.
What gap is legally risky?
Unexplained gaps above ~3–5% attract EEOC-style scrutiny in the US and equal-pay audits in the UK/EU; the number is smaller where disclosure laws bite.
Do raises or lump sums fix it better?
Raises fix the structure permanently and compound; lump sums close the reported gap while leaving the mechanism intact.
Does this Pay Equity Analysis tool store my numbers?
No. The entire calculation runs in your browser session; nothing entered above is transmitted or saved, which is the correct behaviour for sensitive Business & Entrepreneurship figures and the reason you can re-run this page as often as you like.
Glossary for Pay Equity Analysis
- Gap per person
- $7,000 for the Pay Equity Analysis worked case; it sits next to the main result because it carries context the headline leaves out.
- Annual cost to equalize Group B at parity
- $623,000 in the default Pay Equity Analysis calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Group B total payroll
- $7,209,000 on the example inputs — a companion figure printed beside the headline of Pay Equity Analysis so that no result is read in isolation.
- Raw pay gap
- The primary output of this tool: 8.0 % for the default inputs, computed exactly from Gap = (A − B) ÷ A; remediation cost = gap × group B payroll.
- Worked example
- The step-by-step substitution printed above, so the arithmetic can be re-derived by hand or in a spreadsheet.
- Verdict band
- The plain-language label — here “Closing to parity costs roughly $623,000/yr before role and experience controls” — mapping the computed figure onto experience-based ranges for Business & Entrepreneurship.
- Sensitivity (±10 %)
- How far the Pay Equity Analysis moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
Methodology, sources and limits
The model is published arithmetic implemented exactly as written: Gap = (A − B) ÷ A; remediation cost = gap × group B payroll. Source of record: Equal-pay audit practice (ILO conventions; UK gender pay gap methodology). Specialist software layers extra coefficients on top of the same algebra; none are hidden here, and that transparency is what makes a disagreement traceable to a specific input instead of a black box.
Treat the output as a precise answer to the question you typed: exact arithmetic, transparent formula, published source, no hidden rounding. What that buys you is reproducibility — two people, two browsers, the same arithmetic, and a printed trail from inputs to result that either of them can audit.
Turning this Pay Equity Analysis into a decision
Re-run the calculation with your own figures, stress the two most sensitive inputs by ±10 %, and only then act on the result.
The audience here is people who need one trustworthy number before their next decision. Everything you need to defend the result — formula, substitution, sensitivity, source — is printed above, so the only remaining variable is the honesty of your inputs.