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How to Run a Pay Equity Audit in India (Before the Board Asks)

A pay equity audit is no longer a Western luxury. Between the new wage definition, EU pay-transparency spillover, and a Board that reads the headlines, the question is coming. Better to have the answer first.

There is a predictable moment in a growing company's life when someone on the Board — often prompted by an investor, a customer's procurement questionnaire, or a news cycle — asks: "Do we have a pay gap?" The companies that handle that moment well are the ones who already knew the answer. The ones who scramble to produce a number under pressure usually produce a defensive, unconvincing one. A pay equity audit is how you have the answer ready.

Why this is rising on the agenda in India

Three currents are pushing pay equity up the priority list. The Code on Wages embeds equal-pay principles into the statutory framework. The EU Pay Transparency Directive, while European, reaches Indian companies that serve European customers or compete for European-linked contracts. And diversity conversations in India have matured from headcount ratios toward measurable accountability — which inevitably leads to pay.

The methodology, without the intimidation

Pay equity analysis uses multiple regression, and the word "regression" scares people out of doing it themselves. It shouldn't. The logic is intuitive: you build a model that predicts pay from the legitimate factors — role, level, location, experience, performance — and then you check whether a protected characteristic (gender, for example) still explains a slice of pay after those factors are accounted for. If it does, and the effect is statistically significant, you have an unexplained gap that needs investigation.

You do not need a data-science team to start. Excel familiarity is enough for a first pass on a few hundred employees, and the methodology can be taught from first principles to any senior HR practitioner. The discipline matters more than the tooling.

A practical sequence

1. Assemble clean data. Pay, role, level, location, tenure, performance rating, and the protected characteristics you are testing. Clean data is most of the work.
2. Run the raw gap first. It is easy and it sets context, even though it is not the headline number.
3. Build the adjusted model. Regress pay on the legitimate factors, then test whether the protected characteristic adds explanatory power.
4. Test significance. A small gap in a small sample may be noise. Significance testing tells you whether the gap is real.
5. Drill into cohorts. An organisation-wide "no gap" can hide a sharp gap in one function or level. Look underneath the aggregate.
6. Build the remediation plan. Where you find an unexplained gap, cost the adjustment and stage it. A finding without a plan is a liability, not an insight.

Turning findings into a Board paper

The audit's value is realised in how you present it. A good Board paper states the methodology plainly, distinguishes raw from adjusted, names the gaps honestly, and pairs every finding with a remediation cost and timeline. It does not hide behind statistics or over-claim a clean bill of health. Boards trust the function that brings them an honest number with a plan far more than the one that insists there is nothing to see.

The connection to the wage rule

Running this audit in 2026 has a bonus: you are already restructuring compensation for the 50% wage rule. Doing the equity audit at the same time means you fix structure and fairness in one motion, rather than reopening every salary twice. The two projects share most of the same data.