Summary
Pay transparency laws are moving from publishing salary ranges to explaining why two people in the same role are paid differently. The EU Pay Transparency Directive, in force across member states from June 2026, gives employees the right to ask for the criteria behind pay and progression. Several US states already require ranges in job postings and are extending toward justification. Employers that hold productivity data are asking the obvious question: can that data be one of the criteria? The answer is a qualified yes. It can, if the measure is objective, applied to everyone, disclosed in advance and checked for bias. It cannot if it is a proxy for hours, a manager's impression dressed as a number, or something employees only discover at review time.
E-Monitor's productivity data is the same for every employee in a role and visible to each of them, which is the starting point any pay criterion has to meet. Book a demo →
What Pay Transparency Laws Now Require
Three obligations recur across the newer laws. Employers must publish or disclose pay ranges before hiring. They must be able to explain the criteria used to set pay and progression, in terms an employee can understand. And where a pay gap between comparable roles exceeds a threshold, typically five percent, they must be able to justify it with objective, gender-neutral criteria or fix it.
The EU Directive is the most demanding: employees can request their own pay level and the average for colleagues doing equal work, employers above certain headcounts must report gaps, and unexplained gaps shift the burden of proof to the employer in any dispute. In the US, Colorado, California, New York, Washington and others require ranges in postings, and Illinois and others are adding disclosure of the criteria. The UK consults on similar rules for larger employers.
The common thread is that "performance" as a justification now has to be shown, not asserted. That is where productivity data enters.
Can Productivity Data Be a Pay Criterion?
Yes, under four conditions, and each one is a place where organisations get it wrong.
Objective. The measure has to be a count or a rate of something real: cases closed, revenue influenced, deliverables shipped, quality-adjusted output. A productivity score is objective only if the scoring rules are written down and the same for everyone in the role.
Consistent. Applied to every person in the comparison group using the same tool and the same rules. Measuring one team with activity data and another with manager judgement breaks the comparison.
Disclosed in advance. Employees must know the criterion exists and how it works before the period it applies to. A criterion revealed at review time is not a criterion, it is a rationalisation.
Bias-checked. The measure must not systematically disadvantage a protected group. Any metric that rewards presence, hours or after-hours availability will disadvantage carers and part-time workers, and in most workforces that means it will produce a gender gap by another route. The equitable monitoring guide covers how to test for this.
What Employers Can Show
The data that safely supports a pay difference describes outcomes and is normalised for time worked. Examples that hold up: output per active hour; quality rates; client billing realised; delivery against agreed targets; throughput on a shared queue. Each can be shown to the employee, compared across the group, and explained in a sentence.
Activity data can support these measures indirectly. Time per project from automatic time tracking turns raw output into output per hour, which removes the advantage of simply working longer. Focus-time data explains variation without being the criterion itself. The rule of thumb: activity data can normalise and contextualise a pay criterion; it should not be the criterion.
What Employers Cannot Show
Some numbers look like productivity and are not, and using them as pay criteria creates exactly the exposure the laws were written to catch.
- Hours or active time. Paying more for more hours is paying for presence, and it produces gaps against anyone with caring responsibilities.
- After-hours availability. Same problem, worse, and in several EU countries it conflicts with right-to-disconnect law.
- Raw productivity scores without published rules. If the employee cannot see how the number was made, it will not survive a request for the criteria.
- Screenshot or keystroke intensity. Measures of motion, not output.
- Manager impressions restated as numbers. A rating that is really an opinion inherits the bias of the opinion.
The test for each: could you hand the employee the number, the rule that produced it, and the same numbers for their comparators, and defend the outcome? If not, it is not a pay criterion.
Designing the Criterion
Start from the role's purpose and pick one or two outcome measures that a reasonable person in the role would agree describe good work. Define the measurement rule in writing, including what is excluded and how time is normalised. Run it for a full cycle without attaching pay to it, and publish the distribution so people can see where the measure is noisy.
Then test the distribution against protected characteristics. If the measure shows a gap by gender, age, disability or part-time status that the outcomes themselves do not explain, the measure is biased and cannot be used until it is fixed. Only after that does it go into the pay policy, with the rule and the review cycle disclosed to every employee it applies to. The continuous performance guide covers how to keep the criterion honest between cycles.
A Worked Example: Two Analysts, One Pay Gap
Two analysts in the same team, same title, same grade. One is paid eight percent more than the other. Under the EU Directive that gap has to be explained, and the lower-paid analyst has asked for the criteria.
The organisation's first instinct is to point at the dashboard: the higher-paid analyst logs more active hours and has a higher activity score. Both numbers are true. Neither survives scrutiny. The lower-paid analyst works a four-day compressed week to cover childcare, so hours are structurally lower, and the activity score is a weighted blend of app usage that nobody outside IT can explain. Presented as the justification, the gap looks like a penalty for caring responsibilities, which is precisely what the law is designed to catch.
The second attempt uses the criteria the team had published the year before: reports delivered per active hour, error rate on delivered reports, and stakeholder rating from the quarterly review. Normalised for hours, the two analysts deliver at almost the same rate. The higher-paid analyst has a materially lower error rate and a higher stakeholder rating, and both were visible on each analyst's own dashboard all year. The gap is explained by two published, outcome-based, bias-checked criteria, and the answer to the request runs to a single page.
The lesson is not that productivity data is dangerous. The same system produced both the bad justification and the good one. The difference was which numbers had been chosen as criteria, and whether they had been chosen and disclosed in advance.
Answering a Pay Information Request
Under the EU Directive an employee can ask for the criteria behind their pay and the average for colleagues in equal work, and the employer has two months to answer. Prepare the answer format before the first request arrives. A good answer has three parts: the pay range for the role and where the employee sits; the criteria, each with a one-line definition; and the employee's own values on each criterion alongside the group average.
If productivity data is one criterion, the employee's own dashboard should already show them the number, so the request confirms what they knew rather than revealing something. That is the point of self-view, and it is the difference between a transparency regime that works and one that generates disputes. The legal guide covers the broader disclosure duties.
Keeping It Fair Over Time
Criteria drift. A measure that was fair when the team was in the office may become unfair when half of them are remote and the queue is assigned differently. Review pay criteria annually, rerun the bias test, and publish a one-paragraph note on what changed. Where a criterion is dropped, say so; where one is added, disclose it before the cycle it applies to.
Handled this way, productivity data makes pay transparency easier rather than harder: the numbers are already visible, already consistent, and already explained. Handled the other way, as an undisclosed score that surfaces at review time, it is the first thing a regulator or a tribunal will ask to see.
Frequently Asked Questions
1. What is pay transparency?
A set of laws requiring employers to publish pay ranges, explain the criteria behind pay and progression, and justify or close pay gaps between people doing comparable work. The EU Pay Transparency Directive applies from June 2026; several US states and other countries have similar rules.
2. Can productivity data justify a pay difference?
Yes, if the measure is objective, applied to everyone in the role with the same rules, disclosed before the period it covers, and tested for bias against protected groups. Outcome measures normalised for time worked meet this; hours, availability and undisclosed scores do not.
3. Can employers pay more for more hours worked?
For hourly roles, pay follows hours by definition. As a criterion for salary differences, hours or active time reward presence rather than output and create gaps against carers and part-time workers, which is the pattern transparency laws target.
4. What happens if an employee requests their pay criteria?
Under the EU Directive the employer has two months to provide the criteria, the employee's pay level and the average for equal work. Prepare the format in advance: range, criteria with definitions, and the employee's values against the group.
5. Does monitoring data have to be shared with employees for pay purposes?
If it is a pay criterion, the employee is entitled to see their own values and how they were calculated. The simplest way to meet this is an employee dashboard that shows the data continuously rather than only on request.
6. How do I check a productivity metric for bias?
Run it for a full cycle without attaching pay, then compare the distribution across gender, age, disability and part-time status. A gap the outcomes themselves do not explain means the measure is biased and cannot be used until it is redesigned.
Pay criteria employees can already see E-Monitor shows every employee their own productivity data, calculated the same way for everyone, so transparency requests confirm rather than surprise. Sign up →
