Aging Market Data: How to Age Salary Survey Data to a Common Date

Aging Market Data: How to Age Salary Survey Data to a Common Date

You pull three salary surveys for the same job. One was effective in January, one in July, one last October. The medians do not match, and part of the gap is just time. Aging market data fixes that. It moves every survey number forward to one common date so you compare like with like.

Skip this step and your structure is built on a mix of stale and fresh numbers. You will either underpay against a market that already moved or overshoot because you aged the same data twice. This guide shows you how to pick a target date, choose an aging rate, run the math, and avoid the common traps.

TL;DR

• Aging adjusts survey pay data from its effective date to a target date using an annual salary movement rate.
• Pick the target date first. It is a pay-position decision: beginning of year keeps you competitive early, end of year leads all year, midyear is the usual compromise.
• The formula is simple: adjustment factor = 1 + (months of aging ÷ 12 × annual rate). Multiply each survey figure by its factor.
• Use one defensible rate, document it, and apply it once. Do not age data and then also add a market adjustment on top.
• Aging brings numbers to a common date. It does not fix a bad job match. Start with good survey job matching.

What aging market data means

Survey data is a snapshot. A publisher collects pay as of an effective date, cleans it, and releases it months later. By the time you use it, the market has moved.

Aging applies an expected rate of pay growth to the snapshot. Mercer describes the purpose plainly: salaries change throughout the year, but survey data is collected at different times, so aging puts all of it on a common date so the sources are comparable (Mercer, "Why should I age market data?").

Two things aging is not:

• It is not a raise. You are adjusting the market reference, not employee pay.
• It is not a fix for thin data. If your sample is five incumbents, aging gives you a five-incumbent number on a newer date.

Step 1: Choose your target date

The target date depends on what you are doing with the data.

• Checking pay today. Age every survey to the current date and compare it with current salaries.
• Setting next year's ranges. Age to the beginning, middle, or end of the year the structure will be in effect.

Each choice is a pay-position choice in disguise. Mercer notes that aging to the start of the year keeps ranges competitive then but lagging for the rest of the year. Aging to year-end means you lead the market all year. Midyear is a common compromise: you lead for six months and lag for six.

For most employers with a January 1 structure, aging to July 1 is the sensible default. Write the choice down so next year's analyst does the same thing. This is the same discipline you use when you build a pay policy line: the date is part of your pay philosophy, not an afterthought.

Step 2: Choose an aging rate

You need an annual salary movement rate. Use a recognized source, not a guess.

Good sources include:

• Salary budget surveys from WorldatWork, WTW, Mercer, Gallagher, and others. WorldatWork runs a Salary Budget Survey each year.
• The survey publisher's own guidance. Many publishers state a recommended rate.
• Your own history of structure movement, if you have a stable record.

For context, SHRM reported in August 2026 that WTW projects an average U.S. raise budget of 3.4% for 2027, slightly below 3.5% for 2026, and that Gallagher's survey shows budgets stabilizing around 3% to 3.5% (SHRM). Those are raise budgets, so they are a starting point. Many comp teams use a slightly different figure for structure movement than for merit budgets. Pick one rate, state the source, and stay consistent across all surveys in the same cycle.

If the aging period crosses two calendar years, use the rate for each year in that year. Do not apply one rate to both unless you have a reason.

Step 3: Apply the formula

The math is linear within a period:

Adjustment factor = 1 + (months of aging ÷ 12 × annual rate)

Then multiply each survey statistic by its factor.

Take a Payroll Analyst. You want data aged to July 1, 2027 and you use a 3.4% annual rate.

Look at what happened. Raw, the two medians differ by $2,000, or about 2.4%. After aging, the gap is $674, or about 0.8%. Most of the disagreement was just timing. That is the point.

Check your factor: 18 months ÷ 12 = 1.5, times 3.4% = 5.1%, so the factor is 1.051. And 12 months gives 3.4%, so the factor is 1.034.

When the period crosses years

Mercer's worked example ages a survey effective March 1, 2011 to June 1, 2012. It applies a 4% rate for the remainder of the publication year (factor 1.0335) and a 3% rate for the next year (factor 1.0125), then multiplies them to get a combined factor of 1.0464. Use the same approach if your rate changes by year.

Step 4: Combine the sources

Once every survey sits on the same date, you can blend them. Most teams use a weighted average of the aged medians or means, weighted by sample size or by how well each survey matches your labor market.

Keep the raw figures, the aging factor, and the aged result in one table. An auditor, a CFO, or a new analyst should be able to rebuild any number in a minute.

Then take the blended market value into your structure. If you price from points, market pricing and job evaluation work together: the aged market data feeds the pay line, and the points keep internal equity intact.

Common aging mistakes

1. Aging twice. If you age to July 1 and then add a "market adjustment" to cover the same movement, you pay for it twice. Pick one mechanism.
1. Using the wrong effective date. Use the date the data reflects, not the publication date. They can be months apart.
1. Mixing target dates across jobs. Age every job to the same date or your pay line will wobble for no reason.
1. Aging bonus and total cash like base pay. Variable pay does not always move with base. Age base, then handle incentives separately.
1. Aging a bad match. Time does not fix a job that was never comparable. Fix the match first.
1. Hiding the rate. If no one can explain why you used 3.4%, the number will not survive its first challenge.

How aging fits into the structure cycle

Aging is one step in a yearly loop. You select benchmarks and match them to surveys, age the data, blend it, build or refresh the line, and then decide how far to move the structure. If you are deciding how much to move ranges, see how much to adjust your salary structure. For the full workflow from data to ranges, read salary benchmarking step by step.

Want your job points and market data in one place? See how PointFactors works and score your benchmark jobs before you price them.

FAQ

What is aging market data?

Aging market data means adjusting salary survey figures from their effective date to a chosen target date, using an annual pay movement rate, so surveys from different dates can be compared.

How do you calculate an aging factor?

Use 1 plus the months of aging divided by 12, times the annual rate. For 18 months at 3.4%, the factor is 1 + (1.5 × 0.034) = 1.051. Multiply each survey figure by that factor.

What rate should I use to age data?

Use a documented annual salary movement rate from a recognized source, such as a salary budget survey or the survey publisher's guidance. Many teams land in the 3% to 4% range in current conditions, but use the source you can defend.

What date should I age data to?

If you are setting next year's ranges, pick the start, middle, or end of that year. Midyear is a common compromise because you lead the market for part of the year and lag it for the rest.

Do I age data that is already current?

Only to the extent that its effective date differs from your target date. If a survey is already effective on your target date, the factor is 1.000.

Should I age every compensation element?

No. Age base pay with a base pay movement rate. Treat bonuses, equity, and total cash separately, since they move differently.

How is aging different from a market adjustment?

Aging updates the market reference to a common date. A market adjustment changes employee pay or a range because the reference has moved. Do both for the same movement and you double count.

How often should I age data?

Each time you refresh survey inputs, which is usually once a year, or more often for fast-moving roles.

Ready to tie your market data to defensible job points? Start free with PointFactors or review pricing.

By Justin Hampton, founder and CEO of PointFactors.