How we get our numbers
Every salary figure on this site follows the same documented process. Here it is, in full, including the parts that are estimates.
Base salary data
Each career profile carries three researched anchor figures: a low, median and high annual gross salary for the United States, plus median figures for the United Kingdom and the EU. These are aggregated from public salary sources (government labour statistics, large salary-report datasets and published compensation surveys), reviewed by a human, and rounded to honest precision. We would rather show a defensible round number than a false-precision decimal.
The country model
Pages like "{career} salary in {country}" derive their figures from those anchors. Each of the 12 countries we cover is anchored to the closest researched region (US, UK or EU) and adjusted with a country wage factor that reflects how that market pays relative to its anchor. Entry and senior figures scale using each career's own real pay spread, so careers with flat pay curves stay flat and careers with steep curves stay steep. Local currency conversions use current exchange rates, rounded.
This means country figures are modelled estimates, not surveys. They are calibrated to be realistic for comparison and planning; an actual offer depends on the company, the city and how well you negotiate. When a modelled figure conflicts with strong local data, we correct the model.
The calculator and quiz
The salary calculator and the Am I Underpaid? quiz run on exactly the same model as the salary pages, in your browser. Nothing you type is sent to a server or stored.
Update cadence
Career anchors are reviewed when markets move meaningfully, and every page shows the date its underlying data was last updated. Exchange rates and country factors are reviewed on the same cycle.
Corrections
Spotted a figure that doesn't match reality on the ground? We'd rather fix it than defend it. Reach out via the Paygrade YouTube channel and we'll review it against the sources.
What we don't do
- No pay-per-placement bias: nobody can pay to make a career look better paid.
- No scraping private data: everything derives from public, aggregate sources.
- No fake precision: if the honest answer is a range, we show a range.