Live job-market signals
We aggregate structured data from active APAC job listings instead of relying on annual surveys.
Salary Data
How we build the BetterPay salary dataset
Transparency is part of the product. Below is how we collect data, what we process, how often we refresh it, and where the dataset has limits today.
The four pillars of the dataset
AI-extracted structure
Roles, seniority, sector, skills, and compensation fields are extracted with AI and validated against schema rules.
Continuous refresh
Listings cycle in and out of the dataset continuously. Stale data is aged out automatically.
Privacy-respecting design
User-submitted salary inputs are never sold and never linked back to individuals in published statistics.
How we report ranges
For each role and country combination, we publish a median plus 25th and 75th percentile (P25 / P75). Sample size is shown alongside so you can judge confidence.
When the sample is too thin to support a reliable estimate, we say so directly rather than fabricate a number. Pro-tier personalised analysis layers your specific role, experience, and sector context on top of the public ranges.
All compensation figures default to monthly local-currency basis (e.g. monthly SGD for Singapore), which matches how most APAC offers are quoted.
Known limitations
No salary dataset is perfect. We're explicit about ours so you can use the numbers responsibly.
- Some sectors (e.g. niche government roles) are under-represented and we mark these as low-confidence.
- Country coverage varies. Singapore and Malaysia have the deepest data; other APAC markets are filling in.
- Listings sometimes obscure compensation. We use modelled estimates only when the signal is strong enough; otherwise we abstain.
- Cross-country comparisons require care — currency, tax structure, and bonus norms differ materially.
Working with the data
Researchers, journalists, and partners can request data extracts or methodology briefings.