Tools · Analytics
Salary analytics for international teachers
Compare an offer, explore trends and sanity-check what “competitive” looks like — using real, anonymous submissions from the community.
Compare your offer against similar roles and countries
See patterns with charts, ranges and percentiles
Improve the dataset by flagging obvious errors
Quick actions
Start with the dataset, then come back and compare your offer.
Reminder: salary is only part of the story — housing, flights, tuition, medical and cost of living can change everything.
Jump to a section
Compare an offer
See averages & percentiles
Find out where your offer sits.
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Visualise trends
Charts by country & role
Spot patterns, not anecdotes.
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Improve data quality
Flag suspicious entries
Help keep results reliable.
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Make decisions
Negotiate with context
Use data to sanity-check offers.
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Data notes and how to interpret results
Wondering Staffroom is community-submitted. That’s a feature (real teacher context), but it also means you should interpret outputs sensibly.
Compare like with like: role, country, experience and package structure can shift totals dramatically.
Look beyond averages: a few extreme entries can pull means around. Where possible, use percentiles and ranges.
Benefits matter: housing, flights, tuition and medical can turn a “lower” salary into a stronger overall deal.
Flag obvious errors: if you see a clear typo or unrealistic value, use 🚩 Flag to help keep outputs useful.
My Offer vs. The World
Enter your salary, select a country and role, and compare against anonymous submissions. You’ll typically see a percentile ranking, averages and a shortlist of comparable entries.
🎯 My Offer vs. The World
Compare your salary to real submissions for the same country and role (normalised to USD for accuracy).
Visualise salary patterns
Charts help you spot broad signals: what’s typical, what’s rare and where the market seems to be shifting.
📈 Visualise salary patterns
Trend and benefit signals for a country. Salaries are normalised to USD for consistency.
Insights and data quality
Use the insights tools to explore matching submissions and help improve the dataset. The 🚩 Flag button does not delete data — it helps highlight entries that may be misleading or incorrect.
🧠 Insights and data quality
Country volume, benefit patterns and a simple salary distribution. (USD normalised where possible.)
Using analytics to make decisions
Use this page as a reality check, not a verdict. A package that looks “low” on salary may still be strong once housing, tuition, flights and medical are included — and cost of living can swing take-home value massively.
Negotiation prompt
“Based on typical packages for this country and role, could we review either the base salary or the housing allowance to bring the offer closer to market?”
What to compare
- • Base salary vs allowances
- • Housing value (provided or allowance)
- • Flights, tuition and medical
- • Contract length, gratuity, bonus terms
Frequently asked questions
- What does “My Offer vs. The World” actually compare?
- It compares the salary details you enter against anonymous submissions in the dataset, then shows how your offer sits within that group (for example, typical ranges, averages and where you land in the distribution).
- Is this data verified?
- No — the dataset is community submitted. The goal is transparency and pattern spotting, not perfect auditing. If you notice obvious errors, use 🚩 Flag so it can be reviewed.
- What does “percentile” mean here?
- Percentile describes relative position. If an offer is around the 75th percentile, it appears higher than roughly three quarters of comparable submissions in the dataset.
- Does Analytics include housing, flights and other benefits?
- Some benefits may appear in insights if they exist in submissions, but salary figures should always be interpreted alongside the benefits package and local cost of living.
- How do you handle different currencies?
- Submissions are recorded in the currency provided by the teacher. Some tools may convert or normalise values depending on configuration, so treat mixed-currency comparisons carefully and look for consistent groupings.
- What should I do if I see an entry that looks wrong?
- Use the 🚩 Flag option. It helps highlight suspicious submissions (for example, clear typos or unrealistic values) so the dataset remains useful for everyone.
Explore more on Wondering Staffroom
Useful next steps and related pages.
Browse the dataset
Filter and explore anonymous submissions across countries and roles.
Submit your package
Add your salary and benefits anonymously to strengthen the dataset.
Country salary guides
Readable snapshots of typical packages, benefits and context by country.
News & updates
New tools, country pages and community-driven write-ups as the site grows.
Nothing on this page is financial advice. Figures are indicative and based on anonymous teacher submissions; always validate details for a specific school, city and contract.
