Teacher Workforce Tides

1 · Data sources

All workforce inputs come from the World Bank Open Data API: primary teachers (SE.PRM.TCHR), secondary teachers (SE.SEC.TCHR), enrollment (SE.PRM.ENRL, SE.SEC.ENRL), pupil-teacher ratios (SE.PRM.ENRL.TC.ZS, SE.SEC.ENRL.TC.ZS), net primary enrollment (SE.PRM.TENR), crude birth rate (SP.DYN.CBRT.IN) and total population (SP.POP.TOTL). Only countries with teacher data from 2018 or later are included — 111 countries in total.

The "Teaching abroad" panel adds: international school counts estimated from ISC Research public reports and community sources; cost-of-living, quality-of-life and relocation scores from WhereNext (getwherenext.com), used under CC BY 4.0; and salary tiers with monthly savings from teacher-reported medians where available, or a directional estimate model otherwise (see §7).

2 · How projections are calculated

1. School-age population: primary students in year Y are the sum of births from Y−11 to Y−6; secondary students are births from Y−17 to Y−12. A 98% survival rate to school age is applied, multiplied by enrollment rates (country-specific net enrollment for primary, 85% default for secondary).
2. Future births: for years without observed births, annual births are projected as 2024 population × the trailing 3-year average birth rate (2022–2024).
3. Teachers needed: projected students ÷ pupil-teacher ratio, held constant at the most recently reported value.
4. Surplus / shortage: current teacher stock minus teachers needed, under three adjustment scenarios (below).

3 · The three scenarios

The dashboard shows plain-language names; the technical model names are kept in brackets for anyone citing the data.

4 · Reading the demand gauge

• < 60% — severe surplus: needs less than 60% of the current workforce
• 60–80% — moderate surplus
• 80–120% — balanced
• 120–150% — moderate shortage
• > 150% — severe shortage: must hire 50%+ more teachers

5 · Limitations

• Immigration and emigration are not captured — demand in the USA, UK, Germany and the UAE is likely understated.
• World Bank data often covers public-sector teachers only; countries with large private sectors (India, Pakistan) may hide additional demand. India's surplus figure carries a LOW CONFIDENCE badge for this reason, alongside hidden demand from out-of-school children.
• Subject-specific shortages (STEM, special education) are not captured; surplus primary teachers cannot always retrain into secondary roles.
• Data quality varies — figures for Nigeria, DR Congo and Afghanistan are low-confidence estimates.
• Birth rates are assumed to stabilize at the 2022–2024 average; continued declines (China, South Korea) or recoveries (Russia, Hungary) will shift outcomes.

6 · Confidence levels

High: teacher data 2022+, enrollment >90%, stable conditions (China, USA, Japan, Western Europe). Medium: data from 2019–2021 or enrollment 70–90% (India, Indonesia, Brazil, Mexico). Low: pre-2019 data, enrollment <70%, or conflict-affected (Afghanistan, DRC, parts of West Africa).

7 · Salary & savings estimates

Salary figures come in two kinds, and every country is labelled with which kind it has:

• Teacher-reported (39 countries, green badge): the median of real packages submitted by teachers to internationalteachersalary.com (697 records, 97 countries, extracted September 2026). Countries with 3 or more submissions use the reported median; the badge always shows the sample size, and samples of 3–4 should be read with extra caution. Tier spreads are estimated around the median using fixed ratios (Tier 1 ≈ 1.43× and Tier 3 ≈ 0.71× the reported median). Cross-validated against the r/Internationalteachers community salary sheet (673 submissions): where the two datasets overlap, they agree within ~5–15%.
• Modeled estimate (72 countries, amber badge): a statistical fit to the 1,370 teacher-reported packages above. The reported data shows pay multiples fall as living costs rise, so each country's Tier 2 estimate is its monthly living cost multiplied by a fitted factor (11.54 − 1.197 × ln(cost of living); R² = 0.47), with Tier 1 ≈ 1.43× and Tier 3 ≈ 0.71× the Tier 2 figure. This replaced an earlier flat 5× / 3.5× / 2.5× multiplier model, which overstated salaries in high-cost countries by up to 3× (see below). Monthly savings = estimated package minus estimated living costs.

Cost of living: figures reflect a single person's expat-standard living costs in the Tier-1/Tier-2 hub cities where international schools concentrate (Shanghai rather than provincial China, Tokyo rather than rural Japan), not national averages, and are cross-checked against Numbeo city-level data. Figures include housing; Numbeo's published per-city "monthly cost" figures exclude rent, so they are not directly comparable.

Known model error, published for honesty: the original flat-multiplier model was validated against the reported data and landed within ±15% of the reported median for only 10 of 39 comparable countries, running >50% high for 13 of them, including Japan (+208%), Spain (+203%), Estonia (+133%), Italy (+130%), Singapore (+90%) and the UAE (+84%). The current fitted model corrects the systematic part of that error (high-cost countries are no longer overestimated by design), but it cannot capture country-specific factors: it would still predict Spain at roughly double what teachers actually report. Treat every amber badge as an approximation that real submissions replace.

Live: teachers can now submit their real package — anonymously, by country, tier and position. Submissions are stored write-only, reviewed before publishing, and a country switches from "modeled" to "teacher-reported" once it reaches 3 verified submissions.

Sources: internationalteachersalary.com teacher submissions · r/Internationalteachers community salary sheet · WhereNext cost-of-living data (CC BY 4.0) cross-checked against Numbeo city-level indices · salary model fitted to teacher-reported packages.