How can you know a country's top 10% salary from two numbers?

Income within a country is closely log-normal. Two public World Bank figures pin the whole curve: GNI per capita fixes the mean, and the Gini index fixes the spread (σ = √2·Φ⁻¹((Gini+1)/2)). From the resulting log-normal you can read any percentile — median, top 10%, top 1% — exactly.

What's the difference between the survival floor and the minimum wage?

The survival floor is what a single person's essentials actually cost locally (rent, food, transport, utilities — from our Pint & Property basket). The statutory minimum is the legal wage floor, which can sit above or below survival, and some countries (Nordics, Switzerland, Singapore) have none, relying on collective bargaining.

Is this real payroll data?

No — it's a transparent model calibrated from World Bank GNI and Gini (updated 2026-09-07), covering 165 countries. It's excellent for setting fair local salary bands or benchmarking yourself, but a specific role or city can differ. Treat it as a well-grounded estimate, then validate against local job data.

How do I use this to set fair local salary bands?

Open the HR band builder, pick a percentile range (e.g. p50–p75 for a solid mid-market band), and read the local salary range in USD and local currency. The Fair Pay cross-check also shows where a cost-of-living-adjusted offer would land in the local market.

Doesn't the Gini index measure different things in different countries?

Yes — and we correct for it. Some countries survey income (US, most of Europe and Latin America); others survey consumption (China, India, much of Asia and Africa), which looks more equal because households smooth spending. Using each country's World Bank welfare basis, we widen consumption-based Gini figures by ~7 points to an income-equivalent so top-decile pay isn't understated. We also use each country's actual employment-to-population ratio rather than a global assumption.

Global Pay · Local Reality

What does a good salary look like — locally?

Fair Pay scales a salary between countries. This shows the whole local ladder: survival, minimum, median, comfortable, top 10%, top 1% — so companies set bands fairly and locals see exactly where they stand.

Local Salary Benchmarker

What does a good salary actually look like on the ground? From survival to the top 1% — modelled from World Bank income data, so you can set fair local bands or see where you stand.

In Poland, the median wage is about $1,809/month; a top-10% earner clears $3,506/month, and the top 1% $6,013/month.

Survival floor $1,054/mo 3,964 PLN

Statutory minimum $1,241/mo 4,667 PLN

Median (p50) $1,809/mo 6,804 PLN

Comfortable (p75) $2,562/mo 9,636 PLN

Top 10% (p90) $3,506/mo 13,186 PLN

Top 1% (p99) $6,013/mo 22,615 PLN

Monthly wage across the income distribution. Your salary appears as a marker when entered.

HR band builder

Pick a percentile range to price a fair local salary band.

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Fair Pay cross-check

Where would a cost-of-living-fair offer land in the local market?

Home salary Home country

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Median monthly wage by country (darker = higher).

Lower → higher · click a country to open its page

Hiring or getting paid globally?

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Modelled from World Bank GNI + Gini (2026-09-07) — estimates for benchmarking, not payroll data. Links may be affiliate links.

Measured pay by occupation

Everything else on this page is modelled from World Bank income data. This table is measured — gross monthly earnings actually reported to the ILO, for 35 countries. Dataset updated 2026-09-02.

Median and mean gross monthly earnings by ISCO-08 major occupation group, with the gender pay gap and each group's position on the modelled national wage ladder.
Occupation group	Median / month	Mean / month	Pay gap	Local percentile
All occupations	$3,060	$3,567	25%	39.4
Armed forces	$5,083	$5,625	—	72.2
Managers	$4,613	$5,171	10.8%	66.4
Professionals	$3,957	$4,374	20.4%	56.6
Technicians and associate professionals	$3,153	$3,580	21.8%	41.3
Clerical support workers	$2,448	$2,577	11.3%	25.9
Service and sales workers	$2,052	$2,207	29.7%	17.2
Skilled agricultural, forestry and fishery workers	$2,479	$2,431	33.3%	26.6
Craft and related trades workers	$3,153	$3,360	27.1%	41.3
Plant and machine operators and assemblers	$2,825	$3,088	19.8%	34.3
Elementary occupations	$1,542	$1,699	49.7%	7.7

ILO reference year 2025 — one year across every occupation, so a gap between two rows is a real pay difference rather than years of wage inflation.

How good is the model? Against these measurements the modelled ladder is within a third of the measured median for 22 of 31 countries, and it errs in both directions — overstating pay most in Egypt (by 65%) and understating it most in Vietnam (by 44%).

The large misses are not random: the ILO counts employees only, while the model is built from GNI per capita, which is spread over everyone. Where informal and self-employed work is a big share of the labour market the two are measuring different populations, and they should differ.

About the pay gap column. This is a monthly gap, so it is wider — usually by around ten points — than the hourly gender pay gap Eurostat and national statistics offices publish. A monthly figure also carries the difference in hours worked, and part-time work is far more common among women; the Netherlands, with the EU's highest female part-time rate, diverges most, and Sweden least. It is also raw: it controls for nothing, so it measures what women are paid against what men are paid, not unequal pay for identical work.

Gross pay before tax, employees only — do not compare against a take-home figure. ISCO-08 major groups are broad: “Professionals” covers a surgeon and a junior developer alike. No measured earnings are published for Albania, Algeria, Angola, Armenia, Australia, Austria, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Bulgaria, Burkina Faso, Burundi, Cabo Verde, Cameroon, Canada, Central African Republic, Chad, China, Comoros, Costa Rica, Croatia, Cyprus, DR Congo, Djibouti, Dominican Republic, East Timor, Ecuador, El Salvador, Equatorial Guinea, Estonia, Eswatini, Ethiopia, Fiji, Finland, Gabon, Gambia, Georgia, Ghana, Grenada, Guatemala, Guinea, Guinea-Bissau, Haiti, Honduras, Iceland, Iran, Iraq, Israel, Ivory Coast, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Kosovo, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Lithuania, Luxembourg, Madagascar, Malawi, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Micronesia, Moldova, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, New Zealand, Nicaragua, Niger, North Macedonia, Pakistan, Panama, Paraguay, Peru, Qatar, Republic of the Congo, Romania, Russia, Rwanda, Saint Lucia, Samoa, Senegal, Serbia, Seychelles, Sierra Leone, Slovakia, Slovenia, Solomon Islands, Sri Lanka, Sudan, Suriname, Syria, São Tomé and Príncipe, Tajikistan, Tanzania, Togo, Tonga, Tunisia, Tuvalu, Uganda, Ukraine, United Arab Emirates, Uruguay, Uzbekistan, Vanuatu, Zambia, Zimbabwe, which report no data here rather than a modelled substitute.

How the model works

σ = √2·Φ⁻¹((Gini+1)/2) · μ = ln(mean) − σ²/2 · wage(p) = e^(μ + σ·Φ⁻¹(p))

How can you know a country's top 10% salary from two numbers?: Income within a country is closely log-normal. Two public World Bank figures pin the whole curve: GNI per capita fixes the mean, and the Gini index fixes the spread (σ = √2·Φ⁻¹((Gini+1)/2)). From the resulting log-normal you can read any percentile — median, top 10%, top 1% — exactly.
What's the difference between the survival floor and the minimum wage?: The survival floor is what a single person's essentials actually cost locally (rent, food, transport, utilities — from our Pint & Property basket). The statutory minimum is the legal wage floor, which can sit above or below survival, and some countries (Nordics, Switzerland, Singapore) have none, relying on collective bargaining.
Is this real payroll data?: No — it's a transparent model calibrated from World Bank GNI and Gini (updated 2026-09-07), covering 165 countries. It's excellent for setting fair local salary bands or benchmarking yourself, but a specific role or city can differ. Treat it as a well-grounded estimate, then validate against local job data.
How do I use this to set fair local salary bands?: Open the HR band builder, pick a percentile range (e.g. p50–p75 for a solid mid-market band), and read the local salary range in USD and local currency. The Fair Pay cross-check also shows where a cost-of-living-adjusted offer would land in the local market.
Doesn't the Gini index measure different things in different countries?: Yes — and we correct for it. Some countries survey income (US, most of Europe and Latin America); others survey consumption (China, India, much of Asia and Africa), which looks more equal because households smooth spending. Using each country's World Bank welfare basis, we widen consumption-based Gini figures by ~7 points to an income-equivalent so top-decile pay isn't understated. We also use each country's actual employment-to-population ratio rather than a global assumption.

For AI agents: GET /api/wage-bands.json?country=PL&percentile=90 — see /llms.txt and the calculate_wage_bands MCP tool.