🌍 Geographic Differentials
What is a Geographic Differential?
A geographic differential is a percentage adjustment applied to a national market dataset — used when local data isn’t the best fit (e.g., for remote or international employees).
Instead of benchmarking based on a specific city or country, a geo-diff applies a cost-of-labor adjustment to the US national average to reflect pay expectations in that location.
When to Use a Geo-Diff
Use Geographic Differentials if:
You have a remote-first team across many regions
You want a streamlined way to manage ranges without maintaining local benchmarks for every country or metro
Your goal is to create a tiered, transparent, and scalable global pay structure
You don’t have reliable or representative local market data for a region
How It Works
When Geographic Differential is selected in your Company Profile:
U.S. employees are matched to one of three tiers:
115% (e.g., SF, NYC)
110% (e.g., Seattle, LA, DC)
100% (U.S. national average – used for most other locations)

Non-U.S. employees are matched to a percentage discounted off the U.S. national average (e.g., UK = 80%, India = 40%)(see List of Global Geographic Differentials below)
Where the Numbers Come From
Kamsa’s geo-diff percentages are based on real local market compensation data. We analyze verified HRIS-sourced salary data for each country, then calculate an average discount relative to the U.S. national benchmark. These are then:
Rounded to the nearest 10% for consistency
Reviewed by compensation experts to ensure validity
Updated periodically as labor markets shift
This approach balances simplicity, scalability, and accuracy — especially for lean teams operating globally.
List of Global Geographic Differentials
Location | Default Geographic Differential vs. the U.S. National Average |
San Francisco Bay area (“SF”) | 115% |
Austin, Boston, Los Angeles, Seattle | 110% |
Atlanta, Chicago, Dallas, Denver, Philadelphia, Phoenix, Washington, D.C. metro area | 100% |
United States (US) - All | 100% |
Albania | 40% |
Algeria | 30% |
Argentina | 40% |
Armenia | 40% |
Australia | 80% |
Austria | 80% |
Belarus | 40% |
Belgium | 80% |
Bolivia | 30% |
Bosnia and Herzegovina | 40% |
Brazil | 40% |
Bulgaria | 40% |
Cabo Verde | 30% |
Cambodia | 30% |
Canada (All) | 80% |
Chile | 40% |
China - Tier 1 Cities | 60% |
China (All) | 60% |
Colombia | 30% |
Costa Rica | 40% |
Croatia | 50% |
Cyprus | 50% |
Czechia | 40% |
Denmark | 90% |
Dominican Republic | 30% |
Ecuador | 30% |
Egypt | 30% |
El Salvador | 30% |
Estonia | 50% |
Finland | 70% |
France - Paris | 70% |
France (All) | 60% |
Georgia | 40% |
Germany | 80% |
Ghana | 30% |
Greece | 50% |
Guam | 40% |
Guatemala | 30% |
Honduras | 30% |
Hong Kong | 80% |
Hungary | 40% |
Iceland | 90% |
India - Bengaluru | 40% |
India (All) | 40% |
Indonesia | 30% |
Ireland | 80% |
Israel | 80% |
Italy | 60% |
Jamaica | 30% |
Japan | 70% |
Jordan | 40% |
Kazakhstan | 30% |
Kenya | 30% |
Kosovo | 30% |
Kyrgyzstan | 30% |
Latvia | 50% |
Lebanon | 30% |
Lithuania | 50% |
Luxembourg | 100% |
Malaysia | 40% |
Malta | 60% |
Mauritius | 30% |
Mexico | 40% |
Moldova | 40% |
Montenegro | 40% |
Morocco | 30% |
Namibia | 30% |
Netherlands | 80% |
New Zealand | 70% |
Nicaragua | 30% |
Nigeria | 30% |
North Macedonia | 40% |
Norway | 80% |
Pakistan | 30% |
Panama | 40% |
Peru | 40% |
Philippines | 30% |
Poland | 50% |
Portugal | 60% |
Puerto Rico | 90% |
Qatar | 80% |
Romania | 40% |
Russia | 40% |
Saudi Arabia | 70% |
Serbia | 40% |
Singapore | 80% |
Slovakia | 50% |
Slovenia | 60% |
South Africa | 50% |
South Korea | 60% |
Spain | 60% |
Sri Lanka | 30% |
Suriname | 30% |
Sweden | 80% |
Switzerland | 100% |
Taiwan | 60% |
Thailand | 40% |
Toronto | 90% |
Tunisia | 30% |
Turkey | 30% |
UK - Inner London | 90% |
UK (All) | 80% |
Ukraine | 30% |
United Arab Emirates | 80% |
Uruguay | 40% |
Uzbekistan | 30% |
Vancouver | 90% |
Venezuela | 30% |
Vietnam | 30% |
Zimbabwe | 30% |