What is a Geographic Differential?
A geographic differential, or geo diff, is a percentage used to scale U.S. national market pay data for a specific U.S. or global location.
U.S. National is the 100% baseline. A location with an 80% geographic differential is set at 80% of the U.S. national market rate.
Geographic differentials can be used when local market data is limited or when your company wants a consistent approach to setting pay ranges across multiple locations.
Kamsa reviews geographic differentials at least annually and updates them as market conditions change.
Example
If the U.S. national salary midpoint for a role is $120,000 and the geographic differential for the UK is 80%:
$120,000 × 80% = $96,000
The resulting midpoint for the UK is $96,000, which can then be converted to GBP.
When to use Geographic Differentials
Geographic differentials may be useful when:
Your workforce is remote or distributed across multiple locations
Local market data is limited or unavailable
You want a consistent approach to setting pay ranges across locations
You want to reduce the number of local market datasets your company maintains
How Geographic Differentials Work in Kamsa
In Company Profile, you can select Geographic Differential instead of Local Market Data as your default Market Data Cut Approach.
For U.S. Employees
Kamsa uses three geographic differential tiers:
115%: San Francisco Bay Area and New York City Area
110%: Austin, Boston, Los Angeles, and Seattle
100%: U.S. National, used for most other U.S. locations
These percentages are applied to the US (All) Market Data Cut.
For employees outside the U.S.
International geographic differentials are expressed relative to U.S. National, which is set at 100%.
Examples:
UK (All): 80%
Italy: 60%
India (All): 40%
The geographic differential is applied to the U.S. national market midpoint to determine the corresponding midpoint for that location.
How Kamsa Calculates Geographic Differentials
Kamsa calculates geographic differentials by reviewing local cost of labor, cost of living, and actual market pay relative to U.S. national market data.
For each country, we:
Analyze local compensation data for comparable job families and levels
Review cost of labor and cost of living data for the local market
Compare local pay with the U.S. national market rate
Convert the results into a geographic differential
Round to a standard percentage to keep ranges consistent and manageable
Review differentials at least annually and update them as market conditions change
This approach helps reduce volatility from small or uneven datasets while keeping global pay ranges consistent and manageable.
Global Geographic Differentials
The table below shows the default Geographic Differential Market Data Cut for each available location.
Location | Default Geographic Differential |
San Francisco Bay area (“SF”) | US (All) - 115% |
Austin, Boston, Los Angeles, Seattle | US (All) - 110% |
Atlanta, Chicago, Dallas, Denver, Philadelphia, Phoenix, Washington, D.C. metro area | US (All) |
United States (US) - All | US (All) |
Albania | US (All) - 40% |
Algeria | US (All) - 30% |
Argentina | US (All) - 40% |
Armenia | US (All) - 40% |
Australia | US (All) - 80% |
Austria | US (All) - 80% |
Belarus | US (All) - 40% |
Belgium | US (All) - 80% |
Bolivia | US (All) - 40% |
Bosnia and Herzegovina | US (All) - 40% |
Brazil | US (All) - 40% |
Bulgaria | US (All) - 40% |
Cabo Verde | US (All) - 30% |
Cambodia | US (All) - 30% |
Canada (All) | US (All) - 80% |
Chile | US (All) - 40% |
China - Tier 1 Cities | US (All) - 60% |
China (All) | US (All) - 60% |
Colombia | US (All) - 30% |
Costa Rica | US (All) - 40% |
Croatia | US (All) - 50% |
Cyprus | US (All) - 50% |
Czechia | US (All) - 40% |
Denmark | US (All) - 90% |
Dominican Republic | US (All) - 30% |
Ecuador | US (All) - 30% |
Egypt | US (All) - 30% |
El Salvador | US (All) - 30% |
Estonia | US (All) - 50% |
Finland | US (All) - 70% |
France - Paris | US (All) - 70% |
France (All) | US (All) - 60% |
Georgia | US (All) - 40% |
Germany | US (All) - 80% |
Ghana | US (All) - 30% |
Greece | US (All) - 50% |
Guam | US (All) - 40% |
Guatemala | US (All) - 30% |
Honduras | US (All) - 30% |
Hong Kong | US (All) - 80% |
Hungary | US (All) - 40% |
Iceland | US (All) - 90% |
India - Bengaluru | US (All) - 40% |
India (All) | US (All) - 40% |
Indonesia | US (All) - 30% |
Ireland | US (All) - 80% |
Israel | US (All) - 80% |
Italy | US (All) - 60% |
Jamaica | US (All) - 30% |
Japan | US (All) - 70% |
Jordan | US (All) - 40% |
Kazakhstan | US (All) - 30% |
Kenya | US (All) - 30% |
Kosovo | US (All) - 40% |
Kyrgyzstan | US (All) - 30% |
Latvia | US (All) - 50% |
Lebanon | US (All) - 30% |
Lithuania | US (All) - 50% |
Luxembourg | US (All) |
Malawi | US (All) - 30% |
Malaysia | US (All) - 40% |
Malta | US (All) - 60% |
Mauritius | US (All) - 30% |
Mexico | US (All) - 40% |
Moldova | US (All) - 40% |
Montenegro | US (All) - 40% |
Morocco | US (All) - 30% |
Namibia | US (All) - 30% |
Netherlands | US (All) - 80% |
New Zealand | US (All) - 70% |
Nicaragua | US (All) - 30% |
Nigeria | US (All) - 30% |
North Macedonia | US (All) - 40% |
Norway | US (All) - 80% |
Oman | US (All) - 50% |
Pakistan | US (All) - 30% |
Panama | US (All) - 40% |
Peru | US (All) - 40% |
Philippines | US (All) - 30% |
Poland | US (All) - 50% |
Portugal | US (All) - 60% |
Puerto Rico | US (All) - 90% |
Qatar | US (All) - 80% |
Rwanda | US (All) - 30% |
Romania | US (All) - 40% |
Russia | US (All) - 40% |
Saudi Arabia | US (All) - 70% |
Serbia | US (All) - 40% |
Singapore | US (All) - 80% |
Slovakia | US (All) - 50% |
Slovenia | US (All) - 60% |
South Africa | US (All) - 50% |
South Korea | US (All) - 60% |
Spain | US (All) - 60% |
Sri Lanka | US (All) - 30% |
Suriname | US (All) - 30% |
Sweden | US (All) - 80% |
Switzerland | US (All) |
Taiwan | US (All) - 60% |
Tanzania | US (All) - 30% |
Thailand | US (All) - 40% |
Toronto | US (All) - 90% |
Tunisia | US (All) - 30% |
Turkey | US (All) - 30% |
Uganda | US (All) - 30% |
UK - Inner London | US (All) - 90% |
UK (All) | US (All) - 80% |
Ukraine | US (All) - 30% |
United Arab Emirates | US (All) - 80% |
Uruguay | US (All) - 40% |
Uzbekistan | US (All) - 30% |
Vancouver | US (All) - 90% |
Venezuela | US (All) - 30% |
Vietnam | US (All) - 40% |
Zimbabwe | US (All) - 30% |
FAQs
When should I use Geographic Differentials instead of Local Market Data?
Geographic Differentials may be a good fit if your workforce is distributed across multiple locations, local market data is limited, or you want a consistent approach to setting pay ranges across locations.
Local Market Data may be more appropriate if your company prefers to benchmark each location directly against available local compensation data.
How often are Geographic Differentials updated?
Kamsa reviews Geographic Differentials at least annually and updates them when labor markets or economic conditions change
Should we use the geographic differential, US All at 115%, or the local New York City Area or San Francisco Bay Area market data cut?
The majority of our clients use the Geographic Differential approach if they hire remotely across the U.S.
For example, they use US (All) - 115% Market Data Cut for employees within a 60 mile radius of the New York City or San Francisco Bay areas. The 115% geographic differential applied to U.S. national data (US (All) Market Data Cut) reflects the cost of labor and cost of living in these metro areas.
Companies often use US (All) - 115% instead of the New York City Area or San Francisco Bay Area Market Data Cuts because it’s more scalable for remote hiring. We recommend using US (All) - 115% for employees in these areas.
Can we switch back and forth between local market data and the geographic differential approach?
You can change your default Market Data Cut Approach at any time in Company Profile. Before switching, we recommend downloading both the Census Data export AND Full Data Export in case you need to revert to your previous approach. These reports will capture the full dataset before new Market Data Cuts are applied to each employee.
Note: Changing the default Market Data Cut Approach in the Company Profile may impact any current customMarket Data Cut you have selected for an employee within the Employee Data page. Custom market data cuts can be ap
For additional questions, contact [email protected].