## What is a pay differential?

A pay differential is the ratio of the base salaries for employees performing the same job at the same seniority level in two different locations. Geographic pay differentials are an industry best practice for efficiently managing compensation structures for companies with employees in multiple locations.

## How is a pay differential calculated?

We compare the median salary of employees that are performing the same job at the same level to create a geographic pay differential. Controlling for both job and level to ensure we’re looking at comparable roles across locations is critically important, since job mix or candidate seniority differences between locations can also have significant impacts on salary differences.

## What is included in Pave’s pay differentials?

Our pay differentials focus on differences in base salary. They currently do not include non-salary compensation like bonuses, equity, and benefits or costs like payroll taxes.

## Why are some compensation types not available for all locations?

Pave is committed to showing you high quality data, which means data must pass our accuracy standards before being searchable. Given the limited number of employees in some locations, data may not pass our accuracy standards. We will continue to release additional compensation types across locations as data becomes available and passes our accuracy standards.

## Why does Global Location Insights split by job family, and not by level?

Generally, our observations show that variations in job levels have less impact on geographical pay differences compared to the influence of job families. As a result, we focus on providing job family specific insights to give a more accurate understanding of geographical pay differentials.

## Do pay differentials account for different job makeups across locations?

Yes. The “average pay differential” is calculated by taking the median salary of employees that are performing the same job at the same level to create a geographic pay differential. We control for both job family and job level to ensure that we are looking at comparable roles across locations since job mix or candidate seniority differences between locations can have significant impacts on salary differences.

## How are location tiers created?

Location tiers have been defined based on cost of labor across locations and are not based on cost of living. As an example, San Francisco, New York City, and Seattle are all included in Tier 1 because the cost of labor is similar, even if cost of living varies across all three locations.

## What is the difference between cost of labor and cost of living?

Cost of labor represents the amount it costs for certain jobs in a given location. Cost of living represents the amount it costs for an individual to live comfortably in a given location.Unless otherwise noted, Pave Market Data provides benchmarks on cost of labor as the industry standard for benchmarking purposes.

## Do pay differentials account for currency differences?

Yes. We convert all of the salary data we use to calculate pay differential to a shared basis using a trailing 30 day rolling average exchange rate.

## What if I don't see a location I need?

We are constantly working to add new locations as soon as possible. If there are specific locations you're interested in seeing in Pave, reach out to [support@pave.com](mailto:support@pave.com) for help.

## What do the confidence labels mean?

The Pave Confidence Scale is designed to be an easily understood summary of how reliable a given differential is. The labels let you know at a glance which numbers are exactly correct and which ones might be off by two or three percentage points. We want you to know exactly how much to trust each number. The Pave Confidence Scale labels and their meanings:  ![](https://brainfish-storage-prod.s3-accelerate.amazonaws.com/uploads/634ebdf6-3c6d-407f-94a2-ac567d4f99e3/f3552309-39e4-4a92-b4ac-da4dbeaef892/imported?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIAUPPN5IL3VWZM2N3M%2F20260415%2Fus-east-2%2Fs3%2Faws4_request&X-Amz-Date=20260415T055729Z&X-Amz-Expires=3000&X-Amz-Security-Token=IQoJb3JpZ2luX2VjENT%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMiJHMEUCIQCCqzPSSHXKPpTTu8VIWBy7mCmKcA1Wi%2BoTpYO0ysJhBAIgD%2FnxfIQ2iMWP1kwTsC27a2du2PT20ngYmRt7e%2BSlPKwq5gUInf%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FARACGgwzMDgxMjU5NzUyODciDG2ZeHjXx6mVUmMU2yq6BUSDf%2BeZZMZLkAhwMRVFVLDd6GzVxu6F43mp8KnN4PjJ5kR4fRk42yoV9F7qdJPCJiJU%2F233amgMGrZ8ZJFQLpKt5%2FhPj7kC3NBMewiPEctqcGdus6u9AQEpXbB%2BajcnrrcRzepF4x8VAVyT%2B5ruZfcwbbHcCMD2azaEulsvfY3F7EXBYPdYMeXu2lG3lsjnkwwpob6cO3%2FA01ufU694P0%2BH6U2cFrJmlWLen%2FSVu%2F3oYtQ2moHdhM6pfGaW9JHUlzuX2jc96V6SMHQuOAL79MWCaqmWrQJj%2FZ%2BwyA1EHLqR2ym2RHsxcAyn%2BnkFzlcEb7X3YiSaUbha%2BlJadZXQmZZT2M3OLUynm3EE5lI%2FPjVVeLIUpX4YWr1MpDDNuSUIRQc3ccd5FNLMzQs93pOgvMcQc23fxXbLf79cCKZ2B%2FzqH4B6mj7DkdA3CgBMx1f3rR2IU8xXltQfrSrKrnIZu%2BvGM5alZX4qoI6%2FNqAlMetx2D9WX%2BmavdT0P4wV6TE%2FUTgCYGOvx8IGNBxyURujZZWJ2QGgic%2BQciwL%2F%2FyzStVX2X1kG0d0%2BNpjzXzR4pVJ%2BWvbv6XLJj%2BwzYCGBidFO5e5siCLCybONNC6qT%2BmY9uN%2BfZ2q4WbovG9d%2FKuiuah%2Bkl7K00pW%2FkBKvdV%2BRhTLoYwxkLZUPFMQ9QRNMjF542UOpVV2R4Q0PorDIvIHUzmwe5M7VU8rg5ExG4YezZdhCk4gu%2BBcKq9rexYekzABLTgKJqIRafdnI9OdWEfdrVWWdzBcw4BNcq%2FpFtNVsgSqvJKnMBergvwkfZb8gXPrqofJzDQQbAgcZ%2B6csN4BtTJLh2rnZftU6rGZyRTbtBJlTRwVdDjtBJNKXEVL%2B36aDC%2FwclwVjmb3N22xnFehs%2Bu7Pjc73g5Jrv7Nu6cJaCXyxeykFJbhZdd7QZ6MOOi%2FM4GOpABJLXQSWbytxXoL%2FvokC4SubFJ5fdVxpPWrjD%2BXH65hw%2B7QyZBBjXbtm7ImiCIQAHKRmifitaLeDQOKLa%2FR0mSa8kIyROAM%2FktWPGeBJJvdtCskiMBuSzKIM4q9IZceZRuvQErYgcaoNC3gtx4DuCs7fjsbSf1e2G3UFnMRx636pje55pwwwTRHi5kRx3clJA4&X-Amz-Signature=257000da343312e4fcddd183b873527aca6a60ff4571c616048edde0ae5c81f7&X-Amz-SignedHeaders=host&response-content-disposition=attachment)

## How are the confidence labels calculated?

For determining confidence labels, we start with the well-established statistical basics of sample size and margin of error. We refine this basic understanding using machine learning models that look across our entire dataset to quantify the influence of bias, outliers, and incomplete data on our benchmarks.




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