Overview

Calculated benchmarks are values that are computed using a combination of Pave's real-time data, geo-differentials, level progressions, job family relationships, and other regression models.

Compensation data has a number of strong patterns. US jobs pay more than equivalent roles in the UK. Higher levels get paid more than lower ones. Our team of data scientists leverage machine learning to identify these patterns across our entire dataset and then combine this deep understanding of the trends we see across the market with the raw data in and around a specific benchmark to produce a calculated benchmark in cases where robust market data is not available.

Raw benchmarks, which are also available in Pave, aggregate compensation information pulled directly from the HRIS to produce the various percentiles available within the application.

For each slice of market data, we compute a calculated benchmark and compare that against the raw benchmark if it exists. In cases where both a raw and calculated benchmark are available, we display whichever benchmark has a higher level of consistency on our Consistency Scale, in order to provide you with the most reliable set of market data. Learn more about our Consistency Scale here.

Where to Access

When using the product, you’ll be able to identify whether a given benchmark is raw or calculated in a few different ways.

If calculated values are present in the table view, they will be underlined to help you quickly recognize them. You can click the Learn more
Consistency labels will identify whether a benchmark is calculated, along with the sample sizes within each slice of data
Reports generated will include columns that identify whether each benchmark is raw or calculated, along with the consistency label

For more information on Consistency Labels labels, please view our article on **Explaining Consistency Labels.**

For insight into how we calculate these benchmarks, please view our Calculated Benchmarks FAQ.