Answers to common questions about Market Data benchmarks.

Answers to common questions about Market Data benchmarks.

### Does Market Data account for inflation?

No. Pave does not adjust benchmark data for inflation. The data reflects compensation as reported by connected HR platforms at the time of each monthly data release.

### How is Total Cash calculated?

Total Cash includes both employees who receive variable pay and employees who do not. If one employee earns $100,000 in base salary with no variable pay, and another earns $100,000 in base salary plus $20,000 in variable pay, the Total Cash benchmark includes both $100,000 and $120,000.

Because Total Cash includes both employees with and without variable pay, it does not equal base salary plus variable pay, and its sample size may differ from the sample sizes for base salary or variable pay individually.

### Does Market Data include hourly employees?

No. Market Data benchmarks cover salaried employees only.

### Why are there fewer data points for variable pay than base salary?

Variable pay benchmarks only include employees who receive variable pay. Because not every employee receives variable compensation, the variable pay sample is a subset of the base salary sample and is typically smaller.

### Why does a lower-level role sometimes show higher compensation than a higher-level role?

This can happen for a few reasons:

* **Data composition**: The companies contributing to benchmarks at adjacent levels may differ. If those companies have different compensation philosophies, the progression across levels may not increase linearly.
* **Distribution overlap**: The lower and upper ends of compensation distributions can overlap between adjacent levels, particularly at the 25th and 90th percentiles.
* **Equity appreciation**: For equity benchmarks that reflect current grant values, stock appreciation since the grant date can cause variation between levels.

If you see this pattern, consider looking at adjacent levels together and establishing a progression that fits your organization.

### Why are sample sizes different across equity compensation types?

Each equity type has different eligibility criteria:

* **New hire equity** includes only grants issued to new employees within the past 24 months, so the eligible population is smaller.
* **Ongoing equity** includes all non-new-hire grants issued in the past 12 months, covering all employees regardless of when they joined. This typically produces larger sample sizes.
* **Unvested equity** includes all grants vesting in the future, covering all employees. This typically produces the largest sample sizes.

For more on equity compensation types and settings, see [Equity data benchmarking](equity-data-benchmarking.md).

### What should I do if my search is not returning results?

Pave requires benchmark data to meet accuracy standards before displaying it. Data may not meet these standards if the sample size is too small (fewer than three contributing companies) or if the underlying data has too much variance.

If your search does not return results, try expanding your criteria:

* Broaden the location filter
* Adjust company stage filters to include a wider range
* Select the family blend instead of an individual job family
* Check adjacent job levels

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