## How are location tiers created?

To properly allocate cities within each location tier, Pave completed an in depth data analysis on the different cities within the United States. We then grouped the cities based on locations that had similar labor force dynamics, market trends, and cost of living. Additionally we had numerous HR professionals assess our tiers. After sufficient alignment between the data and professionals, the current location tiering was finalized. This said, Pave’s data team is constantly analyzing our data and reserves the right to adjust the tiering when the data suggest it’s appropriate.

For more specific location data you can also select a location from the “Metros” section within our Market Data tool. This will allow you to see data specific to a certain metro such as the SF Bay Area or NYC Metro. You can also inquire about our premium data feature which opens up 60+ additional countries and metros! Please schedule a demo within your account in the Global Location Insights tab to learn more.

## How large is your dataset?

Pave's dataset increases with each new connection. To view the current count of employee records and participating companies, click here and scroll to the bottom of the page.

## How accurate/specific is the dataset?

Pave developed its universal employee compensation job classification system with [The People Design House](https://www.thepeopledesignhouse.com/), a team of established people operations leaders responsible for managing people operations and compensation for companies like Coinbase and Opendoor. This ensures that you’re benchmarking employee compensation against the right data despite differences in how companies title their employees.

To learn more about how Pave has approached job classifications [here](https://support.pave.com/hc/en-us/articles/17865677460631) and job levels [here](/doc/53f6720f-0960-49ed-8ff7-8701b48b1140).

## Who is in your dataset? Can I view a list of the participating companies?

Overall, the majority of the participating companies in our dataset are VC-backed tech companies. To view a list of participating companies and a breakdown of company statistics you must be an connected Pave customer that is using our Market Data tool. You can sign up [here](https://www.pave.com/benchmarking)!

## How often is Pave's Market Data dataset updated?

Our dataset is updated on a monthly basis.

## Does the Market Data account for inflation?

No. Pave does not adjust the market data to account for inflation. We are strictly pulling the data from the systems of our participating companies. The data is then aggregated and de-identified before it enters the production environment.

## What do the 25th, 40th, 50th, 60th, 75th, and 90th percentiles represent?

These percentiles represent the compensation that falls at the corresponding rank within the dataset. For instance, the 50th percentile for base salary represents the middle record base salary record.

## What is the difference between choosing blended and detailed on the leveling framework filter?

The Blended leveling frameworks blends several job levels together, while the Detailed leveling framework allows you benchmark with a much more granular approach.

* The Blended framework is more applicable for early-stage companies with broader levels and responsibilities.
* The Detailed framework is more applicable for later-stage companies with granular levels and responsibilities.

## How does Pave determine a role level and role family for employee compensation?

For non-Executive compensation market data, Pave uses data within your systems to match and label the existing roles at your company. For example:

* If the role manages people
* What team or department the role is in
* The role title
* The relative seniority of the role within the function

We know that this information isn’t the same at every company, or in every industry, so we use a mixture of machine learning and human validation to ensure that roles are classified accurately.

For Executive compensation, titles are used directly as they appear in data sources (i.e. HRIS/payroll systems)

**Pave's Leveling Schema**

Pave’s employee compensation leveling schema is designed for companies of all sizes. The schema is easily matched to Radford and Advanced HR so that the market data can stay current and accurate to what you might already be using. You can learn more information about our Leveling Schema in [this sheet.](https://docs.google.com/spreadsheets/d/18DZuAtJmpH-rCK2kf2QQkzvOcWMTGZtaQdVu1M4YYkU/edit?gid=575682463#gid=575682463)

Questions? Contact us [here](https://support.pave.com/hc/en-us/requests/new) or via the chat bot for help.

## Do you have data for hourly employees?

No, we do not currently offer data on hourly employees. What we recommend here is using the salary amount and breaking that down to an hourly amount.

## Do you have data for non profits?

Our participating companies are mostly VC-backed tech companies ranging in all sizes from small startup to enterprise, however the dataset does contain a few public companies. Because our Market Data tool was designed to support tech companies, we do not have a strong presence of non-profit companies in the dataset.

## How is Total Cash calculated? Why does it not equal Salary plus Variable?

Total Cash includes both employee records with and without variable pay. If an employee receives $100k base salary and another employee receives $100k base salary + $20k variable pay, the total cash benchmark would include both $100k and $120k as total cash values in the calculation.

Because the Total Cash data also includes the salary data of employees who do not receive variable pay, the sample size for Total Cash is not the exact same sample size for Salary Pay.

## Can I segment the data by industry?

**Employee Market Data**- Yes! The filters listed below are available for Employee Market Data

* Commercial Products & Services (Non-tech)
* Consumer Products & Services (Non-tech)
* Hardware & Devices
* Life Sciences
* Media, Entertainment, Gaming
* Technology: Software Products and Services
* Consumer Technology
* E-Commerce
* Enterprise Technology
* Financial Technology
* Healthcare Technology

**Executive Market Data**- Yes! The filters listed below are available for Executive Market Data:

* Consumer technology
* Enterprise technology
* Life sciences

## Is there a way to see how many years of experience are being considered for each job level?

The Market Data tool does not indicate the amount of experience typically associated with a job level, as we've found that employees with different levels of experience can similar tiles or have similar scopes of responsibility.

Instead of referencing years of experience, the Market Data tool provides common job titles and role descriptions designed to help you find the best fit for your team. To view these descriptions:


1. Select a job family
2. Select a leveling framework
3. Select a job level
4. View the skills and scope associated with the role
5. View the role's common job titles

## How can I benchmark a specific role if I can't find a match in the Market Data tool?

If you can't find an exact match for a specific role, we recommend reviewing [our list of job families](https://support.pave.com/hc/en-us/articles/17865677460631) to see if the role might fall under one of the job families that is currently in our production dataset.

Additionally, if you can’t find a role within Pave, its possible that we might call it something different! In this case, you can review the past roles of a few of your employees who you’re looking to benchmark and see if their experiences match a role we support.

Once you identify the job family that the role may fall under, you can use the role descriptions and common job titles in the Market Data tool to guide your search. To do this:


1. Select a job family
2. Select a job level
3. View the skills and scope associated with the role
4. View the common job titles

Our data diversity is increasing with each new integration. While you may not find an exact 1:1 match for a specific role, there's a good chance the data may be available in an upcoming monthly release.

## Is there a way to filter out founder data?

Yes. For C-level jobs, there is a “founder filter” that allows you to select whether you’re viewing data for founders, non-founders, or all executives. Note: this filter is not available for roles below C-level. This is because we saw that nearly all founders fall into a C-level role and the remaining levels can be assumed to be exclusively non-founders.

## Why is my search not returning results?

Pave is committed to showing you high quality data, which means data must pass our accuracy standards before being searchable. Data might not pass our accuracy standards for a few different reasons:

* Low sample size: Data will not be shown if fewer than three companies contribute to the underlying data. This is to maintain anonymity and provide high quality data.
* High variance: Data will not be shown if there is substantial variance in the underlying data, resulting in inconsistency in the benchmarks. This is to ensure only reliable benchmarks are being shown.

If your search is not returning results, we recommend expanding your search criteria. This can include adjusting the company stage filter, expanding to the blended job family, referencing adjacent job levels, changing location, and more.

## Why is the benchmarking data for a lower level greater than that of a higher level?

While it may seem counter-intuitive, sometimes the benchmark for a lower level job role is higher than that of a higher level role. This can happen for a few different reasons:

* Data Composition: The companies that comprise the benchmarks in a job family may vary across levels. If companies in adjacent levels have different compensation philosophies, the salary by level progression in the data may deviate from the expected level progression for an individual company.
* Data Distribution: Due to the distribution of data, there are instances where the lower and upper ends of distributions may overlap with adjacent levels. This is particularly true for the 10th and 90th percentiles, where the very bottom end of the P3 distribution may stretch lower than that of P2.
* External Factors: In instances where external factors impact the value of equity compensation e.g. stock appreciation, this can result in unintuitive variation between levels. If the data points in a given benchmark are from companies that have experienced high growth since equity was granted, it can cause benchmarks to be higher than adjacent levels, in cases where equity benchmarks are taking into account stock appreciation.

When this happens, we advise customers to look at the adjacent levels for the impacted role and come up with a smooth-line distribution across the varying levels that is appropriate for their organization and its needs.

## Why are there fewer Variable data points than Base Salary?

Variable pay benchmarks represent employees that receive greater than $0 of variable pay, known as undiluted variable pay benchmarks. Because not every employee receives variable pay, this is a subset of the employee records included in base salary and is often a smaller sample of data.

## Why are the sample sizes for new hire equity lower than ongoing equity and unvested equity?

New hire equity is limited to grants that have been issued in the past 24 months and only new employees that joined a company are eligible to be included in these benchmarks.

Ongoing equity includes all non-new hire grants issued in the past 12 months and all employees are eligible to be included in these benchmarks, regardless of when they joined the company. This typically results in larger sample sizes than new hire equity.

Unvested equity includes grants vesting in the future and all employees are eligible to be included in these benchmarks, regardless of when they joined the company. This typically results in larger sample sizes than both new hire equity and ongoing equity.

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