This article will provide insights into how Pave calculates the data in our Market Data product.
Table of contents:
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Who is in your dataset? Can I view a list of the participating companies?
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What do the 10th, 25th, 40th, 50th, 60th, 75th, and 90th percentiles represent?
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What is the difference between choosing blended and detailed on the leveling framework filter?
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How does Pave determine a role level and role family for employee compensation?
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How is Total Cash calculated? Why does it not equal Salary plus Variable?
- Can I segment the data by industry?
- Is there a way to see how many years of experience are being considered for each job level?
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How is equity represented and calculated within the Market Data tool?
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How can I benchmark a specific role if I can't find a match in the Market Data tool?
- How is Total Cash calculated? Why does it not equal Salary+Variable?
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, 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 and job levels here.
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!
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 10th, 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.
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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.
Questions? Contact us here 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?
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- Not yet! Our team is currently working on a feature that will allow the market data to be filtered by industry. While we don't yet have an expected launch date for the update, we'll certainly send an email to all Pave admins once the feature is live.
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:
- Select a job family
- Select a leveling framework
- Select a job level
- View the skills and scope associated with the role
- View the role's common job titles
How is equity represented and calculated within the Market Data tool?
Pave offers 4 options when Benchmarking for equity: Total Equity, New Hire Equity, Refresh Equity and Unvested Equity
Total Equity is displayed as the total gross value of all grants. This is calculated as the total number of granted shares multiplied by the preferred share price from a company's most recent round of funding. This includes the impact of stock appreciation as well as any additional grants that may have been received.
New Hire Equity is displayed as the value of the first grant received by an employee at the time that it was received. This is calculated as the number of shares in the employee’s first grant (options or common stock) multiplied by the preferred share price of the company at the time of issuance. There is no additional grants or stock appreciation impact.
Unvested Equity is displayed as the value of all the equity shares received by an employee that have not yet vested
Refresh Equity is displayed as the value of the non-new hire grants received by an employee in the past 12 months.
*Unvested and Refresh Equity are only available to Premium Data subscribers*
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 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:
- Select a job family
- Select a job level
- View the skills and scope associated with the role
- 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.
How is Total Cash Calculated? Why does it not equal Salary+Variable?
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.