Crowd Cut Data at your Fingertips!

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PayScale Crowd Cut Data is a crowdsourced data set that has been curated, modeled, and delivered in a format easily digested by compensation professionals. Currently, the Crowd Cut Data is delivered directly into PayScale MarketPay, or as an excel file.

Due to the nature of the PayScale survey offering, there are several areas where our data are particularly strong. White-collar, health care and tech jobs tend to be very well represented in the PayScale data set. Additionally, any job that faces a tight labor market and strong competition is well represented in our data.

  • Product Details
  • Pricing / Buy Now
  • Data Validation
  • Incumbent and Employer Counts/Cuts:
  • Data Reporting
  • Compensation Model

Product Details

Survey Statistics

  • Technology Crowd Cut
    • Information Technology / Cyber Security (168)
    • Software Development / Architecture (95)
    • Data Analysis / Modeling (67)
    • Quality Assurance / Control (34)
    • User Experience / Interface (27)
    • Gaming (33)
  • Digital Marketing (119)
  • Health Care Crowd Cut
    • Administrative / Support (360)
    • Assistants / Aids (96)
    • Leadership (327)
    • Nursing / Advanced Practice Providers (270)
    • Pharmacy (45)
    • Professional (Non-Physician) (267)
    • Technicians (258)
  • Warehouse, Shipping, Transportation, and Logistics
    • Warehouse
    • Transportation
  • Manufacturing
    • Apparel and Textile (105)
    • Chemical (309)
    • Food and Beverage (248)
    • Fabricated Metal (205)
  • Essential Staff
    • Maintenance (169)
    • Housekeeping, Grounds keeping, and Security
    • Food Workers (142)
  • Accounting, Legal, and Human Resources
    • Accounting
    • Legal
    • Human Resources

Position Families Analyzed

Data Collection: PayScale’s Crowdsourced Data currently has 60 million salary profiles, spanning 15,000 job titles. Every month, PayScale collects 300,000 new validated data points from users filling out the online salary survey to understand their price in the labor market. Each user sees, on average, 35 questions which asks them about their compensation, their job, their employer, their demographics, and multiple unique compensable factors (e.g., skills, certifications, management responsibilities, etc.). The survey leverages decision tree logic, which means the questions asked change according to the answers provided. For example, if a user tells us they are an airplane pilot, we ask what type of aircraft they fly as this information helps to define their compensation. As another example, we ask a set of follow-up questions to those who directly supervise people, such as whether they are responsible for hiring and firing and how many people they supervise. Workers can report wages for contract work, hourly pay, base salary, bonuses, commissions, tips, and more.


Accounting, Legal, and HR$1,000
  • Accounting $500
  • Legal $500
  • Human Resources $500
Digital Marketing $2,000
Health Care
  • Administrative / Support $2,000
  • Assistants / Aides $1,000
  • Leadership $2,000
  • Nursing / Advanced Practice Providers $2,000
  • Pharmacy $1,000
  • Professional (Non-Physician) $2,000
  • Technicians $2,000
Essential Staff $2,000
  • Housekeeping, Groundskeeping,
    and Security $1,000
  • Food Workers $1,000
  • Maintenance $1,000
$4,000 (incl'd warehouse and transportation)
$2,000 (pick 1 + warehouse and transportation)
  • Apparel and Textile $1,000
  • Chemical $2,000
  • Food and Beverage $1,500
  • Fabricated Metal $1,500
Warehouse, Shipping, Transportation & Logistics
  • Warehouse $1,000
  • Transportation $1,000
Technology $4,750
  • Information-Technology-Cyber Security $2,250
  • Software Development / Architecture $1,250
  • Data Analysis / Modeling $1,750
  • Quality Assurance / Control $1,000
  • User Experience / Interface $1,000
  • Gaming $1,000

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Data Validation

We validate each survey that comes in using a combination of machine learning and human validation.

  • Each survey passes through 25 hard coded validation points. If any survey fails any of these validation points, it is removed from the data set.
  • Within each job title, surveys that are deemed to be outliers, based on the reported compensation, are also removed.
  • PayScale uses a proprietary finger printing technology to detect and remove multiple submissions.
  • PayScale tracks submissions trends to identify and remove any questionable data.

Incumbent and Employer Counts/Cuts:

Counts: The compensation model uses all available data within a job/country combination to produce specific pay range estimates. Although the model focuses more heavily on those profiles that most closely match the details of the position and cut, all profiles within the job/country combination are utilized. Therefore, the incumbent counts listed for each specific cut represents the number of profiles available in the job/country combination.

The number of different employers represented is at least the number shown here. As mentioned above, we do not require data providers to disclose their employer name so the number we report is the minimum number of employers included.

Cuts: PayScale's Crowd Cut Data will report multiple cuts (labor markets) for each position. Location and employer type are crucial when pricing a health care positions.  We produced reports for more than 200 Metropolitan Statistical Areas (MSAs), which are regional areas defined by the United States Office of Management and Budget (OMB). You can learn more about MSAs here: The employer types found in each crowd cut are extensive and can be found in the corresponding file located in the Detailed Files section.

Data Reporting

We produce the following statistics for salary, bonus paid, and total cash compensation:

  • Minimum Number of Companies
  • Number of Incumbents
  • 10th Percentile
  • 25th Percentile
  • 50th Percentile
  • 75th Percentile
  • 90th Percentile
  • Average

Compensation Model

Compensation Model: PayScale employs a proprietary parametric Bayesian model for constructing pay ranges and estimates. This model produces pay ranges for individual positions conditional on the data provided. We model pieces of compensation both individually and at the aggregate level, so we have separate models at the job title/country level for base, bonus, and total cash compensation.

The model prioritizes both the most current and the most salient data, meaning recent profiles that most closely match the respondent’s compensable characteristics are factored more heavily in the creation of the conditional salary range. We assume a distribution from the double-Pareto log-normal family of distributions for compensation. This allows the data to follow an asymmetric bell curve that can have a variety of different shapes contingent on job title and location.

  • IsOrderingSetYes
  • Ordering TypeMultiLevelMultiSelection
  • Enable ParticipationNo
  • Sub TitleCrowd Cut Data at your Fingertips!
  • RegionUS
  • Short DescriptionA crowdsourced data set that has been curated, modeled, and delivered in a format easily digested by compensation professionals.