PayScale API¶
Welcome to PayScale's Jobalyzer API documentation.
About Payscale¶
Launched in 2002 and headquartered in Seattle, Washington, PayScale, Inc. runs the industry’s largest online compensation survey and maintains over 60 million current pay records, collecting more than 200,000 new profiles per month. PayScale provides employees and employers across the globe with instant access to accurate salary data that includes never before available information on pay influencers such as years in field, skills, education, and certifications. PayScale’s patented methodology for compiling, analyzing, and aggregating compensation data allows users to search for data that matches the specific attributes of a company or position, providing them with a precise snapshot of current market pay.
Jobalyzer API¶
Jobalyzer is an HTTP RESTful PayScale service for generating various compensation reports. Users provide compensable factors such as job title, city, state, country, and skills and the service returns detailed values around pay. Jobalyzer interfaces with PayScale internal data architecture and our proprietary model to generate these reports. The service also provides several non-pay related reports, such as finding the top skills for a given job title and autocomplete for compensable factors. All inputs and outputs are in JSON format.
Jobalyzer User's Guide¶
This part of the documentation, which is mostly prose, begins with some background information about PayScale, and covers step-by-step instructions for communicating with the API.
Data and Predictive Salary Model Methodology¶
Payscale's Crowdsource Data¶
The data leveraged within Jobalyzer comes from the PayScale online salary survey. People complete a salary profile on PayScale’s website for many reasons, but mostly to prepare to ask for a raise, evaluate a job offer, or just to know how they stack up against others in similar positions. Upon completing PayScale’s salary survey, individuals receive a series of reports that show how their salary compares to other people with similar education, skills and work experience. Individuals can also explore how changes such as moving to a different city, getting a promotion and going back to school can affect their future earning potential. This survey, which is ongoing, incentivizes respondents to provide their information by offering an individualized report of how people like them with the same or a very similar job title are compensated.
The main body of the survey collects demographic information (e.g. age, ethnicity, degrees obtained, major, educational institution) as well as other relevant worker characteristics (e.g. job title, years’ experience, job title, skills critical for the job, certifications, management responsibilities) and labor market traits (e.g. location, industry, company size). The survey logic is responsive to each respondent’s job title and previous answers. For example, PayScale collects information on the type of aircraft that airline pilots fly as that is one of the chief determinants of pay. Similarly, PayScale will ask a set of follow-up questions to those who directly supervise people, such as whether they are responsible for hiring and firing.
Payscale's Salary Model¶
PayScale employs a proprietary parametric Bayesian model for constructing pay ranges and estimates. Although the model has the flexibility to produce estimated conditional distributions for a range of variables, Jobalyzer primarily relies on the model to produce pay ranges for individual respondents; conditional on the data they provide. PayScale model’s pieces of compensation both individually and at the aggregate level, so PayScale has developed 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 requested compensable characteristics are factored more heavily in the creating the conditional salary range. Compensation for the majority of job titles follows a double-Pareto lognormal distribution. This allows the data to follow an asymmetric bell curve that can have a variety of different shapes contingent on job title and location, which provides granularity in the pay predictions.