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PayScale API
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Welcome to PayScale's Jobalyzer API documentation.
About Payscale
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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
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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.
.. toctree::
:caption: Jobalyzer
:maxdepth: 2
jobalyzer/index
Data and Predictive Salary Model Methodology
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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.