How We Process Our Data

Institutions provide information to us in a variety of formats and following many different conventions.

Accordingly, we take several steps to standardize this data for use on our site.


We understand that the processing we apply may result in a loss of fidelity or specificity, especially in regards to race. This standardization allows us to build reports about gender- and race-based pay gaps, which we believe to be essential when discussing equitable pay.

Additionally, despite our best efforts, institutions occasionally make mistakes or misreport information.

For instance, an employee may only work a period of 9 months per year, but their salary may be inflated and reported as if they work for a period of 12 months.

We're constantly refining our requests and processes to avoid these types of errors, but we know we can't catch everything.

If you have any feedback, we'd love to hear it at

Standardization Process

General Transformations

  • If the institution does not collect, report, or provide the specific data requested; or if the information provided is unusable, we transform this data to "Not Provided"
  • If the institution provides data contain universal pre- or postfixes, we remove them
  • We process the data into a UTF-8 compliant format

Name Transformations

  • We transform the data provided to be in "First Middle Last" order
  • Our processing may unintentionally remove diacritical marks

Salary Transformations

  • We round all salary data up to the nearest whole cent
  • When making calculations based on salary data, such as determining a median salary, we exclude any salaries below $1,000

Employment Time Transformations

  • We report 40 hours per week as "full-time" and anything less than 40 hours per week as "part-time"
  • We are unable to directly report data for hours-worked due to variances in institutional reporting

Demographics Transformations

  • We match the definitions used by the U.S. Census for collecting data on race, with the addition of "Hispanic or Latino" and "Two or more races", as most institutions do not discretely report race and ethnicity
  • We transform employee records containing multiple specified races, e.g., "Black, White", to "Two or more races"

Data Retention and Removal

  • We do not intentionally collect nor report data for hourly employees or student workers and take steps to delete this data if it is provided to us
    • We never request data regarding student workers and we specify that we are not seeking data about student workers when submitting requests to institutions
  • We respond to reasonable removal requests, but we do not ever remove data for presidents, executives, or other very highly paid employees
  • We investigate reported inaccuracies, but we do not update individual entries on-demand
  • We do not retain historic data regarding former employees nor positions/salaries formerly held by current employees
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