What is salary?

In Custom Roles, salary represents the estimated annual base salary range, excluding bonuses, benefits, or additional compensation. We define the low salary as the 25th percentile and the high salary as the 75th percentile.

What can I use this data for?

You can use salary data to understand how compensation varies for different roles or locations. 

Why does Custom Roles have its own AboutTheData documentation?

For certain metrics, our methodologies differ significantly between Custom Roles and our self-service platforms, Plan and Recruit.

Generally, Custom Roles reports provide labor market data on locations that aren’t available in Plan or Recruit, or on roles that are too niche to be represented by job posting data. Because of this, we sometimes rely on different sources and more manual processes to calculate labor market metrics. We therefore don’t recommend comparing data between Custom Roles and Recruit or Plan.

What is the methodology for salary?

We gather salary data from multiple sources, including:

  • Government entities like the United States Bureau of Labor Statistics, the Australian Bureau of Statistics, and the Brazilian Institute of Geography and Statistics

  • Job postings on job boards, corporate job sites, and staffing sites

  • Literature from organizations like universities, financial institutions, and non-governmental organizations

  • Our own historical salary datasets

Because salary data varies from location to location, it must be standardized before it can be analyzed. To do this, we run a number of checks and conversions to account for differences in currency, pay period, and more.

  • Currency: For example, distinguishing between the United States dollar and the Canada dollar

  • Pay period: For example, converting hourly wages to annual pay

  • Abbreviations: For example, converting 10K to 10,000, and 1.5 lakh to 150,000

  • Numerical formatting: For example, converting 1.500,00 to 1,500.00

Because we gather a large number of salary data points for each role, we assume that our dataset has a normal distribution. To reduce the impact of any outliers on the estimated salary range, we calculate the 25th and 75th percentiles to represent the low and high salaries, respectively.

If we are only able to gather a limited number of salary data points for a role, we increase the sample size by considering salary data for similar roles. We also use our historical datasets to identify and investigate any abnormalities in the data.