Canada Geographic Salary Differential Survey

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Canada Geographic Salary Differential Survey image The Canada Geographic Salary Differential Survey provides an analysis of pay differentials for about 77 Canadian locations, including provinces, regions and cities. Rather than using data interpolation, this survey provides specific salary differential information based on actual market data collected each year in Mercer's compensation surveys. This salary survey methodology ensures that the information reflects the ever changing Canadian compensation landscape.

With an increasing number of companies with operations across Canada, you need credible salary differential information to stay competitive. With this salary survey, you can consistently determine the cost of your staff all while handling employee relocation across the country. Design your effective compensation strategy so that you can attract and retain the best talent no matter what market you're in.

The survey has been enhanced to now include:

  • 1,964 jobs (79 jobs added in 2017).
  • Increased data pool used for geographic analysis.
  • Updated methodology to a single regression line.
  • Excel delivery.
  • Hyperlinks in the report.
  • More streamlined appearance and intuitive user interface with updated functionality.
  • Comparative analysis updated to limit the number of pay rates with default increments added.
  • A fixed data point with the national median figures.

GEO Features:

  • Analyse geographic salary differentials for about 77 locations in Canada.
  • Compare cities, regions and provinces to each other and to the national median
  • Compare differentials at varying salary levels you define - up to $150,000
  • View salary increase percentages for each location from Mercer's Canada Compensation Planning Survey
  • Create reports and graphs of data for the locations you select
  • Print or export the results
  • Accessible anytime, anywhere over the Internet

Survey Schedule
  • Date Effective: Jan 1*
  • Report Available: May 25
  • *2016 data has been aged to Jan 1, 2017

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