Gender pay gap methodology

The calculation of the gender pay gap involves linking datasets from the Income Tax Instalment Scheme (ITIS), Social Security contributions, and Manpower returns. These datasets are supplied by Revenue Jersey and the Employment, Social Security, and Housing Departments. Records are matched at the individual job level, and the necessary data is available from January 2022 onwards.

Data sources

  • Manpower returns: Employers submit monthly returns to the Government, providing details on all employees, including contract type (full-time, part-time, or zero-hour). Since January 2022, these returns have included contracted hours for dependently employed jobs.
  • Social Security contributions data: This dataset includes all dependently employed jobs where earnings are subject to Social Security. It contains company information, and employee earnings on a monthly basis.
  • ITIS returns: ITIS data records earnings for any job where a salary or wage has been paid. Unlike Social Security contributions, ITIS data does not have an upper earnings limit or a lower threshold below which earnings are not subject to Social Security. By combining ITIS and Social Security data, a complete picture of dependently employed jobs can be created, allowing for the calculation of both medians and means.
  • Demographic information is collated from various sources for our population estimates. To maintain consistency with other reports these demographic values of “best information available” are used. See Population and migration – Methodology and quality: December 2024 for additional details.

Whilst for the vast majority of employees (over 99.9%) gender is based on sex at birth provided by Employment, Social Security and Housing (ESSH), where a majority of other administrative sources hold different records to ESSH, then the recorded sex from those is used instead. Prior to June 2025, gender pay gap reports only used the sex at birth provided by ESSH.

Data cleansing and linking process

  • Jobs where no Social Security contributions were paid, and where the individual was classified as Class 2 (mainly self-employed), are excluded for that month.
  • Remaining jobs from the contributions dataset are matched with Manpower return data using a pseudo-anonymised ID for the individual and the company’s TIN or manpower code as unique identifiers.
  • This linking process ensures that employment sector (SIC 2007 classification), contract type, and contracted hours from the Manpower returns can be accurately matched to the Social Security contributions data.
  • Any inconsistencies, such as a full-time contract with contracted hours of less than 25, are flagged for imputation. Jobs missing contracted hours in their Manpower submission are also marked for imputation.
  • ITIS data is then linked using the previously assigned unique identifier. Jobs with no earnings in either ITIS or Social Security contributions are excluded, as they indicate no employment income for that month.
  • Jobs appearing solely in ITIS data and the individual is classified as Class 2 (mainly self-employed) are excluded, as they represent self-employment cases where a regular salary is taken.
  • Where earnings values differ between ITIS and Social Security contributions, ITIS values are used.

Imputation process

Records flagged for imputation are processed using a random forest algorithm1 to estimate missing contracted hours. The model incorporates multiple indicators and undergoes validation checks to ensure reliability. For example, the distribution of imputed values is compared to originally submitted values to maintain consistency.

Gender pay gap calculations

The gender pay gap is calculated as the percentage difference between male and female hourly rates of pay, calculated by dividing male average earnings by the corresponding average earnings for females. A positive value indicates that males earn more, while a negative value indicates that females earn more.

To ensure statistical reliability, results are only presented for groups containing at least 100 males and 100 females. Groups with fewer individuals are excluded due to potential large uncertainties, which could lead to misleading interpretations.2

Methodological changes

The current methodology used since the June 2024 report differs from that in our earlier experimental reports. The current methodology focuses on hourly rates of pay, which is considered best practice for gender pay statistics. In experimental analyses up to June 2023, we did not have the capability to calculate hourly pay and instead used overall monthly earnings, adjusted for full-time equivalency.

While this earlier approach partially accounted for differences in full-time and part-time employment between men and women, it did not adjust for variations in hours worked among full-time employees. The new methodology addresses this issue. For example, under the new method, if two employees are paid the same hourly rate but one works 40 hours per week and the other 38, no pay gap is recorded. Under the previous method, both would have been classified as full-time, leading to an artificial pay gap.

On average, men work more hours than women, both overall and within each sector. As a result, the current methodology generally produces slightly lower gender pay gap estimates compared with the approach used before June 2024.

Administrative changes

In February 2025 the Government of Jersey implemented the Contributions Function Integration (CFI). CFI moved Social Security contributions data from Employment, Social Security and Housing to Revenue Jersey’s Revenue Management System.

These changes impacted contributions data from July 2024 onwards, with an approximate reduction in the number of jobs in our final dataset by 0.4% and a reduction in median earnings by around 0.9%. As such, revisions to previously published results relating to January 2022 to June 2024 have been necessary to maintain a consistent series. The consistent series used in reports since June 2025, and in data tables on OpenData; reports prior to the June 2025 report have not been revised.

Data tables

Data tables and supplementary information associated with this release can be found on our open data site: Gender pay gap in Jersey – Datasets – Open Data

  1. https://onlinelibrary.wiley.com/doi/abs/10.1002/sam.11348↩︎

  2. Recommendation 8 https://rss.org.uk/RSS/media/News-and-publications/Publications/Reports%20and%20guides/RSS-s-10-Proposed-Reforms-for-Gender-Pay-Gap-Reporting.pdf↩︎