Creating tables and charts showing the following comparisons for men and women can be useful:
- Distribution of employees by job classification
- Distribution of employees by grade
- Average annual full-time equivalent remuneration by job classification
- Average annual full-time equivalent remuneration by job grade
- Proportion of employees in bands of hours
- Percentage of employees in each level
- Term of employment
- Percentage of those receiving and not receiving bonuses
- Composition of full time equivalent annual remuneration
- Employees in red-circled or market-rated jobs
- Job size by full time equivalent remuneration
- Employees by grade by average hourly remuneration by average job size
- Ratio of hourly pay by occupation
- Occupation by starting rate.
Although these charts and tables provide examples of analysis that have been found useful in pay and employment equity reviews, as they reflect factors associated with pay and employment outcomes for women and men, you may find other types of analyses that are relevant to your particular organisation or industry.
You can also use more advanced types of analysis such as ‘R’ regression analysis and scatter plots; box-and-whisker plots can be used to identify and show how remuneration relates to the other variables collected in the data.
If an organisation needs help with generating these charts the pay and employment equity review analysis suite may be useful.
The pay and employment equity review analysis suite
This analysis suite includes an Excel workbook (Excel 2003) [XLS 6.8MB] and an associated user guide.
An organisation enters their payroll and HR data into the workbook and then the data is analysed by gender. Note that the Ministry of Business, Innovation and Employment (MBIE) cannot provide assistance or technical support in relation to these workbooks.
Whether you choose to use the Excel workbooks or analyse your data by another method, the user guide will assist you with the process of collecting your data, preparing a gender profile and analysing the data. You may also wish to use R to complete your statistical analysis.