The Fair Pay Matrix
To measure and eliminate pay discrimination, a whole range of informative indicators and characteristics of discrimination can be enlisted to identify unequal treatment.
The following overview gives possible variables and key performance indicators (KPIs) that can be used to assess progress in equal pay implementation and gauge the success of equality policies.
Calculating differences in income
The basis for calculating the variables and indicators is formed by employee data that are often routinely captured digitally via HR software. In the UK, for example, the BBC (the public service broadcaster) breaks down very precisely which characteristics lead to which discrepancies. Thus in 2020, differences in income amounting to 4.9% were attributable to a disability, 3% to ethnic origin, 3.9% to part-time employment, and -0.3% to sexual orientation. Often, several grounds of discrimination (such as gender or ethnic origin) are compounded for individual employees, resulting in even greater income disparities. Overall, pay gaps based on ethnic origin are much more closely monitored in the USA or New Zealand than has been the case to date in most other countries.
Keeping an eye on discrimination
The Fair Pay Matrix brings together various types of discrimination in a simple overview and displays the relevant data like a car dashboard. This enables decision-makers and HR managers to map and monitor fair pay implementation. The analysis is possible for companies with at least 50 employees. It is based on the statistical method of multiple regression analysis, using Blinder-Oaxaca decomposition, which is regarded as the benchmark in gender pay gap analysis. The Fair Pay Matrix serves as a basis for analyzing the pay structures in a company or organization; the variables and indicators shown here can be expanded.
Expanding room for maneuver
The matrix can serve as the basis for the test methods that show companies and organizations where the action is needed and where adjustments can be made in order to implement equality and fair pay.
The indicators can also be matched to the UN Sustainable Development Goals 5 (Achieve gender equality and empower all women and girls), 8 (Promote sustained, inclusive, and sustainable economic growth, full and productive employment, and decent work for all), and 10 (Reduce inequality within and among countries). The matrix can thus be used to monitor how well a company is aligned to the SDGs. The implementation of the whole United Nations Agenda 2030 depends decisively on whether equality can be achieved as an across-the-board objective.
In addition, the indicators can be matched to the Global Reporting Initiative, or the metrics developed by the World Economic Forum.
Variables | Connection to UN Sustainable Development Goals | Connection to other metrics and indices | Basis of discrimination |
---|---|---|---|
Indicators of possible unequal treatment | |||
Year of birth / age | 8.5, 10.2 | GRI 405-1 WEF Metrics |
Discrimination based on age |
Sex | 8.5 | GRI 405-1 WEF Metrics |
Discrimination based on sex |
Disability | 8.5, 10.2 | GRI 405-1 WEF Metrics |
Discrimination based on disability |
Sexual orientation | 8.5, 10.2 | GRI 405-1 WEF Metrics |
Discrimination based on sexual orientation |
Ethnic origin | 8.5, 10.2 | GRI 405-1 WEF Metrics |
Discrimination based on ethnic origin |
Nationality | 8.5, 10.2 | GRI 405-1 WEF Metrics |
Discrimination based on nationality |
Indicators of structural inequalities | |||
Education | - | - | |
Further training | 5.5 | Restricted access to continuing professional development | |
Role | 5.5 | GRI 405-2 WEF Metrics GDKA |
Vertical discrimination |
Position | 5.5 | GRI 405-2 WEF Metrics GDKA |
Vertical discrimination |
Level of qualifications | 5.5 | Horizontal discrimination | |
Time out, e.g. parental leave, nursing leave | 5.4 | Personal responsibilities that affect career development | |
Years of service / tenure | 5.4 | Tenure is affected by time out | |
Contract type | - | Variable that has a significant influence on the pay gaps | |
Working hours | - | Variable that has a significant influence on the pay gaps | |
Public / private sector | - | Variable that has a significant influence on the pay gaps | |
Company size | - | Variable that has a significant influence on the pay gaps | |
Location | - | Variable that has a significant influence on the pay gaps | |
Dependent variables | |||
Basic salary | All those named | All those named | Sum total of all factors |
Salary components, incl. bonuses and allowances | All those named | All those named | Sum total of all factors |
Total compensation | All those named | All those named | Sum total of all factors |