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Home - Fair-Pay-Matrix

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, the Catalyst Gender and Diversity KPI Alliance, 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

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