Workforce analytics is an evolving measurement approach in human resource (HR) planning and strategy implementation. Workforce analytics can help organizations manage one of their most important resources: their human capital. The purpose of this paper is to propose a diversity metric, called the D-Metric, as a new tool for HR planning. The D-Metric can be used to assess the demographic representativeness of employees across skill categories of an organization’s workforce compared to its relevant labor markets.
The authors present a real example and discuss possible applications of the D-Metric in HRM strategic planning and diversity research.
The D-Metric is a statistic useful in assessing demographic representativeness in the occupational categories of an organization’s workforce compared to the demographics of its relevant labor markets. The methodology could be implemented to assess an organization’s work force representativeness on dimensions such as race, sex, age and pay levels. When the labor market is unitary, without measurable variance, a substitute metric, the U-Metric also presented in this paper, can be used.
Use of the D-Metric requires publicly available labor market data with variance across labor market segments.
There currently is no published metric that evaluates the representativeness of an organization’s work force relative to its relevant labor markets. Many organizations seek a demographically representative workforce to better understand their diverse customer segments. Monitoring the representativeness of an organization’s work force, as captured in Equal Employment Opportunity (EEO-1) forms in the USA, for example, is an important component of HR management strategy. From a legal perspective, the D-Metric or the alternative U-Metric, could be useful in showing progress toward a demographically representative work force.
Buttner, E. and Tullar, W. (2018), "A representative organizational diversity metric: a dashboard measure for executive action", Equality, Diversity and Inclusion, Vol. 37 No. 3, pp. 219-232. https://doi.org/10.1108/EDI-04-2017-0076Download as .RIS
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