An Approach to Gender Pay Equity Analysis Using Bayesian Hierarchical Regression

TL;DR

This paper introduces a Bayesian hierarchical regression model for gender pay equity analysis, addressing industry data challenges and enabling faster, more comprehensive pay adjustment decisions in large organizations.

Contribution

It presents a novel Bayesian hierarchical approach that improves pay equity analysis by handling small samples and gender imbalance in industry data.

Findings

Reduced manual review time for pay adjustments

Effective analysis across large, diverse workforce

Addresses industry data limitations

Abstract

Diversity and inclusion, or D and I, is a topic that sparks the interest of companies, research groups, and individuals alike. Recently in the United States, renewed focus has been placed on fair and equitable pay practices, which are a key component of promoting diversity in the workplace. Despite the increased demand for reliable pay equity analysis, the challenges of conducting this type of analysis on industry data have not been adequately addressed. This paper explains a few limitations of current approaches to pay equity analysis by gender and improves on them with a Bayesian hierarchical regression model. Using global workforce data from a large U.S. semiconductor company, Micron Technology, Inc., the paper demonstrates how the model provides a holistic view of gender pay equity across the organization, while overcoming issues more common in industry data, such as small sample size and poor gender representation. When compared to a prior analysis of Micron's U.S. workforce, this approach decreased the amount of manual review required, enabling decision makers to finalize pay adjustments across a workforce of 31,738 people within four weeks of receiving preliminary model results.