  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.
