论文标题
使用贝叶斯分层回归的性别薪酬平等分析方法
An Approach to Gender Pay Equity Analysis Using Bayesian Hierarchical Regression
论文作者
论文摘要
多样性和包容性或D和我是一个引发公司，研究小组和个人的兴趣的话题。最近，在美国，重新集中于公平，公平的薪酬实践，这是促进工作场所多样性的关键组成部分。尽管对可靠的薪资公平分析的需求增加了，但对行业数据进行此类分析的挑战尚未得到充分解决。本文解释了当前通过性别支付权益分析的方法的一些局限性，并通过贝叶斯分层回归模型对其进行改进。该论文使用了美国大型半导体公司Micron Technology，Inc。的全球劳动力数据，展示了该模型如何提供整个组织中性别薪酬平等的整体观点，同时克服了在行业数据中更常见的问题，例如样本量和性别较差的性别代表性不佳。与先前对Micron的美国劳动力的分析相比，这种方法减少了所需的手动审查量，使决策者能够在收到初步模型结果的四个星期内完成薪酬调整的31,738人。
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.
