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Local linear Peters-Belson regression and its applications to employment discrimination cases.

机译：局部线性Peters-Belson回归及其在就业歧视案例中的应用。

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摘要

In discrimination cases concerning equal pay, the Peters-Belson (PB) regression method is used to estimate the pay disparities between minority and majority employees after accounting for major covariates (e.g., seniority, education). Unlike the standard approach, which uses a dummy variable to indicate protected group status, the PB method first fits a linear regression model for the majority group. The resulting regression equation is then used to predict the salary of each minority employee by using their individual covariates in the equation. The difference between the actual and the predicted salaries of each minority employee estimates the pay differential for that minority employee, which takes into account legitimate job-related factors. The average difference estimates a measure of pay disparity. In practice, however, a linear regression model may not be sufficient to capture the actual pay-setting practices of the employer. Therefore, we use a locally weighted regression model in the PB approach as a specific functional form of the relationship between pay and relevant covariates is no longer needed. The statistical properties of the new procedure are developed and compared to those of the standard methods. The method also extends to the case with a binary (1-0) response, e.g., hiring or promotion. Both simulation studies and re-analysis of actual data show that, in general, the locally weighted PB regression method reflects the true mean function more accurately than the linear model, especially when the true function is not a linear or logit (for a 1-0 response) model. Moreover, only a small loss of efficiency is incurred when the true relation follows a linear or logit model.
机译：在涉及同等报酬的歧视案例中，在考虑了主要协变量（例如年资，学历）之后，使用了Peters-Belson（PB）回归方法来估计少数族裔与多数雇员之间的薪资差距。与使用伪变量指示受保护组状态的标准方法不同，PB方法首先适合大多数组的线性回归模型。然后，将所得的回归方程式用于通过在方程式中使用他们各自的协变量来预测每个少数雇员的薪水。每个少数族裔雇员的实际工资与预期工资之间的差额估计了该少数族裔雇员的工资差异，其中考虑了与工作相关的合法因素。平均差异估计了薪资差异的程度。但是，在实践中，线性回归模型可能不足以捕捉雇主的实际薪酬设定做法。因此，我们在PB方法中使用局部加权回归模型，因为不再需要薪酬与相关协变量之间关系的特定函数形式。开发了新程序的统计属性，并将其与标准方法的统计属性进行比较。该方法还扩展到具有二进制（1-0）响应的情况，例如雇用或晋升。仿真研究和对实际数据的重新分析均表明，一般而言，局部加权PB回归方法比线性模型更准确地反映真实均值函数，尤其是当真实函数不是线性或对数时（对于1- 0响应）模型。此外，当真实关系遵循线性或对数模型时，效率损失很小。

著录项

  • 作者

    Hikawa, Hiroyuki.;

  • 作者单位

    The George Washington University.;

  • 授予单位 The George Washington University.;
  • 学科 Statistics.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 268 p.
  • 总页数 268
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 统计学;
  • 关键词

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