Age- and time-varying proportional hazards models for employment discrimination

TL;DR

This paper introduces a Bayesian smoothness-based proportional hazards model that captures how age and time influence employment termination risks, aiding legal discrimination analyses.

Contribution

It generalizes previous models by allowing age and time effects to vary continuously and smoothly, using a thin-plate spline approach with Bayesian priors.

Findings

Model effectively captures age-time effects on termination hazard.

Application to real discrimination case demonstrates practical utility.

Provides a flexible framework for legal employment discrimination analysis.

Abstract

We use a discrete-time proportional hazards model of time to involuntary employment termination. This model enables us to examine both the continuous effect of the age of an employee and whether that effect has varied over time, generalizing earlier work [Kadane and Woodworth J. Bus. Econom. Statist. 22 (2004) 182--193]. We model the log hazard surface (over age and time) as a thin-plate spline, a Bayesian smoothness-prior implementation of penalized likelihood methods of surface-fitting [Wahba (1990) Spline Models for Observational Data. SIAM]. The nonlinear component of the surface has only two parameters, smoothness and anisotropy. The first, a scale parameter, governs the overall smoothness of the surface, and the second, anisotropy, controls the relative smoothness over time and over age. For any fixed value of the anisotropy parameter, the prior is equivalent to a Gaussian process with linear drift over the time--age plane with easily computed eigenvectors and eigenvalues that depend only on the configuration of data in the time--age plane and the anisotropy parameter. This model has application to legal cases in which a company is charged with disproportionately disadvantaging older workers when deciding whom to terminate. We illustrate the application of the modeling approach using data from an actual discrimination case.