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Compensation fairness is a universal preoccupation in today’s workplace, from whispers around the water cooler to kabuki in the C-suite. Gender discrimination takes center stage in discussions of internal pay equity, but many other protected characteristics may be invoked as grounds for alleging discrimination: age, race, disability, physical appearance, and more. This broad range of vulnerability to discrimination charges is often neglected in corporate assessments of how well compensation systems comply with the law and satisfy employee norms of fairness. Blind spots in general equity constitute a serious threat to organizational performance and risk management. In Compensating Your Employees Fairly, a respected practitioner and consultant lays out in practical terms everything you need to know to protect your company along the full spectrum of internal pay equity issues, including all the technical methods you need to optimize compliance and minimize risk.

Compensating Your Employees Fairly is a timely survey and comprehensive handbook for compensation specialists, HR professionals, EEO compliance officers, and in-house counsel. It provides all the information you need to ensure that compensation systems are equitable, auditable, internally consistent, and externally compliant with equal employment opportunity laws and regulations. The author presents technical information—both legal and statistical—in common-sense terms. Her non-technical breakdown of complex statistical concepts distills just as much as practitioners need to know in order to effectively deploy and interpret the standard applications of statistical analysis to internal pay equity. The focus throughout the book is on real-world application, current examples, and up-to-the-minute information on recent and pending wrinkles in the evolving legal landscape.

Readers of Compensating Your Employees Fairly will learn:

Why internal equity in compensation matters How to detect intentional and non-intentional discrimination in compensation The basics of statistical inference and multiple regression analysis The essentials of data availability, measurability, and collection The criteria for assessing compensation systems for internal equity How to investigate potential problems and react to formal complaints and actions How to avoid litigation and put in place ongoing measures for proactive self-auditing

Inhaltsverzeichnis

Frontmatter

Chapter 1. Why Equity in Compensation Matters

Abstract
Do you compensate your employees fairly?
Stephanie R. Thomas

Chapter 2. Types of Discrimination in Compensation

Abstract
Under U.S. law, two main theories of discrimination are recognized by the courts. According to Ramona Paetzhold and Steven Willborn, these theories “reflect the two different conceptions of the behaviors and processes that produce discriminatory results.” One theory focuses on the intent of the decision maker; discrimination occurs when a decision maker acts with discriminatory intent, known as disparate treatment. The other theory focuses on the policies and procedures used by the decision maker; discrimination occurs when a given policy or procedure has disproportionate effects on members of different groups, called disparate impact.
Stephanie R. Thomas

Chapter 3. Multiple Regression Analysis

Abstract
How do we examine compensation data for the presence or absence of discrimination?
Stephanie R. Thomas

Chapter 4. The Data

Abstract
A good data set is the foundation of a successful statistical review of compensation. The old adage of “garbage in, garbage out” could not be more fitting. Any quantitative analysis is only as good as the underlying data set. Inaccuracies and inconsistencies in the data not only can render the analysis inaccurate and unreliable but can also lead to incorrect inferences regarding the relationships being investigated. If these incorrect inferences are applied to business decisions, an employer could unintentionally create the very situation it was trying to remedy.
Stephanie R. Thomas

Chapter 5. Regression Models of Equal Pay

Abstract
Since 1975 multiple regression analysis has been the preferred statistical technique for identifying compensation discrimination based on protected class status. This technique is used by plaintiffs and defendants to demonstrate discrimination (or lack thereof), and was approved by the U.S. Supreme Court for analysis of pay discrimination in 1986.
Stephanie R. Thomas

Chapter 6. Other Tests of Equal Pay

Abstract
Although multiple regression analysis is the preferred statistical technique for examining questions of internal pay equity, a variety of other statistical and nonstatistical techniques are often used. Some common tools for examining disparate treatment in compensation include a comparison of means and medians, t-tests, cohort analyses, and tipping point and threshold tests.
Stephanie R. Thomas

Chapter 7. Analysis Follow-Up

Abstract
Analysis follow-up is a crucial step in the compensation review process. If the results of a given analysis indicate the possibility of a disparity, either by protected group status or within the context of overall equity, that disparity should be investigated. Without proper follow-up, the opportunities to learn from the analysis and correct potential problem areas are lost.
Stephanie R. Thomas

Chapter 8. The Changing Landscape of Pay Equity Enforcement

Abstract
Since the late 2000s and early 2010s, there have been major changes in the legal and regulatory environment regarding compensation discrimination, and there are even more on the horizon. These changes encompass both individual claims and claims of systemic compensation discrimination.
Stephanie R. Thomas

Chapter 9. Causes of the Gender Pay Gap

Abstract
The movement of women into the labor force has been referred to by some as the greatest social transformation of our time.
Stephanie R. Thomas

Chapter 10. Litigation Avoidance and Proactive Self-Analysis

Abstract
Self-analysis of internal pay equity is a valuable tool in the employer’s risk management toolbox. Unfortunately, few organizations make use of this strategy. In light of the changing landscape of pay equity enforcement, coupled with dramatic increases in employment litigation and regulatory investigation, employers can no longer afford to ignore this important tool.
Stephanie R. Thomas

APPENDIX A. The Basics of Statistical Inference

Abstract
Statistics is a branch of mathematics that focuses on the collection, presentation, and analysis of quantitative information. Generally speaking, there are two kinds of statistics: descriptive and inferential. Descriptive statistics summarize information; examples include averages, medians, minimums and maximums, percentages, charts, and graphs. Descriptive statistics are used to describe what is going on in the data.
Stephanie R. Thomas

Backmatter

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