By Stephanie R. Thomas
Repayment equity is a common preoccupation in today’s place of work, from whispers round the water cooler to kabuki within the C-suite. Gender discrimination takes middle level in discussions of inner pay fairness, yet many different safe features will be invoked as grounds for alleging discrimination: age, race, incapacity, actual visual appeal, and extra. This wide diversity of vulnerability to discrimination fees is usually ignored in company exams of the way good reimbursement platforms conform to the legislations and fulfill worker norms of equity. Blind spots ordinarily fairness represent a major risk to organizational functionality and chance administration. In Compensating Your staff particularly, a revered practitioner and advisor lays out in useful phrases every thing you want to be aware of to guard your organization alongside the total spectrum of inner pay fairness concerns, together with all of the technical tools you must optimize compliance and reduce chance. Compensating Your staff Fairly is a well timed survey and complete guide for repayment experts, HR pros, EEO compliance officials, and in-house suggestions. It offers all of the info you want to make sure that reimbursement platforms are equitable, auditable, internally constant, and externally compliant with equivalent employment chance legislation and rules. the writer offers technical information—both felony and statistical—in commonsense phrases. Her non-technical breakdown of advanced statistical innovations distills simply up to practitioners want to know so that it will successfully installation and interpret the normal functions of statistical research to inner pay fairness. the focal point during the e-book is on real-world software, present examples, and up to date info on fresh and pending wrinkles within the evolving felony panorama. Readers of Compensating Your staff Fairly will learn: Why inner fairness in repayment concerns find out how to notice intentional and non-intentional discrimination in reimbursement the fundamentals of statistical inference and a number of regression research The necessities of knowledge availability, measurability, and assortment the standards for assessing repayment platforms for inner fairness tips to examine strength difficulties and react to formal proceedings and activities easy methods to stay away from litigation and installed position ongoing measures for proactive self-auditing What you’ll learnReaders of Compensating Your staff Fairly will research: Why inner fairness in reimbursement concerns easy methods to observe intentional and non-intentional discrimination in repayment how one can examine capability difficulties and react to formal lawsuits and activities the right way to stay away from litigation and installed position ongoing measures for proactive self-auditing Who this booklet is for HR pros, reimbursement experts, EEO compliance officials, in-house suggestions, and employment lawyers will locate worthy the specialist author’s non-technical therapy of the technical matters which are necessary to realizing all features of inner pay fairness. with no operating knowing of the way to make their info inform a transparent tale, those numerous execs can't make sure that their reimbursement structures are equitable, auditable, and demonstrably compliant with equivalent employment chance legislation and rules. desk of Contents Why fairness in reimbursement concerns sorts of Discrimination in reimbursement a number of Regression research the information Regression types of equivalent Pay Other Tests of equivalent Pay research Follow-Up The altering panorama of Pay fairness Enforcement factors of the Gender Pay hole Litigation Avoidance and Proactive Self-Analysis the fundamentals of Statistical Inference
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Additional info for Compensating Your Employees Fairly: A Guide to Internal Pay Equity
27 International Brotherhood of Teamsters v. S. 324, 339–340, 1977. 28 City of Richmond v. A. S. 469, 501, 1989. 23 Compensating Your Employees Fairly Within the context of compensation discrimination, plaintiffs typically offer statistical analysis in the form of a multiple regression to demonstrate the employer’s pattern or practice of discrimination and establish a prima facie case. 29 Lower courts have followed suit in cases of individual and systemic disparate treatment. In Sweat v. S. 30 Defendant employers also frequently use statistical analysis to demonstrate affirmative defenses.
For ease of explanation, I initially will limit the discussion to a two-variable regression analysis, focusing only on the hourly rate of pay and seniority. The discussion will later be expanded to a three-variable analysis that examines the relationship between the hourly rate of pay, seniority, and gender. 13 aetzhold and Willborn argue that the phrase “independent variable” is misleading, P because the explanatory variables are not independent of the dependent variable, and it is the relationship between the dependent and explanatory variables that is being examined (The Statistics of Discrimination, p.
It may also be the case that the employer refused to hire qualified female applicants before three years ago based on the gender of the applicant. This would be a discriminatory reason for differences in seniority. Although this may support a claim of hiring discrimination, this is a separate issue unrelated to the claim of compensation discrimination. 8 For simplicity in explanation, I focus on a two-variable model. The discussion will be expanded to a multivariable model later in this chapter.