Compensating Your Employees Fairly : A Guide to Internal Pay Equity / / by Stephanie R. Thomas

• Autore: Thomas Stephanie R
• Pubblicazione: Berkeley, CA : , : Apress : , : Imprint : Apress, , 2013
• Edizione: 1st ed. 2013.
• Descrizione fisica: 1 online resource (295 p.)
• Disciplina: 658.3/22
• Soggetto topico: Business; Management science; Business and Management, general
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Contents; About the Author; Acknowledgments; Chapter 1: Why Equity in Compensation Matters; Federal Equal Pay Laws and Regulatory Climate of the Twentieth Century; Equal Pay Laws and Regulations in the Twenty-first Century; The Ledbetter Fair Pay Act; National Equal Pay Enforcement Task Force; Proposed Legislation; The Fair Pay Act of 2011; The Paycheck Fairness Act; The Business Case for Internal Pay Equity; Gender Equity versus Overall Equity; Why Gender Equity Is the Focus; Why You Should Be Concerned About General Equity; Chapter 2: Types of Discrimination in Compensation ; Disparate TreatmentThe Theory of Disparate Treatment; Stages of a Disparate Treatment Claim; The Prima Facie Case; The Prima Facie Case under the Equal Pay Act; The Prima Facie Case under Title VII, ADEA, and ADA; Affirmative Defenses; Pretext; Use of Statistics in Disparate Treatment Claims; Identifying and Preventing Disparate Treatment; Disparate Impact; The Theory of Disparate Impact; Stages of a Disparate Impact Claim; Use of Statistics in Disparate Impact Claims; Disparate Impact in Compensation; Identifying and Preventing Disparate Impact; Chapter 3: Multiple Regression Analysis ; Correlation and CausalityCorrelation; Causality; The Basics of Regression Analysis; Two-Variable Analysis; Three-Variable Analysis; The Classical Linear Regression Model; Assumptions of the Classical Linear Regression Model; Violating Assumption 1: Misspecification; Omitted and/or Irrelevant Variables; Omitted Variables; Inclusion of Irrelevant Variables; Structural Change; Nonlinear Models; Violating Assumption 2: Nonzero Error; Errors in Measurement of Dependent Variable; Errors in Measurement of Independent Variables; Violating Assumption 3: Nonspherical Disturbances; Heteroscedasticity ; AutocorrelationViolating Assumption 4: Multicollinearity; Violating Assumption 5: Simultaneous Equations; Interpretation of Regression Results; Coefficients; Statistical Significance; Practical Significance; Sample Size; Explanatory Power; Inferences of Discrimination; Tools for Performing Multiple Regression; Chapter 4: The Data; Similarly Situated Employee Groupings; Compensation Metrics; Base Pay; Overtime Earnings; Variable Pay; Total Compensation; Rates vs. Raises; Factors Explaining Compensation; Human Capital; Tainted Variables; Dummy Variables; Data Measurability ; Accessibility of DataData Collection and Assembly; Data Cleaning and Verification; Chapter 5: Regression Models of Equal Pay; The Classical Model; Separate Equations Model; The Interaction Model; The Overall Equity Model; Other Models; Chapter 6: Other Tests of Equal Pay; Other Disparate Treatment Tests; Comparison of Means and Medians; t -Test; Independent Samples and Equal Variances; Independent Samples and Unequal Variances; Calculation; Tipping Points and Threshold Tests; Cohort Analysis; Disparate Impact Models; The Four-Fifths Rule; Origin of the Four-Fifths Rule; Calculation ; Limitations of the Four-Fifths Rule
• Sommario/riassunto: 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.
• Altri titoli varianti: Guide to internal pay equity
• Titolo autorizzato: Compensating Your Employees Fairly
• ISBN: 1-4302-5042-9
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910739408303321
• Lo trovi qui: Univ. Federico II