Compensating Your Employees Fairly A Guide to Internal Pay Equity
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 dis...
| Autor principal: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Berkeley, CA :
Apress
2013.
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| Edición: | 1st ed. 2013. |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009469564406719 |
Tabla de Contenidos:
- 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
