Use of Statistics in Equal Employment Opportunity Litigation

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by Walter B. Connolly, Jr., David W. Peterson, Michael J. Connolly

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Use of Statistics in Equal Employment Opportunity Litigation examines legal precedents for the use of statistics, the plaintiff's burden of establishing a prima facie case, and statistical concepts. Featuring charts and diagrams, it includes in-depth coverage of: the hiring process; job assignment, transfer and promotion; discipline and discharge; equal pay, wage mobility and pay awards; age discrimination, including corporate “downsizings”; estimating the racial composition of an employer's labor pool; and testing and selection procedures. A special section analyzes various models for detecting disparate treatment and evaluating employer alternatives.

Book #00553; looseleaf, one volume, 900 pages; published in 1979, updated as needed.
ISBN: 978-1-58852-006-7

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  • Availability: Available
  • Brand: Law Journal Press
  • Product Type: Books
  • Edition: 0
  • Page Count: 900
  • ISBN: 978-1-58852-006-7
  • Pub#/SKU#: 553
  • Volume(s): 1

Author Image
  • Walter B. Connolly, Jr.
Walter B. Connolly, Jr. is a member of the Detroit office of Connolly, Rodgers & Scharman, PLLC.

Also by Walter B. Connolly, Jr.:
Practical Guide to The Occupational Safety and Health Act
Practical Guide to Equal Employment Opportunity

Author Image
  • David W. Peterson
Mr. Peterson is an independent statistical consultant based in the Research Triangle area of North Carolina. He is a former Duke University Professor known for his work on the courtroom use of scientific evidence.

Author Image
  • Michael J. Connolly

Michael J. Connolly is a member of the Detroit office of Connolly, Rodgers & Scharman PLLC. He was formerly General Counsel of the EEOC.

Also by Michael J. Connolly:
Practical Guide to Equal Employment Opportunity

Survey of the Use of Statistics in Civil Rights Cases

§ 1.01 Introduction: Some History on the Use of Statistics in Civil Rights Litigation
§ 1.02 The Statistical Approach in Litigating Employment Discrimination Cases

Plaintiff’s Task: The Establishment of aPrima Facie Case

§ 2.01 Introduction: Precedents and Patterns
[1] Statistics Alone May Be Enough to Establish a Prima Facie Case
[2] Supreme Court Cases After Griggs
[3] When Is the Sample Large Enough to Make Statistical Proof Valid?
[4] The “Statistics Plus” Standard for a Prima Facie Case
[5] Disparate Treatment Versus Disparate Impact
[6] Necessity of Expert Testimony
[7] Use of Statistics From Employer’s Own Records
[8] Conclusions
§ 2.02 Shifting the Burden of Proof
[1] Introduction
[2] Using Statistics to Establish Pretext
[3] The Two-Stage Approach to Shifting the Burden of Persuasion

Defendant’s Task: Responding to the Prima Facie Case

§ 3.01 In General
§ 3.02 Alternative Statistical Showings
[1] Examples: Defendants Using Data Showing New Members or New Hires
[2] Examples: Employer Using Work Force Data
[3] Examples: Challenging Plaintiff’s Statistics
§ 3.03 Defending Business Necessity or Bona Fide Occupational Qualification (BFOQ)
[1] Responding to Disparate Impact: A Showing of Business Necessity
[2] Bona Fide Occupational Qualification
§ 3.04 Bona Fide Seniority System Defense
§ 3.05 Other Defenses
[1] Timeliness of Challenges and Post-Act Discrimination
[2] Unsuccessful Defenses May Suggest Alternative Approaches
§ 3.06 Responding to the Prima Facie Case

The Hiring Process

§ 4.01 Introduction
§ 4.02 Basic Approach: Comparing an Employer’s Work Force with a Sample Group
[1] Establishing a Prima Facie Case with Work Force Comparisons
[2] Comparing Recent Hires with an External Group
[3] Demographic and Labor Pool Data
[4] Selection of the Geographic Area
§ 4.03 Applicant Flow Analysis
§ 4.04 The Impact of Hiring Prerequisites
§ 4.05 Analyzing the Hiring Process
[1] A Fundamental Framework and Some Data Requirements
[2] Labor Pool—Applicant Pool Disparities
[3] Applicant Pool—New Hires Disparities
[4] Interpreting Statistical Data Pertaining to Hiring
§ 4.06 Summary

Approaches to Estimating the Racial Composition of an Employer’s Labor Pool

§ 5.01 Determinants of the Labor Pool
[1] Skill
[2] Distance from the Plant
[3] Population Density
[4] Other Factors
§ 5.02 Census Data on Labor Force Commuting Patterns in the Detroit Area
§ 5.03 The Disk-Shaped Employment Area
§ 5.04 The Stratified Disk-Shaped Employment Area
§ 5.05 An Applicant-Defined Labor Pool Density: Township Units
§ 5.06 Another Applicant-Defined Labor Pool Density: Rectangular Groups of Census Tracts
§ 5.07 The Racial Composition of the Applicant Group
§ 5.08 Summary of Methods
§ 5.09 Refinements in the Estimation Process
[1] The Labor Force with Special Skills
[2] Effects of Affinity Group Preferences on Labor Pool Composition
§ 5.10 Summary of Chapter

Discipline and Discharge

§ 6.01 Introduction
§ 6.02 Discipline and Discharge Cases: Precedents
[1] Introduction
[2] Statistics Used to Establish a Prima FacieCase
[3] Using Statistics to Rebut a Prima FacieCase
§ 6.03 Measuring the Equitability of Discipline
§ 6.04 Analyzing Discharges
§ 6.05 Summary

Job Assignment, Transfers and Promotions

§ 7.01 Introduction
§ 7.02 Job Assignments, Transfers and Promotions
[1] Introduction
[2] The Current Distribution of Employees
[3] Comparisons with External Work Force Data
[4] Weaknesses with Work Force Comparisons
[5] Rates of Promotion, Assignment and Transfer, and Combination Approaches
§ 7.03 Methods of Analysis
[1] The Initial Job Assignment
[2] Job Transfers
[3] Promotions

Testing and Selection Procedures

§ 8.01 Introduction
[1] Tests Influence Management Decisions
[2] Early Federal Guidelines
§ 8.02 The Uniform Guidelines
[1] Validation
[2] Adverse Impact
[3] Forms of Validation
[4] Recordkeeping
§ 8.03 Testing and Selection Procedures: Precedents
[1] Use of Tests Varied and Widespread
[2] Use of Tests Criticized
[3] The Griggs Case
[4] Cases Following Griggs
[5] The Impact of Washington v. Davis
§ 8.04 Measuring a Test’s Effects
[1] Detecting a Discriminatory Impact
[2] Validating Criteria

Equal Pay, Wage Mobility, and Pay Awards

§ 9.01 Introduction
§ 9.02 Equal Pay
[1] Order of Proof
[2] Job Content
[3] An Example of an Equal Pay Analysis
§ 9.03 Wage Mobility
[1] Comparing Similarly Situated People: The Cohort
[2] Aggregating Comparisons Across Cohorts
[3] Dealing with a Continuum of Qualifications: The Diffuse Cohort
§ 9.04 Pay Awards
[1] Back Pay Law and Case Precedents
[2] Calculating Pay Awards
§ 9.05 Conclusion

Age Discrimination

§ 10.01 Introduction
§ 10.02 Establishing a Prima Facie Case
[1] Historical Approaches
[2] Evidentiary Burden
[3] The McDonnell Douglas Criteria for aPrima Facie Case
[4] Applications of the McDonnell DouglasCriteria
[5] Employee’s Qualifications to Perform the Duties Required by the Job
[6] Plaintiff Replaced by a Younger Person
[7] Using Statistics in Private Individual Claims Under the ADEA
[8] Specific Acts of Age Discrimination and Evidence of Discriminatory Intent
§ 10.03 Shifting the Burden of Going Forward with Evidence
§ 10.04 Observations Regarding Large Reduction in Force Cases
[1] More than McDonnell Douglas Criteria Required to Establish a Prima FacieCase
[2] Plaintiff Required to Prove Job Qualifications
[3] Reduction Conducted for Legitimate Economic Consideration
[4] Performance Evaluations; Treatment of Comparable Employees
[5] Disparate Impact Analysis in Reduction in Force Cases
§ 10.05 Measurement of Age Discrimination
[1] Aspects Unique to Age Discrimination
[2] Comparing Age Profiles
[3] Redefining a Comparison

Some Concluding Observations

§ 11.01 In General
§ 11.02 The Practical Value of Statistically Significant Differences
§ 11.03 The Great Appeal and Dangerous Pitfalls of Regression Analysis
§ 11.04 Significance Testing Using Monte Carlo Simulation
§ 11.05 Norms for Evaluating Personnel Practices Derived from Computer Simulation of Workforce Evolution
§ 11.06 Sex as an Underlying Cause in Race Cases
§ 11.07 Pressures for Unequal Pay Resulting from Labor Market Thinness
§ 11.08 Standard Deviation Analysis: An Approximation to P-Value Analysis, A Preferable Alternative
§ 11.09 Imbalance Under Equitable Personnel Practices
§ 11.10 Modeling the Decision Process in Disparate Treatment Cases
[1] The Ideal Paradigm for Detecting Disparate Treatment
[2] Peeling the Onion: The Practical Aspects of Detecting Disparate Treatment
§ 11.11 One Tail or Two? Or Does It Really Matter?
[1] Introduction
[2] Statistical Theory of One- and Two-Tailed Tests
[3] Discrimination Litigation Conventions
§ 11.12 No Statistical Proof that an Employer Does Not Discriminate

Table of Tables
Table of Figures
Table of Maps
Table of Cases