1. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Statistical Analyses of Compensation and Employee Selection – Practical Tips Paul F. White, Ph.D. Edward Bierhanzl, Ph.D. March 11, 2010

2. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Presenters  Edward Bierhanzl, Ph.D. – Principal Compensation  Paul White, Ph.D. – Managing Director Employee Selections Presenters are from ERS Group’s Washington, DC office. 2

3. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com 3 Basic Concepts

4. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com 4 Fair Employment Practices  In a non-discriminatory setting, the outcome of employment decisions don’t depend on protected-group status.  For compensation, this means pay depends on a variety of factors other than protected-group status.  For selections, the representation of protected employees among those “selected” (hired, promoted, terminated, etc.) should be close to their representation among those eligible for selection.  Statistical analysis can determine if these propositions are true.  A statistical model should reflect the actual decision-making process as closely as possible given available data. Similarly-situated employees Factors considered in process At what level were decisions made?

5. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Summary of Probability and Statistical Significance  Comparison of expected outcome versus actual outcome.  Is the difference between the actual and expected outcome consistent with chance, i.e., is there statistical significance?  As the number of events increases, statistically significant outcomes are more likely because the acceptable range for variation becomes relatively smaller. ? ? 5

6. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Examination of Outcomes  Selections: Toss a coin 100 times, expect 50 heads, actual ??? Pool of applicants is 50% female (expected); are the hires also 50% female (actual)? Pool of promotion candidates is 30% minority (expected); are the promotions also 30% minority (actual)? Pool of candidates considered for layoff is 60% age 40 or older (expected); are the layoffs also 60% age 40 or older (actual)?  Compensation: Average compensation level is $70,000 (expected); are the average salaries of men and women also $70,000 (actual)? 6

7. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com 7 Measures of Statistical Significance  Number of standard deviations (statistical measurement of difference between actual and expected) Greater than “two or three” standard deviations is statistically significant  Probability of occurring by chance: Less than 5% is statistically significant  In practice: Greater than 2 standard deviations or less than 5% probability level

8. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com 8 Typical Bell Curve Unlikely to Occur By Chance Statistically Significant Statistically Significant Unlikely to Occur by Chance Statistically Insignificant Likely to Occur by Chance Number of Heads50554540356065 -3-20132 Number of Standard Deviations Difference from 500+5-5-10-15+10+15

9. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Compensation Edward Bierhanzl 9

10. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Do Similar Employees Receive Similar Pay? 10  The answer is in the data  But how do we know which employees are “similarly situated”?  And what’s the best way to analyze their pay?  We answer those questions by building a model of the compensation process.

11. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Pay equity laws and regulations require that similarly situated employees receive equal pay for substantially equal work. Similarly Situated: Perform similar work (job content) Similar skills/qualifications Similar level of responsibility Other pertinent factors (e.g., full-time status, permanent) 11 Similarly Situated

12. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Choose Your Model 12  Agencies typically define employee groups broadly Limited details when defining “similarly situated” This makes disparities look worse  A model that mirrors actual compensation policy will define groups more narrowly Employees grouped by characteristics related to pay Use statistical techniques to account for other differences

13. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Statistical Test! Average Salary Difference Between Females and Males 13 Difference = -$13,000 Number of Standard Deviations = -13.00 Hypothetical data – for illustration only

14. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com What Does This Test Really Tell You?  A statistically significant difference indicates that the observed difference is not likely to have occurred by chance in a neutral salary-setting process. If not chance, then…  Protected status is a factor that influences pay; AND/OR  These groups differ in some way that’s correlated with gender and is relevant to pay. 14

15. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com 15 Shortcomings of Average Compensation Analysis  Does not reflect the salary determination process of the company Important factors not considered: Pay grade Organization Starting level (career path) Specific occupation, etc.  Averages may be influenced by “outliers” People not similarly-situated to the others Data errors

16. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com 16 Multiple Regression Analysis  A statistical tool that allows the analyst to: Quantify the protected/non-protected salary difference after “filtering out” differences that are attributable to other measurable factors that influence pay. Account for differences between protected and non-protected employees in factors such as: Job family Pay grade Company experience (time in grade, time in job, tenure) Education Prior relevant experience Performance Organizational unit (e.g., division, department, etc.)  Effectiveness depends on data availability and how well it reflects the salary-determination process

17. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com If You Do Have the Data 17  What outcome do you focus on?  How do you define similarly situated?  How much detail is too much detail?  Who doesn’t belong in the comparison?  What if you don’t have good data?

18. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Company-wide Salary Regression Analysis 18 Hypothetical data – for illustration only

19. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com 19 Influential Observations  A few employees may contribute substantially more than others to the protected/unprotected salary gap. These employees may be outliers.  The salaries of employees with the greatest influence can be investigated. Is it: Measurement error? Unusual compensation plans? Unusual or atypical jobs? Something left out of the model?  Employees who unduly influence the equation can be identified by using more advanced regression techniques.

20. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Actual v. Predicted Salary Specific Job Family MalesFemales 20 (Hypothetical data – for illustration only)

21. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Tainted Variables? 21  Compensation decisions are affected by evaluations, discipline actions, and on-the-job experience/training  Are these components neutral with respect to protected groups?  Each area can be subjected to its own analysis

22. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com 22 Employee Evaluations  An important determinant of compensation under the direct control of the employer Different from education, tenure, & past experience  Often alleged in litigation to be a “tainted variable” Can be the subject of a separate analysis  Highlights the importance of objective measures in comp and evaluation policy

23. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com 23 Are They Objective?  Objective evaluation criteria “Breadth of experience” – internal experience with different jobs, location, business units No judgment needed  Quasi-objective “Technical skills” – inventory of KSAs combination of self-reported info and supervisor judgment  Subjective Simple “Performance Evaluation”

24. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com 24 Model the Right Population  Model should reflect the actual compensation decision structure  Appropriate to run a separate regression for each decision level  Statistical tests can show whether “pooling” the regressions is OK

25. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com A Dynamic Workforce 25  A changing workforce complicates the analysis of compensation  Cohort analysis can address some issues  Different workforce dynamics can dramatically affect the results of static analysis  Note when there are changes at the business entity level and at the employee level

26. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Mergers, Acquisitions, and Divestments 26  How is the new organization different from the old organization? How much integration has there been?  What to do about legacy data Data migration policy and documentation  Changes in HR and pay policy over time

27. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com 27 Looking at Stayers, Leavers, and Newcomers  How are new employees different from existing employees? Recruiting policies, job requirements, and unobserved characteristics  How are the employees who left different from the ones who stayed? Another signal of unobserved characteristics

28. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com 28 Changing Employee Patterns Before Attrition Years in Position 12Avg. WageCountWageCountWage Female$10.00100$12.00100$11.00 Male$10.00100$12.00100$11.00 After Attrition Years in Position 12Avg. WageCountWageCountWage Female$10.00100$12.0070$10.82 Male$10.0070$12.00100$11.18 (Hypothetical data – for illustration only)

29. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com 29 Changing Pay Patterns Hypothetical data – for illustration only

30. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com 30 Pay Adjustments  Who gets the adjustment? All protected group members or just the “underpaid”  How much do they get? Eliminate the disparity or just eliminate the significance Uniform amount or specific to the individual  How is it paid out? Rolled into a raise or paid out separately

31. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com 31 Why Disparities Occur  Pro-active monitoring can tell you when something is wrong.  The PROCESS can give rise to disparities  The DATA itself can give rise to disparities  EMPLOYEE CHARACTERISTICS can give rise to disparities

32. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com 32 Other Compensation Analyses Starting Pay Merit Pay Bonus Promotion Pay Commission

33. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Employee Selections Paul White 33

34. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Type of Selection Decisions  Selection decisions span the spectrum of employment processes Hiring Promotion Termination Performance score Discipline Bonus award Job Assignment Training 34

35. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Key Challenges  Data availability  Similarly situated employees  Methodological considerations 35

36. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Selections Data Availability 36

37. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Types of Data  Access to complete and accurate data is often a significant hurdle Job applications Job history Job descriptions Ranking sheets Performance appraisals Discipline records Education Level 37

38. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Key Fields 38  Hiring analysis Race/gender/age Application date Selection date and selection indicator Posting number – target job Prior experience and wage Education/credentials Interview and Test outcome Reference and background check outcomes Disposition

39. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Key Fields 39  Promotion analysis Performance measures Job and department Tenure in job/department/firm Location Any other measures of similarly-situated  Ideally – all hiring and promotion decisions are captured in a comprehensive applicant tracking system (ATS)

40. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Key Fields 40  Termination analysis Race/gender/age Selection date and selection indicator Organization Status  Job  Department Measure of Performance Disposition  Voluntary, Involuntary, Eligible for Rehire

41. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Incomplete Data 41  Hiring Missing application  Promotion Placement decision made outside ATS  Terminations Discarded ranking sheets Lack of clarity on which separations are part of RIF  Bonus Awards and Performance Reviews Lack of measurable performance metric

42. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Selections Similarly Situated 42

43. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Identifying Pools 43  Pools of similarly situated individuals  Requires understanding of the process  Potentially relevant criteria Hiring – minimum qualifications Promotion – posting Termination – department Bonus – business unit Discipline – supervisor  Why is this important?

44. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Identifying Pools 44  When dissimilar groups are combined, an adverse impact analysis may: Inappropriately find a statistically significant difference when one did not occur, or Mask statistically significant differences within sub- groups.

45. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com 45 Example 1: Combining Groups Reveals Significant Differences Workforce Examined Percent Age 40 or Older in Workforce Percent Age 40 or Older Among RIF Selections Statistically Significant? Overall Facility55%45%Yes Engineers60%58%No Accountants40%38%No Clerical34%31%No Hypothetical Example—For Illustration Purposes Only

46. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com 46 Example 2: Combining Groups Masks Significant Differences Workforce Examined Percent Age 40 or Older in Workforce Percent Age 40 or Older Among RIF Selections Statistically Significant? Overall Facility45%46%No Engineers60%75%Yes Accountants40%20%Yes Clerical30%31%No Hypothetical Example—For Illustration Purposes Only

47. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Selections Methodology 47

48. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Statistical Analysis 48  Test of Proportions Modest data requirements Not reliable for small counts  Adverse Impact Ratio Straight-forward to calculate No statistical significance measure Not reliable for small counts  Fisher’s Exact Test Measure of statistical significance Reliable for small counts

49. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Hypothetical Case Study 49  Hiring Analysis of Company ABC  1,000 Openings for Technicians  Process: Application Minimum Qualifications Employment Test Interview Hire

50. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Census Benchmark 50 Hypothetical data – for illustration only

51. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Applicant Flow 51 Hypothetical data – for illustration only

52. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Applicant to Hire 52 Hypothetical data – for illustration only

53. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Met Minimum Qualifications 53 Hypothetical data – for illustration only

54. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Minimum Qual. to Hire 54 Hypothetical data – for illustration only

55. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Passed Test 55 Hypothetical data – for illustration only

56. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Passed Test to Hire 56 Hypothetical data – for illustration only

57. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Given Interview 57 Hypothetical data – for illustration only

58. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Passed Interview to Hire 58 Hypothetical data – for illustration only

59. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Given Offer 59 Hypothetical data – for illustration only

60. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Accepted Offer 60 Hypothetical data – for illustration only

61. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Passed Interview to Offer 61 Hypothetical data – for illustration only

62. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Other Potential Influences 62

63. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Lessons Learned 63  Understanding the decision-making process  Identifying similarly-situated employees  Maintaining sufficient data and documentation

64. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Logistic Regression 64  Logistic Regression Analysis As in compensation, controls for multiple factors Models an outcome, such as a selection  1 if person is promoted  0 if not

65. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Logistic Regression  Does the protected group have a statistically significantly higher/lower probability of being selected after accounting for other relevant factors?  Effectiveness depends on data availability  Goal: Construct a regression model that reflects (as closely as possible) the selection process What factors were considered? Who makes the selection decisions? What employees are similarly situated? 65

66. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Logistic Regression - Example  Identify factors considered in RIF Most recent performance score Number of discipline action in last year Safety test score  Add a factor for the protected group 40 and Older indicator  Estimate model – what do we learn? Coefficient on age indicator – is it significant – is it positive or negative? What is the explanatory power (fit) of the model? 66

67. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Self-Monitoring  Key decisions Scope of analyses Periodicity of analyses Dissemination of analyses Plan to address adverse outcomes  Privileged considerations GC and HR and IT GC and outside counsel 67

68. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Special Issues in Promotion Analyses 68

69. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Special Issues in Promotion Analyses Posting System in Place  Equal access to job announcements  Keeping track of applicants Qualified applicants Interviews Job offers vs. actual placements Status at each stage of process Consistency between the lists of people at each stage 69

70. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Special Issues in Promotion Analyses Posting System in Place  Internal versus external applicants  Laterals, Demotions also apply to same position  Decision maker(s) and factors used to determine who passed on to the next stage 70

71. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Special Issues in Promotion Analyses No Posting System  Reviewing traditional job movement patterns  Establish estimates of employees eligible for promotion  Channeling Issues 71

72. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Special Issues in Promotion Analyses How to Define a Promotion  Increase in grade only?  Increase in grade plus increase in pay rate?  No change in job but substantial increase in pay rate?  Permanent vs. temporary  Are there similar moves made by others that are not identified as promotion in the database? 72

73. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Special Issues in Promotion Analyses Discouraged Applicant Allegations  Compare representation of applicants to representation of traditional jobs that feed into target jobs 73

74. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Special Issues in Hiring Analyses 74

75. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Special Issues in Hiring Analyses Applicant Flow Data Available  Information on preferred job  Information on prior work experience Type of job Relevance to job applying for  Information on schedule limitations  Information on desired wage rate 75

76. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Special Issues in Hiring Analyses Applicant Flow Data Available  Information on other factors relevant to hire Education Field of study Etc.  Separate tear-off card to report race and gender  Analysis of job offers vs. actual hires 76

77. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Special Issues in Hiring Analyses Application Data Not Available  2000 U.S. Census Special files by occupation and geography Small geographic area considerations Mapping Census occupation categories Which counties to include: − Where employees come from − Concentric circles 77

78. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Special Issues in Hiring Analyses Application Data Not Available  Industry studies  Commuter patterns for general population 78

79. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Special Issues in Termination Analyses 79

80. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Special Issues in Termination Analyses Rating System in Place  Rankings/Evaluations  Validity of evaluation process  Voluntary versus involuntary terminations  Voluntary separation plan prior to involuntary terminations 80

81. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Employee Data Issues 81

82. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Employee Data Issues Completeness  Work history on employees  Unique identifiers  Effective dates  Pay levels  Location/department/division  Job code  Reason for changes in job code  Factors that identify qualifications  Reason for changes in pay level 82

83. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com  Other quantifiable factors that may determine pay or selections (varies across employers and processes) evaluations education training discipline 83 Employee Data Issues Completeness (Continued)

84. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Employee Data Issues Accuracy  How is the information originally entered into the database by the operators?  What steps are taken to verify the accuracy of the data entry?  Payroll data vs. HR data 84

85. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Employee Data Issues Legacy Systems  When did the transition occur?  Was the migration all at once or over a period of time?  Efforts taken to migrate old data into new system Employees active at time of the migration Employees terminated at the time of migration 85

86. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Pitfalls and Lessons Learned 86  Pitfalls Unreliable or incomplete data Failing to properly define SSEGs Failing to co-ordinate (HR and Legal)  Lessons Learned Buy-in from senior management Allow time and resources for adjustments Know where you stand

87. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com Questions or Comments? 87

88. © 2010 ERS Group. All rights reserved. Reproduction or use of these materials, including in-house training, without authorization of the authors is prohibited. www.ersgroup.com ERS Group Presenters 88 Edward Bierhanzl, Ph.D. Dr. Bierhanzl is a Principal in ERS Group’s Labor and Employment practice. He joined ERS Group as a Research Economist in the Washington, D.C. office in 2005. His work at ERS Group has focused on wage and hour and employment discrimination litigation, including compensation and employment selection monitoring, and analysis related to OFCCP and EEOC investigations. Dr. Bierhanzl has also prepared analyses for firms involved in labor contract negotiations, and for cases involving taxation, government expenditures, and public finance. Paul F. White, Ph.D. Dr. White is the Manager of ERS Group’s Washington, D.C. office and a Director in the firm’s Labor and Employment practice. He has been with ERS Group since 1993. His work at ERS Group has focused on wage and hour and employment discrimination litigation, including compensation and employment selection monitoring, and analysis related to OFCCP and EEOC investigations. Dr. White has testified numerous times across the country in matters involving the statistical analysis of alleged employment discrimination, wage and hour issues, and economic damages.