tilg meeting september 11, 2014 cohen - compensation...•central parking system of louisiana inc....

TILG Meeting September 11, 2014

Presenter: David Cohen

DCI Consulting Group, Inc. 1920 I Street, NW

Washington, D.C. 20006

Disclaimer

The information in this PowerPoint is provided for general information purposes only and these materials are not intended to provide legal advice. These copyrighted materials may not be reproduced, copied or used without prior permission from the authors.

COPYRIGHT © 2014 DCI CONSULTING ALL RIGHTS RESERVED 2

Who We Are: DCI Consulting Group

Location Matters • Strategically located blocks from the Department of Labor in Washington, DC • Allows timely access to federal compliance changes, updates, and strategies

DCI Qualifications • DCI’s staff of Master’s- and Ph.D.-level Industrial-Organizational Psychologists

provide daily EEO compliance and risk management consulting • Our staff have extensive knowledge and experience on workplace processes and

outcomes such as affirmative action, compensation, employee selection, training and development, and diversity and inclusion

DCI Experience • Established 2001 • Our Fortune 500 clients span all OFCCP geographic regions • Clients represent defense, manufacturing, pharmaceutical, food processing,

health care and high technology industries • Highly experienced with complex HR systems and organizational structures


Agenda

• Proposed Equal Pay Report

• OFCCP Enforcement Update

• Directive 307 Overview

• Conducting a Proactive Compensation Analysis


Proposed Equal Pay Report

5

Presidential Memorandum

• On April 8, 2014 President Obama sent a Presidential Memorandum with the subject:

– Advancing Pay Equality Through Compensation Data Collection

• Directed the Secretary to propose a regulation within 120 days of the memo

• OFCCP proposed a regulation on August 8th

– Comments are due in November

6

Equal Pay Report

• Supplement the Employer Information Report (EEO-1 Report) with summary compensation data paid to employees*

• Report would collect data by: – Form W-2 Wage and Tax Statement (W-2) – Sex, Race and Ethnicity – Specified Job Categories (proposed EEO-1) – Total Number of Employees

• SAME SNAPSHOT AS THE EEO-1 REPORT

– Total Hours worked • Exempt – may default to 2080 • Non-exempt – actual hours worked

© 2014 The OFCCP Institute 7

Filing Period

• The EPR can filed anytime between January 1 to March 31 (1st Quarter)

– W-2s are due January 31

• Practically contractors would have 2 months to collect data and submit

• W-2 includes all income (base, bonus, commission, car allowance, etc.)


Equal Pay Report

EEO-1 Job

Category

Job

Cat

ego

ry N

um

ber

Section D – Male Employees

Hispanic or

Latino

Non-Hispanic or Latino

Total

White

Black or

African

American

Native

Hawaiian or

Other Pacific

Islander

Asian

American

Indian or

Alaska

Native

Two or

More Races

To

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W-2

Pai

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W-2

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W-2

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W-2

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W-2

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W-2

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W-2

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W-2

Pay

To

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Wo

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Executive/Sr

Level Officials

& Mgrs

1.1

First/Mid-Level

Officials & Mgrs 1.2

Professionals 2

Technicians 3

Sales Workers 4

Administrative

Support Workers 5

Craft Workers 6

Operatives 7 Laborers and

Helpers 8

Service Workers 9

Total 10


A Review of OFCCP’s Public Enforcement Database

10 COPYRIGHT © 2014 DCI CONSULTING ALL RIGHTS RESERVED

Enforcement Over Time

Fiscal Year Closure Letter3

Notice of Violation Total

Compliance Evaluations

Conciliation Agreement Consent Decree Financial Remedy

# % # % # % # % #

2004 4,938 93.63% 277 5.25% 0 0.00% 59 1.12% 5,274

2005 1,921 90.61% 146 6.89% 0 0.00% 53 2.50% 2,120

2006 3,559 88.64% 383 9.54% 0 0.00% 73 1.82% 4,015

2007 4,390 89.17% 471 9.57% 0 0.00% 62 1.26% 4,923

2008 3,701 85.57% 539 12.46% 5 0.12% 80 1.85% 4,325

2009 3,204 82.01% 618 15.82% 9 0.23% 76 1.95% 3,907

2010 4,019 81.32% 839 16.98% 3 0.06% 81 1.64% 4,942

2011 2,898 72.32% 999 24.93% 9 0.22% 101 2.52% 4,007

2012 2,676 66.78% 1199 29.92% 6 0.15% 126 3.14% 4,007

2013 2,965 72.32% 1037 25.29% 2 0.05% 96 2.34% 4,100

2014 2,385 86.00% 346 12.47% 2 0.08% 40 1.44% 2,773*

Total 36,656 82.49% 6854 15.51 36 0.08% 847 1.92% 44,393


Findings of Discrimination

FY Hiring Promotion Termination Selection Salary Total

# % # % # % # % # % # %

2004 50 87.72% 0 0.00% 1 1.75% 1 1.75% 5 8.77% 57 100%

2005 41 80.39% 0 0.00% 0 0.00% 1 1.96% 9 17.65% 51 100%

2006 61 87.14% 0 0.00% 0 0.00% 3 4.29% 6 8.57% 70 100%

2007 57 86.36% 0 0.00% 0 0.00% 5 7.58% 4 6.06% 66 100%

2008 68 82.93% 1 1.22% 1 1.22% 6 7.32% 6 7.32% 82 100%

2009 65 84.42% 0 0.00% 0 0.00% 8 10.39% 4 5.19% 77 100%

2010 55 73.33% 2 2.67% 1 1.33% 2 2.67% 15 20.00% 75 100%

2011 54 60.67% 2 2.25% 0 0.00% 6 6.74% 27 30.34% 89 100%

2012 55 59.14% 1 1.08% 0 0.00% 6 6.45% 31 33.33% 93 100%

2013 41 60.29% 2 2.94% 1 1.47% 5 7.35% 19 27.94% 68 100%

2014 24 70.58% 0 0.00% 0 0.00% 3 8.82% 7 20.58% 34 100%

Total 571 74.93% 8 1.04% 4 0.52% 46 6.03% 133 17.45% 762 100%


$0

$2,000,000

$4,000,000

$6,000,000

$8,000,000

$10,000,000

$12,000,000

$14,000,000

$16,000,000

2007 2008 2009 2010 2011 2012 2013

Total Settlements from 2007-2013


FY 2013 Compensation Cases

13 Compensation Cases

$494,402.03

• 10 Race/Ethnicity

• 3 Sex

• Average length to closure: 1.73

• Median length to closure: 1.14

• Grouping 13 Job Title

• Systemic 1 using regression


Number of People Compensated, Cont.

Conciliation Agreements

12 Cases (25 People)

• 3 Females

• 5 Blacks

• 2 Hispanics

• 8 Minorities

Consent Decree

1 Case

• 78 Hispanics


Directive 307


OFCCP Directive 307

• Directive 307: – Effective Date: February 28, 2013.

– Subject: Procedures for Reviewing Compensation Systems and Practices.

– Purpose: To outline the procedures for reviewing contractor compensation systems and practices during a compliance evaluation.

• A copy of the Directive and related material can be found at http://www.dol.gov/ofccp/regs/compliance/ directives/dir307.htm.


http://www.dol.gov/ofccp/regs/compliance/directives/dir307.htm

http://www.dol.gov/ofccp/regs/compliance/directives/dir307.htm

What is a Directive?

• A directive is not a regulation.

– United Space Alliance, LLC v. Solis (D.D.C. 2011).

• Attributes of Directive 307 include: – Affects how OFCCP will conduct compensation analyses. – It is not law and carries limited legal weight. – It is not designed as guidance for contractors. – Rulemaking procedures that include input from outside

experts and contractor comments were not followed. • Not subject to the Paperwork Reduction Act—no Notice and

Comment period or review/approval by Office of Management & Budget.


David’s Take on 307

• Large analytical units that will almost always yield statistically significant differences

• Arbitrary removal of legitimate non-discriminatory variables

• Steering, steering and more steering investigations

• Compensation interviews and an increase in on-site investigations

• Politics and the support of the White House


• From an enforcement perspective, small group cohorts may be a thing of the past – “The mere fact that there are pay differences between

comparators, without any other evidence of pretext or other indicia of possible discrimination, generally is not sufficient to find a violation of E.O. 11246. Individual or small group pay disparities typically are analyzed under the disparate treatment theory of discrimination.”

• It appears that OFCCP is “screening in” less contractors after the desk audit phase of the review

Directive 307: The Good


• Large multiple regression analyses using pay

analysis groups as the level of analysis. • Emphasis on steering and placement issues • No requirement to find anecdotal evidence of

discrimination • The ability for the CO to remove legitimate non-

discriminatory variables – Tainted – Non significant in the model

• No way for a contractor to model what OFCCP will do during the course of a compliance review

Directive 307: The Ugly


OFCCP’s “Goldilocks” Approaches

Stage 1: Analyze for Potential Systemic Discrimination in Large

Groups

Stage 2: May analyze for Smaller Group or Unit

Discrimination

Stage 3: May analyze for Individual (cohort)

Discrimination


OFCCP Compensation Interviews


OFCCP Interviews on Pay

• Who should speak for the Contractor? – HR – Compensation – Other

• Pre-interview prep • Topics to cover and documents to review

– Organization, department, job structure at establishment – Company policies and guidelines (provided to OFCCP) – Comp factors and how used – Decision-making level on decisions affecting pay

• Interview – Right to be present – Taking complete notes of Q&A – Signing statement, if asked


Pay Factor Bingo

Employee Pay

Education

Experience

Negotiation

Quality of Production

Acquisition Employee

Job Level

Budget

Geography Skill

Performance Market

Focal Review/R

anking

Org; Unit: Supervisor

Certification or License

Retention

Job & Promotion

History

Prior Salary


Steering


What is Steering?

• Women hired into entry level grocery store positions are disproportionately assigned to the bakery department. Men are assigned to the meat department where pay and promotion opportunities are better.

• Black sales workers are disproportionately assigned to territories with less potential.


Analysis of Opportunity

• The CO should examine employee access to opportunities affecting compensation, such as: – Higher paying positions; – Job classifications; – Work assignments; – Training; – Preferred or higher paid shift work; and – Other opportunities.

• The CO should also examine policies and practices that unfairly limit a group’s opportunity to earn higher pay, such as “glass ceiling” issues, access to overtime hours, pay increases, incentive compensation and higher commission or desired sales territories.

• The CO may investigate any observed differences in pay, other earnings or benefits, job assignment/placement, training/advancement opportunities, differences in opportunities to increase compensation or other unexplained differences.


• Central Parking System of Louisiana Inc. settles hiring and pay discrimination case with US Department of Labor (9/14)

• Company will pay $275,000 in back wages and interest to 195 female and black applicants • NEW ORLEANS — The U.S. Department of Labor's Office of Federal Contract Compliance Programs

announced today that Central Parking System of Louisiana Inc. has agreed to pay $275,000 in back wages and interest to settle allegations of placement and hiring discrimination affecting 104 women and 91 African Americans who were rejected as valets at the company's New Orleans location.

• "I am pleased with this settlement, which reflects a mutual commitment between the department and Central Parking to ensure that all workers have a fair shot at competing for good jobs," said OFCCP Southwest and Rocky Mountain Regional Director Melissa L. Speer. "Outdated notions about race and gender don't belong in any workplace, even when those workplaces are parking garages."

• An OFCCP investigation found that qualified African Americans, who applied for jobs as valets between 2007 and 2009, were hired at a significantly lower rate than similarly situated applicants of other races. Simultaneously, qualified women who applied for these positions were steered into cashier positions, which do not earn tips, leading to lower earnings compared with men hired into the valet positions. These hiring and placement practices violate Executive Order 11246, which prohibits federal contractors such as Central Parking from discriminating in employment based on race and gender.

29

http://www.dol.gov/ofccp/

http://www.dol.gov/ofccp/regs/compliance/ca_11246.htm

• November 2013 G&K to Pay Laundry Workers $290K to Settle Wage Discrimination Charges

• DOL has reached a settlement with federal contractor G&K Services on allegations of gender-based pay discrimination. An OFCCP investigation determined that G&K officials steered female laundry workers into lower-paying, "light duty" jobs and male workers into higher-paying "heavy duty jobs" – regardless of the respective worker's qualifications. G&K will pay nearly $266,000 to 59 women and almost $24,000 to 331 men who were affected by this unfair practice.

30

Tainted Variables


Type of Compensation Factors

• Human Capital versus Establishment approach

– Human Capital factors are the things that an individual brings to the table

• Education, Certifications, Prior Experience, etc.

– Establishment factors are the variables that a company controls and says they pay for

• Time in Job, Performance, Training, etc.

• Plaintiffs and EEOC/OFCCP may argue that establishment factors are inherently tainted


Potential Salary Predictors

• Experience

– Time in company

– Time in grade

– Time in current job

– Previous relevant experience

• Performance Ratings

• Career Path

– New hire

– Promotion/Demotion

• Training

– Education

– Certifications/Licenses

– Skills

– Security Clearance

• Market Information

– Salary survey median

– Geographic adjustments

– Line of business

– Merger/Acquisition


Directive 307 and Factors

• When using factors as controls in a regression analysis, the statistical analyst tests the factors to make certain they are predictive for pay and that the factors are not potentially tainted by discrimination.9 The statistical analysts and OFCCP investigators work together, and consult with RSOL as needed, to determine what factors to include in the final analytical model. When conducting a comparative analysis, the CO likewise evaluates factors offered by the contractor as to their relevance to compensation and whether they were consistently applied. – 9Where statistical testing identifies evidence that a factor results

in adverse impact, further investigation may be needed to determine whether it is appropriate to incorporate it into the model.


https://www.dol.gov/ofccp/regs/compliance/directives/dir307.htmftn.id9

Pay Analysis Groups


What is a Pay Analysis Group?

– Directive 307

• A group of employees (potentially from multiple job titles, units, categories and/or job groups) who are comparable for purposes of the contractor's pay practices. Regression analysis may be performed on different types of pay analysis groups. A pay analysis group may be limited to a single job or title, or may include multiple distinct units or categories of workers. A pay analysis group may combine employees in different jobs or groups, with statistical controls to ensure that workers are similarly situated.


Pay Analysis Groups

• OFCCP will group job titles into larger groups for analysis.

• The groupings will be a function of OFCCP’s judgment and could cross grade, job group or level.

– OFCCP will try to control for differences in grade, group, title and level in its regression analyses.

– OFCCP believes that dissimilar jobs can be made “similar” by controlling for the areas of dissimilarity in the regression analysis rather than in the actual grouping.

• OFCCP’s job groupings will be a moving target.

– “As a compliance evaluation moves from a desk audit to an onsite investigation and a final determination regarding compliance, COs review and refine the approach, including the determination of the appropriate pay analysis groups…”


Pay Analysis Groups (cont.)

• Serial job titles (e.g., Accountant I, Accountant II) seem to be likely candidates for grouping.

• OFCCP likely will use company documents and interviews to determine if there are formal compensation differences based on such variables as department, division and job title.

– Although such factors are often important, they are seldom documented as formal compensation policy.

– Contractors might want to think about formally documenting the factors that can impact pay. For example:

• The pay assigned to a particular employee is a function of many factors including, but not limited to, experience, performance, company division, geographic market, job market, economic market, education and special skills. Because of the complexity and nuances of these factors, pay will be established on a case-by-case basis.


SSEG versus PAG Approach

• 2006 Standards. – OFCCP is required to do a multiple regression analysis by

SSEG. The example below would result in three multiple regression analyses: • SSEG 1 = Electrical Engineer.

• SSEG 2 = Chemical Engineer.

• SSEG 3 = Software Engineer.

• 2013 Directive. – OFCCP has the ability to group data at a larger aggregate

than SSEG. • Engineer Group:

– Control for Electrical Engineer, Chemical Engineer, Software Engineer in the regression.


Pay Analysis Group Example

• Example of a data set used to conduct an analysis of all Engineers in one model.

Race Sex

Time in

Company

Time in

Grade

Time in

Job Age Job Title

Electrical

Engineer

Chemical

Engineer

W M 12.12 8.45 3.45 43 Electrical Engineer 1 0

B M 13.45 13.45 5.67 34 Software Engineer 0 0

A F 1.23 1.23 1.23 45 Chemical Engineer 0 1

N F 3.45 3.45 3.45 47 Software Engineer 0 0

W F 11.23 3.33 3.33 38 Electrical Engineer 1 0

H F 7.68 4.56 2.34 55 Chemical Engineer 0 1


Desk Audit Screen


Item 11

• Please provide annualized compensation data (wages, salaries, commissions, and bonuses) by salary range, rate, grade or level showing total number of employees** by race and gender and total compensation by race and gender. Present these data in a manner that is most consistent with your compensation system. If the information is maintained in electronic format, please submit in that format. See 41 CFR 60-1.4(a)(1). You may also include any other information you have already prepared that would assist us in understanding your compensation system(s).

**For this purpose, the method used to determine employee totals by the contractor should be the same as that used to determine employee totals in the workforce analysis for the AAP.


Item 11 Data

• According to OFCCP data must be submitted either by job group or by salary grade/band, or OFCCP will skip the initial screen and request individual-level data.

– Requiring submission by job group or band would contradict the current Scheduling Letter and the PRA.

– What if a contractor does not have grades for every job?

– No organization uses AAP job group for compensation purposes and planning. • State that in the cover letter

– Does this require OMB approval?


Desk Audit Screen

• Does OFCCP have internal standards for these thresholds? – They must because the Directive states, “OFCCP

periodically may adjust the size and weight of the factors used for these quantitative comparisons, based on the review of the results of investigations, the results of quality audits, and other factors such as agency resources and priorities.” • “Size” implies that there are thresholds, and “weight” implies

that there is a formula.

• If not, there will be a lack of consistency in audits.

• If so, there is a lack of the transparency because OFCCP has not released these thresholds.


Desk Audit

• OFCCP will look at:

– The size of overall average pay differences.

– The largest pay difference.

– The number of job groups or grades where average pay differences exceed a certain threshold.

– The number of employees affected by average pay differences.

• Important question:

– Will these criteria result in every contractor failing the preliminary analysis screen or will the compensation part of the audit actually close for some contractors based on these criteria?


Desk Audit Screen (cont.)

• In addition or instead of the screen, OFCCP may evaluate qualitative factors:

– Compliance history.

– OFCCP or EEOC complaints.

– Anecdotal evidence.

– Potential violations involving other employment practices.

– Data integrity issues.

– Other factors may also be considered.

• These qualitative factors seem to provide OFCCP with the opportunity to focus on certain contractors regardless of the data submitted.


Desk Audit Screen (cont.)

• After the preliminary analysis, the CO may request that the contractor provide additional information necessary to evaluate compensation issues, including:

– Individual compensation data for employees who are covered by the compliance evaluation.

– Other additional data not provided in response to the Scheduling Letter.

– Information regarding the factors used to determine compensation.

– Information about the contractor’s policies and practices related to compensation.

• This is similar to the “12-factor” request but will also likely include requests for other components of pay beyond base compensation, such as bonus, commissions, etc.


Key Takeaways

• Strategically decide how Item 11 data will be submitted

• Standardize policies and procedures to counter “steering” claims

– Requisition system for applicants

• Thoroughly prep all managers that will be interviewed by OFCCP

• Understand all pay factors that are drivers of pay

– This will most likely be different from one position to another


Conducting a Proactive EEO Compensation Analysis


What Are Federal Contractor’s Regulatory Obligations?

• Under 60-2.17(b) contractors must meet the following requirement: • (b) Identification of problem areas. The contractor

must perform in-depth analyses of its total employment process to determine whether and where impediments to equal employment opportunity exist. At a minimum the contractor must evaluate: • (3) Compensation system(s) to determine whether there are

gender-, race- or ethnicity-based disparities.

• There is no requirement to conduct any statistical analyses or produce them during an audit


Covered Contractor Obligations

• Obligation to undertake regular review of pay practices

• “Regular” is not defined

• Employers often align with their compensation review cycle

• Do you make pay increases on an annual basis? Semi-annually?

• Not an obligation to pay people equally but to pay them in a non-discriminatory manner


Conducting Compensation Compliance Self-Audits

• Properly conducted compensation compliance self-audits, are a key tool in understanding and assessing legal compliance and related risks

• Benefits: • Opportunity to undertake self-directed remedial actions, and

thereby mitigate or eliminate possible legal risks

• Understand and address, in advance, the legal challenges that could be faced in an OFCCP audit, or Title VII or Equal Pay Act claims by the EEOC or private claimants

• Good faith compliance


Establishing the Attorney-Client Privilege


The Sliding Scale of Privilege

Not Privileged Argument for Privilege Privileged No Attorney Involvement In-House Counsel (on surface) In-House Counsel (substance) Outside Counsel (on surface) Outside Counsel (substance)


Using Attorney Privileges to Conduct A Confidential Self-Audit

• Conduct compensation analyses under the attorney-related privileges – Protects the confidentiality of the data as compiled and grouped, the

analyses conducted, and the conclusions reached – Attorney-client privilege – Attorney work product doctrine

• Audit is undertaken at the direction of in-house or outside counsel – Steps to structure the analyses under the attorney privileges must be

undertaken at the outset, and best practice is confirmation in writing – Attorney should direct that communications be kept confidential – Restrict communications about the audit within the company to

individuals with a need to know – Outside consultants, e.g., industrial psychologists, are retained on an

attorney-privileged basis


Confidential Self-Audit: The Audit Team

• Determine appropriate team to handle:

• Legal (in house counsel and/or outside attorneys)

• Key corporate team with responsibilities for compensation determinations and implementation decisions

• Keep it to those who “need to know”:

• Human Resources

• Compensation

• Operations

• Industrial psychologists, labor economists, and/or statisticians


Conducting the Analysis


Data to be Analyzed

• What are you going to analyze?

– Base pay

– Hours worked

– Bonus

– Starting Salary

– Merit Increase

– Performance Ratings

– Territory

– Total Compensation


Things to Consider

• Do you have the support from upper level management?

• If needed, do you have a pool of money to make corrections?

• When are you going to conduct do the analysis?

– AAP cycle

– Prior to merit


Which Protected Groups to Analyze


Which Protected Groups Should You Compare?

• Previously, OFCCP used two comparisons – Men v. Women – Whites v. All Minorities Combined

• OFCCP recently moved toward subgroup comparisons for race/ethnicity

• This makes sense legally and philosophically – “Minority” is not a legally protected class – Hispanic is an ethnicity, not a race – Asians have the highest average salaries in the U.S.

• But, it makes analyses more complex and raises issues regarding how analyses should be conducted



Career Path

• New hire

• Promotion

• Demotion

Experience

• Time in company

• Time in grade

• Time in current job

• Previous relevant experience (age?)

Performance Ratings

Training

• Education

• Certifications

• Security clearance

Market Information

• Salary survey median

• Geographic adjustments

• Line of business

• Merger/acquisition

• H1-B Visa

• Economy during time of hire

Job (if groups are non-similarly situated)


Effect of Starting Pay Female Male Merit

Year Hire $ Hire $ Increase Difference

1990 40,000$ 47,000$ 3% (7,000)$

1991 41,200$ 48,410$ 3% (7,210)$

1992 42,436$ 49,862$ 3% (7,426)$

1993 43,709$ 51,358$ 3% (7,649)$

1994 45,020$ 52,899$ 3% (7,879)$

1995 46,371$ 54,486$ 3% (8,115)$

1996 47,762$ 56,120$ 4% (8,358)$

1997 49,673$ 58,365$ 3% (8,693)$

1998 51,163$ 60,116$ 3% (8,953)$

1999 52,698$ 61,920$ 3% (9,222)$

2000 54,279$ 63,777$ 3% (9,499)$

2001 55,907$ 65,691$ 5% (9,784)$

2002 58,702$ 68,975$ 3% (10,273)$

2003 60,463$ 71,044$ 3% (10,581)$

2004 62,277$ 73,176$ 3% (10,899)$

2005 64,146$ 75,371$ 3% (11,225)$

2006 66,070$ 77,632$ 3% (11,562)$

2007 68,052$ 79,961$ 3% (11,909)$

2008 70,094$ 82,360$ 3% (12,266)$

2009 72,196$ 84,831$ 3% (12,634)$

2010 74,362$ 87,376$ 3% (13,013)$

2011 76,593$ 89,997$ 3% (13,404)$

2012 78,891$ 92,697$ 3% (13,806)$

2013 81,258$ 95,478$ 3% (14,220)$

Total (245,581)$


Which Employees Should You Compare?

• Groupings are a key determination that are selected based on functional and legal considerations

• A contractor’s options include: • Job title

• Salary grade/band

• Serial title (e.g., Accountant I, Accountant II)

• Job family

• Goals • Narrow enough that similar jobs are kept together

• Broad enough to allow for sufficient sample size to conduct statistical analyses


• Start at the job title • Be careful that jobs with the same title are actually the “same

job” • Others jobs can be

• Lumped together into one grouping, or • Broken into job families or functions

• If you have to cross grades • Start with serial titles (Engineer I, Engineer II) • Then group according to family or function • Be sure to include either grade midpoint or a dummy-coded

variable in your regression

Our Recommendation


Job Title Job Family Broad Group Grade

Fin Analyst I (10) Finance (19)

Admin (30)

Exempt 1 (141)

Accountant I (9)

HR Rep I (5) HR (11)

Benefits Rep I (6)

Mechanical Eng I (45) Engineering (80)

Technical (111) Electrical Eng I (35)

Electrician (21) Maintenance (31)

HVAC Specialist (10)

Comparison Example


• Similarity Component – Groupings make sense; pass the “next-door-neighbor”

rule

– No indication that the groupings were formed to hide group differences • Have an internal committee of experts review the groupings

• Do not let them see any of the salary analyses

• Document the process

• Remember – You know your jobs better than the OFCCP!

– Five “consultants” will yield five different groupings

– Different from OFCCP does not make you wrong

Justifying Your Groupings


Gathering Data: Database Elements

Employee Information:

• ID & name

• Race/ethnicity

• Gender

Job Information:

• Job title

• Salary grade/band

• Pay comparison grouping

Salary

(annualized FTE)

Position Information:

• Department

• Business unit

• Location

• Full/part-time

• FLSA status

Merit Variables:

• Experience

• Education

• Etc.

Data Snapshot Date


Factors Used to Explain Pay

• What is your compensation philosophy?

• Do you pay for: • Performance? • Education? • Previous experience? • Time in company?

• Do you have different pay systems for exempt and non-exempt? Admin versus technical?

• What data are available? • Currently in HRIS • Not in HRIS but could easily be obtained • Could be obtained with much time, effort, and expense



• Career Path: • New hire • Promotion • Demotion

• Experience: • Time in company • Time in grade • Time in current job • Previous relevant experience

(age?)

• Performance Ratings

• Training: • Education • Certifications • Security clearance

• Market Information: • Salary survey median • Geographic adjustments • Line of business • Merger/acquisition • H1-B Visa • Economy during time of hire


Average Correlations with Salary

Merit Variable Average Correlation

Experience .24

Age .22

Time in Company .21

Time in Grade .17

Education .10

Performance .08


Education Issues

• Education will probably not matter for most jobs as it is a requirement to get the job rather than a determinant of pay in a job

• Education and certifications should be separate variables

• Does the degree field matter? (e.g., engineering v. art)

• Does the quality of the school matter? (e.g., Stanford v. Mississippi St.)

• Three ways to code education • Years of education approach • Degree approach

• None (0), HS Diploma (1), One year degree (2), associate’s degree (3), bachelor’s degree (4), master’s degree (5)

• Dummy variable approach (Number of codes-1) • High school degree (don’t code) • Associates (0,1) • Bachelor’s (0,1)

• Key is to code education on the basis of the way the company makes compensation decisions


Performance Rating Issues

• One or two years? • Average across years if data are missing

• New employees will often not have evaluations • Exclude them (not the best option)

• Be sure to document these exclusions

• Use a Proxy • Median rating in SSEG, or • Meets expectations

• Two choices for analysis • Use the actual rating • Dummy code rating categories

• Each category, or • Above expectations (ratings 4 & 5) • Below expectations (ratings 1 & 2)

• You need to know the rating distribution for each grouping

• Are performance ratings tainted?


Experience Issues

• Types of Experience • Total years of relevant experience

• Time consuming to obtain • Reliability problems in coding

• Time in company (TiC) • Service date • Hire date

• Time in grade/band/job family • Are these accurate or did the HRIS set values to latest version or change?

• Time in current job

• Age can be used as a proxy for total years of experience • Age may overestimate years of experience for women • Several methods of using age

• Age • Age – TiC • Age – TiC – 18 (you can substitute any number for 18) • Age – TiC – 18 – years of college


DCI Research on Age

• OFCCP currently does not like using age as a proxy for previous experience

• Age is commonly used by employers

• Age as a proxy is supported by case law as well as research

• Our basic findings

– Correlation between age and experience exceeds .80

– Median correlation with salary is .28 • Highest correlations are for jobs with salaries lower than $80,000

• Correlations vary by job family: Highest for admin jobs and lowest for IT jobs


Experience Issues

• You may want to test for curvilinear relationships

• These relationships often reflect salary compression or salary inversion

• Important if salaries of highly tenured employees are frozen or slowed

– At top of grade


$0.00

$10,000.00

$20,000.00

$30,000.00

$40,000.00

$50,000.00

$60,000.00

$70,000.00

$80,000.00

0.00 5.00 10.00 15.00 20.00 25.00 30.00

Time in Company

Sal

ary

Log.

(Sal

ary)

Curvilinear Relationship


Analyze Data

• Three potential types of analyses:

• Regression analysis for larger groups • Controls for merit variables

• Fisher’s exact test (FET) for smaller groups • Compares percentage of men above the median versus the

percentage of women above the median

• Does not control for merit variables

• If FET is significant, will need to conduct a cohort analysis

• Cohort analyses for groups with only one or two members of a gender or race/ethnicity • Rank orders employees by salary

• Manually compare salary with experience and other merit variables


The Concept of Regression

• Advantages • Enables prediction

• Allows combinations of small correlations

• Accounts for overlap of variables

• Two common types in HR • Least squares

• Used when evaluating interval or ratio data

• Salary, ratings of job performance

• Logistic (Logit) • Used in evaluating dichotomous decisions

• Hire/not hire, interview/not interview, promote/not promote


What is Multiple Regression?

• Multiple regression analysis is a statistical tool used to understand the relationship between two or more variables

• Regression will help explain the observed salary differences between two groups (Male/Female or Minority/Non-minority) • Male - $50,000

• Female - $40,000

• Regression will help determine whether those differences in pay are the result of legitimate non-discriminatory variables • Time in Company, Time in Grade, Performance, etc.


Regression Example

• OFCCP Audit Scenario – in the electrical engineering job title we have observed the following: • Male average salary - $50,000

• Female average salary - $40,000

• Difference - $10,000

• Statistically significant? – Yes

• Is this the result of discrimination? • Multiple regression will help answer this question


Regression Formula

Y = a + (b1) (x1)+ (b2) (x2)

Y = predicted criterion score

a = constant (intercept)

b = weight (slope)

x = score on the predictor


Predicting Salary from Time in Job Sa

lary

Time in Job


Considerations

• Number of employees in the analysis – Total number (30 is probably the minimum) – Number of employees in each group (5 is the minimum)

• Employee-to-variable ratio – Minimum is a ratio of 5:1 – 10:1 is more comfortable

• Missing variables

• Inclusion of non-significant variables

• Multicollinearity

• Balance of simplicity versus statistical power


What to Look for in a Regression Analysis

• Is race or sex a significant predictor of pay?

• Typically look for t-value of 2.0 or greater

• If t-value is greater than 2.0, did your merit variables explain some of the salary difference?

• Does your model explain enough of the variance in pay? • Ideally you want an R2 value that is high and statistically

significant • The typical R2 for TiC, TiJ, TiG, and age is .24


Salary

Time in Job

Performance Rating


Multiple Regression Analysis

35%

15% 10% 5%

35%

Time in Grade

Time in Company

Performance

Education

Other

Variability in Salary


Is Our R2 Statistically Significant?

ANOVA df SS MS F Sig of F

Regression 5 3,434,436,542 686,887,308 25.80 .01

Residual 113 3,008,138,683 26,620,696

Total 118 6,442,575,225

Tells us if our regression model is significant

(<=.05)


What If Our R2 is Low?

• Two criteria • Significant F • Magnitude of R2

• If low R2

• Have we grouped the jobs properly? • Do we have the right variables in the equation? • Do we have outliers?

• In salary? • In one of the merit variables?

• Implications of a low R2

• Cannot use regression to make salary adjustments • Is our pay practice subjective?

• Starting salaries • Raises


Possible Regression Outcomes

• Merit variables reduce t-value for sex or race below 2.0

• Merit variables reduce t-value for sex or race but it is still above 2.0

• Merit variables increase a previously non-significant t-value for sex or race above 2.0


Regression Example


Cohort Analyses

• A cohort analysis is a method of comparing individuals within a grouping to highlight those who might be overpaid or underpaid

• Typically used for groups • Too small for FET or regression

• With a significant FET

• The Process • Compute median for salary and merit variables for all employees

• Individuals are flagged if:

Employee's salary is below the median, and

Employee’s merit variable(s) are above the median of the group

• Flag an employee regardless of gender or race


Cohort Report Cohort Report

Cohort Report

Job Title: Driving Supervisor

ID Sex Annualized Salary TiC Age

37327 M $85,000.08 12 37.492 43036 F $81,089.04 11 38 96068 M $80,000.16 11 35 43389 F $80,000.16 10 38.168 61423 M $75,790.08 6.174 35 48104 M $75,000.00 6 31.269 60268 F $75,000.00 4 31.269 33794 M $73,500.00 4.2 31.269 70860 F $73,467.12* 5 34 92053 M $72,359.04* 5 38.92 70568 M $72,100.08 3 27.997 30015 M $71,069.04 2 33.667 32408 M $70,000.08 4.2 33 60487 F $70,000.08* 7 34 32676 F $65,500.08* 8 33 47188 F $65,000.00 2 30.99 50276 F $63,756.00 2 28 57352 F $63,700.00 2 25.599 Total 18 *All predicting variables greater

Medians are

based on the

total group

*Males and

Females who

have higher TiC

and est. prior

experience than

the median


Making Salary Adjustments


Making Salary Adjustments

• Remediate any discrepancies found

• Need to commit to effective, appropriate and timely remedy

• Budgetary modifications

• Job changes

• Validating tainted variables

• Maintain appropriate and complete recordkeeping to assist in explaining any compensation differences


Questions


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