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Special Topics Annual Report: The State of Federal Sector Age Discrimination

 

Office of Federal Operations ● Fiscal Year 2017

Contents

PREFACE

Laws

Goal

EXECUTIVE SUMMARY

INTRODUCTION

Overview

Scope

THE ADEA: IMPORTANCE AND IMPACT

A COMPARATIVE ANALYSIS OF THE 40 AND OVER WORKFORCE: THE FEDERAL SECTOR VS. THE CIVILIAN LABOR FORCE

METHODOLOGY

While the Federal Sector Outperforms the CLF in Representation, Pay Disparities Persist Among Federal Employees 40 and Over Despite Educational Attainment

Pay Disparities Persist in the Federal Sector, Even When Education Level is Comparable

AGE DISCRIMINATION COMPLAINTS IN THE FEDERAL SECTOR VS. THE PRIVATE SECTOR

METHODOLOGY

Age Complaints and Settlements are Higher in the Federal Sector, Though the Private Sector Experiences More Findings

PREDICTING FEDERAL SECTOR AGE-BASED COMPLAINTS

METHODOLOGY

Perceptions of Fairness May Be Associated with Age Complaints. Agency Reporting Structure Is a Consistent Predictor

Preliminary Analysis

Predicting Agency Level Age Complaints

Summary

CONCLUSIONS

In General, the Federal Sector Outperforms the Private Sector and the CLF in EEO for Older Workers

Despite Favorable EEO Performance, there is Much Room for Improvement in the Federal Sector

EEOC Regulations, Management Directive 110 and Management Directive 715 May Influence Federal Sector EEO Performance for Older Workers

APPENDIX 1. Federal Employee Viewpoint Survey Fair Workplace Measures

APPENDIX 2. T-Test Results: Age Complaints by FEVS Scores

APPENDIX 3. Predicting Age Complaints Among FEVS Agencies: Poisson Regression Results

APPENDIX 4. Predicting Age Complaints Among Form 462 Agencies: Poisson Regression Results

APPENDIX 5. Gender and Education: Federal Sector vs. CLF, FY2017

REFERENCES

List of Tables

Table 1. Top 3 Issues Alleged in the Federal vs. Private Sector, FY2017

Table 2. Descriptive Statistics for FEVS Agencies’ Analysis

Table 3. Mean Comparison of FEVS Scores for Agency Age Complaint Rates Above the Sample Mean and Agencies Below the Sample Mean (Equal Variances Not Assumed), FY2017

Table 4. Descriptive Statistics for Poisson Regression Analysis: Form 462 Agencies

List of Figures

Figure 1. Federal Sector Rate of Employment for Employees 40 Years and Older, FY2017

Figure 2. Federal Pay Across Age Groups, FY2017

Figure 3. Sex Distribution for Employees 40 and Over: Federal vs. Civilian Labor Force, FY2017

Figure 4. Race and National Origin Distributions for Employees 40 and Over: Federal Sector vs. CLF, FY2017

Figure 5. Mean Earnings by Sex for Employees 40 and Over, Federal Sector, FY2017

Figure 6. Mean Earnings by Race and National Origin for Employees 40 and Over, Federal Sector, FY2017

Figure 7. Percentage of Employees 40 and Over with a Bachelor’s Degree or Higher, Federal Sector, FY2017

Figure 8. Percentage of Employees 40 and Over with a Bachelor’s Degree or Higher by Race and National Origin, Federal, FY2017

Figure 9. Gender Differences in Mean Earnings Among Employees 40 and Over with a Bachelor’s Degrees or Higher, Federal, FY2017

Figure 10. Differences in Mean Earnings by Race and National Origin among Employees 40 and Over with a Bachelor’s Degrees or Higher, Federal, FY2017

Figure 11. Federal vs. Private Sector Age Complaints, FY2013-FY2017

Figure 12. Federal vs. Private Sector Age-Based Settlements, 2013-2017

Figure 13. Federal vs. Private Sector Age-Based Findings, FY2013 - FY2017

PREFACE

Laws

The U.S. Equal Employment Opportunity Commission (EEOC) is responsible for enforcing federal laws that make it illegal to discriminate against a job applicant or an employee because of the person's race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), national origin, age (40 or older), disability, or genetic information.  It is also illegal to discriminate against a person because the person complained about discrimination, filed a charge of discrimination, or participated in an employment discrimination investigation or lawsuit.  The EEOC’s responsibilities extend not only to private employers, but also to agencies in the federal government.  The federal anti-discrimination laws applicable to federal government employment are as follows:

The Equal Pay Act of 1963 (EPA), as amended, which prohibits employment discrimination on the basis of gender in compensation for work requiring substantially equal skill, effort, and responsibility that is performed under similar conditions;

Section 717 of Title VII of the Civil Rights Act of 1964 (Title VII), as amended, which prohibits employment discrimination based on race, color, religion, sex (including gender identity and sexual orientation), and national origin;

Section 15 of the Age Discrimination in Employment Act of 1967 (ADEA), as amended, which prohibits employment discrimination on the basis of age (40 years and older);

Section 501 of the Rehabilitation Act of 1973 (Rehabilitation Act), as amended, which prohibits employment discrimination against federal employees and applicants with disabilities and requires that reasonable accommodations be provided ( Act applies the same standards as the Americans with Disabilities Act, which prohibits discrimination based on disability by private and state or local government employers);

The Pregnancy Discrimination Act (1978 Amendment to Title VII of Civil Rights Act), which prohibits treating a woman unfavorably because of pregnancy, childbirth, or a medical condition related to pregnancy or childbirth; and

The Genetic Information Nondiscrimination Act of 2008 (GINA), which prohibits employment discrimination based on genetic information, including family medical history.

Goal

This Special Topics Report profiles federal sector employees 40 years and over for the fiscal year (FY) 2017.  Specifically, this report:

1.     Compares federal sector workforce distributions for employees 40 and over to the civilian labor force (CLF);

2.     Compares charges of age discrimination in the federal sector to that of the private sector;

3.     Analyzes organizational and socio-cultural factors that may contribute to federal sector age complaints;

4.     Discusses the impact of MD-110 and MD-715 on federal sector diversity and EEO complaints. 

The EEOC intends for this report to serve as a resource for proactive prevention of age discrimination and other inequalities among employees 40 and over by increasing awareness of emerging problem areas.  This report begins by providing a brief history of the ADEA and its impact on the U.S. workforce.  A comparative analysis between federal and private sectors on age-based complaints, workforce distributions, and diversity for people 40 and over is then reported.  Finally, organizational factors and agency FEVS data are used to predict age discrimination complaints.  This report concludes by explaining the impact of MD-110 and MD-715 on older federal sector workers.

EXECUTIVE SUMMARY

This report profiles the federal sector workforce, 40 and over, while providing a comparative analysis between the federal sector and the private sector in diversity and representation and rates of age discrimination complaints, settlements, and findings. Organizational and socio-cultural factors are used to predict age discrimination complaints in the federal sector as well.  Below are highlights from this report:

While the Federal Sector Outperforms the Civilian Labor Force[1] (CLF) in Representation, We Found that Pay Disparities Persist Among Federal Employees 40 and Over

  • Employees 40 and over enjoyed greater representation in the federal sector than in the CLF.
  • There was a tendency for federal employees to earn more as they age.
  • The employment gap between women and men 40 and over was larger than it was in the CLF.
  • Generally, the 40 and over federal sector workforce was more diverse than the CLF, with most race and national origin groups participating in the federal sector at rates equal to or greater than the CLF.
  • There were pay gaps among the race and national origin groups in the federal sector, with Asians and Whites earning more than other groups. This pattern persisted among college degree holders.

Age Complaints and Settlements are Higher in the Federal Sector, Though the Private Sector Experiences More Findings

  • Between 2013 and 2017, inclusively, age complaints in the federal sector have consistently ranged between 8% to 9% higher in volume than in the private sector.
  • Age was the second leading basis alleged in complaints in FY2017, with non-sexual harassment, disciplinary action, and promotion-selection being the top issues alleged in age-based complaints.
  • While the federal sector reported more complaints and settlements, the private sector reported more findings.  The private sector had a finding rate for age complaints between 2.5% and 3% between 2013 and 2017. Federal sector findings have remained under 1% during this same period.

Perceptions of Fairness May Be Associated with Age Complaints and Agency Reporting Structure Is a Consistent Predictor

  • General job satisfaction, confidence in agency leadership, and perceptions of workplace inclusion, as indicated by Federal Employee Viewpoint Survey results, are associated with fewer age complaints among federal agencies.
  • Having an EEO director report directly to the agency head is associated with a 73% decreased likelihood of having an age complaint among agencies surveyed by OPM through the Federal Employee Viewpoint Survey and a 24% decreased likelihood of having an age complaint among agencies that are required to submit Form 462 Reports to EEOC.

INTRODUCTION

Overview

The federal government is the largest employer in the United States, with close to 3 million employees.  With over 70% of its permanent general schedule (GS) workforce being at or above the age of 40 and the average age being 47.5 years (Office of Personnel Management, 2019), it becomes important to understand some of the challenges facing older federal workers. While significant advancements have been made to afford protections to this population, age discrimination remains a basis in over 30% of all federal sector complaints and is the second leading basis in FY2017 behind retaliation—with 4,980 age complaints alleged (Equal Employment Opportunity Commission, 2019). Further, an analysis of federal sector workforce statistics suggests that, while older employees earn more than the government-wide average, gender and race/ethnicity inequalities persist within this demographic. This report highlights some of the successes and challenges in providing equal employment opportunities to this population of workers.

The Age Discrimination in Employment Act (ADEA) of 1967 forbids age discrimination against people who are age 40 or older. The law prohibits discrimination in any aspect of employment, including hiring, firing, pay, job assignments, promotions, layoff, training, benefits, and any other term or condition of employment.  Further, any employment policy or practice that applies to everyone, regardless of age, can be considered illegal if it has a negative impact on applicants or employees age 40 or older and is not based on a reasonable factor other than age (Equal Employment Opportunity Commission, 2019).

The ADEA is an integral part of equal employment opportunity law.  Laws such as the Equal Pay Act of 1963 and the Civil Rights Act of 1964, when paired with the ADEA, transformed the workplace by requiring that employers maintain an environment of equality and fairness through continual opportunity, improvement and growth. Victoria A. Lipnic, the former Acting Chair for the U.S. Equal Employment Opportunity Commission (EEOC), produced the report “The State of Age Discrimination and Older Workers in the U.S. 50 Years After the Age Discrimination in Employment Act (ADEA)” in June of 2018. This report discusses the impact of ADEA on the workplace since its enactment and the current state of age discrimination in the private sector. Acting Chair Lipnic reported that, in the private sector, the older population of workers are more diverse, educated, healthier, and are working and living longer than in previous periods in U.S. history.  Unfortunately, this increased longevity and vitality also means that older workers, of all races, genders, and ethnicities, are at a greater risk of experiencing age discrimination.

The  report describes the federal sector workforce based primarily on age, while providing comparative analyses of key EEO indicators across time and between the federal and non-federal sectors.  Sex, race, and ethnic disparities in representation and pay among this population are also noted.  A prediction model is used to determine what socio-structural factors predict agency age discrimination complaint rates. The information presented can help Congress, stakeholder agencies, and EEOC leadership identify emerging issues in age discrimination and provide benchmarks for measuring federal sector performance.  EEO professionals interested in proactive prevention may also find value in this report.

The data presented in this report was drawn from the following sources:

  • Management Directive 715 Federal Agency Annual Equal Employment Opportunity Program Status Reports (MD-715), a database of workforce statistics from 229 federal agencies and subcomponents filed with the EEOC at the end of FY 2017.
  • Federal Equal Employment Opportunity Statistical Report of Discrimination Complaints Reports (Form 462), a database of complaint statistics from 268 federal agencies and subcomponents filed with the EEOC at the end of FY 2017.
  • The United States Census Bureau’s Current Population Survey Table Creator, a tool for generating data tables based on the monthly survey conducted by the U.S. Census Bureau located at https://www.census.gov/cps/data/cpstablecreator.html.
  • The Office of Personnel Management’s FedScope Data Cubes, December 2017, a data warehouse managed by the Office of Personnel Management that stores human resources, payroll, and training data from Executive Branch agencies in the federal sector https://www.fedscope.opm.gov/ibmcognos/bi/v1/disp?b_action=powerPlayService&m_encoding=UTF-8&BZ=1AAABnPQ_DIp42oVOTW_CQBT8M~uwPdS.
  • EEOC’s Private Sector Enforcement and Litigation Statistics, a summary of all discrimination charges in the private sector, created by the EEOC and located at www.eeoc.gov.
  • The FY2017 Federal Employee Viewpoint Survey Data, an annual survey of federal employees measuring their perceptions of successful organizational conditions at their workplace.

Scope

The goal of this report is to profile the federal sector workforce, age 40 and over, while providing benchmarks against which individual agencies and the federal sector can gauge their EEO performance.  All analyses are done at the agency or sector level. Analyses at the employee level are outside of the scope of this report.  As such, data is reported in the following manner:     

All data are reported in aggregate form; 

5-year trends are presented where appropriate;

Federal sector employment data is based on employees comprising the GS-pay scale workforce at those agencies required to report to OPM—i.e., Executive Agencies;

Non-federal sector data reported is based on the 2017 CLF statistics;

 

 

 

 THE ADEA: IMPORTANCE AND IMPACT

 

The workforce today looks very different from previous periods in U.S. history.  The modern workforce is older and more age-diverse.  It has been projected that, by the year 2025, almost half of the U.S. population will be over the age of 40 (U.S. Census, 2018).  Further, the typical retirement age is increasing due to workers having healthier lifestyles and longer life expectancies and Social Security eligibility (for non-reduced benefits) starting at later ages now than in previous years.  Pension plans are also declining as retirement responsibilities continue to shift from the employer to the employee (Maestas, 2010).  These data suggest that Americans are expected to remain active in the labor force for much longer than their predecessors.  As such, there is an increased importance of the Age Discrimination in Employment Act (ADEA) as a way to ensure that employment opportunities remain commensurate with these expectations.

The challenges of ensuring an equal opportunity workplace within an aging population is compounded when we consider the changing labor force with respect to race, sex, and ethnicity. It has been projected that, by the year 2024, women 55 and over will comprise a quarter of the labor force (Department of Labor, 2008) and a third of the labor force will be made up of Black, Asian, non-White Hispanics, and other non-White workers (Tossi, 2018).  Kunze, Boehm, and Bruch (2011) theorized on what to expect when researching age discrimination in the context of diversity within the U.S. The authors presented a theory that, while age discrimination increases with age diversification, it becomes less salient in the presence of other more socially significant identities.  Due to historical, social, and political processes in the United States, age may not be as important when factors such as sex and race are present, allowing such discriminations to persist even within age groups.  While individuals at a workplace carry multiple group memberships and discriminations are based on allegiance to such memberships, these allegiances are prioritized based on forces that shape our perceptions of the relative value of each membership. In an American socio-political context, race and sex allegiances may be stronger than age allegiances and, as such, members of marginalized race and sex groups will likely face discrimination even from those of a common age group.

While current anti-discrimination laws have proved valuable for reducing some of the impact of age discrimination, it is important that we remain aware of any emerging issues affecting this population. The results that follow are valuable as an examination of age discrimination in the federal sector. In conjunction with former Acting Chair Lipnic’s report, this report will be valuable to those interested in designing policies that promote equal employment opportunities amongst an aging population.

A COMPARATIVE ANALYSIS OF THE 40 AND OVER WORKFORCE: THE FEDERAL SECTOR VS. THE CIVILIAN LABOR FORCE

METHODOLOGY

This section compares the federal sector 40 and over workforce to the civilian labor force 40 and over across a variety of statistical measures related to diversity.  Comparisons between the federal sector and CLF data were drawn using two data sources. Federal sector data was compiled using OPM’s FedScope Employment Cubes.[2]  This data system is created using OPM’s Enterprise Human Resources Integration (EHRI) data warehouse, a government-wide shared platform in which human resources data for all executive agencies are stored (OPM, 2019).  Updated quarterly, this warehouse is the primary source of federal sector workforce data.  The “Employment Cube” and the “Diversity Cube” for December 2017 were utilized to generate all federal sector data tables.  Each cube was filtered with “Age 40 and over” as a custom subset.  Race, sex, national origin, educational attainment, and earnings were extracted, and contingency tables developed.

The civilian labor force statistics were compiled using the U.S. Census Current Population Survey CPS Table Creator.[3]  The CPS data is collected annually by the U.S. Census Bureau and the U.S. Bureau of Labor Statistics and is the primary source of labor force statistics for the population of the United States.  The CPS is administered using a probability sample of about 60,000 households from across all 50 states and the District of Columbia. Household data is weighted to allow for generalizations to the entire U.S. population (U.S. Census Bureau, 2019). For our comparisons, only 2017 data was used, with the population Civilian Labor Force[4] measure and “Age 40 to 80+” used as the primary filters. Afterwards, race, sex, national origin, and education attainment were selected, and contingency tables were developed.       

The results of our comparison of federal sector and civilian labor force workforce distributions follows.

While the Federal Sector Outperforms the CLF in Representation, Pay Disparities Persist Among Federal Employees 40 and Over Despite Educational Attainment[5]

Overall, employees 40 and over enjoy greater representation in the federal sector than in the civilian labor force (Figure 1). Persons 40 and over comprise 54% of the civilian labor force, compared to 72% in the federal sector.  Further, federal employees in this protected age group earned, on average, $87,886 a year and there was a tendency for federal employees in this category to earn more as they get older, with earnings peaking at the 65 and older age group. 

Bar chart comparing the CLF to the Federal Sector in the percent of workers 40 and over. CLF = 54%; Federal = 72%. Detailed table immediately follows.

Sector

Workforce 40+

Total Workforce

% Total Workforce 40+

Civilian Labor Force

 86,139,521

160,127,849

54%

Federal (GS and Equivalent)

 1,457,548

 2,037,246

72%

Figure 1. Federal Sector Rate of Employment for Employees 40 Years and Older, FY2017

Line graph showing pay changes across age groups for employees 40 and over. 40 to 44 = $82,155; 45 to 49 = $84,692; 50 to 54 = $89,079; 55 to 59 = $90,434; 60 to 64 = $90,305; 65 and older = $91,940. Detailed table immediately follows.

 

Age Group

Sector

40 to 44

45 to 49

50 to 54

55 to 59

60 to 64

65+

Federal

$82,155

$84,692

$89,079

$90,434

$90,305

$91,940

Figure 2. Federal Pay Across Age Groups, FY2017

With respect to representation, men in this protected age group participated in the federal sector more than their female counterparts (Figure 3).  Men 40 and over make up 57% of the federal sector workforce, compared with 45% in the CLF, while women 40 and over only make up 43% of the federal sector workforce, compared to 41% in the CLF.[6] While both groups participate at a higher rate in the federal sector than in the CLF, 40 and over men in the federal sector participate at a rate of 12% higher than the CLF, compared to only 2% increased participation among women.

Bar chart comparing the CLF to the federal sector in gender representation. CLF = 45% male and 41% female; Federal = 57% male and 43% female. Detailed table immediately follows. />

Sector

Total

Male

Female

CLF

86,139,521

38,614,582

35,511,169

% CLF

 

45%

41%

Federal

1,457,548

824,576

632,766

% Federal

 

57%

43%

Figure 3. Sex Distribution for Employees 40 and Over: Federal vs. Civilian Labor Force, FY2017[7]

As with gender representation, the federal sector is generally on par with, or ahead of, the CLF with respect to race and national origin for employees 40 and over (Figure 4).  Hispanics, Native Hawaiian or Other Pacific Islanders, Asians, and individuals reporting more than one race are all equally represented in the federal sector and the CLF.  African Americans are represented 8% more in the federal sector than in the CLF, while Whites are underrepresented by 5%. American Indian or Alaska Natives are about 1% higher in the federal sector than in the CLF. Overall, the federal government has maintained comparable representation to the CLF.

Bar chart comparing the CLF to the federal sector in race/ethnic representation. Federal = 63% white, 8% Hispanic, 1% more than one race, less than 1% Native Hawaiian or Pacific Islander, 19% black, 6% Asian, and 2% American Indian or Alaskan Native. CLF = 68% white, 8% Hispanic, 1% more than one race, less than 1% Native Hawaiian or Pacific Islander, 11% black, 6% Asian, and 1% American Indian or Alaskan Native. Detailed table immediately follows.

Sector

Total

American Indian or
Alaskan Native

Asian

Black/
African American

Native Hawaiian or
Pacific Islander

More Than One Race

Hispanic/
Latino

White

CLF

86,139,521

457,393

5,088,134

9,230,584

205,044

784,119

6,802,759

58,360,477

% CLF

 

1%

6%

11%

0%

1%

8%

68%

Federal

1,457,548

25,743

87,258

282,792

6,825

18,190

117,363

918,796

% Fed

 

2%

6%

19%

0%

1%

8%

63%

Figure 4. Race and National Origin Distributions for Employees 40 and Over: Federal Sector vs. CLF, FY2017

With respect to gender, men, on average, earn more than women in the federal sector (Figure 5).  In the federal sector, men 40 and over earn about $7,414 more per year than women, with men earning, on average, $91,250 and women earning $83,836.    

Bar chart comparing average men and women pay in the federal sector. Men = $91,250 and women = $83,836. Detailed table immediately follows.

Sector

Mean

Male

Female

Gender Pay Gap

Federal

$87,886

$91,250

$83,836

$(7,414)

Figure 5. Mean Earnings by Sex for Employees 40 and Over, Federal Sector, FY2017

An analysis of earnings by race and ethnicity reveals pay gaps among these groups as well (Figure 6).  In the federal sector, Asians in the protected age categories, on average, earn more than other groups.  This is followed by Whites, who are the second highest earners, followed by more than one race, Hispanic/Latinos, African Americans, Native Hawaiian or Other Pacific Islanders, and American Indian or Alaska Natives, in that order. 

Bar chart comparing average federal sector pay by race/ethnicity. White = $91,795; Hispanic = $79,945; More than one race = $83,259; Native Hawaiian or Pacific Islander = $73,841; Black = $77,580; Asian = $98,607; and American Indian or Alaskan Native = $69,202. Detailed table immediately follows.

Sector

Mean

American Indian or
Alaska Native

Asian

Black/
African American

Native Hawaiian or
Other Pacific Islander

More Than One Race

Hispanic/
Latino

White

Federal

 $87,886

 $69,202

$98,607

 $77,580

 $73,841

 $83,259

 $79,945

 $91,795

Figure 6. Mean Earnings by Race and National Origin for Employees 40 and Over, Federal Sector, FY2017

Pay Disparities Persist in the Federal Sector, Even When Education Level is Comparable

Federal sector men and women 40 and over are close to equal in their rates of bachelor’s degree or higher holders within each gender group.  In the federal sector 53% of males and 51% of females 40 and over have bachelor’s degrees (Figure 7). In looking at race and national origin, a greater proportion of Asians 40 and over hold a bachelor’s degree or higher than any other group, with 69% of Asian federal employees holding a degree, followed by White employees at 55%, more than one race at 53%, and both Black and Hispanic employees at 42% each. American Indian or Alaska Natives and Native Hawaiian or Other Pacific Islanders held bachelor’s degrees at 33% and 32% respectively (Figure 8, Appendix 5). 

Bar chart comparing federal sector bachelor's degree holders by gender. Men BA holders = 53% and women BA holders = 51%.

Sector

Male

Female

Federal

434,069

319,618

% Federal

53%

51%

Figure 7. Percentage of Employees 40 and Over with a Bachelor’s Degree or Higher, Federal Sector, FY2017

Bar chart comparing federal sector BA holders by race/ethnicity. White = 55%; Hispanic = 42%; More than one race = 53%; Native Hawaiian or Pacific Islander = 32%; Black = 42%; Asian = 69%; American Indian or Alaskan Native = 33%. Detailed table follows immediately.

Sector

Total

American Indian or
Alaska Native

Asian

Black/
African American

Native Hawaiian or
Pacific Islander

More Than One Race

Hispanic/
Latino

White

Federal

753,787

8,461

60,468

118,202

2,216

9,582

48,933

505,617

% Federal

52%

33%

69%

42%

32%

53%

42%

55%

Figure 8. Percentage of Employees 40 and Over with a Bachelor’s Degree or Higher by Race and National Origin, Federal, FY2017

Regarding gender pay among employees with a bachelor’s degree or higher, the pay gap between men and women 40 and over exists (Figure 9).

Bar chart comparing federal sector salaries for BA holders by gender. Male BA holders = $108,647 and female BA holders = $100,304. Detailed table follows immediately.

Sector

Mean

Male

Female

Gender Pay Gap

Federal

$104,965

$108,647

$100,304

$(8,343)

Figure 9. Gender Differences in Mean Earnings Among Employees 40 and Over with a Bachelor’s Degrees or Higher, Federal, FY2017

With regards to race and national origin, pay differences persist among groups holding a bachelor’s degree or higher in the protected age group (Figure 10). Asian Americans 40 and over with bachelor’s degrees or higher, on average, earn more than all other groups, followed closely by Whites, Hispanics, and more than one race. Blacks, Native Hawaiian or Other Pacific Islanders, and American Indian or Alaska Natives, respectively, earn less than all other groups.

Bar chart comparing federal sector salaries for BA holders by race/ethnicity. White = $108,078; Hispanic = $97,617; More than one race = $96,450; Native Hawaiian or Pacific Islander = $91,521; Black = $93,943; Asian = $110,636; American Indian or Alaskan Native = $91,419.  Detailed table follows immediately.

Sector

Mean

American Indian or
Alaskan Native

Asian

Black/
African American

Native Hawaiian or
Pacific Islander

More Than One Race

Hispanic/
Latino

White

Federal

104,965

 $91,419

 $110,636

 $93,943

 $91,521

 $96,450

 $97,617

 $108,078

Figure 10. Differences in Mean Earnings by Race and National Origin among Employees 40 and Over with a Bachelor’s Degrees or Higher, Federal, FY2017

AGE DISCRIMINATION COMPLAINTS IN THE FEDERAL SECTOR VS. THE PRIVATE SECTOR

METHODOLOGY

This section of the report compares the rates of age discrimination complaints, settlements, and findings between the federal and private sectors while providing trends data where appropriate.  The top issues alleged for age complaints are also provided. Data used for the analysis of federal sector and private sector age discrimination complaints was derived from two sources.  Federal sector complaint data was compiled from the Annual Federal Equal Employment Opportunity Statistical Report on Discrimination Complaints (EEOC Form 462). This data is collected annually by the Office of Federal Operations and consists of data on all pre-complaint, formal complaint, and complaint closure activities for all Executive, civilian, military, judicial, and other federal agencies and sub-agencies required to file a Form 462 as identified in 29 C.F.R. 1614.103.  For FY2017, the total number of agencies submitting Form 462 to the EEOC was 268.  

Private sector complaint data was compiled from the Enforcement and Litigation Statistics found at www.eeoc.gov.  These data are compiled annually by EEOC’s Office of Enterprise Data and Analytics and posted publicly on the website in tabular form. The data found on this site represents all discrimination charges filed with the EEOC, including bases, issues, hearings, and closures.  Data from both sources were compiled into contingency tables and analyzed accordingly. The results of these analyses follow.

Age Complaints and Settlements are Higher in the Federal Sector, Though the Private Sector Experiences More Findings

The rate of federal sector age-based EEO complaints remained fairly consistent between FY2013 and FY2017 (Figure 11).  Age discrimination was alleged in 31% of all federal sector complaints, 8% to 9% higher than in the private sector. Non-sexual harassment was a leading issue alleged in age-based complaints, ranking first in the federal sector with 2,094 complaints and third in the private sector with 3,909 complaints (Table 1). 

Line graph comparing federal and private sector age complaints from  FY2013 to FY2017. Federal = 32%, 31%, 32%, 31%, and 31%, respectively. Private = 23%, 23%, 23%, 23%, and 22%, respectively. Detailed table immediately follows.

 

FY2013

FY2014

FY2015

FY2016

FY2017

Total Federal Complaints

15,217

15,008

15,490

15,828

15,482

# Federal Age Complaints

 4,799

4,696

4,959

4,980

4,783

% Federal Age Complaints

32%

31%

32%

31%

31%

Total Private Complaints

93,727

88,778

89,385

91,503

84,254

# Private Age Complaints

21,396

20,588

20,144

20,857

18,376

% Private Age Complaints

23%

23%

23%

23%

22%

Figure 11. Federal vs. Private Sector Age Complaints, FY2013-FY2017

Sector

1st

2nd

3rd

Federal

Non-Sexual Harassment

Disciplinary Action

Promotion-Selection

N Federal

2,094

1,088

1,036

Private

Discharge

Terms/Conditions

Non-Sexual Harassment

N Private

10,091

4,539

3,909

Table 1. Top 3 Issues Alleged in the Federal vs. Private Sector, FY2017

The rates of federal sector settlements for age-based complaints have also been consistent between FY2013 and FY2017, with the federal sector settling between 20% and 23% of all age-based complaints, 13% and 15% more than those settled in the private sector (Figure 12).  Interestingly, while the federal sector has consistently settled more age-based complaints over the past five years, the private sector has consistently had more findings. The rate of age-based complaint findings has been 2.5% to 3% higher in the private sector than in the federal sector.  Federal sector age-based findings have remained less than 1% (Figure 13).

Line graph comparing federal sector and private sector age complaint settlements from FY2013 to FY2017. Federal sector = 23%, 21%, 21%, 22%, and 20%, respectively. Private sector = 8%, 8%, 8%, 7%, and 7%, respectively. Detailed data table immediately follows.

Sector

2013

2014

2015

2016

2017

Federal

1,102

970

1,028

1,076

956

% Age Complaints

22.96%

20.66%

20.73%

21.61%

19.99%

Private

1,781

1,567

1,703

1,445

1,288

% Age Complaints

8.32%

7.61%

8.45%

6.93%

7.01%

Figure 12. Federal vs. Private Sector Age-Based Settlements, 2013-2017

Line graph comparing federal and private sector rates rate of age complaint findings between FY2013 and FY2017. Private sector = 2.5%, 2.7%, 3%, 2.9%, and 2.6%, respectively. Federal = .9%, .7%, .4%, .2%, and .5%, respectively. Detailed data table immediately follows.

Sector

FY2013

FY2014

FY2015

FY2016

FY2017

Federal

41

31

22

8

25

% Federal Age Complaints

0.85%

0.66%

0.44%

0.16%

0.52%

Private

539

552

611

620

485

% Private Age Complaints

2.52%

2.68%

3.03%

2.97%

2.64%

Figure 13. Federal vs. Private Sector Age-Based Findings, FY2013 - FY2017

PREDICTING FEDERAL SECTOR AGE-BASED COMPLAINTS

METHODOLOGY

After observing the prevalence of age complaints in the federal sector, we sought to determine if the organizational and social environments potentially contributed to this phenomenon. As such, we merged Form 462 complaint data with data from OPM’s Federal Employee Viewpoint Survey (FEVS) to investigate the impact of agency socio-structural characteristics on age complaints. 

The Office of Personnel Management FEVS measures employees’ perceptions of their work environment, including their experiences with their agencies, supervisors, senior leadership, and colleagues (Office of Personnel Management, 2017). Factors such as perceived fairness, diversity, and inclusion; satisfaction with job duties and responsibilities; general organizational satisfaction; and select demographics including race, sex, pay grade, education, and supervisory status are measured and compiled into summary scores. During FY2017, more than 485,000 federal employees across 80 agencies participated for a participation rate of 45.5%. The FEVS results are used to access the pulse of the federal workforce and are often used by agency leadership to identify pain points within the organization’s social climate.

Because Form 462 is aggregate data reported only at the agency level, FEVS data was analyzed at this level as well. Data for the 75 agencies found in FEVS were merged with their Form 462 agency-level responses.  Relationships between the agency characteristics, as identified in Form 462, were analyzed alongside agency-level FEVS scores to predict agency-level age complaints. Agency organizational characteristics used to predict age complaints included:

  • Agency size, as indicated by the total number of permanent and temporary employees at the agency;
  • The total number of formal complaints filed at the agency;
  • The number of EEO staff employed at the agency, as indicated by the total number of employees working as EEO counselors, investigators, or counselors/investigators;
  • The agency’s reporting structure, as indicated by whether or not the agency’s EEO director reports directly to the agency’s head, as required by Management Directive 110.

Indicators of the social climate were all derived from FEVS data and are as follows:

  • Agency-level global satisfaction index (GSI)—a four-item index designed to measure employees’ perceptions that their organization is a good place to work and their general satisfaction with their job, pay, and organization, as a whole (See Appendix 1).  GSI is calculated by averaging the unrounded percent positive of each of the four items (OPM 2016).
  • Human Capital Assessment and Accountability Framework Index (HCAAFI)—an index comprised of four sub-scales designed to measure the employee’s perceived competency of agency leadership, including the leadership’s knowledge; whether the leadership fosters a results-oriented culture; ability to manage agency talent; and overall job satisfaction (Appendix 1). HCAAFI is calculated by averaging the unrounded percent positive of the items that make up the index (Office of Personnel Management, 2016).
  • New Inclusion Quotient (NewIQ), a 20-item index measured across five sub-scales that assesses employees’ perceptions of five habits of inclusion -- fairness, openness, cooperativeness, supportiveness, and empowerment (See Appendix A).  The NewIQ is calculated by averaging the unrounded percent positive of each of the items in the sub-index. Averaging the five unrounded sub-index scores creates the overall NewIQ score (Office of Personnel Management, 2016).

Perceptions of Fairness May Be Associated with Age Complaints. Agency Reporting Structure Is a Consistent Predictor

Upon merging Form 462 and FEVS data, our final sample consisted of 75 agencies.  Among these agencies, the average number of employees was 24,486, with an average complaint rate of 132 complaints (Table 2). The average number of age complaints among these agencies was 39 and the average number of EEO staff per agency was 67.  The average GSI score was 68.3, the average HCAAFI score was 69.3, and the average NewIQ score was 64.8. Results indicated that 74.7% of the sample had an EEO director that reported directly to the agency head.

Variable

# of Agencies

Totals

Means

St. Dev.

Ranges

Total Workforce

75

1,836,474

24,486.3

65,815.1

378,257

Complaints

75

9,896

131.95

378.7

2,570

Age Complaints

75

2,919

38.9

105.4

704

EEO Staff

75

5,024

66.9

161.2

870

GSI

72

--

68.3

8.84

46.6

HCAAFI

72

--

69.3

5.94

34.0

NewIQ

72

--

64.8

7.92

47.8

Director Reports to Agency Head?

Yes = 56 (74.7%)

No  = 19 (25.3%)

          75

--

--

--

--

Table 2. Descriptive Statistics for FEVS Agencies’ Analysis[8]

Preliminary Analysis

For our preliminary analysis, we investigated whether age complaint rates were associated with FEVS scores.  Age complaint rates were calculated by taking the total number of age complaints filed at the agency and dividing by the total number of complaints filed at the agency.  This proportion was then converted to a percent.  For our sample of 80 FEVS agencies, the average age complaint rate was 27%. Agencies with an age complaint rate above this average were dummy coded “1” while agencies with an age complaint rates below this average were dummy-coded “0”.  A t-test[9] was used to test the mean difference in agency HCAAFI score, the overall GSI score, and the NewIQ score. We expected that agencies with rates of complaints above the mean would perform poorer on each of these socio-cultural indicators. 

T-tests comparing the average FEVS scores between agencies above the mean age complaint rate and below the mean age complaint rate revealed this very result (Table 3).  Agencies with age complaint rates above the mean reported 2.4 points (P >. 05) higher on their average HCAAFI scores, 3.1 points (P >. 05) higher on their average GIS scores, and 2.2 points (P >. 05) higher on their NewIQ scores. While these results are not statistically significant, these relationships are true for 72 of the 80 agencies comprising the FEVS dataset.

 

# of Agencies

Mean

Mean Difference

DF

P

Overall HCAAFI

Age Complaint Rate Below Mean

34

70.6

2.4

56.2

1.69

.10

Age Complaint Rate Above Mean

38

68.2

       

Overall GSI

Age Complaint Rate Below Mean

34

69.9

3.1

56.6

1.48

.14

Age Complaint Rate Above Mean

38

66.8

       

NewIQ

Age Complaint Rate Below Mean

34

66.0

2.2

55.8

1.17

.25

Age Complaint Rate Above Mean

38

63.7

       

Table 3. Mean Comparison of FEVS Scores for Agency Age Complaint Rates Above the Sample Mean and Agencies Below the Sample Mean (Equal Variances Not Assumed), FY2017

Predicting Agency-Level Age Complaints

For our predictive model, we sought to identify organizational factors that contribute to agency age complaints using our FEVS sample. With the number of age complaints as the outcome variable, we entered total workforce, total complaints, agency reporting structure, number of EEO professionals at the agency, and the agency’s NewIQ score into a Poisson regression model to predict age complaints.

Poisson regression was used given that, as age complaints  are a count variable, it allows us to predict the chances that an age complaint will occur via the “incident rate ratio” (IRR).  The incident rate ratio is a derivative of the regression coefficient and indicates the chance that a complaint will occur when each predictor in the Poisson regression model is entered into the equation.  Every IRR unit above “1” represents a percent increase per unit increase on the predictor variable, while every unit below “1” represents a percent decrease in a complaint occurring per unit increase in the predictor variable. 

The NewIQ was selected as our only FEVS predictor, due to the NewIQ being the more comprehensive measure of an agency’s EEO climate.  For our regression analysis, reporting structure was coded “0” if the agency’s EEO director did not report to the agency head and “1” if the EEO director did report to the agency head.  All other variables were numeric.

With total workforce, total complaints, number of EEO staff, and NewIQ score all held constant, reporting structure was a substantively and statistically significant predictor of age complaints at the agency. Agencies whose EEO director reported directly to the agency head had a 73% decreased likelihood of reporting an age complaint compared to agencies whose EEO director did not report directly to the agency head (IRR = .267; P < .05). Having an EEO director report directly to the agency head reduces the chances of having an age complaint at the agency, even when agency size, total complaints, number of EEO staff, and NewIQ score are the same. The strength of this relationship is impressive. Appendix 2 provides a full display of the regression results. After discovering the strength of the relationship between reporting structure and age complaints, we decided to investigate whether this relationship existed among our larger population of Form 462 agencies.  As in the previous predictive model, total workforce, total complaints, reporting structure, and number of EEO staff were used as predictors.  NewIQ was dropped from the analysis due to the variable not being available for the majority of the 268 agencies reporting Form 462.

Table 4 displays the characteristics of the Form 462 agencies with respect to our targeted variables. Across the 263 agencies used in the final analysis, the average agency size was 10,603 employees and the average number of complaints were 57.8.  The average number of age complaints was 17.8 and the average number of EEO staff at the agency was 24.7.  Results indicated that 58.2% of the agencies had EEO directors that reported directly to the agency head, while 41.8% of agencies did not.

Variable

Obs

Mean

St. Dev.

Range

Total Workforce

263

10,603.4

35,964

37,8265

Complaints

268

57.8

206.0

2,570

Age Complaints

268

17.8

58.8

704.0

EEO Staff

268

24.7

76.4

870.0

Director Reports to Agency Head?

Yes = 156 (58.2%)

No  = 112 (41.8%)

          268

     

Table 4. Descriptive Statistics for Poisson Regression Analysis: Form 462 Agencies[10]

For our regression analysis, reporting structure was coded “0” if the agency’s EEO director did not report directly to the agency head, and “1” if the EEO director did report directly to the agency head.  All other variables were numeric.

As expected, reporting structure was negatively associated with the number of age complaints. Agencies with an EEO director reporting directly to the agency head were 24% less likely to report an age complaint than agencies without their EEO director reporting directly to the agency head (IRR = -.764; P < .05).  This result is both substantively and statistically significant.  When total workforce, total complaints, and number of EEO staff is the same, agencies with a director reporting directly to the agency head are less likely to experience an age complaint. Appendix 3 displays the full regression results.

Summary

The results above suggest that, when all else is equal, the agency reporting structure is singly important in predicting age complaints among both FEVS and Form 462 agencies.  While agency size, and the number of EEO staff were statistically significant in both samples, neither approached the predictive power of having an EEO director report directly to the agency head.  These results demonstrate the importance of this Management Directive 110 requirement in reducing age discrimination among federal agencies.

CONCLUSIONS

The data presented here suggests that older federal employees enjoy greater representation and diversity than what has been observed in the CLF.  The federal sector outperformed the private sector and the CLF in almost every measure of EEO presented in this report.  However, despite a strong relative performance, the race, sex, and national origin inequalities persist in the federal sector among the 40 and older cohort.     

In General, the Federal Sector Outperforms the Private Sector and the CLF in EEO for Older Workers

Relatively, the federal sector has done an impressive job of delivering EEO to older employees when compared to the private sector and the CLF.  Federal employees 40 and over enjoy greater representation and more diversity than their non-federal sector counterparts.  Further, the data indicating that federal employees earn more as they get older suggests that the federal sector is successful in discouraging pay penalties as we age.  With respect to sex, parity between men and women remains when it comes to pay among workers 40 and over, with men on average earning more than women.  While age complaints are indeed higher in the federal sector, data demonstrating that federal sector settlements are also higher while findings are lower suggest that the federal sector is more effective at resolving age discrimination complaints. The discussion that follows attempts to explain some of the patterns observed. 

Despite Favorable EEO Performance, There is Much Room for Improvement in the Federal Sector

Despite the relative success of the federal sector with regard to EEO delivery to employees 40 and over, the $7,414 pay difference between men and women should not be ignored.  Likewise, the fact that pay differences prevail between race and national origin within protected age groups despite having a college degree must be addressed.  While the workforce distributions in the CLF and public sectors mirror with respect to race and national origin, the persistence of inequality despite the high level of oversight suggests that the EEOC should continue to develop policies and guidance that promote EEO. 

EEOC Regulations, Management Directive 110 and Management Directive 715 May Influence Federal Sector EEO Performance for Older Workers

 

Considering the private sector has no complimentary guidance for managing EEO activity, it is our position that the reason that the federal sector outperforms the private sector in EEO is, at least in part, due to the presence of enhanced oversight by EEOC through these management directives. MD-110 provides federal agencies with guidelines and requirements for processing their EEO complaints while MD-715 provides agencies with program requirements for maintaining a successful EEO program and fair workplace. It is the enforcement of these directives that has encouraged agencies to comply and promote a discrimination-free environment.

Research provided by Lee and Smith (2019) may help explain why the federal sector outperforms the private sector in EEO.  These authors reviewed over 22 types of EEO strategies and identified six strategies that can potentially contribute to a fair and equitable workplace:

  • Authorizing workers’ complaints by allowing third parties to advocate on the part of disadvantaged populations and employees;
  • Creating enhanced penalties for engaging in discrimination;
  • Mandating that employers disclose information to workers about their rights;
  • Having strong anti-retaliation laws;
  • Expanding liability by placing the burden of proof on the employer;
  • Making reporting of employer discrimination data to the public and governing bodies mandatory.

While these recommendations were largely designed to address pay disparities and intended for grass roots advocacy, their recommendations have implications for all forms of workplace inequality.  Further, a review of Management Directive 110 and Management Directive 715 aligns with Lee and Smith’s recommendations. 

Lee and Smith suggested that advocacy organizations should attempt to authorize worker complaints by eliminating barriers to “naming, blaming, and claiming” discrimination complaints. Inequality becomes pervasive when it is not challenged by those most impacted by it.  However, if the proper policies, practices, and procedures are not in place, disadvantaged groups may be discouraged from voicing injustice.  Creating a culture of inclusion is necessary for promoting diversity in the workplace. Organizations that are aware of how broader social, historical, and political inequalities reproduce themselves in the workplace are more likely to be successful in promoting equity (Wong 2019).  The EEOC, through enforcement of its regulations and Management Directive 110, encourages employees to voice their concerns about discrimination  by requiring all agencies to establish an EEO Complaint Program, assign an EEO director to this program, and require that this program exists independent of the agency’s personnel functions. The Commission requires agency EEO offices to operate as independent entities, ensuring that employees feel confident that filing a complaint will not impact their standing at the agency. Further, while an EEO complaint begins at the agency, 29 C.F.R. 1614 allows employees to appeal agency decisions to the EEOC or to take their complaint to federal court, reinforcing fairness in the resolution of a complaint.   

Lee and Smith also recommend that advocacy groups require employers to report data on discrimination complaints and findings to the public and governing agencies as a way of promoting employer accountability. Once again, this is a requirement in MD-110.  According to MD-110, agencies must report discrimination complaints to the EEOC annually, including timeliness in complaint processing and ADR utilization.  Agencies must post their No FEAR Act data on discrimination complaints and outcomes on their public websites, and an annual report on workforce distributions and complaint activity must be provided to Congress, EEOC, and the public. Agencies failing to report such data to the Commission are subject to sanctions, as determined by Congress.  Encouraging agencies to remain transparent, which may improve the culture of EEO, increases agency accountability.

The authors further argue that anti-retaliation laws are essential to encouraging potential victims of discrimination to challenge inequality.  As such, the rise of legislation such as the No FEAR Act of 2003 broadened EEOC’s ability to enforce the anti-retaliation protections already afforded in the Civil Rights Act of 1964.  With the development of the No FEAR Act, the EEOC required agencies to provide mandatory No FEAR Act training to all federal employees biannually, ensuring that employees are knowledgeable of their rights to challenge mistreatment without fear of retaliation.

The effective use of ADR in the federal sector may help explain why age complaint settlements were higher and findings were lower as compared to the private sector.  29 C.F.R. Part 1614.102(b)(2) requires that agencies establish alternative dispute resolution programs and that this option be made available to employees when the informal complaint is submitted; further, employees may participate in ADR at any time during the complaint process. An analysis of Form 462 data for FY2017 reveals that agencies offer ADR to employees at a rate of 88%, and it is accepted 55% of the time, with 64% of ADR acceptances resulting in a resolution.

Finally, our results showed that the primary predictor of age complaints both in our Form 462 agencies and our FEVS agencies was having an EEO director report to the agency head.  This is a requirement of 29 C.F.R. 1614.102(b)(4) and MD-110 that may also contribute to more successful EEO at the agency.  Having an EEO director report directly to the agency head was associated with a 73% decreased likelihood of having an age complaint among FEVS agencies and a 24% decreased likelihood of having an age complaint among Form 462 agencies.

In summary, the effective oversight of the federal sector may help explain the difference in EEO delivery between it and the private sector for employees 40 and over. Additional work remains for this population, as pay disparities persist within gender, race, and national origin groups.  Nevertheless, the EEOC has had success in the federal sector regarding older workers.  The federal sector has greater representation than the national CLF while outperforming the private sector in complaint settlements.  Federal employees, on average, earn more as they get older, suggesting that labor is not undervalued with age.  Finally, the finding that agencies with a compliant reporting structure, on average, report fewer age complaints than non-compliant agencies points to a compliant reporting structure as a best practice for protecting older workers.  The private sector may benefit from looking to the federal government as a model EEO employer.

 

APPENDIX 1. Federal Employee Viewpoint Survey Fair Workplace Measures

FEVS Index

Indicators/Sub-scales

Overall Employee Engagement Index (Overall EEI): assesses the critical conditions conducive for employee engagement (e.g., effective leadership, work which provides meaning to employees, etc.). It is made up of three subfactors: Leaders Lead, Supervisors, and Intrinsic Work Experience.

Leaders Lead Sub-Scale:

q53. In my organization, senior leaders generate high levels of motivation and commitment in the workforce.

q54. My organization’s senior leaders maintain high standards of honesty and integrity.

q56. Managers communicate the goals and priorities of the organization.

q60. Overall, how good a job do you feel is being done by the manager directly above your immediate supervisor?

q61. I have a high level of respect for my organization’s senior leaders.

Supervisors Sub-Scale:

q47. Supervisors in my work unit support employee development.

q48. My supervisor listens to what I have to say.

q49. My supervisor treats me with respect.

q51. I have trust and confidence in my supervisor.

q52. Overall, how good a job do you feel is being done by your immediate supervisor?

Intrinsic Work Experience Sub-Scale:

q3. I feel encouraged to come up with new and better ways of doing things.

q4. My work gives me a feeling of personal accomplishment.

q6. I know what is expected of me on the job.

q11. My talents are used well in the workplace.

q12. I know how my work relates to the agency’s goals and priorities.

Global Satisfaction Index (GSI): Measures employee’s overall job satisfaction

q40. I recommend my organization as a good place to work.

q69. Considering everything, how satisfied are you with your job?

q70. Considering everything, how satisfied are you with your pay?

q71. Considering everything, how satisfied are you with your organization?

Human Capital Assessment and Accountability Framework (HCAAF): Measures employee’s confidence in the agency’s leadership and management, whether the agency has a results-oriented performance culture, whether the agency is successful at managing agency talent, and overall job satisfaction.

Leadership and Knowledge of Management Sub-Scale:

q10. My workload is reasonable.

q35. Employees are protected from health and safety hazards on the job.

q36. My organization has prepared employees for potential security threats.

q51. I have trust and confidence in my supervisor.

q52. Overall, how good a job do you feel is being done by your immediate supervisor/team leader?

q53. In my organization, leaders generate high levels of motivation and commitment in the workforce.

q55. Managers/supervisors/team leaders work well with employees of different backgrounds.

q56. Managers communicate the goals and priorities of the organization.

q57. Managers review and evaluate the organization's progress toward meeting its goals and objectives.

q61. I have a high level of respect for my organization’s senior leaders.

q64. How satisfied are you with the information you receive from management on what's going on in your organization?

q66. How satisfied are you with the policies and practices of your senior leaders?

Results-Oriented Performance Culture Sub-Scale:

q12. I know how my work relates to the agency’s goals and priorities.

q14. Physical conditions (for example, noise level, temperature, lighting, cleanliness in the workplace) allow employees to perform their jobs well.

q15. My performance appraisal is a fair reflection of my performance.

q20. The people I work with cooperate to get the job done.

q22. Promotions in my work unit are based on merit.

q23. In my work unit, steps are taken to deal with a poor performer who cannot or will not improve.

q24. In my work unit, differences in performance are recognized in a meaningful way.

q30. Employees have a feeling of personal empowerment with respect to work processes.

q32. Creativity and innovation are rewarded.

q33. Pay raises depend on how well employees perform their jobs.

q42. My supervisor supports my need to balance work and other life issues.

q44. Discussions with my supervisor/team leader about my performance are worthwhile.

q65. How satisfied are you with the recognition you receive for doing a good job?

Talent Management Sub-Scale:

q1. I am given a real opportunity to improve my skills in my organization.

q11. My talents are used well in the workplace.

q18. My training needs are assessed.

q21. My work unit is able to recruit people with the right skills.

q29. The workforce has the job-relevant knowledge and skills necessary to accomplish organizational goals.

q47. Supervisors/team leaders in my work unit support employee development.

q68. How satisfied are you with the training you receive for your present job?

Job Satisfaction Sub-Scale:

q4. My work gives me a feeling of personal accomplishment.

q5. I like the kind of work I do.

q13. The work I do is important.

q63. How satisfied are you with your involvement in decisions that affect your work?

q67. How satisfied are you with your opportunity to get a better job in your organization?

q69. Considering everything, how satisfied are you with your job?

q70. Considering everything, how satisfied are you with your pay?

New IQ Index (NewIQ): indicates the degree to which an environment is inclusive. Although this is a new index, the items that comprise the New IQ have been on the FEVS in previous years, making trend calculation possible.           

           

Fairness Sub-Scale:

q23. In my work unit, steps are taken to deal with a poor performer who cannot or will not improve.            

q24. In my work unit, differences in performance are recognized in a meaningful way.                 

q25. Awards in my work unit depend on how well employees perform their jobs.   

q37. Arbitrary action, personal favoritism and coercion for partisan political purposes are not tolerated.                       

q38. Prohibited Personnel Practices (for example, illegally discriminating for or against any employee/applicant, obstructing a person’s right to compete for employment, knowingly violating veterans’ preference requirements) are not tolerated.

Openness Sub-Scale:

q32. Creativity and innovation are rewarded.                 

q34. Policies and programs promote diversity in the workplace (for example, recruiting minorities and women, training in awareness of diversity issues, mentoring).            

q45. My supervisor is committed to a workforce representative of all segments of society.                       

q55. Supervisors work well with employees of different backgrounds.                                   

Cooperative Sub-Scale:

q58. Managers promote communication among different work units (for example, about projects, goals, needed resources).

q59. Managers support collaboration across work units to accomplish work objectives.    

Supportive Sub-Scale:

q42. My supervisor supports my need to balance work and other life issues.

q46. My supervisor provides me with constructive suggestions to improve my job performance.

q48. My supervisor listens to what I have to say.

q49. My supervisor treats me with respect.

q50. In the last six months, my supervisor has talked with me about my performance.

Empowering Sub-Scale:

q2. I have enough information to do my job well.

q3. I feel encouraged to come up with new and better ways of doing things.

q11. My talents are used well in the workplace.

q30. Employees have a feeling of personal empowerment with respect to work processes.

APPENDIX 2. T-Test Results: Age Complaints by FEVS Scores

The SAS System

proc ttest data = rstsas192;

            class ragecomplaintrate;

            var overalleei;

run;

proc ttest data = rstsas192;

            class ragecomplaintrate;

            var gsi;

run;

proc ttest data = rstsas192;

            class ragecomplaintrate;

            var hcaafjsi;

run;

proc ttest data = rstsas192;

            class ragecomplaintrate;

            var newiq;

run;

The TTEST Procedure

 

Variable: overalleei (overalleei)

ragecomplaintrate

N

Mean

Std Dev

Std Err

Minimum

Maximum

0

34

72.1885

8.9407

1.5333

55.0311

96.1376

1

38

69.8313

5.8799

0.9538

55.5927

82.5157

Diff (1-2)

 

2.3572

7.4805

1.7659

 

 

 

ragecomplaintrate

Method

Mean

95% CL Mean

Std Dev

95% CL Std Dev

0

 

72.1885

69.0689

75.3081

8.9407

7.2113

11.7684

1

 

69.8313

67.8986

71.7639

5.8799

4.7936

7.6070

Diff (1-2)

Pooled

2.3572

-1.1648

5.8792

7.4805

6.4205

8.9631

Diff (1-2)

Satterthwaite

2.3572

-1.2602

5.9746

 

 

 

 

Method

Variances

DF

t Value

Pr > |t|

Pooled

Equal

70

1.33

0.1862

Satterthwaite

Unequal

56.003

1.31

0.1971

 

Equality of Variances

Method

Num DF

Den DF

F Value

Pr > F

Folded F

33

37

2.31

0.0143

 

 

The SAS System

The TTEST Procedure

 

Variable: gsi (gsi)

ragecomplaintrate

N

Mean

Std Dev

Std Err

Minimum

Maximum

0

34

69.9066

10.1198

1.7355

45.2872

91.9081

1

38

66.7868

7.3455

1.1916

47.7087

81.1631

Diff (1-2)

 

3.1197

8.7635

2.0688

 

 

 

ragecomplaintrate

Method

Mean

95% CL Mean

Std Dev

95% CL Std Dev

0

 

69.9066

66.3756

73.4375

10.1198

8.1624

13.3204

1

 

66.7868

64.3724

69.2012

7.3455

5.9885

9.5032

Diff (1-2)

Pooled

3.1197

-1.0063

7.2458

8.7635

7.5216

10.5004

Diff (1-2)

Satterthwaite

3.1197

-1.0919

7.3314

 

 

 

 

Method

Variances

DF

t Value

Pr > |t|

Pooled

Equal

70

1.51

0.1360

Satterthwaite

Unequal

59.628

1.48

0.1436

 

Equality of Variances

Method

Num DF

Den DF

F Value

Pr > F

Folded F

33

37

1.90

0.0600

 

 

The SAS System

The TTEST Procedure

 

Variable: hcaafjsi (hcaafjsi)

ragecomplaintrate

N

Mean

Std Dev

Std Err

Minimum

Maximum

0

34

70.5935

6.9947

1.1996

55.0896

87.4067

1

38

68.1970

4.6211

0.7496

53.4410

78.2586

Diff (1-2)

 

2.3965

5.8611

1.3836

 

 

 

ragecomplaintrate

Method

Mean

95% CL Mean

Std Dev

95% CL Std Dev

0

 

70.5935

68.1529

73.0340

6.9947

5.6418

9.2070

1

 

68.1970

66.6781

69.7159

4.6211

3.7674

5.9786

Diff (1-2)

Pooled

2.3965

-0.3631

5.1560

5.8611

5.0305

7.0228

Diff (1-2)

Satterthwaite

2.3965

-0.4370

5.2300

 

 

 

 

Method

Variances

DF

t Value

Pr > |t|

Pooled

Equal

70

1.73

0.0877

Satterthwaite

Unequal

56.167

1.69

0.0958

 

Equality of Variances

Method

Num DF

Den DF

F Value

Pr > F

Folded F

33

37

2.29

0.0154

 

 

The SAS System

The TTEST Procedure

 

Variable: newiq (newiq)

ragecomplaintrate

N

Mean

Std Dev

Std Err

Minimum

Maximum

0

34

65.9758

9.4533

1.6212

48.7411

96.5648

1

38

63.7397

6.1862

1.0035

50.8903

77.7616

Diff (1-2)

 

2.2361

7.8967

1.8641

 

 

 

ragecomplaintrate

Method

Mean

95% CL Mean

Std Dev

95% CL Std Dev

0

 

65.9758

62.6774

69.2742

9.4533

7.6248

12.4432

1

 

63.7397

61.7064

65.7731

6.1862

5.0434

8.0034

Diff (1-2)

Pooled

2.2361

-1.4818

5.9540

7.8967

6.7776

9.4618

Diff (1-2)

Satterthwaite

2.2361

-1.5838

6.0559

 

 

 

 

Method

Variances

DF

t Value

Pr > |t|

Pooled

Equal

70

1.20

0.2344

Satterthwaite

Unequal

55.824

1.17

0.2459

 

Equality of Variances

Method

Num DF

Den DF

F Value

Pr > F

Folded F

33

37

2.34

0.0133

 

APPENDIX 3. Predicting Age Complaints Among FEVS Agencies: Poisson Regression Results

Variable

Coeff

IRR

SE

Wald Chi-Square

Pr>ChiSq

Total Workforce

.000

1.00

0.3038

120.93

<.0001

Total Complaints

.000

1.00

0.0000

382.19

0.7787

Reporting Structure

-1.32

0.267

0.0001

0.08

<.0001

Number of EEO Staff

.001

1.00

0.0519

647.29

<.0001

NewIQ

-.003

0.996

0.0001

391.56

0.4713

Predicting Age Complaints (N=72)

 

 

The SAS System

 

proc genmod data = rstsas192;

            *class rreport / param=glm;

            model agecomplaints17 = rtotalworkforce complaints report eeostaff             newiq / type3 dist = poisson;

            store pfevs;

            estimate "rtotalworkforce" rtotalworkforce 1  / exp;

            estimate "complaints" complaints 1  / exp;

            estimate "report" report 1  / exp;

            estimate "eeostaff" eeostaff 1  / exp;

            estimate "newiq" newiq 1 / exp;

            run;

 

The GENMOD Procedure

Model Information

Data Set

WORK.RSTSAS192

 

Distribution

Poisson

 

Link Function

Log

 

Dependent Variable

agecomplaints17

agecomplaints17

 

Number of Observations Read

80

Number of Observations Used

72

Missing Values

8

 

Parameter Information

Parameter

Effect

Prm1

Intercept

Prm2

rtotalworkforce

Prm3

complaints

Prm4

report

Prm5

eeostaff

Prm6

newiq

 

Criteria For Assessing Goodness Of Fit

Criterion

DF

Value

Value/DF

Deviance

66

1331.1261

20.1686

Scaled Deviance

66

1331.1261

20.1686

Pearson Chi-Square

66

1655.3520

25.0811

Scaled Pearson X2

66

1655.3520

25.0811

Log Likelihood

 

11930.8533

 

Full Log Likelihood

 

-762.6722

 

AIC (smaller is better)

 

1537.3445

 

AICC (smaller is better)

 

1538.6368

 

BIC (smaller is better)

 

1551.0045

 

 

Algorithm converged.

 

Analysis Of Maximum Likelihood Parameter Estimates

Parameter

DF

Estimate

Standard Error

Wald 95% Confidence Limits

Wald Chi-Square

Pr > ChiSq

Intercept

1

3.3409

0.3038

2.7455

3.9364

120.93

<.0001

rtotalworkforce

1

0.0000

0.0000

0.0000

0.0000

382.19

<.0001

complaints

1

0.0000

0.0001

-0.0001

0.0002

0.08

0.7787

report

1

-1.3205

0.0519

-1.4222

-1.2188

647.29

<.0001

eeostaff

1

0.0015

0.0001

0.0014

0.0017

391.56

<.0001

newiq

1

-0.0035

0.0049

-0.0131

0.0061

0.52

0.4713

Scale

0

1.0000

0.0000

1.0000

1.0000

 

 

Note:

The scale parameter was held fixed.

 

LR Statistics For Type 3 Analysis

Source

DF

Chi-Square

Pr > ChiSq

rtotalworkforce

1

287.81

<.0001

complaints

1

0.08

0.7783

report

1

708.69

<.0001

eeostaff

1

358.76

<.0001

newiq

1

0.52

0.4710

 

Contrast Estimate Results

Label

Mean Estimate

Mean

L'Beta Estimate

Standard Error

Alpha

L'Beta

Chi-Square

Pr > ChiSq

Confidence Limits

   

Confidence Limits

rtotalworkforce

1.0000

1.0000

1.0000

0.0000

0.0000

0.05

0.0000

0.0000

382.19

<.0001

Exp(rtotalworkforce)

 

 

 

1.0000

0.0000

0.05

1.0000

1.0000

 

 

complaints

1.0000

0.9999

1.0002

0.0000

0.0001

0.05

-0.0001

0.0002

0.08

0.7787

Exp(complaints)

 

 

 

1.0000

0.0001

0.05

0.9999

1.0002

 

 

report

0.2670

0.2412

0.2956

-1.3205

0.0519

0.05

-1.4222

-1.2188

647.29

<.0001

Exp(report)

 

 

 

0.2670

0.0139

0.05

0.2412

0.2956

 

 

eeostaff

1.0015

1.0014

1.0017

0.0015

0.0001

0.05

0.0014

0.0017

391.56

<.0001

Exp(eeostaff)

 

 

 

1.0015

0.0001

0.05

1.0014

1.0017

 

 

newiq

0.9965

0.9870

1.0061

-0.0035

0.0049

0.05

-0.0131

0.0061

0.52

0.4713

Exp(newiq)

 

 

 

0.9965

0.0049

0.05

0.9870

1.0061

 

 

APPENDIX 4. Predicting Age Complaints Among Form 462 Agencies: Poisson Regression Results

Variable

Coeff

IRR

SE

Wald Chi-Square

P

Total Workforce

0.0000

1.000

0.0000

14298.3

<.0001

Total Complaints

0.0004

1.000

0.0001

279.25

<.0001

Reporting Structure

-0.7642

0.466

0.0362

19.56

<.0001

Number of EEO Staff

0.0014

1.001

0.0001

445.85

<.0001

Predicting Age Complaints (N=263)

The SAS System

 

proc genmod data = stsas19r;

      *class rreport / param=glm;

      model agecomplaints = rworkforce complaints rreport eeostaff / type3 dist = poisson;

      store p462;

      estimate "rworkforce" rworkforce 1  / exp;

      estimate "complaints" complaints 1  / exp;

      estimate "rreport" rreport 1  / exp;

      estimate "eeostaff" eeostaff 1  / exp;

      run;

The GENMOD Procedure

Model Information

Data Set

WORK.STSAS19R

 

Distribution

Poisson

 

Link Function

Log

 

Dependent Variable

agecomplaints

agecomplaints

 

Number of Observations Read

268

Number of Observations Used

263

Missing Values

5

 

Parameter Information

Parameter

Effect

Prm1

Intercept

Prm2

rworkforce

Prm3

complaints

Prm4

rreport

Prm5

eeostaff

 

Criteria For Assessing Goodness Of Fit

Criterion

DF

Value

Value/DF

Deviance

258

6569.7945

25.4643

Scaled Deviance

258

6569.7945

25.4643

Pearson Chi-Square

258

8758.5901

33.9480

Scaled Pearson X2

258

8758.5901

33.9480

Log Likelihood

 

13889.8792

 

Full Log Likelihood

 

-3599.3795

 

AIC (smaller is better)

 

7208.7591

 

AICC (smaller is better)

 

7208.9926

 

BIC (smaller is better)

 

7226.6199

 

 

Algorithm converged.

 

Analysis Of Maximum Likelihood Parameter Estimates

Parameter

DF

Estimate

Standard Error

Wald 95% Confidence Limits

Wald Chi-Square

Pr > ChiSq

Intercept

1

2.6934

0.0225

2.6493

2.7376

14298.3

<.0001

rworkforce

1

0.0000

0.0000

0.0000

0.0000

279.25

<.0001

complaints

1

0.0004

0.0001

0.0002

0.0005

19.56

<.0001

rreport

1

-0.7642

0.0362

-0.8352

-0.6933

445.85

<.0001

eeostaff

1

0.0014

0.0001

0.0013

0.0016

287.88

<.0001

Scale

0

1.0000

0.0000

1.0000

1.0000

 

 

Note:

The scale parameter was held fixed.

 

LR Statistics For Type 3 Analysis

Source

DF

Chi-Square

Pr > ChiSq

rworkforce

1

205.54

<.0001

complaints

1

21.36

<.0001

rreport

1

461.68

<.0001

eeostaff

1

262.26

<.0001

 

Contrast Estimate Results

Label

Mean Estimate

Mean

L'Beta Estimate

Standard Error

Alpha

L'Beta

Chi-Square

Pr > ChiSq

Confidence Limits

Confidence Limits

rworkforce

1.0000

1.0000

1.0000

0.0000

0.0000

0.05

0.0000

0.0000

279.25

<.0001

Exp(rworkforce)

 

 

 

1.0000

0.0000

0.05

1.0000

1.0000

 

 

complaints

1.0004

1.0002

1.0005

0.0004

0.0001

0.05

0.0002

0.0005

19.56

<.0001

Exp(complaints)

 

 

 

1.0004

0.0001

0.05

1.0002

1.0005

 

 

rreport

0.4657

0.4338

0.4999

-0.7642

0.0362

0.05

-0.8352

-0.6933

445.85

<.0001

Exp(rreport)

 

 

 

0.4657

0.0169

0.05

0.4338

0.4999

 

 

eeostaff

1.0014

1.0013

1.0016

0.0014

0.0001

0.05

0.0013

0.0016

287.88

<.0001

Exp(eeostaff)

 

 

 

1.0014

0.0001

0.05

1.0013

1.0016

 

 

APPENDIX 5. Gender and Education: Federal Sector vs. CLF, FY2017

Bachelor degree holders, 40 and older. Civilian labor force, Percent female BA holders = 45%, Percent male BA holders = 44%; Federal sector, Percent female BA holders = 51%, Percent male BA holders = 53%.

Data Sources: The United States Census Bureau’s Current Population Survey Table Creator, 2017, located at https://www.census.gov/cps/data/cpstablecreator.html, and The Office of Personnel Management’s FedScope Data Cubes, December 2017, located at https://www.fedscope.opm.gov/ibmcognos/cgi-bin/cognosisapi.dll.

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[1] Civilian Labor Force is a term used the by Bureau of Labor Statistics to refer to Americans, 16 years or older, employed and unemployed (Bureau of Labor Statistics, 2019).

[2] OPM’s FedScope Employment Cubes are located at www.fedscope.opm.gov/.

[3] U.S. Census Current Population Survey CPS Table Creator is located at www.census.gov/cps/data/cpstablecreator.html.

[4] Civilian Labor Force measure was labeled “Civilian Labor Force-Recode” in the CPS table creator.

[5] Educational Attainment is defined by having earned a college degree or an advanced graduate degree.

[6] Totals may not equal 100% due to unspecified genders removed from the analysis

[7] Unspecified gender removed from analysis.

[8] *Security Agencies Removed from Analysis Due to Unreported Total Workforce measure.

[9] A t-Test is a statistical procedure used to determine if there is a significant difference between the means to two groups on a specified measure.

[10] *Security Agencies Removed from Analysis Due to Unreported Total Workforce measure.