GAO 

United States Government Accountability Office

Report to Congressional Requesters

WOMEN’S PAY 

Gender Pay Gap in the 

Federal Workforce 

Narrows as 

Differences in 

Occupation, 

Education, and 

Experience Diminish 

March 2009 

GAO-09-279 
Page i GAO-09-279 Women’s Pay 

Contents 

Letter 1

Appendix I Briefing Slides 5 

Appendix II Summary of Methods and Data 39 

Appendix III Cross-sectional Analysis 46 

Appendix IV Cohort Analysis 68 

Appendix V Crosswalk between the Statistics Presented in the 

Briefing Slides and Those Presented in 

Appendices III and IV 81 

Appendix VI Comments from the U.S. Office of Personnel 

Management 91 

Appendix VII Comments from the U.S. Equal Employment 

Opportunity Commission 93 

Appendix VIII GAO Contact and Staff Acknowledgments 100 

Bibliography 101 
Tables 

Table 1: Descriptive Statistics for Selected CPDF Variables Used in 

Our Cross-sectional Analysis 50 

Table 2: Description and Definition of the Alternate Models 53 

Table 3: Main Regression Results 57 

Table 4: Female Coefficient under Alternate Specifications of the 

Model 59 

Table 5: Estimated Female Coefficient within Subgroups Using 

Main Specification 60 

Table 6: Decomposition Results Using Main Specification (with 

contributions of key factors) 63 

Table 7: Decomposition Results Using Alternate Specifications 64 

Table 8: Estimated Total, Unexplained, and Explained Pay Gaps for 

Different Subgroups (using main specification of the 

model) 67 

Table 9: Number of Federal Employees from the 1988 Entry Cohort 

Remaining over 2 Decades in the Status and Dynamic Files 69 

Table 10: Cohort Differences between Men and Women in 

Occupational Categories in 1988 and 2007 72 

Table 11: Cohort Differences between Men and Women in 

Education Levels in 1988 and 2007 73 

Table 12: Descriptive Statistics for Men and Women in 1988 

Entering Cohort 73 

Table 13: Trends in the Female Coefficient for the 1988 Entering 

Cohort before and after Controlling for Differences 

between Men and Women in Measurable Factors 75 

Table 14: Results of the Decomposition: Amount of Gender Pay 

Gap Resulting from Differences between Men’s and 

Women’s Characteristics from 1988 to 2007 77 

Table 15: Summary of Breaks in Services Use among Cohort, Fiscal 

Years 1988 and 2007 79 

Table 16: Example of Precision of Log Difference 82 

Table 17: Crosswalk between Cross-sectional Estimates of the 

Total Pay Gap 83 

Table 18: Crosswalk between Cohort Estimates of the Total Gap in 

Appendix IV and the Briefing Slides 83 

Table 19: Crosswalk between Cross-sectional Estimates of 

Unexplained Gap and the Portions of the Gap Resulting 

from Differences between Men and Women in Measurable 

Factors in Appendix III and the Briefing Slides 84 

Page ii GAO-09-279 Women’s Pay 
Table 20: Crosswalk between Cohort Estimates of Explained Gap 

Resulting from Differences between Men and Women in 

Measurable Factors in Appendix IV and the Briefing Slides 86 

Figures 

Figure 1: Distribution of Occupational Categories in the Entering 

Class of 1988 over 20-year Period 71 

Figure 2: Cost of Taking Unpaid Leave on Pay for Men and Women 79 

Abbreviations 

CPDF Central Personnel Data File 

CPS Current Population Survey 

EEOC Equal Opportunity Employment Commission 

LWOP leave without pay 

OPM Office of Personnel Management 

PATCOB Professional, Administrative, Technical, Clerical, Other 

 White-Collar and Blue-Collar 

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Page iii GAO-09-279 Women’s Pay 
Page 1 GAO-09-279 Women’s Pay 

 

United States Government Accountability Office

Washington, DC 20548 

 

March 17, 2009 

The Honorable Edward M. Kennedy 

Chairman 

Committee on Health, Education, Labor 

 and Pensions 

United States Senate 

The Honorable Tom Harkin 

Chairman 

Subcommittee on Labor, Health and Human 

 Services, Education, and Related Agencies 

Committee on Appropriations 

United States Senate 

The Honorable Carolyn B. Maloney 

Chair 

Joint Economic Committee 

House of Representatives 

Although the pay gap between men and women in the U.S. workforce has 

narrowed since the 1980s, numerous studies have found that a disparity 

still exists. In 2003, we found that women in the general workforce earned, 

on average, 20 cents less for every dollar earned by men in 2000 when 

differences in work patterns, industry, occupation, marital status, and 

other factors were taken into account.1

 Other research indicates that this 

disparity existed for federal workers as well. For example, a 1998 study 

showed that the pay gap between men and women in the federal 

workforce decreased significantly between 1976 and 1995, but in 1995 

white women still earned 14 cents less for every dollar earned by white 

men and African-American women earned 8 cents less for every dollar 

earned by African-American men after available factors related to pay 

were taken into account.2

1

GAO, Women’s Earnings: Work Patterns Partially Explain Difference between Men’s and 

Women’s Earnings, GAO-04-35 (Washington, D.C.: Oct. 31, 2003). 

2

Gregory B. Lewis, “Continuing Progress toward Racial and Gender Pay Equality in the 

Federal Service: An Update,” Review of Public Personnel Administration, vol. 18, no. 2 

(Spring 1998) 23-40. 
21B 

In light of concerns that a pay gap may continue to exist between men and 

women in the workplace, you asked us to examine pay disparity issues and 

the role the federal government has played in enforcing anti-discrimination 

laws. In agreement with your staff, we addressed these questions in two 

separate, consecutive reports, the first of which focused on enforcement 

and outreach efforts in the private sector and among federal contractors.3

This second report addresses the following question: To what extent has the 

pay gap between men and women in the federal workforce changed over 

the past 20 years and what factors account for the gap? 

To answer this question, we used two approaches to analyze data from the 

Central Personnel Data File (CPDF)—maintained by the Office of 

Personnel Management (OPM)—covering a 20-year period. First, we 

looked at “snapshots” of the federal workforce at three points in time 

(1988, 1998, and 2007) to show changes in the federal workforce over a 20-

year period.4

 Second, we examined the cohort (or group) of employees 

who joined the federal workforce in 1988 and tracked their careers over 

the course of 20 years to look for differences in the pay gap in this group. 

We used CPDF data to generate summary statistics on the federal 

workforce and to perform multivariate analyses, which we used to identify 

the amount of the gender pay gap attributable to differences in measurable 

factors—such as work-related and demographic characteristics of men 

and women. To further inform our analyses, we reviewed existing 

literature and reports on gender and pay and interviewed officials at the 

Office of Personnel Management and the Equal Employment Opportunity 

Commission (EEOC). 

We conducted our work from March 2008 to March 2009 in accordance 

with all sections of GAO’s Quality Assurance Framework that are relevant 

to our objectives. The framework requires that we plan and perform the 

engagement to obtain sufficient and appropriate evidence to meet our 

stated objectives and to discuss any limitations in our work. We believe 

 

3

GAO, Women’s Earnings: Federal Agencies Should Better Monitor Their Performance in 

Enforcing Anti-Discrimination Laws,” GAO-08-799 (Washington, D.C.: Aug. 11, 2008). 

4

The CPDF contain personnel data for most of the executive branch departments and 

agencies as well as a few agencies in the legislative branch. For the purposes of this report, 

we refer to workers covered by the CPDF data as the federal workforce. Our “snapshot” 

findings are based on an analysis of a 20 percent random sample of federal employees in 

the CPDF for each of the three points in time. See appendix II for further details on the 

agencies not covered by the CPDF. 

Page 2 GAO-09-279 Women’s Pay 
21B 

that the information and data obtained, and the analysis conducted, 

provide a reasonable basis for our findings and conclusions. 

On January 26, 2009, we briefed your staff on the results of our work. This 

report formally conveys the information provided during that briefing (see 

app. I). In summary, we found: 

• From 1988 to 2007, the gender pay gap—the difference between men’s and 

women’s average annual salary in the federal workforce—declined from 

28 cents to 11 cents on the dollar. For each year we examined, all but 

about 7 cents of the gap can be accounted for by differences in measurable 

factors such as the occupations of men and women and, to a lesser extent, 

other factors such as years of federal experience and level of education. 

The pay gap narrowed as men and women in the federal workforce 

increasingly shared similar characteristics in terms of the jobs they held, 

their levels of experience, and educational attainment. Factors for which 

we lacked data or are difficult to measure, such as work experience 

outside the federal government and discrimination, may account for some 

or all of the remaining 7 cent gap. 

• Our case study analysis of workers who entered the federal workforce in 

1988 showed that their pay gap grew from 22 cents in 1988 to a maximum 

of 28 cents in 1993 through 1996 and then declined to 25 cents in 2007. As 

with the federal workforce, differences between men and women that can 

affect pay, especially occupation, accounted for a significant portion of the 

pay gap over the 20-year period. In addition, our analysis found that 

differences in the use of leave without pay and breaks in federal service 

accounts for little of the pay gap for this group. The portion of the gap that 

we could not explain increased over time from 2 cents in 1988 to 9 cents in 

2007. However, the results of the 1988 cohort are not necessarily 

representative of other cohorts 

Ultimately, the gender pay gap for the entire federal workforce has 

declined primarily because the men and women in the federal workforce 

are more alike in characteristics related to pay than in past years. We 

cannot be sure why a persistent unexplained pay gap remains for both our 

analyses, but this may be due to the inability to account for certain factors 

that cannot effectively be measured or for which data are not available. 

We received written comments on a draft of this report from OPM, which 

manages the CPDF data that were used in our analysis, and from EEOC. 

OPM reviewed our methodology and found our use of the CPDF data to be 

appropriate. They had two suggestions regarding variables in our analysis, 

which we considered carefully. As a result of their comments, we clarified 

Page 3 GAO-09-279 Women’s Pay 
21B 

our discussion of the empirical results in the appendices, but did not alter 

the main findings of our report. OPM’s full comments and our responses 

to them are presented in appendix VI. 

EEOC stated that our study has a solid research design and modeling 

analysis and will serve as an important source of information to the federal 

sector. In addition, EEOC suggested that we expand our report to show 

how the gender pay gap evolved for different protected groups. We 

acknowledge that the difference in wages between men and women may 

vary further by race, age, disability status, and other factors that we 

analyzed. However, to appropriately report on the influence of factors 

related to other protected groups would require substantial analysis that is 

beyond the scope of our study’s objective. EEOC also provided technical 

comments for our consideration. Their full comments and our responses 

to them are presented in appendix VII. 

As agreed with your offices, unless you publicly announce the contents of 

this report earlier, we plan no further distribution of this report until 30 

days from the report date. At that time, we will provide copies to the Chair 

of EEOC, the Director of OPM, relevant congressional committees, and 

other interested parties. We will make copies available to others upon 

request. In addition, the report will be available at no charge on GAO’s 

Website at http://www.gao.gov. 

If you or your staff have any questions about this report, please contact me 

at (202) 512-7215 or sherrilla@gao.gov. Contacts for our Offices of 

Congressional Relations and Public Affairs may be found on the last page 

of this report. GAO staff who made major contributions to this report are 

Andrew Sherrill 

listed in appendix VIII. 

Director, Education, Workforce, 

 and Income Security Issues 

Page 4 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

Page 5 GAO-09-279 Women’s Pay 

Appendix I: Briefing Slides 

WOMEN’S PAY: Gender Pay Gap in the Federal 

Workforce Narrows as Differences in Occupation, 

Education, and Experience Diminish

Briefing for Congressional Requesters

January 26, 2009

*The briefing slides were subsequently updated to reflect comments that EEOC provided on our draft report. 

See appendix VII for EEOC’s comments and our response.
Appendix I: Briefing Slides 

2

Overview

• Key Question

• Scope and Methodology

• Summary of Results

• Background

• Findings

• Entire Federal Workforce

• Case Study

• Concluding Observations

Page 6 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

3

Key Question

In response to your request, we answered this question:

• To what extent has the pay gap between men and women in 

the federal workforce changed over the past 20 years and 

what factors account for the gap?

Page 7 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

4

Scope and Methodology

• To answer our key question, we looked at data covering the 

last 20 years in two different ways:

1. We examined the federal workforce at 3 points in time 

(1988, 1998, and 2007) to show changes in the pay gap 

within the federal workforce as a whole over a 20-year 

perioda

2. We examined a cohort (group) of federal workers, i.e., 

those who entered the federal workforce in 1988, to look 

for differences in the pay gap for this group over timeb

aFor this analysis, we used a 20 percent random sample of federal employees in the CPDF for each of the 3 years.

bWe followed the careers of workers in this group, including those who left the federal workforce and later returned.

Page 8 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

5

Scope and Methodology (cont.)

• Our data came from the Central Personnel Data File (CPDF), which: 

• Is maintained by the Office of Personnel Management.

• Contains information on gender, annual salary, and other demographic 

and occupational factors for federal workers.

• Covers federal employees within most of the executive branch as well 

as a few agencies in the legislative branch, but does not cover 

employees in the judicial branch and federal contractors.a

• We used CPDF data to compute the overall pay gap between men and

women. We then performed multivariate analysis to estimate how much of 

the overall pay gap could be explained by demographic, occupational, and 

other measurable factors for which we have data.

aFor the purposes of this briefing, we refer to workers covered by the CPDF data as the federal workforce. 

See appendix II for further details on our data and data reliability analyses, as well as the employees excluded 

from the CPDF.

Page 9 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

6

Scope and Methods (cont.)

• To inform our analyses, we:

• Reviewed existing literature and reports on gender and pay

• Consulted officials at the Office of Personnel Management and 

the Equal Employment Opportunity Commission—agencies 

that are in part responsible for overseeing the employment 

practices of federal agencies

Page 10 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

7

Summary of Results

• Our analysis of the federal workforce shows that:

• From 1988 to 2007, the gender pay gap—the difference between 

men’s and women’s average paya before controlling for other factors—

narrowed from 28 cents to 11 cents on the dollar.

• For each year we analyzed, all but about 7 cents of the gap was 

accounted for by differences in measurable factors—predominantly 

the occupations of men and women and, to a lesser extent, other 

factors such as experience and education.

• Factors that we could not measure may have accounted for some or

all of the unexplained 7 cent gap.

aPay refers to annual salary. 

Page 11 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

8

Summary of Results (cont.)

• Our case study analysis of one cohort of employees, i.e., those who entered the federal workforce in 1988, showed that between 1988 and 

2007:

• The gender pay gap grew from 22 cents in 1988 to a maximum of 28 cents in 1993 and then declined to 25 cents in 2007. 

• After controlling for differences between men and women, all but 2 to 9 cents (depending on the year) of the pay gap over this period was 

accounted for by differences in measurable factors. Occupation is the 

measurable factor that contributed most to the gap.

• Differences in usage of unpaid leave and breaks in federal service 

accounted for less than 1 cent of this pay gap.

• These results are not necessarily representative of other cohorts.

Page 12 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

9

Background: Previous Studies Have Sought to 

Measure the Pay Gap between Men and Women

• For the entire U.S. workforce:

• Previously, GAO found that after accounting for certain measurable 

differences such as years of experience and part-time work status, 

women earned about 20 cents less for every dollar earned by men in 

2000a

• For the federal workforce:

• Research shows that the gap dropped significantly between 1976 and 

1995, but in 1995 white women still earned 14 cents less for every 

dollar earned by white men, and African-American women earned 8 

cents less for every dollar earned by African-American men after 

accounting for differences in measurable factors between men and

women 

a GAO, Women’s Earnings: Work Patterns Partially Explain Difference between Men’s and Women’s Earnings, GAO-04-35 

(Washington, D.C.: Oct. 31, 2003).

Page 13 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

10

Background: Federal Workers Are Classified 

in Six General Categories

Blue-collar Occupations comprising the crafts, trades, and manual labor, including foremen.

Includes positions that do not fall into other white-collar groups. Most of these 

positions are related to law enforcement or protective services.

Other white-collar

Involves structured work in support of office, business, or fiscal operations. 

Examples include typists, dispatchers, and clerks.

Clerical

Occupations typically associated with and supportive of a professional or 

administrative field. Includes medical technicians, safety technicians, and food 

inspectors.

Technical

Does not have a specific educational requirement, but involves skills typically 

gained through general college education. Examples include human resources 

management and budget analysis.

Administrative

Requires knowledge in a specific discipline, typically acquired through a bachelor’s 

or higher degree in a specialized field. Examples include accounting and 

engineering.

Professional

Occupational category Description

Source: OPM.

Page 14 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

11

Background: Federal Employees are Increasingly 

Concentrated in Professional and Administrative Jobs

• However, the proportion of clerical and blue-collar jobs decreased 

significantly

Source: GAO analysis of CPDF data.

Page 15 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

12

Background: Federal Employees Are Increasingly 

Concentrated in Professional and Administrative Jobs 

(cont.)

• The decline in clerical and blue-collar employment may be 

due to the following trends:

• Many defense-related jobs being phased out following the 

end of the Cold War

• Government efforts to increase efficiency through 

automation and by contracting out jobs

Page 16 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

13

Background: The Federal Workforce Has 

Increasingly More Education

Source: GAO analysis of CPDF data.

• The proportion of federal workers with a bachelor’s degree 

or higher increased from 33% in 1988 to 44% in 2007

Page 17 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

Page 18 GAO-09-279 Women’s Pay 

14

Background: The Federal Workforce Has 

Become More Experienced

Source: GAO analysis of CPDF data.

• In addition, the average years of federal experience increased from 

about 13 years in 1988 to 15 years in 2007
Appendix I: Briefing Slides 

Page 19 GAO-09-279 Women’s Pay 

15

The Pay Gap—before Accounting for Differences 

between Men and Women in Factors Related to Pay—

Has Decreased Significantly Since 1988

Source: GAO analysis of CPDF data.

Findings: Federal Workforce
Appendix I: Briefing Slides 

16

The Pay Gap Does Not Take into Account Differences 

in Measurable Factors between Men and Women

• The gap is a measure of the differences in pay for all 

men and all women in the federal workforce before 

accounting for any factors, such as differences in 

occupation or education

• We found that some of the gap can be accounted for by 

differences in measurable factors

Findings : Federal Workforce

Page 20 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

17

We Used Multivariate Analysis to Account for 

the Following Factors: 

• Work characteristics including occupational category,

agency, and state

• Worker characteristics including education level, federal 

experience, bargaining unit status, part-time work status, and 

veteran status

• Demographic characteristics including gender, age, race and 

ethnicity, and disability status

Findings: Federal Workforce

Page 21 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

18

Measurable Factors Account for a Significant 

Portion of the Gap

Source: GAO analysis of CPDF data.

Findings: Federal Workforce

Page 22 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

19

Occupation, Education, and Experience are the 

Measurable Factors that Contribute Most to the Gap

Source: GAO analysis of CPDF data.

Findings: Federal Workforce

Page 23 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

20

Other Factors That We Could Not Measure May 

Account for the Persistent Unexplained 7 Cent Gapa

• Factors for which we lacked data or are difficult to measure, 

such as work experience outside the federal government and 

discriminatory practices, could account for some of the 

unexplained gap

• Our analysis neither confirms nor refutes the presence of 

discriminatory practices

Findings: Federal Workforce

aThe size of the unexplained gap varies slightly depending on the number of occupational categories used in 

the analysis. See appendix III for further details.

Page 24 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

21

Converging Characteristics of Men and Women in the 

Workplace Help Explain the Narrowing Gap

• Men and women in the federal workforce became more alike 

in several characteristics, especially in:

• The occupations they hold,

• Their educational attainment, and

• Their years of federal work experience

Findings: Federal Workforce

Page 25 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

22

Some Federal Occupational Categories Have 

Become More Integrated by Gender

Findings: Federal Workforce

• Professional, administrative, and clerical occupations—which 

accounted for 68 percent of federal jobs in 2007—have become

more integrated by gender since 1988. For example, between 

1988 and 2007, the proportion of females in professional 

positions rose from 30 to 43 percent and in administrative 

positions rose from 38 to 45 percent

• Other occupations—accounting for 32 percent of the workforce in 

2007—have become or remained less integrated. Between 1988 

and 2007, the proportion of females in technical occupations rose 

from 52 to 60 percent, in blue collar occupations ranged between

9 to 10 percent, and in other white-collar occupations rose 

slightly from 12 to 13 percent.

Page 26 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

23

The Decline of the Clerical Workforce 

Accounts for a Large Reduction in the Gap

• In 1988, there were 312,000 female clerical workers in the federal 

workforce, accounting for 38% of all women in the government

• By 2007, this number dropped to 97,000, with female clerical 

workers accounting for only 13% of all female federal employees 

• Clerical workers are primarily female (85% in 1988 and 69% in 

2007)

• Clerical workers are among the lowest paid group in the federal 

government

Findings: Federal Workforce

Page 27 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

24

Men and Women in the Federal Workforce 

Have Increasingly Similar Levels of Education

Source: GAO analysis of CPDF data.

Findings: Federal Workforce

Page 28 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

25

Men and Women in the Federal Workforce Have 

Increasingly Similar Levels of Federal Experience

Source: GAO analysis of CPDF data.

Findings: Federal Workforce

Page 29 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

26

Analysis of Pay Gap among the Employees Who 

Began Working for the Federal Government in 1988

• To better understand changes in the gender pay gap over 

time, we compiled a data set on the people who began 

working for the federal government in 1988, which allowed us 

to track their federal pay and leave patterns over a 20-year 

perioda

• We accounted for differences between men and women in 

leave patterns (unpaid leave and breaks in serviceb) as well 

as occupation, agency, region, education level, bargaining 

unit status, part-time work status, veteran status, gender, 

age, race and ethnicity, disability status

Findings: Case Study

aData on work patterns came from the CPDF dynamics file.

bA break in service happens when an employee leaves the federal government and later returns.

Page 30 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

27

The 1988 Cohort Is Different from Our Analysis of the 

Entire Government in Important Ways

• The cohort only includes individuals who started working for 

the federal government in 1988, and as a result:

• This group became much smaller over time due to 

workers leaving the government, declining from about 

90,000 in 1988 to about 29,000 in 2007

• By definition, new workers did not enter this group over 

the study period

• Additionally, this cohort is not necessarily representative of 

other cohorts

Findings: Case Study

Page 31 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

28

Analysis of the 1988 Entering Class Shows that the 

Pay Gap Increased in Earlier Years Before Declining

Findings: Case Study

Source: GAO analysis of CPDF data.

Page 32 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

29

Differences Between Men and Women in Measurable Factors 

Account for A Significant but Declining Portion of the Gap

Findings: Case Study

Source: GAO analysis of CPDF data.

Page 33 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

30

For the 1988 Entering Class, Differences in 

Occupation Account for Much of the Pay Gap

Findings: Case Study

Source: GAO analysis of CPDF data.

• The portion of the gap that cannot be explained grew from 2¢ in 1988 to 9¢ in 2007

Page 34 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

31

For Women in the Entering Cohort of 1988, the Decrease 

in the Clerical Workforce Was also Significant

Findings: Case Study

Source: GAO analysis of CPDF data.

• Over the same period, the number of female administrative workers increased

Page 35 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

32

Women in the 1988 Entering Cohort Were More Likely to 

Take Unpaid Leave or Have a Break in Service, but Neither 

Significantly Affected the Pay Gap

• In spite of differences in leave patterns between men and 

women, taking unpaid leave and having a break in service 

consistently accounted for less than 1 cent of the pay gap 

for this cohort of federal workersa

17%

18%

Women

Had a Break in Service at Least 

Once between 1988-2007

Took Unpaid Leave at Least Once 

between 1988-2007

15%

11%

Men

Source: GAO analysis of CPDF data.

Findings: Case Study

aSee appendix IV for additional explanation of these results.

Page 36 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

33

Our Data Do Not Allow Us to Describe Why the 

Unexplained Pay Gap Grew

• As with our analysis of the federal workforce, other factors 

not captured by our data, such as experience outside the 

federal government and discrimination, could account for 

some of the unexplained pay gap

• Our analysis neither confirms nor refutes the presence of 

discriminatory practices

• We could not accurately measure the duration of instances of 

unpaid leave or determine why it was taken

Findings: Case Study

Page 37 GAO-09-279 Women’s Pay 
Appendix I: Briefing Slides 

34

Concluding Observations

• The decline in the pay gap for the federal workforce is 

primarily due to men and women in the federal workforce 

becoming more alike in characteristics related to pay

• We cannot be sure why a persistent unexplained pay gap 

remains for both analyses, but this may be due to the inability 

to account for certain factors that cannot effectively be 

measured or for which data are not available

Page 38 GAO-09-279 Women’s Pay 
Appendix II: Summary of Methods and Data Appendix II: Summary of Methods and Data 

To determine the extent to which the pay gap between men and women in 

the federal workforce changed over the past 20 years and the factors that 

accounted for the gap, we developed several models to estimate gender 

differences in annual salaries before and after controlling for other factors 

that affect pay. These models employed multivariate regression and 

decomposition1

 methods. The factors that affect the pay gap and are used 

in our models include: (1) work characteristics (i.e., the occupation men 

and women worked in, and the agency and state in which they worked); 

(2) worker characteristics (i.e., their education level, years of federal 

experience, bargaining unit status, full-time or part-time work status, and 

veteran status); and (3) additional demographic or background 

characteristics of federal employees (i.e., their gender, age, race/ethnicity, 

and disability status). We conducted a literature review of relevant 

research to inform these analyses and collaborated with GAO 

methodologists and consulted with OPM officials at various stages in 

doing this work.2

For our analysis, we examined federal personnel data covering a 20-year 

period—using both multivariate regression and decomposition methods—

in the following ways: 

1. We computed background statistics on the federal government for 

1988, 1998, and 2007 using information on every federal worker in our 

data. 

2. We then conducted a cross sectional analysis of gender differences in 

salaries for workers in the federal workforce at three points in time 

(September of 1988, 1998, and 2007) using a 20 percent sample of the 

federal workers in our data. 

3. Finally, we analyzed gender differences in salaries for a cohort of 

federal workers who entered the federal workforce in 1988. We 

examined these workers annually for a 20-year period to examine how 

the pay gap evolved over the course of their careers and whether it 

was produced by differences in work patterns (i.e., unpaid leave and 

breaks in service). 

 

1

Decomposition allowed us to analyze men’s and women’s salaries in separate regressions 

and provided an additional tool for determining which attributes were the key explanations 

of the differences between men’s and women’s salaries. 

2

See the bibliography for a list of relevant articles and reports. 

Page 39 GAO-09-279 Women’s Pay 
Appendix II: Summary of Methods and Data 

Appendix III provides a detailed discussion of our cross sectional analysis 

for the federal workforce, and appendix IV provides a detailed discussion 

of the cohort analysis. Appendix V presents the conversion of statistical 

output in appendices III and IV into the estimates of the pay gap that are 

presented in the briefing slides. In this appendix, we describe the data we 

used for our analyses, excluded data, our assessment of the reliability of 

the data, and the limitations of our analysis. 

The data we analyzed came from the Central Personnel Data File (CPDF). 

The CPDF is maintained by the Office of Personnel Management, and 

represents the primary government source of information on federal 

employees. We used two separate sets of files contained in the CPDF—the 

annual status files and the annual dynamics files. The status files consist of 

data elements describing all employees who were present in the federal 

workforce in September of each year, with some notable exclusions 

described below. These elements include information on the federal 

employee’s adjusted basic pay, agency, age, education level, disability 

status, occupation, race or national origin, gender, veteran’s preference 

and status, bargaining unit status, and work schedule as of a certain date 

each year. We used these elements from the status files for 1988, 1998, and 

2007 to construct the data for the cross sectional analysis. 

Data 

The annual dynamics files consist of data elements describing each 

personnel action taken by an agency for the time period covered by the 

file. Personnel actions are the official records of events that occur over the 

course of employees’ careers, and the dynamics file includes indicators 

and dates of hires, unpaid leaves of over 30 days, promotions, 

reassignments, pay changes, resignations, and retirements. We used some 

of these elements from the annual dynamics files, in combination with 

elements described above from the status files, for each year from 1988 to 

2007 to construct the data for the cohort analysis. 

While the CPDF is considered to be the most comprehensive, 

authoritative, and up-to-date database of federal executive branch 

employees, it does not include information for: (1) certain executive 

branch agencies, such as the intelligence services; (2) agencies in the 

Exclusions 

Page 40 GAO-09-279 Women’s Pay 
Appendix II: Summary of Methods and Data 

judicial branch; and (3) most agencies in the legislative branch.3

Ultimately, of the approximately 2.7 million federal employees, the CPDF 

covers roughly 1.6 million of them. The CPDF also does not include 

information on an estimated 10.5 million federal contractors and grantees, 

1.4 million members of the armed forces, and 1 million reservists. 

In addition to those exclusions, for purposes of consistency we performed 

some fairly routine data cleaning by systematically excluding certain 

observations from our analysis that were missing important information.4

We assessed the reliability of the CPDF data elements that were critical to 

our analyses and determined that, despite the limitations outlined below, 

they were sufficiently reliable for the purposes of our analyses. 

Specifically, we: 

Data Reliability 

• Reviewed documentation on the data elements included in the CPDF and 

past GAO analyses of the reliability of the CPDF data; 

• Interviewed OPM officials knowledgeable about the CPDF data and 

consulted these officials periodically throughout the course of our study; 

 

3

Specifically, CPDF coverage of the executive branch currently includes all agencies except 

the Board of Governors of the Federal Reserve, the Central Intelligence Agency, the 

Defense Intelligence Agency, Foreign Service personnel at the State Department, the 

National Geospatial-Intelligence Agency, the National Security Agency, the Office of the 

Director of National Intelligence, the Office of the Vice President, the Postal Rate 

Commission, the Tennessee Valley Authority, the U.S. Postal Service, and the White House 

Office. Also excluded are the Public Health Service’s Commissioned Officer Corps, nonappropriated fund employees, and foreign nationals overseas. CPDF coverage of the 

legislative branch is limited to the Government Printing Office, the U.S. Tax Court, and 

selected commissions. 

4

Specifically, in both analyses, we excluded individuals for whom we had no wage data 

(less than one-half of 1 percent of the individuals in the cross-section analysis). For the 

cohort analysis, when wage data were not available from the status file, we were able to 

use wage data from the dynamics file. For individuals in the status file with more than one 

record in a given year and for whom the wage data on those records differed, we selected 

the higher of the two wages. For those individuals in the dynamics file with more than one 

personnel action in a given year and for whom the wage data on the different actions 

differed, we selected the last reported personnel action with data on wages. In the cross 

sectional analysis we excluded individuals that had missing data on federal experience, 

race, veteran’s preference, and disability status (totaling less than 1 percent of the 

observations). For the cohort analysis, we were able to impute values for missing variables 

using data from other years. 

Page 41 GAO-09-279 Women’s Pay 
Appendix II: Summary of Methods and Data 

• Conducted our own electronic data testing to assess the accuracy and 

completeness of the data used in our analyses; and 

• Consulted with GAO staff knowledgeable about these data sets. 

As a result of these efforts, we identified the following limitations with our 

data: 

• Education data. A GAO report issued in 1998 found that the CPDF data for 

education level were both inaccurate and, in many cases, understated. 

OPM officials we consulted reported that education was sometimes 

understated because employee information was not updated after the time 

of hiring. Therefore, the education data do not always reflect additional 

education acquired during federal service. They also noted that the 

education data and the degree major data were self-reported and therefore 

subject to error. OPM officials stated that no changes have been made to 

enhance the accuracy of the education variable in response to GAO’s 1998 

finding. However, in a 1996 CPDF accuracy survey, OPM noted that 

“education level values appear reliable for determining general education 

groups (e.g., less than high school, high school graduate, some college), 

but less reliable when used to determine the precise education level.” As a 

result, in our analysis, we did not use information on the employee’s 

precise education level, but instead used broad categories to distinguish 

education groups.5

• Duration of Leave without Pay Period. For approximately one-quarter of 

the personnel actions for the employees we analyzed to determine 

whether they took leave without pay (LWOP), there was no corresponding 

personnel action indicating that the employee returned to duty. While we 

attempted to use various proxies in lieu of return to duty actions, we could 

not be certain that these proxies were accurate. Ultimately, we decided 

that it would not be possible for us to reliably measure the impact of the 

duration of LWOP on salaries, and we opted instead to look more simply 

at the effect of taking LWOP, regardless of its duration. Appendix IV 

provides a detailed discussion of our analyses of LWOP. 

 

5

Despite this assurance, however, we undertook an additional analysis to determine 

whether the underreporting of the education variable might affect our results. We used 

data from the Census Bureau’s Current Population Survey (CPS), whose reliability we did 

not assess, to run a similar model with more recent self-reported education data, for a 

sample of self-reported federal workers. The results of the CPS and CPDF analyses were 

similar enough to provide us with considerable confidence that the broad education 

variable was accurate for the purposes of our report. 

Page 42 GAO-09-279 Women’s Pay 
Appendix II: Summary of Methods and Data 

This analysis was not intended to be used to determine whether or not 

discrimination exists in the federal workforce, and the existence of a 

persistent unexplained pay gap in both our cross-sectional and cohort 

analyses after we controlled for as many factors as our data allowed 

means that we can neither rule out nor confirm the possibility that women 

are being treated unequally. A few limitations, some of which are common 

to almost all multivariate analyses, prevent us from definitively 

determining whether unexplained differences in pay by sex are due to 

discrimination or to other factors. First, discrimination is not usually 

overt, and as such direct measures of it generally do not exist. Second, we 

lack data on several factors that may legitimately influence wages, such as 

experience outside of the federal workforce and individual priorities. 

Third, certain variables that were included in our model—such as 

occupation, education level, and part-time status—may have been 

imprecisely measured or reported. Although there is no way to fully 

address these limitations with the data we were using, we took various 

steps to explore the latter two. 

Limitations of the 

Analysis 

With respect to the second set of limitations described above, we 

conducted two sets of cross-sectional analyses to further explore the 

impact of individual priorities on the pay gap, such as personal obligations 

outside of work, and found they had only a minor impact. The CPDF data 

do not contain information on marital status and number of children, 

variables that are commonly regarded as proxies for personal obligations 

and have been included in wage models in some literature.6

 To address 

this potential shortcoming, we used data from the Current Population 

Survey (CPS) to run a similar model with additional variables for marital 

status and number of children. We found that including these variables in 

the model had only a slight effect on the unexplained pay gap (i.e., it was 

reduced by less than 1 percent). We also analyzed a variable in the CPDF 

that indicates whether a federal employee is enrolled in a federal health 

benefit plan for single or family benefits. The health plan variable is a 

rough proxy of whether an individual has a family because individuals may 

receive family health benefits through a spouse. The results of this analysis 

corroborate our analysis of the variables for martial status and number of 

 

6

See, for example, June O’Neill, “The Gender Gap in Wages, circa 2000,” The American 

Economic Review. Vol. 93, No .2, Papers and Proceedings of the One Hundred Fifteenth 

Annual Meeting of the American Economic Association, Washington, D.C.: January 3-5, 

2003 (May 2003), pp. 309-314, and Audrey Light and Manuelita Ureta, “Early-Career Work 

Experience and Gender Way Differentials,” Journal of Labor Economics, Vol. 13, No. 1. 

(January 1995), pp. 121-154. 

Page 43 GAO-09-279 Women’s Pay 
Appendix II: Summary of Methods and Data 

children in the CPS data. Including the health care variable in the model 

reduced the unexplained pay gap by less than 1 percent. In contrast to the 

above analyses, we did not have proxies for motivation and work 

performance that were independent from the process used to determine 

an individual’s salary; therefore, we could not test the effect of these 

factors on the pay gap.7

Also with respect to the second limitation, some of the wage gap may be 

affected by the possibility that women, or certain women and men, may be 

more or less likely to enter the federal government. However, because our 

scope was limited to effects for men and women already employed by the 

federal government, we did not attempt to explain the impact that 

propensity to enter the workforce may have had on the gap. 

With respect to the third limitation, we conducted additional crosssectional analyses of the CPDF data to better understand the degree to 

which different measures of key variables might impact our results. For 

example, we tested several different specifications of the occupation 

variable. We found, using the most detailed occupation data, that the 

unexplained pay gap declined from 7 percent to 5 percent for all 3 years of 

analysis. Although more precise measures of occupation reduced the pay 

gap more than broad measures, we opted to use a broader specification 

because the occupation category variable itself may reflect discriminatory 

practices. Specifically, the fact that men and women are hired into or 

remain in (albeit decreasingly) different occupations may itself reflect 

some level of discrimination associated with hiring, promotion, or other 

employer practices.8

 As such, using a more precise measure of occupation 

in the model might hide the contribution of any such discrimination to the 

pay gap, and thereby understate the unexplained gap. To shed light on this, 

we estimated our model with no control for occupation, which would 

represent an upper-bound on the unexplained pay gap. We found that, with 

 

7

While the CPDF include data on performance ratings and grade information, which reflect 

promotions, these decisions feed directly into determining (and are therefore nearly 

synonymous with) salary. Therefore, it is more appropriate to evaluate these variables as 

dependent variables (in the same way that we are evaluating salary). However, such an 

analysis was beyond the scope of our report. 

8

For discussions of sex discrimination in hiring, see Claudia Goldin and Cecilia Rouse, 

“Orchestrating Impartiality: The Impact of ‘Blind’ Auditions on Female Musicians,” 

American Economic Review, vol. 90, no. 4 (2000); and David M. Neumark, “Sex 

Discrimination in Restaurant Hiring: An Audit Study,” Quarterly Journal of Economics, 

vol. 111, no. 3 (1996). 

Page 44 GAO-09-279 Women’s Pay 
Appendix II: Summary of Methods and Data 

no control for occupation, the unexplained pay gap was 20 percent in 

1988, 14 percent in 1998, and 11 percent in 2007. (See app. III, table 7, for 

further details on these results.) Ultimately, in an effort to strike a balance 

between the two extremes—either no control for occupation or the most 

detailed control for occupation—we used the occupational category 

variable in our model. This occupation variable was also relatively simple 

to interpret because it had significantly fewer categories than the most 

detailed occupation variable.9

We also tested whether additional information on education and 

geography reduced the pay gap. Specifically, we included in the model a 

variable for an individual’s educational major, which was only available for 

our 2007 cross-sectional analysis. For that year, we found that educational 

major reduced the unexplained gap by less than 1 percent. (See app. III, 

table 7, for further details on these results.) We also included a more 

detailed measure of geography—the county in which an employee works. 

We found that the more specific control for geography had no impact on 

the pay gap. 

In addition, certain variables in our model reflect personal decisions that 

may be correlated with salary, such as whether an employee chooses to 

work part-time. Including such variables in the model has the potential to 

lead to biased estimates.10 Although we ultimately decided to keep these 

variables in the main model used in our briefing slides, we ran different 

versions of the model without these variables. (See app. III, table 7, for 

these results.) 

 

9

OPM categorizes occupations into one of six occupational categories: Professional, 

Administrative, Technical, Clerical, Other White-Collar and Blue-Collar (PATCOB). In 

applying decomposition methods, the occupation variable with fewer categories also had 

advantages. Specifically, any category that contains only men or women is excluded when 

computing the decomposition. Using more disaggregated occupation data therefore results 

in the exclusion of individuals that are in an all-female or all-male occupation category. 

10Because of this bias, the academic literature sometimes does not include controls for 

marital status and family size in analyses of the pay gap. See, for example, Francine D. Blau 

and Lawrence M. Kahn, “The U.S. Gender Pay Gap in the 1990’s: Slowing Convergence,” 

Industrial and Labor Relations Review, Vol. 60, No. 1 (October 2006). 

Page 45 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

Page 46 GAO-09-279 Women’s Pay 

Appendix III: Cross-sectional Analysis 

In order to perform our cross-sectional analysis of differences in salaries 

between men and women in the federal government as a whole over a 

20-year period, and the extent those differences can be explained by other 

factors, we employed two separate techniques. Both techniques involved 

multivariate regression, and controlled for many factors that might affect 

pay, such as level of education or occupation. The data used for both 

techniques come from the status file of the Central Personnel Data File 

(CPDF), as described in appendix II. 

The first technique involved regression analysis on a data set which 

included men and women. In this analysis, we used a variable for gender 

to measure the average difference between men and women’s salaries. 

Then by adding additional variables to the regression, we controlled for 

other characteristics of men and women to determine the extent to which 

the difference is (or is not) explained by the addition of those variables. 

The second technique, called a decomposition, analyzed men’s and 

women’s salaries in separate regressions. This method provides an 

additional tool for determining which attributes were the key explanations 

of the differences between men and women’s salaries, and also what 

percentage of men and women’s salary remains unexplained by the 

characteristics measured in our data. 

The data for the analysis come from the status file of the CPDF. As 

described in appendix II, this data set is produced by the Office of 

Personnel Management as a central source of information regarding the 

federal workforce.1

 For the cross-sectional analysis, we selected a 20 

percent random sample of federal employees in the CPDF for each of the 3 

years of the analysis.2

Data Used in the 

Cross-Sectional 

Analysis and 

Descriptive Statistics 

Table 1 shows descriptive statistics for men and women in our sample, for 

the 3 years we used in our analysis. As the table shows, there has been a 

significant narrowing in the gap between the characteristics of men and 

women in the federal workforce in almost all categories, although gaps 

remain. Specifically: 

 

1

For more information regarding the CPDF, exclusions, and the steps we took to ascertain 

whether it was sufficiently reliable for our purposes, see appendix II. 

2

Due to computational limitations associated with conducting sophisticated econometric 

analyses with a large dataset, we selected a 20 percent sample using the random generator 

function in SAS. 
Appendix III: Cross-sectional Analysis 

• Average salary: We performed our analysis using adjusted basic pay as 

recorded in the CPDF, which takes into account various differences in pay 

based on locality and special rates and takes into account existing pay 

caps. This figure reflects that amount an individual would have earned had 

he or she worked a complete year. It does not reflect their actual earnings, 

which are not available in the CPDF data. We deflated the salary using the 

consumer price index. 

• There has been a narrowing of the gap in average salary between men 

and women in the federal workforce. The difference in the average log 

earnings of men and women was about 0.33 in 1988, 0.21 in 1998, and 

0.12 in 2007. 

• The standard interpretation of the log difference is that it is equivalent 

to the percent difference; however, at larger values this value will differ 

somewhat from the precise percent difference. As presented in the 

briefing slides, the percent difference was negative 28 percent in 1988, 

negative 19 percent in 1998, and negative 11 percent in 2007.3

• Age: We computed age using the month and year of birth, and the date the 

data were drawn (September of each year). 

• There has been a narrowing of the difference in the average age 

between men and women. In 1988 it was more than 3 years—by 2007, it 

was less than a year. 

• Federal experience: We measured federal experience by the months 

between the service computation date and the date the data were drawn 

(September of each year). 

• As table 1 shows, there has been a narrowing of the difference in years 

of federal experience between men and women. Specifically, the 

average years of experience for a man was almost 3-1/2 years greater 

than a woman in 1988, about 2 years in 1998, and by 2007 there was no 

appreciable difference. 

• Race and ethnicity: We measured race and ethnicity using the CPDF 

definitions. These definitions do not allow for multiple races. Unlike many 

data sets, they do not record Hispanic status distinctly from race. 

• There appears to be less change in differences in racial composition 

between men and women in the federal workforce. In general, there 

appears to be a decline in the percentage that is white with an increase 

 

3

To transform the coefficient to more exactly equal the percent difference, we applied the 

following formula: exp(difference in logarithms)-1. 

Page 47 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

in the percentage that is Hispanic and Asian/Pacific Islander. This holds 

for both men and women. 

• Education: We used the CPDF definition of the highest degree obtained by 

the employee. 

• There has been a narrowing of the difference in degree attained 

between men and women over the past 20 years. For example, in 1988, 

almost twice as many men in the federal workforce had bachelor’s, 

master’s, professional, or doctoral degrees (40 percent versus 23 

percent). By 2007, the difference was less than 10 percentage points 

(46 versus 40 percent). In some specifications, we included educational 

major within each degree type. However, this measure was only 

available in 2007. 

• Rates of disability: We defined disability by whether the employee did or 

did not have a CPDF code for a disability condition and whether that 

condition indicated a targeted disability as defined by EEOC’s 

Management Directive 715. Only targeted disabilities were counted as 

disabled. 

• There has been a slight narrowing of the difference in the rates of 

disability, as slightly more men and slightly fewer women are classified 

as having no condition. 

• Work schedule: Employees were classified by whether they worked fulltime, part-time or held a flexible schedule (such as seasonal, intermittent, 

on-call, etc.). 

• There has been a slight narrowing of the difference in the work 

schedule of employees, as men are classified as 4 percentage points 

more likely to work full-time in 2007, and about 6 percentage points in 

1988. 

• Occupation: Our main analysis defined occupation using occupational 

category in the CPDF, which groups occupations into six categories: 

Professional, Administrative, Technical, Clerical, Other White-collar, and 

Blue-collar. For the purposes of our analysis, we called this categorical 

variable PATCOB. 

• One of the most striking changes in the composition of the federal 

workforce has been the narrowing of the difference between 

occupations held by men and by women. Much of the narrowing is the 

result of a diminishing clerical sector in the federal workforce. In 1988, 

about 38 percent of women in the federal workforce were in a clerical 

occupation. By 2007, that number was 13 percent. Similarly, in 1988 

Page 48 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

almost 28 percent of men in the federal workforce were in the “BlueCollar” field; by 2007, that number was 17 percent.4

• While PATCOB is a rough measure of occupation, with six categories, 

we also experimented with more disaggregated measures. For 

example, we created “job family level”—a categorical variable that had 

about 50 different occupation categories—and “job series”—another 

categorical variable with more than 700 occupation categories.5

 In 

other specifications, we included the percentage of occupation that 

was female as an additional covariate. 

• Marital status and number of children: A variable containing information 

on the family of the employee was not available. However, as a proxy, in 

some specifications we included a measure of whether that individual had 

registered for health insurance for their family or themselves or had 

declined health insurance coverage. Declined coverage may imply that the 

employee receives coverage through a spouse. 

• In all the years, men are much more likely than women to participate in 

a family plan. In 1988, women were more than twice as likely to have 

declined coverage, although this gap has closed in the most recent 

year. 

• Percentage female: We used the CPDF classification of the gender of the 

employee. 

• The percentage of the federal workforce that is female has risen from 

42 percent to 44 percent over the past 20 years. 

Additionally the following variables were included in the analysis but do 

not appear in the descriptive statistics table: 

 

4

An index of dissimilarity is an alternate way to demonstrate the convergence of the 

occupational structure. The index of dissimilarity is defined as the fraction of either men or 

women that would have to switch occupations to make the distributions identical. The 

range of values are 1 (meaning that the 100 percent of men or women would have to 

switch) to 0 (meaning that the distributions are identical). Using PATCOB, the dissimilarity 

index fell from 40 percent in 1988 to 30 percent in 1998 to almost 20 percent in 2007, 

indicating that the distributions are much closer today. 

5

“Job family level” was constructed by combining PATCOB with the “occupational group” 

variable in the CPDF data, and collapsing blue-collar occupations into a single category. An 

occupational group is a set of occupations in a related field such as engineering or health 

care. In addition, those occupations that individually represented 0.35 percent of the 

population were combined into an “other” category. The number of categories included in a 

regression depended on whether that category had any individuals in a particular year. 

Page 49 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

• Veteran status: Veteran status was categorized into three types defined 

by whether or not the employee was a veteran, and whether or not the 

employee qualified for a veteran’s preference in CPDF data. 

• Geography: An employee’s geographic location, such as state, was 

defined using the location of the employment, which may or may not 

be the location of residence. 

Table 1: Descriptive Statistics for Selected CPDF Variables Used in Our Cross-sectional Analysis 

1988 1998 2007 

Men Women Men Women Men Women

Annual adjusted salary 55,862 39,750 62,595 50,540 70,109 62,021

Log of salary 10.847 10.520 10.957 10.745 11.059 10.938

Age 42.980 39.772 45.707 43.823 46.707 46.148

Federal experience 14.035 10.469 16.143 14.260 14.901 14.995

Race/ethnicity 

African-American .115 .232 .113 .232 .122 .238

Asian Pacific Islander .035 .030 .047 .043 .053 .055

Hispanic .055 .048 .066 .060 .080 .072

Native American .016 .021 .018 .025 .016 .026

White .779 .668 .756 .639 .726 .606

Other .000 .001 .0005 .000 .003 .003

Education 

Less than high school .042 .031 .017 .016 .011 .011

High school diploma .265 .350 .251 .315 .283 .276

Trade degree .047 .076 .030 .052 .023 .041

Some college .236 .300 .231 .287 .194 .239

Bachelor degree .258 .168 .279 .207 .272 .243

Masters degree .082 .044 .102 .070 .124 .114

Professional degree .038 .015 .050 .029 .032 .025

Doctorate degree .023 .006 .029 .011 .031 .020

Other education .010 .010 .011 .013 .031 .030

Occupation (PATCOB) 

Administrative .257 .213 .305 .284 .349 .354

Blue-collar .283 .046 .215 .031 .173 .026

Clerical .048 .381 .035 .202 .045 .126

Other white-collar .029 .002 .040 .004 .051 .006

Professional .234 .143 .266 .214 .244 .242

Technical .148 .216 .139 .264 .138 .246

Page 50 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

1988 1998 2007 

Men Women Men Women Men Women

Work schedule 

Full time .938 .886 .932 .885 .939 .900

Part time .019 .056 .017 .048 .020 .048

Another type .043 .058 .051 .067 .040 .051

Disability status 

None .927 .951 .930 .945 .936 .946

Disabled not targeted .060 .038 .058 .044 .054 .044

Disabled .012 .010 .012 .011 .010 .010

Health plan 

Family plan .600 .317 .598 .364 .524 .357

Self plan .194 .326 .222 .347 .235 .371

Declined coverage .105 .230 .104 .203 .161 .186

Pending .031 .045 .020 .022 .030 .033

Not eligible .070 .082 .056 .063 .045 .052

Percentage female 42% 44% 44%

Number of observations 241,611 175,776 199,153 158,460 205,767 162,822

Source: GAO analysis of CPDF data. 

Regression Analysis 

Approach and Results 

Description of 

Econometric Method 

In order to determine the extent to which gender differences persist when 

characteristics of men and women are taken into account, we performed a 

multivariate regression analysis for 3 years of data: 1988, 1998, and 2007. 

Consistent with the usual practice in studies of the determinants of 

earnings, we attempted to explain the differences by predicting the 

logarithm of annual adjusted pay on characteristics of federal workers. 

Equation 1: 

Ln(annual pay) = α + β*(female) 

 + δ1*(set of work characteristics) 

 + δ2*(set of worker characteristics) 

 + δ3*(set of demographic characteristics) 

Page 51 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

The standard interpretation of β, the coefficient on female, is that it 

represents the average percent difference in earnings between men and 

women, after controlling for the other variables in the model. However, 

similar to the descriptive statistics above, the coefficient in a model such 

as this will differ somewhat from the precise percent change at larger 

values. Consequently, for discussion purposes, as in the briefing slides, we 

perform a transformation on the coefficients to accurately present percent 

changes.6

 This transformation is described in detail in appendix V. 

The following variables were included as controls: 

1. “Work characteristics”: These were characteristics of the individual 

that were dependent on the specific position held, including 

occupation, the agency, and the state in which they worked. 

2. “Worker characteristics”: These were characteristics of the individual, 

rather than the position held, and included years of federal experience, 

educational degree attained, bargaining unit, part-time status, and 

veterans status. 

3. “Demographic characteristics”: These were characteristics of the 

individual that were associated with demography and included 

race/ethnicity, disability status, and age.7

In choosing the variables included in our model, we had to balance two 

competing ideas. As described by Blau and Kahn (2000)8

, the difference 

between male and female wages can be decomposed into two categories: 

what is explained by measured characteristics and what is unexplained by 

those characteristics that may be due to discrimination. However, if a 

study estimates that a portion is unexplained, that finding can be 

challenged if some important explanatory variable has been excluded from 

the analysis, such as occupation. Conversely, including a variable that is 

itself a result of discrimination would cause the unexplained portion to be 

understated. For example, if women are denied access to certain 

 

6

To transform the coefficient to more exactly equal the percent difference, we applied the 

following formula: exp(β)-1. 

7

Although age is a demographic variable, it could have been classified as a worker 

characteristic. This is because our measure of experience only includes experience in the 

federal government. Therefore, the age variable could likely be a proxy for other 

experience. 

8

See Francine Blau and Lawrence Kahn, 2000, “Gender Differences in Pay,” The Journal of 

Economic Perspectives 14 (4): pp.75-99. 

Page 52 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

occupations, controlling for occupation might be explaining away the 

effect of discrimination. In our main analysis, we chose a broad category 

of occupation, PATCOB, in order to balance these competing ideas. 

Alternate definitions of occupation or of other variables could yield 

different results. For example, it may be that defining education using 

educational major in addition to degree reduces the unexplained pay gap. 

Because of questions regarding the appropriateness of certain variables or 

variable definitions in the analysis, and the possibility that our basic 

results could be changed by an alternate specification, we re-analyzed the 

data using varying sets of explanatory variables, as described in table 2. 

Table 2: Description and Definition of the Alternate Models 

Types of factors included (controlled for) in the model 

Name of the model Demographic characteristics “Worker” characteristics “Work” characteristics 

1 Main Race/ethnicity 

Disability 

Age 

Federal exp. 

Degree 

Veteran 

Bargaining unit & work 

schedule 

PATCOB 

Agency 

State 

2 Job family level Race/ethnicity 

Disability 

Age 

Federal exp. 

Degree 

Veteran 

Bargaining unit & work 

schedule 

Job family level 

Agency 

State 

3 Disaggregated occupation, 

but with grouped blue-collar 

Race/ethnicity 

Disability 

Age 

Federal exp. 

Degree 

Veteran 

Bargaining unit & work 

schedule 

Job series (w/ grouped bluecollar) 

Agency 

State 

4 Most disaggregated 

occupation 

Race/ethnicity 

Disability 

Age 

Federal exp. 

Degree 

Veteran 

Bargaining unit & work 

schedule 

Job series 

Agency 

State 

5 In addition to PATCOB, we 

included the proportion of 

women in the occupation 

Race/ethnicity 

Disability 

Age 

Federal exp. 

Degree 

Veteran 

Bargaining unit & work 

schedule 

PATCOB 

Agency 

Proportion of women in 

occupation 

State 

Page 53 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

 Types of factors included (controlled for) in the model 

 Name of the model Demographic characteristics “Worker” characteristics “Work” characteristics 

6 Geography measured by 

county 

Race/ethnicity 

Disability 

Age 

Federal exp. 

Degree 

Veteran 

Bargaining unit & work 

schedule 

PATCOB 

Agency 

County 

7 The addition of educational 

major to the model 

Race/ethnicity 

Disability 

Age 

Federal exp. 

Degree & major 

Veteran 

Bargaining unit & work 

schedule 

PATCOB 

Agency 

State 

8 The addition of educational 

major to the model, with job 

family level 

Race/ethnicity 

Disability 

Age 

Federal exp. 

Degree & major 

Veteran 

Bargaining unit & work 

schedule 

Job family level 

Agency 

State 

9 The addition of educational 

major to the model, with 

grouped blue-collar 

Race/ethnicity 

Disability 

Age 

Federal exp. 

Degree & Major 

Veteran 

Bargaining unit & work 

schedule 

Job series (w/ grouped bluecollar) 

Agency 

State 

10 Excluding agency and 

occupation 

Race/ethnicity 

Disability 

Age 

Federal exp. 

Degree 

Veteran 

State 

11 Excluding agency and 

occupation, but major was 

added 

Race/ethnicity 

Disability 

Age 

Federal exp. 

Degree & major 

Veteran 

State 

12 Only age, federal 

experience, and degree 

Age Federal exp. 

Degree 

State 

13 Only federal experience, 

PATCOB, and degree 

Federal exp. 

Degree 

PATCOB 

14 Health plan was added to 

the model 

Race/ethnicity 

Disability 

Age 

Health plan 

Federal exp. 

Degree 

Veteran 

Bargaining unit & work 

schedule 

PATCOB 

Agency 

State 

Source: GAO analysis. 

Page 54 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

As noted in model 5, table 2, we estimated a model that contained a 

variable to measure the proportion of women in each occupation.9

 This 

variable allowed us to measure the degree to which the proportion of 

women in an occupation accounted for some of the wage gap. There is 

some debate about the appropriateness of including this variable in 

addition to the variable that controls for being female in the model 

because it can be interpreted as double-counting the impact of being 

female.10 Because of this, we chose to exclude it from our main model that 

we discuss in the briefing slides. 

Finally, in addition to the alternate models described above, we also 

examined the gap in salaries within subgroups of the federal workforce. 

Specifically, we examined the gender gap by race and ethnicity, 

occupation, agency, and employees in the federal workforce with less than 

a year of federal work experience. The results of this, as well as the main 

and alternative regression analyses, are provided below. 

Regression Analysis 

Results 

Main Specification of the Model 

Table 3 presents the coefficients and standard errors for the main 

regression results from estimating equation 1. As described above, the 

coefficient on female can be interpreted as the percent difference between 

women’s and men’s annual salary, after accounting for all of the 

measurable characteristics of men and women that we controlled for in 

the model. Additionally, table 3 presents values and standard errors of the 

coefficients associated with all of the other characteristics in the main 

specification model. 

 

9

The occupation category that was used to measure the proportion of women in each 

occupation was “job series,” which was more disaggregated than PATCOB. 

10The inclusion of this variable makes the coefficient on female difficult to interpret. As 

others have noted, the share of women in a particular occupation may be correlated with 

some unobserved characteristics of workers that also influences pay. Since these 

unobserved characteristics may also be captured by the coefficient on the variable for 

female—and since by definition women will tend to be in occupations with more women—

we may be simply introducing two measures of the same thing. This could result in a lower 

measured effect of the female variable and therefore could be misleading. See Altonji, J. G., 

and R. M. Blank, “Race and Gender in the Labor Market,” in Handbook of Labor 

Economics, Volume 3C, O. Ashenfelter and D. Card, eds. (Amsterdam: North-Holland, 

1999), p. 3222. 

Page 55 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

• As the table shows, the percent difference between women’s and men’s 

salary, controlling for the factors in the main specification, has fallen over 

the past 20 years. A negative value indicates that women’s salary was less 

than men’s. Specifically, the coefficient on female changed from 

approximately negative 10.9 percent in 1988, to negative 8.8 in 1998, and 

negative 8.3 in 2007. It is important to note that these results differ slightly 

from our Oaxaca decomposition results, which are discussed in the 

briefing slides. We chose to highlight the results of the Oaxaca 

decomposition in the briefing slides because, unlike with the simple 

regression analysis presented here, the decomposition allows us to 

quantify the amount that each factor in our model contributes to the pay 

gap. 

• Many of the other parameters associated with the control variables are in 

the expected direction. Higher education levels are associated with higher 

levels of salary. For example, after controlling for the other factors in the 

model, in 2007 a federal worker with a BA had a salary that was 18 percent 

higher than the salary of a person who did not complete high school. A 

person with an MA, in 2007, had a salary that was 25 percent higher than 

the salary of a person who did not complete high school. Salary increases 

at higher levels of federal experience and age, but the marginal effect of an 

additional year decreases as the years increase (as indicated by the 

negative sign of the estimate for the squared terms for age and 

experience). 

• As would be expected, there were differences in pay between occupations, 

even after the controls were introduced. For example, clerical workers 

tend to be paid significantly less in the 3 years analyzed. Specifically, 

clerical workers were paid 15.6 percent less than technical workers in 

1988, 16.3 percent less in 1998 and 20.4 percent less in 2007. On the other 

hand, in 1988, professional workers were paid 37.0 percent more than 

technical workers, 39.7 percent more in 1998 and 43.2 more in 2007, after 

controlling for the other factors. 

• Similar to gender, there are disparities by racial and ethnic groups, as well 

as by disability status. For example, in 2007, the salary for an AfricanAmerican employee was 7.4 percent lower than the salary of a white 

person, after controlling for the other factors in the model. 

Page 56 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

Table 3: Main Regression Results 

1988 1998 2007 

Estimate Standard error Estimate Standard error Estimate Standard error

Female -.109 .001 -.088 .001 -.083 .001

Experience and age 

Age .018 .001 .041 .001 .050 .001

Age squared -.0002 .00002 -.0007 .00002 -.0008 .00002

Age cubed 6.46E-7 1.53E-7 3.64E-7 1.73E-7 4.31E-6 1.59E-7

Federal experience .035 .0002 .031 .0003 .029 .0003

Federal exp. squared -.001 .00002 -.001 .00002 .001 1.6E-6

Federal exp. cubed .00001 2.60E-7 .00001 2.82E-7 .00001 2.62E-7

Race/Ethnicity (white is omitted) 

African-American -.079 .001 -.074 .001 -.074 .001

Asian Pacific Islander -.015 .002 -.022 .002 -.005 .002

Hispanic -.045 .001 -.042 .001 -.028 .001

Native American -.033 .002 -.042 .002 -.055 .003

Other -.043 .014 -.057 .016 -.037 .007

Education (less than high school is omitted)

High school .078 .002 .074 .003 .076 .003

Trade degree .112 .002 .112 .003 .112 .004

Some college .110 .002 .112 .003 .114 .004

Bachelor degree .182 .002 .193 .003 .182 .004

Masters degree .258 .002 .272 .003 .247 .004

Professional degree .456 .003 .442 .003 .561 .004

Doctorate degree .411 .003 .418 .004 .398 .004

Other education .035 .004 .058 .004 .091 .004

Occupation (technical is omitted) 

Administrative .260 .001 .318 .001 .363 .001

Blue-collar .095 .001 .053 .001 .036 .001

Clerical -.156 .001 -.163 .001 -.204 .002

Other white-collar -.124 .002 .006 .002 .097 .002

Professional .370 .001 .397 .001 .432 .001

Work schedule (part time is omitted) 

Full time .040 .002 .023 .002 .040 .002

Another type -.097 .002 -.171 .002 -.085 .003

Disability status (targeted disability is omitted)

None .085 .003 .102 .003 .090 .004

Page 57 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

1988 1998 2007 

Estimate Standard error Estimate Standard error Estimate Standard error

Disabled not targeted .062 .003 .076 .003 .061 .004

Observations 417,387 357,613 368,589 

R –Square 79% 78% 77% 

Source: GAO analysis of CPDF data. 

Note: In addition to the variables listed above, the regression included a measure of state, larger 

agencies, bargaining unit, and veteran status. 

Alternate Specifications of the Model 

In addition to the model above, we estimated a set of models with differing 

groups of covariates, as shown in table 4. Each cell in the table reflects the 

coefficient on female of a separate regression. 

• As the table shows, the models that included additional variables, or more 

disaggregated occupation, generally yielded smaller unexplained differences 

between men’s and women’s salaries. For example, the model with 

disaggregated occupation, but grouped blue-collar occupation, resulted in an 

unexplained disparity of about 5.5 percent in 2007. On the other hand, in the 

specification that excluded education and occupation, the model produced a 

disparity of about 11.3 percent. The main analysis model, with PATCOB as the 

occupation variable, was close to the midpoint, at 8.3 percent. 

• Almost all of the models showed the same trend as in the main specification, 

with declines from 1988 to 1998, and less of a decline from 1998 to 2007. 

• The models with fewer controls saw larger unexplained disparities, and 

also larger declines of the disparity that is unexplained by the included 

variables. For example, the model that excluded agency and occupation 

saw declines of 19 percent in 1988 to 11 percent in 2007. 

• The addition of the “health plan” variable, the closest proxy we could 

construct to a marriage variable, had the effect of reducing the coefficient 

on female. For example, the female coefficient decreased by 8 percent in 

2007 with the addition of the “health plan” variable to the model. 

• The largest single effect was introducing the “percent female” as an 

additional explanatory variable. This had the effect of almost reducing the 

Page 58 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

coefficient by 30 to 40 percent, a result that is consistent with literature.11

However, as noted earlier, it is difficult to interpret this coefficient. 

Table 4: Female Coefficient under Alternate Specifications of the Model 

Female coefficient 

(standard error) 

Specification 1988 1998 2007

Main -.109

(.001)

-.088

(.001)

-.083

(.001)

Job family level -.097

(.001)

-.076

(.001)

-.072

(.001)

Disaggregated occupation, but with grouped blue-collar -.084

(.001)

-.064

(.001)

-.055

(.001)

Most disaggregated occupation -.073

(.001)

-.056

(.001)

-.048

(.001)

In addition to PATCOB, we included the percent female 

in the occupation 

-.070

(.001)

-.054

(.001)

-.049

(.001)

The addition of geographic location by countya-.109

(.001)

-.088

(.001)

-.081

(.001)

The addition of educational major to the model -.076

(.001)

The addition of educational major to the model, with job 

family level 

-.066

(.001)

The addition of educational major to the model, with 

grouped blue-collar 

-.053

(.001)

Excluding agency and occupation -.190

(.001)

-.134

(.001)

-.113

(.001)

Excluding agency and occupation, but major was added -.105

(.001)

Only age, federal experience, and degree -.175

(.001)

-.116

(.001)

-.108

(.001)

Only federal experience, PATCOB, and degree -.112

(.001)

-.085

(.001)

-.089

(.001)

Health plan was added to the model -.102

(.001)

-.082

(.001)

-.076

(.001)

Source: GAO analysis of CPDF data. 

a

Approximately 250 dummy variables were created for the individual counties that represent the vast 

majority of federal workers in the U.S. (accounting for roughly 80 percent of all federal employees). 

For the remaining 20 percent of federal employees, dummy variables for state were used. 

 

11See Gregory B. Lewis, “Gender Integration of Occupations in the Federal Civil Service: 

Extent and Effects on Male-Female Earnings,” Industrial and Labor Relations Review, vol. 

49, no. 3 (April, 1996): 472-483 

Page 59 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

Subgroup Analysis 

To investigate whether the disparity between men and women was 

different among certain types of federal workers, we also estimated the 

main specification model for alternate subgroups of the data. The results 

are shown in table 5. As above, each cell of the table represents the 

coefficient on female in a separate regression for that subgroup, 

controlling for the factors included in the main specification. As the table 

shows, we found a great deal of variation among subgroups. 

• Less than 1 year: The unexplained disparity among those with less than a 

year of service tended to be less than the general population. For example, 

the unexplained gap among new hires was 4.3 percent in 2007, about half 

of the general workforce. 

• Race/ethnicity: By stratifying the data by race/ethnicity, we generally 

found the largest disparity was among white employees, and the lowest 

disparity among African-Americans over 20 years. The gaps among 

African-Americans and among Native Americans has grown slightly over 

the past 20 years and fallen among the other groups. 

• Occupation: There were significant negative unexplained disparities 

among four of the six occupation classes, with the largest among technical 

workers at 10.2 percent in 2007. Female clerical workers tended to be paid 

more than male, by about 2 percent. 

Table 5: Estimated Female Coefficient within Subgroups Using Main Specification 

1988 1998 2007 

Experience Estimate Standard error Estimate Standard error Estimate Standard error

Less than 1 year -.037 .003 -.051 .004 -.043 .004

Race/Ethnicity 

African-American -.058 .002 -.061 .002 -.066 .003

Asian Pacific Islander -.110 .004 -.081 .040 -.076 .004

Hispanic -.087 .004 -.068 .003 -.065 .003

Native American -.080 .006 -.095 .006 -.092 .006

White -.121 .001 -.097 .001 -.090 .001

Occupation (PATCOB) 

Administrative -.128 .002 -.103 .002 -.096 .001

Blue-collar -.102 .002 -.106 .003 -.096 .004

Clerical .015 .001 .021 .002 .017 .002

Other white-collar -.019 .008 -.016 .005 -.009 .004

Page 60 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

1988 1998 2007 

Experience Estimate Standard error Estimate Standard error Estimate Standard error

Professional -.099 .002 -.070 .002 -.074 .002

Technical -.127 .002 -.116 .002 -.102 .002

Source: GAO analysis of CPDF data. In addition to the subgroups described above, we also performed subgroup analysis on the larger 

agencies. 

Decomposition Approach 

and Results 

Description of our econometric method 

One possible explanation for the gap could be that women have different 

levels of important attributes, like years of experience, than men. 

Alternatively, women could have the same level of attributes, but women’s 

attributes were treated differently. For example, the effect of an additional 

year of experience might be different for a woman than a man. In order to 

determine whether the difference between men’s and women’s pay is a 

function of men and women having different levels of characteristics, or 

different effects of those characteristics, we asked the following questions: 

• What would the difference in wages be if we took women’s average level 

of characteristics, assigned them men’s effects of those characteristics and 

calculated the difference with women’s average wages? This is referred to 

as the parameter, or unexplained difference. 

• What would the difference in wages be if we took women’s average level 

of characteristics, assigned them men’s effects of those characteristics and 

calculated the difference with men’s average wages? This is referred to as 

the characteristic, or explained difference. 

This methodology, widely used in the discrimination literature, is often 

referred to as “Oaxaca decomposition.”12 In order to apply the “Oaxaca 

decomposition,” we followed these steps: 

• First, we estimated two versions of equation (1), one on women in our 

sample and one on men in our sample. This provided us with two sets of 

regression coefficients, one for men and one for women. 

• Second, we applied the regression coefficients for men to the average 

values of characteristics for men. This gave us the average wages of men. 

 

12For details on this technique see “Male-Female Wage Differentials in Urban Labor 

Markets,” Ronald Oaxaca, International Economic Review, Volume 14, Issue 3 (October 

1973), pp. 693-709. 

Page 61 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

We repeated this analysis for women, producing the average wages for 

women. 

• Third, we then applied the coefficients for men to the average values for 

the characteristics for women. This gave us a new predicted wage—the 

predicted wage for women if they had the same effects of characteristics 

as men. 

With these three values, we were able to decompose the total difference 

between the average of male and female wages into two parts: 

Equation 2: 

(Average Female Wages) – 

(Male returns with female 

characteristics) 

= “Unexplained” or due to parameter 

differences between women and men 

+ (Male returns with female 

characteristics) – (Male Average 

Wages) 

= + “Explained” or due to characteristics 

difference between men and women 

(Average Female Wages) – 

(Average Male Wages) 

= Total 

Similar to the regression case above, the standard interpretation of this 

analysis is that it represents a decomposition of the percent change in 

earnings between men and women. However, at larger values, this value 

will differ somewhat from the precise percent difference. Consequently, 

for discussion purposes in the briefing slides, we scaled the decomposition 

to be proportional to the actual percent difference. 

We performed the decomposition using the same specifications as 

outlined in table 2. In addition, we performed the analysis on the same 

subgroups. 

Results of Decomposition 

Approach 

Main Specification 

Table 6 reports on the results of applying the decomposition methodology 

as outlined in equation 2. As the table shows, the overall conclusions 

drawn from both approaches were similar. Under both the regression and 

the decomposition approaches, differences remain between men and 

women’s salaries, even after correcting for a wide range of characteristics, 

as a negative value indicates that the salary of women was less than men. 

The first row details the total difference, the unexplained or parameter 

difference, and the explained or characteristic difference in each year. The 

other rows indicate the contribution of each of the factors. 

Page 62 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

• As the table shows, using the decomposition methodology, the 

unexplained percentage has been remarkably constant over the past 20 

years. Specifically, it was 7.8 percent in 1988, 8.1 percent in 1998, and 7.5 

percent in 2007. After scaling these numbers to be proportional to the 

actual percent difference (as described in detail in app. V), this gap was 

6.7, 7.3, and 7.1, respectively. 

• Consequently, because the pay gap has been falling, the percentage of the 

gap explained by measurable characteristics has been decreasing. For 

example, the percentage explained by measurable characteristics was 76 

percent (-.249/-.327) in 1988 and 37 percent (-.045/-.121) in 2007. 

• The contribution of occupation is the largest component of any of the 

explanatory variables, accounting for over half of the explained difference 

in the gender pay gap in each year. Specifically, the contribution of 

occupation was 14.5 percentage points in 1988, 7.1 percentage points in 

1998 and 2.9 percentage points in 2007. 

• The geographic location of employment (as measured by the state in 

which the federal worker was employed) had a minimal contribution in 

explaining the gender pay gap for all 3 years. 

Table 6: Decomposition Results Using Main Specification (with contributions of key factors) 

1988 1998 2007 

Total gap 

Unexplained 

gap 

Explained 

gap Total gap

Unexplained 

gap 

Explained 

gap Total gap 

Unexplained 

gap 

Explained 

gap

Total -.327 -.078 -.249 -.211 -.081 -.13 -.121 -.075 -.045

Detailed 

factors 

Intercept -.106 -.106 0 .0622 .0622 0 -.04 -.04 0

Age -.162 -.143 -.019 -.236 -.227 -.009 -.096 -.096 4E-7a

Federal 

experience 

-.06 -.006 -.054 -.001 .024 -.022 .015 .014 .001

Race/ 

ethnicity 

.004 .016 -.012 -3E-6 .011 -.011 -.002 .009 -.011

Education -.034 -.01 -.024 -.039 -.016 -.023 -.013 -.005 -.008

Occupation -.06 .085 -.145 -.01 .060 -.071 .0168 .0456 -.029

Work 

schedule 

.024 .027 -.003 .0258 .029 -.004 -.024 -.021 -.003

Disability 

status 

-.015 -.015 .001 -.041 -.042 .001 -.032 -.032 .0003

State .0129 .0106 .002 -.017 -.019 .002 .0332 .030 .0031

Page 63 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

1988 1998 2007 

Total gap 

Unexplained 

gap 

Explained 

gap Total gap

Unexplained 

gap 

Explained 

gap Total gap 

Unexplained 

gap 

Explained 

gap

Veteran 

status 

.028 .002 .027 .011 -.015 .026 -.008 -.028 .019

Bargaining 

unit 

.029 .040 -.012 .028 .035 -.007 .019 .024 -.005

Agency .013 .023 -.011 .005 .018 -.013 .01 .023 -.013

Source: GAO analysis of CPDF data. 

a

E reflects multiplication by 10 to that power. For example, “-3E-6” refers to -3 multiplied by 10 to the 

negative 6th power. 

Alternate Specifications 

As with the main regression, we also performed the decomposition using 

alternate models. The results—which are consistent with those using the 

main specification regression model—are shown in table 7. 

• While the size of the unexplained gap varied, in almost all of the 

specifications that included agency and occupation, the size of the 

unexplained gap remained roughly constant over time. However, because 

the percentage explained by characteristics has decreased, the total gap 

has been falling. 

• In the models without agency and occupation, the unexplained gap has 

fallen over the past 20 years. For example, in the model without agency or 

occupation, the size of the unexplained gap has fallen from about 20 

percent to about 11 percent. However, the percentage explained by 

characteristics has fallen at a faster rate. Consequently, the explained 

portion of the gap was almost 40 percent in 1988, and less than 10 percent 

in 2007. 

Table 7: Decomposition Results Using Alternate Specifications 

Specification Total gap Unexplained gap Explained gap

Percentage 

explained

Main 

1988 -.327 -.078 -.249 .761

1998 -.211 -.081 -.130 .616

2007 -.121 -.075 -.045 .372

Job family level 

1988 -.327 -.064 -.263 .803

1998 -.211 -.066 -.145 .688

Page 64 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

Specification Total gap Unexplained gap Explained gap

Percentage 

explained

2007 -.121 -.067 -.053 .443

Disaggregated occupation, but with grouped blue-collar 

1988 -.326 -.053 -.273 .836

1998 -.211 -.054 -.157 .745

2007 -.120 -.048 -.072 .601

Most disaggregated occupation—job series 

1988 -.328 -.047 -.281 .857

1998 -.211 -.050 -.162 .764

2007 -.120 -.046 -.076 .622

In addition to PATCOB, included the percent female in the occupation 

1988 -.327 -.022 -.305 .934

1998 -.211 -.038 -.173 .818

2007 -.120 -.035 -.085 .705

Geography measured by county 

1988 -.327 -.080 -.247 .756

1998 -.211 -.081 -.130 .616

2007 -.121 -.074 -.047 .390

The addition of educational major to the model 

2007 -.121 -.069 -.052 .428

The addition of educational major to the model, with job family level 

2007 -.121 -.060 -.061 .504

The addition of educational major to the model, with grouped blue-collar 

2007 -.121 -.046 -.074 .615

The addition of educational major to the model, with the most disaggregated occupation 

2007 -.122 -.045 -.077 .634

Excluding agency and occupation 

1988 -.327 -.195 -.131 .403

1998 -.211 -.141 -.070 .332

2007 -.120 -.112 -.008 .070

Excluding agency and occupation, but major was added 

2007 -.120 -.099 -.021 .175

Only age, federal experience, and degree 

1988 -.327 -.174 -.152 .466

1998 -.211 -.118 -.093 .440

2007 -.120 -.107 -.013 .112

Page 65 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

Specification Total gap Unexplained gap Explained gap

Percentage 

explained

Only federal experience, PATCOB, and degree 

1988 -.327 -.065 -.262 .801

1998 -.211 -.067 -.144 .681

2007 -.120 -.084 -.036 .303

Health plan was added to the model 

1988 -.328 -.077 -.251 .766

1998 -.211 -.076 -.135 .638

2007 -.121 -.069 -.051 .428

Source: GAO analysis of CPDF data. 

Subgroup Analysis of the Main Specification of the Model 

We also performed decompositions on sets of subgroups, as shown in table 8. 

• Less than 1 year: Among workers with less than 1 year of federal 

experience, the gap between male and female salaries that is unexplained 

by characteristics has grown over the past 20 years, from 2.5 percent to 3.7 

percent. However, this measure peaked in 1998 at 4.8 percent. 

• Race/ethnicity: By stratifying the data by race/ethnicity, we found that in 

2007 the largest disparity been men and women (as measured with the 

unexplained gap) was among white employees, at 8.6 percent, and the 

lowest disparity among African-Americans, at 5.7 percent. Noteworthy is 

that in 2007, while African-American women were paid less than AfricanAmerican men on average, the explained gap among African-Americans is 

positive. This implies that contrary to the other groups, African-American 

women have higher average levels of those characteristics included in the 

model that tend to explain the gap, such as education and experience, than 

African-American men. 

• Occupation: There were disparities among all of the occupation classes. 

Noteworthy is that for clerical workers all of the gaps are positive, 

indicating that male clerical workers are paid less than their female 

counterparts, by about 8 percent in 2007. The portion of the gap 

unexplained by characteristics is about 2 to 4 percentage points. 

Page 66 GAO-09-279 Women’s Pay 
Appendix III: Cross-sectional Analysis 

Table 8: Estimated Total, Unexplained, and Explained Pay Gaps for Different Subgroups (using main specification of the 

model) 

1988 1998 2007 

Total 

gap 

Unexplained 

gap 

Explained 

gap Total gap

Unexplained 

gap

Explained 

gap Total gap 

Unexplained 

gap

Explained 

gap

Experience 

Less than 1 

year 

-.208 -.025 -.182 -.193 -.048 -.145 -.109 -.037 -.072

Race/ethnicity 

AfricanAmerican -.163 -.035 -.128 -.071 -.050 -.021 -.002 -.057 .055

Asian Pacific -.286 -.089 -.197 -.193 -.088 -.106 -.109 -.076 -.033

Hispanic -.241 -.063 -.180 -.149 -.065 -.083 -.080 -.061 -.019

Native 

American 

-.240 -.056 -.183 -.180 -.067 -.113 -.130 -.070 -.059

White -.345 -.092 -.252 -.220 -.094 -.126 -.127 -.086 -.041

Occupation (PATCOB) 

Administrative -.197 -.131 -.066 -.118 -.110 -.008 -.075 -.100 .025

Blue-collar -.210 -.099 -.111 -.250 -.108 -.142 -.238 -.097 -.140

Clerical .057 .023 .034 .092 .036 .056 .082 .029 .054

Professional -.273 -.092 -.180 -.197 -.069 -.128 -.162 -.073 -.089

Technicala-.193 -.115 -.078 -.121 -.103 -.018 -.091 -.090 -.001

Source: GAO analysis of CPDF data. 

a

In addition to the subgroups described above, we also performed subgroup analysis on the larger 

agencies. Because of the small number of women in the “Other White-collar” category, a 

decomposition analysis for that category was not presented.

Page 67 GAO-09-279 Women’s Pay 
ohort Analysis 

Page 68 GAO-09-279 Women’s Pay 

Appendix IV: C Appendix IV: Cohort Analysis 

To examine the effect of leave patterns on the pay gap between men and 

women and to further understand changes in the pay gap, we constructed 

and analyzed a dataset on a cohort of workers who entered the federal 

workforce for the first time in 1988. In addition to the variables included in 

our cross sectional analysis, this dataset included controls for leave 

patterns, i.e., unpaid leave and breaks in service. As with the cross 

sectional analysis, we employed linear regression models and 

decomposition methods. In contrast to the cross sectional analysis, the 

cohort analysis used data for each fiscal year from 1988 to 2007, rather 

than at three points in time. 

We used data from the CPDF status and dynamics files to construct a 

longitudinal dataset containing information on the same federal workers 

over a 20-year period. (See app. II for a description of the CPDF data.) 

Specifically, we selected a cohort1

 of 89,356 federal employees hired in 

1988 and tracked their annual salary and leave patterns through the end of 

fiscal year 2007. The dataset contains information on the same individuals 

for each fiscal year that they worked for the federal government. The data 

do not contain information on individuals during fiscal years when they 

did not work in the federal government, including periods following 

retirement or separation and during breaks in service (i.e., when a worker 

separates then returns to the federal workforce). Table 9 shows the 

number of workers remaining in the data over time. By 2007, only 29,009 

of the employees who joined the federal workforce in 1988 remained. 

Data used in Cohort 

Analysis and 

Descriptive Statistics 

 

1

We define this cohort as the group of people who entered the federal workforce in the 

same period. The characteristics of the 1988 federal hiring cohort might not be 

representative of other hiring cohorts, especially those after 2000 because of the change in 

the occupational structure of the federal workforce. This cohort also may not be 

representative of the federal workforce as a whole in any given year. 
Appendix IV: Cohort Analysis 

Table 9: Number of Federal Employees from the 1988 Entry Cohort Remaining over 

2 Decades in the Status and Dynamic Files 

Fiscal year Male Female Total 

1988 38,687 50,669 89,356 

1989 32,996 41,812 74,808

1990 28,351 34,212 62,563 

1991 25,541 29,973 55,514 

1992 23,483 27,133 50,616 

1993 22,149 25,389 47,538 

1994 21,013 23,995 45,008 

1995 19,928 22,790 42,718 

1996 19,052 21,649 40,701 

1997 18,195 20,597 38,792 

1998 17,485 19,586 37,071 

1999 16,816 18,774 35,590 

2000 16,329 18,088 34,417 

2001 15,872 17,571 33,443 

2002 15,558 17,074 32,632 

2003 15,247 16,715 31,962 

2004 14,957 16,374 31,331 

2005 14,612 15,987 30,599 

2006 14,233 15,605 29,838 

2007 13,828 15,181 29,009 

Source: GAO analysis of CPDF data. 

Note: In our data, unpaid leave was indicated with a Leave-Without-Pay personnel action. 

The cohort analysis included two variables on leave patterns, which were 

not available in the data used for our cross sectional analyses: 

• Unpaid leave: We measured the use of unpaid leave (e.g., when an 

individual was absent from work for over 30 consecutive workdays 

without receiving pay) with an indicator that represented whether the 

individual had taken unpaid leave in either the current or any previous 

fiscal year.2

 

2

In our data, unpaid leave was indicated with a Leave-Without-Pay personnel action. 

Page 69 GAO-09-279 Women’s Pay 
Appendix IV: Cohort Analysis 

• Break in service: We measured the cumulative duration of breaks in 

federal service over the 20-year period. We define a break in service to be 

when a federal worker separates from the federal government and is 

appointed back into federal service at some later date. Transfers between 

agencies do not represent breaks in service.3

In comparison with the cross sectional analysis, we estimated a slightly more 

parsimonious model for the cohort because the number of observations in the 

cohort decreases by more than 65 percent over the study period. The 

remaining variables in the cohort analysis were identical to those used in the 

cross sectional analysis, with the following exceptions: 

• Geography was measured by region (rather than state) of duty station, in 

order to reduce the number of variables in the model. The regions were: 

Northeast, South, Midwest, West, and other. 

• Agency was measured by a dummy variable that had the value of 1 if the 

agency was large and 0 if the agency was not large, again to reduce the 

number of variables in the model.4

• Because the members of the cohort all began their federal service in the 

same fiscal year, we did not include a control for federal experience 

because we assume that the federal experience would increase by the 

same amount for each person. Differences in federal experience caused by 

extended periods of unpaid leave or breaks in service were accounted for 

in our analyses directly with the variables that control for unpaid leave 

and breaks in service. 

The overall characteristics of the study population changed over time 

because, as noted earlier, the cohort analysis only included individuals as 

long as they remained in the federal workforce and about two-thirds of the 

people left over the period. In particular, the study population changed 

with respect to occupation and education characteristics. 

The distribution of occupational categories (PATCOB) for the cohort 

changed dramatically over the period as a result of both individuals 

leaving the federal workforce and changing occupations within it. Figure 1 

 

3

The duration of a break in service was computed using the effective date of the separation 

action as the start of the break and the effective date of the subsequent appointment action 

as the end of the break. 

4

The size of the agency was determined by the relative number of federal workers in that 

agency in 1988. 

Page 70 GAO-09-279 Women’s Pay 
Appendix IV: Cohort Analysis 

shows the number of people in each occupational category over the 

period. The number of employees declined in five of the six categories. 

The largest decline was within the clerical category in which more than 90 

percent of the workers who began in 1988 separated from the government 

or changed occupational category over the period. The administrative 

category received a net gain of about 10 percent through workers, and 

primarily women switching from the technical and clerical categories. 

Figure 1: Distribution of Occupational Categories in the Entering Class of 1988 over 20-year Period 

Number of employees (in thousands)

Year

Technical

Professional

Other white-collar

Clerical

Blue-collar

Administrative

0

10

20

30

40

50

60

70

80

90

100

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Source: GAO analysis of CPDF data.

Table 10 shows the proportions of men and women working in each 

occupational category in 1988 and 2007. In comparison to the men and 

women that were in the cohort in 1988, the percentages of men and 

women who remained in the cohort in 2007 were more similar in five out 

of six categories they occupied, especially in the clerical and 

administrative categories. 

Page 71 GAO-09-279 Women’s Pay 
Appendix IV: Cohort Analysis 

Table 10: Cohort Differences between Men and Women in Occupational Categories in 1988 and 2007 

1988 2007 

Men Women

Difference between 

men and women Men Women 

Difference between 

men and women

Occupational Category (PATCOB) 

Professional 34% 17% 17% 35% 23% 12%

Administrative 18% 7% 11% 42% 38% 4%

Technical 10% 11% -1% 9% 26% -17%

Clerical 18% 59% -41% 2% 11% -9%

Other white-collar 10% 3% 7% 5% 0% 5%

Blue-collar 10% 4% 6% 7% 1% 6%

Source: GAO analysis of CPDF data. 

Note: Figures may not sum to 100 percent due to rounding. 

Table 11 shows the education levels of men and women in the 1988 entry 

cohort in 1988 and 2007. In comparison to the men and women that were 

in the cohort in 1988, the men and women who remained in the cohort in 

2007 had higher levels of education.5

 For example, in 1988, 30 percent of 

women in the 1988 entering cohort had a bachelor’s degree or higher, 

while 53 percent of men had a bachelor’s degree or higher. By 2007, of 

those remaining in the federal workforce, 41 percent of women and 63 

percent of men had a bachelor’s degree or higher. 

 

5

The shift in the educational distribution among the cohort members over the 20-year 

period is due to two factors. People with higher levels of education, particularly with 

bachelor’s degrees, stayed in the federal workforce while those with lower levels of 

education were more likely to leave the federal workforce. In addition, people who stayed 

in the federal workforce over the entire period gained advanced degrees over their careers. 

The exception to the trend toward a more educated workforce is that men and women with 

professional degrees were more likely to leave the federal workforce. 

Page 72 GAO-09-279 Women’s Pay 
Appendix IV: Cohort Analysis 

Table 11: Cohort Differences between Men and Women in Education Levels in 1988 and 2007 

1988 2007 

Education level Men Women 

Difference between 

men and women Men Women 

Difference between 

men and women

Less than high school 1% 3% -2% 0% 1% -1%

High school graduate 21% 40% -19% 18% 32% -14%

Some college 24% 28% -4% 20% 26% -6%

Bachelor’s degree 30% 21% 9% 42% 28% 14%

Master’s degree 6% 4% 2% 12% 9% 3%

Doctorate degree 2% 1% 1% 4% 1% 3%

Professional degree 15% 4% 11% 5% 3% 2%

Bachelor’s degree or higher 53% 30% 23% 63% 41% 22%

Source: GAO analysis of CPDF data. 

Note: Figures may not sum to 100 percent due to rounding. 

Table 12 shows the remaining characteristics of the men and women in the 

cohort at the beginning and end of the study period. As the table shows, 

the relative characteristics of men and women varied in any given year and 

over time. 

Table 12: Descriptive Statistics for Men and Women in 1988 Entering Cohort 

 1988 2007 

 Men Women Men Women

Number of observations 38,687 50,669 13,828 15,181

Annual salary 37,320 28,154 89,364 68,468

Age 31 30 48 48

Race/ethnicity 

Nonhispanic White 75% 62% 77% 59%

Nonhispanic African-American 11% 26% 9% 28%

Native American 1% 2% 1% 2%

Asian/Pacific Islander 6% 4% 6% 5%

Hispanic 6% 6% 7% 7%

Other Race/Ethnicity 1% 1% 0% 0%

Large agency 74% 76% 75% 75%

Region 

Northeast 19% 20% 13% 15%

South 42% 40% 49% 50%

Page 73 GAO-09-279 Women’s Pay 
Appendix IV: Cohort Analysis 

 1988 2007 

 Men Women Men Women

Midwest 16% 14% 15% 15%

West 21% 18% 21% 18%

Other 2% 7% 2% 2%

Bargaining Unit Status 63% 72% 52% 63%

Veteran Status 15% 1% 19% 3%

Disability Status 1% 1% 1% 1%

Part-Time Work Schedule 11% 12% 1% 4%

Ever Used Unpaid Leave 4% 3% 11% 21%

Source: GAO analysis of CPDF data. 

Regression Analysis 

Approach and Results 

Empirical Methods To determine the extent to which gender pay disparities existed among the 

1988 entering cohort of federal employees and to examine the effect of 

leave patterns on the pay gap, we performed the following analyses for 

each year between 1988 and 2007. 

• First we computed the pay gap before controlling for other factors. 

• Then, as described in detail in appendix III, we estimated Equation 1 using 

multivariate regression, with two additional variables included to capture 

(1) whether or not an individual took unpaid leave that year or in previous 

years and (2) the cumulative duration of breaks in service over the course 

of an individual’s career. (See app. III for details on the factors that we 

controlled for.) 

• Finally, to determine how much each measurable factor in our 

econometric model accounted for the pay gap, we performed the Oaxaca 

decomposition, as described in detail in appendix III. 

Trends in Pay Gap and 

Contributing Factors 

Table 13 shows the regression coefficient on the dummy variable for being 

female (i.e., the female coefficient) for the 1988 entering cohort after 

controlling for differences between men and women in measurable factors 

for each year of the analysis. As explained in appendix III, the female 

coefficient can be interpreted as the percent difference between women’s 

Page 74 GAO-09-279 Women’s Pay 
Appendix IV: Cohort Analysis 

and men’s annual salaries once the measurable characteristics of men and 

women are controlled for. 

As the table shows, for the 1988 entering cohort, the total pay gap (as 

measured by the female coefficient before accounting for differences 

between men and women) rose from 25 percent in 1988 to a peak of 33 

percent in 1994 and declined to 28 percent by 2007. (Note that these 

figures and those presented in tables 13 and 14 are different from those 

presented in the slides. See app. V for further details on how we converted 

these numbers to precise estimates of the pay gap.) 

After accounting for differences between men and women in measurable 

factors the female coefficient rose from negative 4 percent in 1988 to 

negative 12 percent in 2007.6 It is important to note that these results differ 

slightly from our Oaxaca decomposition results, which are discussed 

below and in the briefing slides. However the overall trend—that the 

unexplained pay gap between men and women steadily increases 

throughout the study period—is consistent between both analyses. We 

chose to highlight the results of the Oaxaca decomposition in the brief 

slides because, coupled with the simple regression analysis presented 

here, the decomposition allows us to quantify the amount that each factor 

in our model contributes to the pay gap. 

Table 13: Trends in the Female Coefficient for the 1988 Entering Cohort before and after Controlling for Differences between 

Men and Women in Measurable Factors 

Fiscal Year 

Female coefficienta

(Before controlling for differences between 

men and women in measurable factors)

Female coefficient

 (After controlling for differences between 

men and women in measurable factors)

1988 -0.25 -0.04

1989 -0.27 -0.05

1990 -0.29 -0.05

1991 -0.30 -0.06

1992 -0.32 -0.07

1993 -0.32 -0.08

 

6

This analysis was replicated using a subset of the data excluding individuals from further 

observation after they had a break in service. The trend in the pay gap was similar between 

the subset and the full dataset. Among the subset, the pay gap was within 1 to 2 percentage 

points lower each year after 1992. Because we were interested in testing the effect of 

having a break in federal service on the pay gap, the results presented here are based on all 

observations including those following a break in service. 

Page 75 GAO-09-279 Women’s Pay 
Appendix IV: Cohort Analysis 

Fiscal Year 

Female coefficienta

(Before controlling for differences between 

men and women in measurable factors)

Female coefficient

 (After controlling for differences between 

men and women in measurable factors)

1994 -0.33 -0.08

1995 -0.32 -0.09

1996 -0.32 -0.09

1997 -0.32 -0.09

1998 -0.31 -0.09

1999 -0.31 -0.10

2000 -0.31 -0.11

2001 -0.31 -0.11

2002 -0.30 -0.11

2003 -0.30 -0.12

2004 -0.29 -0.12

2005 -0.29 -0.12

2006 -0.28 -0.11

2007 -0.28 -0.12

Source: GAO analysis of CPDF data. 

a

All the figures presented in this table are statistically significant at the 0.01 alpha-level. 

To determine the extent to which specific factors account for differences 

between the salaries of men and women in the federal workforce, table 14 

shows the results of the decomposition analysis for individuals in the 1988 

entering cohort.7

 Similar to the regression results above, the standard 

interpretation of this analysis is that it represents a decomposition of the 

percent change in earnings between men and women. However, at larger 

values, this value will differ somewhat from the precise percent difference. 

Consequently, for discussion purposes in the briefing slides, we scaled the 

decomposition to be proportional to the actual percent difference. See 

appendix V for further details. 

Notably, the data show that: 

• The unexplained percentage—i.e., that which could not be explained by 

differences between men and women in measurable factors—rose from 3 

percentage points in 1988 to 11 percentage points in 2007. 

 

7

These data are presented in graphic format on slide 30. 

Page 76 GAO-09-279 Women’s Pay 
Appendix IV: Cohort Analysis 

• Differences between men’s and women’s occupations accounted for most 

of the pay gap. The effect of occupation differences declined over this 

period as the distribution of men and women within occupational 

categories became more similar. 

• After occupation, differences in education contributed to almost as much 

of the gap as all other remaining factors. 

• The use of unpaid leave and breaks in service, which is included under “all 

other characteristics” in table 14, in fact contributed a very small amount 

to the pay gap among men and women in this cohort of federal workers. 

Table 14: Results of the Decomposition: Amount of Gender Pay Gap Resulting from Differences between Men’s and Women’s 

Characteristics from 1988 to 2007 

Total pay gap Unexplained pay gap Occupation Education 

All other 

characteristics

1988 -0.25 -0.03 -0.18 -0.02 -0.02

1989 -0.27 -0.03 -0.20 -0.02 -0.02

1990 -0.29 -0.02 -0.22 -0.02 -0.03

1991 -0.31 -0.03 -0.22 -0.02 -0.03

1992 -0.32 -0.04 -0.23 -0.03 -0.03

1993 -0.32 -0.04 -0.23 -0.03 -0.03

1994 -0.33 -0.04 -0.23 -0.03 -0.03

1995 -0.33 -0.06 -0.22 -0.03 -0.03

1996 -0.32 -0.06 -0.21 -0.03 -0.02

1997 -0.32 -0.06 -0.21 -0.03 -0.02

1998 -0.31 -0.07 -0.20 -0.03 -0.03

1999 -0.31 -0.08 -0.19 -0.03 -0.03

2000 -0.31 -0.09 -0.18 -0.02 -0.03

2001 -0.31 -0.09 -0.17 -0.02 -0.03

2002 -0.30 -0.09 -0.16 -0.02 -0.03

2003 -0.30 -0.10 -0.15 -0.03 -0.03

2004 -0.29 -0.10 -0.14 -0.02 -0.03

2005 -0.29 -0.10 -0.13 -0.02 -0.03

2006 -0.28 -0.10 -0.13 -0.02 -0.03

2007 -0.28 -0.11 -0.12 -0.02 -0.03

Source: GAO analysis of CPDF data. 

Page 77 GAO-09-279 Women’s Pay 
Appendix IV: Cohort Analysis 

One reason why unpaid leave may not have impacted the overall pay gap is 

that the differences between men’s and women’s propensity to use unpaid 

leave and the cost of unpaid leave for men and women may have cancelled 

each other out. As shown in slide 32, 18 percent of women took unpaid 

leave at least once between 1988 and 2007 while only 11 percent of men 

took unpaid leave over this period, a difference that one would expect to 

contribute to the pay gap. However, the cost of taking unpaid leave was 

higher for men than women as shown in figure 2. The y-axis of figure 2 

represents the cost of taking unpaid leave (i.e., the difference between the 

average salaries of those who took leave versus those who did not, after 

controlling for other factors). Figure 2 shows that, in the first year of 

federal service, the salaries of men who took unpaid leave were about 9 

percent lower than the salaries of men who did not take unpaid leave that 

year.8

 In contrast, in the first year of federal service, the salaries for 

women who took unpaid leave were about 4 percent lower than those of 

women who did not take unpaid leave that year. We also were not able to 

identify the reason for using unpaid leave in most cases.9

 Because using 

unpaid leave had a larger negative effect on men’s pay than women’s pay, 

it likely cancelled out the effect of men’s lower propensity to take it. 

Exploring the Use and 

Cost of Unpaid Leave for 

Men and Women 

 

8

The cost of unpaid leave is represented by the coefficient for ever using unpaid leave on 

pay after controlling for all other factors in the model. The coefficient in 1988 cannot fully 

be attributed to the effect of unpaid leave on pay because pay for that year might have been 

determined before the leave was taken. 

9

The CPDF data contain information on the legal authorities for LWOP personnel actions. 

For more than 80 percent of the LWOP actions among the cohort, the legal authority did 

not provide any information on the reason for the LWOP or indication of how the employee 

would be using the time on unpaid leave. 

Page 78 GAO-09-279 Women’s Pay 
Appendix IV: Cohort Analysis 

Figure 2: Cost of Taking Unpaid Leave on Pay for Men and Women 

0

2

4

6

8

10

12

14

16

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Cents

Source: GAO analysis of CPDF data.

Fiscal year Men

Women

Similarly, we also explored the effect of having a break in federal service on pay 

for men and women. With the exception of the first year, the effect of a month 

of break in service on pay is similar for men and women. Furthermore, similar 

percentages of men and women had breaks in service. As a result, less than one 

percent of the pay gap can be attributed to differences between men and 

women in their propensity to take breaks in service. While women’s breaks 

were longer than men’s breaks on average as shown in table 15, this did not 

have an effect on the pay gap between them. 

Table 15: Summary of Breaks in Services Use among Cohort, Fiscal Years 1988 and 2007 

Number of people who had a 

break in service

Average cumulative duration 

of break in months

1988 330

Male 111 2.02

Female 219 2.21

2007 427

Male 146 86.4

Female 281 97.8

Source: GAO analysis of CPDF data. 

Page 79 GAO-09-279 Women’s Pay 
Appendix IV: Cohort Analysis 

Our findings on the effect of leave patterns on the pay gap differ from 

those of the 2003 GAO report, which showed work patterns contributing 

significantly to the differences in men and women’s pay in the general 

population. This could be due to the fact that our data differed from the 

2003 data in three ways. First, our data only follow people through their 

federal careers and do not account for time they spend in other 

employment sectors. Second, unlike the 2003 report, we could not 

differentiate periods of unemployment from time out of the workforce for 

other reasons. Third, the dependent variable for this analysis is annual 

salary while the dependent variable for the 2003 report was earnings. 

The Effects of Workforce 

Attrition and Job 

Switching 

As noted earlier, the number of employees in 5 of the 6 occupational 

categories declined while the administrative category received a net gain 

of more than 1,000 employees—largely as a result of employees switching 

their jobs from the technical and clerical categories. Further, we found 

that, on average, men and women who switched to the administrative 

category earned a lower salary than those who were originally hired into 

the category. This may be due in part to differences in education levels 

between the individuals who switched and the individuals who were 

originally hired into the category. Among people who switched, men also 

had more education than women. Specifically, about 50 percent of the men 

who switched to the administrative category had at least a bachelor’s 

degree compared with 36 percent of the women. 

To determine whether changing jobs or leaving the workforce had an effect on 

the pay gap, we tested two alternative specifications of our model. We included 

(1) an indicator for whether a person changed occupational categories in the 

current fiscal year and (2) an indicator for whether a person left the federal 

workforce through a separation action in the current fiscal year. The latter 

indicator is an attempt to measure the effect of the propensity to leave the 

workforce on the pay gap, i.e., whether those who leave the workforce may 

have some unobservable characteristic that affects the pay gap. Although we 

cannot measure the unobservable characteristic, we can control for the fact 

that the individual is about to leave the workforce, thereby isolating the effect 

of their imminent departure on the pay gap. We found that the inclusion of each 

of these two indicator variables explained less than 1 percentage point of the 

pay gap.10

 

10Both variables for changing jobs and leaving the workforce were statistically significant 

predictors of annual salary. 

Page 80 GAO-09-279 Women’s Pay 
Appendix V: Crosswalk between the Statistics 

Presented in the Briefing Slides and Those Appendix V: Crosswalk between the Presented in Appendices III and IV 

Statistics Presented in the Briefing Slides and 

Those Presented in Appendices III and IV 

This appendix presents the conversion of statistical output in appendices 

III and IV into the estimates of the pay gap that are presented in the 

briefing slides. 

In using regression analysis to understand variation in pay among a group of 

people, it is common to express the dependent variable (e.g., salary) in log 

form (i.e., the log of salary). An advantage of expressing a dependent variable 

like salary in log form is that it allows the coefficients of the explanatory 

variables to be interpreted in a consistent way regardless of the value of the 

variable, e.g., as a percent rather than dollar difference. In any regression 

where the dependent variable is in log form (e.g., the log of salary), 

economists often interpret the coefficient on a particular explanatory (or 

independent) variable as the average percent change in the dependent 

variable that results from a one-unit change in the explanatory variable. As 

such, in our regression analysis—where log of salary is the dependent 

variable and gender is an explanatory variable with two possible values (0 for 

male, 1 for female)—the coefficient on the variable for gender represents the 

percent difference in salary between men and women.1

However, this interpretation of the coefficient—i.e., as a percent 

difference when the dependent variable is in log form—loses its precision 

in certain cases. In cases where the difference in average log salary 

between two comparison groups (in our case men and women) is small, 

the size of the coefficient accurately reflects the percent difference in 

salary. However, as the difference in average log salary becomes larger, 

the coefficient will increasingly differ from the precise percent difference 

that would result from a one-unit change in the explanatory variable. 

When the dependent variable in log form takes on large values, a formula 

can be used to convert the coefficient of the explanatory variable (β) into 

a more precise estimate of the percentage difference in the actual salaries 

caused by a one-unit change in the explanatory variable: 

(exp(β)-1) 

 

1

If the dependent variable were not expressed in log form, the coefficient on the variable 

for gender would represent the dollar difference in salary that results when gender is 

female (1) instead of male (0). The dollar difference is less informative because it does not 

convey the relative magnitude of the salary difference. For example, it might indicate that 

there was a $10 difference in salary between two groups, but it would not indicate whether 

$10 represented a small or large proportion of the salary (1 percent or 10 percent, for 

example). 

Page 81 GAO-09-279 Women’s Pay 
Appendix V: Crosswalk between the Statistics 

Presented in the Briefing Slides and Those 

Presented in Appendices III and IV 

where exp is the base of the natural logarithm (commonly known as e) 

and β is the coefficient of the explanatory variable. (Note: all logarithms in 

this paper are natural logarithms, i.e., to the base e.) 

Table 16 illustrates how the above conversion formula allows us to precisely 

report the pay gap (salary differences) using two hypothetical examples: one 

involving small (10 percent) salary differences between two persons; the 

other large (50 percent). In example 1, where the salary difference is small, 

the difference in log salary (.10) precisely reflects the actual percent change in 

actual salary (10 percent)—such that the two can be used interchangeably. In 

example 2, where the salary difference is large, the difference in log salary 

(.41) does not precisely reflect the percent change (50 percent). When the 

conversion formula is applied to both examples, the result precisely reflects 

the actual change in salary (10 percent=.10 and 50 percent=.50). 

Table 16: Example of Precision of Log Difference 

 Person 1 Person 2

Difference in log 

salary 

Percentage 

difference in 

$ salary 

Converted 

difference

Example 1: 10% Difference in salaries 

Salary $11,000 $10,000 10% 

Log salary 9.31 9.21 0.10 0.10 

Example 2: 50% Difference in salaries 

Salary $15,000 $10,000 50% 

Log salary 9.62 9.21 0.41 0.50 

Source: GAO. 

Note: Figures presented above may be rounded. 

For our cross-sectional analysis, we applied the conversion formula to the 

coefficient associated with the total pay gap for the 3 years: 1988, 1998, 

and 2007. Table 17 shows the crosswalk between the results from 

appendix III to the results in slides 15 and 18 for which we used the 

conversion formula. 

Page 82 GAO-09-279 Women’s Pay 
Appendix V: Crosswalk between the Statistics 

Presented in the Briefing Slides and Those 

Presented in Appendices III and IV 

Table 17: Crosswalk between Cross-sectional Estimates of the Total Pay Gap 

Log gap (as presented in 

table 6 of app. III, under 

“Total Gap”) 

Converted log gap (as presented in 

slides 15 and 18 as the “Pay Gap”) 

Fiscal year Converted log gap=exp(Log gap)-1

1988 -0.327 -0.279

1998 -0.211 -0.190

2007 -0.121 -0.114

Source: GAO analysis of CPDF data. 

Note: In the briefing slides, we present the pay gap as a positive number. Figures presented above 

may be rounded. 

As with the cross-sectional analysis above, for the cohort analysis, we 

used the formula to convert the estimates of the total pay gap for each 

year between 1988 and 2007. The crosswalk between the results in 

appendix IV and our slides are presented in table 18. 

Table 18: Crosswalk between Cohort Estimates of the Total Gap in Appendix IV and 

the Briefing Slides 

Log gap (as presented in 

table 14 of app. IV) 

Converted log gap (as presented in 

slides 28 and 29) 

Fiscal year Converted log gap=exp(Log gap)-1

1988 -0.25 -0.22

1989 -0.27 -0.24

1990 -0.29 -0.25

1991 -0.31 -0.26

1992 -0.32 -0.27

1993 -0.32 -0.28

1994 -0.33 -0.28

1995 -0.33 -0.28

1996 -0.32 -0.28

1997 -0.32 -0.27

1998 -0.31 -0.27

1999 -0.31 -0.27

2000 -0.31 -0.27

2001 -0.31 -0.26

2002 -0.30 -0.26

2003 -0.30 -0.26

Page 83 GAO-09-279 Women’s Pay 
Appendix V: Crosswalk between the Statistics 

Presented in the Briefing Slides and Those 

Presented in Appendices III and IV 

Log gap (as presented in 

table 14 of app. IV) 

Converted log gap (as presented in 

slides 28 and 29) 

Fiscal year Converted log gap=exp(Log gap)-1

2004 -0.29 -0.25

2005 -0.29 -0.25

2006 -0.28 -0.25

2007 -0.28 -0.25

Source: GAO analysis of CPDF data. 

Note: In the briefing slides, we present the pay gap as a positive number. Figures presented above 

may be rounded. 

In contrast to the method used above to convert the total pay gap in tables 17 

and 18, we used a different approach to convert the results of the Oaxaca 

decomposition—the unexplained gap and the relative contributions of 

various factors to the gap.2

 We did not use the approach above because the 

converted contributions of each factor to the pay gap would not add up to the 

total converted pay gap. Instead, consistent with standard practices, we 

scaled each portion of the pay gap by multiplying the results of our 

decomposition approach by the ratio of the converted log gap to the log gap. 

The crosswalk between the results of our cross-sectional analysis of the 

unexplained gap and the contributions of various factors to the gap (table 

6, app. III) and our slides are presented in table 19. 

Table 19: Crosswalk between Cross-sectional Estimates of Unexplained Gap and the Portions of the Gap Resulting from 

Differences between Men and Women in Measurable Factors in Appendix III and the Briefing Slides 

Gap (as presented in table 6 

of app. III, under 

“Explained Gap”)

Converted log gap 

Log gap 

Converted gap (as 

presented in slides 

18 and 19)

Unexplained gap 

1988 0.078 x 0.279

0.327 

= 0.067

1998 0.081 x 0.190

0.211 

= 0.073

 

2

This is consistent with the approach taken by outside researchers. See, for example, “The 

Family Gap for Young Women in the United States and Britain: Can Maternity Leave Make 

a Difference?” by Jane Waldfogel, published in the Journal of Labor Economics, Vol. 16. 

No. 3 (July 1998), pp. 505-545. 

Page 84 GAO-09-279 Women’s Pay 
Appendix V: Crosswalk between the Statistics 

Presented in the Briefing Slides and Those 

Presented in Appendices III and IV 

Gap (as presented in table 6 

of app. III, under 

“Explained Gap”)

Converted log gap 

Log gap 

Converted gap (as 

presented in slides 

18 and 19)

2007 0.075 x 0.114

0.121 

= 0.071

Part of the Gap Resulting from Differences in Occupations 

1988 0.145 x 0.279

0.327 

= 0.124

1998 0.071 x 0.190

0.211 

= 0.064

2007 0.029 x 0.114

0.121 

= 0.027

Part of the Gap Resulting from Differences in Education Levels 

1988 0.024 x 0.279

0.327 

= 0.020

1998 0.023 x 0.190

0.211 

= 0.021

2007 0.008 x 0.114

0.121 

= 0.008

Part of the Gap Resulting from Differences in Experience Levels 

1988 0.054 x 0.279

0.327 

= 0.046

1998 0.022 x 0.190

0.211 

= 0.020

2007 0.000 x 0.114

0.121 

= 0.000

Part of the Gap Resulting from Differences in Other Characteristics 

1988 0.026 x 0.279

0.327 

= 0.022

1998 0.014 x 0.190

0.211 

= 0.013

2007 0.008 x 0.114

0.121 

= 0.008

Source: GAO analysis of CPDF data. 

Note: Figures presented above may be rounded. 

As with the cross-sectional analysis, for the cohort analysis, we computed the 

unexplained gap and the contributions of each factor to the pay gap by 

scaling the results of the decomposition approach by the ratio of the 

Page 85 GAO-09-279 Women’s Pay 
Appendix V: Crosswalk between the Statistics 

Presented in the Briefing Slides and Those 

Presented in Appendices III and IV 

converted log gap to the log gap. The crosswalk between the results of our 

cohort analysis (table 14 of app. IV) and the slides are presented in table 20. 

Table 20: Crosswalk between Cohort Estimates of Explained Gap Resulting from Differences between Men and Women in 

Measurable Factors in Appendix IV and the Briefing Slides 

Portion of gap (as 

presented in table 14 of 

app. IV) 

Converted log gap

Log gap 

(from table 18) 

Converted portion 

of gap 

(as presented in 

slide 30) 

Unexplained gap

1988 0.03 x 0.22

0.25 

= 0.02 

1989 0.03 x 0.24

0.27 

= 0.03 

1990 0.02 x 0.25

0.29 

= 0.02 

1991 0.03 x 0.26

0.31 

= 0.02 

1992 0.04 x 0.27

0.32 

= 0.03 

1993 0.04 x 0.28

0.32 

= 0.04 

1994 0.04 x 0.28

0.33 

= 0.04 

1995 0.06 x 0.28

0.33 

= 0.05 

1996 0.06 x 0.28

0.32 

= 0.05 

1997 0.06 x 0.27

0.32 

= 0.05 

1998 0.07 x 0.27

0.31 

= 0.06 

1999 0.08 x 0.27

0.31 

= 0.07 

2000 0.09 x 0.27

0.31 

= 0.07 

2001 0.09 x 0.26

0.31 

= 0.08 

Page 86 GAO-09-279 Women’s Pay 
Appendix V: Crosswalk between the Statistics 

Presented in the Briefing Slides and Those 

Presented in Appendices III and IV 

Portion of gap (as 

presented in table 14 of 

app. IV) 

Converted log gap

Log gap 

(from table 18) 

Converted portion 

of gap 

(as presented in 

slide 30) 

2002 0.09 x 0.26

0.30 

= 0.08 

2003 0.10 x 0.26

0.30 

= 0.09 

2004 0.10 x 0.25

0.29 

= 0.09 

2005 0.10 x 0.25

0.29 

= 0.09 

2006 0.10 x 0.25

0.28 

= 0.09 

2007 0.11 x 0.25

0.28 

= 0.09 

Part of the Gap Resulting From Differences in Occupations 

1988 0.18 x 0.22

0.25 

= 0.16 

1989 0.20 x 0.24

0.27 

= 0.17 

1990 0.22 x 0.25

0.29 

= 0.19 

1991 0.22 x 0.26

0.31 

= 0.19 

1992 0.23 x 0.27

0.32 

= 0.19 

1993 0.23 x 0.28

0.32 

= 0.19 

1994 0.23 x 0.28

0.33 

= 0.19 

1995 0.22 x 0.28

0.33 

= 0.18 

1996 0.21 x 0.28

0.32 

= 0.18 

1997 0.21 x 0.27

0.32 

= 0.18 

1998 0.20 x 0.27

0.31 

= 0.17 

Page 87 GAO-09-279 Women’s Pay 
Appendix V: Crosswalk between the Statistics 

Presented in the Briefing Slides and Those 

Presented in Appendices III and IV 

Portion of gap (as 

presented in table 14 of 

app. IV) 

Converted log gap

Log gap 

(from table 18) 

Converted portion 

of gap 

(as presented in 

slide 30) 

1999 0.19 x 0.27

0.31 

= 0.16 

2000 0.18 x 0.27

0.31 

= 0.15 

2001 0.17 x 0.26

0.31 

= 0.14 

2002 0.16 x 0.26

0.30 

= 0.14 

2003 0.15 x 0.26

0.30 

= 0.13 

2004 0.14 x 0.25

0.29 

= 0.12 

2005 0.13 x 0.25

0.29 

= 0.12 

2006 0.13 x 0.25

0.28 

= 0.11 

2007 0.12 x 0.25

0.28 

= 0.11 

Part of the Gap Resulting From Differences in Education Levels 

1988 0.02 x 0.22

0.25 

= 0.02 

1989 0.02 x 0.24

0.27 

= 0.02 

1990 0.02 x 0.25

0.29 

= 0.02 

1991 0.02 x 0.26

0.31 

= 0.02 

1992 0.03 x 0.27

0.32 

= 0.02 

1993 0.03 x 0.28

0.32 

= 0.02 

1994 0.03 x 0.28

0.33 

= 0.02 

1995 0.03 x 0.28

0.33 

= 0.02 

Page 88 GAO-09-279 Women’s Pay 
Appendix V: Crosswalk between the Statistics 

Presented in the Briefing Slides and Those 

Presented in Appendices III and IV 

Portion of gap (as 

presented in table 14 of 

app. IV) 

Converted log gap

Log gap 

(from table 18) 

Converted portion 

of gap 

(as presented in 

slide 30) 

1996 0.03 x 0.28

0.32 

= 0.02 

1997 0.03 x 0.27

0.32 

= 0.02 

1998 0.03 x 0.27

0.31 

= 0.02 

1999 0.03 x 0.27

0.31 

= 0.02 

2000 0.02 x 0.27

0.31 

= 0.02 

2001 0.02 x 0.26

0.31 

= 0.02 

2002 0.02 x 0.26

0.30 

= 0.02 

2003 0.03 x 0.26

0.30 

= 0.02 

2004 0.02 x 0.25

0.29 

= 0.02 

2005 0.02 x 0.25

0.29 

= 0.02 

2006 0.02 x 0.25

0.28 

= 0.02 

2007 0.02 x 0.25

0.28 

= 0.02 

Part of the Gap Resulting From Differences in Other Characteristics 

1988 0.02 x 0.22

0.25 

= 0.02 

1989 0.02 x 0.24

0.27 

= 0.02 

1990 0.03 x 0.25

0.29 

= 0.03 

1991 0.03 x 0.26

0.31 

= 0.03 

1992 0.03 x 0.27

0.32 

= 0.03 

Page 89 GAO-09-279 Women’s Pay 
Appendix V: Crosswalk between the Statistics 

Presented in the Briefing Slides and Those 

Presented in Appendices III and IV 

Portion of gap (as 

presented in table 14 of 

app. IV) 

Converted log gap

Log gap 

(from table 18) 

Converted portion 

of gap 

(as presented in 

slide 30) 

1993 0.03 x 0.28

0.32 

= 0.03 

1994 0.03 x 0.28

0.33 

= 0.02 

1995 0.03 x 0.28

0.33 

= 0.02 

1996 0.02 x 0.28

0.32 

= 0.02 

1997 0.02 x 0.27

0.32 

= 0.02 

1998 0.03 x 0.27

0.31 

= 0.02 

1999 0.03 x 0.27

0.31 

= 0.02 

2000 0.03 x 0.27

0.31 

= 0.02 

2001 0.03 x 0.26

0.31 

= 0.02 

2002 0.03 x 0.26

0.30 

= 0.02 

2003 0.03 x 0.26

0.30 

= 0.02 

2004 0.03 x 0.25

0.29 

= 0.02 

2005 0.03 x 0.25

0.29 

= 0.03 

2006 0.03 x 0.25

0.28 

= 0.03 

2007 0.03 x 0.25

0.28 

= 0.03 

Source: GAO analysis of CPDF data. 

Note: Figures presented above may be rounded. 

Page 90 GAO-09-279 Women’s Pay 
Appendix VI: Comments from the U.S. Office Appendix VI: Comments from the U.S. Office of Personnel Management 

of Personnel Management 

Note: GAO comments 

supplementing those in 

the report text appear at 

the end of this appendix. 

See comment 1. 

See comment 2.

Page 91 GAO-09-279 Women’s Pay 
Appendix VI: Comments from the U.S. Office 

of Personnel Management 

The following are GAO’s comments to the Office of Personnel 

Management’s letter dated February 23, 2009. 

1. OPM suggested that it may be useful to examine whether certain pay 

plans that provide higher pay are populated by a greater percentage of 

males vs. females. We agree that such an analysis would be useful in 

understanding gender disparities within the federal government, 

particularly with regard to promotions of women and men into careers 

with higher pay structures. However, as we explain in appendix II, an 

analysis of promotion was beyond the scope of this review. We also 

considered incorporating the pay plan variable into our statistical 

model of pay, but ultimately decided that we could control more 

directly for the underlying sources of variation in pay plans by using 

variables for occupation, geographic location, and agency. 

GAO Comments 

2. OPM also expressed the concern that we provided too little information 

on the role of geographical location in explaining gender pay disparities 

in the federal government. In our main cross-sectional analysis, we 

tested two controls for geographical location—state of employment 

and county of employment—in different models. These controls had 

only a minimal effect on the pay gap. We have clarified our definition 

of the county-level control and added a discussion of the state results in 

appendix III. 

Page 92 GAO-09-279 Women’s Pay 
Appendix VII: Comments from the U.S. 

Equal Employment Opportunity Appendix VII: Comments from the U.S. Equal Commission 

Employment Opportunity Commission 

Note: GAO comments 

supplementing those in 

the report text appear at 

the end of this appendix. 

See comment 1. 

Page 93 GAO-09-279 Women’s Pay 
Appendix VII: Comments from the U.S. 

Equal Employment Opportunity 

Commission 

Page 94 GAO-09-279 Women’s Pay 
Appendix VII: Comments from the U.S. 

Equal Employment Opportunity 

Commission 

See comment 2.

See comment 3. 

See comment 4. 

See comment 5. 

Page 95 GAO-09-279 Women’s Pay 
Appendix VII: Comments from the U.S. 

Equal Employment Opportunity 

Commission 

See comment 6. 

See comment 7. 

See comment 8. 

See comment 9. 

See comment 10. 

See comment 11. 

See comment 12. 

Page 96 GAO-09-279 Women’s Pay 
Appendix VII: Comments from the U.S. 

Equal Employment Opportunity 

Commission 

The following are GAO’s comments to the Equal Employment Opportunity 

Commission’s letter dated February 25, 2009. 

1. As we stated in our letter, we acknowledge that the difference in wages 

between men and women may vary further by race, age, disability 

status, and other factors that we analyzed. However, to appropriately 

report on the influence of factors related to other protected groups 

would require substantial analysis that is beyond the scope of our 

study’s objective. 

GAO Comments 

2. We agree that it would be useful to include additional explanation of 

our regression results pertaining to occupational categories and have 

done so in appendix III. 

3. We agree that graphical depictions of the converging characteristics of 

men and women are generally useful. In fact, slides 24 and 25 contain 

figures depicting the education and experience levels of men and 

women in the federal government over the study period. For 

occupational categories, however, the trends were mixed depending on 

the occupation, and therefore we chose to describe these trends with 

text. As we point out in slides 22 and 23, the professional, 

administrative, and clerical occupations became more integrated by 

gender since 1988, and blue and other white-collar occupations 

remained less integrated. In response to EEOC’s comments, we added 

information to slide 22 on changes in the proportion of women 

working in technical and administrative occupations. 

4. We agree with EEOC’s interpretation of the coefficients on the female 

variable in table 13 of appendix IV (the fifth table in app. IV as stated in 

EEOC’s comments), i.e., that for the 1988 entering cohort, the 

unexplained pay gap steadily increased over the study period. This 

finding was consistent for the main regression analysis and the Oaxaca 

decomposition analysis pertaining to the cohort, as depicted in slide 

29. We have clarified this consistency in appendix IV. 

5. We agree with EEOC’s interpretation of our graphical depiction of the 

explained and unexplained pay gap over the study period. However, 

after experimenting with several graphical depictions of the pay gap, 

we believe our depictions of the pay gap in slides 18, 19, 29, and 30 

appropriately convey both the actual magnitude of the gap and the 

rising share of the unexplained gap. As to EEOC’s question regarding 

whether “unexplained differences” included uncontrolled differences 

such as non-federal work experience, we believe that it may, as we 

Page 97 GAO-09-279 Women’s Pay 
Appendix VII: Comments from the U.S. 

Equal Employment Opportunity 

Commission 

point out in slide 20 (for the cross-sectional analyses) and 33 (for the 

cohort analysis). 

6. In designing our study, we considered including interaction terms in 

our models, but ultimately decided to restrict our attention and our 

presentation of results to “main-effects” models, which omit 

interactions, for several reasons. In addition to being consistent with 

the central objective of our study, the main effects models explained a 

very large portion of the variation in wages without resorting to 

complex interaction terms. Further, our simpler models have the 

advantage of providing easily interpretable estimates of the average 

difference in wages between men and women across all races and 

occupational categories and agencies, after the separate effects of all 

of these other characteristics on wages are controlled for. 

7. We agree that the growing unexplained gap for the 1988 entering 

cohort is perplexing. In slide 34, we listed factors that cannot 

effectively be measured or for which data were not available, that may 

account for this trend. Further, in appendix II, we discussed our 

inability to sufficiently control for personal priorities, as measured by 

number of children and marital status. If men and women differ in 

their personal priorities and these priorities have a cumulative impact 

on pay over the course of a career, it is possible that the growing 

unexplained pay gap that we observe among the cohort is an artifact of 

our inability to control for these unmeasured factors. However we 

were unable to test this hypothesis with the CPDF data. 

8. Due to significant computational limitations associated with 

conducting sophisticated econometric analyses with an enormous 

dataset, we used a 20 percent sample. As a result of EEOC’s comment, 

we expanded the second footnote in appendix III to include more 

information on our sampling technique. 

9. We agree that for future research examining gender differences in 

drop-out rates may provide useful insights into the gender pay gap. 

10. Regarding EEOC’s suggestion that we test how the exclusion of 

clerical workers would impact our results, we conducted a Oaxaca 

decomposition separately for each occupational group. These results 

are presented in table 8 of appendix III. They reveal a very consistent 

story for non-clerical occupation groups. Specifically, for each of these 

groups, the unexplained pay gap fell over the study period to between 

7.3 and 10 percentage points (depending on the occupational category) 

by 2007. 

Page 98 GAO-09-279 Women’s Pay 
Appendix VII: Comments from the U.S. 

Equal Employment Opportunity 

Commission 

11. With regard to EEOC’s comment about examining other dominant job 

categories, it is worth noting that, in addition to the PATCOB 

categories, we tested three different specifications of the occupational 

variable. Each of these specifications was more disaggregated than 

PATCOB. The most detailed specification contained over 700 

occupational categories. These results are presented in table 4 of 

appendix III. 

12. We agree that future longitudinal research on the gender pay gap could 

benefit from examining the initial grade or step of individual workers, 

particularly within job categories and agencies. We did not include 

grade and step in our cohort analysis due to the variation in the 

definitions of these categories across job categories and agencies over 

the study period. 

Page 99 GAO-09-279 Women’s Pay 
Appendix VIII: 

A

GAO Contact and Staff 

cknowledgments 

Page 100 GAO-09-279 Women’s Pay 

Appendix VIII: GAO Contact and Staff 

Acknowledgments 

Andrew Sherrill, Director (202) 512-7215 or sherrilla@gao.gov

Michele Grgich (Assistant Director) and Erin Godtland (Analyst-in-Charge) 

managed this engagement. In addition, the following people made 

significant contributions to this work: Daniel R. Concepcion, Anne-Marie 

Lasowski, Kathleen Van Gelder, and Monique B.Williams, Education, 

Workforce, and Income Security; and Benjamin Bolitzer, Grant Mallie, 

Rhiannon Patterson, Douglas Sloane, Shana Wallace, and Gregory H. 

Wilmoth, Applied Research and Methods. Also contributing to this work 

were: Jeremy Conley, Christoph Hoashi-Erhardt, Cynthia Fagnoni, and 

Gene Kuehneman, Education, Workforce, and Income Security; James 

Rebbe, Office of General Counsel; Ronald Fecso, Chief Statistician; Belva 

Martin, George Stalcup, and Tamara Stenzel, Strategic Issues; Carolyn 

Taylor, Office of Opportunity and Inclusion; Cynthia Heckmann, Human 

Capital Office; and Jennifer Popovic and Wayne Turowski, Applied 

Research and Methods. 

GAO Contact 

Staff 

Acknowledgments 
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(130833) Page 103 GAO-09-279 Women’s Pay 
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