Women's Pay
Gender Pay Gap in the Federal Workforce Narrows as Differences in Occupation, Education, and Experience Diminish Gao ID: GAO-09-279 March 17, 2009Although 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. 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. In light of concerns that a pay gap may continue to exist between men and women in the workplace, Congress asked us to examine pay disparity issues and the role the federal government has played in enforcing anti-discrimination laws. In agreement with Congressional 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. 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. 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).
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. (2) 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 1998 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.
GAO-09-279, Women's Pay: Gender Pay Gap in the Federal Workforce Narrows as Differences in Occupation, Education, and Experience Diminish
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Report to Congressional Requesters:
United States Government Accountability Office:
GAO:
March 2009:
Women's Pay:
Gender Pay Gap in the Federal Workforce Narrows as Differences in
Occupation, Education, and Experience Diminish:
GAO-09-279:
Contents:
Letter:
Appendix I: Briefing Slides:
Appendix II: Summary of Methods and Data:
Appendix III: Cross-sectional Analysis:
Appendix IV: Cohort Analysis:
Appendix V: Crosswalk between the Statistics Presented in the Briefing
Slides and Those Presented in Appendices III and IV:
Appendix VI: Comments from the U.S. Office of Personnel Management:
Appendix VII: Comments from the U.S. Equal Employment Opportunity
Commission:
Appendix VIII: GAO Contact and Staff Acknowledgments:
Bibliography:
Tables:
Table 1: Descriptive Statistics for Selected CPDF Variables Used in Our
Cross-sectional Analysis:
Table 2: Description and Definition of the Alternate Models:
Table 3: Main Regression Results:
Table 4: Female Coefficient under Alternate Specifications of the Model:
Table 5: Estimated Female Coefficient within Subgroups Using Main
Specification:
Table 6: Decomposition Results Using Main Specification (with
contributions of key factors):
Table 7: Decomposition Results Using Alternate Specifications:
Table 8: Estimated Total, Unexplained, and Explained Pay Gaps for
Different Subgroups (using main specification of the model):
Table 9: Number of Federal Employees from the 1988 Entry Cohort
Remaining over 2 Decades in the Status and Dynamic Files:
Table 10: Cohort Differences between Men and Women in Occupational
Categories in 1988 and 2007:
Table 11: Cohort Differences between Men and Women in Education Levels
in 1988 and 2007:
Table 12: Descriptive Statistics for Men and Women in 1988 Entering
Cohort:
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:
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:
Table 15: Summary of Breaks in Services Use among Cohort, Fiscal Years
1988 and 2007:
Table 16: Example of Precision of Log Difference:
Table 17: Crosswalk between Cross-sectional Estimates of the Total Pay
Gap:
Table 18: Crosswalk between Cohort Estimates of the Total Gap in
Appendix IV and the Briefing Slides:
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:
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:
Figures:
Figure 1: Distribution of Occupational Categories in the Entering Class
of 1988 over 20-year Period:
Figure 2: Cost of Taking Unpaid Leave on Pay for Men and Women:
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:
[End of section]
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.[Footnote 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.[Footnote 2]
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.[Footnote 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.[Footnote 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 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 appendix 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 1998 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 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 [hyperlink,
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 listed in appendix VIII.
Signed by:
Andrew Sherrill:
Director, Education, Workforce, and Income Security Issues:
[End of section]
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].
Overview:
Key Question:
* Scope and Methodology:
* Summary of Results:
* Background:
* Findings:
- Entire Federal Workforce;
- Case Study;
* Concluding Observations.
[End of overview]
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?
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 period[A]
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 time[B].
[A] For this analysis, we used a 20 percent random sample of federal
employees in the CPDF for each of the 3 years.
[B] We followed the careers of workers in this group, including those
who left the federal workforce and later returned.
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.
[A] For 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.
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.
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 pay[A] 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.
[A] Pay refers to annual salary.
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 28cents
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.
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
2000[A].
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).
Table: Background: Federal Workers Are Classified in Six General
Categories:
Occupational category: Professional;
Description: Requires knowledge in a specific discipline, typically
acquired through a bachelor‘s or higher degree in a specialized field.
Examples include accounting and engineering.
Occupational category: Administrative;
Description: Does not have a specific educational requirement, but
involves skills typically gained through general college education.
Examples include human resources management and budget analysis.
Occupational category: Technical;
Description: Occupations typically associated with and supportive of a
professional or administrative field. Includes medical technicians,
safety technicians, and food inspectors.
Occupational category: Clerical;
Description: Involves structured work in support of office, business,
or fiscal operations. Examples include typists, dispatchers, and
clerks.
Occupational category: Other white-collar;
Description: Includes positions that do not fall into other white-
collar groups. Most of these positions are related to law enforcement
or protective services.
Occupational category: Blue-collar;
Description: Occupations comprising the crafts, trades, and manual
labor, including foremen.
Source: OPM.
[End of table]
Background: Federal Employees are Increasingly Concentrated in
Professional and Administrative Jobs:
However, the proportion of clerical and blue-collar jobs decreased
significantly.
Figure: Proportion of Federal Workers by Occupational Category:
[Refer to PDF for image: vertical bar graph]
Occupational category: Professional;
Proportion of workforce, 1988: 19%;
Proportion of workforce, 1998: 23%;
Proportion of workforce, 2008: 24%.
Occupational category: Administrative;
Proportion of workforce, 1988: 25%;
Proportion of workforce, 1998: 31%;
Proportion of workforce, 2008: 36%.
Occupational category: Technical;
Proportion of workforce, 1988: 17%;
Proportion of workforce, 1998: 19%;
Proportion of workforce, 2008: 18%.
Occupational category: Clerical;
Proportion of workforce, 1988: 19%;
Proportion of workforce, 1998: 11%;
Proportion of workforce, 2008: 8%.
Occupational category: Other White-Collar;
Proportion of workforce, 1988: 2%;
Proportion of workforce, 1998: 3%;
Proportion of workforce, 2008: 4%.
Occupational category: Blue-Collar;
Proportion of workforce, 1988: 18%;
Proportion of workforce, 1998: 13%;
Proportion of workforce, 2008: 11%.
Source: GAO analysis of CRDF data.
[End of figure]
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.
Background: The Federal Workforce Has Increasingly More Education:
The proportion of federal workers with a bachelor‘s degree or higher
increased from 33% in 1988 to 44% in 2007.
Figure: Educational levels of Federal Workforce:
[Refer to PDF for image: vertical bar graph]
Education level: High School or Less;
Proportion of workforce, 1988: 40%;
Proportion of workforce, 1998: 34%;
Proportion of workforce, 2008: 33%.
Education level: Some College;
Proportion of workforce, 1988: 27%;
Proportion of workforce, 1998: 27%;
Proportion of workforce, 2008: 23%.
Education level: Bachelors Degree;
Proportion of workforce, 1988: 23%;
Proportion of workforce, 1998: 26%;
Proportion of workforce, 2008: 27%.
Education level: Graduate Degree;
Proportion of workforce, 1988: 10%;
Proportion of workforce, 1998: 14%;
Proportion of workforce, 2008: 17%.
Source: GAO analysis of CPDF data.
[End of figure]
Background: The Federal Workforce Has Become More Experienced:
Figure: Years of Federal Experience:
[Refer to PDF for image: vertical bar graph]
Years of Federal Experience: 0-4 years;
Percent of workforce, 1988: 21%;
Percent of workforce, 1998: 10%;
Percent of workforce, 2008: 20%.
Years of Federal Experience: 5-10 years;
Percent of workforce, 1988: 25%;
Percent of workforce, 1998: 21%;
Percent of workforce, 2008: 20%.
Years of Federal Experience: 11-20 years;
Percent of workforce, 1988: 33%;
Percent of workforce, 1998: 39%;
Percent of workforce, 2008: 26%.
Years of Federal Experience: Over 20 years;
Percent of workforce, 1988: 21%;
Percent of workforce, 1998: 31%;
Percent of workforce, 2008: 34%.
In addition, the average years of federal experience increased from
about 13 years in 1988 to 15 years in 2007.
Source: GAO analysis of CPDF data.
[End of figure]
Findings: Federal Workforce:
The Pay Gap”before Accounting for Differences between Men and Women in
Factors Related to Pay”Has Decreased Significantly Since 1988:
Figure: Total Pay Gap between Men and Women in the Federal Workforce:
[Refer to PDF for image: vertical bar graph]
Year: 1988;
Pay gap between men and women (in cents): 28 cents.
Year: 1998;
Pay gap between men and women (in cents): 19 cents.
Year: 2007;
Pay gap between men and women (in cents): 11 cents.
Source: GAO analysis of CPDF data.
[End of figure]
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.
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.
Measurable Factors Account for a Significant Portion of the Gap:
Figure: Federal Workforce: Proportion of Pay Gap that Can and Cannot be
Explained by Available Data:
[Refer to PDF for image: vertical bar graph]
Year: 1998;
Part of the gap resulting from differences in measurable factors: 21
cents;
Part of the gap that is unexplained: 7 cents;
Total pay gap: 28 cents.
Year: 1998;
Part of the gap resulting from differences in measurable factors: 12
cents;
Part of the gap that is unexplained: 7 cents;
Total pay gap: 19 cents.
Year: 2007;
Part of the gap resulting from differences in measurable factors: 4
cents;
Part of the gap that is unexplained: 7 cents;
Total pay gap: 11 cents.
Source: GAO analysis of CPDF data.
[End of figure]
Occupation, Education, and Federal Experience Are the Measurable
Factors That Contribute Most to the Gap:
Figure: Federal Workforce: Proportion of Pay Gap Due to Differences in
Measurable Factors between Men and Women:
[Refer to PDF for image: vertical bar graph]
Unexplained Pay Gap:
Year: 1988;
Part of the Pay Gap Resulting from Differences in Occupations: 14.5
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2.4
cents;
Part of the Pay Gap Resulting from Differences in Experience Levels:
5.4 cents;
Part of the Pay Gap Resulting from Differences in Experience Levels:
2.6 cents;
Part of the Pay Gap Resulting from Differences in Other
Characteristics: 7.8 cents.
Year: 1998;
Part of the Pay Gap Resulting from Differences in Occupations: 7.1
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2.3
cents;
Part of the Pay Gap Resulting from Differences in Experience Levels:
2.2 cents;
Part of the Pay Gap Resulting from Differences in Experience Levels:
1.4 cents;
Part of the Pay Gap Resulting from Differences in Other
Characteristics: 8.1 cents.
Year: 2007;
Part of the Pay Gap Resulting from Differences in Occupations: 2.9
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 0.8
cents;
Part of the Pay Gap Resulting from Differences in Experience Levels: 0;
Part of the Pay Gap Resulting from Differences in Experience Levels:
0.8 cents;
Part of the Pay Gap Resulting from Differences in Other
Characteristics: 7.5 cents.
Source: GAO analysis of CPDF data.
[End of figure]
Other Factors That We Could Not Measure May Account for the Persistent
Unexplained 7 Cent Gap[A]:
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.
[A] The size of the unexplained gap varies slightly depending on the
number of occupational categories used in the analysis. See appendix
III for further details.
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.
Some Federal Occupational Categories Have Become More Integrated by
Gender:
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.
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.
Men and Women in the Federal Workforce Have Increasingly Similar Levels
of Education:
Figure: Proportion of Men and Women in the Federal Workforce with a
Bachelor's Degree or Higher:
[Refer to PDF for image: vertical bar graph]
Year: 1988;
Men: 40%;
Women: 23%.
Year: 1998;
Men: 46%;
Women: 32%.
Year: 2007;
Men: 47%;
Women: 41%.
Source: GAO analysis of CPDF data.
[End of figure]
Men and Women in the Federal Workforce Have Increasingly Similar Levels
of Federal Experience:
[Refer to PDF for image: vertical bar graph]
Year: 1988;
Average federal experience: Men: 14.4. years;
Average federal experience: Women: 10.8 years.
Year: 1998;
Average federal experience: Men: 16.7 years;
Average federal experience: Women: 14.8 years.
Year: 2007;
Average federal experience: Men: 15.2 years;
Average federal experience: Women: 15.5 years.
Source: GAO analysis of CPDF data.
[End of figure]
Findings: Case Study:
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 period[A].
We accounted for differences between men and women in leave patterns
(unpaid leave and breaks in service[B]) as well as occupation, agency,
region, education level, bargaining unit status, part-time work status,
veteran status, gender, age, race and ethnicity, disability status
[A] Data on work patterns came from the CPDF dynamics file.
[B] A break in service happens when an employee leaves the federal
government and later returns.
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.
Analysis of the 1988 Entering Cohort Shows That the Pay Gap Increased
in Earlier Years, Then Declined Somewhat:
Figure: 1988 Entering Class: Total Pay Gap Between Male and Female
Workers:
[Refer to PDF for image: vertical bar graph]
Year: 1988;
Pay Gap: 22 cents.
Year: 1989;
Pay Gap: 24 cents.
Year: 1990;
Pay Gap: 25 cents.
Year: 1991;
Pay Gap: 26 cents.
Year: 1992;
Pay Gap: 27 cents.
Year: 1993;
Pay Gap: 28 cents.
Year: 1994;
Pay Gap: 28 cents.
Year: 1995;
Pay Gap: 28 cents.
Year: 1996;
Pay Gap: 28 cents.
Year: 1997;
Pay Gap: 27 cents.
Year: 1998;
Pay Gap: 27 cents.
Year: 1999;
Pay Gap: 27 cents.
Year: 2000;
Pay Gap: 27 cents.
Year: 2001;
Pay Gap: 26 cents.
Year: 2002;
Pay Gap: 26 cents.
Year: 2003;
Pay Gap: 26 cents.
Year: 2004;
Pay Gap: 25 cents.
Year: 2005;
Pay Gap: 25 cents.
Year: 2006;
Pay Gap: 25 cents.
Year: 2007;
Pay Gap: 25 cents.
Source: GAO analysis of CPDF data.
[End of figure]
Differences between Men and Women in Measurable Factors Account for a
Significant but Declining Portion of the Gap:
Figure: 1988 Entering Class: Proportion of Pay Gap Thant Can and Cannot
Be Explained by Available Data:
[Refer to PDF for image: stacked vertical bar graph]
Year: 1988;
Part of gap resulting from differences in measurable factors: 20 cents;
Part of gap that is unexplained: 2 cents.
Year: 1989;
Part of gap resulting from differences in measurable factors: 21 cents;
Part of gap that is unexplained: 3 cents.
Year: 1990;
Part of gap resulting from differences in measurable factors: 24 cents;
Part of gap that is unexplained: 2 cents.
Year: 1991;
Part of gap resulting from differences in measurable factors: 24 cents;
Part of gap that is unexplained: 2 cents.
Year: 1992;
Part of gap resulting from differences in measurable factors: 24 cents;
Part of gap that is unexplained: 3 cents.
Year: 1993;
Part of gap resulting from differences in measurable factors: 24 cents;
Part of gap that is unexplained: 4 cents.
Year: 1994;
Part of gap resulting from differences in measurable factors: 24 cents;
Part of gap that is unexplained: 4 cents.
Year: 1995;
Part of gap resulting from differences in measurable factors: 23 cents;
Part of gap that is unexplained: 5 cents.
Year: 1996;
Part of gap resulting from differences in measurable factors: 22 cents;
Part of gap that is unexplained: 5 cents.
Year: 1997;
Part of gap resulting from differences in measurable factors: 22 cents;
Part of gap that is unexplained: 5 cents.
Year: 1998;
Part of gap resulting from differences in measurable factors: 21 cents;
Part of gap that is unexplained: 6 cents.
Year: 1999;
Part of gap resulting from differences in measurable factors: 20 cents;
Part of gap that is unexplained: 7 cents.
Year: 2000;
Part of gap resulting from differences in measurable factors: 19 cents;
Part of gap that is unexplained: 7 cents.
Year: 2001;
Part of gap resulting from differences in measurable factors: 19 cents;
Part of gap that is unexplained: 8 cents.
Year: 2002;
Part of gap resulting from differences in measurable factors: 18 cents;
Part of gap that is unexplained: 8 cents.
Year: 2003;
Part of gap resulting from differences in measurable factors: 17 cents;
Part of gap that is unexplained: 9 cents.
Year: 2004;
Part of gap resulting from differences in measurable factors: 17 cents;
Part of gap that is unexplained: 9 cents.
Year: 2005;
Part of gap resulting from differences in measurable factors: 16 cents;
Part of gap that is unexplained: 9 cents.
Year: 2006;
Part of gap resulting from differences in measurable factors: 16 cents;
Part of gap that is unexplained: 9 cents.
Year: 2007;
Part of gap resulting from differences in measurable factors: 16 cents;
Part of gap that is unexplained: 9 cents.
Source: GAO analysis of CPDF data.
[End of figure]
For the 1988 Entering Cohort, Differences in Occupation Account for
Much of the Pay Gap:
* The portion of the gap that cannot be explained grew from 2 cents in
1988 to 9 cents in 2007.
Figure: 1988 Entering Class: Proportion of Pay Gap Due to Differences
in Measurable Factors Between Men and Women:
[Refer to PDF for image: stacked vertical bar graph]
Year: 1988;
Part of the Pay Gap Resulting from Differences in Occupations: 16
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2
cents;
Part of the Pay Gap Resulting from Differences in Other Characteristics
Including Leave: 2 cents;
Unexplained Pay Gap: 2 cents.
Year: 1989;
Part of the Pay Gap Resulting from Differences in Occupations: 7 cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2
cents;
Part of the Pay Gap Resulting from Differences in Other Characteristics
Including Leave: 2 cents;
Unexplained Pay Gap: 3 cents.
Year: 1990;
Part of the Pay Gap Resulting from Differences in Occupations: 19
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2
cents;
Part of the Pay Gap Resulting from Differences in Other Characteristics
Including Leave: 3 cents;
Unexplained Pay Gap: 2 cents.
Year: 1991;
Part of the Pay Gap Resulting from Differences in Occupations: 9 cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2
cents;
Part of the Pay Gap Resulting from Differences in Other Characteristics
Including Leave: 3 cents;
Unexplained Pay Gap: 2 cents.
Year: 1992;
Part of the Pay Gap Resulting from Differences in Occupations: 19
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2
cents;
Part of the Pay Gap Resulting from Differences in Other Characteristics
Including Leave: 3 cents;
Unexplained Pay Gap: 3 cents.
Year: 1993;
Part of the Pay Gap Resulting from Differences in Occupations: 19
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2
cents;
Part of the Pay Gap Resulting from Differences in Other Characteristics
Including Leave: 3 cents;
Unexplained Pay Gap: 4 cents.
Year: 1994;
Part of the Pay Gap Resulting from Differences in Occupations: 19
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2
cents;
Part of the Pay Gap Resulting from Differences in Other Characteristics
Including Leave: 2 cents;
Unexplained Pay Gap: 4 cents.
Year: 1995;
Part of the Pay Gap Resulting from Differences in Occupations: 18
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2
cents;
Part of the Pay Gap Resulting from Differences in Other Characteristics
Including Leave: 2 cents;
Unexplained Pay Gap: 5 cents.
Year: 1996;
Part of the Pay Gap Resulting from Differences in Occupations: 18
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2
cents;
Part of the Pay Gap Resulting from Differences in Other Characteristics
Including Leave: 2 cents;
Unexplained Pay Gap: 5 cents.
Year: 1997;
Part of the Pay Gap Resulting from Differences in Occupations: 18
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2
cents;
Part of the Pay Gap Resulting from Differences in Other Characteristics
Including Leave: 2 cents;
Unexplained Pay Gap: 5 cents.
Year: 1998;
Part of the Pay Gap Resulting from Differences in Occupations: 17
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2
cents;
Part of the Pay Gap Resulting from Differences in Other Characteristics
Including Leave: 2 cents;
Unexplained Pay Gap: 6 cents.
Year: 1999;
Part of the Pay Gap Resulting from Differences in Occupations: 16
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2
cents;
Part of the Pay Gap Resulting from Differences in Other Characteristics
Including Leave: 2 cents;
Unexplained Pay Gap: 7 cents.
Year: 2000;
Part of the Pay Gap Resulting from Differences in Occupations: 15
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2
cents;
Part of the Pay Gap Resulting from Differences in Other Characteristics
Including Leave: 2 cents;
Unexplained Pay Gap: 7 cents.
Year: 2001;
Part of the Pay Gap Resulting from Differences in Occupations: 14
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2
cents;
Part of the Pay Gap Resulting from Differences in Other Characteristics
Including Leave: 2 cents;
Unexplained Pay Gap: 8 cents.
Year: 2002;
Part of the Pay Gap Resulting from Differences in Occupations: 14
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2
cents;
Part of the Pay Gap Resulting from Differences in Other Characteristics
Including Leave: 2 cents;
Unexplained Pay Gap: 8 cents.
Year: 2003;
Part of the Pay Gap Resulting from Differences in Occupations: 13
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2
cents;
Part of the Pay Gap Resulting from Differences in Other Characteristics
Including Leave: 2 cents;
Unexplained Pay Gap: 9 cents.
Year: 2004;
Part of the Pay Gap Resulting from Differences in Occupations: 12
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2
cents;
Part of the Pay Gap Resulting from Differences in Other Characteristics
Including Leave: 2 cents;
Unexplained Pay Gap: 9 cents.
Year: 2005;
Part of the Pay Gap Resulting from Differences in Occupations: 12
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2
cents;
Part of the Pay Gap Resulting from Differences in Other Characteristics
Including Leave: 3 cents;
Unexplained Pay Gap: 9 cents.
Year: 2006;
Part of the Pay Gap Resulting from Differences in Occupations: 11
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2
cents;
Part of the Pay Gap Resulting from Differences in Other Characteristics
Including Leave: 3 cents;
Unexplained Pay Gap: 9 cents.
Year: 2007;
Part of the Pay Gap Resulting from Differences in Occupations: 11
cents;
Part of the Pay Gap Resulting from Differences in Education Levels: 2
cents;
Part of the Pay Gap Resulting from Differences in Other Characteristics
Including Leave: 3 cents;
Unexplained Pay Gap: 9 cents.
Source: GAO analysis of CPDF data.
[End of figure]
For Women in the Entering Cohort of 1988, the Decrease in the Clerical
Workforce Was also Significant:
* Over the same period, the number of female administrative workers
increased.
Figure: Distribution of Occupational Categories for Women in the
Entering Class of 1998:
[Refer to PDF for image: stacked vertical bar graph]
Number of female employees by occupational category:
Year: 1988;
Administrative: 3,530;
Blue Collar: 1,838;
Clerical: 29,808;
Other White Collar: 1,464;
Professional: 8,408;
Technical: 5,560.
Year: 1989;
Administrative: 3,328;
Blue Collar: 1,365;
Clerical: 23,273;
Other White Collar: 1,465;
Professional: 7,492;
Technical: 4,881.
Year: 1990;
Administrative: 3,275;
Blue Collar: 1,164;
Clerical: 17,482;
Other White Collar: 862;
Professional: 6,600;
Technical: 4,822.
Year: 1991;
Administrative: 3,280;
Blue Collar: 865;
Clerical: 14,048;
Other White Collar: 434;
Professional: 6,170;
Technical: 5,168.
Year: 1992;
Administrative: 3,450;
Blue Collar: 736;
Clerical: 11,519;
Other White Collar: 263;
Professional: 5,612;
Technical: 5,541.
Year: 1993;
Administrative: 3,598;
Blue Collar: 648;
Clerical: 9,777;
Other White Collar: 170;
Professional: 5,220;
Technical: 5,972.
Year: 1994;
Administrative: 3,713;
Blue Collar: 596;
Clerical: 8,637;
Other White Collar: 135;
Professional: 4,949;
Technical: 5,959.
Year: 1995;
Administrative: 3,789;
Blue Collar: 533;
Clerical: 7,612;
Other White Collar: 116;
Professional: 4,792;
Technical: 5,946.
Year: 1996;
Administrative: 3,905;
Blue Collar: 465;
Clerical: 6,583;
Other White Collar: 100;
Professional: 4,572;
Technical: 6,023.
Year: 1997;
Administrative: 3,921;
Blue Collar: 427;
Clerical: 5,780;
Other White Collar: 91;
Professional: 4,390;
Technical: 5,987.
Year: 1998;
Administrative: 4,027;
Blue Collar: 391;
Clerical: 5,087;
Other White Collar: 88;
Professional: 4,194;
Technical: 5,798.
Year: 1999;
Administrative: 4,256;
Blue Collar: 357;
Clerical: 4,479;
Other White Collar: 77;
Professional: 4,053;
Technical: 5,546.
Year: 2000;
Administrative: 4,444;
Blue Collar: 331;
Clerical: 3,917;
Other White Collar: 78;
Professional: 3,932;
Technical: 5,382.
Year: 2001;
Administrative: 4,759;
Blue Collar: 294;
Clerical: 3,441;
Other White Collar: 72;
Professional: 3,868;
Technical: 5,132.
Year: 2002;
Administrative: 5,129;
Blue Collar: 264;
Clerical: 2,915;
Other White Collar: 65;
Professional: 3,726;
Technical: 4,925.
Year: 2003;
Administrative: 5,392;
Blue Collar: 234;
Clerical: 2,571;
Other White Collar: 61;
Professional: 3,634;
Technical: 4,791.
Year: 2004;
Administrative: 5,542;
Blue Collar: 219;
Clerical: 2,324;
Other White Collar: 61;
Professional: 3,586;
Technical: 4,639.
Year: 2005;
Administrative: 5,648;
Blue Collar: 206;
Clerical: 2,064;
Other White Collar: 62;
Professional: 3,550;
Technical: 4,456.
Year: 2006;
Administrative: 5,727;
Blue Collar: 200;
Clerical: 1,854;
Other White Collar: 64;
Professional: 3,499;
Technical: 4,255.
Year: 2007;
Administrative: 5,841;
Blue Collar: 188;
Clerical: 1,666;
Other White Collar: 62;
Professional: 3,429;
Technical: 3,992.
Source: GAO analysis of CPDF data.
[End of figure]
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:
Table:
Took Unpaid Leave at Least Once between 1988-2007:
Women: 18%;
Men: 11%.
Had a Break in Service at Least Once between 1988-2007:
Women: 17%;
Men: 15%.
Source: GAO analysis of CPDF data.
[End of table]
* 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 workers[A].
[A] See appendix IV for additional explanation of these results.
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.
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.
[End of section]
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 decomposition[Footnote 5] 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.
[Footnote 6]
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).
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.
Data:
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.
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.
Exclusions:
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
judicial branch; and (3) most agencies in the legislative branch.
[Footnote 7] 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.[Footnote 8]
Data Reliability:
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:
* 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;
* 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.
[Footnote 9]
* 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.
Limitations of the Analysis:
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.
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.[Footnote 10] 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 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.[Footnote 11]
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 cross-
sectional 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.[Footnote 12] 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 understand 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 no control for occupation, the unexplained pay gap was 20 percent
in 1988, 14 percent in 1998, and 11 percent in 2007. (See appendix 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.[Footnote
13]
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 appendix 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.[Footnote 14] 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 appendix III, table 7, for these results.)
[End of section]
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.
Data Used in the Cross-Sectional Analysis and Descriptive Statistics:
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.[Footnote 15] 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.[Footnote 16]
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:
* 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.[Footnote 17]
* 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 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 full-
time, 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
almost 28 percent of men in the federal workforce were in the "Blue-
Collar" field; by 2007, that number was 17 percent.[Footnote 18]
- 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.[Footnote 19] 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:
* 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:
Annual adjusted salary;
1988: Men: 55,862;
1988: Women: 39,750;
1998: Men: 62,595;
1998: Women: 50,540;
2007: Men: 70,109;
2007: Women: 62,021.
Log of salary;
1988: Men: 10.847;
1988: Women: 10.520;
1998: Men: 10.957;
1998: Women: 10.745;
2007: Men: 11.059;
2007: Women: 10.938.
Age;
1988: Men: 42.980;
1988: Women: 39.772;
1998: Men: 45.707;
1998: Women: 43.823;
2007: Men: 46.707;
2007: Women: 46.148.
Federal experience;
1988: Men: 14.035;
1988: Women: 10.469;
1998: Men: 16.143;
1998: Women: 14.260;
2007: Men: 14.901;
2007: Women: 14.995.
Race/ethnicity:
African-American;
1988: Men: .115;
1988: Women: .232;
1998: Men: .113;
1998: Women: .232;
2007: Men: .122; 2007:
Women: .238.
Asian Pacific Islander;
1988: Men: .035;
1988: Women: .030;
1998: Men: .047;
1998: Women: .043;
2007: Men: .053;
2007: Women: .055.
Hispanic;
1988: Men: .055;
1988: Women: .048;
1998: Men: .066;
1998: Women: .060;
2007: Men: .080;
2007: Women: .072.
Native American;
1988: Men: .016;
1988: Women: .021;
1998: Men: .018;
1998: Women: .025;
2007: Men: .016;
2007: Women: .026.
White;
1988: Men: .779;
1988: Women: .668;
1998: Men: .756;
1998: Women: .639;
2007: Men: .726;
2007: Women: .606.
Other;
1988: Men: .000;
1988: Women: .001;
1998: Men: .0005;
1998: Women: .000;
2007: Men: .003;
2007: Women: .003.
Education:
Less than high school;
1988: Men: .042;
1988: Women: .031;
1998: Men: .017;
1998: Women: .016;
2007: Men: .011;
2007: Women: .011.
High school diploma;
1988: Men: .265;
1988: Women: .350;
1998: Men: .251;
1998: Women: .315;
2007: Men: .283;
2007: Women: .276.
Trade degree;
1988: Men: .047;
1988: Women: .076;
1998: Men: .030;
1998: Women: .052;
2007: Men: .023;
2007: Women: .041.
Some college;
1988: Men: .236;
1988: Women: .300;
1998: Men: .231;
1998: Women: .287;
2007: Men: .194;
2007: Women: .239.
Bachelor degree;
1988: Men: .258;
1988: Women: .168;
1998: Men: .279;
1998: Women: .207;
2007: Men: .272;
2007: Women: .243.
Masters degree;
1988: Men: .082;
1988: Women: .044;
1998: Men: .102;
1998: Women: .070;
2007: Men: .124;
2007: Women: .114.
Professional degree;
1988: Men: .038;
1988: Women: .015;
1998: Men: .050;
1998: Women: .029;
2007: Men: .032;
2007: Women: .025.
Doctorate degree;
1988: Men: .023;
1988: Women: .006;
1998: Men: .029;
1998: Women: .011;
2007: Men: .031;
2007: Women: .020.
Other education;
1988: Men: .010;
1988: Women: .010;
1998: Men: .011;
1998: Women: .013;
2007: Men: .031;
2007: Women: .030.
Occupation (PATCOB):
Administrative;
1988: Men: .257;
1988: Women: .213;
1998: Men: .305;
1998: Women: .284;
2007: Men: .349;
2007: Women: .354.
Blue-collar;
1988: Men: .283;
1988: Women: .046;
1998: Men: .215;
1998: Women: .031;
2007: Men: .173;
2007: Women: .026.
Clerical;
1988: Men: .048;
1988: Women: .381;
1998: Men: .035;
1998: Women: .202;
2007: Men: .045;
2007: Women: .126.
Other white-collar;
1988: Men: .029;
1988: Women: .002;
1998: Men: .040;
1998: Women: .004;
2007: Men: .051;
2007: Women: .006.
Professional;
1988: Men: .234;
1988: Women: .143;
1998: Men: .266;
1998: Women: .214;
2007: Men: .244;
2007: Women: .242.
Technical;
988: Men: .148;
1988: Women: .216;
1998: Men: .139;
1998: Women: .264;
2007: Men: .138;
2007: Women: .246.
Work schedule:
Full time;
1988: Men: .938;
1988: Women: .886;
1998: Men: .932;
1998: Women: .885;
2007: Men: .939;
2007: Women: .900.
Part time;
1988: Men: .019;
1988: Women: .056;
1998: Men: .017;
1998: Women: .048;
2007: Men: .020;
2007: Women: .048.
Another type;
1988: Men: .043;
1988: Women: .058;
1998: Men: .051;
1998: Women: .067;
2007: Men: .040;
2007: Women: .051.
Disability status:
None;
1988: Men: .927;
1988: Women: .951;
1998: Men: .930;
1998: Women: .945;
2007: Men: .936;
2007: Women: .946.
Disabled not targeted;
1988: Men: .060;
1988: Women: .038;
1998: Men: .058;
1998: Women: .044;
2007: Men: .054;
2007: Women: .044.
Disabled;
1988: Men: .012;
1988: Women: .010;
1998: Men: .012;
1998: Women: .011;
2007: Men: .010;
2007: Women: .010.
Health plan:
Family plan;
1988: Men: .600;
1988: Women: .317;
1998: Men: .598;
1998: Women: .364;
2007: Men: .524;
2007: Women: .357.
Self plan;
1988: Men: .194;
1988: Women: .326;
1998: Men: .222;
1998: Women: .347;
2007: Men: .235;
2007: Women: .371.
Declined coverage;
1988: Men: .105;
1988: Women: .230;
1998: Men: .104;
1998: Women: .203;
2007: Men: .161;
2007: Women: .186.
Pending;
1988: Men: .031;
1988: Women: .045;
1998: Men: .020;
1998: Women: .022;
2007: Men: .030;
2007: Women: .033.
Not eligible;
1988: Men: .070;
1988: Women: .082;
1998: Men: .056;
1998: Women: .063;
2007: Men: .045;
2007: Women: .052.
Percentage female;
1988: 42%;
1988: 44%;
2007: 44%.
Number of observations;
1988: Men: 241,611;
1988: Women: 175,776;
1998: Men: 199,153;
1998: Women: 158,460;
2007: Men: 205,767;
2007: Women: 162,822.
Source: GAO analysis of CPDF data.
[End of table]
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) = a + b*(female):
+ d1*(set of work characteristics):
+ d2*(set of worker characteristics):
+ d3*(set of demographic characteristics):
The standard interpretation of b, 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.[Footnote 20] 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.[Footnote 21]
In choosing the variables included in our model, we had to balance two
competing ideas. As described by Blau and Kahn (2000)[Footnote 22], 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 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:
1;
Name of the model: Main;
Demographic characteristics:
* Race/ethnicity;
* Disability;
* Age;
"Worker" characteristics:
* Federal experience;
* Degree;
* Veteran;
* Bargaining unit & work schedule;
"Work" characteristics:
* PATCOB;
* Agency;
* State.
2;
Name of the model: Job family level;
Demographic characteristics:
* Race/ethnicity;
* Disability;
* Age;
"Worker" characteristics:
* Federal experience;
* Degree;
* Veteran;
* Bargaining unit & work schedule;
"Work" characteristics:
* Job family level;
* Agency;
* State.
3;
Name of the model: Disaggregated occupation, but with grouped blue-
collar;
Demographic characteristics:
* Race/ethnicity;
* Disability;
* Age;
"Worker" characteristics:
* Federal experience;
* Degree;
* Veteran;
* Bargaining unit & work schedule;
"Work" characteristics:
* Job series (w/ grouped blue-collar);
* Agency;
* State.
4;
Name of the model: Most disaggregated occupation;
Demographic characteristics:
* Race/ethnicity;
* Disability;
* Age;
"Worker" characteristics:
* Federal experience;
* Degree;
* Veteran;
* Bargaining unit & work schedule;
"Work" characteristics:
* Job series;
* Agency;
* State.
5;
Name of the model: In addition to PATCOB, we included the proportion of
women in the occupation;
Demographic characteristics:
* Race/ethnicity;
* Disability;
* Age;
"Worker" characteristics:
* Federal experience;
* Degree;
* Veteran;
* Bargaining unit & work schedule;
"Work" characteristics:
* PATCOB;
* Agency;
* Proportion of women in occupation;
* State.
6;
Name of the model: Geography measured by county;
Demographic characteristics:
* Race/ethnicity;
* Disability;
* Age;
"Worker" characteristics:
* Federal experience;
* Degree;
* Veteran;
* Bargaining unit & work schedule;
"Work" characteristics:
* PATCOB;
* Agency;
* County.
7;
Name of the model: The addition of educational major to the model;
Demographic characteristics:
* Race/ethnicity;
* Disability;
* Age;
"Worker" characteristics:
* Federal experience;
* Degree & major;
* Veteran;
* Bargaining unit & work schedule;
"Work" characteristics:
* PATCOB;
* Agency;
* State.
8;
Name of the model: The addition of educational major to the model, with
job family level;
Demographic characteristics:
* Race/ethnicity;
* Disability;
* Age;
"Worker" characteristics:
* Federal experience;
* Degree & major;
* Veteran;
* Bargaining unit & work schedule;
"Work" characteristics:
* Job family level;
* Agency;
* State.
9;
Name of the model: The addition of educational major to the model, with
grouped blue-collar;
Demographic characteristics:
* Race/ethnicity;
* Disability;
* Age;
"Worker" characteristics:
* Federal experience;
* Degree & Major;
* Veteran;
* Bargaining unit & work schedule;
"Work" characteristics:
* Job series (w/ grouped blue-collar);
* Agency;
* State.
10;
Name of the model: Excluding agency and occupation;
Demographic characteristics:
* Race/ethnicity;
* Disability;
* Age;
"Worker" characteristics:
* Federal experience;
* Degree;
* Veteran;
"Work" characteristics:
* State.
11;
Name of the model: Excluding agency and occupation, but major was
added;
Demographic characteristics:
* Race/ethnicity;
* Disability;
"Worker" characteristics:
* Age;
* Federal experience;
* Degree & major;
* Veteran;
"Work" characteristics:
* State.
12;
Name of the model: Only age, federal experience, and degree;
Demographic characteristics:
* Age;
"Worker" characteristics:
* Federal experience;
* Degree;
"Work" characteristics:
* State.
13;
Name of the model: Only federal experience, PATCOB, and degree;
Demographic characteristics: [Empty];
"Worker" characteristics:
* Federal experience;
* Degree;
"Work" characteristics:
* PATCOB.
14;
Name of the model: Health plan was added to the model;
Demographic characteristics:
* Race/ethnicity;
* Disability;
* Age;
* Health plan;
"Worker" characteristics:
* Federal experience;
* Degree;
* Veteran;
* Bargaining unit & work schedule;
"Work" characteristics:
* PATCOB;
* Agency;
* State.
Source: GAO analysis.
[End of table]
As noted in model 5, table 2, we estimated a model that contained a
variable to measure the proportion of women in each occupation.
[Footnote 23] 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.[Footnote 24] 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.
* 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 African-American employee was 7.4 percent lower than the salary of a
white person, after controlling for the other factors in the model.
Table 3: Main Regression Results:
Female:
1988: Estimate: -.109;
1988 Standard error: .001;
1998: Estimate: -.088;
1998 Standard error: .001;
2007: Estimate: -.083;
2007 Standard error: .001.
Experience and age: Age;
1988: Estimate: .018;
1988 Standard error: .001;
1998: Estimate: .041;
1998 Standard error: .001;
2007: Estimate: .050;
2007 Standard error: .001.
Experience and age: Age squared;
1988: Estimate: -.0002;
1988 Standard error: .00002;
1998: Estimate: -.0007;
1998 Standard error: .00002;
2007: Estimate: -.0008;
2007 Standard error: .00002.
Experience and age: Age cubed;
1988: Estimate: 6.46E-7;
1988 Standard error: 1.53E-7;
1998: Estimate: 3.64E-7;
1998 Standard error: 1.73E-7;
2007: Estimate: 4.31E-6;
2007 Standard error: 1.59E-7.
Experience and age: Federal experience;
1988: Estimate: .035;
1988 Standard error: .0002;
1998: Estimate: .031;
1998 Standard error: .0003;
2007: Estimate: .029;
2007 Standard error: .0003.
Experience and age: Federal experience squared;
1988: Estimate: -.001;
1988 Standard error: .00002;
1998: Estimate: -.001;
1998 Standard error: .00002;
2007: Estimate: .001;
2007 Standard error: 1.6E-6.
Experience and age: Federal experience cubed;
1988: Estimate: .00001;
1988 Standard error: 2.60E-7;
1998: Estimate: .00001;
1998 Standard error: 2.82E-7;
2007: Estimate: .00001;
2007 Standard error: 2.62E-7.
Race/Ethnicity (white is omitted): African-American;
1988: Estimate: -.079;
1988 Standard error: .001;
1998: Estimate: -.074;
1998 Standard error: .001;
2007: Estimate: -.074;
2007 Standard error: .001.
Race/Ethnicity (white is omitted): Asian Pacific Islander;
1988: Estimate: -.015;
1988 Standard error: .002;
1998: Estimate: -.022;
1998 Standard error: .002;
2007: Estimate: -.005;
2007 Standard error: .002.
Race/Ethnicity (white is omitted): Hispanic;
1988: Estimate: -.045;
1988 Standard error: .001;
1998: Estimate: -.042;
1998 Standard error: .001;
2007: Estimate: -.028;
2007 Standard error: .001.
Race/Ethnicity (white is omitted): Native American;
1988: Estimate: -.033;
1988 Standard error: .002;
1998: Estimate: -.042;
1998 Standard error: .002;
2007: Estimate: -.055;
2007 Standard error: .003.
Race/Ethnicity (white is omitted): Other;
1988: Estimate: -.043;
1988 Standard error: .014;
1998: Estimate: - .057;
1998 Standard error: .016;
2007: Estimate: -.037;
2007 Standard error: .007.
Education (less than high school is omitted): High school;
1988: Estimate: .078;
1988 Standard error: .002;
1998: Estimate: .074;
1998 Standard error: .003;
2007: Estimate: .076;
2007 Standard error: .003.
Education (less than high school is omitted): Trade degree;
1988: Estimate: .112;
1988 Standard error: .002;
1998: Estimate: .112;
1998 Standard error: .003;
2007: Estimate: .112;
2007 Standard error: .004.
Education (less than high school is omitted): Some college;
1988: Estimate: .110;
1988 Standard error: .002;
1998: Estimate: .112;
1998 Standard error: .003;
2007: Estimate: .114;
2007 Standard error: .004.
Education (less than high school is omitted): Bachelor degree;
1988: Estimate: .182;
1988 Standard error: .002;
1998: Estimate: .193;
1998 Standard error: .003;
2007: Estimate: .182;
2007 Standard error: .004.
Education (less than high school is omitted): Masters degree;
1988: Estimate: .258;
1988 Standard error: .002;
1998: Estimate: .272;
1998 Standard error: .003;
2007: Estimate: .247;
2007 Standard error: .004.
Education (less than high school is omitted): Professional degree;
1988: Estimate: .456;
1988 Standard error: .003;
1998: Estimate: .442;
1998 Standard error: .003;
2007: Estimate: .561;
2007 Standard error: .004.
Education (less than high school is omitted): Doctorate degree;
1988: Estimate: .411;
1988 Standard error: .003;
1998: Estimate: .418;
1998 Standard error: .004;
2007: Estimate: .398;
2007 Standard error: .004.
Education (less than high school is omitted): Other education;
1988: Estimate: .035;
1988 Standard error: .004;
1998: Estimate: .058;
1998 Standard error: .004;
2007: Estimate: .091;
2007 Standard error: .004.
Occupation (technical is omitted): Administrative;
1988: Estimate: .260;
1988 Standard error: .001;
1998: Estimate: .318;
1998 Standard error: .001;
2007: Estimate: .363;
2007 Standard error: .001.
Occupation (technical is omitted): Blue-collar;
1988: Estimate: .095;
1988 Standard error: .001;
1998: Estimate: .053;
1998 Standard error: .001;
2007: Estimate: .036;
2007 Standard error: .001.
Occupation (technical is omitted): Clerical;
1988: Estimate: -.156;
1988 Standard error: .001;
1998: Estimate: -.163;
1998 Standard error: .001;
2007: Estimate: -.204;
2007 Standard error: .002.
Occupation (technical is omitted): Other white-collar;
1988: Estimate: -.124;
1988 Standard error: .002;
1998: Estimate: .006;
1998 Standard error: .002;
2007: Estimate: .097;
2007 Standard error: .002.
Occupation (technical is omitted): Professional;
1988: Estimate: .370;
1988 Standard error: .001;
1998: Estimate: .397;
1998 Standard error: .001;
2007: Estimate: .432;
2007 Standard error: .001.
Work schedule (part time is omitted): Full time;
1988: Estimate: .040;
1988 Standard error: .002;
1998: Estimate: .023;
1998 Standard error: .002;
2007: Estimate: .040;
2007 Standard error: .002.
Work schedule (part time is omitted): Another type;
1988: Estimate: -.097;
1988 Standard error: .002;
1998: Estimate: -.171;
1998 Standard error: .002;
2007: Estimate: -.085;
2007 Standard error: .003.
Disability status (targeted disability is omitted): None;
1988: Estimate: .085;
1988 Standard error: .003;
1998: Estimate: .102;
1998 Standard error: .003;
2007: Estimate: .090;
2007 Standard error: .004.
Disability status (targeted disability is omitted): Disabled not
targeted;
1988: Estimate: .062;
1988 Standard error: .003;
1998: Estimate: .076;
1998 Standard error: .003;
2007: Estimate: .061;
2007 Standard error: .004.
Observations;
1988: 417,387;
1998: 357,613;
2007: 368,589.
R-Square;
1988: 79%;
1998: 78%;
2007: 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.
[End of table]
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 coefficient by 30 to 40 percent, a result that is consistent with
literature.[Footnote 25] However, as noted earlier, it is difficult to
interpret this coefficient.
Table 4: Female Coefficient under Alternate Specifications of the Model:
Specification: Main;
Female coefficient (standard error): 1988: -.109; (.001);
Female coefficient (standard error): 1998: -.088; (.001);
Female coefficient (standard error): 2007: -.083; (.001).
Specification: Job family level;
Female coefficient (standard error): 1988: -.097; (.001);
Female coefficient (standard error): 1998: -.076; (.001);
Female coefficient (standard error): 2007: -.072; (.001).
Specification: Disaggregated occupation, but with grouped blue-collar;
Female coefficient (standard error): 1988: -.084; (.001);
Female coefficient (standard error): 1998: -.064; (.001);
Female coefficient (standard error): 2007: -.055; (.001).
Specification: Most disaggregated occupation;
Female coefficient (standard error): 1988: -.073; (.001);
Female coefficient (standard error): 1998: -.056; (.001);
Female coefficient (standard error): 2007: -.048; (.001).
Specification: In addition to PATCOB, we included the percent female in
the occupation;
Female coefficient (standard error): 1988: -.070; (.001);
Female coefficient (standard error): 1998: -.054; (.001);
Female coefficient (standard error): 2007: -.049; (.001).
Specification: The addition of geographic location by county[A];
Female coefficient (standard error): 1988: -.109; (.001);
Female coefficient (standard error): 1998: -.088; (.001);
Female coefficient (standard error): 2007: -.081; (.001).
Specification: The addition of educational major to the model;
Female coefficient (standard error): 1988: [Empty];
Female coefficient (standard error): 1998: [Empty];
Female coefficient (standard error): 2007: -.076; (.001).
Specification: The addition of educational major to the model, with job
family level;
Female coefficient (standard error): 1988: [Empty];
Female coefficient (standard error): 1998: [Empty];
Female coefficient (standard error): 2007: -.066; (.001).
Specification: The addition of educational major to the model, with
grouped blue-collar;
Female coefficient (standard error): 1988: [Empty];
Female coefficient (standard error): 1998: [Empty];
Female coefficient (standard error): 2007: -.053; (.001).
Specification: Excluding agency and occupation;
Female coefficient (standard error): 1988: -.190; (.001);
Female coefficient (standard error): 1998: -.134; (.001);
Female coefficient (standard error): 2007: -.113; (.001).
Specification: Excluding agency and occupation, but major was added;
Female coefficient (standard error): 1988: [Empty];
Female coefficient (standard error): 1998: [Empty];
Female coefficient (standard error): 2007: -.105; (.001).
Specification: Only age, federal experience, and degree;
Female coefficient (standard error): 1988: -.175; (.001);
Female coefficient (standard error): 1998: -.116; (.001);
Female coefficient (standard error): 2007: -.108; (.001).
Specification: Only federal experience, PATCOB, and degree;
Female coefficient (standard error): 1988: -.112; (.001);
Female coefficient (standard error): 1998: -.085; (.001);
Female coefficient (standard error): 2007: -.089; (.001).
Specification: Health plan was added to the model;
Female coefficient (standard error): 1988: -.102; (.001);
Female coefficient (standard error): 1998: -.082; (.001);
Female coefficient (standard error): 2007: -.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.
[End of table]
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:
Experience: Less than 1 year;
1988: Estimate: -.037;
1988 Standard error: .003;
1998: Estimate: -.051;
1998 Standard error: .004;
2007: Estimate: -.043;
2007 Standard error: .004.
Race/Ethnicity: African-American;
1988: Estimate: -.058;
1988 Standard error: .002;
1998: Estimate: -.061;
1998 Standard error: .002;
2007: Estimate: -.066;
2007 Standard error: .003.
Race/Ethnicity: Asian Pacific Islander;
1988: Estimate: -.110;
1988 Standard error: .004;
1998: Estimate: -.081;
1998 Standard error: .040;
2007: Estimate: -.076;
2007 Standard error: .004.
Race/Ethnicity: Hispanic;
1988: Estimate: -.087;
1988 Standard error: .004;
1998: Estimate: -.068;
1998 Standard error: .003;
2007: Estimate: -.065;
2007 Standard error: .003.
Race/Ethnicity: Native American;
1988: Estimate: -.080;
1988 Standard error: .006;
1998: Estimate: -.095;
1998 Standard error: .006;
2007: Estimate: -.092;
2007 Standard error: .006.
Race/Ethnicity: White;
1988: Estimate: -.121;
1988 Standard error: .001;
1998: Estimate: -.097;
1998 Standard error: .001;
2007: Estimate: -.090;
2007 Standard error: .001.
Occupation (PATCOB): Administrative;
1988: Estimate: -.128;
1988 Standard error: .002;
1998: Estimate: -.103;
1998 Standard error: .002;
2007: Estimate: -.096;
2007 Standard error: .001.
Occupation (PATCOB): Blue-collar;
1988: Estimate: -.102;
1988 Standard error: .002;
1998: Estimate: -.106;
1998 Standard error: .003;
2007: Estimate: -.096;
2007 Standard error: .004.
Occupation (PATCOB): Clerical;
1988: Estimate: .015;
1988 Standard error: .001;
1998: Estimate: .021;
1998 Standard error: .002;
2007: Estimate: .017;
2007 Standard error: .002.
Occupation (PATCOB): Other white-collar;
1988: Estimate: -.019;
1988 Standard error: .008;
1998: Estimate: -.016;
1998 Standard error: .005;
2007: Estimate: -.009;
2007 Standard error: .004.
Occupation (PATCOB): Professional;
1988: Estimate: -.099;
1988 Standard error: .002;
1998: Estimate: -.070;
1998 Standard error: .002;
2007: Estimate: -.074;
2007 Standard error: .002.
Occupation (PATCOB): Technical;
1988: Estimate: -.127;
1988 Standard error: .002;
1998: Estimate: -.116;
1998 Standard error: .002;
2007: Estimate: -.102;
2007 Standard error: .002.
Source: GAO analysis of CPDF data. In addition to the subgroups
described above, we also performed subgroup analysis on the larger
agencies.
[End of table]
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."[Footnote 26] 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. 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):
equals "Unexplained" or due to parameter differences between women and
men;
plus:
(Average Female Wages) - (Male returns with female characteristics):
(Male returns with female characteristics) - (Male Average Wages);
equals "Explained" or due to characteristics difference between men and
women;
equals;
(Average Female Wages) - (Male returns with female characteristics):
(Average Female Wages) - (Average Male Wages); equals: Total.
[End of table]
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.
* 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 appendix
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):
Total;
1988 Total gap: -.327;
1988 Unexplained gap: -.078;
1988 Explained gap: -.249;
1998 Total gap: -.211;
1998 Unexplained gap: -.081;
1998 Explained gap: -.13;
2007 Total gap: -.121;
2007 Unexplained gap: -.075;
2007 Explained gap: -.045.
Detailed factors: Intercept;
1988 Total gap: -.106;
1988 Unexplained gap: -.106;
1988 Explained gap: 0;
1998 Total gap: .0622;
1998 Unexplained gap: .0622;
1998 Explained gap: 0;
2007 Total gap: -.04;
2007 Unexplained gap: -.04;
2007 Explained gap: 0.
Detailed factors: Age;
1988 Total gap: -.162;
1988 Unexplained gap: -.143;
1988 Explained gap: -.019;
1998 Total gap: -.236;
1998 Unexplained gap: -.227;
1998 Explained gap: -.009;
2007 Total gap: -.096;
2007 Unexplained gap: -.096;
2007 Explained gap: 4E-7[A].
Detailed factors: Federal experience;
1988 Total gap: -.06;
1988 Unexplained gap: -.006;
1988 Explained gap: -.054;
1998 Total gap: -.001;
1998 Unexplained gap: .024;
1998 Explained gap: -.022;
2007 Total gap: .015;
2007 Unexplained gap: .014;
2007 Explained gap: .001.
Detailed factors: Race/ethnicity;
1988 Total gap: .004;
1988 Unexplained gap: .016;
1988 Explained gap: -.012;
1998 Total gap: -3E-6;
1998 Unexplained gap: .011;
1998 Explained gap: -.011;
2007 Total gap: -.002;
2007 Unexplained gap: .009;
2007 Explained gap: -.011.
Detailed factors: Education;
1988 Total gap: -.034;
1988 Unexplained gap: -.01;
1988 Explained gap: -.024;
1998 Total gap: -.039;
1998 Unexplained gap: -.016;
1998 Explained gap: -.023;
2007 Total gap: -.013;
2007 Unexplained gap: -.005;
2007 Explained gap: -.008.
Detailed factors: Occupation;
1988 Total gap: -.06;
1988 Unexplained gap: .085;
1988 Explained gap: -.145;
1998 Total gap: -.01;
1998 Unexplained gap: .060;
1998 Explained gap: -.071;
2007 Total gap: .0168;
2007 Unexplained gap: .0456;
2007 Explained gap: -.029.
Detailed factors: Work schedule;
1988 Total gap: .024;
1988 Unexplained gap: .027;
1988 Explained gap: -.003;
1998 Total gap: .0258;
1998 Unexplained gap: .029;
1998 Explained gap: -.004;
2007 Total gap: -.024;
2007 Unexplained gap: -.021;
2007 Explained gap: -.003.
Detailed factors: Disability status;
1988 Total gap: -.015;
1988 Unexplained gap: -.015;
1988 Explained gap: .001;
1998 Total gap: -.041;
1998 Unexplained gap: -.042;
1998 Explained gap: .001;
2007 Total gap: -.032;
2007 Unexplained gap: -.032;
2007 Explained gap: .0003.
Detailed factors: State;
1988 Total gap: .0129;
1988 Unexplained gap: .0106;
1988 Explained gap: .002;
1998 Total gap: -.017;
1998 Unexplained gap: -.019;
1998 Explained gap: .002;
2007 Total gap: .0332;
2007 Unexplained gap: .030;
2007 Explained gap: .0031.
Detailed factors: Veteran status;
1988 Total gap: .028;
1988 Unexplained gap: .002;
1988 Explained gap: .027;
1998 Total gap: .011;
1998 Unexplained gap: -.015;
1998 Explained gap: .026;
2007 Total gap: -.008;
2007 Unexplained gap: -.028;
2007 Explained gap: .019.
Detailed factors: Bargaining unit;
1988 Total gap: .029;
1988 Unexplained gap: .040;
1988 Explained gap: -.012;
1998 Total gap: .028;
1998 Unexplained gap: .035;
1998 Explained gap: -.007;
2007 Total gap: .019;
2007 Unexplained gap: .024;
2007 Explained gap: -.005.
Detailed factors: Agency;
1988 Total gap: .013;
1988 Unexplained gap: .023;
1988 Explained gap: -.011;
1998 Total gap: .005;
1998 Unexplained gap: .018;
1998 Explained gap: -.013;
2007 Total gap: .01;
2007 Unexplained gap: .023;
2007 Explained gap: -.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.
[End of table]
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: Main: 1988;
Total gap: -.327;
Unexplained gap: -.078;
Explained gap: -.249;
Percentage explained: .761.
Specification: Main: 1998;
Total gap: -.211;
Unexplained gap: -.081;
Explained gap: -.130;
Percentage explained: .616.
Specification: Main: 2007;
Total gap: -.121;
Unexplained gap: -075;
Explained gap: -.045;
Percentage explained: .372.
Specification: Job family level: 1988;
Total gap: -.327;
Unexplained gap: -.064;
Explained gap: -.263;
Percentage explained: .803.
Specification: Job family level: 1998;
Total gap: -.211;
Unexplained gap: -.066;
Explained gap: -.145;
Percentage explained: .688.
Specification: Job family level: 2007;
Total gap: -.121;
Unexplained gap: -.067;
Explained gap: -.053;
Percentage explained: .443.
Specification: Disaggregated occupation, but with grouped blue-collar:
1988;
Total gap: -.326;
Unexplained gap: -.053;
Explained gap: -.273;
Percentage explained: .836.
Specification: Disaggregated occupation, but with grouped blue-collar:
1998;
Total gap: -.211;
Unexplained gap: -.054;
Explained gap: -.157;
Percentage explained: .745.
Specification: Disaggregated occupation, but with grouped blue-collar:
2007;
Total gap: -.120;
Unexplained gap: -.048;
Explained gap: -.072;
Percentage explained: .601.
Specification: Most disaggregated occupation-job series: 1988;
Total gap: -.328;
Unexplained gap: -.047;
Explained gap: -.281;
Percentage explained: .857.
Specification: Most disaggregated occupation-job series: 1998;
Total gap: -.211;
Unexplained gap: -.050;
Explained gap: -.162;
Percentage explained: .764.
Specification: Most disaggregated occupation-job series: 2007;
Total gap: -.120;
Unexplained gap: -.046;
Explained gap: -.076;
Percentage explained: .622.
Specification: In addition to PATCOB, included the percent female in
the occupation: 1988;
Total gap: -.327;
Unexplained gap: -.022;
Explained gap: -.305;
Percentage explained: .934.
Specification: In addition to PATCOB, included the percent female in
the occupation: 1998;
Total gap: -.211;
Unexplained gap: -.038;
Explained gap: -.173;
Percentage explained: .818.
Specification: In addition to PATCOB, included the percent female in
the occupation: 2007;
Total gap: -.120;
Unexplained gap: -.035;
Explained gap: -.085;
Percentage explained: .705.
Specification: Geography measured by county: 1988;
Total gap: -.327;
Unexplained gap: -.080;
Explained gap: -.247;
Percentage explained: .756.
Specification: Geography measured by county: 1998;
Total gap: -.211;
Unexplained gap: -.081;
Explained gap: -.130;
Percentage explained: .616.
Specification: Geography measured by county: 2007;
Total gap: -.121;
Unexplained gap: -.074;
Explained gap: -.047;
Percentage explained: .390.
Specification: The addition of educational major to the model: 2007;
Total gap: -.121;
Unexplained gap: -.069;
Explained gap: -052;
Percentage explained: .428.
Specification: The addition of educational major to the model, with job
family level: 2007;
Total gap: -.121;
Unexplained gap: -.060;
Explained gap: -.061;
Percentage explained: .504.
Specification: The addition of educational major to the model, with
grouped blue-collar: 2007;
Total gap: -.121;
Unexplained gap: -.046;
Explained gap: -.074;
Percentage explained: .615.
Specification: The addition of educational major to the model, with the
most disaggregated occupation: 2007;
Total gap: -.122;
Unexplained gap: -.045;
Explained gap: -.077;
Percentage explained: .634.
Specification: Excluding agency and occupation: 1988;
Total gap: -.327;
Unexplained gap: -.195;
Explained gap: -.131;
Percentage explained: .403.
Specification: Excluding agency and occupation: 1998;
Total gap: -.211;
Unexplained gap: -.141;
Explained gap: -.070;
Percentage explained: .332.
Specification: Excluding agency and occupation: 2007;
Total gap: -.120;
Unexplained gap: -.112;
Explained gap: -.008;
Percentage explained: .070.
Specification: Excluding agency and occupation, but major was added:
2007;
Total gap: -.120;
Unexplained gap: -.099;
Explained gap: -.021;
Percentage explained: .175.
Specification: Only age, federal experience, and degree: 1988;
Total gap: -.327;
Unexplained gap: -.174;
Explained gap: -.152;
Percentage explained: .466.
Specification: Only age, federal experience, and degree: 1998;
Total gap: -.211;
Unexplained gap: -.118;
Explained gap: -.093;
Percentage explained: .440.
Specification: Only age, federal experience, and degree: 2007;
Total gap: -.120;
Unexplained gap: -.107;
Explained gap: -.013;
Percentage explained: .112.
Specification: Only federal experience, PATCOB, and degree: 1988;
Total gap: -.327;
Unexplained gap: -.065;
Explained gap: -.262;
Percentage explained: .801.
Specification: Only federal experience, PATCOB, and degree: 1998;
Total gap: -.211;
Unexplained gap: -.067;
Explained gap: -.144;
Percentage explained: .681.
Specification: Only federal experience, PATCOB, and degree: 2007;
Total gap: -.120;
Unexplained gap: -.084;
Explained gap: -.036;
Percentage explained: .303.
Specification: Health plan was added to the model: 1988;
Total gap: -.328;
Unexplained gap: -.077;
Explained gap: -.251;
Percentage explained: .766.
Specification: Health plan was added to the model: 1998;
Total gap: -.211;
Unexplained gap: -.076;
Explained gap: -.135;
Percentage explained: .638.
Specification: Health plan was added to the model: 2007;
Total gap: -.121;
Unexplained gap: -.069;
Explained gap: -.051;
Percentage explained: .428.
Source: GAO analysis of CPDF data.
[End of table]
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 African-
American 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.
Table 8: Estimated Total, Unexplained, and Explained Pay Gaps for
Different Subgroups (using main specification of the model):
Experience: Less than 1 year;
1988: Total gap: -.208;
1988: Unexplained gap: -.025;
1988: Explained gap: -.182;
1998: Total gap: -.193;
1998: Unexplained gap: -.048;
1998: Explained gap: -.145;
2007: Total gap: -.109;
2007: Unexplained gap: -.037;
2007: Explained gap: -.072.
Race/ethnicity: African-American;
1988: Total gap: -.163;
1988: Unexplained gap: -.035;
1988: Explained gap: -.128;
1998: Total gap: -.071;
1998: Unexplained gap: -.050;
1998: Explained gap: -.021;
2007: Total gap: -.002;
2007: Unexplained gap: -.057;
2007: Explained gap: .055.
Race/ethnicity: Asian Pacific;
1988: Total gap: -.286;
1988: Unexplained gap: -.089;
1988: Explained gap: -.197;
1998: Total gap: -.193;
1998: Unexplained gap: -.088;
1998: Explained gap: -.106;
2007: Total gap: -.109;
2007: Unexplained gap: -.076;
2007: Explained gap: -.033.
Race/ethnicity: Hispanic;
1988: Total gap: -.241;
1988: Unexplained gap: -.063;
1988: Explained gap: -.180;
1998: Total gap: -.149;
1998: Unexplained gap: -.065;
1998: Explained gap: -.083;
2007: Total gap: -.080;
2007: Unexplained gap: -.061;
2007: Explained gap: -.019.
Race/ethnicity: Native American;
1988: Total gap: -.240;
1988: Unexplained gap: -.056;
1988: Explained gap: -.183;
1998: Total gap: -.180;
1998: Unexplained gap: -.067;
1998: Explained gap: -.113;
2007: Total gap: -.130;
2007: Unexplained gap: -.070;
2007: Explained gap: -.059.
Race/ethnicity: White;
1988: Total gap: -.345;
1988: Unexplained gap: -.092;
1988: Explained gap: -.252;
1998: Total gap: -.220;
1998: Unexplained gap: -.094;
1998: Explained gap: -.126;
2007: Total gap: -.127;
2007: Unexplained gap: -.086;
2007: Explained gap: -.041.
Occupation (PATCOB): Administrative;
1988: Total gap: -.197;
1988: Unexplained gap: -.131;
1988: Explained gap: .066;
1998: Total gap: -.118;
1998: Unexplained gap: -.110;
1998: Explained gap: -.008;
2007: Total gap: -.075;
2007: Unexplained gap: -.100;
2007: Explained gap: .025.
Occupation (PATCOB): Blue-collar;
1988: Total gap: -.210;
1988: Unexplained gap: -.099;
1988: Explained gap: -.111;
1998: Total gap: -.250;
1998: Unexplained gap: -.108;
1998: Explained gap: -.142;
2007: Total gap: -.238;
2007: Unexplained gap: -.097;
2007: Explained gap: -.140.
Occupation (PATCOB): Clerical;
1988: Total gap: .057;
1988: Unexplained gap: .023;
1988: Explained gap: .034;
1998: Total gap: .092;
1998: Unexplained gap: .036;
1998: Explained gap: .056;
2007: Total gap: .082;
2007: Unexplained gap: .029;
2007: Explained gap: .054.
Occupation (PATCOB): Professional;
1988: Total gap: -.273;
1988: Unexplained gap: -.092;
1988: Explained gap: -.180;
1998: Total gap: -.197;
1998: Unexplained gap: -.069;
1998: Explained gap: -.128;
2007: Total gap: -.162;
2007: Unexplained gap: -.073;
2007: Explained gap: -.089.
Occupation (PATCOB): Technical[A];
1988: Total gap: -.193;
1988: Unexplained gap: -.115;
1988: Explained gap: -.078;
1998: Total gap: -.121;
1998: Unexplained gap: -.103;
1998: Explained gap: -.018;
2007: Total gap: -.091;
2007: Unexplained gap: -.090;
2007: Explained gap: -.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.
[End of table]
[End of section]
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.
Data used in Cohort Analysis and Descriptive Statistics:
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 appendix II for a description of the CPDF
data.) Specifically, we selected a cohort[Footnote 27] of 89,532
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.
Table 9: Number of Federal Employees from the 1988 Entry Cohort
Remaining over 2 Decades in the Status and Dynamic Files:
Fiscal year: 1988;
Male: 38,687;
Female: 50,669;
Total: 89,356.
Fiscal year: 1989;
Male: 32,996;
Female: 41,812;
Total: 74,808.
Fiscal year: 1990;
Male: 28,351;
Female: 34,212;
Total: 62,563.
Fiscal year: 1991;
Male: 25,541;
Female: 29,973;
Total: 55,514.
Fiscal year: 1992;
Male: 23,483;
Female: 27,133;
Total: 50,616.
Fiscal year: 1993;
Male: 22,149;
Female: 25,389;
Total: 47,538.
Fiscal year: 1994;
Male: 21,013;
Female: 23,995;
Total: 45,008.
Fiscal year: 1995;
Male: 19,928;
Female: 22,790;
Total: 42,718.
Fiscal year: 1996;
Male: 19,052;
Female: 21,649;
Total: 40,701.
Fiscal year: 1997;
Male: 18,195;
Female: 20,597;
Total: 38,792.
Fiscal year: 1998;
Male: 17,485;
Female: 19,586;
Total: 37,071.
Fiscal year: 1999;
Male: 16,816;
Female: 18,774;
Total: 35,590.
Fiscal year: 2000;
Male: 16,329;
Female: 18,088;
Total: 34,417.
Fiscal year: 2001;
Male: 15,872;
Female: 17,571;
Total: 33,443.
Fiscal year: 2002;
Male: 15,558;
Female: 17,074;
Total: 32,632.
Fiscal year: 2003;
Male: 15,247;
Female: 16,715;
Total: 31,962.
Fiscal year: 2004;
Male: 14,957;
Female: 16,374;
Total: 31,331.
Fiscal year: 2005;
Male: 14,612;
Female: 15,987;
Total: 30,599.
Fiscal year: 2006;
Male: 14,233;
Female: 15,605;
Total: 29,838.
Fiscal year: 2007;
Male: 13,828;
Female: 15,181;
Total: 29,009.
Source: GAO analysis of CPDF data.
Note: In our data, unpaid leave was indicated with a Leave-Without-Pay
personnel action.
[End of table]
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.[Footnote 28]
* 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.[Footnote 29]
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.[Footnote 30]
* 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 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:
[Refer to PDF for image: stacked vertical bar graph]
Year: 1988;
Administrative: 10,323;
Blue Collar: 5,641;
Clerical: 36,644;
Other White Collar: 5,329;
Professional: 21,726;
Technical: 9,599.
Year: 1989;
Administrative: 9,354;
Blue Collar: 4,535;
Clerical: 28,016;
Other White Collar: 5,044;
Professional: 19,514;
Technical: 8,329.
Year: 1990;
Administrative: 9,086;
Blue Collar: 4,183;
Clerical: 20,476;
Other White Collar: 3,415;
Professional: 17,250;
Technical: 8,141.
Year: 1991;
Administrative: 9,028;
Blue Collar: 3,692;
Clerical: 16,152;
Other White Collar: 2,262;
Professional: 15,974;
Technical: 8,396.
Year: 1992;
Administrative: 9,374;
Blue Collar: 3,325;
Clerical: 13,166;
Other White Collar: 1,673;
Professional: 14,465;
Technical: 8,601.
Year: 1993;
Administrative: 9,539;
Blue Collar: 3,100;
Clerical: 11,062;
Other White Collar: 1,371;
Professional: 13,448;
Technical: 9,013.
Year: 1994;
Administrative: 9,665;
Blue Collar: 2,882;
Clerical: 9,730;
Other White Collar: 1,236;
Professional: 12,638;
Technical: 8,845.
Year: 1995;
Administrative: 9,732;
Blue Collar: 2,664;
Clerical: 8,519;
Other White Collar: 1,162;
Professional: 12,044;
Technical: 8,595.
Year: 1996;
Administrative: 9,923;
Blue Collar: 2,427;
Clerical: 7,383;
Other White Collar: 1,091;
Professional: 11,411;
Technical: 8,462.
Year: 1997;
Administrative: 9,855;
Blue Collar: 2,257;
Clerical: 6,488;
Other White Collar: 1,033;
Professional: 10,831;
Technical: 8,325.
Year: 1998;
Administrative: 9,946;
Blue Collar: 2,129;
Clerical: 5,711;
Other White Collar: 992;
Professional: 10,300;
Technical: 7,991.
Year: 1999;
Administrative: 10,188;
Blue Collar: 1,989;
Clerical: 5,018;
Other White Collar: 937;
Professional: 9,864;
Technical: 7,587.
Year: 2000;
Administrative: 10,381;
Blue Collar: 1,885;
Clerical: 4,388;
Other White Collar: 903;
Professional: 9,537;
Technical: 7,315.
Year: 2001;
Administrative: 10,726;
Blue Collar: 1,746;
Clerical: 3,856;
Other White Collar: 868;
Professional: 9,293;
Technical: 6,948.
Year: 2002;
Administrative: 11,189;
Blue Collar: 1,633;
Clerical: 3,256;
Other White Collar: 851;
Professional: 9,028;
Technical: 6,614.
Year: 2003;
Administrative: 11,464;
Blue Collar: 1,510;
Clerical: 2,868;
Other White Collar: 826;
Professional: 8,823;
Technical: 6,423.
Year: 2004;
Administrative: 11,616;
Blue Collar: 1,421;
Clerical: 2,603;
Other White Collar: 803;
Professional: 8,727;
Technical: 6,156.
Year: 2005;
Administrative: 11,655;
Blue Collar: 1,352;
Clerical: 2,305;
Other White Collar: 780;
Professional: 8,630;
Technical: 5,875.
Year: 2006;
Administrative: 11,655;
Blue Collar: 1,269;
Clerical: 2,082;
Other White Collar: 773;
Professional: 8,490;
Technical: 5,556.
Year: 2007;
Administrative: 11,651;
Blue Collar: 1,200;
Clerical: 1,881;
Other White Collar: 754;
Professional: 8,306;
Technical: 5,214.
Source: GAO analysis of CPDF data.
[End of figure]
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.
Table 10: Cohort Differences between Men and Women in Occupational
Categories in 1988 and 2007:
Occupational Category (PATCOB): Professional;
1988: Men: 34%;
1988: Women: 17%;
1988: Difference between men and women: 17%;
2007: Men: 35%;
2007: Women: 23%;
2007: Difference between men and women: 12%.
Occupational Category (PATCOB): Administrative;
1988: Men: 18%;
1988: Women: 7%;
1988: Difference between men and women: 11%;
2007: Men: 42%;
2007: Women: 38%;
2007: Difference between men and women: 4%.
Occupational Category (PATCOB): Technical;
1988: Men: 10%;
1988: Women: 11%;
1988: Difference between men and women: -1%;
2007: Men: 9%;
2007: Women: 26%;
2007: Difference between men and women: -17%.
Occupational Category (PATCOB): Clerical;
1988: Men: 18%;
1988: Women: 59%;
1988: Difference between men and women: -41%;
2007: Men: 2%;
2007: Women: 11%;
2007: Difference between men and women: -9%.
Occupational Category (PATCOB): Other white-collar;
1988: Men: 10%;
1988: Women: 3%;
1988: Difference between men and women: 7%;
2007: Men: 5%;
2007: Women: 0%;
2007: Difference between men and women: 5%.
Occupational Category (PATCOB): Blue-collar;
1988: Men: 10%;
1988: Women: 4%;
1988: Difference between men and women: 6%;
2007: Men: 7%;
2007: Women: 1%;
2007: Difference between men and women: 6%.
Source: GAO analysis of CPDF data.
Note: Figures may not sum to 100 percent due to rounding.
[End of table]
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.[Footnote 31] 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.
Table 11: Cohort Differences between Men and Women in Education Levels
in 1988 and 2007:
Education level: Less than high school;
1988: Men: 1%;
1988: Women: 3%;
1988: Difference between men and women: -2%;
2007: Men: 0%;
2007: Women: 1%;
2007: Difference between men and women: -1%.
Education level: High school graduate;
1988: Men: 21%;
1988: Women: 40%;
1988: Difference between men and women: -19%;
2007: Men: 18%;
2007: Women: 32%;
2007: Difference between men and women: -14%.
Education level: Some college;
1988: Men: 24%;
1988: Women: 28%;
1988: Difference between men and women: -4%;
2007: Men: 20%;
2007: Women: 26%;
2007: Difference between men and women: -6%.
Education level: Bachelor's degree;
1988: Men: 30%;
1988: Women: 21%;
1988: Difference between men and women: 9%;
2007: Men: 42%;
2007: Women: 28%;
2007: Difference between men and women: 14%.
Education level: Master's degree;
1988: Men: 6%;
1988: Women: 4%;
1988: Difference between men and women: 2%;
2007: Men: 12%;
2007: Women: 9%;
2007: Difference between men and women: 3%.
Education level: Doctorate degree;
1988: Men: 2%;
1988: Women: 1%;
1988: Difference between men and women: 1%;
2007: Men: 4%;
2007: Women: 1%;
2007: Difference between men and women: 3%.
Education level: Professional degree;
1988: Men: 15%;
1988: Women: 4%;
1988: Difference between men and women: 11%;
2007: Men: 5%;
2007: Women: 3%;
2007: Difference between men and women: 2%.
Education level: Bachelor's degree or higher;
1988: Men: 53%;
1988: Women: 30%;
1988: Difference between men and women: 23%;
2007: Men: 63%;
2007: Women: 41%;
2007: Difference between men and women: 22%.
Source: GAO analysis of CPDF data.
Note: Figures may not sum to 100 percent due to rounding.
[End of table]
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:
Number of observations;
1988: Men: 38,687;
1988: Women: 50,669;
2007: Men: 13,828;
2007: Women: 15,181.
Annual salary;
1988: Men: 37,320;
1988: Women: 28,154;
2007: Men: 89,364;
2007: Women: 68,468.
Age;
1988: Men: 31;
1988: Women: 30;
2007: Men: 48;
2007: Women: 48.
Race/ethnicity: Nonhispanic White;
1988: Men: 75%;
1988: Women: 62%;
2007: Men: 77%;
2007: Women: 59%.
Race/ethnicity: Nonhispanic African-American;
1988: Men: 11%;
1988: Women: 26%;
2007: Men: 9%;
2007: Women: 28%.
Race/ethnicity: Native American;
1988: Men: 1%;
1988: Women: 2%;
2007: Men: 1%;
2007: Women: 2%.
Race/ethnicity: Asian/Pacific Islander;
1988: Men: 6%;
1988: Women: 4%;
2007: Men: 6%;
2007: Women: 5%.
Race/ethnicity: Hispanic;
1988: Men: 6%;
1988: Women: 6%;
2007: Men: 7%;
2007: Women: 7%.
Race/ethnicity: Other Race/Ethnicity;
1988: Men: 1%;
1988: Women: 1%;
2007: Men: 0%;
2007: Women: 0%.
Large agency;
1988: Men: 74%;
1988: Women: 76%;
2007: Men: 75%;
2007: Women: 75%.
Region: Northeast;
1988: Men: 19%;
1988: Women: 20%;
2007: Men: 13%;
2007: Women: 15%.
Region: South;
1988: Men: 42%;
1988: Women: 40%;
2007: Men: 49%;
2007: Women: 50%.
Region: Midwest;
1988: Men: 16%;
1988: Women: 14%;
2007: Men: 15%;
2007: Women: 15%.
Region: West;
1988: Men: 21%;
1988: Women: 18%;
2007: Men: 21%;
2007: Women: 18%.
Region: Other;
1988: Men: 2%;
1988: Women: 7%;
2007: Men: 2%;
2007: Women: 2%.
Bargaining Unit Status;
1988: Men: 63%;
1988: Women: 72%;
2007: Men: 52%;
2007: Women: 63%.
Veteran Status;
1988: Men: 15%;
1988: Women: 1%;
2007: Men: 19%;
2007: Women: 3%.
Disability Status;
1988: Men: 1%;
1988: Women: 1%;
2007: Men: 1%;
2007: Women: 1%.
Part-Time Work Schedule;
1988: Men: 11%;
1988: Women: 12%;
2007: Men: 1%;
2007: Women: 4%.
Ever Used Unpaid Leave;
1988: Men: 4%;
1988: Women: 3%;
2007: Men: 11%;
2007: Women: 21%.
Source: GAO analysis of CPDF data.
[End of table]
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 appendix 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 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 appendix 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.[Footnote 32] 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: 1988;
Female coefficient[A](Before Controlling For Differences Between men
and women in measurable factors): -0.25;
Female coefficient (After controlling for differences between men and
women in measurable factors): -0.04.
Fiscal Year: 1989;
Female coefficient[A](Before Controlling For Differences Between men
and women in measurable factors): -0.27;
Female coefficient (After controlling for differences between men and
women in measurable factors): -0.05.
Fiscal Year: 1990;
Female coefficient[A](Before Controlling For Differences Between men
and women in measurable factors): -0.29;
Female coefficient (After controlling for differences between men and
women in measurable factors): -0.05.
Fiscal Year: 1991;
Female coefficient[A](Before Controlling For Differences Between men
and women in measurable factors): -0.30;
Female coefficient (After controlling for differences between men and
women in measurable factors): -0.06.
Fiscal Year: 1992;
Female coefficient[A](Before Controlling For Differences Between men
and women in measurable factors): -0.32;
Female coefficient (After controlling for differences between men and
women in measurable factors): -0.07.
Fiscal Year: 1993;
Female coefficient[A](Before Controlling For Differences Between men
and women in measurable factors): -0.32;
Female coefficient (After controlling for differences between men and
women in measurable factors): -0.08.
Fiscal Year: 1994;
Female coefficient[A](Before Controlling For Differences Between men
and women in measurable factors): -0.33;
Female coefficient (After controlling for differences between men and
women in measurable factors): -0.08.
Fiscal Year: 1995;
Female coefficient[A](Before Controlling For Differences Between men
and women in measurable factors): -0.32;
Female coefficient (After controlling for differences between men and
women in measurable factors): -0.09.
Fiscal Year: 1996;
Female coefficient[A](Before Controlling For Differences Between men
and women in measurable factors): -0.32;
Female coefficient (After controlling for differences between men and
women in measurable factors): -0.09.
Fiscal Year: 1997;
Female coefficient[A](Before Controlling For Differences Between men
and women in measurable factors): -0.32;
Female coefficient (After controlling for differences between men and
women in measurable factors): -0.09.
Fiscal Year: 1998;
Female coefficient[A](Before Controlling For Differences Between men
and women in measurable factors): -0.31;
Female coefficient (After controlling for differences between men and
women in measurable factors): -0.09.
Fiscal Year: 1999;
Female coefficient[A](Before Controlling For Differences Between men
and women in measurable factors): -0.31;
Female coefficient (After controlling for differences between men and
women in measurable factors): -0.10.
Fiscal Year: 2000;
Female coefficient[A](Before Controlling For Differences Between men
and women in measurable factors): -0.31;
Female coefficient (After controlling for differences between men and
women in measurable factors): -0.11.
Fiscal Year: 2001;
Female coefficient[A](Before Controlling For Differences Between men
and women in measurable factors): -0.31;
Female coefficient (After controlling for differences between men and
women in measurable factors): -0.11.
Fiscal Year: 2002;
Female coefficient[A](Before Controlling For Differences Between men
and women in measurable factors): -0.30;
Female coefficient (After controlling for differences between men and
women in measurable factors): -0.11.
Fiscal Year: 2003;
Female coefficient[A](Before Controlling For Differences Between men
and women in measurable factors): -0.30;
Female coefficient (After controlling for differences between men and
women in measurable factors): -0.12.
Fiscal Year: 2004;
Female coefficient[A](Before Controlling For Differences Between men
and women in measurable factors): -0.29;
Female coefficient (After controlling for differences between men and
women in measurable factors): -0.12.
Fiscal Year: 2005;
Female coefficient[A](Before Controlling For Differences Between men
and women in measurable factors): -0.29;
Female coefficient (After controlling for differences between men and
women in measurable factors): -0.12.
Fiscal Year: 2006;
Female coefficient[A](Before Controlling For Differences Between men
and women in measurable factors): -0.28;
Female coefficient (After controlling for differences between men and
women in measurable factors): -0.11.
Fiscal Year: 2007;
Female coefficient[A](Before Controlling For Differences Between men
and women in measurable factors): -0.28;
Female coefficient (After controlling for differences between men and
women in measurable factors): -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.
[End of table]
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.[Footnote 33] 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.
* 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:
1988;
Total pay gap: -0.25;
Unexplained pay gap: -0.03;
Occupation: -0.18;
Education: -0.02;
All other characteristics: -0.02.
1989;
Total pay gap: -0.27;
Unexplained pay gap: -0.03;
Occupation: -0.20;
Education: -0.02;
All other characteristics: -0.02.
1990;
Total pay gap: -0.29;
Unexplained pay gap: -0.02;
Occupation: -0.22;
Education: -0.02;
All other characteristics: -0.03.
1991;
Total pay gap: -0.31;
Unexplained pay gap: -0.03;
Occupation: -0.22;
Education: -0.02;
All other characteristics: -0.03.
1992;
Total pay gap: -0.32;
Unexplained pay gap: -0.04;
Occupation: -0.23;
Education: -0.03;
All other characteristics: -0.03.
1993;
Total pay gap: -0.32;
Unexplained pay gap: -0.04;
Occupation: -0.23;
Education: -0.03;
All other characteristics: -0.03.
1994;
Total pay gap: -0.33;
Unexplained pay gap: -0.04;
Occupation: -0.23;
Education: -0.03;
All other characteristics: -0.03.
1995;
Total pay gap: -0.33;
Unexplained pay gap: -0.06;
Occupation: -0.22;
Education: -0.03;
All other characteristics: -0.03.
1996;
Total pay gap: -0.32;
Unexplained pay gap: -0.06;
Occupation: -0.21;
Education: -0.03;
All other characteristics: -0.02.
1997;
Total pay gap: -0.32;
Unexplained pay gap: -0.06;
Occupation: -0.21;
Education: -0.03;
All other characteristics: -0.02.
1998;
Total pay gap: -0.31;
Unexplained pay gap: -0.07;
Occupation: -0.20;
Education: -0.03;
All other characteristics: -0.03.
1999;
Total pay gap: -0.31;
Unexplained pay gap: -0.08;
Occupation: -0.19;
Education: -0.03;
All other characteristics: -0.03.
2000;
Total pay gap: -0.31;
Unexplained pay gap: -0.09;
Occupation: -0.18;
Education: -0.02;
All other characteristics: -0.03.
2001;
Total pay gap: -0.31;
Unexplained pay gap: -0.09;
Occupation: -0.17;
Education: -0.02;
All other characteristics: -0.03.
2002;
Total pay gap: -0.30;
Unexplained pay gap: -0.09;
Occupation: -0.16;
Education: -0.02;
All other characteristics: -0.03.
2003;
Total pay gap: -0.30;
Unexplained pay gap: -0.10;
Occupation: -0.15; Education: -0.03;
All other characteristics: -0.03.
2004;
Total pay gap: -0.29;
Unexplained pay gap: -0.10;
Occupation: -0.14; Education: -0.02;
All other characteristics: -0.03.
2005;
Total pay gap: -0.29;
Unexplained pay gap: -0.10;
Occupation: -0.13; Education: -0.02;
All other characteristics: -0.03.
2006;
Total pay gap: -0.28;
Unexplained pay gap: -0.10;
Occupation: -0.13; Education: -0.02;
All other characteristics: -0.03.
2007;
Total pay gap: -0.28;
Unexplained pay gap: -0.11;
Occupation: -0.12; Education: -0.02;
All other characteristics: -0.03.
Source: GAO analysis of CPDF data.
[End of table]
Exploring the Use and Cost of Unpaid Leave for Men and Women:
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
canceled 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.[Footnote 34] 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.[Footnote 35] Because using
unpaid leave had a larger negative effect on men's pay than women's
pay, it likely canceled out the effect of men's lower propensity to
take it.
Figure 2: Cost of Taking Unpaid Leave on Pay for Men and Women:
Refer to PDF for image: line graph]
Year: 1988;
Men: 9.1 cents;
Women: 4.0 cents.
Year: 1989;
Men: 11.3 cents;
Women: 1.8 cents.
Year: 1990;
Men: 13.9 cents;
Women: 2.0 cents.
Year: 1991;
Men: 12.2 cents;
Women: 3.2 cents.
Year: 1992;
Men: 11.2 cents;
Women: 3.2 cents.
Year: 1993;
Men: 8.6 cents;
Women: 2.9 cents.
Year: 1994;
Men: 6.5 cents;
Women: 3.2 cents.
Year: 1995;
Men: 5.1 cents;
Women: 3.1 cents.
Year: 1996;
Men: 3.8 cents;
Women: 3.1 cents.
Year: 1997;
Men: 3.8 cents;
Women: 2.8 cents.
Year: 1998;
Men: 3.5 cents;
Women: 3.1 cents.
Year: 1999;
Men: 4.1 cents;
Women: 3.1 cents.
Year: 2000;
Men: 4.5 cents;
Women: 3.2 cents.
Year: 2001;
Men: 4.1 cents;
Women: 3.0 cents.
Year: 2002;
Men: 3.9 cents;
Women: 3.4 cents.
Year: 2003;
Men: 3.8 cents;
Women: 3.5 cents.
Year: 2004;
Men: 3.5 cents;
Women: 3.5 cents.
Year: 2005;
Men: 3.8 cents;
Women: 3.9 cents.
Year: 2006;
Men: 4.2 cents;
Women: 4.0 cents.
Year: 2007;
Men: 3.2 cents;
Women: 4.4 cents.
Source: GAO analysis of CPDF data.
[End of figure]
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:
1988;
Number of people who had a break in service: 330;
Average cumulative duration of break in months: [Empty].
Male; Number of people who had a break in service: 111;
Average cumulative duration of break in months: 2.02.
Female; Number of people who had a break in service: 219;
Average cumulative duration of break in months: 2.21.
2007;
Number of people who had a break in service: 427;
Average cumulative duration of break in months: [Empty].
Male; Number of people who had a break in service: 146;
Average cumulative duration of break in months: 86.4.
Female; Number of people who had a break in service: 281;
Average cumulative duration of break in months: 97.8.
Source: GAO analysis of CPDF data.
[End of table]
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.[Footnote 36]
[End of section]
Appendix V: Crosswalk between the 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.[Footnote 37]
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 (b) into a more precise estimate of the
percentage difference in the actual salaries caused by a one-unit
change in the explanatory variable:
(exp(b)-1):
where exp is the base of the natural logarithm (commonly known as e)
and b 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:
Example 1: 10% Difference in salaries:
Salary;
Person 1: $11,000;
Person 2: $10,000;
Percentage difference in $ salary: 10%.
Log salary;
Person 1: 9.31;
Person 2: 9.21;
Difference in log salary: 0.10;
Converted difference: 0.10.
Example 2: 50% Difference in salaries:
Salary;
Person 1: $15,000;
Person 2: $10,000;
Percentage difference in $ salary: 50%.
Log salary;
Person 1: 9.62;
Person 2: 9.21;
Difference in log salary: 0.41;
Converted difference: 0.50.
Source: GAO.
Note: Figures presented above may be rounded.
[End of table]
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.
Table 17: Crosswalk between Cross-sectional Estimates of the Total Pay
Gap:
Fiscal year: 1988;
Log gap (as presented in table 6 of appendix III, under "Total Gap"):
-0.327;
Converted log gap (as presented in slides 15 and 18 as the "Pay Gap"):
Converted log gap=exp(Log gap)-1: -0.279.
Fiscal year: 1998;
Log gap (as presented in table 6 of appendix III, under "Total Gap"):
-0.211;
Converted log gap (as presented in slides 15 and 18 as the "Pay Gap"):
Converted log gap=exp(Log gap)-1: -0.190.
Fiscal year: 2007;
Log gap (as presented in table 6 of appendix III, under "Total Gap"):
-0.121;
Converted log gap (as presented in slides 15 and 18 as the "Pay Gap"):
Converted log gap=exp(Log gap)-1: -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.
[End of table]
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:
Fiscal year: 1988;
Log gap (as presented in table 14 of appendix IV): -0.25;
Converted log gap (as presented in slides 28 and 29): Converted log
gap=exp(Log gap)-1: -0.22.
Fiscal year: 1989;
Log gap (as presented in table 14 of appendix IV): -0.27;
Converted log gap (as presented in slides 28 and 29): Converted log
gap=exp(Log gap)-1: -0.24.
Fiscal year: 1990;
Log gap (as presented in table 14 of appendix IV): -0.29;
Converted log gap (as presented in slides 28 and 29): Converted log
gap=exp(Log gap)-1: -0.25.
Fiscal year: 1991;
Log gap (as presented in table 14 of appendix IV): -0.31;
Converted log gap (as presented in slides 28 and 29): Converted log
gap=exp(Log gap)-1: -0.26.
Fiscal year: 1992;
Log gap (as presented in table 14 of appendix IV): -0.32;
Converted log gap (as presented in slides 28 and 29): Converted log
gap=exp(Log gap)-1: -0.27.
Fiscal year: 1993;
Log gap (as presented in table 14 of appendix IV): -0.32;
Converted log gap (as presented in slides 28 and 29): Converted log
gap=exp(Log gap)-1: -0.28.
Fiscal year: 1994;
Log gap (as presented in table 14 of appendix IV): -0.33;
Converted log gap (as presented in slides 28 and 29): Converted log
gap=exp(Log gap)-1: -0.28.
Fiscal year: 1995;
Log gap (as presented in table 14 of appendix IV): -0.33;
Converted log gap (as presented in slides 28 and 29): Converted log
gap=exp(Log gap)-1: -0.28.
Fiscal year: 1996;
Log gap (as presented in table 14 of appendix IV): -0.32;
Converted log gap (as presented in slides 28 and 29): Converted log
gap=exp(Log gap)-1: -0.28.
Fiscal year: 1997;
Log gap (as presented in table 14 of appendix IV): -0.32;
Converted log gap (as presented in slides 28 and 29): Converted log
gap=exp(Log gap)-1: -0.27.
Fiscal year: 1998;
Log gap (as presented in table 14 of appendix IV): -0.31;
Converted log gap (as presented in slides 28 and 29): Converted log
gap=exp(Log gap)-1: -0.27.
Fiscal year: 1999;
Log gap (as presented in table 14 of appendix IV): -0.31;
Converted log gap (as presented in slides 28 and 29): Converted log
gap=exp(Log gap)-1: -0.27.
Fiscal year: 2000; Log gap (as presented in table 14 of appendix IV): -
0.31;
Converted log gap (as presented in slides 28 and 29): Converted log
gap=exp(Log gap)-1: -0.27.
Fiscal year: 2001;
Log gap (as presented in table 14 of appendix IV): -0.31;
Converted log gap (as presented in slides 28 and 29): Converted log
gap=exp(Log gap)-1: -0.26.
Fiscal year:
2002; Log gap (as presented in table 14 of appendix IV): -0.30;
Converted log gap (as presented in slides 28 and 29): Converted log
gap=exp(Log gap)-1: -0.26.
Fiscal year: 2003;
Log gap (as presented in table 14 of appendix IV): -0.30;
Converted log gap (as presented in slides 28 and 29): Converted log
gap=exp(Log gap)-1: -0.26.
Fiscal year: 2004;
Log gap (as presented in table 14 of appendix IV): -0.29;
Converted log gap (as presented in slides 28 and 29): Converted log
gap=exp(Log gap)-1: -0.25.
Fiscal year: 2005;
Log gap (as presented in table 14 of appendix IV): -0.29;
Converted log gap (as presented in slides 28 and 29): Converted log
gap=exp(Log gap)-1: -0.25.
Fiscal year: 2006;
Log gap (as presented in table 14 of appendix IV): -0.28;
Converted log gap (as presented in slides 28 and 29): Converted log
gap=exp(Log gap)-1: -0.25.
Fiscal year: 2007;
Log gap (as presented in table 14 of appendix IV): -0.28;
Converted log gap (as presented in slides 28 and 29): Converted log
gap=exp(Log gap)-1: -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.
[End of table]
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.[Footnote 38] 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, appendix 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:
Unexplained gap:
1988;
Gap (as presented in table 6 of appendix III, under "Explained Gap"):
0.078; times;
Converted log gap Log gap: 0.279; 0.327; equals;
Converted gap (as presented in slides 18 and 19): 0.067.
1998;
Gap (as presented in table 6 of appendix III, under "Explained Gap"):
0.081; times;
Converted log gap Log gap: 0.190; 0.211; equals;
Converted gap (as presented in slides 18 and 19): 0.073.
2007;
Gap (as presented in table 6 of appendix III, under "Explained Gap"):
0.075; times;
Converted log gap Log gap: 0.114; 0.121; equals;
Converted gap (as presented in slides 18 and 19): 0.071.
Part of the Gap Resulting from Differences in Occupations:
1988;
Gap (as presented in table 6 of appendix III, under "Explained Gap"):
0.145; times;
Converted log gap Log gap: 0.279; 0.327; equals;
Converted gap (as presented in slides 18 and 19): 0.124.
1998;
Gap (as presented in table 6 of appendix III, under "Explained Gap"):
0.071; times;
Converted log gap Log gap: 0.190; 0.211; equals;
Converted gap (as presented in slides 18 and 19): 0.064.
2007;
Gap (as presented in table 6 of appendix III, under "Explained Gap"):
0.029; times;
Converted log gap Log gap: 0.114; 0.121; equals;
Converted gap (as presented in slides 18 and 19): 0.027.
Part of the Gap Resulting from Differences in Education Levels:
1988;
Gap (as presented in table 6 of appendix III, under "Explained Gap"):
0.024; times;
Converted log gap Log gap: 0.279; 0.327; equals;
Converted gap (as presented in slides 18 and 19): 0.020.
1998;
Gap (as presented in table 6 of appendix III, under "Explained Gap"):
0.023; times;
Converted log gap Log gap: 0.190; 0.211; equals;
Converted gap (as presented in slides 18 and 19): 0.021.
2007;
Gap (as presented in table 6 of appendix III, under "Explained Gap"):
0.008; times;
Converted log gap Log gap: 0.114; 0.121; equals;
Converted gap (as presented in slides 18 and 19): 0.008.
Part of the Gap Resulting from Differences in Experience Levels:
1988;
Gap (as presented in table 6 of appendix III, under "Explained Gap"):
0.054; times;
Converted log gap Log gap: 0.279; 0.327; equals;
Converted gap (as presented in slides 18 and 19): 0.046.
1998;
Gap (as presented in table 6 of appendix III, under "Explained Gap"):
0.022; times;
Converted log gap Log gap: 0.190; 0.211; equals;
Converted gap (as presented in slides 18 and 19): 0.020.
2007;
Gap (as presented in table 6 of appendix III, under "Explained Gap"):
0.000; times;
Converted log gap Log gap: 0.114; 0.121; equals;
Converted gap (as presented in slides 18 and 19): 0.000.
Part of the Gap Resulting from Differences in Other Characteristics:
1988;
Gap (as presented in table 6 of appendix III, under "Explained Gap"):
0.026; times;
Converted log gap Log gap: 0.279; 0.327; equals;
Converted gap (as presented in slides 18 and 19): 0.022.
1998;
Gap (as presented in table 6 of appendix III, under "Explained Gap"):
0.014; times;
Converted log gap Log gap: 0.190; 0.211; equals;
Converted gap (as presented in slides 18 and 19): 0.013.
2007;
Gap (as presented in table 6 of appendix III, under "Explained Gap"):
0.008; times;
Converted log gap Log gap: 0.114; 0.121; equals;
Converted gap (as presented in slides 18 and 19): 0.008.
Source: GAO analysis of CPDF data.
Note: Figures presented above may be rounded.
[End of table]
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 converted log gap to the log gap. The crosswalk between
the results of our cohort analysis (table 14 of appendix 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:
Unexplained gap:
1988;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.22; 0.25; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1989;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.24; 0.27; equals;
Converted portion of gap (as presented in slide 30): 0.03.
1990;
Portion of gap (as presented in table 14 of appendix IV): 0.02; times;
Converted log gap Log gap (from table 18): 0.25; 0.29; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1991;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.26; 0.31; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1992;
Portion of gap (as presented in table 14 of appendix IV): 0.04; times;
Converted log gap Log gap (from table 18): 0.27; 0.32; equals;
Converted portion of gap (as presented in slide 30): 0.03.
1993;
Portion of gap (as presented in table 14 of appendix IV): 0.04; times;
Converted log gap Log gap (from table 18): 0.28; 0.32; equals;
Converted portion of gap (as presented in slide 30): 0.04.
1994;
Portion of gap (as presented in table 14 of appendix IV): 0.04; times;
Converted log gap Log gap (from table 18): 0.28; 0.33; equals;
Converted portion of gap (as presented in slide 30): 0.04.
1995;
Portion of gap (as presented in table 14 of appendix IV): 0.06; times;
Converted log gap Log gap (from table 18): 0.28; 0.33; equals;
Converted portion of gap (as presented in slide 30): 0.05.
1996;
Portion of gap (as presented in table 14 of appendix IV): 0.06; times;
Converted log gap Log gap (from table 18): 0.28; 0.32; equals;
Converted portion of gap (as presented in slide 30): 0.05.
1997;
Portion of gap (as presented in table 14 of appendix IV): 0.06; times;
Converted log gap Log gap (from table 18): 0.27; 0.32; equals;
Converted portion of gap (as presented in slide 30): 0.05.
1998;
Portion of gap (as presented in table 14 of appendix IV): 0.07; times;
Converted log gap Log gap (from table 18): 0.27; 0.31; equals;
Converted portion of gap (as presented in slide 30): 0.06.
1999;
Portion of gap (as presented in table 14 of appendix IV): 0.08; times;
Converted log gap Log gap (from table 18): 0.27; 0.31; equals;
Converted portion of gap (as presented in slide 30): 0.07.
2000;
Portion of gap (as presented in table 14 of appendix IV): 0.09; times;
Converted log gap Log gap (from table 18): 0.27; 0.31; equals;
Converted portion of gap (as presented in slide 30): 0.07.
2001;
Portion of gap (as presented in table 14 of appendix IV): 0.09; times;
Converted log gap Log gap (from table 18): 0.26; 0.31; equals;
Converted portion of gap (as presented in slide 30): 0.08.
2002;
Portion of gap (as presented in table 14 of appendix IV): 0.09; times;
Converted log gap Log gap (from table 18): 0.26; 0.30; equals;
Converted portion of gap (as presented in slide 30): 0.08.
2003;
Portion of gap (as presented in table 14 of appendix IV): 0.10; times;
Converted log gap Log gap (from table 18): 0.26; 0.30; equals;
Converted portion of gap (as presented in slide 30): 0.09.
2004;
Portion of gap (as presented in table 14 of appendix IV): 0.10; times;
Converted log gap Log gap (from table 18): 0.25; 0.29; equals;
Converted portion of gap (as presented in slide 30): 0.09.
2005;
Portion of gap (as presented in table 14 of appendix IV): 0.10; times;
Converted log gap Log gap (from table 18): 0.25; 0.29; equals;
Converted portion of gap (as presented in slide 30): 0.09.
2006;
Portion of gap (as presented in table 14 of appendix IV): 0.10; times;
Converted log gap Log gap (from table 18): 0.25; 0.28; equals;
Converted portion of gap (as presented in slide 30): 0.09.
2007;
Portion of gap (as presented in table 14 of appendix IV): 0.11; times;
Converted log gap Log gap (from table 18): 0.25; 0.28; equals;
Converted portion of gap (as presented in slide 30): 0.09.
Part of the Gap Resulting From Differences in Occupations:
1988;
Portion of gap (as presented in table 14 of appendix IV): 0.18; times;
Converted log gap Log gap (from table 18): 0.22; 0.25; equals;
Converted portion of gap (as presented in slide 30): 0.16.
1989;
Portion of gap (as presented in table 14 of appendix IV): 0.20; times;
Converted log gap Log gap (from table 18): 0.24; 0.27; equals;
Converted portion of gap (as presented in slide 30): 0.17.
1990;
Portion of gap (as presented in table 14 of appendix IV): 0.22; times;
Converted log gap Log gap (from table 18): 0.25; 0.29; equals;
Converted portion of gap (as presented in slide 30): 0.19.
1991;
Portion of gap (as presented in table 14 of appendix IV): 0.22; times;
Converted log gap Log gap (from table 18): 0.26; 0.31; equals;
Converted portion of gap (as presented in slide 30): 0.19.
1992;
Portion of gap (as presented in table 14 of appendix IV): 0.23; times;
Converted log gap Log gap (from table 18): 0.27; 0.32; equals;
Converted portion of gap (as presented in slide 30): 0.19.
1993;
Portion of gap (as presented in table 14 of appendix IV): 0.23; times;
Converted log gap Log gap (from table 18): 0.28; 0.32; equals;
Converted portion of gap (as presented in slide 30): 0.19.
1994;
Portion of gap (as presented in table 14 of appendix IV): 0.23; times;
Converted log gap Log gap (from table 18): 0.28; 0.33; equals;
Converted portion of gap (as presented in slide 30): 0.19.
1995;
Portion of gap (as presented in table 14 of appendix IV): 0.22; times;
Converted log gap Log gap (from table 18): 0.28; 0.33; equals;
Converted portion of gap (as presented in slide 30): 0.18.
1996;
Portion of gap (as presented in table 14 of appendix IV): 0.21; times;
Converted log gap Log gap (from table 18): 0.28; 0.32; equals;
Converted portion of gap (as presented in slide 30): 0.18.
1997;
Portion of gap (as presented in table 14 of appendix IV): 0.21; times;
Converted log gap Log gap (from table 18): 0.27; 0.32; equals;
Converted portion of gap (as presented in slide 30): 0.18.
1998;
Portion of gap (as presented in table 14 of appendix IV): 0.20; times;
Converted log gap Log gap (from table 18): 0.27; 0.31; equals;
Converted portion of gap (as presented in slide 30): 0.17.
1999;
Portion of gap (as presented in table 14 of appendix IV): 0.19; times;
Converted log gap Log gap (from table 18): 0.27; 0.31; equals;
Converted portion of gap (as presented in slide 30): 0.16.
2000;
Portion of gap (as presented in table 14 of appendix IV): 0.18; times;
Converted log gap Log gap (from table 18): 0.27; 0.31; equals;
Converted portion of gap (as presented in slide 30): 0.15.
2001;
Portion of gap (as presented in table 14 of appendix IV): 0.17; times;
Converted log gap Log gap (from table 18): 0.26; 0.31; equals;
Converted portion of gap (as presented in slide 30): 0.14.
2002;
Portion of gap (as presented in table 14 of appendix IV): 0.16; times;
Converted log gap Log gap (from table 18): 0.26; 0.30; equals;
Converted portion of gap (as presented in slide 30): 0.14.
2003;
Portion of gap (as presented in table 14 of appendix IV): 0.15; times;
Converted log gap Log gap (from table 18): 0.26; 0.30; equals;
Converted portion of gap (as presented in slide 30): 0.13.
2004;
Portion of gap (as presented in table 14 of appendix IV): 0.14; times;
Converted log gap Log gap (from table 18): 0.25; 0.29; equals=;
Converted portion of gap (as presented in slide 30): 0.12.
2005;
Portion of gap (as presented in table 14 of appendix IV): 0.13; times;
Converted log gap Log gap (from table 18): 0.25; 0.29; equals;
Converted portion of gap (as presented in slide 30): 0.12.
2006;
Portion of gap (as presented in table 14 of appendix IV): 0.13; times;
Converted log gap Log gap (from table 18): 0.25; 0.28; equals;
Converted portion of gap (as presented in slide 30): 0.11.
2007;
Portion of gap (as presented in table 14 of appendix IV): 0.12; times;
Converted log gap Log gap (from table 18): 0.25; 0.28; equals;
Converted portion of gap (as presented in slide 30): 0.11.
Part of the Gap Resulting From Differences in Education Levels:
1988;
Portion of gap (as presented in table 14 of appendix IV): 0.02; times;
Converted log gap Log gap (from table 18): 0.22; 0.25; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1989;
Portion of gap (as presented in table 14 of appendix IV): 0.02; times;
Converted log gap Log gap (from table 18): 0.24; 0.27; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1990;
Portion of gap (as presented in table 14 of appendix IV): 0.02; times;
Converted log gap Log gap (from table 18): 0.25; 0.29; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1991;
Portion of gap (as presented in table 14 of appendix IV): 0.02; times;
Converted log gap Log gap (from table 18): 0.26; 0.31; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1992;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.27; 0.32; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1993;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.28; 0.32; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1994;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.28; 0.33; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1995;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.28; 0.33; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1996;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.28; 0.32; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1997;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.27; 0.32; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1998;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.27; 0.31; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1999;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.27; 0.31; equals;
Converted portion of gap (as presented in slide 30): 0.02.
2000;
Portion of gap (as presented in table 14 of appendix IV): 0.02; times;
Converted log gap Log gap (from table 18): 0.27; 0.31; equals;
Converted portion of gap (as presented in slide 30): 0.02.
2001;
Portion of gap (as presented in table 14 of appendix IV): 0.02; times;
Converted log gap Log gap (from table 18): 0.26; 0.31; equals;
Converted portion of gap (as presented in slide 30): 0.02.
2002;
Portion of gap (as presented in table 14 of appendix IV): 0.02; times;
Converted log gap Log gap (from table 18): 0.26; 0.30; equals;
Converted portion of gap (as presented in slide 30): 0.02.
2003;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.26; 0.30; equals;
Converted portion of gap (as presented in slide 30): 0.02.
2004;
Portion of gap (as presented in table 14 of appendix IV): 0.02; times;
Converted log gap Log gap (from table 18): 0.25; 0.29; equals;
Converted portion of gap (as presented in slide 30): 0.02.
2005;
Portion of gap (as presented in table 14 of appendix IV): 0.02; times;
Converted log gap Log gap (from table 18): 0.25; 0.29; equals;
Converted portion of gap (as presented in slide 30): 0.02.
2006;
Portion of gap (as presented in table 14 of appendix IV): 0.02; times;
Converted log gap Log gap (from table 18): 0.25; 0.28; equals;
Converted portion of gap (as presented in slide 30): 0.02.
2007;
Portion of gap (as presented in table 14 of appendix IV): 0.02; times;
Converted log gap Log gap (from table 18): 0.25; 0.28; equals;
Converted portion of gap (as presented in slide 30): 0.02.
Part of the Gap Resulting From Differences in Other Characteristics:
1988;
Portion of gap (as presented in table 14 of appendix IV): 0.02; times;
Converted log gap Log gap (from table 18): 0.22; 0.25; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1989;
Portion of gap (as presented in table 14 of appendix IV): 0.02; times;
Converted log gap Log gap (from table 18): 0.24; 0.27; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1990;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.25; 0.29; equals;
Converted portion of gap (as presented in slide 30): 0.03.
1991;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.26; 0.31; equals;
Converted portion of gap (as presented in slide 30): 0.03.
1992;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.27; 0.32; equals;
Converted portion of gap (as presented in slide 30): 0.03.
1993;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.28; 0.32; equals;
Converted portion of gap (as presented in slide 30): 0.03.
1994;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.28; 0.33; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1995;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.28; 0.33; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1996;
Portion of gap (as presented in table 14 of appendix IV): 0.02; times;
Converted log gap Log gap (from table 18): 0.28; 0.32; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1997;
Portion of gap (as presented in table 14 of appendix IV): 0.02; times;
Converted log gap Log gap (from table 18): 0.27; 0.32; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1998;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.27; 0.31; equals;
Converted portion of gap (as presented in slide 30): 0.02.
1999;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.27; 0.31; equals;
Converted portion of gap (as presented in slide 30): 0.02.
2000;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.27; 0.31; equals;
Converted portion of gap (as presented in slide 30): 0.02.
2001;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.26; 0.31; equals;
Converted portion of gap (as presented in slide 30): 0.02.
2002;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.26; 0.30; equals;
Converted portion of gap (as presented in slide 30): 0.02.
2003;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.26; 0.30; equals;
Converted portion of gap (as presented in slide 30): 0.02.
2004;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.25; 0.29; equals;
Converted portion of gap (as presented in slide 30): 0.02.
2005;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.25; 0.29; equals;
Converted portion of gap (as presented in slide 30): 0.03.
2006;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.25; 0.28; equals;
Converted portion of gap (as presented in slide 30): 0.03.
2007;
Portion of gap (as presented in table 14 of appendix IV): 0.03; times;
Converted log gap Log gap (from table 18): 0.25; 0.28; equals;
Converted portion of gap (as presented in slide 30): 0.03.
Source: GAO analysis of CPDF data.
Note: Figures presented above may be rounded.
[End of table]
[End of section]
Appendix VI: Comments from the U.S. Office of Personnel Management:
Note: GAO comments supplementing those in the report text appear at the
end of this appendix.
United States Office Of Personnel Management:
Washington, DC 20415:
"Our mission is to ensure the Federal Government has an effective
workforce."
[hyperlink, http://www.opm.gov]
[hyperlink, http://www.usajobs.gov]
February 23, 2009:
Mr. Andrew Sherrill,
Director Education. Workforce, and Income Security Issues:
Government Accountability Office:
Washington, DC 20548:
Dear Mr. Sherrill:
Thank you for the opportunity to review the Government Accountability's
draft report, Women's Pay: Gender Pay Gap in the Federal Workforce
Narrows as Differences in Occupation, Education, and Experience
Diminish (GAO-09-279).
OPM has reviewed the methodology employed in the draft report and the
use of Civilian Personnel Data File (CPDF) data generally appears
appropriate. We have two comments regarding the variables used in the
analysis.
While occupational category was a factor in the analysis, it does not
appear that pay plan code was used as a variable. It may be useful to
examine whether certain pay plans that provide higher pay are populated
by a greater percentage of males vs. females. [See comment 1]
Since adjusted basic pay varies by geographic location. we are
concerned about the possibility that the male-female employee
distribution might vary geographically. The report indicates the
geography variable (based on county in which the employee's workplace
is located) was tested. and GAO determined it had no effect on the pay
gap (Page 7 of Appendix II); however, we do not have enough information
to confirm this conclusion. [See comment 2]
Again, we appreciate the opportunity to comment on the draft report.
Sincerely,
Signed by:
Kathie Ann Whipple:
Acting Director:
The following are GAO's comments to the Office of Personnel
Management's letter dated February 23, 2009.
GAO Comments:
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.
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.
[End of section]
Appendix VII: Comments from the U.S. Equal Employment Opportunity
Commission:
Note: GAO comments supplementing those in the report text appear at the
end of this appendix.
U.S. Equal Employment Opportunity Commission:
Office of the Chair:
Washington, DC 20507:
February 25 2009:
Andrew Sherrill,
Director Education, Workforce, and Income Security Issues:
U.S. Government Accountability Office:
Washington, D.C. 20548:
Dear Mr. Sherrill:
Thank you for the opportunity to comment on GAO's report entitled:
Women's Pay: Gender Pay Gap in the Federal Workforce Narrows as
Differences in Occupation, Education, and Experience Diminish (GAO-09-
279).
We believe that the report will be a useful source of information to
the federal sector, particularly as federal agencies continue to
address barriers to EEO in their workplaces. We believe it may be made
even more useful if more information is set forth in the report that
reflects your research findings on the pay gap based on the
intersection of gender with age, disability or race/ethnicity. For
example, on page 9, the study notes:
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....
Thus, we recommend that the report be expanded to show how the gender
pay gap evolved for different protected groups. In addition, we
recommend that the study look at inter-group gender differences, and
not merely differences within the same group. For example, rather than
merely comparing white men to white women and African American men to
African American women, the study should also compare white men to
African American women and to African American men. [See comment 1]
Additionally, while the report concludes that the gender pay gap has
declined significantly, EEOC nevertheless remains dedicated to
identifying and eliminating any part of the pay gap that is due to
discrimination. It is troubling that the portion of the gender pay gap
that cannot be explained in one snapshot remained persistent and
unchanged at 7 cents from 1988 to 2007. We hope future studies cab
isolate the factors accounting for this unexplained portion of the pay
gap.
Perhaps additional research may resolve some of the questions that GAO
did not address, particularly the effects on federal pay gender
differences caused by employees moving from federal to private
employment and subsequently being dropped from the Office of Personnel
Management (OPM) database, primarily affecting upper-level wage
earners. We also hope that future studies may allow for further
refinement such as using EEOC/Census occupational comparisons rather
than the OPM PATCOB approach.
Sincerely,
Signed by:
Stuart J. Ishimaru:
Acting Chairman:
Technical Comments:
Women's Earnings: Gender Pay Gap in the Federal Workforce Narrows as
Differences in Occupation, Education, and Experience Diminish (GAO-09-
279):
This study has a solid research design and two reasonably good
regression models. Our office has just a few minor suggestions.
* It would be useful to see a more focused analysis in the main text in
plain English on the importance of occupations in the pay gap through a
thorough analysis of the five regression coefficients representing the
six federal occupations: their directions, relative strengths, and,
maybe, a dollar amount. For example, what is the meaning of a
coefficient of -.156 for clerical work or the meaning of +.37 for
administrative work in a pay gap model? [See comment 2]
* It would also be useful to see gender comparisons in some of the
graphs demonstrating the "converging trend" in the federal labor force.
For instance, the three percentage bar graphs on pages 11, 13, and 14
can be made more informative if they are presented in separate portions
of women and men. Or perhaps add a couple of time series graphs in the
main text, demonstrating the declining gender difference in the six job
groups in the federal work force since GAO identified gender converging
in occupations to be the predominate factor in reducing the pay gap in
the federal labor force. [See comment 3]
* Appendix IV, Table 5, page 10 (page 82 on the .pdf file) is a bit
confusing especially in contrast to the Powerpoint slides. It is not
entirely clear what the values labeled as female coefficients
represent. Taken at face value it might suggest that when being a women
is treated as an independent variable with a dependent variable of
earnings, the results seem quite different and rather than the gap
being persistent, it appears that things are actually getting worse for
women. This is not to argue against the use of the Oaxaca decomposition
model which seems appropriate but that the other results may warrant
more attention. [See comment 4]
* We have some reservations on GAO's reading of the graph entitled
"Measurable Factors Account for a Significant Portion of the Gap" on
page 18. This is a critical chart, however its meaning is not
transparent. It might be helpful to use proportions to interpret the
over-time unexplained portion of wage gap after controlling for
employee's education, work experiences, and occupation. The unexplained
portion increased in 2007 to 63.0 percent and using terms like
"persistent unexplained 7 cent gap" may be misleading (page 20). For
example, one might interpret the results presented here as that while
women are becoming more like men in terms of human capital, the
disparity attributed to gender remains constant at 7 cents. Thus,
instead of things getting better, the gap persists even with
improvement in the human capital of women. There may be similar issues
with their discussion on their cohort model on page 30. Relatedly, on a
more general note, the terminology at times seems a bit confusing. The
report notes that there are some uncontrolled differences such as
external (non federal) work experiences. It is not clear if the
"unexplained differences" interpreted as gender differences include the
uncontrolled variables such as external work experiences. [See comment
5]
* On GAO's regression models, it may be helpful to add a few
interaction terms to their multivariate regression models to capture
the effects of being female Black or female Asian and the effects of
being a male administrator or male professionals. This is of special
importance when a number of previous studies have clearly identified
those double disadvantages of being female minorities in their
regression models. Cross sectional discrimination can be an important
area for future research but there certainly are reasons to suspect
that African American women and White women do not have the same
experiences. [See comment 6]
* The increase in the portion of unexplained pay gap is
counterintuitive and needs to be addressed. One might suspect that
these changes might be associated with measurement error given the long
time frame involved. (See page 30 of introductory slides.) [See comment
7]
* It would benefit the reader if there was more discussion of the
reason for the 20 percent sample for the regression as well as the
sampling plan. [See comment 8]
* On the cohort part of study, there was an overall dropout rate of 66
percent from the study sample. Though this rate is not unusually high
among longitudinal studies with a 20-year span, there is a difference
of five percentage point between men and women. Did women dropout of
the federal work force because of the low pay? Or, do women follow a
different career track than men. It might actually be the dropouts that
provide useful insights into the pay gap. It may be helpful to see some
types of comparison of women vs. men dropouts in future research. [See
comment 9]
* In examining job group segregation indexes, it is very clear that
women dominate clerical positions. It would be interesting to see how
the exclusion of that job group might impact the results. [See comment
10] Also, it might be interesting to see how a similar methodology
might be applied within certain job titles not controlled for in this
study - an examination of dominant categories across agencies like
attorneys would be interesting. [See comment 11]
* Another area for future research on policy issues on federal
employment is the impact of job and grade on earnings. Clearly job and
grade is a near perfect predictor of earnings in the federal sector but
initial grade or step might be used to determine if men and women with
similar qualifications end up with different grade/step and earning
histories. This might be particularly useful in a cohort type of
analysis. It should be noted however, that the cohort period (beginning
in 1988) might be too short to observe some impacts of disparities in
initial grade. [See comment 12]
The following are GAO's comments to the Equal Employment Opportunity
Commission's letter dated February 25, 2009.
GAO Comments:
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.
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 appendix
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
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.
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.
[End of section]
Appendix VIII: GAO Contact and Staff Acknowledgments:
GAO Contact:
Andrew Sherrill, Director (202) 512-7215 or sherrilla@gao.gov:
Staff Acknowledgments:
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.
[End of section]
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Lewis, Gregory B. "Continuing Progress Toward Racial and Gender Pay
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Light, Audrey and Manuelita Ureta, "Early-Career Work Experience and
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Neumark, David, Roy J. Bank, and Kyle D. Van Nort. "Sex Discrimination
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[End of section]
Footnotes:
[1] GAO, Women's Earnings: Work Patterns Partially Explain Difference
between Men's and Women's Earnings, [hyperlink,
http://www.gao.gov/products/GAO-04-35] (Washington, D.C.: Oct. 31,
2003).
[2] Gregory B. Lewis, "Continuing Progress toward Racial and Gender Pay
Equality in the Federal Workforce," Review of Public Personnel
Administration, vol. 18, no. 2.
[3] GAO, Women's Earnings: Federal Agencies Should Better Monitor
Performance and Strengthen Enforcement of Anti-Discrimination Laws in
the Private Sector," [hyperlink,
http://www.gao.gov/products/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.
[5] 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.
[6] See the bibliography for a list of relevant articles and reports.
[7] 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, non-
appropriated 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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).
[13] 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.
[14] Because 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).
[15] 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.
[16] 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.
[17] To transform the coefficient to more exactly equal the percent
difference, we applied the following formula: exp(difference in
logarithms)-1.
[18] 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.
[19] "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.
[20] To transform the coefficient to more exactly equal the percent
difference, we applied the following formula: exp(b)-1.
[21] 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.
[22] See Francine Blau and Lawrence Kahn, 2000, "Gender Differences in
Pay," The Journal of Economic Perspectives 14 (4): pp.75-99.
[23] The occupation category that was used to measure the proportion of
women in each occupation was "job series," which was more disaggregated
than PATCOB.
[24] The 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.
[25] See Gregory Lewis (1996), "Gender integration of occupations in
the federal civil service: extent effects on male-female earnings,"
Industrial and Labor Relations Review 49 (3): pp. 472-483.
[26] For 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.
[27] 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.
[28] In our data, unpaid leave was indicated with a Leave-Without-Pay
personnel action.
[29] 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.
[30] The size of the agency was determined by the relative number of
federal workers in that agency in 1988.
[31] 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.
[32] 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.
[33] These data are presented in graphic format on slide 30.
[34] 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.
[35] 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.
[36] Both variables for changing jobs and leaving the workforce were
statistically significant predictors of annual salary.
[37] 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).
[38] 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.
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