Report to Congressional Requesters

United States General Accounting Office 

GAO 

October 2003 WOMEN’S EARNINGS 

Work Patterns 

Partially Explain 

Difference between 

Men’s and Women’s 

Earnings 

GAO-04-35 
Page i GAO-04-35 Women's Earnings 

Letter 1

Appendix I Briefing Slides 4 

Appendix II GAO Analysis of the Earnings Difference between 

Men and Women 21 

Review of Other Research on Earnings Differences 21

Data Used in Our Analysis 23

Results of Our Analysis 29

Limitations of Our Analysis 54

Appendix III GAO Analysis of Women’s Workplace Decisions 56 

Purpose 56

Scope and Methodology 56

Summary of Results 57

Background 57

Working Women Make a Variety of Decisions to Manage Work and 

Family Responsibilities 59

Related Research 65

Appendix IV GAO Contact and Staff Acknowledgments 75 

GAO Contact 75

Staff Acknowledgments 75

Tables 

Table 1: Descriptive Statistics for Selected PSID Variables 26

Table 2: Overall and Separate Model Results for Men and Women 34

Table 3: Summary of Decomposition Results 45

Table 4: Decomposition Results Using Regression Coefficients 46

Table 5: Decomposition Results Using Alternative Estimates 50

Contents 
Page ii GAO-04-35 Women's Earnings 

Abbreviations 

CPS Current Population Survey 

OLS ordinary least squares 

PSID Panel Study of Income Dynamics 

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Page 1 GAO-04-35 Women's Earnings 

October 31, 2003 

The Honorable Carolyn B. Maloney 

The Honorable John D. Dingell 

House of Representatives 

Despite extensive research on the progress that women have made toward 

equal pay and career advancement opportunities over the past several 

decades, there is no consensus about the magnitude of earnings 

differences between men and women and why differences may exist. 

According to data from the Department of Labor’s Current Population 

Survey (CPS), women have typically earned less than men.1

 Specifically, in 

2001, the published CPS data showed that for full-time wage and salary 

workers, women’s weekly earnings were about three-fourths of men’s.2

However, this difference does not reflect key factors, such as work 

experience and education, that may affect the level of earnings individuals 

receive. Studies that attempt to account for key factors have provided a 

more comprehensive estimate of the earnings difference. However, recent 

information is lacking because many studies on earnings differences relied 

on data that predated the mid-1990s. But, even when accounting for these 

factors, questions remain about the size of and reasons for any earnings 

difference. To provide insight into these issues, you asked that we 

examine the factors that contribute to differences in men’s and women’s 

earnings. On October 2, 2003, we briefed you on the results of our analysis. 

This report formally conveys the information provided during that briefing 

(see app. I). 

To address this issue, we carried out two types of analyses. We performed 

a quantitative analysis to determine differences in earnings by gender and 

what factors may account for these differences. The statistical model we 

 1

The CPS is a monthly survey that obtains key labor force data, such as employment, 

wages, and occupations. 

2

This figure represents weekly earnings of full-time workers, but considering different 

populations may result in different earnings differences. For example, according to a GAO 

calculation based on CPS data from 2000 using both full-time and part-time workers, 

women’s annual earnings were about half of men’s. 

United States General Accounting Office

Washington, DC 20548 
Page 2 GAO-04-35 Women's Earnings 

developed used data from the Panel Study of Income Dynamics (PSID),3 a 

nationally representative longitudinal data set that includes a variety of 

demographic, family, and work-related characteristics for individuals over 

time. We tracked work and life histories of individuals who were between 

ages 25 and 65 at some point between 1983 and 2000. Using our statistical 

model, we estimated how earnings differ between men and women after 

controlling for numerous factors that can influence an individual’s 

earnings. (For more information about this analysis and its limitations, see 

app. II.) To supplement this analysis, we reviewed the literature and 

interviewed a variety of individuals with expertise on earnings and other 

workplace issues4

 to obtain a broad range of perspectives on reasons why 

workers make certain career and workplace decisions that could affect 

earnings. In addition, we contacted employers to discuss these issues as 

well as to identify what policies employers offered to help workers 

manage work and other life responsibilities. (For more information about 

this analysis, see app. III.) We conducted our work from September 

2002 to October 2003 in accordance with generally accepted government 

auditing standards. 

In summary, we found: 

• Of the many factors that account for differences in earnings between men 

and women, our model indicated that work patterns are key. Specifically, 

women have fewer years of work experience, work fewer hours per year, 

are less likely to work a full-time schedule, and leave the labor force for 

longer periods of time than men. Other factors that account for earnings 

differences include industry, occupation, race, marital status, and job 

tenure. When we account for differences between male and female work 

patterns as well as other key factors, women earned, on average, 

80 percent of what men earned in 2000. While the difference fluctuated in 

each year we studied, there was a small but statistically significant decline 

in the earnings difference over the time period. (See table 2 in app. II.) 

• Even after accounting for key factors that affect earnings, our model could 

not explain all of the difference in earnings between men and women. Due 

to inherent limitations in the survey data and in statistical analysis, we 

cannot determine whether this remaining difference is due to 

 3

The PSID is a survey of a sample of U.S. individuals that collects economic and 

demographic data, with substantial detail on income sources and amounts, employment, 

family composition changes, and residential location. 

4

These individuals will be referred to as “experts” throughout the remainder of this report. 
Page 3 GAO-04-35 Women's Earnings 

discrimination or other factors that may affect earnings. For example, 

some experts said that some women trade off career advancement or 

higher earnings for a job that offers flexibility to manage work and family 

responsibilities. 

In conclusion, while we were able to account for much of the difference in 

earnings between men and women, we were not able to explain the 

remaining earnings difference. It is difficult to evaluate this remaining 

portion without a full understanding of what contributes to this difference. 

Specifically, an earnings difference that results from individuals’ decisions 

about how to manage work and family responsibilities may not necessarily 

indicate a problem unless these decisions are not freely made. On the 

other hand, an earnings difference may result from discrimination in the 

workplace or subtler discrimination about what types of career or job 

choices women can make. Nonetheless, it is difficult, and in some cases, 

may be impossible, to precisely measure and quantify individual decisions 

and possible discrimination. Because these factors are not readily 

measurable, interpreting any remaining earnings difference is problematic. 

As arranged with your offices, unless you announce its contents earlier, 

we plan no further distribution of this report until 30 days after the date of 

this report. At that time, we will provide copies of this report to the 

Secretary of Labor and other interested parties. We will also make copies 

available to others upon request. In addition, the report will be available at 

no charge on GAO’s Web site at http://www.gao.gov. 

Please contact me or Lori Rectanus on (202) 512-7215 if you or your staff 

have any questions about this report. Other contacts and staff 

acknowledgments are listed in appendix IV. 

Robert E. Robertson 

Director, Education, Workforce, and 

 Income Security Issues 
Appendix I: Briefing Slides 

Page 4 GAO-04-35 Women's Earnings 

Appendix I: Briefing Slides 

1

GAO Congressional Briefing 

Representative John D. Dingell and

Representative Carolyn B. Maloney

Analysis of the Earnings Difference

between Men and Women 

October 2, 2003
Appendix I: Briefing Slides 

Page 5 GAO-04-35 Women's Earnings 

2

Introduction

• Despite extensive research on the progress women have 

made toward equal pay, no consensus exists about the size 

of any earnings difference between men and women

• Some earnings studies have not accounted for key factors 

that affect earnings, such as work experience and education

• Even when accounting for such key factors, questions remain 

about the size of and reasons for any difference 
Appendix I: Briefing Slides 

Page 6 GAO-04-35 Women's Earnings 

3

Key Question

• What factors contribute to differences in men’s and women’s 

earnings?
Appendix I: Briefing Slides 

Page 7 GAO-04-35 Women's Earnings 

4

Scope and Methodology

• We developed a statistical model to estimate how earnings 

differ between men and women after controlling for a 

comprehensive set of demographic, family, and work-related 

factors that can influence an individual’s earnings

• We used the Panel Study of Income Dynamics, a nationally 

representative longitudinal data set that includes a variety of 

demographic, family, and work-related characteristics

• We tracked work and life histories of individuals who were 

between ages 25 and 65 at any point during the period 1983 

through 2000
Appendix I: Briefing Slides 

Page 8 GAO-04-35 Women's Earnings 

5

Scope and Methodology (continued)

• To supplement our model, we reviewed literature and 

interviewed a variety of individuals to obtain a broad range of 

perspectives on why workers make certain career and 

workplace decisions that could affect earnings

• Experts reviewed our work 

• We conducted our work from September 2002 to October 

2003 in accordance with generally accepted government 

auditing standards 
Appendix I: Briefing Slides 

Page 9 GAO-04-35 Women's Earnings 

6

Summary of Results 

• Work patterns are important when accounting for some of the 

earnings difference between men and women

• After accounting for factors affecting earnings, women 

earned an average of 80 percent of what men earned in 2000

• Our model could not explain all of the earnings difference 

between men and women due to inherent limitations in the 

survey data and in statistical analysis
Appendix I: Briefing Slides 

Page 10 GAO-04-35 Women's Earnings 

7

Many Factors Account for Earnings 

Difference, but Work Patterns Are Key

• While many factors account for the earnings difference 

between men and women, work patterns are key

• Some of the other factors include industry, occupation, race, 

marital status, and job tenure

• Some of the factors that contribute to an earnings difference 

affect men and women differently, but we cannot explain why
Appendix I: Briefing Slides 

Page 11 GAO-04-35 Women's Earnings 

8

Work Patterns Are Important When 

Accounting for Earnings Difference 

• Men’s and women’s work patterns differ:

• Women have fewer years of work experience

• Women work fewer hours per year

• Women are less likely to work a full-time schedule

• Women leave the labor force for longer periods of time 
Appendix I: Briefing Slides 

Page 12 GAO-04-35 Women's Earnings 

9

Work Patterns (continued)

• Years of work experience and hours worked per year differ 

for men and women 

9

Work Patterns (continued)

• Years of work experience and hours worked per year differ 

for men and women 
Appendix I: Briefing Slides 

Page 13 GAO-04-35 Women's Earnings 

10

Work Patterns (continued)

• Men and women vary in terms of their full-time work and time 

out of the labor force
Appendix I: Briefing Slides 

Page 14 GAO-04-35 Women's Earnings 

11

Perspectives on Why Work Patterns 

Differ

• Although the model could not explain why work patterns 

differ, according to experts and the literature, women are 

more likely to work part time or take leave from work to 

manage home and family responsibilities, such as caring for 

children 

• According to employers, even when they offer part-time work 

or leave from work to all employees, women are more likely 

than men to use these options, although both men and 

women use other work arrangements
Appendix I: Briefing Slides 

Page 15 GAO-04-35 Women's Earnings 

12

Men’s and Women’s Earnings Differ 

Even after Accounting for Key Factors

As the graph shows, there were fluctuations in the earnings difference for each year we studied. Over the time period, there 

was a small but statistically significant decline in the average earnings difference between men and women.

Note: Data were collected annually through 1997 and then biennially starting in 1999. 
Appendix I: Briefing Slides 

Page 16 GAO-04-35 Women's Earnings 

13

Potential Reasons for the Remaining 

Earnings Difference 

• Our model could not explain all of the earnings difference 

between men and women due to inherent limitations in the 

survey data and in statistical analysis 

• Some experts and literature identified potential reasons for 

an earnings difference: 

• some women trade off advancement or higher earnings 

for a job that offers flexibility to manage work and family 

responsibilities

• discrimination resulting from societal views about 

acceptable roles for men and women or views about 

women in the workplace may affect women’s earnings
Appendix I: Briefing Slides 

Page 17 GAO-04-35 Women's Earnings 

14

Some Women Trade off Earnings for 

Flexibility 

• According to some experts and literature, some women trade 

off career advancement or higher earnings for a flexible job 

• For example, a woman may choose a human resources 

job that requires less travel and time in the office than an 

online position in the company, but offers less opportunity 

for advancement and higher earnings 

• For example, in medicine, a woman may choose family 

practice because it may be more accommodating to 

home and family than the surgical specialty, which offers 

relatively higher earnings. Surgeons’ work is generally 

less predictable because it may require treating 

emergencies at all hours 
Appendix I: Briefing Slides 

Page 18 GAO-04-35 Women's Earnings 

15

Discrimination May Also Affect Women’s 

Earnings 

• According to some experts and literature, those who work in 

traditionally female-dominated occupations generally receive 

less earnings

• Also, according to some experts, discrimination against 

women in the workplace negatively affects women’s job 

opportunities, advancement, and therefore, earnings 
Appendix I: Briefing Slides 

Page 19 GAO-04-35 Women's Earnings 

16

Concluding Observations 

• While we could account for much of the earnings difference 

between men and women, we cannot explain all of the 

difference due to inherent limitations in the survey data and in

statistical analysis

• It is difficult to evaluate the remaining difference without a full 

understanding of what contributes to the difference

• An earnings difference resulting from individual decisions 

about how to manage work and family may not be a 

problem, unless the decisions are not freely made

• An earnings difference may result from workplace 

discrimination or subtler discrimination about job choices 

women can make 
Appendix I: Briefing Slides 

Page 20 GAO-04-35 Women's Earnings 

17

Concluding Observations (continued)

• It is difficult to measure and quantify individual decisions and

possible discrimination

• Because these factors are not readily measurable, 

interpreting any remaining earnings difference between men 

and women is problematic 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 21 GAO-04-35 Women's Earnings 

To analyze earnings differences between men and women, we conducted 

multivariate regression analyses of the determinants of individuals’ annual 

earnings. The regression analyses relate individuals’ annual earnings to 

many variables thought to influence earnings, such as number of hours 

worked, occupation, education, and experience. In an analysis of data that 

included men and women, we used a variable for gender to measure the 

average difference in earnings between men and women after accounting 

for the influence of other variables in the model. We also analyzed both 

men’s and women’s earnings in separate regressions and applied a 

frequently used decomposition method to the results to identify the 

important factors leading to earnings differences by gender. 

This appendix provides information on (1) our findings from a review of 

previous research on earnings of men and women, (2) the data we used in 

our analysis, (3) the econometric model we developed, (4) the results from 

our model, and (5) the limitations of our analysis. 

Our literature search consisted primarily of research in peer reviewed 

journals, chiefly in economics, sociology, and psychology. We 

concentrated on research about gender-related earnings differences, as 

opposed to, for example, race-related or age-related earnings differences. 

We focused on studies of populations within the United States, 

particularly, but not limited to, studies using the Panel Study of Income 

Dynamics (PSID)1

 or the Current Population Survey (CPS) databases, and 

studies conducted within the past 10 years. We also included any seminal 

work in the area. We reviewed each study’s primary methodological 

approach (whether it used cross-sectional or panel data and whether it 

used general regression, time series, or other analytic estimation 

methods), the specific databases used, the years included in the study, the 

key variables in the analysis, and the principal results. 

To study earnings differences, most of the studies we reviewed estimated 

a wage or earnings equation that relates individuals’ wages or earnings to 

several independent variables, such as education, experience, occupation, 

 1

The PSID is a longitudinal survey, ongoing since 1968, of a representative sample of U.S. 

individuals and the families they reside in. The central focus of the data is economic and 

demographic, with substantial detail on income sources and amounts, employment, family 

composition changes, and residential location. PSID data were collected annually through 

1997 and biennially starting in 1999. The most recent survey available is 2001, which 

includes data from 2000. 

Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Review of Other 

Research on Earnings 

Differences 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 22 GAO-04-35 Women's Earnings 

industry, and region. In contrast to simple comparisons between the 

average wages or earnings of men and women, these studies attempted to 

determine whether a wage or earnings difference existed after accounting 

for differences between men and women in these variables. 

The wage or earnings difference between men and women can be 

identified in two ways. Studies that pool data for men and women together 

can include a variable denoting the gender of the individuals. In a 

multivariate regression analysis, the coefficient on the gender variable 

represents the difference in earnings between men and women, holding 

constant the effects of the other variables. Alternatively, separate 

regression models can be estimated for men and women and a 

decomposition analysis can compare the results for the two genders. 

Our review of the literature did not uncover much disagreement over the 

existence of an earnings difference after holding constant the effects of 

other variables. Rather, debate centered on the size of any difference and 

factors that might explain it. We found that the size of a difference can 

vary by model estimation procedures, the years included in the analysis, 

and the data set used. The wage or earnings difference, after controlling 

for several factors, varied from 2.5 percent to 47.5 percent. Few of the 

studies used data more recent than the mid-1990s. 

The results of some studies on wage and earnings differences used 

ordinary least squares (OLS) regressions for analysis. Compared to 

analyses of uncontrolled wage and earnings data, OLS regression is an 

improvement because it allows for the control of some factors in the data. 

The strength of findings from OLS approaches has been questioned, 

however, because of at least three potentially significant biases.2

 First, the 

estimates can be biased if some factors that are related to individuals’ 

earnings and that differ between men and women are omitted from the 

analysis (omitted variable bias or unobserved heterogeneity). Second, 

several of the independent variables may be closely interrelated with 

earnings (endogeneity). For example, earnings may be related to the 

number of hours an individual works, but the number of hours one 

chooses to work may depend on how much is earned by working. An OLS 

analysis assumes that no such interrelationships exist. If they do exist, 

OLS can produce biased estimates. Third, in the context of individuals’ 

 2

Moon-Kak Kim and Solomon W. Polachek, “Panel Estimates of Male-Female Earnings 

Functions,” Journal of Human Resources 29:2 (1994): 406–28. 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 23 GAO-04-35 Women's Earnings 

work decisions, OLS estimation can produce biased estimates when 

unobserved factors affect both the level of earnings and the probability 

that someone chooses to work (selection bias). 

To conduct our analysis, we used the PSID rather than the CPS for two 

main reasons. First, by using data that follow individuals over a period of 

time, we can take into account individual work and life histories more 

specifically than CPS or other data sources. Several researchers have 

analyzed gender wage and earnings differences and have attempted to 

address potential unobserved heterogeneity bias using longitudinal data 

such as the PSID. Second, the PSID includes questions that can be used to 

measure actual past work experience, which may be a key factor in 

explaining the gender earnings difference but is not available in the CPS. 

We assessed the reliability of the PSID data by reviewing documentation 

and performing electronic tests in order to check for missing data, 

outliers, or other potential problems that might adversely affect our 

estimates. Based on these tests we determined that the data were 

sufficiently reliable for the purposes of our work. 

In our sample, individuals between the ages of 25 and 65 were tracked 

from 1983 to 2000.3

 Data for some individuals were available for all of 

these years, while data for other individuals were available for some years 

only. This is because some individuals entered the sample after 1983. 

Individuals were not included in the sample until they formed an 

independent household and reached age 25. We did not use data on 

individuals after they reached age 65. 

The dependent variable we focused on is a measure of an individual’s 

annual earnings. As measured in the PSID, annual earnings include an 

individual’s wages and salaries as well as income from bonuses, overtime 

pay, tips, commissions, and other job-related income. It also includes 

earnings from self-employment and farm-related income. We took inflation 

into account by using the consumer price index to adjust annual earnings 

to year 2000 dollars. We also developed an alternative definition of 

earnings for individuals who reported that they were “self-employed only” 

in a particular industry. For these individuals, we multiplied annual hours 

worked by the average hourly earnings for the particular industry they 

 3

The lower limit of the age range was set at 25 because the PSID does not include detailed 

information for dependent college students, posing potential selection bias issues. 

Data Used in Our 

Analysis 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 24 GAO-04-35 Women's Earnings 

worked in using U.S. Department of Labor and U.S. Department of 

Agriculture data.4

To determine why an earnings difference between men and women may 

exist, our model controlled for a range of variables, which can be grouped 

into three variable sets. The first set of independent variables consisted of 

demographic characteristics, including gender, age, and race. We also 

included an education variable that indicated the highest number of years 

of education each respondent attained by the end of the sample period. 

Family-related demographic variables included marital status, number of 

children, and the age of the youngest child in the household. We also 

included other income (defined as family income minus a respondent’s 

own personal earnings), the region where individuals lived (i.e., in the 

South or not), and whether they lived in a rural or urban area (i.e., in a 

metropolitan area or not). 

The second set of independent variables pertained to past work 

experience. Total work experience was defined as the actual number of 

years an individual worked for money since age 18. This variable was 

computed as self-reported experience as reported in 1984 (or the year the 

individual entered the panel), augmented by hours of work divided by 

2,000 in each subsequent year. We also included a variable measuring job 

tenure, defined as the length of time an individual had spent in his or her 

current job. 

The third set of independent variables included labor market activity 

reported in a given survey year. Variables included hours worked in the 

past year, weeks out of the labor force in the past year, and weeks 

unemployed in the past year. For our analysis, we considered time spent 

unemployed and time out of the labor force as work “interruptions,” but 

we did not include time off for one’s own illness or a family member’s 

illness, vacation and other time off, or time out because of strike. We also 

included a variable that accounted for an individual’s full-time or part-time 

employment status, defined as the average number of hours an individual 

worked per week on his or her main job. Individuals were considered to 

have worked part-time if they worked fewer than 35 hours per week and 

full-time if they worked 35 hours or more per week. Other variables in this 

 4

The Department of Agriculture data are from the National Agricultural Statistics Service 

data series “Annual All Hired Workers Wage Rates, U.S. Level” and the Department of 

Labor data are from the Bureau of Labor Statistics data series “Average Hourly Earnings of 

Production Workers.” 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 25 GAO-04-35 Women's Earnings 

category included the individual’s industry, occupation, and an indicator of 

union membership. We also accounted for self-employment status, defined 

as whether respondents worked for someone else, for themselves, or for 

both themselves and someone else. Table 1 shows descriptive statistics for 

selected PSID data used in our analysis. 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 26 GAO-04-35 Women's Earnings 

Table 1: Descriptive Statistics for Selected PSID Variables 

Men Women 

Variable 

Means

(averages)

Standard 

deviation 

Means 

(averages) 

Standard 

deviation 

All individuals (workers and nonworkers)

Annual earnings (in 2000 dollars) 35,942 34,630 16,554 18,510 

Age of individual (in years) 41.3 11.3 42.0 11.5 

Age of youngest child (in years) 3.3 4.9 4.0 5.2 

Number of children 0.9 1.2 1.1 1.2 

Married (percent) 70.1 45.8 61.2 48.7 

Metropolitan area of residence 

(percent) 64.7 48.1 67.1 47.0 

Full-time main job (percent) 74.9 43.3 47.2 49.9 

Time unemployed (in weeks) 1.9 7.0 1.8 6.9 

Time out of the labor force (in 

weeks) 2.4 9.9 6.1 15.3 

Annual hours worked 1,931 926 1,226 957 

Job tenure (in months) 80.1 102.2 55.1 80.3 

Work experience (in years) 16.8 10.2 11.2 8.4 

Highest education (in years) 12.9 2.7 12.7 2.4 

Number of observations 42,394 54,986 

Number of individuals 5,032 6,033 

Workers only

Annual earnings (in 2000 dollars) 40,426 34,334 22,782 18,316 

Age of individual (in years) 40.2 10.6 40.4 10.5 

Age of youngest child (in years) 3.5 5.0 4.3 5.2 

Number of children 1.0 1.2 1.0 1.2 

Married (percent) 72.2 44.9 60.9 48.8 

Metropolitan area of residence 

(percent) 64.5 47.8 68.1 46.6 

Full-time main job (percent) 87.6 33.0 66.8 47.1 

Time unemployed (in weeks) 1.8 6.4 1.9 6.7 

Time out of the labor force (in 

weeks) 0.91 5.1 2.8 9.1 

Annual hours worked 2,154 697 1,672 716 

Job tenure (in months) 89.3 104.2 74.1 85.6 

Work experience (in years) 16.4 9.8 12.1 8.0 

Highest education (in years) 13.2 2.6 13.1 2.3 

Number of observations 35,726 36,793 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 27 GAO-04-35 Women's Earnings 

Men Women 

Variable 

Means

(averages)

Standard 

deviation 

Means 

(averages) 

Standard 

deviation 

Number of individuals 4,477 4,884 

Source: GAO analysis of PSID data. 

We used the Hausman-Taylor model to analyze the earnings difference 

between men and women.5

 The Hausman-Taylor model was developed to 

analyze panel data and to take into account unobserved heterogeneity and 

endogeneity while permitting the estimation of coefficients for factors that 

do not vary over time, such as gender. As is usual practice in studies of the 

determinants of earnings and earnings differences between groups, we 

related the natural logarithm of the dependent variable (annual earnings in 

this case) to several independent variables. The specific equation we 

estimated was 

 5

Jerry A. Hausman and William E. Taylor, “Panel Data and Unobservable Individual 

Effects,” Econometrica 49:6 (November 1981). Light and Ureta use this model to analyze 

the relationship between experience and wage differences (see Audrey Light and Manuelita 

Ureta, “Early-Career Work Experience and Gender Wage Differentials,” Journal of Labor 

Economics 13:1 (1995): 121-154). 

Description of Our 

Econometric Model 

ln (real earningsit) = X1itβ1 + X2itβ2 + Z1iδ1 + Z2iδ2 + μi + νit

where subscripts i and t denote individuals and time periods,

X1it are exogenous time-varying variables assumed to be uncorrelated with μi

and νit,

X2it are endogenous time-varying variables possibly correlated with μi

 but not 

with νit,

Z1i are exogenous time-invariant variables assumed to be uncorrelated with 

μi

 and νit,
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 28 GAO-04-35 Women's Earnings 

In our specification of the model, we allowed annual hours worked, time 

out of labor force, work experience, and the square of experience to be 

time-varying endogenous variables. Highest education achieved was 

treated as a time-invariant endogenous variable. The other independent 

variables were treated as exogenous. 

To account for possible selection bias arising from not accounting for an 

individual’s choice of whether to work, we used a Heckman selection bias 

correction. To do this, we estimated the probability of working in a 

particular year for all individuals in the data set.6

 We then used a term that 

was estimated in this equation (the inverse Mills ratio) as an additional 

independent variable in the Hausman-Taylor earnings equation. The 

Hausman-Taylor model was then estimated for individuals with positive 

annual hours of work and positive earnings in a given year. 

Two academic labor economists reviewed a preliminary version of the 

econometric model and the results. One of the reviewers has published 

extensively on gender wage differences and has used the PSID in his work. 

The other reviewer has published widely on labor economics topics 

generally, also using the PSID. Both reviewers thought that the model and 

results were sound and reasonable. To the extent possible, we have 

incorporated their suggestions for clarifications and additional analysis. 

 6

The probability that an individual worked was modeled as a function of age, the number of 

children and the age of the youngest child in the household, marital status, additional 

family income, work experience, education, race, region and urban-rural indicators, and a 

work disability indicator. This model was estimated separately for men and women for 

each of the years in the sample. 

Z2i are endogenous time-invariant variables possibly correlated with μi but 

not with νit,

β and δ represent coefficients on the respective variables,

μi

 is an individual-specific random error term designed to take unobserved 

individual heterogeneity into account, and

νit is a random error term.
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 29 GAO-04-35 Women's Earnings 

We found that before controlling for any variables that may affect 

earnings, on average, women earned about 44 percent less than men over 

the time period we studied—1983 to 2000. However, after controlling for 

the independent variables that we included in our model, we found that 

this difference was reduced to about 21 percent over this time period. The 

model results indicated a small but statistically significant decline in the 

earnings difference over this period. 

Table 2 shows the regression results for the overall model that included 

observations on men and women combined and the results for men and 

women separately. For each variable in each regression, the table shows 

the coefficient (estimate β), the estimated standard error for the 

coefficient, the p-value, and an alternative coefficient estimate. For each of 

the regressions, the first column of results shows the coefficient estimates. 

The standard interpretation of the regression coefficients in models of this 

type is that they represent the average percentage change in earnings that 

would result from a small increase in an independent variable. The 

estimated standard error and the p-value are shown in the second and 

third columns. A p-value of less than 0.05 indicates that the regression 

coefficient is statistically significantly different from zero, which would 

indicate that the variable has a statistically significant effect on earnings. 

In the fourth column, we show an alternative estimate for the average 

percentage change based on a transformation of the regression 

coefficients, which the literature shows is a more precise measure than the 

standard coefficient estimate.7

 For this reason, we emphasize the 

alternative estimates in the discussion of the results. 

The gender coefficient in the overall model shows the difference in 

earnings between men and women in each year after accounting for the 

effect of the other variables in the model. As shown in the alternative 

estimate column of the overall model results of table 2, the estimated 

coefficient for the gender variable was –0.2025 for the year 2000. This 

means that, holding all other variables in the model constant except for 

gender, women earned an average of about 20.3 percent less than men in 

2000. The estimated coefficients were statistically significantly different 

from zero for each of the years. Overall, the model results indicated that 

there was a small but statistically significant decline in the earnings 

 7

Peter E. Kennedy, “Estimation with Correctly Interpreted Dummy Variables in 

Semilogarithmic Equations,” American Economic Review, 71:4 (September 1981): 801. The 

alternative estimator g = exp(β – 1⁄2 V(β)) – 1, where V(β) is the estimated variance of the 

regression coefficient. 

Results of Our 

Analysis 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 30 GAO-04-35 Women's Earnings 

difference between 1983 and 2000. The analysis indicated that the 

difference declined by about 0.3 percentage points per year, on average. 

The next set of variables, included in the overall model and in the separate 

regressions for men and women, deal with work patterns. In our analysis, 

work patterns included years of work experience, hours worked per year, 

length of time out of the labor force, and whether the individual worked a 

full-time or part-time schedule. In addition, length of unemployment and 

tenure were also considered to be work patterns. For the hours worked, 

time out of the labor force, length of unemployment, and tenure variables, 

the coefficient estimate shown represents the estimated percentage 

change in earnings that would result from a one-unit change (hours or 

weeks) in the particular variable. For example, as shown in table 2 in the 

alternative estimate column of the overall model results, the coefficient for 

time out of the labor force was –0.0226. This means that earnings would 

decrease by about 2.3 percent for each additional week out of the labor 

force, holding all other factors constant—including annual hours worked. 

The coefficients on the experience variables indicate that each additional 

year of work experience is generally associated with increased earnings, 

but this increase declines as the level of experience increases.8

 The 

working full-time variable measures the effect of having a full-time main 

job relative to having a part-time job as a main job. All the work pattern 

variables are estimated to have a statistically significant effect on earnings. 

The next set of variables includes other work-related characteristics. 

Several of these variables are categorical in nature, such as occupation, 

industry, and self-employment status. For these variables, the coefficient 

for a particular category is an estimate of the effect of being in that 

category relative to the omitted category. For example, as shown in 

table 2 in the alternative estimate column of the overall model results, the 

coefficient was -0.09 for those individuals working in service/private 

household occupations. This indicates that individuals working in 

service/private household occupations earned 9 percent less, on average, 

 8

The effect of an additional year of experience on earnings is the sum of the effect of the 

experience and experience-squared variables. The amount that an additional year of 

experience will increase the value of the experience-squared variable will vary with the 

level of experience. For example, an additional year of experience would increase 

experience-squared by 1 for someone with no prior experience, and it will increase the 

experience-squared variable by 41 for someone with 20 years of experience 

(i.e., 441 – 400 = 41). Taking into account the effect of both variables, these estimates 

would indicate that an additional year of experience would increase earnings for men with 

less than 33 years of experience and for women with less than 31 years of experience. 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 31 GAO-04-35 Women's Earnings 

than individuals working in professional and technical occupations (the 

omitted occupation category), holding all other variables in the model 

constant. On the other hand, nonfarm managers and administrators earned 

about 2.5 percent more, on average, than professional and technical 

workers, holding other factors constant. 

Also shown in table 2 are coefficients for demographic variables and other 

independent variables that were included in the model, such as age of 

individual, age of youngest child, number of children, metropolitan area, 

marital status, and region. Several of the coefficients in this category, such 

as age of youngest child and number of children, were not found to be 

statistically significant in the overall model. However, other coefficients 

were statistically significant, such as age of individual, living in a 

metropolitan area, living in the South, being married, and being black. For 

example, in table 2 in the alternative estimate column of the overall model 

results, the coefficient for living in a metropolitan area was 0.0229. This 

means that individuals living in a metropolitan area were estimated to earn 

about 2.3 percent more than those living in non-metropolitan areas, and 

this difference was statistically significant. Also, according to the model, 

individuals living in the South were estimated to earn about 4.2 percent 

less than those not living in the South, and this difference was statistically 

significant. 

Table 2 also shows the regression results of the separate analysis of men 

and women. Most of the variables had coefficients that were both positive 

or both negative for men and women, indicating that the variables affected 

earnings in the same direction. This is the case for all work pattern 

variables. For example, as shown in table 2 in the alternative estimate 

columns for men and women, the estimated coefficients for the work 

experience variable were positive for men and women (0.0264 and 0.0249 

respectively) and the coefficient for the square of work experience is 

negative for both men and women. As discussed above, earnings for both 

men and women generally increase with additional experience, but that 

increase declines the higher the level of work experience (for example, the 

gain between the fifth and sixth year of work experience is larger than 

between the 25th and 26th year of work experience). Estimated coefficients 

for other variables were also negative for both men and women. For 

example, as shown in table 2 in the alternative estimate columns for men 

and women separately, the coefficients for black individuals (relative to 

white—the omitted category) were as follows: -0.1385 for men and 

–0.0661 for women. This means that black men earned about 13.9 percent 

less than white men, while black women earned about 6.6 percent less 

than white women. 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 32 GAO-04-35 Women's Earnings 

The relationship between earnings and number of children is one example 

where the coefficients are not of the same sign. As shown in table 2 in the 

overall model results for men and women combined, the coefficient on the 

number of children variable was statistically insignificant. However, in the 

separate regression analysis of men and women, number of children was 

associated with about a 2.1 percent increase in earnings for men and about 

a 2.5 percent decrease for women, with both estimates being significant. In 

addition, married men earned about 8.3 percent more than never married 

men, while the earnings difference between married and never married 

women was statistically insignificant. 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 33 GAO-04-35 Women's Earnings 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 34 GAO-04-35 Women's Earnings 

Table 2: Overall and Separate Model Results for Men and Women 

 Overall model 

Variable Estimate β Standard error p-value 

Alternative 

estimate g

Gender: women vs. men

2000 –0.2260 0.0227 0.000 -0.2025

1999a 

1998 –0.1716 0.0229 0.000 -0.1579

1997a

1996 –0.2264 0.0230 0.000 -0.2028

1995 –0.2176 0.0215 0.000 -0.1958

1994 –0.2311 0.0213 0.000 -0.2065

1993 –0.2132 0.0214 0.000 -0.1922

1992 –0.2556 0.0210 0.000 -0.2257

1991 –0.2478 0.0209 0.000 -0.2197

1990 –0.2277 0.0209 0.000 -0.2038

1989 –0.2315 0.0209 0.000 -0.2068

1988 –0.2534 0.0210 0.000 -0.2240

1987 –0.2503 0.0211 0.000 -0.2216

1986 –0.2708 0.0210 0.000 -0.2374

1985 –0.2810 0.0212 0.000 -0.2452

1984 –0.2921 0.0212 0.000 -0.2534

1983 –0.2179 0.0222 0.000 -0.1960

Work patterns 

Experience 

(years) 0.0231 0.0019 0.000 0.0234

Experience 

squared –0.0003 0.0000 0.000 -0.0003

Hours worked 

(per year) 0.0004 0.0000 0.000 0.0004

Time out of 

labor force 

(weeks) -0.0228 0.0003 0.000 -0.0226

Length of 

unemployment 

(weeks) –0.0156 0.0004 0.000 -0.0155

Tenure 

(months) 0.0009 0.0000 0.000 0.0009
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 35 GAO-04-35 Women's Earnings 

Men Women 

Estimate βm

Standard 

error p-value 

Alternative 

estimate gm Estimate βf

Standard 

error p-value 

Alternative 

estimate gf

 

0.0260 0.0025 0.000 0.0264 0.0246 0.0031 0.000 0.0249 

–0.0004 0.0000 0.000 -0.0004 –0.0004 0.0001 0.000 -0.0004 

0.0003 0.0000 0.000 0.0003 0.0005 0.0000 0.000 0.0005 

–0.0175 0.0006 0.000 -0.0174 –0.0224 0.0004 0.000 -0.0222 

–0.0171 0.0005 0.000 -0.0170 –0.0143 0.0005 0.000 -0.0142 

0.0010 0.0000 0.000 0.0010 0.0009 0.0001 0.000 0.0009 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 36 GAO-04-35 Women's Earnings 

 Overall model 

Variable Estimate β

Standard 

error p-value 

Alternative 

estimate g

Working full time 

(main job) 0.1519 0.0063 0.000 0.1640

Other work related 

Mother’s education –0.0194 0.0057 0.001 -0.0193

Father’s education –0.0044 0.0051 0.385 -0.0044

Highest education 

(years) 0.1475 0.0058 0.000 0.1590

Self-employment 

status 

 

Works for 

someone else 

onlyb

 

Self-employed 

only 0.0142 0.0103 0.166 0.0142

Missing –0.3272 0.0128 0.000 -0.2791

Both 0.0191 0.0239 0.424 0.0190

Union member 0.1435 0.0090 0.000 0.1542

Occupation 

Professional, 

technicalb

 

Service/private 

household 

workers –0.0949 0.0116 0.000 -0.0906

Farm laborers 

and foremen –0.1761 0.0399 0.000 -0.1622

Farmers and farm 

management –0.3805 0.0469 0.000 -0.3172

Nonfarm laborers –0.0907 0.0162 0.000 -0.0869

Transport 

equipment 

operators –0.0869 0.0179 0.000 -0.0834

Operators, 

nontransport –0.0588 0.0136 0.000 -0.0572

Craftsmen –0.0108 0.0122 0.376 -0.0108
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 37 GAO-04-35 Women's Earnings 

Men Women 

Estimate βm

Standard 

error p-value 

Alternative 

estimate gm Estimate βf

Standard 

error p-value 

Alternative 

estimate gf

0.1724 0.0094 0.000 0.1881 0.1180 0.0086 0.000 0.1252

 

–0.0107 0.0075 0.155 -0.0106 –0.0256 0.0081 0.001 -0.0253

0.0039 0.0067 0.557 0.0039 –0.0117 0.0071 0.102 -0.0116

0.1355 0.0072 0.000 0.1451 0.1603 0.0087 0.000 0.1738

 

 

–0.1056 0.0123 0.000 -0.1003 0.2168 0.0169 0.000 0.2419

–0.2823 0.0187 0.000 -0.2461 –0.3413 0.0175 0.000 -0.2892

0.0506 0.0266 0.057 0.0516 –0.0846 0.0443 0.056 -0.0820

0.1388 0.0113 0.000 0.1488 0.1405 0.0140 0.000 0.1507

 

–0.1061 0.0176 0.000 -0.1008 –0.0975 0.0158 0.000 -0.0930

–0.1928 0.0422 0.000 -0.1761 –0.0602 0.0850 0.479 -0.0618

–0.3434 0.0479 0.000 -0.2915 –0.1690 0.1156 0.144 -0.1611

–0.0823 0.0178 0.000 -0.0791 –0.0627 0.0380 0.099 -0.0615

–0.0576 0.0192 0.003 -0.0562 –0.1840 0.0468 0.000 -0.1690

–0.0458 0.0168 0.007 -0.0449 –0.0657 0.0217 0.003 -0.0638

0.0016 0.0138 0.909 0.0015 –0.0180 0.0290 0.534 -0.0183
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 38 GAO-04-35 Women's Earnings 

 Overall model 

Variable Estimate β

Standard 

error p-value 

Alternative 

estimate g

Clerical workers –0.0438 0.0104 0.000 -0.0429

Sales workers –0.0718 0.0145 0.000 -0.0694

Nonfarm 

managers, 

administrators 0.0243 0.0100 0.015 0.0246

Do not 

know/missing –0.1329 0.0280 0.000 -0.1248

Industry 

Wholesale/retail 

tradeb

 

Public 

administration 0.0702 0.0147 0.000 0.0726

Professional 

services 0.0516 0.0107 0.000 0.0529

Entertainment –0.0378 0.0275 0.168 -0.0375

Personal services 0.0172 0.0156 0.270 0.0172

Business and 

repair services 0.0561 0.0129 0.000 0.0576

Finance, 

insurance, real 

estate 0.1081 0.0149 0.000 0.1141

Transportation/ 

communications/

public utilities 0.1692 0.0145 0.000 0.1842

Manufacturing 0.1369 0.0104 0.000 0.1467

Construction 0.1472 0.0150 0.000 0.1584

Mining/agriculture 0.0303 0.0234 0.195 0.0305

Do not 

know/missing 0.0835 0.0251 0.001 0.0868

Mills ratio –0.2834 0.0218 0.000 -0.2470

Demographic and 

other controls 

Age of individual 

(years) –0.0023 0.0011 0.043 -0.0023

Age of youngest 

child (years) 0.0006 0.0005 0.257 0.0006
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 39 GAO-04-35 Women's Earnings 

Men Women 

Estimate βm

Standard 

error p-value 

Alternative 

estimate gm Estimate βf

Standard 

error p-value 

Alternative 

estimate gf

–0.0608 0.0178 0.001 -0.0592 –0.0497 0.0138 0.000 -0.0486 

–0.0343 0.0187 0.066 -0.0339 –0.0931 0.0218 0.000 -0.0891 

0.0373 0.0125 0.003 0.0379 0.0165 0.0157 0.295 0.0165 

–0.1107 0.0370 0.003 -0.1054 –0.1276 0.0414 0.002 -0.1205 

 

0.0104 0.0183 0.571 0.0102 0.1641 0.0233 0.000 0.1780 

0.0172 0.0164 0.294 0.0172 0.0707 0.0146 0.000 0.0731 

0.0044 0.0337 0.896 0.0039 –0.0756 0.0436 0.083 -0.0737 

–0.0307 0.0301 0.308 -0.0306 –0.0097 0.0196 0.623 -0.0098 

0.0705 0.0158 0.000 0.0729 0.0488 0.0208 0.019 0.0498 

0.0562 0.0219 0.010 0.0575 0.1489 0.0202 0.000 0.1604 

0.1713 0.0163 0.000 0.1867 0.1865 0.0280 0.000 0.2046 

0.1417 0.0126 0.000 0.1521 0.1332 0.0174 0.000 0.1423 

0.1708 0.0160 0.000 0.1861 0.0673 0.0384 0.079 0.0689 

0.0481 0.0247 0.051 0.0489 0.0178 0.0517 0.730 0.0166 

0.1106 0.0323 0.001 0.1164 0.0712 0.0378 0.060 0.0730 

–0.3307 0.0285 0.000 -0.2819 –0.1584 0.0352 0.000 -0.1470 

 

–0.0016 0.0019 0.394 -0.0016 –0.0058 0.0015 0.000 -0.0057 

–0.0013 0.0007 0.048 -0.0013 0.0023 0.0007 0.003 0.0023 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 40 GAO-04-35 Women's Earnings 

 Overall model 

Variable Estimate β

Standard 

error p-value 

Alternative 

estimate g

Number of 

children 0.0004 0.0029 0.897 0.0004

Additional family 

income (inflation 

adjusted in 

thousands of 

dollars) –0.0006 0.0001 0.000 -0.0006

Metropolitan area 0.0226 0.0067 0.001 0.0229

Excellent health 0.0088 0.0057 0.123 0.0089

Marital status 

Never marriedb 

Married 0.0403 0.0113 0.000 0.0410

Other 0.0245 0.0127 0.053 0.0247

Region: South –0.0428 0.0120 0.000 -0.0420

Race 

Whiteb 

Black –0.1031 0.0171 0.000 -0.0981

Other 0.0739 0.0585 0.207 0.0748

Year, compared to 

1983 

2000 0.0410 0.0191 0.032 0.0417

1999a 

1998 –0.0223 0.0187 0.233 -0.0222

1997a

1996 –0.0837 0.0187 0.000 -0.0804

1995 –0.0705 0.0177 0.000 -0.0682

1994 –0.0794 0.0170 0.000 -0.0764

1993 –0.0664 0.0168 0.000 -0.0643

1992 –0.0477 0.0161 0.003 -0.0467

1991 –0.0867 0.0157 0.000 -0.0832

1990 –0.0839 0.0154 0.000 -0.0806

1989 –0.0569 0.0151 0.000 -0.0555

1988 –0.0277 0.0149 0.064 -0.0274

1987 –0.0318 0.0148 0.031 -0.0314

1986 –0.0205 0.0146 0.160 -0.0204
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 41 GAO-04-35 Women's Earnings 

Men Women 

Estimate βm

Standard 

error p-value 

Alternative 

estimate gm Estimate βf

Standard 

error p-value

Alternative 

estimate gf

0.0210 0.0037 0.000 0.0212 –0.0254 0.0047 0.000 -0.0251 

–0.0009 0.0001 0.000 -0.0009 –0.0001 0.0001 0.403 -0.0001 

0.0171 0.0086 0.047 0.0173 0.0305 0.0102 0.003 0.0309 

0.0149 0.0072 0.038 0.0150 0.0062 0.0088 0.483 0.0062 

 

0.0800 0.0142 0.000 0.0831 –0.0011 0.0176 0.950 -0.0013 

0.0685 0.0162 0.000 0.0707 –0.0009 0.0192 0.962 -0.0011 

–0.0522 0.0155 0.001 -0.0510 –0.0377 0.0173 0.030 -0.0371 

 

–0.1487 0.0242 0.000 -0.1385 –0.0682 0.0230 0.003 -0.0661 

0.0491 0.0843 0.560 0.0466 0.0972 0.0762 0.202 0.0989 

 

0.0188 0.0192 0.328 0.0188 0.0621 0.0222 0.005 0.0638 

 

–0.0406 0.0186 0.029 -0.0399 0.0298 0.0215 0.165 0.0300 

–0.1045 0.0185 0.000 -0.0994 –0.0733 0.0205 0.000 -0.0709 

–0.0813 0.0175 0.000 -0.0782 –0.0618 0.0194 0.001 -0.0601 

–0.0973 0.0167 0.000 -0.0928 –0.0759 0.0188 0.000 -0.0733 

–0.0854 0.0165 0.000 -0.0820 –0.0495 0.0184 0.007 -0.0484 

–0.0693 0.0156 0.000 -0.0671 –0.0625 0.0180 0.001 -0.0608 

–0.1023 0.0150 0.000 -0.0974 –0.0921 0.0180 0.000 -0.0881 

–0.0960 0.0146 0.000 -0.0917 –0.0737 0.0174 0.000 -0.0712 

–0.0691 0.0142 0.000 -0.0669 –0.0524 0.0171 0.002 -0.0512 

–0.0359 0.0140 0.010 -0.0354 –0.0516 0.0169 0.002 -0.0504 

–0.0389 0.0137 0.005 -0.0383 –0.0561 0.0165 0.001 -0.0546 

–0.0248 0.0135 0.066 -0.0246 –0.0632 0.0164 0.000 -0.0613 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 42 GAO-04-35 Women's Earnings 

 Overall model 

Variable Estimate β

Standard 

error p-value 

Alternative 

estimate g

1985 –0.0249 0.0145 0.086 -0.0247

1984 –0.0219 0.0144 0.127 -0.0218

Intercept 7.4055 0.0783 0.000 7.4055
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 43 GAO-04-35 Women's Earnings 

Men Women 

Estimate βm

Standard 

error p-value 

Alternative 

estimate gm Estimate βf

Standard 

error p-value

Alternative 

estimate gf

–0.0282 0.0134 0.035 -0.0279 –0.0822 0.0163 0.000 -0.0791 

–0.0237 0.0131 0.070 -0.0235 –0.0847 0.0160 0.000 -0.0813 

7.5910 0.0983 0.000 7.5910 6.9846 0.1179 0.000 6.9846 

Source: GAO analysis of PSID data.

a

Data not available. 

b

Category omitted. 

Tables 3, 4, and 5 show a decomposition analysis of the earnings 

difference derived from the separate regression analysis for men and 

women. This statistical technique—the Blinder-Oaxaca decomposition—

has been commonly used in analyses of wage or earnings differences 

between men and women. The decomposition divides the (logged) 

earnings difference between men and women into two parts: a part 

reflecting differences in characteristics between men and women and a 

part reflecting differences in parameters (or return to earnings) between 

men and women.9

 This decomposition is represented as follows: 

We estimated the logged earnings difference between men and women 

from 1983 and 2000 to be approximately 0.69 (i.e. the left hand side of the 

equation above). The analysis showed that about two-thirds of this 

difference, or 0.45 out of 0.69, reflected differences between men and 

women’s characteristics (the first term on the right hand side of the 

equation). The remaining one-third, about 0.24 out of 0.69, reflected 

differences in parameters, i.e., how the variables affected earnings 

 9

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

Labor Economics (Amsterdam: Elsevier Science, 1999), vol. 3C, pp. 3153–61. 

where Xm and Xf represent the mean values of the independent variables for 

men and women, respectively, and βm and βf are the estimated regression 

coefficients for men and women for all the variables.

ln Em − ln Ef = (Xm − Xf) ́βm + Xf ́(βm − βf) ˆ ˆ ˆ
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 44 GAO-04-35 Women's Earnings 

differently for men and women (the second term on the right hand side of 

the equation). 

Table 3 summarizes how several categories of variables contributed to the 

earnings difference through differences in characteristics and differences 

in parameters. Positive values indicate an earnings advantage for men 

while negative values indicate an advantage for women. For example, in 

table 3, the difference in earnings due to characteristics from the work 

pattern variables is equal to 0.2729, which indicates that men have an 

earnings advantage. This figure represents the sum—for all the work 

pattern variables—of the difference in men’s and women’s mean 

characteristics multiplied by the men’s regression coefficients. The effect 

of the work pattern variables accounted for most of the difference in 

characteristics between men and women (due to different characteristics: 

about 0.27 out of 0.45). Relatively little of the earnings difference was 

attributable to differences in demographic characteristics (about 0.03 out 

of 0.45). 

Table 3 also shows the differences in earnings due to differences in 

parameters (0.2446 in the total row at the bottom of table 3). The table 

shows that women have a relative advantage due to parameters from the 

work pattern variables. In the table, -0.2302 represents the sum—for all the 

work pattern variables—of the difference in men and women’s parameters 

multiplied by the women’s mean value of the variable. Women’s 

advantages in the work pattern and other work-related variable categories 

are outweighed by disadvantages due to the parameters for demographic 

factors and from the intercept of the regressions. The relatively large 

advantage to men in the intercepts of the regressions indicates that a 

predictable earnings difference remains even after taking differences in 

characteristics and relative returns into account. 

This second part of the decomposition allows us to describe how the 

remaining earnings difference results from how each factor affects 

earnings differently for men and women. According to Altonji and Blank, 

this component is often mistakenly attributed to the “share due to 

discrimination” but actually “captures both the effects of discrimination 

and unobserved differences in productivity and tastes.”10 They also point 

out that it may be misleading to label only this second component as the 

result of discrimination, since discriminatory barriers in the labor market 

 10Altonji and Blank, p. 3156. 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 45 GAO-04-35 Women's Earnings 

and elsewhere in the economy can affect the mean values of the 

characteristics. 

Table 3: Summary of Decomposition Results 

Differences in earnings 

Variable categories 

Due to 

characteristics

Due to 

parameters 

Work patternsa 0.2729 -0.2302 

Other work relatedb 0.1539 -0.3218 

Demographic and other controlsc 0.0272 0.1902 

Intercept N/A 0.6065 

Total 0.4540 0.2446 

Source: GAO Analysis of PSID data. 

Note: These summary results are based on the more detailed analysis shown in table 4. 

a

The work patterns category includes: work experience (years), experience squared, time out of the 

labor force (weeks), length of unemployment (weeks), working full time (main job), tenure (months), 

and hours worked (per year). 

b

The other work related category includes: highest education (years), mother’s education, father’s 

education, self-employment status, union membership, industry, occupation, and the Mill’s ratio. 

c

The demographic and other controls category includes all other variables, except the intercept, which 

is a parameter only. 

Table 4 shows more detailed decomposition results.11 In table 4 in the 

column labeled difference due to characteristics, the variables measuring 

work patterns, including experience (0.108), hours worked (0.134), 

working full-time versus part-time (0.036), and length of time out of the 

labor force (0.034), made large contributions to explaining gender 

differences in earnings. Table 4 shows that, on average, men in our sample 

worked about 2,147 hours per year, women about 1,675 hours per year. 

The analysis showed that the difference between men and women, based 

on hours worked, resulted in a relative advantage for men of about 

0.134. In other words, about one-fifth of the uncontrolled logged earnings 

difference (0.134 out of 0.69) results from the greater number of hours 

men worked compared to women. 

 11Table 5 uses the alternative estimates reported in table 2. Because the alternative 

estimates are a transformation of the regression coefficients, the sum of the differences 

due to characteristics and parameters need not sum to the total difference in logged 

earnings as it does in the standard decomposition. 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 46 GAO-04-35 Women's Earnings 

Table 4 also shows how the variables affected earnings differently for men 

and women. Positive values in the difference due to parameters column 

would indicate that men would gain more from an increase in a particular 

variable than would women. For example, compared to women, men 

receive a greater estimated return to their earnings resulting from having 

children. However, we found several large negative values indicating that 

women have a relative advantage over men in terms of how other factors 

affect earnings. The largest negative values in this column resulted from 

the greater estimated return for each additional year of education and the 

greater estimated return for an additional hour of work for women. As 

mentioned above, the relative advantage for women for some of the 

variables in the model is offset when the difference in the intercept terms 

of the separate regressions is added. The difference in the intercept terms 

captures gender differences and other unmeasured effects that we cannot 

identify in the regressions. 12

Table 4: Decomposition Results Using Regression Coefficients 

 Estimate Means (averages) Difference 

Variable 

Men 

βm

Women 

βf

Men 

Xm

Women

Xf

Between 

means 

(averages)

(Xm – Xf)

Due to 

characteristics 

(Xm – Xf) βm

Between 

parameters 

(βm – βf

) 

Due to 

parameters 

(returns) 

Xf (βm– βf) 

Work patterns 

Experience (years) 0.0260 0.0246 16.2891 12.1342 4.1548 0.1081 0.0014 0.0170 

Experience squared –0.0004 –0.0004 359.5914 210.6411 148.9504 –0.0558 0.0001 0.0120 

Hours worked (per 

year) 0.0003 0.0005 2,147.3100 1,674.8000 472.5100 0.1340 -0.0002 -0.3057 

Time out of labor 

force (weeks) –0.0175 –0.0224 0.9262 2.8345 –1.9083 0.0335 0.0049 0.0139 

Length of 

unemployment 

(weeks) –0.0171 –0.0143 1.8149 1.8887 –0.0739 0.0013 -0.0028 -0.0054 

Tenure (months) 0.0010 0.0009 91.4775 74.4278 17.0497 0.0163 0.0000 0.0015 

Working full time (in 

main job) 0.1724 0.1180 0.8761 0.6701 0.2059 0.0355 0.0543 0.0364 

 12Oaxaca and Ransom showed that the size of the intercept terms in decompositions is 

sensitive to the choice of the omitted categorical variables used as reference groups in the 

analysis. See Ronald L. Oaxaca and Michael R. Ransom, “Identification in Detailed Wage 

Decompositions,” Review of Economics and Statistics 81:1(February 1999): 154–57. 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 47 GAO-04-35 Women's Earnings 

 Estimate Means (averages) Difference 

Variable 

Men 

βm

Women 

βf

Men 

Xm

Women

Xf

Between 

means 

(averages)

(Xm – Xf)

Due to 

characteristics 

(Xm – Xf) βm

Between 

parameters 

(βm – βf) 

Due to 

parameters 

(returns) 

Xf (βm– βf) 

Other work related 

Mother’s education –0.0107 –0.0256 3.5458 3.4941 0.0516 –0.0005 0.0150 0.0524 

Father’s education 0.0039 –0.0117 3.3364 3.2447 0.0917 0.0004 0.0156 0.0506 

Highest education 

(years) 0.1355 0.1603 13.1455 13.0880 0.0575 0.0078 –0.0248 -0.3242 

Self-employment 

status 

Works for someone else onlya

 

Self-employed only –0.1056 0.2168 0.1177 0.0579 0.0597 –0.0063 –0.3224 -0.0187 

Missing –0.2823 –0.3413 0.0648 0.1230 –0.0582 0.0164 0.0590 0.0073 

Both 0.0506 –0.0846 0.0094 0.0042 0.0052 0.0003 0.1352 0.0006 

Union member 0.1388 0.1405 0.1773 0.1187 0.0587 0.0081 –0.0017 -0.0002 

Occupation 

Professional, 

technicala

 

Service/private 

household workers –0.1061 –0.0975 0.0763 0.2034 –0.1271 0.0135 –0.0087 -0.0018 

Farm laborers and 

foremen –0.1928 –0.0602 0.0121 0.0023 0.0098 –0.0019 –0.1326 -0.0003 

Farmers and farm 

management –0.3434 –0.1690 0.0124 0.0008 0.0116 –0.0040 –0.1745 -0.0001 

Nonfarm laborers –0.0823 –0.0627 0.0547 0.0083 0.0464 –0.0038 –0.0195 -0.0002 

Transport 

equipment 

operators –0.0576 –0.1840 0.0680 0.0084 0.0596 –0.0034 0.1264 0.0011 

Operators, 

nontransport –0.0458 –0.0657 0.0877 0.0879 –0.0002 0.0000 0.0198 0.0017 

Craftsmen 0.0016 –0.0180 0.2049 0.0171 0.1879 0.0003 0.0196 0.0003 

Clerical workers –0.0608 –0.0497 0.0497 0.2565 –0.2068 0.0126 –0.0111 -0.0028 

Sales workers –0.0343 –0.0931 0.0469 0.0409 0.0059 –0.0002 0.0588 0.0024 

Nonfarm 

managers, 

administrators 0.0373 0.0165 0.1609 0.0922 0.0687 0.0026 0.0208 0.0019 

Do not 

know/missing –0.1107 –0.1276 0.0468 0.0906 –0.0439 0.0049 0.0169 0.0015 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 48 GAO-04-35 Women's Earnings 

 Estimate Means (averages) Difference 

Variable 

Men 

βm

Women 

βf

Men 

Xm

Women

Xf

Between 

means 

(averages)

(Xm – Xf)

Due to 

characteristics 

(Xm – Xf) βm

Between 

parameters 

(βm – βf) 

Due to 

parameters 

(returns) 

Xf (βm– βf) 

Industry 

Wholesale/retail 

tradea

 

Public 

administration 0.0104 0.1641 0.0799 0.0607 0.0192 0.0002 –0.1538 -0.0093 

Professional 

services 0.0172 0.0707 0.1211 0.3467 –0.2256 –0.0039 –0.0535 -0.0186 

Entertainment 0.0044 –0.0756 0.0095 0.0061 0.0034 0.0000 0.0800 0.0005 

Personal services –0.0307 –0.0097 0.0130 0.0678 –0.0549 0.0017 –0.0210 -0.0014 

Business and 

repair services 0.0705 0.0488 0.0585 0.0340 0.0245 0.0017 0.0217 0.0007 

Finance, 

insurance, real 

estate 0.0562 0.1489 0.0394 0.0641 –0.0248 –0.0014 –0.0928 -0.0059 

Transportation/ 

communications/ 

public utilities 0.1713 0.1865 0.0976 0.0353 0.0622 0.0107 –0.0152 -0.0005 

Manufacturing 0.1417 0.1332 0.2444 0.1341 0.1103 0.0156 0.0085 0.0011 

Construction 0.1708 0.0673 0.0963 0.0101 0.0862 0.0147 0.1034 0.0010 

Mining/agriculture 0.0481 0.0178 0.0474 0.0075 0.0399 0.0019 0.0302 0.0002 

Do not 

know/missing 0.1106 0.0712 0.0513 0.0954 –0.0441 –0.0049 0.0394 0.0038 

Mills ratio –0.3307 –0.1584 0.1628 0.3771 –0.2143 0.0709 –0.1723 -0.0650 

Demographic and 

other controls 

Age of individual 

(years) –0.0016 –0.0058 40.1442 40.3309 –0.1867 0.0003 0.0041 0.1669 

Age of youngest child 

(years) –0.0013 0.0023 3.4902 4.2042 –0.7140 0.0010 –0.0036 -0.0152 

Number of children 0.0210 –0.0254 0.9659 1.0469 –0.0810 –0.0017 0.0464 0.0486 

Additional family 

income (inflation 

adjusted in thousands 

of dollars) –0.0009 –0.0001 25.1172 34.9156 –9.7984 0.0086 –0.0008 -0.0284 

Metropolitan area 0.0171 0.0305 0.6476 0.6806 –0.0330 –0.0006 –0.0133 -0.0091 

Excellent health 0.0149 0.0062 0.2613 0.2041 0.0572 0.0009 0.0088 0.0018 

Marital status 

Never marrieda 

Married 0.0800 –0.0011 0.7196 0.6101 0.1095 0.0088 0.0811 0.0495 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 49 GAO-04-35 Women's Earnings 

 Estimate Means (averages) Difference 

Variable 

Men 

βm

Women 

βf

Men 

Xm

Women

Xf

Between 

means 

(averages)

(Xm – Xf)

Due to 

characteristics 

(Xm – Xf) βm

Between 

parameters 

(βm – βf) 

Due to 

parameters 

(returns) 

Xf (βm– βf) 

Other 0.0685 –0.0009 0.1327 0.2424 –0.1097 –0.0075 0.0694 0.0168 

Region: South –0.0522 –0.0377 0.4142 0.4551 –0.0409 0.0021 –0.0145 -0.0066 

Race 

Whitea 

Black –0.1487 –0.0682 0.2666 0.3602 –0.0936 0.0139 –0.0806 -0.0290 

Other 0.0491 0.0972 0.0140 0.0152 –0.0011 –0.0001 –0.0481 -0.0007 

Year, compared to 

1983 

2000 0.0188 0.0621 0.0537 0.0538 –0.0001 –0.0000 –0.0433 -0.0023 

1999b 

1998 –0.0406 0.0298 0.0536 0.0515 0.0021 –0.0001 –0.0704 -0.0036 

1997b

1996 –0.1045 –0.0733 0.0468 0.0514 –0.0046 0.0005 –0.0312 -0.0016 

1995 –0.0813 –0.0618 0.0613 0.0622 –0.0009 0.0001 –0.0194 -0.0012 

1994 –0.0973 –0.0759 0.0615 0.0655 –0.0040 0.0004 –0.0214 -0.0014 

1993 –0.0854 –0.0495 0.0597 0.0641 –0.0044 0.0004 –0.0359 -0.0023 

1992 –0.0693 –0.0625 0.0662 0.0684 –0.0022 0.0002 –0.0068 -0.0005 

1991 –0.1023 –0.0921 0.0668 0.0675 –0.0007 0.0001 –0.0103 -0.0007 

1990 –0.0960 –0.0737 0.0672 0.0686 –0.0015 0.0001 –0.0224 -0.0015 

1989 –0.0691 –0.0524 0.0675 0.0680 –0.0006 0.0000 –0.0167 -0.0011 

1988 –0.0359 –0.0516 0.0669 0.0667 0.0002 –0.0000 0.0157 0.0010 

1987 –0.0389 –0.0561 0.0666 0.0660 0.0006 –0.0000 0.0171 0.0011 

1986 –0.0248 –0.0632 0.0668 0.0654 0.0014 –0.0000 0.0384 0.0025 

1985 –0.0282 –0.0822 0.0666 0.0646 0.0020 –0.0001 0.0540 0.0035 

1984 –0.0237 –0.0847 0.0656 0.0631 0.0025 –0.0001 0.0609 0.0038 

Sum before 

intercept -0.3618 

Intercept 7.5910 6.9846 0.6065 

Sum 0.4540 0.2446 

Source: GAO analysis of PSID data. 

a

Category omitted. 

b

No data available. 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 50 GAO-04-35 Women's Earnings 

Table 5: Decomposition Results Using Alternative Estimates 

Variable 

Alternative 

estimate 

 Means 

(averages) Difference 

Men 

gm

Women 

gf

Men

Xm

Women 

Xf

Between 

means 

(averages) 

(Xm – Xf

) 

Due to 

characteristics 

(Xm – Xf) gm

Between 

parameters 

(gm – gf) 

Due to 

parameters 

(returns) 

Xf (gm – gf) 

Work Patterns 

Experience (years) 0.0264 0.0249 16.2891 12.1342 4.1548 0.1095 0.0014 0.0175 

Experience 

squared –0.0004 –0.0004 359.5914 210.6411 148.9504 –0.0558 0.0001 0.0120 

Hours worked (per 

year) 0.0003 0.0005 2,147.3100 1,674.8000 472.5100 0.1340 –0.0002 -0.3058 

Time out of labor 

force (weeks) –0.0174 –0.0222 0.9262 2.8345 –1.9083 0.0332 0.0048 0.0136 

Length of 

unemployment 

(weeks) –0.0170 –0.0142 1.8149 1.8887 –0.0739 0.0013 –0.0028 -0.0053 

Tenure (months) 0.0010 0.0009 91.4775 74.4278 17.0497 0.0163 0.0000 0.0015 

Working full time 

(in main job) 0.1881 0.1252 0.8761 0.6701 0.2059 0.0387 0.0628 0.0421 

Other work 

related 

Mother’s education –0.0106 –0.0253 3.5458 3.4941 0.0516 –0.0005 0.0147 0.0515 

Father’s education 0.0039 –0.0116 3.3364 3.2447 0.0917 0.0004 0.0155 0.0504 

Highest education 

(years) 0.1451 0.1738 13.1455 13.0880 0.0575 0.0083 –0.0287 -0.3757 

Self-employment 

status 

Works for 

someone else 

onlya

 

Self-employed 

only –0.1003 0.2419 0.1177 0.0579 0.0597 –0.0060 –0.3422 -0.0198 

Missing –0.2461 –0.2892 0.0648 0.1230 -0.0582 0.0143 0.0432 0.0053 

Both 0.0516 –0.0820 0.0094 0.0042 0.0052 0.0003 0.1336 0.0006 

Union member 0.1488 0.1507 0.1773 0.1187 0.0587 0.0087 –0.0019 -0.0002 

Occupation 

Professional, 

technicala

 

Service/private 

household 

workers –0.1008 –0.0930 0.0763 0.2034 –0.1271 0.0128 –0.0079 -0.0016 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 51 GAO-04-35 Women's Earnings 

Variable 

Alternative 

estimate 

 Means 

(averages) Difference 

Men 

gm

Women 

gf 

Men

Xm

Women 

Xf

Between 

means 

(averages) 

(Xm – Xf) 

Due to 

characteristics 

(Xm – Xf) gm

Between 

parameters 

(gm – gf) 

Due to 

parameters 

(returns) 

Xf (gm – gf) 

Farm laborers 

and foremen –0.1761 –0.0618 0.0121 0.0023 0.0098 –0.0017 –0.1143 -0.0003 

Farmers and 

farm 

management –0.2915 –0.1611 0.0124 0.0008 0.0116 –0.0034 –0.1304 -0.0001 

Nonfarm 

laborers –0.0791 –0.0615 0.0547 0.0083 0.0464 –0.0037 –0.0176 -0.0001 

Transport 

equipment 

operators –0.0562 –0.1690 0.0680 0.0084 0.0596 –0.0033 0.1128 0.0009 

Operators, 

nontransport –0.0449 –0.0638 0.0877 0.0879 –0.0002 0.0000 0.0188 0.0017 

Craftsmen 0.0015 –0.0183 0.2049 0.0171 0.1879 0.0003 0.0198 0.0003 

Clerical workers –0.0592 –0.0486 0.0497 0.2565 –0.2068 0.0122 –0.0106 -0.0027 

Sales workers –0.0339 –0.0891 0.0469 0.0409 0.0059 –0.0002 0.0552 0.0023 

Nonfarm 

managers, 

administrators 0.0379 0.0165 0.1609 0.0922 0.0687 0.0026 0.0214 0.0020 

Do not 

know/missing –0.1054 –0.1205 0.0468 0.0906 –0.0439 0.0046 0.0151 0.0014 

Industry 

Wholesale/retail 

tradea

 

Public 

administration 0.0102 0.1780 0.0799 0.0607 0.0192 0.0002 –0.1678 -0.0102 

Professional 

services 0.0172 0.0731 0.1211 0.3467 –0.2256 –0.0039 –0.0560 -0.0194 

Entertainment 0.0039 –0.0737 0.0095 0.0061 0.0034 0.0000 0.0775 0.0005 

Personal 

services –0.0306 –0.0098 0.0130 0.0678 –0.0549 0.0017 –0.0208 -0.0014 

Business and 

repair services 0.0729 0.0498 0.0585 0.0340 0.0245 0.0018 0.0231 0.0008 

Finance, 

insurance, real 

estate 0.0575 0.1604 0.0394 0.0641 –0.0248 –0.0014 –0.1028 -0.0066 

Transportation/ 

communication/ 

public utilities 0.1867 0.2046 0.0976 0.0353 0.0622 0.0116 -0.0178 -0.0006 

Manufacturing 0.1521 0.1423 0.2444 0.1341 0.1103 0.0168 0.0098 0.0013 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 52 GAO-04-35 Women's Earnings 

Variable 

Alternative 

estimate 

 Means 

(averages) Difference 

Men 

gm

Women 

gf 

Men

Xm

Women 

Xf

Between 

means 

(averages) 

(Xm – Xf) 

Due to 

characteristics 

(Xm – Xf) gm

Between 

parameters 

(gm – gf) 

Due to 

parameters 

(returns) 

Xf (gm – gf) 

Construction 0.1861 0.0689 0.0963 0.0101 0.0862 0.0160 0.1172 0.0012 

Mining/ 

agriculture 0.0489 0.0166 0.0474 0.0075 0.0399 0.0020 0.0323 0.0002 

Do not 

know/missing 0.1164 0.0730 0.0513 0.0954 –0.0441 –0.0051 0.0434 0.0041 

Mills ratio –0.2819 –0.1470 0.1628 0.3771 –0.2143 0.0604 –0.1348 -0.0508 

Demographic and 

other controls 

Age of individual 

(years) –0.0016 –0.0057 40.1442 40.3309 –0.1867 0.0003 0.0041 0.1662 

Age of youngest 

child (years) –0.0013 0.0023 3.4902 4.2042 –0.7140 0.0010 –0.0036 -0.0152 

Number of children 0.0212 –0.0251 0.9659 1.0469 –0.0810 –0.0017 0.0463 0.0485 

Additional family 

income (inflation 

adjusted in 

thousands of 

dollars) –0.0009 -0.0001 25.1172 34.9156 –9.7984 0.0086 –0.0008 -0.0284 

Metropolitan area 0.0173 0.0309 0.6476 0.6806 –0.0330 –0.0006 –0.0136 -0.0093 

Excellent health 0.0150 0.0062 0.2613 0.2041 0.0572 0.0009 0.0089 0.0018 

Marital status 

Never marrieda 

Married 0.0831 –0.0013 0.7196 0.6101 –0.1097 –0.0091 0.0844 0.0515 

Other 0.0707 –0.0011 0.1327 0.2424 0.0000 0.0000 0.0718 0.0174 

Region: South –0.0510 –0.0371 0.4142 0.4551 0.1095 –0.0056 –0.0139 -0.0063 

Race 

Whitea 

Black –0.1385 –0.0661 0.2666 0.3602 –0.0936 0.0130 –0.0723 -0.0260 

Other 0.0466 0.0989 0.0140 0.0152 –0.0011 –0.0001 –0.0523 -0.0008 

Year, compared to 

1983 

2000 0.0188 0.0638 0.0537 0.0538 –0.0001 0.0000 –0.0450 -0.0024 

1999b 

1998 –0.0399 0.0300 0.0536 0.0515 0.0021 –0.0001 –0.0699 -0.0036 

1997b

1996 –0.0994 –0.0709 0.0468 0.0514 –0.0046 0.0005 –0.0285 -0.0015 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 53 GAO-04-35 Women's Earnings 

Variable 

Alternative 

estimate 

 Means 

(averages) Difference 

Men 

gm

Women 

gf 

Men

Xm

Women 

Xf

Between 

means 

(averages) 

(Xm – Xf) 

Due to 

characteristics 

(Xm – Xf) gm

Between 

parameters 

(gm – gf) 

Due to 

parameters 

(returns) 

Xf (gm – gf) 

1995 –0.0782 –0.0601 0.0613 0.0622 –0.0009 0.0001 –0.0181 -0.0011 

1994 –0.0928 –0.0733 0.0615 0.0655 –0.0040 0.0004 –0.0196 -0.0013 

1993 –0.0820 –0.0484 0.0597 0.0641 –0.0044 0.0004 –0.0335 -0.0021 

1992 –0.0671 –0.0608 0.0662 0.0684 –0.0022 0.0002 –0.0063 -0.0004 

1991 –0.0974 –0.0881 0.0668 0.0675 –0.0007 0.0001 –0.0093 -0.0006 

1990 –0.0917 –0.0712 0.0672 0.0686 –0.0015 0.0001 –0.0205 -0.0014 

1989 –0.0669 –0.0512 0.0675 0.0680 –0.0006 0.0000 –0.0157 -0.0011 

1988 –0.0354 –0.0504 0.0669 0.0667 0.0002 –0.0000 0.0151 0.0010 

1987 –0.0383 –0.0546 0.0666 0.0660 0.0006 –0.0000 0.0164 0.0011 

1986 –0.0246 –0.0613 0.0668 0.0654 0.0014 –0.0000 0.0368 0.0024 

1985 –0.0279 –0.0791 0.0666 0.0646 0.0020 –0.0001 0.0512 0.0033 

1984 –0.0235 –0.0813 0.0656 0.0631 0.0025 –0.0001 0.0578 0.0036 

Sum before 

intercept -0.3943 

Intercept 7.5910 6.9846 0.6065 

Sumc 0.4311 0.2122 

Source: GAO analysis of PSID data. 

a

Category omitted. 

b

No data available. 

c

Sum need not equal the log difference in earnings due to the transformation of the coefficients. 

To determine whether our results would change significantly if the model 

were specified slightly differently, we changed the specification in several 

ways and compared those results with the results in the report. In all the 

alternative specifications we developed, work patterns were important in 

accounting for some of the earnings difference between men and women. 

In addition, a significant gender earnings difference remained after 

controlling for the effects of the variables in the model. 

We developed several different specifications of the Hausman-Taylor 

model presented in the report. In one particular alternative, we used a 

linear time trend and the national unemployment rate instead of the year 

specific dummy variables to control for the effects of national economic 

conditions and other year-specific effects that are not reflected in the 

other variables in the model. The results of this alternative specification 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 54 GAO-04-35 Women's Earnings 

also showed a slight narrowing of the earnings difference over time, but 

they showed a decline in the difference in 1998 and 2000. We chose to 

report the specification using dummy variables for each year because it is 

more general than a linear time trend specification. However, this shows 

that the results for certain years may be sensitive to the exact specification 

chosen. 

In other variants of the Hausman-Taylor model, we excluded occupation 

and industry variables from the model, excluded observations from selfemployed individuals, limited the analysis to the Survey Research Center 

portion of the PSID, and dropped the selection bias correction term from 

the analysis. In these cases, the average earnings difference increased by 

about 1 to 5 percentage points. As in the results we report, we found a 

small downward trend in the difference in each case. 

We also computed OLS regressions by year, using the same variables as in 

the model we report. The earnings difference was smaller than the results 

shown in table 2 (averaging about 14 percent over the period), and there 

was a small downward trend in the difference over time. 

While our analysis used what we consider to be the most appropriate 

methods and data set available for our purposes, our analysis has both 

data and methodological limitations that should be noted. Specifically, 

although the PSID has many advantages over alternative data sets, like any 

data set, it did not include certain data elements that would have allowed 

us to further define reasons for earnings differences. For example, until 

recently, the PSID did not contain data on fringe benefits—most 

importantly, health insurance and pension coverage. Because data on 

fringe benefits were not available for each year that we studied, we did not 

include it for any year. If more women than men worked in jobs that 

offered a greater percentage of total compensation in the form of fringe 

benefits, part of the remaining gender earnings difference could be 

explained by differences in the receipt of fringe benefits. Similarly, the 

PSID does not contain data on job characteristics such as flexibility that 

men and women may value differently. 

In addition, the PSID does not contain data on education quality or field of 

study, such as college major. It also does not contain data on cognitive 

ability or measures of social skills, all of which may affect earnings. For 

Limitations of Our 

Analysis 
Appendix II: GAO Analysis of the Earnings 

Difference between Men and Women 

Page 55 GAO-04-35 Women's Earnings 

example, studies of earnings differences that used the National 

Longitudinal Survey of Youth have used a measure of ability in addition to 

work experience, education, and demographic variables.13 This data set, 

however, follows a specific cohort of individuals over time and is 

therefore not representative of the population as a whole. 

Our model is also limited in that the industry and occupation categories 

that we used are broad. Gender earnings differences within these 

categories are not reflected and could account for some amount of the 

remaining difference. In addition, we did not explicitly model an 

individual’s choice of occupation and industry and how these choices 

relate to earnings differences. Also, although PSID collects information on 

work interruptions, the detail of some of the survey questions limited our 

ability to fully explore reasons why individuals were out of the labor force. 

We used dummy variables for years to control for general economic 

conditions and year-specific effects. In some specifications of the model, 

we added national unemployment rate data to the PSID sample in order to 

control for national labor market conditions. We did not access the PSID 

Geocode Match file, which contains more detailed information on the 

location of residence of survey respondents. We could not, therefore, 

incorporate a measure of local unemployment rates in the analyses. 

 13See Altonji and Blank, pp. 3160–62, and June O’Neill, “The Gender Gap in Wages, circa 

2000,” American Economic Review 93:2 (May 2003): 309-314 
Appendix III: GAO Analysis of Women’s 

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Page 56 GAO-04-35 Women's Earnings 

Our analysis of data from the PSID identified factors that contribute to the 

earnings difference between men and women, but cannot fully explain the 

underlying reasons why these factors differ. For example, the model 

results indicated that earnings differ, in part, because men and women 

tend to have different work patterns (such as women are more likely to 

work part time) and often work in different occupations. However, the 

model could not explain why women worked part time more often or took 

jobs in certain occupations. In addition, the analysis could not explain why 

a remaining earnings difference existed after accounting for a range of 

demographic, family, and work-related factors. To gain perspective on 

these issues, we conducted additional work to gather information on why 

individuals make certain decisions about work and how those decisions 

may affect their earnings. 

We conducted a multipronged effort, including a literature review, 

interviews with employers as well as individuals with expertise on 

earnings and other workplace issues,1

 and a review of our work by 

additional knowledgeable individuals. Specifically, we reviewed literature 

on work-related decisions, including using alternative work arrangements, 

and how these decisions may affect advancement or earnings. We also 

conducted 10 interviews with a variety of experts—industry groups, 

advocacy groups, unions, and researchers—to obtain a broad range of 

perspectives on reasons why workers make certain career and workplace 

decisions that could affect their earnings. In selecting experts, we targeted 

those who have conducted research on earnings issues and have different 

viewpoints. 

We also interviewed employers from eight companies, as well as a group 

of employees from one of these companies, about policies and practices, 

including alternative work arrangements (such as part time and leave), 

that may affect workers’ workplace decisions and earnings. We targeted 

companies that are recognized leaders in work-life practices; for example, 

those on Working Mother magazine’s “100 Best Companies for Working 

Mothers” and on Fortune magazine’s “100 Best Companies to Work For” 

list. In our selection, we also sought participation from a variety of sectors, 

including: 

• financial/professional services 

 1

These individuals will be referred to as “experts” throughout this appendix. 

Appendix III: GAO Analysis of Women’s 

Workplace Decisions 

Purpose 

Scope and 

Methodology 
Appendix III: GAO Analysis of Women’s 

Workplace Decisions 

Page 57 GAO-04-35 Women's Earnings 

• health care 

• information technology 

• manufacturing 

• media/advertising 

• pharmaceuticals/biotechnology 

• travel/hospitality 

Based on the literature and our interviews, we developed key themes 

about workplace culture, decisions about work, and how these decisions 

may affect career advancement and earnings. We vetted the themes with 

11 experts—who are well known in the area of earnings and work-life 

issues and represent views of researchers, advocacy groups, and 

employers—to determine if the themes were consistent with their 

experience or existing research and to identify areas of disagreement to 

broaden our understanding of the issues. 

According to experts and the literature, women are more likely than men 

to have primary responsibility for family, and as a result, working women 

with family responsibilities must make a variety of decisions to manage 

these responsibilities. For example, these decisions may include what 

types of jobs women choose as well as decisions they make about how, 

when, and where they do their work. These decisions may have specific 

consequences for their career advancement or earnings. However, debate 

exists whether these decisions are freely made or influenced by 

discrimination in society or in the workplace. 

The tremendous growth in the number of women in the labor force in 

recent decades has dramatically changed the world of work. The number 

of women—particularly married women with children—who work has 

increased, in many cases leaving no one at home to handle family and 

other responsibilities. Single-headed households, in which only one parent 

is available to handle both work and home responsibilities, are also 

increasingly common. As a result, an increasing number of workers face 

the challenge of trying to simultaneously manage responsibilities both 

inside and outside the workplace. 

At the same time, however, many employers continue to have certain 

expectations about how much priority workers should give to work in 

relation to responsibilities outside the workplace. While workplace culture 

varies from one workplace to another, research indicates that in some 

cases an “ideal worker” perception exists. According to this perception, an 

Summary of Results 

Background 
Appendix III: GAO Analysis of Women’s 

Workplace Decisions 

Page 58 GAO-04-35 Women's Earnings 

ideal worker places highest priority on work, working a full-time 

9-to-5 schedule throughout their working years, and often working 

overtime. Ideal workers take little or no time off for childbearing or 

childrearing, and they appear—whether true or not—to have few 

responsibilities outside of work. While this perception applies to all 

workers, most experts and literature agree that it disproportionately 

affects women because they often have or take primary responsibility for 

home and family, such as caring for children, even when they are 

employed outside of the home. However, some research indicates that 

men are now more likely than in the past to participate in childcare, 

eldercare, and housework and are beginning to adjust their work in 

response to family obligations. 

Some employers, however, have taken note of the multiple needs of 

workers and have begun to offer alternative work arrangements to help 

workers manage both work and other life responsibilities. These 

arrangements can benefit workers by providing them with flexibility in 

how, when, and where they do their work. One type of alternative work 

arrangement allows workers to reduce their work hours from the 

traditional 40 hours per week, such as part-time work or job sharing.2

Similarly, some employers offer workers the opportunity to take leave 

from work for a variety of reasons, such as childbirth, care for elderly 

relatives, or other personal reasons. Some arrangements, such as flextime, 

allow employees to begin and end their workday outside the traditional 

9-to-5 work hours. Other arrangements, such as telecommuting from 

home, allow employees to work in an alternative location. Childcare 

facilities are also available at some workplaces to help workers with their 

caregiving responsibilities. In addition to benefiting workers, these 

arrangements may also benefit employers by helping them recruit and 

retain workers. For example, according to an industry group for attorneys, 

law firms may lose new attorneys—particularly women who plan to have 

children—if they do not offer workplace flexibility. This is costly to firms 

due to substantial training investments they make in new attorneys, which 

they may not recoup if workers quit early on. 

Nonetheless, research suggests that many workplaces still maintain the 

same policies, practices, and structures that existed when most workers 

 2

Part-time work schedules allow employees to reduce their work hours from the traditional 

40 hours per week in exchange for a reduced salary and possibly pro-rated benefits. Job 

sharing—a form of part-time work—allows two employees to share job responsibilities, 

salary, and benefits of one full-time position. 
Appendix III: GAO Analysis of Women’s 

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Page 59 GAO-04-35 Women's Earnings 

were men who worked full time, 40-hours per week. As a result, there may 

be a “mismatch” between the needs of workers with family responsibilities 

and the structure of the workplace. 

Working women make a variety of decisions to manage both their work 

and home or family responsibilities. According to some experts and 

literature, some women work in jobs that are more compatible with their 

home and family responsibilities. In addition, some women use alternative 

work arrangements such as working a part-time schedule or taking leave 

from work. Experts indicate that these decisions may result in women as a 

group earning less than men. However, debate exists about whether 

women’s work-related decisions are freely made or influenced by 

discrimination. Some experts believe that women and men generally have 

different life priorities—women choose to place higher priority on home 

and family, while men choose to place higher priority on career and 

earnings. These women may voluntarily give up potential for higher 

earnings to focus on home and family. However, other experts believe that 

men and women have similar life priorities, and instead indicate that 

women as a group earn less because of underlying discrimination in 

society or in the workplace. 

According to some experts and literature, some women choose to work in 

jobs that are compatible with their home or family responsibilities, and 

may trade off career advancement or higher earnings for these jobs. Some 

experts and literature indicate that jobs that offer flexibility tend to be 

lower paying and offer less career advancement.3

Women choose jobs with different kinds of flexibility based on their 

needs. According to some researchers, some jobs are less demanding or 

less stressful than others, which may allow women who choose these jobs 

to have more time and energy for responsibilities outside of work. For 

example, a woman may work in an off-line, staff position, such as a human 

resources job, because it requires less travel and less time in the office 

than an online position in the company. Off-line positions may offer 

flexibility, but less opportunity for advancement and higher earnings. One 

expert also indicated that, within a certain field, some women are more 

 3

In contrast, other experts indicate that flexibility is often available in higher paying jobs, 

particularly those where workers have more authority and autonomy. 

Working Women 

Make a Variety of 

Decisions to Manage 

Work and Family 

Responsibilities 

Certain Jobs May Offer 

Flexibility but May Also 

Affect Earnings 
Appendix III: GAO Analysis of Women’s 

Workplace Decisions 

Page 60 GAO-04-35 Women's Earnings 

likely to choose jobs that allow them more flexibility but lower earnings 

potential. For example, according to this expert, within the medical field, 

the family practice specialty is typically more accommodating to home and 

family responsibilities than the surgical specialty, which offers relatively 

higher earnings. Surgeons’ work is generally less predictable because 

surgeons are often called in the middle of the night to treat emergencies. 

The work is also less flexible because surgeons tend to see the same 

patients throughout their treatment, while family practice doctors can rely 

on other doctors in the practice to treat their patients if necessary. Experts 

also noted that some women may start their own businesses, in part, to 

gain flexibility in when and where they work. 

According to some experts and literature, women may choose jobs that 

allow them to quit (for example, to care for a child) and easily reenter the 

labor force with minimal earnings loss when they return to work. Given 

that job skills affect earnings, some suggest that certain women may 

choose jobs in which skills deteriorate or become outdated less quickly. 

As a result, this may allow women to leave and return to work while 

minimizing any effect on their earnings. 

Another way that women manage work and family responsibilities is by 

choosing to use alternative work arrangements, which may affect their 

career advancement and earnings.4

 For example, some women choose to 

work a part-time schedule, take leave from work, or use flextime. While 

some research indicates that certain arrangements may help women 

maintain their careers during times when they need flexibility, other 

research suggests that there may be negative effects. 

No single, national data source exists that provides information about all 

workers who use alternative work arrangements. However, some data 

exist from narrowly scoped studies that focus on particular types of work 

arrangements, types of employees, or individual companies. Even when 

employers offer alternative arrangements to all workers, some research 

and the companies we interviewed indicate that women are more likely 

than men to use certain arrangements, while both men and women use 

others in similar proportions. Specifically, women are more likely than 

men to take leave from work for family reasons and to work part time for 

 4

Since women are more likely than men to use certain alternative work arrangements, any 

effects apply disproportionately to women in these cases. 

Alternative Work 

Arrangements Offer 

Flexibility but Some May 

Affect Earnings 
Appendix III: GAO Analysis of Women’s 

Workplace Decisions 

Page 61 GAO-04-35 Women's Earnings 

family reasons even when these options are available to both men and 

women. According to our interviews and some literature, some workers—

particularly men—are reluctant to use alternative arrangements because 

they perceive that their advancement and earnings will be negatively 

affected. This may help to explain why men tend to use personal days, sick 

days, or vacation time instead of taking family leave. On the other hand, 

similar proportions of men and women use flextime and telecommuting 

when these options are available. However, according to some research, 

men are more likely than women to work in the jobs, organizations, or 

high-level, high-paying positions that have these options available. 

Comprehensive, national data are lacking on how career advancement and 

earnings may be affected by using alternative work arrangements, but 

some limited research does exist. Certain researchers indicate that using 

certain work arrangements may have some beneficial career effects if they 

help workers maintain career linkages or skills that they might otherwise 

lose. For example, for women who would have left the workforce or 

changed jobs if they did not have access to alternative arrangements that 

could help them manage work and family, part-time work5

 may allow them 

to maintain job skills, knowledge, or career momentum. In addition, 

women who can take leave with the guarantee of returning to a similar job 

benefit because they maintain links with an employer where they have 

built up specific job-related skills. 

Other research indicates that using certain alternative work arrangements 

may have negative effects on career advancement and earnings. 

Specifically, employers may view these workers as not conforming to the 

ideal worker norm because they are not at work as much or during the 

same work hours as their managers or co-workers. Research indicates that 

some arrangements, such as leave, part-time work, and telecommuting, 

reduce workers’ “face time”—the amount of time spent in the workplace.6

Given that some employers use face time as an indicator of workers’ 

productivity, those who lack face time may experience negative career 

effects. According to some experts and literature, some employers may 

 5

Research indicates that different types of part-time work exist. Some part-time jobs 

require relatively low skills, and offer low pay and little opportunity for advancement. In 

contrast, other part-time jobs are work schedules that employers create to retain or attract 

workers who cannot or do not want to work full time. These jobs are often higher skilled 

and higher paying with advancement potential. 

6

The idea of “face time” may apply primarily to certain types of jobs, such as professional, 

white-collar jobs or those that require contact with clients or customers. 
Appendix III: GAO Analysis of Women’s 

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view women who use alternative arrangements as less available, less 

valuable, or less committed to their work. This may result in less 

challenging work, fewer career opportunities, fewer promotions, and less 

pay. However, one company representative that we interviewed told us 

that workers using these arrangements are not necessarily less committed 

and that, in some cases, they work harder. For example, several of the 

women we interviewed who were scheduled to work less than full time 

noted that they sometimes came into the office or worked at home on 

their scheduled days off. 

Although existing research is limited and often narrow in scope, following 

are examples of studies that address advancement and earnings effects 

that are associated with using certain alternative arrangements. 

• One study—which tracked a small group of working women for 7 years 

after they gave birth—found that flextime, telecommuting, and reduced 

work hours had some negative impact on wage growth for some 

mothers. Flextime showed a neutral or mild impact on wage growth, 

while telecommuting and reduced work hours—which result in less 

face time—showed large pronounced negative effects, but only for 

some workers. For all three arrangements, managers or professionals 

experienced more negative wage effects than nonmanagerial or 

nonprofessional workers. 

• Another study of 11,815 managers in a large financial services 

organization found that leaves of absence were associated with fewer 

subsequent promotions and smaller raises. This was true regardless of 

the reason for the leave (i.e., a worker’s illness or family 

responsibilities) or whether the leave taker was a man or woman—

though most of the managers taking leave were women. Taking leave 

negatively affected workers’ performance evaluations, but only for the 

year that they took the leave. Even when accounting for any potential 

differences in the performance evaluations of those who did and did 

not take leave, leave takers received fewer promotions and smaller 

raises. 

Managerial support for use of alternative work arrangements is important 

when considering any effects on advancement and earnings. According to 

our company interviews, some managers do not support use of these 

arrangements because they are seen as accommodations to certain 

workers—even though the company’s leadership views them as part of the 

overall business strategy. Workers who use these arrangements may 

experience negative effects if managers place limits on the types of work 
Appendix III: GAO Analysis of Women’s 

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Page 63 GAO-04-35 Women's Earnings 

and responsibilities they receive. For example, one worker we interviewed 

noted that she has not been assigned a high-profile project because she 

works a part-time schedule. Most of the companies we interviewed noted 

the importance of managers in implementing alternative work 

arrangements, and as a result, many train managers on this topic. For 

example, several companies train managers to focus on the quality of an 

individual’s work rather than on when (i.e., what time of day) or where 

(i.e., at home or at the workplace) they do their work. One company also 

revised managers’ performance criteria to include their response to 

flexible work arrangements. 

On the other hand, some workers do not have the option to use alternative 

work arrangements for several reasons. For example, some managers do 

not allow workers to use alternative arrangements because they want to 

directly monitor their workers, they fear that too many others will also 

request these arrangements, or they do not understand how it relates to 

the company’s bottom line. In addition, some workers—often those who 

are lower paid—do not have the option to use alternative arrangements 

because the nature of their job does not allow it. For example, 

telecommuting may not be feasible for administrative assistants because 

they must be in the office to support their bosses. Furthermore, low-paid 

workers often cannot afford to choose a work arrangement that reduces 

their pay. For example, some women in lower-paying jobs cannot afford to 

take any unpaid maternity leave, or to take it for an extended period of 

time, because of their financial situation. 

Debate exists whether decisions that women make to manage work and 

family responsibilities are freely made or influenced by underlying 

discrimination. Some experts believe that women are free to make choices 

about work and family, and willingly accept the earnings consequences. 

Specifically, certain experts believe that some women place higher priority 

on home and family, and voluntarily trade off career advancement and 

earnings to focus on these responsibilities. Other experts believe that 

some women place similar priority on family and career. Alternatively, 

other women place higher priority on career and may delay or decide not 

to have children. However, other experts believe that underlying 

discrimination exists in the presumption that women have primary 

responsibility for home and family, and as a result, women are forced to 

make decisions to accommodate these responsibilities. One example of 

this is a woman who must work part time for childcare reasons, but would 

have preferred to work full time if she did not have this family 

responsibility. In addition, some experts also suggest that women face 

Potential for Direct Or 

Indirect Discrimination 
Appendix III: GAO Analysis of Women’s 

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other societal and workplace discrimination that may result in lower 

earnings. However, according to other experts, although women may still 

face discrimination in the workplace, it is not a systematic problem and 

legal remedies are already in place. For example, Title VII of the Civil 

Rights Act of 1964 prohibits employment discrimination based on gender. 

According to some experts and literature, women face societal 

discrimination that may affect their career advancement and earnings. 

Some research suggests that the career aspirations of men and women 

may be influenced by societal norms about gender roles. For example, 

parents, peers, or institutions (such as schools or the media) may teach 

them that certain occupations—such as nursing or teaching, which tend to 

be relatively lower-paying—are identified with women while others are 

identified with men. As a result, men and women may view different fields 

or occupations as valuable or socially acceptable. According to some 

experts, societal discrimination may help explain why men and women 

tend to be concentrated in different occupations. For example, some 

research has found that women tend to be over-represented in clerical and 

service jobs, while men are disproportionately employed in blue-collar 

craft and laborer jobs.7

 Other research suggests that gender differences 

exist even among those who are college educated. For example, men tend 

to be concentrated in majors such as engineering and mathematics, while 

women are typically concentrated in majors such as social work and 

education. Research indicates that men and women who work in femaledominated occupations earn less than comparable workers in other 

occupations. 

Additionally, some experts and literature suggest that women face 

discrimination in the workplace. This type of discrimination may affect 

what type of jobs women are hired into or whether they are promoted. In 

some cases, employers or clients may underestimate women’s abilities or 

male co-workers may resist working with women, particularly if women 

are in higher-level positions. Employers may also discriminate based on 

their presumptions about women as a group in terms of family 

responsibilities—rather than considering each woman’s individual 

situation. For example, employers may be less likely to hire or promote 

 7

Notably, research indicates that women tend to be concentrated in service-producing 

occupations, such as retail trade and government, which lose relatively few jobs or actually 

gain jobs during recessions. However, men tend to be concentrated in goods-producing 

industries, such as construction and manufacturing, which often lose jobs during 

recessions. 
Appendix III: GAO Analysis of Women’s 

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women because they assume that women may be less committed or may 

be more likely to quit for home and family reasons. To the extent that 

employers who offer higher-paying jobs discriminate against women in 

this way, women may not have the same earnings opportunities as men. 

Finally, other experts suggest that both men and women who are parents 

face discrimination in the workplace due to their family responsibilities in 

terms of hiring, promotions, and terminations on the job. 

According to some literature, discrimination may occur if employers enact 

policies or practices that have a disproportionately negative impact on one 

group of workers, such as women with children. For example, if an 

employer has a policy that excludes part-time workers from promotions, 

this could have a significant effect on women because they are more likely 

to work part time. Other experts suggest that workplace practices 

reflecting ideal worker norms—such as requiring routine overtime for 

promotion—could be considered discrimination. This could impact 

women more (particularly mothers) and may result in a disproportionate 

number of men in high-level positions. 

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Appendix IV: GAO Contact and Staff 

Acknowledgments 

Page 75 GAO-04-35 Women's Earnings 

Linda Siegel, Analyst in Charge (202) 512-7150 

The following individuals also made important contributions to this report: 

Patrick DiBattista, R. Scott McNabb, Corinna Nicolaou, and Caterina 

Pisciotta, Education, Workforce, and Income Security Issues. In addition, 

the following individuals played a key role in developing the statistical 

model and conducting the analysis: Brandon Haller, Ed Nannenhorn, 

MacDonald Phillips, and Wendy Turenne, Applied Research and Methods; 

Scott Farrow, Chief Economist; and Robert Parker, Chief Statistician. 

Appendix IV: GAO Contact and Staff 

Acknowledgments 

GAO Contact 

Staff 

Acknowledgments 

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