Women's Earnings: Work Patterns Partially Explain Difference	 
between Men's and Woman's Earnings (31-OCT-03, GAO-04-35).	 
                                                                 
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. 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. 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, Congress asked that we	 
examine the factors that contribute to differences in men's and  
women's earnings.						 
-------------------------Indexing Terms------------------------- 
REPORTNUM:   GAO-04-35						        
    ACCNO:   A08819						        
  TITLE:     Women's Earnings: Work Patterns Partially Explain	      
Difference between Men's and Woman's Earnings			 
     DATE:   10/31/2003 
  SUBJECT:   Compensation					 
	     Data collection					 
	     Financial analysis 				 
	     Occupational surveys				 
	     Statistical data					 
	     Women						 
	     Womens rights					 
	     Working conditions 				 
	     Department of Labor's Current Population		 
	     Survey						 
                                                                 

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GAO-04-35

United States General Accounting Office

GAO

                       Report to Congressional Requesters

October 2003

WOMEN'S EARNINGS

 Work Patterns Partially Explain Difference between Men's and Women's Earnings

GAO-04-35

Contents

                                    Letter 1

  Appendix I Briefing Slides

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

Abbreviations

CPS Current Population Survey
OLS ordinary least squares
PSID Panel Study of Income Dynamics

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separately.

United States General Accounting Office Washington, DC 20548

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

1The CPS is a monthly survey that obtains key labor force data, such as
employment, wages, and occupations.

2This 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.

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:

o  	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.)

o  	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

3The 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.

4These individuals will be referred to as "experts" throughout the
remainder of this report.

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

    9 Work Patterns (continued)  o  Years of work experience and hours worked 
                                            per year differ for men and women 

Appendix II: GAO Analysis of the Earnings Difference between Men and Women

  Review of Other Research on Earnings Differences

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,

1The 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.

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'

2Moon-Kak Kim and Solomon W. Polachek, "Panel Estimates of Male-Female
Earnings Functions," Journal of Human Resources 29:2 (1994): 406-28.

  Data Used in Our Analysis

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

3The 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.

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

4The 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."

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.

     Table 1: Descriptive Statistics for Selected PSID Variables Men Women

Means (averages)

                               Standard deviation

Means (averages)

                               Standard deviation

Variable

                    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 
                          Number of children      0.9   1.2       1.1 
                           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 
             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 
                Highest education (in years)     12.9   2.7      12.7 
                      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  

                                Men Women Means

Standard deviation Means Standard Variable (averages) (averages) deviation

                       Number of individuals 4,477 4,884

                       Source: GAO analysis of PSID data.

                      Description of Our Econometric Model

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

ln (real earningsit) = X1itb1 + X2itb2 + Z1id1 + Z2id2+ ui+ nit

where subscripts i and t denote individuals and time periods,

X1it are exogenous time-varying variables assumed to be uncorrelated with
ui and nit,

X2it are endogenous time-varying variables possibly correlated with ui but
not with nit,

Z1i are exogenous time-invariant variables assumed to be uncorrelated with
ui and nit,

5Jerry 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).

Z2i are endogenous time-invariant variables possibly correlated with ui
but not with nit,

b and d represent coefficients on the respective variables,

ui is an individual-specific random error term designed to take unobserved
individual heterogeneity into account, and

nit is a random error term.

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.

6The 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.

  Results of Our Analysis

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 b), 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

7Peter E. Kennedy, "Estimation with Correctly Interpreted Dummy Variables
in Semilogarithmic Equations," American Economic Review, 71:4 (September
1981): 801. The alternative estimator g = exp(b -  1/2 V(b)) - 1, where
V(b) is the estimated variance of the regression coefficient.

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,

8The 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.

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.

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.

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

Alternative Variable Estimate b Standard error p-value 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 

                                   Men Women

Standard Alternative Standard Alternative Estimate b error p-value
estimate gm Estimate bf error p-value estimate gf

m

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

                        Overall model Other work related

                                            Standard              Alternative 
                     Variable  Estimate b    error     p-value     estimate g 
            Working full time                                   
                   (main job)    0.1519      0.0063       0.000        0.1640 

              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 Occupation Professional, technicalb

            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 

                 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 

Men Women

Standard Alternative Standard Alternative Estimate b error p-value
estimate gm Estimate bf error p-value estimate gf

m

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

                                 Overall model

                                            Standard              Alternative 
                     Variable  Estimate b    error     p-value     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 

Men Women

           Standard         Alternative          Standard         Alternative 
  Estimate    error p-value estimate gm Estimate  error   p-value estimate gf 
    b m                                    bf                     
  -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

                                 Overall model

                                         Standard                 Alternative 
               Variable   Estimate b      error      p-value       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 

                                   Men Women

Standard error p-value

Alternative estimate gm Estimate bf

Standard error p-value

                       Alternative estimate gfEstimate b

m

            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 

                                 Overall model

                                         Standard                 Alternative 
               Variable   Estimate b      error      p-value       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 

                              Men Women Estimate b

m

Standard error p-value

Alternative estimate gm Estimate bf

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.

aData not available.

bCategory 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:

ln Em  - ln Ef = (Xm  - Xf)'b^ m + Xf'(b^ m  -b^ f)

where Xm and Xf represent the mean values of the independent variables for
men and women, respectively, and bm and bf are the estimated regression
coefficients for men and women for all the variables.

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

9J. 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.

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.

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

       Table 3: Summary of Decomposition Results Differences in earnings

                                                          Due to       Due to 
                          Variable categories    characteristics   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.

aThe 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).

bThe 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.

cThe 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.

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 Means

                                    (averag

Estimate Difference Variable

                                       bm

Women bf 

                                     Women

Between means (averages) (Xm - Xf)

Due to characteristics (Xm - Xf) bm

         Between parameters (bm - bf) Due to parameters (returns) Xf (bm- bf)

Work patterns

     Experience    0.0260 0.0246   16.2891    4.1548  0.1081  0.0014   0.0170 
      (years)                      12.1342                            
     Experience   -0.0004 -0.0004 359.5914   148.9504 -0.0558 0.0001   0.0120 
      squared                     210.6411                            
    Hours worked                                                      
        (per                                                          
       year)       0.0003 0.0005  2,147.3100 472.5100 0.1340  -0.0002 -0.3057 
                                  1,674.8000                          
    Time out of                                                       
       labor                                                          
force (weeks)  -0.0175 -0.0224   0.9262   -1.9083  0.0335  0.0049   0.0139 
                                    2.8345                            
     Length of                                                        
    unemployment                                                      
      (weeks)     -0.0171 -0.0143   1.8149   -0.0739  0.0013  -0.0028 -0.0054 
                                    1.8887                            
       Tenure      0.0010 0.0009   91.4775   17.0497  0.0163  0.0000   0.0015 
      (months)                     74.4278                            
    Working full                                                      
      time (in                                                        
                   0.1724 0.1180    0.8761    0.2059  0.0355  0.0543   0.0364 
     main job)                      0.6701                            

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.

                                     Means

                                    (averag

                              Estimate Difference

Variable

                                       bm

Women bf  

                                     Women

Between means (averages) (Xm - Xf)

Due to characteristics (Xm - Xf) bm

         Between parameters (bm - bf) Due to parameters (returns) Xf (bm- bf)

                               Other work related

    Mother's   -0.0107 -0.0256 3.5458  3.4941  0.0516 -0.0005 0.0150   0.0524 
    education                                                         
    Father's    0.0039 -0.0117 3.3364  3.2447  0.0917 0.0004  0.0156   0.0506 
    education                                                         
     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 -0.1056 0.2168  0.1177 0.0579 0.0597  -0.0063 -0.3224 -0.0187 
           only                                                       
     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    -0.1061 -0.0975 0.0763 0.2034 -0.1271 0.0135  -0.0087 -0.0018 
     workers                                                           
  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     -0.0823 -0.0627 0.0547 0.0083 0.0464  -0.0038 -0.0195 -0.0002 
    laborers                                                           
    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     -0.0608 -0.0497 0.0497 0.2565 -0.2068 0.0126  -0.0111 -0.0028 
     workers                                                           
  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 

                                     Means

                                    (averag

                              Estimate Difference

Variable

                                       bm

Women bf  

                                     Women

Between means (averages) (Xm - Xf)

Due to characteristics (Xm - Xf) bm

         Between parameters (bm - bf) Due to parameters (returns) Xf (bm- bf)

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

                                     Means

                                    (averag

                              Estimate Difference

Variable

                                       bm

Women bf  

                                     Women

Between means (averages) (Xm - Xf)

Due to characteristics (Xm - Xf) bm

         Between parameters (bm - bf) Due to parameters (returns) Xf (bm- bf)

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

a

    White 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.

aCategory omitted.

bNo data available.

           Table 5: Decomposition Results Using Alternative Estimates

Alternative Mean  Variable  estimate (averages) Difference

                                                               Between Due to
                                              means Due to Between parameters
          Men Women Men Women (averages) characteristics parameters (returns)
             gm   gf    m  Xf   (Xm - Xf) (Xm - Xf) gm  (gm - gf) f (gm - gf)

Work Patterns Other work related

    Experience    0.0264 0.0249    16.2891    4.1548   0.1095 0.0014   0.0175 
      (years)                      12.1342                            
    Experience                                                        
      squared    -0.0004 -0.0004 359.5914    148.9504 -0.0558 0.0001   0.0120 
                                 210.6411                             
Hours worked                                                       
       (per                                                           
       year)      0.0003 0.0005  2,147.3100  472.5100  0.1340 -0.0002 -0.3058 
                                 1,674.8000                           
    Time out of                                                       
       labor                                                          
force (weeks) -0.0174 -0.0222   0.9262    -1.9083   0.0332 0.0048   0.0136 
                                   2.8345                             
     Length of                                                        
unemployment                                                       
      (weeks)    -0.0170 -0.0142   1.8149    -0.0739   0.0013 -0.0028 -0.0053 
                                   1.8887                             
      Tenure      0.0010 0.0009    91.4775   17.0497   0.0163 0.0000   0.0015 
     (months)                      74.4278                            
Working full                                                       
       time                                                           
                  0.1881 0.1252    0.8761     0.2059   0.0387 0.0628   0.0421 
(in main job)                   0.6701                             

    Mother's   -0.0106 -0.0253 3.5458  3.4941  0.0516 -0.0005 0.0147   0.0515 
    education                                                         
    Father's    0.0039 -0.0116 3.3364  3.2447  0.0917  0.0004 0.0155   0.0504 
    education                                                         
     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

Alternative Mean  Variable estimate (averages) Difference

                                                               Between Due to
                                              means Due to Between parameters
      Men  Women  Men  Women  (averages) characteristics parameters (returns)
                 gm  gf  m  Xf  (Xm - Xf) (Xm - Xf) gm  (gm - gf) f (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    -0.0592 -0.0486 0.0497 0.2565 -0.2068  0.0122 -0.0106 -0.0027 
     workers                                                           
  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       0.1867 0.2046  0.0976 0.0353 0.0622   0.0116 -0.0178 -0.0006 
    utilities                                                          
  Manufacturing   0.1521 0.1423  0.2444 0.1341 0.1103   0.0168 0.0098   0.0013 

Alternative Mean  Variable estimate (averages) Difference

                                                               Between Due to
                                              means Due to Between parameters
      Men  Women  Men  Women  (averages) characteristics parameters (returns)
                 gm  gf  m  Xf  (Xm - Xf) (Xm - Xf) gm  (gm - gf) f (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    -0.0013 0.0023  3.4902  4.2042  -0.7140  0.0010 -0.0036 -0.0152 
    (years)                                                           
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 

Alternative Mean  Variable estimate (averages) Difference

                                                               Between Due to
                                              means Due to Between parameters
      Men  Women  Men  Women  (averages) characteristics parameters (returns)
                 gm  gf  m  Xf  (Xm - Xf) (Xm - Xf) gm  (gm - gf) f (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.

aCategory omitted.

bNo data available.

cSum 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

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.

  Limitations of Our Analysis

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

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 Workplace Decisions

Purpose 	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.

  Scope and Methodology

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:

o  financial/professional services

1These individuals will be referred to as "experts" throughout this
appendix.

  Summary of Results

Background

o  health care

o  information technology

o  manufacturing

o  media/advertising

o  pharmaceuticals/biotechnology

o  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

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

2Part-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.

  Working Women Make a Variety of Decisions to Manage Work and Family
  Responsibilities

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.

    Certain Jobs May Offer Flexibility but May Also Affect Earnings

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

3In contrast, other experts indicate that flexibility is often available
in higher paying jobs, particularly those where workers have more
authority and autonomy.

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.

    Alternative Work Arrangements Offer Flexibility but Some May Affect 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

4Since women are more likely than men to use certain alternative work
arrangements, any effects apply disproportionately to women in these
cases.

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

5Research 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.

6The 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.

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.

o  	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.

o  	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

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.

                Potential for Direct Or Indirect Discrimination

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

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

7Notably, 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.

  Related Research

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

GAO Contact

  Staff Acknowledgments

(130187)

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.

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