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entitled 'Women's Earnings: Work Patterns Partially Explain Difference 
between Men's and Woman's Earnings' which was released on November 20, 
2003.

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Report to Congressional Requesters:

United States General Accounting Office:

GAO:

October 2003:

WOMEN'S EARNINGS:

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

Women's Earnings:

GAO-04-35:

Contents:

Letter:

Appendix I: Briefing Slides:

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

Review of Other Research on Earnings Differences:

Data Used in Our Analysis:

Results of Our Analysis:

Limitations of Our Analysis:

Appendix III: GAO Analysis of Women's Workplace Decisions:

Purpose:

Scope and Methodology:

Summary of Results:

Background:

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

Related Research:

Appendix IV: GAO Contact and Staff Acknowledgments:

GAO Contact:

Staff Acknowledgments:

Tables:

Table 1: Descriptive Statistics for Selected PSID Variables:

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

Table 3: Summary of Decomposition Results:

Table 4: Decomposition Results Using Regression Coefficients:

Table 5: Decomposition Results Using Alternative Estimates:

Abbreviations:

CPS: Current Population Survey:

OLS: ordinary least squares:

PSID: Panel Study of Income Dynamics:

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.[Footnote 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.[Footnote 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 developed used data from the Panel Study of Income 
Dynamics (PSID),[Footnote 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 
issues[Footnote 4] to obtain a broad range of perspectives on reasons 
why workers make certain career and workplace decisions that could 
affect earnings. In addition, we contacted employers to discuss these 
issues as well as to identify what policies employers offered to help 
workers manage work and other life responsibilities. (For more 
information about this analysis, see app. III.) We conducted our work 
from September 2002 to October 2003 in accordance with generally 
accepted government auditing standards.

In summary, we found:

* Of the many factors that account for differences in earnings between 
men and women, our model indicated that work patterns are key. 
Specifically, women have fewer years of work experience, work fewer 
hours per year, are less likely to work a full-time schedule, and leave 
the labor force for longer periods of time than men. Other factors that 
account for earnings differences include industry, occupation, race, 
marital status, and job tenure. When we account for differences between 
male and female work patterns as well as other key factors, women 
earned, on average, 80 percent of what men earned in 2000. While the 
difference fluctuated in each year we studied, there was a small but 
statistically significant decline in the earnings difference over the 
time period. (See table 2 in app. II.):

* Even after accounting for key factors that affect earnings, our model 
could not explain all of the difference in earnings between men and 
women. Due to inherent limitations in the survey data and in 
statistical analysis, we cannot determine whether this remaining 
difference is due to 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:

Signed by Robert E. Robertson: 

[End of section]

Appendix I: Briefing Slides:

[See PDF for images]

[End of section]

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

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.

Review of Other Research on Earnings Differences:

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)[Footnote 5] 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, 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.[Footnote 6] 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' 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).

Data Used in Our Analysis:

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.[Footnote 7] 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 worked in using U.S. Department of Labor 
and U.S. Department of Agriculture data.[Footnote 8]

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 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:

Variable: All individuals (workers and nonworkers): 

Variable: Annual earnings (in 2000 dollars): Men: Means (averages): 
35,942; Men: Standard deviation: 34,630; Women: Means (averages): 
16,554; Women: Standard deviation: 18,510.

Variable: Age of individual (in years): Men: Means (averages): 41.3; 
Men: Standard deviation: 11.3; Women: Means (averages): 42.0; Women: 
Standard deviation: 11.5.

Variable: Age of youngest child (in years): Men: Means (averages): 
3.3; Men: Standard deviation: 4.9; Women: Means (averages): 4.0; 
Women: Standard deviation: 5.2.

Variable: Number of children; Men: Means (averages): 0.9; Men: 
Standard deviation: 1.2; Women: Means (averages): 1.1; Women: Standard 
deviation: 1.2.

Variable: Married (percent): Men: Means (averages): 70.1; Men: 
Standard deviation: 45.8; Women: Means (averages): 61.2; Women: 
Standard deviation: 48.7.

Variable: Metropolitan area of residence (percent); Men: Means 
(averages): 64.7; Men: Standard deviation: 48.1; Women: Means 
(averages): 67.1; Women: Standard deviation: 47.0.

Variable: Full-time main job (percent); Men: Means (averages): 74.9; 
Men: Standard deviation: 43.3; Women: Means (averages): 47.2; Women: 
Standard deviation: 49.9.

Variable: Time unemployed (in weeks); Men: Means (averages): 1.9; Men: 
Standard deviation: 7.0; Women: Means (averages): 1.8; Women: Standard 
deviation: 6.9.

Variable: Time out of the labor force (in weeks); Men: Means 
(averages): 2.4; Men: Standard deviation: 9.9; Women: Means 
(averages): 6.1; Women: Standard deviation: 15.3.

Variable: Annual hours worked; Men: Means (averages): 1,931; Men: 
Standard deviation: 926; Women: Means (averages): 1,226; Women: 
Standard deviation: 957.

Variable: Job tenure (in months); Men: Means (averages): 80.1; Men: 
Standard deviation: 102.2; Women: Means (averages): 55.1; Women: 
Standard deviation: 80.3.

Variable: Work experience (in years); Men: Means (averages): 16.8; 
Men: Standard deviation: 10.2; Women: Means (averages): 11.2; Women: 
Standard deviation: 8.4.

Variable: Highest education (in years); Men: Means (averages): 12.9; 
Men: Standard deviation: 2.7; Women: Means (averages): 12.7; Women: 
Standard deviation: 2.4.

Variable: Number of observations; Men: Means (averages): 42,394; Men: 
Standard deviation: [Empty]; Women: Means (averages): 54,986; Women: 
Standard deviation: [Empty].

Variable: Number of individuals; Men: Means (averages): 5,032; Men: 
Standard deviation: [Empty]; Women: Means (averages): 6,033; Women: 
Standard deviation: [Empty].

Variable: Workers only: 

Variable: Annual earnings (in 2000 dollars); Men: Means (averages): 
Workers only: 40,426; Men: Standard deviation: 34,334; Women: Means 
(averages): 22,782; Women: Standard deviation: All individuals 
(workers and nonworkers): 18,316.

Variable: Age of individual (in years); Men: Means (averages): 40.2; 
Men: Standard deviation: 10.6; Women: Means (averages): 40.4; Women: 
Standard deviation: 10.5.

Variable: Age of youngest child (in years); Men: Means (averages): 
3.5; Men: Standard deviation: 5.0; Women: Means (averages): 4.3; 
Women: Standard deviation: 5.2.

Variable: Number of children; Men: Means (averages): 1.0; Men: 
Standard deviation: 1.2; Women: Means (averages): 1.0; Women: Standard 
deviation: 1.2.

Variable: Married (percent); Men: Means (averages): 72.2; Men: 
Standard deviation: 44.9; Women: Means (averages): 60.9; Women: 
Standard deviation: 48.8.

Variable: Metropolitan area of residence (percent); Men: Means 
(averages): 64.5; Men: Standard deviation: 47.8; Women: Means 
(averages): 68.1; Women: Standard deviation: 46.6.

Variable: Full-time main job (percent); Men: Means (averages): 87.6; 
Men: Standard deviation: 33.0; Women: Means (averages): 66.8; Women: 
Standard deviation: 47.1.

Variable: Time unemployed (in weeks); Men: Means (averages): 1.8; Men: 
Standard deviation: 6.4; Women: Means (averages): 1.9; Women: Standard 
deviation: 6.7.

Variable: Time out of the labor force (in weeks); Men: Means 
(averages): 0.91; Men: Standard deviation: 5.1; Women: Means 
(averages): 2.8; Women: Standard deviation: 9.1.

Variable: Annual hours worked; Men: Means (averages): 2,154; Men: 
Standard deviation: 697; Women: Means (averages): 1,672; Women: 
Standard deviation: 716.

Variable: Job tenure (in months); Men: Means (averages): 89.3; Men: 
Standard deviation: 104.2; Women: Means (averages): 74.1; Women: 
Standard deviation: 85.6.

Variable: Work experience (in years); Men: Means (averages): 16.4; 
Men: Standard deviation: 9.8; Women: Means (averages): 12.1; Women: 
Standard deviation: 8.0.

Variable: Highest education (in years); Men: Means (averages): 13.2; 
Men: Standard deviation: 2.6; Women: Means (averages): 13.1; Women: 
Standard deviation: 2.3.

Variable: Number of observations; Men: Means (averages): 35,726; Men: 
Standard deviation: [Empty]; Women: Means (averages): 36,793; Women: 
Standard deviation: [Empty].

Variable: Number of individuals; Men: Means (averages): 4,477; Men: 
Standard deviation: [Empty]; Women: Means (averages): 4,884; Women: 
Standard deviation: [Empty].

Source: GAO analysis of PSID data.

[End of table]

Description of Our Econometric Model:

We used the Hausman-Taylor model to analyze the earnings difference 
between men and women.[Footnote 9] 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:

[See PDF for image]

[End of equation]

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.[Footnote 10] 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.

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.[Footnote 11] 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 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.[Footnote 12] 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, 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:

[See PDF for image]

Source: GAO analysis of PSID data.

[A] Data not available.

[B] Category omitted.

[End of table]

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.[Footnote 13] This decomposition is represented as 
follows:

We estimated the logged earnings difference between men and women from 
1983 and 2000 to be approximately 0.69 (i.e. the left hand side of the 
equation above). The analysis showed that about two-thirds of this 
difference, or 0.45 out of 0.69, reflected differences between men and 
women's characteristics (the first term on the right hand side of the 
equation). The remaining one-third, about 0.24 out of 0.69, reflected 
differences in parameters, i.e., how the variables affected earnings 
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."[Footnote 14] 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 and elsewhere in the 
economy can affect the mean values of the characteristics.

Table 3: Summary of Decomposition Results:

Variable categories: Work patterns[A]; Differences in earnings: Due to 
characteristics: 0.2729; Differences in earnings: Due to parameters: -
0.2302.

Variable categories: Other work related[B]; Differences in earnings: 
Due to characteristics: 0.1539; Differences in earnings: Due to 
parameters: -0.3218.

Variable categories: Demographic and other controls[C]; Differences in 
earnings: Due to characteristics: 0.0272; Differences in earnings: Due 
to parameters: 0.1902.

Variable categories: Intercept; Differences in earnings: Due to 
characteristics: N/A; Differences in earnings: Due to parameters: 
0.6065.

Variable categories: Total; Differences in earnings: Due to 
characteristics: 0.4540; Differences in earnings: Due to parameters: 
0.2446.

Source: GAO Analysis of PSID data.

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

[A] The work patterns category includes: work experience (years), 
experience squared, time out of the labor force (weeks), length of 
unemployment (weeks), working full time (main job), tenure (months), 
and hours worked (per year).

[B] The other work related category includes: highest education 
(years), mother's education, father's education, self-employment 
status, union membership, industry, occupation, and the Mill's ratio.

[C] The demographic and other controls category includes all other 
variables, except the intercept, which is a parameter only.

[End of table]


Table 4 shows more detailed decomposition results.[Footnote 15] 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.

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.[Footnote 16]

Table 4: Decomposition Results Using Regression Coefficients:

Variable: Work patterns: 

Variable: Experience (years); Estimate: Men Beta(sub m): 0.0260; 
Estimate: Women Beta(sub f): 0.0246; Means (averages): Men X(sub m): 
16.2891; Means (averages): Women X(sub f): 12.1342; Difference: 
Between means (averages) [X(sub m) - X(sub f)]: 4.1548; Difference: 
Due to characteristics [X(sub m) - X(sub f)] Beta(sub m): 0.1081; 
Difference: Between parameters [Beta(sub m) - Beta(sub f)]: 0.0014; 
Difference: Due to parameters (returns) X(sub f) [Beta(sub m) - 
Beta(sub f)]: 0.0170.

Variable: Experience squared; Estimate: Men Beta(sub m): -0.0004; 
Estimate: Women Beta(sub f): -0.0004; Means (averages): Men X(sub m): 
359.5914; Means (averages): Women X(sub f): 210.6411; Difference: 
Between means (averages) [X(sub m) - X(sub f)]: 148.9504; Difference: 
Due to characteristics [X(sub m) - X(sub f)] Beta(sub m): -0.0558; 
Difference: Between parameters [Beta(sub m) - Beta(sub f)]: 0.0001; 
Difference: Due to parameters (returns) X(sub f) [Beta(sub m) - 
Beta(sub f)]: 0.0120.

Variable: Hours worked (per year); Estimate: Men Beta(sub m): 
0.0003; Estimate: Women Beta(sub f): 0.0005; Means (averages): 
Men X(sub m): 
2,147.3100; Means (averages): Women X(sub f): 1,674.8000; Difference: 
Between means (averages) [X(sub m) - X(sub f)]: 472.5100; Difference: 
Due to characteristics [X(sub m) - X(sub f)] Beta(sub m): 0.1340; 
Difference: Between parameters [Beta(sub m) - Beta(sub f)]: -0.0002; 
Difference: Due to parameters (returns) X(sub f) [Beta(sub m) - 
Beta(sub f)]: -0.3057.

Variable: Time out of labor force (weeks); 
Estimate: Men Beta(sub m): -0.0175; 
Estimate: Women Beta(sub f): -0.0224; 
Means (averages): Men 
X(sub m): 0.9262; 
Means (averages): Women X(sub f): 2.8345; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -1.9083; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0335; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0049; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m)  Beta(sub f)]: 0.0139.

Variable: Length of unemployment (weeks); 
Estimate: Men Beta(sub m): -0.0171; 
Estimate: Women Beta(sub f): -0.0143; 
Means (averages): Men X(sub m): 1.8149; 
Means (averages): Women X(sub f): 1.8887; 
Difference: Between means (averages) 

[X(sub m) - X(sub f)]: -0.0739; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0013; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0028; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0054.

Variable: Tenure (months); 
Estimate: Men Beta(sub m): 0.0010; 
Estimate: Women Beta(sub f): 0.0009; 
Means (averages): Men X(sub m): 91.4775; 
Means (averages): Women X(sub f): 74.4278; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 17.0497; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0163; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0000; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0015.

Variable: Working full time (in main job); 
Estimate: Men Beta(sub m): 0.1724; 
Estimate: Women Beta(sub f): 0.1180; 
Means (averages): Men X(sub m): 0.8761; 
Means (averages): Women X(sub f): 0.6701; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.2059; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0355; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0543; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0364.

Variable: Other work related: 

Variable: Mother's education; 
Estimate: Men Beta(sub m): -0.0107; 
Estimate: Women Beta(sub f): -0.0256; 
Means (averages): Men X(sub m): 3.5458; 
Means (averages): Women X(sub f): 3.4941; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0516; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0005; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0150; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0524.

Variable: Father's education; 
Estimate: Men Beta(sub m): 0.0039; 
Estimate: Women Beta(sub f): -0.0117; 
Means (averages): Men X(sub m): 3.3364; 
Means (averages): Women X(sub f): 3.2447; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0917; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0004; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0156; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0506.

Variable: Highest education (years); 
Estimate: Men Beta(sub m): 0.1355; 
Estimate: Women Beta(sub f): 0.1603; 
Means (averages): Men X(sub m): 13.1455; 
Means (averages): Women X(sub f): 13.0880; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0575; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0078; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0248; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.3242.

Variable: Self-employment status:

Variable: Works for some-one else only[A]: [Empty].

Variable: Self-employed only; 
Estimate: Men Beta(sub m): -0.1056; 
Estimate: Women Beta(sub f): 0.2168; 
Means (averages): Men X(sub m): 0.1177; 
Means (averages): Women X(sub f): 0.0579; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0597; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0063; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.3224; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0187.

Variable: Missing; 
Estimate: Men Beta(sub m): -0.2823; 
Estimate: Women Beta(sub f): -0.3413; 
Means (averages): Men X(sub m): 0.0648; 
Means (averages): Women X(sub f): 0.1230; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0582; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0164; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0590; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0073.

Variable: Both; 
Estimate: Men Beta(sub m): 0.0506; 
Estimate: Women Beta(sub f): -0.0846; 

Means (averages): Men X(sub m): 0.0094; 
Means (averages): Women X(sub f): 0.0042; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0052; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0003; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.1352; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0006.

Variable: Union member; 
Estimate: Men Beta(sub m): 0.1388; 
Estimate: Women Beta(sub f): 0.1405; 
Means (averages): Men X(sub m): 0.1773; 
Means (averages): Women X(sub f): 0.1187; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0587; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0081; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0017; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0002.

Variable: Occupation: 

Variable: Professional, technical[A]: [Empty].

Variable: Service/private household workers; 
Estimate: Men Beta(sub m): -0.1061; 
Estimate: Women Beta(sub f): -0.0975; 
Means (averages): Men X(sub m): 0.0763; 
Means (averages): Women X(sub f): 0.2034; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.1271; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0135; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0087; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0018.

Variable: Farm laborers and foremen; 
Estimate: Men Beta(sub m): -0.1928; 
Estimate: Women Beta(sub f): -0.0602; 
Means (averages): Men X(sub m): 0.0121; 
Means (averages): Women X(sub f): 0.0023; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0098; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0019; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.1326; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0003.

Variable: Farmers and farm management; 
Estimate: Men Beta(sub m): -0.3434; 
Estimate: Women Beta(sub f): -0.1690; 
Means (averages): Men X(sub m): 0.0124; 
Means (averages): Women X(sub f): 0.0008; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0116; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0040; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.1745; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0001.

Variable: Nonfarm laborers; 
Estimate: Men Beta(sub m): -0.0823; 
Estimate: Women Beta(sub f): -0.0627; 
Means (averages): Men X(sub m): 0.0547; 
Means (averages): Women X(sub f): 0.0083; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0464; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0038; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0195; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0002.

Variable: Transport equipment operators; 
Estimate: Men Beta(sub m): -0.0576; 
Estimate: Women Beta(sub f): -0.1840; 
Means (averages): Men X(sub m): 0.0680; 
Means (averages): Women X(sub f): 0.0084; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0596; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0034; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.1264; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0011.

Variable: Operators, nontransport; 
Estimate: Men Beta(sub m): -0.0458; 
Estimate: Women Beta(sub f): -0.0657; 
Means (averages): Men X(sub m): 0.0877; 
Means (averages): Women X(sub f): 0.0879; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0002; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0000; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0198; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0017.

Variable: Craftsmen; 
Estimate: Men Beta(sub m): 0.0016; 
Estimate: Women Beta(sub f): -0.0180; 
Means (averages): Men X(sub m): 0.2049; 
Means (averages): Women X(sub f): 0.0171; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.1879; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0003; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0196; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0003.

Variable: Clerical workers; 
Estimate: Men Beta(sub m): -0.0608; 
Estimate: Women Beta(sub f): -0.0497; 
Means (averages): Men X(sub m): 0.0497; 
Means (averages): Women X(sub f): 0.2565; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.2068; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0126; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0111; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0028.

Variable: Sales workers; 
Estimate: Men Beta(sub m): -0.0343; 
Estimate: Women Beta(sub f): -0.0931; 
Means (averages): Men X(sub m): 0.0469; 
Means (averages): Women X(sub f): 0.0409; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0059; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0002; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0588; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0024.

Variable: Nonfarm managers, administrators; 
Estimate: Men Beta(sub m): 0.0373; 
Estimate: Women Beta(sub f): 0.0165; 
Means (averages): Men X(sub m): 0.1609; 
Means (averages): Women X(sub f): 0.0922; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0687; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0026; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0208; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0019.

Variable: Do not know/missing; 
Estimate: Men Beta(sub m): -0.1107; 
Estimate: Women Beta(sub f): -0.1276; 
Means (averages): Men X(sub m): 0.0468; 
Means (averages): Women X(sub f): 0.0906; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0439; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0049; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0169; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0015.

Variable: Industry: 

Variable: Wholesale/retail trade[A]: [Empty].

Variable: Public administration; 
Estimate: Men Beta(sub m): 0.0104; 
Estimate: Women Beta(sub f): 0.1641; 
Means (averages): Men X(sub m): 0.0799; 
Means (averages): Women X(sub f): 0.0607; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0192; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0002; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.1538; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0093.

Variable: Professional services; 
Estimate: Men Beta(sub m): 0.0172; 
Estimate: Women Beta(sub f): 0.0707; 
Means (averages): Men X(sub m): 0.1211; 
Means (averages): Women X(sub f): 0.3467; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.2256; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0039; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0535; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0186.

Variable: Entertainment; 
Estimate: Men Beta(sub m): 0.0044; 
Estimate: Women Beta(sub f): -0.0756; 
Means (averages): Men X(sub m): 0.0095; 
Means (averages): Women X(sub f): 0.0061; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0034; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0000; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0800; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0005.

Variable: Personal services; 
Estimate: Men Beta(sub m): -0.0307; 
Estimate: Women Beta(sub f): -0.0097; 
Means (averages): Men X(sub m): 0.0130; 
Means (averages): Women X(sub f): 0.0678; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0549; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0017; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0210; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0014.

Variable: Business and repair services; 
Estimate: Men Beta(sub m): 0.0705; 
Estimate: Women Beta(sub f): 0.0488; 
Means (averages): Men X(sub m): 0.0585; 
Means (averages): Women X(sub f): 0.0340; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0245; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0017; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0217; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0007.

Variable: Finance, insurance, real estate; 
Estimate: Men Beta(sub m): 0.0562; 
Estimate: Women Beta(sub f): 0.1489; 
Means (averages): Men X(sub m): 0.0394; 
Means (averages): Women X(sub f): 0.0641; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0248; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0014; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0928; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0059.

Variable: Transportation/ communications/ public utilities; 
Estimate: Men Beta(sub m): 0.1713; 
Estimate: Women Beta(sub f): 0.1865; 
Means (averages): Men X(sub m): 0.0976; 
Means (averages): Women X(sub f): 0.0353; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0622; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0107; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0152; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0005.

Variable: Manufacturing; 
Estimate: Men Beta(sub m): 0.1417; 
Estimate: Women Beta(sub f): 0.1332; 
Means (averages): Men X(sub m): 0.2444; 
Means (averages): Women X(sub f): 0.1341; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.1103; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0156; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0085; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0011.

Variable: Construction; 
Estimate: Men Beta(sub m): 0.1708; 
Estimate: Women Beta(sub f): 0.0673; 
Means (averages): Men X(sub m): 0.0963; 
Means (averages): Women X(sub f): 0.0101; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0862; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0147; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.1034; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0010.

Variable: Mining/agriculture; 
Estimate: Men Beta(sub m): 0.0481; 
Estimate: Women Beta(sub f): 0.0178; 
Means (averages): Men X(sub m): 0.0474; 
Means (averages): Women X(sub f): 0.0075; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0399; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0019; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0302; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0002.

Variable: Do not know/missing; 
Estimate: Men Beta(sub m): 0.1106; 
Estimate: Women Beta(sub f): 0.0712; 
Means (averages): Men X(sub m): 0.0513; 
Means (averages): Women X(sub f): 0.0954; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0441; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0049; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0394; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0038.

Variable: Mills ratio; 
Estimate: Men Beta(sub m): -0.3307; 
Estimate: Women Beta(sub f): -0.1584; 
Means (averages): Men X(sub m): 0.1628; 
Means (averages): Women X(sub f): 0.3771; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.2143; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0709; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.1723; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0650.

Variable: Demographic and other controls: 

Variable: Age of individual (years); 
Estimate: Men Beta(sub m): -0.0016; 
Estimate: Women Beta(sub f): -0.0058; 
Means (averages): Men X(sub m): 40.1442; 
Means (averages): Women X(sub f): 40.3309; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.1867; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0003; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0041; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.1669.

Variable: Age of youngest child (years); 
Estimate: Men Beta(sub m): -0.0013; 
Estimate: Women Beta(sub f): 0.0023; 
Means (averages): Men X(sub m): 3.4902; 
Means (averages): Women X(sub f): 4.2042; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.7140; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0010; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0036; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0152.

Variable: Number of children; 
Estimate: Men Beta(sub m): 0.0210; 
Estimate: Women Beta(sub f): -0.0254; 
Means (averages): Men X(sub m): 0.9659; 
Means (averages): Women X(sub f): 1.0469; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0810; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0017; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0464; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0486.

Variable: Additional family income (inflation adjusted in 
thousands of dollars); 
Estimate: Men Beta(sub m): -0.0009; 
Estimate: Women Beta(sub f): -0.0001; 
Means (averages): Men X(sub m): 25.1172; 
Means (averages): Women X(sub f): 34.9156; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -9.7984; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0086; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0008; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0284.

Variable: Metropolitan area; 
Estimate: Men Beta(sub m): 0.0171; 
Estimate: Women Beta(sub f): 0.0305; 
Means (averages): Men X(sub m): 0.6476; 
Means (averages): Women X(sub f): 0.6806; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0330; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0006; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0133; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0091.

Variable: Excellent health; 
Estimate: Men Beta(sub m): 0.0149; 
Estimate: Women Beta(sub f): 0.0062; 
Means (averages): Men X(sub m): 0.2613; 
Means (averages): Women X(sub f): 0.2041; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0572; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0009; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0088; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0018.

Variable: Marital status: Variable: Never married[A]: 

Variable: Married; 
Estimate: Men Beta(sub m): 0.0800; 
Estimate: Women Beta(sub f): -0.0011; 
Means (averages): Men X(sub m): 0.7196; 
Means (averages): Women X(sub f): 0.6101; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.1095; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0088; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0811; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0495.

Variable: Other; 
Estimate: Men Beta(sub m): 0.0685; 
Estimate: Women Beta(sub f): -0.0009; 
Means (averages): Men X(sub m): 0.1327; 
Means (averages): Women X(sub f): 0.2424; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.1097; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0075; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0694; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0168.

Variable: Region: South; 
Estimate: Men Beta(sub m): -0.0522; 
Estimate: Women Beta(sub f): -0.0377; 
Means (averages): Men X(sub m): 0.4142; 
Means (averages): Women X(sub f): 0.4551; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0409; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0021; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0145; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0066.

Variable: Race: 

Variable: White[A]: [Empty].

Variable: Black; 
Estimate: Men Beta(sub m): -0.1487; 
Estimate: Women Beta(sub f): -0.0682; 
Means (averages): Men X(sub m): 0.2666; 
Means (averages): Women X(sub f): 0.3602; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0936; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0139; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0806; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0290.

Variable: Other; 
Estimate: Men Beta(sub m): 0.0491; 
Estimate: Women Beta(sub f): 0.0972; 
Means (averages): Men X(sub m): 0.0140; 
Means (averages): Women X(sub f): 0.0152; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0011; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0001; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0481; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0007.

Variable: Year, compared to 1983: 

Variable: 2000; 
Estimate: Men Beta(sub m): 0.0188; 
Estimate: Women Beta(sub f): 0.0621; 
Means (averages): Men X(sub m): 0.0537; 
Means (averages): Women X(sub f): 0.0538; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0001; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0000; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0433; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0023.

Variable: 1999[B]: [Empty].

Variable: 1998; 
Estimate: Men Beta(sub m): -0.0406; 
Estimate: Women Beta(sub f): 0.0298; 
Means (averages): Men X(sub m): 0.0536; 
Means (averages): Women X(sub f): 0.0515; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0021; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0001; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0704; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0036.

Variable: 1997[B]: [Empty].

Variable: 1996; 
Estimate: Men Beta(sub m): -0.1045; 
Estimate: Women Beta(sub f): -0.0733; 
Means (averages): Men X(sub m): 0.0468; 
Means (averages): Women X(sub f): 0.0514; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0046; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0005; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0312; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0016.

Variable: 1995; 
Estimate: Men Beta(sub m): -0.0813; 
Estimate: Women Beta(sub f): -0.0618; 
Means (averages): Men X(sub m): 0.0613; 
Means (averages): Women X(sub f): 0.0622; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0009; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0001; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0194; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0012.

Variable: 1994; 
Estimate: Men Beta(sub m): -0.0973; 
Estimate: Women Beta(sub f): -0.0759; 
Means (averages): Men X(sub m): 0.0615; 
Means (averages): Women X(sub f): 0.0655; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0040; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0004; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0214; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0014.

Variable: 1993; 
Estimate: Men Beta(sub m): -0.0854; 
Estimate: Women Beta(sub f): -0.0495; 
Means (averages): Men X(sub m): 0.0597; 
Means (averages): Women X(sub f): 0.0641; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0044; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0004; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0359; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0023.

Variable: 1992; 
Estimate: Men Beta(sub m): -0.0693; 
Estimate: Women Beta(sub f): -0.0625; 
Means (averages): Men X(sub m): 0.0662; 
Means (averages): Women X(sub f): 0.0684; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0022; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0002; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0068; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0005.

Variable: 1991; 
Estimate: Men Beta(sub m): -0.1023; 
Estimate: Women Beta(sub f): -0.0921; 
Means (averages): Men X(sub m): 0.0668; 
Means (averages): Women X(sub f): 0.0675; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0007; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0001; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0103; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0007.

Variable: 1990; 
Estimate: Men Beta(sub m): -0.0960; 
Estimate: Women Beta(sub f): -0.0737; 
Means (averages): Men X(sub m): 0.0672; 
Means (averages): Women X(sub f): 0.0686; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0015; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0001; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0224; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0015.

Variable: 1989; 
Estimate: Men Beta(sub m): -0.0691; 
Estimate: Women Beta(sub f): -0.0524; 
Means (averages): Men X(sub m): 0.0675; 
Means (averages): Women X(sub f): 0.0680; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0006; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.0000; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: -0.0167; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.0011.

Variable: 1988; 
Estimate: Men Beta(sub m): -0.0359; 
Estimate: Women Beta(sub f): -0.0516; 
Means (averages): Men X(sub m): 0.0669; 
Means (averages): Women X(sub f): 0.0667; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0002; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0000; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0157; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0010.

Variable: 1987; 
Estimate: Men Beta(sub m): -0.0389; 
Estimate: Women Beta(sub f): -0.0561; 
Means (averages): Men X(sub m): 0.0666; 
Means (averages): Women X(sub f): 0.0660; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0006; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0000; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0171; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0011.

Variable: 1986; 
Estimate: Men Beta(sub m): -0.0248; 
Estimate: Women Beta(sub f): -0.0632; 
Means (averages): Men X(sub m): 0.0668; 
Means (averages): Women X(sub f): 0.0654; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0014; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0000; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0384; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0025.

Variable: 1985; 
Estimate: Men Beta(sub m): -0.0282; 
Estimate: Women Beta(sub f): -0.0822; 
Means (averages): Men X(sub m): 0.0666; 
Means (averages): Women X(sub f): 0.0646; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0020; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0001; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0540; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0035.

Variable: 1984; 
Estimate: Men Beta(sub m): -0.0237; 
Estimate: Women Beta(sub f): -0.0847; 
Means (averages): Men X(sub m): 0.0656; 
Means (averages): Women X(sub f): 0.0631; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0025; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): -0.0001; 
Difference: Between parameters 
[Beta(sub m) - Beta(sub f)]: 0.0609; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.0038.

Variable: Sum before intercept: Difference: Due to 
parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: -0.3618.

Variable: Intercept; 
Estimate: Men Beta(sub m): 7.5910; 
Estimate: Women Beta(sub f): 6.9846; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.6065.

Variable: Sum; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] Beta(sub m): 0.4540; 
Difference: Due to parameters (returns) X(sub f) 
[Beta(sub m) - Beta(sub f)]: 0.2446.

Source: GAO analysis of PSID data.

[A] Category omitted.

[B] No data available.

[End of table]

Table 5: Decomposition Results Using Alternative Estimates:

Variable: Work Patterns: 

Variable: Experience (years); 
Alternative estimate: Men g(sub m): 0.0264; 
Alternative estimate: Women g(sub f): 0.0249; 
Means (averages): Men X(sub m): 16.2891; 
Means (averages): Women X(sub f): 12.1342; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 4.1548; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.1095; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0014; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0175. 

Variable: Experience squared; 
Alternative estimate: Men g(sub m): -0.0004; 
Alternative estimate: Women g(sub f): -0.0004; 
Means (averages): Men X(sub m): 359.5914; 
Means (averages): Women X(sub f): 210.6411; 
 Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 148.9504; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0558; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0001; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0120. 

Variable: Hours worked (per year); 
Alternative estimate: Men g(sub m): 0.0003; 
Alternative estimate: Women g(sub f): 0.0005; 
Means (averages): Men X(sub m): 2,147.3100; 
Means (averages): Women X(sub f): 1,674.8000; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 472.5100; 
 Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.1340; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0002; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.3058. 



Variable: Time out of labor force (weeks); 
Alternative estimate: Men g(sub m): -0.0174; 
Alternative estimate: Women g(sub f): -0.0222; 
Means (averages): Men X(sub m): 0.9262; 
Means (averages): Women X(sub f): 2.8345; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -1.9083; 
 Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0332; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0048; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0136. 

Variable: Length of unemployment (weeks); 
Alternative estimate: Men g(sub m): -0.0170; 
Alternative estimate: Women g(sub f): -0.0142; 
Means (averages): Men X(sub m): 1.8149; 
Means (averages): Women X(sub f): 1.8887; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0739; 
 Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0013; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0028; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0053. 

Variable: Tenure (months); 
Alternative estimate: Men g(sub m): 0.0010; 
Alternative estimate: Women g(sub f): 0.0009; 
Means (averages): Men X(sub m): 91.4775; 
Means (averages): Women X(sub f): 74.4278; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 17.0497; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0163; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0000; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0015. 

Variable: Working full time (in main job); 
Alternative estimate: Men g(sub m): 0.1881; 
Alternative estimate: Women g(sub f): 0.1252; 
Means (averages): Men X(sub m): 0.8761; 
Means (averages): Women X(sub f): 0.6701; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.2059; 
 Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0387; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0628; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0421. 

Variable: Other work related: 

Variable: Mother's education; 
Alternative estimate: Men g(sub m): -0.0106; 
Alternative estimate: Women g(sub f): -0.0253; 
Means (averages): Men X(sub m): 3.5458; 
Means (averages): Women X(sub f): 3.4941; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0516; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0005; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0147; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0515. 

Variable: Father's education; 
Alternative estimate: Men g(sub m): 0.0039; 
Alternative estimate: Women g(sub f): -0.0116; 
Means (averages): Men X(sub m): 3.3364; 
Means (averages): Women X(sub f): 3.2447; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0917; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0004; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0155; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0504. 

Variable: Highest education (years); 
Alternative estimate: Men g(sub m): 0.1451; 
Alternative estimate: Women g(sub f): 0.1738; 
Means (averages): Men X(sub m): 13.1455; 
Means (averages): Women X(sub f): 13.0880; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0575; 
 Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0083; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0287; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.3757. 

Variable: Self-employment status: 

Variable: Works for someone else only[A]: [Empty] 

Variable: Self-employed only; 
Alternative estimate: Men g(sub m): -0.1003; 
Alternative estimate: Women g(sub f): 0.2419; 
Means (averages): Men X(sub m): 0.1177; 
Means (averages): Women X(sub f): 0.0579; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0597; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0060; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.3422; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0198. 

Variable: Missing; 
Alternative estimate: Men g(sub m): -0.2461; 
Alternative estimate: Women g(sub f): -0.2892; 
Means (averages): Men X(sub m): 0.0648; 
 Means (averages): Women X(sub f): 0.1230; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0582; 
Difference: Due to characteristics 

[X(sub m) - X(sub f)] g(sub m): 0.0143; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0432; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0053. 

Variable: Both; 
Alternative estimate: Men g(sub m): 0.0516; 
Alternative estimate: Women g(sub f): -0.0820; 
Means (averages): Men X(sub m): 0.0094; 
 Means (averages): Women X(sub f): 0.0042; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0052; 
Difference: Due to characteristics 

[X(sub m) - X(sub f)] g(sub m): 0.0003; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.1336; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0006. 

Variable: Union member; 
Alternative estimate: Men g(sub m): 0.1488; 
Alternative estimate: Women g(sub f): 0.1507; 
Means (averages): Men X(sub m): 0.1773; 
Means (averages): Women X(sub f): 0.1187; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0587; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0087; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0019; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0002. 

Variable: Occupation: 

Variable: Professional, technical[A]: [Empty].

Variable: Service/private household workers; 
Alternative estimate: Men g(sub m): -0.1008; 
Alternative estimate: Women g(sub f): -0.0930; 
Means (averages): Men X(sub m): 0.0763; 
Means (averages): Women X(sub f): 0.2034; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.1271; 
 Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0128; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0079; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0016. 

Variable: Farm laborers and foremen; 
Alternative estimate: Men g(sub m): - 0.1761; 
Alternative estimate: Women g(sub f): -0.0618; 
Means (averages): Men X(sub m): 0.0121; 
Means (averages): Women X(sub f): 0.0023; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0098; 
 Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0017; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.1143; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0003. 

Variable: Farmers and farm management; 
Alternative estimate: Men g(sub m): - 0.2915; 
Alternative estimate: Women g(sub f): -0.1611; 
Means (averages): Men X(sub m): 0.0124; 
Means (averages): Women X(sub f): 0.0008; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0116; 
 Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0034; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.1304; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0001. 

Variable: Nonfarm laborers; 
Alternative estimate: Men g(sub m): -0.0791; 
Alternative estimate: Women g(sub f): -0.0615; 
Means (averages): Men X(sub m): 0.0547; 
Means (averages): Women X(sub f): 0.0083; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0464; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0037; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0176; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0001. 

Variable: Transport equipment operators; 
Alternative estimate: Men g(sub m): -0.0562; 
Alternative estimate: Women g(sub f): -0.1690; 
Means (averages): Men X(sub m): 0.0680; 
Means (averages): Women X(sub f): 0.0084; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0596; 
 Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0033; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.1128; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0009. 

Variable: Operators, nontransport; 
Alternative estimate: Men g(sub m): - 0.0449; 
Alternative estimate: Women g(sub f): -0.0638; 
Means (averages): Men X(sub m): 0.0877; 
Means (averages): Women X(sub f): 0.0879; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0002; 
 Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0000; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0188; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0017. 

Variable: Craftsmen; 
Alternative estimate: Men g(sub m): 0.0015; 
Alternative estimate: Women g(sub f): -0.0183; 
Means (averages): Men X(sub m): 0.2049; 
 Means (averages): Women X(sub f): 0.0171; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.1879; 
Difference: Due to characteristics 

[X(sub m) - X(sub f)] g(sub m): 0.0003; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0198; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0003. 

Variable: Clerical workers; 
Alternative estimate: Men g(sub m): -0.0592; 
Alternative estimate: Women g(sub f): -0.0486; 
Means (averages): Men X(sub m): 0.0497; 
Means (averages): Women X(sub f): 0.2565; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.2068; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0122; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0106; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0027. 

Variable: Sales workers; 
Alternative estimate: Men g(sub m): -0.0339; 
Alternative estimate: Women g(sub f): -0.0891; 
Means (averages): Men X(sub m): 0.0469; 
Means (averages): Women X(sub f): 0.0409; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0059; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0002; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0552; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0023. 

Variable: Nonfarm managers, administrators; 
Alternative estimate: Men g(sub m): 0.0379; 
Alternative estimate: Women g(sub f): 0.0165; 
Means (averages): Men X(sub m): 0.1609; 
Means (averages): Women X(sub f): 0.0922; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0687; 
 Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0026; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0214; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0020. 

Variable: Do not know/missing; 
Alternative estimate: Men g(sub m): -0.1054; 
Alternative estimate: Women g(sub f): -0.1205; 
Means (averages): Men X(sub m): 0.0468; 
Means (averages): Women X(sub f): 0.0906; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0439; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0046; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0151; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0014. 

Variable: Industry: 

Variable: Wholesale/retail trade[A]: [Empty]. 

Variable: Public administration; 
Alternative estimate: Men g(sub m): 0.0102; 
Alternative estimate: Women g(sub f): 0.1780; 
Means (averages): Men X(sub m): 0.0799; 
Means (averages): Women X(sub f): 0.0607; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0192; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0002; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.1678; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0102. 

Variable: Professional services; 
Alternative estimate: Men g(sub m): 0.0172; 
Alternative estimate: Women g(sub f): 0.0731; 
Means (averages): Men X(sub m): 0.1211; 
Means (averages): Women X(sub f): 0.3467; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.2256; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0039; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0560; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0194. 

Variable: Entertainment; 
Alternative estimate: Men g(sub m): 0.0039; 
Alternative estimate: Women g(sub f): -0.0737; 
Means (averages): Men X(sub m): 0.0095; 
Means (averages): Women X(sub f): 0.0061; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0034; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0000; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0775; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0005. 

Variable: Personal services; 
Alternative estimate: Men g(sub m): -0.0306; 
Alternative estimate: Women g(sub f): -0.0098; 
Means (averages): Men X(sub m): 0.0130; 
Means (averages): Women X(sub f): 0.0678; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0549; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0017; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0208; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0014. 

Variable: Business and repair services; 
Alternative estimate: Men g(sub m): 0.0729; 
Alternative estimate: Women g(sub f): 0.0498; 
Means (averages): Men X(sub m): 0.0585; 
Means (averages): Women X(sub f): 0.0340; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0245; 
 Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0018; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0231; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0008. 

Variable: Finance, insurance, real estate; 
Alternative estimate: Men g(sub m): 0.0575; 
Alternative estimate: Women g(sub f): 0.1604; 
Means (averages): Men X(sub m): 0.0394; 
Means (averages): Women X(sub f): 0.0641; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0248; 
 Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0014; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.1028; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0066. 

Variable: Transportation/ communication/ public utilities; 
Alternative estimate: Men g(sub m): 0.1867; 
Alternative estimate: Women g(sub f): 0.2046; Means 
(averages): Men X(sub m): 0.0976; 
Means (averages): Women X(sub f): 0.0353; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0622; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0116; 
 Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0178; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0006. 

Variable: Manufacturing; 
Alternative estimate: Men g(sub m): 0.1521; 
Alternative estimate: Women g(sub f): 0.1423; 
Means (averages): Men X(sub m): 0.2444; 
Means (averages): Women X(sub f): 0.1341; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.1103; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0168; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0098; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0013. 

Variable: Construction; 
Alternative estimate: Men g(sub m): 0.1861; 
Alternative estimate: Women g(sub f): 0.0689; 
Means (averages): Men X(sub m): 0.0963; 
Means (averages): Women X(sub f): 0.0101; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0862; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0160; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.1172; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0012. 

Variable: Mining/ agriculture; 
Alternative estimate: Men g(sub m): 0.0489; 
Alternative estimate: Women g(sub f): 0.0166; 
Means (averages): Men X(sub m): 0.0474; 
Means (averages): Women X(sub f): 0.0075; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0399; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0020; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0323; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0002. 

Variable: Do not know/missing; 
Alternative estimate: Men g(sub m): 0.1164; 
Alternative estimate: Women g(sub f): 0.0730; 
Means (averages): Men X(sub m): 0.0513; 
Means (averages): Women X(sub f): 0.0954; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0441; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0051; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0434; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0041. 

Variable: Mills ratio; 
Alternative estimate: Men g(sub m): -0.2819; 
Alternative estimate: Women g(sub f): -0.1470; 
Means (averages): Men X(sub m): 0.1628; 
Means (averages): Women X(sub f): 0.3771; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.2143; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0604; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.1348; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0508. 

Variable: Demographic and other controls: 

Variable:Age of individual (years); 
Alternative estimate: Men g(sub m): - 0.0016; 
Alternative estimate: Women g(sub f): -0.0057; 
Means (averages): Men X(sub m): 40.1442; 
Means (averages): Women X(sub f): 40.3309; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.1867; 
 Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0003; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0041; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.1662. 

Variable:Age of youngest child (years); 
Alternative estimate: Men g(sub m): -0.0013; 
Alternative estimate: Women g(sub f): 0.0023; 
Means (averages): Men X(sub m): 3.4902; 
Means (averages): Women X(sub f): 4.2042; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.7140; 
 Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0010; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0036; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0152. 

Variable: Number of children; 
Alternative estimate: Men g(sub m): 0.0212; 
Alternative estimate: Women g(sub f): -0.0251; 
Means (averages): Men X(sub m): 0.9659; 
Means (averages): Women X(sub f): 1.0469; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0810; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0017; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0463; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0485. 

Variable:Additional family income (inflation 
adjusted in thousands of dollars); 
Alternative estimate: Men g(sub m): -0.0009; 
Alternative estimate: Women g(sub f): -0.0001; 
Means (averages): Men X(sub m): 25.1172; 
Means (averages): Women X(sub f): 34.9156; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -9.7984; 
Difference: Due to characteristics 

[X(sub m) - X(sub f)] g(sub m): 0.0086; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0008; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0284. 

Variable: Metropolitan area; 
Alternative estimate: Men g(sub m): 0.0173; 
Alternative estimate: Women g(sub f): 0.0309; 
Means (averages): Men X(sub m): 0.6476; 
Means (averages): Women X(sub f): 0.6806; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0330; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0006; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0136; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0093. 

Variable: Excellent health; 
Alternative estimate: Men g(sub m): 0.0150; 
Alternative estimate: Women g(sub f): 0.0062; 
Means (averages): Men X(sub m): 0.2613; 
Means (averages): Women X(sub f): 0.2041; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0572; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0009; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0089; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0018. 

Variable: Marital status: 

Variable: Never married[A]: [Empty]. 

Variable: Married; 
Alternative estimate: Men g(sub m): 0.0831; 
Alternative estimate: Women g(sub f): -0.0013; 
Means (averages): Men X(sub m): 0.7196; 
 Means (averages): Women X(sub f): 0.6101; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.1097; 
Difference: Due to characteristics 

[X(sub m) - X(sub f)] g(sub m): -0.0091; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0844; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0515. 

Variable: Other; 
Alternative estimate: Men g(sub m): 0.0707; 
Alternative estimate: Women g(sub f): -0.0011; 
Means (averages): Men X(sub m): 0.1327; 
 Means (averages): Women X(sub f): 0.2424; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0000; 
Difference: Due to characteristics 

[X(sub m) - X(sub f)] g(sub m): 0.0000; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0718; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0174. 

Variable: Region: South; 
Alternative estimate: Men g(sub m): -0.0510; 
Alternative estimate: Women g(sub f): -0.0371; 
Means (averages): Men X(sub m): 0.4142; 
Means (averages): Women X(sub f): 0.4551; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.1095; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0056; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0139; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0063. 

Variable: Race: 

Variable: White[A]: [Empty]. 

Variable: Black; 
Alternative estimate: Men g(sub m): -0.1385; 
Alternative estimate: Women g(sub f): -0.0661; 
Means (averages): Men X(sub m): 0.2666; 
 Means (averages): Women X(sub f): 0.3602; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0936; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0130; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0723; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0260. 

Variable: Other; 
Alternative estimate: Men g(sub m): 0.0466; 
Alternative estimate: Women g(sub f): 0.0989; 
Means (averages): Men X(sub m): 0.0140; 
 Means (averages): Women X(sub f): 0.0152; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0011; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0001; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0523; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0008. 

Variable: Year, compared to 1983: 

Variable: 2000; 
Alternative estimate: Men g(sub m): 0.0188; 
Alternative estimate: Women g(sub f): 0.0638; 
Means (averages): Men X(sub m): 0.0537; 
 Means (averages): Women X(sub f): 0.0538; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0001; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0000; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0450; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0024. 

Variable: 1999[B]: [Empty]. 

Variable: 1998; 
Alternative estimate: Men g(sub m): -0.0399; 
Alternative estimate: Women g(sub f): 0.0300; 
Means (averages): Men X(sub m): 0.0536; 
 Means (averages): Women X(sub f): 0.0515; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0021; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0001; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0699; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0036. 

Variable: 1997[B]: [Empty]. 

Variable: 1996; 
Alternative estimate: Men g(sub m): -0.0994; 
Alternative estimate: Women g(sub f): -0.0709; 
Means (averages): Men X(sub m): 0.0468; 
 Means (averages): Women X(sub f): 0.0514; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0046; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0005; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0285; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0015. 

Variable: 1995; 
Alternative estimate: Men g(sub m): -0.0782; 
Alternative estimate: Women g(sub f): -0.0601; 
Means (averages): Men X(sub m): 0.0613; 
 Means (averages): Women X(sub f): 0.0622; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0009; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0001; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0181; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0011. 

Variable: 1994; 
Alternative estimate: Men g(sub m): -0.0928; 
Alternative estimate: Women g(sub f): -0.0733; 
Means (averages): Men X(sub m): 0.0615; 
 Means (averages): Women X(sub f): 0.0655; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0040; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0004; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0196; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0013. 

Variable: 1993; 
Alternative estimate: Men g(sub m): -0.0820; 
Alternative estimate: Women g(sub f): -0.0484; 
Means (averages): Men X(sub m): 0.0597; 
 Means (averages): Women X(sub f): 0.0641; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0044; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0004; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0335; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0021. 

Variable: 1992; 
Alternative estimate: Men g(sub m): -0.0671; 
Alternative estimate: Women g(sub f): -0.0608; 
Means (averages): Men X(sub m): 0.0662; 
 Means (averages): Women X(sub f): 0.0684; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0022; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0002; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0063; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0004. 

Variable: 1991; 
Alternative estimate: Men g(sub m): -0.0974; 
Alternative estimate: Women g(sub f): -0.0881; 
Means (averages): Men X(sub m): 0.0668; 
 Means (averages): Women X(sub f): 0.0675; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0007; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0001; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0093; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0006. 

Variable: 1990; 
Alternative estimate: Men g(sub m): -0.0917; 
Alternative estimate: Women g(sub f): -0.0712; 
Means (averages): Men X(sub m): 0.0672; 
 Means (averages): Women X(sub f): 0.0686; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0015; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0001; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0205; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0014. 

Variable: 1989; 
Alternative estimate: Men g(sub m): -0.0669; 
Alternative estimate: Women g(sub f): -0.0512; 
Means (averages): Men X(sub m): 0.0675; 
 Means (averages): Women X(sub f): 0.0680; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: -0.0006; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.0000; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: -0.0157; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: -0.0011. 

Variable: 1988; 
Alternative estimate: Men g(sub m): -0.0354; 
Alternative estimate: Women g(sub f): -0.0504; 
Means (averages): Men X(sub m): 0.0669; 
 Means (averages): Women X(sub f): 0.0667; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0002; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0000; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0151; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0010. 

Variable: 1987; 
Alternative estimate: Men g(sub m): -0.0383; 
Alternative estimate: Women g(sub f): -0.0546; 
Means (averages): Men X(sub m): 0.0666; 
 Means (averages): Women X(sub f): 0.0660; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0006; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0000; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0164; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0011. 

Variable: 1986; 
Alternative estimate: Men g(sub m): -0.0246; 
Alternative estimate: Women g(sub f): -0.0613; 
Means (averages): Men X(sub m): 0.0668; 
 Means (averages): Women X(sub f): 0.0654; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0014; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0000; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0368; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0024. 

Variable: 1985; 
Alternative estimate: Men g(sub m): -0.0279; 
Alternative estimate: Women g(sub f): -0.0791; 
Means (averages): Men X(sub m): 0.0666; 
 Means (averages): Women X(sub f): 0.0646; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0020; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0001; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0512; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0033. 

Variable: 1984; 
Alternative estimate: Men g(sub m): -0.0235; 
Alternative estimate: Women g(sub f): -0.0813; 
Means (averages): Men X(sub m): 0.0656; 
 Means (averages): Women X(sub f): 0.0631; 
Difference: Between means (averages) 
[X(sub m) - X(sub f)]: 0.0025; 
Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): -0.0001; 
Difference: Between parameters 
[g(sub m) - g(sub f)]: 0.0578; 
 Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.0036. 

Variable: Sum before intercept: Difference: Due to parameters 
(returns) X(sub f) [g(sub m) - g(sub f)]: -0.3943. 

Variable: Intercept; 
Alternative estimate: Men g(sub m): 7.5910; 
Alternative estimate: Women g(sub f): 6.9846; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.6065. 

Variable: Sum[C]: Difference: Due to characteristics 
[X(sub m) - X(sub f)] g(sub m): 0.4311; 
Difference: Due to parameters (returns) X(sub f) 
[g(sub m) - g(sub f)]: 0.2122. 


Source: GAO analysis of PSID data.

[A] Category omitted.

[B] No data available.

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

[End of table]

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 self-
employed 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.[Footnote 17] 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.

[End of section]

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,[Footnote 18] and a review of our 
work by additional knowledgeable individuals. Specifically, we reviewed 
literature on work-related decisions, including using alternative work 
arrangements, and how these decisions may affect advancement or 
earnings. We also conducted 10 interviews with a variety of experts--
industry groups, advocacy groups, unions, and researchers--to obtain a 
broad range of perspectives on reasons why workers make certain career 
and workplace decisions that could affect their earnings. In selecting 
experts, we targeted those who have conducted research on earnings 
issues and have different viewpoints.

We also interviewed employers from eight companies, as well as a group 
of employees from one of these companies, about policies and practices, 
including alternative work arrangements (such as part time and leave), 
that may affect workers' workplace decisions and earnings. We targeted 
companies that are recognized leaders in work-life practices; for 
example, those on Working Mother magazine's "100 Best Companies for 
Working Mothers" and on Fortune magazine's "100 Best Companies to Work 
For" list. In our selection, we also sought participation from a 
variety of sectors, including:

* financial/professional services:

* health care:

* information technology:

* manufacturing:

* media/advertising:

* pharmaceuticals/biotechnology:

* travel/hospitality:

Based on the literature and our interviews, we developed key themes 
about workplace culture, decisions about work, and how these decisions 
may affect career advancement and earnings. We vetted the themes with 
11 experts--who are well known in the area of earnings and work-life 
issues and represent views of researchers, advocacy groups, and 
employers--to determine if the themes were consistent with their 
experience or existing research and to identify areas of disagreement 
to broaden our understanding of the issues.

Summary of Results:

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.

Background:

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.[Footnote 19] 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 
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 Work and Family 
Responsibilities:

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.[Footnote 20]

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 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.[Footnote 21] 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 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 
work[Footnote 22] 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.[Footnote 23] 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 view women who use alternative 
arrangements as less available, less valuable, or less committed to 
their work. This may result in less challenging work, fewer career 
opportunities, fewer promotions, and less pay. However, one company 
representative that we interviewed told us that workers using these 
arrangements are not necessarily less committed and that, in some 
cases, they work harder. For example, several of the women we 
interviewed who were scheduled to work less than full time noted that 
they sometimes came into the office or worked at home on their 
scheduled days off.

Although existing research is limited and often narrow in scope, 
following are examples of studies that address advancement and earnings 
effects that are associated with using certain alternative 
arrangements.

* One study--which tracked a small group of working women for 7 years 
after they gave birth--found that flextime, telecommuting, and reduced 
work hours had some negative impact on wage growth for some mothers. 
Flextime showed a neutral or mild impact on wage growth, while 
telecommuting and reduced work hours--which result in less face time--
showed large pronounced negative effects, but only for some workers. 
For all three arrangements, managers or professionals experienced more 
negative wage effects than nonmanagerial or nonprofessional workers.

* Another study of 11,815 managers in a large financial services 
organization found that leaves of absence were associated with fewer 
subsequent promotions and smaller raises. This was true regardless of 
the reason for the leave (i.e., a worker's illness or family 
responsibilities) or whether the leave taker was a man or woman--though 
most of the managers taking leave were women. Taking leave negatively 
affected workers' performance evaluations, but only for the year that 
they took the leave. Even when accounting for any potential differences 
in the performance evaluations of those who did and did not take leave, 
leave takers received fewer promotions and smaller raises.

Managerial support for use of alternative work arrangements is 
important when considering any effects on advancement and earnings. 
According to our company interviews, some managers do not support use 
of these arrangements because they are seen as accommodations to 
certain workers--even though the company's leadership views them as 
part of the overall business strategy. Workers who use these 
arrangements may experience negative effects if managers place limits 
on the types of work 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.[Footnote 24] 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 female-dominated 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 
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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[End of section]

Appendix IV: GAO Contact and Staff Acknowledgments:


FOOTNOTES

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

[2] This figure represents weekly earnings of full-time workers, but 
considering different populations may result in different earnings 
differences. For example, according to a GAO calculation based on CPS 
data from 2000 using both full-time and part-time workers, women's 
annual earnings were about half of men's. 

[3] The PSID is a survey of a sample of U.S. individuals that collects 
economic and demographic data, with substantial detail on income 
sources and amounts, employment, family composition changes, and 
residential location. 

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

[5] The PSID is a longitudinal survey, ongoing since 1968, of a 
representative sample of U.S. individuals and the families they reside 
in. The central focus of the data is economic and demographic, with 
substantial detail on income sources and amounts, employment, family 
composition changes, and residential location. PSID data were collected 
annually through 1997 and biennially starting in 1999. The most recent 
survey available is 2001, which includes data from 2000.

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

[7] The lower limit of the age range was set at 25 because the PSID 
does not include detailed information for dependent college students, 
posing potential selection bias issues.

[8] The Department of Agriculture data are from the National 
Agricultural Statistics Service data series "Annual All Hired Workers 
Wage Rates, U.S. Level" and the Department of Labor data are from the 
Bureau of Labor Statistics data series "Average Hourly Earnings of 
Production Workers."

[9] Jerry A. Hausman and William E. Taylor, "Panel Data and 
Unobservable Individual Effects," Econometrica 49:6 (November 1981). 
Light and Ureta use this model to analyze the relationship between 
experience and wage differences (see Audrey Light and Manuelita Ureta, 
"Early-Career Work Experience and Gender Wage Differentials," Journal 
of Labor Economics 13:1 (1995): 121-154).

[10] The probability that an individual worked was modeled as a 
function of age, the number of children and the age of the youngest 
child in the household, marital status, additional family income, work 
experience, education, race, region and urban-rural indicators, and a 
work disability indicator. This model was estimated separately for men 
and women for each of the years in the sample. 

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

[12] The effect of an additional year of experience on earnings is the 
sum of the effect of the experience and experience-squared variables. 
The amount that an additional year of experience will increase the 
value of the experience-squared variable will vary with the level of 
experience. For example, an additional year of experience would 
increase experience-squared by 1 for someone with no prior experience, 
and it will increase the experience-squared variable by 41 for someone 
with 20 years of experience (i.e., 441 - 400 = 41). Taking into account 
the effect of both variables, these estimates would indicate that an 
additional year of experience would increase earnings for men with less 
than 33 years of experience and for women with less than 31 years of 
experience. 

[13] J. G. Altonji and R. M. Blank, "Race and Gender in the Labor 
Market," The Handbook of Labor Economics (Amsterdam: Elsevier Science, 
1999), vol. 3C, pp. 3153-61.

[14] Altonji and Blank, p. 3156. 

[15] Table 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. 

[16] Oaxaca 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.

[17] See 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

[18] These individuals will be referred to as "experts" throughout this 
appendix. 

[19] Part-time work schedules allow employees to reduce their work 
hours from the traditional 40 hours per week in exchange for a reduced 
salary and possibly pro-rated benefits. Job sharing--a form of part-
time work--allows two employees to share job responsibilities, salary, 
and benefits of one full-time position. 

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

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

[22] Research indicates that different types of part-time work exist. 
Some part-time jobs require relatively low skills, and offer low pay 
and little opportunity for advancement. In contrast, other part-time 
jobs are work schedules that employers create to retain or attract 
workers who cannot or do not want to work full time. These jobs are 
often higher skilled and higher paying with advancement potential. 

[23] The idea of "face time" may apply primarily to certain types of 
jobs, such as professional, white-collar jobs or those that require 
contact with clients or customers. 

[24] Notably, research indicates that women tend to be concentrated in 
service-producing occupations, such as retail trade and government, 
which lose relatively few jobs or actually gain jobs during recessions. 
However, men tend to be concentrated in goods-producing industries, 
such as construction and manufacturing, which often lose jobs during 
recessions. 

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