Women's Earnings
Work Patterns Partially Explain Difference between Men's and Woman's Earnings
Gao ID: GAO-04-35 October 31, 2003
Despite extensive research on the progress that women have made toward equal pay and career advancement opportunities over the past several decades, there is no consensus about the magnitude of earnings differences between men and women and why differences may exist. According to data from the Department of Labor's Current Population Survey (CPS), women have typically earned less than men. Specifically, in 2001, the published CPS data showed that for full-time wage and salary workers, women's weekly earnings were about three-fourths of men's. However, this difference does not reflect key factors, such as work experience and education, that may affect the level of earnings individuals receive. Studies that attempt to account for key factors have provided a more comprehensive estimate of the earnings difference. However, recent information is lacking because many studies on earnings differences relied on data that predated the mid-1990s. But, even when accounting for these factors, questions remain about the size of and reasons for any earnings difference. To provide insight into these issues, Congress asked that we examine the factors that contribute to differences in men's and women's earnings.
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. 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.
GAO-04-35, Women's Earnings: Work Patterns Partially Explain Difference between Men's and Woman's Earnings
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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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Monthly Labor Review 120:4 (1997): 15-24.
[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 - ½ 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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