oversight

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

Published by the Government Accountability Office on 2003-10-31.

Below is a raw (and likely hideous) rendition of the original report. (PDF)

               United States General Accounting Office

GAO            Report to Congressional Requesters




October 2003
               WOMEN’S EARNINGS

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




GAO-04-35
Contents


Letter                                                                                1


Appendix I     Briefing Slides                                                        4



Appendix II    GAO Analysis of the Earnings Difference between
               Men and Women                                                         21
               Review of Other Research on Earnings Differences                      21
               Data Used in Our Analysis                                             23
               Results of Our Analysis                                               29
               Limitations of Our Analysis                                           54

Appendix III   GAO Analysis of Women’s Workplace Decisions                           56
               Purpose                                                               56
               Scope and Methodology                                                 56
               Summary of Results                                                    57
               Background                                                            57
               Working Women Make a Variety of Decisions to Manage Work and
                 Family Responsibilities                                             59
               Related Research                                                      65

Appendix IV    GAO Contact and Staff Acknowledgments                                 75
               GAO Contact                                                           75
               Staff Acknowledgments                                                 75


Tables
               Table 1: Descriptive Statistics for Selected PSID Variables           26
               Table 2: Overall and Separate Model Results for Men and Women         34
               Table 3: Summary of Decomposition Results                             45
               Table 4: Decomposition Results Using Regression Coefficients          46
               Table 5: Decomposition Results Using Alternative Estimates            50




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

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




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Page ii                                                    GAO-04-35 Women's Earnings
United States General Accounting Office
Washington, DC 20548




                                   October 31, 2003

                                   The Honorable Carolyn B. Maloney
                                   The Honorable John D. Dingell
                                   House of Representatives

                                   Despite extensive research on the progress that women have made toward
                                   equal pay and career advancement opportunities over the past several
                                   decades, there is no consensus about the magnitude of earnings
                                   differences between men and women and why differences may exist.
                                   According to data from the Department of Labor’s Current Population
                                   Survey (CPS), women have typically earned less than men.1 Specifically, in
                                   2001, the published CPS data showed that for full-time wage and salary
                                   workers, women’s weekly earnings were about three-fourths of men’s.2
                                   However, this difference does not reflect key factors, such as work
                                   experience and education, that may affect the level of earnings individuals
                                   receive. Studies that attempt to account for key factors have provided a
                                   more comprehensive estimate of the earnings difference. However, recent
                                   information is lacking because many studies on earnings differences relied
                                   on data that predated the mid-1990s. But, even when accounting for these
                                   factors, questions remain about the size of and reasons for any earnings
                                   difference. To provide insight into these issues, you asked that we
                                   examine the factors that contribute to differences in men’s and women’s
                                   earnings. On October 2, 2003, we briefed you on the results of our analysis.
                                   This report formally conveys the information provided during that briefing
                                   (see app. I).

                                   To address this issue, we carried out two types of analyses. We performed
                                   a quantitative analysis to determine differences in earnings by gender and
                                   what factors may account for these differences. The statistical model we




                                   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.



                                   Page 1                                                     GAO-04-35 Women's Earnings
    developed used data from the Panel Study of Income Dynamics (PSID),3 a
    nationally representative longitudinal data set that includes a variety of
    demographic, family, and work-related characteristics for individuals over
    time. We tracked work and life histories of individuals who were between
    ages 25 and 65 at some point between 1983 and 2000. Using our statistical
    model, we estimated how earnings differ between men and women after
    controlling for numerous factors that can influence an individual’s
    earnings. (For more information about this analysis and its limitations, see
    app. II.) To supplement this analysis, we reviewed the literature and
    interviewed a variety of individuals with expertise on earnings and other
    workplace issues4 to obtain a broad range of perspectives on reasons why
    workers make certain career and workplace decisions that could affect
    earnings. In addition, we contacted employers to discuss these issues as
    well as to identify what policies employers offered to help workers
    manage work and other life responsibilities. (For more information about
    this analysis, see app. III.) We conducted our work from September
    2002 to October 2003 in accordance with generally accepted government
    auditing standards.

    In summary, we found:

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

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


    3
     The PSID is a survey of a sample of U.S. individuals that collects economic and
    demographic data, with substantial detail on income sources and amounts, employment,
    family composition changes, and residential location.
    4
        These individuals will be referred to as “experts” throughout the remainder of this report.




    Page 2                                                           GAO-04-35 Women's Earnings
discrimination or other factors that may affect earnings. For example,
some experts said that some women trade off career advancement or
higher earnings for a job that offers flexibility to manage work and family
responsibilities.

In conclusion, while we were able to account for much of the difference in
earnings between men and women, we were not able to explain the
remaining earnings difference. It is difficult to evaluate this remaining
portion without a full understanding of what contributes to this difference.
Specifically, an earnings difference that results from individuals’ decisions
about how to manage work and family responsibilities may not necessarily
indicate a problem unless these decisions are not freely made. On the
other hand, an earnings difference may result from discrimination in the
workplace or subtler discrimination about what types of career or job
choices women can make. Nonetheless, it is difficult, and in some cases,
may be impossible, to precisely measure and quantify individual decisions
and possible discrimination. Because these factors are not readily
measurable, interpreting any remaining earnings difference is problematic.


As arranged with your offices, unless you announce its contents earlier,
we plan no further distribution of this report until 30 days after the date of
this report. At that time, we will provide copies of this report to the
Secretary of Labor and other interested parties. We will also make copies
available to others upon request. In addition, the report will be available at
no charge on GAO’s Web site at http://www.gao.gov.

Please contact me or Lori Rectanus on (202) 512-7215 if you or your staff
have any questions about this report. Other contacts and staff
acknowledgments are listed in appendix IV.




Robert E. Robertson
Director, Education, Workforce, and
 Income Security Issues




Page 3                                             GAO-04-35 Women's Earnings
                Appendix I: Briefing Slides
Appendix I: Briefing Slides




          GAO Congressional Briefing
       Representative John D. Dingell and
       Representative Carolyn B. Maloney

         Analysis of the Earnings Difference
             between Men and Women



                                              October 2, 2003


                                                                           1




                Page 4                                GAO-04-35 Women's Earnings
                 Appendix I: Briefing Slides




Introduction

• Despite extensive research on the progress women have
  made toward equal pay, no consensus exists about the size
  of any earnings difference between men and women

• Some earnings studies have not accounted for key factors
  that affect earnings, such as work experience and education

• Even when accounting for such key factors, questions remain
  about the size of and reasons for any difference




                                                                     2




                 Page 5                          GAO-04-35 Women's Earnings
                 Appendix I: Briefing Slides




Key Question


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




                                                                     3




                 Page 6                          GAO-04-35 Women's Earnings
                  Appendix I: Briefing Slides




Scope and Methodology

• We developed a statistical model to estimate how earnings
  differ between men and women after controlling for a
  comprehensive set of demographic, family, and work-related
  factors that can influence an individual’s earnings

• We used the Panel Study of Income Dynamics, a nationally
  representative longitudinal data set that includes a variety of
  demographic, family, and work-related characteristics

• We tracked work and life histories of individuals who were
  between ages 25 and 65 at any point during the period 1983
  through 2000


                                                                         4




                  Page 7                             GAO-04-35 Women's Earnings
                  Appendix I: Briefing Slides




Scope and Methodology (continued)

• To supplement our model, we reviewed literature and
  interviewed a variety of individuals to obtain a broad range of
  perspectives on why workers make certain career and
  workplace decisions that could affect earnings

• Experts reviewed our work

• We conducted our work from September 2002 to October
  2003 in accordance with generally accepted government
  auditing standards



                                                                         5




                  Page 8                            GAO-04-35 Women's Earnings
                 Appendix I: Briefing Slides




Summary of Results


• Work patterns are important when accounting for some of the
  earnings difference between men and women

• After accounting for factors affecting earnings, women
  earned an average of 80 percent of what men earned in 2000

• Our model could not explain all of the earnings difference
  between men and women due to inherent limitations in the
  survey data and in statistical analysis


                                                                      6




                 Page 9                          GAO-04-35 Women's Earnings
                  Appendix I: Briefing Slides




Many Factors Account for Earnings
Difference, but Work Patterns Are Key

• While many factors account for the earnings difference
  between men and women, work patterns are key

• Some of the other factors include industry, occupation, race,
  marital status, and job tenure

• Some of the factors that contribute to an earnings difference
  affect men and women differently, but we cannot explain why




                                                                       7




                  Page 10                          GAO-04-35 Women's Earnings
                 Appendix I: Briefing Slides




Work Patterns Are Important When
Accounting for Earnings Difference
• Men’s and women’s work patterns differ:

   • Women have fewer years of work experience

   • Women work fewer hours per year

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

   • Women leave the labor force for longer periods of time



                                                                      8




                 Page 11                          GAO-04-35 Women's Earnings
                 Appendix I: Briefing Slides




Work Patterns
Work Patterns (continued)
              (continued)

•• Years
   Years of
         of work
            work experience
                 experience and
                            and hours
                                hours worked
                                      worked per
                                             per year
                                                 year differ
                                                      differ
   for men
   for men and
           and women
               women




                                                                     9
                                                                     9




                 Page 12                         GAO-04-35 Women's Earnings
                 Appendix I: Briefing Slides




Work Patterns (continued)

• Men and women vary in terms of their full-time work and time
  out of the labor force




                                                                    10




                 Page 13                         GAO-04-35 Women's Earnings
                 Appendix I: Briefing Slides




Perspectives on Why Work Patterns
Differ
• Although the model could not explain why work patterns
  differ, according to experts and the literature, women are
  more likely to work part time or take leave from work to
  manage home and family responsibilities, such as caring for
  children

• According to employers, even when they offer part-time work
  or leave from work to all employees, women are more likely
  than men to use these options, although both men and
  women use other work arrangements



                                                                    11




                 Page 14                         GAO-04-35 Women's Earnings
                               Appendix I: Briefing Slides




Men’s and Women’s Earnings Differ
Even after Accounting for Key Factors




   As the graph shows, there were fluctuations in the earnings difference for each year we studied. Over the time period, there
   was a small but statistically significant decline in the average earnings difference between men and women.
   Note: Data were collected annually through 1997 and then biennially starting in 1999.


                                                                                                                                  12




                               Page 15                                                                GAO-04-35 Women's Earnings
                  Appendix I: Briefing Slides




Potential Reasons for the Remaining
Earnings Difference
• Our model could not explain all of the earnings difference
  between men and women due to inherent limitations in the
  survey data and in statistical analysis

• Some experts and literature identified potential reasons for
  an earnings difference:
   • some women trade off advancement or higher earnings
     for a job that offers flexibility to manage work and family
     responsibilities
   • discrimination resulting from societal views about
     acceptable roles for men and women or views about
     women in the workplace may affect women’s earnings

                                                                       13




                  Page 16                           GAO-04-35 Women's Earnings
                  Appendix I: Briefing Slides




Some Women Trade off Earnings for
Flexibility
• According to some experts and literature, some women trade
  off career advancement or higher earnings for a flexible job
   • For example, a woman may choose a human resources
       job that requires less travel and time in the office than an
       online position in the company, but offers less opportunity
       for advancement and higher earnings
   • For example, in medicine, a woman may choose family
       practice because it may be more accommodating to
       home and family than the surgical specialty, which offers
       relatively higher earnings. Surgeons’ work is generally
       less predictable because it may require treating
       emergencies at all hours


                                                                        14




                  Page 17                            GAO-04-35 Women's Earnings
                 Appendix I: Briefing Slides




Discrimination May Also Affect Women’s
Earnings
• According to some experts and literature, those who work in
  traditionally female-dominated occupations generally receive
  less earnings

• Also, according to some experts, discrimination against
  women in the workplace negatively affects women’s job
  opportunities, advancement, and therefore, earnings




                                                                     15




                 Page 18                          GAO-04-35 Women's Earnings
                   Appendix I: Briefing Slides




Concluding Observations

• While we could account for much of the earnings difference
  between men and women, we cannot explain all of the
  difference due to inherent limitations in the survey data and in
  statistical analysis
• It is difficult to evaluate the remaining difference without a full
  understanding of what contributes to the difference
    • An earnings difference resulting from individual decisions
       about how to manage work and family may not be a
       problem, unless the decisions are not freely made
    • An earnings difference may result from workplace
       discrimination or subtler discrimination about job choices
       women can make

                                                                          16




                   Page 19                             GAO-04-35 Women's Earnings
                  Appendix I: Briefing Slides




Concluding Observations (continued)

• It is difficult to measure and quantify individual decisions and
  possible discrimination

• Because these factors are not readily measurable,
  interpreting any remaining earnings difference between men
  and women is problematic




                                                                         17




                  Page 20                            GAO-04-35 Women's Earnings
                       Appendix II: GAO Analysis of the Earnings
Appendix II: GAO Analysis of the Earnings
                       Difference between Men and Women



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.


                       Our literature search consisted primarily of research in peer reviewed
Review of Other        journals, chiefly in economics, sociology, and psychology. We
Research on Earnings   concentrated on research about gender-related earnings differences, as
                       opposed to, for example, race-related or age-related earnings differences.
Differences            We focused on studies of populations within the United States,
                       particularly, but not limited to, studies using the Panel Study of Income
                       Dynamics (PSID)1 or the Current Population Survey (CPS) databases, and
                       studies conducted within the past 10 years. We also included any seminal
                       work in the area. We reviewed each study’s primary methodological
                       approach (whether it used cross-sectional or panel data and whether it
                       used general regression, time series, or other analytic estimation
                       methods), the specific databases used, the years included in the study, the
                       key variables in the analysis, and the principal results.

                       To study earnings differences, most of the studies we reviewed estimated
                       a wage or earnings equation that relates individuals’ wages or earnings to
                       several independent variables, such as education, experience, occupation,



                       1
                        The PSID is a longitudinal survey, ongoing since 1968, of a representative sample of U.S.
                       individuals and the families they reside in. The central focus of the data is economic and
                       demographic, with substantial detail on income sources and amounts, employment, family
                       composition changes, and residential location. PSID data were collected annually through
                       1997 and biennially starting in 1999. The most recent survey available is 2001, which
                       includes data from 2000.




                       Page 21                                                     GAO-04-35 Women's Earnings
Appendix II: GAO Analysis of the Earnings
Difference between Men and Women




industry, and region. In contrast to simple comparisons between the
average wages or earnings of men and women, these studies attempted to
determine whether a wage or earnings difference existed after accounting
for differences between men and women in these variables.

The wage or earnings difference between men and women can be
identified in two ways. Studies that pool data for men and women together
can include a variable denoting the gender of the individuals. In a
multivariate regression analysis, the coefficient on the gender variable
represents the difference in earnings between men and women, holding
constant the effects of the other variables. Alternatively, separate
regression models can be estimated for men and women and a
decomposition analysis can compare the results for the two genders.

Our review of the literature did not uncover much disagreement over the
existence of an earnings difference after holding constant the effects of
other variables. Rather, debate centered on the size of any difference and
factors that might explain it. We found that the size of a difference can
vary by model estimation procedures, the years included in the analysis,
and the data set used. The wage or earnings difference, after controlling
for several factors, varied from 2.5 percent to 47.5 percent. Few of the
studies used data more recent than the mid-1990s.

The results of some studies on wage and earnings differences used
ordinary least squares (OLS) regressions for analysis. Compared to
analyses of uncontrolled wage and earnings data, OLS regression is an
improvement because it allows for the control of some factors in the data.
The strength of findings from OLS approaches has been questioned,
however, because of at least three potentially significant biases.2 First, the
estimates can be biased if some factors that are related to individuals’
earnings and that differ between men and women are omitted from the
analysis (omitted variable bias or unobserved heterogeneity). Second,
several of the independent variables may be closely interrelated with
earnings (endogeneity). For example, earnings may be related to the
number of hours an individual works, but the number of hours one
chooses to work may depend on how much is earned by working. An OLS
analysis assumes that no such interrelationships exist. If they do exist,
OLS can produce biased estimates. Third, in the context of individuals’



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




Page 22                                                GAO-04-35 Women's Earnings
                   Appendix II: GAO Analysis of the Earnings
                   Difference between Men and Women




                   work decisions, OLS estimation can produce biased estimates when
                   unobserved factors affect both the level of earnings and the probability
                   that someone chooses to work (selection bias).


                   To conduct our analysis, we used the PSID rather than the CPS for two
Data Used in Our   main reasons. First, by using data that follow individuals over a period of
Analysis           time, we can take into account individual work and life histories more
                   specifically than CPS or other data sources. Several researchers have
                   analyzed gender wage and earnings differences and have attempted to
                   address potential unobserved heterogeneity bias using longitudinal data
                   such as the PSID. Second, the PSID includes questions that can be used to
                   measure actual past work experience, which may be a key factor in
                   explaining the gender earnings difference but is not available in the CPS.
                   We assessed the reliability of the PSID data by reviewing documentation
                   and performing electronic tests in order to check for missing data,
                   outliers, or other potential problems that might adversely affect our
                   estimates. Based on these tests we determined that the data were
                   sufficiently reliable for the purposes of our work.

                   In our sample, individuals between the ages of 25 and 65 were tracked
                   from 1983 to 2000.3 Data for some individuals were available for all of
                   these years, while data for other individuals were available for some years
                   only. This is because some individuals entered the sample after 1983.
                   Individuals were not included in the sample until they formed an
                   independent household and reached age 25. We did not use data on
                   individuals after they reached age 65.

                   The dependent variable we focused on is a measure of an individual’s
                   annual earnings. As measured in the PSID, annual earnings include an
                   individual’s wages and salaries as well as income from bonuses, overtime
                   pay, tips, commissions, and other job-related income. It also includes
                   earnings from self-employment and farm-related income. We took inflation
                   into account by using the consumer price index to adjust annual earnings
                   to year 2000 dollars. We also developed an alternative definition of
                   earnings for individuals who reported that they were “self-employed only”
                   in a particular industry. For these individuals, we multiplied annual hours
                   worked by the average hourly earnings for the particular industry they



                   3
                    The lower limit of the age range was set at 25 because the PSID does not include detailed
                   information for dependent college students, posing potential selection bias issues.




                   Page 23                                                     GAO-04-35 Women's Earnings
Appendix II: GAO Analysis of the Earnings
Difference between Men and Women




worked in using U.S. Department of Labor and U.S. Department of
Agriculture data.4

To determine why an earnings difference between men and women may
exist, our model controlled for a range of variables, which can be grouped
into three variable sets. The first set of independent variables consisted of
demographic characteristics, including gender, age, and race. We also
included an education variable that indicated the highest number of years
of education each respondent attained by the end of the sample period.
Family-related demographic variables included marital status, number of
children, and the age of the youngest child in the household. We also
included other income (defined as family income minus a respondent’s
own personal earnings), the region where individuals lived (i.e., in the
South or not), and whether they lived in a rural or urban area (i.e., in a
metropolitan area or not).

The second set of independent variables pertained to past work
experience. Total work experience was defined as the actual number of
years an individual worked for money since age 18. This variable was
computed as self-reported experience as reported in 1984 (or the year the
individual entered the panel), augmented by hours of work divided by
2,000 in each subsequent year. We also included a variable measuring job
tenure, defined as the length of time an individual had spent in his or her
current job.

The third set of independent variables included labor market activity
reported in a given survey year. Variables included hours worked in the
past year, weeks out of the labor force in the past year, and weeks
unemployed in the past year. For our analysis, we considered time spent
unemployed and time out of the labor force as work “interruptions,” but
we did not include time off for one’s own illness or a family member’s
illness, vacation and other time off, or time out because of strike. We also
included a variable that accounted for an individual’s full-time or part-time
employment status, defined as the average number of hours an individual
worked per week on his or her main job. Individuals were considered to
have worked part-time if they worked fewer than 35 hours per week and
full-time if they worked 35 hours or more per week. Other variables in this


4
 The Department of Agriculture data are from the National Agricultural Statistics Service
data series “Annual All Hired Workers Wage Rates, U.S. Level” and the Department of
Labor data are from the Bureau of Labor Statistics data series “Average Hourly Earnings of
Production Workers.”




Page 24                                                     GAO-04-35 Women's Earnings
Appendix II: GAO Analysis of the Earnings
Difference between Men and Women




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.




Page 25                                           GAO-04-35 Women's Earnings
Appendix II: GAO Analysis of the Earnings
Difference between Men and Women




Table 1: Descriptive Statistics for Selected PSID Variables

                                               Men                      Women
                                        Means       Standard         Means    Standard
Variable                            (averages)      deviation    (averages)   deviation
All individuals (workers and nonworkers)
Annual earnings (in 2000 dollars)       35,942        34,630         16,554     18,510
Age of individual (in years)                 41.3        11.3          42.0        11.5
Age of youngest child (in years)              3.3         4.9           4.0         5.2
Number of children                            0.9         1.2           1.1         1.2
Married (percent)                            70.1        45.8          61.2        48.7
Metropolitan area of residence
(percent)                                    64.7        48.1          67.1        47.0
Full-time main job (percent)                 74.9        43.3          47.2        49.9
Time unemployed (in weeks)                    1.9         7.0           1.8         6.9
Time out of the labor force (in
weeks)                                        2.4         9.9           6.1        15.3
Annual hours worked                         1,931        926          1,226        957
Job tenure (in months)                       80.1      102.2           55.1        80.3
Work experience (in years)                   16.8        10.2          11.2         8.4
Highest education (in years)                 12.9         2.7          12.7         2.4
Number of observations                  42,394                       54,986
Number of individuals                       5,032                     6,033
Workers only
Annual earnings (in 2000 dollars)       40,426        34,334         22,782     18,316
Age of individual (in years)                 40.2        10.6          40.4        10.5
Age of youngest child (in years)              3.5         5.0           4.3         5.2
Number of children                            1.0         1.2           1.0         1.2
Married (percent)                            72.2        44.9          60.9        48.8
Metropolitan area of residence
(percent)                                    64.5        47.8          68.1        46.6
Full-time main job (percent)                 87.6        33.0          66.8        47.1
Time unemployed (in weeks)                    1.8         6.4           1.9         6.7
Time out of the labor force (in
weeks)                                       0.91         5.1           2.8         9.1
Annual hours worked                         2,154        697          1,672        716
Job tenure (in months)                       89.3      104.2           74.1        85.6
Work experience (in years)                   16.4         9.8          12.1         8.0
Highest education (in years)                 13.2         2.6          13.1         2.3
Number of observations                  35,726                       36,793




Page 26                                                     GAO-04-35 Women's Earnings
                     Appendix II: GAO Analysis of the Earnings
                     Difference between Men and Women




                                                                    Men                        Women
                                                              Means      Standard            Means    Standard
                         Variable                         (averages)     deviation       (averages)   deviation
                         Number of individuals                   4,477                        4,884
                     Source: GAO analysis of PSID data.




Description of Our   We used the Hausman-Taylor model to analyze the earnings difference
Econometric Model    between men and women.5 The Hausman-Taylor model was developed to
                     analyze panel data and to take into account unobserved heterogeneity and
                     endogeneity while permitting the estimation of coefficients for factors that
                     do not vary over time, such as gender. As is usual practice in studies of the
                     determinants of earnings and earnings differences between groups, we
                     related the natural logarithm of the dependent variable (annual earnings in
                     this case) to several independent variables. The specific equation we
                     estimated was

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

                     where subscripts i and t denote individuals and time periods,

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

                     X2it are endogenous time-varying variables possibly correlated with µi but not
                     with νit,

                     Z1i are exogenous time-invariant variables assumed to be uncorrelated with
                     µi and νit,




                     5
                      Jerry A. Hausman and William E. Taylor, “Panel Data and Unobservable Individual
                     Effects,” Econometrica 49:6 (November 1981). Light and Ureta use this model to analyze
                     the relationship between experience and wage differences (see Audrey Light and Manuelita
                     Ureta, “Early-Career Work Experience and Gender Wage Differentials,” Journal of Labor
                     Economics 13:1 (1995): 121-154).




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Z2i are endogenous time-invariant variables possibly correlated with µi but
not with νit,

β and δ represent coefficients on the respective variables,

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

νit is a random error term.

In our specification of the model, we allowed annual hours worked, time
out of labor force, work experience, and the square of experience to be
time-varying endogenous variables. Highest education achieved was
treated as a time-invariant endogenous variable. The other independent
variables were treated as exogenous.

To account for possible selection bias arising from not accounting for an
individual’s choice of whether to work, we used a Heckman selection bias
correction. To do this, we estimated the probability of working in a
particular year for all individuals in the data set.6 We then used a term that
was estimated in this equation (the inverse Mills ratio) as an additional
independent variable in the Hausman-Taylor earnings equation. The
Hausman-Taylor model was then estimated for individuals with positive
annual hours of work and positive earnings in a given year.

Two academic labor economists reviewed a preliminary version of the
econometric model and the results. One of the reviewers has published
extensively on gender wage differences and has used the PSID in his work.
The other reviewer has published widely on labor economics topics
generally, also using the PSID. Both reviewers thought that the model and
results were sound and reasonable. To the extent possible, we have
incorporated their suggestions for clarifications and additional analysis.




6
  The probability that an individual worked was modeled as a function of age, the number of
children and the age of the youngest child in the household, marital status, additional
family income, work experience, education, race, region and urban-rural indicators, and a
work disability indicator. This model was estimated separately for men and women for
each of the years in the sample.




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                 We found that before controlling for any variables that may affect
Results of Our   earnings, on average, women earned about 44 percent less than men over
Analysis         the time period we studied—1983 to 2000. However, after controlling for
                 the independent variables that we included in our model, we found that
                 this difference was reduced to about 21 percent over this time period. The
                 model results indicated a small but statistically significant decline in the
                 earnings difference over this period.

                 Table 2 shows the regression results for the overall model that included
                 observations on men and women combined and the results for men and
                 women separately. For each variable in each regression, the table shows
                 the coefficient (estimate β), the estimated standard error for the
                 coefficient, the p-value, and an alternative coefficient estimate. For each of
                 the regressions, the first column of results shows the coefficient estimates.
                 The standard interpretation of the regression coefficients in models of this
                 type is that they represent the average percentage change in earnings that
                 would result from a small increase in an independent variable. The
                 estimated standard error and the p-value are shown in the second and
                 third columns. A p-value of less than 0.05 indicates that the regression
                 coefficient is statistically significantly different from zero, which would
                 indicate that the variable has a statistically significant effect on earnings.
                 In the fourth column, we show an alternative estimate for the average
                 percentage change based on a transformation of the regression
                 coefficients, which the literature shows is a more precise measure than the
                 standard coefficient estimate.7 For this reason, we emphasize the
                 alternative estimates in the discussion of the results.

                 The gender coefficient in the overall model shows the difference in
                 earnings between men and women in each year after accounting for the
                 effect of the other variables in the model. As shown in the alternative
                 estimate column of the overall model results of table 2, the estimated
                 coefficient for the gender variable was –0.2025 for the year 2000. This
                 means that, holding all other variables in the model constant except for
                 gender, women earned an average of about 20.3 percent less than men in
                 2000. The estimated coefficients were statistically significantly different
                 from zero for each of the years. Overall, the model results indicated that
                 there was a small but statistically significant decline in the earnings


                 7
                  Peter E. Kennedy, “Estimation with Correctly Interpreted Dummy Variables in
                 Semilogarithmic Equations,” American Economic Review, 71:4 (September 1981): 801. The
                 alternative estimator g = exp(β – ½ V(β)) – 1, where V(β) is the estimated variance of the
                 regression coefficient.




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difference between 1983 and 2000. The analysis indicated that the
difference declined by about 0.3 percentage points per year, on average.

The next set of variables, included in the overall model and in the separate
regressions for men and women, deal with work patterns. In our analysis,
work patterns included years of work experience, hours worked per year,
length of time out of the labor force, and whether the individual worked a
full-time or part-time schedule. In addition, length of unemployment and
tenure were also considered to be work patterns. For the hours worked,
time out of the labor force, length of unemployment, and tenure variables,
the coefficient estimate shown represents the estimated percentage
change in earnings that would result from a one-unit change (hours or
weeks) in the particular variable. For example, as shown in table 2 in the
alternative estimate column of the overall model results, the coefficient for
time out of the labor force was –0.0226. This means that earnings would
decrease by about 2.3 percent for each additional week out of the labor
force, holding all other factors constant—including annual hours worked.
The coefficients on the experience variables indicate that each additional
year of work experience is generally associated with increased earnings,
but this increase declines as the level of experience increases.8 The
working full-time variable measures the effect of having a full-time main
job relative to having a part-time job as a main job. All the work pattern
variables are estimated to have a statistically significant effect on earnings.

The next set of variables includes other work-related characteristics.
Several of these variables are categorical in nature, such as occupation,
industry, and self-employment status. For these variables, the coefficient
for a particular category is an estimate of the effect of being in that
category relative to the omitted category. For example, as shown in
table 2 in the alternative estimate column of the overall model results, the
coefficient was -0.09 for those individuals working in service/private
household occupations. This indicates that individuals working in
service/private household occupations earned 9 percent less, on average,


8
  The effect of an additional year of experience on earnings is the sum of the effect of the
experience and experience-squared variables. The amount that an additional year of
experience will increase the value of the experience-squared variable will vary with the
level of experience. For example, an additional year of experience would increase
experience-squared by 1 for someone with no prior experience, and it will increase the
experience-squared variable by 41 for someone with 20 years of experience
(i.e., 441 – 400 = 41). Taking into account the effect of both variables, these estimates
would indicate that an additional year of experience would increase earnings for men with
less than 33 years of experience and for women with less than 31 years of experience.




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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
                th      th
between the 25 and 26 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.


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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.




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

                                             Overall model
                                                                        Alternative
Variable               Estimate β    Standard error          p-value    estimate g
Gender: women vs. men
2000                      –0.2260           0.0227             0.000       -0.2025
     a
1999
1998                      –0.1716           0.0229             0.000       -0.1579
     a
1997
1996                      –0.2264           0.0230             0.000       -0.2028
1995                      –0.2176           0.0215             0.000       -0.1958
1994                      –0.2311           0.0213             0.000       -0.2065
1993                      –0.2132           0.0214             0.000       -0.1922
1992                      –0.2556           0.0210             0.000       -0.2257
1991                      –0.2478           0.0209             0.000       -0.2197
1990                      –0.2277           0.0209             0.000       -0.2038
1989                      –0.2315           0.0209             0.000       -0.2068
1988                      –0.2534           0.0210             0.000       -0.2240
1987                      –0.2503           0.0211             0.000       -0.2216
1986                      –0.2708           0.0210             0.000       -0.2374
1985                      –0.2810           0.0212             0.000       -0.2452
1984                      –0.2921           0.0212             0.000       -0.2534
1983                      –0.2179           0.0222             0.000       -0.1960
Work patterns
Experience
(years)                     0.0231          0.0019             0.000        0.0234
Experience
squared                   –0.0003           0.0000             0.000       -0.0003
Hours worked
(per year)                  0.0004          0.0000             0.000        0.0004
Time out of
labor force
(weeks)                    -0.0228          0.0003             0.000       -0.0226
Length of
unemployment
(weeks)                   –0.0156           0.0004             0.000       -0.0155
Tenure
(months)                    0.0009          0.0000             0.000        0.0009




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                     Men                                                         Women
              Standard               Alternative                            Standard                     Alternative
Estimate βm       error    p-value   estimate gm        Estimate βf             error       p-value      estimate gf




    0.0260      0.0025       0.000        0.0264             0.0246           0.0031          0.000          0.0249

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

    0.0003      0.0000       0.000        0.0003             0.0005           0.0000          0.000          0.0005


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


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


    0.0010      0.0000       0.000        0.0010             0.0009           0.0001          0.000          0.0009




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                                                Overall model
                                            Standard                     Alternative
Variable               Estimate β               error       p-value      estimate g
Working full time
(main job)                  0.1519            0.0063            0.000        0.1640
Other work related
Mother’s education        –0.0194             0.0057            0.001       -0.0193
Father’s education        –0.0044             0.0051            0.385       -0.0044
Highest education
(years)                     0.1475            0.0058            0.000        0.1590
Self-employment
status
   Works for
   someone else
   onlyb
   Self-employed
   only                     0.0142            0.0103            0.166        0.0142
   Missing                –0.3272             0.0128            0.000       -0.2791
   Both                     0.0191            0.0239            0.424        0.0190
Union member                0.1435            0.0090            0.000        0.1542
Occupation
   Professional,
            b
   technical
   Service/private
   household
   workers                –0.0949             0.0116            0.000       -0.0906
   Farm laborers
   and foremen            –0.1761             0.0399            0.000       -0.1622
   Farmers and farm
   management             –0.3805             0.0469            0.000       -0.3172
   Nonfarm laborers       –0.0907             0.0162            0.000       -0.0869
   Transport
   equipment
   operators              –0.0869             0.0179            0.000       -0.0834
   Operators,
   nontransport           –0.0588             0.0136            0.000       -0.0572
   Craftsmen              –0.0108             0.0122            0.376       -0.0108




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                    Men                                                                Women
              Standard               Alternative                                Standard                       Alternative
Estimate βm       error   p-value    estimate gm           Estimate βf              error      p-value         estimate gf

    0.1724      0.0094      0.000         0.1881                0.1180            0.0086         0.000             0.1252


   –0.0107      0.0075      0.155        -0.0106               –0.0256            0.0081         0.001            -0.0253
    0.0039      0.0067      0.557         0.0039               –0.0117            0.0071         0.102            -0.0116

    0.1355      0.0072      0.000         0.1451                0.1603            0.0087         0.000             0.1738




   –0.1056      0.0123      0.000        -0.1003                0.2168            0.0169         0.000             0.2419
   –0.2823      0.0187      0.000        -0.2461               –0.3413            0.0175         0.000            -0.2892
    0.0506      0.0266      0.057         0.0516               –0.0846            0.0443         0.056            -0.0820
    0.1388      0.0113      0.000         0.1488                0.1405            0.0140         0.000             0.1507




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

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

   –0.3434      0.0479      0.000        -0.2915               –0.1690            0.1156         0.144            -0.1611
   –0.0823      0.0178      0.000        -0.0791               –0.0627            0.0380         0.099            -0.0615


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

   –0.0458      0.0168      0.007        -0.0449               –0.0657            0.0217         0.003            -0.0638
    0.0016      0.0138      0.909         0.0015               –0.0180            0.0290         0.534            -0.0183




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                                                Overall model
                                            Standard                     Alternative
Variable                Estimate β              error       p-value      estimate g
   Clerical workers       –0.0438             0.0104            0.000       -0.0429
   Sales workers          –0.0718             0.0145            0.000       -0.0694
   Nonfarm
   managers,
   administrators           0.0243            0.0100            0.015        0.0246
   Do not
   know/missing           –0.1329             0.0280            0.000       -0.1248
Industry
   Wholesale/retail
        b
   trade
   Public
   administration           0.0702            0.0147            0.000        0.0726
   Professional
   services                 0.0516            0.0107            0.000        0.0529
   Entertainment          –0.0378             0.0275            0.168       -0.0375
   Personal services        0.0172            0.0156            0.270        0.0172
   Business and
   repair services          0.0561            0.0129            0.000        0.0576
   Finance,
   insurance, real
   estate                   0.1081            0.0149            0.000        0.1141
   Transportation/
   communications/
   public utilities         0.1692            0.0145            0.000        0.1842
   Manufacturing            0.1369            0.0104            0.000        0.1467
   Construction             0.1472            0.0150            0.000        0.1584
   Mining/agriculture       0.0303            0.0234            0.195        0.0305
   Do not
   know/missing             0.0835            0.0251            0.001        0.0868
Mills ratio               –0.2834             0.0218            0.000       -0.2470
Demographic and
other controls
Age of individual
(years)                   –0.0023             0.0011            0.043       -0.0023
Age of youngest
child (years)               0.0006            0.0005            0.257        0.0006




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                    Men                                                                Women
              Standard               Alternative                                Standard                       Alternative
Estimate βm       error   p-value    estimate gm           Estimate βf              error      p-value         estimate gf
   –0.0608      0.0178      0.001        -0.0592               –0.0497            0.0138         0.000            -0.0486
   –0.0343      0.0187      0.066        -0.0339               –0.0931            0.0218         0.000            -0.0891


    0.0373      0.0125      0.003         0.0379                0.0165            0.0157         0.295             0.0165

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




    0.0104      0.0183      0.571         0.0102                0.1641            0.0233         0.000             0.1780

    0.0172      0.0164      0.294         0.0172                0.0707            0.0146         0.000             0.0731
    0.0044      0.0337      0.896         0.0039               –0.0756            0.0436         0.083            -0.0737
   –0.0307      0.0301      0.308        -0.0306               –0.0097            0.0196         0.623            -0.0098

    0.0705      0.0158      0.000         0.0729                0.0488            0.0208         0.019             0.0498


    0.0562      0.0219      0.010         0.0575                0.1489            0.0202         0.000             0.1604


    0.1713      0.0163      0.000         0.1867                0.1865            0.0280         0.000             0.2046
    0.1417      0.0126      0.000         0.1521                0.1332            0.0174         0.000             0.1423
    0.1708      0.0160      0.000         0.1861                0.0673            0.0384         0.079             0.0689
    0.0481      0.0247      0.051         0.0489                0.0178            0.0517         0.730             0.0166

    0.1106      0.0323      0.001         0.1164                0.0712            0.0378         0.060             0.0730
   –0.3307      0.0285      0.000        -0.2819               –0.1584            0.0352         0.000            -0.1470




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

   –0.0013      0.0007      0.048        -0.0013                0.0023            0.0007         0.003             0.0023




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                                               Overall model
                                            Standard                     Alternative
Variable                Estimate β              error          p-value   estimate g
Number of
children                    0.0004            0.0029             0.897       0.0004
Additional family
income (inflation
adjusted in
thousands of
dollars)                  –0.0006             0.0001             0.000      -0.0006
Metropolitan area           0.0226            0.0067             0.001       0.0229
Excellent health            0.0088            0.0057             0.123       0.0089
Marital status
                    b
   Never married
   Married                  0.0403            0.0113             0.000       0.0410
   Other                    0.0245            0.0127             0.053       0.0247
Region: South             –0.0428             0.0120             0.000      -0.0420
Race
              b
   White
   Black                  –0.1031             0.0171             0.000      -0.0981
   Other                    0.0739            0.0585             0.207       0.0748
Year, compared to
1983
   2000                     0.0410            0.0191             0.032       0.0417
          a
   1999
   1998                   –0.0223             0.0187             0.233      -0.0222
          a
   1997
   1996                   –0.0837             0.0187             0.000      -0.0804
   1995                   –0.0705             0.0177             0.000      -0.0682
   1994                   –0.0794             0.0170             0.000      -0.0764
   1993                   –0.0664             0.0168             0.000      -0.0643
   1992                   –0.0477             0.0161             0.003      -0.0467
   1991                   –0.0867             0.0157             0.000      -0.0832
   1990                   –0.0839             0.0154             0.000      -0.0806
   1989                   –0.0569             0.0151             0.000      -0.0555
   1988                   –0.0277             0.0149             0.064      -0.0274
   1987                   –0.0318             0.0148             0.031      -0.0314
   1986                   –0.0205             0.0146             0.160      -0.0204




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                   Men                                                            Women
              Standard              Alternative                             Standard                    Alternative
Estimate βm       error   p-value   estimate gm          Estimate βf            error     p-value       estimate gf

    0.0210      0.0037      0.000         0.0212             –0.0254          0.0047        0.000          -0.0251




   –0.0009      0.0001      0.000         -0.0009            –0.0001          0.0001        0.403          -0.0001
    0.0171      0.0086      0.047         0.0173              0.0305          0.0102        0.003           0.0309
    0.0149      0.0072      0.038         0.0150              0.0062          0.0088        0.483           0.0062



    0.0800      0.0142      0.000         0.0831             –0.0011          0.0176        0.950          -0.0013
    0.0685      0.0162      0.000         0.0707             –0.0009          0.0192        0.962          -0.0011
   –0.0522      0.0155      0.001         -0.0510            –0.0377          0.0173        0.030          -0.0371



   –0.1487      0.0242      0.000         -0.1385            –0.0682          0.0230        0.003          -0.0661
    0.0491      0.0843      0.560         0.0466              0.0972          0.0762        0.202           0.0989



    0.0188      0.0192      0.328         0.0188              0.0621          0.0222        0.005           0.0638


   –0.0406      0.0186      0.029         -0.0399             0.0298          0.0215        0.165           0.0300


   –0.1045      0.0185      0.000         -0.0994            –0.0733          0.0205        0.000          -0.0709
   –0.0813      0.0175      0.000         -0.0782            –0.0618          0.0194        0.001          -0.0601
   –0.0973      0.0167      0.000         -0.0928            –0.0759          0.0188        0.000          -0.0733
   –0.0854      0.0165      0.000         -0.0820            –0.0495          0.0184        0.007          -0.0484
   –0.0693      0.0156      0.000         -0.0671            –0.0625          0.0180        0.001          -0.0608
   –0.1023      0.0150      0.000         -0.0974            –0.0921          0.0180        0.000          -0.0881
   –0.0960      0.0146      0.000         -0.0917            –0.0737          0.0174        0.000          -0.0712
   –0.0691      0.0142      0.000         -0.0669            –0.0524          0.0171        0.002          -0.0512
   –0.0359      0.0140      0.010         -0.0354            –0.0516          0.0169        0.002          -0.0504
   –0.0389      0.0137      0.005         -0.0383            –0.0561          0.0165        0.001          -0.0546
   –0.0248      0.0135      0.066         -0.0246            –0.0632          0.0164        0.000          -0.0613




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                                               Overall model
                                            Standard                     Alternative
Variable               Estimate β               error          p-value   estimate g
   1985                   –0.0249             0.0145             0.086      -0.0247
   1984                   –0.0219             0.0144             0.127      -0.0218
Intercept                   7.4055            0.0783            0.000        7.4055




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                                        Men                                                                 Women
                                     Standard                Alternative                              Standard                        Alternative
     Estimate βm                         error p-value       estimate gm          Estimate βf             error       p-value         estimate gf
           –0.0282                     0.0134    0.035             -0.0279            –0.0822             0.0163        0.000             -0.0791
           –0.0237                     0.0131    0.070             -0.0235            –0.0847             0.0160        0.000             -0.0813
             7.5910                    0.0983    0.000             7.5910              6.9846             0.1179        0.000              6.9846
Source: GAO analysis of PSID data.
                                                         a
                                                         Data not available.
                                                         b
                                                         Category omitted.


                                                         Tables 3, 4, and 5 show a decomposition analysis of the earnings
                                                         difference derived from the separate regression analysis for men and
                                                         women. This statistical technique—the Blinder-Oaxaca decomposition—
                                                         has been commonly used in analyses of wage or earnings differences
                                                         between men and women. The decomposition divides the (logged)
                                                         earnings difference between men and women into two parts: a part
                                                         reflecting differences in characteristics between men and women and a
                                                         part reflecting differences in parameters (or return to earnings) between
                                                         men and women.9 This decomposition is represented as follows:


                                                         ln Em − ln Ef = (Xm − Xf)´βˆm + Xf´(βˆm − βˆf)

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

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




                                                         9
                                                          J. G. Altonji and R. M. Blank, “Race and Gender in the Labor Market,” The Handbook of
                                                         Labor Economics (Amsterdam: Elsevier Science, 1999), vol. 3C, pp. 3153–61.




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

Table 3 summarizes how several categories of variables contributed to the
earnings difference through differences in characteristics and differences
in parameters. Positive values indicate an earnings advantage for men
while negative values indicate an advantage for women. For example, in
table 3, the difference in earnings due to characteristics from the work
pattern variables is equal to 0.2729, which indicates that men have an
earnings advantage. This figure represents the sum—for all the work
pattern variables—of the difference in men’s and women’s mean
characteristics multiplied by the men’s regression coefficients. The effect
of the work pattern variables accounted for most of the difference in
characteristics between men and women (due to different characteristics:
about 0.27 out of 0.45). Relatively little of the earnings difference was
attributable to differences in demographic characteristics (about 0.03 out
of 0.45).

Table 3 also shows the differences in earnings due to differences in
parameters (0.2446 in the total row at the bottom of table 3). The table
shows that women have a relative advantage due to parameters from the
work pattern variables. In the table, -0.2302 represents the sum—for all the
work pattern variables—of the difference in men and women’s parameters
multiplied by the women’s mean value of the variable. Women’s
advantages in the work pattern and other work-related variable categories
are outweighed by disadvantages due to the parameters for demographic
factors and from the intercept of the regressions. The relatively large
advantage to men in the intercepts of the regressions indicates that a
predictable earnings difference remains even after taking differences in
characteristics and relative returns into account.

This second part of the decomposition allows us to describe how the
remaining earnings difference results from how each factor affects
earnings differently for men and women. According to Altonji and Blank,
this component is often mistakenly attributed to the “share due to
discrimination” but actually “captures both the effects of discrimination
and unobserved differences in productivity and tastes.”10 They also point
out that it may be misleading to label only this second component as the
result of discrimination, since discriminatory barriers in the labor market


10
     Altonji and Blank, p. 3156.




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

Table 3: Summary of Decomposition Results

                                                             Differences in earnings
                                                                   Due to                Due to
    Variable categories                                    characteristics           parameters
                 a
    Work patterns                                                    0.2729               -0.2302
                      b
    Other work related                                               0.1539               -0.3218
    Demographic and other controlsc                                  0.0272                0.1902
    Intercept                                                           N/A                0.6065
    Total                                                            0.4540                0.2446

Source: GAO Analysis of PSID data.

Note: These summary results are based on the more detailed analysis shown in table 4.
a
 The work patterns category includes: work experience (years), experience squared, time out of the
labor force (weeks), length of unemployment (weeks), working full time (main job), tenure (months),
and hours worked (per year).
b
The other work related category includes: highest education (years), mother’s education, father’s
education, self-employment status, union membership, industry, occupation, and the Mill’s ratio.
c
 The demographic and other controls category includes all other variables, except the intercept, which
is a parameter only.


Table 4 shows more detailed decomposition results.11 In table 4 in the
column labeled difference due to characteristics, the variables measuring
work patterns, including experience (0.108), hours worked (0.134),
working full-time versus part-time (0.036), and length of time out of the
labor force (0.034), made large contributions to explaining gender
differences in earnings. Table 4 shows that, on average, men in our sample
worked about 2,147 hours per year, women about 1,675 hours per year.
The analysis showed that the difference between men and women, based
on hours worked, resulted in a relative advantage for men of about
0.134. In other words, about one-fifth of the uncontrolled logged earnings
difference (0.134 out of 0.69) results from the greater number of hours
men worked compared to women.



11
 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.




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                                           Table 4 also shows how the variables affected earnings differently for men
                                           and women. Positive values in the difference due to parameters column
                                           would indicate that men would gain more from an increase in a particular
                                           variable than would women. For example, compared to women, men
                                           receive a greater estimated return to their earnings resulting from having
                                           children. However, we found several large negative values indicating that
                                           women have a relative advantage over men in terms of how other factors
                                           affect earnings. The largest negative values in this column resulted from
                                           the greater estimated return for each additional year of education and the
                                           greater estimated return for an additional hour of work for women. As
                                           mentioned above, the relative advantage for women for some of the
                                           variables in the model is offset when the difference in the intercept terms
                                           of the separate regressions is added. The difference in the intercept terms
                                           captures gender differences and other unmeasured effects that we cannot
                                           identify in the regressions. 12

Table 4: Decomposition Results Using Regression Coefficients

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




                                           12
                                            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.




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                           Estimate            Means (averages)                                  Difference
                                                                           Between                                              Due to
                                                                             means              Due to       Between       parameters
                          Men Women                  Men    Women        (averages)     characteristics    parameters        (returns)
Variable                   βm     βf                  Xm        Xf          (Xm – Xf)       (Xm – Xf) βm       (βm – βf)     Xf (βm– βf)
Other work related
Mother’s education     –0.0107 –0.0256           3.5458       3.4941         0.0516            –0.0005          0.0150          0.0524
Father’s education      0.0039 –0.0117           3.3364       3.2447         0.0917              0.0004         0.0156          0.0506
Highest education
(years)                 0.1355    0.1603        13.1455     13.0880          0.0575              0.0078        –0.0248         -0.3242
Self-employment
status
  Works for some-
               a
  one else only
  Self-employed only   –0.1056    0.2168         0.1177       0.0579         0.0597            –0.0063         –0.3224         -0.0187
  Missing              –0.2823 –0.3413           0.0648       0.1230        –0.0582              0.0164         0.0590          0.0073
  Both                  0.0506 –0.0846           0.0094       0.0042         0.0052              0.0003         0.1352          0.0006
Union member            0.1388    0.1405         0.1773       0.1187         0.0587              0.0081        –0.0017         -0.0002
Occupation
  Professional,
  technicala
  Service/private
  household workers    –0.1061 –0.0975           0.0763       0.2034        –0.1271              0.0135        –0.0087         -0.0018
  Farm laborers and
  foremen              –0.1928 –0.0602           0.0121       0.0023         0.0098            –0.0019         –0.1326         -0.0003
  Farmers and farm
  management           –0.3434   –0.1690         0.0124       0.0008         0.0116            –0.0040         –0.1745         -0.0001
  Nonfarm laborers     –0.0823 –0.0627           0.0547       0.0083         0.0464            –0.0038         –0.0195         -0.0002
  Transport
  equipment
  operators            –0.0576 –0.1840           0.0680       0.0084         0.0596            –0.0034          0.1264          0.0011
  Operators,
  nontransport         –0.0458 –0.0657           0.0877       0.0879        –0.0002              0.0000         0.0198          0.0017
  Craftsmen             0.0016 –0.0180           0.2049       0.0171         0.1879              0.0003         0.0196          0.0003
  Clerical workers     –0.0608 –0.0497           0.0497       0.2565        –0.2068              0.0126        –0.0111         -0.0028
  Sales workers        –0.0343 –0.0931           0.0469       0.0409         0.0059            –0.0002          0.0588          0.0024
  Nonfarm
  managers,
  administrators        0.0373    0.0165         0.1609       0.0922         0.0687              0.0026         0.0208          0.0019
  Do not
  know/missing         –0.1107 –0.1276           0.0468       0.0906        –0.0439              0.0049         0.0169          0.0015




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                           Estimate            Means (averages)                                  Difference
                                                                           Between                                              Due to
                                                                             means              Due to       Between       parameters
                           Men Women                 Men    Women        (averages)     characteristics    parameters        (returns)
Variable                    βm     βf                 Xm        Xf          (Xm – Xf)       (Xm – Xf) βm       (βm – βf)     Xf (βm– βf)
Industry
   Wholesale/retail
        a
   trade
   Public
   administration        0.0104   0.1641         0.0799       0.0607         0.0192              0.0002        –0.1538         -0.0093
   Professional
   services              0.0172   0.0707         0.1211       0.3467        –0.2256            –0.0039         –0.0535         -0.0186
   Entertainment         0.0044 –0.0756          0.0095       0.0061         0.0034              0.0000         0.0800          0.0005
   Personal services    –0.0307 –0.0097          0.0130       0.0678        –0.0549              0.0017        –0.0210         -0.0014
   Business and
   repair services       0.0705   0.0488         0.0585       0.0340         0.0245              0.0017         0.0217          0.0007
   Finance,
   insurance, real
   estate                0.0562   0.1489         0.0394       0.0641        –0.0248            –0.0014         –0.0928         -0.0059
   Transportation/
   communications/
   public utilities      0.1713   0.1865         0.0976       0.0353         0.0622              0.0107        –0.0152         -0.0005
   Manufacturing         0.1417   0.1332         0.2444       0.1341         0.1103              0.0156         0.0085          0.0011
   Construction          0.1708   0.0673         0.0963       0.0101         0.0862              0.0147         0.1034          0.0010
   Mining/agriculture    0.0481   0.0178         0.0474       0.0075         0.0399              0.0019         0.0302          0.0002
   Do not
   know/missing          0.1106   0.0712         0.0513       0.0954        –0.0441            –0.0049          0.0394          0.0038
Mills ratio             –0.3307 –0.1584          0.1628       0.3771        –0.2143              0.0709        –0.1723         -0.0650
Demographic and
other controls
Age of individual
(years)                 –0.0016 –0.0058         40.1442     40.3309         –0.1867              0.0003         0.0041          0.1669
Age of youngest child
(years)                 –0.0013   0.0023         3.4902       4.2042        –0.7140              0.0010        –0.0036         -0.0152
Number of children       0.0210 –0.0254          0.9659       1.0469        –0.0810            –0.0017          0.0464          0.0486
Additional family
income (inflation
adjusted in thousands
of dollars)             –0.0009 –0.0001         25.1172     34.9156         –9.7984              0.0086        –0.0008         -0.0284
Metropolitan area        0.0171   0.0305         0.6476       0.6806        –0.0330            –0.0006         –0.0133         -0.0091
Excellent health         0.0149   0.0062         0.2613       0.2041         0.0572              0.0009         0.0088          0.0018
Marital status
                    a
   Never married
   Married               0.0800 –0.0011          0.7196       0.6101         0.1095              0.0088         0.0811          0.0495




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                                        Estimate            Means (averages)                                  Difference
                                                                                        Between                                              Due to
                                                                                          means              Due to       Between       parameters
                                        Men Women                 Men        Women    (averages)     characteristics    parameters        (returns)
 Variable                                βm     βf                 Xm            Xf      (Xm – Xf)       (Xm – Xf) βm       (βm – βf)     Xf (βm– βf)
    Other                             0.0685 –0.0009          0.1327         0.2424      –0.1097            –0.0075          0.0694          0.0168
 Region: South                       –0.0522 –0.0377          0.4142         0.4551      –0.0409              0.0021        –0.0145         -0.0066
 Race
               a
    White
    Black                            –0.1487 –0.0682          0.2666         0.3602      –0.0936              0.0139        –0.0806         -0.0290
    Other                             0.0491   0.0972         0.0140         0.0152      –0.0011            –0.0001         –0.0481         -0.0007
 Year, compared to
 1983
    2000                              0.0188   0.0621         0.0537         0.0538      –0.0001            –0.0000         –0.0433         -0.0023
           b
    1999
    1998                             –0.0406   0.0298         0.0536         0.0515       0.0021            –0.0001         –0.0704         -0.0036
    1997b
    1996                             –0.1045 –0.0733          0.0468         0.0514      –0.0046              0.0005        –0.0312         -0.0016
    1995                             –0.0813 –0.0618          0.0613         0.0622      –0.0009              0.0001        –0.0194         -0.0012
    1994                             –0.0973 –0.0759          0.0615         0.0655      –0.0040              0.0004        –0.0214         -0.0014
    1993                             –0.0854 –0.0495          0.0597         0.0641      –0.0044              0.0004        –0.0359         -0.0023
    1992                             –0.0693 –0.0625          0.0662         0.0684      –0.0022              0.0002        –0.0068         -0.0005
    1991                             –0.1023 –0.0921          0.0668         0.0675      –0.0007              0.0001        –0.0103         -0.0007
    1990                             –0.0960 –0.0737          0.0672         0.0686      –0.0015              0.0001        –0.0224         -0.0015
    1989                             –0.0691 –0.0524          0.0675         0.0680      –0.0006              0.0000        –0.0167         -0.0011
    1988                             –0.0359 –0.0516          0.0669         0.0667       0.0002            –0.0000          0.0157          0.0010
    1987                             –0.0389 –0.0561          0.0666         0.0660       0.0006            –0.0000          0.0171          0.0011
    1986                             –0.0248 –0.0632          0.0668         0.0654       0.0014            –0.0000          0.0384          0.0025
    1985                             –0.0282 –0.0822          0.0666         0.0646       0.0020            –0.0001          0.0540          0.0035
    1984                             –0.0237 –0.0847          0.0656         0.0631       0.0025            –0.0001          0.0609          0.0038
 Sum before
 intercept                                                                                                                                  -0.3618
 Intercept                            7.5910   6.9846                                                                                        0.6065
 Sum                                                                                                          0.4540                         0.2446
Source: GAO analysis of PSID data.
                                                        a
                                                        Category omitted.
                                                        b
                                                        No data available.




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Table 5: Decomposition Results Using Alternative Estimates

                       Alternative                Means
Variable                estimate                (averages)                                    Difference
                                                                         Between                                             Due to
                                                                           means              Due to       Between      parameters
                        Men    Women               Men    Women        (averages)     characteristics    parameters       (returns)
                         gm        gf               Xm        Xf          (Xm – Xf)       (Xm – Xf) gm      (gm – gf)    Xf (gm – gf)
Work Patterns
Experience (years)    0.0264    0.0249      16.2891       12.1342          4.1548              0.1095         0.0014         0.0175
Experience
squared              –0.0004   –0.0004     359.5914      210.6411        148.9504            –0.0558          0.0001         0.0120
Hours worked (per
year)                 0.0003    0.0005   2,147.3100 1,674.8000           472.5100              0.1340       –0.0002         -0.3058
Time out of labor
force (weeks)        –0.0174   –0.0222        0.9262       2.8345         –1.9083              0.0332         0.0048         0.0136
Length of
unemployment
(weeks)              –0.0170   –0.0142        1.8149       1.8887         –0.0739              0.0013       –0.0028         -0.0053
Tenure (months)       0.0010    0.0009      91.4775       74.4278         17.0497              0.0163         0.0000         0.0015
Working full time
(in main job)         0.1881    0.1252        0.8761       0.6701          0.2059              0.0387         0.0628         0.0421
Other work
related
Mother’s education   –0.0106   –0.0253        3.5458       3.4941          0.0516            –0.0005          0.0147         0.0515
Father’s education    0.0039   –0.0116        3.3364       3.2447          0.0917              0.0004         0.0155         0.0504
Highest education
(years)               0.1451    0.1738      13.1455       13.0880          0.0575              0.0083       –0.0287         -0.3757
Self-employment
status
   Works for
   someone else
        a
   only
   Self-employed
   only              –0.1003    0.2419        0.1177       0.0579          0.0597            –0.0060        –0.3422         -0.0198
   Missing           –0.2461   –0.2892        0.0648       0.1230         -0.0582              0.0143         0.0432         0.0053
   Both               0.0516   –0.0820        0.0094       0.0042          0.0052              0.0003         0.1336         0.0006
Union member          0.1488    0.1507        0.1773       0.1187          0.0587              0.0087       –0.0019         -0.0002
Occupation
   Professional,
            a
   technical
   Service/private
   household
   workers           –0.1008   –0.0930        0.0763       0.2034         –0.1271              0.0128       –0.0079         -0.0016




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                       Alternative                Means
Variable                estimate                (averages)                                    Difference
                                                                         Between                                             Due to
                                                                           means              Due to       Between      parameters
                        Men    Women               Men   Women         (averages)     characteristics    parameters       (returns)
                         gm        gf               Xm       Xf           (Xm – Xf)       (Xm – Xf) gm      (gm – gf)    Xf (gm – gf)
  Farm laborers
  and foremen        –0.1761   –0.0618        0.0121      0.0023           0.0098            –0.0017        –0.1143         -0.0003
  Farmers and
  farm
  management         –0.2915   –0.1611        0.0124      0.0008           0.0116            –0.0034        –0.1304         -0.0001
  Nonfarm
  laborers           –0.0791   –0.0615        0.0547      0.0083           0.0464            –0.0037        –0.0176         -0.0001
  Transport
  equipment
  operators          –0.0562   –0.1690        0.0680      0.0084           0.0596            –0.0033          0.1128         0.0009
  Operators,
  nontransport       –0.0449   –0.0638        0.0877      0.0879          –0.0002              0.0000         0.0188         0.0017
  Craftsmen           0.0015   –0.0183        0.2049      0.0171           0.1879              0.0003         0.0198         0.0003
  Clerical workers   –0.0592   –0.0486        0.0497      0.2565          –0.2068              0.0122       –0.0106         -0.0027
  Sales workers      –0.0339   –0.0891        0.0469      0.0409           0.0059            –0.0002          0.0552         0.0023
  Nonfarm
  managers,
  administrators      0.0379    0.0165        0.1609      0.0922           0.0687              0.0026         0.0214         0.0020
  Do not
  know/missing       –0.1054   –0.1205        0.0468      0.0906          –0.0439              0.0046         0.0151         0.0014
Industry
  Wholesale/retail
       a
  trade
  Public
  administration      0.0102    0.1780        0.0799      0.0607           0.0192              0.0002       –0.1678         -0.0102
  Professional
  services            0.0172    0.0731        0.1211      0.3467          –0.2256            –0.0039        –0.0560         -0.0194
  Entertainment       0.0039   –0.0737        0.0095      0.0061           0.0034              0.0000         0.0775         0.0005
  Personal
  services           –0.0306   –0.0098        0.0130      0.0678          –0.0549              0.0017       –0.0208         -0.0014
  Business and
  repair services     0.0729    0.0498        0.0585      0.0340           0.0245              0.0018         0.0231         0.0008
  Finance,
  insurance, real
  estate              0.0575    0.1604        0.0394      0.0641          –0.0248            –0.0014        –0.1028         -0.0066
  Transportation/
  communication/
  public utilities    0.1867    0.2046        0.0976      0.0353           0.0622              0.0116        -0.0178        -0.0006
  Manufacturing       0.1521    0.1423        0.2444      0.1341           0.1103              0.0168         0.0098         0.0013




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                                         Difference between Men and Women




                       Alternative                Means
Variable                estimate                (averages)                                    Difference
                                                                         Between                                             Due to
                                                                           means              Due to       Between      parameters
                        Men    Women               Men   Women         (averages)     characteristics    parameters       (returns)
                         gm        gf               Xm       Xf           (Xm – Xf)       (Xm – Xf) gm      (gm – gf)    Xf (gm – gf)
   Construction       0.1861    0.0689        0.0963      0.0101           0.0862              0.0160         0.1172         0.0012
   Mining/
   agriculture        0.0489    0.0166        0.0474      0.0075           0.0399              0.0020         0.0323         0.0002
   Do not
   know/missing       0.1164    0.0730        0.0513      0.0954          –0.0441            –0.0051          0.0434         0.0041
Mills ratio          –0.2819   –0.1470        0.1628      0.3771          –0.2143              0.0604       –0.1348         -0.0508
Demographic and
other controls
Age of individual
(years)              –0.0016   –0.0057      40.1442      40.3309          –0.1867              0.0003         0.0041         0.1662
Age of youngest
child (years)        –0.0013    0.0023        3.4902      4.2042          –0.7140              0.0010       –0.0036         -0.0152
Number of children    0.0212   –0.0251        0.9659      1.0469          –0.0810            –0.0017          0.0463         0.0485
Additional family
income (inflation
adjusted in
thousands of
dollars)             –0.0009   -0.0001      25.1172      34.9156          –9.7984              0.0086       –0.0008         -0.0284
Metropolitan area     0.0173    0.0309        0.6476      0.6806          –0.0330            –0.0006        –0.0136         -0.0093
Excellent health      0.0150    0.0062        0.2613      0.2041           0.0572              0.0009         0.0089         0.0018
Marital status
   Never marrieda
   Married            0.0831   –0.0013        0.7196      0.6101          –0.1097            –0.0091          0.0844         0.0515
   Other              0.0707   –0.0011        0.1327      0.2424           0.0000              0.0000         0.0718         0.0174
Region: South        –0.0510   –0.0371        0.4142      0.4551           0.1095            –0.0056        –0.0139         -0.0063
Race
            a
   White
   Black             –0.1385   –0.0661        0.2666      0.3602          –0.0936              0.0130       –0.0723         -0.0260
   Other              0.0466    0.0989        0.0140      0.0152          –0.0011            –0.0001        –0.0523         -0.0008
Year, compared to
1983
   2000               0.0188    0.0638        0.0537      0.0538          –0.0001              0.0000       –0.0450         -0.0024
        b
   1999
   1998              –0.0399    0.0300        0.0536      0.0515           0.0021            –0.0001        –0.0699         -0.0036
        b
   1997
   1996              –0.0994   –0.0709        0.0468      0.0514          –0.0046              0.0005       –0.0285         -0.0015




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                                                        Difference between Men and Women




                                      Alternative                    Means
 Variable                              estimate                    (averages)                                             Difference
                                                                                                Between                                                    Due to
                                                                                                  means                 Due to         Between        parameters
                                       Men    Women                 Men          Women        (averages)        characteristics      parameters         (returns)
                                        gm        gf                 Xm              Xf          (Xm – Xf)          (Xm – Xf) gm        (gm – gf)      Xf (gm – gf)
     1995                       –0.0782       –0.0601            0.0613          0.0622          –0.0009                  0.0001          –0.0181          -0.0011
     1994                       –0.0928       –0.0733            0.0615          0.0655          –0.0040                  0.0004          –0.0196          -0.0013
     1993                       –0.0820       –0.0484            0.0597          0.0641          –0.0044                  0.0004          –0.0335          -0.0021
     1992                       –0.0671       –0.0608            0.0662          0.0684          –0.0022                  0.0002          –0.0063          -0.0004
     1991                       –0.0974       –0.0881            0.0668          0.0675          –0.0007                  0.0001          –0.0093          -0.0006
     1990                       –0.0917       –0.0712            0.0672          0.0686          –0.0015                  0.0001          –0.0205          -0.0014
     1989                       –0.0669       –0.0512            0.0675          0.0680          –0.0006                  0.0000          –0.0157          -0.0011
     1988                       –0.0354       –0.0504            0.0669          0.0667            0.0002                –0.0000           0.0151              0.0010
     1987                       –0.0383       –0.0546            0.0666          0.0660            0.0006                –0.0000           0.0164              0.0011
     1986                       –0.0246       –0.0613            0.0668          0.0654            0.0014                –0.0000           0.0368              0.0024
     1985                       –0.0279       –0.0791            0.0666          0.0646            0.0020                –0.0001           0.0512              0.0033
     1984                       –0.0235       –0.0813            0.0656          0.0631            0.0025                –0.0001           0.0578              0.0036
 Sum before
 intercept                                                                                                                                                 -0.3943
 Intercept                           7.5910    6.9846                                                                                                          0.6065
        c
 Sum                                                                                                                      0.4311                               0.2122
Source: GAO analysis of PSID data.
                                                        a
                                                            Category omitted.
                                                        b
                                                            No data available.
                                                        c
                                                            Sum need not equal the log difference in earnings due to the transformation of the coefficients.


                                                        To determine whether our results would change significantly if the model
                                                        were specified slightly differently, we changed the specification in several
                                                        ways and compared those results with the results in the report. In all the
                                                        alternative specifications we developed, work patterns were important in
                                                        accounting for some of the earnings difference between men and women.
                                                        In addition, a significant gender earnings difference remained after
                                                        controlling for the effects of the variables in the model.

                                                        We developed several different specifications of the Hausman-Taylor
                                                        model presented in the report. In one particular alternative, we used a
                                                        linear time trend and the national unemployment rate instead of the year
                                                        specific dummy variables to control for the effects of national economic
                                                        conditions and other year-specific effects that are not reflected in the
                                                        other variables in the model. The results of this alternative specification



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                     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.


                     While our analysis used what we consider to be the most appropriate
Limitations of Our   methods and data set available for our purposes, our analysis has both
Analysis             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




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Difference between Men and Women




example, studies of earnings differences that used the National
Longitudinal Survey of Youth have used a measure of ability in addition to
work experience, education, and demographic variables.13 This data set,
however, follows a specific cohort of individuals over time and is
therefore not representative of the population as a whole.

Our model is also limited in that the industry and occupation categories
that we used are broad. Gender earnings differences within these
categories are not reflected and could account for some amount of the
remaining difference. In addition, we did not explicitly model an
individual’s choice of occupation and industry and how these choices
relate to earnings differences. Also, although PSID collects information on
work interruptions, the detail of some of the survey questions limited our
ability to fully explore reasons why individuals were out of the labor force.

We used dummy variables for years to control for general economic
conditions and year-specific effects. In some specifications of the model,
we added national unemployment rate data to the PSID sample in order to
control for national labor market conditions. We did not access the PSID
Geocode Match file, which contains more detailed information on the
location of residence of survey respondents. We could not, therefore,
incorporate a measure of local unemployment rates in the analyses.




13
 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




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Appendix III: GAO Analysis of Women’s
              Workplace Decisions



Workplace Decisions

              Our analysis of data from the PSID identified factors that contribute to the
Purpose       earnings difference between men and women, but cannot fully explain the
              underlying reasons why these factors differ. For example, the model
              results indicated that earnings differ, in part, because men and women
              tend to have different work patterns (such as women are more likely to
              work part time) and often work in different occupations. However, the
              model could not explain why women worked part time more often or took
              jobs in certain occupations. In addition, the analysis could not explain why
              a remaining earnings difference existed after accounting for a range of
              demographic, family, and work-related factors. To gain perspective on
              these issues, we conducted additional work to gather information on why
              individuals make certain decisions about work and how those decisions
              may affect their earnings.


              We conducted a multipronged effort, including a literature review,
Scope and     interviews with employers as well as individuals with expertise on
Methodology   earnings and other workplace issues,1 and a review of our work by
              additional knowledgeable individuals. Specifically, we reviewed literature
              on work-related decisions, including using alternative work arrangements,
              and how these decisions may affect advancement or earnings. We also
              conducted 10 interviews with a variety of experts—industry groups,
              advocacy groups, unions, and researchers—to obtain a broad range of
              perspectives on reasons why workers make certain career and workplace
              decisions that could affect their earnings. In selecting experts, we targeted
              those who have conducted research on earnings issues and have different
              viewpoints.

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

              •      financial/professional services


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




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                     •   health care
                     •   information technology
                     •   manufacturing
                     •   media/advertising
                     •   pharmaceuticals/biotechnology
                     •   travel/hospitality

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


                     According to experts and the literature, women are more likely than men
Summary of Results   to have primary responsibility for family, and as a result, working women
                     with family responsibilities must make a variety of decisions to manage
                     these responsibilities. For example, these decisions may include what
                     types of jobs women choose as well as decisions they make about how,
                     when, and where they do their work. These decisions may have specific
                     consequences for their career advancement or earnings. However, debate
                     exists whether these decisions are freely made or influenced by
                     discrimination in society or in the workplace.


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



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ideal worker places highest priority on work, working a full-time
9-to-5 schedule throughout their working years, and often working
overtime. Ideal workers take little or no time off for childbearing or
childrearing, and they appear—whether true or not—to have few
responsibilities outside of work. While this perception applies to all
workers, most experts and literature agree that it disproportionately
affects women because they often have or take primary responsibility for
home and family, such as caring for children, even when they are
employed outside of the home. However, some research indicates that
men are now more likely than in the past to participate in childcare,
eldercare, and housework and are beginning to adjust their work in
response to family obligations.

Some employers, however, have taken note of the multiple needs of
workers and have begun to offer alternative work arrangements to help
workers manage both work and other life responsibilities. These
arrangements can benefit workers by providing them with flexibility in
how, when, and where they do their work. One type of alternative work
arrangement allows workers to reduce their work hours from the
traditional 40 hours per week, such as part-time work or job sharing.2
Similarly, some employers offer workers the opportunity to take leave
from work for a variety of reasons, such as childbirth, care for elderly
relatives, or other personal reasons. Some arrangements, such as flextime,
allow employees to begin and end their workday outside the traditional
9-to-5 work hours. Other arrangements, such as telecommuting from
home, allow employees to work in an alternative location. Childcare
facilities are also available at some workplaces to help workers with their
caregiving responsibilities. In addition to benefiting workers, these
arrangements may also benefit employers by helping them recruit and
retain workers. For example, according to an industry group for attorneys,
law firms may lose new attorneys—particularly women who plan to have
children—if they do not offer workplace flexibility. This is costly to firms
due to substantial training investments they make in new attorneys, which
they may not recoup if workers quit early on.

Nonetheless, research suggests that many workplaces still maintain the
same policies, practices, and structures that existed when most workers


2
 Part-time work schedules allow employees to reduce their work hours from the traditional
40 hours per week in exchange for a reduced salary and possibly pro-rated benefits. Job
sharing—a form of part-time work—allows two employees to share job responsibilities,
salary, and benefits of one full-time position.




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                           Workplace Decisions




                           were men who worked full time, 40-hours per week. As a result, there may
                           be a “mismatch” between the needs of workers with family responsibilities
                           and the structure of the workplace.


                           Working women make a variety of decisions to manage both their work
Working Women              and home or family responsibilities. According to some experts and
Make a Variety of          literature, some women work in jobs that are more compatible with their
                           home and family responsibilities. In addition, some women use alternative
Decisions to Manage        work arrangements such as working a part-time schedule or taking leave
Work and Family            from work. Experts indicate that these decisions may result in women as a
                           group earning less than men. However, debate exists about whether
Responsibilities           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     According to some experts and literature, some women choose to work in
Flexibility but May Also   jobs that are compatible with their home or family responsibilities, and
Affect Earnings            may trade off career advancement or higher earnings for these jobs. Some
                           experts and literature indicate that jobs that offer flexibility tend to be
                           lower paying and offer less career advancement.3

                           Women choose jobs with different kinds of flexibility based on their
                           needs. According to some researchers, some jobs are less demanding or
                           less stressful than others, which may allow women who choose these jobs
                           to have more time and energy for responsibilities outside of work. For
                           example, a woman may work in an off-line, staff position, such as a human
                           resources job, because it requires less travel and less time in the office
                           than an online position in the company. Off-line positions may offer
                           flexibility, but less opportunity for advancement and higher earnings. One
                           expert also indicated that, within a certain field, some women are more



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




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                           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           Another way that women manage work and family responsibilities is by
Arrangements Offer         choosing to use alternative work arrangements, which may affect their
Flexibility but Some May   career advancement and earnings.4 For example, some women choose to
                           work a part-time schedule, take leave from work, or use flextime. While
Affect Earnings            some research indicates that certain arrangements may help women
                           maintain their careers during times when they need flexibility, other
                           research suggests that there may be negative effects.

                           No single, national data source exists that provides information about all
                           workers who use alternative work arrangements. However, some data
                           exist from narrowly scoped studies that focus on particular types of work
                           arrangements, types of employees, or individual companies. Even when
                           employers offer alternative arrangements to all workers, some research
                           and the companies we interviewed indicate that women are more likely
                           than men to use certain arrangements, while both men and women use
                           others in similar proportions. Specifically, women are more likely than
                           men to take leave from work for family reasons and to work part time for



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




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family reasons even when these options are available to both men and
women. According to our interviews and some literature, some workers—
particularly men—are reluctant to use alternative arrangements because
they perceive that their advancement and earnings will be negatively
affected. This may help to explain why men tend to use personal days, sick
days, or vacation time instead of taking family leave. On the other hand,
similar proportions of men and women use flextime and telecommuting
when these options are available. However, according to some research,
men are more likely than women to work in the jobs, organizations, or
high-level, high-paying positions that have these options available.

Comprehensive, national data are lacking on how career advancement and
earnings may be affected by using alternative work arrangements, but
some limited research does exist. Certain researchers indicate that using
certain work arrangements may have some beneficial career effects if they
help workers maintain career linkages or skills that they might otherwise
lose. For example, for women who would have left the workforce or
changed jobs if they did not have access to alternative arrangements that
could help them manage work and family, part-time work5 may allow them
to maintain job skills, knowledge, or career momentum. In addition,
women who can take leave with the guarantee of returning to a similar job
benefit because they maintain links with an employer where they have
built up specific job-related skills.

Other research indicates that using certain alternative work arrangements
may have negative effects on career advancement and earnings.
Specifically, employers may view these workers as not conforming to the
ideal worker norm because they are not at work as much or during the
same work hours as their managers or co-workers. Research indicates that
some arrangements, such as leave, part-time work, and telecommuting,
reduce workers’ “face time”—the amount of time spent in the workplace.6
Given that some employers use face time as an indicator of workers’
productivity, those who lack face time may experience negative career
effects. According to some experts and literature, some employers may


5
 Research indicates that different types of part-time work exist. Some part-time jobs
require relatively low skills, and offer low pay and little opportunity for advancement. In
contrast, other part-time jobs are work schedules that employers create to retain or attract
workers who cannot or do not want to work full time. These jobs are often higher skilled
and higher paying with advancement potential.
6
The idea of “face time” may apply primarily to certain types of jobs, such as professional,
white-collar jobs or those that require contact with clients or customers.




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

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

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

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

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



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                          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   Debate exists whether decisions that women make to manage work and
Indirect Discrimination   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


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other societal and workplace discrimination that may result in lower
earnings. However, according to other experts, although women may still
face discrimination in the workplace, it is not a systematic problem and
legal remedies are already in place. For example, Title VII of the Civil
Rights Act of 1964 prohibits employment discrimination based on gender.

According to some experts and literature, women face societal
discrimination that may affect their career advancement and earnings.
Some research suggests that the career aspirations of men and women
may be influenced by societal norms about gender roles. For example,
parents, peers, or institutions (such as schools or the media) may teach
them that certain occupations—such as nursing or teaching, which tend to
be relatively lower-paying—are identified with women while others are
identified with men. As a result, men and women may view different fields
or occupations as valuable or socially acceptable. According to some
experts, societal discrimination may help explain why men and women
tend to be concentrated in different occupations. For example, some
research has found that women tend to be over-represented in clerical and
service jobs, while men are disproportionately employed in blue-collar
craft and laborer jobs.7 Other research suggests that gender differences
exist even among those who are college educated. For example, men tend
to be concentrated in majors such as engineering and mathematics, while
women are typically concentrated in majors such as social work and
education. Research indicates that men and women who work in 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


7
  Notably, research indicates that women tend to be concentrated in service-producing
occupations, such as retail trade and government, which lose relatively few jobs or actually
gain jobs during recessions. However, men tend to be concentrated in goods-producing
industries, such as construction and manufacturing, which often lose jobs during
recessions.




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                   women because they assume that women may be less committed or may
                   be more likely to quit for home and family reasons. To the extent that
                   employers who offer higher-paying jobs discriminate against women in
                   this way, women may not have the same earnings opportunities as men.
                   Finally, other experts suggest that both men and women who are parents
                   face discrimination in the workplace due to their family responsibilities in
                   terms of hiring, promotions, and terminations on the job.

                   According to some literature, discrimination may occur if employers enact
                   policies or practices that have a disproportionately negative impact on one
                   group of workers, such as women with children. For example, if an
                   employer has a policy that excludes part-time workers from promotions,
                   this could have a significant effect on women because they are more likely
                   to work part time. Other experts suggest that workplace practices
                   reflecting ideal worker norms—such as requiring routine overtime for
                   promotion—could be considered discrimination. This could impact
                   women more (particularly mothers) and may result in a disproportionate
                   number of men in high-level positions.


                   Anderson, Deborah J., Melissa Binder, and Kate Krause. “The Motherhood
Related Research   Wage Penalty Revisited: Experience, Heterogeneity, Work Effort, and
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                   Appelbaum, Eileen, ed. “The New Realities of Family Life and the
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                   Appelbaum, Eileen, and Lonnie Golden. “The Standard Workday or the
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                   Appelbaum, Eileen. “The Transformation of Work and Employment
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                   and Well-Being, Washington, D.C.: June 16-18, 2003,
                   http://www.popcenter.umd.edu/conferences/nichd/agenda.html.




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Bailyn, Lotte, Robert Drago, and Thomas A. Kochan. “Integrating Work
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Blair-Loy, Mary, and Amy S. Wharton. “Employees’ Use of Work-Family
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Gornick, Janet C., and Marcia K. Meyers. “Parental Care of Children:
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Leibowitz, Arleen A. “An Economic Perspective on Work, Family, and
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Ruhm, Christopher J. “How Well Do Parents With Young Children
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                  Appendix IV: GAO Contact and Staff
Appendix IV: GAO Contact and Staff
                  Acknowledgments



Acknowledgments

                  Linda Siegel, Analyst in Charge (202) 512-7150
GAO Contact
                  The following individuals also made important contributions to this report:
Staff             Patrick DiBattista, R. Scott McNabb, Corinna Nicolaou, and Caterina
Acknowledgments   Pisciotta, Education, Workforce, and Income Security Issues. In addition,
                  the following individuals played a key role in developing the statistical
                  model and conducting the analysis: Brandon Haller, Ed Nannenhorn,
                  MacDonald Phillips, and Wendy Turenne, Applied Research and Methods;
                  Scott Farrow, Chief Economist; and Robert Parker, Chief Statistician.




(130187)
                  Page 75                                          GAO-04-35 Women's Earnings
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