|| A) Extent
of Disparities and Factors. In considering the questions
|| "The extent
of wage disparities, both in the public and private
sectors, between men and women and between minorities
and nonminorities, and
|| Those factors
which cause, or which tend to cause, the disparities,
including segregation between women and men and between
minorities and nonminorities across and within occupations,
payment of lower wages for work in female-dominated
occupations, child-rearing responsibilities, the number
of women who are heads of households, education, hours
worked, and years on the job;"
|on the basis
of the research conducted, this is what we know:
||a) Nationally -
Wage Gap - Gender-Based
According to a study on gender-based wage
disparities conducted by the United States General Accounting
Office, without adjusting for certain relevant factors that
affect wages, women in the U.S. earned 44% less than men during
the period of the 1983-2000 (GAO, 44). However, once certain
relevant factors were incorporated into the equation, the
gap dropped to 21%. Among the significant factors were work
patterns, choice of industry, choice of occupation, race,
marital status, and job tenure. The two major factors seemingly
affecting wages are the differences in industries and occupations
females and males choose, and the work patterns they have
at those jobs (GAO, 10). The differentiation that occurs in
terms of education and the differences in choice of industry
and occupation and in work patterns are explored below.
Differences in career choices between men and women are
documented at the college level. Men more often choose majors
that are hard sciences; while women choose those involving
humanities and education. In 2000, women earned only 36%
of all physical science degrees, 27% of all degrees in computer
and information sciences, and a mere 17% in engineering (BPWF, 6).
Choice of Industry and Occupation
Gender roles are still clearly visible within the job market
as women and men are often concentrated into occupations
and job titles that they do not share with the opposite
sex. So called "women's jobs" and "men's jobs" still exist
within the market, and typically those traditionally held
by men tend to pay more than those traditionally held by women.
In "Still a Man's Labor Market," Rose and Hartman look
at the job market in terms of three tiers - elite, good,
and less-skilled jobs. They find that in the elite tier,
women are concentrated in teaching and nursing; while men
are business executives, scientists, doctors, and lawyers.
In middle tier jobs, women are secretaries, while men are
blue collar workers; and in the lower tier, women are sales
clerks, while men work in factory jobs. Within each of the
six gender-tier categories, at least 75% of the workers
are of one gender; and in each tier, women's jobs pay significantly
less than those of male counterparts (Rose, iv).
Whether the differences in the choices made by men and
women are a result of conforming to societal norms or are
free choices cannot be definitively concluded, but they
exist. Still, the question of why professions typically
chosen by women pay less remains. Rose and Hartman's "Still
a Man's Labor Market" suggests that jobs chosen by men within
each tier of the labor force are typically more skilled
or onerous than those chosen by women.
The other major factor affecting earning differences between
men and women is work patterns including the number of hours
worked per year, years of experience in the labor force,
and the amount of leave taken. The GAO study found that
women on average have fewer years of work experience than
men (men have 16 years of experience, while women have 12),
work fewer hours per year (men work 2147, while women work
1675 - a difference of 472 hours per year), are less likely
to work a full-time schedule, and leave the labor force
for longer periods of time than men (GAO, 11-12). Taking
these differences into consideration, may partially explain
why women earn less than men, since they work fewer hours than men.
A fifteen-year longitudinal study conducted by the IWPR
and summarized in "Still a Man's Labor Market" found that
women who spent most of the study period married earned
less because they had more years out of the labor force;
whereas, women who were only married for a few years spent
more time in the work force. Along the same lines, women
who had children present for ten to fifteen years during
the study period had the lowest earnings, while women who
had children for two years or less earned nearly $9,000
more per working year on average.
National research conducted by IWPR showed that 52% of
women have at least one complete calendar year without any
earnings in comparison to only 16% of men. A career interruption
of one year or more can have a serious impact on one's career
and earnings regardless of whether it is a man or a woman
(Rose, iii). In addition, the demands of motherhood lead
women to make other choices that affect their careers. According
to Furchtgott-Roth and Stolba in "Women's Figures," in order
to accommodate familial needs, women tend to choose occupations
where job flexibility is high, salaries are lower, and job
skills deteriorate at a slower rate than others (Furchtgott-Roth, 13).
In research conducted by the Maryland Federation of Business
and Professional Women, results showed that 77.85% of working
women reported that flexible work schedules are of moderate
or major importance to them, while half of those women reported
that having opportunities to work part-time is of moderate
or major importance to them (BPWF, 5).
To sum up, women in many professions are making decisions
to balance work and family priorities and those decisions
result in fewer women reaching the top of their fields.
The fact that women 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, affects both
the amount of money women make and the perception of their
value in the work force.
In the GAO report, once measurable factors such as choice
of industry, choice of occupation, and work patterns were
added into the equation, the 44% difference between the
earnings of men and women dropped to 21% (GAO, 29). Other
studies have found approximately the same results. So, how
can the other 21% be explained? Simply, not all factors
that could possibly affect wage disparity are measurable.
Moreover, it is virtually impossible to come up with every
factor that could possibly affect wages (GAO, 19-20). Certainly,
other factors exist that have yet to be studied and tested.
In addition, there is the possibility of discrimination
("just because you are a woman, I will pay you less"). However,
measuring that possibility by examining statistical aggregates,
either nationally or in a particular state, is complicated
because of the number of variables involved.
- Wage Gap - Race/Ethnicity Based>
Just as a wage gap can be found in earnings
of men and women, a wage gap exists among some racial and
ethnic groups in America. In some instances, research suggests
various answers as to what factors impact the wage gap - education,
differences in work patterns, differences in choice of industry/occupation,
skill disparity, language disparity, economic changes and
discrimination. Each of these possibilities has different policy implications.
Enrollment and Completion Rates
Level of education plays an important role in how much
one earns and will earn in the future. U.S. rates of enrollment
are very similar among all groups for high school; however
Hispanics' and blacks' rates of high school completion are
lower than those of whites and Asians. In terms of college
enrollment, college enrollment of whites is at 23%, of blacks
is at 20%, of Hispanics is at 16%, and of Asians is at 35%.
For full time college enrollment, whites' is at 16%, blacks'
at 13%, Hispanics' at 10%, and Asians' at 26% (U.S. Census
- 2). As demonstrated below, in data from the Integrated
Postsecondary Education Data System (IPEDS) Graduation Rate
Survey published in 2003, rates of enrollment do not tell
the whole story; completion rates provide insight into educational differences:
Table 1: Group Completion Rates
* 18- through 24-year-olds
who had completed high school, by race/ethnicity: October 2000
** First-Time-In-College, Bachelor-Degree-Seeking
Students Enrolled fall 1997 Who Graduated from the same College
or University by August 2003, IPEDS GRS.
A gap exists also in advanced degrees. According
to the U.S. Census Survey of Income and
Program Participation of 2001, out of the total 16,180,000
advanced degrees held by people in America, 82.4% were held
by whites, 6% were held by blacks, 3.6% were held by Hispanics,
and the rest by other minorities (U.S. Census - 1). As the
data reveals, at practically all levels of education, blacks
and Hispanics have a lower level of participation and completion.
Education - Outcomes
According to various research, level of education and earnings
have a positive correlation. A study conducted by the U.S.
Census Bureau and published in "The Big Payoff: Educational
Attainment and Synthetic Estimates of Work-Life Earnings"
displayed this correlation. Although blacks and Hispanics
earn less than whites of roughly the same level of education,
there is a great return on education for all racial and
ethnic groups. In fact, the return on education is greater
for blacks and Hispanics, because in calculating the increase
in earnings of a person who starts out without even a high
school degree and then works his way up to an advanced degree,
the increase in earnings for whites is 280%, while for blacks
and Hispanics it is 315%. While various data demonstrate
that blacks and Hispanics have less education than whites
and Asians when measuring by degrees earned, the question
that remains is why an earnings gap remains for people of
roughly the same level of education but of different racial
or ethnic groups. One explanation is that the data available
often does not control for both level of education and years
of experience, nor does it control for quality of education.
Education - Parents
Wages are not only affected by the level education of the
individual, but also correlate to the level of education
of the individual's parents. For whites and blacks whose
parents had less than a college education, whites consistently
earn more than blacks. However, in a situation where the
parents had some college education or more, blacks earn
more than their white counterparts (Black, 19).
Choice of Industry and Occupation
As shown in Table 2, differences between racial and ethnic
groups can be found in their choices of industry and occupation.
Table 2: Occupational Data for Employed
Population 16 and over*
*Original Source: U.S.
Census Bureau, 2000. "Profile of Selected Economic Characteristics."
Census 2000, Summary File 4, QT-P28.
These statistics beg the question why people
of different races end up in different occupations. One answer
is obvious - differences in education; because a great percentage
of blacks and Hispanics do not acquire a high school or a
college degree, they work jobs in service, production, transportation,
and material moving. Another reason may be the existence of
so called "ethnic niches." New York City provides a broad
example of ethnic niches; there, Hispanics predominantly work
in construction, Asians run laundromats and dry cleaning businesses,
fire fighters are generally white males. While such niches
can help members of the prevalent racial or ethnic group at
that job obtain a job by providing training and shelter from
discrimination, the ethnic segregation may lead to lower pay
and may constrain job mobility. Once an ethnic niche is created
in a certain occupation or industry, the desirability and
availability of the job becomes limited (Spalter-Roth, 5).
Labor Force Participation
Various resources, including the U.S. Census Bureau, show
that a greater percentage of black and Hispanic men than
white and Asian men do not participate in the labor force;
of those people who are in the labor force, there are twice
as many blacks unemployed as whites. Moreover, blacks and
Hispanics tend to work fewer weeks per year and fewer hours
per week, are overrepresented in temporary and on-call work,
and tend to be unemployed for longer periods of time than
whites. Rates of participation in the labor market, as well
as rates of employment and unemployment are one way to compare
work experience among racial and ethnic groups, which could
explain some of the gap in wages and earnings. Whether it
is by choice or due to other factors, statistically, black,
Hispanic, and, to a lesser extent, Asian people overall
are employed less than whites (Spalter-Roth, 2).
Table 3: Labor Force Participation, Employment,
Unemployment in 2000*
*Original Source: U.S.
Census Bureau, 2000. "Profile of Selected Economic Characteristics."
Census 2000, Summary File 4, DP-3.
Number of Weeks Worked
The differences in number of weeks worked per year and
number of hours worked per week by the different racial
and ethnic groups may also reveal information about the
gap in wages and earnings. According to the California labor
market data, among all working men compared in 2000, blacks
worked 46 weeks per year on average, while whites worked
48. In terms of hours worked per week, blacks and Hispanics
worked about 41 hours per week, while whites worked 44 hours
per week (Reed). This is also reflected when hourly wages
are compared to annual earnings. According to "Basic Skills
and the Black-White Earning Gap" by Neal and Johnson, black
men in America earn 48% less per year than whites of the
same age, even though their wages are only 24% lower (Johnson,
12). This statistic suggests that black men may be working
less time overall.
Temporary and Part-time Jobs
The type of jobs people hold can greatly affect their wages
also. According to "The Big Payoff," the earnings of workers
who work full time year round tend to be significantly higher
than the earnings of workers who work part time or just
part of the year (Cheeseman Day, 2). When compared to whites,
the participation of blacks and Hispanics in non-standard
work (regular part-time, temporary help agency, on-call/day
labor, self employed, independent contractor) is proportional
to the size of its population, and maybe even slightly low.
However, in two worst areas of non-standard jobs - temporary
and on-call labor - both of which tend to pay little and
offer few benefits, if any, blacks and Hispanics are over
represented. While blacks made up only 12% of the U.S. population
in 1997, they made up 20% of all temporary workers in the
U.S. In the same year, Hispanics represented 13% of the
temporary workers and were 15% of all on-call/day laborers
(Hudson, 12). Moreover, whether people work full-time or
non-standard jobs is often closely tied to their level of
education. For example, according to "The Big Payoff," high
school dropouts are less likely to work full time and year
round than people with bachelor's degrees. While only 65%
of high school dropouts worked full time and year round
in 2000, 77% of people with bachelor's degrees worked the
same amount (Cheeseman Day, 2).
One important factor affecting the wage gap between racial
and ethnic groups is skill. While looking at the level of
education has been the traditional and common way to determine
one's ability level and predict future wages, recent researchers
have contended that this information can be misleading because
the quality of schools and intensity of education in different
schools vary greatly in America. Just as age is not a valid
predictor of one's level of education, the amount of schooling
one has does not truly reveal ability.
In "The Role of Premarket Factors in Black-White Wage Differences"
Derek Neal and William Johnson discuss a different measure
of education - skill. For their research, Neal and Johnson
used the scores from the Armed Forces Qualification Test
(AFQT) found in the National Longitudinal Survey of Youth,
to examine the black-white wage gap among workers in their
late twenties (age 26-29). The AFQT is known to be a racially
unbiased measure of basic skills that helps predict job
performance, and is often used in military testing. The
data set included a sample of individuals who were tested
at ages 16-18, just before they entered the labor force
full time or began their secondary education. Testing for
math and reading skills, the results of the test revealed
that three-fourth of the racial wage gap for men is due
to a skill disparity. For women, the test scores explained
all of the wage disparity. In fact, when the AFQT scores
were held constant for white, black, and Hispanic women,
black and Hispanic women earned more than white women.
The information on skill disparity begs for some explanation
for the cause of the skill disparity between racial and
ethnic groups. According to Neal and Johnson, the ability
disparity can be explained by varying school and home environments.
In fact, the authors found that children's scores on the
AFQT correlated with the level of education and the professional
status of their parents, the number of children in the family,
measures of family reading material, and school characteristics
of the children (including student/teacher ratio, disadvantaged
student ratio, dropout rate, teacher turnover rate) (Neal,
887). These factors may vary by race and ethnicity. According
to Carneiro, Heckman, and Masterov, however, most of the
important factors would be those related to the family environment,
since ability gaps are substantial before children even
enter school. Among the factors they mention are measures
of family background, family income, mother's level of education,
home environment, and mother's cognitive ability.
Disparity exists among racial and ethnic groups before
school begins. Now we must address why this gap widens as
the children get older and obtain more education. The positive
effect of schooling on test scores is much larger for whites
and Hispanics than it is for blacks. This could be explained
by the fact that whites, and blacks and Hispanics start
school at different levels; since blacks and Hispanics start
with lower skills than whites, their subsequent progress
and success is less than that of whites. The quality of
schools attended by black and Hispanic children in comparison
to white children could also explain the lower effect of
schooling on the former group relative to the latter group.
Thus, differential initial conditions and differential school
quality may also be important determinant of the adult black-white
skill gap (Carneiro, 14-17).
Immigration and Language Disparity
Language disparity plays an important role in wage determination.
According to "Labor Market Costs of Language Disparity:
An Interpretation of Hispanic Earnings Differences," language
ability explains up to one-third of the relative wage difference
between Whites and Hispanics in America. The wage disparity
that is usually attributed to ethnicity, nativity, and time
in the United States can in fact be explained by differences
associated with English language skills. (McManus 818)
Similar results were found in "Why Do Minority Men Earn
Less?" Here, the authors found that the status of immigration
and whether English is spoken at home both affect earnings.
Generally for non-immigrants, if a language other than English
is spoken at home, the people earn less than those who speak
only English at home. When comparing all immigrants, those
who do not speak English at home earn substantially less
than those who do. Moreover, when all people who do not
speak English at home are compared, the immigrants earn
substantially less than non-immigrants. Thus, it can be
concluded that one's immigration status as well as what
language one speaks at home both affect earnings. (Black 16-17)
According to the U.S. Department of Labor, there are other
things that could affect the wage disparity, and in fact
made earnings more unequal during the 1980's and 1990's
- these are technological change, trade liberalization,
increased immigration, value of the minimum wage, and declining
unionization. The economy has transitioned from being driven
by manufacturing to information. Thus, as technology continues
to advance, the demand for skilled workers who are able
to operate the advanced technology and contribute to its
development continues to grow. Moreover, technological advancements
are causing the replacement of lesser-skilled jobs with
automated devices, and thus demand for lesser-skilled workers
is dropping. This situation is aggravated by the increase
in immigration that has been occurring since 1965. Particularly,
less-skilled workers with lower education levels have and
continue to immigrate to the U.S., which increases the competition
for unskilled jobs and drives wages down for unskilled-workers.
Expanded trade also drives down the wages of low-skilled
workers because it displaces the goods they produce. A decline
in unionization in the 1980's has also contributed to increased
wage inequality, because fewer workers are impacted by collective
bargaining. Finally, the minimum wage fell in real terms
during both the 1970's and 1980's reaching a level in 1990
significantly below its 1960 level.
relied on several general sources of materials regarding
wage disparities and the issue of equal pay in Maryland.
Two sources were specifically developed for the Commission
to consider Maryland-specific information and are discussed
herein. These are the study conducted by IWPR on behalf
of the Commission and two memoranda prepared by staff
of Maryland Human Relations Commission.
||i) IWPR Study
|Based on data
analysis exploring relative earnings of women and men
in Maryland, as well as earnings differences by race
and ethnicity, and by sector of employment prepared
by the Institute for Women's Policy Research ( from
a dataset from the 2002 through 2004 files of the American
Community Survey) the key findings in the IWPR report
are included below::
- "More than one-fifth of the difference in women's
and men's earnings cannot be explained by differences
in their education, potential work experience, job
characteristics, or other measurable factors. A
smaller, but still meaningful, portion of earnings
differences between whites and workers of color
is not explained by observed demographic and job characteristics.
- Men's annual earnings and hourly wages are higher
than women's. This is true when comparing all women
and men; when evaluating only those working full-time
for the whole year (FTFY workers); and when comparing
women and men by sector (public and private), within
racial/ethnic groups, by level of education, and
by occupation. (The only exceptions are wages of
African Americans and Hispanics and both earnings
and wages of Laborers.)
- Asian American men out-earn white, African American,
and Hispanic men. Among women, earnings are similar
for whites and Asian Americans, but much lower for
African Americans and Hispanics.
- Women work nearly as many hours and weeks as men.
Among full-time full-year workers, women work 2.6
fewer hours per week than men, and the same number
of weeks per year.
- Educational attainment varies enormously among
racial and ethnic groups and, to a lesser degree, by gender.
- Women of all races and men of color do better
relative to white men in the public sector than
in private-sector employment.
- Pay is generally higher in the public sector than
in the private sector, reflecting the fact that
public-sector workers are older than their private-sector
counterparts, have more years of potential work
experience, are more concentrated in professional
occupations, and have higher educational attainment.
- Occupational segregation by both gender and race/ethnicity
is a very strong feature of Maryland's employment.
- Pay differences between men and women employed
in the same occupation are large, as are differences
between workers of different race/ethnic groups
employed in the same occupation." (IWPR 1-2)
||ii) Maryland Human Relations
Commission (MCHR) Reports
Glendora C. Hughes, General Counsel, Maryland Human
Relations Commission (MCHR) provided statistics on complaints
processed by the MCHR that involve wages. In the period
between January 1, 2004 and December 7, 2005, the statistics
show that out of 829 total issues involving race, 56
involved wage issues and out of 636 involving sex, 35
involved wage issues. The Division of Labor and Industry
reports receiving no Maryland Equal Pay Act complaints
during the past ten years.
MCHR staff provided two memoranda on existing law
and case law regarding the Maryland Equal Pay Act
(MEPA) and race-based wage disparity complaints. The
memo prepared by MCHR legal staff regarding gender-based
Equal Pay Complaints concludes that it "appears that
most employees are either unaware of MEPA, are using
the federal EPA to file a claim, or are mistakenly
filing a claim under MEPA but are establishing a prima
facie case under federal EPA elements. In addition,
the lack of appellate case law can probably be attributed
to the lack of claims under the MEPA."
In the race-based wage MCHR memo, MCHR Commission
Counsel staff did not find as much information as
in the gender-based memorandum. They attribute this
to three possible causes: "First, Title VII claims
are construed in harmony with EPA in spite of Title
VII prohibiting a broader range of discrimination.
Second, Exhibits 1 and 2 suggest data for Title VII
does exist suggesting Title VII suits have been filed;
however, statistics do not further distinguish the
type of discrimination. For example, the U.S. Equal
Employment Opportunity Commission [hereinafter EEOC]
race discrimination statistical data in Exhibit 1
could encompass race discrimination in hiring, promotion,
or compensation. However, there is no distinction
among each category. The same can be echoed with the
EEOC national origin discrimination statistical data
in Exhibit 2. Research of cases from around the country
and law reviews was conducted; however, the focus
was wage discrimination in light of gender instead
of race. Last, the lack of race-wage discrimination
cases may also be the result of potential plaintiffs
being discouraged from discussing their salaries or
not being aware that race-wage discrimination has
or is occurring." (MCHR 6 Appendix F)
These reviews of gender-based and race-based complaint
systems point to two separate but intertwined recommendations.
First, the MEPA needs to be carefully reviewed to
determine what impediments exist to filing claims
and those impediments need to be addressed. From a
preliminary discussion with DLLR staff, it is clear
that there is no funding for administration or enforcement
of the Maryland Equal Pay Law. In addition there are
parts of the law that need to be reviewed and may
need to be strengthened. In the review of other state
laws, ways of strengthening the Maryland EPA law are
studied. Secondly, it is clear that data is an underlying
impediment to understanding the Equal Pay Issue. It
appears that improvements both on the federal and
state level on data retention may be desirable.
to the consequences of the disparities on the economy
and families affected, the Commission believes that
given the lack of Maryland-specific economic data available
and the complexity of the causes leading to wage disparities,
few conclusions can be drawn on the consequences of
the disparities. Although the Commission looked at estimates
of the dollar losses to women due to wage disparities,
it is difficult to draw specific reliable conclusions
related to the Maryland economy from those materials.
The Commission does believe that given the fact that
data shows that more minority families are headed
by female single parents, the wage disparities serve
to amplify the existing unequal distribution of income.
This coupled with inferior educational opportunities
and limited mobility suggests that the disparities
will remain unchanged or increase, unless intervention occurs.
From an economic perspective, the Commission does
not believe that addressing the disparities will necessarily
increase the overall percentage of GDP that is spent
on wages. Rather, there would be a re-distribution
of wages without increasing total wage expenditures
in the economy or the total number of workers employed.
One additional impact the Commission would comment
on is the impact on those who discriminate against
women and minorities in terms of wages. According
to Art Diamond's web log, in "The Economics of Discrimination,"
Gary Becker argued that "those who discriminate in
the labor market pay a price for their prejudice in
the form of having to pay higher wages. Those who
do not discriminate have open to them an additional
pool of workers, whose talents will contribute to
the firm's bottom line." (Diamond 1)
Analysis - Actions that may lead to the elimination
and prevention of disparities
Assembly asked the Commission to report on "actions
that are likely to lead to the elimination and prevention
of the disparities." In researching this charge, the
Commission relied on a review of international, national
and local literature to identify actions to assist in
the elimination and prevention of disparities. A number
of organizations identified possible strategies with
the potential to reduce disparities. The strategies
are highlighted below.
Strengthen Legal Remedies - Legislative initiatives,
which would lead to more effective enforcement of
equal pay laws, including model legislation to:
- provide for enhanced penalties for violating the
equal pay act,
- require employers to post rights and remedies
and conduct regular equal pay reviews,
- establish alternative dispute resolution methods,
- allow claims to be brought on behalf of groups
Remedy Wage Disparities -- Implementing wage
adjustment to correct inequities, raising the
Minimum Wage, and bargaining strategies.
Work Life Initiatives -Supporting part time
and flexible working, including telecommuting options;
and providing for accessible, affordable and high
quality childcare options for women.
Education of Workers - Educating workers about
rights and remedies and developing and supporting
adequate community outreach education capacity.
Pay Equity Audits -Encouraging or requiring
the use of pay equity self-audits, providing technical
assistance to employers, creating and using software
to analyze pay structures, and developing an individualized
plan to address audit findings.
Best Practices - Documenting best practices
for employers and developing model
policies for the public and private sectors, recognizing
employers that have best practices and providing technical
assistance to employers.
Data Collection - Improving data collection
systems and requirements.
Education - Insuring equal educational opportunities
for women and minorities and providing professional
Public Relations - Educating the public about
the extent of disparities and prevention strategies.
Government Procurement Practices -Enhancing
employment opportunities for underrepresented workers
in the higher-paying non traditional jobs, apprenticeships
and the trades, on federal projects; and promoting
gender equality by contracting government projects
to those companies that comply with gender and race