State offices and all DLLR physical locations will be closed to the public December 24 & December 25, 2014. However, Unemployment Insurance telephone and Web operations WILL be available on Wednesday, December 24.

DLLR's Division of Labor and Industry

 

Findings - Report of the Equal Pay Commission

 
IV) Findings
 
 A) Extent of Disparities and Factors. In considering the questions of:
 
1) "The extent of wage disparities, both in the public and private sectors, between men and women and between minorities and nonminorities, and
 
2) 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.

Education

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.

Work Patterns

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.

Unexplained Disparity

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.

 
b)  Nationally - 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.

Education

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

 
Race/Ethnicity High School* College **
White 91.8% 59%
Black 83.7% 40%
Hispanic 64.1% 42%
Asian 94.6% 64%
 

* 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*

 
Race/Ethnicity Management/
Professional
Service Production/
Transportation/
Materials Moving
White 35.6% 13.4% 13.6%
Black 25.2% 22.0% 18.6%
Hispanic 18.1% 21.8% 21.2%
Asian 44.6% 14.1% 13.4%

*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).

Work Patterns

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*

 
Race/Ethnicity In Labor Force Employed Unemployed
White 64.6% 61.1% 3.0%
Black 60.2% 52.5% 6.9%
Hispanic 61.4% 55.2% 5.7%
Asian 63.3% 59.7% 3.2%

*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).

Skill Disparity

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)

Economic Changes

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.

 
c)  Maryland-Specific
 
The Commission 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::
 

Key Findings

  • "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
 

Commission member 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.

 
B)  Consequences
 

With regard 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)

 
C)  Literature Analysis - Actions that may lead to the elimination and prevention of disparities
 

The General 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, and
  • allow claims to be brought on behalf of groups of employees.

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 development opportunities.

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 equality policies.

 
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