Mohamad G. Alkadry
Leslie E. Tower
West Virginia University
Essays on
Equity, Gender,
and Diversity
Unequal Pay: The Role of Gender
Mohamad G. Alkadry is an associate
professor and director of the master of
public administration program at West
Virginia University. His teaching and
research interests include organization
theory and behavior, social justice issues,
and citizen participation.
E-mail: malkadry@mail.wvu.edu.
Pay disparities between men and women persist in
the U.S. workforce despite comparable pay legislation,
advocacy, and social change. This article discusses theories
of gender pay disparities, such as the glass ceiling, position segregation, agency segregation, and human capital.
Using an online national survey, 1,600 responses were
collected for four groups of public procurement professionals. The gender wage gap ranged from $5,035 to
$9,577. Multiple regression of the data show that gender
continues to play a major role in predicting the salaries of
public officials in similar positions. Gender and human
capital variables predicted between 36.5 percent and
53.9 percent of the variance in pay.
Leslie E. Tower is an assistant professor in
the Division of Social Work and Division of
Public Administration at West Virginia
University. Her research interests include
domestic violence, health care administration, and women’s issues.
E-mail: LETower@mail.wvu.edu
M
ore than 40 years after the passage of the
Equal Pay Act of 1963, pay disparities
between men and women persist in the
U.S. workforce (Gibelman 2003). A 2003 study by
the General Accounting Office (now the Government
Accountability Office) found that women earned 79.7
percent of what men earned, even after controlling for
occupation, industry, years of work experience, job
tenure, number of work hours, time off for childbearing, race, marital status, and education. By comparison, women’s earnings in 1983 equaled 80.3 percent
of men’s earnings, an indication that the wage gap is
not shrinking (GAO 2003b; see also Schiller 1989).
Pay disparities are often attributed to an upward mobility glass ceiling or to the segregation of women in
certain “female-dominated” occupations, positions, or
agencies. Pay disparities may also be driven by disparities in such human capital variables as professional
skills, education, and experience.
Attempts to rectify gender pay disparities have been
fought largely through legislation, regulation, and
litigation. Federal laws include the Equal Pay Act of
1963, which guarantees equal work for equal pay, and
Title VII of the Civil Rights Act of 1964, which
prohibits sex-based employment discrimination (e.g.,
hiring, firing, training, promotion, and wages). The
Equal Employment Opportunity Commission
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Public Administration Review • November | December 2006
(EEOC) enforces these two laws. Lawsuits filed by the
EEOC may have a greater impact on employment
practices than the law itself or lawsuits filed by private
law firms. However, the number of EEOC lawsuits is
declining, while the number of private lawsuits is
rising (Blau, Ferber, and Winkler 2002).
This article reviews the existing literature on the major
drivers of pay disparities among men and women.
This literature includes studies of the glass ceiling and
position segregation, agency segregation, and human
capital as drivers of pay disparities between men and
women, as well as policy options to decrease these
barriers to pay equity. The current study analyzes data
from a survey of 1,600 public employees to test the
effect of gender (among a number of other variables)
on the pay of individuals who hold comparable positions in comparable agencies. This article examines
the persistence of pay inequity even when men and
women have comparable human capital characteristics
and have attained similar positions in similar fields.
The Glass Ceiling and Position Segregation
Pay differences between women and men have traditionally been attributed to the limited number of
women in the higher-paying upper levels of organizations. Women are concentrated in lower-echelon
positions because of initial hiring at the entry level
and a lack of upward mobility within organizations
(Guy 1993; Naff 1994; Newman 1994). This traditional conception of pay inequity begs three questions: Are women truly concentrated in the lower
levels or positions of organizations? Why are they
entering into these lower levels or positions and not
elsewhere? And, why do women who enter lower-level
positions not advance to higher positions within their
organizations?
Despite years of equal opportunity and affirmative
action efforts, women remain concentrated in certain
lower-level positions. In a study of the federal senior
service, Mani (1997) noted that women occupied 85
percent of all clerical positions but only 13 percent
of the Senior Executive Service positions in the
federal public service. Groshen (1991) argued that
position or occupational status could account for
one-half to two-thirds of the pay gap between men
and women. Orazem and Mattila studied occupational segregation within state governments and
found that “much of the wage differential between
men and women is tied to the different employers
and occupational labor markets that men and
women inhabit and not to disparate treatment
within given employers” (1998, 96). In their study of
data from all 18,365 employees in the West Virginia
state government, Alkadry, Nolf, and Condo (2002)
reported that women accounted for 85.7 percent of
“administrative support” jobs and 30 percent of
“officials and administrators.” This corroborates the
findings of previous studies on the segregation of
women in lower-level positions. And, concentration
in lower-level positions often means segregation in
lower-paying positions. For instance, Alkadry, Nolf,
and Condo (2002) reported that only 6 percent of all
West Virginia state government employees earning
more than $50,000 were women.
Historically, women’s have gained access to the public
sector through the lower ranks (Guy 1993; Naff 1994;
Newman 1994). Guy has noted that “social pushes
and pulls result in women gaining entrance to administrative positions while [the] wage gap continues to
reveal the relationship between gender and salary”
(1993, 285). Most of the sociocultural barriers faced
by women entering the public service are centered on
the gender typing of employees. Heilman et al.
(2004), for example, found that women who broke
from traditionally female jobs and succeeded in
traditionally male jobs were liked less and personally
derogated more often than their male counterparts.
Furthermore, they reported, these negative feelings
often affected the women’s salaries. Stivers (1993) has
argued that women are viewed in society as caring and
sensible individuals, whereas images of leaders in the
public sector are associated with characteristics that
are mostly masculine. Therefore, gender typing and
socialization tend to result in the segregation of
women in certain agencies, occupations, and
positions.
Upward mobility within organizations may combat
gender segregation, resulting in the progress of women
into upper-level and better-paying positions. However, many organizational and sociocultural factors
deny women the benefits of upward mobility. Newman (1993) found that a greater proportion of
women than men were handicapped in their career
advancement by domestic constraints and received
lower wages than their male counterparts. Lennon and
Rosenfield (1994) studied 13,017 households and
found that although married women performed twice
the housework as their husbands, both men and
women viewed their workloads as fair. In another
study, Noonan (2001) found that women performed
one and a half times more housework than men and
spent more time on female tasks than men spent on
male tasks. Noonan also found that the women she
studied had less full-time experience, more part-time
experience, took more employment breaks, and were
less likely to travel extensively for work. In her sample
of 10,008 households, women earned an average of
$11.55 per hour and men earned an average of
$17.56 per hour. For every one-hour increase in the
amount of housework performed by women, their
hourly wage decreased by 0.3 percent. On the other
hand, housework did not have a significant effect on
men’s salaries (Noonan 2001).
Organizational barriers such as career-development
patterns, workplace policies, and mentoring directly
affect women’s ability to progress in organizations
(Guy 1993). Stroh, Brett, and Reilly (1992) examined
the career progression of 1,029 male and female
managers employed by 20 Fortune 500 companies.
They found that despite similar education and work
background, there was a disparity in men and
women’s salary increases. Kelly et al. (1991) found
that mobility into elite positions occurs at a higher
rate for men than women. Guy (1993) also suggested
that men seem better able to climb the ladder, whereas
women seem less adept. Men are advantaged, after
controlling for other variables, in both pay levels and
wage growth in all jobs, regardless of gender composition (Kelly et al. 1991).
Newman (1993) studied career advancement in the
Florida Civil Service and found differences between
men’s and women’s barriers to career advancement.
Budig (2002) studied female-dominated, genderbalanced, and male-dominated positions and found
that men are more likely than women “to be promoted into rewarding male and female jobs, regardless
of the gender composition of the job held prior to
promotion” (Budig 2002, 274). Stroh, Brett, and
Reilly (1992) have suggested that women may have
done all they can to break barriers to pay equity and
that corporations may need to break some of the
barriers to the promotion of women.
Not all studies concur that women in organizations
are not advancing into higher positions because of
organizational barriers. Lewis and Park (1989) examined the effects of age, length of service, education,
salary, and gender on differences in turnover rates for
men and women. Studying a sample of 1 percent of
the entire federal civil service, they concluded that
gender is a minor factor in explaining turnover,
whereas age, experience, and salary are all more likely
to affect turnover. These variables are mostly human
capital barriers that some have used to explain the
gender pay disparity.
Unequal Pay 889
It is obvious from previous studies that women remain
concentrated in lower-echelon positions for cultural,
organizational, and human capital reasons. They face
barriers to advancement, and when they do advance,
they generally proceed at a slower pace than men.
These barriers form what is labeled the “glass ceiling,”
a metaphoric barrier that keeps women in lower-level
positions. The segregation of women in lower-level
positions causes the pay gap between men and women
to widen. This gap may be corrected by strengthening
the enforcement of existing equal opportunity laws
(Rose and Hartmann 2003). Affirmative action strategies that encourage the placement of women in upperlevel and better-paying positions may help shatter the
glass ceiling and reduce position segregation.
Organizations can also encourage the hiring, retention, and advancement of women by adopting policies
that are friendly to women in the workplace. Such
policies may include “making work places more
‘family friendly’ through more flexible hours, providing
more job-guaranteed and paid leaves of absence for
sickness and family care, encouraging men to use
family leave more, increasing subsidies for childcare
and early education, encouraging the development of
more part-time jobs that pay well and also have good
benefits, and improving outcomes for mothers and
children after divorce” (Rose and Hartmann 2003, v).
Nonstandard work schedules, job sharing, and homebased employment may offer additional flexibility to
workers with family responsibilities (Blau, Ferber, and
Winkler 2002).
Agency Segregation
The gender typing of women not only affects the types
of occupations they pursue but also the types of agencies they work for. The image of “caring” women
results in women working in agencies that provide
services such as education and social services (Newman
1994; see also Stivers 1993). “Agency segregation” is
the term used to refer to the segregation of women in
traditionally female agencies.
Newman (1994) has argued that women are more
likely to be employed in redistributive agencies than
in regulatory or distributive agencies. Newman’s taxonomy of agencies is based on Lowi’s (1985) framework of administrative structures. According to Lowi,
redistributive agencies are those concerned with
health, welfare, or education and primarily concern
themselves with the reallocation of money and provision of services to certain segments of society. Regulatory agencies include environmental agencies, law
enforcement agencies, or taxing authorities, which
primarily focus on implementing control and regulatory policies. Distributive agencies, such as transportation and parks and recreation agencies, focus on
service to the general population. In their study of
West Virginia state agencies, Alkadry, Nolf, and
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Public Administration Review • November | December 2006
Condo (2002) found that 65.7 percent of employees
in West Virginia redistributive agencies were women,
compared to 23.2 percent of employees in distributive
agencies and 33 percent in regulatory agencies.
Cornwell and Kellough (1994) looked at clerical,
blue-collar, technical, administrative, and professional
jobs but found that the nature and number of clerical
and technical jobs had a positive effect on the percentage of women in these agencies. They concluded in
their study of women and minorities in federal agencies that the female employment share was higher in
agencies with a larger proportion of clerical jobs.
Agency size, the number of new hires, and union
strength did not significantly affect the percentage of
women employed in a federal agency.
Departments with primarily female-dominated occupations are likely to pay lower wages than agencies
with primarily male-dominated positions (Orazem
and Mattila 1998). Kelly et al. (1991) examined six
states (California, Alabama, Arizona, Texas, Wisconsin, and Utah) and found that female-dominated jobs
had lower average wages than male-dominated jobs.
Budig’s (2002) findings reaffirm position segregation
arguments but also introduce the idea that the integration of women into male-dominated fields and
men into female-dominated fields would not close the
gap in pay. Men’s economic advantage may persist
even after position integration.
Research has also shown a considerable gap in pay
between men and women even in female-dominated
fields such as social work (Becker 1961; Gibelman
2003; Koeske and Krowinski 2004; Gibelman and
Whiting 1997). Becker interviewed members of the
National Association of Social Workers (NASW) in
1960 and found that women earned 86 percent of
the salaries of men. Almost 20 years later, the
NASW found that men earned 30.5 percent more
than women. More recently, Koeske and Krowinski
reported an average pay gap of $3,665 between male
and female social workers, even when controlling for
years of experience, job role (i.e., administrative or
direct practice), age, and years with a master’s
degree.
Miller, Kerr, and Reid (1999) used the metaphor of a
“glass wall” to describe the segregation of women in
certain agencies. Based on data from the EEOC, they
studied administrative and professional positions in
U.S. cities between 1985 and 1993. The authors
found that women were severely underrepresented in
distributive and regulatory agencies. This finding is
consistent with the findings of Newman (1994) and
Orazem and Mattila (1998). These researchers also
found that salaries in redistributive agencies were
lower, on average, than those in distributive and
regulatory agencies.
Policies to rectify agency segregacomparable worth policies have
Policies to rectify agency segre- the potential of reducing gender–
tion aim to increase access to
education or training in highpay disparities even with the
gation aim to increase access
paying fields in which women
persistence of agency and occupato education or training in
are underrepresented and to
tional segregation. Women in
high-paying fields in which
develop new legal remedies to
female-dominated agencies or
women are underrepresented
establish comparable worth
occupations will have the potenand to develop new legal remedies tial to earn comparable salaries to
(Rose and Hartmann 2003).
to establish comparable worth. men or women in comparable
Comparable worth extends the
concept of equal pay for equal
male-dominated agencies or
work, codified in the Equal Pay
occupations.
Act, to include equal pay for comparable work. Comparable worth is determined by conducting detailed
Human Capital
evaluations of job descriptions and calculating the
The barriers to pay equity and equal employment
composite effort, skill, responsibility, and work enviopportunity for women and men are far too complironment and then adjusting the pay for different but
cated and interconnected to permit the construction
comparable jobs (Blau, Ferber, and Winkler 2002;
of a useful typology. Organizational barriers are interKillingsworth 2002). Achieving comparable worth at
connected with sociocultural and human capital barrithe federal level is unlikely, as federal courts have ruled ers. A discussion of pay equity may attribute the pay
that existing antidiscrimination laws do not require
gap to women’s tenure in the workforce compared to
comparable worth. In addition, federal comparable
men’s. Other explanations might attribute it to educaworth bills, such as the Fair Pay Act and the Paycheck
tional differences or differences in work experience.
Fairness Act, which contain provisions for the develHuman capital theories suggest that investments in
opment of guidelines to help employers who volunone’s human capital, such as education, responsibility,
tarily engage in comparable worth practices, continue
experience, age, and leadership abilities, explain differto receive little attention.
ences in success (Kelly 1991). Therefore, it is important to review the literature on human capital drivers
Comparable worth at the state level, however, may
and their effect on salaries.
have more promise. The National Committee on Pay
Equity has identified more than 20 states that have
The type and quality of education seem to play a
implemented some comparable worth pay adjustrole in salary gaps (Rumberger and Thomas 1993;
ments. However, Killingsworth (2002) has argued
Solomon and Wachtel 1975). Amirault (1994) and
that many programs have had a limited and unclear
Nieva and Gutek (1981) linked pay inequity to educaimpact, even among the six states with the most ambi- tional disparities. Although education may influence
tious initiatives. State comparable worth efforts are
pay (Morgan 1997), there is no foundation for arguing
likely to continue to encounter cost barriers of implethat women are less educated than their male colmentation, exacerbated by state deficits, as well as
leagues. Education is a very important variable that
opposition from labor unions and others. Comparable needs to be controlled for whenever studying pay
worth, however, would have no impact on workers
disparities between men and women. However, educawho attain the same positions across different organition is relevant to pay disparities only if men and
zations. Examining such a scenario is what makes our
women can be shown to have different levels of
current study distinct.
education.
On a related front, the Office of Federal Contract
Compliance Programs (OFCCP 2004), which
enforces Executive Order 11246, related federal
affirmative action, has proposed standards for interpreting systemic compensation discrimination. The
OFCCP defines compensation discrimination as
“dissimilar treatment of individuals who are similarly
situated, based on similarity in work performed, skills,
and qualification involved in the job and responsibility level” (OFCCP 2004, 67249; emphasis added).
Although comparable worth has clearly been rejected
by federal courts and law makers, compensation analysis of similar jobs may become more rigorous, relying
on multiple pooled regression analysis. If adopted,
these regulations may build capacity for future administrations that are interested in pay equity. In general,
Company size has an effect not only on pay disparities
but also on pay itself (Langer 2000). Bertrand and
Hallock (2001) found that 75 percent of the wage gap
in pay between male and female executives of larger
companies was explained by company size and by the
fact that women were less likely to be chief executive
officer, chair, vice chair, or president of these companies. They found no evidence that industry segregation had any effect on the wage gap. Once women
executives’ age and seniority were controlled for, the
gap in pay fell to less than 5 percent.
The level of responsibility an individual bears is likely
to affect compensation. Sales volume and organizational size influence an employee’s workload and,
consequently, the compensation of that employee
Unequal Pay 891
(Langer 2000; Ogden, Zsidisin, and Hendrick 2002).
Supervisory responsibilities also influence the
compensation of employees (Langer 2000; Ogden,
Zsidisin, and Hendrick 2002). People with more
responsibility, be it supervisory or financial, would
reasonably be compensated at higher levels than those
with fewer responsibilities. Years of experience in one’s
field and current job tenure also play a role in determining the salaries of individuals (Holzer 1990).
Studying purchasing professionals primarily in the
private sector, Fitzgerald (1998) found that the gap
between the average salary paid to women and men
was $17,600. He found that the women in purchasing
occupations whom he studied were younger and less
experienced than men, had fewer supervisory responsibilities, were in charge of lower purchasing volumes,
were less educated, and did not hold as many senior
positions as men. However, even when these factors
were taken into account, the average compensation
of women remained lower than that of men in the
purchasing field.
Other human capital barriers include leadership abilities (Powell 1988; Rosener 1990) and managerial
aptitude (Kelly et al. 1991). Groshen has even argued
that “in the human capital model, the wage gap is
associated with occupation and with the individual,
unless establishments or job cells are sorted by
quality” (2001, 468).
Conceptual Model
The foregoing literature review suggests several directions. First, position segregation is responsible for part
of the disparities in pay between men and women.
However, this form of segregation plays only a minor
role when comparing pay within specified positions or
levels. Second, agency segregation plays an important
role in widening the gap between male pay and female
pay. This role is also more relevant when comparing
people in different agency types. Finally, the human
capital characteristics of employees, their skills, and
their experiences play a major role in establishing the
pay of most employees. The literature on human
capital drivers of pay and pay disparities does not rule
out the potential for these drivers to influence the
salaries of men and women. However, it is important
to isolate the effect of these drivers from the effect of
gender on salaries.
This article focuses on human capital and its effect on
wage disparities between men and women. The human capital variables include experience, supervisory
responsibilities, financial responsibilities, education,
organization size, age, and certification. Because respondents were drawn from different labor markets,
the labor market competitiveness of respondents and
the cost of living in the respondents’ counties must be
controlled for (Agron 1996).
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Public Administration Review • November | December 2006
However, any study of human capital drivers of pay
inequity needs to control for position and agency
segregation. Controlling for both position and agency
segregation may be accomplished by studying pay
disparities within the same position levels and the
same agency types. Therefore, this study focuses on
public procurement officials in different positions.
Although the sample used in this study includes procurement officials at all levels of the procurement
organization, the results are analyzed separately for
each position level.
Because the study sample consists of public employees in the purchasing field, three major issues that are
unique to public sector wage determination and wage
disparities must be noted. First, employees in the
public sector are likely to earn less money than those
in the private sector (Leenders and Fearon 1997;
Muller 1991; Ogden, Zsidisin, and Hendrick 2002).
Because the sample used in the present study was
drawn exclusively from the public sector, this difference is not likely to have an impact on the internal
validity of the findings. Second, there is an expectation that public sector practices will be more sensitive
to issues of representation and fairness than those of
the private sector (Frederickson 1997; Riggs 1970;
Wise 1990). Third, the federal government and
several states have historically taken more steps to
reduce pay gaps than private sector companies
(Killingsworth 2002). The latter two issues mean,
if anything, that the pay disparities reflected in
public sector samples may be more severe in the
private sector.
Population and Methodology
To gather the data, an online survey instrument consisting of 36 questions was used. The National Institute for Government Procurement (NIGP) is the
major professional association for public procurement
officials. It was established in 1944 to provide education, professional certification, and technical assistance for public organizations (Thai 2001). An e-mail
message with a hyperlink to the online survey was sent
to all individuals who work in NIGP member organizations and whose e-mail addresses were available to
the NIGP. The e-mail message was sent to 6,747
members; a total of 1,673 individuals responded to
the survey, resulting in a response rate of 25 percent.
After adjusting for invalid addresses, this response rate
increased to 28 percent.
As recommended by Lindner, Murphy, and Briers
(2001), steps were taken to account for possible
nonresponse bias, given the low response rate. The
gender, geographic, and agency distributions of respondents were compared to the distribution of the
original sample, and there were no apparent signs of
nonresponse bias. Furthermore, the average salary of
respondents within each of the job classifications was
compared to the average salary of the original sample,
and again the lack of substantial discrepancies revealed no nonresponse bias (Alkadry and Beach
2003). The total number of respondents represents
28 percent of the entire population, not 28% of a
sample drawn from that population. This percentage
is considered very reasonable given historical response
rates for online surveys (Andrews, Nonnecke, and
Preece 2003).
The data analyzed included 374 responses from heads
of procurement offices, 333 responses from purchasing
supervisors and materials managers, 462 responses from
buyers, and 160 responses from specialists, technicians,
and assistant buyers. To control for the effect of position segregation, the data were analyzed for each of
these position classifications and not across positions.
Among the respondents, 25 percent worked in state
procurement agencies, 22 percent worked in county
and regional agencies, and 29 percent worked in
municipal agencies. Forty-nine percent of all respondents were female and 51 percent were male. The
mean age of respondents was 47 years, and the median was 48 years. The average salary for all respondents was $42,896. On average, respondents had
15.19 years of education and 15 years of purchasing
experience. The average tenure with the current
employer was 10.77 years.
Results
As a prelude to the study, it is important to highlight
any pay differentials among the different positions in
the sample. Analysis of variance was used to analyze
the variance of mean salaries among men and women
in different positions. As table 1 reports, the pay gap,
which consistently favored male employees, was statistically significant for all positions except senior buyers.
Depending on the position, this gap ranged from
$5,788 to $9,577. Female executives earned 86.5
percent of their male counterparts, female managers
earned 87.3 percent of their male counterparts, female
buyers earned 87.2 percent of their male counterparts,
and female technicians earned 86.6 percent of their
male counterparts. Such results are extremely useful in
establishing the prevalence of pay inequity in this
population.
Differences in human capital are the cornerstone of
arguments that pay disparities between men and
women are driven by factors other than gender. Therefore, this study analyzed mean variances of the key
human capital variables. In table 2, the difference for
many of these variables is not reported because of its
lack of statistical significance. For the variables that
were significant, the difference was not always in favor
of men. For the heads of purchasing units, the only
difference that was significant was years of experience
and years of education. Men in this position had more
experience and slightly more years of education. For
managers and supervisors, men had less experience
with current employer, more experience in purchasing, more subordinates, more education, and were
older on average. Male senior buyers and associate
buyers had more purchasing experience, more education, and were older. Male technicians had slightly
higher levels of education than their female
counterparts.
Like the disparities in pay, human capital disparities
also corroborate some previous findings. However,
how much of the pay disparities are driven by disparities in human capital characteristics remains unclear.
To study this effect, multiple regression analysis was
used to predict salary using several human capital
variables but also using gender and controlling for
labor market competitiveness and cost of living.
The regression model was built using 2002 salary
(including bonuses) as the dependent variable. Gender
and certification were entered as dummy independent
variables. Certification captured whether employees
held any special purchasing-related expertise. Total
years with current employer and total years of experience in purchasing were entered to capture the effect
of experience in predicting salaries. To capture the
effect of the amount of responsibility attached to
respondents, this study used the number of subordinates, annual procurement volume, and number of
levels between the respondent and the chief executive
officer of the agency. The size of the organization was
captured through the number of staff in purchasing
units. Age and the number of years of education were
also entered. Because the respondents came from
different parts of the country, there was a need to
Table 1 Pay Disparities in Procurement Positions
Heads of purchasing units
Managers and supervisors
Senior buyers
Buyers
Technicians, specialists,
and assistant buyers
All classifications
Average Male Salary
(including bonuses)
Average Female Salary
(including bonuses)
Female Salary as a Percentage
of Male Salary
Pay Gap (Sig.)
$70,741
$58,809
$48,030
$45,262
$49,318
$61,164
$51,323
$45,812
$39,474
$42,689
86.5
87.3
95.4
87.2
86.6
$9,577 (< 0.0005)
$7,486 (< 0.0005)
$2,218 (not sig.)
$5,788 (< 0.0005)
$6,629 (< 0.011)
$58,106
$47,712
82.1
$10,394 (< 0.0005)
Unequal Pay 893
Table 2 Human Capital Disparities
Gender
Years with current employer
Years of purchasing experience
Number of subordinates
Years of education
Hierarchy
Annual procurement volume
Number of staff in purchasing unit
Number of staff in jurisdiction
Age
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Heads of
Purchasing Units
Managers and
Supervisors
18.93
12.64
16.44
15.08
Senior
Buyers
Buyers
10.03
13.77
17.84
15.49
9.68
6.26
15.76
14.71
18.14
14.30
14.56
11.04
15.50
14.84
15.04
14.11
48.86
46.09
50.35
45.37
48.35
44.69
Technicians, Specialists,
and Assistant Buyers
15.58
14.39
Note: All reported means are statistically significant at the 0.05 level or better. Nonreported means are not statistically significant
(or have a statistical significance greater than 0.05).
control for cost of living and labor market competitiveness and their effect on salary variance. The median housing value and the median household income
were used to measure cost of living and labor market
competitiveness, respectively.
run for these two populations. The remaining three
populations included the heads of purchasing units,
supervisors and managers, and buyers. Table 3 lists
the results of the multiple regression model for these
three populations. All assumptions of linearity and
homoscedasticity are met for the three models.
The result is a multiple regression model with one
dependent variable and 12 independent variables.
Using the standard of a minimum of 10 responses per
predictor, the minimum sample size for each regression model is 120 valid cases. Sample sizes for the
senior buyer and technician populations were smaller
than 120 cases (89 and 94 valid cases, respectively).
Therefore, multiple regression models could not be
Gender had a statistically significant effect (a standardized beta of –0.146, –0.165, and –0.135, respectively)
on the variance of salaries of heads of purchasing,
supervisors and managers, and buyers.
For heads of purchasing units, the significant betas
were the number of subordinates (0.226), median
Table 3 Standardized Beta Values for Predicting Variance in Salaries
Heads of Purchasing Units
R2:
Adj. R2:
Iteration
Valid N
0.415
0.396
8
251
Female (yes/no)
Total years with current employer
Total years of experience in purchasing
Number of subordinates
Number of years of education
Hierarchy
Annual procurement volume
Number of staff in purchasing unit
Age
Certified (yes/no)
Median household income
Median housing value
Significant beta totals
Beta
–0.146
0.161
0.117
0.226
0.192
Not sig.
Not sig.
0.198
Not sig.
Not sig.
0.224
0.145
1.117
0.396
0.375
7
209
%
10
11
8
16
14
0
0
14
0
0
16
10
100
Note: Reported betas are statistically significant at the 0.05 level or better.
894
Public Administration Review • November | December 2006
Supervisors and Managers
Beta
–0.165
0.166
0.167
0.216
0.249
Not sig.
Not sig.
Not sig.
Not sig.
Not sig.
0.304
0.161
1.098
Buyers
0.539
0.52
7
179
%
12%
12%
12
15
17
0
0
0
0
0
21
11
100
Beta
–0.135
0.273
0.279
0.193
0.197
–0.153
Not sig.
Not sig.
Not sig.
Not sig.
Not sig.
0.461
1.115
%
10
20
20
14
14
–11
0
0
0
0
0
33
100
household income (0.224), number of years of education (0.192), number of staff in the purchasing unit
(0.198), total years with current employer (0.161),
gender (–0.146), median housing value (0.145), and
total years of experience in purchasing (0.117).
For purchasing supervisors and materials managers,
the significant betas were median household income
(0.304), number of years of education (0.249),
number of subordinates (0.216), gender (–0.165),
total years with current employer (0.166), total years
of experience in purchasing (0.167), and median
housing value (0.161).
For buyers, the significant betas were median housing
value (0.461), total years of experience in purchasing
(0.279), total years with current employer (0.273),
number of years of education (0.197), number of
subordinates (0.193), hierarchy (–0.153), and gender
(–0.135).
Discussion
This research is unique in two ways. First, it studies
pay inequities within the same occupation and within
similar positions. Although respondents were not all
working for the same agency, they were all involved in
the purchasing occupation in purchasing departments.
Furthermore, the analysis was conducted within position level or rank, and it controlled for the human
capital characteristics that are often associated with
gender pay disparity. Female salaries were consistently
lower than male salaries for the position categories
covered in this study.
However, the challenge that this research undertook
was not to confirm that a disparity existed. In previous
studies, the wage gap has been as low as $3,665 in a
sample of social workers (Koeske and Krowinski 2004)
and as high as $17,600 in a sample of private sector
purchasing professionals (Fitzgerald 1998). Instead, the
challenge was to isolate the effect of gender on pay
disparity from the effects of other human capital and
cost of living variables. Seniority, experience in field,
supervisory responsibilities, education, hierarchy, size
of organization, cost of living, labor market competitiveness, and gender were used as predictors of variance
in pay. This study found that women and men in
similar positions in the same field of work made different salaries for several reasons—one of which is gender.
The regression results across positions were similar,
with gender significantly contributing to salary for the
three populations covered by the regression analysis.
In some cases, it was as important as human capital
predictors such as experience and job responsibilities.
The regression analysis also confirmed that other
human capital variables contribute to these disparities.
In other words, disparities are driven by human capital and cost of living variables, but they are also driven
by the gender of the employee. The predictors
included in the three regression models explained 40
percent to 55 percent of the variance in the dependent
variable. This means that some of the variance in
compensation is driven by factors other than those
explored in the literature and used in this article.
Human capital variables such as education, years with
the current employer, years of experience in purchasing, and the number of subordinates all contributed
to the variance in compensation in our sample. Previous studies have inconsistently documented the
impact of these differences on men’s and women’s
salaries. Some scholars have reported an association
between education and salary (e.g., Amirault 1994;
Nieva and Gutek 1981), whereas others have not
found any significant association (GAO 2003; Stroh,
Brett, and Reilly 1992). Holzer (1990) found that
years of experience in one’s field plays a role in determining the salaries of individuals, but Koeske and
Krowinski (2004) found that experience does not play
such a role.
In this study, age, certification, hierarchy, annual
procurement volume, and number of staff in the unit
did not explain the variance in the salaries of individuals. Researchers have reported that age does not significantly affect the wage gap (Bertrand and Hallock
2001; Koeske and Krowinski 2004). Of men and
women in purchasing, Fitzgerald (1998) noted that
women tend to be younger, less experienced, have
fewer supervisory responsibilities, have responsibility
for fewer purchasing dollars, are less educated, and
hold less senior positions than men. Even when these
factors are taken into account, average compensation
of women remains lower than that of men. Although
the literature inconsistently reports the significance
of human capital variables on the wage gap, the wage
gap persists.
The results of the current study should be interpreted
in light of sampling limitations. The first limitation
concerns sampling coverage. Individuals not belonging to the National Institute for Governmental Purchasing did not have an opportunity to respond to the
survey. However, this is the most comprehensive
sampling frame for this population. The NIGP is the
largest and only national purchasing organization that
has individual members. Individuals automatically
become members when their agencies join the NIGP.
The survey was sent to the entire sampling frame. The
second limitation concerns the response rate. To control for this limitation, we compared key characteristics of the respondents to those of the full sampling
frame. There was no indication of any nonresponse
bias. Coverage error and nonresponse error should be
addressed in all survey research, but scholars recommend that more attention be paid to these two errors
in the case of online data collection (Granello and
Unequal Pay 895
Wheaton 2004; Bachmann, Elfrink, and Vazzana
1996; Crawford, Couper, and Lamias 2001). As
discussed previously, this study took several steps to
uncover the existence of sampling bias, and none
was discovered.
Conclusion
Barriers to pay equity are complicated because organizational barriers are interconnected with sociocultural
and human capital barriers. Reporting the direct
contribution of gender to pay disparity does not
relieve policy makers from minimizing these barriers.
Although this study held constant the impact of position and agency segregation on pay disparities, forms
of segregation remain a serious problem in organizations. Gibelman (2003) has suggested that a multipronged response is needed that would include (1)
education and advocacy activities (e.g., to broaden
professional and public awareness of the wage gap);
(2) group activity (e.g., women working together
within and with employers, government, women’s
organizations, and unions); (3) professional activity
(e.g., collaboration among professional groups to
addresses the wage gap); and (4) policy activity
(e.g., comparable worth, which seeks to influence
position and agency segregation).
they may not have an important impact on the labor
market.
We do not want to shift the debate away from the
important policies cited here; rather, we wish to start
another front in the “war on wage sexism.” This front
must take into consideration that even when women
overcome barriers to accessing upper-echelon jobs or
traditionally male-dominated occupations, pay disparities are likely to persist. The research in this study
did not focus on one government level or one type of
government. Instead, it studied purchasing professionals at the state, local, and regional levels. This
makes the potential for corrective policy action even
more complicated. If the problem were concentrated
in one agency, the solution could come from within
that agency. However, expecting change to occur
from within all of these agencies at once is unrealistic,
as many of these agencies may be oblivious to the
inequities that exist. In this case, change is needed
from outside these organizations. Federal standards
must be adopted to specifically address pay inequity
at all levels of government and even in the private
sector.
As we advocate a larger role for federal employment
regulation, the federal government seems to be movBetter enforcement of existing laws (e.g., the Equal
ing in the opposite direction. There is evidence of a
Pay Act and Title VII) and regulations (i.e., affirmabelief that gender-conscious policies are discriminative action), as well as stronger laws (i.e., the Paycheck tory, as signaled in recent executive, judicial, and
Fairness Act and Fair Pay Act) are needed to address
legislative actions, as well as public opinion. The fedthis issue. Such reforms, however, are unlikely in the
eral government appears to be retreating from its role
current political climate (Killingsworth 2002). Addiin protecting equal rights. For example, the National
tionally, state comparable worth efforts are likely to
Council for Research on Women (2004) has
continue to encounter barriers. Comparable worth,
documented a change in the mission of the U.S.
although very important and sorely needed, is not
Department of Labor’s Women’s Bureau, from a
enough to contend with the issues raised in the cur“responsibility to advocate and inform women dirent study. This study has shown that even for people
rectly, and the public as well, of women’s rights and
who have attained the same positions, although in
employment issues” to a “responsibility to promote
different organizations, gender continues to play a role profitable employment opportunities for women, to
in salary determinations.
empower them by enhancing their skills and improving their working conditions, and to provide employResorting to legal action may be
ers with more alternatives to
effective in dealing with compameet their labor needs.” Absent
Resorting to legal action may
rable worth within the same
from the new mission and vision
be effective in dealing with
organization or jurisdiction, but
is the intention to conduct reit may prove ineffective and
search about workplace rights,
comparable worth within the
impractical because the disparisame organization or jurisdiction, disseminate those findings, and
ties revealed in this article can be
propose policies that benefit
but it may prove ineffective
found across different organizaworking women. As a result,
and impractical because the
tions and jurisdictions. In this
more than 25 publications and
disparities revealed in this article fact sheets about women’s rights
case, class-action lawsuits, which
can be found across different
tend to have more impact than
and employment equity are
individual lawsuits, may not be
organizations and jurisdictions. no longer distributed by the
feasible. Class-action lawsuits are
Women’s Bureau.
usually based on many employees
and one employer (or a few employers). Individual
Finally, this study has left some very important
wage discrimination cases are very expensive to pursue research questions unanswered. We found that women
and difficult to argue. When private cases are won,
working in comparable occupations in comparable
896
Public Administration Review • November | December 2006
positions continue to receive lower pay than men.
Some of the variables that drive that difference are
related to individual skills and cost of living, but
gender itself continues to affect salaries. This is an
important finding, but unfortunately, it falls short of
recommending ways to eliminate the effect of gender
on employees’ salaries. Why do men make more
money than women even when women break the glass
ceiling and many glass walls? This is a compelling
research question that needs to be explored. Without
some clear answers to this question, these disparities
may not be remedied.
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