Gender Discrimination

Explain what the gender pay gap is and how it has changed over time.In the article, “The Gender Pay Gap: Challenging the Rationalizations,” (article attached), Lips argues that the human capital model is insufficient for understanding the persistence of the gender pay gap. Similarly, the AAUW report we read, “The Simple Truth About the Gender Pay Gap,” (https://www.aauw.org/resource/the-simple-truth-abo…)notes that the pay gap cannot simply be explained by different career and life choices men and women make. Why are the human capital and “choices” argument insufficient? What other factors, such as gender-based discrimination, explain the persistence of the gender pay gap?Considering the consequences of the pay gap, for both women and men, why is it important to address this issue? Discuss the workplace policies you think would be most effective for reducing the pay gap between men and women.Minimum 350 words.Include references, APA
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Sex Roles (2013) 68:169–185
DOI 10.1007/s11199-012-0165-z
FEMINIST FORUM
The Gender Pay Gap: Challenging the Rationalizations. Perceived
Equity, Discrimination, and the Limits of Human Capital Models
Hilary M. Lips
Published online: 26 April 2012
# Springer Science+Business Media, LLC 2012
Abstract A gender gap in earnings has proven both
persistent and universal. This paper relies mainly on
U.S. data, but a gap between women’s and men’s earnings exists in every country. There is a continuing debate
as to the extent to which the gap reflects merely the
inevitable and reasonably fair result of differing work
patterns and behaviors by women and men or the impact
of employment discrimination against women. The human capital approach, in which various explanatory variables are used to shrink the perceived size of the gap, is
often used to argue that much of the gap is due, not to
discrimination, but to differing investments in employment by women and men. However, neither “investments” nor “outcomes” can be assessed in genderneutral ways and the model’s underlying notion of
rational choices made against the backdrop of a genderneutral playing field is flawed. Discrimination appears to
be entwined with gendered work patterns and behaviors;
many of the human capital “explanatory” variables themselves require explanation. Understanding the gap
requires recognition of the limitations of human capital
models, and a willingness both to take a more sophisticated approach to such models, and to think beyond this
approach.
Keywords Gender . Employment . Discrimination . Gender
pay gap
H. M. Lips (*)
Department of Psychology, Radford University,
Box 6946, Radford, VA 24142, USA
e-mail: hlips@radford.edu
Introduction
Median annual earnings of women fulltime workers in the
United States are currently reported to be 75.7 % of men’s
earnings (U.S. Census Bureau 2011a, Table 36). Although this
represents an improvement over the 64.5 % of men’s earnings
paid to women in the mid-1950s, in recent years the number
has fluctuated little from a percentile in the high 70s, and the
significant remaining gap stubbornly refuses to close. The
situation is similar in other countries. Women in Europe earned
an average of 82 % as much as men in 2008 (Eurostat 2010). In
Australia, one recent study indicated that women’s 2010 earnings were 82 % of those of their male counterparts (“Gender
pay gap getting worse, not better: Australian study” 2010). In
Canada, among all workers (part-time and full-time), women’s
earnings averaged only 64.5 % of men’s in 2008 (Statistics
Canada 2010), and in the United Kingdom, full-time women
workers earned 89.8 % of comparable men’s hourly earnings;
among all workers, women’s earnings were 80.2 % of men’s
in 2010 (Office for National Statistics 2010a). Across all
industrialized countries, the Organization for Economic Cooperation and Development reported recently that women’s
median full-time earnings were 82.4 % of men’s (Rampell
2010). Earnings statistics for 150 countries worldwide show
that, without exception, women’s earnings lag behind those
of men (United Nations Development Program 2009).
Whereas the statistics available for different countries are
not strictly comparable, they are presented here to illustrate
the universality of the problem.
Debate continues to swirl, not only around the size of the
gap, but around whether the gap is unfair or fair: whether it
reflects discrimination visited on women or simply differences in human capital “investments” that women and men
bring to the workplace. In this debate, there is a concern,
170
implicitly grounded in the tenets of an equity-based approach to distributive justice, with finding the correct,
gender-neutral way to measure equal units of input from
women and men so we can fairly assess whether their outcomes (in terms of pay) are equivalent.
This paper reviews and analyzes the major conceptual
and technical issues implicit in the human capital model—
the model often used as a framework for explaining and
rationalizing the gender pay gap. I begin with an overview
of the assumptions, built into the model, that objective,
gender-neutral measures of investments and outcomes can
be identified and applied to understanding the gender pay
gap. Next, I turn to an analysis of the outcomes side of the
model: how is pay best measured, and do the measures we
choose have implications for the perceived size of the gap?
This is followed by an examination of the input side of the
model: what variables should be considered as relevant
investments when comparing the pay of women and men,
and are women and men rewarded similarly for similar
levels of investment? Finally, I turn to the an examination
of the assumption, implicit in the human capital model, that
women’s and men’s investments in paid work represent
individual choices, based on personal priorities and values,
rather than behavior that is constrained by necessity in many
ways. In these analyses, the data cited come from U.S.
sources, unless otherwise noted. A concluding section
includes suggestions for new ways of thinking about the
gender pay gap, and for needed research. Taking a social
psychological perspective, I argue that a new approach to
the use of the human capital model is required—one that
both acknowledges the complexity (and perhaps impossibility) of a completely gender-neutral analysis of inputs
and outcomes, and incorporates psychological, social and
cognitive factors to identify the broad range of influences
that affect the earnings of both women and men. An
understanding of the limits of traditional human capital
approaches, as well as a broader vision of the forces that
influence earnings, is critical to the search for policy
approaches aimed at narrowing the gap between women’s and men’s earnings.
The Human Capital Model as an Explanatory
Framework
The human capital model posits that workers’ earnings are
directly related to their investments in employment, so that
greater investments in terms of education and training, skill,
work experience, and so on produce greater earnings (e.g.,
Mincer 1974). The model rests on the idea that worker
contributions and merit can be quantified and that rewards
are then distributed in a rational, bias-free way that reflects
this quantification. The human capital approach to
Sex Roles (2013) 68:169–185
compensation is invoked as the driving force behind a wide
range of pay discrepancies: why college graduates earn
more than high school graduates, full professors earn more
than instructors, individuals with rare skills and talents earn
more than their counterparts with more common skills (e.g.,
Becker 1964; Schultz 1995). Yet, it bears admitting that this
model represents an ideal of fairness that may seldom be
achieved, that strong disagreements may exist on how to
value worker investments or contributions, and that inconsistencies and hypocrisy can often be found in the way the
model is applied to various situations. Highly public examples of such inconsistencies can be found in the recent pay
increases and bonuses offered to CEOs of companies whose
abysmal performance would appear to prescribe slashing rather than raising the compensation of the persons in charge: a
67 % increase in compensation for the CEO of Bank of
America, despite the company’s loss of 2.2 billion dollars in
2010, a bonus of 4 million dollars for the CEO of the recently
bailed out Royal Bank of Scotland (Schaefer 2011).
The human capital model provides the basis for comments made by economists and policy analysts who have
argued, for example, that it would be unfair to judge gender
discrimination by wage differences not adjusted for differences in worker skills or characteristics (Stanley and Jarrell
1998), length of the average work week for women and men
(Oi 1991), or the average number of weeks per year worked
by women and men (Blau and Beller 1988; Jarrell and
Stanley 2004). When analyzing the gender wage gap, proponents of the human capital approach aim to explain as
much of the gap as possible by controlling for female–male
differences in such tangible inputs as education, work hours,
and years of experience. The observed gap is decomposed
into various explanatory factors, and the remaining, unexplained gap, or residual, is thought to represent discrimination (e.g., Neuman and Oaxaca 2004; Oaxaca 1973).
Discrimination may include discriminatory preferences of
employers, co-workers or customers and statistical discrimination based on employers’ anticipation that women will be
less productive and/or less committed to full-time employment. It may also include wage depression in femaledominated jobs that results when qualified women are excluded from male-dominated occupations, leading to an
oversupply of available workers for female-dominated jobs
(Blau and Kahn 2000). The decomposition approach has
been used in ever more sophisticated models (e.g., Machin
and Puhani 2003; Kunze 2005; Miyoshi 2007) in an attempt
to specify more precisely the variables that lead to the
earnings differential between women and men. Using the
human capital approach, analysts have variously determined
that the portion of the gap that might be attributable to
discrimination (at least until some new explanatory factor
is discovered) may range from 38 % (Blau and Kahn 2000)
to 40 % (Kunze 2005).
Sex Roles (2013) 68:169–185
The assumption that gender-neutral units of input can be
found is itself questionable, however. Indeed, decisions
about which input and outcome measures to use when
comparing women’s and men’s earnings are unlikely to be
completely neutral. Rather, like so many decisions in scientific research, they are entwined with various implicit and
value-laden assumptions (Neuman and Oaxaca 2004). The
focus on such seemingly concrete measures as hours worked
and workforce experience can obscure a host of social
psychological issues such as social influence, perceived
choice, expectations, stereotyping, self-views, and social
capital (Fortin 2005). Furthermore, scrutiny of the earnings
measures (outcomes) used to delineate the pay gap suggests
that even these are not wholly objective and can reflect
gender bias. Indeed, feminist economists and political scientists have long argued that conventional economic
approaches tend to be limited and to neglect the ways in
which societies’ constructions of gender are integral to
women’s and men’s labor market behavior and experience,
making such a “neutral” analysis inadequate and inappropriate (e.g., Figart 2005; Whitehouse 2003).
Beneath issues of identification and measurement of
appropriate inputs and outcomes lies the fundamental flaw
in the application of the traditional human capital model: the
assumption that, when women’s and men’s contributions
and compensation are compared, they are set against a
neutral background–a level playing field on which women
and men participate under equal conditions and are judged
fairly and without bias. Yet, as the following sections elaborate, this is not the case. Even when the standard human
capital variables are considered and controlled for, gender
differences in pay remain.
Measuring Outcomes
Size and Stability of the Pay Gap
As noted by Blau and Kahn (2007), published government
statistics on annual and weekly earnings of fulltime workers
in the United States reveal that the ratio of women’s to men’s
earnings decreased between 1955 and 1960, remained relatively stable until the late 1970s, then gradually increased until
the early 1990s. Since that time it has fluctuated within a range
of 75 to 79 %. Attempts to forecast the closing of the gap
suggest that there is currently no clear trend toward closing or
widening (Blau and Kahn 2007; Lips 2003).
The size of the gap appears different depending on the
unit of measurement used. For example, data reported by the
U.S. Census Bureau (2011a, Table 38) and the U.S. Bureau
of Labor Statistics (2010a, Tables 16 & 18) indicates that
among full-time year-round workers aged 16 and older in
2009, women’s earnings were 85.5 % of men’s when
171
measured as median hourly pay, but were 80.2 % of men’s
when measured as median weekly earnings and only 77.0 %
of men’s when measured as median annual income. Thus,
the measure chosen to report the gap alters its perceived size
and seriousness.
Hourly Earnings: An Unbiased Measure?
Economists have often stated a preference for the hourly
measure of earnings, asserting that it is less biased toward
exaggerating the gap (e.g., Blau and Kahn 2007; Jarrell and
Stanley 2004). This preference is based on the twin assumptions that hourly earnings can be measured accurately and
that they reflect worker input in a fair, gender-neutral way.
For example, Jarrell and Stanley (2004) argue that “Best
research practice suggests that annual and weekly salaries
should not form the basis of discrimination estimates; otherwise, gender differences in weeks worked per year or
hours worked per week may bias these estimates” (p. 836),
and Blau and Kahn (2007) note that “Ideally, we would like
a measure of wages or an hourly rate of pay” (p. 8).
At first blush, such arguments may seem eminently reasonable. After all, the use of hourly earnings should control for
female–male differences in the number of hours worked—and
if women work fewer hours than men, surely they deserve less
pay. Yet, there are several problems with this approach. First,
many worker’s earnings are not computed based on hourly
rates. Many full-time, full-year salaried workers (about 39 %
of employed women and 45 % of employed men in the U.S.;
U.S. Bureau of Labor Statistics 2010a) are paid on the basis of
an annual salary. Although there is a general expectation that
their work averages from 35 to 40 hr per week, many of these
workers would probably say that their work hours can vary
widely from week to week and that the important thing is
getting the necessary tasks done. Junior academics, for example, often joke that if they computed their hourly rate of pay,
they would be making less than a dollar an hour during their
first few years of employment. On the other end of the
continuum, translating the compensation packages for CEOs
of the S&P top 500 companies, which, according to the
Associated Press, averaged $8.4 million in 2007 (Beck and
Fordahl 2008), into hourly wages results in an estimate of
more than $4038 per hour for a fictional 52-week year (no
vacations) at 40 hr per week. When observers complain that
no worker can be worth so much per hour, defenders of the
high compensation are quick to argue that it is not the value of
hourly work, but the value of the individual’s talent and vision
that makes the salary appropriate. Thus, there is a tacit admission, in at least some instances, that thinking of pay in terms of
hourly wages is not necessarily appropriate.
Equality in terms of hourly earnings may not represent
equality in terms of total labor-market rewards (Whitehouse
2003). For example, hourly wage rates often do not capture
172
completely the value of a paycheck in terms of benefits such
as available retirement and health care plans. Such benefits
are often the fruits of labor union negotiations. Indeed, some
research suggests that unionized women workers are 18.8 %
more likely to have health insurance coverage and 24.7 %
more likely to have pension coverage than their nonunionized counterparts (Schmitt 2008). Men are more likely
than women to belong to unions, although that gender
difference has been eroding as the rate of unionization drops
more quickly for men than women (Schmitt and Warner
2009). Furthermore, there are sectors of the labor force,
such as finance, insurance, and retail, that are largely nonunionized and where women form a large majority of workers (Bronfenbrenner 2005). In situations where men, but not
women, are unionized the same rate of hourly pay may
translate into more discretionary income for men than
women.
The use of hourly pay as an indicator is also unlikely to
reflect the availability of bonuses, stock options, and other
aspects of compensation (e.g., company car, cell phone) that
are not figured into salary, except perhaps on an annual
basis. Men are more likely than women to hold positions
in which such bonuses are available. For example, a study of
CEOs of a national nonprofit with 207 autonomous member
agencies in 24 countries revealed that female CEOs were
paid less, both in salaries and in bonuses, even controlling
for differences in human capital, organizational size, and
performance variables (Mesch and Rooney 2004). Another
study of more than 5500 individuals employed in the private
sector in the United States showed that women workers
were more likely than men to be paid piece rates and less
likely to be paid commissions and bonuses (Geddes and
Heywood 2003). For those very few women who make it
to the highest corporate positions, this difference in extrasalary compensation may not exist: One recent study found
that, even when including aspects of extra-salary compensation such as stock options and performance incentives,
there was no difference in compensation between the 61
female and 4,643 male CEOs of firms included in Standard
and Poor’s ExecuComp database, which includes firms in
the S&P 500, midcap 400, and S&P small cap 600 indices
(Adams et al. 2007). There was, however, a gender gap in
compensation among the larger group of non-CEO top
executives in this study.
Another problem with hourly pay as an indicator of the
wage gap is overtime pay. In some jobs, workers who put in
more than their normal number of hours per day or week or
who work on holidays are paid at a higher rate than their
usual hourly wage. Recent data from Great Britain show that
24.1 % of men, but only 12.7 % of women who work fulltime take home overtime pay (Office for National Statistics
2010b). In the United States, workers covered by the Fair
Labor Standards Act are entitled to time and a half for the
Sex Roles (2013) 68:169–185
extra hours if they work more than 40 hr per week. (U.S.
Department of Labor 2008). However, union-negotiated
overtime rates for skilled jobs are sometimes higher—and,
traditionally, men have been more likely than women to be
in jobs covered by such negotiations. Thus, men’s “extra”
hours are paid more highly than women’s (Brereton 1990;
Grimshaw and Rubery 2001). In fact, data from the U.S.
Bureau of Labor Statistics (2010, Table 5) shows that,
among individuals working more than 40 hr per week, the
gender pay gap increases with the number of hours worked
at the primary job: among those working 41 to 44 hr per
week, women’s weekly earnings are 92 % of men’s, whereas
among those working 60 or more hours per week, women’s
weekly earnings are only 82 % of men’s. In making this
comparison, the reliance on usual hourly wages greatly
underestimates the wage gap.
Suppose, however, that we accept hourly pay as the best
indicator for assessing the discrepancy between the earnings
of women and men. How is such an hourly rate computed
for all those people whose compensation is not defined as an
hourly wage? One approach is to assume a 40-hour week
and divide weekly compensation by that amount. As noted
above, however, 40 hr may not be the correct estimate for
many workers. Another approach is to take employees’ selfreports of total compensation and divide by their selfreported number of hours worked to obtain a measure of
work volume. Using this approach, Drolet (2001) found that
men full-time workers reported working an average of
43.1 hr per week in 1997, whereas women reported an
average of 39.0 hr. Controlling for work volume, Drolet
found, reduced the apparent size of the 1997 gender wage
gap from 32 % to 19.6 %. However, Drolet does not provide
evidence as to whether w …
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