EARNINGS INEQUALITY AMONG WOMEN: DOES MARRIAGE MATTER?
A Thesis by
Sabina Thapa
B.A., Wichita State University, 2008
Submitted to the Department of Sociology
and the faculty of the Graduate School of
Wichita State University
in partial fulfilment of
the requirements for the degree of
Master of Arts
May 2012
© Copyright 2012 by Sabina Thapa
All Rights Reserved
EARNINGS INEQUALITY AMONG WOMEN: DOES MARRIAGE MATTER?
The following faculty members have examined the final copy of this thesis for form and
content, and recommend that it be accepted in partial fulfilment of the requirement for the
degree of Master of Arts with a major in Sociology.
_______________________________
Twyla Hill, Committee Chair
_______________________________
Jodie Hertzog, Committee Member
_______________________________
Carolyn Shaw, Committee Member
iii
DEDICATION
To my parents Ramesh Kumar Thapa and Shyama Thapa
iv
ACKNOWLEDGEMENT
I owe a deep debt of gratitude to Dr. Twyla Hill for her constant encouragement, guidance
and valuable supervision. I would like to express my sincere indebtedness to Dr. Jodie Hertzog and
Dr. Charles Koeber for constant mentoring, Dr. David Wright and Dr. Ron Matson for
encouragement, and Dr. Carolyn Shaw for comments and suggestions. I am grateful to my parents for
continuous inspiration, love, and support they have been providing me and bringing me up to this
stage. Therefore I would like to dedicate this thesis work to my parents. I am also grateful to my
husband for being so much supportive to my study. Needless to say, I alone am responsible for any
deficiencies that may have remained in this study.
v
ABSTRACT
This study aims to investigate the effect of marriage on women’s earnings. Using
Current Population Survey (CPS) 2010 data, three sets of hypotheses are tested to address the
effect of individual level factors, structure level factors and gender/race factors. The results
suggest that education, experience, level of occupation, and size of business, among others,
are the important factors explaining earnings inequality among women. Marriage has a
significant effect on women’s earnings and married women have consistently higher income
than unmarried women. Some interesting and striking results of this study hold significant
sociological and policy importance.
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TABLE OF CONTENTS
SECTION
Page
1.
INTRODUCTION
1
2.
LITERATURE REVIEW
2
2.1 Individualist Approach
2.1.1 Human Capital Theory
2.1.2 Selectivity Hypothesis
2.1.3 Productivity Hypothesis
2.2 Structural Approach
2.2.1 Dual Economy Theory
2.2.2 Segmented Labor Market Theory
2.3 Gender Model
2.4 A Composite Model and Research Hypotheses
3
3
4
5
7
8
9
11
15
RESEARCH METHODOLOGY
17
3.1 Data and Sample
3.2 Variables
3.2.1 Dependent Variable
3.2.2 Independent Variables
3.3 Methods of Analysis
17
18
18
19
21
RESULTS AND DISCUSSION
21
4.1 Univariate and Bivariate Results
4.2 Multivariate Results
4.2.1 OLS Regression Results
4.2.2 Comparison of Models
4.2.3 Partitioning of Variance
4.3 Discussion
4.3.1 Individual Level
4.3.2 Structural Level
4.3.3 Gender/Race
21
26
26
27
30
31
31
32
34
CONCLUSION
35
5.1 Implication
5.2 Limitations
36
36
3
4.
5.
REFERENCES
38
APPENDIX
43
vii
1.
Introduction
Over the last several decades, there has been a significant shift in gender-role attitudes
about women’s role in household work and childrearing, and involvement of women in the
labor force. The shift towards more egalitarian gender-role attitudes has helped women to
pursue higher education and better their career opportunities (Fan and Marini, 2000). The
increase in the age of first marriage of women and the tendency of increasing cohabitation
indicate that women these days are foregoing or delaying marriage (Fan and Marini, 2000;
Waite, 1995). However, a persistent question arises - does foregoing or delaying marriage
provide economic benefits to women or does marriage improve women’s earnings? Is there
any wage gap between married and unmarried or single women1? Becker (1985) states that
married men will have higher earnings than unmarried or single men because of the
comparative advantage of marriage available to married men. But the question still remains
as to whether this premium applies to married women.
While the effect of marriage on earnings has received considerable attention from
sociologists, the focus has been on the marriage premium for men. Empirical studies often
find higher wages for married men than unmarried or single men (Chun and Lee, 2001;
Korenman and Neumark, 1991). But the theoretical underpinnings and empirical analysis of
earnings differences between married and unmarried or single women has not received
considerable attention. Some earlier studies focused mainly on gender role specialization, the
motherhood penalty and/or racial differences. For example, Hill (1979) reported that married
women have weaker labor force attachment and greater absenteeism than single women.
Similarly, women with children have had lower wages than childless women (Council of
Economic Advisors, 1998). Alon and Haberfeld (2007) reported that minority women earn
1
In this study, ‘unmarried’ women and/or ‘single’ women means ‘never married’ women.
1
less than White women. A few studies have provided evidence regarding the effect of
marriage on women’s earnings. For example, Waite (1995) states that Black women enjoy a
marriage premium of about three percent, whereas White women experience a penalty of
about four percent. Korenman and Neumark (1992) and Toutkoushian (1998) in their studies
did not observe a significant marriage premium for women. Furthermore, Correll, Benard and
Paik (2007) state that mothers experience disadvantages in the workplace in addition to those
commonly associated with gender and employed mothers suffer a per-child wage penalty of
approximately five percent (Budig and England, 2001). However, still there is a lack of a
detailed study about the effect of marriage on women’s earnings considering other individual
level, structural level, and race and ethnic variables. To this end, this thesis uses the 2010
Current Population Survey (CPS) to examine the effect of marriage on women’s earnings.
This thesis is organized as follows: with this introduction, Section two presents a
review of theories on the wage gap which will be used to construct a model and hypotheses
that will be tested in this research; Section three explains the data and methodology; Section
four presents the univariate, bivariate, and multivariate results and discussions. Finally,
Section five concludes the thesis.
2.
Literature Review
There are various theoretical explanations for earnings differences which can be
grouped into three basic approaches – individualist approach, structuralist approach and
gender approach. The individualist approach explains the earnings gap based upon the
individual level characteristics such as education and work experience, whereas structuralist
models explain the earnings gap based on occupational roles. Furthermore, the gender
models explain the earnings gap based on sex, race and/or ethnicity. Though these models
2
generally explain the earnings gap between married and unmarried men, these models have
some relevancies while explaining the earnings difference between married and unmarried
women. In the following sections I have reviewed these models and attempted to connect
those models with potential earnings gaps between married and unmarried or single women.
2.1
Individualist Approach
The individualist approach describes sociology as a study of individuals in a social
setting where society is regarded as the sum total of the individuals living in the society
(Mayhew, 1980). Hence, individual characteristics or human motives are important while
studying social behaviors (Mayhew, 1980; Reskin, 2003). In this context, individual models
state that earnings difference is the effect of individual level characteristics such as
productivity and selectivity (Becker 1985; Nakosteen and Zimmerman, 1997). Within the
Individualist approach there are three major theoretical explanations for income (wage)
inequality. They are Human Capital Theory, Selectivity Hypothesis and Productivity
Hypothesis which are explained below.
2.1.1 Human Capital Theory
Human Capital Theory advanced by Becker (1985) states that individuals are rational
beings. Therefore, they make choices to invest in human capital (i.e. education and training)
in order to increase their productivity in their jobs thereby future earnings. Individuals with
higher productivity are rewarded with higher pay (i.e. those who have invested in human
capital will receive higher wages because wage is the reward given for the use of labor
productivity) (Becker, 1985). Similarly, it is assumed that as the number of years in work
increases, it increases the level of skill through experience. Hence more experienced workers
will be more productive and will have higher pay.
3
While linking marital status with earnings, the Human Capital Theory states that the
attitude of investment in human capital by married and unmarried or single men varies.
Married men tend to spend more time and money to gain market specific knowledge and skill
because their wives allocate more time and energy to household responsibilities (Becker,
1985; Gray, 1996; Korenman and Neumark, 1991). Waite (1995) further argues that married
men have lower rates of alcohol abuse, lower mortality rates and higher sexual satisfaction. It
is also argued that employers perceive married men as more committed, motivated and
productive (Becker, 1985). As a result married men receive higher wages in comparison to
unmarried men. However, this may not be the case for women. Unmarried women tend to
invest more in human capital for enhancing their level of education, skills and knowledge for
career development in comparison to married women because married women prioritize
family commitment over career commitment (Stickney and Konrad, 2007). Married women,
in general, prefer part-time work after having a child particularly until the child goes to
school (Treas and Widmer, 2000). This makes married women less experienced and less
productive at the work place. As level of education and work experience are important factors
responsible for wage differences of women (Browne and Askew, 2005), married women tend
to earn lower wages than unmarried women.
2.1.2 Selectivity Hypothesis
The second explanation for the marriage premium is put forward by the Selectivity
Hypothesis, which states that men with higher earnings, better and more secure jobs, and
stronger economic perspective are likely to marry because they are valued more in the labor
and marriage market (Becker, 1985; Ginther and Zavodny 2001; White and Rogers, 2000).
Nakosteen and Zimmerman (1997) interpret the observance of higher wages for married men
as an effect of the mate selection process done by women. Therefore, men with higher
4
earnings are more often selected for marriage. For men, their likelihood of marriage depends
on their earnings and some personal traits (Chun and Lee, 2001). The attributes that lead to
success in the workplace (responsibility, honesty, dedication, etc.) overlap with the attributes
that lead to success in finding and keeping a spouse (Becker, 1985; Ginther and Zavodny,
2001). As men with higher income are also less likely to divorce than men with lower income
(Waite and Gallagher, 2000), males with higher incomes are most likely to get married and
when married, less likely to divorce, therefore are more likely to have higher earnings than
unmarried men.
However, the Selectivity Hypothesis is silent regarding the link between personal
attributes and a marriage premium for women. If high earnings or financial security increases
the value of men in marriage market, then women with high earnings or with higher
productivity potential are likely to defer to marry or prefer not to marry. Women who are able
to support themselves through their own work (or through welfare benefits) will feel less
pressure to marry for economic need and may choose not to marry or to form families
through cohabitations or non-marital childbearing (Cherlin, 1992 cited in White and Rogers,
2000). They put education advancement and career development in the forefront in order to
establish themselves firmly in a career (Lauer and Lauer, 2011).
2.1.3 Productivity Hypothesis
The productivity hypothesis is based on the role of traditional household specialization
or division of labor by sex where men are assumed to join the workforce whereas women are
assumed to go into domestic labor. Therefore, men are regarded as more productive in the
labor market as they spend more time on their career and labor market goals (Chun and Lee,
2001). Furthermore, married men compared to unmarried or single men have more
commitment to their jobs, they are seldom fired and frequently promoted, and in addition
5
receive a larger share of the profits distributed according to individual performance (Becker,
1985). Becker (1985) states that men have a competitive advantage in the labor market as
women are in-charge of household responsibilities as a result of the division of labor. In this
regard, the unmarried or single men have to engage in both the labor market and household
work, which causes them to exert more time and energy. Waite (1995) also suggests that
married men are likely to benefit from both economic and social benefits such as less alcohol
abuse, less stress, longer life expectancy, and higher income and wealth.
However, does marriage increase the productivity of women? The existing literature on
productivity hypothesis is less responsive to this question. Some empirical studies provide
some explanations to this question. Brown and Dickman (2010) in a study of emerging
adults’ work and family commitments reported that college men and women are equally
committed to work and family. Similarly Stickney and Konrad (2007) found that women
with egalitarian attitudes (career-oriented and independent) have significantly higher earnings
than women with traditional attitudes (family oriented and dependent on their men). Despite
the increasing labor force attachment of women and increasing egalitarian gender role
attitudes, marriage and/or motherhood has significant impacts on productivity and therefore
on earnings of women. According to Treas and Widmer (2000), married women are likely to
prefer to stay at home or work part-time once they have preschool children and they prefer to
go back to full time work only after the children leave home. This discontinuation at work
and preference for part-time work may reduce the productivity of married women (Corell,
Benard, and Paik, 2007; Peterson and Morgan, 1995).
The literature on the motherhood penalty also indicates that mothers looking for
employment are less likely to be hired, are offered lower salaries and are perceived as being
less committed to a job than fathers or women without children (Correll, Benard, and Paik,
6
2007). The motherhood penalty is often linked with decreased productivity of working
mothers (Budig and England, 2001; Correll, Benard, and Paik, 2007). Working pregnant
women face further disadvantage regarding promotions and discrimination may lead to be
fired (Byron, 2010). These circumstances suggest that pregnant women and mothers tend to
have lower earnings than non-mothers.
In sum, the above explanations of individual models state that individual level
characteristics, like level of education and training, work experience and behavioral traits are
the factors influencing earnings inequality between married and unmarried women.
Unmarried or single women focus on education and career, so have higher human capital
whereas married women who may need to give time for family, and working mothers may
have to face a motherhood penalty. Therefore, unmarried (non-mothers) women are likely to
have higher earnings than married women and working mothers.
2.2
Structural Approach
The structural approach assumes that individuals are shaped by the bigger structures of
society. Structural theories then, focus on the interrelationships between the larger social
structures or institutions of the society, and how these structures and institutions affect
individuals in the society (Ritzer and Goodman, 2004). However, in the structural approach,
the individual is not the subject matter of analysis in both research and theory construction.
Thus the structural approach argues that earnings inequality is a structural phenomenon, and
it is determined by organizations and organizational structure. The job positions in
organizational structure are based on an organizational hierarchy with owners at the top level,
managers at the middle level, and workers at the bottom or floor level. In this structural
setting people in higher levels/positions receive higher wages than people in lower
levels/positions. Furthermore, Coverdill (1988) states that wages are affected by the structure
7
of the market where the company is operating. There are two models of the structural
approach that explain the wage difference – Dual Economy Theory and Segmented Labor
Market Theory, which are explained below.
2.2.1 Dual Economy Theory
The dual economy theory assumes that the economy is not homogenous and, therefore,
can be divided into a monopoly sector and a competitive sector (Gordon, Edwards, and
Reich, 1982). Sorting of a particular firm in either of the categories depends upon the nature
of business, size of the firm, industrial location, and market concentration (Tolbert, Horan,
and Beck, 1980). In the monopoly sector or concentrated markets, the company will have
high profit, therefore employees in the monopoly sector earn higher wages, have better
benefits, more opportunities for mobility, and greater work satisfaction than employees in the
competitive sector (Reid and Rubin, 2003). In addition, the monopoly sector requires a stable
and trainable workforce, which means education and work experience are the important
aspects of gaining entry into the monopoly sector (Coverdill, 1988). In contrast, the
competitive market contains small firms with limited markets, low wages, little or no training
and skills, minimal job security, and limited career development opportunities (Reid and
Rubin, 2003). Hodson (1983) further states that monopoly firms have a higher rate of
unionization than competitive firms which may lead to higher wages and greater benefits
being provided to workers. Therefore, the gender wage gap is the result of the
disproportionate allocation of women to peripheral jobs (Coverdill, 1988).
Linking the above theoretical explanations with the marriage premium, as married men
are preferred by employers, married men are more likely to be attracted to and employed by
the monopoly sector than unmarried men. The attraction of married men in the monopoly
sector is also linked with prestige, higher pay and benefits. According to Coverdill (1988),
8
monopoly markets may have some institutional barriers created through job specification,
recruitment process, career development, etc. In contrast in competitive markets, these
institutional barriers are less. Therefore women get more job opportunities in the competitive
markets than in the monopoly markets. The dual economic theory also suggests that the
greater labor force attachment of women in competitive markets is the result of
discrimination and gender constraints, not because women have lower human capital
(Coverdill, 1988). In addition, with respect to the labor force attachment of married and
unmarried women, unmarried women are more likely to get jobs in the monopoly sector than
the married women because of discrimination and institutional constraints in the labor
markets (Coverdill, 1988).
According to O'Connor (O’Connor, 1973, cited in Coverdill, 1988), the supply of labor
in competitive industries is inflated by workers such as married women, students, and retired
workers who want, and will accept lower wages to obtain, irregular work. Because married
women are generally involved much more deeply than unmarried women in family activities
such as childcare and housework, they may be willing or be forced to forego the wage
advantages of monopoly sector workplaces in order to work close to home, have flexible
work hours, and so on (Coverdill, 1988).
2.2.2 Segmented Labor Market Theory
Another structural explanation for earnings inequality is the Segmented Labor Market
(SLM) theory. The theory states that there are different job markets and different
professionals work in these different job markets. These different job markets are often
segmented based on occupation, geography and nature of the industry. The occupational
labor markets arise from the division of labor, increasing differentiation and specialization.
Since each occupational labor market requires specific skills and knowledge, the workers are
9
less likely to switch into another occupational labor market. It also applies in geographic
market segments and industry-wise market segments. Therefore, this theory suggests that a
wage is directly related to professions and positions in the labor market, not to the workers’
attributes (Weitzman, 1989). The employees in so called white-collar professions and whitecollar positions are likely to have higher earnings than employees in so called blue-collar
professions and blue-collar positions. This theory further segregates labor markets into
primary labor markets and secondary labor markets. The jobs in primary labor market are
characterized by higher wages, better working conditions, more stable employment, and
higher return to human capital (Weitzman, 1989). However, importantly, the SLM theory
states that earnings in the secondary labor market, unlike in the primary market, are not
related to productivity (Boston, 1990).
The empirical literature shows that pay differs significantly for different occupations.
The white collar occupations or positions pay comparatively higher wages than the blue
collar occupations or positions. The gender wage gap is often attributed to this occupational
segregation (Petersen and Morgan, 1995).The occupations that have more female employees
are lower paid than the occupations that have more male employees (Baron and Newman,
1990). In other words, women have disproportionately worked in occupations such as
teachers, nurses, secretaries, and retail sales clerks that pay relatively low wages and men
have disproportionately worked in occupations such as executives, managers, doctors,
lawyers, engineers, and scientists that pay comparatively high wages (CONSAD, 2009). In
addition, within the white collar professions there are gender wage gaps. The empirical
literature on white-collar professions such as lawyers (Noonan, Cocoran, and Courant 2005),
physicians (Boulis and Jacobs 2003), scientists (Prokos and Padavic 2005), financial
professionals on Wall Street (Roth 2003), and faculty in higher education (Toutkoushian
10
1998) indicate that women are earning less than their male counterparts. Furthermore, there is
a wage gap between women working in white-collar jobs and blue-collar jobs. The size of
this disparity on women’s earning is higher than the disparity that exists between men
working in white-collar and blue-collar jobs (Petersen and Morgan, 1995). The disparity
emerges quickly with a small gender wage gap among college graduates then widens over
time as women’s professional careers progress (Peterson and Morgan, 1995).
The structural explanations of segmented labor theory are silent about the effect of
marriage on women’s earnings. But as stated by Boston (1990), having never been married is
one of the most significant factors in determining the likelihood of upward mobility from
blue-collar to white-collar positions and professions because never married women are
assumed to be more committed to their work and have less job turnover. Therefore, the
unmarried women may have higher earnings than the married women.
To sum up, the structural models suggest that occupational level and type, market
structure and labor market conditions are responsible for earning differences. Unmarried
women are likely to enter into white-collar professions and get jobs in monopoly markets
whereas married women are likely to enter into blue-collar profession and get jobs in the
competitive market. These disparities in labor markets and occupational segregations result in
higher earnings for unmarried women than married women.
2.3
Gender Model
In addition to individual and structural approaches, gender theories also explain the
earnings inequality between men and women. The gender models are based on gender
discrimination at work settings and gender role attitudes in family settings.
Regarding gender discrimination within work settings, gender models state that the
gender wage gap is caused by discrimination in several processes. Petersen and Morgan
11
(1995) outline three major forms of discrimination that cause the gender wage gap. First,
women are differently allocated to occupations and establishments through differential access
to job markets which is often termed as ‘allocative discrimination’. Women face limited
access to attractive positions within a work organization at the time of entry and/or at the time
of promotion (Hultin and Szulkin, 1999). Women receive less preference for high pay and
high ranking positions (Beggs, 2001). “Allocative discrimination thus denotes unequal
treatment of women in decisions about recruitment and promotion that in turn lead to women
generally being employed in occupations, establishments, and jobs with relatively low
earnings levels” (Hultin and Szulkin, 1999, pg. 455). Among working women, married
women and working mothers face more allocative discrimination than unmarried and or nonmothers.
Second, the occupations that are primarily held by women are paid lower wages than
the similar skilled required occupations that are primarily held by men. This type of
discrimination is called ‘valuative discrimination’ or ‘evaluative discrimination’. Although
the value of a job should be assessed by the different aspects of the work content that are of
importance in the wage-setting process such as demands of qualifications and responsibilities,
women are devalued at work by employers or perspective employers assuming women are
less productive and less committed than men (Hultin and Szulkin, 1999; South and Spitze,
1994). Therefore, women earn less than men. In addition, the motherhood penalty in terms of
pay and promotion is an established example of discrimination and institutional constraints
responsible for earnings gap between working mothers and non-mothers.
Third, women get lower wages than men within the same occupation and within the
same establishment. This form of discrimination is called ‘within-job wage discrimination’.
Although this kind of discrimination at work is legally prohibited, the increasing trend of
12
discrimination charge filings over last decade (EEOC, 2009, cited in Curtis, 2010) indicates
the presence of ‘within-job wage discrimination’ at work. There is lack of transparency on
the recruitment and promotion process. The literature suggests that pregnant women and
working mothers face even more “within-job wage discrimination’ than single and nonmothers (EEOC, 2009, cited in Curtis, 2010).
Besides the above explanations, in a family setting, gender role attitudes play an
important role in women’s participation in the labor force. Traditionally, women are
responsible for household work whereas men are responsible for external affairs like jobs and
earnings. These family expectations influence the choices’ women can make. This separation
of women from the labor force systematically decreases women’s earnings (South and Spitze,
1994). Women’s greater participation in household work than in labor force attachment is an
important factor in the gender wage gap (Shelton and Firestone, 1989). However, recent
studies show increasing egalitarian gender role attitudes in the family settings and women
have an increasing trend of career orientation and labor force attachment (Stickney and
Konrad, 2007).
Gender theories can also be used to explain the earning differences between married
and never married women. Corell, Benard, and Paik (2007) state that mothers experience
disadvantages in the workplace in addition to those commonly associated with gender. These
women engage more in household work than non-mother women because of childrearing
responsibilities. Furthermore, mothers need to give more time to their children, and young
children (under 6) demand more time from mothers than older children. Literature suggests
that a motherhood wage gap exists and factors such as reduced investment in human capital
by mothers, lower work effort by mothers, and discrimination against mothers by employers
are responsible for lower earnings of mothers compared with non-mothers (Budig and
13
England, 2001; Corell, Benard, and Paik, 2007). In contrast, single non-mother women and/or
unmarried women can continue their job and can further invest in human capital for career
growth. Never married women are often career oriented. The continuation at work further
increases the productivity. These gender based attributes, functions and responsibilities may,
therefore, lead to lower pay for married women/working mothers than unmarried or single
women without children.
In addition to gender discrimination, racial discrimination at work is another important
factor for understanding gender earnings inequality (Avery, 2003). The forms of gender
discrimination are also applicable in racial discrimination. Traditionally, disadvantaged
groups such as Blacks and Hispanics have lesser wages than their White counterparts.
Previous studies suggest that Whites have higher earnings than minorities (Blinder 1973;
Blau and Kahn, 2002). This differential in earnings is often linked with racial differences in
human capital accumulation (Darity, 1982). For example, as described by human capital
theories, earnings are tied to human capital acquired by an individual. According to this
argument, Blacks tend to acquire or accumulate less human capital therefore they are paid
less (Darity, 1982).However, Blacks and Hispanics have less access to accumulate human
capital because of socio-economic circumstances. Even if Blacks and Hispanics have
competitive human capital than Whites and Asians, Black and Hispanics have less access to
better jobs and face discrimination in pay and promotion. Gloria and Hird (1999) found that
White students have significantly higher career success than racial and ethnic minority
students. Furthermore, Alon and Haberfeld (2007) state that there is a persistent racial and
ethnic wage gap among women. These wage gaps are even larger among women with no
college education. The effect of marriage on earnings differs for racial groups. Surprisingly,
Waite (1995) states that Black women enjoy a marriage premium of about three percent
14
whereas White women experience a penalty of about four percent. In sum, assessing gender
and racial discriminations is important to understanding income inequality.
2.4
A Composite Model and Research Hypotheses
The individual models state that individual characteristics such as education and skill,
work experience, and behavioral traits (honesty, commitment, etc.) are important factors
explaining wage differences whereas structural models state that structural variables like job
position (rank), occupation, and market structure are important factors to explain this wage
gap. The gender models further state that the bias towards particular gender, race or ethnicity,
and marital status affect wage differences. Since these models individually are not sufficient
to fully explain the wage gap between married and unmarried women, this thesis attempts to
synthesize individual models, structural models and gender models, and proposes an
alternative model for earnings inequality.
The composite model presented in Figure 1 below shows that individual level
characteristics like level of education, and work experience determine the level of earnings of
individuals. This relationship is further affected by gender issues like race, ethnicity, marital
status, etc. as explained by gender theories. The structural variables like occupation level,
industry, etc. determine the level of earnings as explained by structural theories. The
structural variables may also influence the relationship between individual level
characteristics and level of earnings. Furthermore, as explained by gender theories, gender
issues also influence the structural variables thereby the level of earning.
As explained by individual theories, individuals having higher education qualification,
and more work experience will have higher earnings than individuals with lower education
qualification and less work experience. Similarly, as explained by structural theories,
individuals working in a higher position in the job hierarchy and working in white-collar
15
professions will have higher earnings. Furthermore, as stated by gender theories, unmarried
women will attain higher job positions in organizational hierarchy. When mapping these
relationships, it is hypothesized that unmarried women will be found to invest more in human
capital, be sorted into higher positions and white-collar professions, and have less household
responsibilities. Therefore, unmarried or single women will have higher earnings than
married women.
Figure 1: A Composite Model
Independent Variables
Dependent Variable
Individual level variables
Education
Work experience
Structural variables
Job position
Job occupation
Wage gap (women)
Gender/Race variables
Gender
Race
Marital status
The three sets of hypotheses are formulated as follows and will be tested in this thesis.
Hypothesis 1a: Net of other factors, with an increase in level of education, there will be an
increase in earnings.
Hypothesis 1b: Net of other factors, with an increase in number of years at work (measured
by age), there will be an increase in earnings.
Hypothesis 2a: Net of other factors, women in white-collar positions (in organizational
structure) will have higher earnings than that of blue-collar positions.
16
Hypothesis 2b: Net of other factors, women in high-skill occupations will have higher
earnings than that of low-skill occupations.
Hypothesis 2c: Net of other factors women working in large size organizations will earn
more than women working in small size organizations.
Hypothesis 2d: Net of other factors women working in government sector will earn more than
women working in private sector.
Hypothesis 2e: Net of other factors, women in the Midwest region will earn less than women
in other regions.
Hypothesis 2f: Net of other factors, women working full time full year will earn higher than
women working part time or part year.
Hypothesis 3a: Net of other factors, unmarried women will have higher earnings than
married women.
Hypothesis 3b: Net of other factors, women without a child under 6 will have higher earnings
than women with a child under 6.
Hypothesis 3c: Net of other factors, minority women will earn less than White and Asian
women.
3
Research Methodology
3.1
Data and Sample
This study uses the Current Population Survey (CPS) 2010 data set (Bureau of Census,
2010). The CPS is a random, representative sample survey of the American population
conducted by US Bureau of Census for the Bureau of Labor Statistics. This survey mainly
focuses on factors such as individuals, families and households, employment, salary/wages,
occupations, working hours, education level, etc. The CPS is comprised of a nationally
17
representative sample. There are a total of 158,879 observations in the CPS data set.
However, as per the objective of the study some sample restrictions are being imposed.
The sample restrictions imposed are as follows: only individual level data is selected; only
the female respondents (age range 18 through 64) are selected and responses from noncivilians and military spouses are excluded; only the female respondents who are ‘currently
married’ and ‘never married’ are selected, and, only those respondents with annual earnings
greater than $258 and less than $200,000 are selected. To make the sample more
representative of the true population, a relative weight has been applied to all cases. The
relative weight index is created by dividing standard weight by mean of standard weight.
Therefore the final sample consists of 32,862 cases comprising 64.75 percent married women
and about 35.25 percent unmarried women. Of 32862 total cases, 11734 cases (35.7 percent)
are from the South region, 7716 cases (23.5 percent) from the Midwest region, 7243 cases
(22.1 percent) from the West region, and 6169 cases (18.8 percent) from the Northeast
region. The regional sample distribution is similar to the total population distribution.
3.2
Variables
Based on the literature surveyed, data available in the CPS data set, and the scope of the
study the following variables are used in this study.
3.2.1 Dependent Variable
The dependent variable of this study is annual income or earnings which is measured
by total annual wages (as available in CPS data set) in raw U.S. dollars. This is an interval
level variable with values ranging from $258 to $200,000, exclusive.
18
3.2.2 Independent Variables
To capture the theoretical explanations posited by three major theoretical models
(individual models, structural models, and gender models), three sets of independent
variables are used in this study which are explained below.
Individual level variables: There are three individual level variables – level of
education attainment, age, and agecentered which are considered in this study. The CPS
survey originally has 17 categories for the “level of education attainment” variable. For
convenience, this variable is grouped into 5 categories (“less than high school diploma”,
“high school diploma or equivalent”, “some college degree”, “college/bachelor degree”, and
“advance degree”) in ordinal level. Another individual level variable is “age” which is a
proxy measure for work experience. This variable is a ratio level variable value ranging from
18 to 64 inclusive. The sample restriction on this variable is guided from general understating
about the age for entering into the formal labor force and retiring from the labor force.
Furthermore, as suggested by the literature, there is a non-linear relationship between age and
earnings. Therefore a new “Agecentered” variable is created to capture this non-linear effect
by standardizing and squaring the variable “age”.
Structural level variables: There are five major structural variables used in this study
namely size of the business, level of occupations (4 levels occupations), working status (full
time/part time), employer’s type (government/private), and geographic region. The size of
business is represented as the number of workers in the company. For methodological
purpose, the size of business is divided into small business (employees 1-99), medium
business (employees 100-499), and large business (employees above 500). To capture the
effect of size of business on earnings, two dummy variables are created as “medium
business” and “large business” considering “small business” as a reference category, that is,
19
“medium business” takes the value 1 if size of business is medium otherwise takes a value of
0. Similarly, in the “large business” dummy variable, large business takes the value 1 if the
size of business is large otherwise takes a value of 0.
To capture the effect of 4 levels of occupation, three dummy variables are created
considering “Blue collar low skill” job as a reference category. The first dummy variable
“White collar high skill” takes value 1 if the level of occupation is white-collar high skill,
otherwise takes value 0. Similarly another dummy variable “White collar low skill” takes
value 1 if the level of occupation is white-collar low skill, otherwise takes value 0. The next
dummy variable “Blue collar high skill” takes value 1 if the level of occupation is blue-collar
high skill, otherwise takes value 0. Similarly, for the working status of women, a dummy
variable is created that takes value 1 if she is working full-time full-year and takes value 0
otherwise. For employers’ type, a dummy variable is created that takes value 1 if respondent
is working in government organization (federal, state, and local) and takes value 0 if working
in private sector. Finally, to capture the effect of geographic region on earnings, a dummy
variable is created which takes value 1 if respondent is from the Midwest region and takes
value 0 otherwise.
Gender/Race variables: As explained by gender theories, gender/race variables such as
gender, marital status, having children, and race and ethnicity are important factors
explaining earnings inequality. There are three gender/race variables considered in this study.
They are marital status of women, having a child age under 6, and race/ethnicity. A dummy
variable that takes value 0 if the woman is ‘never married’ and value 1 if she is married.
Another dummy variable is created that takes value 0 if a woman doesn’t have a child age
under 6, and value 1 if a woman has children age under 6. For race/ethnicity, the available
race/ethnicity variable is transformed into minority and nonminority (binary) based dummy
20
variable. The Whites and Asians are considered as non-minority and Blacks, Hispanic and
‘Others’ are considered as minority. This dummy variable takes value 0 if respondent is
White and/or Asian and takes value 1 otherwise. The motive behind putting White and Asian
in the same reference category is both of these racial categories have similar earnings.
3.3
Methods of Analysis
In this study the analysis of data is undertaken at three stages. First univariate analyses
are performed where frequency, mean, median and standard deviation (where applicable) are
computed and analyzed. At the next stage, bivariate analyses are performed between/among
dependent and independent variables and the bivariate relationships are tested by t-tests, and
ANOVA tests. At the final stage the multivariate analyses are performed developing three
multiple regression models: ‘total model’, ‘unmarried women only model’ and ‘married
women only model'. Ordinary Least Square (OLS) method is used to estimate the parameters.
4.
Results and Discussion
4.1
Univariate and Bivariate Results
Table 1 and Table 2 present univariate and bivariate results. The average annual income
of women in the sample is about $ 29600.12 with standard deviation of $ 21071.97. The
average annual income of married women is $32811.95 and unmarried women is $23699.86.
The t-test for the mean difference of annual income of married and unmarried women has tvalue of -39.54 (p<0.001, df=26103). This suggests that the null hypothesis of equality of
mean of annual income of married and unmarried women is rejected at 0.001 level of
significance. Therefore, married women have significantly higher earnings than unmarried
women.
21
Within individual level factors, about two-thirds women in the sample have less than a
bachelor’s degree and about 23 percent have bachelor’s degree and only about 11 percent
have advance degrees. This pattern is similar within married and unmarried sub-sample
groups. The average annual income of women with less than a high school diploma is
$15381, with a high school diploma is $22569, with some college is $25180, with a
bachelor’s degree is $37354, and with an advanced degree is $52692. The One-way ANOVA
test results for the equality of average annual income with different levels of education
attainment suggest that level of annual earnings differs significantly across the different
levels of education attainment (F =2476.8, p<0.001, df= (4; 32856)). Furthermore, posthoc
analysis (Scheffe test) indicates that there are no homogeneous subsets. The results suggest
that level of income increases as level of education attainment increases.
The average age of women, in sample, is about 38.1 years with standard deviation of
12.7 years. The average age of unmarried women is 29.4 years whereas the average age of
married women 42.3 years. Therefore, unmarried women are younger than married women,
and therefore, married women could potentially have greater work experience than unmarried
women (t= -106.3, p<0.001, df=24208).
Based on four levels of occupations, about 43.9 percent of women are working in
“white-collar high-skill” occupations and about 33.3 percent are working in “white-collar
low-skill” occupations. Therefore, about 77.2 (=43.9+33.3) percent of women are working in
“white-collar” level occupations, that is, the majority of working women are working in
“white-collar” level occupations. There are very few, only about 3.2 percent of women
working in “blue-collar high-skill” level and about 19.6 percent women are working in “bluecollar low-skill” level occupations. The pattern of distribution among married women is
similar to the full sample. However, in the case of unmarried women, about 35 percent work
22
in “white-collar high-skill” level occupations and about 26 percent women work in “bluecollar low-skill” level occupations.
The average earnings of women working in White-collar high skill occupations is
$39354, White-collar low skill occupations is $25046, Blue-collar high skill occupations is
$27402, and Blue-collar low skill is $15882. The F statistic (ANOVA test) value is
2619.45(p<0.001, df = (3, 2849)) which indicates that the average earnings of women are not
equal for different levels of occupation. The posthoc analysis results indicate that the earnings
are significantly different across the different levels of occupations. Women working in
White-collar high skill occupations earn the highest income whereas women working in
Blue-collar low skill occupations earn the lowest income. Interestingly, women working in
Blue-collar high skill occupations earn higher income than women working in White-collar
low skill occupations.
The majority of women in the sample are working in large sized businesses in the
sample. About 51.7 percent are working in large sized organizations. About 34.4 percent
women are working in small sized organizations, with only about 13.9 percent are working in
medium sized business. The pattern is similar within sample sub-sets. The average earnings
of women for small business, medium business and large business categories are $23683,
$31403 and $33049 respectively. The F-test for the equality of average annual earnings of
women across the size of business they employed has F-statistic 719.87 (p<0.001, df= (2,
32858)). The significant F-value suggests that the average annual earnings of women are not
equal across size of businesses. The posthoc analysis suggests that the women’s earnings vary
by the size of business they work for. Specifically, the annual earnings of women increase as
the size of business increases.
23
On an average women work about 38 hours per week with a standard deviation of
10.4 hours. However some women work only about one hour per week and some women
work about 99 hours per week. About 60.4 percent women are working in a full time job
throughout the year whereas about 15.4 percent women are working in a part time job
throughout the year. About 12.6 percent of women work full time job partly throughout the
year and about 11.5 percent of women work a part time job partly throughout the year. The
first two categories suggest the continuous labor force attachment of women whereas the
latter two categories may suggest discontinuity of women in the labor force. Therefore, about
75.8 (=60.4+15.4) percent of women are attached to the labor force throughout the year. The
Chi-square test (χ2 =380.095, p<0.001, df = 3) suggests that the labor force attachment of
women is related to marital status of women. About 54 percent of unmarried women work
full time jobs throughout the year whereas about 60 percent married women work full time
jobs throughout the year. Similarly about 17 percent of unmarried women work part time
jobs throughout the year whereas about 15 percent of married women work part time jobs
throughout the year.
Within structural model factors, as shown in Table 2, there is earnings inequality
between women living in Midwest region and other regions (Northeast, South, and West).
The t-test of equality of average income of women between Midwest region and other regions
suggests that women in the Midwest region have lower earnings than women in other
regions. The t-value is 5.72 (p<0.001, df=32860). However, the Cohen’s d suggests that the
t-test result of significant difference is not meaningful.
In the sample, about 80 percent of women are working in private sector jobs whereas
about 20 (=2.2+6.3+11.6) percent of working women are working in government sectors.
The t-test result (t = -26.77, p<0.001, df=32280) for the equality of women’s earnings based
24
on the employers’ type (private sector and government sector) suggests that women working
in government sector jobs earn more than women working in the private sector. The annual
wages for women working in government sector is $35930.70 whereas for women working in
private sector is $28012.6.
For the gender/race model, about 80 percent of working women in the sample do not
have children under 6. The average annual income of women with a child under 6 is about
$28087 and women who do not have a child under 6 is about $29970 (t = 6.44, p<0.001,
df=32860). The t-test results indicate that women without a child have a higher annual
income than women with a child. However, in both cases Cohen’s d values suggest that effect
size is small. Therefore, this statistically significant difference is not meaningful.
Finally, the racial distribution of women in the sample is categorized into five
categories. About 68.3 percent of women in the sample are “White non-Hispanic”, about 12.7
percent are “Hispanic”, about 12.2 percent are “Black non-Hispanic” and only about 4.8
percent are “Asian”. There are also about 2 percent who are identified as “Other nonHispanic” women in the sample. When combined, there are about 73 percent ‘White, and
Asian” women and about 27 percent minority women in the sample. The t-test shows that the
annual income between minority women and non-minority (White and Asian women) is not
equal. The t-value is 26.7 (p<0.001, df = 32860). That means, minority women have
significantly lower annual earnings than non-minority (White and Asian) women. The
average annual income of minority women is $24935.40 whereas the average annual income
of non-minority women is $31315.80.
25
4.2
Multivariate Results
4.2.1 OLS Regression Results
A multiple regression was conducted to evaluate how well the individual level,
structural level and gender/race level variables predict the annual wages of women. When
the dependent variable, annual wages, was tested for normality, the null hypothesis of normal
distribution was rejected. But the sample contains more than 32000 cases and the test of
residuals suggests that this was not a problem. For the multicollinearity test, the value of VIF
was less than 10 and Tolerance value was greater than 0.1 for all the independent variables.
In addition, none of the independent variables were correlated over 0.70 with any other
independent variable. Therefore, multicollinearity is not a problem. Test of outliers were also
performed. The maximum found in the Mahalonobis distance test was 59.57 but the
maximum Cook’s distance test was less than 1 and central leverage value was greater than 3
times of average value but not greater than 0.03. There were only 213 cases as outliers which
are less than 5 percent of total sample size (32862 cases). So the outliers were not removed
from the sample. The OLS regression results are reported in Table 3.
As the effect of individual level variables on women’s earnings, for every one level
increase in educational attainment, annual wages increase by $6098.20. The level of
education attainment is measured in ordinal level. For each yearly increase in age, the level of
annual wages increases by $297.30. To capture the non-linear relationship between annual
wages and age, a new variable ‘Agecentered’ was created by standardizing and squaring age.
For each unit increase in the new centered age variable, the annual wages decrease by $12.80.
Both of the relationships between income and age are statistically significant at 0.001 level.
Regarding the structural variables, the women working in medium size organizations
earn $3250.40 more than women working in small size organizations. Similarly women
26
working in large size business earn $4899.10 than women working in small size business.
The women in white-collar high skill jobs earn $8308.2 more than women in blue-collar low
skill jobs. The women in white-collar low skill jobs earn $1495.10 more than women in bluecollar low skill jobs. Similarly, the women in blue-collar high skill jobs earn $3405.50 more
than women in blue-collar low skill jobs. The women who work full-time full year earn
$18422.40 more than women working in other reference work status categories. The women
working for government (federal, state, and local) organizations earn $2306.60 less than the
women working in the private sector. Similarly women in the Midwest region earn $1558.10
less than women from other regions.
Regarding the gender level variables, the married women have mean annual wages
$527.30 more than unmarried women. For women with a child under 6, the mean annual
income is $1666.80 more than for women without a child under 6. About the effect of
race/ethnicity, minority women earn $15917.20 on average less than White and Asian
women.
The adjusted R2of the model is 0.529 (F=2638.2, p<0.001), so that about 53 percent of
the variation in the annual wages is explained by these independent variables. The full-time
full year working status, level of education attainment, white-collar high skill jobs, age, and
large size business are the largest contributing variables to explain variation in annual wages.
The standardized coefficients for these variables are greater than 0.10.
4.2.2 Comparison of Models
The OLS regression is run separately for unmarried and married women. Both of the
models are statistically significant at 0.001 level. In the ‘Unmarried women only’ model, the
independent variables explain about 57.7 percent variation in unmarried women’s annual
wages whereas in the ‘Married women only’ model, the independent variables explain about
27
48.5 percent variation in married women’s annual wages. These variables have more
explanatory power for never married women than married women.
At the individual level, each one level increase in education increases annual wages by
$5154.80for unmarried women and by $6453.90 for married women. The modified Chow test
showed that these coefficients are statistically significantly different. Therefore married
women benefit more ($1299.10) from higher educational attainment than unmarried women.
However, for each additional year of age, unmarried women have mean annual wages
increase of $337.40 and married women have mean annual wages increase of $289.90. The
modified Chow test shows that these coefficients are statistically significantly different.
Therefore, unmarried women benefit more from experience (age) than married women. The
results could be reflective of the higher average age of married women than of unmarried
women and the non-linear relationship between age and earnings. For each unit increase in
the new centered age variable, annual wages decrease by $14.50 for unmarried women and
$12.10 for married women; however the modified Chow test showed that these coefficients
are not statistically significantly different.
At the structural level, unmarried women working in medium size organizations earn
$2388.60 more than unmarried women working in small size business whereas married
women working in medium size organizations earn $3699.00 more than married women
working in small size business. The modified chow test shows that these coefficients are
statistically significantly different. Similarly unmarried women in large size business earn
$2809.40 higher than unmarried women working in small size business whereas married
women working in large size business earn $6022.90 higher than married women working in
small size business. The modified chow test suggests that these coefficients are statistically
28
significantly different. Therefore, married women benefit more from working for larger
businesses.
For the effect of level of occupations, unmarried women working in white-collar high
skill jobs earn $7166.50 more than unmarried women working in the blue-collar low skill
jobs. This difference is higher for married women that is $9233.30. The modified Chow test
shows that the difference is statistically significant at 0.001 level. Similarly, unmarried
women working in white-collar low skill jobs earn $1020.40 more than unmarried women
working in blue-collar low skill jobs. In the case of married women, women in white-collar
low skill jobs earn $2287.40 more than women in blue-collar low skill jobs. The modified
Chow test shows that these coefficients are statistically significantly different. Moreover,
unmarried women in blue-collar high skill jobs earn $3692.40 more than unmarried working
in blue-collar low skill jobs whereas married women in blue-collar high skill jobs earn
$3423.30 more than married women in blue-collar low skill jobs. However, the modified
Chow test shows the coefficients are not statistically different. These results in different level
of occupations, therefore, suggest that married women benefit more than unmarried women,
at least in white-collar occupations, and women in high skill jobs are earning more than
women in low skill jobs.
The unmarried women who work full-time full year earn $16378.10 more on average
than unmarried women who work part-time or partly in a year whereas the married women
who work full-time full year earn $19546.30 more on average than unmarried women who
work part-time or partly in a year. The modified Chow test shows that these coefficients are
statistically significantly different. Therefore, the married women who work full-time full
year benefits more than unmarried women with the same work status. The unmarried women
working in government organizations earn $559.40 less on average than unmarried women
29
working in private sector but married women working in government organizations earn quite
a lot lower (that is, $3204.40) than married women working in the private sector. The wage
gap between government sector and private sector is higher for married women than for
unmarried women. However, the coefficient in ‘Unmarried women only’ model is not
statistically significant.
The unmarried women in the Midwest region earn $2010.30 less than unmarried
women in other regions but married women in Midwest region earn only $1358.00 less than
married women in other regions. However the modified Chow test suggests that there is no
statistically significant difference in the size of coefficients.
At the gender level, unmarried women with children under 6 earn $309.70 more than
unmarried women without a child age under 6 whereas married women with children under 6
earn $1898.00 more than married women without a child under 6. The unmarried minority
women earn $1841.50 less on average than unmarried White and Asian women but married
minority women earn $2156.20 less on average than married White and Asian women.
Therefore, there is a racial wage gap for both married and unmarried women.
4.2.3 Partitioning of Variance
The adjusted R square is 0.530 for the OLS regression model containing three model
segments, so about 53 percent of variance in annual wages (dependent variable) is explained
by all these variables combined. The first three variables are individual level variables, the
second eight variables are the structure level variables and the last three variables are
gender/race variable. When the individual model segment was removed, the R square
decreased to 0.428. When the structural segment was removed, the R square decreased to
0.306. When the gender/race segment was removed, the R square decreased very slightly to
0.528. Therefore, the structural segment has a greater impact on annual wages than the
30
individual or gender/race model segment. The partitioning of unique variance of dependent
variable in Model 1 (full sample) shows that the individual segment explains about 32.6
percent variation whereas the structural segment explains about 66.6 percent variation and the
gender/race segment explains only about 0.8% variation. Table 4 shows the partitioning of
unique variance.
4.3
Discussion
Despite increasing egalitarian gender role attitudes and increasing labor force
attachment of women, existing research indicates a persistent gender wage gap (Beggs, 2001;
Petersen and Morgan, 1995). The literature further indicates income inequality among
women. The current study explored income inequality among women, particularly married
and never married. There is a lack of explicit sociological theories that explain income
inequality between married and unmarried women. Classical sociological theories provide
some possible understandings of this issue. Therefore different sociological theories
(individualist theories, structuralist theories and gender theories) have been used to assess the
factors affecting income inequality between married and unmarried women. This study finds
some assumptions of these theories can be used to explain differences in income among
women. However, some results are quite surprising and challenge existing theoretical
understandings of earnings inequality among women.
4.3.1 Individual Level
At individual level, Human Capital theories suggest that investment in human capital
such as education, and experience are important factors for income determination (Backer,
1992). Those who have invested more in human capital have higher income. Under this
theoretical basis, this study hypothesized: as level of education increased, the income would
increase, net of other factors. The results of the study supported this hypothesis (see Table 3).
31
The results were consistent in all three models (full sample, married women only, and
unmarried women only) and consistent with previous studies (Browne and Askew, 2005).
However the benefit of education varies between married and unmarried women. Married
women benefited more from investments in education than unmarried women. A possible
explanation for this could be linked with average age of career startup, and level of education
attainment. The average age of married women is higher than the average age of unmarried
women (43 years versus 26 years – median). Women often enter into the workforce at an
early age and continue their studies (for example, working part-time and studying). The
increase in the level of education helps them to get a better job and follow different strategies
(for example, late marriage, delaying having children) to avoid discriminations or constraints
at work. Therefore once they are established in their career (with higher education, higher
skills and experience), then they can get married where marital status will have less impact on
their pay and promotion.
The above explanation of higher earnings for married women can be substantiated by
the results on age and age-centered variables. As hypothesized, age (experience) has positive
effect on earnings. Unmarried women benefit more from experience (age is the proxy
variable) then married women because the average age of unmarried women is lower than
married women, and there is non-linear (quadratic) relationship between age and earnings.
4.3.2 Structural Level
Structural theories state that structural variables such as the size of business, level of
occupation, and nature of work are important factors determining the level of income.
Consistent with structural theories and as hypothesized, this study observed that women
working in larger businesses earned more than women working in smaller businesses. A
possible reason for higher earnings in larger size business is that these business organizations
32
might be benefited from the scale and scope of economies therefore having higher profits.
The workers (employees) in such businesses may receive higher incentives (salary, bonus and
allowances). Similarly as hypothesized, women working in Blue-collar high-skill jobs earned
more than women working in Blue-collar low skill jobs, and the increase was true also for
White-collar low-skill and White-collar high-skill jobs. However, women working in bluecollar high skill occupations earned more than women working in white-collar low-skill
occupations. The possible reasons for theses earnings differences could be linked with
education (knowledge and expertise) and technical skills associated with these occupations
and demand and supply in such labor markets. The workers in high-skill occupations have to
have acquired high skills which are often referred as investment in education and skill
development (for example, engineers and technicians).
Interestingly, this study found that married women have significantly higher earnings
than unmarried women while considering size of business and level of occupations. Why are
these disparities? There is a lack of theoretical understanding for these findings. Perhaps,
married women are more successful than unmarried women in their career front within the
same level of occupations. It is also possible that married women who are established in their
career are preferred by the employer.
Interestingly, contrary to the hypothesis, this study found that women workings in
government organizations earn less than women working in the private sector. It was
expected that the job security including pay and promotion in government sector is more than
in private sector. The multivariate results suggest that private sector jobs benefit women more
than government sector jobs, at least for married women (the coefficient is not statistically
significant in unmarried women only model, see Table 3). Finally, as hypothesized, this study
found regional earnings inequality among women. Women in Midwest region had
33
significantly lower income than women in other regions. A possible reason for this
geographic income disparity among women is lack of job opportunities available in local job
markets.
4.3.3 Gender/Race
Gender theories state that in addition to education, experience, and organizational
settings, the gender level attributes like gender, race, marital status, family structure
(marriage, children, etc.) are very important factors determining women’s earnings.
Theoretical explanations indicate both direct and indirect linkage between earnings and
gender level variables. Often gender level variables affect earnings through individual level
variables and structural level variables. Therefore, after controlling for the effect of individual
level variables and structural level variables, this study found a significant effect of marriage,
having children under 6, and race on women’s earnings. Contrary to the hypotheses, marriage
provided a significant contribution to women’s earnings and women with a child had
significantly higher income than women without a child. The effect of race on earnings,
however, was as expected (hypothesized), minority women earned significantly lower income
than White and Asian women. The results relating to marriage suggest that like married men,
married women also enjoy a marriage premium. Married women enjoyed about a $527
marriage premium; and women with a child age under 6 earned $1667 more income than
women without a child. These findings ask for new insights into gender understandings of
earnings inequality, and suggest that there is a significant benefit of family (marriage and
having children) on earnings. These findings further support Waite’s (1995) arguments on
benefits of marriage. There could be a couple of explanations for marriage premium for
women. First could be the changing gender role attitudes where women are also focusing
more on career betterment. Second could be delayed marriage. Women who choose to marry
34
after an established career may be less affected by marriage in their work/performance. Third
could be shared family responsibilities and family wellbeing. Married women may have
shared and vested interest on wellbeing of their family than unmarried or single women.
Married women along with her husband think about future prospects of themselves and their
children which make them work harder and earn more.
5.
Conclusion
This study examined the earnings inequality between married and never married
women considering individual model factors, structural model factors, and gender/race model
factors. Three sets of hypotheses were developed and tested by using the Current Population
Survey (CPS) 2010 data. The results indicate that full time job (working status), level of
education, white-collar high skill professions, and experience, among others, are the
important factors for earnings. The structural level variables better explain the earnings
inequality among women, followed by individual level variables.
This study has offered two surprising and interesting results. First, while addressing the
main research question of this thesis, as its title, “Earnings inequality among women: Does
marriage matter?”, the answer is found that Yes! Marriage has a significant positive impact
on women’s earnings. Like married men, married women enjoy the marriage premium.
However, understanding earnings inequality between married and unmarried women is not as
clear as earning inequality between married and unmarried men. There is a lack of
sociological theories explaining the marriage premium for women, however, some possible
reasons were offered in this study. Second, in contrast to sociological understandings, this
study observed higher income for women with children than women without children. These
empirical results which are sharply contrast to previous sociological understandings of
35
income inequality requires further research that may lead to a new gender theory of income
inequality.
5.1
Implication
The results of this study provide a significant contribution to sociological
understandings of women’s labor force attachment and their wellbeing. There is great
concern among sociologists about the current trend of marriage in the US where the ratio of
unmarried women is increasing over the years. The results of this study indicate that women
can benefit financially from marriage. The study also indicates that there is regional earnings
inequality among women which alerts government agencies to develop the plans and policies
to reduce such regional inequality. The policy should encourage business organizations to
operate businesses in Midwest region and provide job opportunities to local women.
Furthermore, minority women have constantly lower income than their White and Asian
counter parts. It is believed that there is unequal access in the job market for minority people,
especially women and lack of competitive human capital by minorities are often used as
rationales for their lower income. So government agencies should work to bring minority
women into primary sector by providing career oriented training and development programs,
implementing equal employment opportunity act more effectively, and by providing easier
access to education, particularly higher education. Since research in income inequality holds
great policy implications, further research on marriage premium for minority women could
shed more light on income equality among women.
5.2
Limitations
There are some limitations of this study that may have affected the results. First, this
study includes only currently married and never married women. It excludes the divorced and
separated women from the study. Including these categories of women in the sample may
36
further help to understand earning inequality among women. It is also possible that never
married women may be cohabiting. Second, this study relies on the Current Population
Survey (CPS) 2010 data. The limitations and caveats on CPS data set apply to this study. For
example, the respondents may not provide the actual data, say total annual income; therefore
there could be a measurement error. Third, it is sometimes difficult to quantify the qualitative
measures like work experience. For example, the older women may not necessarily be more
experienced than younger women. Forth, this study uses Ordinary Least Squares method
including T-test, F-test and Chi-square to test the hypotheses and derive the conclusions.
Hence the limitations associated with these statistical tools are inherent in this study. For
example, issues of endogenity of independent variables could be important such as having a
child and the marital status; race and education attainment; etc.
37
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42
APPENDIX
43
Table1: Univariate Results
Sample
Sample size (n)
Percentage
Full Sample
32862
100.00%
Unmarried
11583
35.25%
Married
21279
64.75%
$29,600.12
$25,000.00
$21,071.97
$23,699.86
$20,000.00
$19,139.78
$32,811.95
$30,000.00
$21,379.28
6.33%
26.70%
33.19%
23.20%
10.58%
100.00%
38.09
38.00
12.74
7.05%
25.47%
38.69%
21.73%
7.05%
100.00%
29.39
26.00
10.86
5.93%
27.37%
30.19%
24.00%
12.51%
100.00%
42.82
43.00
11.09
34.38%
13.89%
51.73%
100.00%
43.88%
33.31%
3.20%
19.61%
100.00%
18.77%
23.48%
35.71%
22.04%
100.00%
60.38%
15.45%
12.64%
11.53%
100.00%
79.95%
2.19%
6.25%
11.61%
100.00%
34.79%
12.46%
52.75%
100.00%
35.31%
34.92%
3.56%
26.22%
100.00%
20.47%
22.57%
34.48%
22.48%
100.00%
54.15%
16.76%
13.73%
15.37%
100.00%
85.23%
1.82%
5.40%
7.54%
100.00%
34.16%
14.68%
51.17%
100.00%
48.55%
32.44%
3.00%
16.01%
100.00%
17.85%
23.98%
36.37%
21.80%
100.00%
63.77%
14.74%
12.05%
9.44%
100.00%
77.07%
2.40%
6.72%
13.82%
100.00%
80.35%
19.65%
100.00%
68.31%
12.22%
12.70%
4.80%
1.97%
100.00%
85.50%
14.50%
100.00%
58.12%
20.59%
14.88%
3.82%
2.59%
100.00%
77.54%
22.46%
100.00%
73.86%
7.66%
11.52%
5.33%
1.63%
100.00%
Dependent variable
Annual
wages
Mean
Median
Std. Deviation
Individual level variable
Less than high school diploma
High school diploma or equiv.
Some college degree
Education
Bachelor's degree
Advance degree (master & above)
Total
Mean
Age
Median
Std. Deviation
Structural level variables
Small (employee 1-99)
Medium (employee 100-499)
Size of business
Large (employee 500 & above)
Total
White-collar high skill
White-collar low skill
Blue-collar high skill
4 level occupation
Blue-collar low skill
Total
Northeast
Midwest
Region
South
West
Total
Full time Full Year
Part time Full Year
Work status
Full time Part Year
Part time Part Year
Total
Private
Federal
Employer's type
State
Local
Total
Gender level variable
No
Has child under 6
Yes
Total
White non-Hispanic
Black non-Hispanic
Hispanic
Race/Ethnicity
Asian
Other non-Hispanic
Total
44
Table 2: Bivariate results
Variable
Level of education
Level of occupation
Business size
Employers' type
Region
Marital status
Has child age under 6
Race/Ethnicity
Categories
Less than high school diploma
High school diploma or equiv.
Some college degree
Bachelor's degree
Advance degree (master & above)
White-collar high skill
White-collar low skill
Blue-collar high skill
Blue-collar low skill
Small (1-99)
Medium (100-499)
Large (500 & above)
Private sector
Government (Fed/State/Local)
Midwest
Other regions
(Northeast, South, and West)
Married
Unmarried
Yes
No
Minority
Non-minority (White and Asian)
45
Annual wages
Std.
Mean
Dev
15381.0 10890.0
22569.0 14528.0
25180.0 17570.0
37354.0 21387.0
52692.0 25487.0
39354.0 22976.0
25046.0 16418.0
27401.0 17189.0
15882.0 11831.0
23683.3 17660.4
31403.1 20818.7
33048.5 22332.0
28012.6 20616.2
35930.7 21673.0
28435.6 20182.8
29957.5
32812.0
23699.9
28087.4
29970.1
24935.4
31315.8
21325.0
21379.3
19139.8
21335.9
20990.7
18165.7
21795.4
T-test/F-test
Cohen’s
d
F = 2476.8***
F = 2619.45***
F = 719.87***
t = -26.77***
0.37
t = 5.72***
0.07
t = -39.54***
0.43
t = 6.44***
0.09
t = 26.7***
0.29
46
Model 1: Both married
Model 2: Unmarried
Model 3: Married
Models => and unmarried women
women only
women only
Independent variables
b
SE(b) β
b
SE(b) β
ϯ b
SE(b)
Individual level variables
Education
6098.2***
86.7
0.313
5154.8***
132.9
.274
^ 6453.9***
112.6
Age
297.3***
8.2
0.180
337.4***
11.9
.191
^ 289.9***
13.1
Agecentered
-12.8***
.6
-0.090 -14.5***
1.0
-.102
-12.1***
.9
Structural level variable
Medium business (100-499)
3250.4***
256.9
0.053
2388.6***
385.6
.041
^ 3699.0***
333.9
Large business (500 & above)
4899.1***
185.7
0.116
2809.4***
265.4
.073
^ 6022.9***
247.7
White-collar high-skill
8308.2***
249.6
0.196
7166.5***
338.4
.179
^ 9233.3***
348.0
White-collar low-skill
1495.1***
234.0
0.033
1020.4***
308.0
.025
^ 2287.4***
332.6
Blue-collar high-skill
3405.5***
485.1
0.028
3692.4***
661.3
.036
3423.3***
666.3
Worked full time full year
18422.4***
171.2
0.428
16378.1***
255.9
.426
^ 19456.3***
224.7
Govt. worker
-2306.6***
217.0
-0.044 -559.4
350.7
-.010
-3204.4***
272.7
Midwest region
-1558.1***
190.8
-0.031 -2010.3***
280.8
-.044
-1358.0***
250.0
Gender/race level variables
Married
527.3*
213.3
0.012
Child age under 6
1666.8***
218.6
0.031
309.7
344.8
.006
1898.0***
289.5
Minority
-1995.3***
192.6
-0.042 -1841.5***
255.8
-.047
-2156.2***
272.0
(Constant)
-15917.2*** 416.4
-11106.0*** 653.8
-18132.7***
681.7
R-square
0.529
0.577
0.485
Adjusted R-square
0.529
0.567
0.485
F-stat (sig.)
2638.2***
1213.2***
1512.7***
No. of cases
32862
11583
21279
***, **, * indicate the statistical significance at 0.001, 0.01 and 0.05 level respectively. β is the standardized coefficient
Ϯ = significant difference between unmarried and married at 0.001 level (modified Chow test)
.037
-.041
.061
.141
.216
.050
.027
.437
-.063
-.027
.336
.150
-.087
β
Table 3: OLS Regression Results for the effect of individual level, structural level and gender/race level variables on
annual wages
Table 4: Partitioning Unique Variance of Model 1
Predictors
Individual level variables
Education
Age
Agecentered
Structural level variable
Medium business (100-499)
Large business (500 & above)
White-collar high-skill
White-collar low-skill
Blue-collar high-skill
Worked full time full year
Govt. worker
Midwest region
Gender level variable
Married
Child under 6
Minority
per model
segment
percent
of total
0.0709
0.0190
0.0069
0.0968
32.6%
.048
.100
.126
.024
.027
.407
-.040
-.031
0.0023
0.0100
0.0159
0.0006
0.0007
0.1658
0.0016
0.0010
0.1978
66.6%
.009
.012 .013
.029
.031 .000
-.042 .000 -.039
total unique variance
shared variance
total variance
0.0001
0.0008
0.0015
0.2971
0.2319
0.529
0.0025
0.8%
100.0%
B
b
sig.
part
sq part
6098.2
297.3
-12.8
.313
.180
-.090
.000
.000
.000
.266
.138
-.083
3250.4
4899.1
8308.2
1495.1
3405.5
18422.4
-2306.6
-1558.1
.053
.116
.196
.033
.028
.428
-.044
-.031
.000
.000
.000
.000
.000
.000
.000
.000
527.3
1666.8
-1995.3
47