WAGE DIFFERENTIALS BETWEEN MALES AND FEMALES IN THE
INFORMATION AND TECHNOLOGY SECTOR
A Thesis by
Danielle Ely
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 fulfillment of
the requirements for the degree of
Master of Arts
May 2010
© Copyright 2010 by Danielle Ely,
All Rights Reserved
WAGE DIFFERENTIALS BETWEEN MALES AND FEMALES IN THE
INFORMATION AND TECHNOLOGY SECTOR
The following faculty members have examined the final copy of this thesis for form and content,
and recommend that it be accepted in partial fulfillment of the requirement for the degree of
Master of Arts with a major in Sociology.
_____________________________
David Wright, Committee Chair
_____________________________
Twyla Hill, Committee Member
_____________________________
Ravi Pendse, Committee Member
iii
ABSTRACT
This research was conducted to examine the different factors that attribute to the
wage gap between men and women in the information and technology sector. Three
type of factors were included in the analysis: individual level (e.g. age, education etc.),
structural level (e.g. the size of business, job title, etc), and gender level (e.g. sex,
occupational sex segregation). The Current Population Survey (CPS) data from years
2007 to 2009 was used for the analysis. Two sample t-tests and OLS (Ordinary Least
Square) regression were used to analyze the CPS data. The findings from the analysis
indicate that the wage gap out of the full sample of I.T. workers is 85.4%, meaning that
women are making about 85 cents to a man’s dollar. Net of other factors, being female
will decrease potential earnings by $8066 per year. Women are sorted into inferior,
lower paid positions than men in the information and technology sector. The variables
that had the largest impact on annual earnings for the entire sample were the individual
level factors, followed by structural level factors.
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TABLE OF CONTENTS
SECTIONS
Page
1. INTRODUCTION ................................................................................................. 1
2. LITERATURE REVIEW ........................................................................................ 2
2.1 Individualist model .......................................................................................... 2
2.2 Structuralist model .......................................................................................... 6
2.3 Gender Model ............................................................................................... 10
2.4 Conceptual Model ......................................................................................... 13
2.5 Hypotheses ................................................................................................... 14
3. DATA AND METHODOLOGY ............................................................................ 15
3.1 Data .............................................................................................................. 15
3.2 Variables ....................................................................................................... 15
3.2.1 Individual level variables ..................................................................... 16
3.2.2 Structural level variables .................................................................... 16
3.2.3 Gender level variables ........................................................................ 18
3.3 Methodology ................................................................................................. 18
4. RESULTS ........................................................................................................... 19
5. CONCLUSION.................................................................................................... 23
5.1 Discussion .................................................................................................... 23
5.2 Limitations .................................................................................................... 27
5.3 Policy ............................................................................................................ 28
6. REFERENCES ................................................................................................... 30
7. APPENDIX ......................................................................................................... 35
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1. Introduction
The information technology (IT) sector has witnessed above average
employment growth over the last 40 years (Figure 1A) (Bureau of Labor Statistics
2009). However, males and females have not equally benefitted in this growth. While
males’ share of employment in IT has continued to climb, females’ share of employment
began to decline in the early 1990s and has only recently begun to increase (Bureau of
Labor Statistics 2009). As Figure 1B shows, this instability in females’ IT employment
was due to shifts in the types of IT jobs available. In the 1980s females were heavily
concentrated in computer operators and support positions (Bureau of Labor Statistics
2009). These positions became increasingly obsolete with the rise of the modern PC.
However, recently, females have witnessed increased employment growth in system
analyst positions, although lagging males’ growth in these IT occupations (Bureau of
Labor Statistics 2009).
The IT sector has also seen above average median earnings over the last 40
years and steady increasing income since the early 1990s (Figure 2) (Bureau of Labor
Statistics 2009). Females’ earnings were lower than the national median during the
1970s and 1980s largely due to the occupancy of lower IT positions (computer
operators and support) but have risen since the 1990s as females have made inroads
into the higher paying system analyst related jobs (Bureau of Labor Statistics 2009).
The positive trends in employment and earnings within the IT sector have
benefited females, providing them employment opportunities that increase their odds of
being economically independent. However, females still lag males in both employment
and earnings within the IT sector which raises the question of why. Are these
1
differences a result of different decisions that males and females make in terms of the
investment in skills and employment choice? Are males and females segregated into
different occupations which cause a difference in income or employment growth
possibilities? Are the differences attributable to processes of gender stratification such
as discrimination and sorting? This study examines why differences in earnings among
males and females in the IT sector exist.
Three theoretical models have emerged to explain wage differentiation between
males and females. Individualist theories, including rational choice, human capital, and
comparative advantage, state that wage differences between males and females are
due to the different choices in productive skills made by males and females.
Structuralist theories, such as dual economy and segmented labor market, argue that
the position an individual occupies dictates the income they will receive independent of
individual attributes. Gender theories argue that females are systemically devalued in
the labor market and sorted into inferior economic positions relative to men. From these
three schools of thought a composite model is created to examine difference in earnings
among males and females in the IT sector using data from the Current Population
Survey (CPS) from the years 2007 to 2009.
2. Literature Review
2.1 Individualist model
Rational choice theorists argue that individuals make choices about their actions
based on the costs and benefits of completing the action, which are influenced by the
desires and beliefs of the individual (Coleman 1986, Parsons 2005). Different choices
lead to different skills, varying in marketability, which would be a factor in the difference
2
in wages among males and females. For example, a difference in wages between
males and females that is relevant to rational choice theory would be that males
concentrate on training or furthering their marketable skill set whereas females
concentrate on household or domestic responsibilities. Regardless of the track taken,
for rational choice theorists, one’s current state of being is always a consequence of
past choices taken.
Human capital acknowledges the role of choice but goes one step further in
arguing that certain choices lead to increases in productivity that employers find
attractive in increasing profits which then are compensated by increased earnings for
the worker (Becker, 1992). The commonly cited skill based investment for human
capitalist are on the job training and educational attainment. These investments allow
the potential employee to be more attractive to an employer, and make the potential
employee more likely to obtain a position with the employer.
On the job training increases possible financial returns with a smaller private cost
for the individual (since job training is provided by the company) than formal education
which is largely financed by the individual (Mincer 1962). On the job training is an
important source of large increases in earnings because workers are more valuable to
the company because they have been given specific knowledge about the company
(Becker 1992). This specific knowledge makes the individual more valuable to the
company because they will be more skilled and productive than workers who have not
had the training (Mincer 1958). The longer an individual is with a company can also
increase the amount of on the job training they will receive, so the company is
increasing knowledge in its workers who have more seniority (Mincer 1958). This type
3
of training has been said to be as important, or more important, as an extended
education, as education is thought of as a beginning, with generalized knowledge
whereas on the job training is company specific (Mincer 1962). This on the job training
is less likely to be given to employees who are not likely to stay with the company, or to
those who are in temporary, short-term employment (Ono and Zavodny 2005).
An extended education can increase financial returns for an individual (Mincer
1958). Education, in terms of being human capital, also allows individuals to earn more
because it provides knowledge, skills and a means of analyzing issues that are not
available to individuals who do not further their education (Becker 1992). Females have
benefited greatly from advancing their education and have become involved in
traditionally “male” fields such as math, the sciences, economics, law, and medical
fields (Becker 1992). Advanced education has also been seen to be beneficial in
industry sectors that have changing technologies and advancement in productivity
(Becker 1992). Technology is affecting the way different sectors value general skills,
and those individuals without these general skills are finding themselves unable to find
work (Gould 2002). In the past, these general skills would have varied across
professions. For example, a factory worker would have needed to have physical
strength and endurance, whereas today they would depend on computers and their
ability to oversee the equipment doing the work (Gould 2002). The information and
technology sector is one field for which people acquire an extended education in
addition to receiving on the job training. This field, like most others, will reward people
who have these types of training, as well as those who are older who have more
experience and more time to receive the training (Balmaceda 2005).
4
So for the rational choice and human capital theorists, wage difference between
males and females are a reflection of different investment strategies. But why do males
and females have different investments in human capital? For the individualist,
comparative advantage provides the answer. Comparative advantage argues that like
countries, individuals will produce labor that is cheapest to the individual so they can
earn the maximum amount of profit (Ricardo 1919). The tasks that an individual
performs best will be the ones that they will choose to have as an occupation, because
they will put forth the least amount of effort in gaining their income (Sattinger 1978).
When applied to males’ and females’ wages, comparative advantage describes
females’ lower wages as a feature of the amount of labor they spend on housework and
childcare instead of paid labor, which causes the decrease in their productivity and their
wages (Becker 1985). Regardless whether one is arguing a biological (maternal
instinct) or a sex-based division of labor in the home caused by the separation of work
and family in modern times, females can provide domestic labor (especially childcare) at
lower cost than males.
Females’ unpaid labor requires them to find paid work that is flexible in hours,
sick leave, etc. and those employers pay them less because of the flexibility that is
offered (Becker 1985). Females with children spend less time in the job market due to
their household and childcare duties, therefore they have less experience, training,
continuous employment and opportunity to advance in a company, leading to the
differentiation in wages between males and females (Mincer and Polachek 1974).
Experience, also referred to as loyalty when an individual stays with a particular
company for an extended amount of time, is highly rewarded, so if females are
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spending less time with a company, they will not receive the rewards given to males
who do not take the time off from working (Anderson et. al. 2009). Human capitalists
would make the argument that the majority of females are not choosing to go into the
field of information and technology, and that the females who are in the field do not
spend a majority of their efforts at work, but at home, and are spending less time in the
workforce, therefore decreasing their potential income.
A final note on discrimination is warranted before leaving the individualist model.
While other theories argue that wage differences are based on discriminatory actions of
employers, individualists argue that discrimination would be a cost to employers,
ultimately forcing out of business employers who discriminate (Becker, 1971).
Employers who paid males higher wages would face labor cost competition from
employers who paid males and females equally.
Market equilibrium would cause any
discrimination that did exist to wither away. So in the absence of discrimination, wage
differences must be a reflection of different investments in productivity enhancing skill
among individuals.
2.2 Structuralist Model
Structuralists argue that wage differences between males and females are from
the structures, or company settings, in which they work. For structuralist, organizations
are economic hierarchies in which each economic position has a range of income
independent of individual attributes. Thus, income is first and foremost a product of the
position one occupies (Piore 1973). Structuralism as related to income is based on two
different but related dimensions of production—the technical relations of production
(dual economy theory) and the social relations of production (segmented labor market
6
theory. The technical relations of production are rooted in the physical infrastructure of
the firm and affects all workers. The social relations of production are rooted in the
social organization (occupational hierarchies) of the firm and impacts economic position
differentially.
Dual Economy theory argues that there are two sectors that have emerged over
time. One sector is called the monopoly sector (also known as the core) which is a
capital intensive, technologically advanced industry with differentiated wage structures,
job ladders and on the job training (Tolbert, Horan and Beck 1980; Oster 1979). The
other sector is called the competitive sector (also known as the periphery) which is not
capital intensive but labor intensive, with low skill jobs and instability in employment
(Tolbert, Horan and Beck 1980; Oster 1979). These two sectors have significant
differences in earning levels and the composition of the labor force (Beck, Horan and
Tolbert 1978). Under this theory, monopoly sector industries are concerned with
technical relations of production, which influences their ability to give higher wages
through surplus from production (Edwards 1973).
Dual economists focus on the organization of production and the industrial
structure (Tolbert, Horan, and Beck 1980). These corporations have also integrated
their companies to ensure they will have the needed raw materials to make their
products, as well as institutionalizing research and development of products to stay
ahead of technological developments outside of the firm (Edwards 1973). By having
control of the materials needed for production, these monopolistic firms can have a
higher profit potential, which is also attributed to by their high market share and their
involvement in national and international markets (Edwards 1973). Similarly, in the IT
7
sector, large IT firms have economies of scale that make production more efficient and
less costly allowing for support of higher wage structures whereas small IT firms have
limited ability to expand production and lower cost making it difficult to support high
wage structures.
Dual economy theory discusses how large firms in the monopoly sector can
influence the competitive sector through controlling the amount of competition in the
market (Edwards 1973). The competitive sector is not able to compete with the
monopoly sector, due to their involvement in local markets and inability to pay their
workers in a higher wage structure because of a low ratio of capital to labor (Tolbert,
Horan and Beck 1980; Oster 1979). An example of how this applies to the information
and technology sector is if a small corporation such as Ribbit’s Computers (a small
computer sales company located in Wichita, Ks) is compared to the Microsoft
corporation. Ribbit’s Computers cannot offer employees the pay or benefits that
Microsoft can offer due to the smaller amount of surplus from sales and the amount of
labor it took to make that capital and the small, localized labor market. Microsoft can
offer more to employees because they are a part of national and international markets
that allow for greater capital with less labor.
The segmented labor market theory argues that there are two main labor
markets, primary and secondary markets. These markets are based on the social
relations of production, or the occupational position hierarchy that exists within
companies and the labor market. The primary market has high wages and credentials
required for the positions available (Bygren and Kumlin 2005). The secondary market
includes task oriented work and low wages (Bygren and Kumlin 2005). The primary and
8
secondary markets have different sets of rules, different ways of getting information,
different skill requirements and different behavior requirements for their workers
(Edwards 1973). The primary market mainly consists of white males with progressive
careers, where they are given more responsibilities, authority, pay and an increased
status the longer they are in the labor force. The primary market also has more
employment stability and autonomy in decision making for the employees (Bygren and
Kumlin 2005: Edwards 1973). The secondary market tends to be comprised of females,
blacks, teenagers, and the urban poor who are denied opportunities of vertical
movement (Edwards 1973). The secondary market does not allow the same
opportunities as the primary market as there is less employment stability, lower wages
and defined rules for decision making (Edwards 1973). There have been indications that
the reason females are not equally paid is due to sex discrimination in the primary
market (Beck, Horan and Tolbert 1978). Females are more likely to be in the periphery
or secondary market, where they earn a lower income than they would in the primary
market (Coverdill 1988). When females are in the same position as males in the
secondary market, they are still earning less than males (Coverdill 1988).
The primary sector of segmented labor markets is focused highly on internal
labor markets, inside one specific firm, that reduces the possibility of vertical movement
between the two sectors and the possibility of higher wages (Levitan, Magnum, and
Marshall 1972; Edwards 1973). Females are less likely to be hired in the primary sector
because of the possibility they will take time off for maternity leave or other family
obligations, and in the areas with rapidly changing technology it will cost the employer to
retrain the females when they come back into the job from leave (Estevez-Abe 2005).
9
As information and technology is a field with rapidly changing technology, it can be
assumed that females will be less likely to be hired because of possible interruptions in
their career and the possibility that they will leave for time off.
2.3 Gender model
Individualist and structuralist theorists see gender as a variable whose possible
negative effects can be modified or overcome in the labor market. Gender theorists
argue that gender is a process that systemically devalues women and sorts them into
inferior economic positions. Gender theorists state that minorities and females become
disadvantaged through devaluation and sorting processes that occur on all levels,
personal to organizational. Throughout their lives, these groups go through processes of
having their skills and attributes devalued in the workforce and are sorted into lower
paying jobs, which lead to the economic disadvantage for these females and minorities.
The gender theories that address the pay differences among males and females include
crowding theory, revolving door theory and job queuing theory.
The devaluation of females occurs in many ways, for example through
objectification and using females to sell products through sexualized images, the
socialization of young children into gender-based roles, and the use of language that
favors male pronouns. The division of household labor is a good example of how
females are devalued and sorted into inferior positions. Household labor is unpaid and
has historically been relegated to females. While males have increased their share of
household labor in recent decades, females continue to perform the majority of this
unpaid labor. The lack of labor support from males is further exasperated by only few
companies providing benefits to reduce this burden, especially childcare. In addition,
10
studies show that within the work setting, females are often relegated to performing
domestic labor in the workplace regardless of their occupational position (Greenstein
2000). This unpaid labor creates a disadvantage for females as they are spending a
“second shift” of work at home beyond their paid labor shift (Reskin and Roos 1992,
Hochschild 1989).
Devaluation is often a prelude to sorting. Some of the most common sorting
theories applied to wage differences between males and females are Crowing theory,
Revolving Door syndrome and Job/Gender ques. Crowding theory is based on the idea
that disadvantaged groups, such as females, are crowded into smaller job sectors than
males, causing an overabundance of labor (Bergmann 1971). This overabundance
causes the pay scale to decrease and the group is unable to obtain higher wages
because of the discrimination they face (Bergmann 1971). By discrimination being
present, females are crowded into the secondary labor market which influences how
much they will earn, the amount of autonomy they have and whether they have a “good”
job, one that they prefer to be in. Females are less likely to be in the information and
technology sector because, according to crowding theory, information and technology is
largely comprised of males making it a primary labor market, which leads to females
being under valued in the field, facing discrimination from employers and employees,
and feeling forced into another area that will pay less.
The “revolving door” theory, introduced by Jerry Jacobs, asserts that events
throughout a female’s life influence which career direction she will believe is appropriate
for her to take (Jacobs 1989). Gender roles, or sex roles, are taught to males and
females at a young age which indicate what occupations are appropriate for males and
11
females (Jacobs 1989). Due to the gendered expectations, females are more likely to
choose a job that is socially acceptable, such as teaching, rather than economically
useful, such as engineering. For example, the area containing work with computer
expertise is dominated by males because it is technological in its nature and females
are taught through role expectations that it is not an appropriate field for them to work in
(Blackburn and Jarman 2006).
Although today females have greater access to traditionally male dominated
fields, their turnover from male traditional to gender neutral or female dominated jobs is
high. Jerry Jacobs found that for every 11 women who enter a male dominated job, 10
ultimately leave for gender neutral or female dominated jobs (Jacobs 1989). This high
turn-over rate for females is a product of several factors. A female being present in a
male-dominated field can intimidate the male workers who will then use coercion of
different sorts such as mockery or exclusion tactics to force the female out of the
position (Jacobs 1989). A female will also begin to have feelings of cognitive
dissonance, wondering if it is appropriate to be in a male dominated field when she is a
female (Jacobs 1989). Another reason for the high turnover rate could be due to
employers having second thoughts about having a female employee, leading them to
question the females’ abilities in comparison to the males’ (Jacobs 1989). Both males
and females attribute higher levels of information and communication technology use
and expertise as a masculine trait (Selwyn 2007). Similar to crowding theory, the
revolving door theory claims that the reason females are less likely to be in the
information and technology sector is due to discrimination, but because females are the
12
minority in the field, they will either leave voluntarily or be forced out of the position
because of their minority status.
In the book “Job Queues, Gender Queues” Reskin and Roos (1990) describe the
process in which employers and employees rank both the job and applicants.
Employers rank potential employees from those with the most desirable individual
characteristics to the least desirable individual characteristics. The highest desired
quality is to have a male employee and then after all male options are exhausted an
employer will take females (Reskin and Roos 1990). Employees also rank the
attractiveness of the job and will attempt to get a job that is highly attractive to them,
which is true for both males and females (Reskin and Roos 1990). If an employee is not
able to get their first choice of job attractiveness, they will continue moving down into
less attractive jobs until they become employed (Reskin and Roos 1990). For females,
this means that they will not have the opportunity to make as much money as a male,
due to being sorted into lower prestige, and paid, positions from the processes used by
employers. This applies to the information and technology sector, as well as many other
areas, because the most desired worker is a white male, and females are not the first
desired choice to be chosen to fill a position.
2.4 Conceptual Model
By combining the previous models, a conceptual model (Figure 3) is formed with
individual, structural and gender components. Individualists argue that individuals are
rational actors and make choices to invest in human capital to increase their
productivity, which is expected to increase income. Examples of individual level factors
would include age (a proxy for on-the-job training) and education. It is argued that as
13
education increases it would be expected that income would increase. Income is thus a
reflection of choices that increases one’s productive capacity. Structuralists argue that
organizations are hierarchies of economic positions, each position having a range of
income that is independent of individual attributes. Thus, income is first and foremost
determined by the position one occupies. Structural level factors would include hours
worked, industry, company size and occupational position. These components have
both direct and indirect effects on income. Factors reflective of the technical relations of
production impact all workers. For example, as company size increases wage
structures increase. Factors reflective of the social relations of production impact
workers based on their economic position. For example, managers received greater
economic return from investment in education than production workers. The gender
perspective argues that gender processes systemically devalued females and sorted
them into different position than males. Females are consistently sorted into inferior
economic positions relative to males and even within the same position receive less
economic returns on individual attributes than males.
2.5 Hypotheses:
Individual model:
1. As age increases, income will increase, net of other factors.
2. As education increases, income will increase, net of other factors.
Structural model:
3. As company size increases, income will increase, net of other factors.
4. As occupational skill position increases, income will increase, net of other
factors.
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Gender model:
5. Females will be in inferior positions.
6. Females will earn less income than males, net of other factors.
3. Data and methodology
3.1 Data
The data for this study comes from the Current Population Survey (CPS) March
Annual Socio-Economic Supplement, a national probability survey conducted by the
Bureau of the Census (Bureau of Labor Statistics, 2009). CPS data are collected from
approximately 60,000 households on a monthly basis to obtain national estimates on
household, family and person demographics, employment and earnings. Population
restrictions for this study include civilians between the age of 18 through 65 who had
work experience in the previous year in IT occupations. In order to have sufficient
sample size for analysis, this study uses a pooled CPS sample from the years 2007 to
2009 which yields 7,927 respondents. The study restricts the range of annual earnings
to eliminate the effects from potential outliers. Annual earnings are restricted to a
minimum of $9,012.50 (earnings at minimum wage for one year) and a maximum of
$300,000 (only 70 respondents exceeded this earnings amount). The final sample size
for this study is 7,233 respondents.
3.2 Variables
The dependent variable for this study is an interval measure (in dollars) of annual
earnings based on wages, salaries and self-employment earnings. CPS earnings from
2007 and 2008 have been deflated to 2009 dollars. Most scholars log earnings to
compensate for their highly skewed nature, but given the sample restrictions, skew is
15
minimized eliminating the need for a logarithmic transformation. In addition, the
standardized residuals from the regression models used in the study were normally
distributed. For further examination, the study creates both quintiles and centiles of
earnings.
3.2.1 Individual level variables
Individual level factors include age, education, region and urban/rural residence.
Age is an interval level variable that, in this study, was restricted to those between the
ages of 18 to 65. Age is used as a proxy for experience. Education is a 5 level ordinal
variable measuring whether the respondents has (1) less than a high school diploma,
(2) high school diploma or equivalent, (3) some college education or associate degree,
(4) bachelor’s degree or (5) advanced college degree such as a master’s, Ph.D. or
professional degree. Binaries (0,1) were constructed for each education category for
bivariate and multivariate analysis. Since earnings are impacted by the standard of
living depending upon location of residence this study uses the region (northeast,
midwest, south, west) and urban/rural variables available in the CPS. For IT employed
respondents, the midwest region and rural residence have statistically the lowest
earnings and binaries (0,1) for each have been created for bivariate and multivariate
analysis in order to control for cost of living by location.
3.2.2 Structural-level variables
Structural level factors include annual hours worked, hours worked per week, full
time status, self employed, government worker, company size, industry, and
occupational position. Annual hours worked, an interval level variable, indicates the total
amount of hours the respondent worked for the year. Full-time and part-time status are
16
derived from the usual weekly work hours (35 or more hours equal full-time
employment). Binaries (0,1) are created for self-employed respondents and
respondents who work for a government agencies (federal, state, and local). Company
size is an interval level variable that was used to construct binaries of small, medium
and large businesses. Small business is defined as businesses with one to 99
employees, medium business is defined as businesses with 100 to 499 employees, and
large business is defined as businesses with 500 or more employees. Since earnings
are impacted by industrial location, a binary (0,1) is created to indicate whether the
respondent works in the goods producing sector (construction, manufacturing and
mining).
The CPS provides data on ten IT related occupational positions: 1) chief
information officer (CIO), 2) software engineer, 3) hardware engineer, 4) computer
scientist and system analyst, 5) computer programmers, 6) database administrator, 7)
network system administrator, 8) network system analyst, 9) computer support
specialist and 10) computer operators. For presentation, these occupations are grouped
into two clusters based on education, skill level and functional authority as defined by
the ONET classification system (U.S. Department of Labor, 2009). The first cluster
includes 1) Chief Information Officer, 2) engineers (software and hardware), 3)
programmers and related (computer scientist, system analyst and programmers), 4)
network related (network administrators and system analyst), and 5) computer support
(support specialist and operators). The second cluster includes 1) CIO and engineers,
2) programmers and network related, and 3) support IT occupations. Binaries (0,1) are
created for each of the above clusters for bivariate and multivariate analysis.
17
3.2.3 Gender variables
Gender level variables include sex, occupational sex segregation, marital status,
having children under 6, minority member and immigration status. A binary (0,1) was
created to indicate whether the respondent was female. Occupational sex segregation
is the percent of females within a 4 digit occupation divided by the percent of females in
the workforce; values under 1 indicate that females are underrepresented, values over 1
indicate females are over represented and 1 indicates males and females are equally
represented. Marital status, a nominal variable, contains the categories of married, ever
married (divorced, separated, widowed) and never married. Binaries (0,1) were created
from the marital status variable for analysis purposes. A binary (0,1) was created to
indicate if the respondent has a child under the age of 6.
The CPS provides information on the race and ethnicity of a respondent.
Traditionally, Caucasians are classified as non-minority and African Americans, Asians,
and Hispanics are classified as minority. However, as is usually the case when
examining earnings, Asians have earnings that are equivalent to Whites and including
them among minorities inflates earnings. So for this study Asians are classified as a
non-minority group along with non-Hispanic Caucasians and only African Americans
and Hispanics are classified as minority members. A minority binary (0,1) is created for
bivariate and multivariate analysis. In addition to race/ethnicity, immigrant status is
constructed as a binary (0,1).
3.3 Methodology
This study utilizes several different statistical techniques to examine income
differences between males and females in the IT sector. Univariate analysis provides
18
summaries and distributional patterns for the full sample and separately by males and
females. In cases of comparing males and females, group-means-comparison tests (ttest and anova) are used to identify whether statistically significant differences exist
between males and females. Differences are also evaluated based on effect size (.20
or higher). Ordinary Least Square (OLS) regression is used to examine multivariate
relationships in order to identify whether factors have an independent effect net of
controls and what magnitude in terms of explained variance each independent effect
contributes to the estimation models. The saturated regression model is ran against the
full sample and separately for males and females. A modified chow test is used to
determine statistical difference comparing males’ and females’ unstandardized betas
(1.96 and -1.96 or higher).
4. Results
Tables 1A through 1C provide univariate and bivariate results that compare
males and females in IT occupations. Table 1A shows that females earn $59,609
annually whereas males earn $70,587 annually, a statistically significant difference at
the .001 level and creating a pay gap of 84.4%. This pay difference is similar for both
median earnings and for full-time workers.
Among individual-level factors, Table 1A shows that females are more likely than
males to have a High school diploma or less (13.0% versus 8.5%) and less likely to
have a bachelors degree (42.9% versus 47.2%). There are no statistically significant
differences among males and females within the remaining educational categories, nor
by age or region. Females are more likely to live in rural areas than males (7% versus
4.7%).
19
Among structural level factors, Table 1B shows that females worked fewer
annual hours (2,053 versus 2,159), weekly hours (40.8 versus 42.6) and were less likely
to work full-time (93.9% versus 97.6%). Females were more likely to work within the
government sector (16.5% versus 10.7%) but less likely to be self-employed (2.4%
versus 4.1%). In regards to company size, females are less likely to be in small
businesses (16.8% versus 22.5%) but more likely to be in large businesses (70.1%
versus 63.3%). Males and females were equally likely to be employed in medium size
companies. Females are less likely to be employed in the higher-income goodsproducing sector than males (13% versus 16%).
In occupational positions, females and males have the same likelihood to be a
Chief Information officer. However, females are less likely to be an engineer (20.8%
versus 26.7%), more likely to be programmers (38.1% versus 35%), less likely to be in
network related jobs (12.9% versus 15.8%), and more likely to be employed as
computer support (17.2% versus 11.2%). When the five occupational categories are
collapsed into 3 categories based on the position’s skill, females are more likely to be
concentrated in the lower positions relative to males lending support to hypothesis #5
which states that females will be concentrated in lower economic positions. Females
are less likely to be in the high IT group (31.8% versus 37.9%) and more likely to be in
the low IT group (17.2% versus 11.2%) relative to males.
Among gender-level factors, Table 1C shows that females are more likely to work
in occupations that have greater percentages of female workers (0.60 versus 0.55).
Females are more likely to be members of a minority group (17.5% versus 13.5%).
While there are no differences between males and females for being married, females
20
are more likely to have ever been married (15.4% versus 8.9%) and less likely to be
never married (20.7% versus 25.2%). Females are less likely to have children under
the age of 6 (18.7% versus 22.1%) but equally likely to have children under the age of
18. Males and females have the same likelihood of being an immigrant.
Figure 4 shows the median annual earnings of full time IT workers based on age.
Individualists argue that as one acquires age, they acquire experience which is
rewarded with higher earnings. In the early ages, males and females start with equitable
wages, but as they age income diverges into separate trajectories of earning ending
with a pay gap of 68.8%.
Table 2 provides univariate and bivariate results based on full time full year IT
workers (chosen to keep a uniform sample, and based on the small percentage of the
sample that was not full time full year) stratified by educational category. With the
exception of the doctoral degree level, females earn less than males for each
educational degree level. When collapsed into a category indicating a graduate level
degree (master and doctoral), there is no statistically significant difference between
males and females in educational attainment, but there is a significant difference in
earnings for males and females with graduate degrees with females earning less
($75,000 versus $86,000). When collapsed into a category indicating a college degree,
there is a significant difference in educational attainment between males and females
and a significant difference in earnings.
Table 3 shows the earnings related to occupational position for full time full years
IT workers. Although males and females have the same likelihood of being a CIO,
females earn significantly less than males ($80,000 versus $86,000). On the other
21
hand, there are several IT occupations where females and males statistically earn the
same whether or not they are equally likely to be in the occupational category. Females
and males earn the same among hardware engineers, computer system analyst,
computer programmers and computer support specialist. However, in addition to CIOs,
females continue to earn less than males among software engineers, database
administrators, network system administrators, network system analyst, and computer
operators.
Table 4 shows the ordinary least squares (OLS) regression analysis, regressing
income on to the individual, structural and gender level factors. In the full saturated
model for the full model, the adjusted r square is .469, indicating that the model can
explain almost 47% of the variation in the dependent variable annual earnings. The
study hypothesizes that females will earn less income net of other factors, which was
supported in Table 4 showing that females earn $8,489 less than males, net of other
factors. This negative income effect could be attributed to 3 factors; 1) data error, 2)
exclusion of other relevant factors, or 3) discrimination.
The study hypothesizes that with increases in age income increases, which is
supported by the study. For each year of increase in age there is an increase in income
of $700. This holds true for both males and females. The study hypothesizes that as
education level increases income will increase, net of other factors, which is supported.
Using less than a college degree as a reference group, income increases for each
additional educational degree attained. This pattern exists for both males and females.
The study hypothesizes that as company size increases, income will increase,
net of other factors, which is supported. Using small sized businesses as the reference
22
group, people working in medium business earn $3,438 more than the reference group
and people working in a large business earn $7,991 more than the reference group. The
relationship between company size and earnings holds true for both males and females
within large businesses but only for males in medium size businesses.
This study hypothesizes that as occupational position increases income will
increase, net of other factors, which is supported. Using computer support workers as
the reference group, people working in network related areas witness an increase of
$8,607, programmers and related earn an additional $9,825, engineers earn an
additional $16,195 and chief information officers earn an additional $20,764 more than
the reference group. While this pattern exists for males and females among the CIOs,
Programmers, Network and Support positions, for Engineers females earn considerably
less than males ($9,349 versus $18,604 above the earnings of computer support
positions).
Figure 5 shows the partitioning of the shares of unique variance in annual
earnings in order to test the 3 models in the full sample, as well as for males and
females. In the full sample, structural level factors have the highest explanatory power
(48.3%) followed by individual level factors (44.9%) and then gender level factors
(6.8%). There is a difference in the male and female models. For males, individual-level
factors provide more explanation than females (51.8% versus 46.3%) and for females,
structural level factors carry more weight (49.5% versus 46%). Females also have a
greater impact from gender level factors than males (4.2% versus 2.2%).
5. Conclusion
5.1 Discussion
23
The IT sector is an important employment option for both males and females as it
has above average employment and earnings growth. However, females have lagged
males in the IT sector and clearly have different employment and earnings trajectory
than males. This study has sought to shed light on the earnings dimension using a
unified model approach that incorporates individual-, structural- and gender-level
components.
The individualist model argues that earnings are a reflection of human capital
investment choices which lead to increasing ones productivity and thereby, earnings.
Often educational attainment and on the job training are used as proxies for skill
development. Accordingly, this study created two hypotheses to test the individual
model’s assumption, both of which were supported by findings in Figure 4 and Tables 2
and 4. Figure 4 and Table 4 showed that as age increases income increases
supporting hypothesis #1. Figure 4 showed that males and females have different
earnings trajectories over their lifetimes with males having a sustained earnings
increase throughout the prime work years in contrast to females. One explanation for
this pattern of a rising gap in wages could be the exiting from the labor market by
females related to child birth. However, most studies show when females exit for
childbirth related factors it is for a very small period of time ranging from less than 12
weeks to a maximum of 6 months for a majority of females that return to the work force
(Hofferth and Curtin 2006, McGovern et al 2000). A more likely explanation, given the
cross-sectional nature of the CPS data, is that females have fewer years of labor market
experience in IT occupations than similar age males, leading to the difference in wages
by age cohort. However, Table 4 showed that when controlling for other factors, the
24
earnings differences caused by age were equivalent for both males and female. It is
likely that the differential earnings trajectories shown in Figure 4 are an artifact of the
cross-sectional nature of the data.
Educational attainment plays an important role for individualist theorists and is a
requirement for employment in the IT sector. While males and females do have
different concentrations within educational degrees, table 2 showed that even within
similar degrees, females earned less than males except for those with doctoral degrees.
However, moving from bivariate (Table 2) to multivariate (Table 4) analysis showed that
earnings increases from advances in educational attainment equally benefit males and
females supporting hypothesis #2.
The structuralist model argues that earnings are a reflection of the economic
position one occupies independent of individual attributes. The technical relations of
production thesis states that firms with large economies of scale structures (such as
large companies) affords the ability to increase profits which can support higher wage
structures leading to increase earnings for all workers regardless of their individual
human capital (hypothesis #3 which states that as company size increases, income will
increase). Similarly, the social relations of production thesis states that earnings are
segmented by economic position (such as occupational position) to recruit different
labor markets and control risk to structural assets (hypothesis #4 which states that as
occupational skill position increases, income will increase). Table 4 showed that as
company size increases, earnings increase, supporting hypothesis #3. However, this
increase did not hold equally true for males and females. While male earnings
increased as they move from small to large companies, females witnessed increased
25
earnings only in large companies. A similar pattern emerged when examining earnings
by economic position as shown in Table 4. Support for hypothesis #4, as occupational
skill position increases, income will increase, net of other factors, comes from table 4,
showing that with each increase in position, there is an increase in income. Supporting
hypothesis #5, earnings increased as occupational skill level increased from the lowest
paid computer support positions to the highest paid CIOs. Yet, this pattern of increase
held true for males and females only among programmers and CIOs. Among
engineers, males received a substantial pay dividend over females and among network
position females received no additional increase in earnings over computer support
positions.
Finally, the Gender model argues that gender is a process (in contrast to an
attribute) of systemic devaluation and sorting. Females will be sorted into inferior
economic positions and even within the same position will receive less economic
returns on individual attributes than males. Accordingly this study created two
hypotheses to test the Gender model’s assumptions concerning occupational
concentration (hypothesis #5) and inferior economic returns (hypothesis #6) both of
which were supported by Tables 1B, 3 and 4. As shown in Table 1B, females are more
likely to be concentrated in lower skill positions than males lending support to
hypothesis #5. Interestingly, females have the same probability of being in the top CIO
position as males, but their lower levels of participation among engineers and greater
likelihood of computer support places them at a disadvantage. This disadvantage is
especially prominent when one considers that this study found that female engineers
earn less than male engineers net of controls, meaning they are in positions that are not
26
economically advantageous. Most damaging for females is that simply being female
incurs economic cost, reflective of the devaluation thesis, shown clearly in Table 4 and
lending support to hypothesis #6. Being female incurs a negative cost of $8,489 net of
controls. While it is important to note, as with all multivariate coefficients, that this
negative coefficient could be the result of measurement error or misspecification of the
model, the negative coefficient can also be attributable to discrimination.
5.2 Limitations
As with any research this study has limitations. Even though the CPS collects
data monthly, it is not a longitudinal or panel study but a snapshot at one point in time, a
cross-sectional survey design. This means that factor effects caused by recency are
minimized. For example, if the respondent changed from full-time to part-time
employment the week before the survey, the survey will not have had time to measure
the negative income effects from the change in work status.
While the CPS provides the most comprehensive data on employment and
earnings nationally, it is missing some important data elements for examining earnings.
The CPS does not provide information on labor history such as how long a respondent
has been in the labor force, how many times they have exited and for how long, how
many jobs they have had and how long they have worked for their present employer.
Entering into IT has many various paths, and while a small majority of the individuals
who are in IT went to school and entered the field, there is evidence that many people
have employment or education in other areas and then move over to the IT field
(Moncarz 2002). This would indicate that age may not be a valid proxy for experience or
tenure in the IT field. The different earnings trajectories in Figure 4 are possibly
27
reflective of females’ later entry into IT occupations rather than exits related to child
bearing but the CPS does not provide data to measure this effect, however in previous
studies there has been evidence suggesting that a person will either enter into IT almost
immediately after high school or some college education or the person will have several
jobs in different fields before entering into IT (Messersmith et al 2008). Nor does the
CPS provide data on domestic settings in which the respondent lives, such as the
household division of labor or the management and care of children, which would affect
the work hours a respondent is available.
5.3 Policy
Without question the IT sector has provided females the opportunity to have
positive employment growth and above average earnings. However, while females
have the same likelihood of being Chief Information Officers as males, females are still
concentrated in lower economic positions. In addition, while the pay differences
between males and females are small in most of the IT occupations, females do earn
substantially less than male engineers, an IT occupation that garners high earnings
within this sector. These findings of representation and earnings can be changed by
more aggressively pushing females into engineering fields. Along with other efforts to
encourage females into the IT sector, by focusing greater effort on engineering
occupations, females are able to increase their representation in higher level positions
and reap the financial rewards of higher earnings such positions provide.
One way females are currently being encouraged into higher positions in IT is
through federal support that is giving money to colleges to increase the number of
female students who go into science or engineering programs (June 2010). Another
28
way that females are being encouraged into science and technology fields is through
programs such as the Society of Women Engineers (SWE) who, with alliances from
other groups, create and maintain interest for young adults (more specifically females)
in these fields. The SWE has made several policy implications to encourage females
into the STEM (Science, Technology, Engineering and Mathematics) areas, these
include providing incentives, encouraging school officials to provide STEM materials to
students in K through 12 grades, supporting magnet schools that have the STEM
curriculum and creating public-private alliances that will foster interest in the STEM
areas (Society of Women Engineers 2006). Incentives such as financial backing make
education fields with higher wages more appealing to female students and will
encourage females to study and work in these previously male dominated fields.
29
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30
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34
APPENDIX
35
36
Number of Employed Civilians
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
3,500,000
1971
1972
1973
1974
1975
1976
f emale
male
Year
Figure 1A
Employment in IT Occupations by Sex, 1971 to 2009
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
37
Number of Employed Civilians
0
500,000
1,000,000
1,500,000
Female support
Female programmer
Female system analyst
Male support
1981
1980
1979
1978
1977
1976
2,000,000
Male programmer
Male system analyst
1982
2,500,000
Figure 1B
IT Occupational Employment by Sex, 1971 to 2009
1990
1989
Year
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1988
1987
1986
1985
1984
1983
1975
1974
1973
1972
1971
38
Median Annual Earning in 2008 Constant Dollars
$0
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
$70,000
$80,000
1971
1972
1973
1974
all workers (incl non-IT)
female
male
Year
Figure 2
Annual Median Earnings among Full-time Full-year IT Occupations by Sex, 1971 to 2009
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Figure 3
Income Determination Model
Gender
Structural
Individual
Income
(Wright, 1992)
39
Table 1A
Full Sample Values for Workers by Sex
Variables:
Full Sample
Dependent Variables
Annual Earnings (mean):
$67,745
Annual Earnings (median):
$65,000
Annual Earnings Centile (median):
50
Males
1
2
Females
$70,587 *** ^ $59,609
$70,000
$57,000
54
39
(stddev)
(29889)
(30111)
(27682)
FT Workers Annual Earnings (median):
$69,000
$70,001
$60,000
39.32
38.85
40.65
(10.5)
(100%)
(10.6)
(100%)
(10.3)
(100%)
Individual-level Factors:
Age (years)
High school Dipl or less (0,1)
9.7%
(0.3)
Some college (0,1)
24.1%
24.4%
23.1%
Bachelor degree (0,1)
46.1%
(0.5)
(0.5)
(0.5)
Master degree (0,1)
18.4%
17.9%
19.5%
(0.4)
(0.4)
(0.4)
Doctoral degree (0,1)
1.8%
2.0%
1.4%
(0.1)
(0.1)
(0.1)
% Midwest region (0,1)
22.6%
22.6%
22.6%
Rural Residence (0,1)
5.3%
(0.2)
(0.2)
(0.3)
Sample N (weighted):
7,233
100.0%
5,361
74.1%
1,872
25.9%
(0.4)
2
13.0%
(0.3)
(0.4)
1
8.5% ***
(0.3)
= *** p < 0.001, ** p < 0.01, * p<0.05
effect size greater than => .20
40
(0.4)
47.2% ***
(0.4)
4.7% ***
(0.4)
42.9%
(0.4)
7.0%
(pay-gap)
84.4%
81.4%
85.7%
Table 1B
Full Sample Values for Workers by Sex
Variables:
Full Sample
Structural-level Factors
Annual hours worked
2,132
(median)
2,080
(434.8)
Weekly hours
(median)
42.1
40
% Full time (0,1)
96.6%
% Government Worker (0,1)
12.2%
% Self-Employed (0,1)
3.7%
(6.8)
(0.2)
(0.3)
% Small Business (0,1)
Females
2,159 *** ^
2,080
2,053
2,080
(433.1)
(429.9)
42.6 *** ^
40
(6.9)
97.6% ***
(0.2)
10.7% ***
(0.3)
4.1% ***
40.8
40
(6.4)
93.9%
(0.2)
16.5%
(0.4)
2.4%
(0.2)
(100%)
(0.2)
(100%)
21.0%
22.5% ***
16.8%
(0.4)
(0.4)
(0.4)
% Medium Business (0,1)
14.0%
14.2%
13.1%
% Large Business (0,1)
65.0%
% Goods producing industry (0,1)
15.0%
16.0% ***
13.0%
(0.4)
(100%)
(0.4)
(100%)
(0.3)
(100%)
11.2%
11.3%
11.0%
(0.3)
(0.3)
(0.3)
(0.5)
% CIO (0,1)
% Engineers (0,1)
25.1%
(0.4)
% Programmers & Related (0,1)
35.8%
(0.5)
% Network & Related(0,1)
15.1%
(0.4)
% Computer Support (0,1)
12.8%
(0.3)
(0.3)
63.3% ***
(0.5)
26.7% ***
(0.4)
35.0% **
(0.5)
15.8% ***
(0.4)
11.2% ***
(0.3)
(0.3)
70.1%
(0.5)
20.8%
(0.4)
38.1%
(0.5)
12.9%
(0.3)
17.2%
(0.4)
(100%)
(100%)
(100%)
% High [CIO & Engineers] (0,1)
36.3%
37.9% ***
31.8%
(0.5)
(0.5)
(0.5)
% Medium [Prog & Network Related] (0,1)
50.9%
50.9%
51.0%
% Low [Support] (0,1)
12.8%
(0.3)
(0.3)
(0.4)
Sample N (weighted):
7,233
100.0%
5,361
74.1%
1,872
25.9%
(0.5)
2
1
(0.2)
(100%)
(0.3)
1
2
Males
= *** p < 0.001, ** p < 0.01, * p<0.05
effect size greater than => .20
41
(0.5)
11.2% ***
(0.5)
17.2%
Table 1C
Full Sample Values for Workers by Sex
Variables:
Full Sample
Gender-level factors:
Occupational Sex Segregation
0.56
(0.1)
% Minority (0,1)
% Married (0,1)
Females
0.55 *** ^
0.60
(0.1)
(0.2)
13.5% ***
17.5%
(0.4)
(100%)
(0.3)
(100%)
(0.4)
(100%)
65.4%
65.9%
64.0%
(0.5)
(0.5)
10.6%
% Never Married (0,1)
24.0%
% With children under 6 (0,1)
21.2%
(0.4)
(0.4)
(0.4)
% With children under 18 (0,1)
43.7%
43.8%
43.5%
(0.5)
(0.5)
(0.5)
% Immigrant (0,1)
21.5%
21.5%
21.3%
(0.4)
(0.4)
(0.4)
Sample N (weighted):
7,233
100.0%
5,361
74.1%
1,872
25.9%
= *** p < 0.001, ** p < 0.01, * p<0.05
effect size greater than => .20
42
8.9% ***
(0.5)
% Ever Married (0,1)
(0.4)
2
1
14.5%
(0.3)
1
2
Males
(0.3)
25.2% ***
(0.4)
22.1% ***
15.4%
(0.4)
20.7%
(0.4)
18.7%
Annual Median Earnings
43
$0
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
$70,000
$80,000
$90,000
under 25
25 to 29
30 to 34
35 to 39
Age Cohorts
40 to 44
45 to 49
50 to 54
pay_gap
f emale
male
55 to 59
Figure 4
Median Annual Earnings of Full-time Full-Year IT Workers by Age Cohort
60 & over
0%
20%
40%
60%
80%
100%
120%
Pay-gap
44
2
1
1,298
4,269
Graduate degree (master & doctoral)
College degree (bachelor to doctoral)
effect size greater than => .20
= *** p < 0.001, ** p < 0.01, * p<0.05
643
1,536
2,971
1,177
120
FTFY
HS Diploma or less
Some college (incl assoc deg)
Bachelor degree
Master degree
Doctoral degree
Education:
963
3,276
424
1,161
2,313
865
97
Male
335
993
219
375
658
312
23
19.3%
65.7%
10.4%
24.0%
46.4%
17.5%
1.8%
(100%)
Female FTFY
1
19.8%
67.4% ***
8.7% ***
23.9%
47.6% ***
17.8%
2.0%
(100%)
% of all
males
2
21.1%
62.6%
13.8%
23.6%
41.5%
19.6%
1.5%
(100%)
% all
females
$85,000
$75,000
$50,000
$55,000
$70,000
$83,000
$90,000
FTFY
***
***
***
***
1
^
^
^
^
2
$86,000 *** ^
$77,000 *** ^
$53,000
$56,000
$75,000
$85,000
$90,000
Male
Table 2
Educational Attainment and Median Annual Earnings of Full-time Full-year IT Workers
$75,000
$70,000
$44,000
$50,000
$65,000
$75,000
$85,001
87.2%
90.9%
83.0%
89.3%
86.7%
88.2%
94.4%
Female Pay-gap
45
2,349
3,304
794
High (CIO & Engineers)
Medium (Programmers & Network)
Low (Support)
2
effect size greater than => .20
= *** p < 0.001, ** p < 0.01, * p<0.05
739
1,610
2,317
988
794
(1) CIO
(2) Engineers
(3) Programmers & Related
(4) Network & Related
(5) Computer Support
1
739
1,514
96
1,293
858
165
349
638
573
220
FTFY
Chief Information Officer CIO (1)
Software engineer (2)
Hardware engineer (2)
Computer scientist & system analyst (3)
Computer programmers (3)
Database administrator (3)
Network system administrator (4)
Network system analyst (4)
Computer support specialist (5)
Computer operators (5)
Occupational Group:
1,844
2,498
519
557
1,286
1,714
784
519
557
1,210
76
939
666
109
287
497
410
108
Male
505
806
275
182
324
603
204
275
182
304
20
354
192
56
62
141
163
112
Female
36.4%
51.3%
12.3%
11.5%
25.0%
35.9%
15.3%
12.3%
11.5%
23.5%
1.5%
20.1%
13.3%
2.6%
5.4%
9.9%
8.9%
3.4%
(100%)
2
***
*
***
*** ^
*
*** ^
**
**
**
***
1
37.9% ***
51.4%
10.7% ***
11.5%
26.5%
35.3%
16.1%
10.7%
11.5%
24.9%
1.6%
19.3%
13.7%
2.2%
5.9%
10.2%
8.4%
2.2%
(100%)
% of all
FTFY males
31.8%
50.8%
17.3%
11.5%
20.4%
38.0%
12.8%
17.4%
11.5%
19.2%
1.2%
22.3%
12.1%
3.6%
3.9%
8.9%
10.3%
7.1%
(100%)
% all
females
0.48
0.58
0.75
0.60
0.42
0.64
0.44
0.75
0.60
0.43
0.32
0.66
0.57
0.85
0.34
0.49
0.62
1.09
occ sex
seg
index
$80,000
$65,000
$45,000
$85,000
$80,000
$65,000
$60,000
$45,000
$85,000
$80,000
$79,007
$65,000
$65,000
$70,000
$60,000
$62,000
$50,000
$34,000
FTFY
2
***
***
***
***
***
^
^
^
^
*** ^
*** ^
** ^
*** ^
*
*** ^
*** ^
1
$85,000 *** ^
$65,000 *** ^
$45,000 *** ^
$86,000
$82,000
$67,000
$65,000
$45,000
$86,000
$82,000
$79,810
$65,000
$65,000
$80,000
$60,000
$65,000
$50,000
$40,000
Male
Table 3
Occupational Position and Median Annual Earnings of Full-time Full-year IT Workers
$71,796
$60,000
$42,267
$80,000
$70,000
$62,929
$50,000
$42,267
84.5%
92.3%
93.9%
93.0%
85.4%
93.9%
76.9%
93.9%
$80,000 93.0%
$70,000 85.4%
$66,833 83.7%
$65,000 100.0%
$65,000 100.0%
$55,000 68.8%
$53,747 89.6%
$50,000 76.9%
$50,000 100.0%
$31,000 77.5%
Female Pay-gap
TABLE 4
OLS Regression Analysis for the Income Determination Model
(Dependent variable = annual earnings)
Variables:
Individual-level factors:
Age (years)
Age centered square
Doctoral degree (0,1)
Master degree (0,1)
Bachelor degree
Less than College Degree
Midwest (0,1)
Rural (0,1)
Full sample
unstd. 1
std.
Males
unstd.
1
std.
$700 ***
0.247
$718 ***
0.252
-$31 ***
-0.128
-$34 ***
-0.138
$18,804 ***
0.084 $19,726 ***
0.091
$18,062 ***
0.234 $17,331 ***
0.221
$11,707 ***
0.195 $11,156 ***
0.185
ref grp
ref grp
-$3,545 ***
-0.050 -$3,603 ***
-0.050
-$10,405 ***
-0.078 -$10,488 ***
-0.074
2
Females
unstd. 1
std.
$612 ***
0.228
-$24 ***
-0.106
$14,780 ***
0.063
$19,765 ***
0.283
$12,796 ***
0.229
ref grp
-$3,149 ***
-0.048
-$9,677 ***
-0.089
Structural-level factors:
Annual Hours Worked
$21 ***
0.308
$20 ***
0.292 ^
$23 ***
0.356
Government Worker -$5,878 ***
-0.064 -$6,742 ***
-0.069
-$3,969 **
-0.053
Self Employed -$4,847 ***
-0.030 -$4,475 ***
-0.029
-$4,653
-0.026
Large Business (0,1)
$7,991 ***
0.127
$8,408 ***
0.135
$6,568 ***
0.109
Medium Business (0,1)
$3,438 ***
0.040
$4,091 ***
0.047
$977
0.012
Small Business (0,1)
ref grp
ref grp
ref grp
Goods-producing (0,1)
$1,653 *
0.020
$1,231
0.015
$2,815 *
0.034
CIO (0,1) $20,764 ***
0.219 $21,921 ***
0.230
$17,103 ***
0.193
Engineers (0,1) $16,195 ***
0.235 $18,604 ***
0.273 ^
$9,349 ***
0.137
Programmers & Related (0,1)
$9,825 ***
0.158 $10,192 ***
0.161
$8,369 ***
0.147
Network & Related (0,1)
$8,607 ***
0.103 $11,214 ***
0.136
$1,139
0.014
Computer Support (0,1)
ref grp
ref grp
ref grp
Gender-level factors:
Female (0,1)
Occ.Sex-Seg.Index
Minority (0,1)
Immigrant (0,1)
Married (0,1)
Ever-married (0,1)
Never-married (0,1)
% with child under age 6 (0,1)
-$8,489 ***
-$5,191
-$4,369 ***
-$258
$4,887 ***
ref grp
-$3,190 ***
-$617
-0.124
-0.026
-0.051
-0.004
0.078
-0.046
-0.008
(Constant): -$18,370 ***
Adjusted R-sq.
0.469 ***
$609
-$4,400 ***
-$569
$4,957 ***
ref grp
-$4,171 ***
-$301
-$20,332 ***
0.457 ***
1
= *** p < 0.001; ** p < 0.01; * p < 0.05
2
significant difference betw een men and w omen at the .05 level
46
0.003
-0.050
-0.008
0.078
-0.060
-0.004
-$16,547
-$4,932
$648
$4,388
ref grp
-$35
-$2,587
***
***
***
-0.103
-0.068
0.010
0.076
*
-0.001
-0.036
-$20,129 **
0.461 ***
Figure 5
Shares of Unique Variance Explained
Full sample:
6.8%
Gender
48.3%
Structural
44.9%
Individual
Income
(Wright, 1992)
Males only:
2.2%
Gender
46.0%
Structural
51.8%
Individual
Income
(Wright, 1992)
47