08832323.2011.630433
Journal of Education for Business
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Identifying Differences in Business Students’
Salary Expectations
Nicholas Khosrozadeh , Jeanna McGinnis , Oliver Schnusenberg & Lynn
Comer Jones
To cite this article: Nicholas Khosrozadeh , Jeanna McGinnis , Oliver Schnusenberg & Lynn
Comer Jones (2013) Identifying Differences in Business Students’ Salary Expectations, Journal
of Education for Business, 88:1, 16-25, DOI: 10.1080/08832323.2011.630433
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Date: 11 January 2016, At: 20:54
JOURNAL OF EDUCATION FOR BUSINESS, 88: 16–25, 2013
C Taylor & Francis Group, LLC
Copyright
ISSN: 0883-2323 print / 1940-3356 online
DOI: 10.1080/08832323.2011.630433
Identifying Differences in Business Students’ Salary
Expectations
Nicholas Khosrozadeh
Fidelity Investments, Jacksonville, Florida, USA
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:54 11 January 2016
Jeanna McGinnis
UBS, New York, New York, USA
Oliver Schnusenberg and Lynn Comer Jones
The University of North Florida, Jacksonville, Florida, USA
The authors investigated preparedness variables affecting business students’ salary expectations
by utilizing a sample of 209 finance students from a regional university and 51 students
attending the Financial Management Association Leaders’ Conference in New York in 2011.
Students who network more, are applying for a higher level job, and perceive their mathematical
ability to be higher expect to earn more 1 and 5 years after graduation. However, students who
perceive the difficulty of finding a job to be higher have lower expectations for salaries when
graduating. These relationships are more pronounced for men than for women. However,
female finance students expect to earn higher salaries than male finance students, holding these
variables constant.
Keywords: mathematical ability, networking relationships, salary differences, salary
expectations
A variety of studies have investigated variables that influence career success (Calkins & Welki, 2006; Kirchmeyer,
1998), while other studies have investigated the factors that
determine choices of college majors (Hunjra, Ur-Rehman,
Ahmad, Safwan, & Ur-Rehman, 2010; Malgwi, Howe, &
Burnaby, 2005; Jackson et al., 1992). However, there is a relative absence of studies investigating the factors influencing
students’ salary expectations. Specifically, it is interesting to
investigate if networking relationships, self-perceived mathematical ability, and perceived difficulty in finding a job influence the salaries students expect to earn upon graduation.
For decades, gender has been the foundation from which
these determinants of career success and major choice have
been analyzed. Specific influencing factors such as human
capital gain, individual variables, interpersonal relationships,
and family obligations are used in studies such as Chenevert and Tremblay (2002) and Kirchmeyer (1998). The
Correspondence should be addressed to Oliver Schnusenberg, The University of North Florida, Department of Accounting and Finance, 1 UNF
Drive, Jacksonville, FL 32224, USA. E-mail: [email protected]
influencing factors have had positive and negative effects
on men and women within the business career industry.
Although the business major is number 1 on the top 10 list
of college majors for women (Tulshyan, 2010), there is still
much to be learned in terms of what motivates women to enter
the business major and eventually enter the field of finance.
According to the Chartered Institute of Management Accountants’ (CIMA, 2010) recent survey of over 4,500 finance
and business professionals from across the globe on use of
leadership skills and career progression strategies by gender,
women are six times less likely than their male counterparts
to be working as chief financial officers or chief executive
officers. Moreover, on average, CIMA male members earn
24% more than female members in the United Kingdom
and 39% more in Ireland; in South Africa and Sri Lanka the
difference is an even wider (47%). The study also found that
women still lag behind men in terms of seniority and salary,
which becomes particularly significant after 10 years’ work
experience. Such stark salary differences may be justified
if women are unable to perform as well as men in the same
job. However, CIMA also found that having more women
in senior roles is linked to stronger financial performance.
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STUDENT SALARY EXPECTATIONS
Nonetheless, the finance industry has historically been
dominated by men. Consequently, understanding the reasoning behind women’s success and motivation, educational
institutions and professional corporations will potentially be
able to promote increased advancements for women finance
professionals.
Our primary objective was to investigate the relationship
between various factors related to preparedness, such as the
amount of networking students engage in and the involvement with student organizations, and the expected earnings
percentiles of 1 year, 5 years, and 10 years after graduation. A
secondary objective was to investigate whether this relationship differs by gender. Subjects involved within the survey
included female and male students at a regional university
in Florida enrolled in finance degree courses and female and
male students who attended the Financial Management Association (FMA) Leaders’ Conference in New York in March
2011. Investigating these issues is important to graduating
students and employers alike.
The remainder of this article is structured as follows. We
present a review of related literature, the hypotheses and data,
the results, and then the conclusions and implications.
REVIEW OF RELATED LITERATURE
Our primary objective was to investigate the factors that relate to student preparedness for the job market, while our
secondary objective was to investigate gender differences in
these factors. In the literature, these two objectives are not
clearly separable. A multitude of studies, for example, have
investigated factors influencing students’ choice of major
and gender differences in these choices. Other studies have
focused on career success factors and gender differences in
these factors. However, we were aware of virtually no studies
that have investigated career success factors without controlling for gender differences. Overall, the existing research
seems to indicate that women choose their major in college
more out of present and social interests. However, to our
knowledge, no research to date has investigated how future
salary expectations differ between male and female college
students based on their level of motivation and other personal characteristics. Specifically, we were not aware of any
research that had investigated what the differences in salary
expectations are once students are highly motivated students
in their field and close to graduation.
Several studies have investigated factors influencing students’ choice of majors. Calkins and Welki (2006) attempted
to find these factors and explore just why some students never
consider career paths within the realm of economics. Findings reveal that the most important factors in the choice of
major are interest in the subject, career concerns, the student’s performance in major classes, and the teaching reputation and approachability of the faculty. Similarly, Malgwi
et al. (2005) investigated the different factors influencing
17
business students’ choice of major. Their findings showed
that interest in the subject was the most important factor for
incoming freshmen. Even with students that changed to a
business major from a nonbusiness major, credit is given to
the positive dispositions toward the new major, rather than
negative feelings toward their old major. The authors also
reinforced the fact that high schools do little to nothing in
influencing students to pursue a career in business. They
found, to their surprise, that high school courses, high school
advisors, and even parents do not appear to be particularly
influential in the initial major choice. The main influences
from high school on selecting a major seems limited to only
the general coursework studied in the curriculum. The researchers suggest that more out-of-the-box type exposure to
subject study should be granted to students so as to not limit
their choices to just the general coursework.
In Hunjra et al.’s (2010) research of the general factors
explaining the choice of a finance major, it was discovered
that the majority of students are interested in the field of finance for the personal benefits rather than playing a positive
and participating role in society. The research also reinforces
the perception that finance is a profit-driven field. Hunjra
et al. also found the respondents to perceive the field as less
theoretical and more mathematical. Jackson et al.’s (1992)
variables for perceived job inputs included, but were not limited to, basic job skills, previous work experience, business
sophistication, preparation, and qualifications.
A variety of studies have investigated gender differences
in factors of career success and student choice of major. Chenevert and Tremblay (2002), for example, found that female
students have a considerable amount of control over their
future success in their finance career, and that the amount
of control over their success can be influenced by a majority of factors, including support from educators and family, the continuation of further education and involvement,
changes within the family structure (marriage or children),
and others. Relatedly, Kirchmeyer (1998) studied those individuals who “worked for a variety of industries within
manufacturing, health care, financial and other professional
services, and banking being most common, and mostly in
general management and the functional specialties of accounting and finance” (p. 680). To determine career success
and rate importance, Kirchmeyer measured the importance of
career progression, perceived career success, human capital
variables, gender roles, supportive relationships, family status variables, and sex. Findings indicated that human capital
variables have stronger effects on men’s components of success than on women’s. In addition, supportive relationships,
such as mentors and peer networks, are more pronounced for
men. However, gender roles have stronger effects on success
for women. Last, the findings show no differential effect of
family status.
In their research, Bansak and Starr (2010) attempted to
look deeper into the gender differences in predispositions
toward economics. While economics is a separate field of
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18
N. KHOSROZADEH ET AL.
study from finance, and economics departments in universities are often not located in the business school, finance is
a subfield of economics and therefore a field relevant to the
present research. Bansak and Starr found that students view
economics as a field that prioritizes math skills and making
money—a combination that they find to be unappealing to
women, but not so much for men. They found women’s predispositions toward studying economics to be reflective of
their concerns about subject difficulty and disinterest. They
also found a general disinterest in women in the types of jobs
associated with an economics major, the main concern being
unfriendly workplaces. The common misconception of fears
about the work–family balance in the economic workplace
was proven to not be so important in scaring away female
interest in economics. Jackson et al. (1992) found that men
have higher job-performance expectations than women based
on their perceived job inputs (i.e., their beliefs about what
they had to contribute to the job). Malgwi et al. (2005) also
found that for women a very influential factor in choice of
major was aptitude in the subject. Men happened to be significantly more influenced by the major’s potential for career
advancement and job opportunities and the level of compensation in the field. In the study mentioned previously, Calkins
and Welki (2006) also addressed gender differences in major
choices. Specifically, for female respondents, more emphasis was put on subject interest, good class performance, high
school exposure, and the encouragement of a high school
teacher, whereas male respondents were more concerned
with the perceived marketability and expected income associated with the subject.
Jackson et al. (1992) and Heckert et al. (2002) find that
controlling for perceived others’ pay eliminates gender differences in entry-level pay. Jackson et al. suggested that peak
career pay gender differences diminished slightly as a function of business sophistication. This suggests that job inputs
related to business sophistication should increase women’s
perceptions on the ability to earn higher salaries. We conclude
that more research is needed to test the impact of perceived
job inputs on the gender gaps in peak pay expectations.
HYPOTHESES AND METHOD
Hypotheses
To our knowledge, no studies to date have investigated to
what extent students actually expect their salaries to vary
based on a variety of factors related to preparedness, such
as networking with companies prior to graduation or mathematical ability. The various studies included in the literature
review on career success, while very informative, have not
investigated if there are actually any differences in salary expectations based on factors that would render students more
employable. In the present study we sought to investigate the
specific relationship between salary expectations and several
variables related to preparedness. Our primary hypothesis is
that factors that render students more attractive to potential
employers based on their level of preparedness for the job
would result in expectations of higher salaries:
Hypothesis 1: The more prepared a student is to enter the
workforce, the higher the earnings percentile that student
would expect after graduation.
Moreover, we investigate earnings percentile expectations
for 1, 5, and 10 years after graduation. Once a student has
entered the workforce, his or her salary will be influenced by
factors other than initial preparedness (Chenevert & Tremblay, 2002). Therefore, we developed a second hypothesis.
Hypothesis 2: The relationship between preparedness variables and salary expectations would be less pronounced
the longer a student has been in the workforce.
We also investigated whether there is a difference in salary
expectations by gender. The extant literature suggests that
salary expectations gender differences (peak pay) are mitigated in specialty areas (Major & Konar, 1984). Moreover,
Jackson et al. (1992) found that the level of business sophistication is positively related to peak pay expectations, but
not to entry pay expectations. For entry pay, Jackson et al.
and Major and Konar found that there are no gender differences in salary expectations once perceived others’ pay is
controlled for. Thus, collectively, the extant literature suggests there should be no differences between women’s and
men’s pay expectations at entry or peak pay once specialty
area, perceived others’ pay, and business sophistication are
controlled for.
Therefore, we formulated a third hypothesis.
Hypothesis 3: There would be no gender difference in entry
or peak pay expectations.
Last, we investigated whether there is a difference in the
relationships between salary expectations and preparedness
variables between the genders. Since this is the first study to
investigate finance salary difference expectations, we did not
focus our hypotheses on the specific variables that may have
a differential impact on salary expectations by gender.
Specifically, we sought to test the following fourth hypothesis:
Hypothesis 4: The relationship between preparedness variables and salary expectations would be the same for men
as for women, particularly for highly motivated finance
students.
Participants
The participants include general business students at a regional Florida university enrolled in an introductory finance
course, finance majors enrolled in an upper level behavioral
finance course at a regional Florida university, and students
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STUDENT SALARY EXPECTATIONS
who attended the March 2011 FMA Leaders’ Conference
in New York. As an inducement, introductory students were
provided extra credit for completing the survey and behavioral students were provided participation points. Students
who did not complete the survey were offered other comparable extra credit opportunities instead. The response rate
for courses at the regional Florida university were 65.38%
(221 responses in the introductory course) and 66.67% (22
responses in the behavioral finance course). The response
rate at the FMA conference was 26.77% (83 responses from
310 registrants). The relatively low response rate can be explained, at least in part, by the absence of any extra credit opportunities. However, students were informed that we would
gladly share the results of the survey. We considered the 83
students to be especially motivated and an appropriate sample. Moreover, our response rate was similar to Jackson et al.
(1992), 447 of 1,588.
Incomplete and nonsensical answers reduced the final
sample to 260 students (209 from the regional university
in Florida, 19 of whom were upper level behavioral finance
students, and 51 from the FMA Finance Leaders’ Conference). Sixty-one percent of the final sample was men, 12%
were married, and 10% had children. Forty-four percent of
the respondents indicated they had some work experience in
their field (including internships; see Table 1). Our sample
is perfect for investigating the choices of upper classmen, as
98% of the sample were either juniors or seniors.
Procedures
The survey included an introduction explaining the purpose
of the research, which was being conducted by the accounting
and finance department at the regional Florida university. The
purpose of the research was described as obtaining information about student preparation for the job market. Anonymity
and confidentiality were guaranteed, and students were allowed to withdraw consent and responses without penalty or
prejudice. Moreover, in a separate informed consent form,
students were given contact information for the principal investigator and institutional review board chair, and told orally
they could ask questions about the research.
Instrument
The survey is included in the Appendix, together with the
script students received with the survey. The survey includes
10 questions related to job market preparedness, regarding,
for example, high school and college grade point averages
(GPAs); the amount of networking relationships students
have with companies; the employment level sought (e.g.,
entry level vs. executive); the students’ intention to pursue
a higher degree; utilization of a university’s career management center; self-perceived mathematical ability; levels of
support from employers, professors, and family and friends to
pursue a career; and involvement with student organizations
are all variables that reflect a student’s level of preparedness.
Arguably, students who are performing better on these vari-
19
TABLE 1
Summary Statistics for Selected Variables (n = 260)
Variable
Male
Married
Children
Work experience
High School GPAa
College GPAa
Networking relationshipsb
Higher degreec
Utilize Career Management
Centerd
Math abilitye
Career supportf
Educational supportf
Personal supportf
School club memberg
Employment levelh
Difficulty finding job?i
%
M
SD
Median
Minimum
Maximum
3.15
2.90
2.30
1.68
2.69
0.87
0.82
1.19
0.89
1.23
3.00
3.00
2.00
1.00
3.00
1
1
1
1
1
4
4
5
3
5
7.27
3.15
3.44
4.22
2.32
1.97
3.16
1.78
1.47
1.33
1.11
1.55
0.87
1.10
8.00
3.00
4.00
5.00
1.00
2.00
3.00
1
1
1
1
1
1
1
10
5
5
5
5
4
5
60.8
11.9
9.6
43.5
aChoices were 1 (less than 2.5), 2 (2.5–2.9), 3 (3.0–3.4), and (3.5 or
greater). bFive-point Likert-type scale ranging from 1 (none) to 5 (a lot).
cChoices were 1 (yes, master’s only), 2 (yes, master’s and doctoral), and 3
(no, neither). Frequencies were 60%, 12%, and 29% for the three choices,
respectively (error due to rounding). dFive-point Likert-type scale ranging
from 1 (never) to 5 (always). eChoices were from 1 (low) to 10 (high). fFivepoint Likert-type scale ranging from 1 (low support) to 5 (high support).
gFive-point Likert-type scale ranging from 1 (no, I am not a member of
such an organization) to 5 (I’m very active and an officer of our student
organization). hChoices were 1 (entry level), 2 (associate), 3 (management),
and 4 (executive). Frequencies were 37%, 31%, 29%, and 2% for the four
choices, respectively. iFive-point Likert-type scale ranging from 1 (not at
all difficult) to 5 (very difficult).
ables should expect a higher salary level. In addition to these
variables, we also included two other variables that should
influence the expected salary: the employment level sought
(e.g., entry level vs. executive) and the perceived difficulty
of finding a job were also included as control variables.
As shown in Table 1, the median high school and cumulative college GPA for students in the sample was between 3.0
and 3.4. However, the average student has few networking
relationships. Seventy-two percent of the sample intended to
pursue either a master’s or doctoral degree, and the median
student sometimes utilized the career management center
at the university. Students perceived their own mathematical
ability to be rather high, with a median score of 8 of 10. Family and friends appeared supportive of students’ careers, with
a median score of 5 on a 5-point scale; this was followed by
support from the academic environment (median score of 4)
and the work environment (median score of 3). The average
student was not very involved in the student organizations
at their university. Somewhat surprisingly, only 37% of the
sample was seeking an entry-level position, while 31% and
29% were seeking associate and management positions, respectively. On average, students in the sample did not think
it would be either overly difficult or easy to find a job.
To investigate the degree to which student salary expectations can be predicted using the preparedness variables
20
N. KHOSROZADEH ET AL.
TABLE 2
Percentage Frequency Distribution of Student Salary Expectations for Various Time Periods Postgraduation (n = 260)
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:54 11 January 2016
1 year
5 years
10 years
10% (lowest)
20%
30%
40%
50%
60%
70%
80%
90%
100% (highest)
3.5
0.0
0.4
9.6
3.1
0.4
11.9
3.8
2.7
20.0
6.2
1.9
26.9
14.2
4.6
13.1
17.3
6.2
9.2
21.5
11.9
5.4
21.5
23.5
0.4
9.2
23.5
0.0
3.1
25.0
described previously, it is useful to assess the salary students
are expecting after graduation. For this purpose, Table 2
presents the percentage frequency distribution of earnings
deciles for 1 year, 5 years, and 10 years after graduation.
Clearly, students expected the earnings decile they are in to
increase dramatically the longer they are in the workforce.
In the case of our sample, 1 year after graduation, students
expected to be, on average, in the fifth earnings decile. Five
years after graduation, they expected to be in decile 7, on
average. Ten years after graduation, they expected to be in
decile 8, on average. Moreover, Table 2 shows that 25% of
students in our sample expected to be in the highest earnings
decile 10 years after graduation.
where SALi is the salary expectation (earnings decile) of
student i for alternatively 1 year, 5 years, and 10 years postgraduation; NETi is the networking relationships of student i,
measured using a 5-point Likert-type scale; ELEVELi is the
employment level sought by student i, expressed as a variable
ranging from 1 (entry level) to 4 (executive); JDIFFi is the
perceived difficulty of finding a job for student i, measured
using a 5-point Likert-type scale; MATHi is the perceived
mathematical ability for student i, measured from 1 (low) to
10 (high); and εi is the error term for student i.
Note that Equation 1 does not contain the level of personal support, which was shown in Table 3 to influence the
responses to the salary expectations for five years after graduation. Moreover, Equation 1 includes the self-perceived
RESULTS
Our ultimate goal was to isolate those preparedness-related
variables that explain the expected salaries 1 year, 5 years,
and 10 years after graduation. Including all 13 variables as
independent variables in a regression would result in some
methodological challenges, as the sample consisted of only
260 observations. In order to identify those variables whose
response distribution is significantly different for the three
salary expectation variables, we therefore first conducted chisquare tests. The results of these tests are show in Table 3.
As shown in Table 3, for the earnings decile expected by
students 1 year after graduation, only 3 of the 13 included
variables were significant. Specifically, the distribution of
responses to the salary expectation differed for networking
relationships, the initial employment level (e.g., entry level
vs. executive) and the perceived difficulty of finding a job.
For the earnings deciles expected 5 years after graduation,
networking relationships, the level of personal support, and
the perceived difficulty in finding a job influenced the distribution of responses. For 10 years after graduation, none of
the 13 variables resulted in significantly different responses
to the expected earnings percentile.
Relationship Between Preparedness Variables
and Salary Expectations for Various Time
Periods Postgraduation (Hypotheses 1 and 2)
Based on the results from Table 3, we next utilized the following regression model:
SALi = a0 + a1 NETi + a2 ELEVELi + a3 JDIFFi
+ a4 MATHi + εi ,
(1)
TABLE 3
Chi-Square Tests for Differences in Distribution of
Variables Affecting Preparedness
1 year
Variable
High school GPAa
College GPAa
Networking
relationshipsb
Higher degreec
Utilize Career
Management Centerd
Math abilitye
Career supportf
Educational supportf
Personal supportf
School club memberg
Work experience
Employment levelh
Difficulty finding job?i
χ2
5 years
p
χ2
10 years
p
χ2
p
21.50
24.51
46.54∗
.609 19.64
.433 22.57
.047 45.09∗
.717 29.94
.546 26.47
.062 40.80
.317
.493
.268
17.57
27.29
.350 14.63
.704 31.93
.552 8.08
.470 39.65
.977
.310
86.99
27.58
21.85
34.18
32.24
9.69
47.29∗∗
48.45∗
.110
.690
.911
.363
.455
.288
.003
.031
96.45
29.33
22.63
33.26
46.22
9.62
29.69
34.38
.116
.777
.960
.599
.118
.382
.328
.546
86.71
25.37
17.73
46.43∗
20.03
4.04
28.11
49.09∗
.114
.791
.981
.048
.951
.854
.256
.027
aChoices were 1 (less than 2.5), 2 (2.5–2.9), 3 (3.0–3.4), and (3.5 or
greater). bFive-point Likert-type scale ranging from 1 (none) to 5 (a lot).
cChoices were 1 (yes, master’s only), 2 (yes, master’s and doctoral), and 3
(no, neither). Frequencies were 60%, 12%, and 29% for the three choices,
respectively (error due to rounding). dFive-point Likert-type scale ranging
from 1 (never) to 5 (always). eChoices were from 1 (low) to 10 (high). fFivepoint Likert-type scale ranging from 1 (low support) to 5 (high support).
gFive-point Likert-type scale ranging from 1 (no, I am not a member of
such an organization) to 5 (I’m very active and an officer of our student
organization). hChoices were 1 (entry level), 2 (associate), 3 (management),
and 4 (executive). Frequencies were 37%, 31%, 29%, and 2% for the four
choices, respectively. iFive-point Likert-type scale ranging from 1 (not at
all difficult) to 5 (very difficult).
∗ p < .05. ∗∗∗ p < .01. N = 260.
STUDENT SALARY EXPECTATIONS
21
TABLE 4
Regression Results for Salary Expectations and Preparedness Variables
1 year
5 years
10 years
Intercept
p
NET
p
ELEVEL
p
JDIFF
p
MATH
p
Adj. R2
F
p
2.818∗∗
5.072∗∗
7.052∗∗
.000
.000
.000
.158†
.167†
.122
.077
.082
.216
.466∗∗
.329∗
.202
.000
.011
.129
–.205∗
–.222∗
–.126
.038
.036
.249
.162∗∗
.157∗
.104
.006
.013
.109
12.10%
8.50%
2.60%
9.896∗∗
7.049∗∗
2.702∗
.000
.000
.031
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:54 11 January 2016
Note. The model is SALi = a0 + a1 NETi + a2 ELEVELi + a3 JDIFFi + a4 MATHi + εi , where SALi = the salary expectation (earnings decile) of student
i for alternatively 1 year, 5 years, and 10 years postgraduation; NETi = the networking relationships of student i, measured using a 5-point Likert-type scale;
ELEVELi = the employment level sought by student i, expressed as a variable from 1 (entry level) to 4 (executive); JDIFFi = the perceived difficulty of
finding a job for student i, measured using a five-point Likert-type scale; MATHi = the perceived mathematical ability for student i, measured from 1 (low)
to 10 (high); and εi = error term for student i.
†p < .1. ∗ p < .05. ∗∗∗ p < .01.
mathematical ability, which was marginally significant in
Table 3, with p values of .11 for all three salary variables.1
The results from estimating Equation 1 are shown in
Table 4. As indicated on the first row of Table 4, the four
variables included as independent variables explain about
12.1% of the salary expected 1 year after graduation.2 All
four included variables are significant at conventional levels,
with the expected sign; students who networked more, were
applying for a higher level job, and perceived their mathematical ability to be higher expected to be in a higher earnings
percentile 1 year after graduation. However, students who
perceived the difficulty of finding a job to be higher had lower
expectations for salaries when graduating. The explanatory
power of these four variables dropped to 8.5% and 2.6% for
the earnings percentiles expected 5 and 10 years after graduation, respectively, which only makes sense, given that many
other variables will factor into salary expectations once students are actually employed. However, while none of the four
variables appeared to affect expected salaries 10 years after
graduation, the four variables were still significant (although
mostly to a lesser extent) in influencing the expected salaries
5 years postgraduation. For the total sample, we therefore
found strong support for Hypothesis 1, that better preparation is associated with higher salary expectations. However,
there was only partial support for Hypothesis 2: the relationship between preparedness variables and salary expectations
was equally strong for salary expectations 1 and 5 years after
graduation, although they became insignificant 10 years after
graduation. This is an interesting finding that indicates that
although students may also consider additional variables in
the future, they at least also consider their present level of
preparedness to influence their salaries even 5 years ahead.
Relationship Between Preparedness Variables
and Salary Expectations by Gender (Hypotheses
3 and 4)
In order to investigate whether salary expectations differed
by gender (Hypothesis 3) and whether the relationship between preparedness variables and salary expectations dif-
fered across genders (Hypothesis 4), we utilized the following regression model:
SALi = a0 + a1 INDEXi + a2 GINDEXi
+ a3 GENDERi + εi , where
(2)
where SALi is the salary expectation (earnings decile) of student i for alternatively 1 year, 5 years, and 10 years postgraduation; INDEXi is an index that is equal to the sum of the variables NET, ELEVEL, and MATH, less the variable JDIFF for
student i from Table 5; GINDEXi is INDEXi × GENDERi ;
GENDERi is a dummy variable equal to unity for women
and zero otherwise; and εi is the error term for student i.
Notice that the variable INDEX contains all four variables that were previously included as separate regressors
in Table 4.3 In Equation 2, the coefficient a1 estimates the
relationship between the index and SAL for men only; the coefficient a2 for the interaction term GINDEX indicates how
much more or less pronounced the relationship between the
variables contained in the index and salary expectations is for
women than for men; the coefficient a3 indicates how much
more or less pronounced the salary expectations for women
are, holding the variable INDEX constant. We expected a1 to
be positive and significant and a2 to be negative and significant, which would be consistent with most of the variables
being significant for men, but not for women in Table 5.
Although previous studies show that women earn less than
men, indicating that the coefficient a3 should be negative and
significant, and that women expect higher salaries 1 year after
graduation. Thus, to the extent that this higher salary was not
driven by the level of the preparedness variables, coefficient
a3 was expected to be positive and significant.4
The results from estimating Equation 2 for the entire sample of 260 students are shown in Panel A of Table 5. The
rows in Table 5 show the regression results for alternative
dependent variables for earnings deciles 1, 5, and 10 years
after graduation. As shown in the first row of Table 5, the
independent variables explain about 12% of the variation in
salary expectations 1 year after graduation. This value drops
to 11% for 5 years after graduation and to 6% for 10 years
after graduation, indicating that the variables forming the
22
N. KHOSROZADEH ET AL.
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TABLE 5
Regression Results for Salary Expectations and an Index of Preparedness Variables
Panel A: Total sample (n = 260)
1 year
5 years
10 years
Panel B: Business majors (n = 190)
1 year
5 years
10 years
Panel C: Highly motivated finance
majors (n = 70)
1 year
5 years
10 years
Intercept
p
Index
p
GIndex
p
Gender
p
Adj. R2
F
p
2.411∗∗∗
4.217∗∗∗
6.342∗∗∗
.000
.000
.000
.252∗∗∗
.252∗∗∗
.184∗∗∗
.000
.000
.000
–.133∗
–.147∗
–.144∗
.068
.058
.070
1.336∗∗
1.712∗∗
1.696∗∗
.036
.011
.015
11.80%
11.30%
5.80%
12.590∗∗∗
11.981∗∗∗
6.349∗∗∗
.000
.000
.000
2.580∗∗∗
4.433∗∗∗
6.302∗∗∗
.000
.000
.000
.264∗∗∗
.245∗∗∗
.174∗∗∗
.000
.000
.003
–.131∗
–.123
–.135
.071)
.137
.136
1.047∗
1.397∗
1.774∗∗
.098
.052
.025
16.10%
11.20%
5.60%
13.096∗∗∗
8.936∗∗∗
4.755∗∗∗
.000
.000
.003
2.063∗∗
3.633∗∗∗
6.383∗∗∗
.033
.000
.000
.228∗∗
.284∗∗∗
.207∗∗
.031
.005
.015
–.295
–.343
–.119
.281
.187
.586
3.350
3.603
1.066
.172
.122
.584
5.60%
9.10%
4.80%
2.374∗∗
3.298∗∗
2.154
.078
.026
.102
Note. The model is SALi = a0 + a1 I N DEXi + a2 GI N DEXi + a3 GEN DERi + εi , where SALi = the salary expectation (earnings decile) of student
i for alternatively 1 year, 5 years, and 10 years postgraduation; I N DEXi = an index that is equal to the sum of the variables NET, ELEVEL, and MATH,
less the variable JDIFF for student i from Table 5; GI N DEXi = I N DEXi × GEN DERi ; GEN DERi = a dummy variable equal to unity for women
and zero otherwise; εi = error term for student i.
†p < .1. ∗ p < .05. ∗∗∗ p < .01.
constructed index lose some of their explanatory power the
longer students are in the work force. Again, given that future salaries depend on a myriad of factors once students are
employed, this result is hardly surprising.
Despite the decreasing explanatory power, the results displayed in Table 5 are surprisingly consistent. In all three
regressions, the variables INDEX and GINDEX have the expected positive and negative and significant coefficients, respectively. This indicates that the relationship between the
INDEX and salary expectations is more pronounced for men
than for women. Again, it is possible that other variables
not included here would do a better job at explaining salary
expectations for women. The coefficient for GENDER is positive and significant, which indicates that women expect to
be in a higher earnings decile than men 1 year, 5 years, and
10 years after graduation. To the extent women’s job input
and business sophistication perceptions are enhanced, these
results are not surprising.
Hypothesis 3, that there would be no gender difference
in entry or peak salary expectations, was not supported; the
coefficient a3 is positive and significant in all regressions
in Panel A, indicating that women expected to earn higher
salaries than men. This is inconsistent with findings in the
previous literature in that previous findings indicate that there
should not be a gender difference once specialty area, business sophistication, and perceived others’ pay is controlled
for. Hypothesis 4, that the relationship between preparedness
variables and salary expectations would be the same for men
and women, was also not supported, at least for the total
sample; the coefficient a2 is consistently negative and significant, indicating that the relationship between preparedness
variables and salary expectations was less pronounced for
women than for men. One possible explanation for the finding in Panel A that women expect to earn more than men
is the lack of control for a specialty area. While we controlled for the level of business sophistication (through the
preparedness variables) and indirectly for the perceived others’ pay,5 we have not yet controlled for a specialty area. In
order to accomplish this, we partitioned the total sample into
two subsamples.
Panel B shows the regression results for the subsample of
190 business majors, while Panel C shows the results for the
subsample of 70 upper level finance majors, consisting of the
upper level course at the regional Florida university and the
FMA conference participants. If lack of a specialty area is
the cause of the higher salary expectations women display
in Panel A, then the subsample of finance majors should not
display a significant coefficient for the GENDER variable.
Indeed, Panel C of Table 5 reveals that this is true; a3 is not
significant for salary expectations either 1 year, 5 years, or
10 years after graduation for the subsample of finance majors. Conversely, the results for business majors in Panel B
are similar to those for the total sample in Panel A. Notably,
however, the coefficient a2 is not significant for salary expectations 5 years and 10 years after graduation, implying that
the relationship between preparedness variables and salary
expectations is equally strong for men and women for those
salaries. Panel C also reveals that Hypothesis 4 is supported
for highly motivated finance majors; the relationship between
the variables included in INDEX and salary expectations is
not significantly different for the two genders for salaries
expected 1 year, 5 years, and 10 years after graduation.
CONCLUSION AND IMPLICATIONS
To our knowledge, this is the first study to focus on student workforce preparedness variables and the relationship
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STUDENT SALARY EXPECTATIONS
with salary expectations. We hypothesized that more prepared students would expect higher salaries postgraduation
(Hypothesis 1) and that this relationship would become less
pronounced the longer a student has been in the workforce
(Hypothesis 2). Moreover, we hypothesized that there would
be no gender differences in entry or peak pay expectations
(Hypothesis 3), and that the relationship between preparednesss variables and salary expectations would be the same for
men as for women, particularly for highly motivated finance
students (Hypothesis 4).
The findings reported here for job market preparation and
salary expectations are encouraging; students who prepare
more for the job market in terms of networking relationships
and mathematical ability expect higher salaries for expected
salaries up to 5 years postgraduation, which strongly supports Hypothesis 1. The existing literature on major choices
indicates that interest in the subject (Calkins & Welki, 2006;
Malgwi et al., 2005), career concerns and performance in
major classes (Calkins & Welki, 2006), the level of preparation required (Malgwi et al., 2005), and personal benefits
(Hunjra et al., 2010) all influence major choices. The finding
that higher preparation is associated with higher expected
salary levels adds an interesting aspect to this literature. In
particular, it appears that students are cognizant of the fact
that the more prepared they are for the job market within
their major, the higher the salaries can be that they expect.
In other words, once the major is chosen, students appear to
associate success (in terms of salary) with more preparation
in and prior to the job market.
Furthermore, while we find some support for Hypothesis
2, that the relationship between expected salaries and
factors associated with job market preparation becomes
less pronounced for salaries expected more than 1 year
after graduation, that relationship only tapers off gradually;
5 years after graduation, greater job market preparation is
still associated with higher expected salaries. Thus, while
other factors, such as job performance, probably influence
expected salaries for years after graduation, the perceived relationship between these preparedness factors and expected
salaries is still surprisingly strong. Notice that this finding
is different from those reported by Chenevert and Tremblay
(2002), who found that salaries are influenced by factors
other than initial preparedness once a student has entered the
workforce. Our finding indicates that the salaries students
expect to earn (prior to being employed) years after their
initial employment are still very much influenced by their
level of preparedness. For educators, this is a very encouraging finding, as it indicates that students perceive job market
preparation to be highly valued even after they are employed.
In turn, this should provide educators with additional opportunities to provide students with often much-needed exposure
to potential employers, and career management centers.
When investigating gender differences in entry or peak
salary expectations, we found that women expected to earn
higher salaries than men, rejecting Hypothesis 3. This is a
23
surprising finding in light of the studies by Jackson et al.
(1992) and Major and Konar (1984), who find no gender
differences in pay once controlling for mitigating factors,
and Heckert et al. (2002), who found that peak career pay
gender differences diminish as a function of business sophistication. In our study, these gender differences persisted
even after controlling for the employment level and the selfperceived mathematical ability. Moreover, we found that the
relationship between preparedness variables and salary expectations was more pronounced for men than for women,
rejecting Hypothesis 4. Taken together, the findings that
women expected to earn more, but that the relationship between our preparedness variables and expected salaries was
stronger for men, indicates that women’s higher expected
salaries may be driven by factors other than the ones included
here.
One possible answer lies in the factors that cause individuals to choose their major in the first place. Women have
been found to choose their major more based on subject interest and class performance (Calkins & Welki, 2006) and
aptitude in the subject (Malgwi et al., 2005), while men appear to choose their major more based on the ability to make
money (Bansak & Starr, 2010) and the potential for career
advancement and the level of compensation (Malgwi et al.).
Thus, it is possible that the female subsample of students
utilized here made a more deliberate decision in choosing a
major such as finance. Consequently, they might view themselves relatively more prepared than their male counterparts
along other variables not measured here, which results in
their higher salary expectations. This would also explain why
the relationship between our included preparedness variables
and expected salaries was more pronounced for the male
subsample.
This study contributes to the extant literature because we
used a more robust sample of highly motivated finance students. Previous literature has investigated choices of majors
and factors that determine career success, but no study to our
knowledge has investigated the relationship between preparedness variables and salary expectations within a major.
As such, in this study we further refined the specialty area
and our results provide generalizability for highly motivated
finance students. Within this major, however, the findings are
rather interesting, and we document very pronounced differences in salary expectations and gender differences in these
salary expectations. Nonetheless, future research is needed
to determine whether the results hold across other specialty
areas.
Other results reported in this study suggest that further
investigation of networking and career services use may
be helpful in explaining salary expectations and women’s
demand for fair pay. Such research would be important
to students, university career centers, and future employers. Specifically, future researchers should address why men
are more inclined to engage in networking outlets, whereas
women are more inclined to utilize career management
24
N. KHOSROZADEH ET AL.
services, and whether students perceive these activities as
enhancing their pay.
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:54 11 January 2016
NOTES
1. The level of personal support was initially included as
a variable in all three regressions in Table 4 but was
insignificant and added no explanatory power.
2. To ensure that the results from Table 3 were not misleading in deciding on the regression model utilized
here, all 13 variables were included as independent
variables in Equation 1. The results for the total sample, available from the authors upon request, confirm
that the four variables included in Equation 1 were the
only significant variables. The regressions were also
conducted individually by gender using all 13 independent variables. The results are not substantially different from the results for the total sample and show that
the four variables included in Table 4 are highly significant. The only difference to the total sample results
is that the intention to obtain a higher degree results in
marginally significant lower expected salaries one year
after graduation for men.
3. We also conducted individual regressions for each gender and for each time period, which are available from
the authors upon request. Overall, the results of these
regressions indicate that the four predictors of expected
salaries identified previously worked especially well
for men, explaining almost one fifth of the expected
earnings percentile 1 year after graduation. For women,
however, only perceived mathematical ability appeared
important, and inconsistently so.
4. An additional advantage of this regression model over
a regression model including all of the individual regressors is that it eliminates potential problems resulting from multicollinearity due to the high correlations
between the independent variables.
5. Recall from the discussion of Table 1 that women utilize the career management center more and may therefore be more familiar with average salaries.
REFERENCES
Heckert, T. M., Droste, H. E., Adams, P. J., Griffin, C. M., Roberts, L. L.,
Mueller, M. A., & Wallis, H. A. (2002). Gender differences in anticipated
salary: Role of salary estimates for others, job characteristics, career paths,
and job inputs. Sex Roles, 47, 139–151.
Hunjra, A. I., Ur-Rehman, K., Ahmad, A., Safwan, N., & Ur-Rehman, I.
(2010). Factors explaining the choice of finance major: Students’ perception toward finance profession. Interdisciplinary Journal of Contemporary Research in Business, 2, 439–455.
Jackson, L. A., Gardner, P. D., & Sullivan, L. A. (1992). Explaining gender
differences in self-pay expectations: Social comparison standards and
perceptions of fair pay. Journal of Applied Psychology, 77, 651–663.
Kirchmeyer, C. (1998). Determinants of managerial career success: Evidence and explanation of male/female differences. Journal of Management, 24, 673–692.
Major, B., & Konar, E. (1984). An investigation of sex differences in pay
expectations and their possible causes. Academy of Management Journal,
27, 777–792.
Malgwi, C. A., Howe, M. A., & Burnaby, P. A. (2005). Influences on
students’ choice of college major. Journal of Education for Business, 80,
275–282.
Tulshyan, R. (2010). Top 10 college majors for women. Retrieved
from http://www.forbes.com/2010/03/02/top-10-college-majors-womenforbes-woman-leadership-education.html
APPENDIX
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Student Preparation for the Job Market
The purpose of this study is to investigate how students prepare themselves for the job market. The survey is about measuring students’ personal actions taken to make themselves
more marketable. Your responses to the survey will be anonymous. No identifying information including your name will
be on the sur
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Identifying Differences in Business Students’
Salary Expectations
Nicholas Khosrozadeh , Jeanna McGinnis , Oliver Schnusenberg & Lynn
Comer Jones
To cite this article: Nicholas Khosrozadeh , Jeanna McGinnis , Oliver Schnusenberg & Lynn
Comer Jones (2013) Identifying Differences in Business Students’ Salary Expectations, Journal
of Education for Business, 88:1, 16-25, DOI: 10.1080/08832323.2011.630433
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JOURNAL OF EDUCATION FOR BUSINESS, 88: 16–25, 2013
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Copyright
ISSN: 0883-2323 print / 1940-3356 online
DOI: 10.1080/08832323.2011.630433
Identifying Differences in Business Students’ Salary
Expectations
Nicholas Khosrozadeh
Fidelity Investments, Jacksonville, Florida, USA
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:54 11 January 2016
Jeanna McGinnis
UBS, New York, New York, USA
Oliver Schnusenberg and Lynn Comer Jones
The University of North Florida, Jacksonville, Florida, USA
The authors investigated preparedness variables affecting business students’ salary expectations
by utilizing a sample of 209 finance students from a regional university and 51 students
attending the Financial Management Association Leaders’ Conference in New York in 2011.
Students who network more, are applying for a higher level job, and perceive their mathematical
ability to be higher expect to earn more 1 and 5 years after graduation. However, students who
perceive the difficulty of finding a job to be higher have lower expectations for salaries when
graduating. These relationships are more pronounced for men than for women. However,
female finance students expect to earn higher salaries than male finance students, holding these
variables constant.
Keywords: mathematical ability, networking relationships, salary differences, salary
expectations
A variety of studies have investigated variables that influence career success (Calkins & Welki, 2006; Kirchmeyer,
1998), while other studies have investigated the factors that
determine choices of college majors (Hunjra, Ur-Rehman,
Ahmad, Safwan, & Ur-Rehman, 2010; Malgwi, Howe, &
Burnaby, 2005; Jackson et al., 1992). However, there is a relative absence of studies investigating the factors influencing
students’ salary expectations. Specifically, it is interesting to
investigate if networking relationships, self-perceived mathematical ability, and perceived difficulty in finding a job influence the salaries students expect to earn upon graduation.
For decades, gender has been the foundation from which
these determinants of career success and major choice have
been analyzed. Specific influencing factors such as human
capital gain, individual variables, interpersonal relationships,
and family obligations are used in studies such as Chenevert and Tremblay (2002) and Kirchmeyer (1998). The
Correspondence should be addressed to Oliver Schnusenberg, The University of North Florida, Department of Accounting and Finance, 1 UNF
Drive, Jacksonville, FL 32224, USA. E-mail: [email protected]
influencing factors have had positive and negative effects
on men and women within the business career industry.
Although the business major is number 1 on the top 10 list
of college majors for women (Tulshyan, 2010), there is still
much to be learned in terms of what motivates women to enter
the business major and eventually enter the field of finance.
According to the Chartered Institute of Management Accountants’ (CIMA, 2010) recent survey of over 4,500 finance
and business professionals from across the globe on use of
leadership skills and career progression strategies by gender,
women are six times less likely than their male counterparts
to be working as chief financial officers or chief executive
officers. Moreover, on average, CIMA male members earn
24% more than female members in the United Kingdom
and 39% more in Ireland; in South Africa and Sri Lanka the
difference is an even wider (47%). The study also found that
women still lag behind men in terms of seniority and salary,
which becomes particularly significant after 10 years’ work
experience. Such stark salary differences may be justified
if women are unable to perform as well as men in the same
job. However, CIMA also found that having more women
in senior roles is linked to stronger financial performance.
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STUDENT SALARY EXPECTATIONS
Nonetheless, the finance industry has historically been
dominated by men. Consequently, understanding the reasoning behind women’s success and motivation, educational
institutions and professional corporations will potentially be
able to promote increased advancements for women finance
professionals.
Our primary objective was to investigate the relationship
between various factors related to preparedness, such as the
amount of networking students engage in and the involvement with student organizations, and the expected earnings
percentiles of 1 year, 5 years, and 10 years after graduation. A
secondary objective was to investigate whether this relationship differs by gender. Subjects involved within the survey
included female and male students at a regional university
in Florida enrolled in finance degree courses and female and
male students who attended the Financial Management Association (FMA) Leaders’ Conference in New York in March
2011. Investigating these issues is important to graduating
students and employers alike.
The remainder of this article is structured as follows. We
present a review of related literature, the hypotheses and data,
the results, and then the conclusions and implications.
REVIEW OF RELATED LITERATURE
Our primary objective was to investigate the factors that relate to student preparedness for the job market, while our
secondary objective was to investigate gender differences in
these factors. In the literature, these two objectives are not
clearly separable. A multitude of studies, for example, have
investigated factors influencing students’ choice of major
and gender differences in these choices. Other studies have
focused on career success factors and gender differences in
these factors. However, we were aware of virtually no studies
that have investigated career success factors without controlling for gender differences. Overall, the existing research
seems to indicate that women choose their major in college
more out of present and social interests. However, to our
knowledge, no research to date has investigated how future
salary expectations differ between male and female college
students based on their level of motivation and other personal characteristics. Specifically, we were not aware of any
research that had investigated what the differences in salary
expectations are once students are highly motivated students
in their field and close to graduation.
Several studies have investigated factors influencing students’ choice of majors. Calkins and Welki (2006) attempted
to find these factors and explore just why some students never
consider career paths within the realm of economics. Findings reveal that the most important factors in the choice of
major are interest in the subject, career concerns, the student’s performance in major classes, and the teaching reputation and approachability of the faculty. Similarly, Malgwi
et al. (2005) investigated the different factors influencing
17
business students’ choice of major. Their findings showed
that interest in the subject was the most important factor for
incoming freshmen. Even with students that changed to a
business major from a nonbusiness major, credit is given to
the positive dispositions toward the new major, rather than
negative feelings toward their old major. The authors also
reinforced the fact that high schools do little to nothing in
influencing students to pursue a career in business. They
found, to their surprise, that high school courses, high school
advisors, and even parents do not appear to be particularly
influential in the initial major choice. The main influences
from high school on selecting a major seems limited to only
the general coursework studied in the curriculum. The researchers suggest that more out-of-the-box type exposure to
subject study should be granted to students so as to not limit
their choices to just the general coursework.
In Hunjra et al.’s (2010) research of the general factors
explaining the choice of a finance major, it was discovered
that the majority of students are interested in the field of finance for the personal benefits rather than playing a positive
and participating role in society. The research also reinforces
the perception that finance is a profit-driven field. Hunjra
et al. also found the respondents to perceive the field as less
theoretical and more mathematical. Jackson et al.’s (1992)
variables for perceived job inputs included, but were not limited to, basic job skills, previous work experience, business
sophistication, preparation, and qualifications.
A variety of studies have investigated gender differences
in factors of career success and student choice of major. Chenevert and Tremblay (2002), for example, found that female
students have a considerable amount of control over their
future success in their finance career, and that the amount
of control over their success can be influenced by a majority of factors, including support from educators and family, the continuation of further education and involvement,
changes within the family structure (marriage or children),
and others. Relatedly, Kirchmeyer (1998) studied those individuals who “worked for a variety of industries within
manufacturing, health care, financial and other professional
services, and banking being most common, and mostly in
general management and the functional specialties of accounting and finance” (p. 680). To determine career success
and rate importance, Kirchmeyer measured the importance of
career progression, perceived career success, human capital
variables, gender roles, supportive relationships, family status variables, and sex. Findings indicated that human capital
variables have stronger effects on men’s components of success than on women’s. In addition, supportive relationships,
such as mentors and peer networks, are more pronounced for
men. However, gender roles have stronger effects on success
for women. Last, the findings show no differential effect of
family status.
In their research, Bansak and Starr (2010) attempted to
look deeper into the gender differences in predispositions
toward economics. While economics is a separate field of
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18
N. KHOSROZADEH ET AL.
study from finance, and economics departments in universities are often not located in the business school, finance is
a subfield of economics and therefore a field relevant to the
present research. Bansak and Starr found that students view
economics as a field that prioritizes math skills and making
money—a combination that they find to be unappealing to
women, but not so much for men. They found women’s predispositions toward studying economics to be reflective of
their concerns about subject difficulty and disinterest. They
also found a general disinterest in women in the types of jobs
associated with an economics major, the main concern being
unfriendly workplaces. The common misconception of fears
about the work–family balance in the economic workplace
was proven to not be so important in scaring away female
interest in economics. Jackson et al. (1992) found that men
have higher job-performance expectations than women based
on their perceived job inputs (i.e., their beliefs about what
they had to contribute to the job). Malgwi et al. (2005) also
found that for women a very influential factor in choice of
major was aptitude in the subject. Men happened to be significantly more influenced by the major’s potential for career
advancement and job opportunities and the level of compensation in the field. In the study mentioned previously, Calkins
and Welki (2006) also addressed gender differences in major
choices. Specifically, for female respondents, more emphasis was put on subject interest, good class performance, high
school exposure, and the encouragement of a high school
teacher, whereas male respondents were more concerned
with the perceived marketability and expected income associated with the subject.
Jackson et al. (1992) and Heckert et al. (2002) find that
controlling for perceived others’ pay eliminates gender differences in entry-level pay. Jackson et al. suggested that peak
career pay gender differences diminished slightly as a function of business sophistication. This suggests that job inputs
related to business sophistication should increase women’s
perceptions on the ability to earn higher salaries. We conclude
that more research is needed to test the impact of perceived
job inputs on the gender gaps in peak pay expectations.
HYPOTHESES AND METHOD
Hypotheses
To our knowledge, no studies to date have investigated to
what extent students actually expect their salaries to vary
based on a variety of factors related to preparedness, such
as networking with companies prior to graduation or mathematical ability. The various studies included in the literature
review on career success, while very informative, have not
investigated if there are actually any differences in salary expectations based on factors that would render students more
employable. In the present study we sought to investigate the
specific relationship between salary expectations and several
variables related to preparedness. Our primary hypothesis is
that factors that render students more attractive to potential
employers based on their level of preparedness for the job
would result in expectations of higher salaries:
Hypothesis 1: The more prepared a student is to enter the
workforce, the higher the earnings percentile that student
would expect after graduation.
Moreover, we investigate earnings percentile expectations
for 1, 5, and 10 years after graduation. Once a student has
entered the workforce, his or her salary will be influenced by
factors other than initial preparedness (Chenevert & Tremblay, 2002). Therefore, we developed a second hypothesis.
Hypothesis 2: The relationship between preparedness variables and salary expectations would be less pronounced
the longer a student has been in the workforce.
We also investigated whether there is a difference in salary
expectations by gender. The extant literature suggests that
salary expectations gender differences (peak pay) are mitigated in specialty areas (Major & Konar, 1984). Moreover,
Jackson et al. (1992) found that the level of business sophistication is positively related to peak pay expectations, but
not to entry pay expectations. For entry pay, Jackson et al.
and Major and Konar found that there are no gender differences in salary expectations once perceived others’ pay is
controlled for. Thus, collectively, the extant literature suggests there should be no differences between women’s and
men’s pay expectations at entry or peak pay once specialty
area, perceived others’ pay, and business sophistication are
controlled for.
Therefore, we formulated a third hypothesis.
Hypothesis 3: There would be no gender difference in entry
or peak pay expectations.
Last, we investigated whether there is a difference in the
relationships between salary expectations and preparedness
variables between the genders. Since this is the first study to
investigate finance salary difference expectations, we did not
focus our hypotheses on the specific variables that may have
a differential impact on salary expectations by gender.
Specifically, we sought to test the following fourth hypothesis:
Hypothesis 4: The relationship between preparedness variables and salary expectations would be the same for men
as for women, particularly for highly motivated finance
students.
Participants
The participants include general business students at a regional Florida university enrolled in an introductory finance
course, finance majors enrolled in an upper level behavioral
finance course at a regional Florida university, and students
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STUDENT SALARY EXPECTATIONS
who attended the March 2011 FMA Leaders’ Conference
in New York. As an inducement, introductory students were
provided extra credit for completing the survey and behavioral students were provided participation points. Students
who did not complete the survey were offered other comparable extra credit opportunities instead. The response rate
for courses at the regional Florida university were 65.38%
(221 responses in the introductory course) and 66.67% (22
responses in the behavioral finance course). The response
rate at the FMA conference was 26.77% (83 responses from
310 registrants). The relatively low response rate can be explained, at least in part, by the absence of any extra credit opportunities. However, students were informed that we would
gladly share the results of the survey. We considered the 83
students to be especially motivated and an appropriate sample. Moreover, our response rate was similar to Jackson et al.
(1992), 447 of 1,588.
Incomplete and nonsensical answers reduced the final
sample to 260 students (209 from the regional university
in Florida, 19 of whom were upper level behavioral finance
students, and 51 from the FMA Finance Leaders’ Conference). Sixty-one percent of the final sample was men, 12%
were married, and 10% had children. Forty-four percent of
the respondents indicated they had some work experience in
their field (including internships; see Table 1). Our sample
is perfect for investigating the choices of upper classmen, as
98% of the sample were either juniors or seniors.
Procedures
The survey included an introduction explaining the purpose
of the research, which was being conducted by the accounting
and finance department at the regional Florida university. The
purpose of the research was described as obtaining information about student preparation for the job market. Anonymity
and confidentiality were guaranteed, and students were allowed to withdraw consent and responses without penalty or
prejudice. Moreover, in a separate informed consent form,
students were given contact information for the principal investigator and institutional review board chair, and told orally
they could ask questions about the research.
Instrument
The survey is included in the Appendix, together with the
script students received with the survey. The survey includes
10 questions related to job market preparedness, regarding,
for example, high school and college grade point averages
(GPAs); the amount of networking relationships students
have with companies; the employment level sought (e.g.,
entry level vs. executive); the students’ intention to pursue
a higher degree; utilization of a university’s career management center; self-perceived mathematical ability; levels of
support from employers, professors, and family and friends to
pursue a career; and involvement with student organizations
are all variables that reflect a student’s level of preparedness.
Arguably, students who are performing better on these vari-
19
TABLE 1
Summary Statistics for Selected Variables (n = 260)
Variable
Male
Married
Children
Work experience
High School GPAa
College GPAa
Networking relationshipsb
Higher degreec
Utilize Career Management
Centerd
Math abilitye
Career supportf
Educational supportf
Personal supportf
School club memberg
Employment levelh
Difficulty finding job?i
%
M
SD
Median
Minimum
Maximum
3.15
2.90
2.30
1.68
2.69
0.87
0.82
1.19
0.89
1.23
3.00
3.00
2.00
1.00
3.00
1
1
1
1
1
4
4
5
3
5
7.27
3.15
3.44
4.22
2.32
1.97
3.16
1.78
1.47
1.33
1.11
1.55
0.87
1.10
8.00
3.00
4.00
5.00
1.00
2.00
3.00
1
1
1
1
1
1
1
10
5
5
5
5
4
5
60.8
11.9
9.6
43.5
aChoices were 1 (less than 2.5), 2 (2.5–2.9), 3 (3.0–3.4), and (3.5 or
greater). bFive-point Likert-type scale ranging from 1 (none) to 5 (a lot).
cChoices were 1 (yes, master’s only), 2 (yes, master’s and doctoral), and 3
(no, neither). Frequencies were 60%, 12%, and 29% for the three choices,
respectively (error due to rounding). dFive-point Likert-type scale ranging
from 1 (never) to 5 (always). eChoices were from 1 (low) to 10 (high). fFivepoint Likert-type scale ranging from 1 (low support) to 5 (high support).
gFive-point Likert-type scale ranging from 1 (no, I am not a member of
such an organization) to 5 (I’m very active and an officer of our student
organization). hChoices were 1 (entry level), 2 (associate), 3 (management),
and 4 (executive). Frequencies were 37%, 31%, 29%, and 2% for the four
choices, respectively. iFive-point Likert-type scale ranging from 1 (not at
all difficult) to 5 (very difficult).
ables should expect a higher salary level. In addition to these
variables, we also included two other variables that should
influence the expected salary: the employment level sought
(e.g., entry level vs. executive) and the perceived difficulty
of finding a job were also included as control variables.
As shown in Table 1, the median high school and cumulative college GPA for students in the sample was between 3.0
and 3.4. However, the average student has few networking
relationships. Seventy-two percent of the sample intended to
pursue either a master’s or doctoral degree, and the median
student sometimes utilized the career management center
at the university. Students perceived their own mathematical
ability to be rather high, with a median score of 8 of 10. Family and friends appeared supportive of students’ careers, with
a median score of 5 on a 5-point scale; this was followed by
support from the academic environment (median score of 4)
and the work environment (median score of 3). The average
student was not very involved in the student organizations
at their university. Somewhat surprisingly, only 37% of the
sample was seeking an entry-level position, while 31% and
29% were seeking associate and management positions, respectively. On average, students in the sample did not think
it would be either overly difficult or easy to find a job.
To investigate the degree to which student salary expectations can be predicted using the preparedness variables
20
N. KHOSROZADEH ET AL.
TABLE 2
Percentage Frequency Distribution of Student Salary Expectations for Various Time Periods Postgraduation (n = 260)
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:54 11 January 2016
1 year
5 years
10 years
10% (lowest)
20%
30%
40%
50%
60%
70%
80%
90%
100% (highest)
3.5
0.0
0.4
9.6
3.1
0.4
11.9
3.8
2.7
20.0
6.2
1.9
26.9
14.2
4.6
13.1
17.3
6.2
9.2
21.5
11.9
5.4
21.5
23.5
0.4
9.2
23.5
0.0
3.1
25.0
described previously, it is useful to assess the salary students
are expecting after graduation. For this purpose, Table 2
presents the percentage frequency distribution of earnings
deciles for 1 year, 5 years, and 10 years after graduation.
Clearly, students expected the earnings decile they are in to
increase dramatically the longer they are in the workforce.
In the case of our sample, 1 year after graduation, students
expected to be, on average, in the fifth earnings decile. Five
years after graduation, they expected to be in decile 7, on
average. Ten years after graduation, they expected to be in
decile 8, on average. Moreover, Table 2 shows that 25% of
students in our sample expected to be in the highest earnings
decile 10 years after graduation.
where SALi is the salary expectation (earnings decile) of
student i for alternatively 1 year, 5 years, and 10 years postgraduation; NETi is the networking relationships of student i,
measured using a 5-point Likert-type scale; ELEVELi is the
employment level sought by student i, expressed as a variable
ranging from 1 (entry level) to 4 (executive); JDIFFi is the
perceived difficulty of finding a job for student i, measured
using a 5-point Likert-type scale; MATHi is the perceived
mathematical ability for student i, measured from 1 (low) to
10 (high); and εi is the error term for student i.
Note that Equation 1 does not contain the level of personal support, which was shown in Table 3 to influence the
responses to the salary expectations for five years after graduation. Moreover, Equation 1 includes the self-perceived
RESULTS
Our ultimate goal was to isolate those preparedness-related
variables that explain the expected salaries 1 year, 5 years,
and 10 years after graduation. Including all 13 variables as
independent variables in a regression would result in some
methodological challenges, as the sample consisted of only
260 observations. In order to identify those variables whose
response distribution is significantly different for the three
salary expectation variables, we therefore first conducted chisquare tests. The results of these tests are show in Table 3.
As shown in Table 3, for the earnings decile expected by
students 1 year after graduation, only 3 of the 13 included
variables were significant. Specifically, the distribution of
responses to the salary expectation differed for networking
relationships, the initial employment level (e.g., entry level
vs. executive) and the perceived difficulty of finding a job.
For the earnings deciles expected 5 years after graduation,
networking relationships, the level of personal support, and
the perceived difficulty in finding a job influenced the distribution of responses. For 10 years after graduation, none of
the 13 variables resulted in significantly different responses
to the expected earnings percentile.
Relationship Between Preparedness Variables
and Salary Expectations for Various Time
Periods Postgraduation (Hypotheses 1 and 2)
Based on the results from Table 3, we next utilized the following regression model:
SALi = a0 + a1 NETi + a2 ELEVELi + a3 JDIFFi
+ a4 MATHi + εi ,
(1)
TABLE 3
Chi-Square Tests for Differences in Distribution of
Variables Affecting Preparedness
1 year
Variable
High school GPAa
College GPAa
Networking
relationshipsb
Higher degreec
Utilize Career
Management Centerd
Math abilitye
Career supportf
Educational supportf
Personal supportf
School club memberg
Work experience
Employment levelh
Difficulty finding job?i
χ2
5 years
p
χ2
10 years
p
χ2
p
21.50
24.51
46.54∗
.609 19.64
.433 22.57
.047 45.09∗
.717 29.94
.546 26.47
.062 40.80
.317
.493
.268
17.57
27.29
.350 14.63
.704 31.93
.552 8.08
.470 39.65
.977
.310
86.99
27.58
21.85
34.18
32.24
9.69
47.29∗∗
48.45∗
.110
.690
.911
.363
.455
.288
.003
.031
96.45
29.33
22.63
33.26
46.22
9.62
29.69
34.38
.116
.777
.960
.599
.118
.382
.328
.546
86.71
25.37
17.73
46.43∗
20.03
4.04
28.11
49.09∗
.114
.791
.981
.048
.951
.854
.256
.027
aChoices were 1 (less than 2.5), 2 (2.5–2.9), 3 (3.0–3.4), and (3.5 or
greater). bFive-point Likert-type scale ranging from 1 (none) to 5 (a lot).
cChoices were 1 (yes, master’s only), 2 (yes, master’s and doctoral), and 3
(no, neither). Frequencies were 60%, 12%, and 29% for the three choices,
respectively (error due to rounding). dFive-point Likert-type scale ranging
from 1 (never) to 5 (always). eChoices were from 1 (low) to 10 (high). fFivepoint Likert-type scale ranging from 1 (low support) to 5 (high support).
gFive-point Likert-type scale ranging from 1 (no, I am not a member of
such an organization) to 5 (I’m very active and an officer of our student
organization). hChoices were 1 (entry level), 2 (associate), 3 (management),
and 4 (executive). Frequencies were 37%, 31%, 29%, and 2% for the four
choices, respectively. iFive-point Likert-type scale ranging from 1 (not at
all difficult) to 5 (very difficult).
∗ p < .05. ∗∗∗ p < .01. N = 260.
STUDENT SALARY EXPECTATIONS
21
TABLE 4
Regression Results for Salary Expectations and Preparedness Variables
1 year
5 years
10 years
Intercept
p
NET
p
ELEVEL
p
JDIFF
p
MATH
p
Adj. R2
F
p
2.818∗∗
5.072∗∗
7.052∗∗
.000
.000
.000
.158†
.167†
.122
.077
.082
.216
.466∗∗
.329∗
.202
.000
.011
.129
–.205∗
–.222∗
–.126
.038
.036
.249
.162∗∗
.157∗
.104
.006
.013
.109
12.10%
8.50%
2.60%
9.896∗∗
7.049∗∗
2.702∗
.000
.000
.031
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Note. The model is SALi = a0 + a1 NETi + a2 ELEVELi + a3 JDIFFi + a4 MATHi + εi , where SALi = the salary expectation (earnings decile) of student
i for alternatively 1 year, 5 years, and 10 years postgraduation; NETi = the networking relationships of student i, measured using a 5-point Likert-type scale;
ELEVELi = the employment level sought by student i, expressed as a variable from 1 (entry level) to 4 (executive); JDIFFi = the perceived difficulty of
finding a job for student i, measured using a five-point Likert-type scale; MATHi = the perceived mathematical ability for student i, measured from 1 (low)
to 10 (high); and εi = error term for student i.
†p < .1. ∗ p < .05. ∗∗∗ p < .01.
mathematical ability, which was marginally significant in
Table 3, with p values of .11 for all three salary variables.1
The results from estimating Equation 1 are shown in
Table 4. As indicated on the first row of Table 4, the four
variables included as independent variables explain about
12.1% of the salary expected 1 year after graduation.2 All
four included variables are significant at conventional levels,
with the expected sign; students who networked more, were
applying for a higher level job, and perceived their mathematical ability to be higher expected to be in a higher earnings
percentile 1 year after graduation. However, students who
perceived the difficulty of finding a job to be higher had lower
expectations for salaries when graduating. The explanatory
power of these four variables dropped to 8.5% and 2.6% for
the earnings percentiles expected 5 and 10 years after graduation, respectively, which only makes sense, given that many
other variables will factor into salary expectations once students are actually employed. However, while none of the four
variables appeared to affect expected salaries 10 years after
graduation, the four variables were still significant (although
mostly to a lesser extent) in influencing the expected salaries
5 years postgraduation. For the total sample, we therefore
found strong support for Hypothesis 1, that better preparation is associated with higher salary expectations. However,
there was only partial support for Hypothesis 2: the relationship between preparedness variables and salary expectations
was equally strong for salary expectations 1 and 5 years after
graduation, although they became insignificant 10 years after
graduation. This is an interesting finding that indicates that
although students may also consider additional variables in
the future, they at least also consider their present level of
preparedness to influence their salaries even 5 years ahead.
Relationship Between Preparedness Variables
and Salary Expectations by Gender (Hypotheses
3 and 4)
In order to investigate whether salary expectations differed
by gender (Hypothesis 3) and whether the relationship between preparedness variables and salary expectations dif-
fered across genders (Hypothesis 4), we utilized the following regression model:
SALi = a0 + a1 INDEXi + a2 GINDEXi
+ a3 GENDERi + εi , where
(2)
where SALi is the salary expectation (earnings decile) of student i for alternatively 1 year, 5 years, and 10 years postgraduation; INDEXi is an index that is equal to the sum of the variables NET, ELEVEL, and MATH, less the variable JDIFF for
student i from Table 5; GINDEXi is INDEXi × GENDERi ;
GENDERi is a dummy variable equal to unity for women
and zero otherwise; and εi is the error term for student i.
Notice that the variable INDEX contains all four variables that were previously included as separate regressors
in Table 4.3 In Equation 2, the coefficient a1 estimates the
relationship between the index and SAL for men only; the coefficient a2 for the interaction term GINDEX indicates how
much more or less pronounced the relationship between the
variables contained in the index and salary expectations is for
women than for men; the coefficient a3 indicates how much
more or less pronounced the salary expectations for women
are, holding the variable INDEX constant. We expected a1 to
be positive and significant and a2 to be negative and significant, which would be consistent with most of the variables
being significant for men, but not for women in Table 5.
Although previous studies show that women earn less than
men, indicating that the coefficient a3 should be negative and
significant, and that women expect higher salaries 1 year after
graduation. Thus, to the extent that this higher salary was not
driven by the level of the preparedness variables, coefficient
a3 was expected to be positive and significant.4
The results from estimating Equation 2 for the entire sample of 260 students are shown in Panel A of Table 5. The
rows in Table 5 show the regression results for alternative
dependent variables for earnings deciles 1, 5, and 10 years
after graduation. As shown in the first row of Table 5, the
independent variables explain about 12% of the variation in
salary expectations 1 year after graduation. This value drops
to 11% for 5 years after graduation and to 6% for 10 years
after graduation, indicating that the variables forming the
22
N. KHOSROZADEH ET AL.
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TABLE 5
Regression Results for Salary Expectations and an Index of Preparedness Variables
Panel A: Total sample (n = 260)
1 year
5 years
10 years
Panel B: Business majors (n = 190)
1 year
5 years
10 years
Panel C: Highly motivated finance
majors (n = 70)
1 year
5 years
10 years
Intercept
p
Index
p
GIndex
p
Gender
p
Adj. R2
F
p
2.411∗∗∗
4.217∗∗∗
6.342∗∗∗
.000
.000
.000
.252∗∗∗
.252∗∗∗
.184∗∗∗
.000
.000
.000
–.133∗
–.147∗
–.144∗
.068
.058
.070
1.336∗∗
1.712∗∗
1.696∗∗
.036
.011
.015
11.80%
11.30%
5.80%
12.590∗∗∗
11.981∗∗∗
6.349∗∗∗
.000
.000
.000
2.580∗∗∗
4.433∗∗∗
6.302∗∗∗
.000
.000
.000
.264∗∗∗
.245∗∗∗
.174∗∗∗
.000
.000
.003
–.131∗
–.123
–.135
.071)
.137
.136
1.047∗
1.397∗
1.774∗∗
.098
.052
.025
16.10%
11.20%
5.60%
13.096∗∗∗
8.936∗∗∗
4.755∗∗∗
.000
.000
.003
2.063∗∗
3.633∗∗∗
6.383∗∗∗
.033
.000
.000
.228∗∗
.284∗∗∗
.207∗∗
.031
.005
.015
–.295
–.343
–.119
.281
.187
.586
3.350
3.603
1.066
.172
.122
.584
5.60%
9.10%
4.80%
2.374∗∗
3.298∗∗
2.154
.078
.026
.102
Note. The model is SALi = a0 + a1 I N DEXi + a2 GI N DEXi + a3 GEN DERi + εi , where SALi = the salary expectation (earnings decile) of student
i for alternatively 1 year, 5 years, and 10 years postgraduation; I N DEXi = an index that is equal to the sum of the variables NET, ELEVEL, and MATH,
less the variable JDIFF for student i from Table 5; GI N DEXi = I N DEXi × GEN DERi ; GEN DERi = a dummy variable equal to unity for women
and zero otherwise; εi = error term for student i.
†p < .1. ∗ p < .05. ∗∗∗ p < .01.
constructed index lose some of their explanatory power the
longer students are in the work force. Again, given that future salaries depend on a myriad of factors once students are
employed, this result is hardly surprising.
Despite the decreasing explanatory power, the results displayed in Table 5 are surprisingly consistent. In all three
regressions, the variables INDEX and GINDEX have the expected positive and negative and significant coefficients, respectively. This indicates that the relationship between the
INDEX and salary expectations is more pronounced for men
than for women. Again, it is possible that other variables
not included here would do a better job at explaining salary
expectations for women. The coefficient for GENDER is positive and significant, which indicates that women expect to
be in a higher earnings decile than men 1 year, 5 years, and
10 years after graduation. To the extent women’s job input
and business sophistication perceptions are enhanced, these
results are not surprising.
Hypothesis 3, that there would be no gender difference
in entry or peak salary expectations, was not supported; the
coefficient a3 is positive and significant in all regressions
in Panel A, indicating that women expected to earn higher
salaries than men. This is inconsistent with findings in the
previous literature in that previous findings indicate that there
should not be a gender difference once specialty area, business sophistication, and perceived others’ pay is controlled
for. Hypothesis 4, that the relationship between preparedness
variables and salary expectations would be the same for men
and women, was also not supported, at least for the total
sample; the coefficient a2 is consistently negative and significant, indicating that the relationship between preparedness
variables and salary expectations was less pronounced for
women than for men. One possible explanation for the finding in Panel A that women expect to earn more than men
is the lack of control for a specialty area. While we controlled for the level of business sophistication (through the
preparedness variables) and indirectly for the perceived others’ pay,5 we have not yet controlled for a specialty area. In
order to accomplish this, we partitioned the total sample into
two subsamples.
Panel B shows the regression results for the subsample of
190 business majors, while Panel C shows the results for the
subsample of 70 upper level finance majors, consisting of the
upper level course at the regional Florida university and the
FMA conference participants. If lack of a specialty area is
the cause of the higher salary expectations women display
in Panel A, then the subsample of finance majors should not
display a significant coefficient for the GENDER variable.
Indeed, Panel C of Table 5 reveals that this is true; a3 is not
significant for salary expectations either 1 year, 5 years, or
10 years after graduation for the subsample of finance majors. Conversely, the results for business majors in Panel B
are similar to those for the total sample in Panel A. Notably,
however, the coefficient a2 is not significant for salary expectations 5 years and 10 years after graduation, implying that
the relationship between preparedness variables and salary
expectations is equally strong for men and women for those
salaries. Panel C also reveals that Hypothesis 4 is supported
for highly motivated finance majors; the relationship between
the variables included in INDEX and salary expectations is
not significantly different for the two genders for salaries
expected 1 year, 5 years, and 10 years after graduation.
CONCLUSION AND IMPLICATIONS
To our knowledge, this is the first study to focus on student workforce preparedness variables and the relationship
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STUDENT SALARY EXPECTATIONS
with salary expectations. We hypothesized that more prepared students would expect higher salaries postgraduation
(Hypothesis 1) and that this relationship would become less
pronounced the longer a student has been in the workforce
(Hypothesis 2). Moreover, we hypothesized that there would
be no gender differences in entry or peak pay expectations
(Hypothesis 3), and that the relationship between preparednesss variables and salary expectations would be the same for
men as for women, particularly for highly motivated finance
students (Hypothesis 4).
The findings reported here for job market preparation and
salary expectations are encouraging; students who prepare
more for the job market in terms of networking relationships
and mathematical ability expect higher salaries for expected
salaries up to 5 years postgraduation, which strongly supports Hypothesis 1. The existing literature on major choices
indicates that interest in the subject (Calkins & Welki, 2006;
Malgwi et al., 2005), career concerns and performance in
major classes (Calkins & Welki, 2006), the level of preparation required (Malgwi et al., 2005), and personal benefits
(Hunjra et al., 2010) all influence major choices. The finding
that higher preparation is associated with higher expected
salary levels adds an interesting aspect to this literature. In
particular, it appears that students are cognizant of the fact
that the more prepared they are for the job market within
their major, the higher the salaries can be that they expect.
In other words, once the major is chosen, students appear to
associate success (in terms of salary) with more preparation
in and prior to the job market.
Furthermore, while we find some support for Hypothesis
2, that the relationship between expected salaries and
factors associated with job market preparation becomes
less pronounced for salaries expected more than 1 year
after graduation, that relationship only tapers off gradually;
5 years after graduation, greater job market preparation is
still associated with higher expected salaries. Thus, while
other factors, such as job performance, probably influence
expected salaries for years after graduation, the perceived relationship between these preparedness factors and expected
salaries is still surprisingly strong. Notice that this finding
is different from those reported by Chenevert and Tremblay
(2002), who found that salaries are influenced by factors
other than initial preparedness once a student has entered the
workforce. Our finding indicates that the salaries students
expect to earn (prior to being employed) years after their
initial employment are still very much influenced by their
level of preparedness. For educators, this is a very encouraging finding, as it indicates that students perceive job market
preparation to be highly valued even after they are employed.
In turn, this should provide educators with additional opportunities to provide students with often much-needed exposure
to potential employers, and career management centers.
When investigating gender differences in entry or peak
salary expectations, we found that women expected to earn
higher salaries than men, rejecting Hypothesis 3. This is a
23
surprising finding in light of the studies by Jackson et al.
(1992) and Major and Konar (1984), who find no gender
differences in pay once controlling for mitigating factors,
and Heckert et al. (2002), who found that peak career pay
gender differences diminish as a function of business sophistication. In our study, these gender differences persisted
even after controlling for the employment level and the selfperceived mathematical ability. Moreover, we found that the
relationship between preparedness variables and salary expectations was more pronounced for men than for women,
rejecting Hypothesis 4. Taken together, the findings that
women expected to earn more, but that the relationship between our preparedness variables and expected salaries was
stronger for men, indicates that women’s higher expected
salaries may be driven by factors other than the ones included
here.
One possible answer lies in the factors that cause individuals to choose their major in the first place. Women have
been found to choose their major more based on subject interest and class performance (Calkins & Welki, 2006) and
aptitude in the subject (Malgwi et al., 2005), while men appear to choose their major more based on the ability to make
money (Bansak & Starr, 2010) and the potential for career
advancement and the level of compensation (Malgwi et al.).
Thus, it is possible that the female subsample of students
utilized here made a more deliberate decision in choosing a
major such as finance. Consequently, they might view themselves relatively more prepared than their male counterparts
along other variables not measured here, which results in
their higher salary expectations. This would also explain why
the relationship between our included preparedness variables
and expected salaries was more pronounced for the male
subsample.
This study contributes to the extant literature because we
used a more robust sample of highly motivated finance students. Previous literature has investigated choices of majors
and factors that determine career success, but no study to our
knowledge has investigated the relationship between preparedness variables and salary expectations within a major.
As such, in this study we further refined the specialty area
and our results provide generalizability for highly motivated
finance students. Within this major, however, the findings are
rather interesting, and we document very pronounced differences in salary expectations and gender differences in these
salary expectations. Nonetheless, future research is needed
to determine whether the results hold across other specialty
areas.
Other results reported in this study suggest that further
investigation of networking and career services use may
be helpful in explaining salary expectations and women’s
demand for fair pay. Such research would be important
to students, university career centers, and future employers. Specifically, future researchers should address why men
are more inclined to engage in networking outlets, whereas
women are more inclined to utilize career management
24
N. KHOSROZADEH ET AL.
services, and whether students perceive these activities as
enhancing their pay.
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:54 11 January 2016
NOTES
1. The level of personal support was initially included as
a variable in all three regressions in Table 4 but was
insignificant and added no explanatory power.
2. To ensure that the results from Table 3 were not misleading in deciding on the regression model utilized
here, all 13 variables were included as independent
variables in Equation 1. The results for the total sample, available from the authors upon request, confirm
that the four variables included in Equation 1 were the
only significant variables. The regressions were also
conducted individually by gender using all 13 independent variables. The results are not substantially different from the results for the total sample and show that
the four variables included in Table 4 are highly significant. The only difference to the total sample results
is that the intention to obtain a higher degree results in
marginally significant lower expected salaries one year
after graduation for men.
3. We also conducted individual regressions for each gender and for each time period, which are available from
the authors upon request. Overall, the results of these
regressions indicate that the four predictors of expected
salaries identified previously worked especially well
for men, explaining almost one fifth of the expected
earnings percentile 1 year after graduation. For women,
however, only perceived mathematical ability appeared
important, and inconsistently so.
4. An additional advantage of this regression model over
a regression model including all of the individual regressors is that it eliminates potential problems resulting from multicollinearity due to the high correlations
between the independent variables.
5. Recall from the discussion of Table 1 that women utilize the career management center more and may therefore be more familiar with average salaries.
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APPENDIX
SCRIPT FOR PROJECT IRB
Student Preparation for the Job Market
The purpose of this study is to investigate how students prepare themselves for the job market. The survey is about measuring students’ personal actions taken to make themselves
more marketable. Your responses to the survey will be anonymous. No identifying information including your name will
be on the sur