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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
To link to this article: http://dx.doi.org/10.1080/08832323.2011.630433
Published online: 19 Nov 2012.
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JOURNAL OF EDUCATION FOR BUSINESS, 88: 16–25, 2013 CopyrightC Taylor & Francis Group, LLC
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
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 influ-ence 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 rel-ative absence of studies investigating the factors influencing students’ salary expectations. Specifically, it is interesting to investigate if networking relationships, self-perceived math-ematical ability, and perceived difficulty in finding a job in-fluence 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 Chen-evert and Tremblay (2002) and Kirchmeyer (1998). The
Correspondence should be addressed to Oliver Schnusenberg, The Uni-versity 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 Ac-countants’ (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.
Nonetheless, the finance industry has historically been dominated by men. Consequently, understanding the rea-soning 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 involve-ment 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 relation-ship 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 Asso-ciation (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 re-late 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 control-ling 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 per-sonal 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 stu-dents’ 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. Find-ings reveal that the most important factors in the choice of major are interest in the subject, career concerns, the stu-dent’s performance in major classes, and the teaching repu-tation and approachability of the faculty. Similarly, Malgwi et al. (2005) investigated the different factors influencing
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 re-searchers 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 fi-nance 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 lim-ited 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. Ch-enevert 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 major-ity of factors, including support from educators and fam-ily, the continuation of further education and involvement, changes within the family structure (marriage or children), and others. Relatedly, Kirchmeyer (1998) studied those in-dividuals 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 ac-counting 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 sta-tus variables, and sex. Findings indicated that human capital variables have stronger effects on men’s components of suc-cess 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
18 N. KHOSROZADEH ET AL.
study from finance, and economics departments in universi-ties 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 pre-dispositions 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 sig-nificantly more influenced by the major’s potential for career advancement and job opportunities and the level of compen-sation in the field. In the study mentioned previously, Calkins and Welki (2006) also addressed gender differences in major choices. Specifically, for female respondents, more empha-sis 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 as-sociated with the subject.
Jackson et al. (1992) and Heckert et al. (2002) find that controlling for perceived others’ pay eliminates gender dif-ferences in entry-level pay. Jackson et al. suggested that peak career pay gender differences diminished slightly as a func-tion of business sophisticafunc-tion. 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 mathe-matical 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 ex-pectations 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 & Trem-blay, 2002). Therefore, we developed a second hypothesis.
Hypothesis 2: The relationship between preparedness vari-ables 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 miti-gated in specialty areas (Major & Konar, 1984). Moreover, Jackson et al. (1992) found that the level of business sophis-tication 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 differ-ences in salary expectations once perceived others’ pay is controlled for. Thus, collectively, the extant literature sug-gests 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 hy-pothesis:
Hypothesis 4: The relationship between preparedness vari-ables 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 re-gional 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
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 behav-ioral students were provided participation points. Students who did not complete the survey were offered other com-parable 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 ex-plained, at least in part, by the absence of any extra credit op-portunities. 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 sam-ple. 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’ Confer-ence). 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 informa-tion about student preparainforma-tion for the job market. Anonymity and confidentiality were guaranteed, and students were al-lowed to withdraw consent and responses without penalty or prejudice. Moreover, in a separate informed consent form, students were given contact information for the principal in-vestigator 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 manage-ment 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-TABLE 1
Summary Statistics for Selected Variables (n=260)
Mini-
Maxi-Variable % M SD Median mum mum
Male 60.8
Married 11.9
Children 9.6
Work experience 43.5
High School GPAa 3.15 0.87 3.00 1 4
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).f Five-point 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 cumula-tive 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. Fam-ily 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, re-spectively. 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 expec-tations 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)
10% (lowest) 20% 30% 40% 50% 60% 70% 80% 90% 100% (highest)
1 year 3.5 9.6 11.9 20.0 26.9 13.1 9.2 5.4 0.4 0.0
5 years 0.0 3.1 3.8 6.2 14.2 17.3 21.5 21.5 9.2 3.1
10 years 0.4 0.4 2.7 1.9 4.6 6.2 11.9 23.5 23.5 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.
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 chi-square 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 distri-bution 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 fol-lowing regression model:
SALi =a0+a1NETi+a2ELEVELi+a3JDIFFi
+a4MATHi+εi, (1)
where SALi is the salary expectation (earnings decile) of
studentifor alternatively 1 year, 5 years, and 10 years post-graduation;NETiis the networking relationships of studenti,
measured using a 5-point Likert-type scale;ELEVELiis the
employment level sought by studenti, expressed as a variable ranging from 1 (entry level) to 4 (executive);JDIFFi is the
perceived difficulty of finding a job for studenti, measured using a 5-point Likert-type scale; MATHiis the perceived
mathematical ability for studenti,measured from 1 (low) to 10 (high); andεiis the error term for studenti.
Note that Equation 1 does not contain the level of per-sonal support, which was shown in Table 3 to influence the responses to the salary expectations for five years after grad-uation. Moreover, Equation 1 includes the self-perceived
TABLE 3
Chi-Square Tests for Differences in Distribution of Variables Affecting Preparedness
1 year 5 years 10 years
Variable χ2 p χ2 p χ2 p
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).f Five-point 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.
TABLE 4
Regression Results for Salary Expectations and Preparedness Variables
Intercept p NET p ELEVEL p JDIFF p MATH p Adj.R2 F p
1 year 2.818∗∗ .000 .158† .077 .466∗∗ .000 –.205∗ .038 .162∗∗ .006 12.10% 9.896∗∗ .000
5 years 5.072∗∗ .000 .167† .082 .329∗ .011 –.222∗ .036 .157∗ .013 8.50% 7.049∗∗ .000
10 years 7.052∗∗ .000 .122 .216 .202 .129 –.126 .249 .104 .109 2.60% 2.702∗ .031
Note.The model isSALi=a0+a1NETi+a2ELEVELi+a3JDIFFi+a4MATHi+εi,whereSALi=the salary expectation (earnings decile) of student ifor alternatively 1 year, 5 years, and 10 years postgraduation;NETi=the networking relationships of studenti, measured using a 5-point Likert-type scale; ELEVELi=the employment level sought by studenti, expressed as a variable from 1 (entry level) to 4 (executive);JDIFFi=the perceived difficulty of finding a job for studenti, measured using a five-point Likert-type scale;MATHi=the perceived mathematical ability for studenti,measured from 1 (low) to 10 (high); andεi=error term for studenti.
†p<.1.∗p<.05.∗∗∗p<.01.
mathematical ability, which was marginally significant in Table 3, withpvalues 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 mathemat-ical 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 gradu-ation, respectively, which only makes sense, given that many other variables will factor into salary expectations once stu-dents 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 prepara-tion is associated with higher salary expectaprepara-tions. However, there was only partial support for Hypothesis 2: the relation-ship 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 be-tween preparedness variables and salary expectations
dif-fered across genders (Hypothesis 4), we utilized the follow-ing regression model:
SALi =a0+a1INDEXi+a2GINDEXi
+a3GENDERi+εi,where (2)
whereSALiis the salary expectation (earnings decile) of
stu-dentifor alternatively 1 year, 5 years, and 10 years postgrad-uation;INDEXiis an index that is equal to the sum of the
vari-ablesNET,ELEVEL, andMATH, less the variableJDIFFfor studentifrom Table 5;GINDEXi isINDEXi×GENDERi;
GENDERi is a dummy variable equal to unity for women
and zero otherwise; andεiis the error term for studenti.
Notice that the variable INDEX contains all four vari-ables that were previously included as separate regressors in Table 4.3In Equation 2, the coefficient a
1 estimates the
relationship between the index andSALfor men only; the co-efficienta2for the interaction termGINDEX 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 coefficienta3indicates how much
more or less pronounced the salary expectations for women are, holding the variableINDEXconstant. We expecteda1to
be positive and significant anda2to be negative and
signif-icant, 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 coefficienta3should 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
a3was expected to be positive and significant.4
The results from estimating Equation 2 for the entire sam-ple 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.
TABLE 5
Regression Results for Salary Expectations and an Index of Preparedness Variables
Intercept p Index p GIndex p Gender p Adj.R2 F p
Panel A: Total sample (n=260)
1 year 2.411∗∗∗ .000 .252∗∗∗ .000 –.133∗ .068 1.336∗∗ .036 11.80% 12.590∗∗∗ .000
5 years 4.217∗∗∗ .000 .252∗∗∗ .000 –.147∗ .058 1.712∗∗ .011 11.30% 11.981∗∗∗ .000
10 years 6.342∗∗∗ .000 .184∗∗∗ .000 –.144∗ .070 1.696∗∗ .015 5.80% 6.349∗∗∗ .000
Panel B: Business majors (n=190)
1 year 2.580∗∗∗ .000 .264∗∗∗ .000 –.131∗ .071) 1.047∗ .098 16.10% 13.096∗∗∗ .000
5 years 4.433∗∗∗ .000 .245∗∗∗ .000 –.123 .137 1.397∗ .052 11.20% 8.936∗∗∗ .000
10 years 6.302∗∗∗ .000 .174∗∗∗ .003 –.135 .136 1.774∗∗ .025 5.60% 4.755∗∗∗ .003
Panel C: Highly motivated finance majors (n=70)
1 year 2.063∗∗ .033 .228∗∗ .031 –.295 .281 3.350 .172 5.60% 2.374∗∗ .078
5 years 3.633∗∗∗ .000 .284∗∗∗ .005 –.343 .187 3.603 .122 9.10% 3.298∗∗ .026
10 years 6.383∗∗∗ .000 .207∗∗ .015 –.119 .586 1.066 .584 4.80% 2.154 .102
Note.The model isSALi=a0+a1I N DEXi+a2GI N DEXi+a3GEN DERi+εi, whereSALi=the salary expectation (earnings decile) of student ifor alternatively 1 year, 5 years, and 10 years postgraduation;I N DEXi=an index that is equal to the sum of the variablesNET,ELEVEL, andMATH, less the variableJDIFFfor studentifrom 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 studenti.
†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 fu-ture salaries depend on a myriad of factors once students are employed, this result is hardly surprising.
Despite the decreasing explanatory power, the results dis-played in Table 5 are surprisingly consistent. In all three regressions, the variablesINDEXandGINDEXhave the ex-pected positive and negative and significant coefficients, re-spectively. This indicates that the relationship between the
INDEXand 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 forGENDERis pos-itive 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, busi-ness 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 coefficienta2is consistently negative and
signif-icant, indicating that the relationship between preparedness variables and salary expectations was less pronounced for women than for men. One possible explanation for the find-ing in Panel A that women expect to earn more than men
is the lack of control for a specialty area. While we con-trolled for the level of business sophistication (through the preparedness variables) and indirectly for the perceived oth-ers’ pay,5we 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 GENDERvariable. Indeed, Panel C of Table 5 reveals that this is true;a3is not
significant for salary expectations either 1 year, 5 years, or 10 years after graduation for the subsample of finance ma-jors. Conversely, the results for business majors in Panel B are similar to those for the total sample in Panel A. Notably, however, the coefficienta2is not significant for salary
expec-tations 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 inINDEX 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 stu-dent workforce preparedness variables and the relationship
with salary expectations. We hypothesized that more pre-pared 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 prepared-nesss 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 sup-ports 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 prepa-ration 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 re-lationship 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 encourag-ing findencourag-ing, 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 oppor-tunities 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
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 so-phistication. In our study, these gender differences persisted even after controlling for the employment level and the self-perceived mathematical ability. Moreover, we found that the relationship between preparedness variables and salary ex-pectations 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 be-tween 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 indi-viduals to choose their major in the first place. Women have been found to choose their major more based on subject in-terest and class performance (Calkins & Welki, 2006) and aptitude in the subject (Malgwi et al., 2005), while men ap-pear 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 them-selves 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 stu-dents. Previous literature has investigated choices of majors and factors that determine career success, but no study to our knowledge has investigated the relationship between pre-paredness 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 differ-ences in salary expectations and gender differdiffer-ences 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 employ-ers. 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.
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
mis-leading in deciding on the regression model utilized here, all 13 variables were included as independent variables in Equation 1. The results for the total sam-ple, 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 indepen-dent variables. The results are not substantially differ-ent from the results for the total sample and show that the four variables included in Table 4 are highly sig-nificant. 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 gen-der 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 re-gressors is that it eliminates potential problems result-ing from multicollinearity due to the high correlations between the independent variables.
5. Recall from the discussion of Table 1 that women uti-lize the career management center more and may there-fore be more familiar with average salaries.
REFERENCES
Bansak, C., & Starr, M. (2010). Gender differences in perdispositions toward economics.Eastern Economic Journal,36, 33–57.
Calkins, L. N., & Welki, A. (2006). Factors that influence choice of major: Why some students never consider economics.International Journal of Social Economics,33, 547–564.
Chartered Institute of Management Accountants. (2010). Female fi-nance professionals six times less likely than men to make it to the top. Retrieved from http://www.cimaglobal.com/en-gb/Thought-leadership/women/Breaking-glass/
Chenevert, D., & Tremblay, M. (2002). Managerial career success in Cana-dian organizations: Is gender a determinant?The International Journal of Human Resource Management,13, 920–941.
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’ percep-tion toward finance profession.Interdisciplinary Journal of Contempo-rary 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:
Evi-dence and explanation of male/female differences.Journal of Manage-ment,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-women-forbes-woman-leadership-education.html
APPENDIX
SCRIPT FOR PROJECT IRB
Student Preparation for the Job Market
The purpose of this study is to investigate how students pre-pare themselves for the job market. The survey is about mea-suring students’ personal actions taken to make themselves more marketable. Your responses to the survey will be anony-mous. No identifying information including your name will be on the survey. All data will be retained in locked facilities under the control of the principal investigator. Feel free to skip any question in the survey you are not comfortable an-swering. Participation in this study is voluntary and choosing not to participate will result in no penalties or loss of benefits to which you are otherwise entitled. Students will receive ex-tra credit for participating in this study but other comparable extra credit opportunities will be available if you do not wish to participate. For FMA Conference participants, there are no direct benefits from participating in this study. Participants must be at least 18 years old to participate.
1. Select your gender.
a. Male b. Female 2. Are you married?
a. Yes b. No 3. Do you have children?
a. Yes b. No
4. Do you have work experience in your field, including internships?
a. Yes b. No
5. What was your GPA upon graduation from high school?
a. less than 2.5 b. 2.5–2.9 c. 3.0–3.4 d. 3.5 or greater 6. What is your cumulative college GPA? (Freshmen
need not respond.)
a. less than 2.5 b. 2.5–2.9 c. 3.0–3.4 d. 3.5 or greater 7. Are you utilizing the university’s career management
center? a. Never b. Rarely c. Sometimes d. Often e. Always
8. Are you actively involved with a business-oriented student organization?
a. No, I am not a member of such an organization b. I am a member, but I rarely attend meetings c. I occasionally go to meetings
d. I’m an active member of a student organization e. I’m very active and an officer of our student
organization
9. Do you have any networking relationships with orga-nizations in your field?
a. None b. Few c. Some d. Several e. A lot
10. What level job will you be applying for when you graduate?
a. Entry level b. Associate c. Management d. Executive
11. Do you intend to earn a Masters or doctoral degree? a. Yes, Masters only b. Yes, Masters and doctoral c. No, neither
12. Who supports your decision to pursue a career in your field? Career level supporter include your boss or co-workers, educational level supporters include professors, academic advisors, and tutors, personal level supporters include your spouse and parents. For each of these groups indicate the level of support from 1 (low support) to 5 (high support).
a. Career Level Supporter 1 2 3 4 5 b. Educational Level Supporter 1 2 3 4 5 c. Personal Level Supporter 1 2 3 4 5 13. How would you rate your math ability?
a. 1 (low) b. 2 c. 3 d. 4 e. 5 f. 6 g. 7 h. 8 i. 9 j. 10 (high) 14. What earnings percentile do you expect to earn one
year after graduation? Five years after graduation? Ten years after graduation?
a. 10% (lowest) b. 20% c. 30% d. 40% e. 50% f. 60% g. 70% h. 80% i. 90% j. 100%
First Year: Five Years: Ten Years:
