anexaminationoftheschool-to-work ... · pdf fileuniversity of guelph, ontario, canada ......

An Examination of the School-to-Work

Transitions of Male and Female College

and University Graduates of Applied and

Liberal Arts Programs in Canada

David WaltersDepartment of Sociology and Anthropology, College of Social and Applied Human Services,

University of Guelph, Ontario, Canada

E-mail: [email protected]

This study compares the school-to-work transitions of recent male and femalepostsecondary graduates of various levels of schooling. Gender comparisons inearnings are also made between graduates of applied and technical fields with thoseof liberal arts fields. The results of this study suggest that the earnings of universitygraduates of all levels are similar for both men and women. However, there remainsa large gender gap in earnings among community college graduates of all fields,even after controlling for a variety of structural and work-related characteristics.The policy implications of these findings are also explored.Higher Education Policy (2006) 19, 225–250. doi:10.1057/palgrave.hep.8300121

Keywords: Postsecondary education; field of study; gender; labor-market outcomes;Canada; school–work transitions

Introduction

Following high school graduation, students face many important decisionsregarding which type of postsecondary education to pursue. Not only mustthey choose between 2-year community college programs vs full universitydegree programs, they must also choose among a variety of different fields ofstudy. Such decisions will ultimately provide them with different labor-marketoutcomes. Past research has identified clear patterns regarding the earnings ofgraduates of various postsecondary programs. For example, it is welldocumented in the research literature that graduates with higher-levelpostsecondary credentials have better labor-market outcomes than graduatesof lower levels of postsecondary schooling. That is, graduates with higher leveluniversity degrees (i.e. master’s and doctorates) have better labor-marketoutcomes than graduates with undergraduate degrees, who in turn, have betterlabor-market outcomes than graduates of community college programs(Finnie, 2000a, b; Christie and Shannon, 2001; Walters, 2004). At the same

Higher Education Policy, 2006, 19, (225–250)r 2006 International Association of Universities 0952-8733/06 $30.00

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time, past studies have also shown that field of study has a major impact onlabor-market outcomes (Jacobs, 1995; Davies and Guppy, 1997; Christie andShannon, 2001; Drolet, 2002; Lin et al., 2003; Walters, 2003). Although somerecent research supports the assertion that the new economy has increasinglybeen requiring graduates with critical thinking and communications skillswhich are commonly provided by liberal arts programs (Allen, 1996, 1997,1999b; Axelrod et al., 2001; Lin et al., 2003), the majority of studies continue toshow that graduates of applied and technical programs earn considerably morethan their counterparts of the liberal arts programs.

However, it is unclear if the difference in earnings between graduates ofapplied and liberal arts programs are similar for men and women at variouslevels of postsecondary schooling. Indeed, very little research is available whichcompares the early labor-market experiences of male and female postsecondarygraduates of different levels of schooling and different fields of study;particularly among those who have made their school-to-work transitions inthe new economy. Thus, it is still not fully understood whether men andwomen are disproportionately rewarded for certain skills more so than others.This is an especially important issue since men and women are disproportio-nately concentrated in different types of postsecondary programs, and facevery different labor-market prospects following graduation.

The purpose of this study is to build on the previous research in the area bycomparing the earnings of male and female college and university graduates ofvarious fields of study. A key issue addressed in this paper is whether theearnings premium associated with applied programs is similar for men andwomen, and if this relationship is consistent across various levels of schooling.This study will also identify the earnings advantages for males and females whoacquire higher levels of schooling in the same field of study. The results of thisstudy are intended to provide social investigators with a better understandingof the labor-market outcomes of recent graduates, and to assist both studentsand policy-makers in making important decisions that will have lastingimplications.

Literature Review

The extent to which gender differences in employment outcomes areattributable to gender differences in educational attainment has received agrowing amount of attention in both the theoretical and research literature.The importance of emphasizing gender differences in earnings is highlighted bythe fact that women have historically been made less than men. In fact,economic life is organized around gender in every known human society. Sincethe rise of industrial capitalism, production and reproduction were divided

David WaltersExamination of the School-to-Work Transitions of College and University Graduates

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Higher Education Policy 2006 19

between the female household and the male economy (Oakley, 1975), and thereis no single labor market in which men and women compete on equal terms(Rubinson and Browne, 1994, 609).

For the most part, gender inequality has declined considerably over the lastquarter century. This decline has corresponded with a shift in women’sfinancial and economic aspirations, from finding a suitable husband toachieving independence and autonomy in their own careers. Women are nowmuch more ambitious, they are much more independent and career-oriented,and have a different perspective on what constitutes an acceptable occupation.Factors contributing to this shift include higher divorce rates, more liberalideals regarding women in the workforce, and the greater need for a dual-income family.

Declining gender inequality has corresponded with the increase inattainment of educational credentials by women. In fact, the postsecondaryenrolment levels for women have surpassed men at the college andundergraduate levels (Guppy and Davies, 1998). At the same time, womenhave recently caught up to, and in some cases surpassed, men in many of theonce traditionally male-dominated professional programs such as medicine,pharmacy, and law (see Davies et al., 1996).1

Still, there is compelling evidence to suggest that gender differences inearnings persist. One explanation for the gender gap in pay is that women aremore likely to be concentrated in part-time and temporary forms ofemployment than men (Redpath, 1994; Krahn, 1995; Duffy et al., 1997;Finnie, 2000a). This is believed to be attributable to women’s looserattachment to the labor force (see Finnie, 2000a, b, 206), which may beexplained by the fact that women are much more likely than men to beresponsible for childrearing and domestic chores (see also Krahn and Lowe,1998a, 159–164). Nevertheless, even among full-time workers, women still earnless than men, and this is also likely because women are more likely to havetheir careers interrupted by childrearing.2 As well, gender differences in regardto job interruption and willingness to relocate have also been identified as twokey reasons why the returns to education are different for men and women (seeJacobs, 1995).

Human capital theory offers valuable insight into why there are genderdisparities in labor-market outcomes. The central tenant of the human capitaltheory of education is that individuals who have obtained the most valuableand sought-after forms of human capital, namely education, enjoy the bestlabor-market prospects (see Mincer, 1958; Becker, 1964; Schultz, 1971). In fact,research indicating that women’s labor-market outcomes have improved indirect correspondence with their shift into higher levels of education isconsistent with the human capital argument. Indeed, gender differences inhuman capital, particularly education, have been found to explain a significant


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portion of the gender gap in earnings (see Rubinson and Browne, 1994;Christie and Shannon, 2001).3,4,5

However, human capital theory has difficulty explaining why men andwomen with similar levels of human capital have different labor-marketoutcomes. Likewise, human capital theory has been challenged for assumingthat workers generally compete in a single, open labor market, and forimplying that employers always make completely rational hiring decisionsbased entirely on ability (see Krahn and Lowe, 1998a, 111, 184). Bothstructuralists and dual-labor-market theorists have been quick to note thathuman capital characteristics do not account for all gender differences inearnings, while discrimination in the labor market is believed to be a majorexplanation for the remaining gender gap in pay (see Smith (1990) for a furtherdiscussion on this issue).6

Nevertheless, the acquisition of higher education has been quite promisingfor women, particularly for those with the highest-level credentials. While thereis a substantial gender gap in earnings among men and women with lowerlevels of schooling (i.e. high school or less), the gap narrows considerablyamong men and women with postsecondary credentials. Among postsecondarygraduates, the wage gap between men and women is smaller for those withundergraduate degrees than for those with college diplomas or tradescertificates, while gender differences in the earnings of graduates with thehighest-level credentials (i.e. master’s and doctorate) are nearly negligible(Christie and Shannon, 2001).

However, despite their shift into higher levels of schooling, women still tendto be heavily concentrated in certain fields of study. For example, mostundergraduates in the arts and humanities are women (Redpath, 1994, Daviesand Guppy, 1997). Likewise, female undergraduates are also highlyconcentrated in nursing and primary education programs, whereas maleundergraduates are heavily concentrated in engineering and applied sciencesprograms (Redpath, 1994). Indeed, men are generally more likely than womento enter more selective fields of study that provide higher payoffs (Davies andGuppy, 1997),7 while fields with high female-to-male gender ratios are oftenperceived by employers to be less valuable, and consequently are underpaid inthe labor market (see Kilbourne et al., 1994).8

The fact that men and women are highly concentrated in different programshas had a strong impact on their labor-market outcomes. For example,Boothby (2000) found that the earnings structure for men and women is quitesimilar for graduates of most fields of study, but only under certain conditions.Among graduates of fields with a significant representation of both femalesand males, the earnings patterns of women and men are generally similar.However, the earnings structures of women and men are much less similar forprograms with an unequal gender balance (see Boothby, 2000).9


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Gender segregation in academic programs can also lead to striking genderdifferences in occupational destinations, technological training and computerskills (Lowe and Krahn 1989a, b). Although some evidence suggests thatgender stratification has declined in many professional occupations (Rubinsonand Browne, 1994, 604), women are less likely than men to be in higher skilledpositions across all occupational sectors, and are heavily concentrated in lowerlevel white-collar occupations (Boyd, 1990). Women also represent themajority of nurses, clerical workers, elementary school teachers, as well assales and service workers. They also tend to choose to work in such fields associal work, psychology, and human relations, while men tend to selectengineering and accounting fields (see Redpath, 1994).

Persistent gender segregation in both the acquisition and use of various typesof skills is a key theoretical and policy concern, particularly as the issue of skillutilization in the knowledge-based economy has become a pressing issue. Atthe same time, the importance of considering field of study as a predictor ofgender inequality is receiving more attention in the research literature (seeFarkas, 1996; Christie and Shannon, 2001; Drolet, 2002).10 However, therelationship between field of study and earnings for recent male and femalepostsecondary graduates is still largely unexplored in the research literature. Infact, much of the existing research in the area primarily involves comparisonsamong all postsecondary graduates, whereby recent graduates are pooledtogether with graduates of earlier cohorts. For example, results that are basedon census data (Allen, 1999a, b; Christie and Shannon, 2001) or the Survey ofLabor and Income Dynamics (Drolet, 2002) generally do not accurately reflectthe labor-market experiences of recent graduates entering the labor market ofthe knowledge-based economy.

At the same time, research that has examined the school-to-work transitionsis also limited because it does not provide detailed comparisons among bothcollege and university graduates (Krahn and Lowe, 1998b; Boothby, 2000;Hay, 2000; Butlin, 2001; Finnie, 2001; Lin et al., 2003). One study by Walters(2003) has examined field of study differences when comparing the earnings ofcollege graduates with those who have obtained university undergraduatedegrees.11 His results showed that the earnings of college graduates of appliedfields (i.e. business and engineering) are higher than those of universityundergraduate liberal arts programs. However, the extent to which thesefindings apply to both men and women is yet to be determined. Furthermore,these findings underestimate the true earnings potential of a liberal arts degree,because many university undergraduates pursue higher levels of schooling (i.e.,they enter master’s and PhD programs), whereas graduates of communitycollege programs do not have this option. Thus, when investigating the labor-market prospects of university graduates, it is important that the earnings ofmaster’s and PhD graduates are considered as well.


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Some studies reviewed above, which have made gender comparisons, lacksufficient controls (Allen, 1996, 1999a, b; Finnie, 2000a, b). Results basedprimarily on descriptive statistical information, such as frequency tables andcross tabulations, are less able to aid researchers in explaining their findings.This can be problematic as human capital, structuralist, and dual labor-markettheorists argue that gender differences in labor-market outcomes areattributable to a variety of individual and structural characteristics. Thus,when examining gender differences in labor-market outcomes, it is alsoimportant to account for as many theoretically relevant characteristics aspossible. By doing so, researchers are better able to determine whether genderdifferences in earnings are attributable to gender differences in earnings-determining characteristics, or to differential earnings payoffs to men andwomen.

While past research has identified that the gender gap in earnings is smallerfor those with higher-level credentials, little is known regarding the extent towhich this finding varies by field of study because research that has madegender comparisons among graduates of different levels of schooling hasgenerally not made field of study distinctions at each level. However, it wouldbe useful for both policy-makers and students to know the extent to whichgender inequality is related to both level of schooling and field of study. Thus,the statistical analysis in this paper will provide earnings profiles of recent maleand female community college and university graduates (bachelors, masters,and PhDs) of various fields of study. The descriptive analysis will be followedby a series of regression models, which will be used to reproduce the earningsprofiles after controlling for a number of theoretically important socio-demographic and work-related variables.12 In concordance with past research,it is expected that gender earnings inequality will be lower among graduates ofhigher-level postsecondary programs. However, it is also expected that thesegender differences in earnings will be explained by both the structural andhuman capital characteristics included in the regression analysis.

Methods

The 1995 National Graduates Survey (NGS) is the source of data for thisanalysis. The NGS includes more than 43,000 respondents who graduated in1995 and were surveyed 2 years following graduation (in 1997). It is the mostextensive survey available in Canada relating to the school-to-work transitionsof postsecondary graduates, and is representative of all provinces andterritories. This analysis includes only the respondents who had not obtainedan additional postsecondary credential following the credential that theyobtained in 1995.13


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Ordinary least squares regression is used to estimate the statistical models inthis study, treating the log (base 10) of earnings in 1997 dollars as thedependent variable. The sociodemographic independent variables include sex,marital status, age, region, mother’s education, father’s education, visibleminority status, and the presence of dependent children. Descriptions of thesevariables are provided in Appendix A.

The education variables available in the NGS are particularly useful fordealing with the limitations of past research. For example, when field of studycomparisons have been made, there has also been a tendency for someresearchers to group broadly different fields into a single category.14 Suchgroupings provide for limited comparisons because they do not capture labor-market differences that might exist among graduates of distinct programs.15

The field of study and level of schooling variables in the NGS overcome theselimitations by providing a thorough distinction among graduates of variouspostsecondary programs.

The level of schooling variable consists of four different categories: (1)community college graduates; (2) university undergraduates (treated as thereference category in the regression models); (3) graduates of master’sprograms; and (4) graduates of PhD programs.16,17,18 The field of studyvariable was derived by Statistics Canada such that homogeneous fields ofstudy are grouped together into the following nine different categories:(1) education, recreational and counseling services; (2) fine and applied arts;(3) humanities and related fields; (4) social sciences and related fields;(5) commerce, management and business administration; (6) agricultural andbiological sciences; (7) engineering and applied sciences, technologies andtrades; (8) health professions, sciences and technologies; and (9) mathematicsand physical sciences. These fields of study categories are consistent across alllevels of schooling.19,20

Two occupation-related variables are also included to control for the factthat men and women have different occupational destinations. The firstvariable is a measure of employment status (i.e. whether the respondents areemployed full-time or part-time). The second variable assesses the respondents’occupational status using the SOC occupational classification codes developedby Statistics Canada (See Table 2 for the categories of this variable).

Lastly, two additional variables are included in the analysis to assess theamount of gender segregation in both education and the labor market. Thefirst variable measures the gender ratio of each respondent’s academicprogram.21 The second variable is an assessment of the gender ratio of eachrespondent’s occupation. This variable captures labor-market segregation bygender, while also tapping into gender differences in occupational training.22

Both gender segregation variables consist of five categories, with each categoryrepresenting a specified proportion of women in each group. The five


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categories are as follows: (1) less than 21 % female; (2) between 21 and 40 %female; (3) between 41 and 60 % female; (4) between 61 and 80 5 female; and(5) more than 80 % female.

Results

Table 1 provides the earnings profiles of male and female college and universitygraduates of nine fields of study. Some of the more noteworthy findings fromthis table are discussed below.

Columns 1 and 2 of Table 1 show that, in general, graduates with higherlevels of schooling report higher earnings.23 They also suggest that for bothmen and women, graduates of technical and applied fields (i.e. business,engineering, and mathematics) have higher earnings than graduates of theliberal arts fields (arts, humanities, and social sciences). As mentioned earlier,these results are highly consistent with that of past research. Column 3 showsthe proportion of women in each field of study. Women generally outnumbermen in most fields of study at the college, undergraduate and master’s levels.Exceptions are graduates of engineering and mathematics-related fields at eachof the three levels and graduates of business programs at the master’s level,where the proportion of graduates is higher for males than females.

The last two columns of Table 1 provide some very interesting results. Theydisplay the percentage change in earnings of male (column 4) and female(column 5) postsecondary graduates, in comparison with their field of studycounterparts of the previous (next lowest) level. The results show that, onaverage, women improve their earnings much more than men by obtaining auniversity degree rather than a community college diploma. For example, theearnings of females with undergraduate degrees are approximately 31% higherthan the earnings of female community college graduates, whereas therespective figure for males is 14%. The largest increase for women is forgraduates of technical fields such as education, health, engineering andbusiness; however, women also experience modest earnings premiums if theyobtain liberal arts degrees, primarily in the humanities and social sciences. Menwith undergraduate degrees in the technical fields also experience considerableearnings improvements over their college counterparts; however, unlikewomen, they do not experience an earnings premium if they obtain a liberalarts undergraduate degree rather than a community college diploma in acomparable liberal arts field.

In general, there is a very substantial earnings premium among both maleand female graduates with master’s level degrees, in comparison with thosewith undergraduate degrees. The primary exception is male fine arts graduateswith a master’s degree. They have slightly lower earnings (by 3%) than males


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Table 1 Earnings (1997 dollars) by gender according to field of study and level of schooling

Growth

(in %)

Growth

(in %)

Earnings

Ratio

Males Females Female (%) Males Females (in %)

College (all) 29,976 22,257 51 — — 74.25

Education. Rec. Counseling 22,794 19,700 84 — — 86.43

Fine Arts 25,155 19,371 62 — — 77.01

Humanities 30,421 20,983 59 — — 68.98

Social Sciences 28,385 22,206 66 — — 78.23

Commerce/Business 29,077 21,439 70 — — 73.73

Agricultural/Bio Sciences 25,093 20,519 54 — — 81.77

Engineering/Applied Sciences 32,129 27,131 15 — — 84.44

Health Professions 29,591 24,599 85 — — 83.13

Math and Physical Sciences 33,912 27,734 43 — — 81.78

University undergraduate 34,062 29,203 55 14 31 85.73

Education. Rec. Counseling 33,278 28,658 67 46 45 86.12

Fine Arts 25,143 20,564 64 0 6 81.79

Humanities 28,876 24,784 66 �5 18 85.83

Social Sciences 28,758 27,103 63 1 22 94.25

Commerce/Business 37,372 31,286 50 29 46 83.72

Agricultural/Bio Sciences 30,086 24,062 57 20 17 79.98

Engineering/Applied Sciences 39,584 38,881 19 23 43 98.22

Health Professions 41,222 37,398 85 39 52 90.72

Math and Physical Sciences 37,229 33,814 26 10 22 90.83

Master’s (all) 51,206 42,709 53 50 46 83.40

Education. Rec. counseling 48,225 47,592 64 45 66 98.69

Fine arts 24,374 24,348 65 �3 18 99.89

Humanities 31,608 31,823 66 9 28 100.68

social sciences 39,113 34,041 62 36 26 87.03

Commerce/Business 65,341 49,426 43 75 58 75.64

Agricultural/Bio sciences 40,004 32,901 52 33 37 82.24

Engineering/applied sciences 48,353 42,501 20 22 9 87.90

Health professions 70,119 53,678 79 70 44 76.55

Math and Physical sciences 44,572 38,218 31 20 13 85.74

PhD 47,423 46,119 33 �7 8 97.25

Education. Rec. Counseling 52,539 53,415 56 9 12 101.67

Fine Arts 38,410 38,237 42 58 57 99.55

Humanities 40,381 41,130 39 28 29 101.85

Social sciences 49,705 47,339 46 27 39 95.24

Commerce/business 76,235 59,967 30 17 21 78.66

Agricultural/bio sciences 38,833 39,708 36 �3 21 102.25

Engineering/Applied Sciences 52,112 48,897 10 8 15 93.83

Health Professions 44,257 44,300 42 �37 �17 100.10

Math and Physical Sciences 45,620 44,120 17 2 15 96.71


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with fine arts undergraduate degrees. In fact, males with a master’s degree inthe fine arts even earn slightly less than male college graduates with a fine artsdiploma, 2 years following graduation. Males with a master’s degree in thehumanities show a very modest improvement (9%) over their counterpartswith an undergraduate degree. These figures suggest that it is not all thatrewarding (economically) for men to pursue master’s degrees in these areas.However, there is a pronounced earnings premium for male social sciencegraduates with a master’s degree over their undergraduate counterparts. Theirearnings improve by 36%. In comparison, female fine arts, humanities andsocial science graduates report earnings that are 18, 28, and 26% higher thantheir undergraduate counterparts. Thus, it is much more advantageous forwomen to pursue master’s degrees in the fine arts and humanities than it is formen, particularly since master’s programs are generally shorter in length thanundergraduate programs.

Among both males and females, but particularly among males, master’sgraduates of business and health-related fields experience the greatest earningsimprovement, relative to their counterparts with undergraduate degrees. Menwith master’s degrees in both business and health-related fields improve theirearnings by more than 70%. Women improve their earnings by 58 and 44% inthese fields, respectively. The greatest earnings improvement for females with amaster’s level degree is for those in education. Their earnings are 66% higherthan the earnings of females with an undergraduate degree in education andrelated fields.

The information in columns 4 and 5 also show that, on average, female PhDgraduates improve their earnings by 8% over their master’s counterparts, whilemales earn approximately 7% less than males with a master’s degree. Thesefigures are somewhat discouraging considering a PhD is generally at least a 4-year commitment.24 However, the chances of improving ones earnings byobtaining a PhD vs a master’s degree vary considerably by field of study.Indeed, a PhD does provide a strong improvement in earnings for graduates ofall liberal arts fields, particularly those of the fine arts. In fact, both males andfemales with a doctorate in the fine arts earn more than 50% of theircounterparts with only a master’s degree. Indeed, this is the only instancewhere it may be economically viable for males to pursue a higher-level degreein the fine arts. Nevertheless, the earnings of graduates with a fine artsdoctorate are still the lowest of all graduates at this level.

Men and women with doctorates in humanities fields report earnings thatare 28 and 29% higher, respectively, than their gendered counterparts withmaster’s degrees in these areas. Likewise men and women with PhD’s in thesocial sciences improve their earnings over their male and female counterpartswith a master’s degree by 27 and 39%, respectively. Finally, health fields arethe only programs where both male and female PhD graduates earn less than


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do their gendered counterparts with only a master’s degree (males earnapproximately 37% less, whereas females earn 17% less). Thus, from aneconomic standpoint, it makes little sense for master’s graduates, both maleand female, in health-related areas to pursue a PhD.

The gender earnings ratios are provided in the last column of Table 1. Onaverage, the earnings of men and women are more equitable among graduateswith higher levels of schooling. There is, however, a great deal of variationaccording to field of study within each level of schooling. At the communitycollege level, the field with the highest gender earnings ratio is education, wherewomen can expect to earn approximately 86% of what men earn. Conversely,community college graduates of humanities fields have the lowest genderearnings ratio — women earn approximately 69% of what men earn. Amonggraduates with a baccalaureate, women of engineering fields experience thehighest gender earnings ratio; they earn approximately 98% of what men earn.In contrast, the highest gender gap in earnings is among graduates ofagricultural and biological science programs, where women earn slightly lessthan 80% of what men earn.

Some particularly noteworthy observations are made when comparing thegender earnings ratios among male and female graduates of master’s and PhDprograms. Surprisingly, the gender earnings ratio is lowest among graduates ofhealth and business programs at the master’s level, and business graduates atthe PhD level. In each group, women earn less than 80% of what men earn.These findings are somewhat unexpected, considering that these are among themost economically rewarding fields for women.

Of course, the above findings are based on earnings profiles that do notcontrol for structural and employment differences that exist between men andwomen.25 The regression models below are used to determine whether thegender differences identified above remain, after controlling for importantstructural, human capital, and employment variables discussed earlier.

Regression results

Table 2 provides the OLS regression estimates for the two models identifiedbelow. The coefficients for both models are obtained, after controlling formarital status, age, region, mother’s education, father’s education, number ofchildren, visible minority status, and language (whether the respondents speakEnglish or French).26 The regression coefficients for these variables areavailable from the author upon request.

Model 1 assesses the effect of each of the independent variables, sex, field ofstudy, level of schooling, gender segregation by education, employment status,occupational status, and gender segregation by occupation, on the log ofearnings, while controlling for the variables mentioned above. The results from


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Table 2 OLS regression of log of earnings on gender, field of study, level of schooling,

employment status, occupational status, gender segregation by education and occupation, and the

interactions among gender, level of schooling, and field of study. These results are obtained

controlling for the sociodemographic variables (see Appendix A)

Model 1 Model 2

Gender b s.e. (b) P*** b s.e. (b) P***

Female �0.05 0.003 �0.05 0.007

Field of Study *** ***

Education 0.03 0.005*** 0.04 0.009***

Fine Arts �0.04 0.008*** �0.07 0.016***

Humanities �0.04 0.006*** �0.06 0.011***

Commerce 0.04 0.006*** 0.06 0.009***

Agricultural/Bio Sci �0.04 0.007*** �0.04 0.012***

Engineering/Ap Science 0.05 0.008*** 0.06 0.010***

Health professions 0.08 0.008*** 0.10 0.015***

Math 0.02 0.009* 0.03 0.011*

Social Sciences

Level of schooling *** ***

College �0.08 0.004*** �0.01 0.009

Master’s 0.09 0.005*** 0.07 0.013***

PhD 0.06 0.013*** 0.13 0.031***

Undergraduate

Employment status *** ***

Part-time �0.31 0.004 �0.30 0.004

Full-time

Occupational status *** ***

Manager, admin 0.00 0.007 0.00 0.007

Natural Sci, Engineering �0.01 0.008 �0.01 0.008

Religion �0.15 0.028*** �0.14 0.028***

Teaching and Related �0.03 0.008*** �0.03 0.008***

Medicine and Health 0.04 0.009*** 0.04 0.009***

Art, literary, recreation �0.01 0.010 �0.01 0.010

Clerical and related �0.07 0.007*** �0.06 0.007***

Sales �0.07 0.008*** �0.07 0.008***

Service �0.08 0.007*** �0.08 0.007***

Manual labour �0.07 0.008*** �0.07 0.008***

Social sciences

Gender segregation (Occupation) *** ***

Occupation >80% Female �0.03 0.006*** �0.03 0.006***

Occupation 61–80% Female �0.05 0.004*** �0.05 0.004***

Occupation 20–39% Female 0.04 0.005*** 0.04 0.005***

Occupation o20% Female 0.04 0.007*** 0.04 0.007***

Occupation 40–60% Female

Gender segregation (Education) *

Educ Program >80% female �0.01 0.007 0.00 0.007

Educ Program 61–80% Female 0.00 0.004 0.00 0.005


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Table 2 (Continued)

Model 1 Model 2

Educ Program 20–39% Female 0.00 0.006 0.00 0.007

Educ Program o20% Female 0.01 0.008 0.02 0.008*

Educ program 40–60% Female

Level (Gender) ***

College (Women) �0.03 0.007***

Master’s (Women) 0.01 0.010

PhD (Women) 0.04 0.028

Field of study (gender) ***

Education (Women) 0.02 0.010

Fine arts (Women) 0.01 0.016

Humanities (Women) 0.05 0.012***

Commerce (Women) �0.01 0.009

Agricultural/Bio Sci (Women) 0.00 0.014

Engineering/Ap Science (Women) 0.03 0.011***

Health Professions (Women) 0.02 0.014

Math (Women) 0.03 0.016

Field of study (Level) ***

Education (College) �0.07 0.012***

Education (Master’s) 0.02 0.017

Education (PhD) �0.02 0.056

Fine Arts (College) 0.03 0.017

Fine Arts (Master’s) �0.13 0.036***

Fine Arts (PhD) �0.09 0.107

Humanities (College) 0.03 0.017

Humanities (Master’s) �0.02 0.019

Humanities (PhD) �0.07 0.050

Commerce (College) �0.07 0.010***

Commerce (Master’s) 0.07 0.015***

Commerce (PhD) 0.02 0.079

Agricultural (College) 0.00 0.016

Agricultural (Master’s) 0.00 0.027

Agricultural (PhD) �0.09 0.045

Engineering (College) �0.07 0.011***

Engineering (Master’s) �0.04 0.019*

Engineering (PhD) �0.09 0.042*

Health (College) �0.09 0.012***

Health (Master’s) 0.04 0.021

Health (PhD) �0.19 0.047***

Math (College) �0.03 0.021

Math (Master’s) �0.04 0.024

Math (PhD) �0.11 0.041**

*Po0.05; N=17,169; N=17,169;

**Po0.01; F=184.38; F=135.65;

***Po0.001; Prob >F=0; Prob >F=0;

Adj R2=0.469; Adj R2=0.481.


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this model indicate that, even after controlling for the other variables in themodel, men earn more than do women (Po0.001). The effect of field of studyon the log of earnings is also statistically significant (Po0.001). The dummycoefficients for this variable indicate that graduates of the fine arts, humanities,and agricultural and biological sciences earn the least. When controlling for theother variables in the model, these graduates have earnings that aresignificantly lower than social science graduates, the reference category(Po0.001).27 Graduates of mathematics and applied science programs earnslightly more than do social science graduates (Po0.05). Graduates of health-related programs, followed by engineering, commerce, and education programsearn the most, respectively. The difference in earnings between social sciencegraduates and graduates of each of these fields is statistically significant(Po0.001).

The variable used to assess gender segregation within academic programs isjust barely statistically significant (Po0.05).28 The dummy coefficients for thisvariable suggest that graduates of programs with higher proportions of malesrelative to females generally earn more than graduates of programs with higherproportions of females relative to males.

All of the employment outcome variables are statistically significant. Asexpected, the coefficient for part-time workers is statistically significant(Po0.001), indicating that full-time workers earn significantly more thanpart-time workers. Likewise, the occupational status variable also has a strongeffect on earnings (Po0.001). The parameter estimates for this variableindicate that the earnings of graduates employed in religious, service, sales,clerical, and manual labor positions are among the lowest of all employedgraduates. In fact, graduates employed in each of these fields earn significantlyless than do graduates employed in social science fields, the reference category(Po0.001). Graduates employed in medicine and health-related fields reportthe highest level of earnings. This is the only employment sector wheregraduates earn significantly more than graduates employed in social sciencefields (Po0.001). The earnings difference between graduates employed insocial science fields and graduates employed in each of business, engineering,and literary arts occupations is not statistically significant.

Finally, the effect of the occupational gender segregation on the log ofearnings is also statistically significant (Po0.001). As expected, the parameterestimates for this variable suggest that graduates employed in occupations withhigher proportions of women earn less than graduates employed inoccupations with higher proportions of men.

Since the central purpose of this paper is determine whether the relationshipbetween gender and earnings depends on both level of schooling and field ofstudy, Model 2 includes interactions among gender, field of study, and level ofschooling. Each of the interactions is statistically significant, as is the test for


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the three-way interaction among all three interaction terms (Po0.001). Thestatistically significant interaction terms indicate that the association betweenany pair of the variables and the log of earnings depends on the level of thethird. When adding the interaction terms in the model, all of the other variablesremained statistically significant at their previous levels, except for the variablethat measures the amount of gender segregation in the respondents’ academicprograms; it is no longer statistically significant.

Figure 1 plots the regression coefficients involved in the interactions, holdingthe other variables constant at their means. To make the results moremeaningful, the logged coefficients are converted back to actual earnings. Thetwo graphs in the figure represent the earnings of graduates of college,university undergraduate, masters and PhD programs, broken down by field ofstudy, separately for men (Figure 1a) and women (Figure 1b). The figuresdisplay a similar earnings pattern for men and women by field of study;however, the variability within each field of study, and across each levelappears to be greater for females than males. This implies that womengenerally improve their earnings more than do men by obtaining higher levelsof education. Thus, the rate of return on education within each field appears tobe higher for women than for men, even after controlling for gender differencesin sociodemographic and employment characteristics.

Among those with college diplomas and undergraduate degrees, graduateswith credentials in health-related fields report the highest earnings, whereasgraduates of the fine arts, humanities, and agricultural/biological sciencesreport the lowest earnings of both men and women. Incidentally, social sciencegraduates with university undergraduate degrees can expect, on average, toearn as much as the highest paid community college graduates, whencontrolling for other structural and employment characteristics.

The graphs in Figure 1 also show that certain fields offer high rates of returnto graduates who pursue higher-level degrees (i.e. master’s and PhD). Forexample, the earnings of master’s and PhD graduates (both males and females)of business-related programs are among the highest of all graduates, indicatingthat there is a clear earnings premium associated with these degrees that is notexplained by the other variables in the regression models. However, there doesnot appear to be much of an earnings premium for men who obtain a PhD inbusiness, as opposed to a master’s degree (MBA) in business, after controllingfor the other variables.

Of the three liberal arts fields, the social sciences provide the highest returnto those who pursue higher-level degrees (both master’s degrees and earndoctorates). Graduates of fine arts and humanities programs are less likely toimprove their earnings by obtaining higher-level degrees. In fact, whencontrolling for the other variables in the model, both male and femalegraduates with master’s degrees in the fine arts earn less than their gendered


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counterparts of all other programs. Another noteworthy finding is that, whencontrolling for the other variables, health-related fields are not nearly aseconomically rewarding for PhD graduates as they are for graduates with

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master’s level degrees. Both men and women with such doctorates reportearning less than those even with undergraduate degrees in these fields.

The graphs in Figure 2 are created using the same regression coefficients asthose used to create the graphs in Figure 1. They are included to provide anadditional perspective on gender differences by field of study for graduates ofeach level of schooling. Despite the statistically significant interactions, thegraphs in Figure 2 show quite similar earnings patterns for men and womenacross the various fields of study. The primary exception is among collegegraduates where there appears to be some notable gender discrepancies inearnings by field of study. Indeed, there are very clear gender differences inearnings among graduates of most fields of study at the college level. In fact,

Community College University: Undergraduate

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Figure 2. Earnings of male and female graduates of various fields of study, presented separately

for community college, undergraduate, master’s and PhD graduates.


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gender differences in earnings are statistically significant for every field of studyexcept for graduates of humanities programs.29

One key pattern that can be identified in Figure 2 is that the gender gap inearnings generally declines for most fields of study among graduates withhigher levels of schooling. In comparison with community college graduates,the gender gap in earnings is much smaller for every field of study among thosewith undergraduate degrees. In fact, business, agricultural/biological sciences,and social sciences are the only undergraduate fields where gender differencesin earnings are statistically significant.30

At both the master’s and PhD levels, gender differences in earnings arenearly identical for every field of study. In fact, among both sets of graduatesonly master’s graduates of business (Po0.01) and social science-related fields(Po0.05) are statistically significant. Indeed, an interesting finding from theregression analysis is that the large gender gap in earnings among master’sgraduates of both health and business programs and PhD graduates ofbusiness programs, has declined considerably after controlling for genderdifferences in sociodemographic and employment characteristics.

Discussion

The knowledge-based economy has a tremendous impact on the school-to-work transitions of recent postsecondary graduates (see Walters, 2004). Whileresearch on this issue is still in the early stages, understanding the relationshipbetween postsecondary education and the early labor-market outcomes ofrecent postsecondary graduates is all the more important, as both fundingdecisions and students’ educational choices and are based, in part, on theexpected returns to a postsecondary education. The results of this studycontribute to the existing literature in the area by tapping into genderdisparities in earnings among recent graduates, broken down by both field ofstudy and level of schooling, while also controlling for a variety ofsociodemographic and employment-related characteristics. This study hasprovided a number of new and interesting findings that are highlightedbelow.

As mentioned earlier, past research has suggested that field of study is animportant predictor of earnings inequality, particularly in Canada (see alsoDavies and Hammack, 2005). However, the existing body of literature has notthoroughly compared field of study outcomes among male and femalegraduates of various levels of postsecondary education, and prior to thisstudy it was largely unknown whether field of study is more important at onelevel of postsecondary schooling than at another level. In both the controlledand uncontrolled analyses, the results of this study indicate that, for both males


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and females, field of study is a more important predictor of inequality amonggraduates with higher levels of postsecondary education than among graduateswith lower levels of postsecondary education. In fact, there is a clear earningspremium associated with obtaining higher-level credentials in technical fields,particularly those related to business. Likewise, the regression results, whichalso control for a variety of occupation-related characteristics, indicate thatthis finding is independent of the possibility that specialized and technicalprograms are able to provide their graduates with tighter connections to thelabor market.31,32

The regression results also show that the earnings patterns by field of studyfor men and women are very similar, particularly among university graduates.Thus, for the most part, it does not appear that the new economy is favouringone form of credential (i.e. technical vs liberal arts) differently for women andmen. However, this analysis suggests that obtaining a higher level ofpostsecondary education generally improves the earnings more for womenthan for men for most fields of study. In other words, the acquisition of higherpostsecondary credentials is more beneficial for women than for men in thenew economy.

The statistical analysis also suggests that much of the gender differences inthe earnings of university graduates observed in the descriptive analysis areexplained by the sociodemographic and work-related variables included in theregression models. Specifically, after controlling for sociodemographiccharacteristics (i.e. age, region language, etc.), employment and occupationalstatus characteristics, and gender segregation in education and occupation, theearnings differentials between male and female university graduates (identifiedin the descriptive analysis) of health-related fields, and to some degreegraduates of business fields, largely disappear. However, there remains apronounced gender difference in earnings among community college graduatesof every field of study, except those of humanities programs, even aftercontrolling for these characteristics. In fact, other things being equal, womenearn less with a community college diploma in any of the technical fields thenmen earn with a community college diploma in any of the liberal arts fields,including the fine and applied arts. In contrast, the regression results show thatgender differences in the earnings of university graduates of most fields aremuch smaller. While there is some discrepancy in pay for male and femaleundergraduates in business, university undergraduate programs of all fields aregenerally more egalitarian than are community college programs. At the sametime, university undergraduate programs offer an additional benefit in thatthey provide opportunities for women to pursue higher levels of schoolingwhere the earnings for men and women are quite similar.

Thus, a key question remaining following this analysis is why is there still aconsiderable gender earnings gap in pay among male and female community


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college graduates, even after controlling for a variety of sociodemographic,field of study and labor-market characteristics? This gap is likely attributableto variables that could not be controlled for in the statistical analysis. Mostnotably, variables that explicitly tap into gender discrimination are notavailable in the 1995 NGS. It would have been ideal to have some way to tapinto employers’ hiring practices, as graduates with community collegepostsecondary credentials may experience a very different labor market thangraduates with university credentials. For example, it is quite possible thatgraduates of community college programs are more likely to enter the labormarket in lower-level positions, where pay scales are uncommon andemployment equity programs are generally not implemented. In contrast,graduates of university programs, particularly those of higher levels (i.e. thosewith master’s and PhD degrees), might be more likely to enter higher-leveloccupations, where pay scales and affirmative action policies are routinelyenforced. Such characteristics of the labor market are not completely capturedby the occupation-related variables included in the analysis, and more directmeasures of occupational prestige are not available in the NGS.33

The results of this study also raise some important policy issues regardingthe viability of liberal arts credentials outside of the social sciences, particularlyfor men.34 From an economic standpoint, these programs do not providestrong economic returns for recent graduates, even for those with higher-leveldegrees in these fields. Of course, students who enroll in arts and humanitiesprograms may be motivated by something other than economic incentives,such as the pursuit of knowledge for the sake of learning. At the same time,these programs provide a number of benefits to graduates that are all too oftenignored or underemphasized in labor-market policy research. These benefitsinclude the ability to read, write and communicate effectively; not to mentionthe stimulation of both creativity and critical thought. Moreover, theseprograms provide numerous, yet virtually immeasurable, cultural benefits forsociety as a whole. However, considering that both male and female graduatesof fine arts and humanities programs have especially low earnings, includingmaster’s and PhD graduates, policy-makers and postsecondary institutionofficials might want to consider ways to make these programs economicallyviable options for students. For example, they might consider adjusting thetuition levels of these programs to correspond with their expected labor-marketoutcomes. By doing so, graduates of these programs will be less likely to beburdened with excessive student loans, which they may have difficulty repayingfollowing graduation.35

Furthermore, information should be made available to graduates of fine artsand humanities programs on how to find jobs that will utilize the skills andknowledge that they have obtained in school, and how to better market theseskills to employers. These graduates also need to be better informed about


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recruitment, promotion, hiring, training and other employment-relateddecisions. At the same time, it is important to inform employers that graduateswith a liberal postsecondary education are not very different from theircounterparts with technical and applied training in terms of the employabilityskills that they have obtained in school (see Lin et al., 2003, 79).

The results of this study have important policy implications that should lendto further social inquiries regarding gender, field of study and inequality.Policy-makers and postsecondary institution officials should take particularnote of the fact that the students’ choices regarding field of study are muchmore important at higher levels of schooling. This is a new and importantdiscovery that was identified through making field of study comparisonsamong graduates of master’s and PhD programs. Investigators interested inthis finding might want to explore this issue further in the future.

Finally, while it is assuring to know that the new economy does notdifferentially reward men and women with similar credentials at the universitylevel, the findings for female community college graduates are somewhatdiscouraging, particularly since they are obtained after controlling formany important individual, structural, and occupation-related characteristics(including gender segregation). Thus, these results suggest, at least in terms ofpay equity, females should be encouraged to pursue a university leveleducation, while immediate policy initiatives might be directed at improvingthe labor-market prospects of females who graduate from community collegeprograms.

Notes

1 However, men still obtain the vast majority of doctoral-level degrees (Guppy and Davies,

1998, 93).

2 Likewise, women may have less incentive to seek out jobs that are in keeping with their

credentials if they anticipate that their careers will be interrupted by childrearing. This may also

explain why they have a lower rate of return on their investment in education.

3 Other factors, such as gender differences in geographical mobility, differences in experience and

training (Royalty, 1996), and labour force attachment (Duncan et al., 1993; Kilbourne et al.,

1994) have also been used to explain gender differences in employment outcomes.

4 The studies reviewed by Rubinson and Browne (1994, 587) suggest that approximately half of

the gender gap in earnings is explained by human capital variables.

5 Education is the most common form of human capital investigated in the research literature.

However, other forms of human capital include job training, migration, health, and economic

information (see Schultz, 1971).

6 The true extent to which gender discrimination is responsible for gender differences in earnings

is largely unknown because discrimination is generally inferred from the unexplained gender gap

in pay that is left over after human capital variables are accounted for in statistical models

(see Rubinson and Browne, 1994).

7 Some traditionally male-dominated fields such as medicine, pharmacy, and law are undergoing

feminization, however (Davies et al., 1996).


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8 For example, using panel data of approximately 10,000 respondents from the 1966–1981

National Longitudinal Survey of Youth, Kilbourne et al. (1994) found that there is a negative

effect associated with the ‘nurturant social skills’ that are disproportionately acquired by

women.

9 On a similar note, women tend to be penalized if they are employed in traditionally male

occupations (Duncan et al., 1993; Kanter, 1977), whereas, men are penalized if they are

employed in traditionally ‘female’ occupations (Allen, 1996).

10 For example, recent research relying on traditional decomposition techniques shows that field of

study plays an extremely important role in explaining gender differences in earnings (Christie

and Shannon, 2001; Drolet, 2002). Likewise, Wannell (1990) found that differences in field of

study explain more of the gender gap than differences in level of schooling.

11 All of the above papers relating to school–work transitions draw on data from Statistics

Canada’s NGSs.

12 While discrimination in the labour market is always a key concern, due to the inherent

limitations with the data used in this paper, it is only possible to account for structural and

human capital variables in the statistical analysis.

13 Finnie (2000a, b) and Krahn and Bowlby (1999) used similar criteria to select graduates for their

analyses using NGS data.

14 For example the study by Drolet (2002), using the Survey of Labour and Income Dynamics,

groups graduates of education programs together with those of fine arts programs and

graduates of agricultural fields with those of health-related fields. However, graduates of these

programs have been found to have very different labour market outcomes (Walters, 2003).

15 Often this sort of limitation is attributable to shortcomings of existing surveys. For example,

many surveys do not have enough detailed information (or a large enough sample size) to

distinguish among a broad number of fields of study. Therefore, researchers are often forced to

collapse field of study categories into smaller, more heterogeneous, groups.

16 The analysis excludes graduates of trades and professional programs because they contain

extremely low (or zero) cell counts for many of the field of study categories.

17 Incidentally, Statistics Canada grouped graduates with a bachelor of education degree (i.e.

BEd’s) in the same category as those with undergraduate degrees because they are not

considered to be professional programs similar to that of programs which offer degrees in

medicine (MD), dentistry (DDS, DMD), veterinary medicine (DVM), or law (LLB).

18 The NGS is particularly valuable for making field of study comparisons among graduates with

earned doctorates, because the survey includes more than 2000 respondents with a PhD.

19 In general, it has been less common for researchers to make field of study comparisons between

university and college graduates, because many surveys do not contain field of study categories

at the community college level that are comparable with those available at the university level.

The 1995 NGS overcomes this limitation because it provides a field of study variable that

harmonizes fields of study of community college and university programs.

20 While others have grouped graduates of liberal arts fields (i.e. fine arts, humanities and social

sciences) together (Lin et al., 2003), they are treated as separate in this analysis because

graduates of these fields have been found to have markedly different labour market outcomes

(Walters, 2003).

21 This variable is derived using the respondents’ actual field of study, rather than the aggregated

field of study variable.

22 This variable is derived based on the respondents’ actual occupation, rather than the derived

occupational classification codes.

23 The only exception is for comparisons between male master’s and PhD graduates, where

master’s graduates, on average, report higher earnings than do PhD graduates. This is likely

attributable to the large proportion of master’s graduates in business (MBA) programs.


246


24 However, these findings are likely attributable to the fact that there are a relatively large number

of graduates in the masters degrees in business (MBAs).

25 For example, the low gender earnings ratio among college graduates could be attributable to the

fact that women with these credentials are more likely to work part-time, or because they are

employed in different occupations than their male counterparts.

26 Incidentally, the regression models were also estimated including a variable that taps into the

respondents’ willingness to relocate for the purpose of career advancement. For this question,

the respondents’ were asked: Would you move to another city or town to improve your career/

job prospects? The response options for this question are: (1) yes; (2) no; and (3) maybe. This

variable was included because it reflects one’s ability to migrate for job purposes, which is

considered as a component of human capital (see Schultz, 1971). This variable was not

statistically significant in any of the models estimated. Moreover, considering the subjective

nature of this variable and the uncertainty regarding its causal relationship with earnings, it was

subsequently removed from the analyses.

27 When not otherwise stated, all of the results obtained in the regression models are interpreted as

controlling for the other variables in the model.

28 A separate analysis reveals that this variable is just barely statistically significant because much

of the effect of this variable is explained by the variable that accounts for gender segregation in

the labour market. When the latter variable was removed from the analysis, this variable

becomes highly significant (Po0.001).

29 The coefficient for graduates of mathematics programs is statistically significant at Po0.05. All

other coefficients are statistically significant at Po0.001. The statistical significance levels for all

of these contrasts are obtained from independent F-tests.

30 The difference for both commerce and social science graduates is statistically significant at

Po0.001. The difference for agricultural and biological science graduates is statistically

significant at Po0.01. These significance tests are obtained from independent F-tests.

31 Collins (1979), for example, provides a thorough discussion regarding the techniques

used by some program officials (i.e. increasing admission requirements to limit the supply of

graduates with particular credentials) to stimulate a labour market demand for their

graduates.

32 This finding might be attributable to the possibility that business and other technical fields are

able to acquire higher quality applicants through more rigorous admission requirements.

Unfortunately, the NGS does not include any variables that tap into program requirements

or other indicators of ability. Such measures might have also been valuable for explaining

why graduates with higher levels of schooling earn more than those with lower levels of

schooling.

33 At the same time, one must always be careful when implying that the remaining gender

differences in earnings are attributable to gender discrimination, as it is always

possible that some other variables that were not controlled for in the analysis may also

explain much of these differences. For example, questions that identify whether the

respondents were employed in public sector, private sector, or in unionized jobs, might have

helped explain some of the remaining gender disparities in earnings among community college

graduates.

34 Incidentally, the fact that social science graduates report markedly higher earnings than fine arts

and humanities graduates suggests that it is all the more important for future researchers to treat

these groups of liberal arts graduates separately.

35 Incidentally, social science graduates fair quite well in the labour market, particularly those with

higher-level credentials. In particular, men and women with an earned doctorate in a social

science field greatly improve the earnings over their gendered counterparts with only a master’s

degree.


247


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Wannell, T. (1990) ‘Male-female earnings gap among recent university graduates’, Perspectives on

Labour and Income 2(2): 19–31.

Lin, Z., Sweet, R. and Anisef, P. (2003) ‘Consequences and policy implications for university

students who have chosen liberal or vocational education in Canada: labour market outcomes

and employability skills’, Higher Education Policy 16: 55–85.


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Appendix A

Sociodemographic control variables

Sex Age

Men 20–45

Women

Marital status Minority status

Married Visible Minority

Separated/Widowed/Divorced Non-Minority

Single

Number of Children Mother’s and Father’s educationa

No children High school

One child Some postsecondary

Two children Trade

Three or more children College

University

Region Master’s

Eastern provinces Professional

Quebec PhD

Western provinces

Ontario

Language of interview

English

French

aThe father’s and mother’s education variables include an additional category for respondents who

either did not know or did not report their father’s or mother’s education.


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