Introduction

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A marked gender gap in educational attainment exists today, with women attending postsecondary (PSE) education in greater numbers than men. However, this has not always been the case; in the past, men enrolled in university at higher rates than women (Clark 2000) but, today, because of higher female high-school graduation rates and greater participation in university, females have increased their overall level of education relative to men.  Turcotte (2011), for example, cites figures from the Labour Force Survey showing that, in 1990, men and women, aged 25 to 34, graduated from university in about equal proportions (at 15%); however, by 2012, the rate for women had increased to 37%  while, for men, the proportion increased but only to 26%. A similar reversal in the gender gap has also been found elsewhere (see Buchmann, DiPrete and McDaniel 2008; Goldin, Katz, and Kuziemko 2006; Jacob 2002).

Yet, despite all the gains in women’s level of education relative to men, for the most part, they still lag behind men in terms of earnings.  Williams (2010) finds, for instance, that the average total income of men has been consistently higher than that of their female counterparts for the past several decades. In 2008, for example, men earned over $45,000 on average, while women earned about $30,000 on average. Among young men and women under the age of 34, the gender gap in income was smaller than for older age groups, but a significant income gap of about $10,000 prevailed in 2008. Other Canadian research echoes these findings. Hango (2010), for instance, found that among a recent cohort of Canadian young adults, females earned less, and were less likely to be employed than their male counterparts several years after leaving full-time education. There is other evidence to suggest, however, that the gender gap in pay and inequality may be narrowing (Cooke-Reynolds and Zukewich 2004; Drolet 2011).

Part of the explanation for the continued earnings gap may lie in the postsecondary program choices made by young Canadian adults.¹  Not all university programs lead to similar labour market outcomes (Finnie 2001; Frenette and Coulombe 2007; Gerber and Cheung 2008; Giles and Drewes 2001; Walters 2004).  This continued economic discrepancy across program types leads to poorer female economic outcomes, because women have traditionally entered postsecondary programs that have lower returns in the labour market (Bobbit-Zeher 2007; Christie and Shannon 2001; Davies and Guppy 1997; Gerber and Cheung 2008; Frenette and Coulombe 2007; McMullen, Gilmore and Petit 2010; OECD 2011; Turner and Bowen 1999). McMullen and her colleagues (2010) found that, in 2007, over 80% of all university graduates in the health professions were women, while between 60% and 70% of the social and behavioural sciences and humanities graduates were women. In contrast, 30% or less of the mathematics, computer and information science, or engineering graduates, were women. These figures are also echoed at the doctoral level in Canada (Desjardins and King 2011) and in the United States (England, Allison, Li, Mark, Thompson, Budig and Sun 2007). Christie and Shannon (2001), meanwhile, used the 1986 and 1991 Canadian Censuses, and found that gender differences in industry and occupation of employment as well as field of study are the largest contributors to the explained portion of the gender gap in earnings; much more so than differences in level of educational attainment.  In other words, women’s earnings are typically lower than men’s based on their choice of university major, not on the level of degree obtained.²

Over time, the gender gap by field of study has narrowed, but continues with women and men still choosing traditional university fields of study (Andres and Adamuti-Trache 2007; Frenette and Coulombe 2007; Zarifa 2012). For example, England et al. (2007) found that, in the United States, even though female graduation rates in traditionally male fields, such as engineering and mathematics, have increased since the early 1970s, they are still substantially lower than male graduation rates in these disciplines. Recent Canadian data, meanwhile, from the 2011 National Household Survey (NHS) found that women held a higher share of university degrees among younger university STEM graduates than among older ones, but men still held the majority of university STEM degrees—young women aged 25 to 34 represented 39.1% of university STEM degrees in that age group (Ferguson and Zhao 2013).

Part of the explanation for gender differences in university program choice may lie in academic interests and ability in high school.  Past research has found, not surprisingly, that youth with strong mathematical and/or science abilities are more likely to go into university programs where mathematical and science skills are essential (Trusty 2002; Trusty, Robinson, Plata and Ng 2000).  However, a great deal of past work has found that girls score lower than boys on tests related to mathematical skills, yet do better on tests related to reading (Bussière, Knighton and Pennock 2007; Downey and Vogt Yuan 2005; Niederle and Vesterlund 2010; OECD 2009; OECD 2012).  Meanwhile, in terms of science knowledge, not much overall difference between boys and girls has been found (Bussière, Knighton and Pennock 2007; OECD 2009; OECD 2012).  However, is it lower ability in mathematics related disciplines that drives girls away from choosing university programs where these skills are necessary, or is it that girls are simply not as interested in mathematics as boys, or perhaps have less confidence in their abilities than boys?   

This paper will examine first university program choice of young men and women, by paying particular attention to their mathematical and science ability at age 15. This paper cannot isolate all potential characteristics that may or may not affect program choice in university; however, it can help uncover the link between gender, mathematical and science ability and program choice, while controlling for a wide range of factors.

Section two summarizes recent literature that focuses on the link between mathematical and science ability and program choice, with a focus on gender differences. Section three describes the data and methodology used for the analysis, followed by the concluding section, which presents the results and analytical discussion.

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Notes

1. Other explanations related to family formation and the presence of children affecting female labour force participation are also important and have been well studied, but remain outside the scope of the current paper and will not be examined further (see Phipps, Burton, and Lethbridge 2001; Waldfogel 1998; Zhang 2009).
2. Interestingly this same finding was observed much earlier by Wannell (1990), who discovered that differences in field of study were much more important than differences in level of education in explaining the wage gap.