Occupational Interest And The Gender Divide
Published by Najib A. Mozahem
College of Business Administration, Rafik Hariri University, Damour, Lebanon
These findings are described in the article entitled Gender differences in career choices among students in secondary school, recently published in the International Journal of School & Educational Psychology (International Journal of School & Educational Psychology 6 (2018)). This work was conducted by Najib A. Mozahem, Dana K. Kozbar, and Ahmad W. Al Hassan from Rafik Hariri University, and Laila A. Mozahem from Ahliah School.
Over the past few decades, two dynamics seem to have taken place in higher education. The first is that, in many countries, the female disadvantage has been reversed. In a large number of countries, more females are graduating from universities than males (OECD, 2012).
The second dynamic is that while there are growing numbers of females students in universities, the same cannot be said about females in certain majors. In fact, the proportion of females in science, technology, engineering, and mathematics (STEM) continues to be low (Ceci, Williams, & Barnett, 2009). For some reason, female students seem to gravitate more towards education, humanities, and health.
These findings have led researchers to investigate the reasons that lead to such choices. Traditionally, the biological explanation has been used to argue that these gender differences were innate. This line of thought argued that females are simply not good at math, and that STEM fields require a certain level of mathematical abilities (Nosek, Banaji, & Greenwald, 2002). This stereotype strongly exists (Lane, Goh, & Driver-Linn, 2012) despite the fact that there is a vast array of studies that have systematically documented that this is simply not true (Hyde at al., 2008).
Given the finding that females are as adept as math as males, researchers started looking at larger social forces that might be affecting career-related decisions. One of the most dominant theories is Social Career Cognitive Theory (SCCT; Lent, Brown, & Hackett, 1994). This theory argues that while females may be as adept as males at working these majors, they nonetheless seem to have lower self-efficacies in these fields. The reason for this is that females in society are in many instances discouraged from engaging in certain activities that might lead them to strengthen their self-efficacy in these specific domains. The reason why they are discouraged from engaging in these activities is that these activities are perceived as being masculine (Betz & Hackett, 1981). SCCT argues that perceived self-efficacy is more important than actual abilities because people avoid activities that they believe they are not good at and choose to engage in those activities that they believe they are good at. This leads to the result that females avoid traditionally male-dominated occupations since they are discouraged, or at least not encouraged, from engaging them.
This process of socialization, where females are discouraged from taking part in masculine acts, has an important time dimension. During the development process, individuals gather information from several sources and use this information to develop their own sense of self-efficacy. There is strong evidence that the interaction of parents and teachers with children is determined by the gender of the child (Gunderson, Ramirez, Levine, & Beilock, 2012), and that parents have different expectations from girls and boys (PISA, 2006). As a result, as the children grow, girls will develop lower levels of self-efficacy in STEM fields and, as such, their interest in pursuing these fields wanes over time.
As an assistant professor in a university, it has struck me how many females are in my regular business classes as opposed to how few females are in my business class that is given to engineering students. Prior research that I had conducted in the university indicated that female students in both the engineering and the business schools get higher grades on math than their male counterparts in both of these colleges respectively. This finding, together with my observation of the gender imbalance in the engineering class, was the catalyst to a research paper that I conducted with three colleagues.
In order to capture the effect that time might have, we collected data from secondary and high school students who were aged between 11 and 18 years old. In our survey, we indicated a list of 22 occupations, and we asked the students to indicate how likely they were to pursue each of these occupations once it was time to enroll in university. These surveys were distributed in three private schools in Lebanon. Like most countries, the number of female university graduates in Lebanon today exceeds the number of male graduates, but the proportion of females in STEM fields in considerably low.
The 22 occupations that were included in our study were classified into five categories: creative (music, artist, author, and television), medical (dentist, medical doctor, biological research, and nurse), child-mentoring and rehabilitative (kindergarten teacher, social services, working with sick children and with people with disabilities), clerical-sales (banker, accountant, sales agent, and office manager), and technological (mathematician, computer programmer, mechanical engineer, civil engineer, and communications engineer).
The results of our study were, unfortunately, not shocking. These results could be divided into two parts. In the first part, we ignored the time dimension by evaluating the results without taking the age of the respondents into consideration. We found that boys display a significantly higher interest in the technological category, while girls display a significantly higher interest in the creativity and the child-mentoring and rehabilitative categories.
The interesting, and also expected, finding was when we introduced age into the model. What we found was that the differences between the genders manifested themselves with time. Initially, for respondents who were 11 and 12 years old, there was no significant difference between girls and boys with regards to their interest in the occupations in the technology category. What we found was that this gap developed and grew as the age of the respondents increased.
This finding has important implications on the social surroundings of these students. Given that the gender gap develops over time, the results provide strong support for the argument that these differences are a result of different socialization processes that children go through. Given the crucial role that science and technology play in our society, it is imperative that the social structure does not discourage students who are adept in a certain field to pursue careers in it.
- Betz, N. E., & Hackett, G. (1981). The relationship of career-related self-efficacy expectations to perceived career options in college women and men. Journal of Counseling Psychology, 28(5), 399-410. https://content.apa.org:443/journals/cou/28/5/399
- Ceci, S. J., Williams, W. M., & Barnett, S. M. (2009). Women’s underrepresentation in science: sociocultural and biological considerations. Psychological Bulletin, 135(2), 218-261. http://doi.apa.org/getdoi.cfm?doi=10.1037/a0014412
- Hyde, J. S., Lindberg, S. M., Linn, M. C., Ellis, A. B., & Williams, C. C. (2008). Gender similarities characterize math performance. Science, 321(5888), 494-495.
- Gunderson, E. A., Ramirez, G., Levine, S. C., & Beilock, S. L. (2012). The role of parents and teachers in the development of gender-related math attitudes. Sex roles, 66(3-4), 153-166. https://link.springer.com/article/10.1007%2Fs11199-011-9996-2
- Lane, K. A., Goh, J. X., & Driver-Linn, E. (2012). Implicit science stereotypes mediate the relationship between gender and academic participation. Sex Roles, 66(3-4), 220-234. https://link.springer.com/article/10.1007%2Fs11199-011-0036-z
- Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79-122. https://linkinghub.elsevier.com/retrieve/pii/S000187918471027X
- Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002). Math= male, me= female, therefore math≠ me. Journal of personality and social psychology, 83(1), 44-59.
- Organization for Economic Cooperation and Development (2012). Education at a Glance 2012: OECD Indicators, OECD Publishing. https://www.oecd-ilibrary.org/education/education-at-a-glance-2012_eag-2012-en
- PISA. (2006). Retrieved from http://www.oecd.org/pisa/pisaproducts/database-pisa2006.htm