The results presented in the preceding section confirm that there are large differences in labour market outcomes between men and women in the Australian graduate labour market, despite the economy’s success in closing the education gap between these two groups in the workforce. In particular, our results show that women take longer to find full-time employment after tertiary level graduation and to achieve important benchmarks in the labour market such as finding permanent jobs, finding jobs that match their area of tertiary training, achieving above-average earnings and feeling secure in the job that they currently have. Our semi-parametric duration analysis identifies several confounding factors. Being a parent, being a migrant from a non-English speaking nation, living in major cities, living in areas with a high unemployment rate and year of graduation are each found to exert significant negative influences on employment outcomes generally, but more acutely for women. Most notable of these is the parent factor for women – motherhood appears consistently as the main cause of lengthy delays (of as much as six months) for women in reaching important milestones in the labour market. In contrast, our results also showed that being a parent is inconsequential for men’s labour market outcomes – that is, fatherhood neither improves nor worsens men’s ability to find full-time, permanent employment, or to achieve above average earnings or feel secure in the jobs they currently have.
These results make intuitive sense given traditional gender roles in typical Australian household – that is, women continue to take the larger share of unpaid household and care work, while men are relied on as main breadwinners and thus continue to spend longer hours in paid work (Baxter and Hewitt 2013; Craig and Brown 2017; Craig and Churchill 2021). We however need to qualify these results because of selection bias and other unobserved factors that could not be accounted for. Selection bias in labour force studies arises because data on wages and related outcomes are available only for a self selected group of labour force participants. This data inherently rests on the individuals’ decisions on whether or not to participate in the labour force, which are in turn determined by one’s tastes, preferences, work and family values, sense of competitiveness and/or attitudes to risk (Stam et.al. 2014; Blau and Kahn 2017, Mitri 2021) all of which are unobserved.1 It is thus highly possible that we have not accounted for the roles that these unobservable variables may have had on the estimated survival rates and therefore our findings should be interpreted with caution.2
That said, some aspects of this study can support a strong causal effect narrative. First, our sample is relatively homogenous and limited to the highly educated. Second, we use longitudinal data and duration modelling techniques. On the first point, we can draw from the findings of Noonan et al. (2005) and Bertrand et al. (2010) in their analysis of observed wage gaps among lawyers and MBA graduates, respectively. Using samples of highly educated men and women, both studies conclusively found that considerable portions of observed gender differences can be explained by labour supply factors like weekly hours and actual post-qualification work experience, which were in turn related to career-family trade-offs. On the second point, we draw on the linked data labour literature – notably the work of Hirsch et al (2010) and Ludsteck (2014), which uses longitudinal linked data and an individual fixed effects approach to address the selection issues. Both articles provide clear empirical evidence confirming that observed wage penalties for women are strongly associated with observed worker and job characteristics. Lastly, we draw on the specialisation-in-the-family argument first raised in Becker (1991). Accordingly, traditional notions of gender roles that view the husband as the primary earner may increase married men’s effort and motivation, and hence improve their labour market outcomes much faster than married women. Overall, the empirical evidence suggests that some portion of the observed relationship between gender and time needed to achieve outcomes in the labour market is causal.
On education, our gender-differentiated regressions show that completing a higher degree certificate or diploma hastens men’s ability to land permanent jobs and achieve above average earnings, while this extra qualification does not make any difference for women’s outcomes. This speaks to the issue of return on investment in higher education, regardless of whether this cost is borne by the government (via scholarships, financial incentives etc.) or by the individual. The immediate implication is that it is more worthwhile for men to study more and qualify for a minor, year-long second tertiary degree, as data shows such investment fast-tracks their career progression after employment. For women, on the other hand, investment in further education does not seem pay career dividends until the qualification pursued is at the highest levels (a masters or PhD degree).
Given that our sample consists of tertiary qualified men and women, our results are consistent with the finding that gender gaps are much slower to close among the highly skilled workforce than the general workforce (Arulampalam et al. 2007; Albrecht, Björklund, and Vroman 2003; Blau and Kahn 2017). For the case, our findings could reflect several possible factors in the Australian labour market that disadvantage women, including the presence of gender discrimination and the existence of a glass ceiling effect. Alternatively, it may also be that such findings result from other unmeasured factors that could lead men and women on the top of the distribution to behave differently. These possibilities will be explored in an extension paper that will investigate in far greater detail the role of higher education, alongside occupation and industry, in accounting for observed gender gaps, and how these variables interact with parenthood and the other relevant covariates.
Interestingly, we also find two other variables that appear to hold back women from achieving employment benchmarks in good time: living in major cities (as opposed to regional centres) and living in areas of high unemployment. For the first case, given higher rates of competition for full-time permanent jobs in large cities, it may be that women’s higher risk aversion profile cause them to accept less than ideal job offers too quickly, as seen in Cortes et. al (2021), which can then prolong the time it takes to land more permanent, full-time jobs that better match their skills. Bertrand (2018) however offers empirical evidence indicating that only 10 to 15 per cent of differences in labour market outcomes are due to psychological attributes such as risk aversion, confidence or competitiveness. Rather, Bertrand (2018) highlights that women have a relatively higher demand on their time outside the labour market (such as in childcare and other forms of nonmarket work), which then leads them to hold stricter conditions of jobs acceptance. These conditions may include more flexible or part-time work, which is harder to find in large cities where there is greater competition. Additionally, these demands on their time can give women less time to spend on job searching and applications. In short, women in cities may be one of those groups experiencing severe and entrenched disadvantage across both inner and outer metropolitan areas.
For job-skill match outcome, we find that age matters for women – indicating lengthier spells for achieving a job-skill match for older women than similarly aged men. On finding a permanent job, we find that being a parent is not significant, but that having a graduate diploma or certificate improves one’s chances of finding a permanent job, particularly for men. Finally, we find that individuals from non-English speaking countries and year of graduation penalises men more than women, while area unemployment rate penalises women more than men.
With regards to the covariate living in areas of high unemployment, this is thought to reflect the delicate balancing act that women have to do to pursue a successful career and attend to family caring duties at the same time. Our results are consistent with previous studies that show commuting times are a strong determinant of the labour supply in US cities (Black et al. 2014). Married women, particularly those with young children, are particularly sensitive to commuting time (Rosenthal and Strange 2012). Married women with young children located in close geographical proximity to their mothers or mothers-in-law can more easily participate in the labour market given the availability of childcare (Compton and Pollak 2014). Indeed, previous work by Le Barbanchon et al. (2019) shows that, on average, women have a lower willingness to commute relative to men.
Footnotes
[1] Possible selection bias in measuring differences in outcomes is an important and complex issue. Thus, it may not be surprising that efforts to address it have not yet achieved a consensus. Some differences arise because each of the reviewed studies not only focuses on a different data set or time period, but each uses a different approach to correcting for selection or implements it differently - including different definitions of the wage sample and different specifications of estimating equations.
[2] The economic literature is however unclear whether the issue of selection produces biased results; a comprehensive review of empirical works concludes that the evidence is mixed (Blau and Kahn 2017).
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