Why we need to be serious about gender stereotyping

Gender stereotyping is an important pervasive force that underlies many persistent gender gaps we observe in social and economic life (for example in education, the labour market, political representation, and the division of labour in households) and unless we take them seriously, make conscious efforts to teach our children about their
existence and effects, and equip them to combat them adequately, we will in fact not make progress. I wrote this piece for an event to mark 100 years of Women’s Rights in October 2020 focussing on the areas I know best through my research and practice, namely education, the world of work and women’s wellbeing, arguing through the lens of my expertise in labour and behavioural economics. What I write below reflects also on my experience as a Diversity and Inclusion Lead at my University, my outreach activities in schools, my role on the Women Committee of the Royal Economic Society, and my life as a woman.

Let me begin with some facts about gender gaps in education.

Although the international evidence on educational attainment generally highlights a closure of the gender gap in education (http://reports.weforum.org/global-gender-gap-report-2016), and although girls systematically report more positive educational attitudes and aspirations than boys (Rampino and Taylor, 2013), there remain key differences in the subjects boys and girls choose to study. In particular, the UK, which already has one of the lowest shares of 15-year
olds intending a STEM career among the OECD countries, actually lags behind most OECD countries in women’s aspirations to study a STEM subject and engage in a STEM career (see OECD, 2012). Mendolia and Walker (2012) have indeed shown that the UK ranking of 15- year old pupils in Mathematics and Science in the OECD’s PISA tests has been falling from 2000 to 2009 and was just below the OECD average in Mathematics and only slightly above in Science. A key contributing factor to the UK’s deficit is the very low General Certificate of Secondary Education (GCSE) performance at 16 in Science and Mathematics subjects. High level passes in these subjects at this level is a pre-requisite for further study in these same subjects, so understanding what happens at GSCE level is clearly important. What is really striking about the statistics is that after GSCEs girls select OUT of maths even when they actually do BETTER than boys in these subjects (Institute of Physics, 2013; Smith and Golding, 2015; Reuben et al 2014). This suggests that girls’ beliefs about their own performance may be biased and not based on their actual abilities.

Before we turn to how these beliefs are formed, let us consider the consequences of this ‘choice’ by girls not to study mathematics further than is compulsory. Evidence from a large body of research shows that maths skills play an important role in determining a person’s earnings, over and above their level of overall educational attainment (Joensen and Skyt Nielsen, 2009). Opting out of maths, of course, also affects the selection of women out of STEM subjects at University and in the labour market, with important effects on pay gaps (STEM jobs pay better!). These continue into career and pension gaps, and of course determine a range of other decisions within couples (who will stay home with the baby; who will arrange their working patterns around school and other activities for a couple of decades thereafter?). These decisions are driven by financial considerations as well as the sheer bargaining power of
women and men (Petrongolo and Olivetti, 2006; Ceci and Williams, 2010). So being better paid is not just good in itself but it affords a different negotiating platform for the division of household chores and, importantly, it makes families and kids better off in the long run also. This is the time frame that is clearly in their mind when taking out mortgages, but for some reason not when deciding on parental leave and work arrangements, as witnessed by the child penalty data which for mothers stands (in the long term) at a whopping 44% (Kleven et al, 2019). This is by no means a UK story: UNESCO reported in 2017 that women represent only 35 percent of all students enrolled in STEM-related fields of higher education, and only 28 percent in the critical information and communications technology field, which
means that any support to these critical fields unintentionally benefits men more than women.

Collective welfare suffers too: as neuroscientist Gina Rippon explains so well in The Gendered Brain, science itself is all the poorer for not having enough women in it given they bring not just more talent but different perspectives and directions of research (Rippon, 2019). Moreover, as a report commissioned by the IMF (Ostry et al., 2018) attests, countries where the gender gap is higher have lower productivity and innovation.

Science finds no evidence of ‘innate’ gender differences in ability (for example, spatial ability, intuition, etc., see Gina Rippon, 2019). The evidence rather suggests that gender gaps in maths are closely aligned to the following:

• parents’ expectations – more gender equal parents have daughters that do better at maths (Cornwell and Mustard, 2013; González de San Román and De La Rica, 2012; Fryer and Levitt, 2010)

• teachers’ expectations – teachers that have positive expectations increase the performance of pupils (Figlio, 2005; Sprietsma, 2009; Campbell, 2015; Hannah and Linden, 2012) and more gender egalitarian teachers increase the performance and uptake of STEM by girls (Alan et al., 2018; Carlana, 2018)

• school composition – in gender segregated schools girls choose STEM more and boys choose humanities more (Favara, 2012)

• pupils’ expectations – these affect performance independently of previous attainment and parental and other characteristics (Jacob and Wilder, 2010).

As the evidence reveals, gender stereotyping clearly has a great deal to do with all this. Stereotypes are cognitive shortcuts; we use them automatically to generate expectations of other’s behaviour, and we thus attach the expectation of what we think a group does to a person who comes from that group (Kahnemann, 2011; Schmader, 2010; Schneider, 2005). Cerebral networks used to process self-identity are different to those used to process more general knowledge and they are harder to change: a direct consequence of this is that correcting identity-related stereotypes with direct experience is difficult (Rippon, 2019). The relevant example in the present context is the gender and maths stereotype. Bohren et al. (2018) have shown through experiments in Mathematics Stack Exchange, an online forum of maths Q&A with 10 million participants, that for some users providing more information on the competence
of a female user changes the stereotype and even reverses it; however, for other users, this does not happen and instead they question the rating mechanism of the forum.

What happens at the receiving end of stereotyping? Stereotype threat affects performance both in positive terms (if it is manipulated to convince pupils to believe they belong to a group that has a natural advantage, they perform better) and in negative terms (if pupils are reminded of their gender, they do worse in the subjects in which they are expected to do badly) (Johns et al., 2005; Jussim et al., 2015). This starts early: girls aged 4 have worse performance in spatial
skills test if they colour in a girl playing with a doll before taking the test (Shenouda & Danovitch, 2014). Evidence shows that exposure to bias toward one’s group affects effort, self confidence, and productivity (Carlana, 2018; Bordalo et al., 2018; Glover et al., 2017), and bias is not just found in subjective elements of evaluation but in algorithms too (Schelsinger et al, 2018). This is perhaps unsurprising given they are designed by humans and fed human produced data.

The message is, I hope, clear: we need work to identify and combat bias and stereotyping, including in making schoolchildren aware that it exists and in preparing them to face it (teacher training reading lists on gender should include Rippon, 2019, Criado-Perez, 2019 and Bohnet, 2016). We do not rely on chance when our children cross the road: we tell them it is dangerous and we hold their hands in crossing it for many years before we let them do it by themselves. We should do the same with gender stereotyping and help our girls (and our boys) step into life
prepared to deal with the oncoming traffic.