Is language a source of gender bias?

Unconscious bias has been implicated as one of the drivers of gender inequality. Where does this unconscious bias arise from? Given that it is unconscious, it must be part of our identity or our culture - things which are difficult to change (at least in the short term). That might seem like a cop-out, but it does explain why, despite the hype, most interventions to prevent unconscious bias don't work (although, if you follow the link, you'll see that some may do).

One important aspect of culture is language, and languages differ in their treatment of gender. So-called 'gendered languages' attach genders to objects (even inanimate objects). In Spanish, think of el toro (the bull, a masculine noun), or la casa (the home, a feminine noun). English doesn't attach genders to objects like that (although we do sometimes refer to objects, like ships, as a particular gender). So if unconscious bias arises (in part) from culture, and a key aspect of culture is language, then this raises the question: do the words we use, or how we use them, affect gender bias?

This interesting question was addressed in a recent working paper by Pamela Jakiela (University of Maryland) and Owen Ozier (World Bank). They pulled together a dataset on over 4300 languages, spoken by over 99 percent of the world's population. [*] They then used their dataset to explore whether gendered language was associated with women's educational attainment, women's labour force participation, and gender attitudes among men and women. They found:

...a robust negative relationship between grammatical gender and female labor force participation. Our preferred specification suggests that grammatical gender is associated with a 12 percentage point reduction in women's labor force participation and an almost 15 percentage point increase in the gender gap in labor force participation... Taken at face value, our coefficient estimates suggest that gender languages keep approximately 125 million women around the world out of the labor force...

We find a far more muted cross-country relationship between grammatical gender and women's educational attainment. This may be due to the fact that the average within-country gender gap in educational attainment is much smaller than the gender gap in labor force participation | since many wealthy countries have no gender gap in educational attainment, particularly at the primary school level. The prevalence of gender languages is negatively associated with the gender gap in primary school completion after controlling for continent fixed effects, but the estimated relationship is only marginally statistically significant.

Using data from the World Values Survey (WVS), we show that grammatical gender predicts support for traditional gender roles. The coefficient estimate is large in magnitude, suggesting that differences in language could explain the entire gap in gender attitudes between Ukraine (at the 55th percentile of WVS countries in terms of support for gender equality) and Trinidad and Tobago (at the 80th percentile).

So, not only did gendered language explain some of the differences in gender attitudes and female labour force participation between countries, the size of the effects is meaningful. Jakiela and Ozier also showed that the results were similar when looking within countries with language heterogeneity (where some local languages are gendered and others are not), including Kenya, Nigeria, Niger, Uganda, and India. The results are correlations so fall a little short of demonstrating causality, although the paper does include a section that shows that a causal explanation is likely. Jakiela and Ozier conclude that:

Our results are consistent with research in psychology, linguistics, and anthropology suggesting that languages shape patterns of thought in subtle and subconscious ways.

The obvious policy implication to draw from their results, if they are indeed causal, is that gendered languages (e.g. Spanish, German) should immediately drop the gendered treatment of nouns. It couldn't be that simple though, could it?

[HT: Development Impact last June, although they referred to an earlier version of the same working paper]

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[*] As an aside, this data seems like it would be a fantastic resource for all sorts of other research, particularly where an instrumental variable for gender bias is required.