Languages use different systems for classifying nouns. Gender languages assign many — sometimes all — nouns to distinct sex-based categories, masculine and feminine. Drawing on a broad range of historical and linguistic sources, this paper constructs a measure of the proportion of each country’s population whose native language is a gender language. At the cross-country level, this paper documents a robust negative relationship between the prevalence of gender languages and women’s labor force participation. It also shows that traditional views of gender roles are more common in countries with more native speakers of gender languages. In African countries where indigenous languages vary in terms of their gender structure, educational attainment and female labor force participation are lower among those whose native languages are gender languages. Cross-country and individual-level differences in labor force participation are large in both absolute and relative terms (when women are compared to men), suggesting that the observed patterns are not driven by development or some unobserved aspect of culture that affects men and women equally. Following the procedures proposed by Altonji, Elder, and Taber (2005) and Oster (2017), this paper shows that the observed correlations are unlikely to be driven by unobservables. Using a permutation test based on the structure of the language tree and the distribution of languages across countries, this paper demonstrate that the results are not driven by spurious correlations within language families. Gender languages appear to reduce women’s labor force participation and perpetuate support for unequal treatment of women.
That is from a new World Bank working paper by Pamela Jakiela and Owen Ozier, via the excellent Kevin Lewis. Kevin also points us to a new study indicating that East Asians smile fifty percent less than Americans.