Owen Ozier, World Bank
Owen Ozier, World bank
Languages use different systems for classifying nouns. Gender languages assign many — sometimes all — nouns to distinct sex-based categories, masculine and feminine. We construct a new data set, documenting this property for more than four thousand languages which together account for more than 99 percent of the world’s population. At the cross-country level, we document a robust negative relationship between prevalence of gender languages and women’s labor force participation. We also show that traditional views of gender roles are more common in countries with more native speakers of gender languages. Our cross-country data also permit a novel permutation test, demonstrating that the patterns we find are robust to statistical correction for correlation in linguistic structure within language families. We also conduct within-country analysis in two regions where indigenous languages vary in terms of their gender structure. In four countries in Sub-Saharan Africa and in India, we show that educational attainment and female labor force participation are lower among those whose native languages are gender languages.