Gender equality remains a largely unattained ideal in society and politics. While most voters are supportive of women candidates, sexism played a marked role in recent elections and in public debates. Yet we know very little of (1) how attitudes about gender sexism at the macro level affect opportunities for women in politics and (2) how the political context affects sexist attitudes and attitudes about gender. This project brings together existing data on sexism and related attitudes to construct a ‘sexist mood’ over time and space using Bayesian dynamic latent trait modeling. This novel measure of the level of sexism at a given time and country can be used as both an explanatory variable of political progress of women, and an outcome variable country level trends.
