Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We develop an index of local sexism for the United States using publicly available Google Trends data. We shed light on the correlates of local sexism and find that the most important factors that predict it are the economic outcomes of men. Finally, we show that online sexism is associated with higher levels of the residual gender wage gap, the wage gap after controlling for education, occupation, industry, and age. We find evidence for a direct association of sexism with the wage gap, consistent with labor market discrimination and an indirect association that works through household decisions which themselves are associated with wages.