Abstract

This paper introduces an index for assessing local attitudes toward women in the United States, leveraging the Google search index and a machine learning methodology. Exploiting the constructed measure of sexism, our investigation reveals that the #MeToo movement garnered greater attention in areas characterized by low measured sexism in the pre-MeToo era. Additionally, a substantial increase in reported sex crimes is observed in those areas post-MeToo compared to those with higher sexism measures. Further empirical findings indicate that the surge in documented sex crimes primarily stems from changes in reporting behavior rather than substantive shifts in actual incidents.

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