UC Irvine

In my first chapter, I construct a novel measure of misogyny using Google Trends data on searches that include derogatory terms for women. I show that misogyny is an economically meaningful and statistically significant predictor of the wage gap, and use it to test the predictions of two influential labor market discrimination models. I find that the gender wage gap is inconsistent with the Becker model of taste-based discrimination, but that it fits Black's search model of discrimination which allows for discrimination from even a small group of misogynists to result in a wage gap. In my second chapter, I investigate the impact of COVID-19 lockdowns on domestic violence by leveraging Google Trends data on search interest in domestic violence resources. I find that COVID-19 lockdown orders result in a meaningful and statistically significant increase in search interest in domestic violence hotlines but a decrease in search interest for other resources such as protective orders and domestic violence shelters, which require victims to leave their homes in the midst of a global pandemic. Moreover, I find that increased exposure between victim and perpetrator exacerbate domestic violence and that economic hardship and a lack of independent earning potential make women especially vulnerable. Finally, I find that search interest in terms likely to be searched by third-party reporters drastically decrease as a result of COVID-19 lockdowns, suggesting that external mechanisms for identifying and reporting domestic violence are affected by lockdown orders.