This paper investigates how targeted groups respond politically to hate crimes occurred in their local communities. We combine incident-level administrative data on hate crimes in Los Angeles County from 2014 to 2022 with L2 individual voter files, leveraging the hyperlocal variation in the location and timing of these incidents. Our findings reveal that serious anti-Hispanic hate crimes result in a 1.6 percentage point increase in voter turnout among nearby Hispanic voters compared to their White counterparts. This mobilization effect is primarily driven by communities with a high Hispanic population density and the presence of Hispanic advocacy and community service organizations. We find no evidence of changes in the voter registration or migration patterns. Additionally, we show that hate crimes change voters' policy preferences on combating crime and affirmative action. Lastly, we find that more Hispanic candidates are running for city offices in cities that have experienced hate crimes. However, we do not find significant changes in political participation among Black and American Asian communities following anti-Black and anti-Asian hate crimes, respectively.

We study favoritism in the selection of elite scientific talent, by examining the relationship between host institution affiliation and performance in the Chinese Science Olympiad, where a gold medal guarantees a student's admission to top universities. Using hand-collected participant-level data (2003 - 2021), we find that students affiliated with the host province have a significantly higher winning probability, and the effect is more pronounced in host provinces where corruption norms are more prevalent. We further present evidence suggestive of cheating behavior using a portion of the contest vulnerable to information leakage, as well as the centralized post-Olympiad selection outside the control of host provinces. Together, our findings shed light on the crucial role of the organizational structure in designing equitable assessment systems for talent.

I study whether voters' cognitive biases affect political candidates' entry decisions. Building off the insight that in down-ballot elections, voters tend to choose the first-listed candidate due to choice fatigue and the primacy effect, I conjecture that potential candidates with late-alphabet surnames, expecting positional disadvantages on an alphabetically ordered ballot, are less likely to run for office. Using within-state variation in ballot order rules and data on 341,156 candidates running for U.S. state legislatures from 1967 to 2022, I find that  alphabetically ordered ballots have an impact on candidate entry, resulting in a 3.68 percentage-point decrease in the representation of late-alphabet candidates (equivalent to a 16.4\% reduction). Moreover, alphabetically ordered ballots may unintentionally impact minority candidate entry, due to these candidates' distinctive distribution of surname initials.

We show that, in contrast to classic models of voting and political advertising, mere exposure to (and thus familiarity with) a candidate may lead to greater support. Using data on sub-national elections in Louisiana and names used for Atlantic tropical storms from 1982 to 2020, we find that a down-ballot candidate receives a 12.1-percentage-points higher vote share in elections where a same-name hurricane hit Louisiana before the election. This result holds after considering name-specific popularity and potential behavioral responses from candidates. Our result contributes to our understanding of political campaigning and advertising markets more generally.

This paper examines whether gender composition may influence cognitive performance in a real-world high-stakes setting. We use unique administrative data on students taking the college entrance examination in China, who are randomly assigned to test rooms with varying gender compositions. Our findings reveal that an increased presence of male students in the test room leads to a decreased performance of female students, but does not affect males. The presence of males is a widely used cue for triggering stereotype threat in lab experiments, and additional evidence suggests our results are consistent with this concept: female students who are more inclined to endorse the math-gender stereotype are more strongly affected by gender composition. This study identifies a previously unexplored passive gender composition effect, providing new insights into the debate on education policy regarding single-sex versus mixed-sex schools.