Introduction and Theoretical Framework

Hate crimes against Asian Americans have deep historical roots in the United States, dating back to the era of Asian immigration. Notable incidents, such as the Rock Springs Massacre of 1885, resulted in the deaths of at least 28 Chinese miners and widespread property destruction, while institutionalized discrimination—exemplified by the Immigration Act of 1924—severely restricted Asian immigration.

A recent surge in anti-Asian hate crimes began with the COVID-19 pandemic in early 2020, when the virus was erroneously linked to China. Racist labels such as “Chinese virus” and “Kung flu” further fueled public hostility. Between March 2020 and March 2022, Stop AAPI Hate recorded 11,467 incidents targeting Asian Americans and Pacific Islanders. The 2022 American Experiences with Discrimination Survey indicated that 16 percent of Asian American adults and 14 percent of Native Hawaiians and Pacific Islanders experienced hate incidents since early 2021, affecting nearly three million individuals. High-profile attacks, including the 2021 Atlanta Spa Shootings and assaults on elderly Asian Americans, underscore the severity of this threat. In response, the COVID-19 Hate Crimes Act was enacted in 2021.

Despite increased attention, most research has focused on descriptive accounts of anti-Asian violence, with limited theory-informed analysis of the structural factors underlying these crimes. This study addresses this gap by applying Peter Blau’s structural theory to examine city-level racial structural conditions associated with anti-Asian hate crimes in California from 2002 to 2021.

Blau’s theory emphasizes how group size, racial heterogeneity, inequality, and segregation shape intergroup relations. Larger outgroup populations relative to a minority group may increase opportunities for contact and conflict. Heterogeneous environments can facilitate benign interactions that reduce tension, whereas inequality and segregation may exacerbate competition and social distance. Applying this framework allows the present study to investigate how these structural conditions influence the prevalence of anti-Asian hate crimes, providing a macro-level perspective on patterns of racialized violence over time.

Data and Methods

This study examines anti-Asian hate crimes in California using two datasets. The first is incident-level hate crime data reported to the Department of Justice from 2002 to 2021, covering all law enforcement agencies and including city identifiers. Over 20 years, 1,063 hate crimes targeting Asians, Native Hawaiians, and Pacific Islanders were reported. The second dataset combines the 2010 census and ACS 5-year estimates to construct independent variables, using mid-period data to reflect typical social conditions.

Analysis focuses on cities with populations of 25,000 or more, resulting in 246 cities after excluding one outlier. The dependent variable is hate crimes per 10,000 Asian residents. Key independent variables include racial group size, heterogeneity, income inequality, and residential segregation, with controls for gender and age composition, concentrated disadvantage, and residential instability. Negative binomial regression with robust standard errors is used due to the skewed, over-dispersed count outcome. Separate models address multicollinearity between White and Hispanic population shares. Marginal effects quantify the expected change in hate crimes from one-unit changes in predictors. Additional analyses using pre-pandemic data test for COVID-19 confounding.

Results

The percentage of White residents modestly increases anti-Asian hate crimes, while a larger Black population has a stronger positive effect. Hispanic population share is negatively associated with anti-Asian hate crimes. Racial heterogeneity reduces hate crimes, whereas income inequality between Asians and Whites increases them. Residential segregation shows mixed effects: higher Asian-White segregation lowers hate crimes, but higher Asian-Hispanic segregation raises them; Asian-Black segregation is not significant. Among controls, only the percentage of residents aged 15–24 is positively associated with hate crimes.

Pre-pandemic analyses yield similar results, indicating robust effects of racial structural factors. Marginal effect comparisons suggest that Black population size, Asian-White and Asian-Hispanic segregation, and racial heterogeneity have the strongest impacts, though differences in measurement scales warrant cautious interpretation.

Discussion and Conclusion

The results support Blau’s framework, showing that racial structural conditions shape anti-Asian hate crimes. Larger White and Black populations are associated with higher rates, suggesting that greater outgroup size increases opportunities for intergroup contact and potential conflict. Conversely, a larger Hispanic population is linked to lower rates, possibly reflecting substitution with White share or distinct immigrant-status dynamics. Greater racial heterogeneity reduces hate crime, consistent with intergroup contact theory, while deviations from income parity between Asians and Whites increase conflict risk, aligning with resource-competition interpretations. Segregation effects differ by pairing: Asian–White separation reduces contact and conflict, whereas Asian–Hispanic separation may reflect unique spatial processes requiring further study.

Limitations include reliance on California police-reported incidents, which may understate true hate crime prevalence, and the lack of offender-race data, which prevents analysis of specific intergroup dynamics. Future research should extend to national datasets, explore interactions among structural factors, examine mechanisms linking structure to individual offending, and assess how acute events, such as COVID-19, interact with structural conditions.

These findings highlight that group size, heterogeneity, inequality, and segregation help explain variation in anti-Asian hate crimes. While structural change is complex, promoting meaningful intergroup contact through schools, workplaces, and community programs may reduce conflict in diverse settings. Combined with broader efforts to address inequality and spatial segregation, such interventions can help mitigate anti-Asian violence in urban contexts.