Searching for a Roommate: A Correspondence Audit Examining Racial/Ethnic and Immigrant Discrimination among Millennials

Estimating social bias in data sharing behaviours: an open science experiment

Open data sharing is critical for scientific progress. Yet, many authors refrain from sharing scientific data, even when they have promised to do so. Through a preregistered, randomized audit experiment (N = 1,634), we tested possible ethnic, gender and status-related bias in scientists' data-sharing willingness. 814 (54%) authors of papers where data were indicated to be 'available upon request' responded to our data requests, and 226 (14%) either shared or indicated willingness to share all or some data. While our preregistered hypotheses regarding bias in data-sharing willingness were not confirmed, we observed systematically lower response rates for data requests made by putatively Chinese treatments compared to putatively Anglo-Saxon treatments. Further analysis indicated a theoretically plausible heterogeneity in the causal effect of ethnicity on data-sharing. In interaction analyses, we found indications of lower responsiveness and data-sharing willingness towards male but not female data requestors with Chinese names. These disparities, which likely arise from stereotypic beliefs about male Chinese requestors' trustworthiness and deservingness, impede scientific progress by preventing the free circulation of knowledge.

Validated names for experimental studies on race and ethnicity

A large and fast-growing number of studies across the social sciences use experiments to better understand the role of race in human interactions, particularly in the American context. Researchers often use names to signal the race of individuals portrayed in these experiments. However, those names might also signal other attributes, such as socioeconomic status (e.g., education and income) and citizenship. If they do, researchers would benefit greatly from pre-tested names with data on perceptions of these attributes; such data would permit researchers to draw correct inferences about the causal effect of race in their experiments. In this paper, we provide the largest dataset of validated name perceptions to date based on three different surveys conducted in the United States. In total, our data include over 44,170 name evaluations from 4,026 respondents for 600 names. In addition to respondent perceptions of race, income, education, and citizenship from names, our data also include respondent characteristics. Our data will be broadly helpful for researchers conducting experiments on the manifold ways in which race shapes American life.

COVID-19 Vaccine Rollouts and the Reproduction of Urban Spatial Inequality: Disparities Within Large US Cities in March and April 2021 by Racial/Ethnic and Socioeconomic Composition

Rollouts of COVID-19 vaccines in the USA were opportunities to redress disparities that surfaced during the pandemic. Initial eligibility criteria, however, neglected geographic, racial/ethnic, and socioeconomic considerations. Marginalized populations may have faced barriers to then-scarce vaccines, reinforcing disparities. Inequalities may have subsided as eligibility expanded. Using spatial modeling, we investigate how strongly local vaccination levels were associated with socioeconomic and racial/ethnic composition as authorities first extended vaccine eligibility to all adults. We harmonize administrative, demographic, and geospatial data across postal codes in eight large US cities over 3 weeks in Spring 2021. We find that, although vaccines were free regardless of health insurance coverage, local vaccination levels in March and April were negatively associated with poverty, enrollment in means-tested public health insurance (e.g., Medicaid), and the uninsured population. By April, vaccination levels in Black and Hispanic communities were only beginning to reach those of Asian and White communities in March. Increases in vaccination were smaller in socioeconomically disadvantaged Black and Hispanic communities than in more affluent, Asian, and White communities. Our findings suggest vaccine rollouts contributed to cumulative disadvantage. Populations that were left most vulnerable to COVID-19 benefited least from early expansions in vaccine availability in large US cities.

Priming COVID-19 salience increases prejudice and discriminatory intent against Asians and Hispanics

Mounting reports in the media suggest that the COVID-19 pandemic has intensified prejudice and discrimination against racial/ethnic minorities, especially Asians. Existing research has focused on discrimination against Asians and is primarily based on self-reported incidents or nonrepresentative samples. We investigate the extent to which COVID-19 has fueled prejudice and discrimination against multiple racial/ethnic minority groups in the United States by examining nationally representative survey data with an embedded vignette experiment about roommate selection (collected in August 2020; n = 5,000). We find that priming COVID-19 salience has an immediate, statistically significant impact: compared to the control group, respondents in the treatment group exhibited increased prejudice and discriminatory intent against East Asian, South Asian, and Hispanic hypothetical room-seekers. The treatment effect is more pronounced in increasing extreme negative attitudes toward the three minority groups than decreasing extreme positive attitudes toward them. This is partly due to the treatment increasing the proportion of respondents who perceive these minority groups as extremely culturally incompatible (Asians and Hispanics) and extremely irresponsible (Asians). Sociopolitical factors did not moderate the treatment effects on attitudes toward Asians, but prior social contact with Hispanics mitigated prejudices against them. These findings suggest that COVID-19-fueled prejudice and discrimination have not been limited to East Asians but are part of a broader phenomenon that has affected Asians generally and Hispanics as well.