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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.

The authors estimate the associations between community socioeconomic composition and changes in coronavirus disease 2019 (COVID-19) vaccination levels in eight large cities at three time points. In March, communities with high socioeconomic status (SES) had significantly higher vaccination rates than low-SES communities. Between March and April, low-SES communities had significantly lower changes in percentage vaccinated than high-SES communities. Between April and May, this difference was not significant. Thus, the large vaccination gap between communities during restricted vaccine eligibility did not narrow when eligibility opened up. The link between COVID-19 vaccination and community disadvantage may lead to a bifurcated recovery whereby advantaged communities move on from the pandemic more quickly while disadvantaged communities continue to suffer.

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.

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.

Proponents of big data claim it will fuel a social research revolution, but skeptics challenge its reliability and decontextualization. The largest subset of big data is not designed for social research. Data augmentation-systematic assessment of measurement against known quantities and expansion of extant data with new information-is an important tool to maximize such data's validity and research value. Using trained research assistants or specialized algorithms are common approaches to augmentation but may not scale to big data or appease skeptics. We consider a third alternative: data augmentation with online crowdsourcing. Three empirical cases illustrate strengths and limitations of crowdsourcing, using Amazon Mechanical Turk to verify automated coding, link online databases, and gather data on online resources. Using these, we develop best practice guidelines and a reporting template to enhance reproducibility. Carefully designed, correctly applied, and rigorously documented crowdsourcing help address concerns about big data's usefulness for social research.

Survey research finds that millennials have less prejudiced views of racial/ethnic minorities than other generations, leading some to label millennials as postracial. However, attitudinal survey research may be subject to social desirability bias because it documents statements or beliefs instead of actions. Moreover, most audit studies focus on people who make hiring decisions or own rental property and are therefore often older than millennials. This study uses a correspondence audit to investigate discrimination among millennials via "roommate wanted" advertisements. We sent over 4,000 emails and found a tiered pattern of discrimination against Asian (Indian and Chinese), Hispanic, and Black room-seekers. However, whether Asian and Hispanic room-seekers face significant discrimination varies based on whether they use predominantly White first names or traditional first names. Our findings shed light on the future of our racial system, expand our knowledge of discrimination beyond the traditional Black/White binary, and illustrate the persistence of anti-Blackness.

Existing research linking SES with work primarily focuses on the precursors (educational attainment) and outcomes (income) of work, rather than asking how diverse facets of work influence health. Using four waves of data from the Wisconsin Longitudinal Study, we evaluate whether multiple measures of respondent job characteristics, respondent preferences for those characteristics, and their interaction substantially improve the fit of sociological models of men's and women's physical and mental health at midlife and old age compared to traditional models using educational attainment, parental SES, and income. We find that non-wage job characteristics predict men's and women's physical and mental health over the lifecourse, although we find little evidence that the degree to which one's job accords with one's job preferences matters for health. These findings expand what we know about how work matters for health, demonstrating how the manner and condition under which one works has lasting impacts on wellbeing.

This study examines how COVID-induced and general attitudes toward Asians have changed over the course of the pandemic using nationally representative survey experiments in 2020 and 2022. First, we measured COVID-induced anti-Asian attitudes as the effect of a treatment reminding respondents of the pandemic on whether respondents would be willing to live or work with someone who is East or South Asian. The results suggest that the COVID-19 treatment worsened attitudes toward East and South Asians in the social domain and toward East Asians in the economic domain in 2020, but not in 2022. Second, we measured change in general attitudes toward Asians by comparing the control group responses in 2020 and 2022. The results demonstrate that, over the same period, general attitudes toward Asians have not improved despite growing attention toward anti-Asian biases. This finding underscores the persistence of general negative attitudes toward Asians beyond the immediate context of the pandemic and the ongoing imperative to actively address deeply ingrained biases against Asians. [ABSTRACT FROM AUTHOR]

Despite common conceptions, evidence on whether No Child Left Behind (NCLB) has had adverse effects for low achieving students is mixed. We hypothesize that the incentive to shift attention away from the lowest achieving students increases with the rigor of state standards. Using panel data from students in North Carolina, we exploit two natural experiments: increases in the rigor of standards in math in 2006 and then again in reading in 2008. We report an increase in test score gaps between low and high achievers and students near grade level. Adverse effects on low achievers are largest in the lowest achieving schools. We discuss the policy implications of our findings given the widespread adoption of more rigorous Common Core Standards.

Objective: On US college campuses, mental health problems are highly prevalent, appear to be increasing, and are often untreated. Concerns about student mental health are well documented, but little is known about potential variations across the diversity of institutions of higher education. Participants: Participants were 43,210 undergraduates at 72 campuses that participated in the Healthy Minds Study from 2007 to 2013. Methods: Multivariable logistic regressions focus on associations between institutional characteristics and student mental health and treatment utilization. Results: The following institutional characteristics are associated with worse mental health: doctoral-granting, public, large enrollment, nonresidential, less competitive, and lower graduation rates. Among students with apparent mental health problems, treatment utilization is higher at doctorate-granting institutions, baccalaureate colleges, institutions with small enrollments, and schools with strong residential systems. Conclusions: Although high rates of mental health problems and low treatment utilization are major concerns at all types of institutions of higher education, substantial variation occurs across campuses.

The theory of action behind the No Child Left Behind Act of 2001 is that "shining a light" on subgroup performance will increase reading and math test scores for minority and disadvantaged students. Using a panel of all students in Grades 3 through 8 in North Carolina from 2000 to 2008 (N = 1.7 million students in 1,800 schools), the authors estimate double- and triple-differenced models with school fixed effects to examine whether subgroup-specific accountability threats increase high-stakes test scores. These sanctions are found to have positive effects for minority and disadvantaged students. Larger positive effects emerge for the lowest achieving schools rather than schools near the margin of passing. Some evidence of adverse effects is also found for low and high achievers in math, but not in reading, a finding attributed to the combination of increases in the rigor of state standards in math and responses to an accountability metric based on test score status rather than growth. The implications of the findings for the design of educational accountability systems are discussed. (Contains 2 figures, 5 tables, and 15 notes.)

Researchers studying discrimination and bias frequently conduct experiments that use racially distinctive names to signal race or ethnicity. The evidence that these studies provide about racial discrimination depends on the assumption that the names researchers use differ only based on perceived race and not some other factor. In this article, we assess this common assumption using data from five different studies (n = 1,004; 2,002; 1,035; 5,631; 1,858) conducted at different times across four separate survey platforms (Lucid Marketplace, Lucid Theorem, MTurk, and Prolific). We find evidence that names commonly used to signal race/ethnicity also influence perceptions about socioeconomic status and social class. Specifically, we observe that Americans tend to think that individuals with names typically used by Black and Hispanic people have lower educational attainment and income and are of a lower social class. Even when we present respondents with the educational attainment of a named individual, respondents still perceive Black people as lower social class than White people. We discuss the implications of these findings for past and future experimental work that uses names to signal race. We also articulate the importance of choosing names that best approximate the quantity that scholars want to estimate. [ABSTRACT FROM AUTHOR]