Perceptions of racial bias have been linked to poorer circulatory health among Blacks compared with Whites. However, little is known about whether Whites' actual racial bias contributes to this racial disparity in health. We compiled racial-bias data from 1,391,632 Whites and examined whether racial bias in a given county predicted Black-White disparities in circulatory-disease risk (access to health care, diagnosis of a circulatory disease; Study 1) and circulatory-disease-related death rate (Study 2) in the same county. Results revealed that in counties where Whites reported greater racial bias, Blacks (but not Whites) reported decreased access to health care (Study 1). Furthermore, in counties where Whites reported greater racial bias, both Blacks and Whites showed increased death rates due to circulatory diseases, but this relationship was stronger for Blacks than for Whites (Study 2). These results indicate that racial disparities in risk of circulatory disease and in circulatory-disease-related death rate are more pronounced in communities where Whites harbor more explicit racial bias.

Large-scale data collection has enabled social scientists to examine psychological constructs at broad, regional levels. However, because constructs and their measures initially operationalized at the individual level may have qualitatively and quantitatively different properties at other levels of analysis, the validity of constructs must be established when they are operationalized at new levels. To this end, the current research presents evidence of construct validity for explicit and implicit racial bias at region levels. Following classic measurement theory, we examine the substantive, structural, and external evidence of construct validity for regional biases. We do so with responses from ϳ2 million Black and White North Americans collected over 13 years. Though implicit measures typically demonstrate low retest reliability at the individual level, our analyses reveal conventionally acceptable levels of retest reliability at the highest levels of regional aggregation. Additionally, whereas previous meta-analyses find relatively low explicit-implicit correlations at the individual level, the present research uncovered strong explicitimplicit correlations at regional levels. The findings have implications for how we interpret measures of racial bias at regional levels.

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a b s t r a c tRationale: Research suggests that, among Whites, racial bias predicts negative ingroup health outcomes. However, little is known about whether racial bias predicts ingroup health outcomes among minority populations. Objective: The aim of the current research was to understand whether racial bias predicts negative ingroup health outcomes for Blacks. Method: We compiled racial bias responses from 250,665 Blacks and 1,391,632 Whites to generate county-level estimates of Blacks' and Whites' implicit and explicit biases towards each other. We then examined the degree to which these biases predicted ingroup death rate from circulatory-related diseases. Results: In counties where Blacks harbored more implicit bias towards Whites, Blacks died at a higher rate. Additionally, consistent with previous research, in counties where Whites harbored more explicit bias towards Blacks, Whites died at a higher rate. These links between racial bias and ingroup death rate were independent of county-level socio-demographic characteristics, and racial biases from the outgroup in the same county. Conclusion: Findings indicate that racial bias is related to negative ingroup health outcomes for both Blacks and Whites, though this relationship is driven by implicit bias for Blacks, and explicit bias for Whites.

This research examined how the typicality of gender cues in politicians’ faces related to their electoral success. Previous research has shown that faces with subtle gender-atypical cues elicit cognitive competition between male and female categories, which perceivers resolve during face perception. To assess whether this competition adversely impacted politicians’ electoral success, participants categorized the gender of politicians’ faces in a hand-tracking paradigm. Gender-category competition was indexed by the hand’s attraction to the incorrect gender response. Greater gender-category competition predicted a decreased likelihood of votes, but only for female politicians. Time-course analyses revealed that this outcome was evident as early as 380 ms following face presentation (Study 1). Results were replicated with a national sample, and effects became more pronounced as the conservatism of the constituency increased (Study 2). Thus, gender categorization dynamics during the initial milliseconds after viewing a female politician’s face are predictive of her electoral success, especially in more conservative areas.