Perceptions of candidate strength in job recruitment: Does candidate race moderate the attractiveness bias in White women?

The attractiveness bias suggests that people who are more attractive will be positively favored across life outcomes. This study sought to test whether candidate attractiveness, sex, and race, affect perceptions of candidate strength in a job recruitment task. In total, 338 White women (M_age = 20.94 ± 5.65) were asked to make judgements of a potential candidate for an administrative job (resume with candidate photograph). The vignettes differed in terms of candidate ability (strong/weak), sex (male/female), race (Black/White), and attractiveness (attractive/less attractive). Participants rated perceived candidate strength and likelihood to invite for interview. Results showed no significant main effects for attractiveness. However, there was a significant interaction for target attractiveness and race, such that attractive/White candidates were more likely to be invited for interview than less attractive/White candidates. There was also a significant main effect for race such that Black candidates were rated as stronger and more likely to be interviewed. Sensitivity analyses (with nonheterosexual women removed from the sample) also showed a main effect for target sex such that female candidates were favored over male candidates. Overall, these findings provide evidence that attractiveness, sex, and race have important, albeit complex, effects on hiring decisions in the workplace.     

Make‐or‐Break: Chasing Risky Goals or Settling for Safe Rewards?

Humans regularly pursue activities characterized by dramatic success or failure outcomes where, critically, the chances of success depend on the time invested working toward it. How should people allocate time between such make‐or‐break challenges and safe alternatives, where rewards are more predictable (e.g., linear) functions of performance? We present a formal framework for studying time allocation between these two types of activities, and we explore optimal behavior in both one‐shot and dynamic versions of the problem. In the one‐shot version, we illustrate striking discontinuities in the optimal time allocation policy as we gradually change the parameters of the decision‐making problem. In the dynamic version, we formulate the optimal strategy—defined by a giving‐up threshold—which adaptively dictates when people should stop pursuing the make‐or‐break goal. We then show that this strategy is computationally inaccessible for humans, and we explore boundedly rational alternatives. We compare the performance of the optimal model against (a) a myopic giving‐up threshold that is easier to compute, and even simpler heuristic strategies that either (b) only decide whether or not to start pursuing the goal and never give up or (c) consider giving up at a fixed number of control points. Comparing strategies across environments, we investigate the cost and behavioral implications of sidestepping the computational burden of full rationality.