Can Information Reduce Ethnic Discrimination? Evidence from Airbnb

We use data from Airbnb to identify the mechanisms underlying discrimination against ethnic minority hosts. Within the same neighborhood, hosts from minority groups charge 3.2 percent less for comparable listings. Since ratings provide guests with increasingly rich information about a listing’s quality, we can measure the contribution of statistical discrimination, building upon Altonji and Pierret (2001). We find that statistical discrimination can account for the whole ethnic price gap: ethnic gaps would disappear if all unobservables were revealed. Also, three-quarters (2.5 points) of the initial ethnic gap can be attributed to inaccurate beliefs of potential guests about hosts’ average group quality. (JEL D83, J15, L84)

Studying APOE ɛ4 Allele Dose Effects with a Univariate Morphometry Biomarker

Background: A univariate neurodegeneration biomarker (UNB) based on MRI with strong statistical discrimination power would be highly desirable for studying hippocampal surface morphological changes associated with APOE ɛ4 genetic risk for AD in the cognitively unimpaired (CU) population. However, existing UNB work either fails to model large group variances or does not capture AD induced changes. Objective: We proposed a subspace decomposition method capable of exploiting a UNB to represent the hippocampal morphological changes related to the APOE ɛ4 dose effects among the longitudinal APOE ɛ4 homozygotes (HM, N = 30), heterozygotes (HT, N = 49) and non-carriers (NC, N = 61). Methods: Rank minimization mechanism combined with sparse constraint considering the local continuity of the hippocampal atrophy regions is used to extract group common structures. Based on the group common structures of amyloid-β (Aβ) positive AD patients and Aβ negative CU subjects, we identified the regions-of-interest (ROI), which reflect significant morphometry changes caused by the AD development. Then univariate morphometry index (UMI) is constructed from these ROIs. Results: The proposed UMI demonstrates a more substantial statistical discrimination power to distinguish the longitudinal groups with different APOE ɛ4 genotypes than the hippocampal volume measurements. And different APOE ɛ4 allele load affects the shrinkage rate of the hippocampus, i.e., HM genotype will cause the largest atrophy rate, followed by HT, and the smallest is NC. Conclusion: The UMIs may capture the APOE ɛ4 risk allele-induced brain morphometry abnormalities and reveal the dose effects of APOE ɛ4 on the hippocampal morphology in cognitively normal individuals.

You are not worth the risk: The ethics of statistical discrimination in organisational selection of applicants

<p>Your job application is rejected unseen because you ticked a box admitting you smoke. The employer screened out applicants who ticked the 'smoker' box, because she had read empirical studies that suggest smokers, as a group, are a higher productivity risk than non-smokers. What distinctive ethical concerns inhere in the organisational practice of discriminating against applicants on the basis of group risk statistics? I argue that risk-focussed statistical discrimination is morally undesirable due to the lack of respect for applicants as unique autonomous agents. However, I argue further that the decision-making context affects the morality of this discrimination. Other things being equal, the morality of statistical discrimination varies depending on the purpose of the organisation, the level of detail in the discrimination, and whether the discrimination is transparent to applicants and includes some benefit for applicants. Because organisations may have good reason to use risk-focussed statistical discrimination when assessing applicants, I present some recommendations for decision-makers to mitigate the lack of respect for applicants as individual agents. Organisational decision-makers can focus on the extent to which the statistical data they use comprise i) factors that feature efforts and achievements of the applicant; ii) dynamic rather than static factors; and iii) data drawn from the applicant’s own history and actions over time.</p>

You are not worth the risk: The ethics of statistical discrimination in organisational selection of applicants

<p>Your job application is rejected unseen because you ticked a box admitting you smoke. The employer screened out applicants who ticked the 'smoker' box, because she had read empirical studies that suggest smokers, as a group, are a higher productivity risk than non-smokers. What distinctive ethical concerns inhere in the organisational practice of discriminating against applicants on the basis of group risk statistics? I argue that risk-focussed statistical discrimination is morally undesirable due to the lack of respect for applicants as unique autonomous agents. However, I argue further that the decision-making context affects the morality of this discrimination. Other things being equal, the morality of statistical discrimination varies depending on the purpose of the organisation, the level of detail in the discrimination, and whether the discrimination is transparent to applicants and includes some benefit for applicants. Because organisations may have good reason to use risk-focussed statistical discrimination when assessing applicants, I present some recommendations for decision-makers to mitigate the lack of respect for applicants as individual agents. Organisational decision-makers can focus on the extent to which the statistical data they use comprise i) factors that feature efforts and achievements of the applicant; ii) dynamic rather than static factors; and iii) data drawn from the applicant’s own history and actions over time.</p>

Foreign Accents in the Early Hiring Process: A Field Experiment on Accent-Related Ethnic Discrimination in Germany

Based on a field experiment conducted in Germany between October 2014 and October 2015, this article focuses on the disadvantages associated with the presence of a foreign accent in the early hiring process, when applicants call in response to a job advertisement to ask whether the position is still available. We examine whether a foreign accent influences employers’ behaviors via productivity considerations and/or whether foreign-accented speech is related to statistical discrimination or tastes among employers or customers that translate into differential treatment. To address these processes, we supplement our field-experimental data with information on job and firm characteristics from the texts of vacancy announcements and advertising companies’ homepages, on labor supply from the Federal Employment Agency, and on anti-immigrant attitudes from the German General Social Survey. Results suggest that while calling with a Turkish name did not result in a lower rate of positive replies, this rate was reduced for candidates who called with a Turkish accent. Turkish-accented applicants were told more often than the advertised position was already filled. Our findings suggest that the difference in response rates was not due to productivity considerations related to how well individuals understood foreign-accented speakers. Instead, results support the notion that the observed disadvantages were linked to discrimination based on employers’ ethnic tastes. While we found no indications pointing to the relevance of customer tastes or statistical discrimination, we cannot rule out these processes altogether. Our findings demonstrate that language cues can be more relevant than applicants’ names in shaping employers’ initial responses. They, thereby, highlight the need to consider multiple ethnic cues and different stages of the hiring process.

First impression biases in the performing arts: taste-based discrimination and the value of blind auditioning

I develop a game-theoretic framework to study the repercussions of an evaluator’s bias against a specific group of applicants. The evaluator decides upfront between holding an informed or a blind audition. In the latter, the evaluator learns neither the applicant’s ability nor the gender. I show that, above a threshold bias, the evaluator prefers a blind audition to provide high effort incentives exclusively for high-ability applicants. Consequently, committing to no information can be beneficial for the evaluator. I also show that a highly biased evaluator’s preferences align with those of a highly able female. I extend the framework to performance uncertainty and gender-blind CVs and compare blind auditions to affirmative action. The framework is relevant for auditory-based applications: my results can explain why blind auditions have increased the probability of a female orchestra musician being hired via taste-based discrimination and challenge explanations grounded in statistical discrimination.

Where Does Taste-Based Discrimination Come From?

As the last chapter has revealed, the reason why a decision-maker makes use of statistical discrimination is easily comprehensible. If a decision situation underlies uncertainty, he has to assess the probabilities of possible scenarios with some degree of vagueness. In this process, group memberships of providers can serve as a proxy for these probabilities.