3 resultados para statistical discrimination

em CentAUR: Central Archive University of Reading - UK


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Caste based quotas in hiring have existed in the public sector in India for decades. Recently there has been debate about introducing similar quotas in private sector jobs. This paper uses a correspondence study to determine the extent of caste based discrimination in the Indian private sector. On average low-caste applicants need to send 20% more resumes than high-caste applicants to get the same callback. Differences in callback which favor high-caste applicants are particularly large when hiring is done by male recruiters or by Hindu recruiters. This finding provides evidence that differences in callback between high and low-caste applicants are not entirely due to statistical discrimination. High-caste applicants are also differentially favored by firms with a smaller scale of operations, while low-caste applicants are favored by firms with a larger scale of operations. This finding is consistent with taste-based theories of discrimination and with commitments made by large firms to hire actively from among low-caste groups.

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We present the first empirical study to reveal the presence of implicit discrimination in a non-experimental setting. By using a large dataset of in-match data in the English Premier League, we show that white referees award significantly more yellow cards against non-white players of oppositional identity. We argue that this is the result of implicit discrimination by showing that this discriminatory behaviour: (i) increases in how rushed the referee is before making a decision, and (ii) it increases in the level of ambiguity of the decision. The variation in (i) and (ii) cannot be explained by any form of conscious discrimination such as taste-based or statistical discrimination. Moreover, we show that oppositional identity players do not differ in their behaviour from other players along several dimensions related to aggressiveness and style of play providing further evidence that this is not statistical discrimination.

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The notions of resolution and discrimination of probability forecasts are revisited. It is argued that the common concept underlying both resolution and discrimination is the dependence (in the sense of probability theory) of forecasts and observations. More specifically, a forecast has no resolution if and only if it has no discrimination if and only if forecast and observation are stochastically independent. A statistical tests for independence is thus also a test for no resolution and, at the same time, for no discrimination. The resolution term in the decomposition of the logarithmic scoring rule, and the area under the Receiver Operating Characteristic will be investigated in this light.