The Statistical Theory of Racism and Sexism

By Edmund S. Phelps. 1972

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Citations in the corpus

Fairness in Machine Learning: Lessons from Political Philosophy (2017, background): “As economists have argued on the basis of models, such generalisations about groups can be an efficient means for firms to reduce risk when more direct evidence about an individual is lacking Phelps (1972). Despite the potential efficiency benefits of generalisation, it is widely regarded as wrong i”
A Short-term Intervention for Long-term Fairness in the Labor Market (2017, background): “Since most forms of explicit wage-discrimination are now illegal in the U.S. and genetic accounts of group di erences have been largely discredited [12], modern theories of labor market discrimination have updated the classical works—Becker’s “tastebased” discrimination [13] and Phelps’ model of exo”
The Problem of Infra-marginality in Outcome Tests for Discrimination (2016, background): “The hit rate (i.e., the search 1Taste-based discrimination stands in contrast to statistical discrimination (Arrow, 1973; Phelps, 1972), in which officers might use a driver’s race to improve their estimate that he is carrying contraband.”
Impartial Predictive Modeling: Ensuring Fairness in Arbitrary Models (2016, background): “It is interesting to note that the data case we consider that allows observable diﬀer-ences in merit between groups is regarded as the trivial case and lacks signiﬁcant research (Phelps, 1972).”
Managers’ Political Beliefs and Gender Inequality among Subordinates: Does His Ideology Matter More Than Hers? (2018, unknown): “…Davis and Greenstein, 2009), making them less likely to believe that women lack the temperament needed to succeed in leadership roles and less likely to believe that women will eventually leave the firm to pursue family responsibilities (e.g., Phelps, 1972; Ridgeway, 2001; Eagly and Karau, 2002).”
A field experiment to study sex and age discrimination in the Madrid labour market (2011, background): “If the discrimination were statistical (Arrow 1972; Phelps 1972), the slight interest in hiring women would be based on the fact that employers believe that if they have small children the mothers may try to make paid work compatible with housework to a greater extent than fathers (so that this woul”
Paying More to Get Less (2011, background): “Much other research has explored how ascriptive characteristics such as race and gender are also used as indicators in hiring (Phelps, 1972; Heilman, 1980; Petersen and Saporta, 2004).”
Sexual orientation discrimination in the labour market (2009, background): “These are distaste for the minority (Becker, 1957) and statistical discrimination (Arrow, 1973; Phelps, 1972; Aigner and Cain, 1977).”
Discrimination in the Age of Algorithms (2018, unknown): “See, e.g., Arrow (1973); Phelps (1972).”
Racial differences in the effects of job contacts: Conflicting evidence from cross-sectional and longitudinal data (2002, background): “If information about workers abilities is costly to obtain, then expectations of racial differences in ability (real or imagined) may result in employers using race as a cheap screening device (Phelps, 1972).”
Can Gender Parity Break the Glass Ceiling?: Evidence from a Repeated Randomized Experiment (2010, background): “Again, according to our results this would be an explanation in case it is male beauty only that counts.(50) Unfortunately, no proxy for candidate beauty is available in the data set.”
Reducing Minority Discrimination at the Front Line—Combined Survey and Field Experimental Evidence (2019, background): “This explanation sees discrimination as a conscious and rational cost-benefit analysis based on relatively accurate beliefs and on 10 the fact that the cost of obtaining more information does not outweigh the benefit of a more precise evaluation of a client or a customer (Phelps 1972).”
The Effectiveness of Jobs Reservation: Caste, Religion and Economic Status in India (2007, background): “…by considering a group whose members, though as deprived and poorly qualified as 11 This preference might be engendered by a distaste for persons from such groups (bigotry: Becker, 1971) or by a belief that employees from such groups were inferior workers (statistical discrimination: Phelps, 1972).”