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Revision as of 22:51, 11 January 2019 by Loraof (talk | contribs) (→top: distinguishing the behavior from the theory of the behavior)(diff) ← Previous revision | Latest revision (diff) | Newer revision → (diff)Statistical discrimination is a theorized behavior in which racial or gender inequality results when economic agents (consumers, workers, employers, etc.) have imperfect information about individuals they interact with. According to this theory, inequality may exist and persist between demographic groups even when economic agents are rational and non-prejudiced.
The theory of statistical discrimination was pioneered by Kenneth Arrow and Edmund Phelps This type of discrimination can result in a self-reinforcing vicious circle over time, as the atypical individuals from the discriminated group are discouraged from participating in the market, or improving their skills as their (average) return on investment (education etc.) is less than for the non-discriminated group.
A related form of (theorized) statistical discrimination is based on group variances, assuming equal averages. For discrimination to occur in this scenario, the decision maker needs to be risk averse; such a decision maker will prefer the group with the lower variance. Even assuming two theoretically identical group distributions (in all respects, including average and variance), a risk averse decision maker will prefer the group for which a measurement (test) exists that minimizes the error term. For example, if two groups, A and B, have theoretically identical distributions of test scores well above the average for the entire population, but group A's estimate is considered more reliable because a large amount of data may be available for group A in comparison to group B, then if two people, one from A and one from B apply for a job, using statistical discrimination, A is hired, because it is perceived that his group score is a more reliable estimate, so a risk-averse decision maker sees group B's group score as more likely to be luck. Conversely, if the two groups are below average, B is hired, because group A's negative score is believed to be a better estimate.
It has been suggested that home mortgage lending discrimination against African Americans, which is illegal in the United States, may be partly caused by statistical discrimination.
Market forces are expected to penalize some forms of statistical discrimination; for example, a company capable and willing to test its job applicants on relevant metrics is expected to do better than one that relies only on group averages for employment decisions.
References
- Fang, Hanming and Andrea Moro, 2011, "Theories of Statistical Discrimination and Affirmative Action: A Survey," in Jess Benhabib, Matthew Jackson and Alberto Bisin, eds: Handbook of Social Economics, Vol. 1A, Chapter 5, The Netherlands: North Holland, 2011, pp. 133-200. Available as NBER Working Papers 15860, National Bureau of Economic Research, Inc.
- William M. Rodgers (2009). Handbook on the Economics of Discrimination. Edward Elgar Publishing. p. 223. ISBN 978-1-84720-015-0.
- K. G. Dau-Schmidt (2009). Labor and Employment Law and Economics. Edward Elgar Publishing. p. 304. ISBN 978-1-78195-306-8.
- ^ Paula England (1992). Comparable Worth: Theories and Evidence. Transaction Publishers. pp. 58–60. ISBN 978-0-202-30348-2.
- Rooting Out Discrimination in Home Mortgage Lending -
- Thomas J. Nechyba (2010). Microeconomics: An Intuitive Approach. Cengage Learning. p. 514. ISBN 0-324-27470-X.
Further reading
- Arrow, K. J. (1973), "The Theory of Discrimination", in O. Ashenfelter and A. Rees (eds.), Discrimination in Labor Markets, Princeton, NJ: Princeton University Press.
- Coate, Steven and Glenn Loury, 1993, Will affirmative-action policies eliminate negative stereotypes?,The American Economic Review, 1220–1240.
- Glenn Loury, The Anatomy of Racial Inequality, Princeton University Press. Informally illustrates the theory in the context of United States' racial differences.
- Phelps, Edmund S. (1972). "The Statistical Theory of Racism and Sexism" (PDF). American Economic Review. 62: 659–661.