Statistical discrimination is a theorized behavior in which group inequality arises when economic agents (consumers, workers, employers, etc.) have imperfect information about individuals they interact with.[1] According to this theory, inequality may exist and persist between demographic groups even when economic agents are rational. This is distinguished from taste-based discrimination which emphasizes the role of prejudice (sexism, racism, etc.) to explain disparities in labour market outcomes between demographic groups.[2]

The theory of statistical discrimination was pioneered by Kenneth Arrow (1973) and Edmund Phelps (1972).[3] The name "statistical discrimination" relates to the way in which employers make employment decisions. Since their information on the applicants' productivity is imperfect, they use statistical information, both current and historical, on the group they belong to in order to infer productivity. If a minority group is less productive initially (due to historic discrimination or having navigated a bad equilibrium), each individual in this group will be assumed to be less productive and discrimination arises.[4] 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,[5] or from improving their skills as their (average) return on investment (education etc.) is less than for the non-discriminated group.[6]

A related form of statistical discrimination is based on differences in the signals that applicants send to employers. These signals report the applicant's productivity, but they are noisy. Discrimination can occur if groups differ on means, even if applicants have identical nominal above-average signals: regression to the mean will imply that a member of a higher-mean group will regress less as they are more likely to have a higher true value, while the lower-mean group member will regress more and the signal will overestimate their value if the group membership is ignored ("Kelley's paradox"[7]). Discrimination can also occur on group variances in the signals (i.e. in how noisy the signal is), even assuming equal averages. For variance-based discrimination to occur, the decision maker needs to be risk averse; such a decision maker will prefer the group with the lower variance.[8] Even assuming two theoretically identical groups (in all respects, including average and variance), a risk averse decision maker will prefer the group for which a measurement (signal, test) exists that minimizes the signal error term.[8] For example, assume two individuals, A and B, have theoretically identical test scores well above the average for the entire population, but individual A's estimate is considered more reliable because a large amount of data may be available for their group in comparison to the group of B. Then if two people, one from A and one from B, apply for the same job, A is hired, because it is perceived that their score is a more reliable estimate, so a risk-averse decision maker sees B's 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. This generates differences in employment chances, but also in the average wages of different groups - a group with a lower signal precision will be disproportionately employed to lower paying jobs.[9]

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.[10]

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.[11][verification needed]

According to a 2020 study, managers who had experience with statistical discrimination theory were more likely to believe in the accuracy of stereotypes, accept stereotyping, and engage in gender discrimination in hiring. When managers were informed of criticisms against statistical discrimination, these effects were reduced.[12]

See also


  1. ^ Mankiw, N. Gregory (2020). Principles of Economics (9 ed.). Cengage Learning. pp. 392–393. ISBN 9780357133804. Retrieved 19 September 2021.
  2. ^ Thijssen, Lex (2016). "Taste-based versus Statistical Discrimination: Placing the Debate into Context" (PDF). GEMM Project.
  3. ^ 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.
  4. ^ Lang, Lehmann (2012). "Racial Discrimination in the Labour Market: Theory and Empirics" (PDF). Journal of Economic Literature. 50 (4): 959–1006. doi:10.1257/jel.50.4.959. JSTOR 23644909.
  5. ^ William M. Rodgers (2009). Handbook on the Economics of Discrimination. Edward Elgar Publishing. p. 223. ISBN 978-1-84720-015-0.
  6. ^ K. G. Dau-Schmidt (2009). Labor and Employment Law and Economics. Edward Elgar Publishing. p. 304. ISBN 978-1-78195-306-8.
  7. ^ Wainer & Brown 2006, "Three Statistical Paradoxes in the Interpretation of Group Differences: Illustrated with Medical School Admission and Licensing Data"
  8. ^ a b Paula England (1992). Comparable Worth: Theories and Evidence. Transaction Publishers. pp. 58–60. ISBN 978-0-202-30348-2.
  9. ^ Phelps, Edmund (1972). "The Statistical Theory of Racism and Sexism". The American Economic Review. 62 (4): 659–661. JSTOR 1806107.
  10. ^ Rooting Out Discrimination in Home Mortgage Lending -
  11. ^ Thomas J. Nechyba (2010). Microeconomics: An Intuitive Approach. Cengage Learning. p. 514. ISBN 978-0-324-27470-7.
  12. ^ Tilcsik, András (2020-12-11). "Statistical Discrimination and the Rationalization of Stereotypes". American Sociological Review. 86: 93–122. doi:10.1177/0003122420969399. ISSN 0003-1224.

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. ISBN 0-691-04170-9
  • Coate, Steven and Glenn Loury, 1993, Will affirmative-action policies eliminate negative stereotypes?, The American Economic Review, 1220–1240. JSTOR 2117558
  • 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". American Economic Review. 62 (4): 659–661. JSTOR 1806107.