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. It stands in contrast with taste-based discrimination which uses racism, sexism and the likes to explain different labour market outcomes of groups. The theory of statistical discrimination was pioneered by Kenneth Arrow (1973) and Edmund Phelps (1972). 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 on the group they belong to in order to infer productivity. If the 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. 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 from 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 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"). 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. 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. For example, assume two individuals, A and B, have theoretically identical test scores well above the average for the entire