The authors presented a simple classroom game in which students are randomly designated as employers, purple workers, or green workers. This environment may generate statistical discrimination if workers of one color tend not to invest because they anticipate lower opportunities in the labor market, and these beliefs are self-confirming as employers learn that it is, on average, less profitable to hire workers of that color. Such discriminatory equilibria may arise even when workers are ex-ante identical, and the employer has no prior information regarding potential workers. The exercise typically generates a lively discussion about discrimination and how it may be addressed by alternative public policies.

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Measuring the intensity and impact of overt racism and discrimination has been the subject of much debate over the 20th century. Few disagree that historical discrimination and other forms of social ostracism partly explain current disparities on a myriad of economic, social, and health related outcomes. Disagreement arises in explaining the underlying reasons behind the discriminatory treatment.

Uncovering mechanisms behind discriminatory actions is difficult because attitudes about race, gender, and other characteristics that often serve as a basis for differential treatment are not easily observed or measured. Therefore, laboratory experiments have been particularly useful in the study of discrimination under conditions where experience, perceived status, and group identity can be partially measured and controlled. For example, cleverly designed experiments allow one to distinguish the effects of underlying biases in preferences for one's in-group from the effects of information-based forms of discrimination (e.g., statistical profiling and social categorization) This paper surveys laboratory studies of discrimination in psychology and economics.

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The literature on statistical discrimination shows that ex-ante identical groups may be differentially treated in discriminatory equilibria. This paper constructs a dynamic model of statistical discrimination and explores what happens to the individuals who nonetheless overcome the initial discrimination...

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Employment or admission "goals" are often preferred to affirmative action as a way of obtaining diversity. By constructing a simple model of employer-auditor interaction, it is shown that when an auditor has imperfect information regarding employers' proclivities to discriminate and the fraction of qualified minorities in each employers applicant pool, goals are synonymous with quotas. Technically speaking, any equilibrium of the auditing game involves a non-empty set of employers that hire so that they do not trigger an audit by rejecting qualified non-minorities, hiring unqualified minorities, or both. Further, under some assumptions, explicit quotas (those mandated by an auditor) are more efficient than implicit quotas (goals settled upon in equilibrium by employers wishing to avoid an audit).

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There is a wealth of research demonstrating that agents process information with the aid of categories. In this paper we study this phenomenon in two parts. First, we build a model of how experiences are sorted into categories and how categorization affects decision making. Second,in a series of results that partly characterize an optimal categorization, we show that specific biases emerge from categorization. For instance, types of experiences and objects that are less frequent in the population tend to be more coarsely categorized and lumped together. As a result, decision makers make less accurate predictions when confronted with such objects. This can result in discrimination against minority groups even when there is no malevolent taste for discrimination. However, such comparative statics are highly sensitive to the particular situation; optimal categorizations can change in surprising ways. For instance, increasing agroups population, holding all else constant, can lead a decision maker to make less accurate predictions about that group.

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The Ku Klux Klan reached its heyday in the mid-1920s, claiming millions of members. In this paper, we analyze the 1920s Klan, those who joined it, and the social and political impact that it had. We utilize a wide range of newly discovered data sources including information from Klan membership roles, applications, robe-order forms, an internal audit of the Klan by Ernst and Ernst, and a census that the Klan conducted after an internal scandal. Combining these sources with data from the 1920 and 1930 U.S. Censuses, we find that individuals who joined the Klan were better educated and more likely to hold professional jobs than the typical American. Surprisingly, we find few tangible social or political impacts of the Klan. There is little evidence that the Klan had an effect on black or foreign born residential mobility, or on lynching patterns. Historians have argued that the Klan was successful in getting candidates they favored elected. Statistical analysis, however, suggests that any direct impact of the Klan was likely to be small. Furthermore, those who were elected had little discernible effect on legislation passed. Rather than a terrorist organization, the 1920s Klan is best described as a social organization built through a wildly successful pyramid scheme fueled by an army of highly-incentivized sales agents selling hatred, religious intolerance, and fraternity in a time and place where there was tremendous demand.

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