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Unintended consequences of corruption indices

Chapkovski, Philipp (2022): Unintended consequences of corruption indices.

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Using the results of a pre-registered online experiment, this paper examines how information about a corruption in a group can affect intergroup relations. Corruption indices are not only a valuable tool for investors and policymakers to make informed decisions, but can also lead to statistical discrimination: Individuals from a more "corrupt" region may be perceived as less trustworthy or more prone to dishonest behavior. To test this hypothesis, we manipulated the amount of information participants had about their potential partners' regions of origin and asked them to (a) estimate the proportion of participants in each region who report a more profitable outcome in a coin toss game and (b) transfer money to a partner in each region in a trust game. The presence of a regional corruption index led participants to significantly overestimate the degree of dishonesty by participants from more corrupt regions and to reduce trust towards them. The results show how corruption indices can be a source of statistical discrimination against outgroups despite the well-meaning intentions of their creators.

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