Affirmative action in large population tullock contests

Abstract

We compare equal treatment and affirmative action policies in Tullock contests. Equal treatment means that agents who exert equal effort have an equal probability of success. In affirmative action, agents who incur an equal cost of effort have an equal probability of success. Finite player contests with non-linearities in impact and cost functions cannot be solved in closed form. Instead, we approximate them with large population contests with measure zero agents. Affirmative action reduces aggregate effort in such contests, which can be solved. However, it ensures equality without any significant loss of aggregate welfare. We verify these findings for finite player contests through numerical simulations. For a sufficiently large number of players, the numerical simulations support the results of the large population analysis.