Learning and Coordination in the Presidential Primary System

In elections with three or more candidates, coordination among like-minded voters is an important problem.
We analyze the trade-off between coordination and learning about candidate quality under different temporal election systems in the context of the U.S. presidential primary system. In our model, candidates with different policy positions and qualities compete for the nomination, and voters are uncertain about the candidates’ valence. This setup generates two effects: Vote-splitting (i.e., several candidates in the same policy position compete for the same voter pool) and voter learning (as the results in earlier elections help voters to update their beliefs on candidate quality). Sequential voting minimizes vote-splitting in late districts, but voters may coordinate on a low quality candidate. Using the parameter estimates obtained from the 2000–2008 Democratic presidential primaries, we conduct policy experiments such as replacing the current system with a simultaneous system, adopting the reform proposal of the National Association of Secretaries of State, or imposing party rules that lead to candidate withdrawal when pre-specified conditions are met.

Advance Access at OUP

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