a1 Bocconi University
Is electoral competition good for political selection? To address this issue, we introduce a theoretical model where ideological parties select and allocate high-valence (experts) and low-valence (party loyalists) candidates into electoral districts. Voters care about a national policy (e.g., party ideology) and the valence of their district's candidates. High-valence candidates are more costly for the parties to recruit. We show that parties compete by selecting and allocating good politicians to the most contestable districts. Empirical evidence on Italian members of parliament confirms this prediction: politicians with higher ex ante quality, measured by years of schooling, previous market income, and local government experience, are more likely to run in contestable districts. Indeed, despite being different on average, politicians belonging to opposite political coalitions converge to high-quality levels in close electoral races. Furthermore, politicians elected in contestable districts have fewer absences in parliament, due to a selection effect more than to reelection incentives.
c1 Vincenzo Galasso is Associate Professor of Political Economics, Bocconi University, Director of Dondena—Centre for Research on Social Dynamics, and associated with IGIER and CEPR; Via Rontgen 1, 20136 Milan, Italy ([email protected]).
We thank Alberto Alesina, Tim Besley, Sandro Brusco, Ernesto Dal Bo, Simon Hix, Alessandro Lizzeri, Andrea Mattozzi, Gerard Padro i Miquel, the editor Gary Cox, three anonymous referees, and seminar participants at Basel, UC Berkeley, CERGE-EI Prague, Erasmus University Rotterdam, London School of Economics, MPSA 2009 Chicago, IGIER, IMT, Paris School of Economics, and Stockholm University for their insightful suggestions. We also thank Andrea Di Miceli for excellent research assistance and “ERE – Empirical Research in Economics” for providing the data. Financial support by the European Research Council (Grant No. 230088) is gratefully acknowledged. The remaining errors are ours and follow a random walk.