Grégoire Rota-Graziosi

Endogenizing Leadership in Tax Competition: A Timing Game Perspective

In this paper we extend the standard approach of horizontal tax competition by endogenizing the timing of decisions made by the competing jurisdictions. Following the literature on the endogenous timing in duopoly games, we consider a pre-play stage, where jurisdictions commit themselves to more early or late, i.e. to fix their tax rate at a first or second stage. We highlight that at least one jurisdiction experiments a second-mover advantage. We show that the Subgame Perfect Equilibria (SPEs) correspond to the two Stackelberg situations yielding to a coordination problem. In order to solve this issue, we consider a quadratic specification of the production function, and we use two criteria of selection: Pareto-dominance and risk-dominance. We emphasize that at the safer equilibrium the less productive or smaller jurisdiction leads and hence loses the second-mover advantage. If asymmetry among jurisdictions is sufficient, Pareto-dominance reinforces risk-domination in selecting the same SPE. Three results may be deduced from our analysis: (i) the downward pressure on tax rates is less severe than predicted; (ii) the smaller jurisdiction leads; (iii) the big-country-higher-tax-rate rule does not always hold. Classification-JEL: H30, H87, C72.

Endogenous timing in contests: Applications

This second volume of the Handbook includes original contribution by experts in the field. It provides up-to-date surveys of the most relevant applications of game theory to industrial organization. The book covers both classical as well as new IO topics such as mergers in markets with homogeneous and differentiated goods, leniency and coordinated effects in cartels and mergers, static and dynamic contests, consumer search and product safety, strategic delegation, platforms and network effects, auctions, environmental and resource economics, intellectual property, healthcare, corruption, experimental industrial organization and empirical models of R&D.