BORDERS AS BOUNDARIES TO FISCAL POLICY INTERACTIONS? AN EMPIRICAL ANALYSIS OF POLITICIANS’ OPINIONS ON RIVALS IN THE COMPETITION FOR FIRMS

Authors


  • The paper was written while Steffen Osterloh was working at ZEW Mannheim. The opinions expressed in this paper reflect the personal views of the authors and not necessarily those of the German Council of Economic Experts. We are grateful to Jan Brueckner, Micheal Collins, Christina Gathmann, Friedrich Heinemann, Bruno Heyndels, Eckhard Janeba, Jordi Jofre Monseny, Federico Revelli, and Per Tovmo as well as seminar participants at ZEW Mannheim, the annual AFSE meeting (Lyon, 2012), the IIPF congress (Ann Arbor, 2012) and three anonymous referees for helpful suggestions. Steffen Osterloh gratefully acknowledges the financial support from the Deutsche Forschungsgemeinschaft (DFG) through SFB 884 “Political Economy of Reforms”. Benny Geys is grateful to FWO Vlaanderen (grant nr. G.0022.12) for financial support. The usual caveat applies.

ABSTRACT

Studies of spatial policy interdependence in (local) public policies usually concentrate on the relations between jurisdictions within a single analyzed region, and disregard possible extraregional effects. However, the theoretical spatial statistics literature shows that biased estimates might emerge if spatial interactions extend beyond the boundaries of the available data (i.e., the boundary value problem). This paper empirically assesses the practical relevance of this concern by studying German local politicians’ assessments of their jurisdictions’ main competitors in the struggle to attract firms. We find that location near a border significantly undermines politicians’ perception that the fiercest competitive pressure derives from jurisdictions within their own state. This effect sets in about 20 km (10.2 km) from a national (international) border. These results indicate that nearest municipalities perceive each other as competitors regardless of the state or country where they are located, which has important implications for estimating spatial dependence models.

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