Abstract

Innovation policy is inherently a highly experimental endeavour. In an increasingly complex, intertwined, fast-changing and uncertain world, policy-makers pursuing innovations in support of economic development need to engage in a systematic process of policy learning. Benchmarking with peers is a powerful learning channel in regional innovation policy, provided that the identification of suitable peer regions is based on similarity in the structural dimensions influencing innovation policy. While several regional benchmarking methodologies and rankings are currently available, regions for comparison are most often not selected adequately. Either they are compared based just on innovation and economic performance measures, paying insufficient attention to the context in which performance is or can be achieved. Or they are chosen based on a mix of variables of different nature not suitable for supporting effective learning. To overcome these limitations, the chapter proposes a methodology to identify peer regions in the European Union focusing on similarity in innovation-relevant structural characteristics. The authors construct a novel database covering all European Union regions, and compute a full matrix of regional pairwise distances resulting from the aggregation of several dimensions. They discuss selected cases and the related policy implications for the design and implementation of regional innovation policy.