Quantitative Methods for Place-Based Innovation Policy
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Quantitative Methods for Place-Based Innovation Policy

Measuring the Growth Potential of Regions

Edited by Roberta Capello, Alexander Kleibrink and Monika Matusiak

Place-based innovation policy design requires an in-depth understanding of territories and their complexity. Traditional statistics, with a lack of publicly available data at the disaggregated (sub-sectoral and regional) level, often do not provide adequate information. Therefore, new methods and approaches are required so that scientists and experts that can inform decision-makers and stakeholders in choosing priorities and directions for their innovation strategies. The book replies to such a need by offering advanced mapping methodologies for innovation policies with a special focus on approaches that take into account place-based policies.
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Chapter 7: Learning from similar regions: how to benchmark innovation systems beyond rankings

Susana Franco, Carlo Gianelle, Alexander Kleibrink and Asier Murciego


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.

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