A.Navarro, M., Gibaja, J.J., Franco, S., Murciego, A., Gianelle, C., Hegyi, F.B., Kleibrink, A.
S3 Working Paper
English // Keywords: regional benchmarking, structural similarity, policy learning, smart specialisation
Description
One of the most basic conditions required for drawing lessons from regional benchmarking is to compare homogeneous regions and learn from equivalents. This condition is not met when regions for comparison are chosen based on their high performance, overlooking their regional context or structural conditions. This paper aims to provide a new methodology for the identification of homogeneous regions for regional benchmarking; identifying groups of homogeneous regions using variables that are similar in nature; focusing solely on structural conditions, thereby overcoming the flaws produced by mixing variables of a different nature (comparing structural indicators with performance and / or behavioural indicators). Thus, regional benchmarking can be of great help in making strategic decisions within the process of the design and implementation of regional Research and Innovation Strategies for Smart Specialisation (RIS3), taking into account the relative position of the region to other regions in Europe. Following the RIS3 approach of looking beyond the regional administrative boundaries, benchmarking based on structural similarity enables the region to identify its competitive advantages through systematic comparisons with other regions or to map the national and international context in search of examples to learn from, or to mark a difference with.
