REGIO- INDUSTRY 4.0 INDEX

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1 REGIO- INDUSTRY 4.0 INDEX HOW EU REGIONS PREPARE TO INDUSTRY 4.0 CONTEXT The idea behind a region-industry 4.0 index is that a better knowledge of the determinants of industrial change across EU regions can lead to a better coordination of national policy actions and to more effective crossregional collaborations. A central part of European Union s challenge to boost growth and harness innovation in its industrial base lies in the effort to support Industry 4.0 type of initiatives which require the transformation of industry and value chains along new digital business models, exploiting the rise of cyber physical systems, the internet of things and cloud computing. The uptake and implementation of Industry 4.0 is not just a challenge for the business sector, but also for regions that host the industrial and innovation ecosystems that are bound to be transformed. WHY A REGIO-INDUSTRY INDEX Although several studies and analyses have been produced over the last years to account for the ongoing transformation, the need is felt to start measure not only the diversity of contexts across regions, but also the key factors that could act as enabling forces for the new digital revolution in manufacturing.

2 The idea behind a region-industry 4.0 index is that a better knowledge of the determinants of industrial change across EU regions can lead to a better coordination of national policy actions and to more effective cross-regional collaborations. THE METHODOLOGY The region-industry 4.0 index adopts a multifactor criteria analysis and applies it to the present transformation of industrial bases in EU regions The region-industry 4.0 index adopts a multifactor criteria analysis and applies it to the present transformation of industrial bases in EU regions. Since the goal is to improve the capacity to coordinate industry 4.0 policies, the baseline is represented by EU regions with an advanced manufacturing and advanced materials specialisation according to the respective smart specialisation regional agendas that have been communicated to the European Commission. There are 123 regions that have and advanced manufacturing specialisation in Europe. The sample used to build the index is made up of 61 regions at NUTS2 level (47% of total regions with an advanced manufacturing specialisation). The main data of these 61 regions refer to the relevance of high tech employment with regard to their respective manufacturing bases. Sample main variables mean CV High tech manufacturing employment in 61 EU 0,7 0,65 regions* Perc. of High tech patents** 16,94 1,29 Total Labour force with ,92 tertiary education** Change in high tech manuf. 0,02 13,34 Employment Manufacturing employment 2015 (thousand) 298,57 1,10 *= data from Eurostat; **= data from OECD The index is based on the residuals of a linear regression having the high-tech manufacturing employment as the dependent variable and controlling for all the above indicated dimensions. The model specification is tested against several statistical tests. 1 1 Results of the OLS regression are available upon request. 1

3 RESULTS There is a significant correlation in the model (R 2 =44%) between the present share of high-tech manufacturing employment and the adopted control variables. In particular the strongest coefficient is given by the presence of labour force with tertiary education followed by the rate of change of high-tech employment from 2011 to 2015 and by the percentage of hightech patents. A scenario of a transformation not only linked to the digital optimisation of produtive processes but also to skills and knowledge is confirmed, highlighting the need to accompany the digital uptake with an effective skill-based upgrading policy. Interestingly, the share of high-tech employment has a negative and significant correlation with a standardised measure of manufacturing specialisation across the 61 regions, which is an indication of how the current high-tech transformation does not occur in traditional manufacturing regions, but tend to benefit from other location features and ecosystem externalities. A scenario of a transformation not only linked to the digital optimisation of produtive processes but also to skills and knowledge is confirmed, highlighting the neeed to accompany the digital uptake with an effective skill-based upgrading policy 2

4 REGIO-INDUSTRY 4.0 INDEX (INDEX MEAN =100) EU region (NUTS2) 40index EU region (NUTS2) 40index FR10 - Île de France FI1D - Pohjois- ja Itä-Suomi ES30 - Comunidad de Madrid DEB - Rheinland-Pfalz FR52 - Bretagne ES52 - Comunidad Valenciana NO06 - Trøndelag FR43 - Franche-Comté AT1 - Ostösterreich FI19 - Länsi-Suomi FR71 - Rhône-Alpes DEF - Schleswig-Holstein BE2 - Vlaams Gewest FI1C - Etelä-Suomi DE6 - Hamburg FR51 - Pays de la Loire DE3 - Berlin ITH5 - Emilia-Romagna SE12 - Östra Mellansverige ITF1 - Abruzzo IE - Ireland ITF3 - Campania FI1B - Helsinki-Uusimaa ES24 - Aragón DE7 - Hessen PL41 - Wielkopolskie DEA - Nordrhein-Westfalen ITC1 - Piemonte DE2 - Bayern SE31 - Norra Mellansverige UKL - Wales PL51 - Dolnoslaskie FR61 - Aquitaine NL1 - Noord-Nederland FR62 - Midi-Pyrénées ITI1 - Toscana BE3 - Région wallonne HU - Hungary DED - Sachsen ITH4 - Friuli-Venezia Giulia DE1 - Baden-Württemberg EE - Estonia DE4 - Brandenburg ITI3 - Marche DEG - Thüringen DEE - Sachsen-Anhalt DE9 - Niedersachsen ES22 - Comunidad Foral de Navarra ES21 - País Vasco NO04 - Agder og Rogaland ES11 - Galicia UKN0 - Northern Ireland (UK) ITC4 - Lombardia PL22 - Slaskie PL11 - Lódzkie PT11 - Norte ITH3 - Veneto CZ02 - Strední Cechy ITF4 - Puglia PL61 - Kujawsko-Pomorskie CZ08 - Moravskoslezsko

5 Regio Industry 4.0 MAP Map of high-tech manufacturing employment change Map of change of manufacturing employment