Smart specialization strategy and its operationalization in the regional policy : case Finland

Copyright © 2017 The Authors. Published by VGTU Press. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The material cannot be used for commercial purposes. Smart Specialization Strategy and itS operationalization in the regional policy: caSe Finland Jari KaiVo-oJa1, Saku VÄhÄSantanen2, ari Karppinen3, teemu haUKioJa4 1Finland Futures Research Centre, Turku School of Economics, University of Turku 2Regional Council of Satakunta, Pori, Finland 3School of Management, University of Tampere, FI-33014 Tampereen yliopisto, Tampere, Finland 4Pori Unit, Turku School of Economics, University of Turku, P.o. Box 170 (Pohjoisranta 11A), FI-28101 Pori, Finland E-mails: 1[email protected] (corresponding author); 2[email protected]; 3[email protected]; 4[email protected] Received 6 February 2017; accepted 7 May 2017 abstract. In the European Union, smart specialization is an important concept in regional policy. Its primary aim is to achieve inclusive and sustainable economic growth. There is a lack of convenient region specific measures to operationalize smart specialization startegies (S3). The purpose of the paper is to find “indices of smart specialization” on a regional level. We propose indices that are based on (1) the rate of industrial diversification, (2) revealed comparative advantage and (3) regions’ overall relative specialization. In the empirical part, we analyze smart specialization in Finland using structural data provided by Statistics Finland for seventy sub-regions (LAU1) and 24 sub-industries in manufacturing. These industries are the most important for exports, productivity, and regional economic performance for a small country. The following indices are used in empirical evaluations: Herfindahl-Hirschman Index (HHI) for regional diversity, BalassaHoover Index (BHI) for revealed comparative advantage, and Region’s Relative Specialization Index (RRSI) for aggregate regional specialization differences. The concept of smart specialization is related to these measures. Index analyses reveal that many growing sub-regions have similar comparative advantages. This means inter-regional synergy, and it enables opportunities for strategic cooperation between regions. To develop smart specialization strategies for Europe’s regions, we need these kinds of empirical knowledge-based management tools and planning approaches. Keywords: smart specialization, Finland, manufacturing, sub-industry, LAU1, Herfindahl-Hirschman index (HHI), Balassa-Hoover index (BHI), revealed comparative advantage, Region’s relative specialization index (RRSI). Jel Classification: B41, C18, C43, D20, D78, F14, F43, F63, H70, I23, I25, I52, L60, L78, N80, O12, O32, O33, R10, R11, R12, R58, Z18. Business, ManageMent and education ISSN 2029-7491 / eISSN 2029-6169 2017, 15(1): 28–41 doi:10.3846/bme.2017.362 29 Business, Management and Education, 2017, 15(1): 28–41 1. Introduction The European Union has a cohesion policy which underlines integrated and placebased economic convergence. The general dimensions of National/Regional Research and Innovation Strategies for Smart Specialisation (S3 strategies) are policy support and investments, spatial strengths, competitive advantage and excellence, innovation activities, stakeholders’ participation, and experiments and evidence-based policy (European Commission 2014: 1). We focus here on the evidence-based strategy tools for RIS3. We shall demonstrate the use of some regional planning and management tools, which are suitable for smart specialization (S3) analysis and management. We expect these methods are useful in the regional policy processes of the European Union. The EU regions are facing many challenges of the global economy and global political landscape. The spatial context of our analysis is Finland and its sub-regions (LAU1). Earlier, Karppinen and Vähäsantanen (2015) have studied the resilience of Finnish sub-regions by focusing on manufacturing industries. This means that our analysis can be applied in all the LAU1-level sub-regions in the European Union. There is large and growing literature on the issue of smart specialization (Capello 2014; McCann, Ortega-Argilés 2015; Foray 2015; Gianelle et al. 2016). In addition to the EU, also the OECD has paid attention to this issue in its report (OECD 2013). In broader international perspective, smart specialization has been seen a policy relevant topic. Our framework is closely linked to the mainstream thinking of economics. We emphasize the identification of potential domains of specialization that might be beneficial for the country given its productive assets (Foray 2011). We focus on the industries, the most important element of Quartet Helix, when analysing smart specialization strategy in the regional context. Other key elements of the helix are the academia, the government, and civil society with citizens (see e.g. Foray et al. 2009; Foray 2011, 2012, 2014, 2015; Santonen et al. 2014; Virkkala et al. 2014; S3 Platform 2015; Johnson 2015; McCann, Ortega-Argilés 2015). The principle of comparative advantage1 and the structure of spatial economic activity have been key topics in the field of regional economics. Our aim is to link these two important approaches to the discussion on smart specialization strategy. This critical aspect of smart specialization has been lacking in many scientific articles, which have analysed and elaborated smart specialization strategy in the European Union (see e.g. Jucevicius, Galbuogiene 2014; Sipilova 2015; Gule 2015; Paliokaite et al. 2015; Paliokaite et al. 2016; Gheorghiu et al. 2016). We shall provide a new empirical tool package for the analysis of smart specialization strategy in the European Union. Measurement of these economic issues is important for the smart specialization strategy in EU-countries and EU-regions (Gianelle et al. 2016). 1 Comparative advantage is a traditional concept in the field of international trade. We apply the hypothesis of revealed comparative advantage, which is parallel to competitive advantage in a regional context (see e.g. Thissen et al. 2013: 34–35). 30 J. Kaivo-oja et al. Smart specialization strategy and its operationalization in the regional policy: case Finland Political decision-makers in European Union are today very interested in key success factors like economic growth, jobs, and investments (see European Commission 2017). These economic success factors are always linked to regional economies in the European Union. The idea of smart specialization can be considered a strategic planning policy instrument. Our motivation is to show how regions in the European Union can analyse the actual contents of smart specialization policy in their local activities. In the practice of regional economic policy, we can improve economic productivity and export success through smart specialization. We argue that smart specialization strategy must be based on the knowledge of basic realities of regional economy and place-based industrial activity/structure. 2. theoretical framework In scientific literature, the concepts of innovation, competitive advantage, and spatial development are closely interconnected (Borsekova et al. 2016). These concepts have various common features and are the subject of many debates among practioners and academics. This article focuses on competitive advantage and spatial development, but we understand that all kinds of innovation activities are shaping local economies and industries. Current industrial structures and regional competitive advantages are outputs of innovation activities (Fig. 1). The smart specialization strategy depends on the three key elements described below. If (1) there is not much innovation and R&D activity, if (2) there are not much competitive advantages in local economies, and if (3) spatial development is not effective and economy is not delivering economic growth, welfare, and happiness, the development process is not fully based on smart specialization strategy. Our empirical analysis is focused directly on the measurement of competitive advantage and spatial development. Innovation activities are driving forces for spatial economic growth, but in this article we are focusing on realized competitiveness. The elements of (1) creative destruction and entrepreneurial discovery (see e.g. Maliranta 2005) and (2) flexible serendipity management can play regenerative roles in spatial development (see e.g. Kakko et al. 2016; Roth et al. 2017; Gheorghiu et al. 2016). These aspects do not eliminate the role of strategic planning in the context of spatial development. Fig. 1. Interconnections among innovation, competitive advantage, and spatial development (Borsekova et al. 2016). 31 Business, Management and Education, 2017, 15(1): 28–41 In next sections, we define our empirical analysis tools for the smart specialization strategy. Smart specialization of the EU regions does not happen in a vacuum. Globalization, hypercompetition, and rapid technological changes reshape the conditions of regions and local economies continuously (see Fig. 2 above). Mindsets of local stakeholders play an important role in the formulation of smart specialization strategies (see Lahari, Perez-Nordtvedt 2008; Virkkala et al. 2014). Based on Fig. 2 we can understand that it is important that the mindsets of stakeholders (global mindset, virtual mindset, innovation mindset, and collaboration mindset) correlate with the realities of globalization, hypercompetition, and rapid technological change. Our empirical approach directly supports regional and local reality checks in this respect. 3. Index framework of the study All the following indices are based on Karppinen and Vähäsantanen (2015). Herfindahl-Hirschman Index (HHI) We apply the Herfindahl-Hirchman Index (HHI) to the Finnish sub-region data. Our data includes 71 sub-regions and 24 industrial sectors. The Herfindahl-Hirschman index (HHI) is a commonly accepted measure of market concentration. We are not analyzing market shares but the industry shares of sub-regions. The HHI formula is the following: 2 1=  =  ∑ ni ss i x HHI x  (1) where xi is the number of employed people in industrial sector (i) and x is total number of people employed in all industrial sectors in region (s) and n is number of industrial sectors (n). HHI-index is calculated as the sum of squared industry shares for each sub-region. In other words, it measures the diversification of industry structure of a Fig. 2. Framework showing relationships between competitive challenges, mindsets, and smart specialization challenges (modified, see Lahari, Perez-Nordtvedt 2008: 313). 32 J. Kaivo-oja et al. Smart specialization strategy and its operationalization in the regional policy: case Finland sub-region. HHI takes values between [0,1]. The smaller the value, the more diversified region, and vice versa. Balassa-Hoover Index (BHI) The key index in this study is the Balassa-Hoover Index (BHI). The formula of BHI is the following: BHIsi = si i s x X x X (2) where xsi is the number of employed people in region s and in industry i, (Xsi/Xi) is corresponding share for all sub-regions. If BHIsi ≥ 1, there is revealed comparative advantage in relation to all regions. We use BHI with industrial labor data of Statistics Finland (2017). Region’s Relative Specialization Index (RRSI) We use RRSI as a measure of comparative specialization of sub-regions. RRSI can be calculated in the following way: ( ) ( ) ( ) ( ) 22 2 12 11 1= − + − +…+ − i i in s RRSI BHI BHI BHI        (3) The higher the RRS index is the more specialized the structure of manufacturing industry is in the region. If the structure of a region is homogenous, the RRSI obtains value zero. If the RRSI ≠ 0, the industrial structure of a region differs from the country’s average. The higher the RRSI value, the more a region is different from the whole country. 4. Empirical demonstrations: case Finland In this section, we present some empirical results based on smart specialization tools presented above. We focus on key results in the case of Finland. First, we figure out the HHI index for sub-regions in Finland (Fig. 3). This indicator is important for resilience analysis. Small HHI value means stronger resilience potential and higher index numbers mean weaker resilience. The HHI is an indicator of industrial diversity. From the Figure 3, we can observe that in 2014 the seven most diversified subregions in Finland were Kuopio, Turku, Lahti, Helsinki, Tampere, Hämeenlinna and Pori. Except in Helsinki and Tampere, they all have been growth-led sub-regions in the recent recession. The GDP rate of growth in Finland was 2.07% in 2011–2014. Corresponding figures for the sub-regions are: 4.94% in Turku, 4.19% in Hämeenlinna, 3.81% in Jyväskylä, 3.80% in Kuopio, 3.63% in Pori, and 2.27% in Lahti. The rates of GDP growth in Helsinki (1.97%) and in Tampere (1.86%) were slightly less than the average (Statistics Finland 2017). Since the late 2000s Oulu region has been one of the growth centers in Finland due to Nokia and the rapid growth of the ICT-sector. During 2011–2014, its rate of growth (0.53%) deviated 70% from the country average. 33 Business, Management and Education, 2017, 15(1): 28–41 The HHI-index tells that the region is the least diversified among the group of large sub-regions. On the the hand, Salo sub-region was hit by the worst negative rate of growth, –9.04% in 2011–2014. Other sub-regions with negative rates of growth were Porvoo (–7.05%), Loviisa (–5.46%), Ålands Skärgård (–5.25%), Etelä-Pirkanmaa (–4.64%), Sydösterbotten (–0.86%), Raahe (–0.68%), Kouvola (–0.66%), and Äänekoski (–0.26%). From the Figure 3 we can observe that except in Kouvola and Salo, these sub-regions have relatively large values for the HHI, i.e. their industrial structures are less diversified than in average. 0,867 0.000 0.050 0.1000.150 0.2000.250 0.300 0.3500.400 0.4500.500 Kuopio Turku Lah Helsinki Tampere Hämeelinna Pori Jakobstad Joensuu Lounais-Pirkanmaa Kotka-Hamina Keski-Karjala Mikkeli Kokkola Suupohja Lappeenranta Ylä-Savo Mariehamns stad Luoteis-Pirkanmaa Riihimäki Jyväskylä Pohjois-Satakunta Raasepori Ylivieska Kajaani Kouvola Pielisen Karjala Joutsa Rauma Rovaniemi Pohjois-Lappi Kyrönmaa Pieksämäki Salo Vakka-Suomi Savonlinna Itä-Lappi Seinäjoki Etelä-Pirkanmaa Ylä-Pirkanmaa Porvoo Forssa Saarijärvi-Viitasaari Torniojokilaakso Oulu Äänekoski Kehys-Kainuu Sisä-Savo Haapavesi-Siikalatva Varkaus Järviseutu Kausnen Kuusiokunnat Vaasa Tunturi-Lappi Loviisa Nivala-Haapajärvi Sydösterboen Keuruu Jämsä Koillis-Savo Kemi-Tornio Loimaa Oulunkaari Ålands landsbygd Imatra Koillismaa Åboland-Turunmaa Raahe Ålands skärgård Fig. 3. Herfindahl-Hirschman Index of sub-regions (LAU1) based on the analysis of 24 industrial sectors in Finland, 2014. 34 J. Kaivo-oja et al. Smart specialization strategy and its operationalization in the regional policy: case Finland Next, we present Balassa-Hoover Index for industrial production (24 manufacturing sectors) in eight urban regions (Fig. 4). With the exception of Oulu, these regions have more diversified industrial structure than in average (see Fig. 3). In Figure 4, if the BHI values for industries exceed eight then comparative advantage is revealed. 13 such manufacturing industries can be found. As we can see, the BHI values are not evenly distributed among the regions. We can interpret to mean that when the value of BHI > 8, the industries as aggregate reveal comparative advantage, but not necessarily in a specific region. To get this information, we need to inspect Figure 5, where cases for BHI > 1 are exposed. Few observations can be made. First, no industry exsits as a source of revealed comparative advantage in all eight regions. Second, three industrial sectors (“Machinery and equipment repair, maintenance and installation”, “Manufacturing of furniture”, and “Manufacturing of chemicals and chemical products”) give impetus to revealed comparative advantage in five regions. This result can be interpreted in terms of smart specialization. One one hand, these five Fig. 4. Balassa-Hoover Index (BHI) of industrial production (24 manufacturing sectors) in some urban sub-regions in Finland, 2014. 0246810 12 14 16 18 20 10 Manufacturing of food products 11 Manufacring of beverages 12 Manufactoring of of tobacco products 13 Manufacturing of texles 14 Manufactoring of clothes 15 Leather and leather products manufacturing 16 Manufacturing of wood and cork products (except… 17 Manufacturing of paper and paper products 18 Prinng and reproducon of recorded media 19 Manufacturing of coke and refined petroleum… 20 Manufacturing of chemicals and chemical products 21 Manufacturing of basic pharmaceucal products and… 22 Rubber and plasc products 23 Manufacturing of other non-metallic mineral products 24 Metal processing 25 Fabricated metal products (excl. machinery and… 26 Computer, electronic and opcal products 27 Manufacturing of electrical equipment 28 Manufacturing of other machinery and equipment 29 Manufacturing of motor vehicles, trailers and semi-… 30 Manufacturing of other transport equipment 31 Manufacturing of furniture 32 Other producon 33 Machinery and equipment repair, maintenance and… Helsinki Tampere TurkuOulu JyväskyläKuopio Pori Rovaniemi 35 Business, Management and Education, 2017, 15(1): 28–41 regions have been successful in competition as compared to the whole country. On the other hand, these five regions have an opportunity to exploit inter-regional synergies. That is, Helsinki, Turku, Oulu, Kuopio and Pori could try to seek strategic alliances to cooperate in activities where synergies can be found; in manufacturing of chemicals and chemical products, for example. Figures 7–9 describe corresponding interregional synergy potentials for all 70 LAU1 regions, and some manufacturing industries. Third, as we combine the results of Figure 3 and 5, it can be found that less diversified sub-regions Oulu and Rovaniemi (relatively high HHI values) have less revealed comparative advantage in industries. Further, there are some dominating industries in their regional economy: “Computer, electronic and optical products” in Oulu (BHI = 5.2), “Manufacturing of motor vehicles, trailers and semi-trailers” (BHI = 13.7), and unspecified “Other production” in Rovaniemi (BHI = 10.5). Correspondingly, in Helsinki sub-region there are nine manufacturing industries, which belong to revealed comparative advantage sectors, and the range of the BHI value is from 1.1 to 2.5. This kind of information is highly valuable for smart specialization discussions in Finland. Similar smart specialization analysis can be applied to all European subregions and urban centres. Fig. 5. Revealed comparative advantage (BHI > 1) in industrial sectors for some key Finnish urban sub-regions 2014 (BHI values inside columns) 1.1 2.5 1.1 1.7 1.6 1.9 2.4 1.7 1.3 3.4 2.2 1.2 1.3 1.2 1.7 1.4 1.5 1.2 1.5 1.7 2.9 1.6 6.1 1.2 1.5 3.9 1.2 1.3 1.9 1.1 2.1 1.1 1.3 1.6 1.1 1.1 1.4 5.2 1.3 1.1 3,7 1.8 2.9 1.8 2.0 1.2 1.1 1.5 2.0 1.4 1.2 1.2 2.0 1.0 4.5 1.2 2.4 1.7 1.1 1,1 13.7 1.2 10.5 0.02.0 4.06.0 8.0 10.0 12.0 14.0 16.0 18.0 20.0 10 Manufacturing of food products 11 Manufacring of beverages 13 Manufacturing of texles 14 Manufacturing of clothes 15 Leather and leather products manufacturing 16 Manufacturing of wood and cork products (except… 17 Manufacturing of paper and paper products 18 Prinng and reproducon of recorded media 19 Manufacturing of coke and refined petroleum… 20 Manufacturing of chemicals and chemical products 21 Manufacturing of basic pharmaceucal products and… 22 Rubber and plasc products 23 Manufacturing of other non-metallic mineral products 24 Metal processing 25 Fabricated metal products (excl. machinery and… 26 Computer, electronic and opcal products 27 Manufacturing of electrical equipment 28 Manufacturing of other machinery and equipment 29 Manufacturing of motor vehicles, trailers and semi-… 30 Manufacturing of other transport equipment 31 Manufacturing of furniture 32 Other producon 33 Machinery and equipment repair, maintenance and… Helsinki TampereTurkuJyväskyläOulu Kuopio Pori Rovaniemi 36 J. Kaivo-oja et al. Smart specialization strategy and its operationalization in the regional policy: case Finland In Figures 6a and 6b, we figure out the key findings of the Region’s Relative Specilization Index (RRSI) for Finnish sub-regions. The higher the RRS index the more specialized the structure of manufacturing industry is in a region as compared to the whole country (see Equation 3). The BHI index can be used to industry specific analyses. Instead, the RRS index can be used to measure the specialization intensity of the whole regional economy. The RSS index can also be used to measure the aggregate deviation of specialization from country’s avarerage. RSS index analyses can reveal useful information for regional smart specialization strategies. The HHI analysis reflects the risk potential that is embedded in region’s economic structure, if it confronts the exogenous economic shocks. Instead, the RRSI analysis reflects regions’ risk for exogenous (national or supranational) industrial or regional policy. Therefore, smart specialization policy should be region specific. All Finnish regions are covered in the RRSI analysis in Figures 6a and 6b. Fig. 6a. RRS Index, 35 sub-regions in Finland, the index number refers to the most specialized sub-region, rankings 1–35, 2014. 0,05,0 10,015,020,025,030,0 35,040,0 45,050,0 Åboland-Turunmaa Koillis-Savon Porvoo Ylivieska Kausnen Sydösterboen Rovaniemi Raahe Jämsä Vakka-Suomi Kyrönmaa Kajaani Kemi-Tornio Lounais-Pirkanmaa Mikkeli Tunturi-Lappi Imatra Ålands landsbygd Ålands skärgård Koillismaa Torniolaakso Oulunkaari Ylä-Savo Itä-Lappi Loviisa Pohjois-Lappi Suupohja Pohjois-Satakunta Mariehamn Luoteis-Pirkanmaa Kehys-Kainuu Kuusiokunnat Kokkola Turku Ylä-Pirkanmaa 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 ...225,7