E u r o p e a n I n t e g r a t i o n S t u d i e s 2 0 1 8 / 1 2 50 Submitted 04/2018 Accepted for publication 11/2018 European Integration Studies No. 12 / 2018 pp. 50-62 DOI 10.5755/j01.eis.0.12.21872 Identification of Smart Regions with Resilience, Specialisation and Labour Intensity in a Globally Competitive Sector – Examination of LAU-1 Regions in Finland EIS 12/2018 Abstract Identification of Smart Regions with Resilience, Specialisation and Labour Intensity in a Globally Competitive Sector – Examination of LAU-1 Regions in Finland http://dx.doi.org/10.5755/j01.eis.0.12.21872 Teemu Haukioja Turku School of Economics, University of Turku Jari Kaivo-oja Futures Research Centre, Turku School of Economics, University of Turku Ari Karppinen Turku School of Economics, University of Turku Saku Vähäsantanen Regional Council of Satakunta The purpose of the study was to construct smart specialisation indicators for LAU-1 regions in Fin- land. Established indices are based on indicators of the regions’ revealed comparative advantage and the degree of diversification in the sub-regional industrial structure. Furthermore, we introduce an indicator that can be used to assess the socio-economic importance (employment) of diversification and specialisation for a region. The indices data is based on Statistics Finland (2015) data for the 70 Local Administrative Unit level 1 (LAU1) sub-regions in Finland. The potential S3 indices measured here reveal the position of each sub-region’s smart specialisation among the 70 sub-regions in 2015. It is common economic knowledge that manufacturing industries are the most export-oriented, highly productive and thus can approximate a region’s success in international trade and competitive advan- tages. The study is based on three smart specialisation indices: the Herfindahl-Hirschman Index for regional resilience (HHI), the regional relative specialisation index (RRSI) based on the Balassa-Hoover Index (B-H), and the relative employment volume index in the manufacturing sector (LIMI). Through in- dex examination, we can obtain knowledge about a region’s smart specialisation status and potential. The results reveal that each sub-region has its own smart specialisation characteristics with a different risk profile. Sub-regions like Helsinki and Tampere have a similar industrial structure to Finland as a whole and are resilient: they will benefit from nationwide economic and industrial policy, and they have a good capability of resisting economic shocks. Our study reveals that there are some other similar smaller (LAU-1) sub-regions in Finland – for example Rauma. As such, it is critical that this kind of research-based basic information be taken into account when constructing sustainable strategies for regional development. Similar calculations could be performed for all regions in Europe. 51 E u r o p e a n I n t e g r a t i o n S t u d i e s 2 0 1 8 / 1 2 KEYWORDS: Finland, Herfindahl-Hirschman Index, Balassa-Hoover Index, industrial diversification, European Union, regional development, revealed comparative advantage, smart specialisation. A smart region must be able to specialise enough to succeed in international competition and at the same time be sufficiently diverse in its industrial (i.e. competitive) structure to adapt to exter- nal shocks. This study concerns the specialisation and diversification of the competitive/industrial sector in LAU-1 sub-regions in Finland concerning their position in terms of smart specialisation. Smart specialisation has been a strategic (S3) challenge in European regional development and policy for several years. Despite intensive research and numerous financed projects, the actual content of S3 remains somewhat obscure. On the one hand, the goal is that each European re- gion finds its own competitive advantages and fields of economic specialisation (Foray et al. 2009, Foray 2011, 2012, 2014, 2015, Capello 2014, Gule 2015, European Commission 2014, European Commission 2017). The ultimate goals are economic growth, investments and jobs (see e.g. Mc- Cann & Ortega-Argilés 2015, Kaivo-oja, Haukioja & Karppinen 2017). On the other hand, any given region should be resilient to external shocks. In practice, this means that the industrial structure of the region should be diverse enough to maintain its ability to recover from setbacks. There is a strong foothold in S3 thinking according to which European regions are supposed to find their local strengths by adopting Entrepreneurial Discovery Processes (EDP) and renewing their co-op- eration practices with stakeholders in the spirit of the Quadruple Helix Approach. The aim of most S3 projects is to emphasise the importance of micro-based qualitative indicators, because they can produce unique local knowledge. However, interest is also shifting towards quantitative macro indicators and statistics, because only they can reveal a region’s relative potential and economic success in relation to other regions. Both kinds of information are important in order to produce sustainable Smart Specialisation Strategies (see OECD 2013, Borsekova et al. 2017). The construction of ‘smart’ indicators for Europe’s Smart Specialisation Strategies (S3) is a cur- rent but challenging topic both for academic research and for the people involved in the prac- tice of regional development. The need for S3 indicators has been recognized but the common ground for implementing a systematic set of indicators is still lacking. There is an obvious reason for this: it is hard to create an integrated indicator system that is based on a bottom-up approach and a subjective process where local stakeholders are supposed to create a common compre- hension of relevant objects of measurement and the right indicators for that purpose. Unavoida- bly, many such subjective-based indicators must be qualitative in nature. If this is a challenging task for one region, it is almost a ‘mission impossible’ for the whole of the European Union. In Europe there are numerous regions whose cultural and socio-economic features may be highly distinctive; what fits well for one region may be totally inadequate for another. Despite these difficulties, in its handbook ‘Implementing Smart Specialisation’ (Gianelle et al. 2016, Paliokaite, Martinaitis & Reimeris 2015, Santonen, Kaivo-oja & Suomala 2014, Virkkala et al. 2014), the Eu- ropean Commission presents a framework and the desirable properties that are expected from smart specialisation indicators. A bottom-up approach makes local tailoring possible, because understanding can be increased about the specific characteristics of a given region. The problem, however, is that this micro-based information cannot be used effectively to understand other regions. Consequently, comparisons between regions must also be rather arbitrary. Some Smart Specialisation scholars have even taken quite a critical attitude to macro-based indicators, almost denying their usefulness in Smart Specialisation considerations (OECD 2013, 77). However, quantifiable macro-indicators may at their best present a quantitative and ‘objective’ measuring tool for some phenomenon of interest, which makes a region’s relative differences and features visible, and can provide important comparative knowledge about a region’s relative position among a group of ‘smart’ regions. In contrast to such Introduction E u r o p e a n I n t e g r a t i o n S t u d i e s 2 0 1 8 / 1 2 52 indoctrination, we see that there is no convincing research-based evidence to justify neglecting the macro aspects in academic S3 research and in the practice of regional development projects. On the contrary, we see that such omissions may even be harmful for viable and sustainable regional strategies. We propose that both approaches are needed, and the omission of one may give an incomplete, inaccurate and biased picture about what is going on in a region. Thus, all aspects of micro, macro, qualitative and quantitative approaches should be exploited, because they are not exclusionary, but complementary. In order to commit to a high quality strategy building process, a comprehensive ‘both-and’ mindset needs to be adopted. As a conclusion, by using an index ap- proach we can obtain regional profiles of smart specialisation that are revealed by general statis- tics, instead of local or individual qualitative assessments that are incomparable with each other. The knowledge creation in this study is based on the idea that regions in Europe need relevant and as objective data as possible to recognize their true competitive advantages. Competitive advantages are not isolated from other regions; rather they are related to other regions. Thus, sustainable and successful Smart Specialisation strategies for individual regions must be com- patible with this knowledge. The index approach can reveal a region’s present economic struc- ture in industrial production. It can also support relevant background information, which enables realistic discoveries about potential new business models that originate in regions’ strengths (see e.g. Johnson 2015, Gheorghiu et al. 2016, Jucevicius & Galbuogiene 2014, Paliokaite, Mar- tinaitis & Sarpong 2016). This provides the foundation for knowledge-based management and strategy building, supporting bold openings for experiments and the regional strengthening of the Entrepreneurial Discovery Processes. The S3 approach aims to support the building blocks of regional competitiveness. Typical sources of competitiveness are (1) innovation and creativity, (2) agglomeration economics, (3) foreign direct investment (FDI), (4) clusters, specialisation and concentration, (5) networks and trans- portation costs, (6) education and research, (7) size and available resources, (8) economic struc- ture and (9) interregional structure. Regions have many ways to improve their competitiveness (Thissen et al. 2013, S3 Platform 2015). Clear indications of competitiveness are (1) research and technological development, (2) institutions and social capital, (3) foreign direct investment and (4) infrastructure and human capital. These fundamental factors lead regions towards revealed comparative advantage with improved labour productivity and employment rate. Finally, these two key factors guide regions to improvement of regional performance (gross regional product). In the final stage, the target outcome is welfare and quality of life (see Thissen et al. 2013, p. 50). Regional development strategies can be based on clustering, openness, diversification and specialisation. These four domains give rise to our region- al strategic options: (1) cluster strategy, where a region combines specialisation and openness, (2) cluster strategy, where a region combines specialisation and clustering, (3) self-sufficiency strate- gy, where a region combines clustering and diversification, and finally (4) trade-dependent diversi- fication strategy, where a region combines diversification and openness (Thissen et al. 2013, p. 89). Theoretical framework Method and data For a Smart Specialisation Strategy to work in regions, it is of great importance to find the best indicators that show the status of a region as plainly as possible among the comparison group. We have applied the index approach to the LAU-1 regions of Finland. Industrial production is emphasised, which can be characterised as an export-oriented high productivity sector. Three statistical Smart Specialisation indicators are examined: Equation 1 is based on the Herfindahl-Hirschman Index (HHI). Originally, the HHI was used to meas- ure market concentration, i.e. the market shares of the firms in an industry. It also can be used to describe a region’s economic resilience. This index can be used to identify ex ante the region's ability to 53 E u r o p e a n I n t e g r a t i o n S t u d i e s 2 0 1 8 / 1 2 cope with external shocks, such as a financial crisis, the closure of large mills or industrial accidents. The calculation of the HHI index takes into account the extent to which the individual industries in the sub-sector employ the population in relation to the entire industrial sector's labour force in the region. The smaller the value of HHI becomes, the more versatile industrial structure the region has. A versatile industry structure reflects the economic resilience of the region: "all the eggs" are not put "into the same basket". Resilience calculation enables better risk management in regional policy and regional economics (see also Lahari et al. 2008, Karppinen, & Vähäsanta- nen 2015, Maliranta 2005, Kakko, Kaivo-oja & Mikkelä 2016). Information on economic resilience in the region is useful for both public and private sector decision-makers, who should be aware of the instability of the global economy when they are developing and implementing regional strategies. The data for this study comes from Statistics Finland (2017, 2018). Herfindahl-Hirschman Index (HHI) We have applied the Herfindahl-Hirchman Index (HHI) to describe the ex ante resilience properties of the Finnish sub-regions against asymmetric external economic shocks (Herfindahl 1950, Hirch- man 1964). Our data includes 70 sub-regions and 24 industrial sectors. The HHI measures the in- dustry-wide diversification of a competitive sector in the sub-region. The HHI formula is as follows: (1) where x i is the number of people employed in the industrial sector (i), x is the total number of people employed in all industrial sectors in the region (s) and (n) is the number of industrial sec- tors. The HHI index is calculated as the sum of squared industry shares for each sub-region. The HHI uses values between [0,1]. The smaller the value, the more diversified the region, and vice versa (see e.g. Kaivo-oja et al. 2017). Region’s Relative Specialisation Index (RRSI) As the HHI reflects a region’s ability to resist external shocks in an ex ante sense, we have ap- plied RRSI as an indicator of the revealed comparative specialisation of sub-regions in an ex post sense. The RRSI measures the observable amount of the relative deviation of the region’s industrial structure compared to that of the whole country. The aim of the RRSI is to identify whether the region has succeeded in specialising to a sufficient degree to survive in global competitive markets. In the terminology of economics, the relative comparative advantage is revealed. The RRSI measures the relative disparity of the industri- al structure of the region with respect to the broad industrial structure of the whole country. The higher the value RRSI index becomes, the more specialised the region's industrial produc- tion base is in relation to the entire national economy. For industrial activities, this means high productivity and economic success in good times, but on the reverse side, there is a possibly of weaker economic resilience in the event of a recession or other economic downturn. This approach is in line with the terminology of intelligent specialisation. It can be calculated in the following way (see e.g. Balassa & Noland 1989): = [√∑ (1 − )2=1 ] (2) where BHI i is the Balassa-Hoover Index (BHI) for industry (i). The formula for the BHIsi is as follows: Information on economic resilience in the region is useful for both public and private sector decision- makers, who should be aware of the instability of the global economy when they are developing and implementing regional strategies. The data for this study comes from Statistics Finland (2017, 2018). Herfindahl-Hirschman Index (HHI) We have applied the Herfindahl-Hirchman Index (HHI) to describe the ex ante resilience properties of the Finnish sub-regions against asymmetric external economic shocks (Herfindahl 1950, Hirchman 1964). Our data includes 70 sub-regions and 24 industrial sectors. The HHI measures the industry-wide diversification of a competitive sector in the sub-region. The HHI formula is as follows: 𝐻𝐻𝐻𝐻𝐻𝐻� = ∑ ���� �� � ���� (1) where xi is the number of people employed in the industrial sector (i), x is the total number of people employed in all industrial sectors in the region (s) and (n) is the number of industrial sectors. The HHI index is calculated as the sum of squared industry shares for each sub-region. The HHI uses values between [0,1]. The smaller the value, the more diversified the region, and vice versa (see e.g. Kaivo-oja et al. 2017). Region’s Relative Specialisation Index (RRSI) As the HHI reflects a region’s ability to resist external shocks in an ex ante sense, we have applied RRSI as an indicator of the revealed comparative specialisation of sub-regions in an ex post sense. The RRSI measures the observable amount of the relative deviation of the region’s industrial structure compared to that of the whole country. The aim of the RRSI is to identify whether the region has succeeded in specialising to a sufficient degree to survive in global competitive markets. In the terminology of economics, the relative comparative advantage is revealed. The RRSI measures the relative disparity of the industrial structure of the region with respect to the broad industrial structure of the whole country. The higher the value RRSI index becomes, the more specialised the region's industrial production base is in relation to the entire national economy. For industrial activities, this means high productivity and economic success in good times, but on the reverse side, there is a possibly of weaker economic resilience in the event of a recession or other economic downturn. This approach is in line with the terminology of intelligent specialisation. It can be calculated in the following way (see e.g. Balassa & Noland 1989): 𝑅𝑅𝑅𝑅𝑅𝑅𝐻𝐻� = ��∑ �1 − 𝐵𝐵𝐻𝐻𝐻𝐻� ������ �� (2) where BHIi is the Balassa-Hoover Index (BHI) for industry (i). The formula for the BHIsi is as follows: 𝐵𝐵𝐻𝐻𝐻𝐻�� = ��� �� �� � � (3) where xsi/Xs is the share of employed in the region (s) in industry (i), and (xi/X) is the corresponding share for the country as a whole. If BHIsi ≥ 1, there is a revealed comparative advantage for that industry in a sub- region (s) compared to the sum of all the regions. We have used the BHI with industrial labour data. The higher the RRS index, the more specialised the structure of manufacturing industry is revealed to be in the region. If the structure of a region is similar to that of the country as a whole, the RRSI obtains a value of zero. If the RRSI ≠ 0, the industrial structure of a region differs from the country’s average. Equation 4 is a simple indicator of employment in industrial production in a given region. We call this indicator the ‘Labour Intensity of Manufacturing Index’ (LIMIs) for region (s): (3) E u r o p e a n I n t e g r a t i o n S t u d i e s 2 0 1 8 / 1 2 54 where x si /X s is the share of employed in the region (s) in industry (i), and (x i /X) is the correspond- ing share for the country as a whole. If BHI si ≥ 1, there is a revealed comparative advantage for that industry in a sub-region (s) compared to the sum of all the regions. We have used the BHI with industrial labour data. The higher the RRS index, the more specialised the structure of manufacturing industry is re- vealed to be in the region. If the structure of a region is similar to that of the country as a whole, the RRSI obtains a value of zero. If the RRSI ≠ 0, the industrial structure of a region differs from the country’s average. Equation 4 is a simple indicator of employment in industrial production in a given region. We call this indicator the ‘Labour Intensity of Manufacturing Index’ (LIMIs) for region (s): 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿� ���𝐸𝐸��� 𝑇𝑇𝐸𝐸� �� (4) where 𝐸𝐸��� measures employment in manufacturing and TE is total employment. As a third dimension, the significance of employability with the features revealed by the two previous indicators in the region still needs to be considered. The Labour Intensity of Manufacturing Index (LIMI) measures the region's industrial workforce's share of the entire workforce in the region. The empirical material is based on the employment statistics of industry (TOL2) produced by Statistics Finland (LAU1, statistical definition, which is used by the European Statistical Office in Eurostat Finland). The data sets are for 2015. Results In Fig. 1 we have reported the Herfindahl-Hirschman Index analysis of the regional economy in Finland (70 LAU1 regions) in 2015. (4) where E man measures employment in manufacturing and TE is total employment. As a third dimension, the significance of employability with the features revealed by the two pre- vious indicators in the region still needs to be considered. The Labour Intensity of Manufacturing Index (LIMI) measures the region's industrial workforce's share of the entire workforce in the region. The empirical material is based on the employment statistics of industry (TOL2) produced by Statistics Finland (LAU1, statistical definition, which is used by the European Statistical Office in Eurostat Finland). The data sets are for 2015. In Fig. 1 we have reported the Herfindahl-Hirschman Index analysis of the regional economy in Finland (70 LAU1 regions) in 2015. In Fig. 2 we have reported the regions’ Relative Specialisation Index (RRSI) in Finland in 2015. In Fig. 3 we have reported the regions’ labour intensity in Finland in 2015. Fig. 4 is based on the indices (HHI) and (LIMI) presented in Figs. 1 and 3 and equations (1) and (3). We have classified both the resilience and labour intensity of the manufacturing properties of smart sub-regions into four sub-areas (I-IV) based on their median. If a sub-region is in sub-area I or II, its S3 properties are strong resilience in the sub-region’s economy and high/weak labour intensity of manufacturing, respectively. Correspondingly, sub-areas III and IV consist of weak resilience and weak/strong labour intensity properties, respectively. For more detailed classification purposes, the upper and lower quartiles are also shown in the figure. The coloures indicate the location of the sub-region with respect to the LIMI index median/quartiles classification. The names of some smart sub-regions – the ten most diversified and the five least diversified – are expressed according to the LIMI classification. Each of the sub-regions can find a distinctive position for their smart spe- cialisation in 2015. For example, the smart specialisation properties for the economy of the Jakob- stad sub-region can be characterised by a very strong labour intensity and quite high resilience in manufacturing. In Tables 1 and 2 these sub-regions are explored in more detail. Fig. 5 is based on the indices (RRSI) and (LIMI) presented in Figs. 2 and 3 and equations (2) and (3). In order to achieve convenient comparisons with respect to sub-regional smart specialisa- tion properties (i.e. HHI and RRSI with respect to LIMI), we have also classified here two smart specialisation features. In the case of Fig. 5, the relative sub-regional specialisation and labour intensity of manufacturing properties of the smart sub-regions are classified into four sub-areas (I-IV) based on their median. If a sub-region is in sub-area I or II, its S3 properties are a significant Results 55 E u r o p e a n I n t e g r a t i o n S t u d i e s 2 0 1 8 / 1 2 Fig. 1. Herfindahl-Hirschman Index Analysis of the Regional Economy in Finland (70 LAU1 regions) in 2015. 0 10 20 30 40 50 60 Ålands skärgård Raahe Imatra Koillis-Savo Koillismaa Åboland-Turunmaa Ålands landsbygd Loimaa Kemi-Tornio Jämsä Keuruu Sydösterbotten Tunturi-Lappi Nivala-Haapajärvi Vaasa Oulunkaari Haapavesi-Siikalatva Kuusiokunnat Järviseutu Kehys-Kainuu Saarijärvi-Viitasaari Oulu Sisä-Savo Itä-Lappi Äänekoski Forssa Varkaus Kaustinen Torniolaakso Porvoo Ylä-Pirkanmaa Savonlinna Seinäjoki Vakka-Suomi Etelä-Pirkanmaa Pohjois-Lappi Kajaani Pieksämäki Pielisen Karjala Riihimäki Loviisa Kyrönmaa Kouvola Rauma Jyväskylä Ylivieska Mariehamns stad Joutsa Pohjois-Satakunta Ylä-Savo Rovaniemi Salo Suupohja Kokkola Lappeenranta Luoteis-Pirkanmaa Raasepori Keski-Karjala Kotka-Hamina Joensuu Jakobstadsregionen Mikkeli Pori Lounais-Pirkanmaa Hämeenlinna Helsinki Tampere Lahti Turku Kuopio Finland Figure 1 Herfindahl-Hirschman Index Analysis of the Regional Economy in Finland (70 LAU1 regions) in 2015 E u r o p e a n I n t e g r a t i o n S t u d i e s 2 0 1 8 / 1 2 56 In Fig. 2 we have reported the regions’ Relative Specialisation Index (RRSI) in Finland in 2015. Fig. 2. Regions’ Relative Specialisation Index (RRSI) in Finland in 2015. 0 5 10 15 20 25 30 35 40 Helsinki Jyväskylä Riihimäki Tampere Rauma Joensuu Varkaus Salo Loimaa Pieksämäki Lappeenranta Forssa Hämeenlinna Pori Kotka-Hamina Kouvola Etelä-Pirkanmaa Oulu Jakobstadsregionen Sisä-Savo Äänekoski Koillis-Savo Seinäjoki Raasepori Lahti Saarijärvi-Viitasaari Pielisen Karjala Keski-Karjala Järviseutu Nivala-Haapajärvi Savonlinna Keuruu Vaasa Loviisa Itä-Lappi Ylä-Pirkanmaa Ylä-Savo Turku Luoteis-Pirkanmaa Kuusiokunnat Haapavesi-Siikalatva Mariehamns stad Joutsa Kokkola Suupohja Kehys-Kainuu Oulunkaari Pohjois-Lappi Koillismaa Kuopio Torniolaakso Pohjois-Satakunta Lounais-Pirkanmaa Ålands landsbygd Imatra Mikkeli Tunturi-Lappi Kemi-Tornio Ålands skärgård Kajaani Åboland-Turunmaa Rovaniemi Jämsä Vakka-Suomi Raahe Kyrönmaa Sydösterbotten Ylivieska Kaustinen Porvoo Figure 2 Regions’ Relative Specialisation Index (RRSI) in Finland in 2015 57 E u r o p e a n I n t e g r a t i o n S t u d i e s 2 0 1 8 / 1 2 In Fig. 3 we have reported the regions’ labour intensity in Finland in 2015. Fig. 3. Labour intensity in the sub-regions of Finland. Fig. 4 is based on the indices (HHI) and (LIMI) presented in Figs. 1 and 3 and equations (1) and (3). We have classified both the resilience and labour intensity of the manufacturing properties of smart sub- regions into four sub-areas (I-IV) based on their median. If a sub-region is in sub-area I or II, its S3 properties are strong resilience in the sub-region’s economy and high/weak labour intensity of manufacturing, respectively. Correspondingly, sub-areas III and IV consist of weak resilience and weak/strong labour intensity properties, respectively. For more detailed classification purposes, the upper and lower quartiles are also shown in the figure. The coloures indicate the location of the sub-region with respect to the LIMI index median/quartiles classification. The names of some smart sub-regions – the ten most diversified and the five least diversified – are expressed according to the LIMI classification. Each of the sub-regions can find a distinctive position for their smart specialisation in 2015. For example, the smart specialisation properties for the economy of the Jakobstad sub-region can be characterised by a very strong labour intensity and quite high resilience in manufacturing. In Tables 1 and 2 these sub-regions are explored in more detail. 0% 5% 10% 15% 20% 25% 30% 35% Ålands skärgård Tunturi-Lappi Pohjois-Lappi Mariehamns stad Rovaniemi Torniolaakso Itä-Lappi Kajaani Kuopio Helsinki Joutsa Koillismaa Ålands landsbygd Oulunkaari Jyväskylä Koillis-Savo Kehys-Kainuu Oulu Kyrönmaa Mikkeli Turku Kaustinen Keski-Karjala Loviisa Lappeenranta Nivala-Haapajärvi Pieksämäki Saarijärvi-Viitasaari Kotka-Hamina Kouvola Savonlinna Joensuu Tampere Sisä-Savo Raasepori Hämeenlinna Kuusiokunnat Kokkola Pielisen Karjala Luoteis-Pirkanmaa Sydösterbotten Riihimäki Åboland-Turunmaa Pori Seinäjoki Lahti Järviseutu Ylä-Savo Ylivieska Pohjois-Satakunta Keuruu Suupohja Loimaa Salo Vaasa Forssa Lounais-Pirkanmaa Kemi-Tornio Porvoo Imatra Haapavesi-Siikalatva Rauma Varkaus Ylä-Pirkanmaa Jämsä Etelä-Pirkanmaa Jakobstadsregionen Raahe Äänekoski Vakka-Suomi Figure 3 Labour intensity in the sub-regions of Finland relative specialisation in the sub-region’s economy and high/weak labour intensity of manufac- turing, respectively. Correspondingly, sub-areas III and IV consist of minor relative deviation in the sub-region’s industrial structure and weak/strong labour intensity properties, respectively. For more detailed classification purposes, the upper and lower quartiles are also shown in the figure. The colours indicate the location of the sub-region with respect to the LIMI index median/ quartiles classification. The names of some smart sub-regions – the ten most relatively special- ised and the five least specialised – are expressed according to the LIMI classification. Also in this case, each of the sub-regions can find a distinctive position for their smart specialisation in 2015. For example, the smart specialisation properties for the economy of the Porvoo sub-region can be characterised by a very high relative specialisation compared to Finland as a whole and the quite high importance of industry for its sub-regional economy. E u r o p e a n I n t e g r a t i o n S t u d i e s 2 0 1 8 / 1 2 58 Notes: III Quite similar industrial structure compared to the whole economy and weak labour intensity of manufacturing in a sub-regional economy. IV Quite similar industrial structure compared to the whole economy and strong labour intensity of manufacturing in a sub-regional economy. Fig. 5. Classification of smart sub-regions (2) by revealed comparative advantage (i.e. sub-regional specialisation, RRSI) and labour intensity of manufacturing (LIMI) Comparing the sub-regions’ smart specialisation properties using HHI and RRSI (i.e. resilience and specialisation) with the labour intensity of manufacturing in the sub-regions’ economy it can be observed, firstly, that the RRSI index for sub-regions – as a description of smart specialisation – produces a distribution whose kurtosis is much higher with a remarkably higher specialisation tail. Concretely, this means that the industrial structure of many Finnish sub-regions is quite similar in terms of the industrial structure in Finland as whole but some specific LAU-1 regions differ significantly from the average structure. Potentially, these sub-regions have great potential to succeed in international competition because of their relative specialisation, but simultaneously they may be vulnerable. They have the potential to emerge as an area of sudden structural change. Secondly, if the distribution of smart sub- regions with respect to specialisation is very peaked, it can be assumed that nationwide industrial and economic policy also supports the economic growth of many such sub-regions. These sub-regions are explored more specifically in Tables 1 and 2. Notes: III Quite similar industrial structure compared to the whole economy and weak labour intensity of manufacturing in a sub-regional economy. IV Quite similar industrial structure compared to the whole economy and strong labour intensity of manufacturing in a sub-regional economy. Figure 5 Classification of smart sub-regions (2) by revealed comparative advantage (i.e. sub- regional specialisation, RRSI) and labour intensity of manufacturing (LIMI) Fig. 4. Classification of smart sub-regions (1) by resilience (HHI) and labour intensity of manufacturing (LIMI) Fig. 5 is based on the indices (RRSI) and (LIMI) presented in Figs. 2 and 3 and equations (2) and (3). In order to achieve convenient comparisons with respect to sub-regional smart specialisation properties (i.e. HHI and RRSI with respect to LIMI), we have also classified here two smart specialisation features. In the case of Fig. 5, the relative sub-regional specialisation and labour intensity of manufacturing properties of the smart sub-regions are classified into four sub-areas (I-IV) based on their median. If a sub-region is in sub-area I or II, its S3 properties are a significant relative specialisation in the sub-region’s economy and high/weak labour intensity of manufacturing, respectively. Correspondingly, sub-areas III and IV consist of minor relative deviation in the sub-region’s industrial structure and weak/strong labour intensity properties, respectively. For more detailed classification purposes, the upper and lower quartiles are also shown in the figure. The colours indicate the location of the sub-region with respect to the LIMI index median/quartiles classification. The names of some smart sub-regions – the ten most relatively specialised and the five least specialised – are expressed according to the LIMI classification. Also in this case, each of the sub-regions can find a distinctive position for their smart specialisation in 2015. For example, the smart specialisation properties for the economy of the Porvoo sub-region can be characterised by a very high relative specialisation compared to Finland as a whole and the quite high importance of industry for its sub-regional economy. Figure 4 Classification of smart sub-regions (1) by resilience (HHI) and labour intensity of manufacturing (LIMI) 59 E u r o p e a n I n t e g r a t i o n S t u d i e s 2 0 1 8 / 1 2 Table 1 TOP 10 ´Traffic lights´ regional development. TOP 10 regions of HHI and RRSI in Finland in 2015 Rank Subregion TOP10 HHI LIMI Rank Subregion TOP10 RRSI LIMI 1 Kuopio 7.93 7.1% 1 Porvoo 35.46 21.1 % 2 Turku 8.67 12.3% 2 Kaustinen 34.35 12.4 % 3 Lahti 8.92 17.5% 3 Ylivieska 32.61 17.7 % 4 Tampere 9.38 14.3% 4 Sydösterbotten 19.72 15.7 % 5 Helsinki 9.44 7.9% 5 Kyrönmaa 16.51 11.8 % 6 Hämeenlinna 9.99 15.0% 6 Raahe 16.08 31.0 % 7 Lounais-Pirkanmaa 10.50 21.1% 7 Vakka-Suomi 14.44 31.6 % 8 Pori 10.96 16.7% 8 Jämsä 14.21 27.8 % 9 Mikkeli 10.96 12.2% 9 Rovaniemi 13.06 4.8 % 10 Jakobstadsregionen 11.01 29.7% 10 Åboland-Turunmaa 12.71 16.1 % Comparing the sub-regions’ smart specialisation properties using HHI and RRSI (i.e. resilience and specialisation) with the labour intensity of manufacturing in the sub-regions’ economy it can be observed, firstly, that the RRSI index for sub-regions – as a description of smart specialisa- tion – produces a distribution whose kurtosis is much higher with a remarkably higher special- isation tail. Concretely, this means that the industrial structure of many Finnish sub-regions is quite similar in terms of the industrial structure in Finland as whole but some specific LAU-1 regions differ significantly from the average structure. Potentially, these sub-regions have great potential to succeed in international competition because of their relative specialisation, but si- multaneously they may be vulnerable. They have the potential to emerge as an area of sudden structural change. Secondly, if the distribution of smart sub-regions with respect to specialisation is very peaked, it can be assumed that nationwide industrial and economic policy also supports the economic growth of many such sub-regions. These sub-regions are explored more specifi- cally in Tables 1 and 2. Rank Subregion HHI LIMI Rank Subregion RRSI LIMI 66 Koillismaa 30.67 8.9% 66 Rauma 3.89 24.5% 67 Koillis-Savo 31.40 10.8% 67 Tampere 3.82 14.3% 66 Imatra 32.71 22.6% 66 Riihimäki 3.68 15.9% 69 Raahe 48.15 31.0% 69 Jyväskylä 3.49 10.7% 70 Ålands skärgård 55.10 1.04% 70 Helsinki 3.43 7.9% Table 2 RANKING 66-70 ´Traffic lights´ regional development. The bottom five sub-regions of HHI and RRSI in Finland in 2015 In Table 1 the TOP 10 ´Traffic lights´ of regional development of HHI and RRSI in Finland in 2015 are listed. According to Table 1, the Kuopio sub-region is the most diversified (of the 70 sub-regions) but the economic significance of the industrial sector is minimal in its regional economy. The Lahti sub-region, on the other hand, is diversified and at the same time its labour intensity is significant. In other words, this kind of sub-region is resilient to external shocks according to its industrial structure, and at the same time this structure has major significance for its regional development. The same goes for the Lounais-Pirkanmaa and Jakobstad regions. E u r o p e a n I n t e g r a t i o n S t u d i e s 2 0 1 8 / 1 2 60 In Table 2 the bottom five ranked sub-regions of Finland are listed. Both HHI and RRSI indicators are used as reference points. In Table 2 there are some interesting results when we compare them with Table 1. Firstly, as we can easily see, some sub-regions are represented in both the Top 10 of HHI analysis and also at the bottom of the RRSI analysis list. Such sub-regions are Helsinki and Tampere. Helsinki and Tampere are in the Top Ten group of resilience (HHI), but at the same time in the RRSI these regions are on the list of the bottom five sub-regions. This result is intuitive: the sub-regions are diversified and their industrial structure complies with the industrial structure of the whole country. These areas, Helsinki and Tampere, strongly determine the industrial structure of the whole country, as they are large economies on a Finnish scale. Regions like this benefit from the overall industrial and economic policy of Finland. In the left column, attention is drawn to the sub-regions of Raahe and Imatra. These sub-regions have problems with resilience strategy because the value of the HHI index is high and at the same time the LIMI index has a high value. In Finland, the sub-region of Salo with its earlier large indus- trial Nokia production had a similar economic position to the Imatra and Raahe sub-regions. This spatial analysis covers Finnish sub-regions. The same type of analysis could be presented for all EU member states. Conclusions Balassa, B. & Noland, M. (1989). 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Regional Competitiveness and Specialization in Europe: Place-based Development in Internation- al Networks. New Horizons in Regional Science. Edward Elgar, Cheltenham, UK. https://doi. org/10.4337/9781782545163 Virkkala, S., Mäenpää, A. & Mariussen, Å. (Eds.) (2014). The Ostrobothnian Model of Smart Speciali- sation [online], [cited 07 June 2018]. Proceedings of the University of Vaasa, Reports 196. Vaasa. Avail- able from Internet: http://www.uva.fi/materiaali/ pdf/isbn_978–952–476–577–0.pdf JARI KAIVO-OJA PhD, Research direc- tor, Adjunct professor Finland Futures Re- search Centre, Turku School of Eco- nomics, University of Turku Fields of research interests Rregional and nation- al foresight systems, corporate and technology foresight, futures studies, innovation management, global foresight and interna- tional futures research. He has worked for the European Commission in the FP6, in the FP7 and in the Horizon 2020. He has worked in projects with the European Foundation, the Eurostat, the Nordic Innovation Centre and the European Parliament. Research director Jari Kaivo-oja has published more than 150 articles in academic journals and books Address FI-20014, Finland E-mail: jari.kaivo-oja@utu.fi About the authors TEEMU HAUKIOJA PhD, Assistant Professor University of Turku, Turku School of Eco- nomics, Pori Unit Fields of research interests Economic growth, and sustainable develop- ment. He is the author and co-author of several scientific papers and book chapters that have been published in refer- eed journals and books. He holds a PhD in Economics from Turku School of Economics u School of Economics Address P.o. Box 170, FI-28100, Finland E-mail: teemu.haukioja@utu.fi ARI KARPPINEN M.Sc. Econ., Researcher University of Turku, Turku School of Eco- nomics, Pori Unit Fields of research interests In the fields of regional economics and develop- ment and multinational enterprises. He is the author and co-author of several scientific papers Address P.o. Box 170, FI-28100, Finland E-mail: ari.karppinen@utu.fi SAKU VÄHÄSANTANEN M. Sc. Econ., Regional Advisor at the Regional Council of Satakunta University of Turku, Turku School of Eco- nomics, Pori Unit Fields of research interests Focused on regional economy, regional competitiveness and resilience. He is the author and co-author in numerous studies about regional economy and business cycles. He has been a project researcher at the University of Turku, Pori Unit, but his current work at the Regional Council concentrates on regional development and studies Address Pohjoisranta 11 C, FI-28100 Pori, Finland E-mail: saku.vahasantanen@ satakuntaliitto.fi