The purpose of the study is to construct smart specialization indicators for LAU-1 regions in Finland. Established indices are based on indicators on region’s revealed comparative advantage, and the degree of diversification in its sub-regional industrial structure. Further, we introduce a measure that can be used to assess the socio-economic importance (employment) of diversification and specialization for a region. The data of indices is based on the Statistic Finland (2015) with Local Administrative Unit level 1 (LAU1), 70 regions in Finland. The potential S3 Indices measured here reveal sub-region’s Smart specialization position within 70 sub-region in Finland in 2015. The common economic knowledge states that manufacturing industries are the most export-oriented, highly productive, and thus, can approximate the region’s success in international trade and competitive advantages. The study is based on smart specialization indices: the Herfindahl-Hirschman Index for regional resilience (HHI), regional relative specialization index (RRSI) based on Balassa-Hoover Index (B-H), and the relative employment volume index in manufacturing sector (LIMI). By index examination, we can obtain knowledge about a region’s smart specialization status and potentials. Results reveal that, firstly, each sub-region has its own smart specialization characters with different risk profile. Secondly, specialization strategy (RRSI) in smart specialization has yielded more secure strategy than sub-regional resilience strategy in Finland. Sub-regions like Helsinki and Tampere have similar industrial structure like Finland as whole and they are resilient: they will benefit from nation-wide economic and industrial policy, and they have good ability to resist economic shocks. Our study reveals that there are some other similar smaller (LAU-1) sub-regions in Finland – like Rauma. As such, this kind of research based basic information is critical to been taken into account while constructing sustainable strategies for regional development. Similar calculations can be performed for all regions in Europe. DOI: http://dx.doi.org/10.5755/j01.eis.0.12.21872

中文翻译：

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确定具有全球竞争力的行业的弹性，专业化和劳动强度的智慧区域–芬兰LAU-1地区的检验

研究的目的是为芬兰的LAU-1地区构建智能的专业化指标。既定的指标是基于有关该地区已显示出的比较优势以及该地区次区域产业结构多元化程度的指标。此外，我们引入了一种可用于评估区域多元化和专业化的社会经济重要性（就业）的措施。指数的数据基于芬兰统计局（2015年）的本地行政单位级别1（LAU1），位于芬兰的70个地区。此处测量的潜在S3指数揭示了该区域在2015年在芬兰70个子区域中的Smart专业化地位。共同的经济知识表明，制造业是最注重出口，生产力最高的，因此，可以估计该地区在国际贸易中的成功和竞争优势。该研究基于智能专业化指数：赫芬达尔·赫希曼区域弹性指数（HHI），基于Balassa-Hoover指数（BH）的区域相对专业化指数（RRSI）和制造业相对就业量指数（LIMI） 。通过索引检查，我们可以获得有关区域智能专业化状态和潜力的知识。结果表明，首先，每个子区域都有其自己的智能专业特征，具有不同的风险特征。其次，智能专业化中的专业化战略（RRSI）比芬兰的次区域适应力战略产生了更安全的战略。像赫尔辛基和坦佩雷这样的次区域拥有与整个芬兰类似的产业结构，并且具有韧性：他们将受益于全国范围的经济和产业政策，并且具有抵抗经济冲击的良好能力。我们的研究表明，芬兰还有其他一些类似的较小（LAU-1）子区域，例如劳马。因此，在构建区域发展的可持续战略时，必须考虑到这种基于研究的基本信息。可以对欧洲所有地区执行类似的计算。DOI：http：//dx.doi.org/10.5755/j01.eis.0.12.21872 在构建区域发展的可持续战略时，必须考虑到这种基于研究的基本信息。可以对欧洲所有地区执行类似的计算。DOI：http：//dx.doi.org/10.5755/j01.eis.0.12.21872 在构建区域发展的可持续战略时，必须考虑到这种基于研究的基本信息。可以对欧洲所有地区执行类似的计算。DOI：http：//dx.doi.org/10.5755/j01.eis.0.12.21872