Laszlo Szerb (p), Zsófia Fehér (p), Krisztina Horváth

Our study presents a new index, called Combined Regional Competitiveness Index (CRCI), measuring the competitiveness of 151 European Union regions. The aim of the new index is to explain differences in economic growth. While it is generally believed that the basic unit of territorial competitiveness is the firm, existing country level and regional competitiveness measures focus on the widely interpreted institutional aspects of competitiveness and neglect individual (firm) level characteristics (Annoni 2016, Huggins 2003, 2011). We have created a competitiveness index that combines together the firms’ individual competencies and the regional institutional factors in a systemic way. These CRCI pillars and sub-indices reflect not only to theoretical constructs but also to the availability of data.
The new index comprises four sub-indices, nine pillars and 18 variables each representing a different aspect of the regional competitiveness. Intensity of competition sub-index reflects to two types of competitive pressure one that is coming from existing businesses and the other is deriving from new entry. Growth and internationalization strategy includes the firms’ generally interpreted growth and international aspirations. In the Human capital sub-index, we incorporate the businesses’ level of education and training and the entrepreneurial abilities of the leader of the business. The Innovation sub-index reflects to the firms’ renewal capabilities. It measures the ability of create new technology, new product, and how firms can absorb existing technology.
The cluster analysis shows that the three groups of the 151 EU regions prevail a wide varieties of competitiveness profile based on the ten pillars of competitiveness. The regression analysis shows that the regional employment rate of CRCI has a positive effect on the gross added value per employee in the given region. We can find that CRCI scores explain regional growth both in terms of value added and employment. Moreover, institutional factors’ coefficients are found to be significantly positive in terms of any performance metrics, individual factors have positive but insignificant effects.

Literature:

Huggins, R., & Williams, N. (2011). Entrepreneurship and regional competitiveness: The role and progression of policy. Entrepreneurship & Regional Development, 23(9-10), 907-932.

Huggins, R. (2003). Creating a UK competitiveness index: regional and local benchmarking. Regional Studies, 37(1), 89-96.

Daniela Nepote (p), Filomena Berardi

The economic and financial crisis that hit the world economy in 2008, has led Europe to question itself on the redefinition of the role of companies and their positioning within the economic ecosystem in relation to restarting economies. Amongst the innumerable economic policy tools to promote economic development and to foster growth, the majority of current cluster policies in European countries have targeted emerging industries and emerging technologies. This is especially the case when those policies are strongly related to innovation and R&D support, as well as to Smart Specialization Strategies (S3). Accordingly, the most important measures for clusters support refer to the engagement of SMEs, research and development, and internationalization. It is important to note that interventions may have an effect on economics which are not easy to measure. In fact, according to the “regional innovation paradox” (Oughton C., Landabaso M., & Morgan k., 2002) innovation does not translate automatically and everywhere in better economic performance, posing a problem of a reliable performance’s evaluation. Moreover, quantitative attempts to measure the impact of innovation may be made difficult by the well-known problems of defining innovation, and the obvious pitfalls of using R&D expenditure as a proxy (Boschma and Frenken, 2006). The final scope of this paper is to outline a number of reasons for which a comprehensive approach on cluster evaluation is needed and therefore to elaborate a number of possible strategies to define it. This paper highlights the development of the cluster strategy in Piedmont Region as a case study.

Mitsuhiko Kataoka (p), Ram Basnet

The dairy sector in Nepal has a strong backward linkage in the rural household sector which improves the utilization of local resources and reduces the poverty. Given the vital economic role, the government of Nepal has implemented several dairy development policies for decades. In 1969, it established the public dairy enterprise to promote the industrial development in Nepal’s dairy sector and the dairy production spread across the country. At present, many non-governmental firms operate dairy productions.
We use Data Envelopment Analysis (DEA) to measure the relative efficiencies of Nepal’s dairy firms in the operational performance by location, ownership, and size. We utilize the output-oriented DEA models that employs multiple variables of inputs (physical assets, labour costs, and intermediate input values,) and outputs (total sales and revenues) from National Census of Manufacturing Establishments 2011-2012.
Our preliminary study found that majority of the firms performs far below the efficient managerial performance and operation scale. Classifying all firms by Urban-Rural location, the firms in Kathmandu Valley (Urban) operate show the far higher operation efficiency than non- Kathmandu Valley (Rural). The firms located along the border provinces with India also shows the relatively higher operation efficiency than average.
The non-public firms operate more efficiently in managerial performance than the public firms, on average. All public (non-public) firms operate more efficiently by decreasing (increasing) the operation size. Moreover, our inequality decomposition analysis showed that the interfirm inequality in overall technical efficiency is influenced more by the managerial factors than the scale factors. The promotion of the technical and managerial assistance can be a major policy directions to reduce the interfirm inefficiency gap in operation.