Knowledge-intensive Specialisation and Total Factor Productivity (TFP) in the EU Regional Scope

Kijek, Tomasz; Matras-Bolibok, Anna

doi:10.11118/actaun202068010181

Cited by 10 publications

(13 citation statements)

References 34 publications

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“…A promising approach that aims to boost the regional productivity growth is smart specialisation [57]. For example, Kijek and Bolibok [58] show that specialisation in high-tech manufacturing and high-tech knowledge-intensive services positively affects regional TFP. Application of the fixed-effect model means that significant specific factors influencing TFP for regions are not included in the model.…”

Section: Resultsmentioning

confidence: 99%

Nonlinear Effects of Human Capital and R&D on TFP: Evidence from European Regions

Kijek

2020

Sustainability

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The aim of the paper is to study the impact of human capital and R&D on Total Factor Productivity (TFP) from a nonlinear perspective. In the spirit of the theory of innovation-driven growth and models with thresholds in human capital and low-growth equilibria, we hypothesize that the impact of human capital and R&D on TFP is nonlinear. We also make an attempt to explain complementarities between R&D expenditures and human capital, applying developments in the R&D-based growth models. We use spatial panel data models to estimate the link among R&D, human capital, and TFP across the European regions between 2009 and 2014. Empirical evidence shows that there are decreasing returns to human capital and R&D in the European regional space. Moreover, R&D and human capital turn out to be strategic complements. Finally, regional TFP is found to be positively affected by TFP of neighboring regions.Sustainability 2020, 12, 1808 2 of 14 human capital that cannot be revealed using the standard linear approach. Secondly, we extend our analyses by incorporating the spatial dimension of knowledge spillovers into our empirical model. It gives a new insight into the role of geography in explaining regional TFP differences.Our article is organized in the following way. Part 2 includes the literature review and hypotheses development. Part 3 presents data and empirical models. Part 4 shows empirical findings and discussion, and Section 5 concludes and indicates directions for future research. Literature Review and Hypotheses Development The Link Between R&D and TFP: Some Stylised Facts about Possible NonlinearitiesAs generally accepted, R&D expenditures are targeted toward increasing the stock of knowledge so as to discover new applications and inventions [11]. According to the possibility of the research commercialization into commercial applications, there is a distinction between fundamental research, applied research, and development. Similarly, a distinction is made between R&D directed toward process R&D (the introduction of new methods of production) and product R&D (the invention of new and modified goods). As such, the impact of R&D on productivity is twofold. Firstly, process R&D may increase productivity by enhancing the quality or lowering the average production costs of existing goods. Secondly, product R&D may widen the range of intermediate inputs or final goods available [12].The association between R&D investments and TFP has a conceptual basis in the literature on R&D-based stream of endogenous growth models, which is rooted in the neoclassical growth theory. According to the neoclassical theory, total factor productivity reflects a shift in the production function resulting from technical progress [13]. Since technical progress is closely related to the knowledge stemming from research and development activities, endogenous technical change is regarded to be generated by formal R&D investments. The first generation of R&D-based models assumes that more R&D labor should result in more TFP growth. In other words, th...

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Section: Resultsmentioning

confidence: 99%

Nonlinear Effects of Human Capital and R&D on TFP: Evidence from European Regions

Kijek

2020

Sustainability

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“…They also emphasize the positive relationship between regional specialization based on knowledge-intensive services and productivity. Detailed research in this regard among EU countries was carried out by Kijek and Matras-Bolibok (2020). The analysis of the KIS location patterns in the EU countries was also performed by Merino and Rubalcaba (2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Factor Competitiveness of the Technological Advanced Services Sector in the European Union Countries: A Typological Analysis

Juchniewicz¹

2021

ERSJ

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Purpose:The article presents a significant and current research problem concerning the competitiveness of the technologically advanced services sector. The research aimed to assess the diversity of this phenomenon in the EU Member States and make a typology of Member States based on partial competitiveness indicators. Design/Methodology/Approach: The study uses a universal model of factor competitivenessthe adopted indicators can also be used to assess other sectors of the economy. Typological classification of EU Member States was made using Ward's cluster analysis. Findings: Several countries shape the level of competitiveness of the high-tech services sector in the EU. The first group comprises France, Germany, Spain, the Netherlands, and Italy -it is characterized by a high level of production potential and factor competitiveness indicators. The second group comprises countries with low production potential but with the highest competitiveness indicators among the EU countries. These are Luxembourg, Cyprus, and Malta. Practical Implications: The typology of EU countries in terms of the production potential and competitiveness of the high-tech services sector is a source of information that can shape regional development policies of this sector in individual EU countries. The increasing importance of knowledge-based services in economic development, which includes the studied sector, is a crucial application value of the study. Originality/Value: The level of competitiveness of the technologically advanced services sector and the factors shaping it is a critical issue both in EU policy and in the development of individual countries. Research on the high-tech sector focuses mainly on manufacturing companies (high-tech industry), and to a much lesser extent, on service companies (high-tech services). The considerations contained in the article fill the research gap in this area.

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“…Just as other types of innovation, eco-innovation should be investigated as a spatially embedded process. The tendency observed at the both regional and national levels, that the factors of production are accumulated in spatial proximity, implies that innovation activity is highly concentrated (Kijek and Matras-Bolibok, 2020). Occurrence of positive externalities leads to achievement of higher level of innovativeness only by certain locations, which in turn, contributes to unequal distribution of innovation performance across space, reinforcing the most developed economies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Occurrence of positive spatial externalities is considered to be even more important for eco-innovations implementation compared to other innovations (Horbach, 2014). The observed accumulation of factors of production in a spatial proximity, at the both regional and national levels, contributes to unequal distribution of innovation performance across space (Kijek and Matras-Bolibok, 2020).…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution of Eco-Innovation Performance: Evidence from European Countries

Kijek¹,

Matras-Bolibok²

2020

ERSJ

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Purpose: The aim of the paper is twofold. Firstly, it attempts to assess the spatial pattern of eco-innovation performance in European countries and to identify economies which are efficient in transforming eco-innovation inputs into outputs. Secondly, it endeavours to examine eco-innovation efficiency distribution and existence of spatial externalities across European countries. Design/Methodology/Approach: The sample consists of 21 European countries. We use two eco-innovations inputs and two eco-innovation outputs to measure eco-innovation performance. To calculate eco-innovation efficiency, we apply DEA method. In our research, mapping and Moran's I are employed to find the spatial pattern of eco-innovation performance. Findings: The results show that high and medium-high eco-innovation inputs and ecoinnovation outputs are mainly concentrated in countries in the Northern and West Central Europe, while low and medium-low eco-innovation inputs and eco-innovation outputs are performed in the East Central and Southern European countries. The findings confirm the presence of a negative spatial autocorrelation process in eco-innovation efficiency. Practical Implications: Identification of the eco-innovation distribution in the spatial scope is undoubtedly of high political importance, as it should enable to adjust policy actions aimed at improving eco-innovation efficiency to spatial characteristics of a given economy. Originality/Value: Since the issue of spatial characteristics of eco-innovation is still not sufficiently explored in the relevant literature, our paper attempts to fill a cognitive and methodological gap in the investigation of the spatial aspects of eco-innovation performance in the European countries.

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Knowledge-intensive Specialisation and Total Factor Productivity (TFP) in the EU Regional Scope

Cited by 10 publications

References 34 publications

Nonlinear Effects of Human Capital and R&D on TFP: Evidence from European Regions

Nonlinear Effects of Human Capital and R&D on TFP: Evidence from European Regions

Factor Competitiveness of the Technological Advanced Services Sector in the European Union Countries: A Typological Analysis

Spatial Distribution of Eco-Innovation Performance: Evidence from European Countries

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