50 Years of Regional Convergence in Turkey: New Data Set and Analysis for the Period 1960-2010

Karaca, Orhan

doi:10.17233/sosyoekonomi.343016

Cited by 10 publications

(2 citation statements)

References 18 publications

(6 reference statements)

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“…Logarithms of GDP per capita (PCGDP) and human capital (HC) are added as control variables. The PCGDP variable was measured by using Turkey Regional Economic Dataset (Karaca, 2018). It shows the per capita GDP of regions by NUTS3 level at constant prices (2009 TL).…”

Section: Presents the Results Of The Vif Values Of The Independent Va...mentioning

confidence: 99%

Knowledge Space, Relatedness and Complexity: A Regional Analysis in Turkey

Akgüngör

ABAY

2021

Yildiz Social Science Review

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Regional development policies based on regions’ core strengths is key for innovative and competitive growth. For sustainable growth, each region would discover their own growth paths grounded on their core knowledge base and capabilities. Although there are studies that focus on regional clustering of economic activity and technological dispersion of sectors in Turkey, little is known related to the regions’ potential to attract new technologies based on their core strenghts. There are three objectives of the paper: The first objective is to map knowledge space (relatedness between technologies) in Turkey for 2010 and 2017. The second objective of the paper is to understand relatedness (branching opportunities) and knowledge complexity in Turkey’s regions for 2010 and 2017. The third objective is to demonstrate the relationship of regional innovativeness as measured by patent applications with relatedness and knowledge complexity across Turkey’s NUTS3 regions. The study uses data from the European Patent Office (EPO) REGPAT Database (downloaded in January 2020). In order to map Turkey’s knowledge space, we use technological relatedness between patent classes. The knowledge space is a network where nodes represent technological categories and lines represent links between each pair of technology. Relatedness of the regions is operationalized by relatedness density. Knowledge complexity is operationalized by knowledge complexity index. We use regression analysis to understand the correlation of patent applications with regions’ relatedness density and knowledge complexity. As a control variable, the regression model uses diversity variable that is operationalized by the number of technological classes in which each city has relative technological advantage. The analysis demonstrates that knowledge space in Turkey became denser between 2010 and 2017 and there are variations across regions with respect to branching opportunities and knowledge complexity. Diversity and relatedness density are positively correlated with patent applications while complexity does not have a correlation with regional innovativeness.

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Section: Presents the Results Of The Vif Values Of The Independent Va...mentioning

confidence: 99%

Knowledge Space, Relatedness and Complexity: A Regional Analysis in Turkey

Akgüngör

ABAY

2021

Yildiz Social Science Review

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“…The dependent variable (PATPER) and smart specialization variables (ARD and KCI) are generated using patent data for regions from the OECD-REGPAT January 2020 Edition database (OECD, 2020). PCGDP data at the NUTS-3 level (2009 constant prices) are from Turkstat and are compiled by Karaca (2018). 3 The UNI variable is from the Turkish Council of Higher Education, and the TPARK variable is from the Republic of Türkiye's Ministry of Industry and Technology (2021).…”

Section: Data Sourcesmentioning

confidence: 99%

Correlation of Relatedness and Complexity with Patent Applications: A Regional Analysis from Türkiye

Akgüngör

ABAY

2023

Verimlilik Dergisi

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Purpose: The aim of the paper is to test the hypothesis that regional innovativeness is positively correlated with relatedness (whether a potentially new industry is related to preexisting skills of the region) and complexity (unique and hard to imitate technologies in the region). Methodology: Relationship between innovativeness with relatedness and complexity is investigated with panel regression analysis. The dependent variable is innovativeness (measured by patent applications) and independent variables are average relatedness density and regional knowledge complexity index. We use per capita income, population and universities as control variables. The data is from OECD-REGPAT database January 2020 Edition (OECD, 2020) for the years 1978–2017 covering Turkey’s NUTS-3 regions. Findings: The results confirm that innovativeness and relatedness correlate with each other while complexity coefficient is not statistically significant. Originality: The originality of the paper is that we demonstrate the significance of connection between regional innovations and the extent to which potential new activities are technologically related to a region’s portfolio of activities. The results demonstrate that regional policies with a distinct focus on working with localized preexisting set of skills and capabilities work well in enhancing regional innovations. Conversely, technological uniqueness of the regions does not correlate with innovations.

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Technological paths and smart specialization: analysis of regional entry and exit in Turkey

Abay,

Akgüngör

2023

Asia-Pac J Reg Sci

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50 Years of Regional Convergence in Turkey: New Data Set and Analysis for the Period 1960-2010

Cited by 10 publications

References 18 publications

Knowledge Space, Relatedness and Complexity: A Regional Analysis in Turkey

Knowledge Space, Relatedness and Complexity: A Regional Analysis in Turkey

Correlation of Relatedness and Complexity with Patent Applications: A Regional Analysis from Türkiye

Technological paths and smart specialization: analysis of regional entry and exit in Turkey

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