Cluster Analysis of Countries Inequality due to IT Development (original) (raw)
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Cluster Analysis of Countries Inequality Due to IT Development Through Macros Application
Information and Communication Technologies in Education, Research, and Industrial Applications, 2020
The choice between economic efficiency and social equity has become a key objection in economic development, since in the current economic system, which has become close to the Pareto optimum, the achievement of both of these goals is mutually exclusive. There is only one way to reach both of these goalsthe fundamental change of current system of economic relations and getting access to new curves of production capabilities, which may become quite real within development of Industry 4.0 and 6th technological wave. Nevertheless, nobody can predict the social impact of Industry 4.0 on society, which in the context of future technological changes transforms into Society 4.0. The purpose of this paper is to prepare cluster analysis of countries inequality due to IT development using software package. We researched impact of gross capital formation, research and development expenditure to create innovations, intellectual property and hightechnology exports on inequality of countries using principal component analysis based on open data 2012-2015. We found 4 main clusters of 45 countries which have convergence and divergence attributes due to IT development. It was also revealed the countries which had inequality due to other reasons which are not connected with IT development.
ANALYSIS OF THE DIGITAL ECONOMY AND SOCIETY INDEX (DESI) THROUGH A CLUSTER ANALYSIS
Trakya Üniversitesi Sosyal Bilimler Dergisi, 2021
This study has two main goals. The first one aims to determine how the European Union countries are clustered according to The Digital Economy and Society Index (DESI) 2020 data. The second one aims to determine whether there is a similarity between the DESI cluster of the European Union countries and the social welfare regime classification. METHODS: In the research, the cluster method was used in accordance with DESI 2020 data. RESULTS: Technological and digital investments and initiatives of countries have clustered the European Union countries in 4 different groups. The countries clustered according to DESI data are shaped for investments and spending for digitalization within the scope of sub-dimensions of DESI. In this context, the welfare regimes applied by the countries affect the spending for digitalization. CONCLUSIONS: According to The Digital Economy and Society Index (DESI) 2020 data has proved that there is a similarity between the classification of the European Union countries according to their welfare regimes and digitalization.
ICT in Emerging Countries and Turkey: Cluster Analysis Approach
2016
Changes that came with the new economic order have affected the whole globe. Especially the developments in information and communication technologies (ICT) caused the nations to concentrate in this field to increase their competitiveness. The emphasis placed by emerging countries that have low income but a high development potential on ICT is increasing every passing day, similar to all other countries. In the present study, it was aimed to cluster and analyze emerging economy countries, for which data was available, based on similar macroeconomic variables and information and communications technologies variables and to determine the place Turkey would occupy in this cluster. 2013 data for twenty two countries and twelve variables were analyzed using hierarchical cluster analysis. The analysis based on macroeconomic variable demonstrated that the countries were organized in four clusters and Turkey and many European emerging countries are in the same cluster. Based on the analysis...
Prace Komisji Geografii Przemyslu Polskiego Towarzystwa Geograficznego, 2013
Industrial activities have a notable percentage in whole national economies. Thus, it is necessary to follow the development of the industrial activities to be able to review economic development. Identification of similarities or differences among countries provides the ability to notice more clearly the level of regional development and its problems. However, it makes complex subject with a large number of data and with different national data methodology for each country and year. Statistical analysis is a proven tool to make it easier. The goal was to follow the developments since it has the ability to summarize complex data. In this study, we found out similar characteristics among EU Countries (except Croatia which joined EU in 2013) and Turkey by using export and import rates, industrial production index, recent prices, percent of GDP of industry parameters in industrial sector variables. Squared Euclid distance, Pearson proximity matrix and Ward's method were used to calculate distances between different countries variables and to find out country groups which have similar development characteristics. The analyses were supported with dendogram and maps.
Characterizing the level of economic development of countries
Proceedings of the International Conference on Applied Statistics, 2019
The main purpose of this paper is to provide an objective analysis of the economic development level of countries. This is done by measuring it through a new index and by classifying the countries in an optimal number of clusters, each group characterizing different levels of economic development. The proposed methodology is based on three steps: creating a composite index (by applying the principal component analysis), establishing the optimal number of development groups (based on the number of principal components and on the hierarchical clustering) and clustering countries into them (with the help of k-means analysis). Therefore, this approach solves the difficulty of classifying the countries, complication that is mentioned in the specialized literature. Also, the paper creates a better understanding on the economic development level of countries, as, usually, the papers examine the economic growth level of countries. The analysis is conducted at the level of 60 countries for y...
An Empirical Analysis about Technological Development and Innovation Indicators
Procedia - Social and Behavioral Sciences, 2015
Researches on technological development and innovation indicators that are used as different criteria for measurement such as multivariate statistics methods have increased rapidly in the field of social sciences since 1990s. The concept of indicators is an interesting field of science, which are used to inform us about things that are difficult to measure. Indicators for technology development and innovation may be defined as statistics, which measure quantifiable aspects of technological development and innovation creation. In this research, indicators help us to describe technological development and innovation clearly and enable us to have a better understanding of the impact of policies and programs on technological development and innovation and on the society and the economy in general. The objective of the present paper is to examine whether technological development indicators, which are used as a proxy for economic growth, innovation and the development level of countries, are influenced by the used variables in this analysis. The study is conducted by using a very large data set. It covers a monthly time period of 1996 and 2011. The study includes a variety of variables such as research and development expenditure (RDE), high-technology exports (HTE), long-term unemployment (LTU), patent applications-residents (PA), patent applications-nonresidents (PAF), health expenditure (HE), GNI per capita (PPP), share of women employed in the non-agricultural sector (SWE), stocks traded (ST), internet users (IU), scientific and technical journal articles (STJ). The empirical results which were obtained by using MDS (Multidimensional Scaling) and HCA (Hierarchical Cluster Analysis) methods suggest that the variables of RDE, PA, HE, PPP, SWE, IU and STJ have significant impacts on technological development and innovation and should be reviewed all together.
Uncovering the Innovation Productivity of Asian Countries: A Cluster Analysis Approach
Journal of Educational and Human Resource Development, 2018
The interplay of quality education in science and mathematics, university-industry collaboration in research and development, and a number of patents acquired dictates a country's level of innovation in the global economy. The study explored the innovation efficiency of thirty Asian countries with an attempt to group the nations with similar characteristics and uncover essential associations. Some variables from the Global Competitiveness Index 2017-2018 and the Global Innovation Index 2018 were used as multivariate inputs to the cluster analysis algorithm. Results revealed that there were three clusters of countries derived. Countries with low innovation, six in number, had small scores in the different variables that need to be improved. On the other hand, nations with high innovation, four in number, had the best scores in all the indicators. Further, the twenty countries with average innovation have to continue boosting its quality of Mathematics and Science education and university-industry partnership. Regression models of the different clusters were derived to supplement the results. Much is to be done on the patenting to be at par with the highly innovative countries in the world. In addition, to lessen the innovation gap, nations with high innovation may help the countries with low innovation productivity which is possible due to existing regional intergovernmental organizations in Asia like Association of Southeast Asian Nations (ASEAN), Southeast Asian Ministers of Education Organization (SEAMEO), and the like.
Clustering of Countries in Global Landscape of Knowledge Economy Development
SCIENTIFIC BULLETIN OF POLISSIA
Urgency of the research stems from the need to analyse the complex transformation processes in the world economy system, associated with transition to knowledge economy (KE). Identifying the distinctive features of the countries at the national level is a key prerequisite for generalization of these processes at the global level. Target setting. In the context of changes in the world economy, an assessment of the level and dynamics of KE development parameters in general and by individual components is critical for every country. International comparative analysis allows assessing the global landscape of KE development as well as identifying its key factors. Actual scientific researches and issues analysis. Prominent Ukrainian and foreign scientists-economists formed the theoretic and methodological bases for the study. Numerous studies prove the relevance of the selected topic and significance of the identified scientific and practical problem. Uninvestigated parts of general matters defining. Global landscape of KE development has been studied in a fragmented manner. In particular, it is related to search for its structure and identification of the key factors of knowledge management. The research objective. To implement clastering of countries using KE development parameters and to identify its key factors. The statement of basic materials. Clustering of countries has been implemented using KE development parameters in 2010 and 2014, including such components as education, science, information and communication technologies, manufacturing technologies and innovative business. Based on the assessment of differences between clasters, we have identified the key factors of KE formation. Boundary values of these parameters by clusters enabled us to evaluate a position of Ukraine in the global landscape. Conclusions. Study outcomes characterize the structure and heterogeneity of the global landscape of KE development. We have identified its key factors, boundary values, which are critical while assessing a position of particular countries and identifying target indicators of their strategies.
From industrial clusters to clusters of technology, contexts and prerequisites; Case study of Iran
2018
Given that a significant portion of the economic activities are carried out by small and medium enterprises in most countries of the world, government will always support them logically by different policies in order to enhance their competitiveness so that they can be active in the legal competitiveness so that they can be active in the legal competitive market themselves. According to research, cluster making in term of industrial clusters is one of the methods which have gained useful experience in increasing competitive capability of small and medium enterprises. The fact that all economic activities are based on knowledge, but knowledge-based services and industries are symbols of acquiring knowledge and technology, regarded as knowledge, and are considered as indicators of measuring academic and technological and capability of a country. Modern and advanced technologies lead to increasing the efficiency, productivity and competitiveness of a country. In this paper, in addition of introducing clusters and cluster making, will investigate and review the ways of formation, evolution and all kinds of cluster; cluster evolution suggests that in addition of longitudinal and transverse relationship among enterprises in term of industrial cluster, a new approach has been formed for clustering based on a new attitude in which there is a close relationship among enterprises and their way of organizing the space. This space arrangement allowing providing knowledge-based industries concentration, are known as technology clusters. Therefore, we will consider the discrimination among industrial and technology cluster and identifying the prerequisites and infrastructures of forming a technology cluster. So, indicators and requirements of a technology cluster are known with review of research and survey among the related experts and active clusters in Iran which are the same with technology cluster to some extent. In the following, using multi-criteria decision making, TOPSIS of the current clusters are ranked; thus best Tech clusters are identified and finally technology will provide solutions for the development of industrial clusters.
Clustering of high-tech industrial production: Factors and trends
Journal of Applied Engineering Science, 2018
The purpose of the study is to identify trends and future models for the development of regional industrial clusters in the new technological order. Paper deals with the dynamics of the formation of industrial clusters, the main regularities of the development of high-tech industries are revealed. Particular attention is given to localization of high-tech industries within the framework of regional industrial clusters; scientifi c novelty contains a model of inter-sectoral interaction in the conditions of the innovation economy. The result of the analysis shows, that successful cluster initiatives combine a developed innovative core, an essential industrial basis and a signifi cant number of small and medium-sized forms of innovative entrepreneurship.