The measurement of human development using the Ward method of cluster analysis

Majerová, Ingrid; Nevima, Jan

doi:10.14254/2071-8330.2017/10-2/17

Cited by 25 publications

(21 citation statements)

References 18 publications

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“…In this way, the authors examined hard data values based on statistical indicators and the WEF Global Survey results in the next step of the research. They created clusters out of the data from all countries with IBM SPSS Statistics 26 programme with Ward method founded on the twelve competitiveness pillars (Majerova and Nevima, 2017). They inspected which clusters the analysed countries got into, and added to that, they compared this grouping with the GDP-based clusters created by WEF.…”

Section: Methodsmentioning

confidence: 99%

The Role of Innovation and Human Factor in the Development of East Central Europe

Tóth¹,

Juhász²,

Kálmán³

2020

Montenegrin Journal of Economics

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Schumpeter's one-hundred-year-old modernised theory of economic development is once again having a renaissance. The main factors of this are innovation and capital, which Porter expanded using further factors. In this report the following six countries were examined: Montenegro, Serbia, Russia, the Czech Republic, Poland and Hungary. Between 2007 and 2017, the roles that technological innovation, human factors and higher education played were examined. Indices used in the analysis were expanded using factors added to Schumpeter's theory by Porter, viz., economic growth, the development of the information technology sector, higher education, productivity and innovation, and they give an overview of the modern innovation theories. Due to the objectiveness of statistical indicators, it was these which were predominantly included in the report. The aim of the study was to examine will a given country be likely to have an economic development if the country makes the most of the opportunities that innovation and higher education present.JEL classification: I25, O11, O30, O47, R11

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Section: Methodsmentioning

confidence: 99%

The Role of Innovation and Human Factor in the Development of East Central Europe

Tóth¹,

Juhász²,

Kálmán³

2020

Montenegrin Journal of Economics

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“…The clusterization indicated significant differences in socio-economic development and its result was one of the criteria for the approval of project funds. The comparison of regions in Visegrad Group Plus countries was performed according to the Human Development Index in (Majerova & Nevima, 2017). A combination of regression analysis, Ward and K-means methods was performed to cluster the regional labor and vocational training market in Germany (Kleinert, et al, 2018;Blien, et al 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Rezankova (2014) applied a clustering algorithm on data of enterprise, macroeconomic and economic activity by age groups to European countries, including Lithuania. Majerova and Nevima (2017) applied a hierarchical clustering method by measuring the distance between the clusters as the squared Euclidian distance and Ward method on the human development index with a z-score normalization technique on the data. Augustysnski and Laskos-Grabowski (2018) compared various clustering algorithms and determined that the best results were achieved on their dataset by applying a compression-based dissimilarity measure.…”

Section: Introductionmentioning

confidence: 99%

“…In most research, clusterization was performed for the indicators of a specific year. In the second step, the change between the clusters is usually analyzed (Burinskienė & Rudzkiene, 2004;Majerova & Nevima, 2017). The proposed approach enables clusters of similar municipalities to be determined with respect to the time series of socioeconomic indicators and the relationships between the indicators.…”

Section: Introductionmentioning

confidence: 99%

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Application of Multivariate Time Series Cluster Analysis to Regional Socioeconomic Indicators of Municipalities

Gružauskas

Čalnerytė

Fyleris

et al. 2021

Real Estate Management and Valuation

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The socio-economic development of municipalities is defined by a set of indicators in a period of interest and can be analyzed as a multivariate time series. It is important to know which municipalities have similar socio-economic development trends when recommendations for policy makers are provided or datasets for real estate and insurance price evaluations are expanded. Usually, key indicators are derived from expert experience, however this publication implements a statistical approach to identify key trends. Unsupervised machine learning was performed by employing K-means clusterization and principal component analysis for a dataset of multivariate time series. After 100 runs, the result with minimal summing error was analyzed as the final clusterization. The dataset represented various socio-economic indicators in municipalities of Lithuania in the period from 2006 to 2018. The significant differences were noticed for the indicators of municipalities in the cluster which contained the 4 largest cities of Lithuania, and another one containing 3 districts of the 3 largest cities. A robust approach is proposed in this article, when identifying socio-economic differences between regions where real estate is allocated. For example, the evaluated distance matrix can be used for adjustment coefficients when applying the comparative method for real estate valuation.

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“…She has incorporated the indicator of health personnel as one of 12 variables of index. Majerova and Nevima (2017) focused on the analysis of socio-economic development in V4+ countries using the cluster Ward method and divided the regions to three groups according to the potential for development. Verner and Chudarkova (2013) have dealt with the relationship of human capital (including the health personnel) and economic growth in Visegrad group countries and considered that increases in human capital will tend to economic growth and thus will contribute to the improvement of national competitiveness.…”

Section: Introductionmentioning

confidence: 99%

Socio-economic Development and its Impact on Health Personnel in Regions of Visegrad Group Plus Countries

Majerová

2019

Review of Economic Perspectives

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The quality of life and economic development of economies and their regions is influenced by socio-economic indicators such as unemployment, healthcare and education, environmental issues or household equipment. The question remains whether these indicators within regions can also influence each other and whether they have positive or negative effects. The paper aims to describe the impact of the selected socioeconomic indicators on health personnel, which was also divided from the gender perspective. For this purpose, the regions of the Visegrad Group Plus countries at NUTS II level were selected. The research was performed in the period from 2004 to 2013. According to our results based on the Generalized Method of Moments with panel data, we found out that a change in household income and a change in the number of vehicles in the monitored regions have the greatest (positive) impact on staff in hospitals. The research may contribute to the discussion about levels of regional economic development and the related competitiveness in terms of the socio-economic approach, and it may also provide a basis for further discussion on its measurement and use of appropriate indicators.

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The measurement of human development using the Ward method of cluster analysis

Cited by 25 publications

References 18 publications

The Role of Innovation and Human Factor in the Development of East Central Europe

The Role of Innovation and Human Factor in the Development of East Central Europe

Application of Multivariate Time Series Cluster Analysis to Regional Socioeconomic Indicators of Municipalities

Socio-economic Development and its Impact on Health Personnel in Regions of Visegrad Group Plus Countries

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