The Use Of Cluster Analysis To Assess The Demographic Potential Of Russian Regions

Shubat, Oksana; Bagirova, Anna; Shmarova, Irina

doi:10.7148/2017-0053

Cited by 3 publications

(4 citation statements)

References 4 publications

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“…Regions may be clustered by income level or TFR, with subsequent time series analysis inside each typified group. We have partly used such an approach toward time series analysis in our previous research (Shubat et al 2016;Shubat et al 2017). The results we obtained, which showed a lack of relationship between fertility and income, define the expedience of differentiating groups beforehand.…”

Section: Discussionmentioning

confidence: 99%

Econometric Modelling Of Time Series Relationship Between Fertility And Income For The Russian Population: Methodological Issues

Shubat¹,

Bagirova²

2018

ECMS 2018 Proceedings Edited by Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen

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Finding determinants of demographic processes is a highly topical issue in countries with negative demographic trends. Our research was aimed at studying the relationships between fertility and income indicators in Russia. The period under review was 2000 to 2016. To explore the correlation between the time series, we used the methodology of estimating trend deviation. We applied analytical smoothing to model trends, estimating regression models. To assess the strength of relationship between the time series, we analysed correlation between regressions' residuals. The results of our analysis showed no relationship between people's incomes and fertility rates. The research we carried out into time series dynamics did not confirm the results of other studies based on static data. Accordingly, this raises questions about the methodology for analyzing the relationship between dynamic processes with a high volatility of input data. Evidently to receive reliable and stable results, multidimensional analysis methods should be integrated into the study of relationships between dynamic time series, including preliminary multi-dimensional data classification. This will enable carrying out analysis on homogenous territorial or temporal segments, which would be more methodologically sound.

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Section: Discussionmentioning

confidence: 99%

Econometric Modelling Of Time Series Relationship Between Fertility And Income For The Russian Population: Methodological Issues

Shubat¹,

Bagirova²

2018

ECMS 2018 Proceedings Edited by Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen

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“…Cluster analysis has been widely used by many researchers. Shubat et al (2017) used cluster analysis to assess the demographic potential of Russian regions. Zhang and Li (2014) used cluster analysis to identify Chinese provinces and to investigate the qualitative characteristics of the given populations.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…Cluster analysis allows us to identify the groups of regions that have the best or worst conditions to develop their demographic potential (Shubat et al, 2017). Three years from the time series from 2000 to 2016 were chosen to be analysed.…”

Section: Assessment Of Similarities Across the V4 Regions Using A Cluster Analysismentioning

confidence: 99%

Socio-Demographic Potential of Human Resources in the Visegrad Regions

Koišová

Masárová

Ivanová

2021

Journal of Business Economics and Management

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Human resources are the key factor in the development of countries and regions. They affect the prosperity and competitiveness of the countries and regions. The purpose of the study was to assess the human resource potential in the V4 countries and to identify the groups of regions that share similar socio-demographic characteristics. Changes in these characteristics were tracked in the years of 2000, 2008, 2016 at the NUTS II level. Hierarchical cluster analysis was utilised as the main research method in order to identify the groups of regions where measures need to be taken to maintain regional development. We found out that the best conditions for the potential of human resources are concentrated mainly in metropolitan areas. On the other hand, there are regions that do not make sufficient use of their potential in the area of human resources development, e.g. the region of Východné Slovensko. It is the region with future prospects of human resource potential In the future it is necessary to remove shortcomings and low human resources potential in lowperforming regions.

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“…It should be noted that the latter situation can be found only in certain countries: for instance, some researchers point out differences in the demographic development between northern and southern Italy (Pastuszka 2017), provinces of China (Wang et al 2017), and Russian regions (Shubat et al 2017). In Russian regions, demographic indicator values vary greatly (see table 1).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Modelling Of The Russian Demographic Space

Bagirova¹,

Shubat²,

Akishev³

2018

ECMS 2018 Proceedings Edited by Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen

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Demographic processes are extremely difficult to manage and require the different methods of their research. Our studies aimed at modelling the demographic space of Russian regions by using fuzzy clustering. Our analysis is based on the indicators of the regions' demographic potential. We used our own original methodology combining the statistical procedures of fuzzy clustering and expert survey data. We considered indicators characterizing the reproduction potential and variables characterizing the potential quality of the future population. As a result of fuzzy clustering, five clusters were formed. Our experts evaluated the reproduction potential and the quality of the future population for each cluster. The data for each region were used to calculate their reproduction potential and the quality of their future population. In comparison to hard clustering, fuzzy clustering enhances the flexibility of evaluation: our assesments of each region do not depend exclusively on the potential of the nearest cluster, as we also take into consideration the region's possible similarities with other neighbouring clusters with different potential. Such modelling allows us to identify those Russian regions that could be considered as 'growth points' in the implementation of demographic policy.

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The Use Of Cluster Analysis To Assess The Demographic Potential Of Russian Regions

Cited by 3 publications

References 4 publications

Econometric Modelling Of Time Series Relationship Between Fertility And Income For The Russian Population: Methodological Issues

Econometric Modelling Of Time Series Relationship Between Fertility And Income For The Russian Population: Methodological Issues

Socio-Demographic Potential of Human Resources in the Visegrad Regions

Fuzzy Logic Modelling Of The Russian Demographic Space

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