Analysing the Data on Incomes in the Regional Context by the Principal Component Method
The article focuses on solving the task of analysing statistical data on households’ income and their main components in absolute and relative units. We took into account a number of additional indicators, including social transfers, and applied the principle component method. The analysis’ purpose...
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Russian Academy of Sciences, Institute of Economics of the Ural Branch
2019
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ftdoajarticles:oai:doaj.org/article:f099b0ca3a824b47a1f911660e664757 2023-08-27T04:10:23+02:00 Analysing the Data on Incomes in the Regional Context by the Principal Component Method Baryy Galeevich Ilyasov Elena Anatolyevna Makarova Elena Shavkatovna Zakieva Emma Salavatovna Gizdatullina 2019-06-01T00:00:00Z https://doi.org/10.17059/2019-2-22 https://doaj.org/article/f099b0ca3a824b47a1f911660e664757 EN RU eng rus Russian Academy of Sciences, Institute of Economics of the Ural Branch http://www.economyofregion.com/data/jarticles/3181.pdf https://doaj.org/toc/2072-6414 https://doaj.org/toc/2411-1406 doi:10.17059/2019-2-22 2072-6414 2411-1406 https://doaj.org/article/f099b0ca3a824b47a1f911660e664757 Экономика региона, Vol 15, Iss 2, Pp 601-617 (2019) population income principal component method sample clustering weight coefficient coeffici ent of information content integrated sign scatterplot clusters of regions imitating dynamic model Regional economics. Space in economics HT388 article 2019 ftdoajarticles https://doi.org/10.17059/2019-2-22 2023-08-06T00:45:29Z The article focuses on solving the task of analysing statistical data on households’ income and their main components in absolute and relative units. We took into account a number of additional indicators, including social transfers, and applied the principle component method. The analysis’ purpose was to identify patterns of «clustering». The first step was to identify clusters of the Russian Federation regions, which vary in terms of population’s revenue structure taking into account the volumes of subsidies and subventions. The second step was to determine the generalized characteristics of the revealed clusters and their representation in a form of clustering rules. We have shown that the cluster structure of the households sector at the regional level is sufficiently polarized. We have revealed the small clusters of regions characterized by a high level of households’ monetary income and relatively large population (e. g. Moscow, Khanty-Mansi Autonomous Okrug). Alternatively, there are sufficiently inhabited clusters of regions with both a considerable volume of non-monetary income in a form of food combined and the low or average level of monetary income and small positive dynamics of population (Bryansk, Kursk Oblasts). On the other hand, in the regions with a relatively low monetary income, the revenue structure includes a high share of natural supplies in the form of food (for example, Republic of Dagestan and Republic of Ingushetia). Moreover, in the regions with a high monetary income, there is a small share of the raised funds and spent savings in revenue structure (Yamalo-Nenets Autonomous Okrug and others). We have constructed clusters of regions and established their quantity, structure and generalized characteristics presented in the form of clustering rules. We used that data for defining structural and parametrical characteristics when developing a dynamic model of the households sector and the module of intellectual management. These dynamic model and the module became a part of the system of ... Article in Journal/Newspaper khanty khanty-mansi nenets Nenets Autonomous Okrug Yamalo Nenets Yamalo-Nenets Autonomous Okrug Mansi Directory of Open Access Journals: DOAJ Articles Economy of Region 15 2 601 617 |
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topic |
population income principal component method sample clustering weight coefficient coeffici ent of information content integrated sign scatterplot clusters of regions imitating dynamic model Regional economics. Space in economics HT388 |
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population income principal component method sample clustering weight coefficient coeffici ent of information content integrated sign scatterplot clusters of regions imitating dynamic model Regional economics. Space in economics HT388 Baryy Galeevich Ilyasov Elena Anatolyevna Makarova Elena Shavkatovna Zakieva Emma Salavatovna Gizdatullina Analysing the Data on Incomes in the Regional Context by the Principal Component Method |
topic_facet |
population income principal component method sample clustering weight coefficient coeffici ent of information content integrated sign scatterplot clusters of regions imitating dynamic model Regional economics. Space in economics HT388 |
description |
The article focuses on solving the task of analysing statistical data on households’ income and their main components in absolute and relative units. We took into account a number of additional indicators, including social transfers, and applied the principle component method. The analysis’ purpose was to identify patterns of «clustering». The first step was to identify clusters of the Russian Federation regions, which vary in terms of population’s revenue structure taking into account the volumes of subsidies and subventions. The second step was to determine the generalized characteristics of the revealed clusters and their representation in a form of clustering rules. We have shown that the cluster structure of the households sector at the regional level is sufficiently polarized. We have revealed the small clusters of regions characterized by a high level of households’ monetary income and relatively large population (e. g. Moscow, Khanty-Mansi Autonomous Okrug). Alternatively, there are sufficiently inhabited clusters of regions with both a considerable volume of non-monetary income in a form of food combined and the low or average level of monetary income and small positive dynamics of population (Bryansk, Kursk Oblasts). On the other hand, in the regions with a relatively low monetary income, the revenue structure includes a high share of natural supplies in the form of food (for example, Republic of Dagestan and Republic of Ingushetia). Moreover, in the regions with a high monetary income, there is a small share of the raised funds and spent savings in revenue structure (Yamalo-Nenets Autonomous Okrug and others). We have constructed clusters of regions and established their quantity, structure and generalized characteristics presented in the form of clustering rules. We used that data for defining structural and parametrical characteristics when developing a dynamic model of the households sector and the module of intellectual management. These dynamic model and the module became a part of the system of ... |
format |
Article in Journal/Newspaper |
author |
Baryy Galeevich Ilyasov Elena Anatolyevna Makarova Elena Shavkatovna Zakieva Emma Salavatovna Gizdatullina |
author_facet |
Baryy Galeevich Ilyasov Elena Anatolyevna Makarova Elena Shavkatovna Zakieva Emma Salavatovna Gizdatullina |
author_sort |
Baryy Galeevich Ilyasov |
title |
Analysing the Data on Incomes in the Regional Context by the Principal Component Method |
title_short |
Analysing the Data on Incomes in the Regional Context by the Principal Component Method |
title_full |
Analysing the Data on Incomes in the Regional Context by the Principal Component Method |
title_fullStr |
Analysing the Data on Incomes in the Regional Context by the Principal Component Method |
title_full_unstemmed |
Analysing the Data on Incomes in the Regional Context by the Principal Component Method |
title_sort |
analysing the data on incomes in the regional context by the principal component method |
publisher |
Russian Academy of Sciences, Institute of Economics of the Ural Branch |
publishDate |
2019 |
url |
https://doi.org/10.17059/2019-2-22 https://doaj.org/article/f099b0ca3a824b47a1f911660e664757 |
genre |
khanty khanty-mansi nenets Nenets Autonomous Okrug Yamalo Nenets Yamalo-Nenets Autonomous Okrug Mansi |
genre_facet |
khanty khanty-mansi nenets Nenets Autonomous Okrug Yamalo Nenets Yamalo-Nenets Autonomous Okrug Mansi |
op_source |
Экономика региона, Vol 15, Iss 2, Pp 601-617 (2019) |
op_relation |
http://www.economyofregion.com/data/jarticles/3181.pdf https://doaj.org/toc/2072-6414 https://doaj.org/toc/2411-1406 doi:10.17059/2019-2-22 2072-6414 2411-1406 https://doaj.org/article/f099b0ca3a824b47a1f911660e664757 |
op_doi |
https://doi.org/10.17059/2019-2-22 |
container_title |
Economy of Region |
container_volume |
15 |
container_issue |
2 |
container_start_page |
601 |
op_container_end_page |
617 |
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1775352385314488320 |