Оценка данных о доходах населения в региональном разрезе методом главных компонент
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'...
Published in: | Economy of Region |
---|---|
Main Authors: | , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | Russian |
Published: |
Institute of Economics, Ural Branch of the Russian Academy of Sciences
2019
|
Subjects: | |
Online Access: | http://elar.urfu.ru/handle/10995/91591 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071686879&doi=10.17059%2f2019-2-22&partnerID=40&md5=c95ce1e9890d3186eb20faaf32cc9f5f https://doi.org/10.17059/2019-2-22 |
id |
fturalfuniv:oai:elar.urfu.ru:10995/91591 |
---|---|
record_format |
openpolar |
spelling |
fturalfuniv:oai:elar.urfu.ru:10995/91591 2024-01-21T10:07:48+01:00 Оценка данных о доходах населения в региональном разрезе методом главных компонент Analysing the data on incomes in the regional context by the principal component method Ilyasov, B. G. Makarova, E. A. Zakieva, E. S. Gizdatullina, E. S. Ильясов, Б. Г. Макарова, Е. А. Закиева, Е. Ш. Гиздатуллина, Э. С. 2019 application/pdf http://elar.urfu.ru/handle/10995/91591 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071686879&doi=10.17059%2f2019-2-22&partnerID=40&md5=c95ce1e9890d3186eb20faaf32cc9f5f https://doi.org/10.17059/2019-2-22 ru rus Institute of Economics, Ural Branch of the Russian Academy of Sciences Институт экономики Уральского отделения РАН Экономика региона. 2019. Том 15, выпуск 2 Оценка данных о доходах населения в региональном разрезе методом главных компонент / Б. Г. Ильясов, Е. А. Макарова, Е. Ш. Закиева, Э. С. Гиздатуллина. — DOI 10.17059/2019-2-22. — Текст : электронный // Экономика региона. — 2019. — Том 15, выпуск 2. — С. 601-617. 2411-1406 2072-6414 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071686879&doi=10.17059%2f2019-2-22&partnerID=40&md5=c95ce1e9890d3186eb20faaf32cc9f5f WOS:000472642000022 http://elar.urfu.ru/handle/10995/91591 doi:10.17059/2019-2-22 85071686879 000472642000022 info:eu-repo/semantics/openAccess CLUSTERING CLUSTERS OF REGIONS COEFFICIENT OF INFORMATION CONTENT IMITATING DYNAMIC MODEL INTEGRATED SIGN POPULATION INCOME PRINCIPAL COMPONENT METHOD SAMPLE SCATTERPLOT WEIGHT COEFFICIENT Article info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2019 fturalfuniv https://doi.org/10.17059/2019-2-2210.17059/2019-2-22. 2023-12-26T01:59:50Z 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 Ural Federal University (URFU): ELAR Economy of Region 15 2 601 617 |
institution |
Open Polar |
collection |
Ural Federal University (URFU): ELAR |
op_collection_id |
fturalfuniv |
language |
Russian |
topic |
CLUSTERING CLUSTERS OF REGIONS COEFFICIENT OF INFORMATION CONTENT IMITATING DYNAMIC MODEL INTEGRATED SIGN POPULATION INCOME PRINCIPAL COMPONENT METHOD SAMPLE SCATTERPLOT WEIGHT COEFFICIENT |
spellingShingle |
CLUSTERING CLUSTERS OF REGIONS COEFFICIENT OF INFORMATION CONTENT IMITATING DYNAMIC MODEL INTEGRATED SIGN POPULATION INCOME PRINCIPAL COMPONENT METHOD SAMPLE SCATTERPLOT WEIGHT COEFFICIENT Ilyasov, B. G. Makarova, E. A. Zakieva, E. S. Gizdatullina, E. S. Ильясов, Б. Г. Макарова, Е. А. Закиева, Е. Ш. Гиздатуллина, Э. С. Оценка данных о доходах населения в региональном разрезе методом главных компонент |
topic_facet |
CLUSTERING CLUSTERS OF REGIONS COEFFICIENT OF INFORMATION CONTENT IMITATING DYNAMIC MODEL INTEGRATED SIGN POPULATION INCOME PRINCIPAL COMPONENT METHOD SAMPLE SCATTERPLOT WEIGHT COEFFICIENT |
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 |
Ilyasov, B. G. Makarova, E. A. Zakieva, E. S. Gizdatullina, E. S. Ильясов, Б. Г. Макарова, Е. А. Закиева, Е. Ш. Гиздатуллина, Э. С. |
author_facet |
Ilyasov, B. G. Makarova, E. A. Zakieva, E. S. Gizdatullina, E. S. Ильясов, Б. Г. Макарова, Е. А. Закиева, Е. Ш. Гиздатуллина, Э. С. |
author_sort |
Ilyasov, B. G. |
title |
Оценка данных о доходах населения в региональном разрезе методом главных компонент |
title_short |
Оценка данных о доходах населения в региональном разрезе методом главных компонент |
title_full |
Оценка данных о доходах населения в региональном разрезе методом главных компонент |
title_fullStr |
Оценка данных о доходах населения в региональном разрезе методом главных компонент |
title_full_unstemmed |
Оценка данных о доходах населения в региональном разрезе методом главных компонент |
title_sort |
оценка данных о доходах населения в региональном разрезе методом главных компонент |
publisher |
Institute of Economics, Ural Branch of the Russian Academy of Sciences |
publishDate |
2019 |
url |
http://elar.urfu.ru/handle/10995/91591 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071686879&doi=10.17059%2f2019-2-22&partnerID=40&md5=c95ce1e9890d3186eb20faaf32cc9f5f https://doi.org/10.17059/2019-2-22 |
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_relation |
Экономика региона. 2019. Том 15, выпуск 2 Оценка данных о доходах населения в региональном разрезе методом главных компонент / Б. Г. Ильясов, Е. А. Макарова, Е. Ш. Закиева, Э. С. Гиздатуллина. — DOI 10.17059/2019-2-22. — Текст : электронный // Экономика региона. — 2019. — Том 15, выпуск 2. — С. 601-617. 2411-1406 2072-6414 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071686879&doi=10.17059%2f2019-2-22&partnerID=40&md5=c95ce1e9890d3186eb20faaf32cc9f5f WOS:000472642000022 http://elar.urfu.ru/handle/10995/91591 doi:10.17059/2019-2-22 85071686879 000472642000022 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.17059/2019-2-2210.17059/2019-2-22. |
container_title |
Economy of Region |
container_volume |
15 |
container_issue |
2 |
container_start_page |
601 |
op_container_end_page |
617 |
_version_ |
1788698411676991488 |