Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/13226
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dc.contributor.authorMartynenko, Valentyna-
dc.contributor.authorKovalenko, Yuliia-
dc.contributor.authorChunytska, Iryna-
dc.contributor.authorPaliukh, Oleksandr-
dc.contributor.authorSkoryk, Maryna-
dc.contributor.authorPlets, Ivan-
dc.contributor.authorПлець, Іван Іванович-
dc.date.accessioned2022-11-08T09:52:36Z-
dc.date.available2022-11-08T09:52:36Z-
dc.date.issued2022-
dc.identifier.issn1738-7906-
dc.identifier.urihttp://hdl.handle.net/123456789/13226-
dc.description.abstractThe efficiency of the regional fiscal policy implementation is based on the achievement of target criteria in the formation and distribution of own financial resources of local budgets, reducing their deficit and reducing dependence on transfers. It is also relevant to compare the development of financial autonomy of regions in the course of decentralisation of fiscal relations. The study consists in the cluster analysis of the effectiveness of fiscal policy implementation in the context of 24 regions and the capital city of Kyiv (except for temporarily occupied territories) under conditions of fiscal decentralisation. Clustering of the regions of Ukraine by 18 indicators of fiscal policy implementation efficiency was carried out using Ward's minimum variance method and k-means clustering algorithm. As a result, the regions of Ukraine are grouped into 5 homogeneous clusters. For each cluster measures were developed to increase own revenues and minimize dependence on official transfers to increase the level of financial autonomy of the regions. It has been proved that clustering algorithms are an effective tool in assessing the effectiveness of fiscal policy implementation at the regional level and stimulating further expansion of financial decentralisation of regions.uk_UA
dc.language.isoenuk_UA
dc.publisherINT JOURNAL COMPUTER SCIENCE & NETWORK SECURITY-IJCSNSDAE-SANG OFFICE 301, SANGDO 5 DONG 509-1, SEOUL 00000, SOUTH KOREAuk_UA
dc.relation.ispartofseries22;7-
dc.subjectIndicatorRegionUkraineModelDecentralizationClusterComponentWard's minimum variance methodk-means clustering algorithmuk_UA
dc.titleFiscal Policy Effectiveness Assessment Based on Cluster Analysis of Regionsuk_UA
dc.typeArticleuk_UA
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