Econometric Analysis of Determinants of Competitiveness of Territorial Communities of the Black Sea Economic Region

Assessing the competitiveness of territorial communities is an essential tool for effectively planning and implementing post-war reconstruction programs in Ukraine. The article aims to identify the determinants of the competitiveness of territorial communities of the Black Sea Economic Region using econometric modelling and statistical data for 2004–2023. The research methodology is based on conducting a correlation analysis and applying the least squares method to construct a regression model, with verification of the statistical significance of the coefficients. The gross regional product is used as a dependent variable, while twelve independent factors characterizing demographic, economic, social, infrastructural, and ecological aspects of development were employed as explanatory variables. The strongest correlations were identified with population size (0.967) and labour force (0.935), confirming the central role of human capital in regional competitiveness formation. The constructed regression model demonstrates high statistical significance (R²=0.998, F-criterion 2037.88) and reveals that 99.82% of gross regional product variation is explained by the included variables. The population demonstrates the strongest positive impact, with a coefficient of 0.218, indicating that a one-thousand-person increase in population leads to a gross regional product growth of 218 thousand UAH. Available income shows a positive influence (coefficient 0.0001), while export volume positively affects competitiveness (coefficient 0.0038). However, maritime freight transportation volume demonstrates a negative coefficient (-0.0017), reflecting structural economic changes and modernization needs of traditional infrastructure systems. The paradoxical negative correlation between available income and gross regional product (-0.473) may indicate inflationary processes and structural imbalances in the Black Sea Economic Region. The analysis confirms the necessity of a comprehensive approach to territorial communities’ competitiveness assessment through several factor blocks: economic, social, infrastructural, ecological, innovation, institutional, and region-specific factors. The empirical results substantiate the development of an integrated methodology for competitiveness evaluation that accounts for the unique characteristics of the Black Sea Region, including its coastal location, international trade role, and multi-ethnic population composition.

Authors

Publication Details

Journal
Oblik i finansi
Published
2025-01-01
DOI
https://doi.org/10.33146/2307-9878-2025-2(108)-164-173
Primary Topic
Global Trade and Competitiveness
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Econometric Analysis of Determinants of Competitiveness of Territorial Communities of the Black Sea Economic Region

Г. Б. Ніколенко
Oblik i finansi
Global Trade and Competitiveness
article

Econometric Analysis of Determinants of Competitiveness of Territorial Communities of the Black Sea Economic Region

Г. Б. Ніколенко
article en

Abstract

Assessing the competitiveness of territorial communities is an essential tool for effectively planning and implementing post-war reconstruction programs in Ukraine. The article aims to identify the determinants of the competitiveness of territorial communities of the Black Sea Economic Region using econometric modelling and statistical data for 2004–2023. The research methodology is based on conducting a correlation analysis and applying the least squares method to construct a regression model, with verification of the statistical significance of the coefficients. The gross regional product is used as a dependent variable, while twelve independent factors characterizing demographic, economic, social, infrastructural, and ecological aspects of development were employed as explanatory variables. The strongest correlations were identified with population size (0.967) and labour force (0.935), confirming the central role of human capital in regional competitiveness formation. The constructed regression model demonstrates high statistical significance (R²=0.998, F-criterion 2037.88) and reveals that 99.82% of gross regional product variation is explained by the included variables. The population demonstrates the strongest positive impact, with a coefficient of 0.218, indicating that a one-thousand-person increase in population leads to a gross regional product growth of 218 thousand UAH. Available income shows a positive influence (coefficient 0.0001), while export volume positively affects competitiveness (coefficient 0.0038). However, maritime freight transportation volume demonstrates a negative coefficient (-0.0017), reflecting structural economic changes and modernization needs of traditional infrastructure systems. The paradoxical negative correlation between available income and gross regional product (-0.473) may indicate inflationary processes and structural imbalances in the Black Sea Economic Region. The analysis confirms the necessity of a comprehensive approach to territorial communities’ competitiveness assessment through several factor blocks: economic, social, infrastructural, ecological, innovation, institutional, and region-specific factors. The empirical results substantiate the development of an integrated methodology for competitiveness evaluation that accounts for the unique characteristics of the Black Sea Region, including its coastal location, international trade role, and multi-ethnic population composition.

Oblik i finansi(2(108))
Openalex Percentile: Top 77%
Global Trade and Competitiveness
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.