ON THE MUTUAL INFLUENCE OF FACTORS DETERMINING THE LEVEL OF INCLUSIVE DEVELOPMENT: THE CASE OF CENTRAL AND EASTERN EUROPEAN COUNTRIES

A detailed analysis of interconnections between key indicators shaping the Inclusive Development Index (IDI) was conducted to evaluate the relationships between economic, social, and environmental characteristics of the studied countries. This approach is essential for identifying the main drivers of inclusive development and recognizing factors that may hinder sustainable growth, creating opportunities for more effective policymaking. The analysis demonstrates that some indicators, such as X1 (GDP per capita), X2 (labor productivity), X3 (healthy life expectancy), and X4 (employment rate), exhibit strong positive relationships. For example, labor productivity (X2) strongly correlates with GDP per capita (X1) at 0,938, highlighting productivity as a key factor in economic growth. Additionally, the positive relationship between labor productivity (X2) and healthy life expectancy (X3) (0,680) emphasizes the role of economic growth in improving social conditions and quality of life. However, indicators such as X7 (wealth Gini coefficient) and X5 (income Gini coefficient) show weak or negative correlations with economic and social characteristics, such as X1 and X3, indicating that inequality can limit the positive effects of economic progress on social welfare. Environmental and demographic indicators also play a significant role. Pollution intensity (X10) negatively correlates with GDP (X1) (-0,717) and labor productivity (X2) (-0,708), reflecting the trend of reduced environmental burden in more developed countries. Demographic dependency (X12) negatively correlates with GDP per capita (X1) (-0,489) and labor productivity (X2) (-0,531), illustrating the challenges faced by economies with aging populations and a high proportion of dependents. Overall, economic indicators like GDP per capita (X1) and labor productivity (X2) serve as key drivers of inclusive development, while social inequality (X5, X7) and environmental burden (X10) act as significant challenges. Addressing these barriers and focusing on demographic and social issues are vital for achieving inclusive and sustainable development. Future research aims to develop models for assessing inclusive development using advanced tools such as fuzzy logic and neural networks, ensuring a comprehensive approach to enhancing the IDI.