Aybaniz Gubadova

There are active debates on the scale of inflation-economic growth causality in the short- and long-term perspectives and factors affecting the correlation and effectiveness of anti-inflationary measures depending on initial economic conditions. These scientific debates result in controversial results. This study aims to explore short- and long-run relationships in the inflation-economic growth chain of 12 post-Soviet countries (Azerbaijan, Estonia, Latvia, Lithuania, Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, Belarus, Moldova, Ukraine, and Georgia) to determine the most effective system of anti-inflationary policy. The paper employed statistical analysis and trend line extrapolation (analysis of inflationary trends in 2000–2021 and forecast for 2022–2024), pooled mean group of the autoregressive distributed lag model in the Stata 14.2/SE software (identification of the short and long-run coefficients characterizing relationships between inflation and economic growth), and ordinary least squares regression (country-specific modeling results). Statistical analysis showed that Latvia, Lithuania, and Estonia have the most effective anti-inflationary policy; Azerbaijan, Kazakhstan, Kyrgyzstan, and Moldova demonstrate moderate effectiveness, and the other countries have low effectiveness. It is also established that in the long run, a 1% increase in inflation results in a 0.195% decrease in GDP growth with a 99% confidence probability, while in the short run, this causal relationship is insignificant. Country-specific modeling results revealed that within 12 post-Soviet countries, economic growth in Kazakhstan, Lithuania, and Ukraine in a short-term perspective depends on inflation dynamics. According to the modeling results, Lithuania has the most effective anti-inflationary policy to ensure sustainable economic growth.

Type of the article: Research Article

Abstract
The banking and financial system of the countries of the world is constantly developing, but at different rates and ways, given their differences in the levels of economic, financial, and innovation development. The purpose of this article is to identify factors that link upper-middle- and high-income countries in terms of banking ecosystem digitalization, based on cluster analysis. The research sample includes 40 countries – 20 top-performing upper-middle-income and 20 high-income economies – based on the 2023 ICT Development Index. The analysis is based on 15 standardized indicators characterizing digitalization in the banking ecosystem, sourced from the International Monetary Fund, the World Bank, and the International Telecommunication Union. These indicators cover ICT development, AI readiness, cybersecurity, GovTech maturity, financial development, banking access, and digital transaction activity. Data standardization was performed in Stata (v19.5) using the built-in function to create new variables with a mean of 0 and a standard deviation of 1. Cluster analysis was conducted using the k-means method in Statgraphics (v19), with silhouette scores computed in Python to determine the optimal number of clusters. Cluster analysis revealed four distinct country groups, demonstrating that similarities in banking ecosystem digitalization transcend income levels. Key convergence factors include ICT development, GovTech maturity, mobile banking adoption, and AI readiness. Some upper-middle-income countries exhibit digitalization patterns comparable to high-income economies, highlighting the role of strategic investment and policy, rather than income, as primary drivers of digital financial advancement.