Kalamkas Rakhimzhanova

Type of the article: Research Article

AI is widely regarded by the IMF and the World Bank as a catalyst for growth. AI should be understood as a multidimensional socio-technical system embedded across institutions, industries, and society. Its economic contribution depends on which pillars of the national AI system expand (e.g., R&D capacity, infrastructure, governance, or social acceptance). For this reason, the seven pillars of AI development are measured by the AI Vibrancy subindices, which help avoid reliance on a single composite indicator that may conceal offsetting effects. This study examines how different pillars of the national AI ecosystem shape the architecture of the knowledge economy and its economic outcomes by estimating heterogeneous within-country associations between GDP per capita and seven AI ecosystem pillars, operationalized through AI Vibrancy subindices, using a balanced panel of 36 countries with complete data over the period 2020–2023. Fixed- and random-effects models are estimated using heteroskedasticity-robust and Driscoll-Kraay standard errors. The results indicate that, within countries over time, the R&D (β = –5.676, p < 0.001) and Infrastructure (β = –16.306, p < 0.001) subindices have strong and statistically significant negative associations with GDP per capita, while Public Opinion shows an adverse effect that is significant at the 5% level under heteroskedasticity-robust inference (β = –9.126, p = 0.040) and marginally significant under Driscoll-Kraay inference (p = 0.054). Responsible AI exhibits a marginally positive association (β = 5.773, p = 0.065) in the Driscoll-Kraay specification, whereas Economy, Education, and Policy & Government show no significant within-country effects.

Type of the article: Research Article

Abstract
Whether digital transformation in the public sector and in financial services jointly contributes to banking stability – or whether the two strands proceed along parallel trajectories – remains an open empirical question for post-socialist economies undergoing both reforms simultaneously. This study addresses the question in three components. First, a cross-country mediation analysis covers up to 130 economies over 2018–2024 (853 country-year observations), drawing on the World Bank GovTech Maturity Index, the IMF Financial Access Survey, and the IMF Financial Soundness Indicators, with panel OLS, country-clustered standard errors, and bootstrap mediation tests. Second, the results are decomposed via fixed-effect deviations for three post-Soviet economies from distinct EBRD regions: Ukraine, Armenia, and Kazakhstan. Pre-shock GovTech maturity is positively associated with digital banking adoption (β = +2.91, p = 0.017); sub-pillars differ in channel: core government systems for transaction intensity, public service delivery for account ownership. Bootstrap mediation tests do not support the indirect path through digital banking adoption (six specifications, lowest p = 0.132). GovTech maturity instead shows a substantial direct association with the non-performing-loan ratio – a 13-percentage-point reduction per unit increase in GTMI (p = 0.037) – plausibly operating through institutional infrastructure such as property registries, e-courts, and tax-credit information systems. The two strands are linked but not chained: GovTech is associated with digital banking adoption, yet the route to lower non-performing loans runs through institutional infrastructure. Country-level decomposition reveals heterogeneous GTMI trajectories and identifies reform priorities across public service delivery and core government systems.

Acknowledgment
This article was prepared based on the results of a study funded by the Ministry of Education and Science of Ukraine entitled “GovTech for Ukraine: A Digital, Secure, Transparent, and Equitable State in Times of War and Post-War Reconstruction” (registration number: 0126U000544).