AI ecosystem pillars and economic growth: Implications for knowledge economy architecture from AI vibrancy subindices

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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.

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    • Table 1. Variables and their sources
    • Table 2. Descriptive statistics (from R Studio)
    • Table 3. Summary table of results of FE and RE models
    • Table 4. Outputs of the Driscoll-Kraay and cluster-robust standard errors
    • Table 5. System GMM estimation results
    • Table 6. Descriptive statistics
    • Table 7. Panel regression results for GDP per capita (square root transformation) and Yeo-Johnson-transformed AI Vibrancy subindexes
    • Table 8. Fixed effects model with heteroskedasticity-robust standard errors
    • Table 9. Fixed effects model with Driscoll-Kraay robust standard errors
    • Table 10. Country-specific fixed effects from the fixed effects model
    • Table 11. One-way fixed effects model with time dummies and Driscoll-Kraay standard errors
    • Conceptualization
      Kalamkas Rakhimzhanova, Kalilla Abdullayev, Artsrun Avetikyan, Andrii Zolkover, Alina Danileviča, Mykola Povoroznyk, Yong Zhou
    • Project administration
      Kalamkas Rakhimzhanova
    • Writing – original draft
      Kalamkas Rakhimzhanova, Kalilla Abdullayev, Artsrun Avetikyan, Andrii Zolkover, Alina Danileviča, Mykola Povoroznyk, Yong Zhou
    • Writing – review & editing
      Kalamkas Rakhimzhanova, Kalilla Abdullayev, Artsrun Avetikyan, Andrii Zolkover, Alina Danileviča, Mykola Povoroznyk, Yong Zhou
    • Supervision
      Kalilla Abdullayev
    • Visualization
      Artsrun Avetikyan, Yong Zhou
    • Validation
      Andrii Zolkover, Yong Zhou
    • Funding acquisition
      Alina Danileviča
    • Resources
      Alina Danileviča, Yong Zhou
    • Data curation
      Yong Zhou
    • Formal Analysis
      Yong Zhou
    • Investigation
      Yong Zhou
    • Methodology
      Yong Zhou
    • Software
      Yong Zhou