<u>Source: IMF, AIPI data mapper [https://www.imf.org/external/datamapper/AI_PI@AIPI/ADVEC/EME/LIC]. Notes: The map is checked and</u> validated by the World Bank Cartography Unit as of February 02, 2025.

A methodological decision in this study is the choice of the recent AIPI from the IMF over the AI Readiness Index developed by the private UK-based consulting firm Oxford Insights.<sup>9</sup> This decision is primarily driven by the IMF’s reputation as a globally recognized multilateral institution and supposed to be accountable, which ensures greater scrutiny and transparency in the development of its products. In contrast, the outputs of private entities like Oxford Insights, though valuable, do not operate within the same level of oversight or accountability. Furthermore, the AI Readiness Index is explicitly designed to assess the extent to which governments are prepared to deploy AI in public service delivery.<sup>10</sup> In contrast, the AIPI aims to focus on a more comprehensive evaluation of how countries can harness AI capabilities effectively. That said, the choice of index does not significantly impact the broader contribution of this study, as the proposed methodology remains adaptable and replicable.

Employing Bayesian Model Averaging (BMA), we analyze a comprehensive set of 67 historical, political, and economic indicators from growth determinants literature to identify robust predictors of national AIPI. The BMA framework,<sup>11</sup> as implemented here, enables us to isolate the most robust determinants of AI preparedness from a wide range of potential predictors, as defined and built by Sala-i-Martin, Doppelhofer, and Miller (2004).<sup>12</sup> Figure 2 illustrates the inclusion probabilities of various covariates in the top 5,000 models. The results highlight that initial conditions—such as life expectancy, higher education levels, and economic size in 1960—along with cultural and geographic influences, including Confucian heritage and an East Asian regional dummy, are positively correlated with AIPI. Conversely, the indicator political instability, captured by the frequency of revolutions and military coups, shows a negative association with AIPI.<sup>13</sup>

**Figure 2** : Model Inclusion in BMA on AIPI [hyper- / prior; random model prior] gg nn

> 9 See https://staging2.oxfordinsights.com/ai-readiness/. 10 Ibid.

> 11 BMA addresses the challenge of model uncertainty by allowing for the evaluation of multiple competing models rather than relying on a single model specification. In the context of this study, BMA provides a systematic way to assess the relative importance of various predictors of AI preparedness and readiness, through the AIPI, by calculating each variable’s Posterior Inclusion Probability (PIP). 12 Their database is available on the website of the American Economic Association (AEA): <u>https://www.aeaweb.org/articles?id=10.1257/0002828042002570.</u>

> 13 Furthermore, historical trade openness emerges as a significant driver of AI adoption. However, this correlation should be interpreted cautiously, as one of the AIPI pillars (pillar iii: innovation and economic integration) directly captures aspects of trade openness and liberalization. The exact definitions of variables and their sources are available in (Sala-i-Martin, Doppelhofer, and Miller, 2004, p. 820-821).

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