We also consider alternative scores that could be used for this purpose, in particular the abilitybased scores developed by Felten et al. (2021, 2023a, 2023b, 2018), based on the US O’NET classifications, recently linked to ISCO-08 4-digit occupations by (Cazzaniga et al., 2024), as well as the patent-based scores of exposure to digital technologies by Prytkova et al. (2024), from which a set of technological groups relevant to GenAI could be isolated. Having compared these alternatives, we find that Felten et al. scores are quite aligned with GBB in broad terms, except for catching a much wider group of Managers and Professionals as highly exposed to AI technologies.<sup>11</sup> Since such broad coverage seems somewhat unrealistic in the context of many developing countries, we opt for the scores of GBB, which provide a direct link to ISCO-08 documentation and focus exclusively on GenAI.

This choice is further reinforced by the arguments recently advanced by (Nurski and Ruer, 2024), who find that task- (GBB) and ability-based (Felten, 2023) scores render similar general results in the European context. The authors suggest the task-based analysis using GBB scores is particularly advantageous for evaluating employment impacts, since task bundles are better at representing the daily reality of occupations (see Autor, 2015), and considering the share of affected tasks offers more scope to separate the potential for job transformation or displacement due to technological advancements. Indeed, task-based approaches have been widely used in the literature for this type of analysis (Restrepo, 2023, for concrete examples see Acemoglu and Restrepo, 2022, 2018; Frey and Osborne, 2017;), including the recently increasing use of AI-generated task-level scores (Acemoglu, 2024; Eloundou et al., 2023), used as a blueprint for the GBB scoring method.

In step 1, we tag occupations at 4-digit level in ISCO-08 into three categories established by GBB: “automation potential”, “augmentation potential” and “the big unknown”. We then rely on the ILO harmonized microdata collection<sup>12</sup> to obtain the shares of employment at 4-digit level occupations for 18 countries for each of these three AI exposure categories (Figure 7). We also calculate the shares that such exposed occupations make up in the higher, 2-digit level of occupational classification. From this step, we switch to the harmonized household surveys from the SocioEconomic Database for Latin America and the Caribbean (SEDLAC) to calculate AI exposure across and within 16 Latin American countries.<sup>13</sup> Our sample of SEDLAC data consists of about 900,000 individual survey observations, with details by country provided in the Appendix (Table A1).

The advantage of SEDLAC database is that it contains a host of harmonized variables at the individual level, including the income aggregates used to measure poverty, as well as demographic

> 11 See Figures A2 and A3 in the Appendix for a quick visual comparison of GBB scores to Felten et al. (2023) and Prytkova et al. (2024). Felten et al.’s scores cover a wider range of AI than GenAI covered by GBB. Prytkova et al. (2024) focus on tech abilities in patents, which can be far from readiness for market-level deployment. A detailed analysis of these scores at the 4-digit occupational level is available upon request.

> 12 Calculations of 2-digit employment from ILO Micro data repository by David Bescond, ILO STATISTICS.

> 13 SEDLAC is produced by the University of La Plata’s center for Distributional, Labor and Social Studies (CEDLAS) and The World Bank’s Equitable Growth, Finance and Institutions LCR-POV-Poverty and Equity Group (ELCPV). This project aims to improve the comparability of social and economic statistics across 25 countries in the Latin American and the Caribbean (LAC) region. This involves the harmonization of household survey variables in eight categories: income, demographics, education, employment, infrastructure, durable goods, services, and aggregate welfare.

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