to gender and age, the benefits of potential transformation are more equally distributed across these dimensions than the exposure to the risk of automation. The trends across several other characteristics are similar to the automation exposure, with urban, higher-educated, non-poor, formal occupations and higher income brackets corresponding to relatively higher augmentation potential. An important difference, however, concerns the employment relationships and sectors. Whereas automation was most prevalent among employees, the potential of augmentation is relatively higher among employees and the self-employed, with significantly higher shares of employment observed in education, health and personal services. The exposed self-employed occupations with the largest share of jobs cover a wide range of the skill spectrum: low (e.g. elementary workers not classified elsewhere), middle (e.g. hairdressers, market salespersons, motorcycle drivers) and high (e.g. architects, real estate agents, photographers, musicians).

In summary, having compared the trends across countries and socioeconomic groups, we observe some common patterns regarding overall exposure to GenAI, with important variability across types of exposure. People with higher education and incomes, in urban areas, with jobs in the formal sector, and younger tend to have higher levels of exposure to GenAI, regardless of the type of exposure. The strongest differences in the type of exposure appear across gender, employment status and sectors, with women and salaried employees in banking, finance, insurance services and public administration showing higher exposure to automation, while augmentation exposure is most dominant among salaried and self-employed and in the sector of education, health and personal services.<sup>30</sup>

# 4.2. Impact of digital infrastructure on the potential of transformation

The previous section provided a detailed analysis based on the assumption of equal exposure among all countries within a given ISCO-08 2-digit category. In this section, we apply the calculations that predict the use of a computer at work and therefore introduce variation at the level of individual occupations within countries.<sup>31</sup> Since access to a computer is a crucial starting point for the use of GenAI technologies, by applying this additional criterion, we separate the theoretical exposure of occupations to GenAI from the practical feasibility of such a transformation in a given country context, as a function of available digital infrastructure. We focus this part of analysis on three principal dimensions: country-specific digital characteristics, exposure type and gender. This worker-level approach suggests that the workers’ skills are crucial to determine the digital divide and the effective exposure to GenAI. But it is worth noting this gap could also be determined by firm characteristics or AI investment decisions. Although there is no clear agreement on the effects

> 30 These general patterns also hold when conducting a conditional analysis. In particular, we estimate OLS regressions to simultaneously account for the different drivers of exposure to GenAI by pooling the microdata for all 16 countries (Table A3 in appendix). To consider the sampling frame of the household surveys while at the same time giving the same weight to every country, the population weights are normalized so that they add up to 1 within each country. We do not use the population weights in their raw version because the results would be driven mostly by large countries such as Mexico and Brazil. The main differences from the unconditional figures analyzed above are that women are less exposed to augmentation than men, and that rural and urban workers are equally exposed when controlling for other factors.

> 31 See Section 3.2 for technical details.

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