# 4. Findings

4.1. Cross-country comparisons of the levels of exposure Since we apply the same conceptual framework of exposure as proposed by GBB (automation, augmentation and “the big unknown”) we can calculate the total share of employment exposed to GenAI by adding those three categories in each country. Figure 7 presents the ranking of individual countries, with total exposure ranging between 26-27 percent in Barbados, Ecuador and Bolivia, up to 37-38 percent in Uruguay and Costa Rica respectively.<sup>24</sup> We strongly emphasize that this way of presenting results should not be equated with a statement that “ _up to 40 percent of employment in LAC is exposed to automation_ ”. Quite to the contrary, among these fairly large shares of potential exposure, only a small portion of occupations – between 2 to 5 percent, depending on country context – falls into the potential of full automation. Augmentation figures are consistently higher, ranging between 8 and 14 percent across countries. In addition, the category of the big unknown is rather large, suggesting that in the medium-term, these estimated shares could change quite significantly, as occupations from the big unknown might move into the automation and augmentation categories, depending on whether the technology is applied in a more task-automating manner, or whether it generates new complementary tasks (Acemoglu, 2024). In the short term, however, the proportion of employment in the LAC countries that have the potential to completely disappear to GenAI automation is significantly lower than the jobs that could be transformed by this technology. It is also important to emphasize that these cross-country comparisons regarding GenAI exposure rely on the assumption that the same occupation has a similar exposure in, for example, a high-income country and Guatemala. Some empirical work suggests that this assumption may not always hold, since the tasks of each occupation tend to change with economic development (Caunedo et al., 2023; Lewandowski et al., 2019; Lo Bello et al., 2019).<sup>25</sup> This paper tries to mitigate this issue in Section 4.2 by adjusting these exposure measures with measures of computer use at work.

> 24 Argentina‘s labor force survey covers only larger cities, which is why the exposure to GenAI is significantly larger than for other countries (44%). It was removed from the plot to avoid confusion. Data for Grenada, Guyana, Montserrat, and Suriname are based on ILO micro data, all other countries based on SEDLAC. Total employment for Grenada and Montserrat based on 2020 surveys, for all other countries based on ILO modelled estimates for 2023. 25 However, since the patterns of task specialization across countries differ along several dimensions, it is difficult to assess whether our method tends to under- or over-estimate the exposure to GenAI in the developing world. For example, Caunedo et al (2023) find that occupations tend, on average, to be more intensive in non-routine interpersonal tasks in richer countries. If such tasks are less prone to be automated by GenAI, then our method would be underestimating the exposure to GenAI in developing countries relative to developed ones. On the other hand, occupations tend to be more intensive in non-routine manual tasks in poorer countries. If such tasks are less likely to be automated by GenAI, this would imply that our method would over-estimate the exposure to GenAI in developing countries relative to developed ones.

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