poorer countries, the lack of digital infrastructure might offer a temporary buffer from the risk of imminent automation to some occupations in this category – a trend that likely extends to countries with relatively lower incomes outside Latin America. The plot also re-confirms that such automation-exposed jobs are disproportionately held by women.

The situation is visibly different in the case of occupations with augmentation potential. First, the distribution of such jobs is more equal among women and men. In addition, the shares of jobs that do not use a computer at work are also more evenly distributed across men and women. An intuitive way of thinking about this part of Figure 11 is that the light blue zones (no computer) represent the transformation potential that cannot be attained due to digital infrastructure limitations. If one was to assume that such a transformation could translate into productivity gains in those occupations, the zones without a computer can be seen as an unattainable potential productivity gains.<sup>35</sup> Taking this analysis one step further, we can quantify these effects. For example, if we calculate the unattained augmentation potential as an average value across the LAC countries (weighted by total employment of each country), it corresponds to 6.24 percent of female employment and 6.22 percent of male employment. If we think of this gap as an average share of jobs within all jobs with augmentation potential, it corresponds to a non-negligible 44 percent of such jobs held by women and 50 percent of such jobs held by men. Applying these calculations to the 2023 ILO modelled estimates of total employment in each country, we estimate that there are some 17 million jobs among the 16 LAC countries in our SEDLAC sample that could, in theory, experience additional productivity from the technological transformation with GenAI, but which will not be in position to do so due to the lack of digital infrastructure. Some 7 million such jobs are held by women and nearly 10 million are held by men (see Figure A6 in Appendix for country-level breakdowns).

# 4.3. Within-country patterns

In this section, we present a sample of within-country analysis, using the example of Colombia and Costa Rica (Figure 12). Detailed breakdowns by country are provided in the Appendix and made available in a dynamic way on our online portal.<sup>36</sup> Within each country, the plot can be read as a detailed breakdown of a given category on the vertical axis into the shares of employment represented in horizonal separations. For example, in the case of Colombia, among women, 5.5 percent of employment is exposed to the risk of automation, 11.3 percent of employment is in the augmentation category, and 22.5 percent in the big unknown. The remainder, not presented in the graph, contains all other female-held

> 35 We also implement a robustness check where we use the variable of ”computer ownership at home”, as selfreported by households in SEDLAC, to adjust for digitalization. In particular, we replace the imputed ”computer use at work” variable for this measure, to check if the patterns prevail. As seen in figure A.5 in the appendix, while the absolute values of exposure adjusted by digitalization are different across methods, the relative figures are very similar. In particular, among jobs exposed to GenAI augmentation, the share of workers with a computer at home tends to be higher in richer countries.

> 36 https://pgmyrek.shinyapps.io/AI_Data_Portal_Research/

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