Since there are only four Latin American countries in PIAAC, we extrapolate the measures of computer use at work from PIAAC to the full set of countries in the SEDLAC database. In particular, we estimate a predictive model for the probability of computer use at the individual level using the full set of countries in PIAAC<sup>19</sup> and independent variables that are available both in the PIAAC and SEDLAC databases.<sup>20</sup> We then use the estimated model and the set of independent variables to predict the probability of computer use in the SEDLAC database. More specifically, we first estimate the following Logit model in PIAAC: specifically, we first estimate the following Logit model in PIAAC: Pr (c c c c I , a , GDP , i c ) (1) oo aa cc,ii cc,ii cc,ii cc,ii cc,ii cc cc cc Where c c c c = 1) = ff(I is a binary variable equal to 1 if individual cc , ffc aaffcc , cceecc in country cciic , bbc uses a computer at aaeebbaaiiee work; I I is a vector of 39 dummy variables for each 2-digit ISCO08 occupation21; a is cc,ii a vector of 4 dummy variables indicating age groups; oo is a dummy variable equal to one for ii cc aa cc,ii cc,ii High School graduates; GDP is the log of GDP per capita in 2017 US$ PPP; i c is the rate cc cc,ii of internet users per 100 people, and; is the number of fixed broadband subscriptions cceecc cc cc per 100 people. Since the reference year of the PIAAC surveys varies by country, we use the cciic cc corresponding year of the country-level variables (i.e. GDP, internet and broadband). These bbc aaeebbaaiiee country-level variables are helpful to capture the link between the economy-wide level of digital and economic development with the level of computer use at work.

In the next step, we use the estimated equation (1) to predict the probability of using a computer at work at the individual level in the SEDLAC database.<sup>22</sup> The probability of not using a computer at work is simply 1-Pr(computer=1). When choosing the reference years of the country-level variables of the model, we use the reference year of the SEDLAC surveys. Then, the probability of an individual being exposed to AI and of using a computer at work will be the multiplication of both individual probabilities. For example, take the case of a group of workers (e.g. workers with high education) that, on average, has a 0.23 GenAI augmentation exposure probability (i.e. the average of the binary exposure measure at the 4-digit level). Then let’s assume that, based on the individual predictions from the Logit model, on average, individuals in such group have a 0.7 (0.3) likelihood of using (not using) a computer at work. As a result, we conclude that workers in such group have a 0.161 (=0.7 x 0.23) probability of being exposed to GenAI augmentation and of using a computer at work, while they have a 0.069 (0.3 x 0.23) probability of being exposed to GenAI augmentation and of not using a computer at work.<sup>23</sup>

> increased dramatically since then. In addition, some developed countries with more recent surveys do not have ISCO 08 information at the 4-digit level (e.g. United States). In contrast, New Zealand and Slovenia’s surveys were collected during the second round (2014-2015), which is the same timeframe of Chile’s survey, and closer to the timeframe of Ecuador, Mexico and Peru (2017).

> 19 There are 38 countries with publicly available PIAAC microdata, see <u>https://www.oecd.org/skills/piaac/data/.</u> 20 A similar exercise was implemented by Garrote et al., (2021) to adjust working-from-home measures by internet access rates.

> 21 While there are 43 ISCO08 2-digit level categories, we drop from the sample the categories 01 (Commissioned Armed Forces Officers), 02 (Non-Commissioned Armed Forces Officers) y 03 (Armed Forces Occupations, Other Ranks) since they are not available in PIAAC.

> 22 Table A2 in the appendix contains the estimated coefficients.

> 23 The logic is the same when the measures of GenAI exposure are imputed at the 2-digit ISCO08 level, which are not binary but continuous measures between 0 and 1. That is, the exposure to GenAI augmentation within a group of workers would be the average of the continuous exposure measure.

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