|**_Sector of economic_**<br>**_activity_**|Primary sector<br>Low-tech manufacturing (food, beverages, tobacco, textiles and clothing)<br>Other manufacturing<br>Construction|
|---|---|
||Retail, restaurants, hotels and repairs<br>Utilities, transport and communications<br>Banking, finance, insurance, professional services<br>Public administration<br>Education, health and personals services<br>Domestic service|

# 3.2. Use of a computer at work

The method applied so far enables detailed insights into country-level data on exposure of occupations to GenAI, with further breakdowns by demographic and socioeconomic characteristics of the affected groups (Table 1). At the same time, the variation in AI exposure across and within countries is only driven by the variation in the occupational structures, because the same occupation in different countries uses the same score of GenAI exposure. To address this limitation, in the next step we introduce cross-country variation of occupation-level scores, by accounting for the variability in the use of computer equipment in the sam ~~e o~~ ccupation located in different national contexts.

We first proceed by imputing the GenAI exposure measures at the 4-digit ISCO08 to the microdata from the Programme for the International Assessment of Adult Competencies (PIAAC) collected by the OECD. These surveys include rich information on detailed tasks carried out by people at work, such as whether workers use a computer (and internet)<sup>17</sup> at work. Using this binary indicator, we split each group of GenAI exposure into those who use a computer at work, and those who do not. Not using a computer at work means that even if the worker is in an occupation that is exposed to GenAI augmentation, such potential productivity gains are unlikely to realize given the lack of access to digital infrastructure. We first implement this exercise for the four countries in the LAC region (Chile, Ecuador, Mexico and Peru) and two developed economies (Slovenia and New Zealand) included in the PIAAC dataset.<sup>18</sup> 17 For the main results presented in the paper, we use variable “g_q04”, which contains the response to the following question: “Do you use a computer in your job?/Did you use a computer in your last job?”. We also do robustness checks by creating a binary variable equal to 1 when the worker uses both a computer and internet at work, by using the variables “g_q05a”, “g_q05c”, “g_q05d” and “g_q05h”, which contain information about the frequency (i.e. “never”, “less than once a month”, “less than once a week but at least once a month”, “at least once a week but not every day”, or “every day”) of internet use for mail, work related information, conduct transactions and participate in video calls, respectively. More specifically, when the worker responds that he or she “never” uses the internet for any of those four reasons at work, we consider that the worker does not use internet at work. If the worker responds that he or she uses the internet at work for any of those purposes with any frequency other than “never”, then we consider that the worker uses internet at work. Simultaneous use of computer and internet presents a more restrictive condition, which results in greater digital gaps. These calculations at the country level are shown in Figure A5. More detailed statistics are available upon request.

18 While PIAAC includes several developed economies, we did not choose them for this initial step of the analysis because their surveys were either collected several years earlier (2011-2012) and the adoption of digital technologies 15