WP121 Web · Page 23

20
  ILO Working Paper 121
We first proceed by imputing the GenAI exposure measures at the 4-digit ISCO08 to the micro­
data from the Programme for the International Assessment of Adult Competencies (PIAAC) col­
lected by the OECD. These surveys include rich information on detailed tasks carried out by peo­
ple at work, such as whether workers use a computer (and internet)16 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 occu­
pation 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.17
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 particu­
lar, we estimate a predictive model for the probability of computer use at the individual level us­
ing the full set of countries in PIAAC18 and independent variables that are available both in the 
PIAAC and SEDLAC databases.19 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:
(
)
(
)
computer
f ISCO
age
female
edu
internet
broadband
Pr
= 1 =
,
,
,
,GDP ,
,
c i
c i
o
c i
a
c i
c i
c
c
c
,
,
,
,
,
  (1)
Where computerc i,  is a binary variable equal to 1 if individual i in country c uses a computer at 
work; ISCOc i
o
,  is a vector of 39 dummy variables for each 2-digit ISCO08 occupation20; agec i
a
,  is a 
vector of 4 dummy variables indicating age groups; educ i,  is a dummy variable equal to one for 
High School graduates; GDPc is the log of GDP per capita in 2017 US$ PPP; internet c is the rate of 
internet users per 100 people, and; broadbandc is the number of fixed broadband subscriptions 
per 100 people. Since the reference year of the PIAAC surveys varies by country, we use the cor­
responding year of the country-level variables (i.e. GDP, internet and broadband). These coun­
try-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.21 The probability of not using a comput­
er 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 
16
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 varia­
ble 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 lea