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EXPANDING OPPORTUNITIES
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Figure 2.25 The interaction between technology and jobs varies by occupation
Probability of being computerized and intensity in use of ICT at work, by occupation
Probability of being computerized
(technology substituting for workers)
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Average intensity in
use of ICT at work
Agriculture workers
Waiters and bartenders Clerks
Subsistence farmers
Cooks Metal-processing operators
Food processors
Assemblers
Drivers Shop salespersons
Transport and storage laborers
Cleaners
Garment
workers
Handicraft
workers
Street and related
service workers
Hairdressers,
beauticians
Protective services
workers
Retail and wholesale
trade managers
Secretaries (general)
Tellers
Mining and
construction laborers
Sales and purchasing
agents and brokers
Business services agents
Administration
professionals
Mining, manufacturing, and
Legislators
construction supervisors
Hotel and restaurant
Legal professionals
managers
Secondary Medical doctors
education teachers Sales managers
Numerical clerks
Administration managers
Finance professionals
Sales, marketing, and public
relations professionals
Managing directors
and chief executives
Average probability of
being computerized
Software and applications
developers and analysts
Information and communication
technology service managers
0 2 4 6 8 10 12 14 16
Intensity in use of ICT at work
(technology complementing workers)
Sources: WDR 2016 team, based on STEP household surveys (World Bank, various years) and Frey and Osborne 2013. Data at http://bit.do/WDR2016-Fig2_25.
Note: The probability of being computerized is obtained from Frey and Osborne (2013). ICT intensity is an index between 0 (no use of technology) and 19 (most use of
technology). ICT = information and communication technology. The red lines represent the average values of ICT intensity (x-axis) and of computerization (y-axis) across the
pooled sample of 10 developing countries with STEP household surveys.
whether countries need not just to develop modern
skills among children and youth, but also to come up
with a strategy for the retraining and lifelong learning
of the current stock of (older) workers.
The challenge is to start reforms today to maximize
the digital dividends and to prepare for any
disruptions. Even if expected labor market changes
are similar in Malaysia and South Africa, Poland and
Turkey, or Finland and Italy, skill systems vary widely
and not all are prepared to equip workers with skills
that complement technology. This process needs to
start very early in life, and education and training
systems are notoriously difficult to change. So, any
reform takes many years to have effects, which is why
there is a race between skills and technology. Some
skill systems are well-positioned, but for many others,
skills—and hence, people—are losing the race.
Making the internet work for everyone
To design policy responses to technological change, it
is important to understand who the changes are likely
to affect the most, and how the process plays out both
in terms of employment and earnings. As discussed,
employment is likely to polarize, with routine occupations
losing ground to nonroutine occupations. These
changes in labor demand have in turn implications
for earnings. But employment polarization does not
necessarily mean wage polarization.
Three interrelated factors mediate the impact of
digital technologies on earnings:
• Complementarity with technology. Workers in jobs
that use and complement technology are likely
to see both an increase in employment and an
increase in earnings because of higher productivity.
This is the case for workers who use nonroutine
cognitive skills and ICT skills. Workers in routine
occupations, however, will see less demand for
their skills, bringing down both their employment
and their wages.
• Product demand. If workers produce goods or services
that consumers keep buying as they get richer
or as the price declines, increases in productivity
can translate into increases in wages. This is often
the case for workers with nonroutine skills producing,
say, knowledge, management expertise, or
medical services. If not, increases in productivity
can lead to lower employment and earnings in that
sector because fewer workers can satisfy demand,
as for many agricultural goods.