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How Technology Affects Jobs: A Smarter Future for Skills, Jobs, and Growth in Asia

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Abstract

As the Fourth Industrial Revolution (4IR) unfolds, some types of jobs will disappear while many others will be created. While displacing some jobs in its wake, new technologies will simultaneously unleash countervailing forces that generate more jobs and the net effect at the aggregate level is positive. Shifts in the demand for workforce skills will require adequate skills development or retraining. Although the 4IR provides a unique opportunity for Asia to create new high-quality skills, capitalizing on this will require reforms in national education and training systems to ensure an agile and competent workforce.
Chapter 32
How Technology Affects Jobs: A Smarter
Future for Skills, Jobs, and Growth
in Asia
Sameer Khatiwada
Jobs are in a constant state of evolution. Today’s labor markets are far different
from those of decades past. The types of jobs workers need to sustain their liveli-
hoods, the industries that power economic growth, even the societal expectations of
what a job represents are all subject to constant change. At present, there is nothing
more transformative concerning the nature of jobs and the future of work than the
Fourth Industrial Revolution (4IR)—a new era built on the use of more sophisti-
cated robots and computing power and the automation of tasks once thought to be
uniquely human (ADB 2018). In this essay, we examine the literature on how the
4IR is changing the demand for jobs and explore the implications of new technology
on jobs in Asia and the Pacific.
Throughout history, humans have always endeavored to produce things better,
faster, and cheaper: from the use of water and steam power to mechanize production
in the First Industrial Revolution, to the use of electric power for mass production
in the Second Industrial Revolution, to the use of electronics and information tech-
nology to automate production in the Third Industrial Revolution, and to the extreme
automation, connectivity, and the wider implementation of artificial intelligence in
the 4IR. Just like the industrial revolutions preceding it, the 4IR will profoundly
affect people’s lives.
As the 4IR unfolds, some types of jobs will disappear, while many others will
be created. Recent studies, discussed below, show that new technologies will result
in higher productivity. While displacing some jobs in their wake, new technologies
will simultaneously unleash countervailing forces that generate more jobs, and the
net effect at the aggregate level will be positive.
S. Khatiwada (B
)
Southeast Asia Department, Asian Development Bank, Manila, Philippines
e-mail: SKHATIWADA@adb.org
© Asian Development Bank 2020
B. Panth and R. Maclean (eds.), Anticipating and Preparing for Emerging Skills
and Jobs, Education in the Asia-Pacific Region: Issues, Concerns and Prospects 55,
https://doi.org/10.1007/978-981- 15-7018-6_32
263
264 S. Khatiwada
Issues and Challenges
The Asia and Pacific region is home to more than 60% of the global working-
age population, and six out of ten young people in the world today live in this
region.1The future of work globally is tied to labor market outcomes in Asia and the
Pacific—in fact, there is no other region in the world that highlights the challenges
and opportunities that stem from the 4IR better than Asia. There is one question
that is on everyone’s mind when it comes to the 4IR: Is this time different? As new
technology allows us to automate increasingly complex tasks, should we finally be
worried that “technological unemployment,” as was warned by luminaries such as
David Ricardo, Karl Marx, and John Maynard Keynes, would be borne out in the
near future?
As computing capacity improves and becomes cheaper, its usage in production
and delivery of services will spread across industries. For example, blockchain, which
is “a digital, distributed ledger that keeps a record of all transactions across partic-
ipating peer-to-peer networks,” is set to transform businesses worldwide. But, is
it different compared with technological innovations in the past? Economic history
tells us that the invention of electricity and computers impacted all sectors and indus-
tries and spread across the globe. They gave rise to occupations that did not exist
before, such as electricians, electrical engineers, computer engineers, software devel-
opers, website designers, etc. The technology of today will have similar effects; it is
not evident why things would be different this time. But there is one aspect of the
4IR that is different than before: Increasingly complex tasks can now be automated
(Autor 2015, Acemoglu and Restrepo 2018, ADB 2018). But this is to be expected,
as technology has evolved from electricity and computers to quantum computing
and artificial intelligence (AI). Should we be surprised that humans have invented
increasingly complex machines to make production more efficient and cheaper? With
each generation of technological breakthroughs, this is to be expected, as that is the
very definition of human progress. It also gives rise to new types of jobs—from auto
repair workers in the past to computer engineers and web developers in the present.
Moreover, it is important to keep in mind that not all technology displaces human
labor. For example, magnetic resonance imaging (MRI) or X-ray machines in hospi-
tals perform functions that humans cannot do, but they complement human labor in
the delivery of medical care. Should we be worried about these types of technological
advancements? No. This phenomenon has been described as “deepening of automa-
tion”: when technological improvements increase the productivity of capital in tasks
that have already been automated (Acemoglu and Restrepo 2018). Furthermore, with
sophisticated medical technology, nurses can perform tasks typically performed by
doctors. Also, these types of technologies have had the fastest adoption rates, as they
are used in the delivery of services such as medical care that are in high demand.
Breakthroughs in biotechnology do not destroy jobs, but they augment the value
of human labor. Similarly, technological advancements in the delivery of public
1Youth population refers to people between the ages of 15 and 24.
32 How Technology Affects Jobs: A Smarter Future for Skills, Jobs … 265
services such as health, education, and social security do not necessarily destroy
jobs but enhance the quality and provision of these services.
History suggests that technological advancements have raised labor productivity,
lowered prices for consumers, increased demand, raised incomes, and underpinned
economic growth and job creation. This time should be no different. However,
displacement of workers from jobs that are being automated is real and has conse-
quences on their future employability, income, and living standards (ADB 2018).
Indeed, if history is any guide, the introduction of new technology during the
first industrial revolution led to rising labor demand and wages, but this came
after a protracted period of stagnant wages (Acemoglu and Restrepo 2018). This
phenomenon has been dubbed “Engel’s pause” or the “living standards paradox.” As
in the past, labor regulation and social policy can play a critical role in breaking out
of the “Engel’s pause” such that wages rise with increasing productivity. Technology
is partly responsible for rising income inequality in recent decades (Autor Levy and
Murnane 2003), and whether new advancements in automation will slow this trend
depends critically on whether technology complements or substitutes human labor.
If labor gets substituted in a greater number of occupations than before, then income
distribution depends on whether labor has the bargaining power to reap the benefits
of productivity gains. The role of labor market institutions and skills of the work-
force is key in understanding how wages evolve and whether workers can transition
smoothly to new tasks and occupations.
Recent Evidence
As big data and AI make possible the automation of even highly complex manual
and cognitive tasks, there is increasing public concern that new technologies will
soon take over everyone’s jobs. Further fueling concerns are estimates indicating
that more than two-thirds of jobs in various economies of developing Asia are at risk
of automation. Will automation lead to widespread job displacement, with robots
taking on the role of human workers across industries? How bad is this going to be?
Industrial robots, used largely in manufacturing, have become much more
powerful and sophisticated due to technological advancements in AI and computing
capacity. In recent years, the use of industrial robots has increased considerably:
Between 2010 and 2015, the stock of industrial robots in Asia increased by 70%
to 887,400 units (ADB 2018). The People’s Republic of China (PRC)—the largest
market for industrial robots—accounts for about 43% of all sales of industrial robots
to Asia and the Pacific. Another 24% of sales is accounted for by the Republic of
Korea, followed by 22% by Japan.
So, do robots displace human workers? According to Acemoglu and Restrepo
(2017), the use of industrial robots in the United States between 1990 and 2017 was
negatively associated with employment and wages. They found that an additional
robot reduced employment by six workers, and one new robot per thousand workers
reduced wages by 0.5%. They also found that the negative effects of robots on labor
266 S. Khatiwada
market outcomes were more pronounced in the manufacturing sector and in routine,
manual, and blue-collar jobs. A study by the United Nations Conference on Trade
and Development (UNCTAD) (2017)— which included a sample of 64 countries
between 2005 and 2014—also found that increased robot use was associated with
a slight decline in the share of manufacturing in total employment and in real wage
growth. Further, robots displaced routine tasks usually done by workers on the middle
rungs of the pay scale.
In contrast, Graetz and Michaels (2015) found that industrial robots increased
labor productivity, total factor productivity, and wages in a sample of 17 developed
countries, including 14 European countries, Australia, the Republic of Korea, and
the United States. They showed that robotics accounted for 10% of gross domestic
product growth, 16% of labor productivity growth, and wage growth within industries
with higher robot density. In line with the result of Acemoglu and Restrepo (2017),
they found that robotics reduced the employment of low-skilled workers and, to a
lesser extent, middle-skilled workers, but had no significant effect on high-skilled
workers.
Automation is set to replace more jobs in developing countries than in developed
ones, according to a recent study by Frey and Rahbari (2016). They estimate that the
share of jobs at risk of automation is about 77% in the PRC, 69% in India, and 85%
in Ethiopia. This proportion is lower in Organisation for Economic Co-operation and
Development (OECD) countries, averaging about 57%. Similarly, a recent report by
Chang et al. (2016) showed that new technology poses both risks and opportunities
for the Association of Southeast Asian Nations (ASEAN) countries. Several sectors
with high value-added and providing employment to a large part of the population
face risk of digitization and automation. For instance, in the auto sector alone, more
than 60% of salaried workers in Indonesia and about 73% in Thailand face automation
risks. In the case of Viet Nam, about 75% of workers in electronics 86% in apparel
and footwear are at risk of automation.
However, the overall picture is not too grim. ADB (2018) finds optimism in
developing Asia’s job prospects based on the following four observations: First, new
technologies only automate certain tasks but not the entire job. In fact, the automation
of routine and manual tasks frees up human work toward more complex tasks. Second,
job automation occurs only when it is both technically and economically feasible—a
requirement met mostly in capital-intensive manufacturing, where, according to the
report, employment shares were already low in 2015. Third, rising demand offsets
or compensates for the job displacement effect of automation. Finally, technological
change and economic growth create new occupations and industries, offsetting the
displacement effect of automation.
When new technologies make possible the production of goods using fewer
workers, the job displacement effects are often countered by other forces at play,
with the net effect at the aggregate level being positive. For instance, the study by
Bessen (2017) finds that computer use in the United States between 1984 and 2007
was not only associated with job losses in manufacturing industries but also with
employment growth in nonmanufacturing industries. In particular, computer use was
associated with a 3% per year job loss on average in manufacturing industries, but a
32 How Technology Affects Jobs: A Smarter Future for Skills, Jobs … 267
1% per annum faster employment growth in nonmanufacturing industries. Moreover,
according to the McKinsey Global Institute (2017), automation at the aggregate level
could raise productivity growth by 0.8%–1.4% annually.
As some jobs are made obsolete by new technologies, entirely new categories are
emerging. An analysis of occupation titles in India, Malaysia, and the Philippines
found that 43%–57% of new job titles that emerged in the past 10 years are in ICT
(ADB 2018). For instance, in India, new jobs were driven mainly by different types
of specialized technicians needed to work with computer-controlled machines. Many
more new jobs will arise in healthcare and education and in finance, insurance, real
estate, and other business services. Further, a recent study by Khatiwada and Veloso
(2019) also find evidence that new jobs provide higher wages than old jobs.
Implications for the Future
While we should not necessarily be worried about massive job losses in Asia and the
Pacific due to automation, it is becoming clear that shifts in the demand for workforce
skills require adequate skills development or retraining and that workers with weaker
foundational skills will face hurdles in seizing the opportunities that new technologies
provide. The 4IR is expected to increase the demand for nonroutine cognitive tasks
as well as generate new jobs that pay better wages. However, taking advantage of
these developments requires a supply of agile and competent workers.
Indeed, as some jobs become obsolete, entirely new categories of jobs are
emerging. According to ADB (2018), demand for jobs is shifting towards those
that require nonroutine cognitive, social, and information and communications tech-
nology (ICT) (ICT) tasks. An analysis of four economies in developing Asia shows
that over the past decade, employment in nonroutine cognitive tasks expanded 2.6
times faster than total employment (Figure 32.1, Panel A). Moreover, wages have
also grown faster in nonroutine/cognitive types of jobs than those for manual jobs
(Figure 32.1, Panel B).
ADB (2018) also finds that most new job titles have emerged in the cognitive and
nonroutine category, with as much as 82% of new jobs in Malaysia in this category,
and around 60% in India and the Philippines. A recent study by Khatiwada and
Vel o so ( 2019) categorizes new occupation titles by skill level. The authors find that
majority of new work requires high skills: 62% of new job titles in India, 82% in
Viet Nam, 80% in Malaysia, and 61% in the Philippines (Figure 32.2).
Khatiwada and Veloso (2019) also find evidence that new jobs pay better than old
jobs. For instance, they find that in Viet Nam, across all industries, average monthly
wages in new jobs are higher than those of old jobs. The wage gap is most apparent
in mining, manufacturing, and construction. Even in agriculture, where wages have
been persistently low, new jobs pay much better than old jobs. On average, new jobs
pay 1.5 times more than old jobs in Viet Nam.
268 S. Khatiwada
Panel A: Annual Growth in Employment of Wage Workers by Job Type (%)
Panel B: Change in Average Monthly Wages, constant prices (in US$)
Note: The time frames vary across countries, with Viet Nam the shortest (2007—2015), followed by Thailand (2000—
2010), India (2000—2012), and Indonesia (2000—2014). Asia refers to the four countries included in this analysis.
Source: Asian Development Outlook 2018: How Technology Affects Jobs.
-3
-2
-1
0
1
2
3
Non-routine
cognitive
Manual
Non-routine
cognitive
Manual
Non-routine
cognitive
Manual
Non-routine
cognitive
Manual
Non-routine
cognitive
Manual
ASIA India Indonesia Thailand Viet Nam
0
20
40
60
80
100
Non-
routine
cognitive
Ma n u al No n-
routine
cognitive
Manual Non-
routine
cognitive
Manual Non-
routine
cognitive
Manual
INDIA IND ONE SIA THAILAND V IE T NAM
US $
Figure 32.1 Change in Employment by Task Intensity of Work: Nonroutine Cognitive versus
Manual Work. Note The time frames vary across countries, with Viet Nam the shortest (2007–
2015), followed by Thailand (2000–2010), India (2000–2012), and Indonesia (2000–2014). Asia
refers to the four countries included in this analysis. Source Asian Development Outlook 2018:
How Technology Affects Jobs
Conclusion
The Fourth Industrial Revolution provides a unique opportunity for the region to
create new high-quality jobs and vastly improve the job quality and productivity of
existing work. However, capitalizing on new opportunities in promising sectors will
require strengthening and reforming national education and training systems and
equipping workers with the qualifications and skills to compete for emerging jobs.
Policymakers should enhance the quality of available technical training programs
and ensure that they meet the current and future labor market needs. The inclusion
of technical training in secondary education by developing technical and vocational
32 How Technology Affects Jobs: A Smarter Future for Skills, Jobs … 269
Note: This follows Autor’s (2014) classification of skill levels based on the International
Standard Classification of Occupations (ISCO) Divisions.
Source: Khatiwada and Veloso (2019).
0% 20% 40% 60% 80% 100%
India (1968 - 2004)
Viet Nam (1998 - 2008)
Malaysia (1998 – 2008)
India (2004 - 2015)
Philippines (1990 - 2012)
Low Middle Hig h
Figure 32.2 Share of New Job Titles by Skill Level. Note This follows Autor’s (2014) classification
of skill levels based on the International Standard Classification of Occupations (ISCO) Divisions.
Source Khatiwada and Veloso (2019)
education and training (TVET) hybrid models should be strengthened. High-income
countries are increasingly characterized by knowledge rather than means of produc-
tion. Countries in Asia and the Pacific must also ensure that their workers have
the skills to thrive in the knowledge economy. Delivering TVET through quality
apprenticeships will benefit both workers and potential employers. Moreover, direct
industry involvement in curriculum development and quality assurance ensures that
TVET is in line with labor market demands.
There is also a need to increase the use of ICT in education. Policymakers should
take advantage of the scalability of ICT by making it an integral part of education
delivery. Using ICT can help deliver TVET to a wider audience, create a more open
and flexible learning environment, and allow access to enhanced learning through
interactive content. Such flexibility will produce TVET that is more responsive to
the labor market’s needs.
Link to the presentation material: https://events.development.asia/materials/201
71213/jobs-and-technology-implications-education-and-skills-development.
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The views expressed in this Chapter are those of the authors and do not necessarily reflect the views
and policies of the Asian Development Bank (ADB) or its Board of Governors or the governments
they represent.
ADB does not guarantee the accuracy of the data included in this Chapter and accepts no
responsibility for any consequence of their use. The mention of specific companies or products of
manufacturers does not imply that they are endorsed or recommended by ADB in preference to
others of a similar nature that are not mentioned.
By making any designation of or reference to a particular territory or geographic area, or by
using the term “country” in this Chapter, ADB does not intend to make any judgments as to the
legal or other status of any territory or area.
This work is available under the Creative Commons Attribution 3.0 IGO license (CC BY
3.0 IGO) https://creativecommons.org/licenses/by/3.0/igo/. By using the content of this Chapter,
you agree to be bound by the terms of this license. For attribution, translations, adaptations, and
permissions, please read the provisions and terms of use at https://www.adb.org/terms-use#openac
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is attributed to another source, please contact the copyright owner or publisher of that source for
permission to reproduce it. ADB cannot be held liable for any claims that arise as a result of your
use of the material.
... 4IR technologies, as further explained by Humphreys (2019), contributes to lower social and environmental impacts. Khatiwada (2020) elucidates the manner in which 4IR leads to an evolution of jobs and consequently demands an upgrading of skills as it leads to higher quality jobs. Moreover, Khatiwada (2020) highlights the phenomenon of "deepening of automation", where technological advancements improve the productivity of tasks which were already automated. ...
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... 4IR technologies, as further explained by Humphreys (2019), contributes to lower social and environmental impacts. Khatiwada (2020) elucidates the manner in which 4IR leads to an evolution of jobs and consequently demands an upgrading of skills as it leads to higher quality jobs. Moreover, Khatiwada (2020) highlights the phenomenon of "deepening of automation", where technological advancements improve the productivity of tasks which were already automated. ...
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In this essay, I begin by identifying the reasons that automation has not wiped out a majority of jobs over the decades and centuries. Automation does indeed substitute for labor—as it is typically intended to do. However, automation also complements labor, raises output in ways that leads to higher demand for labor, and interacts with adjustments in labor supply. Journalists and even expert commentators tend to overstate the extent of machine substitution for human labor and ignore the strong complementarities between automation and labor that increase productivity, raise earnings, and augment demand for labor. Changes in technology do alter the types of jobs available and what those jobs pay. In the last few decades, one noticeable change has been a "polarization" of the labor market, in which wage gains went disproportionately to those at the top and at the bottom of the income and skill distribution, not to those in the middle; however, I also argue, this polarization is unlikely to continue very far into future. The final section of this paper reflects on how recent and future advances in artificial intelligence and robotics should shape our thinking about the likely trajectory of occupational change and employment growth. I argue that the interplay between machine and human comparative advantage allows computers to substitute for workers in performing routine, codifiable tasks while amplifying the comparative advantage of workers in supplying problem-solving skills, adaptability, and creativity.
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We apply an understanding of what computers do to study how computerization alters job skill demands. We argue that computer capital (1) substitutes for workers in performing cognitive and manual tasks that can be accomplished by following explicit rules; and (2) complements workers in performing nonroutine problem-solving and complex communications tasks. Provided these tasks are imperfect substitutes, our model implies measurable changes in the composition of job tasks, which we explore using representative data on task input for 1960 to 1998. We find that within industries, occupations and education groups, computerization is associated with reduced labor input of routine manual and routine cognitive tasks and increased labor input of nonroutine cognitive tasks. Translating task shifts into education demand, the model can explain sixty percent of the estimated relative demand shift favoring college labor during 1970 to 1998. Task changes within nominally identical occupations account for almost half of this impact.
Demographics and Robots: Theory and Evidence
  • D Acemoglu
  • P Restrepo
Acemoglu, D., & Restrepo, P. (2017). Demographics and Robots: Theory and Evidence. Work in progress.
International Labour Organization
  • J H Chang
  • G Rynhart
  • P Huynh
Chang, J. H., Rynhart, G., & Huynh, P. (2016). ASEAN in Transformation: How Technology is Changing Jobs and Enterprises. Bureau for Employers' Activities Working Paper No. 10. International Labour Organization.
Do Labor-Saving Technologies Spell the Death of Jobs in the Developing World
  • C B Frey
  • E Rahbari
Frey, C. B., & Rahbari, E. (2016). Do Labor-Saving Technologies Spell the Death of Jobs in the Developing World. Paper prepared for the 2016 Brookings Blum Roundtable. Aspen, Colorado. 3-4 August.
Forthcoming. Robots at Work. Review of Economics and Statistics
  • G Graetz
  • G Michaels
Graetz, G., & Michaels, G., Forthcoming. Robots at Work. Review of Economics and Statistics.
Philippines Market Strategy. Credit Suisse Research Strategy
  • Credit Suisse
Credit Suisse. (2017). Philippines Market Strategy. Credit Suisse Research Strategy, 14 September. https://research-doc.credit-suisse.com/docView?language=ENG&format=PDF&sourceid=csp lusresearchcp&document_id=1079799301&serialid=SHRD8qOpL7xIvFqtbU8Gv7TpPWT eBNx4MjmMmGWQ75w%3D.