Technological change and employment: Creative destruction - ILO Asia-Pacific Working Paper Series - International Labour Organization

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                         Technological change and employment:
                                           Creative destruction

    ILO DWT for South Asia and Country Office for India    i

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ILO Asia-Pacific Working Paper Series

                                    Technological change and employment:
                                                       Creative destruction

                                                                       Dev Nathan1 and Neetu Ahmed2
                                                                                      November 2017

                    ILO DWT for South Asia and Country Office for India

    1 Institute for Human Development, New Delhi; also at the Duke GVC Center, USA and GPN Studies, India.
    2 Ph.D. student, IGNOU. New Delhi.
                                                                                                             i

ii   ILO DWT for South Asia and Country Office for India

Preface
We live in a time of uncertainty, not just in the business world but among ordinary people too. In a
developing country such as India, uncertainty about employment creation adds to the continuing condition
of a poor record in the creation of decent jobs. But some of this uncertainty is also due to the projection of
technical possibilities as short-term economic trends.

This paper shifts the basis of discussion of the impact of technological change on employment from
an extrapolation of technical possibilities to an examination of economic trends. The economic factors
working in a time of technological change are divided into macro-economic, sectoral and firm-level factors.
Bringing firms into the discussion of technological change is important as firm strategies and product
requirements together interact in producing firm-level changes. Firms, it must be emphasized, are the key
actors in carrying out technological change. Of course, they do this in a context of the macro-economy
subject to degrees of global competition. Thus, the paper argues that product requirements, e.g. changing
customer requirements in the IT services industry requiring end-to-end digital solutions, stringent hygienic
parameters in the pharmaceutical industry and quality standards in the automobile industry are all driving
firm-level adoption of automation.

The paper also points out that technological change is not a one-way process. There is not just destruction
of some jobs and even professions, but also the creation of new jobs and professions. The technologies
themselves require new jobs and professions in building the new infrastructure and providing service centres.
Witness the millions of new jobs in the technological infrastructure of mobile telecommunications. The
growth of productivity also leads to higher, though more unequal incomes, which leads to jobs in meeting
growing demands. Information and communication technologies have also made possible flexibility in the
location and even timing of task performance, providing a possible boost to the employment of women in
many sectors. The new platform-based services have created jobs in transport and tourism services. Along
with the spread of India’s digital infrastructure they are promoting a manner of organization in sections of
the unorganized sectors.

However, as the paper points out, there are some important features of technological change that require
urgent policy attention. First, is the inevitable declining employment intensity of production. Every
percentage point in GDP will be brought about by fewer jobs than before. This means that attention needs
to be paid to achieving and maintaining high rates of growth.

The second feature is that of growing polarization in the job market. The returns to high-skilled labour
are likely to increase, while those at the low-skilled end stagnate. This requires attention to strengthening
incomes at the low-skilled end of the labour market.

The third feature is that those who will lose jobs are often not the ones who gain the new jobs. And even
for those who are able to re-train themselves, there will necessarily be time-lags in these processes of re-
training and re-employment. All this together places even more importance on the need for a developing
economy, such as India, to build a comprehensive, universal and portable social security system.

Most of the discussion around the employment impact of technological change has been in the context of
high-income or developed economies. This paper, on the other hand, places the discussion of technological
change in the context of developing economies. In order to carry forward the discussion of technological
change in the context of developing economies, the ILO is pleased to release this paper for discussion as
a Working Paper.

                                                                                                Sher Verick
                                                                                           Officer-in-Charge

 ILO DWT for South Asia and Country Office for India                                                       iii

Contents

Introduction
Technology and creative destruction                                          1
Technological anxiety                                                        2
Technological change and employment: Technical and economic possibilities    3
Types of impact of technological change                                      5
Automation                                                                   6
 Flexibility                                                                 9
 Digital taylorism                                                           9
The platform                                                                11
Job creation                                                                12
Is Automation the end of outsourcing in GVCs?                               14
Current technological transformations in India                              16
 Digital Infrastructure                                                     16
 Platforms                                                                  17
 Robots in manufacturing                                                    18
 Pharmaceutical sector                                                      19
 IT services                                                                19
 Agriculture and food processing                                            20
 Garments and shoes                                                         20
 Start-up ecosystem                                                         21
 E-commerce                                                                 22
 Technological change and women                                             22
Conclusion: Employment effects in India                                     22

ILO DWT for South Asia and Country Office for India                           v

vi   ILO DWT for South Asia and Country Office for India

Acknowledgements

This paper was written for the ILO, New Delhi. Our thanks to Sher Verick, Sudipta Bhadra and Govind
Kelkar for discussions and comments at various stages of writing.

ILO DWT for South Asia and Country Office for India                                              vii

viii   ILO DWT for South Asia and Country Office for India

Introduction
In dealing with the consequences of technological change a lot of attention is given to the destruction
of jobs and of old types of livelihoods, while the simultaneous or sequential creation of new jobs and
livelihoods is often not given as much attention. This paper is based on the understanding that the process
of technological change is one of creative destruction, and not just one of unmitigated destruction alone.
Of course, the losers and gainers are often not the same people, which is a feature of technological
change that must always be kept in mind while fashioning policies to deal with technological change.

One must distinguish between technical possibility and economic likelihood. Just because something is
technically feasible does not mean that it will necessarily occur. What is economically likely depends
on a combination of macro-economic or economy wide factors such as the prices of land, capital and
labour, including separately the price of women’s labour,, and micro-economic factors, such as firm or
enterprise strategies and product markets.

The paper looks at different notions of technology and the appearance of anxiety caused by technological
change, particularly in times of changes in core technology, like the current period. The effects of
automation on job destruction, changing requirements from workers, the flexibility of working, and
the development of digital Taylorism are dealt with in this study. This is followed by considering the
development of Internet-based platforms such as Amazon, Uber, Airbnb, and Ola in e-commerce and
transport services, all of which have created new kinds of jobs and challenge established notions of the
nature of work.

We then turn to the question of whether the new technologies and the development of automation
will lead to the end of outsourcing in GVCs. This discussion is important since it sets the context for
discussing technological changes in India. After a brief discussion of some factors affecting firm-level
adoption of technological changes in India, the paper takes up a number of important technological
transformations that are currently underway in India. Besides employment numbers and types, gender
differences and gaps can also be affected by technological change. In the next section, different aspects
of the interaction of gender relations with technological change are brought together The paper ends by
taking note of the employment effects of these technological transformations in India.

Technology and creative destruction

Technology, according to the standard definition found in the field of economics, is the means of
production. An extended definition would call it a means to fulfil a human purpose (in the singular),
in the plural as an assemblage of practices and components, and in an overall sense as the entire
collection of devices and engineering practices available to a culture (Arthur 2009, 28). Within a set
of technologies or an assemblage of technologies in a period, there are some technologies that have
pervasive effects—effects that are economy-wide in nature or at least affect a large number of sectors.
These were called general purpose technologies (Freeman and Luca 2001, taking up from Bresnahan
and Trajtenberg 1995).

General purpose technologies help identify an era such as that of steam (along with iron and steel) in
the =eighteenth and nineteenth centuries, that of electricity and oil in the twentieth century, and then the
current information and communication technology (ICT) of the first decades of the twenty-first century
(Freeman and Luca 2001; Brynjolfsson and McAfee 2014; McAfee and Brynjolfsson 2017). ICT is
identified as the current general-purpose technology, which in the form of digitalization is affecting all

ILO DWT for South Asia and Country Office for India                                                        1

areas of production and even many aspects of social and political life with the rise of digital interaction
and social media.

It was Schumpeter (and Marx before him) who identified technological change as the key feature of
capitalism, characterizing it as creative destruction—the destruction of old forms of production and
the creation of new ones (1944). Any technological change has this feature of creation and destruction,
but when a general purpose technology is developed, then the change takes the form of a gale-force of
change which affects the entire economy. Some analysts (Freeman and Perez 1988) call this a change in
the techno-economic paradigm, where a general purpose technology makes possible a reordering of the
overall organization of production.

For instance, the development of steam as energy source enabled the development of the factory system,
with the centralized production of energy and centralized production of goods. Electricity led to the
development of the assembly line with its Ford-Taylor system of mass production. The development of
ICTs has cheapened the transaction cost of supervision between firms and promoted the splintering of
production between firms and even between geographies in global value chains (GVCs), the latter in
order to utilize labour arbitrage with different wage rates in different countries (Gereffi 2018, Baldwin
2016, Nathan, Tewari, and Sarkar 2016). ICTs are now being developed and deployed across every
economic sector and over all social, and even political fields. This is a change in core technology that
is spreading its effects all over the economic and society. The latest phase of this change with ICTs is
sometimes identified as the Fourth Industrial Revolution (Schwab, 2016). But in the perspective of
changes in core technologies, the current change due to the development and even generalization of
ICTs can be better as a phase of the deployment of ICTs, rather than as a change in core technology
itself.

Technological anxiety

A time of change is always a difficult time, and creative destruction is always both creation and
destruction. New jobs are created and old jobs are destroyed. However, those who benefit from the
new jobs are often (one might even say usually) different from those who lose the old jobs. When the
horse-cart was replaced by the automobile, Schumpeter pointed out, ‘In general, it is not the owner
of the stage coach who builds railways’ (1944); but this is equally true of workers. One would expect
that it was not the horse-riders who became the car drivers, just as it was not the lead typesetters
who became the new computer text composers in computerized printing. Similarly, when women’s
hand embroidery done at home was replaced by machine embroidery in the factory, those women lost
their function, while men got the jobs to operate the embroidery machines in the factory. The absence
of a coincidence between losers and gainers inevitably leads to technological anxiety and defensive
struggles of displaced workers, as was the case with the Printers’ Unions against early computerization.

Any replacement of a technology by another would lead to some manner of anxiety. However, when
the transformation is in the form of a change in the general purpose technology, which is a change
that is likely to affect most if not all sectors of the economy, then such a period can well be termed a
period of technological anxiety, as Mokyr, Vickers, and Ziebarth call it in their paper on the ‘History of
Technological Anxiety’ (2015). As Freeman and Luca point out, ‘The new techno-economic paradigm
imposes new rhythms of mental and manual work that challenge the traditional norms of production and
lead to defensive struggles’ (2001: 357).

Of course, this is not the first period of technological anxiety, as pointed out in the Mokyr paper. At
the beginning of the mechanization of the textile industry, there were the Luddites and others who
opposed the introduction of machinery and the factory-system as that would destroy old craft jobs.

2                                                              ILO DWT for South Asia and Country Office for India

The late-nineteenth century shift to mass production in the assembly line also led to technological
anxiety. Currently, one is witnessing technological anxiety once again. This is so both in the developed,
high-income countries (HICs) of Western Europe and North America where there exists the fear of
the continuing ICT-enabled shift of jobs to Asia and other parts of the developing world; such anxiety
is also there in India and other developing countries, where there is the fear that the digitization of
production processes, such as additive manufacturing (also called 3-D printing) could lead to a re-
shoring of manufacturing jobs in high-income countries as the advantages of cheaper labour would
vanish in the face of digital automation.

Technological change and employment: Technical and economic
possibilities

Some contemporary analyses deals with what we can call technical possibilities. The Frey-Osbourne
analysis (2013), for instance, lists jobs on a risk scale based on the technical possibilities of replacement
by machines (2013). They do not consider the economic possibilities of such deals. Newspaper and
other reports using the Frey-Osbourne analysis equate the possibilities of replacement by machines with
actual threats to jobs, and that, too, of a relatively immediate nature. An economic analysis, however,
needs to move from technical possibilities through firm-level analysis to establish that what is technically
possible may or may not come about. In a more comprehensive manner one may talk of enterprise-
level analysis, regarding households and the self-employed as enterprises too. At a methodological
level, it is not just about macro-economic tendencies but also involves linking macro-economic with
micro-economic factors, with the interplay of these factors leading to actual outcomes in the adoption
of technologies. This could also be characterized as the replacement of an analysis of supply-side or
technical factors with one that combines supply and demand analysis. This can also be characterized as
opening the ‘black box’ of technological transformation, bringing economic and other social processes
into the discussion (Heeks and Stanforth 2015).

The World Bank does make such a distinction in identifying technical and economic possibilities. In the
World Development Report analysis, about sixty per cent of jobs in India can technically be automated.
However, this comes down to forty-two per cent when time lags are taken into account. The time lags
are due to the difficulties in adoption of the new technologies, lower wages, and a higher prevalence of
jobs based on manual dexterity (World Bank 2016, 126). What is missing in the World Bank analysis,
though, is the introduction of firm or enterprise strategies. The more recent World Bank study (Hallward-
Dreimeier and Nayyar 2017) does bring in firm strategies, particularly in the discussion of the likely
effects of ICT and the Internet of Things (IoT) on global value chains (GVCs).

The McKinsey Global Institute (2017) starts with the position that it is not just technical feasibility that
affects the pace and extent of automation. This is also affected by the cost of deploying automation,
labour market dynamics, the benefits of automation beyond labour reduction, and the social acceptance
of automation.

Under what economic and profit-maximizing firm-level choice conditions is computerization or the
automation of jobs likely to occur (Verick 2017)? Answering this question is necessary to understand
the likely path of employment change. The answer to Autor’s question (2014) ‘why are there so many
jobs?’ may lie in these conditions and not in the technical limits to machine learning in tacit knowledge
tasks put forward by him. This is something that needs to be explored, not by a technical but by an
economic analysis.

Such an economic analysis needs to be carried out with a differentiation between macro-economic
conditions in high-income countries with respect to those in low/medium-income countries and between

ILO DWT for South Asia and Country Office for India                                                        3

firms with different strategies. A good summary illustration of differences in the adoption of automation
is in the density of robots installed in different countries, density being the number of robots installed
per 10,000 employees.

Table 1: Density of robots by country
    S. No.    Country                                           Robot density (2014)                      Rate of growth (%)
    1         Korea                                                      478                                     12.0
    2         Japan                                                      323                                      0.1
    3         Germany                                                    282                                      4.0
    4         US                                                         155                                       11
    5         China                                                       36                                      35
    6         South Africa                                                22                                      22
    7         India                                                         2                                     NA
    Source: IFR World Robotics 2016 Report, quoted in Nasscom, FICCI, EY 2017: 47.

There is a clear difference between the density of robots in high income countries (South Korea, Japan,
Germany and the US), where the density figures are in the hundreds, and middle-income countries
where the figures are in two-digits, which ends up going down to just two per cent in India.

An additional point stands out in the Table 1—the highest rate of growth of robot density is in China.
It is expected that China will possess the largest number of robots in 2018 (in terms of the highest
robot density by country). Is this a sign of the changing macro-economic conditions of China or of
the strategies of Chinese firms seeking to dominate not just domestic markets but also international
markets in many high-tech spheres? Hallward-Driemeier and Nayyar (2017) attribute this to attempts
by Chinese firms to retain low-value capturing manufacturing segments as wage costs go up.

The point being made is that the extent and rate of change of robotisation both need investigation
in terms of macro-economic conditions, firm strategies, and product requirements. This is an area of
analysis where not much attention has been paid. For instance, off-shoring to low-wage countries may
well be an alternative to automation. Under what conditions does on-shoring or near-shoring replace
off-shoring? Under what conditions is the unbundling characteristic of GVCs likely to be replaced by
rebundling in smart factories?

We will look at one example to illustrate this issue. The Indian company SF made high-tensile radiator
caps for GM. In 2011, because it asked GM for USD 1.03 per piece, it was outbid, not by a lower-wage
Asian country, but by a high-wage Austrian firm. The Austrian firm developed an automated production
process which not only produced at a lower cost but also provided greater precision, more consistent
quality, and shorter turn-around times (Tewari, Veeramani, and Singh 2017).

What is interesting is that the Indian company, when deciding to compete, decided to locate its own
automated factory not in high-interest and low-wage India but in a low-interest and high-wage country.
What counts in such decisions is not just wages but the overall cost of production. Interest, wages, and
rents for land all come into the picture when this cost is calculated.

We take another illustration where the high price of land pushed the mechanization of what was a manual
process. The example chosen is the processing of raw cashew, usually carried out by large numbers
of women. In Koraput, one of the poorest districts in India, cashew processing is done manually in
factories in rural locations, but is mechanized in urban locations. The high price of urban land compared
to cheap rural land pushed this decision to mechanize cashew processing.

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Comparing across economies and even regions—low-priced land will retard automation while a high-
priced land will promote it, a low interest rate will promote automation while a high interest rate will
retard it, and a low wage rate will retard automation while a high wage rate will promote it.

The price of labour is an obvious influence on the extent of automation. One would expect that a Toyota
plant or an Amazon warehouse in India would be less automated than their counterparts in Japan or the
USA. That is so. However, a somewhat unexpected factor affecting the extent of automation is that of
the collective strength of the workers. The Maruti plants in the Gurgaon-Manesar area were witness to
a series of strikes in the period 2011-13. Following this, the extent of use of robots in the production
process was increased and from a few hundred it reached 1100, compared to 7000 workers in 2016.

In a way, it might be said that the organized work force and the possibilities of work stoppages trigger
the substitution of workers by robots. Discussions in other automobile-producing clusters reveal that
the management in other firms learnt from the Gurgaon-Manesar events and stepped up the adoption of
robots. Between 2010 and 2014, robot sales to the automobile industry increased by an average of 27
per cent per year (Phillip, 2015a).

Besides the economy-wide factor prices mentioned earlier, there are also firm-specific and product-
specific factors that can affect the adoption of automation. High-quality and high-precision products
would tend to have more automation involved in their production. At the same time, firms may adopt
strategies to move into certain product niches for high-quality and precision products and thus adopt
automation. These effects are not in order to substitute labour, but in order to produce to required
standards and precision.

The driving factor in the adoption of robots in India has probably been the need for high precision
in production. The BMW plant is more automated than other assembly plants in India—the reason
being the need for assuring quality and precision in production. However, the trend of robotization is
spreading to other automotive plants. Shripad Ranade, Head, Automotive & Engineering, Tata Strategic
Management Group, said, ’The changing global and Indian scenario has made it important for the
industry to consider leapfrogging towards the advanced manufacturing trends. It is imperative for
stakeholders to improve the adoption by focusing on driving awareness of these trends, emulating
global best practices, forging industry-academia connect and up-skilling workforce.’ (Tata Strategic
Report, 2016) Also, ’Sharp styling, and usage of new materials for crash and safety requirements
are fuelling the demand for automation,’ said Anil Sinha, Vice-President, Manufacturing Operations,
Passenger Vehicle Business Unit, Tata Motors (Phillip 2015b).

What the factors discussed here show is that it is necessary to look at economy-wide, industry-wide,
and firm-level factors in the adoption of automation and other new technologies. When one looks at the
adoption of new technologies that affect not just one sector but at core technologies that can affect all
sectors of the economy, one can be sure that it is a time of change—a change that will take place varied
speeds in different economies, sectors, and even in firms.

Types of impact of technological change

Three types of issues come up in the discussion of the impact of technological change on employment.
The first set deals with the volume of employment; the second with the skill requirements of the new
employment; and the third with the nature of work in terms of the satisfaction derived by workers. We
will discuss all three issues.

ILO DWT for South Asia and Country Office for India                                                     5

The context of the discussion is that of the current spread of ICT through the digitization of processes
in the production of both goods and services. The technologies include automation systems, robots,
additive printing, artificial intelligence (AI), machine-learning, and cloud computing, all of which
together can be labelled the machine, as in McAfee and Brynjolfsson (2017). These machines have also
been used to create platforms such as Uber, Ola, and Airbnb. These platforms have had very different
effects on employment when compared to those caused by the deployment of ICTs within enterprises.
The effects of platform-based businesses will be dealt with separately as platforms.

Technologies impact the performance of work through a number of processes, such as mechanization,
automation, and flexibilization. Some forms of work can be mechanized—this is an historically old
effect of technological change. With ICT-based digital technologies some work can be automated,
substituting machine for human labour. ICTs can also enable work to be performed remotely or at
different times, thus enabling flexibilization in the performance of work.

Automation

Which segments of workers are more likely to be affected by automation? This depends on the tasks they
perform. Recent analyses of labour markets (Acemoglu and Autor 2010 and Autor 2013) emphasize the
necessity of considering that ‘the fundamental units of production are job tasks, which are combined
to produce output’ (Autor 2013, 3). Tasks are divided into three types—(1) high-skill, analytic, and
problem-solving tasks; (2) middle-skill, routine, or codifiable manual and office tasks; and (3) low-skill,
in-person, service tasks. In order to perform these tasks, workers need to have the required capabilities.
Here, capabilities are defined as the competences that enable a worker to perform a task or utilize a
technology in production (modifying Nubler 2013, 122).

Capabilities are based on the knowledge sets possessed by an individual along with the skill in utilizing
that knowledge to perform tasks. The knowledge set, following Polanyi, is divided into two types—
explicit and tacit knowledge. Explicit knowledge is that which can be formalized in routines, such
as, ‘if x then y’. Tacit knowledge, however, is of the type about which, as Michael Polanyi put it, ‘we
know more than we can tell’ (Polanyi 1966, 4). Based on Polanyi’s paradox, it has been concluded that
automation can impact tasks based on explicit knowledge or routines that can be set down in formulae,
while tasks based on tacit knowledge cannot be automated (Autor 2013). Consequently, the middle-
skill, routine, or codifiable manual and office tasks are the ones that are said to be most susceptible
to automation, leading, in a sense, to the hollowing-out of the middle; high-level analytic tasks and
low-level personally delivered service tasks do not get automated, while the middle-skills tasks are
automated.

More recently, however, with the advent of machine learning, that is, where machines do not just
perform according to embedded algorithms but learn from the patterns that they observe, it is argued
that many high-skill and analytic tasks can also be automated (McAfee and Brynjolfsson 2017). The
nature of machine learning goes beyond performing on the basis of the algorithms that humans feed
into them. Instead, much like the way in which children learn a language not from knowing the rules,
but by observing patterns they observe in people speaking (along with some correction), computers
can also learn from analysing data sets. The more sets of data (big data) there are, the more they can
detect patterns and follow them, without any explicit codification of rules. This, for instance, was the
method followed in teaching computers to translate or to play the Chinese game Go. The result was not
a translation such as one would expect of a literary work, but a workable translation that would do for
most situations. Of course, such machine translation will require human checking to remove any errors
that crop up.

6                                                              ILO DWT for South Asia and Country Office for India

This advancement of AI to what is called machine learning has led to a debate between Autor (2015) and
his MIT Business School colleagues McAfee and Brynjolfsson (2017). Autor asks ‘Why Are There Still
So Many Jobs?’ and answers his question by pointing to the strong complementarity between machines
and labour. In the earlier paragraph we pointed out the role of humans in checking machine translation.
This is a complementarity between machines and labour in performing the task of translation, enabling
higher productivity. Thus, one needs to look not just at substitution between machines and labour but
also complementarities between them, or how machines augment labour. As Autor puts it, ‘Focusing
only on what is lost misses a central economic mechanism by which automation affects the demand for
labour: raising the value of the tasks that workers uniquely supply’ (2015: 5).

However, this mechanism of raising the value of tasks performed does not affect all workers equally—‘…
there’s never been a better time to be a worker with special skills or the right education, because these
people can use technology to create and capture value. However, there’s never been a worse time to be
a worker with only “ordinary skills” and abilities to offer, because computers, robots, and other digital
technologies are acquiring these skills and abilities at an extraordinary rate.’ (Brynjolfsson and McAfee
2014, 11).

The Polanyi paradox of implicit knowledge that cannot be reduced to rules or routines was thought to
set a hard boundary for the kind of tasks could be automated. The advent of machine learning, however,
aided by vast quantities of data and computer programmes of neural networks, have turned this hard
boundary into a soft boundary. For instance, a lot of medical diagnoses can be automated as machines
find patterns between symptoms and diseases. Of course, the machines cannot tell us why those patterns
occur. That is the role of scientific investigation. Machines can tell us, though, which treatments seem to
have worked and their rates of success. These capacities of computers can be used to reduce the burden
on doctors, leaving them the task of making the final decisions on diagnosis and treatments. They would
also reduce the cost of healthcare since highly paid high-skilled doctors would be required to spend less
time on each patient. It would also help spread medical care through tele-medicine to poorly connected
rural areas.

However, even this augmentation involves the substitution of intelligent machines for some part of the
labour. The net result is that for any population, the number of high-skilled doctors required would be
lower than earlier. Furthermore, within an organization there is likely to be some division of labour,
with low- or medium-skill tasks being performed by lower-level professionals while the high-skill tasks
are performed by the higher-level professionals. Due to automation, the number of the former would
decrease. Thus, even the augmentation of capabilities by combining human and machine work would
lead to some substitution of machines for labour performing low- to medium-level tasks.

One effect, however, would be unambiguous—there would be an increase in the skill level required
from those working with the machines. They would need to be able to perform the right kind of data
analysis and there would be redefinition of many jobs. However, there has been a long debate on de-
skilling as the result of mechanization or even the contemporary role of computers.

This debate goes back to Adam Smith who wrote, ‘The man whose whole life is spent in performing
a few simple operations … generally becomes as stupid and ignorant as it is possible for a human
creature to become” (quoted in Mokyr et al 2015, 38). This was echoed by Harry Braverman (1974)
who similarly held that computers by taking over the task of setting industrial machines were leading
to the de-skilling of workers.

Adam Smith was writing in the context of craft work being replaced by the very division of labour
that he espoused as the base of increasing productivity of the factory over household handicraft

ILO DWT for South Asia and Country Office for India                                                       7

production. Piore and Sabel (1994) expected that with the rise of small batch or flexible specialization
as opposed to mass production, there would be a revival of workers with all-round skills. However,
what has happened in garment manufacture, for instance, is that customized production has moved from
artisanally tailored production to customized assembly-line production. Raymonds and Aditya Birla
both produce customized or bespoke garments; but after measurements are taken or details entered in
the order page, the production is carried out in assembly-line production in the factory. There is still a
role for multi-skilled workers, but mainly in order to substitute for workers of any particular skill that
are absent at any time.

The development of automation makes the Braverman thesis irrelevant, since what is occurring is the
replacement of low- and medium-skill workers by automation. It was found that the demand for routine
skills, both physical and cognitive, declined sharply in the USA between 1980 and 2012 (David Deming
quoted in McAfee and Brynjolfsson 2016, 321). This result could be the effect of a combination of
automation and off-shoring, which would tend to primarily affect the work segments that require low to
medium skills (Nathan 2016).

What is different about the current destruction of jobs is that it is not confined to low-skill routine jobs,
but includes a large section of middle-level service jobs. There have been many modelling exercises
that attempt to estimate jobs likely to face automation. The World Bank, the McKinsey Global Institute,
and other such agencies have conducted such exercises.

We summarize a detailed modelling analysis of 702 occupations by Frey and Osborne (2013). They
placed jobs that are likely to face automation in three categories—high risk, medium risk and low
risk categories. The analysis produced the result that forty-seven per cent of U.S. jobs fell in the high-
risk category, that ‘could be automated relatively soon, perhaps over the next decade or so’ (Frey and
Osbourne 2013, 44). Twenty-three per cent of U.S. jobs fell within the low-risk category, while twenty-
nine per cent were in the medium-risk category.

This model predicted that most workers in transport and logistics occupations, along with the bulk of
administrative and office support workers, and labour in production occupations, are in the high-risk
category. The high-risk category also includes many jobs in service occupations.

The low-risk category includes jobs in education, healthcare, the arts and media. Management,
business, and finance are also in the low-risk category. However, these jobs will involve high levels
of complementarity with computers, as many of the routine data analyses that feed these jobs are now
performed by computers. These are jobs that are characterized by a high level of social intelligence (Frey
and Osbourne 2013, 40-41). Engineering and science occupations also fall in the low-risk category and
they are characterized by the high degree of creative intelligence they require.

Tasks requiring manual dexterity, finger dexterity, and cramped work spaces fall in the medium-risk
category. Technology could be developed for robots at some point in time to be able to take over the
handling of fabric, which is soft and pliable. As Frey and Osbourne point out, ‘The computerization
of occupations in the medium risk category will depend on perception and manipulation challenges’
(2013: 39).

However, as pointed out earlier, it is necessary to make a distinction between technical and economic
possibilities, bringing macro-economic variables and firm strategies into the analysis.

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Flexibility

The Internet allows for some flexibility in the location of work. In IT service provision, it is possible for
work to be split among team members and be carried out in different locations. However, the integration
of all work conducted in different locations is not seamless. A knowledgeable team leader is required
to both divide and integrate the output. This can lead to team leaders being valuable. Women in such
positions should be provided some flexibility in working from home during pregnancy and childbirth.
Thus, one should expect that for women with special skills, a flexible location may be an option that
firms may offer to retain such high-skilled women (Nathan et al 2016).

The outsourcing of software and related online work takes place not only to companies in India and
elsewhere, but also to individual workers. This is also a form of the gig economy, where payment is
on the basis of tasks carried out. In an old-fashioned manner this would also be known as piece-rate
payment with flexible work. Economists Otto Kässi and Vili Lehdonvirta of the University of Oxford
created an Online Labor Index (OLI), which measures the utilization of online labour across countries
and occupations by tracking the number of projects and tasks posted on platforms in near-real time.
India is the leading country, with a 24% share of the online labour observed (The iLabour Project
2017). South Asia as a whole accounts for more than fifty per cent of online labour involved in software
development.

However, for many types of work, flexible working hours or flexible locations have resulted in a gender
gap in earnings, leading to the characterization of flexible work with little prospect of promotion as the
mommy track (see https://en.wikipedia.org/wiki/Mommy_track). A study of the Indian IT industry also
pointed to the gendered constraints of parenting on women’s promotion (Kelkar, Shrestha, and Veena
2005). Goldin (2014) points out that there is a non-linear relationship between the ‘number of hours
worked and particular hours worked’ and earnings in a number of sectors. In sectors such as technology,
science, and health, though, there is a linear relationship between hours worked and earnings, with no
extra for the kind of hours worked. In these sectors the gender gap in wages has declined. Consequently,
the potential of flexible location and timing needs to be supplemented by firm policies that create a linear
relationship between hours worked and earnings, so that flexible working hours do not disadvantage
women.

Digital taylorism

The Taylorist doctrine of worker management was based on the division of complex labour
into simple tasks, for example, as done on an assembly line, and the separation of mental and
manual labour. This method of worker management was epitomized in the Charlie Chaplin
classic Modern Times. Henry Ford was famously reported to have asked, ‘When what I want
is two hands, why do I also get a brain?’ This management system changed with the introduc-
tion of what are called High Performance Work Systems, starting with Volvo in Sweden and
made famous as the Toyota method of worker involvement. In place of the assembly line was
the work group, with workers possessing multiple capabilities. Workers were also expected to
use their brains and come up with suggestions for process improvements to reduce costs and
increase productivity. Besides a basic pay scale, worker remuneration also included incentive
payments based on output.

The measurement of workers’ output has taken a leap forward with the switch from analogue to digital
methods of data collection on the work site, whether it is the shop-floor, an office, or even the road.
Sensors can now be used for monitoring the exact time used by workers in performing different tasks,
for example, in answering calls in a call centre. Any time spent off-work, such as in going to the toilet
or drinking water or tea/coffee —can be strictly monitored. Work itself can be divided into fine-grained

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tasks, with time allotted for the completion of each task. The use of sensors and CCTVs at different
work stations enables a system of surveillance that fulfils the Benthamite dream of the Panopticon, a
machine that could see a person’s every move. Digital technology and all the data collected has made
Bentham’s Panopticon a reality, in the form of Digital Taylorism through codifying, capturing, and
digitalizing their work (Brown, Lauder, and Ashton 2010).

All the data can then be used to evaluate worker performance. Those who do not meet the standards can
be eliminated. This use of digital data to eliminate is carried a step forward by linking it with the Bell
Curve—even in high-performance teams that meet the required benchmarks, there will always be some
employees at the left tail end of a Bell Curve. This system has been used to eliminate the bottom five to
ten per cent of employees in a team or section.

This method of worker assessment was introduced into manufacturing by Jack Welch, who was the
CEO of GE at that point. It has been adopted by many IT firms such as Google, Microsoft, Adobe,
and Accenture. The Indian IT service firms TCS, Infosys, and others also adopted Bell Curve methods
of eliminating the employees falling into the left-hand tail area of the curve. The results of employee
burnout with high performance and long hour requirements were highlighted in a New York Times report
on work in Amazon (Kantor and Streitfeld, 2015), a report that led Amazon CEO Jeff Bezos to say that
he did not recognize the company described in that report.

What has happened to Amazon’s employee management system after that exposure is not clear. However,
many IT companies have abandoned the Bell Curve method of assessment. The IT sector is subject to
more rapid technological changes compared to other sectors. The Clock Speed of technological change
has speeded up and any technological gain is only temporary (Fine 1998). Consequently, firms need to
be innovative in order to be ahead of the curve. The decline of once powerful tech firms such as Yahoo,
Nokia, and Blackberry is testimony to the need for constant innovation. Such constant innovation
requires a high degree of worker involvement—something that is not fostered by the Bell Curve method
of eliminating workers. As a result, many IT firms—Google, Microsoft, Adobe, and even some Indian
IT firms, Infosys, TCS and Wipro—have begun to abandon the Bell Curve.

The chief of Cisco’s human resources department is quoted as saying, ‘From an employee’s perspective
it [the bell curve] is the most hated process that you have,’ (Francine Katsoudas, quoted by Sujit John,
2015). Such an employee perspective is to be expected. What is interesting is the next part of her
statement: ‘Even leaders are saying they are not getting what they want from the system.’ The reason
(team) leaders do not get what they want could be that this system promotes competition among team
members when cooperation needs to be fostered. The system forces a team to have some ‘losers’, even
when the team is doing well. This is not good for employee morale and motivation; it may not be good
even for team performance.

Digital Taylorism, however, continues in routine office tasks, such as in call centres. However, there it is
now meeting with individualized resistance from employees. In a Weapons of the Weak-manner (Scott
1985) employees carried out methods of ‘bounded performance, feedback diversions and vacillations’
(Noronha and D’Cruz 2016, 437). The high rate of attrition in the Indian IT and ITES industry the
authors attribute to forms of fight-back against Digital Taylorism—where protest is not possible, the
option is an exit in Hirschmanian terms (1970).

Innovation resulting from R&D in company laboratories and research in university departments was
thought to be the preserve of technologists and scientists. Von Hippel democratized innovation by
bringing in lead consumers into the process (2005). While there is substantial discussion of the role of

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workers’ capabilities in firm performance, we still lack an analysis of the roles of shop-floor workers in
firm-level innovation.

The platform

If the machine and automation changed the nature of work within the firm or enterprise, the nature of
the firm itself has been changed by the development of Internet-based platforms such as Amazon and
Alibaba in e-commerce, Uber and Ola for transport, and Airbnb in hospitality services. What these
platforms enable is the provision of services, bringing together those who supply services and those
who demand them. However, the platforms are not mere clearing-houses like the Walrasian auctioneer,
merely matching price and supply with demand. They are new market-firm hybrids (Sundararajan
2015, 190) that centralize certain activities—branding, trust, payments, and sometimes pricing and
customer service, while decentralizing other activities—supply infrastructure creation and actual
service provision.

The impact of platforms has been very substantial in services provided with products that have high
asset value (for example, houses and cars) and low frequency of use (Gansky, 2010). The result has been
what is called an asset-light economy. While personal cars are generally used onlyfive per cent of the
time, cars with Uber or such transport platforms are used for up to fifty per cent of the time (McAfee
and Brynjolfsson 2017, 197). This results in lower ownership of cars. In the USA, by 2013, those born
in the 1980s or 1990s owned thirteen per cent fewer cars than the generation before them at the same
age (McAfee and Brynjolfsson 2017, 197).

Besides increasing the use of under-utilized assets, the platform systems have also created new jobs
while destroying some old jobs. Traditional taxi drivers have lost jobs. In Los Angeles, within three
years after the arrival of Uber and Lyft, traditional taxi rides went down by thirty per cent. In San
Francisco, , Yellowstone Cab Cooperative (the largest taxi company), filed for bankruptcy in 2016
(McAfee and Brynjolfsson 2017, 201). At the same time, jobs created in the USA due to these online
platforms or due to the need for on-demand workers were estimated atthree million in 2014 and is
expected to be seven million by 2020 (Sundarajan, 2017: 160).

Michael Spence captured the impact of the internet-based platforms as he wrote, ’Indeed those who
fear the job-destroying and job-shifting power of automation, should look at the sharing economy [of
the Internet-based platforms] and heave a sigh of relief’ (2015). Millions of jobs have been created.
However, there have been protests against Uber by traditional taxi services in many cities as the value
of their licenses has fallen. There have also been questions about the distribution of income within the
platform systems, between the platform owners and the service providers. ‘The big money goes to
the corporations that own the software. The scraps go to the on-demand workers,’ said Robert Reich,
former U.S. Secretary of Labour (quoted in Sundarajan, 2017: 161).

With regard to the quality of employment, Steve Kasriel, the CEO of the labour platform Upwork, said
at the World Economic Forum, 2015, ‘The younger generation really aspire for this kind of career. They
don’t want the nine-to-five job, working with the same employer, needing to be on-premise. They like
the flexibility, they like the independence, and the control they have’ (quoted in Sundarajan 2017, 162).

A number of studies have shown that the hourly earnings from the digital labour market, even after
they pay the platform its commissions, are higher than those from the traditional market. A study of
Indonesia’s on-demand transport workers showed that eighty-two per cent of them thought that this paid
better than their previous employment (Fanggidae, Sagala, and Ningrum 2016).

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The chief concerns, however, have been over whether the earnings will amount to a living wage, and the
absence of social security protection. Social security in the USA is linked to being an employee and the
employer-employee relation is denied in the platform world, with service providers being considered
independent contractors or micro-entrepreneurs.

Faced with protests, some on-line services have categorized their service providers as part-time or full-
time employees. This would make them liable to contribute to their social security. There is no doubt,
though, that what we are dealing with is a new category of workers. In India and other developing
countries we are familiar with the category of self-employed workers—those who work with their
own means of production, buy inputs, and sell their outputs on the market. In this case, online service
providers utilize their own means of production, whether they are cars or service instruments. They also
sell their services, but not in the same manner as the independent sellers, since they sell through the
platforms. In addition, as Sundarajan points out, the platform firms carry out branding and some other
non-core functions. Thus, the platforms have a stake in the quality of service provided by the online
workers.

The key question has centred on the absence of social security for these platform workers. This is
not a problem in the Scandinavian countries where the state provides social security. However, in
countries where social security is linked to employment by an identified employer, the lack of proper
employee status becomes a problem. Can the way forward be by providing social security to all workers
(one might add paid and unpaid) by the state, funded by taxes, including the earnings by the platform
companies? A group of prominent individuals in New York proposed a system of benefits that are
portable, independent, and universal, that is, independent of employment status. In addition, they also
proposed that businesses should be allowed to develop their own safety net options (Sundarajan 2017).
The variability of earnings in such individually remunerated performance of tasks (the gig economy
as it is called) has also led to proposals for provision of a basic income (Ulrich Beck) and ways of
protecting income stability. The idea of a basic income has become an issue in contemporary politics in
some European countries.

Platform businesses rely on feedback from customers and service providers evaluating each other. In
this process, it is the evaluation of service providers by their customers that is important, since poor
ratings can not only mean the loss ofbusiness, but can even result in the service provider being dropped
from the platform altogether. This evaluation by customers seems like a good thing, enabling one to
check a service before entering into a contract. However, there is an element of what has been called
Data Darwinism (Sundarajan 2017, 201) in user-generated ratings. ‘The strong get stronger. The fittest
survive.’ The competition may not always be fair and ratings can be manipulated.

Improving working conditions have usually resulted from what Karl Polanyi (1944) called the second
movement, when social forces try to bring market forces under control. Key to this is the role of the
workers themselves. Could there be new forms of associations of on-demand workers? The very large
number of workers involved in on-demand work and the formation of online worker communities, for
example, in China (Dewan and Randolph 2016, 8) point to ways in which the new technologies and
related forms of work organization might promote such new forms of workers’ associations.

Job creation

Is technological change only a matter of job destruction through automation and related processes? In
the example of the Internet-based platforms, it is seen that there has also been also a net creation of jobs.
Jobs of the older type of providers of taxi rides have been destroyed, but new jobs have been created
for new providers. This is not just the replacement of the old with the new, with the numbers remaining

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unchanged. The new form of mobility services seems to have induced some potential car owners and
self-drivers to forsake the thought of owning cars and driving themselves to opting for the one-trip-at-a-
time driven rides. This is an increase in jobs since it substitutes unpaid self-driving for paid chauffeured
rides.

Furthermore, since technological change results in an increase in productivity there is a growth in
income associated with technological change, even if there is also simultaneously an increase in
inequality which that policy needs to address. The increase in income with its concomitant increase
in demand, whether for new forms of consumption or for leisure activities, leads to job creation. With
growing demand, however, there is a change in its structural composition. Food becomes a smaller
portion of the consumption basket. At present, manufactures are also becoming a smaller portion of
world consumption, with an increasing role for services.

This is the base of the growth of new industries that come up to meet the new demands. One can only
point to the growth of television shows, spectator sports, computer games, and so on as new or growing
sectors of the economy. Thus, the growth of new sectors or structural changes in the economy are also
the result of technological change—not just job destruction, but also job creation on a higher scale
than before. Any phase of technological change must be seen in terms of both job destruction and job
creation (Freeman and Luca 2001, Perez 2002, Perez 2016, and Nubler 2017). Some of the job creation
is not due to the increased prosperity but is also created by the technology itself, such as computer
gaming growing out of the ICT industry.

As Brian Arthur (2009) emphasizes, though, job creation is not only for the purpose of meeting human
needs; there are also the needs of the new technology or the new production system itself. First, there
is an investment in possible ways of cheapening the new technology: ‘every technology by its very
existence sets up an opportunity for fulfilling its purpose more cheaply or efficiently; and so for every
technology there exists always an open or new opportunity’ (Arthur 2009, 54).

Every technology also requires a supporting system of technologies for organizing its production and
distribution. All the components and service stations require investment and jobs. Furthermore, there is
the major infrastructural change that accompanies a change in core technology. The railways required
an infrastructure of rail lines, check points, and railway stations, with their complexes of shops and
facilities. Automobile transport required highways, roads, petrol stations and so on. The growth of
Internet-based economic and social interaction requires an investment in the information highway with
its towers for 3G, 4G or broader bandwidth and global fibrr-optic networks.

It is necessary to draw attention to the growth of income, the rise of new industries to serve new
needs, the complementary requirements of each technology, and all the jobs that are created in the
process. Newspaper reports and popular discussions tend to concentrate on job destruction. However, as
emphasized in Schumpeter (and Marx too for that matter) destruction and creation also go on, sometimes
simultaneously, sometimes sequentially. Overall, there is also the expected growth of what is called the
consuming class, that is, those with a per capita consumption above USD 10/day.

A factor in growing demand, pointed out in the McKinsey (2017) study is that of the marketization
of previously unpaid domestic labour, largely performed by women. Large parts of child-care, care
of the aged, cooking and cleaning are being performed through marketed services. In the HICs these
changes took place some time ago, but in emerging economies these processes are currently underway.
This is the reverse of John Stuart Mill’s quip that if a man marries his maid, it would reduce the GDP
as the formerly paid service would now become an unpaid service! In the marketization of portions of
domestic work portions of unpaid work become paid work and thus increase GDP. A process of external

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