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ILO Asia-Pacif ic Working Paper Series

Technological change and employment: Creative destruction

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ILO Asia-Pacific Working Paper Series

Technological change and employment: Creative destruction

ILO DWT for South Asia and Country Office for India

Dev Nathan1 and Neetu Ahmed2

November 2017

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.

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PrefaceWe 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

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Contents

IntroductionTechnology and creative destruction 1Technological anxiety 2Technological change and employment: Technical and economic possibilities 3Types of impact of technological change 5Automation 6

Flexibility 9Digital taylorism 9

The platform 11Job creation 12Is Automation the end of outsourcing in GVCs? 14Current technological transformations in India 16

Digital Infrastructure 16Platforms 17Robots in manufacturing 18Pharmaceutical sector 19IT services 19Agriculture and food processing 20Garments and shoes 20Start-up ecosystem 21E-commerce 22Technological change and women 22

Conclusion: Employment effects in India 22

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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.

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ILO DWT for South Asia and Country Office for India 1

IntroductionIn 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

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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.

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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

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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 countryS. 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.

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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.

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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

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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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provision of these domestic services could occur not only through the private market but, as has been the case in the Scandinavian countries, only through the state provision of child care and care of the aged.

However, what is also true is that in such transitions there are both those who lose jobs and those who gain jobs, and the two are often not the same set of people. Even for those who are likely to be able to shift from older to newer jobs, there is the cost of re-training and the human cost of disruptions and lost income. It is not enough to point to the subsequent or long-term benefits of job creation. It is also necessary to fashion ways in which the human costs of technological changes can be lessened.

Social security measures are a necessary measure to reduce the costs of technological changes. They are doubly useful, not only in lessening the suffering of disruption of old livelihoods, but also in securing social acceptance of the transitions. With respect to England before the Industrial Revolution, t has been argued that a parish-based system of social protection provided a social minimum to English peasant-worker households (Sretzer 2007). This parish-based social provision existed well before the later Poor Laws. Such a social minimum helped make labour more mobile, since entitlements were not location-based. It also reduced opposition to the adoption of more efficient but labour-displacing methods of production such as mechanization. Sretzer contrasts the ease of such transitions that were productivity-increasing in England with the greater resistance to such change in the rest of Europe. There might well be a parallel in the contrast between South and North/Central India in the contemporary Indian situation.

As technological change increases the productivity per worker, in developed economies or high-income countries the current discussion is on the possibilities of a basic income along with a reduction in working hours. In addition, there is the possible rise of the generalist, a person who could be involved in more than one kind of work. In developing economies, however, the focus is on the universalization of social security, even if it is called a ‘Basic Income’ as in Davala et al (2015). This could play the role of enabling workers to shift between old and new jobs and also reduce opposition to technological change.

Is Automation the end of outsourcing in GVCs?

The application of ICTs was critical in developing out-sourcing and the creation of GVCs. ICTs reduced the cost of coordination or governance of production segments. As one would expect from the Coase analysis, a reduction in the cost of coordination of production would promote the shifting of some production segments from hierarchical governance within the firm to market-based governance across firms. The reduction of transport costs further reduced the cost of such coordination across national boundaries. The application of ICTs in conjunction with reduced transport costs led to the growth of outsourcing across geographies where labour and overall production costs differed. Lower-value and more labour-intensive tasks were outsourced to countries, largely in Asia, where wages were lower than in the headquarter economies of the USA, the EU, and Japan. The further spread of ICT technologies reduces coordination costs and thus promotes the fragmentation of production. This factor will continue to have an effect on GVC-based production in the current scenario. However, has the development of IT-based automation transformed the situation? Technological uncertainty in developing countries is largely based on the idea that automation would lead to the end of outsourcing in global value chains. The key point in the technological transformation leading to automation is that the decision on the location of a factory depends on the availability of labour with the necessary capability or knowledge and skills. For garment manufacturing or shoe manufacturing, the availability of skilled labour has not mattered much so far. It takes a very short while to train a poorly educated worker to operate a sewing machine. With, say, 3D or additive manufacturing, though,

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the capability required is much more complex. Given the availability of workers with the requisite capabilities, however, the wage at which the worker can be employed comes into the calculation.

Thus, the development of automation does not mean the end of outsourcing. What it means is that cheap labour will not be the deciding factor any more, but the cheapness of labour of the requisite capability will matter. This will also vary with the share of wages in total costs. Where wages (or salaries) have been a large part of production costs, say, more than fifty per cent of production costs (as in the case of software services), then the fact that Indian software engineers are paid only, say one-tenth of a U.S. engineer, does count in deciding on the location of outsourcing. However, where wage costs are only one or two per cent of the retail price, as in the case of garments, then the wage difference between workers in India or the USA would not matter as much. The fact that it still does matter is shown by the fact that the big brands and retailers are in constant search for the next cheap location for garment manufacture.

However, another factor could count in the decision to move from off-shore to on-shore or near-shore outsourcing. That is the requirement to deliver quickly and in scale. The advent of ‘fast fashion’ emphasizes quick delivery in response to trends on the retail floor. For example, Zara stores are directly linked backwards to internally-controlled production and fulfilment systems, which means that demand data flows unimpeded to the supply chain. Suppliers are forced to conform to shorter lead times. Total control enables the company to respond quickly to changing fashion and customer preferences. This control permits Zara to issue new designs in a short duration of time. Higher transport costs from off-shore locations would then need to be compared to lower costs from on-shore or near-shore locations.

Again, if the cost of production goes down with automation compared to the low-wage manual process then there would be a shift in location, though the production would still be outsourced. This is what has happened with the Indian auto-component manufacturer SF which lost its market for producing a low-value item, radiator caps, to an Austrian firm that automated the process.

Another feature of the changing geographies of outsourcing is that of high-quality production being carried out nearer to the designer, while low-quality products are produced at low-wage locations. This is the structure of high-fashion garment manufacture by Italian designers. The high quality items are produced in Italy, medium quality items are produced in Eastern Europe, while the low-end items are produced in China. The numerous prognoses that discount the possibilities of continued outsourcing do not take into account the manner in which value chain organizing of production, whether global, regional or national, has taken over the production process. ‘Concentrate on core competence and outsource the rest’ has now become the central mantra of industrial organization. The application of ICTs has reduced the cost of outsourced or market-based production compared to firm-based or hierarchical coordination. This has shifted the balance towards outsourced production coordinated by the lead firm with its suppliers as against intra-firm, hierarchical production of the same. When wage differences are added to this organizing principle, the result is a GVC. GVCs do not necessarily mean that production would be spread around the world, though. Areas with existing concentrations of production produce an agglomeration effect (Hallward-Driemeier and Nayyar 2017). China’s ‘supply chain cities’ are good examples of agglomerative concentrations of light manufacturers, which would be difficult to compete against.

Another factor brought into the analysis of the future of producing within GVCs is that of protectionist sentiment, such as seen in Brexit, Donald Trump’s victory in the USA, and the growing nationalist movements in France and Germany. Protectionism could take the shape of either restrictions on the movement of high-skilled persons or higher tariffs on imports. What are the likely effects of such moves?

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Take the case of the outsourced provision of IT services. The Indian IT industry is based on the Global Delivery System (GDS), in which outputs are broken into onshore and offshore tasks. The competitive advantage of the Indian industry lies in being able to perform the bulk of the programming in low-wage Indian locations. This competitive advantage is not affected by restrictions on issuing U.S. H-1B visas.

Coming to higher tariffs, how would they work? Complex products like automobiles have some 30,000 parts. Even smartphones have hundreds of components. These are imported from many parts of the world to wherever the assembly is carried out. Tariffs on the imports of these components would only make manufacturers in the country concerned less competitive on the world market. As Gary Gereffi (2017) points out, the USA exports cars and parts worth about USD 100 billion while importing parts from many countries, with especially large amounts from Mexico and Canada. Any tariffs on those imports would only make it harder to export the final products. Despite the political rhetoric, one would not expect serious tariff-raising in the developed countries.

In addition, one cannot extrapolate from the recent slowdown in growth of world trade. There is still growth, though it is occurring at just one or two per cent per year. This slowdown of trade growth is largely due to the failure of the developed economies to climb out the Great Recession. However, with most of the economies of Asia and Africa growing reasonably fast, these markets, though relatively small, will become more important as markets in which to seek growth.

Some of the analysis of the current situation in which automation is taking place, almost discounts the possibility of India increasing its exports. It is true that India has failed to increase its exports, but that is not because of the inability of world trade to absorb more Indian exports. It is because of the inability of Indian exporters to be competitive. Taking the case of garments alone, there is higher cost due to poor logistics. A McKinsey report points out that logistics costs in India are fourteen per cent of the cost of goods, as against six to eight per cent globally (McKinsey 2014, 9). In addition, Indian manufacturers have a poor a reputation for on-time delivery (Nathan and Harsh 2017). The result is that despite productivity-adjusted wages in China growing by a compound annual growth rate (CAGR) of eleven per cent between 2007 and 2014 (BCG and CII 2017, 34), not much outsourced production from China is moving to India.

The difference in the out-sourcing GVC paradigm is that while cheaper labour still matters in decisions about locating different production segments, the skill requirements of production in automated systems are much higher than that of traditional tailors or assemblers. Thus, it is cheaper labour of the high-skill variety that will count in being a destination for outsourcing in GVCs.

Current technological transformations in India

India has been and is going through a number of technological transformations. These are summarized below1.

Digital Infrastructure

The biometric unique identity system, Aadhaar, covers more than 1 billion people. On top of Aadhaar as a platform, a payments system and an electronic ‘know your customer’ (KYC) system have been built,

1 This summary is based on newspaper reports and analyses by McKinsey (2014), BCG and CII (2017), and Nandan Nilekani (2016).

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together called India Stack. This infrastructure can be used by technology start-ups and app developers to create new business apps and payments systems.

Smartphone usage has grown rapidly to reach 300 million in 2016. This means that Internet access already exceeds the population of the USA. Smart phone usage is expected to keep growing at twenty-five million per quarter to reach 700 million in 2020. It should also be noted, though, that the proportion of Internet usage is still quite low at less than 30 per cent of the population.

In addition, cloud services are spreading, reducing the cost of IT services to a fraction of what it would cost for businesses to set up their own IT systems. McKinsey Global estimates that up to twenty million SMEs in India could access digital business in the cloud (2014).

What are the likely impacts of these technological transformations? It will make it easier for SMEs to access larger markets and inputs, use up-to-date computer systems, and even try out the latest innovations. Identification on platforms can be the basis for formal credit. Tech start-ups can have widespread platforms on which to base their products. Digital payments systems with reliable identification can be developed. All this will enable SMEs and start-ups to become important contributors to growth. Start-ups are important for technology development as they do not have legacy business fields to sustain and can be agile in taking up technology challenges. Of course, when tech start-ups are successful, there is a tendency for established majors in the sector to take them over.

The development of this digital infrastructure will also allow for a reduction in intermediaries. This is one of the banes of India’s business structure, which reduces the revenue that reaches producers. For instance, work outsourced by garment brands to home-workers has so many intermediaries that a piece of work for which the brands pay INR 25 ends up by yielding INR 5 or just twenty per cent to the homeworker. The reduction of intermediaries could also help increase the scope for producer companies and other such collectives to establish themselves as aggregators of individual home-workers, providing them some economies of scale in addition to reducing the share of intermediaries.

Digitization of the logistics infrastructure could help reduce transport and logistics’ costs, provided there are improvements in physical infrastructure and in the ease of dealing with the Indian bureaucracy.

Platforms

Platform-based businesses are growing rapidly. Uber and Ola provide transportation services. Ola reports that it has 450,000 vehicles available through its system (Ola web site). However, this is not a net increase in employment as some jobs have been lost in the conventional taxi trade. The platform-based businesses, however, do promote a shift from the unorganized to the organized sector.

According to the Oxford Online Labour Index, India is the leading country in online workers, with a 24 per cent share of the online labour market of which 50 % are deployed in Software development & Technology (The iLabour Project 2017).

There are internet-based platforms for almost any kind of service, including cleaning, food delivery, and chauffeur services. E-commerce includes not only Indian businesses like Flipkart, but also Amazon. The e-commerce segment is growing rapidly with specialized e-commerce firms as well as the e-commerce arms of brick-and-mortar retail stores.

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The sectors in which digital platforms could spur growth are mobility solutions, food delivery, hospitality, health, personal services, and tourism. The DIPP (2017), as also BCG and CII (2017) all expect dynamic growth in these sectors because of the possibilities opened up by digital platforms.

Robots in manufacturing

The trend of adoption of robots in manufacturing is increasing because the cost of implementing automation is reducing over the years. Companies like Boston-based ‘Rethink Robotics’ are offering agile factory robots for as little as USD 25,000 (www.rehinkrobots.com/baxter), which is equivalent to paying a full-time human worker USD 4 per hour over the lifetime of the machine. With wages in India well below USD 4 per hour, the extent of substitution by robots is bound to be lower than in the USA or South Korea.

According to the International Federation of Robotics (IFR), in 2014, 2100 industrial robots were sold in India. It is estimated that this number could rise to 6000 in 2018. The operational stock of multipurpose industrial robots in India was 11760 in 2014. In 2015, the estimated figure was 14300 and it is expected to rise to 27100 by 2018.

According to the study done by NASSCOM, FICCI & EY (2017) Indian auto companies attested to the deployment of robots on the shop floor. While paint and welding shops are expected to be completely automated by 2020, the use of robots in the assembly line is limited to 20% across companies. Indian companies are also exploring the deployment of robots in the logistics function. With intra-plant logistics becoming automated, logistics costs are expected to reduce by 10%-20% and inventory costs by 30%-50% .

Table 2: Robots in the Indian Automobile IndustryCompany Number of Robots Number of Manual WorkersFord India, Sanad 453 (90% of work is automated) 2500

Hyundai Motor India, TN 400 4848

Volkswagen India, Pune 123 2000

Tata, Pune 100 N/A

Maruti Suzuki, Gurgaon 1100 7000

Bajaj Auto 100-200 Co-bots (Collaborative robots) N/A

Source: ASSOCHAM 2017

Robots are far from taking over manufacturing at automobile factories. In the assembly section, where a finish team fills a vehicle with a combination of parts and features, very few areas are automated. ’The assembly shop is largely manual, with automation accounting for only ten per cent of the total operations because jobs involving quality checks and visual inspection are best performed by people,’ says Sudhakar of Hyundai. The use of co-bots in automobile industry is not only increasing productivity and precision but also making the production process easy. It potentially provides an opportunity for female workers to use easy, flexible co-bots instead of heavy cumbersome robots. Bajaj Auto has installed 120 co-bots which are operational in Bajaj’s three plants. These perform various tasks, and fifty per cent of the workforce working on these assembly lines are women (Nair 2016).

Tata Motors is developing an in-house facility for manufacturing robots. TAL Manufacturing Solutions, a wholly-owned subsidiary of Tata Motors, has launched a Made-in-India robot catering primarily to micro, small, and medium industries. The firm claims that it is priced thirty to forty per cent lower than similar products. ’This is the first robot conceptualised and designed in India. TAL Brabo is suited not

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only for India, but also for manufacturing units across the world,’ said Amit Bhingurde, Chief Operating Officer (Robotics), TAL (Businessline, 2017).

Automation is affecting not only workers on the shop-floor but also management sections. On 23 May, 2017, Tata Motors’s CFO C Ramakrishnan said that the company had cut 1500 managerial jobs as part of restructuring (Kumar and Udaykumar, 2017). While the exact reason for the move wasn’t mentioned, automation of many mid-level jobs could be the reason.

As mentioned earlier, one factor in the use of robots is the management strategy of reducing the associational strength of organized workers. Another factor is that of meeting stringent product quality requirements.

Pharmaceutical sector

India’s Pharmaceutical sector is comparatively advanced in automation. Currently, many large pharmaceutical companies like Zydus Cadila, Torrent Pharma, and Cipla are focusing on automating their production process of drugs, especially in areas where the complete integration of machines and equipment is required to maintain hygienic production conditions. Pharmaceutical factories require tablets to be sorted based on colour, shape, and size, picked and placed accurately in the right containers. Automation using digital image processing through cameras are used in this process for such tasks. Other processes in the pharmaceutical sector deploying automation are RFID tags in the warehouse, weighing and dispensing of raw material to reduce risk of contamination, human error, which in turn reduces batch cycle times and improves data integrity by minimizing the manual process. RFID-tagged pillboxes and cabinets produce data that will enable the automated replenishment of drug stocks.

Global sourcing firms are also testing the possibilities of the delivery of drugs and collection of samples to remote locations through drones. In future, new technologies are expected in this industry like continuous production, personalized medicine, robotics, and additive manufacturing of drugs through 3-D printing (PwC 2016). Digital fabrication or 3-D printing is gaining traction as a valuable technology for making small batches of products which were earlier very costly and impractical to produce (Ehrhardt 2015).

The U.S. Food & Drug administration has approved the first ever 3-D printed prescription drug for the treatment of epilepsy, called Spritam, sold by Aprecia Pharmaceuticals. This new technology of medicine production is best suited for unique diseases and for very small patient populations (Ehrhardt 2015). These new technologies in the pharmaceutical industry affect not only big MNC’s but also small pharmaceutical firms, third party manufacturers, and startups.

In the pharmaceutical sector in India the main factor driving automation is that of maintaining hygienic production conditions such as would meet stringent US FDA certification requirements.

IT services

In the last two years, the IT services industry has been frequently in the news for the fall in recruitment and even lay-offs. Lay-offs are usually denied, but IT firms continue the practice of using bell-curve assessments to drop the lowest performing five per cent or so of employees. By not replacing them, the IT companies have reduced their bench strength of engineers who could be quickly deployed to ramp up production in any project as required. Automated programming does not require bench strength to ramp up production.

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Indian IT majors such as Infosys, TCS, Wipro, and HCL are all developing automated systems of computer programming. They are also developing AI platforms. From programming, they are moving on to providing complete digital solutions or end-to-end services to their clients.

The technological change in the Indian IT industry is due to a combination of two factors. One is to cut costs. Many sections of code are repetitive and can simply be copied from existing programmes. The other factor is changing client requirements. The Indian companies are in transition from their legacy work of providing middle- to lower-programming inputs to designing and providing end-to-end solutions. The latter often requires the use of artificial intelligence platforms, which all the IT majors are in the process of making integral to their functioning. In the process, they are transitioning from being IT service companies to becoming straightforward technology companies, providing both products and services. Of course products and services are not so different—as Arthur (2015) points out, they are two sides of the same coin. Products provide services and the move to cloud-based software-as-a-service only reinforces the two-sides-of-a-coin characterization of products and services.

The chief executive of one big Indian technology company, who asked not to be named, says the drop in jobs due to automation and AI would be worse if bosses were not concerned about the consequences of laying off so many workers. ‘We carried out an audit and found that we could replace half our staff with artificial intelligence,’ he says. ‘We would do it, but for the social fallout it would create.’ The pace of automation is not only an economic matter, but also a social matter.

Agriculture and food processing

Cutting operational costs is also a factor driving mechanization in agriculture—something that has visibly picked up in the last few years. A comparison of the costs of land preparation with the traditional human-bullock power and mechanized ploughing showed that the cost was INR 2000 per acre for manual ploughing and came down to INR 500 per acre for mechanized ploughing—as per discussions with officials of the Agricultural Engineering Department in Odisha. The development of a market for land preparation services, through the rental of tractors or power tillers, has brought down the cost of mechanical land preparation, since farmers no longer have to bear the capital cost of equipment.

High land prices also have had an impact on the adoption of mechanization. The high price of land makes it difficult to continue low-return, small- to medium-scale enterprises in areas with high land prices. This has led to, on the one hand, the shifting of low-return activities, such as garment manufacture, away from metropolitan centres towards peri-urban areas with lower land prices. High land prices can also have the effect of prompting enterprises located in such areas to adopt more capital-intensive and land-saving production methods. This was observed, for instance, in the case of cashew processing in Koraput, Odisha, where town units adopted mechanized processing while rural units had rows of women carrying out manual processing. The constraint was the need to economize on the use of expensive floor space in urban areas.

Garments and shoes

If the need for precision promoted automation in engineering industries, the requirement of standardization can also promote the mechanization of tasks such as embroidery, which was earlier done by hand. This is observed in the manufacture of garments for export where much of the embroidery and embellishment formerly done by hand (usually by women who worked from home) has now been mechanized and brought into the factory.

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The use of robots in garment manufacturing world-wide remains low. This is partly due to a key technical challenge: while automobiles or electronics products—often assembled with the assistance of robots—are largely made up of hard parts, garment manufacturing involves pliable, elastic fabrics that have traditionally made the process difficult to automate. However, technical advances can be expected to solve the problem of robotic handling of soft fabrics.

The spread of robotic sewing machines, or ’sewbots’ will probably happen first in Europe and the USA, as companies seek to bring manufacturing and production closer to their main consumer markets (Bain 2016). Adidas announced that a factory in Germany will begin manufacturing shoes using robots. The ‘Speedfactory’ will employ just 160 people: one robotic production line will make soles, the other production line the upper part of shoes. With an additional factory planned for the USA, it is a scheme Adidas describes as a ‘gamechanger’ (Hoskins 2016)

Sewbots are unlikely to appear in factories in Asia, an ILO report says, but will be installed in destination markets like Europe and the USA. It is such a big threat that the ILO urges Asian countries to start planning to diversify to ‘avoid considerable setbacks in development’ (Hoskins, 2016)

With time-to-market, customization, and cost being the primary drivers in sewn goods production—especially in apparel—it is only a matter of time before low-cost, technologically advanced robots replace traditional tailors around the world. However, at present, and probably for the next few years, garment manufacture in India can grow, driven by the higher wage costs in China; provided Indian manufacturers are able to improve their record on quality and on-time delivery.

At the same time, large Indian garment manufacturers are planning to reduce employment. Textile major Raymond is planning to cut about 10000 jobs in its manufacturing centres in the next three years, replacing them with robots and other technology. One robot could replace around 100 workers. The company employs over 30000 staff in their sixteen manufacturing plants in the country.

The studies discussed here illustrate some of the effects of setting up already existing digital technologies—although when Aadhaar was designed, it was the first instance of a large-scale biometric identification system. The creation of new technologies, however, is a different matter altogether. Indian majors in both the IT and pharmaceutical sectors are struggling to develop new technologies in their respective areas (see chapters on IT services and pharmaceuticals in Nathan, Tewari and Sarkar, forthcoming).

Furthermore, as the DIPP draft points out, it is necessary for Indian firms to develop their own brands. An analysis of development policy with GVCs points out that the bulk of profits in GVCs go to the brands or lead firms, located in what Richard Baldwin terms headquarter economies (Baldwin 2016 and Nathan 2017). Thus, moving up the value chain means that developing countries should develop their own brands.

Start-up ecosystem

India added over 1,000 start-ups in 2017, giving it a total of 5,200 and making it the third-largest in the world, after the US and China (NASSCOM and Zinnov 2017, as reported in Jain, 2017). The start-ups are not creators of large numbers of jobs, but they are important for future growth as India moves on through middle-income status. The start-ups are concentrated in the fields or verticals of health, finance. But there are many start-ups in advanced technology areas, such as Artificial Intelligence (AI), blockchain, analytics, clean energy, and building solutions. These investments in new areas of digital technology are important for future growth of the Indian economy.

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E-commerce

An area of growing importance is that of e-commerce. In terms of direct jobs there may not be many. But there is a large infrastructure of warehouses and transport services, including home delivery that is being built up. The home delivery infrastructure can be counted as an addition to employment, since it substitutes for the former unpaid work of direct shopping.

Technological change and women

At different points in this paper we have referred to the impact of some technological changes on women. The development of IT infrastructure makes it possible for work to be performed from different locations and in a flexible manner. This would help increase the employment of women in sectors that allow both remote and flexible functioning, as the IT services sector (Nathan, Sarepalli and Gurunathan 2016). However, the same flexibility also results in the creation of a secondary and subordinate professional tract for women. The reduction of the need for ‘heavy’ labour through forms of mechanization also makes it possible to employ more women on the shop-floor in industries other than in the so-called ‘traditional’ women’s manufacturing sectors such as garment manufacture. There are now increasing numbers of women in engineering and even metal plants, such as Tata Steel.

The higher proportion of women in the IT services than older forms of engineering is now an old story in Indian development. But women are also going ahead in technology start-ups. Reports of the NASSCOM-Zinnov report on start-ups point out that 10 to 11 per cent of start-ups are by women (Jain, 2017).

An important factor restricting women’s employment in the operation of new technologies is the bias or prejudice that women cannot handle new technologies. This is seen not just in rural India, where women, it is assumed, cannot operate machines (Nathan et al, forthcoming), but also in the high-tech Silicon Valley in the USA, where there is a dramatic gender imbalance in pay and power (Kolhatkar 2017).

While technological change has enabled many women to not just enter into factory and office work and use the benefits of these jobs to redefine traditional gender roles in the household and community, at the same time, it seems that gender prejudices continue to restrict women’s opportunities in such jobs.

Conclusion: Employment effects in India

Automation in manufacturing in India is driven more by product (precision) and production (hygiene) requirements than by a need to reduce costs. In IT services automation is based on reducing costs in using repetitive portions of programming sequences and also changing client requirements that ask for end-to-end solutions.

Automation and the introduction of robots in the so-called heavy industries such as automobiles, steel, or minerals would definitely reduce employment intensity of production. Even in light industries, there would be increased mechanization, if not robotization. However, the skill content of GVC-based production of goods and services will go up. Going by the trends seen in the GDP growth rate and employment generation, in one way or the other, the employment intensity of manufacturing would go down. This would mean that manufacturing growth may not provide much additional employment or, India would have to capture a larger portion of world production in order to provide additional employment.

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At the same time, the educational requirement of jobs is also likely to increase. Labour will need to undertake more complex interactions with machines. One cannot be certain any more, though, that an increase in the educational requirements for jobs will lead to greater security of employment. It has been observed that if the skills required of labour are generic to an industry and not firm-specific, employers can continue with precarious forms of employment..

With computer networks, many tasks can be carried on in remote locations. This would benefit women with domestic responsibilities, who might otherwise have had to give up employment. However, this can also result in the formation of a secondary and inferior professional track for women.

If the proposed mechanization of what was formerly hand-worked comes about, it could result in the displacement of women home-workers. In factories or fields, women are not considered to be skilled workers and do not get trained for the same.

The development of cloud-based IT services, digital payments, and identification systems reduce both the cost of utilizing the latest technology and transaction cost. Platform-based e-commerce reduces the transaction costs needed for establishing brands and finding customers. All this would be beneficial to the formation and growth of SMEs. One can expect to see a proliferation of Internet-based SMEs.

While the platforms will definitely be few in number (for example, just Uber and Ola in transport) the number of service providers can be very large, meaning there can be substantial employment in these growing sectors. Under Ola there are already almost half a million cars and workers. With increased possibilities of working with flexible timings and work periods, more women could participate in these new forms of work. Such paid work, in turn, could strengthen women in their attempts to reduce inequality. Of course, advances in challenging norms and violence that restrict women’s access to public spaces and numerous types of jobs (such as of transport service providers, as by some women taxi-drivers in Mumbai and Chennai) would enable women to participate even more in digital platform-based jobs.

The difference between these jobs created through platforms and the traditional unorganized sector is that the first category consist of jobs in the organized sector. However, there can be problems, such as uncertain incomes and a lack of employer-contributed social security. This makes it all the more necessary for India to develop a system of social security, including income smoothening, healthcare, and education, which is universal and portable, based on the individual and not the household. Where the workers work under platforms, the firms owning the platforms can also be required to contribute to these social security systems.

These platform-based jobs can be quite numerous. In the case of transport, Ola alone provides jobs to 450,000 persons; adding Uber and some smaller players can increase the number to one million. These are large numbers of workers. As seen in China (Dewan and Randolph 2015) Internet communication can be used to create new forms of worker organizations. In India, too, Ola or Uber drivers have come together to put forward their demands to the platforms. The very technologies used to provide individualized employment under the platforms can be the means to bring the workers into collectives that increase their associative power—something that has historically been essential for improving employment conditions.

In contributing to future growth and increased productivity are the start-ups, particularly in advanced technology areas. They do not provide much employment, are important for future growth, but are also likely to increase inequality. Overall, the increasing educational requirements of the new technologies will tend to increase inequality in the economy. This is all the more reason for a strong system of social

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security and even more important, attempts to develop technologies that can raise productivity at the base of the economy.

What has been seen in this paper is that product requirements in automobiles and pharmaceuticals drive automation in these sectors in India; but in the IT software sector there is also the factor of reducing cost requirements in order to maintain margins. Across sectors, there is increasing demand for more skilled labour. Not only is the profit share likely to increase, but so also polarization within workers. Left to market forces alone these will be the trends, but what it points to is the necessity of designing policy to deal with these trends so that inequalities do not widen as, for instance, by providing training and domestic care work arrangements for women in the workforce. For the growing number of on-demand workers or those without clear employment contracts, as in the case of drivers under Ola or Uber, there is the need to fashion systems of social security, such as by extending provident fund and employees’ medical insurance systems, funded by taxes on the platform owners. Above all there is the need for a coherent, universal and portable social security system that could reduce social opposition to technological change.

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Technological Change and Employment: Creative Destruction

ILO Decent Work Team for South Asia and Country Office for India

India Habitat Center, Core 4B, 3rd FloorLodhi Road New Delhi-110003IndiaTel.:+911147509200, Fax: + 911124602111Email: delhi@ilo.orgwww.ilo.org/india