June 5, 2023

Lukmaan IAS

A Blog for IAS Examination



THE CONTEXT: Automation in this era is critical in understanding inequality dynamics as it affects different sections of society and different parts of the industry differently. In some cases as white collar jobs as designer and engineer have become more productive and sophisticated whereas in other cases automation has led to replacement of workers in blue collar jobs. In this context, let’s analyse the different dynamics of automation and the risk of widening inequality.


  • Automation is the substitution of machines and algorithms for tasks previously performed by labor and it is nothing new and has often been seen as an engine of economic growth and at the same time debate about workers being replaced by machines is certainly not new.
  • It started with the Industrial Revolution but however in the past its effect was counterbalanced by other technologies boosting human productivity and employment opportunities but this is not the case today.
  • In fact, next phase of automation, relying on AI and AI-powered machines such as self-driving cars, may be even more disruptive, especially if it is not accompanied by other types of more human-friendly technologies.
  • According to the World Bank Development Report, 77 percent of existing jobs in China 47 percent of US jobs, 69 per cent of Indian jobs and an average of 57 per cent of jobs in OECD countries are susceptible to automation and could be replaced by automated processes and robots.
  • An MIT economist suggests automation has a bigger impact on the labor market and income inequality than the previous industrial revolution. Research indicates and identifies the year 1987 as a key inflection point in this process, the moment when jobs lost to automation stopped being replaced by an equal number of similar workplace opportunities. Within industries adopting automation, the study shows, the average “displacement” or job loss from 1947-1987 was 17 percent of jobs, while the average “reinstatement” (new opportunities) was 19 percent. But from 1987-2016, displacement was 16 percent, while reinstatement was just 10 percent.



Nature of economic growth has become much less shared since the 1980s which led to wider inequality in much of the industrialized world and has led to disappearance of good, high-paying, secure jobs and decline in the real wages of less-educated workers.


Regarding the effects of automation on the composition and nature of work, automation and robotic technologies tend to favour non-routine cognitive tasks while they are reducing demand for manual work. New technologies also have the capability to take over routine tasks, for example manufacturing assembly and back-office work, which fall under the middle-skilled category and thus replaces them.


Automation increases the productivity and income of high-skilled workers but leaves productivity of low-skilled workers unchanged and redistribute income from poor low-skilled workers to rich high-skilled workers.


There is a shift in investment to advanced economies and new technology and automation widening the gap between rich and poor countries by shifting more investment to advanced economies where automation is already established. As a result, investment gets diverted from developing countries to finance this capital and robot accumulation in advanced economies, thus resulting in a transitional decline in GDP in the developing country.


In more recent years there has been some convergence in how quickly countries worldwide adopt new technologies, but once those technologies have been taken up by a minority of users those referred as ‘early adopters’, there is a divergence in how long they take to become widely used by large numbers of the population who are generally left behind.


Opportunities to access technologies that address the needs of low-income groups is very less. Technologies available today do not necessarily respond to the needs of low-income and vulnerable groups. They are often developed by profit-seeking firms and naturally respond to the needs of more affluent markets.


Advances in automation as machine learning technology, coupled with increasingly available big data, could replace knowledge-intensive roles in sectors such as business and health care in the first half of the twenty-first century. Some estimate that machine learning algorithms could displace 140 million knowledge workers globally , further contributing to a ‘hollowing-out’ of middle-income jobs and to growing polarization of the labor market


Though increase in skill and education helps in growth. However, in a heterogeneous society, not everybody is able or willing to obtain higher education and skill and those who due to ability constraints, do not manage to acquire higher education and skills and are left behind.



  • IMF data indicate that female workers are at a significantly higher risk for displacement by automation than male workers, with 11 percent of the female workforce at high risk of being automated given the current state of technology, with significant cross-country heterogeneity.
  • Also recent research at the McKinsey Global Institute finds that automation is likely to displace men and women more or less equally over the next decade. But, as a result of that displacement, women will need to make far more significant transitions compared to men and may find it more difficult to capture new opportunities because of the persistent barriers they face.


  • Poorer regions in advanced economies are no longer catching up to the rich as fast as they used to before automation.
  • As poorer regions tend to specialize in agriculture and manufacturing industries rather than high productivity service sectors such as information technology and communications and finance.
  • Regional disparities has increased in terms of number and quality of new jobs created, unemployment and educational attainment due to automation.
  • As new jobs created by new technology will require highly skilled workers which can cause uneven development and exacerbate regional inequalities.


  • Inequality within countries can also be exacerbated by rapid changes in the nature of work due to automation.
  • Developing countries have pursued rapid growth through manufacturing goods for export to rich country consumer markets. Automation will erode this pathway, as low labour costs become less important for manufacturing.
  • Increasing automation in textile manufacturing, which has expanded opportunities for women in many developing countries, will have negative impacts on gender equality. And for Africa, with a high youth population, not being able to capitalise on low-cost labour to attract manufacturing investment is particularly concerning.
  • Technological advances and automation tend to increase returns to capital (the owners of the machines) and decrease returns to workers. And digital technologies provide a huge boost to highly skilled workers’ productivity in some sectors, leading to lower demand for the less skilled worker leading to global inequality.


  • In the long term, the accelerated development of automation is likely to deepen the “digital divide” between urban and rural areas.
  • Because of the problems of backward rural economic development and unbalanced urban and rural development there is urban–rural income gap created which is too large.
  • It is argued that there are significant urban–rural gaps in network coverage and urban residents have greater employment opportunities than rural residents, leading to an increased urban–rural income gap.
  • From an industry chain perspective, the development of smart technologies and automation will result in significantly higher wage growth rates for high-skilled workers in urban areas than low-skilled workers in rural areas.


  • Education is riddled with inequality as there is wide gap in disparate instructional quality, equipment, and outcomes among different sections and regions. Rather than providing a solution to wealth inequality, education now reinforces it.
  • The effectiveness of online and blending learning is limited, yet ed-tech advocates and investors keep pushing the adoption of these technologies in low-income classrooms which tends to be ineffective.
  • Without acceptance of these automation technologies it is considered that these styles of learning are flawed and real progress cannot be made. Until these learning methods are proven, their adoption will only help increase inequality, rather than help students.
  • Automation is exacerbating the Digital Divide in Classrooms in across the nation as country have been flooded equipped with software, computers and high-speed internet. However, the technological disparity and literacy gap is increasing.



As on one hand Technologies, notably ICT, have brought improved access to basic services such as finance and education but at the same time it has widened inequality as countries differ in terms of investments, policy support or technological capabilities, or because technology is skill- and capital-biased and enables rent seeking, or because certain conditions need to be in place for vulnerable populations to benefit from technology and access to appropriate technology solutions.


Presence of “digital divide” has amplified the “technology divide” and widened inequalities, across all three of its dimensions, and between subregions, countries and people.


Trend of automation to replace routine manufacturing tasks and low-skilled jobs looks set to continue, and technological advances are also making it possible to automate a greater number of non-routine tasks.


Frontier technologies are likely to intensify these impacts because technological capabilities are not equally distributed across countries and people in the region.With Frontier technologies, such as AI, are likely to intensify both the divides and the dividends. New technologies can create and reinforce inequality of outcome and opportunity.


Regulatory frameworks for AI and frontier technology also need to be in place before the digital divide becomes unbridgeable. This is important because automation may prove to be a double burden by reducing employment and limiting opportunities.


Automation though is widening inequality but there are also positive sides of automation which is mentioned below:

  • Creation of New jobs: Automation entails not only innovation in the development of existing products, but also the development of entirely new products and ways of working, and thus the creation of new jobs.
  • Implementation of SDGs: Automation can be used as a means for implementing the Sustainable Development Goals (SDGs) and helps achieve the goals.
  • Can even reduce inequality: There is potential in automation to reduce inequality in opportunities but it is not automatic. It largely depends on the capabilities of the poor to access and use technologies and solutions that respond to their needs.
  • Economic growth: Automation together with the opportunities provided by trade and investment for capital accumulation and productive transformation can help achieve an unprecedented level of economic growth and enabling several countries to catch up with developed nations.


  • Exclusion of low skilled labour: There is low-skilled labor that does not benefit from automation and with the arrival of new machines. Skill-based technological progress induces more high skilled labour and thus a decline of low-skilled labor supply.
  • Demographic transition: This is likely going to be much more challenging for developing countries which have hoped for high dividends from a much-anticipated demographic transition. The growing youth population in developing countries was hailed by policymakers as possibly a big chance to benefit from a transition of jobs and automation could risk that.
  • Rising unemployment: Rising wage inequality due to automation may also trigger rising unemployment and there can be increasing involuntary unemployment of low-skilled individuals.
  • Lack of infrastructure: Reliable and resilient broadband networks are often the foundation for developing and using frontier technologies such as AI. However, the lack of such broadband networks in many parts of the region means that AI uptake is and will continue to be uneven. Frontier technologies are based on huge quantities of real-time data, which are themselves critically dependent on high-speed (broadband) Internet. The existing lack of broadband connectivity is a hindrance.
  • Availability, Affordability and Reliability: Extent of technological inequalities among countries broadly depends on three factors: availability, affordability and reliability of the infrastructure and this is a constant issue which is persisting in this era.
  • Slowdown in economic growth: Excessive automation may also be a cause of the slowdown in productivity growth. This is because automation decisions are not reducing costs and, even more important, because a singular focus on automation technologies may be causing businesses to miss out on productivity gains from new tasks, new organizational forms, and technological breakthroughs that are more complementary to humans.

If current trends continue, AI and other frontier technologies may further increase income, opportunity and impact inequalities and widen development gaps among countries and people.


  • Investment in Infrastructure: To address technology-induced inequality in the region, ICT infrastructure, notably broadband networks, must be affordable, reliable and resilient. Where progress has stagnated, such as in many LDCs and countries with special needs, a big investment push is needed. Without this investment in infrastructure there will be no narrowing of the existing digital divide and mitigation of the widening disparities
  • Redistribution through progressive income taxation: To maintain income equality redistribution through progressive income taxation by implementation of a robot tax has recently been proposed which can be a game changer.
  • Address persistent inequalities in technological capabilities: To catch up with more advanced economies, and thus reduce income inequalities among countries, countries with low technological capabilities should consider strengthening technological learning through public policies that should focus on the adoption, adaptation and diffusion of existing technologies rather than on investing in cutting edge R&D.
  • Domestic firms upgradation: Policies should aim to promote greater learning from trade and FDI, increasing productivity in existing productive sectors, and support the formation and growth of domestic firms by absorptive capacity of domestic knowledge systems, productive diversification and export upgrading.
  • Skills development: It is important pathway to address growing inequalities, particularly in universities and institutes of higher learning. Skills and knowledge acquired should be able to help address challenges associated with automation.
  • Promote regional and international cooperation: Promote regional and international cooperation to exploit technology dividends such as cooperation with States, regional and international partners, including donors, could prioritize funding for trans-border broadband infrastructure.
  • Need of government policies: In lower-income countries, governments are advised to give due priority to the building of domestic technological capabilities and, accordingly, allocate the corresponding budget funds. In more advanced economies, there is a need for governance models to integrate and coordinate technological and innovation policies with other economic and social policies and to give voice to a wide range of agents throughout the policies.

THE CONCLUSION: Policymakers should act to mitigate those risks associated with increasing inequality especially in the face of these new technologically-driven pressures. There is a need for a drastic shift to rapidly improve productivity gains and investment in education and skills development that will capitalize on the much-anticipated demographic transition even in this era of automation.


1. How automation is reinforcing already perceived inequalities and discuss the steps needs to be taken to eliminate or reduce inequalities.
2. Role that automation plays in income and wealth inequality is complex and contested. Explain.

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March 2023