Technological change is an indelible aspect of modern economic growth. While economies may grow in the short term through capital and labor accumulation, healthy long-term growth cannot be achieved without continuous technological improvements. This enables economies to generate more output from a given amount of productive factors, either by designing new products or by inventing new ways of producing the same output. However, technological change has also been accompanied by great fears: the fear of labor displacement, unemployment, and increasing inequality are among the most important.
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Those concerns and expectations have become supercharged in recent weeks, however, with the launch of ChatGPT, an artificial intelligence language tool developed by OpenAI. AI tools represent powerful innovations that can help spur growth and generate new types of jobs but also replace existing human labor by automating processes that previously required human workers. As India and the global economy face significant headwinds – such as slowing growth, rising inflation and inequality – we need urgent consideration and significant policy interventions to deliver on promises and prevent threats of automation.
There is an important academic literature on the impact of automation on employment and inequality. The ability to automate parts of the production process is easier when these tasks are largely routine and do not require cognitive thinking or personal contact. The introduction of robots in factories has reduced the need for factory workers. At the same time, economies have seen an expansion of lower-wage service sector workers in jobs that have retained an important aspect of human interaction — such as restaurant workers — since these jobs cannot be easily automated.
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Automation is also giving rise to new professions that prioritize higher-ranking skills. The introduction of robots in the factory may displace some factory workers, but it will lead to the emergence of engineers and programmers who specialize in operating and maintaining these new machines. A study by economists from MIT, Utrecht University and Northwestern University found that 60% of jobs in the US in 2018 were found in positions that did not exist in 1940.
This phenomenon is known as job polarization, wherein routine operations fall prey to automation, while the economy sees an increase in low-skill and high-skill jobs at either end of the wage distribution.
Automation also has major effects on inequality. Replacing labor with capital – that is, robots for workers – would raise the share of capital in relation to labour. Attracting labor to high-paid, high-skilled and low-wage, low-skilled workers increases inequality among workers. The final outcome will depend on whether the increase in labor productivity is shared with workers in the form of wage increases, as well as the extent of wage gaps between the bottom and top of the wage distribution.
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What does this mean for the Indian labor market? Tasks involving routine software coding and other operations may see a decrease in labor demand. For example, the legal profession may see greater adoption of AI in writing routine legal briefs.
Economist Michael Webb has found greater potential for AI adoption in high-skill occupations, such as laboratory technicians and chemical engineers, while low-skill occupations are not as exposed. One might expect that certain forms of low-skill software jobs — call center work that focuses on human interactions — would be relatively shielded from the current impacts of AI.
There is no doubt that new avenues will emerge. There will be an increased demand for workers trained to work with AI, skilled in knowing what prompts to use to direct AI to perform certain tasks. Automation will see an increase in tasks that rely on cognitive skills that human ingenuity cannot – until now – replace. This will allow workers to focus their energies on tasks of a higher order, freed from the burden of routine operations, thus increasing productivity. But the specter of greater polarization is present, with a smaller proportion of high-skilled workers emerging and a large mass of low-skilled workers.
There is a lot of space for policy development, especially in the field of education. There should be more emphasis on teaching abstract problem-solving skills and how to harness the power of these new technologies, rather than on training students in routine processes. But this, admittedly, is not easy.
Consider the process of automation replacing factory workers but creating new jobs for technicians. The rise or fall of employment depends on whether there are enough factories with new machinery to provide jobs for technicians, and whether the demand for labor in other sectors has expanded enough to absorb the displaced factory workers. The rise or fall of inequality depends on whether the benefits of increased productivity are transmitted in the form of increased wages. The ultimate impact of automation on employment levels and economy-wide inequality depends critically on the macroeconomic policy environment.
These effects have been studied extensively by economist Daron Acemoglu. In the American economy, the period from 1947 to 1987 saw a healthy demand for labor offsetting displacement from automation, with wages rising in parallel with productivity, resulting in relatively flat wage shares. However, the patterns of technological change look very different from 1987 to 2017. Automation replaces capital for labor, while demand for labor in other sectors is relatively weak. This has led to slower wage growth and a lower wage share.
The changing nature of innovation—greater progress in the use of digital technology, for example—may explain the evolving nature of technological change, but one must also look to the policy environment for an explanation. A strong union presence in the immediate post-World War II era ensured that wages rose in line with productivity, while easy access to education ensured a skilled workforce. In the new economy, the shrinking negotiating workforce and the increasing costs of education have been seen by some as factors behind the changing fortunes of workers.
The final effect
The bargaining power of labor in India is low, given the presence of a large informal economy and weak trade union presence. Private consumption and investment growth remains muted, and prospects for future growth remain uncertain. AI-powered automation may exacerbate the injustices that already burden India’s workers. It is essential to ensure that demand for labor increases in the face of technological change—whether through fiscal and monetary policy or interventions that appropriately redistribute productivity gains. It is also important to ensure that the policy supports the final impact on workers. For example, one can imagine increasing taxes on profits in order to ensure a fund that can raise skills and educate and retrain displaced workers.
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One should not think that policy interventions to ensure a measure of distributive justice necessarily represent an economically efficient trade-off. A level playing field can positively influence technological change in the future: Acemoglu and fellow economist Pasquale Restrepo report positive effects of automation in relatively high-wage economies.
The pace of technological change in the digital age can far outpace the ability of institutions and policies to devise blueprints to prepare for it. But the difficulty of the task is no excuse for being late in preparing for the challenge.
(Rahul Menon Associate Professor at OP Jindal International University, Haryana)