The sorrows and desperation we can name will never exceed the richness of reality. There is always an excess. And this excess overflows with possibilities only half disclosed. As we scrutinize the horizon of intelligence, machine learning, and digital skills, what worries us most is a future that works. Time is needed for policies and mechanisms that may counteract the adverse effects of automation-propelled job displacement. Time is needed for these new jobs to define themselves, for workers to retool, and for new entrants into the future of work to prepare.
On the interplay of AI, robotics, and globalization, the political scientist Darrelle West and the economist Frank Levy agree that job displacement in major industries will inevitably fuel major economic disruptions. This in turn will fuel even deeper anti-democratic populist responses and political polarization.
Richard Baldwin’s analysis is far less fatalistic, but still quite alarming. He believes that the nascent interplay of robotics, AI, and globalization is advancing inhumanly fast. And that its explosive pace is such that it is injecting force into the socio-political system via a rate of job displacement that exceeds our ability to absorb workers via job replacement.
A report by the McKinsey Global Institute (2017) titled, “Jobs Lost, Jobs Gains: Workforce Transformation in times of Transition”, estimates that up to one-third of the 2030 workforce in the US and Germany may need to learn new skills and find new occupations. In a parallel paper, “A Future That Works: Automation, Employment and Productivity”, (2017), the McKinsey Global Institute forecasts that as much as fifty per cent of occupations would be affected in one way or another by technological change.
“Robots and Jobs: Evidence from US Labour Markets”, (2020), published in the Journal of Political Economy, 128, No. 6, pp. 2188-2244, examined the period 1990–2007 during which robot density increased by roughly one robot per 1,000 workers. Estimates showed that employment shrank on average by 0.4 per cent – one robot replacing four workers – and wages by 0.8 per cent. In France, Daron Acemoglu, Claire Lelarge, and Pascual Restrepo, (2020) published a study on competitiveness with robots in the American Economic Association Papers and Proceedings 110, pp. 383-88. In a sample that exceeded 50,000 firms between 2010-2015, they found the same negative effects on employment and wages and positive effects on productivity.
A salient concern that is emerging from these discourses is that policy is required that could attenuate potential inegalitarian consequences of digitalization-based technological change. In advanced economies, there is a marked difference between disposable income inequality and the evolution of market income. As it turns out, the former proves more stable than the latter. This means that redistribution was able to offset some inequality shock on market incomes.
For these economies, the puzzle is whether redistribution can be as effective in the futures under construction if inequality shocks on market incomes amplify under the force of globotics, automation, and IoT. François Bourguignon, director of studies at Ecole des Hautes Études en Sciences Sociales and former senior vice president of the World Bank, believes that tax can have a stabilizing role with four aims: to influence the speed and direction of innovation; to capitalize safety nets for occupational transitions; to circumvent an explosion in disposable income inequality; and to make sure that aggregate demand can play a role in compensating for job displacement.
But none of these economic policies can have any effect outside a new ecology of schooling, and lifelong learning that shifts education funding from the lecture hall to the factory floor and office cubicle. The risk is that globotics, and AI may not outpace tax policy, and shifts in the ecology of schooling to avoid the adverse effects of automation-propelled job displacement.
Two MIT Professors of Economics have asked the elementary question: What if the U.S. placed a tax on robots? In Working Paper 25103, titled, “Robots, Trade, and Luddism: A Sufficient Statistic Approach to Optimal Technology Regulation”, by Arnaud Costinot and Iván Werning (2018), it is argued that robot density is increasing and artificial intelligence technologies are spreading rapidly alongside imports from China and other developing economies. They argue that these changes create opportunities for some workers, extinguish chances for others, and generate significant distributional consequences.
Because robots can replace jobs, a tax on robots would incentivize firms to retain human workers, while also compensating for a dropoff in payroll taxes when robots are used. But slowing the increase in robot density with a tax is far from simple. How can a tax be designed that applies exclusively to automation, machinery or devices without affecting capital equipment overall? Is a robot different from an algorithm?
How do you define a robot that separates it from any other tool or piece of equipment? Taxing automation may eventually have to find a passage via increased taxation of capital. Under the present conditions, it is not clear whether the existing tax regime provides extensive incentives to automation investment or to labour-saving equipment in general. South Korea has reduced incentives for firms to deploy robots; European Union policymakers, on the other hand, considered a robot tax but did not enact it.
Dr Fazal Ali completed his Masters in Philosophy at the University of the West Indies, he was a Commonwealth Scholar who attended the University of Cambridge, Hughes Hall, provost of the University of Trinidad and Tobago and the acting president, and chairman of the Teaching Service Commission. He is presently a consultant with the IDB.
