Pandemic and automation: will the lost jobs come back?

Economy

TBS Report
21 October, 2020, 10:25 pm
Last modified: 21 October, 2020, 10:30 pm

The Covid-19 pandemic has hit the economies hard and left millions of people unemployed across the globe. It has now been a growing concern whether the newly unemployed people will get their jobs back or not.

The mass concern has been echoed in a latest report of International Monetary Fund (IMF) released on Wednesday.

The report titled "Regional Economic Outlook Asia and Pacific (Navigating Navigating the Pandemic: A Multispeed Recovery in Asia)" said the tendency of using robots instead of humans has increased significantly across the world. As a result, a fear has been created whether the workers who lost their jobs during the pandemic will get their jobs back.

The report said Robot adoption (measured by new robot installations per 1,000 employees, collected by the International Federation of Robotics) tends to increase after pandemic events, especially when such events are associated with a significant economic contraction.

This is in line with the literature showing that firms tend to undertake restructuring after recessions and adjust production toward labor-saving technologies.

Automation raises productivity, but the analysis suggests that it also increases inequality by displacing workers in routine manual occupations, which have low earnings.  

The COVID-19 pandemic is likely to increase inequality further over the medium term, unless policies succeed in altering historical patterns.

The report said, major epidemics over the past two decades, even though smaller in scale than COVID-19, have led to persistent increases in the Gini coefficient, raised income shares to higher-income deciles, and lowered the employment-to-population ratio for those with basic education compared with those with higher education. One channel through which pandemics may increase inequality is the acceleration in automation and robotization.

 It is also consistent with recent studies showing that pandemic-induced uncertainty could add to the incentives for automation on net, despite its negative effects on aggregate demand, as firms try to anticipate future labor disruptions from pandemics. The increase in inequality over the medium term tends to be larger for economies with higher robot density—above 2.3 per thousand and where robot adoption increases more after the pandemic.

These results suggest that the distributional effects of this pandemic could be sizable in Asia: In 2018 nearly two-thirds of the world's operational stocks of industrial robots were in Asia, and more than 40 percent of the world's new robots were installed in China. Moreover, robot density is rising fast from a low base in several Asian economies.

The report also mentioned about pandemics and social unrest. It said, higher inequality is associated with lower sustainable medium-term growth and can fuel social tensions in countries with already high inequality.

Past major pandemics, by reducing growth and increasing inequality, have led to a significant increase in social unrest in the medium term, as measured by the civil disorder score from International Country Risk Guide.

IMF's analysis also finds that the impact of inequality on social unrest depends on the extent of redistribution (measured as the difference between market Gini coefficient and net Gini coefficient): an increase in inequality is associated with more unrest when redistributive transfers are low, suggesting that redistributive measures indeed help to reduce social tensions.

The report said in an overview that, the coronavirus disease (COVID-19) pandemic is still unfolding around the globe. In Asia, as elsewhere, the virus has ebbed in some countries but surged in others. The global economy is beginning to recover after a sharp contraction in the second quarter of 2020, as nationwide lockdowns are lifted and replaced with more targeted containment measures. The Asia and Pacific region is also starting to recover tentatively, but at multiple speeds. 

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