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Automation of Tasks and Knowledge-Intensive Services: A Sectorial Approach to the Impact of Covid 19 in Argentina

  • Martinez, Ricardo Gabriel (Universidad de Buenos Aires. Facultad de Ciencias Economicas. Departamento de Economia. CONICET-Universidad de Buenos Aires. Instituto Interdisciplinario de Economia Politica de Buenos Aires) ;
  • Leone, Julian Gabriel (Universidad de Buenos Aires. Facultad de Ciencias Economicas. Departamento de Economia. CONICET-Universidad de Buenos Aires. Instituto Interdisciplinario de Economia Politica de Buenos Aires) ;
  • Repeti, Juan Manuel Rodriguez (Universidad de Buenos Aires. Facultad de Ciencias Economicas. Departamento de Economia. CONICET-Universidad de Buenos Aires. Instituto Interdisciplinario de Economia Politica de Buenos Aires)
  • Received : 2021.10.14
  • Accepted : 2021.12.22
  • Published : 2021.12.28

Abstract

The covid 19 pandemic led to an economic collapse and multiple impacts upon Argentina's labour dynamics. As well as in other parts of the region, falls in employment rates (both due to an increase in unemployment and significant withdrawals from the labour force) were combined with wage reductions for those who were able to keep their jobs. Thus, two important processes for the labour market complimented each other: a structural shock associated with a tasks automation as a reorganisation and substitution of factors, with a cyclical recession caused by the pandemic. The international experience shows the amplifying impact the latter has on the former, generating long-term consequences mainly in routine-intensive jobs. However, the knowledge-intensive services sector appears to be the most capable of cushioning the recessionary shock (both in terms of wages and labour absorption), even with nuances within the sector depending on the extent of the shutdown measures and its capability to switch to remote work. Finally, the task approach is decisive in capturing the ability to adapt both the cyclical and structural processes, absorbing a large part of the explanatory potential that sectoral classifications tend to bring about.

Keywords

Acknowledgement

Paula Maliniauskas & Bautista Bassi (fellow research assistants).

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