Is the Covid-19 pandemic fast-tracking automation in developing countries? Evidence from Colombia

BIS Working Papers  |  No 1048  | 
10 November 2022
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 |  34 pages



Recent evidence suggests that the Covid-19 pandemic has tended to accelerate the automation process in developed economies. We assess whether this is also happening in Colombia, a developing country characterised by a combination of low investment in research and development (R&D) and productivity, and high levels of informal labour and unemployment. In this context, the mobility restrictions imposed by the government in order to reduce the spread of the virus and the fear of contagion led to a reduction in labour supply, increasing labour cost relative to capital, which in turn could have enhanced automation.


We contribute to the literature about the pandemic's impact on occupations more prone to automation, providing evidence from a middle-income country like Colombia with high levels of informal labour and unemployment. We measure the demand for new jobs during the pandemic using vacancies by occupations collected by the Colombian Public Employment Services Bureau and the total salaried employment level based on household surveys. The probability of automation of each occupation is measured using the Frey and Osborne (2017) and Nedelkoska and Quintini (2018) methodology adapted to the Colombian case. We evaluate the differential effect of the pandemic on job openings and occupations using event-study methods


During the pandemic, job openings fell more drastically in occupations with a higher potential of automation. These effects are large and persistent, with negative and significant coefficients until our last period of study (August 2021). We also find negative and significant effects on employment, particularly salaried employment. Most of our results are driven by the sectors that were more affected by mobility restrictions. Therefore, in these cases, mobility restrictions amplified the effect of the pandemic on automation. These results are robust to alternative specifications, such as using different measures of automation potential or controlling for sector-specific trends. Finally, we show that the most affected individuals are women over 40 who work in low-wage occupations.


We estimate indicators of aggregate demand and supply conditions based on a structural factor model using a large number of inflation and real activity measures for the United States. We identify demand and supply factors by imposing theoretically motivated sign restrictions on factor loadings. The results provide a narrative of the evolution of the stance of demand and supply over the past five decades. The most recent factor estimates indicate that the inflation surge since mid-2021 has been driven by a combination of extraordinarily expansionary demand conditions and tight supply conditions. We obtain similar results for the euro area, but with a somewhat greater role for tight supply consistent with the greater exposure of the euro area to recent adverse global energy price shocks. We further find that tighter monetary policy and financial conditions dampen both demand and supply conditions.

JEL classification: J23, O30, J60.

Keywords: automation, pandemic, vacancies, employment.