New technologies, potential unemployment and ‘nescience economy’ during and after the 2020 economic crisis

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Abstract

The coronavirus pandemic and the economic crisis in 2020 are accelerating digital transformation. During and after the crisis, there are opportunities and needs for remote work facilities, online services, delivery drones, etc. We discuss how unmanned technologies can cause a long-term employment decrease, and why compensation mechanisms may not work. Using the internationally comparable Frey–Osborne methodology, we estimated that less than a third of employees in Russia work in professions with a high automation probability. Some of these professions can suffer the most during quarantine measures; employment in traditional services can be significantly reduced. By 2030, about half of the jobs in the world and a little less in Russia will need to adapt during the fourth industrial revolution because they are engaged in routine, potentially automated activities. In the regions, specializing in manufacturing, this value is higher; the lowest risk is in the largest agglomerations with a high share of digital economy, greater and diverse labour markets. Accelerating technological change can lead to a long-term mismatch between the exponential increase in automation rate and compensating effects of retraining, new jobs creation and other labour market adaptation mechanisms. Some people will not be ready for a life-long learning and competition with robots, and accordingly there is a possibility of their technological exclusion. The term “nescience economy” and corresponding assessment method were proposed. Using an econometric model, we identified factors that reduce these risks: human capital concentration, favourable business climate, high quality of life and ICT development. Based on these factors, some recommendations for authorities were proposed in the conclusion.

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APA

Zemtsov, S. (2020). New technologies, potential unemployment and ‘nescience economy’ during and after the 2020 economic crisis. Regional Science Policy and Practice, 12(4), 723–743. https://doi.org/10.1111/rsp3.12286

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