Seite - (000071) - in Disrupted Development and the Future of Inequality in the Age of Automation

64 L. SCHLOGL AND A. SUMNER on the tasks performed for each type of job. The authors use these data to impute a score of automatability, as well as the size of the population at “high risk” of automation. Interestingly, Russia’s occupational struc- ture is deemed least automatable of the 21 countries considered, whereas Germany and Austria top the rank. Put differently, the country with the Table 5.2 (continued) Authors Region Findings Chang and Huynh (2016) South East Asia 56% of jobs are at high risk of automation in Association of Southeast Asian Nations (ASEAN) countries Frey et al. (2016) Developing countries “Developing countries are highly suscepti- ble to the expanding scope of automation” (ibid., p. 18) Frey and Rahbari (2016) OECD and Ethiopia, India and China China will lose 77% of jobs to automation, India 69%, Ethiopia 85%, and OECD aver- age 57% jobs lost World Bank (2016) Developing Countries Two-thirds of all jobs susceptible to automation (1.8 bn jobs), but the effects are moderated by lower wages and slower technology adoption Avent (2017) Developing Countries “New technology seems to be making life harder for the emerging world” (ibid., p. 171) World Economic Forum (2017) Africa 41% of all work activities in South Africa susceptible to automation, 44% in Ethiopia, 46% in Nigeria, and 52% in Kenya ADB (2018) Asia In the period of 2005–2015 in 12 Asian economies there were 101 m job losses per annum due to “modern machine tools and ICT equipment” which were offset by 134 m jobs created due to higher demand for goods and services (ibid., pp. 77–78) Global studies Grace et al. (2017) Global 50% chance of AI outperforming humans in all tasks in 45 years and of automating all human jobs in 120 years McKinsey Global Institute (2017a) Global Using existing technologies, around two-thirds of occupations could have one- third of their constitutive tasks automated Source Sources cited