Page - (000072) - in Disrupted Development and the Future of Inequality in the Age of Automation

5 AUTOMATION AND STRUCTURAL TRANSFORMATION … 65 lowest gross domestic product (GDP) per capita (and per worker) in the data set considered by Arntz et al. (2016) shows the highest resilience to automation. Generally, there is no consistent relationship with GDP per capita and their score of automatability, though, in this OECD data set (which is based on a selection of structurally similar economies). The McKinsey Global Institute (2017b) provides estimates of employ- ment that is susceptible to automation for 52 countries, which is the most comprehensive global data set we know of. Overall, McKinsey is consid- erably more pessimistic with their estimates of mean automatability, being on average 10 percentage points above that of Arntz et al. Their estimates are more pessimistic in every country and considerably more pessimistic specifically regarding non-OECD countries.10 Across Western OECD countries only, the estimates of Arntz et al. and McKinsey are, in fact, closely aligned (r2 = 0.5). Their automatability estimates of industrialized economies such as Russia, Korea, and Japan, though, differ significantly, with McKinsey being considerably more pessimistic. Another recent global estimate comes from the World Bank (2016) who provide data for 40 countries and are yet more pessimistic, with average estimates lying 20 percentage points above the McKinsey esti- mate. The overlap of country coverage between the World Bank and the McKinsey estimates is small (nine countries); among those, the shared variance is relatively low at about 12% (Table 5.3 shows selected coun- tries). In addition to automatability estimates, the World Bank also Table 5.3 Estimates of the proportion of employment that is automatable in selected countries Sources As cited MGI (2017c) (%) World Bank (2016) (%) Argentina 48 65 China 51 77 Costa Rica 52 68 Ethiopia 50 85 India 52 69 Malaysia 51 68 Nigeria 46 65 South Africa 41 67 Thailand 55 72