Page - (000073) - in Disrupted Development and the Future of Inequality in the Age of Automation

66 L. SCHLOGL AND A. SUMNER provides adjusted estimates which take into account the different speeds of technology diffusion across countries. In the next section, we explore the McKinsey Global Institute (2017b) and World Bank (2016) data in more detail.11 5.5 empiricAl pAtterns of AutomAtAbility And economic development Instead of focusing on the levels of automatability per se, which remains fairly contentious we next discuss the relationship of automatability and economic development.12 The first observation to make (and one that was also made by Frey, Osborne, & Holmes, 2016) is that automatability estimates show a relationship with the level of GNI per capita across countries in global comparison (Fig. 5.2). Both sets of estimates are highly significantly (p < 0.01) negatively correlated with gross national income (GNI) per capita. Thus, the richer an economy, the less automatable the labor force. That said, McKinsey’s estimates range from a minimum of 41% to a maximum of 56% and the World Bank’s from 55 to 85%, so even the most resilient countries could still see significant labor market disruption. 0F.LQVH\ *OREDO ,QVWLWXWH F DXWRPDWDELOLW\ HVWLPDWHV :RUOG %DQN DXWRPDWDELOLW\ HVWLPDWHVZϸ с Ϭ͘ϮϴϬϯϬ͕ϬϬϬϲϬ͕ϬϬϬϵϬ͕ϬϬϬ ϰϬ ϱϬ ϲϬ'E/ Zϸ с Ϭ͘ϮϭϬϭϬ͕ϬϬϬϮϬ͕ϬϬϬϯϬ͕ϬϬϬ ϱϬ ϳϬ ϵϬ Fig. 5.2 The level of economic development and the share of employment sus- ceptible to automation. Source Authors’ estimates based on sources cited