Page - (000069) - in Disrupted Development and the Future of Inequality in the Age of Automation

62 L. SCHLOGL AND A. SUMNER is cheaper than in high-income countries, thus more competitive vis- à-vis machines, and there is thus less of an incentive to automate; and (ii) conversely, given widespread low-skilled manual routine work, work tasks that are prevalent in developing countries are easier to automate from a technological viewpoint. In other words, the APS will likely be larger in developing countries. Considering the taxonomy that was pro- posed earlier, this means that automation is arguably more technologi- cally but less economically feasible. Empirical estimates and forecasts of the potential impact of auto- mation across the world are presented in Table 5.2 (the table is non- exhaustive). It is immediately evident from the studies in Table 5.2 that there is no consensus on jobs impacts and substantial variation in current estimates. Estimates range from alarming scenarios, according to which there is a “50% chance of AI outperforming humans in all tasks within 45 years” (Grace, Salvatier, Dafoe, Zhang, & Evans, 2017, emphasis added), on the one hand, to contrasting claims of there being “no evidence that automation leads to joblessness” (Mishel & Bivens, 2017, p. 1), and the sarcastic recommendation that “everyone should take a deep breath” (Atkinson & Wu, 2017, p. 23). The seminal study in the recent automation literature is that of Frey and Osborne (2013) for the United States, and subsequent studies have reproduced and refined their methodology. They conclude that almost half of the US employment is “at risk.” In contrast, Arntz, Gregory, and Zierahn (2016) occupies a middle ground in terms of optimism. The authors argue with some plausibility for a “task-based” rather than an— inevitably oversimplified—“occupation-based” approach to estimating automatability risk. Arntz et al. draw on data from an international sur- vey of adult skills conducted across OECD countries which contains data Table 5.1 The labor dynamics of automation in a dual economy Source Authors’ imagination Technology Labor Response Outcome Complementary Adapted Keep/hire Structural stability Substitutive Adaptable Retrain/switch task Structural change Lower wage Non-adaptable Lay off