Automation
Economist John Maynard Keynes remarked that an inevitable consequence of economizing on material needs through productivity gains is the creation of free leisure time, and that the problem of economics is now to solve the new problem of how best to equitably allocate and utilize this new free leisure time (Lee, 2017). The transformations from automation draw greater emphasis on the question of ownership of the machines and the case for redistribution policies to ensure that the benefits of automation are shared equitably (Lee, 2017).
Technological unemployment is a term used by Keynes to describe the elimination of jobs as a consequence of improvements in productivity that are faster than the rate of increased demand for goods (Lee, 2017). Productivity improvements can be achieved by augmenting tasks with automation and by improvements in the problem solving and entrepreneurial activities. Recent technology gains have demonstrated proof of concept capability for substitution of a wide range of tasks. The technological pace of automation is a disruptive force that is accelerating the general transition to the services economy (Lee, 2017). While productivity may improve in a particular sector, the aggregate of jobs left behind by automation severely overlap with the same service jobs that tend to resist further productivity improvements, which could lead to a more permanent slowdown of productivity and income growth (Lawrence, 2017).
In Figure 5.10, tasks are organized based on their relative vulnerability to obsolescence by automation. Tasks that are more vulnerable to being disrupted by automation are characterised by predictable physical environments or data manipulations. Features that are more resilient to substitution are unpredictable physical environments and tasks that employ creative, cognitive, planning, decision-making, managerial and caring roles where humans still outperform machines (Lawrence, 2017). As much as 70% of Singapore’s workforce is potentially substitutable through automation, with a conservative estimate of 25% in the high risk category (Lee, 2017). Industries identified with the highest risk are manufacturing, wholesale, retail and public administration. By age and gender, women and older workers are more at risk of being replaced by automation (Lee, 2017).
A bifurcated labor market
The knowledge economy is a term to describe a service economy that is knowledge intense (Leydesdorff, 2005). While the transition from manufacturing to service is a broad trend observed in many countries the evolution to the knowledge economy may have different outcomes for different countries depending on the path taken and education of the population. The general distribution of new jobs in an automated economy appear to have a bimodal pattern within the services sector. One of the groups is characterised by low cognitive load but depend greatly on physical human capabilities such as navigating unpredictable physical environments for homecare. The other half of new jobs are knowledge intense characterised by higher cognitive load tasks based on design, creative and executive functions. Currently these jobs have widely different compensation rates, so there is a risk that this trend could accelerate wealth inequality, even at high levels of aggregate income growth in absence of any policy intervention (Lawrence, 2017).
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