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As aging populations put downward pressure on growth, policymakers will have increasingly strong incentives to promote the rapid adoption of automation as a means to replace lost workers, improve productivity, and boost GDP. However, with automation poised to add the equivalent of 1.1 to 2.3 billion full-time workers to the world’s largest economies (G19 plus Nigeria) by 2065, they will also need to consider how to support the redeployment of potentially large numbers of displaced workers.
As these forces unfold, it is likely policymakers will misdiagnose their root causes – for example, blaming unfair trade practices or currency manipulation. To be clear, our view is that automation is by far the central force disrupting workers today, and will be for some time.
Policy responses will differ greatly country by country, shaped by local labor markets, corporate climates, and political attitudes. Singapore, for example, is promoting robotics and automation as a means of growing its manufacturing sector despite limited space. With a land area smaller than New York City, the city-state has little room for more workers, which would come in the form of migrant labor; by embracing automation, policymakers aim to reduce demand for these workers, preserving both jobs and high wages for native-Singaporeans while still boosting productivity.
Japan is also likely to pursue robotics and automation, but for very different reasons: with an aging population, machines will be needed to supplement its declining workforce. Meanwhile, the EU is on the opposite end of the spectrum; policymakers there are considering a tax on robots as a means of slowing the effects of automation.
Reskilling has become something of a policy “silver bullet” in response to these disruptions. But for all the hype, economists are yet to have found any successful, scalable programs for reskilling people over 50 years of age. The US alone has already spent tens of billions of dollars on retraining programs, with mixed results.
With jobs shifting rapidly, figuring out a model that allows people to reskill within existing university and technical school systems – or organizations themselves – is a multi-billion-dollar problem. For policymakers, it is imperative; as most countries moving to embrace automation are facing declines in their working-age populations, it will actually be impossible for them to achieve their goals around GDP growth, or to compete in global markets, without leveraging both human and machine labor.
While there are no proven solutions yet, many are exploring new models. Intuitively, we know people can learn new skills, even in old age. But how can we do it well, at scale? What can we expect people to learn in different phases of their lives? In today’s rapidly changing environment, in which “the shelf life of new skills is only five years”, what skills should we even try to teach?
Even if policymakers are able to answer these questions, several challenges still remain. For one, jobs are not uniformly distributed across geographic areas; there may be work, but not necessarily near where you live. This has become particularly pronounced as jobs have moved from small to big cities, and more broadly, from developed to developing countries. In Indiana, for example, thousands of medical device manufacturing jobs are currently unfilled – simply because of a geographical mismatch.
Historically, people have moved to where jobs are, but mobility has significantly decreased in recent years, due in part to forces such as increasing homeownership as well as reliance on state-specific benefits. In this environment, policymakers will have to accept the fact that not all new jobs are created equal, and that policies such as relocation assistance may be needed.
Another challenge for policymakers is to address workers for whom reskilling and/or relocating is not possible. In these scenarios, proposals such as Universal Basic Income (UBI) are increasingly being discussed – despite the fact that they are politically untenable in most countries, including the US. This highlights a particular challenge for policymakers: to devise humane ways to address growing worker displacement while navigating their own political climates and not interfering with fundamental principles of choice, risk, and reward.
Deloitte
2017