The industrialised world, especially the United States, suffered severe economic ills even before the Covid-19 pandemic. Unless we recognise them now, we are unlikely to produce solutions.
Chief among these problems is the nature of economic growth, which has become much less shared since the 1980s. Wider inequality in much of the industrialised world; the disappearance of good, high-paying, secure jobs; and the decline in the real wages of less-educated workers in the United States are all facets of this unshared growth (Acemoglu 2019), which has deepened discontent and sparked protests from both left and right in the years since the Great Recession.
My research with Pascual Restrepo indicates that automation accounts for much of this loss of shared growth, along with such factors as globalisation and the declining power of labour relative to capital (Acemoglu and Restrepo 2019). With the next phase of automation rapidly unfolding, driven by machine learning and artificial intelligence (AI), the world’s economies stand at a crossroads. AI could further exacerbate inequality. Or, properly harnessed and directed through government policies, it could contribute to a resumption of shared growth.
Automation is the substitution of machines and algorithms for tasks previously performed by labour, and it’s nothing new. Ever since weaving and spinning machines powered Britain’s Industrial Revolution, automation has often been an engine of economic growth. In the past, however, it was part of a broad technology portfolio, and its potentially negative effects on labour were counterbalanced by other technologies boosting human productivity and employment opportunities. Not today.
The next phase of automation, relying on AI and AI-powered machines such as self-driving cars, may be even more disruptive, especially if it is not accompanied by other types of more human-friendly technologies. This broad technological platform, with diverse applications and great promise, could help human productivity and usher in new human tasks and competencies in education, health care, engineering, manufacturing, and elsewhere. But it could also worsen job losses and economic disruption if applied exclusively for automation.
The pandemic has certainly given employers more reasons to look for ways of substituting machines for workers, and recent evidence suggests they are doing so (Chernoff and Warman 2020).
Some argue that pervasive automation is the price we pay for prosperity: new technologies will increase productivity and enrich us, even if they dislocate some workers and disrupt existing businesses and industries. The evidence does not support this interpretation.
Despite the bewildering array of new machines and algorithms all around us, the US economy today generates very low total factor productivity growth — economists’ headline measure of the productivity performance of an economy, which gauges how efficiently human and physical capital resources are being used. In particular, total factor productivity growth has been much lower over the past 20 years than during the decades after World War II (Gordon 2017).
Even though information and communication technology has advanced rapidly and is applied in every sector of the economy, industries that rely more intensively on these technologies have not performed better in terms of total factor productivity, output, or employment growth (Acemoglu and others 2014).
The reasons for this recent slow productivity growth are not well understood. But one contributing factor appears to be that many automation technologies, such as self-checkout kiosks or automated customer service, are not generating much total factor productivity growth. Put differently, rather than bringing productivity dividends, automation has been excessive because businesses are adopting automation technologies beyond what would reduce production costs or because these technologies have social costs because they give rise to lower employment and worker wages.
Excessive automation may also be a cause of the slowdown in productivity growth. This is because automation decisions are not reducing costs and, even more important, because a singular focus on automation technologies may be causing businesses to miss out on productivity gains from new tasks, new organisational forms, and technological breakthroughs that are more complementary to humans.
But is automation really excessive? I believe so. First of all, when employers make decisions about whether to replace workers with machines, they do not take into account the social disruption caused by the loss of jobs — especially good ones. This creates a bias toward excessive automation.
Even more important, several factors appear to have fuelled automation beyond socially desirable levels. Particularly important has been the transformation in the corporate strategies of leading US companies. American and world technology is shaped by the decisions of a handful of very large, very successful tech companies that have tiny workforces and a business model built on automation (Acemoglu and Restrepo 2020). Big Tech companies including Amazon, Alibaba, Alphabet, Facebook, and Netflix are responsible for more than $2 of every $3 spent globally on AI (McKinsey Global Institute 2017). — IMF News.