Blog Post

Do we understand the impact of artificial intelligence on employment?

Artificial intelligence is already transforming the world of work, but the future is hard to predict. Some see most jobs at risk of automatisation, while others argue robots will only take on a narrow range of tasks in the coming decades. Nevertheless, we need a broad debate to prepare the appropriate economic policy response to the new industrial revolution.

By: Date: April 27, 2017 Topic: Innovation & Competition Policy

In my previous blog on artificial intelligence (AI), I dealt with the general characteristics of AI and machine learning. Thanks to complex virtual learning techniques, machines are now able to perform a wide range of physical and cognitive tasks. And the efficiency and accuracy of their work is expected to increase as AI systems advance through machine learning, big data and increased computational power.

The benefits are clear, but there are also concerns for the future of human work and employment. If indeed machines continue to improve their performance beyond human levels, a natural question to ask is whether machines will put humans’ jobs at risk and reduce employment. Such a concern is not new but in fact dates back to the 1930s, when John Maynard Keynes postulated his “technological unemployment” theory.

In general, automation affects employment in two opposing ways:

  • Negatively – by directly displacing workers from tasks they were previously performing (displacement effect)
  • Positively – by increasing the demand for labour in other industries or jobs that arise due to automation (productivity effect)

So, the real question is which of the two effects will dominate in the AI era. Before we deal with this question, let’s travel back in time to previous industrial revolutions. Some interesting case studies are reported by The Economist:

  • During the 19th century, the amount of coarse cloth a single weaver in America could produce in an hour increased by a factor of 50, while the amount of labour required per yard of cloth fell by 98%. However, the result was that cloth became cheaper, and demand for it increased. This created four times more jobs in the long run.
  • The introduction of automobiles in daily life led to a decline in horse-related jobs. However, new industries emerged resulting in a positive impact on employment. It was not only that the automobile industry itself grew fast, increasing the available jobs in the sector. Jobs were also created in different sectors because of the growing number of vehicles on the roads. For example, new jobs were created in the motel and fast-food industries that arose to serve motorists and truck drivers.

So, past cases suggest that in the short run the displacement effect may dominate. But, in the longer run, when markets and society are fully adapted to major automation shocks, the productivity effect can dominate and lead to a positive impact on employment.

The invention of cars and automatic looms was long ago, but the Economist article also presents similar case studies of more recent technological developments.

  • The introduction of software capable of analysing large volumes of legal documents reduced the cost of search but increased demand for it. As a result, the number of legal clerks (who before the implementation of the software had to search for the legal documents manually in a more time consuming way) increased by 1.1% on average per year between 2000 and 2013 instead of decreasing due to the displacement effect.
  • Automated teller machines (ATMs) might have been expected to significantly reduce the number of bank clerks by taking over some of their routine tasks. Indeed, in the USA their average number fell from 20 per branch in 1988 to 13 in 2004. But that also reduced the cost of running a bank branch, allowing banks to open more branches in response to customer demand. The number of urban bank branches rose by 43% over the same period, so the total number of employees increased.

So, even in more recent examples, we see that technology leads to new employment opportunities in a way that we could not even imagine few decades ago. The automation of shopping through e-commerce, along with more accurate recommendations, encourages people to buy more and has increased overall employment in retail. (The annual growth of e-commerce in Europe is 22%. See Marcus and Petropoulos, 2016 for relevant statistics and policy discussion.) People can also generate income by supplying services on collaborative economy platforms where the entry barriers are very low. And people can further utilise available assets in an efficient way through the supply-demand matching algorithms in place.

Should we expect that the impact of AI on employment to follow similar patterns? Perhaps not. The McKinsey Global Institute estimates that, compared with the Industrial Revolution of the late 18th and early 19th centuries, AI’s disruption of society is happening ten times faster and at 300 times the scale. That means roughly 3000 times the impact. This very fast technological progress in the AI era raises the question: Is this time different?

Nevertheless, it is difficult to answer this question in a clear and convincing way. Reviewing recent research and economic analyses we see that there is no consensus on the impact of automation on employment.

Acemoglu and Restrepo (2017) deal with industrial robots (“an automatically controlled, reprogrammable, and multipurpose [machine]”) which perform tasks like welding, painting, assembling, handling materials, or packaging. Thus machines that “have a unique purpose, cannot be reprogrammed to perform other tasks, and/or require a human operator” do not fall in this definition of industrial robots.

Using data from the International Federation of Robotics about industrial robots in the post-1990 era, the authors report that Europe has introduced more robots in its labour market than the US. In European countries, robot usage started near 0.6 robots per thousand workers in the early 1990s and increased rapidly to 2.6 robots per thousand workers in the late 2000s. In the US, robot usage is lower but follows a similar trend; it started near 0.4 robots per thousand workers in the early 1990s and increased rapidly to 1.4 robots per thousand workers in the late 2000s. In fact, the US trends are closely mirrored by the 35th percentile of robot usage among the European countries.

Source: Acemoglu and Restrepo (2017)

AI empl pic

The authors conclude that one additional robot per thousand workers reduces the US employment-to-population ratio by about 0.18-0.34% and wages by 0.25-0.5%. The industry most affected by automation is manufacturing. However, interestingly, they only find a weak correlation between the increase of industrial robots and the decline of routine jobs.

When we are trying to interpret these results we should not forget that there are still few industrial robots in the US economy. If the spread of robots proceeds over the next two decades as expected by experts, such as Brynjolfsson and McAfee (2012) and Ford (2015), its aggregate implications for employment will be much larger. These books also warn that “white collar” jobs could be impacted just as much as “blue collar” ones.

Frey and Osborne (2013) predict that about 47% of total US employment is vulnerable to automation over the next 20 years. In contrast to the books mentioned above, the authors of this study also suggest that new advances in technology will primarily damage low-skill, low-wage workers as tasks previously hard to computerise in the service sector become vulnerable to technological advance. Bowles (2014) repeated Frey and Osborne’s empirical exercise for Europe, concluding that 54% of European jobs are at risk because of automation.

Chui, Manyika and Miremadi (2015) estimate that 45% of work activities could be automated using already demonstrated technology. If AI systems were to reach the median level of human performance, an additional 13% of work activities in the US economy could be automated. The study also finds that even the highest-paid occupations in the economy, such as financial managers, physicians, and senior executives, including CEOs, have a significant amount of activity that can be automated.

There are also studies that find a much smaller displacement effect of automation on employment. Arntz, Gregory and Zierahn (2016) predict that on average across the 21 OECD countries, only 9% of jobs are automatable. Atkinson (2016) agrees with this estimate, looking at the next 20 years, as he said  at a recent  Bruegel event about AI.

The big difference between this 9% estimate and 47% reported by Frey and Osborne (2013) is explained as follows: Frey and Osborne focus on whole occupations rather than single job-tasks (occupation based approach) when they estimate the risk of automation. Even if some occupations are labeled as high-risk occupations, they may still contain a substantial share of tasks that are hard to automate. In contrast, Arntz, Gregory and Zierahn adopt a task-based approach which focuses on the risk of specific tasks to be automated . That dramatically reduces the estimated impact of automation.

This makes clear that a crucial point when assessing the impact of automation is to determine what will be technologically feasible in the next decades and how capable the machines will be in replacing humans in their job tasks. Manyika et al (2017) estimate that only a fraction of less than 5% of tasks consist of activities that are 100% automatable, suggesting that a task-based approach can better capture the impact of automation. They also report that about 60% of occupations have at least 30% of their activities that are automatable.

All these studies have focused on the displacement effect of automation. What is even more challenging is to try to also assess the impact of the productivity effect. That would require predicting future market developments based on exact assumptions about the creation of new occupations, industries and tasks facilitated by new technologies that have not yet arrived. This is extremely hard to do! Who would have thought 20 years ago that with smartphones we would be able to perform many different tasks on one device, and that there would be huge new markets related to their function?

Even industry experts seem to be divided over the impact of automation on employment. Smith and Anderson (2014) asked 1900 experts in the field about the impact of AI on employment by 2025. Half (48%) envision a future in which robots and digital agents will have displaced significant numbers of both blue- and white-collar workers—with many expressing concern that this will lead to vast increases in income inequality, masses of people who are effectively unemployable, and breakdowns in social order. The other half of (52%) expects that technology will not displace more jobs than it creates by 2025.

There are also voices which claim that automation will not have any impact. Bob Gordon, for example, trying to explain mediocre US productivity growth concludes that “the benefits of the digital revolution were over by 2005” and that AI will only have a very limited impact. On the other side, the world-famous physicist Stephen Hawking goes several steps further by predicting that “the development of full artificial intelligence could spell the end of the human race.” The debate is broad, to say the least.

The fact that it is difficult to predict the exact impact of AI makes it complex to frame a policy response. But some society-level reaction is surely needed. It is therefore necessary to initiate an open consultation of all involved parties, to define our approach towards the AI era. This process will have several steps.

  1. The first important step is to understand what AI is and what its potential will be.
  1. Then, we need to define a framework of rules for the operation of machines and AI automated systems. These must go far beyond Asimov’s famous three laws of robotics. The Civil Law Rules on Robotics proposed by the European Parliament can also motivate social dialogue about issues related to liability, safety, security and privacy in the coming AI era. Adopting clear rules based on a good understanding of this new era could make the transition easier and mitigate potential concerns. However, adopting rules without good understanding and knowledge of how this new technology will be implemented (first step) would be be counterproductive.
  1. As a third step we need to design and implement those policies that will help us to accommodate new technology possibilities. For example, one way to move forward could be to carefully redesign education and training programs so that they provide the right qualifications for workers to interact and work efficiently alongside machines. This might minimise potential displacement concerns. Such initiatives will require the close interaction of authorities and institutions with major technological firms which have both the know-how and the capacity to contribute to the training. Improved instruments for job search assistance and job reallocation could also be beneficial and would mitigate concerns associated to the displacement effect. Without any doubt the Partnership on AI of the big high-tech companies can help promote the needed social dialogue and coordinate further developments with the participation of multicultural research groups and educational institutions.

At the moment we are far from a political consensus about the challenges of the AI era. The US Treasury Secretary Steve Munchin does not seem concerned at all about the impact of automation on employment. This position clearly contradicts the previous US administration, which even published a comprehensive report about the challenges of the AI era (considering employment one of the main worries). Christine Lagarde, in her talk at the Bruegel/IMF event shortly ahead of the 2017 IMF Spring Meetings, identified the impact of automation on employment as a concern that requires policy actions.

However, we should not rush into a response. The time for policy will come, but at the moment we are still at the first step of understanding the potential of AI and that various ways is might impact on our economy. To deepen this understanding, we should further promote social dialogue among all the involved parties (researchers, policy makers, industry representatives, politicians, etc). This is a vital first step to better grasp the challenges and opportunities of this new industrial revolution. And although we should not rush to conclusions, we must also act swiftly. The speed with which technology advances may introduce disruptive forces in the market earlier than some people might think.


Republishing and referencing

Bruegel considers itself a public good and takes no institutional standpoint. Anyone is free to republish and/or quote this post without prior consent. Please provide a full reference, clearly stating Bruegel and the relevant author as the source, and include a prominent hyperlink to the original post.

View comments
Read about event

Upcoming Event

Jul
9
12:30

Should we revisit the patent system for pharmaceutical products?

Analysis of the legal issues with the current IP system for regulated market authorisations for pharmaceutical products, as well as its economic effects.

Speakers: Christian Jervelund, Margaret K. Kyle, Roberto Romandini, Bruno van Pottelsberghe, Amaryllis Verhoeven and Reinhilde Veugelers Location: Bruegel, Rue de la Charité 33, 1210 Brussels
Read article More on this topic

Blog Post

Robots, ICT and EU employment

Disruptive technologies based on ICT, robots, and artificial intelligence have transformed labour markets through their important effects on employment. As the number of industrial robots continues to rise, our results imply that some measures to facilitate workforce transition and accommodate the rise of automation might be needed to maintain satisfactory labour market outcomes.

By: David Pichler, Georgios Petropoulos and Francesco Chiacchio Topic: Innovation & Competition Policy Date: June 15, 2018
Read article More on this topic More by this author

Podcast

Podcast

Robots: Positive or negative for EU employment?

Bruegel research fellow Georgios Petropoulos features in this episode of ‘The Sound of Economics’ to discuss a study he has co-authored on the impact of robotisation on employment in Europe.

By: The Sound of Economics Topic: Innovation & Competition Policy Date: May 29, 2018
Read about event

Past Event

Past Event

Youth up Europe: The future of work: towards safeguarding young people's rights in the era of increased digitisation

This event will discuss what impact digitisation will have on the employment opportunities for young people and how we can safeguard their rights.

Topic: European Macroeconomics & Governance, Innovation & Competition Policy Location: Av. Rei Humberto II de Itália, 2750-642 Cascais Date: May 10, 2018
Read about event More on this topic

Past Event

Past Event

Protecting EU firms without protectionism

Do we need more effective support for EU companies, more targeted to threatened sectors of strategic importance to the EU? Do we need to revise our competition policy rules on state aid to allow for a more strategic industrial policy support? Do we need new policy approaches to prepare for a changing global environment?

Speakers: Vincent Aussilloux, Tomas Baert, Paolo Casini, Gert-Jan Koopman, André Sapir, Reinhilde Veugelers and Focco Vijselaar Topic: Innovation & Competition Policy Location: Bruegel, Rue de la Charité 33, 1210 Brussels Date: May 3, 2018
Read article Download PDF More on this topic

Working Paper

The impact of industrial robots on EU employment and wages: A local labour market approach

In theory, robots can directly displace workers from performing specific tasks (displacement effect). But they can also expand labour demand through the efficiencies they bring to industrial production (productivity effect). This working paper adopts the local labour market equilibrium approach developed by Acemoglu and Restrepo to assess which effects dominate and the impact of robots on wage growth and employment rate in Europe.

By: Francesco Chiacchio, Georgios Petropoulos and David Pichler Topic: Innovation & Competition Policy Date: April 18, 2018
Read about event More on this topic

Past Event

Past Event

Robots and artificial intelligence: The next frontier for employment and EU economic policy

This event looked at the impact of robotics and artificial intelligence on employment, wages and EU economic policy.

Speakers: Pat Bajari, Julia Bock-Schappelwein, Anna Byhovskaya, Paola Maniga, Mario Mariniello, Clara Neppel, Loukas Stemitsiotis, Georgios Petropoulos and Barry O’Sullivan Topic: Innovation & Competition Policy Location: Bruegel, Rue de la Charité 33, 1210 Brussels Date: April 18, 2018
Read article Download PDF More on this topic More by this author

Policy Contribution

Are European firms falling behind in the global corporate research race?

The author looks at how concentrated corporate R&D is in Europe, compared with sales and employment. The US and China are more likely to produce new R&D leaders that take over some of the top positions from incumbent R&D leaders. How is the EU coping with technology shifts and creating the next generation of new leading firms?

By: Reinhilde Veugelers Topic: Innovation & Competition Policy Date: April 12, 2018
Read article More on this topic

Blog Post

The European Globalisation Adjustment Fund: Time for a reset

It is only in the last decade that the EU has had an active policy to reintegrate workers who lost their jobs as a result of globalisation, through the European Globalisation Adjustment Fund (EGF). In this blog, the authors assess the performance of the Fund and make three recommendations to improve its effectiveness. To be more successful, the Fund should improve its monitoring and widen the scope of its usage.

By: Grégory Claeys and André Sapir Topic: European Macroeconomics & Governance Date: April 11, 2018
Read article Download PDF More on this topic

Policy Contribution

The European Globalisation Adjustment Fund: Easing the pain from trade?

With the European Globalisation Adjustment Fund (EGF), the EU now has an instrument to help workers negatively affected by trade find new jobs. However, only a small proportion of EU workers affected by globalisation receive EGF financing. How to improve the EGF? Revising the eligibility criteria to qualify for EGF assistance, enlarging the scope of the programme beyond globalisation and collecting more and better data to enable a proper evaluation of the programme.

By: Grégory Claeys and André Sapir Topic: European Macroeconomics & Governance Date: March 22, 2018
Read article More on this topic More by this author

Blog Post

Economies of States, Economies of Cities

Both in Europe and the US, economists are starting to notice how the economies of cities have been sometimes diverging from the economies of states. While some areas thrive, others may be permanently left behind. Maybe it is time to adopt a more clearly sub-national perspective. We review recent contributions on this issue.

By: Silvia Merler Topic: Global Economics & Governance Date: February 5, 2018
Read about event More on this topic

Past Event

Past Event

Europe’s immigration and integration challenges: Financial and labour market dimensions

The event, organised by Bruegel in cooperation with the Institute for International Affairs will discuss these and related questions and will also feature the launch in Rome of the study authored by Zsolt Darvas on the impact and integration of migrants in the European Union.

Speakers: Roberto Ciciani, Zsolt Darvas, Marcela Escobari, Tatiana Esposito, Manjula M. Luthria, Carlo Monticelli, James Politi and Nathalie Tocci Topic: European Macroeconomics & Governance Location: Banca Monte dei Paschi di Siena, Via Mighetti 30/A, Rome, Italy Date: February 2, 2018
Load more posts