Blog Post

Artificial intelligence’s great impact on low and middle-skilled jobs

Artificial intelligence and machine learning will significantly transform low-skilled jobs that have not yet been negatively affected by past technological change.

By: and Date: June 29, 2020 Topic: Innovation & Competition Policy

The academic literature suggests that, in the past decades, technological progress has led to job polarisation in European Union countries. While computer technologies and robots have replaced, to some extent, routine middle-skilled jobs such as machine operation, construction work or administrative work, they have also led to an increase in complementary, non-routine high-skilled jobs (eg managers, professionals) and in low-skilled jobs (eg agriculture, cleaning and personal care services). However, our new research suggests that the new technologies that have emerged since 2010 – artificial intelligence and machine learning – are set to change drastically the job landscape over the next few decades. These technologies are likely to have a deeper impact across a wider range of jobs and tasks, including possible destruction of low-skilled jobs.

(…) new technologies that have emerged since 2010 – artificial intelligence and machine learning – are set to change drastically the job landscape over the next few decades. These technologies are likely to have a deeper impact across a wider range of jobs and tasks, including possible destruction of low-skilled jobs.

Artificial intelligence (AI) systems are able to perform tasks that involve decision-making, therefore changing the impact of automation on the workforce. AI-powered technologies can now retrieve information, coordinate logistics, handle inventories, prepare taxes, provide financial services, translate complex documents, write business reports, prepare legal briefs and diagnose diseases. Moreover, they are set to become much better at these tasks in the next few years thanks to machine learning (ML): computers fed by big data can learn, practice skills and ultimately improve their own performances and perform their assigned tasks more efficiently.

Our new working paper evaluates the ‘probability of automation’ for different jobs, using data from 24 European countries. This probability is initially computed at the job task level and then aggregated at the occupational level (Table 1). Since each job consists of a variety of tasks, with different potential for automation, the probability of automation at the job level does not necessarily mean the destruction of jobs, but rather whether automation can significantly transform the nature of those jobs.

Table 1: European jobs with the highest and lowest probabilities of automation


Source: Brekelmans and Petropoulos (2020) based on Nedelkoska and Quintini (2018).

We use this measure of automation in an aggregate framework where jobs are grouped into three different categories of skill: low, middle and high-skilled jobs. Figure 1 shows the results.

Figure 1: Exposure to automation of different skill groups


Source: Brekelmans and Petropoulos (2020).

These results suggest that artificial intelligence and machine learning will have different impacts compared to computer and robotic technologies, which caused job polarisation (drop in routine middle-skilled jobs and increase in low-skilled jobs). In contrast, AI is highly likely to significantly alter not only middle-skilled jobs, but also low-skill employment. Moreover, while the high skilled are relatively less at risk from AI and ML-induced transformation, its impact is still non-negligible for these jobs.

The results also suggest a future transformation of work. In middle and low-skilled jobs, AI systems will complete the easily automated tasks while humans continue to perform those that cannot be automated. A high probability of automation may also be associated with the creation of new tasks and jobs though the productivity gains from adopting AI technologies, but these jobs and tasks will most likely be high-skilled.

The transformative nature of AI and ML requires proactive measures to re-design labour markets. Countries with high degrees of labour flexibility, high quality science education and less pervasive product market regulations tend to have higher skill-oriented job structures and are therefore less exposed to labour transformation due to automation.

The transformative nature of AI and ML requires proactive measures to re-design labour markets. The workforce needs to be prepared for the upcoming changes, while the efficiency gains from these technologies should be harnessed. Countries with high degrees of labour flexibility, high quality science education and less pervasive product market regulations tend to have higher skill-oriented job structures and are therefore less exposed to labour transformation due to automation.

 

This Blog was produced within the project “Future of Work and Inclusive Growth in Europe“, with the financial support of the Mastercard Center for Inclusive Growth. 

Recommended citation
Brekelmans S., G. Petropoulos (2020), ‘Artificial intelligence’s great impact on low and middle-skilled jobs’, Bruegel Blog, 29 June, available at https://www.bruegel.org/2020/06/artificial-intelligences-great-impact-on-low-and-middle-skilled-jobs/


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.

Read article More on this topic More by this author
 

Blog Post

Designing a hybrid work organisation

Post-pandemic hybrid work models should be carefully planned, taking into account individual and organisational needs.

By: Laura Nurski Topic: Innovation & Competition Policy Date: July 5, 2021
Read article More on this topic
 

Blog Post

Workers can unlock the artificial intelligence revolution

Employers and artificial intelligence developers should ensure new technologies work for workers by making them trustworthy, easy to use and valuable in day-to-day work.

By: Mia Hoffmann and Laura Nurski Topic: Innovation & Competition Policy Date: June 30, 2021
Read article More on this topic More by this author
 

Podcast

Podcast

The skills of the future

What challenges and opportunities does technology bring to the labour market?

By: The Sound of Economics Topic: Innovation & Competition Policy Date: June 23, 2021
Read article More on this topic More by this author
 

Blog Post

Algorithmic management is the past, not the future of work

Algorithmic management is the twenty-first century’s scientific management. Job quality measures should be included explicitly in health and safety risk assessments for workplace artificial-intelligence systems.

By: Laura Nurski Topic: Innovation & Competition Policy Date: May 6, 2021
Read about event More on this topic
 

Past Event

Past Event

AI regulation at the service of industrial policy?

What role should the EU play in the regulation of AI?

Speakers: Julia Anderson, Joanna Bryson, Annika Linck and Martin Ulbrich Topic: Innovation & Competition Policy Date: April 22, 2021
Read article More by this author
 

Opinion

We need more bias in artificial intelligence

What makes one vision more desirable than another is not its neutrality, but whether it can better serve one’s goals in the context of where those goals are being pursued.

By: Mario Mariniello Topic: European Macroeconomics & Governance, Innovation & Competition Policy Date: April 21, 2021
Read article More on this topic
 

Blog Post

The impact of COVID-19 on artificial intelligence in banking

COVID-19 has not dampened the appetite of European banks for machine learning and data science, but may in the short term have limited their artificial-intelligence investment capacity.

By: Julia Anderson, David Bholat, Mohammed Gharbawi and Oliver Thew Topic: Finance & Financial Regulation Date: April 15, 2021
Read article More on this topic
 

External Publication

Wealth distribution and social mobility

This report explores the distribution of household wealth in the EU Member States and analyses the role of wealth in social mobility.

By: Zsolt Darvas and Catarina Midões Topic: European Macroeconomics & Governance Date: April 1, 2021
Read article Download PDF More by this author
 

Working Paper

The unequal inequality impact of the COVID-19 pandemic

Less-educated workers have suffered most from job losses in the COVID-19 pandemic, and it is quite likely there was a significant increase in European Union income inequality in 2020.

By: Zsolt Darvas Topic: European Macroeconomics & Governance, Global Economics & Governance Date: March 30, 2021
Read article More on this topic More by this author
 

Blog Post

Self-employment, COVID-19, and the future of work for knowledge workers

The experiences of the self-employed could give a glimpse into the future of work for knowledge workers in a post-pandemic world.

By: Milena Nikolova Topic: Innovation & Competition Policy Date: March 8, 2021
Read article More by this author
 

Opinion

Will COVID accelerate productivity growth?

The COVID-19 pandemic has prompted an increasing number of rich-country firms to reduce their reliance on global supply chains and invest more in robots at home. But it is probably too soon to tell whether this switch will increase productivity growth in advanced economies.

By: Dalia Marin Topic: European Macroeconomics & Governance, Global Economics & Governance Date: February 10, 2021
Read article More on this topic More by this author
 

Blog Post

COVID-19 has widened the income gap in Europe

Workers with low-educational levels suffered far worse than others in terms of COVID-19 related job losses during the first half of 2020 in the EU. Jobs for tertiary-educated workers even increased. Thus, the pandemic has increased income inequality, reinforcing the case for inclusive development.

By: Zsolt Darvas Topic: European Macroeconomics & Governance Date: December 3, 2020
Load more posts