In a study focused on 962 skills and 25,000 workers, researchers at the Oxford Internet Institute, University of Oxford, and the Center for Social Data Science, University of Copenhagen, find that the economic value of a skill is determined by how well it can be combined with other worker competencies, termed “complementarity”. Artificial Intelligence (AI) skills command a high value due to their demand and their ability to be combined with other skills.

Published in the journal Research Policy, the study “What is the Price of a Skill? The Value of Complementarity” used data from nearly 50,000 freelance projects posted on online labour platforms in the USA between 2014 and 2022. The skills were mapped from completed projects to create a network showing their value in US dollars as well as their relation to one another.

The findings suggest that the higher the potential of a skill to be combined with others, the higher its own value. For example, part of the reason why a skill like Data Analytics is valuable is that it can be combined with other skills of high value. In contrast, skills like Photo Retouching can only be combined with a specific set of other skills and therefore have a lower value.

In the context of new technologies requiring new skills and shifting labour demands, the study’s findings can inform workers on where and how they can match their skills to the labour market.

The expansion of novel technologies, in particular AI, was found to be strongly impacting the value of skills. Programming languages and data science, were amongst the most valuable and workers with AI skills were able to command 21% higher wages on average.

Of the frequently in demand AI skills, the top five in terms of economic value (as a percentage increase of hourly wages) to the worker were:

1 Machine Learning (+40%)

2 Tensor Flow[1] (+38%)

3 Deep Learning (+27%)

4 Natural Language Processing (+19%)

5 Data Science (+17%)

Skills with a high general-purpose value across employment sectors were also identified, for example programming in Python (+8%) – a versatile coding language. In addition, the research shows that the worker’s background matters when applying skills. Software & Tech skills, for example, are seven times more valuable for workers in Marketing and ten times as valuable for workers in Finance & Legal than for workers from the tech domain. This also indicates that skills in Software & Tech have high general-purpose value.

Dr Fabian Stephany, co-author commented on the findings: “We know we never apply skills in isolation. Using this data we saw which proficiencies were most sought after and which sets were in demand together, this allowed us to give skills and complementary skills a financial value based on the demands of the labour market.”

Ole Teutloff commented: “Conceptualising the relationship between skills as a network enabled us to show the context dependency of human capital.”

Dr Stephany added: “Our findings have profound implications for individuals, businesses, and policymakers. By recognizing the value of complementarity, we can better guide workers on their individual reskilling journeys in times of technological change.”

Examining the findings in the context of a global workforce equipped for the new economy, three principles emerged:

1. Skill Sets: Most jobs require a combination of skills. Therefore, the value of a skill can only be assessed in the context of its complementary skills.
2. Reskilling Efficiency: As workers adapt to new technologies, they incrementally add new skills to their existing skill sets. Maximising complementarity between old and new skills is crucial for economic efficiency in this process.
3. Strategic Value: As a particular skill’s set of complementary skills becomes more diverse, the more strategic options a worker has for reskilling. This increases their resilience against unforeseen technological changes in the future.

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Notes to editors

The full study published in Research Policy, can be accessed here: What is the Price of a Skill? The Value of Complementarity

Media contact
Sara Spinks/Roz Pacey
Press@oii.ox.ac.uk

This research has gone through the University of Oxford’s ethical review process, the CUREC number associated with this is: SSD/CUREC1A/15-005

With thanks to the funders of this research: ESRC Digit Innovation Fund (G2781-15) and the programme on AI & Work funded by the Dieter Schwarz Foundation.

Dr. Fabian Stephany is a Departmental Research Lecturer in AI & Work at the Oxford Internet Institute (OII), University of Oxford, where he leads the Skill Scale Project (www.skillscale.org) investigating the emergence of new skills and sustainability of novel occupations in times of technological disruption.

Ole Teutloff is a PhD fellow in Social Data Science at SODAS and the University of Edinburgh. He holds an MSc in Social Data Science from Oxford University and a Master of Public Policy from the Hertie School of Governance in Berlin. In his research, he investigates the impact of digital technologies on labour markets.

About the OII

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[1] TensorFlow is a free and open-source software library for machine learning and artificial intelligence.