Mapping Career Causeways: Supporting workers at risk

www.nesta.org.uk/report/mapping-career-causeways-supporting-workers-risk/
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Mapping Career Causeways: Supporting workers at risk

Automation is changing the landscape of work, accelerated by the COVID-19 pandemic. As economies look to recover, millions of workers across Europe will need to retrain in order to transition into new roles. Focusing on occupations at a lower risk of automation will be a better investment for individuals and economies more widely.

To date, most automation research has focused on identifying which occupations are most at risk of automation. This report goes one step further, by providing guidance on how workers (in the UK, France and Italy) can transition out of these occupations and into lower-risk roles. This guidance is made possible by an algorithm that estimates the similarity between over 1,600 jobs, based on the skills and work experiences required in each role. We have visualised these similarities in A map for navigating the labour market, an interactive map that can be used to view worker resilience to a range of shocks, including the impact of COVID-19.

The key finding from our research is that automation risk can be hard to escape. This is because occupations that are at high risk of automation tend to require similar skills (these occupations sit close to each other on the map). As a consequence, at-risk workers, who predominantly reside in sales, customer service and clerical roles, will require more retraining to find an occupation that is at a lower risk.

A new system for supporting job transitions

The career transition recommendation algorithm has three functions:

  • Broadening job search horizons: The algorithm can identify a set of ‘viable’ and ‘desirable’ jobs into which a worker could potentially move. Given a worker’s current role, the ‘viable transitions’ are those roles that involve similar skills requirements, education and experience levels. ‘Desirable transitions’ are a subset of viable transitions and consist of those roles that offer comparable or higher pay to their current role.
  • Providing tailored advice on upskilling: For an individual worker, the algorithm can perform a skills gap analysis and identify the new skills that they may need to make a successful transition. For groups of workers, the model can identify the skills that would expand their viable transitions. This information can be used by a range of stakeholders – including career advisors, employers, policy makers and local governments – to tailor training and upskilling advice.
  • Incorporating the risk of shocks, such as automation and COVID-19: When suggesting transitions, the model can take into account the risk of automation in the destination occupation, as well as the worker’s potential exposure to COVID-19. On the former, the model can pinpoint specific tasks that raise and lower automation risk in any given occupation.

If you are interested in partnering with us to trial and validate this new system, please get in touch.

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Recommendations for policy and practice

Enhancing resilience to labour market disruption
While local contexts shape their decisions, policymakers across Europe can use these findings to design targeted interventions that anticipate change to the demand for skills and enhance regional and individual resilience to labour market disruption.

Building an inclusive and responsive system to support career transitions
Insights from this report can be used to make services for skills development and career transitions more responsive to changes in the demand for skills, and better tailored to workers whose jobs are at risk.

This project is supported by J.P. Morgan as part of their New Skills at Work initiative.

Nesta’s mission is to empower workers to navigate their way through a changing labour market. You can read more in our manifesto for supporting the six million at risk of losing their jobs in the next decade, Precarious to Prepared.

Authors

Karlis Kanders

Karlis Kanders

Karlis Kanders

Senior Data Foresight Lead, Discovery

Karlis is a Senior Data Foresight Lead working in Nesta’s Discovery team

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Jyldyz Djumalieva

Jyldyz Djumalieva

Jyldyz Djumalieva

Data Science Technical Lead, Data Analytics Practice

Jyldyz Djumalieva is the Data Science Technical Lead working in Data Analytics

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Cath Sleeman

Cath Sleeman

Cath Sleeman

Head of Data Discovery, Data Analytics Practice

Dr Cath Sleeman is the Head of Data Discovery.

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Jack Orlik

Jack Orlik

Jack Orlik

Programme Manager - Open Jobs, Data Analytics Practice

Jack was a Programme Manager for Open Jobs.

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