A map for navigating the labour market

Supporting job transitions in uncertain times

Thursday, 26 November 2020

The backdrop of rising unemployment has made urgent the need to understand how workers' skills and experiences can be transferred between jobs. In our new report, Mapping Career Causeways: Supporting workers at risk, we have used machine learning to create a map that captures the similarities between over 1,600 jobs, based on the skills and work activities that make up each role.

In the map, similar occupations are placed close to each other, showing how a worker's skills and experience can be transferred to a range of nearby jobs. These jobs can then be compared along different dimensions, such as their average earnings, risk of automation and potential exposure to COVID-19.

Over the next year, these data-driven measures of occupational-similarity will be verified and tested. Ultimately, we hope that this type of map could help to broaden career horizons and tailor skill recommendations.

A map of the labour market

The map, shown below, captures the relationships between over 1,600 jobs. The closer two occupations are to each other, the greater the overlap in their skills and work activities.

Overlaid on the map is a measure that reflects potential exposure to automation risk. These estimates of risk are derived from the results of a study by Brynjolfsson, Mitchell and Rock (2018), in which the authors used crowdsourcing to rate thousands of tasks on their suitability for machine learning. We translated these estimates to a European set of occupations (by developing a crosswalk between the O*NET and ESCO frameworks).

Whilst these estimates of task automation represent the most detailed ones available, they are still experimental in nature and the translation of these findings to a European context adds additional uncertainty. The complete methodology is detailed in the report, where we also consider the role of 'bottleneck tasks' that are particularly difficult to automate. An occupation may have high overall suitability for machine learning but a handful of bottleneck tasks may slow or even prevent automation.

The full methodology, including definitions of 'suitability for machine learning' and 'safe and desirable' transitions, are detailed in the report: Mapping Career Causeways: Supporting workers at risk.

As shown in the map above, the sales and services sector and the business and administration sector both contain many jobs whose tasks are judged to be suitable for machine learning. The actual likelihood of automation will depend on many other factors, and there may be cultural, societal or financial barriers that prevent or slow automation. Moreover, innovation in job design may ensure that the adoption of machine learning augments these roles instead of displacing them. The estimates of suitability for machine learning will also continue to evolve as machine learning advances. For example, progress in the design and adoption of robotic technology would raise the risk of automation for a number of manually-intensive roles in the construction sector.

The cluster of higher-risk jobs across the map shows that, if workers in these jobs are displaced by machine learning systems, they may find it hard to transition into lower-risk jobs that offer at least a similar level of pay to their current job (which are called 'desirable transitions'). The reason is that many of the desirable transitions for these workers would put them back into jobs whose tasks appear suitable for machine learning. These workers face the seemingly impossible challenge of finding a job that is sufficiently similar to their current role so that it is a viable transition, but also sufficiently dissimilar such that it is less suitable for machine learning. The groups that have the smallest number of desirable transitions into lower-risk roles include sales and services and a subset of clerical business and administrative occupations.

Using the map to provide guidance

In the example below, we show how the map might be used to provide insights about the role of a shop assistant. This job contains a number of tasks that would be suitable for machine learning. It has also been severely impacted by COVID-19. Using the map, we can identify alternative jobs that require similar skills and work activities. We then compare these 'viable transitions' along dimensions such as earnings and employment as well as risk of automation and potential exposure to COVID-19.

Viable transitions for a shop assistant

This role is at high risk of automation and has been severely impacted by COVID-19. If a shop assistant needs to move jobs, there are 54 roles that they may wish to consider. These 'viable transitions' all involve similar skills and/or work activities to those required by a shop assistant.

Top viable transitions

These are the 15 viable transitions which require the most similar skills and/or work activities to those needed by a shop assistant.

Overall similarity

All the roles are similar to a shop assistant, but a sales assistant is the closest. A car leasing agent is the least similar and may require more retraining or upskilling. Overall similarity can be decomposed into two components: work activities and skills.

Similarity of work activities

The work activities in the top viable transitions are all quite similar to those performed by a shop assistant. They include the likes of promoting, selling and purchasing.

Similarity of skills

Skill similarity varies and roles that are further to the left will likely require more retraining. The most transferable skills from a shop assistant's skillset include identifying customer needs, demonstrating products and processing payments.

More than similarity

There are a number of dimensions along which to compare these top viable transitions. These include the number of vacancies available in each position as well as their average levels of pay.

Average annual earnings

Rental service representatives have the highest average annual earnings whilst fuel retail sales workers have the lowest. Most of these jobs have higher average earnings than a shop assistant. However, these are just estimates and actual pay will vary.

Employment level

There is no official data on vacancies by occupation. We can only show a rough estimate of total employment in each job. Sales assistants and specialised sellers are amongst the largest occupations. Next we compare the viable transitions by their exposure to risk.

Suitability for machine learning

Unfortunately, many of the top viable transitions contain tasks that would be suitable for machine learning. The work activities which ensured that these jobs were 'a good fit' are also highly automatable.

Viable transitions at lower risk of automation

Jobs that had high overall risk and few hard-to-automate tasks have been replaced by other viable transitions. These new roles are less similar to a shop assistant, but they are still viable transitions and their tasks are less suitable for machine learning. The new roles include merchandiser and shop manager.

Potential direct exposure to COVID-19

This metric is based on whether the job can be done remotely and whether it involves close interaction with people. While it can highlight jobs with potentially high exposure to COVID-19, it is still an experimental measure and appears to underestimate risk for hotel butlers and club hosts and it does not capture the indirect impact of COVID-19 on jobs.

Viable transitions at lower risk of COVID-19 and automation

Jobs that were judged to have high potential exposure to COVID-19 have been replaced with other viable transitions. These new roles are less similar to a shop assistant than the previous roles, but they are still viable and potentially have lower exposure to both COVID-19 and automation.

Acquiring a new skill

Upskilling will expand a worker's options. If a shop assistant begins to manage staff, then their viable transitions will widen to include office manager and advertising assistant.

Broadening career horizons

We have highlighted a couple of options that a shop assistant may wish to consider. Of course, moving jobs is not as simple as just selecting an occupation; there may be no vacancies in a worker's preferred role. Nevertheless, this type of tool can help to show workers the transferability of their skills and experiences, and it may act as a useful starting point when searching for a job.

The full methodology, including definitions of 'suitability for machine learning' and 'potential direct exposure to COVID-19', are detailed in the report: Mapping Career Causeways: Supporting workers at risk.

Tailoring advice on upskilling

The map can be used to pinpoint the potential skill gaps between a worker's current job and their desired job. We also can identify the skills that would have the greatest broadening effect on a worker's options. For workers in jobs that have high suitability for machine learning, we find that there are four broad skill types that would increase their 'safe and desirable transitions'. The most effective of these core skills can unlock, on average, between two and three new options per occupation. The four groups are:

  • Management skills to manage staff, budgets and projects;
  • Communication skills to build and maintain business relationships, use different communication channels and liaise with managers and authorities;
  • Information analysis and evaluation skills to execute feasibility studies, assess financial viability, analyse risk and perform research;
  • Compliance knowledge to comply with company guidelines, work health and safety standards and environmental legislation.

Taken together, our observations underscore the role of non-routine activities requiring advanced cognitive reasoning, human judgement, and working with other people, in protecting workers against automation risk.

Partner with us

Over the next year, we aim to validate and trial the map of career transitions, and we are actively seeking partners to work with us. This work will involve seeking feedback on the transition pathways, testing different methods for delivering insights about career transitions, and enriching the information that can be overlaid on the map. Through this work, we aim to broaden the information that is available to individuals, businesses and public services, and thereby drive change that helps to connect people to good work.

If you are interested in working with us, or supporting our research, please get in touch by emailing [email protected].