In this study we use a novel and comprehensive method to map out how employment is likely to change, and the implications for skills. We show both what we can expect, and where we should be uncertain. We also show likely dynamics in different parts of the labour market — from sectors like food and health to manufacturing. We find that education, health care, and wider public sector occupations are likely to grow. We also explain why some low-skilled jobs, in fields like construction and agriculture, are less likely to suffer poor labour market outcomes than has been assumed in the past.
More generally, we shine a light on the skills that are likely to be in greater demand, including interpersonal skills, higher-order cognitive skills, and systems skills. Unlike other recent studies, the method also makes it possible to predict with some confidence what kinds of new jobs may come into existence.
The study challenges the false alarmism that contributes to a culture of risk aversion and holds back technology adoption, innovation, and growth; this matters particularly to countries like the US and the UK, which already face structural productivity problems.
Crucially, through the report, we point to the actions that educators, policymakers and individuals can take to better prepare themselves for the future.
Here’s how our methodology works:
We start by reviewing the drivers of change and the interactions that are expected to shape industry structures and labour markets in 2030. We also assemble detailed information about occupations (key tasks, related industries and historical growth patterns). This material is used to contextualise and inform discussions at foresight workshops in the US and UK, our countries of analysis.
At the workshops, panels of experts are presented with three sets of ten individual occupations and invited to debate the future prospects of each in light of the trends. The first set of ten occupations is chosen randomly. Participants then assign labels to the occupations according to their view of its future demand prospects (grow, stay the same, shrink), as well as their level of con dence in their responses. To sharpen prediction, an active learning method is implemented: the subsequent sets of occupations to be labelled are chosen by the algorithm. Speciffically, the algorithm chooses occupations in areas of the skills space about which it is least certain, based on the previously labelled occupations. This process is repeated twice to generate a training set of
We subsequently use this information to train a machine learning classiffer to generate predictions for all occupations. This relies on a detailed data set of 120 skills, abilities and knowledge features against which the U.S. Department of Labor’s O*NET service ‘scores’ occupations. (We also map this data to the closest comparable UK occupations using a ‘cross-walk’.) Together with the predictions about changes in occupational demand, this permits us to estimate the skills that will, by extension, most likely experience growth or decline.
We interpret the machine learning results with particular attention to the discussions from our foresight workshops, and highlight findings that are most relevant for employers, educators and policymakers.
THE FUTURE DEMAND FOR OCCUPATIONS
We predict that around one-tenth of the workforce are in occupations that are likely to grow as a percentage of the workforce. Around one- fth are in occupations that will likely shrink. This latter gure is much lower than recent studies of automation have suggested.
This means that roughly seven in ten people are currently in jobs where we simply cannot know for certain what will happen. However, our ndings about skills suggest that occupation redesign coupled with workforce retraining could promote growth in these occupations.
We find that many of the jobs likely to experience a fall in employment are, unsurprisingly, low- or medium-skilled in nature. However, in challenge to some other studies, not all low- and medium-skilled jobs are likely to face the same fate.
In general, public sector occupations — with some exceptions — feature prominently and are predicted to see growth.
We also expect buoyant demand for some — but not all — professional occupations, re ecting the continued growth of service industries.
THE FUTURE DEMAND FOR SKILLS
We find a strong emphasis on interpersonal skills, higher-order cognitive skills and systems skills in both the US and the UK.
Our findings also confirm the importance of higher-order cognitive skills such as originality, fluency of ideas and active learning.
We show that the future workforce will need broad- based knowledge in addition to the more specialised features that will be needed for specific occupations.
UNCOVERING SKILL COMPLEMENTARITIES
Complementary skills that are most frequently associated with higher demand are customer and personal service, judgement and decision making, technology design, fluency of ideas, science and operations analysis.